Back

Developmental sleep reallocation enables metabolic adaptation in desert flies

Li, S.; Szuperak, M.; Nave, C.; Tang, S. H.; Donlea, J. M.; Kayser, M. S.

2025-10-15 neuroscience
10.1101/2025.10.15.682659 bioRxiv
Show abstract

Sleep is essential for adaptation and survival across the lifespan, yet the ecological pressures shaping sleep ontogeny remain poorly understood. We investigated sleep across early developmental stages in Drosophila mojavensis, a stress-resilient desert-adapted species. While adult D.mojavensis exhibit prolonged and consolidated sleep, along with enhanced starvation tolerance and survival compared to Drosophila melanogaster, the developmental trajectory underlying these adaptation strategies for surviving in harsh environments is unknown. Moreover, during developmental (larval) periods, animals do not encounter the same environmental stressors experienced by adults (e.g., food scarcity, extreme temperatures). We find that in contrast to adults, D.mojavensis larvae exhibit reduced and fragmented sleep relative to D.melanogaster. D.mojavensis larval sleep is also deeper, reflecting a shift toward increased sleep efficiency rather than simple sleep loss. D.mojavensis larvae consume more food than D.melanogaster and survive longer under starvation, suggesting a strategic tradeoff by suppressing sleep to prioritize nutrient intake and energy storage early in life while resources are more abundant. Metabolic analyses reveal elevated triglyceride accumulation in D.mojavensis across their lifespan, indicating enhanced energy storage capacity. These findings provide an example of how, within a fixed genetic background, an animal can reallocate sleep in opposing manners to maximize survival and energetics depending upon ecological pressures unique to each phase of life.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.