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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

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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.

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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.

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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.

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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.

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  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

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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.

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The authors declare no competing interests.

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Supplementary information

Supplementary Information

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

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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.

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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

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