Transcription imparts architecture, function and logic to enhancer units.


Journal

Nature genetics
ISSN: 1546-1718
Titre abrégé: Nat Genet
Pays: United States
ID NLM: 9216904

Informations de publication

Date de publication:
10 2020
Historique:
received: 12 08 2019
accepted: 28 07 2020
pubmed: 23 9 2020
medline: 25 11 2020
entrez: 22 9 2020
Statut: ppublish

Résumé

Distal enhancers play pivotal roles in development and disease yet remain one of the least understood regulatory elements. We used massively parallel reporter assays to perform functional comparisons of two leading enhancer models and find that gene-distal transcription start sites are robust predictors of active enhancers with higher resolution than histone modifications. We show that active enhancer units are precisely delineated by active transcription start sites, validate that these boundaries are sufficient for capturing enhancer function, and confirm that core promoter sequences are necessary for this activity. We assay adjacent enhancers and find that their joint activity is often driven by the stronger unit within the cluster. Finally, we validate these results through functional dissection of a distal enhancer cluster using CRISPR-Cas9 deletions. In summary, definition of high-resolution enhancer boundaries enables deconvolution of complex regulatory loci into modular units.

Identifiants

pubmed: 32958950
doi: 10.1038/s41588-020-0686-2
pii: 10.1038/s41588-020-0686-2
pmc: PMC7541647
mid: NIHMS1616002
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1067-1075

Subventions

Organisme : NIGMS NIH HHS
ID : T32 GM132083
Pays : United States
Organisme : NICHD NIH HHS
ID : T32 HD057854
Pays : United States
Organisme : NIGMS NIH HHS
ID : R37 GM025232
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM025232
Pays : United States
Organisme : NHGRI NIH HHS
ID : UM1 HG009393
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK115398
Pays : United States
Organisme : NHGRI NIH HHS
ID : R01 HG008126
Pays : United States

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Auteurs

Nathaniel D Tippens (ND)

Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA.
Department of Computational Biology, Cornell University, Ithaca, NY, USA.
Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA.
Tri-Institutional Training Program in Computational Biology and Medicine, Cornell University, Ithaca, NY, USA.

Jin Liang (J)

Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA.

Alden King-Yung Leung (AK)

Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA.
Department of Computational Biology, Cornell University, Ithaca, NY, USA.

Shayne D Wierbowski (SD)

Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA.
Department of Computational Biology, Cornell University, Ithaca, NY, USA.

Abdullah Ozer (A)

Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA.

James G Booth (JG)

Department of Statistics and Data Science, Cornell University, Ithaca, NY, USA.

John T Lis (JT)

Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA. johnlis@cornell.edu.
Tri-Institutional Training Program in Computational Biology and Medicine, Cornell University, Ithaca, NY, USA. johnlis@cornell.edu.

Haiyuan Yu (H)

Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA. haiyuan.yu@cornell.edu.
Department of Computational Biology, Cornell University, Ithaca, NY, USA. haiyuan.yu@cornell.edu.
Tri-Institutional Training Program in Computational Biology and Medicine, Cornell University, Ithaca, NY, USA. haiyuan.yu@cornell.edu.

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