Back

Early-life stress alters chromatin modifications in VTA to prime stress sensitivity

Geiger, L. T.; Balouek, J.-A. R.; Farrelly, L. A.; Chen, A. S.; Tang, M.; Bennett, S. N.; Nestler, E. J.; Garcia, B. A.; Maze, I.; Pena, C. J.

2024-03-14 neuroscience
10.1101/2024.03.14.584631 bioRxiv
Show abstract

Early-life stress increases sensitivity to subsequent stress, which has been observed at behavioral, neural activity, and gene expression levels. However, the molecular mechanisms underlying such long-lasting sensitivity are poorly understood. We tested the hypothesis that persistent changes in transcription and transcriptional potential were maintained at the level of the epigenome, through changes in chromatin. We used a combination of bottom-up mass spectrometry, viral-mediated epigenome-editing, RNA-sequencing, patch clamp electrophysiology of dopamine neurons, and behavioral quantification in a mouse model of early-life stress, focusing on the ventral tegmental area (VTA), a dopaminergic brain region critically implicated in motivation, reward learning, stress response, and mood and drug disorders. We found that early-life stress alters histone dynamics in VTA, including enrichment of histone-3 lysine-4 monomethylation -- associated with open chromatin and primed or active enhancers -- and the H3K4 monomethylase Setd7. Mimicking early-life stress through postnatal overexpression of Setd7 and enrichment of H3K4me1 in VTA sensitizes transcriptional, physiological, and behavioral response to adult stress. These findings link early-life stress experience to long-term stress hypersensitivity within the brains dopaminergic circuitry, providing a mechanism by which early-life stress increases risk for mood and anxiety disorders later in life.

Published in Neuron · training set

Matching journals

The top 9 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.