Metabolic network percolation quantifies biosynthetic capabilities across the human oral microbiome
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.
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E. coli auxotrophs growth dataEMBO Press, DOI 10.1038/msb.2010.66.
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Microbial co-occurrence dataPLOS Computional Biology, doi.org/10.1371/journal.pcbi.1002687.
Article and author information
Author details
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
© 2019, Bernstein et al.
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.
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