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Machine learning reveals STAT motifs as predictors for GR-mediated gene repression.
Comp. Struc. Biotech. J. 21, 1697-1710 (2023)
Glucocorticoids are potent immunosuppressive drugs, but long-term treatment leads to severe side-effects. While there is a commonly accepted model for GR-mediated gene activation, the mechanism behind repression remains elusive. Understanding the molecular action of the glucocorticoid receptor (GR) mediated gene repression is the first step towards developing novel therapies. We devised an approach that combines multiple epigenetic assays with 3D chromatin data to find sequence patterns predicting gene expression change. We systematically tested> 100 models to evaluate the best way to integrate the data types and found that GR-bound regions hold most of the information needed to predict the polarity of Dex-induced transcriptional changes. We confirmed NF-κB motif family members as predictors for gene repression and identified STAT motifs as additional negative predictors.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Chipseq ; Epigenomics ; Glucocorticoid Receptor ; Machine-learning ; Repression ; Rnaseq ; Stat; Glucocorticoid-receptor; Chromatin Accessibility; Recruitment; Activation; Mechanisms; Binding
Language
english
Publication Year
2023
HGF-reported in Year
2023
ISSN (print) / ISBN
2001-0370
e-ISSN
2001-0370
Quellenangaben
Volume: 21,
Pages: 1697-1710
Publisher
Research Network of Computational and Structural Biotechnology (RNCSB)
Publishing Place
Radarweg 29, 1043 Nx Amsterdam, Netherlands
Reviewing status
Peer reviewed
Institute(s)
Institute of Diabetes and Endocrinology (IDE)
Institute of Computational Biology (ICB)
Institute of Computational Biology (ICB)
POF-Topic(s)
30202 - Environmental Health
30205 - Bioengineering and Digital Health
30205 - Bioengineering and Digital Health
Research field(s)
Enabling and Novel Technologies
Enabling and Novel Technologies
PSP Element(s)
G-501900-259
G-553500-001
G-503800-001
G-553500-001
G-503800-001
Grants
Helmholtz Association
WOS ID
000965723000001
Scopus ID
85148546544
PubMed ID
36879886
Erfassungsdatum
2023-02-28