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Updated: Feb 21, 2026

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
Published on: August 9, 2019
DNA Sequence Constraints Define Functionally Active Steroid Nuclear Receptor Binding Sites in Chromatin.
Laurel A Coons1,2, Sylvia C Hewitt1, Adam B Burkholder3
1Receptor Biology Section, Reproductive and Developmental Biology Laboratory, National Institute of Environmental Health Sciences/National Institutes of Health, Research Triangle Park, North Carolina 27709.
Understanding gene regulation requires deciphering DNA sequences. This study defines the precise DNA sequence constraints for functional steroid nuclear receptor (sNR) cis-regulatory elements, enabling prediction of transcriptional activity.
Area of Science:
- Genomics
- Molecular Biology
- Epigenetics
Background:
- Gene regulatory programs are encoded in DNA sequences.
- Millions of gene regulatory elements have been identified, but their functions are often unclear.
- Transcription factors interpret cis-regulatory elements to influence cell function.
Purpose of the Study:
- To define the sequence constraints of functional steroid nuclear receptor (sNR) cis-regulatory elements.
- To understand how DNA sequence variations affect transcriptional activity and functionality.
- To develop predictive models for regulatory sequence function.
Main Methods:
- Probed transcriptional activity, DNA-binding competence, and functional activity of nuclear receptor mutants.
- Utilized cellular and animal model systems.
- Analyzed chromatin-interacting sites and their association with transcriptional output.
Main Results:
- Only a small fraction of sNR chromatin-interacting events are associated with transcriptional output.
- Functional sNR cis-regulatory elements differ from consensus palindromic elements by only one or two nucleotides.
- Identified specific sequence variations that dictate functionality.
Conclusions:
- Defined the transcriptional grammar necessary to predict functionality from regulatory sequences.
- These findings have significant implications for understanding gene regulation and disease.
- Provides a framework for predicting the functional impact of genomic variations.
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