Environments that Induce Synthetic Microbial Ecosystems

By Niels Klitgord, Daniel Segrè. PLoS Comput. Biol., 2010

346 citations recorded by the source corpus.

Interactions between microbial species are sometimes mediated by the exchange of small molecules, secreted by one species and metabolized by another. Both one-way (commensal) and two-way (mutualistic) interactions may contribute to complex networks of interdependencies. Understanding these interactions constitutes an open challenge in microbial ecology, with applications ranging from the human microbiome to environmental sustainability. In parallel to natural communities, it is possible to explore interactions in artificial microbial ecosystems, e.g. pairs of genetically engineered mutualistic strains. Here we computationally generate artificial microbial ecosystems without re-engineering the microbes themselves, but rather by predicting their growth on appropriately designed media. We use genome-scale stoichiometric models of metabolism to identify media that can sustain growth for a pair of species, but fail to do so for one or both individual species, thereby inducing putative symbiotic interactions. We first tested our approach on two previously studied mutualistic pairs, and on a pair of highly curated model organisms, showing that our algorithms successfully recapitulate known interactions, robustly predict new ones, and provide novel insight on exchanged molecules. We then applied our method to all possible pairs of seven microbial species, and found that it is always possible to identify putative media that induce commensalism or mutualism. Our analysis also suggests that symbiotic interactions may arise more readily through environmental fluctuations than genetic modifications. We envision that our approach will help generate microbe-microbe interaction maps useful for understanding microbial consortia dynamics and evolution, and for exploring the full potential of natural metabolic pathways for metabolic engineering applications.

Subfields: Biomedical Engineering, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/de9cc45480fb379b250184ba718e9507fed8ca5c

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Citations in the corpus

Fast automated reconstruction of genome-scale metabolic models for microbial species and communities (2018, background): “An emerging application of genome-scale models is the study of cross-feeding and nutrient competition in microbial communities (10–15).”
Mathematical Modeling of Microbial Community Dynamics: A Methodological Review (2014, methodology): “Klitgord and Segre [66] extended the method by Stoylar et al. (by artificially introducing additional compartment that represents environment shared by different species) to explore interspecies metabolite exchanges for various pairwise combinations of seven bacterial species.”
OptCom: A Multi-Level Optimization Framework for the Metabolic Modeling and Analysis of Microbial Communities (2012, methodology): “Unlike earlier FBA-based modeling approaches that rely on a single objective function to describe the entire community [19,30] or separate FBA problems for each microorganism [25,27,31,33], OptCom integrates both species- and communitylevel fitness criteria into a multi-level/objective framework.”
Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli (2013, background): “, microbial species) and environmental conditions (Jain and Srivastava, 2009; Klitgord and Segrè, 2010; Wintermute and Silver, 2010).”
Thousands of microbial genomes shed light on interconnected biogeochemical processes in an aquifer system (2016, background): “Changes in environmental conditions appear to drive selection of pore ﬂuid-associated species from the particle-associated ‘microbial seed bank’ 30,31 .”
Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules (2013, background): “Notably, constraints-based approaches can potentially overcome some of these limitations by explicitly modeling the environment and by incorporating constraints on fluxes and nutrient uptake (38).”
Using Genome-Scale Models to Predict Biological Capabilities (2015, neutral): “Also, design has expanded beyond single populations to microbial communities/ecosystems (Klitgord and Segrè, 2010).”
Identifying Keystone Species in the Human Gut Microbiome from Metagenomic Timeseries Using Sparse Linear Regression (2014, background): “For example, a small molecule that is secreted by one species can be metabolized by another [11].”
Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods (2012, methodology): “COBRA methods are now characterizing the role for metabolism in microbial community structure 118–120 , and these studies are providing insight into mutual-ism 121 , competition 122 , parasitism 81,123 and community evolution 117,124 .”