Circadian VIPergic neurons of the suprachiasmatic nuclei sculpt the sleep-wake cycle
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA644912 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR12174600_1.fastq.gz | Fastqsanger.gz | |||
| SRR12174600_2.fastq.gz | Fastqsanger.gz | |||
| SRR12174601_1.fastq.gz | Fastqsanger.gz | |||
| SRR12174601_2.fastq.gz | Fastqsanger.gz | |||
| SRR12174582_1.fastq.gz | Fastqsanger.gz |
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Project description:Sleep is governed by two processes: a circadian process that times sleep and wake and a homeostatic process that drives sleep as a function of prior wake history. Discovered in the fruit fly Drosophila, the period (per) gene is a “universal” cornerstone of the circadian clock, robustly oscillating at the transcript level, in all organs and tissues and in essentially all animals. We hypothesized that there may be a comparable factor (we term “sleeper”) for sleep homeostasis. To identify sleeper, we performed a wide-ranging transcriptomic analysis to identify genes whose expression tracks sleep-wake history in the Drosophila brain. We use a variety of methods to manipulate sleep-wake and subsequent rebound including mechanical, thermogenetic, optogenetic, pharmacological and circadian/baseline across 7 datasets. Using a log2 fold change threshold of 1, we did not identify any gene that was sleep-wake dependent across all datasets, raising the possibility that genes identified with a single method of sleep manipulation are related to the nature of the manipulation and not to sleep. Nonetheless, we identified genes whose expression changed with sleep-wake in a consistent direction across at least 2 datasets. We did observe significant pairwise overlap in regulated genes across many of the datasets. Gene ontology analysis of genes associated with sleep and wake as well as with sleep rebound. These analyses highlight previous processes implicated in sleep homeostasis as well as provide potentially novel pathways. In addition, we also examine genes whose expression correlates with prior sleep-wake history and/or predicted subsequent sleep rebound across these datasets. Among significantly correlated genes we observed some that were correlated with recent (<3h) sleep history while others were correlated with much longer (>6h) time frames suggesting temporally distinct genetic pathways for integrating waking experience. This analysis also highlights specific GO pathways for sleep/wake/rebound. Applying a novel GPT based paper search algorithm, we call fl.ai, we identified several genes with implicated or demonstrated function in sleep regulation consistent with potential homeostatic function. Collectively, these studies inform the future design of studies on the nature of sleep homeostasis as well as provide unique molecular clues across diverse sleep-wake conditions.
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