bioRxivGenomicsPreprint
Collaborative binding by diverse pioneer transcription factors amplifies Polycomb repression to preserve lineage fidelity
Gerardo Gabriel MirizioiD, Morgan BuckleyiD, Katie Ludwig, Satoshi Matsui, Samuel Sampson, Hee‐Woong LimiD, Makiko IwafuchiiD
Abstract
Cooperative and collaborative modes of transcription factor (TF) binding are central to eukaryotic gene activation, providing regulatory specificity and robustness in cell fate control. However, gene activation is only half the story of transcriptional regulation, and cells also require mechanisms to repress inappropriate gene programs. It remains largely unknown how broadly expressed repressive TFs and epigenetic regulators achieve context-specific repression required for proper cell differentiation. Here, we show that the repressive TF PRDM1 cooperates with lineage- and stage-specific pioneer TFs during human endoderm differentiation. Notably, the number of diverse pioneer TFs bound at enhancers determines the strength of PRDM1-mediated enhancer repression. Mechanistically, collaborative binding by multiple pioneer TFs synergistically promotes local nucleosome remodeling and stabilizes PRDM1 occupancy, culminating in the formation of hyper-bivalent enhancers that are characterized by strongly elevated levels of H3K4me1 and Polycomb-associated modifications. These enhancers reinforce the repression of alternative-lineage, and past and future developmental programs. Together, our findings redefine the roles of TF cooperation and chromatin accessibility beyond the prevailing activation-centric view. We propose a collaborative repression model in which diverse pioneer TFs establish focal chromatin accessibility that reinforces the context-specific assembly of repressive regulators, thereby safeguarding lineage and developmental fidelity.
- organism: human
- assay: ChIP-seq
- assay: DNA methylation
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: cell type annotation
- approach: statistical model
- approach: experimental assay
- Citations
- 0
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