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      Pleiotropy, cooperation, and the social evolution of genetic architecture

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      PLoS Biology
      Public Library of Science

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          Abstract

          Pleiotropy has been suggested as a novel mechanism for stabilising cooperation in bacteria and other microbes. The hypothesis is that linking cooperation with a trait that provides a personal (private) benefit can outweigh the cost of cooperation in situations when cooperation would not be favoured by mechanisms such as kin selection. We analysed the theoretical plausibility of this hypothesis, with analytical models and individual-based simulations. We found that (1) pleiotropy does not stabilise cooperation, unless the cooperative and private traits are linked via a genetic architecture that cannot evolve (mutational constraint); (2) if the genetic architecture is constrained in this way, then pleiotropy favours any type of trait and not especially cooperation; (3) if the genetic architecture can evolve, then pleiotropy does not favour cooperation; and (4) there are several alternative explanations for why traits may be linked, and causality can even be predicted in the opposite direction, with cooperation favouring pleiotropy. Our results suggest that pleiotropy could only explain cooperation under restrictive conditions and instead show how social evolution can shape the genetic architecture.

          Author summary

          Recent research into microbial communities has revealed that the cooperative secretion of molecules—which are produced by individual cells and benefit neighbouring cells—is linked to the production of privately beneficial intracellular enzymes. This pleiotropic link between commonly and privately beneficial traits has been suggested as a novel way for maintaining cooperation in conditions under which it would otherwise be outcompeted by cheating cells. The reason is that cheats, which do not cooperate, would also lose the benefit of producing the private trait and thus suffer a fitness disadvantage. We test the plausibility of this hypothesis with analytical models and individual-based simulations. We find that cooperation can only be stabilised if one makes restrictive assumptions about the genetic architecture, such that the pleiotropic link with a private trait cannot be broken through further evolution. If the genetic architecture can evolve, then natural selection can favour mutants that do not cooperate but that still perform the private trait, leading to the breakdown of cooperation. We discuss a number of alternative explanations for the observation of linkage between cooperative and private traits and show that causality may even arise in the opposite direction to that previously predicted—when cooperation is favoured, this may select for pleiotropy. Our results suggest a novel explanation for why cooperative and private traits may be linked and show how social evolution can shape the genetic architecture.

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          Most cited references64

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          Extreme genome reduction in symbiotic bacteria.

          Since 2006, numerous cases of bacterial symbionts with extraordinarily small genomes have been reported. These organisms represent independent lineages from diverse bacterial groups. They have diminutive gene sets that rival some mitochondria and chloroplasts in terms of gene numbers and lack genes that are considered to be essential in other bacteria. These symbionts have numerous features in common, such as extraordinarily fast protein evolution and a high abundance of chaperones. Together, these features point to highly degenerate genomes that retain only the most essential functions, often including a considerable fraction of genes that serve the hosts. These discoveries have implications for the concept of minimal genomes, the origins of cellular organelles, and studies of symbiosis and host-associated microbiota.
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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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              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: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Biol
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, CA USA )
                1544-9173
                1545-7885
                25 October 2018
                October 2018
                25 October 2018
                : 16
                : 10
                : e2006671
                Affiliations
                [001]Department of Zoology, University of Oxford, Oxford, United Kingdom
                Massachusetts Institute of Technology, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                [¤]

                Current address: Department of Social Psychology and Social Neuroscience, Institute of Psychology, University of Bern, Bern, Switzerland

                Author information
                http://orcid.org/0000-0002-2198-1560
                Article
                pbio.2006671
                10.1371/journal.pbio.2006671
                6219813
                30359363
                1d773f2e-3e6b-4fe3-9f16-d23b45df77b0
                © 2018 dos Santos 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
                : 15 May 2018
                : 11 October 2018
                Page count
                Figures: 5, Tables: 0, Pages: 25
                Funding
                Swiss National Science Foundation http://www.snf.ch/en (grant number P2LAP3_158669). Received by MDS. The funder 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
                Population Biology
                Population Metrics
                Fecundity
                Biology and Life Sciences
                Evolutionary Biology
                Evolutionary Genetics
                Biology and Life Sciences
                Evolutionary Biology
                Evolutionary Processes
                Kin Selection
                Biology and Life Sciences
                Genetics
                Heredity
                Genetic Linkage
                Biology and Life Sciences
                Genetics
                Gene Types
                Regulator Genes
                Biology and Life Sciences
                Developmental Biology
                Life Cycles
                Biology and Life Sciences
                Genetics
                Gene Expression
                Gene Regulation
                Biology and Life Sciences
                Microbiology
                Microbial Evolution
                Biology and Life Sciences
                Evolutionary Biology
                Organismal Evolution
                Microbial Evolution
                Custom metadata
                vor-update-to-uncorrected-proof
                2018-11-06
                The simulated data used in this study (shown in Figs 2 and 4 and Supplementary S2S7 and S9S17 Figs) are available from the OSF data repository (DOI: 10.17605/OSF.IO/6PQS3). The Matlab code used to generate the data used in this study is available from the OSF data repository (DOI: 10.17605/OSF.IO/6PQS3).

                Life sciences
                Life sciences

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