Journal
NUCLEIC ACIDS RESEARCH
Volume 49, Issue 18, Pages 10347-10368Publisher
OXFORD UNIV PRESS
DOI: 10.1093/nar/gkab841
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Funding
- National Institutes of Health [R01GM131398]
- NIH [R01GM131398]
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The study explores the role of protein-protein interactions between transcription factors in enhancer regulation and develops a predictive model for enhancer-promoter interactions. The model shows superior performance, identifies specific TF PPIs involved in long-range regulation, and reveals new mechanistic understandings of enhancer regulation.
Long-range regulation by distal enhancers plays critical roles in cell-type specific transcriptional programs. Computational predictions of genome-wide enhancer-promoter interactions are still challenging due to limited accuracy and the lack of knowledge on the molecular mechanisms. Based on recent biological investigations, the protein-protein interactions (PPIs) between transcription factors (TFs) have been found to participate in the regulation of chromatin loops. Therefore, we developed a novel predictive model for cell-type specific enhancer-promoter interactions by leveraging the information of TF PPI signatures. Evaluated by a series of rigorous performance comparisons, the new model achieves superior performance over other methods. The model also identifies specific TF PPIs that may mediate long-range regulatory interactions, revealing new mechanistic understandings of enhancer regulation. The prioritized TF PPIs are associated with genes in distinct biological pathways, and the predicted enhancer-promoter interactions are strongly enriched with cis-eQTLs. Most interestingly, the model discovers enhancer-mediated trans-regulatory links between TFs and genes, which are significantly enriched with trans-eQTLs. The new predictive model, along with the genome-wide analyses. provides a platform to systematically delineate the complex interplay among TFs, enhancers and genes in long-range regulation. The novel predictions also lead to mechanistic interpretations of eQTLs to decode the genetic associations with gene expression.
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