Back

Alcohol-induced sleep dysregulation in Drosophila is dependent on the neuropeptide PDF

Ramirez-Roman, M. E.; Fuenzalida-Uribe, N. L.; Ayala-Santiago, G.; Agosto, J. L.; Ghezzi, A.

2024-05-05 neuroscience
10.1101/2024.05.05.592572 bioRxiv
Show abstract

Alcohol exposure is known to trigger homeostatic adaptations in the brain that lead to the development of tolerance and dependence. These adaptations are also believed to be the root of a series of disturbances in sleep patterns that often manifest during the development of alcoholism and can have significant clinical and economic consequences. Unfortunately, the neuronal and genetic pathways that control the effects of alcohol on sleep are currently unknown, thus limiting our efforts to find effective treatment. In this study, we conduct a mechanistic exploration of the relationships between alcohol and sleep alterations using a Drosophila model system. We show that the genetic manipulation of the ventral lateral neurons (LNv) --a set of neurons known to control sleep in Drosophila-- disrupts alcohol sensitivity and tolerance. Moreover, we show that alcohol exposure induces a series of alterations in sleep patterns that last for several days. Our results demonstrate that a single alcohol exposure promotes daytime sleep, alters the structure of sleep during the night, and reduces morning anticipatory behavior. In addition, we show that some of these alterations partially depend on the activity of the neuropeptide PDF, a key element in regulating sleep architecture. We propose that alcohol-induced sleep disruption stems from alterations in the activity of the PDF-releasing LNv neurons and that these alterations are similar to those that produce alcohol tolerance.

Matching journals

The top 6 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.