bioRxivEvolutionary BiologyPreprint
Patterns of urine scent mark pheromone evolution in house mice and relatives (Muridae: Mus)
Abstract
Scent marks are important mediators of territorial behavior and sexual selection in many species, especially among mammals. As such, the evolution of compounds used in scent marks has the potential to inform our understanding of signal evolution in relation to social and sexual selection. A major challenge in studies of chemical communication is that the link between semiochemical compounds and genetic changes is often unclear. The major urinary proteins (MUPs) of house mice are elaborated pheromone blends that provide information on sex, status and individual identity. Importantly, MUPs are a direct protein product of genes, providing a clear link between genotype and phenotype. Here we examine the evolution of urinary pheromone signals among house mice and relatives by examining the sequences and patterns of expression of MUPs in the liver, where urine excreted MUPs are produced. MUP patterns have evolved among mouse species both by gene duplication and variation in expression. Notably, the sex-specificity of pheromone expression that has previously been assumed to be male-specific varies considerably across species. Our data reveal that individual identity signals in MUPs evolved prior to 0.35 million years ago and have rapidly diversified through recombining a modest number of perceptually salient amino acid variants. Amino acid variants are much more common on the exterior of the protein where they could interact with vomeronasal receptors, suggesting that perception have played a major role in shaping MUP diversity. Collectively, these data provide new insights into the diverse processes and pressures shaping pheromone signals, and suggest new avenues for using house mice and their wild relatives to probe the evolution of signals and signal processing.
- organism: mouse
- assay: proteomics
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
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