Loss of diversity in the gut microbiome can persist for extended periods after antibiotic treatment, impacting microbiome function, antimicrobial resistance and probably host health. Despite widespread antibiotic use, our understanding of the species and metabolic functions contributing to gut microbiome recovery is limited. Using data from 4 discovery cohorts in 3 continents comprising >500 microbiome profiles from 117 individuals, we identified 21 bacterial species exhibiting robust association with ecological recovery post antibiotic therapy. Functional and growth-rate analysis showed that recovery is supported by enrichment in specific carbohydrate-degradation and energy-production pathways. Association rule mining on 782 microbiome profiles from the MEDUSA database enabled reconstruction of the gut microbial ‘food web’, identifying many recovery-associated bacteria as keystone species, with the ability to use host- and diet-derived energy sources, and support repopulation of other gut species. Experiments in a mouse model recapitulated the ability of recovery-associated bacteria (Bacteroides thetaiotaomicron and Bifidobacterium adolescentis) to promote recovery with synergistic effects, providing a boost of two orders of magnitude to microbial abundance in early time points and faster maturation of microbial diversity. The identification of specific species and metabolic functions promoting recovery opens up opportunities for rationally determining pre- and probiotic formulations offering protection from long-term consequences of frequent antibiotic usage.
Subscribe to Journal
Get full journal access for 1 year
only $8.25 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Illumina sequencing data for this study (mouse models) are available from the Sequence Read Archive under project ID SRP142225. Samples are labelled in SRA with a shorthand (for example, PBS6D22, where ‘PBS’ represents the gavage condition, ‘6’ represents the cage number, and ‘D22’ represents the day of sampling).
Analysis scripts used for generating the figures in this study are available at https://github.com/CSB5/Recovery_Determinants_Study.
Marchesi, J. R. et al. The gut microbiota and host health: a new clinical frontier. Gut 65, 330–339 (2016).
Bäumler, A. J. & Sperandio, V. Interactions between the microbiota and pathogenic bacteria in the gut. Nature 535, 85–93 (2016).
Kampmann, C., Dicksved, J., Engstrand, L. & Rautelin, H. Composition of human faecal microbiota in resistance to Campylobacter infection. Clin. Microbiol. Infect. 22, 61.e1–61.e8 (2016).
Gilbert, J. A. et al. Microbiome-wide association studies link dynamic microbial consortia to disease. Nature 535, 94–103 (2016).
Routy, B. et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 359, 91–97 (2018).
Gopalakrishnan, V. et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 359, 97–103 (2018).
Dethlefsen, L., Huse, S., Sogin, M. L. & Relman, D. A. The pervasive effects of an antibiotic on the human gut microbiota, as revealed by deep 16S rRNA sequencing. PLoS Biol. 6, e280 (2008).
Zaura, E. et al. Same exposure but two radically different responses to antibiotics: resilience of the salivary microbiome versus long-term microbial shifts in feces. mBio 6, e01693-01615 (2015).
Perez-Cobas, A. E. et al. Gut microbiota disturbance during antibiotic therapy: a multi-omic approach. Gut 62, 1591–1601 (2013).
Klein, E. Y. et al. Global increase and geographic convergence in antibiotic consumption between 2000 and 2015. Proc. Natl Acad. Sci. USA 115, E3463–E3470 (2018).
Blaser, M. J. & Falkow, S. What are the consequences of the disappearing human microbiota?. Nat. Rev. Microbiol. 7, 887–894 (2009).
Blaser, M. J. Antibiotic use and its consequences for the normal microbiome. Science 352, 544–545 (2016).
Stevens, V., Dumyati, G., Fine, L. S., Fisher, S. G. & van Wijngaarden, E. Cumulative antibiotic exposures over time and the risk of Clostridium difficile infection. Clin. Infect. Dis. 53, 42–48 (2011).
Smillie, C. S. et al. Ecology drives a global network of gene exchange connecting the human microbiome. Nature 480, 241–244 (2011).
Modi, S. R., Lee, H. H., Spina, C. S. & Collins, J. J. Antibiotic treatment expands the resistance reservoir and ecological network of the phage metagenome. Nature 499, 219–222 (2013).
Raymond, F. et al. The initial state of the human gut microbiome determines its reshaping by antibiotics. ISME J. 10, 707–720 (2016).
Livanos, A. E. et al. Antibiotic-mediated gut microbiome perturbation accelerates development of type 1 diabetes in mice. Nat. Microbiol. 1, 16140 (2016).
Cox, L. M. & Blaser, M. J. Antibiotics in early life and obesity. Nat. Rev. Endocrinol. 11, 182–190 (2015).
Langdon, A., Crook, N. & Dantas, G. The effects of antibiotics on the microbiome throughout development and alternative approaches for therapeutic modulation. Genome Med. 8, 39 (2016).
Dethlefsen, L. & Relman, D. A. Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proc. Natl Acad. Sci. USA 108, 4554–4561 (2011).
Jakobsson, H. E. et al. Short-term antibiotic treatment has differing long-term impacts on the human throat and gut microbiome. PLoS ONE 5, e9836 (2010).
Raymond, F., Deraspe, M., Boissinot, M., Bergeron, M. G. & Corbeil, J. Partial recovery of microbiomes after antibiotic treatment. Gut Microbes 7, 428–434 (2016).
Palleja, A. et al. Recovery of gut microbiota of healthy adults following antibiotic exposure. Nat. Microbiol. 3, 1255–1265 (2018).
Suez, J. et al. Post-antibiotic gut mucosal microbiome reconstitution is impaired by probiotics and improved by autologous FMT. Cell 174, 1406–1423 (2018).
Harvey, E., Gounand, I., Ward, C. L., Altermatt, F. & Cadotte, M. Bridging ecology and conservation: from ecological networks to ecosystem function. J. Appl. Ecol. 54, 371–379 (2017).
Bascompte, J. & Stouffer, D. B. The assembly and disassembly of ecological networks. Phil. Trans. R. Soc. B 364, 1781–1787 (2009).
The Human Microbiome Project Consortium Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).
Wexler, H. M. Bacteroides: the good, the bad, and the nitty-gritty. Clin. Microbiol. Rev. 20, 593–621 (2007).
Faust, K. & Raes, J. Microbial interactions: from networks to models. Nat. Rev. Microbiol. 10, 538–550 (2012).
Solden, L. M. et al. Interspecies cross-feeding orchestrates carbon degradation in the rumen ecosystem. Nat. Microbiol. 3, 1274–1284 (2018).
Adamowicz, E. M., Flynn, J., Hunter, R. C. & Harcombe, W. R. Cross-feeding modulates antibiotic tolerance in bacterial communities. ISME J. 12, 2723–2735 (2018).
Wang, J. & Jia, H. Metagenome-wide association studies: fine-mining the microbiome. Nat. Rev. Microbiol. 14, 508–522 (2016).
David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).
Rothschild, D. et al. Environment dominates over host genetics in shaping human gut microbiota. Nature 555, 210–215 (2018).
Sokol, H. et al. Faecalibacterium prausnitzii is an anti-inflammatory commensal bacterium identified by gut microbiota analysis of Crohn disease patients. Proc. Natl Acad. Sci. USA 105, 16731–16736 (2008).
Takahashi, K. et al. Reduced abundance of butyrate-producing bacteria species in the fecal microbial community in Crohn’s disease. Digestion 93, 59–65 (2016).
El Kaoutari, A., Armougom, F., Gordon, J. I., Raoult, D. & Henrissat, B. The abundance and variety of carbohydrate-active enzymes in the human gut microbiota. Nat. Rev. Microbiol. 11, 497–504 (2013).
Korem, T. et al. Growth dynamics of gut microbiota in health and disease inferred from single metagenomic samples. Science 349, 1101–1106 (2015).
Sicard, J. F., Le Bihan, G., Vogeleer, P., Jacques, M. & Harel, J. Interactions of intestinal bacteria with components of the intestinal mucus. Front. Cell. Infect. Microbiol. 7, 387 (2017).
Karlsson, F. H., Nookaew, I. & Nielsen, J. Metagenomic data utilization and analysis (MEDUSA) and construction of a global gut microbial gene catalogue. PLoS Comput. Biol. 10, e1003706 (2014).
Gauffin Cano, P., Santacruz, A., Moya, A. & Sanz, Y. Bacteroides uniformis CECT 7771 ameliorates metabolic and immunological dysfunction in mice with high-fat-diet induced obesity. PLoS ONE 7, e41079 (2012).
Flint, H. J., Scott, K. P., Duncan, S. H., Louis, P. & Forano, E. Microbial degradation of complex carbohydrates in the gut. Gut Microbes 3, 289–306 (2012).
Tailford, L. E., Crost, E. H., Kavanaugh, D. & Juge, N. Mucin glycan foraging in the human gut microbiome. Front. Genet. 6, 81 (2015).
Arike, L. & Hansson, G. C. The densely O-glycosylated MUC2 mucin protects the intestine and provides food for the commensal bacteria. J. Mol. Biol. 428, 3221–3229 (2016).
Finnie, I. A., Dwarakanath, A. D., Taylor, B. A. & Rhodes, J. M. Colonic mucin synthesis is increased by sodium butyrate. Gut 36, 93–99 (1995).
Willemsen, L. E., Koetsier, M. A., van Deventer, S. J. & van Tol, E. A. Short chain fatty acids stimulate epithelial mucin 2 expression through differential effects on prostaglandin E1 and E2 production by intestinal myofibroblasts. Gut 52, 1442–1447 (2003).
Cornick, S., Tawiah, A. & Chadee, K. Roles and regulation of the mucus barrier in the gut. Tissue Barriers 3, e982426 (2015).
Koh, A., De Vadder, F., Kovatcheva-Datchary, P. & Backhed, F. From dietary fiber to host physiology: short-chain fatty acids as key bacterial metabolites. Cell 165, 1332–1345 (2016).
Wampach, L. et al. Colonization and succession within the human gut microbiome by Archaea, Bacteria, and Microeukaryotes during the first year of life. Front. Microbiol. 8, 738 (2017).
Ridaura, V. K. et al. Gut microbiota from twins discordant for obesity modulate metabolism in mice. Science 341, 1241214 (2013).
Jiang, T. et al. Apple-derived pectin modulates gut microbiota, improves gut barrier function, and attenuates metabolic endotoxemia in rats with diet-induced obesity. Nutrients 8, 126 (2016).
Wei, Y. et al. Pectin enhances the effect of fecal microbiota transplantation in ulcerative colitis by delaying the loss of diversity of gut flora. BMC Microbiol. 16, 255 (2016).
Onrust, L. et al. Steering endogenous butyrate production in the intestinal tract of broilers as a tool to improve gut health. Front. Vet. Sci. 2, 75 (2015).
Scott, K. P., Martin, J. C., Duncan, S. H. & Flint, H. J. Prebiotic stimulation of human colonic butyrate-producing bacteria and bifidobacteria, in vitro. FEMS Microbiol. Ecol. 87, 30–40 (2014).
Van den Abbeele, P. et al. Microbial community development in a dynamic gut model is reproducible, colon region specific, and selective for Bacteroidetes and Clostridium cluster IX. Appl. Environ. Microbiol. 76, 5237–5246 (2010).
Sung, J. et al. Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis. Nat. Commun. 8, 15393 (2017).
Mimee, M., Tucker, A. C., Voigt, C. A. & Lu, T. K. Programming a human commensal bacterium, Bacteroides thetaiotaomicron, to sense and respond to stimuli in the murine gut microbiota. Cell Syst. 1, 62–71 (2015).
Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).
Abubucker, S. et al. Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLoS Comput. Biol. 8, e1002358 (2012).
Segata, N. et al. Metagenomic microbial community profiling using unique clade-specific marker genes. Nat. Methods 9, 811–814 (2012).
Franzosa, E. A. et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods 15, 962–968 (2018).
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).
Langille, M. G. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31, 814–821 (2013).
Wu, G. D. et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105–108 (2011).
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).
Yin, Y. et al. dbCAN: a web resource for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 40, W445–W451 (2012).
Gupta, S. K. et al. ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes. Antimicrob. Agents Chemother. 58, 212–220 (2014).
Wood, D. E. & Salzberg, S. L. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 15, R46 (2014).
Cantarel, B. L., Lombard, V. & Henrissat, B. Complex carbohydrate utilization by the healthy human microbiome. PLoS ONE 7, e28742 (2012).
Hipp, J., Güntzer, U. & Nakhaeizadeh, G. Algorithms for association rule mining–a general survey and comparison. ACM SIGKDD Explor. Newsl. 2, 58–64 (2000).
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
Magnusdottir, S. et al. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat. Biotechnol. 35, 81–89 (2017).
Ravikrishnan, A., Blank, L. M., Srivastava, S. & Raman, K. Investigating metabolic interactions in a microbial co-culture through integrated modelling and experiments. Comput. Struct. Biotechnol. J. 18, 1249–1258 (2020).
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).
Huson, D. H., Mitra, S., Ruscheweyh, H. J., Weber, N. & Schuster, S. C. Integrative analysis of environmental sequences using MEGAN4. Genome Res. 21, 1552–1560 (2011).
Chua, M. C. et al. Effect of synbiotic on the gut microbiota of caesarean delivered infants: a randomized, double-blind, multicenter study. J. Pediatr. Gastroenterol. Nutr. 65, 102–106 (2017).
Xu, J. et al. A genomic view of the human–Bacteroides thetaiotaomicron symbiosis. Science 299, 2074–2076 (2003).
Thomas, F., Hehemann, J. H., Rebuffet, E., Czjzek, M. & Michel, G. Environmental and gut bacteroidetes: the food connection. Front. Microbiol. 2, 93 (2011).
Fernandez-Duarte, K. P., Olaya-Galan, N. N., Salas-Cardenas, S. P., Lopez-Rozo, J. & Gutierrez-Fernandez, M. F. Bifidobacterium adolescentis (DSM 20083) and Lactobacillus casei (Lafti L26-DSL): probiotics able to block the in vitro adherence of rotavirus in MA104 cells. Probiotics Antimicrob. Proteins 10, 56–63 (2017).
Thomas, L. V., Ockhuizen, T. & Suzuki, K. Exploring the influence of the gut microbiota and probiotics on health: a symposium report. Br. J. Nutr. 112, S1–S18 (2014).
Riviere, A., Selak, M., Lantin, D., Leroy, F. & De Vuyst, L. Bifidobacteria and butyrate-producing colon bacteria: importance and strategies for their stimulation in the human gut. Front. Microbiol. 7, 979 (2016).
Lee, D. K. et al. Probiotic bacteria, B. longum and L. acidophilus inhibit infection by rotavirus in vitro and decrease the duration of diarrhea in pediatric patients. Clin. Res. Hepatol. Gastroenterol. 39, 237–244 (2015).
Dewulf, E. M. et al. Insight into the prebiotic concept: lessons from an exploratory, double blind intervention study with inulin-type fructans in obese women. Gut 62, 1112–1121 (2013).
This work was supported by funding from the National Healthcare Group (NHG-CSCS/12008), the National Medical Research Council, the National Research Foundation and A*STAR, Singapore.
The authors declare no competing interests.
Peer review information Peer reviewer reports are available.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Figs. 1–11, Table 1 and Note 1.
Species abundance profile across samples from the different cohorts.
Differentially abundant species in recoverers versus non-recoverers.
Inferred metabolic pathway abundances across samples from the different cohorts.
Inferred CAZyme abundances across samples from the different cohorts.
PTR values for different species and the computed community growth rate per sample from the different cohorts.
Microbial dependency relationships in the gut microbiome predicted via association rule mining on the MEDUSA database.
Metabolic support index values for interactions between various RAB species and the corresponding top 10% of interactions.
About this article
Cite this article
Chng, K.R., Ghosh, T.S., Tan, Y.H. et al. Metagenome-wide association analysis identifies microbial determinants of post-antibiotic ecological recovery in the gut. Nat Ecol Evol 4, 1256–1267 (2020). https://doi.org/10.1038/s41559-020-1236-0
Current Opinion in Biotechnology (2021)
Nature Microbiology (2020)