bioRxivEvolutionary Biology
Unique transcriptional signatures of sleep loss across independently evolved cavefish populations
S. E. McGaugh, C. N. Passow, J. B. Jaggard, B. A. Stahl, A. C. Keene
Abstract
Animals respond to sleep loss with compensatory rebound sleep, and this is thought to be critical for the maintenance of physiological homeostasis. Sleep duration varies dramatically across animal species, but it is not known whether evolutionary differences in sleep duration are associated with differences in sleep homeostasis. The Mexican cavefish, Astyanax mexicanus, has emerged as a powerful model for studying the evolution of sleep. While eyed surface populations of A. mexicanus sleep approximately eight hours each day, multiple blind cavefish populations have converged on sleep patterns that total as little as two hours each day, providing the opportunity to examine whether the evolution of sleep loss is accompanied by changes in sleep homeostasis. Here, we examine the behavioral and molecular response to sleep deprivation across four independent populations of A. mexicanus. Our behavioral analysis indicates that surface fish and all three cavefish populations display robust recovery sleep during the day following nighttime sleep deprivation, suggesting sleep homeostasis remains intact in cavefish. We profiled transcriptome-wide changes associated with sleep deprivation in surface fish and cavefish. While the total number of differentially expressed genes was not greater for the surface population, the surface population exhibited the highest number of uniquely differentially expressed genes than any other population. Strikingly, a majority of the differentially expressed genes are unique to individual cave populations, suggesting unique expression responses are exhibited across independently evolved cavefish populations. Together, these findings suggest sleep homeostasis is intact in cavefish despite a dramatic reduction in overall sleep duration.
- organism: mouse
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
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