Metabolic network percolation quantifies biosynthetic capabilities across the human oral microbiome
- Boston University, United States
- The Forsyth Institute, United States
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.
-
E. coli auxotrophs growth dataEMBO Press, DOI 10.1038/msb.2010.66.
-
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.
Metrics
-
- 4,157
- views
-
- 499
- downloads
-
- 24
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Metabolic network percolation quantifies biosynthetic capabilities across the human oral microbiome
eLife 8:e39733.
https://doi.org/10.7554/eLife.39733
Further reading
-
- Computational and Systems Biology
- Medicine
Sudden death after myocardial infarction (MI) is associated with electrophysiological heterogeneities and ionic current remodelling. Low ejection fraction (EF) is used in risk stratification, but its mechanistic links with pro-arrhythmic heterogeneities are unknown. We aim to provide mechanistic explanations of clinical phenotypes in acute and chronic MI, from ionic current remodelling to ECG and EF, using human electromechanical modelling and simulation to augment experimental and clinical investigations. A human ventricular electromechanical modelling and simulation framework is constructed and validated with rich experimental and clinical datasets, incorporating varying degrees of ionic current remodelling as reported in literature. In acute MI, T-wave inversion and Brugada phenocopy were explained by conduction abnormality and local action potential prolongation in the border zone. In chronic MI, upright tall T-waves highlight large repolarisation dispersion between the border and remote zones, which promoted ectopic propagation at fast pacing. Post-MI EF at resting heart rate was not sensitive to the extent of repolarisation heterogeneity and the risk of repolarisation abnormalities at fast pacing. T-wave and QT abnormalities are better indicators of repolarisation heterogeneities than EF in post-MI.
-
- Computational and Systems Biology
Measuring mitochondrial respiration in frozen tissue samples provides the first comprehensive atlas of how aging affects mitochondrial function in mice.
