A library of human gut bacterial isolates paired with longitudinal multiomics data enables mechanistic microbiome research

M Poyet^#^{1

2

3}, M Groussin^#^{1

2

3}, S M Gibbons^#^{1

2

3

4}, J Avila-Pacheco³, X Jiang^{1

2

3}, S M Kearney^{1

2

3}, A R Perrotta^{1

2}, B Berdy^{1

2

3}, S Zhao^{1

2}, T D Lieberman^{1

2

3}, P K Swanson^{1

5}, M Smith^{5

6}, S Roesemann³, J E Alexander³, S A Rich³, J Livny³, H Vlamakis³, C Clish³, K Bullock³, A Deik³, J Scott³, K A Pierce³, R J Xavier^{7

8

9}, E J Alm^{10

11

12

13

14}

Affiliations

¹ Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
² Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA.
³ The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁴ Institute for Systems Biology, Seattle, WA, USA.
⁵ Finch Therapeutics, Somerville, MA, USA.
⁶ OpenBiome, Somerville, MA, USA.
⁷ Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA. xavier@molbio.mgh.harvard.edu.
⁸ The Broad Institute of MIT and Harvard, Cambridge, MA, USA. xavier@molbio.mgh.harvard.edu.
⁹ Gastrointestinal Unit and Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA. xavier@molbio.mgh.harvard.edu.
¹⁰ Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. ejalm@mit.edu.
¹¹ Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA. ejalm@mit.edu.
¹² The Broad Institute of MIT and Harvard, Cambridge, MA, USA. ejalm@mit.edu.
¹³ Finch Therapeutics, Somerville, MA, USA. ejalm@mit.edu.
¹⁴ OpenBiome, Somerville, MA, USA. ejalm@mit.edu.

^# Contributed equally.

PMID: 31477907
DOI: 10.1038/s41591-019-0559-3

A library of human gut bacterial isolates paired with longitudinal multiomics data enables mechanistic microbiome research

M Poyet et al. Nat Med. 2019 Sep.

. 2019 Sep;25(9):1442-1452.

doi: 10.1038/s41591-019-0559-3. Epub 2019 Sep 2.

Authors

Affiliations

¹ Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
² Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA.
³ The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁴ Institute for Systems Biology, Seattle, WA, USA.
⁵ Finch Therapeutics, Somerville, MA, USA.
⁶ OpenBiome, Somerville, MA, USA.
⁷ Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA. xavier@molbio.mgh.harvard.edu.
⁸ The Broad Institute of MIT and Harvard, Cambridge, MA, USA. xavier@molbio.mgh.harvard.edu.
⁹ Gastrointestinal Unit and Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA. xavier@molbio.mgh.harvard.edu.
¹⁰ Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. ejalm@mit.edu.
¹¹ Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA. ejalm@mit.edu.
¹² The Broad Institute of MIT and Harvard, Cambridge, MA, USA. ejalm@mit.edu.
¹³ Finch Therapeutics, Somerville, MA, USA. ejalm@mit.edu.
¹⁴ OpenBiome, Somerville, MA, USA. ejalm@mit.edu.

^# Contributed equally.

PMID: 31477907
DOI: 10.1038/s41591-019-0559-3

Abstract

Our understanding of how the gut microbiome interacts with its human host has been restrained by limited access to longitudinal datasets to examine stability and dynamics, and by having only a few isolates to test mechanistic hypotheses. Here, we present the Broad Institute-OpenBiome Microbiome Library (BIO-ML), a comprehensive collection of 7,758 gut bacterial isolates paired with 3,632 genome sequences and longitudinal multi-omics data. We show that microbial species maintain stable population sizes within and across humans and that commonly used 'omics' survey methods are more reliable when using averages over multiple days of sampling. Variation of gut metabolites within people over time is associated with amino acid levels, and differences across people are associated with differences in bile acids. Finally, we show that genomic diversification can be used to infer eco-evolutionary dynamics and in vivo selection pressures for strains within individuals. The BIO-ML is a unique resource designed to enable hypothesis-driven microbiome research.

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References

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A library of human gut bacterial isolates paired with longitudinal multiomics data enables mechanistic microbiome research

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A library of human gut bacterial isolates paired with longitudinal multiomics data enables mechanistic microbiome research

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Abstract

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