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      The Evolution of Quorum Sensing as a Mechanism to Infer Kinship

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          Abstract

          Bacteria regulate many phenotypes via quorum sensing systems. Quorum sensing is typically thought to evolve because the regulated cooperative phenotypes are only beneficial at certain cell densities. However, quorum sensing systems are also threatened by non-cooperative “cheaters” that may exploit quorum-sensing regulated cooperation, which begs the question of how quorum sensing systems are maintained in nature. Here we study the evolution of quorum sensing using an individual-based model that captures the natural ecology and population structuring of microbial communities. We first recapitulate the two existing observations on quorum sensing evolution: density-dependent benefits favor quorum sensing but competition and cheating will destabilize it. We then model quorum sensing in a dense community like a biofilm, which reveals a novel benefit to quorum sensing that is intrinsically evolutionarily stable. In these communities, competing microbial genotypes gradually segregate over time leading to positive correlation between density and genetic similarity between neighboring cells (relatedness). This enables quorum sensing to track genetic relatedness and ensures that costly cooperative traits are only activated once a cell is safely surrounded by clonemates. We hypothesize that under similar natural conditions, the benefits of quorum sensing will not result from an assessment of density but from the ability to infer kinship.

          Author Summary

          Bacteria secrete signal molecules into their environment and use these to regulate many of their key phenotypes. This is called quorum sensing and it is thought to evolve because it allows cells to sense their density. Here we propose a new function for quorum sensing that sheds light on its evolution. We develop a realistic model of a bacterial community and show that quorum sensing can function as a way to outcompete neighbors in patches occupied by many different genotypes. Growing aggressively at first makes quorm sensing genotypes a match for competitors. This strategy allows them to surround themselves with clonemates before reallocating resources to costly traits like cooperative secretions. This works because quorum sensing can act as a timer, which cells can use to infer how related they are to their neighbours and tune their investment into costly and exploitable cooperation based on the threat of competition from unrelated genotypes.

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          Most cited references47

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          Pyrosequencing enumerates and contrasts soil microbial diversity.

          Estimates of the number of species of bacteria per gram of soil vary between 2000 and 8.3 million (Gans et al., 2005; Schloss and Handelsman, 2006). The highest estimate suggests that the number may be so large as to be impractical to test by amplification and sequencing of the highly conserved 16S rRNA gene from soil DNA (Gans et al., 2005). Here we present the use of high throughput DNA pyrosequencing and statistical inference to assess bacterial diversity in four soils across a large transect of the western hemisphere. The number of bacterial 16S rRNA sequences obtained from each site varied from 26,140 to 53,533. The most abundant bacterial groups in all four soils were the Bacteroidetes, Betaproteobacteria and Alphaproteobacteria. Using three estimators of diversity, the maximum number of unique sequences (operational taxonomic units roughly corresponding to the species level) never exceeded 52,000 in these soils at the lowest level of dissimilarity. Furthermore, the bacterial diversity of the forest soil was phylum rich compared to the agricultural soils, which are species rich but phylum poor. The forest site also showed far less diversity of the Archaea with only 0.009% of all sequences from that site being from this group as opposed to 4%-12% of the sequences from the three agricultural sites. This work is the most comprehensive examination to date of bacterial diversity in soil and suggests that agricultural management of soil may significantly influence the diversity of bacteria and archaea.
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            Social evolution theory for microorganisms.

            Microorganisms communicate and cooperate to perform a wide range of multicellular behaviours, such as dispersal, nutrient acquisition, biofilm formation and quorum sensing. Microbiologists are rapidly gaining a greater understanding of the molecular mechanisms involved in these behaviours, and the underlying genetic regulation. Such behaviours are also interesting from the perspective of social evolution - why do microorganisms engage in these behaviours given that cooperative individuals can be exploited by selfish cheaters, who gain the benefit of cooperation without paying their share of the cost? There is great potential for interdisciplinary research in this fledgling field of sociomicrobiology, but a limiting factor is the lack of effective communication of social evolution theory to microbiologists. Here, we provide a conceptual overview of the different mechanisms through which cooperative behaviours can be stabilized, emphasizing the aspects most relevant to microorganisms, the novel problems that microorganisms pose and the new insights that can be gained from applying evolutionary theory to microorganisms.
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              Quorum sensing in bacteria: the LuxR-LuxI family of cell density-responsive transcriptional regulators.

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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
                April 2016
                27 April 2016
                : 12
                : 4
                : e1004848
                Affiliations
                [1 ]Computational Biology Program, Sloan-Kettering Institute, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
                [2 ]Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
                [3 ]Department of Zoology, University of Oxford, Oxford, United Kingdom
                [4 ]Department of Fundamental Microbiology, University of Lausanne, Switzerland
                University of Cambridge, UNITED KINGDOM
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: JS APS KRF SM. Performed the experiments: JS. Analyzed the data: JS APS KRF SM. Contributed reagents/materials/analysis tools: JS APS SM. Wrote the paper: JS APS KRF SM.

                Article
                PCOMPBIOL-D-15-00112
                10.1371/journal.pcbi.1004848
                4847791
                27120081
                a7088f72-b822-4324-be8c-51e11403652a
                © 2016 Schluter et al

                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
                : 26 January 2015
                : 4 March 2016
                Page count
                Figures: 5, Tables: 0, Pages: 18
                Funding
                JS and KRF are supported by European Research Council Grant 242670 ( http://erc.europa.eu/) and SM by a Marie Curie Intra-European Fellowship ( http://www.fp7peoplenetwork.eu/marie-curie-actions/) and by an Ambizione fellowship from the Swiss National Science Foundation( http://www.snf.ch/en/funding/careers/ambizione/). JS was also supported by the EPSRC ( http://www.epsrc.ac.uk/) through the DTC Systems Biology at the University of Oxford ( http://www.sysbiodtc.ox.ac.uk/). 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
                Microbiology
                Microbial Physiology
                Quorum Sensing
                Biology and Life Sciences
                Physiology
                Physiological Processes
                Secretion
                Medicine and Health Sciences
                Physiology
                Physiological Processes
                Secretion
                Biology and Life Sciences
                Microbiology
                Microbial Evolution
                Biology and Life Sciences
                Evolutionary Biology
                Organismal Evolution
                Microbial Evolution
                Biology and Life Sciences
                Evolutionary Biology
                Evolutionary Genetics
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Cloning
                Research and Analysis Methods
                Molecular Biology Techniques
                Cloning
                Biology and Life Sciences
                Microbiology
                Biofilms
                Biology and Life Sciences
                Cell Biology
                Cell Processes
                Cell Cycle and Cell Division
                Research and Analysis Methods
                Simulation and Modeling
                Custom metadata
                The computational framework is available on GitHub ( https://github.com/jsevo/ibmQS.git).

                Quantitative & Systems biology
                Quantitative & Systems biology

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