Back

Chromatin context-dependent deacetylation by the asymmetric Rpd3L

Zhao, H.; Li, H.; Wang, C.; Yang, X.; Li, H.; Zou, B.; Dong, S.; Zhang, N.; Zhou, Y.; Yi, L.; Zhang, Y.; Xie, Y.; Qin, D.; Chao, W. C. H.; Pei, D.; He, J.

2026-01-09 molecular biology
10.64898/2026.01.08.698523 bioRxiv
Show abstract

The regulation of gene expression requires precise control of chromatin-associated complexes that respond to diverse structural and epigenetic cues. The Rpd3 Large (Rpd3L) complex is a Sin3 histone deacetylase (HDAC) complex that dynamically adapt to chromatin states to reinforce transcriptional silencing, yet the mechanisms governing the catalytic activation in chromatin context-dependent manner remain unclear. Here we present the cryo-EM structure of Rpd3L bound to both mono- and di-nucleosome substrate at near-atomic resolution, uncovering a substrate-guided allosteric activation mechanism. Rpd3L adopts an asymmetric architecture, in which the proximal catalytic module anchors the first nucleosome, while the Sin3 PAH domains engage linker DNA to reposition a second nucleosome. This spatial configuration brings the distal catalytic module into proximity with chromatin and unlocks its latent deacetylase activity. Biochemical and mass spectrometry analyses confirm that dual nucleosome engagement selectively enhances Rpd3L activity and broadens substrate specificity. Together, these findings establish a hierarchical mechanism by which Rpd3L interprets histone modifications and nucleosome organization to modulate its enzymatic output at promoter regions. Our study provides a framework for understanding higher-order chromatin repression mechanisms by chromatin-regulation complexes and co-repressors.

Published in Nucleic Acids Research (predicted rank #4) · training set

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.