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Co-occupancy by multiple cardiac transcription factors identifies transcriptional enhancers active in heart
Open accessAbstract
Identification of genomic regions that control tissue-specific gene expression is currently problematic. ChIP and high-throughput sequencing (ChIP-seq) of enhancer-associated proteins such as p300 identifies some but not all enhancers active in a tissue. Here we show that co-occupancy of a chromatin region by multiple transcription factors (TFs) identifies a distinct set of enhancers. GATA-binding protein 4 (GATA4), NK2 transcription factor-related, locus 5 (NKX2-5), T-box 5 (TBX5), serum response factor (SRF), and myocyte-enhancer factor 2A (MEF2A), here referred to as “cardiac TFs,” have been hypothesized to collaborate to direct cardiac gene expression. Using a modified ChIP-seq procedure, we defined chromatin occupancy by these TFs and p300 genome wide and provided unbiased support for this hypothesis. We used this principle to show that co-occupancy of a chromatin region by multiple TFs can be used to identify cardiac enhancers. Of 13 such regions tested in transient transgenic embryos, seven (54%) drove cardiac gene expression. Among these regions were three cardiac-specific enhancers of Gata4 , Srf , and swItch/sucrose nonfermentable-related, matrix-associated, actin-dependent regulator of chromatin, subfamily d, member 3 ( Smarcd3 ), an epigenetic regulator of cardiac gene expression. Multiple cardiac TFs and p300-bound regions were associated with cardiac-enriched genes and with functional annotations related to heart development. Importantly, the large majority (1,375/1,715) of loci bound by multiple cardiac TFs did not overlap loci bound by p300. Our data identify thousands of prospective cardiac regulatory sequences and indicate that multiple TF co-occupancy of a genomic region identifies developmentally relevant enhancers that are largely distinct from p300-associated enhancers.
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Heintzman, N. D., Stuart, R. K., Hon, G., Fu, Y., Ching, C. W., Hawkins, R. D., Barrera, L. O., Van Calcar, S., Qu, C., Ching, K. A., et al. (2007) · Nature Genetics · doi:10.1038/ng1966 · Cited by 3,398
Yu, C., Cantor, A. B., Yang, H., Browne, C., Wells, R. A., Fujiwara, Y., & Orkin, S. H. (2002) · The Journal of Experimental Medicine · doi:10.1084/jem.20020656 · Cited by 600
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Farnham, P. J. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2636 · Cited by 519
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Metadata
- Authors
- He, A., Kong, S. W., Ma, Q., & Pu, W. T.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2011-03-17
- Volume
- 108
- Issue
- 14
- Pages
- 5632-5637
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2036145288
- Cited by
- 357
- References
- 3
- FWCI
- 14.58
- Type
- journal article
- Language
- en
- Open access
- green
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