Transcription-factor occupancy at HOT regions quantitatively predicts RNA polymerase recruitment in five human cell lines.
Online Publication Date2013-10-20
Print Publication Date2013
Permanent link to this recordhttp://hdl.handle.net/10754/596828
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AbstractBACKGROUND: High-occupancy target (HOT) regions are compact genome loci occupied by many different transcription factors (TFs). HOT regions were initially defined in invertebrate model organisms, and we here show that they are a ubiquitous feature of the human gene-regulation landscape. RESULTS: We identified HOT regions by a comprehensive analysis of ChIP-seq data from 96 DNA-associated proteins in 5 human cell lines. Most HOT regions co-localize with RNA polymerase II binding sites, but many are not near the promoters of annotated genes. At HOT promoters, TF occupancy is strongly predictive of transcription preinitiation complex recruitment and moderately predictive of initiating Pol II recruitment, but only weakly predictive of elongating Pol II and RNA transcript abundance. TF occupancy varies quantitatively within human HOT regions; we used this variation to discover novel associations between TFs. The sequence motif associated with any given TF's direct DNA binding is somewhat predictive of its empirical occupancy, but a great deal of occupancy occurs at sites without the TF's motif, implying indirect recruitment by another TF whose motif is present. CONCLUSIONS: Mammalian HOT regions are regulatory hubs that integrate the signals from diverse regulatory pathways to quantitatively tune the promoter for RNA polymerase II recruitment.
CitationFoley JW, Sidow A (2013) Transcription-factor occupancy at HOT regions quantitatively predicts RNA polymerase recruitment in five human cell lines. BMC Genomics 14: 720. Available: http://dx.doi.org/10.1186/1471-2164-14-720.
SponsorsWe thank Cheryl Smith, Ed Grow, Noah Spies, and Erik Lehnert for critical reading of this manuscript. We are also grateful to Anshul Kundaje and Phil Lacroute for invaluable technical advice. This work was supported by the Stanford Genome Training Program (NIH/NHGRI T32 HG000044), a subcontract to ENCODE grant HG004695, and a KAUST AEA grant.
PubMed Central IDPMC3826616
CollectionsPublications Acknowledging KAUST Support
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