Rapid profiling of transcription factor-cofactor interaction networks reveals principles of epigenetic regulation.
Melissa M IngeR MillerH HookD BrayJ L KeenanR ZhaoThomas D GilmoreTrevor SiggersPublished in: bioRxiv : the preprint server for biology (2024)
Transcription factor (TF)-cofactor (COF) interactions define dynamic, cell-specific networks that govern gene expression; however, these networks are understudied due to a lack of methods for high-throughput profiling of DNA-bound TF-COF complexes. Here we describe the Cofactor Recruitment (CoRec) method for rapid profiling of cell-specific TF-COF complexes. We define a lysine acetyltransferase (KAT)-TF network in resting and stimulated T cells. We find promiscuous recruitment of KATs for many TFs and that 35% of KAT-TF interactions are condition specific. KAT-TF interactions identify NF-κB as a primary regulator of acutely induced H3K27ac. Finally, we find that heterotypic clustering of CBP/P300-recruiting TFs is a strong predictor of total promoter H3K27ac. Our data supports clustering of TF sites that broadly recruit KATs as a mechanism for widespread co-occurring histone acetylation marks. CoRec can be readily applied to different cell systems and provides a powerful approach to define TF-COF networks impacting chromatin state and gene regulation.
Keyphrases
- single cell
- transcription factor
- gene expression
- high throughput
- rna seq
- dna methylation
- cell therapy
- dna damage
- signaling pathway
- electronic health record
- immune response
- heart rate
- heart rate variability
- genome wide
- machine learning
- blood pressure
- mesenchymal stem cells
- big data
- nuclear factor
- cell proliferation
- toll like receptor
- deep learning
- bone marrow
- drug induced
- sensitive detection