Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Metagenome-wide association analysis identifies microbial determinants of post-antibiotic ecological recovery in the gut

Nature Ecology & Evolution volume 4, pages 1256–1267 (2020)Cite this article

Subjects

Abstract

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Gut microbiome recovery profiles and key associated taxa.
Fig. 2: Mechanistic model linking microbial functions with recovery.
Fig. 3: Role of RABs in ecological recovery via the microbial food web.
Fig. 4: Promoting microbiome recovery in a mouse model using RABs.

Similar content being viewed by others

Data availability

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).

Code availability

Analysis scripts used for generating the figures in this study are available at https://github.com/CSB5/Recovery_Determinants_Study.

References

  1. Marchesi, J. R. et al. The gut microbiota and host health: a new clinical frontier. Gut 65, 330–339 (2016).

    PubMed  Google Scholar 

  2. Bäumler, A. J. & Sperandio, V. Interactions between the microbiota and pathogenic bacteria in the gut. Nature 535, 85–93 (2016).

    PubMed  PubMed Central  Google Scholar 

  3. 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).

    CAS  Google Scholar 

  4. Gilbert, J. A. et al. Microbiome-wide association studies link dynamic microbial consortia to disease. Nature 535, 94–103 (2016).

    CAS  PubMed  Google Scholar 

  5. Routy, B. et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 359, 91–97 (2018).

    CAS  PubMed  Google Scholar 

  6. Gopalakrishnan, V. et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 359, 97–103 (2018).

    CAS  PubMed  Google Scholar 

  7. 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).

    PubMed  PubMed Central  Google Scholar 

  8. 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).

    Google Scholar 

  9. Perez-Cobas, A. E. et al. Gut microbiota disturbance during antibiotic therapy: a multi-omic approach. Gut 62, 1591–1601 (2013).

    CAS  PubMed  Google Scholar 

  10. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Blaser, M. J. & Falkow, S. What are the consequences of the disappearing human microbiota?. Nat. Rev. Microbiol. 7, 887–894 (2009).

    CAS  PubMed  Google Scholar 

  12. Blaser, M. J. Antibiotic use and its consequences for the normal microbiome. Science 352, 544–545 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 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).

    PubMed  Google Scholar 

  14. Smillie, C. S. et al. Ecology drives a global network of gene exchange connecting the human microbiome. Nature 480, 241–244 (2011).

    CAS  PubMed  Google Scholar 

  15. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Raymond, F. et al. The initial state of the human gut microbiome determines its reshaping by antibiotics. ISME J. 10, 707–720 (2016).

    CAS  PubMed  Google Scholar 

  17. Livanos, A. E. et al. Antibiotic-mediated gut microbiome perturbation accelerates development of type 1 diabetes in mice. Nat. Microbiol. 1, 16140 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Cox, L. M. & Blaser, M. J. Antibiotics in early life and obesity. Nat. Rev. Endocrinol. 11, 182–190 (2015).

    PubMed  Google Scholar 

  19. 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).

    PubMed  PubMed Central  Google Scholar 

  20. 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).

    CAS  PubMed  Google Scholar 

  21. 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).

    PubMed  PubMed Central  Google Scholar 

  22. Raymond, F., Deraspe, M., Boissinot, M., Bergeron, M. G. & Corbeil, J. Partial recovery of microbiomes after antibiotic treatment. Gut Microbes 7, 428–434 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Palleja, A. et al. Recovery of gut microbiota of healthy adults following antibiotic exposure. Nat. Microbiol. 3, 1255–1265 (2018).

    CAS  PubMed  Google Scholar 

  24. Suez, J. et al. Post-antibiotic gut mucosal microbiome reconstitution is impaired by probiotics and improved by autologous FMT. Cell 174, 1406–1423 (2018).

    CAS  PubMed  Google Scholar 

  25. 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).

    Google Scholar 

  26. Bascompte, J. & Stouffer, D. B. The assembly and disassembly of ecological networks. Phil. Trans. R. Soc. B 364, 1781–1787 (2009).

    PubMed  PubMed Central  Google Scholar 

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

    PubMed Central  Google Scholar 

  28. Wexler, H. M. Bacteroides: the good, the bad, and the nitty-gritty. Clin. Microbiol. Rev. 20, 593–621 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Faust, K. & Raes, J. Microbial interactions: from networks to models. Nat. Rev. Microbiol. 10, 538–550 (2012).

    CAS  PubMed  Google Scholar 

  30. Solden, L. M. et al. Interspecies cross-feeding orchestrates carbon degradation in the rumen ecosystem. Nat. Microbiol. 3, 1274–1284 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Wang, J. & Jia, H. Metagenome-wide association studies: fine-mining the microbiome. Nat. Rev. Microbiol. 14, 508–522 (2016).

    CAS  PubMed  Google Scholar 

  33. David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).

    CAS  PubMed  Google Scholar 

  34. Rothschild, D. et al. Environment dominates over host genetics in shaping human gut microbiota. Nature 555, 210–215 (2018).

    CAS  PubMed  Google Scholar 

  35. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 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).

    CAS  PubMed  Google Scholar 

  37. 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).

    CAS  PubMed  Google Scholar 

  38. Korem, T. et al. Growth dynamics of gut microbiota in health and disease inferred from single metagenomic samples. Science 349, 1101–1106 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 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).

    PubMed  PubMed Central  Google Scholar 

  40. 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).

    PubMed  PubMed Central  Google Scholar 

  41. 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).

    PubMed  PubMed Central  Google Scholar 

  42. 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).

    PubMed  PubMed Central  Google Scholar 

  43. Tailford, L. E., Crost, E. H., Kavanaugh, D. & Juge, N. Mucin glycan foraging in the human gut microbiome. Front. Genet. 6, 81 (2015).

    PubMed  PubMed Central  Google Scholar 

  44. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Cornick, S., Tawiah, A. & Chadee, K. Roles and regulation of the mucus barrier in the gut. Tissue Barriers 3, e982426 (2015).

    PubMed  PubMed Central  Google Scholar 

  48. 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).

    CAS  PubMed  Google Scholar 

  49. 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).

  50. Ridaura, V. K. et al. Gut microbiota from twins discordant for obesity modulate metabolism in mice. Science 341, 1241214 (2013).

    PubMed  Google Scholar 

  51. 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).

    PubMed  PubMed Central  Google Scholar 

  52. 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).

    PubMed  PubMed Central  Google Scholar 

  53. 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).

    PubMed  PubMed Central  Google Scholar 

  54. 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).

    CAS  PubMed  Google Scholar 

  55. 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).

    PubMed  PubMed Central  Google Scholar 

  56. Sung, J. et al. Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis. Nat. Commun. 8, 15393 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).

    PubMed  PubMed Central  Google Scholar 

  59. Abubucker, S. et al. Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLoS Comput. Biol. 8, e1002358 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Segata, N. et al. Metagenomic microbial community profiling using unique clade-specific marker genes. Nat. Methods 9, 811–814 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Franzosa, E. A. et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods 15, 962–968 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 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).

    CAS  PubMed  Google Scholar 

  63. Langille, M. G. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31, 814–821 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Wu, G. D. et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105–108 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).

    PubMed  PubMed Central  Google Scholar 

  66. Yin, Y. et al. dbCAN: a web resource for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 40, W445–W451 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 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).

    PubMed  PubMed Central  Google Scholar 

  68. Wood, D. E. & Salzberg, S. L. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 15, R46 (2014).

    PubMed  PubMed Central  Google Scholar 

  69. Cantarel, B. L., Lombard, V. & Henrissat, B. Complex carbohydrate utilization by the healthy human microbiome. PLoS ONE 7, e28742 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Hipp, J., Güntzer, U. & Nakhaeizadeh, G. Algorithms for association rule mining–a general survey and comparison. ACM SIGKDD Explor. Newsl. 2, 58–64 (2000).

    Google Scholar 

  71. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Magnusdottir, S. et al. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat. Biotechnol. 35, 81–89 (2017).

    CAS  PubMed  Google Scholar 

  73. 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).

  74. Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).

    CAS  PubMed  Google Scholar 

  75. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 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).

  77. Xu, J. et al. A genomic view of the human–Bacteroides thetaiotaomicron symbiosis. Science 299, 2074–2076 (2003).

    CAS  PubMed  Google Scholar 

  78. Thomas, F., Hehemann, J. H., Rebuffet, E., Czjzek, M. & Michel, G. Environmental and gut bacteroidetes: the food connection. Front. Microbiol. 2, 93 (2011).

    PubMed  PubMed Central  Google Scholar 

  79. 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).

  80. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. 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).

    PubMed  PubMed Central  Google Scholar 

  82. 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).

    PubMed  Google Scholar 

  83. 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).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

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.

Author information

Author notes

  1. These authors contributed equally: Kern Rei Chng, Tarini Shankar Ghosh, Yi Han Tan, Tannistha Nandi.

Authors and Affiliations

  1. Genome Institute of Singapore, Singapore, Singapore

    Kern Rei Chng, Tarini Shankar Ghosh, Tannistha Nandi, Amanda Hui Qi Ng, Chenhao Li, Aarthi Ravikrishnan, Kar Mun Lim, Swaine L. Chen & Niranjan Nagarajan

  2. APC Microbiome Ireland, University College Cork, Cork, Ireland

    Tarini Shankar Ghosh

  3. Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Yi Han Tan, Ivor Russel Lee, David Lye, Louis Chai, Yunn-Hwen Gan & Niranjan Nagarajan

  4. National Centre for Infectious Disease, Singapore, Singapore

    David Lye & Barnaby Young

  5. Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore

    David Lye & Barnaby Young

  6. Tan Tock Seng Hospital, Singapore, Singapore

    David Lye, Timothy Barkham & Barnaby Young

  7. Department of Biotechnology, Bhupat and Jyoti Mehta School of Biological Sciences, Indian Institute of Technology (IIT) Madras, Chennai, India

    Karthik Raman

  8. Initiative for Biological Systems Engineering, IIT Madras, Chennai, India

    Karthik Raman

  9. Robert Bosch Centre for Data Science and Artificial Intelligence (RBC-DSAI), IIT Madras, Chennai, India

    Karthik Raman

  10. Division of Infectious Diseases, University Medicine Cluster, National University Health System, Singapore, Singapore

    Louis Chai

Authors
  1. Kern Rei Chng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tarini Shankar Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yi Han Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tannistha Nandi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ivor Russel Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Amanda Hui Qi Ng
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chenhao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Aarthi Ravikrishnan
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Kar Mun Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. David Lye
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Timothy Barkham
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Karthik Raman
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Swaine L. Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Louis Chai
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Barnaby Young
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Yunn-Hwen Gan
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Niranjan Nagarajan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

N.N., Y.-H.G., B.Y. and S.L.C. planned and designed the project. B.Y., L.C., T.B. and D.L. contributed the clinical cohorts. Y.H.T. and I.R.L. performed the mouse experiments, with resulting data analysed by T.N. under the guidance of Y.-H.G. and N.N. A.H.Q.N. and K.M.L. conducted wet-lab experiments with guidance from K.R.C. and N.N. T.S.G., K.R.C., A.R., C.L. and T.N. coordinated computational analysis with supervision by K.R. and N.N. T.S.G., T.N., A.R., K.R.C. and N.N. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Barnaby Young, Yunn-Hwen Gan or Niranjan Nagarajan.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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 information

Supplementary Information

Supplementary Figs. 1–11, Table 1 and Note 1.

Reporting Summary

Peer Review Information

Supplementary Data 1

Species abundance profile across samples from the different cohorts.

Supplementary Data 2

Differentially abundant species in recoverers versus non-recoverers.

Supplementary Data 3

Inferred metabolic pathway abundances across samples from the different cohorts.

Supplementary Data 4

Inferred CAZyme abundances across samples from the different cohorts.

Supplementary Data 5

PTR values for different species and the computed community growth rate per sample from the different cohorts.

Supplementary Data 6

Microbial dependency relationships in the gut microbiome predicted via association rule mining on the MEDUSA database.

Supplementary Data 7

Metabolic support index values for interactions between various RAB species and the corresponding top 10% of interactions.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-020-1236-0

Search

Advanced search

Quick links

Nature Briefing Microbiology

Sign up for the Nature Briefing: Microbiology newsletter — what matters in microbiology research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Microbiology