Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010).
Mackie, R. I., White, B. A. & Isaacson, R. E. Gastrointestinal Microbiology (Chapman & Hall, New York, 1997).
Wang, J. et al. Dietary history contributes to enterotype-like clustering and functional metagenomic content in the intestinal microbiome of wild mice. Proc. Natl. Acad. Sci. USA 111, E2703–E2710 (2014).
Ridaura, V. K. et al. Gut microbiota from twins discordant for obesity modulate metabolism in mice. Science 341, 1241214 (2013).
Manichanh, C., Borruel, N., Casellas, F. & Guarner, F. The gut microbiota in IBD. Nat. Rev. Gastroenterol. Hepatol 9, 599–608 (2012).
Louis, P., Hold, G. L. & Flint, H. J. The gut microbiota, bacterial metabolites and colorectal cancer. Nat. Rev. Microbiol. 12, 661–672 (2014).
Zhao, L. et al. Gut bacteria selectively promoted by dietary fibers alleviate type 2 diabetes. Science 359, 1151–1156 (2018).
Blaut, M. Ecology and physiology of the intestinal tract. Curr. Top. Microbiol. Immunol. 358, 247–272 (2013).
Furusawa, Y. et al. Commensal microbe-derived butyrate induces the differentiation of colonic regulatory T cells. Nature 504, 446–450 (2013).
Sung, J. et al. Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis. Nat. Commun. 8, 15393 (2017).
Abubucker, S. et al. Metabolic reconstruction for metagenomic data and its application to the human microbiome. Plos Comput. Biol. 8, e1002358 (2012).
Greenblum, S., Turnbaugh, P. J. & Borenstein, E. 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).
Levy, R. & Borenstein, E. Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules. Proc. Natl. Acad. Sci. USA 110, 12804–12809 (2013).
Zelezniak, A. et al. Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proc. Natl. Acad. Sci. USA 112, 6449–6454 (2015).
Heinken, A. & Thiele, I. Systematic prediction of health-relevant human-microbial co-metabolism through a computational framework. Gut Microbes 6, 120–130 (2015).
Kettle, H., Louis, P., Holtrop, G., Duncan, S. H. & Flint, H. J. Modelling the emergent dynamics and major metabolites of the human colonic microbiota. Environ. Microbiol. 17, 1615–1630 (2015).
Magnúsdóttir, S. et al. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat. Biotechnol. 35, 81–89 (2017).
Tramontano, M. et al. Nutritional preferences of human gut bacteria reveal their metabolic idiosyncrasies. Nat. Microbiol 3, 514–522 (2018).
Babaei, P., Shoaie, S., Ji, B. & Nielsen, J. Challenges in modeling the human gut microbiome. Nat. Biotechnol. 36, 682–686 (2018).
Magnúsdóttir, S., Heinken, A., Fleming, R. M. T. & Thiele, I. Reply to “Challenges in modeling the human gut microbiome”. Nat. Biotechnol. 36, 686–691 (2018).
Bleich, A. & Fox, J. The mammalian microbiome and its importance in laboratory animal research. ILAR J. 56, 153–158 (2015).
Nelson, K. E. An update on the status of current research on the mammalian microbiome. ILAR J. 56, 163–168 (2015).
Nguyen, T. L. A., Vieira-Silva, S., Liston, A. & Raes, J. How informative is the mouse for human gut microbiota research? Dis. Model Mech. 8, 1–16 (2015).
Pickard, J. M. et al. Rapid fucosylation of intestinal epithelium sustains host–commensal symbiosis in sickness. Nature 514, 638–641 (2014).
Cullender, T. C. et al. Innate and adaptive immunity interact to quench microbiome flagellar motility in the gut. Cell Host Microbe 14, 571–581 (2013).
Langille, M. G. et al. Microbial shifts in the aging mouse gut. Microbiome 2, 50 (2014).
Rooks, M. G. et al. Gut microbiome composition and function in experimental colitis during active disease and treatment-induced remission. ISME J. 8, 1403–1417 (2014).
Benson, A. K. et al. Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors. Proc. Natl. Acad. Sci. USA 107, 18933–18938 (2010).
Linnenbrink, M., Wang, J., Hardouin, E. A., Künzel, S., Metzler, D. & Baines, J. F. The role of biogeography in shaping diversity of the intestinal microbiota in house mice. Mol. Ecol. 22, 1904–1916 (2013).
Truong, D. T. et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat. Methods 12, 902–903 (2015).
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).
Bouladoux, N., Harrison, O. J. & Belkaid, Y. The mouse model of infection with Citrobacter rodentium. Curr. Protoc. Immunol. 119, 19.15.1–19.15.25 (2017).
Heinken, A., Sahoo, S., Fleming, R. M. T. & Thiele, I. Systems-level characterization of a host-microbe metabolic symbiosis in the mammalian gut. Gut Microbes 4, 28–40 (2013).
Sung, J. et al. Data from: Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis. Dryad Digital Repository, https://doi.org/10.5061/dryad.mc1j9 (2017).
Lim, R. et al. Data from: Large-scale metabolic interaction network of the mouse and human gut microbiota. Dryad Digital Repository, https://doi.org/10.5061/dryad.dr7sqv9v8 (2020).
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
Theriot, C. M. et al. Antibiotic-induced shifts in the mouse gut microbiome and metabolome increase susceptibility to Clostridium difficile infection. Nat. Commun. 5, 3114 (2014).
Kovatcheva-Datchary, P. et al. Simplified intestinal microbiota to study microbe-diet-host interactions in a mouse model. Cell Rep 26, 3772–3783 (2019).
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).
Source: Ecology - nature.com
