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      Integration of Metabolic and Quorum Sensing Signals Governing the Decision to Cooperate in a Bacterial Social Trait

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          Abstract

          Many unicellular organisms live in multicellular communities that rely on cooperation between cells. However, cooperative traits are vulnerable to exploitation by non-cooperators (cheaters). We expand our understanding of the molecular mechanisms that allow multicellular systems to remain robust in the face of cheating by dissecting the dynamic regulation of cooperative rhamnolipids required for swarming in Pseudomonas aeruginosa. We combine mathematical modeling and experiments to quantitatively characterize the integration of metabolic and population density signals (quorum sensing) governing expression of the rhamnolipid synthesis operon rhlAB. The combined computational/experimental analysis reveals that when nutrients are abundant, rhlAB promoter activity increases gradually in a density dependent way. When growth slows down due to nutrient limitation, rhlAB promoter activity can stop abruptly, decrease gradually or even increase depending on whether the growth-limiting nutrient is the carbon source, nitrogen source or iron. Starvation by specific nutrients drives growth on intracellular nutrient pools as well as the qualitative rhlAB promoter response, which itself is modulated by quorum sensing. Our quantitative analysis suggests a supply-driven activation that integrates metabolic prudence with quorum sensing in a non-digital manner and allows P. aeruginosa cells to invest in cooperation only when the population size is large enough (quorum sensing) and individual cells have enough metabolic resources to do so (metabolic prudence). Thus, the quantitative description of rhlAB regulatory dynamics brings a greater understating to the regulation required to make swarming cooperation stable.

          Author Summary

          Although bacteria are not multicellular organisms, they commonly live in large communities and engage in many cooperative behaviors. Cooperation can allow bacteria to access additional nutrients, but it requires the secretion of products that will be shared by the community. How bacteria make the molecular decision to cooperate within a community is still not completely understood. The bacterium Pseudomonas aeruginosa regulates the secretion of one of these shared products, rhamnolipids, using information about population density and nutrient availability in its environment. Expression of the operon rhlAB is required for the bacteria to produce rhamnolipids. We use a combined computational and experimental approach to investigate how P. aeruginosa continually combines current information of population density and nutrient availability to determine if it should express rhlAB. We find that when conditions are nutrient rich, P. aeruginosa uses population density to modulate the amount rhlAB expression, however when the bacteria are starved for nutrients the starvation condition largely determines how the bacteria will express rhlAB. Because the bacteria continually adjust expression based on the current conditions, the molecular decision to produce rhamnolipids can be adjusted if either population density or nutrient conditions change. Our combined computational and experimental approach sheds new light on the rich regulatory dynamics that govern a cellular decision to cooperate.

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          Quorum sensing: cell-to-cell communication in bacteria.

          Bacteria communicate with one another using chemical signal molecules. As in higher organisms, the information supplied by these molecules is critical for synchronizing the activities of large groups of cells. In bacteria, chemical communication involves producing, releasing, detecting, and responding to small hormone-like molecules termed autoinducers . This process, termed quorum sensing, allows bacteria to monitor the environment for other bacteria and to alter behavior on a population-wide scale in response to changes in the number and/or species present in a community. Most quorum-sensing-controlled processes are unproductive when undertaken by an individual bacterium acting alone but become beneficial when carried out simultaneously by a large number of cells. Thus, quorum sensing confuses the distinction between prokaryotes and eukaryotes because it enables bacteria to act as multicellular organisms. This review focuses on the architectures of bacterial chemical communication networks; how chemical information is integrated, processed, and transduced to control gene expression; how intra- and interspecies cell-cell communication is accomplished; and the intriguing possibility of prokaryote-eukaryote cross-communication.
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            Quorum sensing in bacteria: the LuxR-LuxI family of cell density-responsive transcriptional regulators.

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              Cooperation and conflict in quorum-sensing bacterial populations.

              It has been suggested that bacterial cells communicate by releasing and sensing small diffusible signal molecules in a process commonly known as quorum sensing (QS). It is generally assumed that QS is used to coordinate cooperative behaviours at the population level. However, evolutionary theory predicts that individuals who communicate and cooperate can be exploited. Here we examine the social evolution of QS experimentally in the opportunistic pathogen Pseudomonas aeruginosa, and show that although QS can provide a benefit at the group level, exploitative individuals can avoid the cost of producing the QS signal or of performing the cooperative behaviour that is coordinated by QS, and can therefore spread. We also show that a solution to the problem of exploitation is kin selection, if interacting bacterial cells tend to be close relatives. These results show that the problem of exploitation, which has been the focus of considerable attention in animal communication, also arises in bacteria.
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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
                23 June 2015
                June 2015
                : 11
                : 6
                : e1004279
                Affiliations
                [1 ]Program in Immunology and Microbial Pathogenesis, Weill Cornell Graduate School of Medical Sciences, New York, New York, United States of America
                [2 ]Program in Computational Biology, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
                [3 ]Tri-Institutional Training Program in Computational Biology and Medicine, New York, New York, United States of America
                University of Wisconsin-Madison, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: KEB HM JBX. Performed the experiments: KEB HM MD DvD. Analyzed the data: KEB HM MD. Contributed reagents/materials/analysis tools: KEB HM JBX. Wrote the paper: KEB HM JBX.

                Article
                PCOMPBIOL-D-15-00027
                10.1371/journal.pcbi.1004279
                4477906
                26102206
                a281ad9d-623a-444d-af92-cb74a17ae89b
                Copyright @ 2015

                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

                History
                : 12 January 2015
                : 11 April 2015
                Page count
                Figures: 7, Tables: 0, Pages: 26
                Funding
                This work was supported by the Lucille Castori Center for Microbes, Inflammation, and Cancer, and by the Office of the Director, National Institutes of Health of the National Institutes of Health under Award Number DP2OD008440 to JBX, and Award Number T32 AI007621 to KEB and a National Science Foundation Graduate Research Fellowship GRFP DGE-1257284 2012 to HM. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

                Quantitative & Systems biology
                Quantitative & Systems biology

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