Transcription-factor occupancy at HOT regions quantitatively predicts RNA polymerase recruitment in five human cell lines.

Foley, Joseph W; Sidow, Arend

Transcription-factor occupancy at HOT regions quantitatively predicts RNA polymerase recruitment in five human cell lines.

Files

PMC3826616.pdf (3.25 MB)

License

http://creativecommons.org/licenses/by/2.0

Type

Article

Authors

Foley, Joseph W
Sidow, Arend

Online Publication Date

2013-10-20

Print Publication Date

2013

Date

2013-10-20

Abstract

BACKGROUND: 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.

Citation

Foley 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.

Acknowledgements

We 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.