Summarizing internal dynamics boosts differential analysis and functional interpretation of super enhancers.


Journal

Nucleic acids research
ISSN: 1362-4962
Titre abrégé: Nucleic Acids Res
Pays: England
ID NLM: 0411011

Informations de publication

Date de publication:
08 04 2022
Historique:
accepted: 14 02 2022
revised: 14 01 2022
received: 01 11 2021
pubmed: 3 3 2022
medline: 16 4 2022
entrez: 2 3 2022
Statut: ppublish

Résumé

Super enhancers (SEs) are broad enhancer domains usually containing multiple constituent enhancers that hold elevated activities in gene regulation. Disruption in one or more constituent enhancers causes aberrant SE activities that lead to gene dysregulation in diseases. To quantify SE aberrations, differential analysis is performed to compare SE activities between cell conditions. The state-of-art strategy in estimating differential SEs relies on overall activities and neglect the changes in length and structure of SEs. Here, we propose a novel computational method to identify differential SEs by weighting the combinatorial effects of constituent-enhancer activities and locations (i.e. internal dynamics). In addition to overall activity changes, our method identified four novel classes of differential SEs with distinct enhancer structural alterations. We demonstrate that these structure alterations hold distinct regulatory impact, such as regulating different number of genes and modulating gene expression with different strengths, highlighting the differentiated regulatory roles of these unexplored SE features. When compared to the existing method, our method showed improved identification of differential SEs that were linked to better discernment of cell-type-specific SE activity and functional interpretation.

Identifiants

pubmed: 35234924
pii: 6541024
doi: 10.1093/nar/gkac141
pmc: PMC8989535
doi:

Types de publication

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

Langues

eng

Sous-ensembles de citation

IM

Pagination

3115-3127

Subventions

Organisme : NCI NIH HHS
ID : P30 CA076292
Pays : United States
Organisme : NIAID NIH HHS
ID : R01 AI123420
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA262530
Pays : United States

Informations de copyright

© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.

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Auteurs

Xiang Liu (X)

Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA.

Bo Zhao (B)

Division of Infectious Disease, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.

Timothy I Shaw (TI)

Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA.

Brooke L Fridley (BL)

Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA.

Derek R Duckett (DR)

Department of Drug Discovery, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA.

Aik Choon Tan (AC)

Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA.

Mingxiang Teng (M)

Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA.

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Classifications MeSH