Metabolic network percolation quantifies biosynthetic capabilities across the human oral microbiome

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Abstract

The biosynthetic capabilities of microbes underlie their growth and interactions, playing a prominent role in microbial community structure. For large, diverse microbial communities, prediction of these capabilities is limited by uncertainty about metabolic functions and environmental conditions. To address this challenge, we propose a probabilistic method, inspired by percolation theory, to computationally quantify how robustly a genome-derived metabolic network produces a given set of metabolites under an ensemble of variable environments. We used this method to compile an atlas of predicted biosynthetic capabilities for 97 metabolites across 456 human oral microbes. This atlas captures taxonomically-related trends in biomass composition, and makes it possible to estimate inter-microbial metabolic distances that correlate with microbial co-occurrences. We also found a distinct cluster of fastidious/uncultivated taxa, including several Saccharibacteria (TM7) species, characterized by their abundant metabolic deficiencies. By embracing uncertainty, our approach can be broadly applied to understanding metabolic interactions in complex microbial ecosystems.

Data availability

All scripts and metabolic network data used for generating the manuscript results are available on GitHub (https://github.com/segrelab/biosynthetic_network_robustness) (f82f1e0).All genomes used to derive the metabolic networks are available from the Human Oral Microbiome Database (http://www.homd.org/), except for three strains whose genomes are available on NCBI GenBank, with the following accession numbers: Saccharibacteria (TM7) bacterium HMT-488 strain AC001: NCBI CP040003, Saccharibacteria (TM7) bacterium HMT-955 strain PM004: NCBI CP040008, Pseudopropionibacterium propionicum HMT-439 strain F0700: NCBI CP040007.The data shown in the figures are also available in the form of supplementary tables included in the manuscript submission.

The following previously published data sets were used

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http://www.homd.org/
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https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002687#s5

Article and author information

Author details

David B Bernstein

Department of Biomedical Engineering, Boston University, Boston, United States

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0001-6091-4021
Floyd E Dewhirst

The Forsyth Institute, Cambridge, United States

Competing interests
The authors declare that no competing interests exist.
Daniel Segre

Department of Biomedical Engineering, Boston University, Boston, United States

For correspondence
dsegre@bu.edu

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-4859-1914

Funding

National Institute of Dental and Craniofacial Research (R37DE016937)

Floyd E Dewhirst

National Institute of General Medical Sciences (R01GM121950)

Daniel Segre

Defense Advanced Research Projects Agency (HR0011-15-C-0091)

Daniel Segre

Biological and Environmental Research (DE-SC0012627)

Daniel Segre

National Institute of Dental and Craniofacial Research (R01DE024468)

Floyd E Dewhirst
Daniel Segre

National Institute of General Medical Sciences (T32GM008764)

David B Bernstein

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.