bioRxivEvolutionary BiologyPreprint
Pleiotropy constrains the evolution of immune system plasticity while promoting domain modularity
Abstract
Pleiotropic genes control multiple traits. This can result in evolutionary antagonism because adaptation that favors one trait can interfere with the function of another. While pleiotropic genes show statistical signatures of evolutionary constraint, many of them contain multiple domains that may evolve under different selective pressures. This could either strengthen or alleviate gene-level constraint. Here, we study pleiotropy within the immune system of six Drosophila species to disentangle gene and domain-level evolution. We hypothesized that the multifunctional nature of pleiotropic genes may promote within-gene variation in evolutionary rates of their domains compared to non-pleiotropic genes. Consistently, we found a greater within-gene variation in evolutionary rate among domains of pleiotropic genes than other gene classes, despite relatively low between-gene variation in evolutionary rates among pleiotropic genes. Non-pleiotropic genes, on the other hand, show a more heterogeneous selective pressure at the gene level. Regardless of pleiotropy status, domains within antiviral proteins show elevated evolutionary rates, while signaling protein domains show elevated ratios of radical to conservative amino acid substitutions, which likely have a significant effect on protein structure and function. Finally, an examination of plasticity in infection-induced gene expression responses across species revealed that non-pleiotropic genes with elevated evolutionary rates were also more likely to demonstrate variation in plasticity, but this relationship did not extend to pleiotropic genes. Overall, our results identify differences in evolutionary patterns across various biological levels (i.e., gene, domain, protein, and expression), showing that domain-specific evolution can potentially alleviate gene-level constraints.
- organism: Drosophila
- assay: proteomics
- research focus: chromatin & epigenetic regulation
- approach: statistical model
- Citations
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