Omics in gut microbiome analysis.
gut microbiome
metabolome
metagenome
metataxonome
metatranscriptome
omics
Journal
Journal of microbiology (Seoul, Korea)
ISSN: 1976-3794
Titre abrégé: J Microbiol
Pays: Korea (South)
ID NLM: 9703165
Informations de publication
Date de publication:
Mar 2021
Mar 2021
Historique:
received:
04
01
2021
accepted:
10
02
2021
revised:
09
02
2021
entrez:
24
2
2021
pubmed:
25
2
2021
medline:
21
8
2021
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 33624266
doi: 10.1007/s12275-021-1004-0
pii: 10.1007/s12275-021-1004-0
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
292-297Références
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: 32690973
pmcid: 7801254
doi: 10.1038/s41587-020-0603-3
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.
pubmed: 7535888
pmcid: 239358
doi: 10.1128/MR.59.1.143-169.1995
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.
pubmed: 32909848
doi: 10.1161/HYPERTENSIONAHA.120.15885
pmcid: 32909848
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.
pubmed: 31341288
pmcid: 7015180
doi: 10.1038/s41587-019-0209-9
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.
pubmed: 32822584
doi: 10.1016/j.chom.2020.07.020
pmcid: 32822584
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: 32647386
doi: 10.1038/s41575-020-0327-3
pmcid: 32647386
Cani, P.D. 2018. Human gut microbiome: hopes, threats and promises. Gut 67, 1716–1725.
pubmed: 29934437
pmcid: 6109275
doi: 10.1136/gutjnl-2018-316723
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.
pubmed: 29588346
pmcid: 5940265
doi: 10.1084/jem.20171761
Dai, H. and Guan, Y. 2020. The Nubeam reference-free approach to analyze metagenomic sequencing reads. Genome Res. 30, 1364–1375.
pubmed: 32883749
pmcid: 7545149
doi: 10.1101/gr.261750.120
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.
pubmed: 30150717
doi: 10.1038/s41591-018-0160-1
pmcid: 30150717
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: 29209090
pmcid: 5716994
doi: 10.1038/s41467-017-01973-8
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.
pubmed: 30377376
pmcid: 6235447
doi: 10.1038/s41592-018-0176-y
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.
pubmed: 33122357
pmcid: 7898465
doi: 10.1126/science.aay9097
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: 27092134
pmcid: 4823605
doi: 10.3389/fmicb.2016.00484
Kim, M.S. and Bae, J.W. 2018. Lysogeny is prevalent and widely distributed in the murine gut microbiota. ISME J. 12, 1127–1141.
pubmed: 29416123
pmcid: 5864201
doi: 10.1038/s41396-018-0061-9
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.
pubmed: 24115628
doi: 10.1111/1758-2229.12090
pmcid: 24115628
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.
pubmed: 21948823
pmcid: 3208976
doi: 10.1128/AEM.06331-11
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: 32753050
pmcid: 7409454
doi: 10.1186/s40168-020-00894-x
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.
pubmed: 33420064
pmcid: 7794225
doi: 10.1038/s41467-020-20389-5
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.
pubmed: 26982729
doi: 10.1038/nature17193
pmcid: 26982729
Kopylova, E., Noe, L., and Touzet, H. 2012. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217.
pubmed: 23071270
doi: 10.1093/bioinformatics/bts611
pmcid: 23071270
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.
pubmed: 32668218
doi: 10.1016/j.chom.2020.06.001
pmcid: 32668218
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.
pubmed: 33020474
pmcid: 7536225
doi: 10.1038/s41467-020-18754-5
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.
pubmed: 32760003
doi: 10.1038/s41586-020-2564-6
pmcid: 32760003
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: 33329430
pmcid: 7729130
doi: 10.3389/fmicb.2020.571847
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.
pubmed: 32943527
doi: 10.1126/science.abb3763
pmcid: 32943527
Marchesi, J.R. and Ravel, J. 2015. The vocabulary of microbiome research: a proposal. Microbiome 3, 31.
pubmed: 26229597
pmcid: 4520061
doi: 10.1186/s40168-015-0094-5
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: 28983295
pmcid: 5613115
doi: 10.3389/fmicb.2017.01830
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.
pubmed: 29904082
doi: 10.1038/s41579-018-0036-x
pmcid: 29904082
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.
pubmed: 28461690
doi: 10.1038/nrmicro.2017.30
pmcid: 28461690
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.
pubmed: 20173072
pmcid: 2849220
doi: 10.1128/AEM.01754-09
Oh, M. and Zhang, L. 2020. DeepMicro: deep representation learning for disease prediction based on microbiome data. Sci. Rep. 10, 6026.
pubmed: 32265477
pmcid: 7138789
doi: 10.1038/s41598-020-63159-5
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.
pubmed: 6092964
doi: 10.1038/312040a0
pmcid: 6092964
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.
pubmed: 22495754
doi: 10.1093/bioinformatics/bts174
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: 30824884
pmcid: 30824884
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.
pubmed: 26442149
pmcid: 4552756
doi: 10.1016/j.nmni.2015.06.005
Savage, D.C. 1977. Microbial ecology of the gastrointestinal tract. Annu. Rev. Microbiol. 31, 107–133.
pubmed: 334036
doi: 10.1146/annurev.mi.31.100177.000543
pmcid: 334036
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.
pubmed: 22688413
pmcid: 3443552
doi: 10.1038/nmeth.2066
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.
pubmed: 23804561
doi: 10.1136/gutjnl-2012-303839
pmcid: 23804561
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.
pubmed: 29807988
pmcid: 6786971
doi: 10.1038/s41564-018-0171-1
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.
pubmed: 33453153
doi: 10.1016/j.cell.2020.12.011
pmcid: 33453153
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.
pubmed: 27063479
pmcid: 4863679
doi: 10.1016/j.cellimm.2016.03.007
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 .
The Human Microbiome Project Consortium. 2012a. A framework for human microbiome research. Nature 486, 215–221.
doi: 10.1038/nature11209
The Human Microbiome Project Consortium. 2012b. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214.
pmcid: 3564958
doi: 10.1038/nature11234
Tonegawa, S. 1983. Somatic generation of antibody diversity. Nature 302, 575–581.
pubmed: 6300689
doi: 10.1038/302575a0
pmcid: 6300689
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.
pubmed: 23245704
doi: 10.1016/j.tim.2012.11.003
pmcid: 23245704
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: 33225575
pmcid: 33225575
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
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.
pubmed: 28462587
doi: 10.1089/ars.2016.6790
pmcid: 28462587
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.
pubmed: 25428410
pmcid: 4441880
doi: 10.1177/0300985814559404
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: 24928884
pmcid: 4136111
doi: 10.1128/AEM.01226-14
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.
pubmed: 5240844
pmcid: 5240844
doi: 10.1126/science.aad3369