
Abstract Genome streamlining, i.e. genome size reduction, is observed in bacteria with very different life traits, including endosymbiotic bacteria and several marine bacteria, raising the question of its evolutionary origin. None of the hypotheses proposed in the literature is firmly established, mainly due to the many confounding factors related to the diverse habitats of species with streamlined genomes. Computational models may help overcome these difficulties and rigorously test hypotheses. In this work, we used Aevol, a platform designed to study the evolution of genome architecture, to test 2 main hypotheses: that an increase in population size (N) or mutation rate (μ) could cause genome reduction. In our experiments, both conditions lead to streamlining but have very different resulting genome structures. Under increased population sizes, genomes lose a significant fraction of noncoding sequences but maintain their coding size, resulting in densely packed genomes (akin to streamlined marine bacteria genomes). By contrast, under an increased mutation rate, genomes lose both coding and noncoding sequences (akin to endosymbiotic bacteria genomes). Hence, both factors lead to an overall reduction in genome size, but the coding density of the genome appears to be determined by N×μ. Thus, a broad range of genome size and density can be achieved by different combinations of N and μ. Our results suggest that genome size and coding density are determined by the interplay between selection for phenotypic adaptation and selection for robustness.
population size, Population Density, genome architecture, genome architecture genome evolution genome streamlining mutation rate modeling population size, mutation rate, Models, Genetic, Bacteria, modeling, genome evolution, Article, [SDV] Life Sciences [q-bio], Evolution, Molecular, Genome Size, Mutation Rate, genome streamlining, Genome, Bacterial
population size, Population Density, genome architecture, genome architecture genome evolution genome streamlining mutation rate modeling population size, mutation rate, Models, Genetic, Bacteria, modeling, genome evolution, Article, [SDV] Life Sciences [q-bio], Evolution, Molecular, Genome Size, Mutation Rate, genome streamlining, Genome, Bacterial
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