Back

Dynamic regulation of NAT10-mediated mRNA acetylation at synapses by learning and memory in mouse hippocampus

Zhou, H.-Q.; Zhang, J.-W.; Zhu, Z.; Lin, W.-P.; Jin, H.-J.; Ding, Y.-Y.; Chen, D.-S.; Yin, D.-M.

2024-12-06 neuroscience
10.1101/2024.06.01.596932 bioRxiv
Show abstract

Precise regulation of protein synthesis is critical for brain functions such as long-term memory, and its dysregulation is implicated in numerous memory disorders. While mRNA methylation such as N6-methyladenosine (m6A) that has been widely studied in memory, the role of mRNA acetylation remains largely unknown. N4-acetylcytidine (ac4C), the only known form of RNA acetylation in eukaryotes, promotes mRNA stability and translation. In this study, we identified the ac4C epitranscriptome in mouse hippocampal homogenates and synaptosomes through ac4C-RNA immunoprecipitation followed by next-generation sequencing (acRIP-seq). The Morris water maze (MWM) was employed to induce and evaluate memory acquisition and forgetting processes. We show that synaptic ac4C levels are dynamically regulated, increasing after memory formation and returning to baseline after natural forgetting. The dynamic changes of ac4C-mRNAs regulated by memory were validated by ac4C dot-blot, liquid chromatography-tandem mass spectrometry (LC-MS/MS), and acRIP-qPCR analysis. We further demonstrate that NAT10, the ac4C writer, in mouse hippocampus is important for spatial memory via regulating memory-related mRNAs, proteins, and ultimately synaptic plasticity. Lastly, we generated a freely-accessible website (http://ac4catlas.com) that included the dataset of ac4C epitranscriptome in mouse hippocampus. Altogether these results demonstrate that dynamic and localized mRNA acetylation is important for synaptic plasticity and memory.

Published in eLife (predicted rank #3) · training set

Matching journals

The top 3 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.