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      Is Open Access

      Suboptimal community growth mediated through metabolite crossfeeding promotes species diversity in the gut microbiota

      1 , 2 , * , 1 , 2

      PLoS Computational Biology

      Public Library of Science

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The gut microbiota represent a highly complex ecosystem comprised of approximately 1000 species that forms a mutualistic relationship with the human host. A critical attribute of the microbiota is high species diversity, which provides system robustness through overlapping and redundant metabolic capabilities. The gradual loss of bacterial diversity has been associated with a broad array of gut pathologies and diseases including malnutrition, obesity, diabetes and inflammatory bowel disease. We formulated an in silico community model of the gut microbiota by combining genome-scale metabolic reconstructions of 28 representative species to explore the relationship between species diversity and community growth. While the individual species offered a broad range of metabolic capabilities, communities optimized for maximal growth on simulated Western and high-fiber diets had low diversities and imbalances in short-chain fatty acid (SCFA) synthesis characterized by acetate overproduction. Community flux variability analysis performed with the 28-species model and a reduced 20-species model suggested that enhanced species diversity and more balanced SCFA production were achievable at suboptimal growth rates. We developed a simple method for constraining species abundances to sample the growth-diversity tradeoff and used the 20-species model to show that tradeoff curves for Western and high-fiber diets resembled Pareto-optimal surfaces. Compared to maximal growth solutions, suboptimal growth solutions were characterized by higher species diversity, more balanced SCFA synthesis and lower exchange rates of crossfed metabolites between more species. We hypothesized that modulation of crossfeeding relationships through host-microbiota interactions could be an important means for maintaining species diversity and suggest that community metabolic modeling approaches that allow multiobjective optimization of growth and diversity are needed for more realistic simulation of complex communities.

          Author summary

          The gut microbiota serve a critical role in maintaining a healthy state in the human host. The gut contains approximately 1,000 bacterial species that provide a wide range of metabolic capabilities including the breakdown of dietary compounds and the synthesis of useful metabolites. The robust function of the gut community is intimately connected to its diversity, both with respect to the number of species and the relative abundance of those species. The gradual loss of diversity is a key element of microbiota dysbiosis, which has been correlated with a wide range of health problems including inflammatory bowel disease. To investigate species diversity in the gut microbiota, we developed an in silico community model by combining genome-scale metabolic reconstructions of 28 representative species from the most abundant genera in the human gut. Our model predicted that maximal community growth produced low species diversity and no synthesis of the health-promoting metabolite butyrate. After reducing the community model to 20 species, we showed that suboptimal community growth allowed much higher species diversity and butyrate synthesis more consistent with in vivo studies. The model predicted that increased diversity could be achieved through modulation of metabolite crossfeeding relationships between species, an experimentally testable hypothesis.

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          Most cited references 55

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          The gut microbiome in health and in disease

          Recent technological advancements and expanded efforts have led to a tremendous growth in the collective knowledge of the human microbiome. This review will highlight some of the important recent findings in this area of research.
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            Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth.

            Annotated genome sequences can be used to reconstruct whole-cell metabolic networks. These metabolic networks can be modelled and analysed (computed) to study complex biological functions. In particular, constraints-based in silico models have been used to calculate optimal growth rates on common carbon substrates, and the results were found to be consistent with experimental data under many but not all conditions. Optimal biological functions are acquired through an evolutionary process. Thus, incorrect predictions of in silico models based on optimal performance criteria may be due to incomplete adaptive evolution under the conditions examined. Escherichia coli K-12 MG1655 grows sub-optimally on glycerol as the sole carbon source. Here we show that when placed under growth selection pressure, the growth rate of E. coli on glycerol reproducibly evolved over 40 days, or about 700 generations, from a sub-optimal value to the optimal growth rate predicted from a whole-cell in silico model. These results open the possibility of using adaptive evolution of entire metabolic networks to realize metabolic states that have been determined a priori based on in silico analysis.
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              Formation of propionate and butyrate by the human colonic microbiota

              The human gut microbiota ferments dietary non-digestible carbohydrates into short-chain fatty acids (SCFA). These microbial products are utilized by the host and propionate and butyrate in particular exert a range of health-promoting functions. Here an overview of the metabolic pathways utilized by gut microbes to produce these two SCFA from dietary carbohydrates and from amino acids resulting from protein breakdown is provided. This overview emphasizes the important role played by cross-feeding of intermediary metabolites (in particular lactate, succinate and 1,2-propanediol) between different gut bacteria. The ecophysiology, including growth requirements and responses to environmental factors, of major propionate and butyrate producing bacteria are discussed in relation to dietary modulation of these metabolites. A detailed understanding of SCFA metabolism by the gut microbiota is necessary to underpin effective strategies to optimize SCFA supply to the host.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: SoftwareRole: VisualizationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                October 2018
                30 October 2018
                : 14
                : 10
                Affiliations
                [1 ] Department of Chemical Engineering, University of Massachusetts, Amherst, Massachusetts, USA
                [2 ] Institute for Applied Life Sciences, University of Massachusetts, Amherst, Massachusetts, USA
                Ecole Polytechnique Fédérale de Lausanne, SWITZERLAND
                Author notes

                The authors have declared that no competing interests exist.

                Article
                PCOMPBIOL-D-18-00660
                10.1371/journal.pcbi.1006558
                6226200
                30376571
                © 2018 Henson, Phalak

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 6, Tables: 1, Pages: 21
                Product
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: Award U01EB019416
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: Award CBET 1511346
                Award Recipient :
                The authors wish to acknowledge National Institutes of Health (Award U01EB019416) and National Science Foundation (Award CBET 1511346) for partial financial support of this research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Funds from these grants were used to cover the costs to publish in open access.
                Categories
                Research Article
                Biology and Life Sciences
                Ecology
                Ecological Metrics
                Species Diversity
                Ecology and Environmental Sciences
                Ecology
                Ecological Metrics
                Species Diversity
                Biology and Life Sciences
                Nutrition
                Diet
                Medicine and Health Sciences
                Nutrition
                Diet
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Metabolites
                Physical Sciences
                Chemistry
                Chemical Compounds
                Propionates
                Biology and Life Sciences
                Physiology
                Physiological Processes
                Secretion
                Medicine and Health Sciences
                Physiology
                Physiological Processes
                Secretion
                Biology and Life Sciences
                Organisms
                Bacteria
                Gut Bacteria
                Medicine and Health Sciences
                Gastroenterology and Hepatology
                Inflammatory Bowel Disease
                Biology and Life Sciences
                Nutrition
                Nutrients
                Medicine and Health Sciences
                Nutrition
                Nutrients
                Custom metadata
                vor-update-to-uncorrected-proof
                2018-11-09
                SteadyCom is freely available at http://www.maranasgroup.com/software.htm. The MATLAB simulation codes used in this study are included as supplementary materials available from the journal website.

                Quantitative & Systems biology

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