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      The Contribution of High-Order Metabolic Interactions to the Global Activity of a Four-Species Microbial Community

      1 , * , 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 activity of a biological community is the outcome of complex processes involving interactions between community members. It is often unclear how to accurately incorporate these interactions into predictive models. Previous work has shown a range of positive and negative metabolic pairwise interactions between species. Here we examine the ability of a modified general Lotka-Volterra model with cell-cell interaction coefficients to predict the overall metabolic rate of a well-mixed microbial community comprised of four heterotrophic natural isolates, experimentally quantifying the strengths of two, three, and four-species interactions. Within this community, interactions between any pair of microbial species were positive, while higher-order interactions, between 3 or more microbial species, slightly modulated community metabolism. For this simple community, the metabolic rate of can be well predicted only with taking into account pairwise interactions. Simulations using the experimentally determined interaction parameters revealed that spatial heterogeneity in the distribution of cells increased the importance of multispecies interactions in dictating function at both the local and global scales.

          Author Summary

          Many wild microbial ecosystems contain hundreds to thousands of species, suggesting that interactions between species likely play an important role in regulating the behavior of such complex cellular networks. Predicting how these interactions impact the overall activity of microbial communities remains a challenge. Here we quantify the contribution of interactions between more than two species to the overall metabolic rate of a mixture of four freshwater bacteria. We systematically measure interactions between these species and use theoretical models to examine the influence cell-cell interactions on spatially non-uniform microbial populations. Our results demonstrate that although interactions between species are key regulators of system behavior, only considering interactions between pairs of species is sufficient to predict ecosystem activity. Simulations demonstrate that activity at both the single-cell and population level would be strongly influenced by how microbes are distributed in space. These findings improve our understanding of how best to examine groups of microbes that coexist in environments such as soil, water, and the human body.

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          Most cited references 36

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          Weak pairwise correlations imply strongly correlated network states in a neural population

           Gasper Tkacik (2006)
          Biological networks have so many possible states that exhaustive sampling is impossible. Successful analysis thus depends on simplifying hypotheses, but experiments on many systems hint that complicated, higher order interactions among large groups of elements play an important role. In the vertebrate retina, we show that weak correlations between pairs of neurons coexist with strongly collective behavior in the responses of ten or more neurons. Surprisingly, we find that this collective behavior is described quantitatively by models that capture the observed pairwise correlations but assume no higher order interactions. These maximum entropy models are equivalent to Ising models, and predict that larger networks are completely dominated by correlation effects. This suggests that the neural code has associative or error-correcting properties, and we provide preliminary evidence for such behavior. As a first test for the generality of these ideas, we show that similar results are obtained from networks of cultured cortical neurons.
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            Investigation of the Alamar Blue (resazurin) fluorescent dye for the assessment of mammalian cell cytotoxicity.

             I Wilson,  T Orton,  F Pognan (2000)
            We show here the identity of Alamar Blue as resazurin. The 'resazurin reduction test' has been used for about 50 years to monitor bacterial and yeast contamination of milk, and also for assessing semen quality. Resazurin (blue and nonfluorescent) is reduced to resorufin (pink and highly fluorescent) which is further reduced to hydroresorufin (uncoloured and nonfluorescent). It is still not known how this reduction occurs, intracellularly via enzyme activity or in the medium as a chemical reaction, although the reduced fluorescent form of Alamar Blue was found in the cytoplasm and of living cells nucleus of dead cells. Recently, the dye has gained popularity as a very simple and versatile way of measuring cell proliferation and cytotoxicity. This dye presents numerous advantages over other cytotoxicity or proliferation tests but we observed several drawbacks to the routine use of Alamar Blue. Tests with several toxicants in different cell lines and rat primary hepatocytes have shown accumulation of the fluorescent product of Alamar Blue in the medium which could lead to an overestimation of cell population. Also, the extensive reduction of Alamar Blue by metabolically active cells led to a final nonfluorescent product, and hence an underestimation of cellular activity.
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              Bacterial manganese reduction and growth with manganese oxide as the sole electron acceptor.

              Microbes that couple growth to the reduction of manganese could play an important role in the biogeochemistry of certain anaerobic environments. Such a bacterium, Alteromonas putrefaciens MR-1, couples its growth to the reduction of manganese oxides only under anaerobic conditions. The characteristics of this reduction are consistent with a biological, and not an indirect chemical, reduction of manganese, which suggest that this bacterium uses manganic oxide as a terminal electron acceptor. It can also utilize a large number of other compounds as terminal electron acceptors; this versatility could provide a distinct advantage in environments where electron-acceptor concentrations may vary.
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                Author and article information

                Contributors
                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
                13 September 2016
                September 2016
                : 12
                : 9
                Affiliations
                [1 ]Department of Physics, University of Southern California, Los Angeles, California, United States of America
                [2 ]Department of Biological Sciences, University of Southern California, Los Angeles, California, United States of America
                University of Wisconsin-Madison, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                • Conceptualization: XG JQB.

                • Data curation: XG JQB.

                • Formal analysis: XG JQB.

                • Funding acquisition: JQB.

                • Investigation: XG.

                • Methodology: XG JQB.

                • Project administration: JQB.

                • Resources: XG JQB.

                • Software: XG JQB.

                • Supervision: JQB.

                • Validation: XG JQB.

                • Visualization: XG JQB.

                • Writing - original draft: XG JQB.

                • Writing - review & editing: XG JQB.

                Article
                PCOMPBIOL-D-16-00176
                10.1371/journal.pcbi.1005079
                5021341
                27623159
                © 2016 Guo, Boedicker

                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: 4, Tables: 0, Pages: 13
                Product
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000006, Office of Naval Research;
                Award ID: N00014-15-1-2573
                Award Recipient :
                This work was supported by Office of Naval Research award number N00014-15-1-2573, http://www.onr.navy.mil/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Species Interactions
                Biology and Life Sciences
                Cell Biology
                Cell Physiology
                Cell Metabolism
                Biology and Life Sciences
                Ecology
                Ecosystems
                Microbial Ecosystems
                Ecology and Environmental Sciences
                Ecology
                Ecosystems
                Microbial Ecosystems
                Biology and Life Sciences
                Organisms
                Bacteria
                Aeromonas
                Aeromonas Hydrophila
                Biology and Life Sciences
                Microbiology
                Medical Microbiology
                Microbial Pathogens
                Bacterial Pathogens
                Aeromonas Hydrophila
                Medicine and Health Sciences
                Pathology and Laboratory Medicine
                Pathogens
                Microbial Pathogens
                Bacterial Pathogens
                Aeromonas Hydrophila
                Physical Sciences
                Mathematics
                Statistics (Mathematics)
                Confidence Intervals
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Forecasting
                Physical Sciences
                Mathematics
                Statistics (Mathematics)
                Statistical Methods
                Forecasting
                Biology and Life Sciences
                Ecology
                Ecosystems
                Ecology and Environmental Sciences
                Ecology
                Ecosystems
                Computer and Information Sciences
                Network Analysis
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

                Quantitative & Systems biology

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