Evolution of mutation rates in digital genomes: the roles of genetic drift, mutational supply, and genome size
Mutation is the ultimate mechanism that produces genetic novelty, and thus a central ingredient of evolution. Mutation rates are therefore thought to be tuned by natural selection, for example to optimize a delicate balance between the generation of adaptive diversity and the accumulation of deleterious mutations. As this selection occurs over very long time scales, models and simulations have been powerful tools to understand how mutation rate evolves and which factors influence it. Most simulation methods are nevertheless limited by the over-simplicity of the genotype-to-phenotype map they feature, especially regarding the encoding of mutation rate. We modified Aevol, an evolutionary simulator inspired by bacterial genomics with a realistic genome structure and a complex genotype-to-phenotype layer, to allow organisms to evolve genes coding for higher replication fidelity. This setup permits several degrees of realism absent in other models: mutation-rate modifier genes themselves experience a realistic distribution of effects of mutations and diminishing-returns epistasis, similarly to fitness modifiers. Moreover, a lower mutation rate comes with the trade-off of a larger genome to encode the genes improving replication fidelity. We use this setup to test hypotheses regarding the evolution of prokaryotic mutation rate, and its link with genome size and genetic drift. We found that evolution systematically increases replication fidelity, even when this results in lower fitness. We highlight two factors which limit the mutation rate decrease: genetic drift and the supply of gain-of-fidelity mutations. Significance StatementMutation rate is a central parameter governing the evolution of living systems, but it is also itself the product of evolution, as it is determined by enzymatic processes which are subject to hereditary variations and natural selection. Several hypotheses exist to explain how the mutation rate evolves, and which factors govern mutation rate variation between and within species. We propose a "digital genomics" simulation model which permits testing and refining some of these hypotheses, in a setup capturing key constraints such as a realistic supply of mutations and selection pressure for genome space. We found that selection almost always decreases the mutation rate. We highlight the role of two factors in determining the amount of mutation rate reduction, genetic drift and the supply of gain-of-fidelity mutations, as well as a strong relationship with genome size.