Omics in gut microbiome analysis

Abstract

Our understanding of the interactions between microbial communities and their niche in the host gut has improved owing to recent advances in environmental microbial genomics. Integration of metagenomic and metataxonomic sequencing data with other omics data to study the gut microbiome has become increasingly common, but downstream analysis after data integration and interpretation of complex omics data remain challenging. Here, we review studies that have explored the gut microbiome signature using omics approaches, including metagenomics, metataxonomics, metatranscriptomics, and metabolomics. We further discuss recent analytics programs to analyze and integrate multi-omics datasets and further utilization of omics data with other advanced techniques, such as adaptive immune receptor repertoire sequencing, microbial culturomics, and machine learning, to evaluate important microbiome characteristics in the gut.

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References

  1. Almeida, A., Nayfach, S., Boland, M., Strozzi, F., Beracochea, M., Shi, Z.J., Pollard, K.S., Sakharova, E., Parks, D.H., Hugenholtz, P., et al. 2020. A unified catalog of 204,938 reference genomes from the human gut microbiome. Nat. Biotechnol. 39, 105–114.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  2. Amann, R.I., Ludwig, W., and Schleifer, K.H. 1995. Phylogenetic identification and in situ detection of individual microbial cells without cultivation. Microbiol. Rev. 59, 143–169.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. Aryal, S., Alimadadi, A., Manandhar, I., Joe, B., and Cheng, X. 2020. Machine learning strategy for gut microbiome-based diagnostic screening of cardiovascular disease. Hypertension 76, 1555–1562.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  4. Bolyen, E., Rideout, J.R., Dillon, M.R., Bokulich, N.A., Abnet, C.C., Al-Ghalith, G.A., Alexander, H., Alm, E.J., Arumugam, M., Asnicar, F., et al. 2019. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. Bushman, F.D., Conrad, M., Ren, Y., Zhao, C.Y., Gu, C., Petucci, C., Kim, M.S., Abbas, A., Downes, K.J., Devas, N., et al. 2020. Multiomic analysis of the interaction between Clostridioides difficile infection and pediatric inflammatory bowel disease. Cell Host Microbe 28, 422–433.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  6. Cammarota, G., Ianiro, G., Ahern, A., Carbone, C., Temko, A., Claesson, M.J., Gasbarrini, A., and Tortora, G. 2020. Gut microbiome, big data and machine learning to promote precision medicine for cancer. Nat. Rev. Gastroenterol. Hepatol. 17, 635–648.

    PubMed  Article  PubMed Central  Google Scholar 

  7. Cani, P.D. 2018. Human gut microbiome: hopes, threats and promises. Gut 67, 1716–1725.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. Chen, Y., Chaudhary, N., Yang, N., Granato, A., Turner, J.A., Howard, S.L., Devereaux, C., Zuo, T., Shrestha, A., Goel, R.R., et al. 2018. Microbial symbionts regulate the primary Ig repertoire. J. Exp. Med. 215, 1397–1415.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. Dai, H. and Guan, Y. 2020. The Nubeam reference-free approach to analyze metagenomic sequencing reads. Genome Res. 30, 1364–1375.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. Deschasaux, M., Bouter, K.E., Prodan, A., Levin, E., Groen, A.K., Herrema, H., Tremaroli, V., Bakker, G.J., Attaye, I., Pinto-Sietsma, S.J., et al. 2018. Depicting the composition of gut microbiota in a population with varied ethnic origins but shared geography. Nat. Med. 24, 1526–1531.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  11. Duvallet, C., Gibbons, S.M., Gurry, T., Irizarry, R.A., and Alm, E.J. 2017. Meta-analysis of gut microbiome studies identifies diseasespecific and shared responses. Nat. Commun. 8, 1784.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  12. Franzosa, E.A., McIver, L.J., Rahnavard, G., Thompson, L.R., Schirmer, M., Weingart, G., Lipson, K.S., Knight, R., Caporaso, J.G., Segata, N., et al. 2018. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods 15, 962–968.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. Guo, H., Chou, W.C., Lai, Y., Liang, K., Tam, J.W., Brickey, W.J., Chen, L., Montgomery, N.D., Li, X., Bohannon, L.M., et al. 2020. Multi-omics analyses of radiation survivors identify radioprotective microbes and metabolites. Science 370, eaay9097.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. Hilton, S.K., Castro-Nallar, E., Perez-Losada, M., Toma, I., McCaffrey, T.A., Hoffman, E.P., Siegel, M.O., Simon, G.L., Johnson, W.E., and Crandall, K.A. 2016. Metataxonomic and metagenomic approaches vs. culture-based techniques for clinical pathology. Front. Microbiol. 7, 484.

    PubMed  PubMed Central  Article  Google Scholar 

  15. Kim, M.S. and Bae, J.W. 2018. Lysogeny is prevalent and widely distributed in the murine gut microbiota. ISME J. 12, 1127–1141.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. Kim, M.S., Hwang, S.S., Park, E.J., and Bae, J.W. 2013. Strict vegetarian diet improves the risk factors associated with metabolic diseases by modulating gut microbiota and reducing intestinal inflammation. Environ. Microbiol. Rep. 5, 765–775.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  17. Kim, M.S., Park, E.J., Roh, S.W., and Bae, J.W. 2011. Diversity and abundance of single-stranded DNA viruses in human feces. Appl. Environ. Microbiol. 77, 8062–8070.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. Kim, J.Y., Whon, T.W., Lim, M.Y., Kim, Y.B., Kim, N., Kwon, M.S., Kim, J., Lee, S.H., Choi, H.J., Nam, I.H., et al. 2020. The human gut archaeome: identification of diverse haloarchaea in Korean subjects. Microbiome 8, 114.

    PubMed  PubMed Central  Article  Google Scholar 

  19. Kim, H.S., Whon, T.W., Sung, H., Jeong, Y.S., Jung, E.S., Shin, N.R., Hyun, D.W., Kim, P.S., Lee, J.Y., Lee, C.H., et al. 2021. Longitudinal evaluation of fecal microbiota transplantation for ameliorating calf diarrhea and improving growth performance. Nat. Commun. 12, 161.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. Knowles, B., Silveira, C.B., Bailey, B.A., Barott, K., Cantu, V.A., Cobian-Guemes, A.G., Coutinho, F.H., Dinsdale, E.A., Felts, B., Furby, K.A., et al. 2016. Lytic to temperate switching of viral communities. Nature 531, 466–470.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  21. Kopylova, E., Noe, L., and Touzet, H. 2012. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  22. Lee, J.Y., Cevallos, S.A., Byndloss, M.X., Tiffany, C.R., Olsan, E.E., Butler, B.P., Young, B.M., Rogers, A.W.L., Nguyen, H., Kim, K., et al. 2020a. High-fat diet and antibiotics cooperatively impair mitochondrial bioenergetics to trigger dysbiosis that exacerbates pre-inflammatory bowel disease. Cell Host Microbe 28, 273–284.e6.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  23. Lee, G., You, H.J., Bajaj, J.S., Joo, S.K., Yu, J., Park, S., Kang, H., Park, J.H., Kim, J.H., Lee, D.H., et al. 2020b. Distinct signatures of gut microbiome and metabolites associated with significant fibrosis in non-obese NAFLD. Nat. Commun. 11, 4982.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. Li, H., Limenitakis, J.P., Greiff, V., Yilmaz, B., Scharen, O., Urbaniak, C., Zund, M., Lawson, M.A.E., Young, I.D., Rupp, S., et al. 2020. Mucosal or systemic microbiota exposures shape the B cell repertoire. Nature 584, 274–278.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  25. Liang, Q., Zhang, M., Hu, Y., Zhang, W., Zhu, P., Chen, Y., Xue, P., Li, Q., and Wang, K. 2020. Gut microbiome contributes to liver fibrosis impact on T cell receptor immune repertoire. Front. Microbiol. 11, 571847.

    PubMed  PubMed Central  Article  Google Scholar 

  26. Lobel, L., Cao, Y.G., Fenn, K., Glickman, J.N., and Garrett, W.S. 2020. Diet posttranslationally modifies the mouse gut microbial proteome to modulate renal function. Science 369, 1518–1524.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  27. Marchesi, J.R. and Ravel, J. 2015. The vocabulary of microbiome research: a proposal. Microbiome 3, 31.

    PubMed  PubMed Central  Article  Google Scholar 

  28. Marsh, J.W., Humphrys, M.S., and Myers, G.S.A. 2017. A laboratory methodology for dual RNA-sequencing of bacteria and their host cells in vitro. Front. Microbiol. 8, 1830.

    PubMed  PubMed Central  Article  Google Scholar 

  29. Martens, E.C., Neumann, M., and Desai, M.S. 2018. Interactions of commensal and pathogenic microorganisms with the intestinal mucosal barrier. Nat. Rev. Microbiol. 16, 457–470.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  30. Mirzaei, M.K. and Maurice, C.F. 2017. Menage a trois in the human gut: interactions between host, bacteria and phages. Nat. Rev. Microbiol. 15, 397–408.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  31. Nichols, D., Cahoon, N., Trakhtenberg, E., Pham, L., Mehta, A., Belanger, A., Kanigan, T., Lewis, K., and Epstein, S. 2010. Use of ichip for high-throughput in situ cultivation of “uncultivable” microbial species. Appl. Environ. Microbiol. 76, 2445–2450.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. Oh, M. and Zhang, L. 2020. DeepMicro: deep representation learning for disease prediction based on microbiome data. Sci. Rep. 10, 6026.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. Patten, P., Yokota, T., Rothbard, J., Chien, Y., Arai, K., and Davis, M.M. 1984. Structure, expression and divergence of T-cell receptor beta-chain variable regions. Nature 312, 40–46.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  34. Peng, Y., Leung, H.C., Yiu, S.M., and Chin, F.Y. 2012. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 28, 1420–1428.

    CAS  Article  Google Scholar 

  35. Richard, M.L. and Sokol, H. 2019. The gut mycobiota: insights into analysis, environmental interactions and role in gastrointestinal diseases. Nat. Rev. Gastroenterol. Hepatol. 16, 331–345.

    PubMed  PubMed Central  Google Scholar 

  36. Rouli, L., Merhej, V., Fournier, P.E., and Raoult, D. 2015. The bacterial pangenome as a new tool for analysing pathogenic bacteria. New Microbes New Infect. 7, 72–85.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. Savage, D.C. 1977. Microbial ecology of the gastrointestinal tract. Annu. Rev. Microbiol. 31, 107–133.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  38. Segata, N., Waldron, L., Ballarini, A., Narasimhan, V., Jousson, O., and Huttenhower, C. 2012. Metagenomic microbial community profiling using unique clade-specific marker genes. Nat. Methods 9, 811–814.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. Shin, N.R., Lee, J.C., Lee, H.Y., Kim, M.S., Whon, T.W., Lee, M.S., and Bae, J.W. 2014. An increase in the Akkermansia spp. population induced by metformin treatment improves glucose homeostasis in diet-induced obese mice. Gut 63, 727–735.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  40. Sieber, C.M.K., Probst, A.J., Sharrar, A., Thomas, B.C., Hess, M., Tringe, S.G., and Banfield, J.F. 2018. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat. Microbiol. 3, 836–843.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. Stacy, A., Andrade-Oliveira, V., McCulloch, J.A., Hild, B., Oh, J.H., Perez-Chaparro, P.J., Sim, C.K., Lim, A.I., Link, V.M., Enamorado, M., et al. 2021. Infection trains the host for microbiota-enhanced resistance to pathogens. Cell 184, 615–627.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  42. Stauber, J., Shaikh, N., Ordiz, M.I., Tarr, P.I., and Manary, M.J. 2016. Droplet digital PCR quantifies host inflammatory transcripts in feces reliably and reproducibly. Cell. Immunol. 303, 43–49.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. Tanes, C., Bittinger, K., Gao, Y., Friedman, E.S., Nessel, L., Paladhi, U.R., Chau, L., Panfen, E., Fischbach, M.A., Braun, J., et al. 2021. Role of dietary fiber in the recovery of the human gut microbiome and its metabolome. Cell Host Microbe doi: https://doi.org/10.1016/j.chom.2020.12.012.

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

    Article  CAS  Google Scholar 

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

    PubMed Central  Article  CAS  Google Scholar 

  46. Tonegawa, S. 1983. Somatic generation of antibody diversity. Nature 302, 575–581.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  47. Weitz, J.S., Poisot, T., Meyer, J.R., Flores, C.O., Valverde, S., Sullivan, M.B., and Hochberg, M.E. 2013. Phage-bacteria infection networks. Trends Microbiol. 21, 82–91.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  48. Whon, T.W., Kim, H.S., Shin, N.R., Jung, E.S., Tak, E.J., Sung, H., Jung, M.J., Jeong, Y.S., Hyun, D.W., Kim, P.S., et al. 2020. Male castration increases adiposity via small intestinal microbial alterations. EMBO Rep. 22, e50663.

    PubMed  PubMed Central  Google Scholar 

  49. Whon, T.W., Kim, H.S., Shin, N., Sung, H., Kim, M., Kim, J.Y., Kang, W., Kim, P.S., Hyun, D., Seong, H.J., et al. 2021. Calf diarrhea caused by prolonged expansion of autochthonous gut Enterobacteriaceae and their lytic bacteriophages. mSystems. Doi: https://doi.org/10.1128/mSystems.00816-20

  50. Whon, T.W., Shin, N.R., Jung, M.J., Hyun, D.W., Kim, H.S., Kim, P.S., and Bae, J.W. 2017. Conditionally pathogenic gut microbes promote larval growth by increasing redox-dependent fat storage in high-sugar diet-fed drosophila. Antioxid. Redox Signal. 27, 1361–1380.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  51. Williams, J.M., Duckworth, C.A., Burkitt, M.D., Watson, A.J., Campbell, B.J., and Pritchard, D.M. 2015. Epithelial cell shedding and barrier function: a matter of life and death at the small intestinal villus tip. Vet. Pathol. 52, 445–455.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. Yun, J.H., Roh, S.W., Whon, T.W., Jung, M.J., Kim, M.S., Park, D.S., Yoon, C., Nam, Y.D., Kim, Y.J., Choi, J.H., et al. 2014. Insect gut bacterial diversity determined by environmental habitat, diet, developmental stage, and phylogeny of host. Appl. Environ. Microbiol. 80, 5254–5264.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  53. Zhernakova, 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. 2016. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 352, 565–569.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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Acknowledgments

This study was supported by the Main Research Program of the World Institute of Kimchi (KE2101-2 to T.W.W., J.Y.K., and S.W.R.) and the KRIBB Research Initiative Program (KGM5232113 to N.-R.S.), Republic of Korea.

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Correspondence to Seong Woon Roh.

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Whon, T.W., Shin, NR., Kim, J.Y. et al. Omics in gut microbiome analysis. J Microbiol. 59, 292–297 (2021). https://doi.org/10.1007/s12275-021-1004-0

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Keywords

  • omics
  • gut microbiome
  • metagenome
  • metataxonome
  • metatranscriptome
  • metabolome