bioRxivEvolutionary BiologyPreprint
Explaining the rapid evolution of mammalian meiotic recombination proteins
Julien JosephiD, Paola MontoyaiD, Frédéric BaudatiD, Aurora Ruiz‐HerreraiD, Julien ClaveliD
Abstract
Meiotic recombination -- the exchange of genetic material between parental chromosomes during gamete production -- is critical for fertility, genome stability, and evolutionary adaptation. In eukaryotes, meiotic recombination is carried out by a deeply conserved molecular machinery. Despite this conservation, the sequences of proteins involved in meiotic recombination evolve at a remarkably high rate in mammalian species. Several biological processes have been proposed to explain this rapid evolution, but none have been quantitatively tested. In this study, we analyzed the variation in evolutionary rates of 100 recombination proteins across approximately 400 placental mammals. Our results show that this rapid evolution is primarily driven by lower levels of purifying selection compared to the rest of the proteome. We show that, although selective pressures exerted on recombination proteins are generally shared across mammals, a few recombination proteins exhibit strong variation in selective pressures. Contrary to previous hypotheses, we demonstrate that chromosome number and genome-wide recombination rates do not account for much of this variation. Instead, these variations are primarily associated with chromosome pairing and synapsis proteins, which tend to experience increased selective pressures throughout mammalian evolution, especially in large and long-lived species. This pattern probably reflects more intense selection for stability of chromosome pairing proteins in the oocytes of long-lived species, in which chromosomes sometimes need to stay paired for decades.
- organism: mouse
- organism: human
- organism: rat
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: chromatin & epigenetic regulation
- research focus: phylogenetics / evolution
- research focus: gene regulatory network inference
- approach: statistical model
- approach: comparative / phylogenetic analysis
- Citations
- 0
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