Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Dissecting the binding mechanisms of transcription factors to DNA using a statistical thermodynamics framework

Patrick C.N. Martin, Nicolae Radu Zabet
doi: https://doi.org/10.1101/666446
Patrick C.N. Martin
School of biological Sciences, University of Essex, Wivenhoe Park Colchester CO4 3SQ
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Nicolae Radu Zabet
School of biological Sciences, University of Essex, Wivenhoe Park Colchester CO4 3SQ
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: nzabet@essex.ac.uk
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

At the heart of gene regulation are Transcription Factors (TF), proteins which bind to DNA in a sequence specific manner and drive the activation or repression of genes. Here, we present a statistical thermodynamics framework (ChIPanalyser) which models and predicts binding of TFs. We focused on investigating the binding mechanisms of three TFs that are known architectural proteins CTCF, BEAF-32 and su(Hw) in three Drosophila cell lines (BG3, Kc167 and S2). While CTCF preferentially binds only to a subset of high affinity sites located in open chromatin, BEAF-32 binds to most of its high affinity binding sites available in open chromatin. In contrast, su(Hw) binds to both open chromatin and also regions displaying intermediate levels of accessibility. Most importantly, differences in TF binding profiles between cell lines for these TFs are mainly driven by differences in DNA accessibility and not by differences in TF concentrations between cell lines. Finally, we investigated binding of Hox TFs in Drosophila and found that Ubx prefers open chromatin, while Abd-B and Dfd are capable to bind in partially closed chromatin. Overall, our results show that TFs display different binding mechanisms and that our model is able to recapitulate this diverse repertoire of mechanisms.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted June 11, 2019.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Dissecting the binding mechanisms of transcription factors to DNA using a statistical thermodynamics framework
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Dissecting the binding mechanisms of transcription factors to DNA using a statistical thermodynamics framework
Patrick C.N. Martin, Nicolae Radu Zabet
bioRxiv 666446; doi: https://doi.org/10.1101/666446
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Dissecting the binding mechanisms of transcription factors to DNA using a statistical thermodynamics framework
Patrick C.N. Martin, Nicolae Radu Zabet
bioRxiv 666446; doi: https://doi.org/10.1101/666446

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Genomics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4119)
  • Biochemistry (8828)
  • Bioengineering (6532)
  • Bioinformatics (23484)
  • Biophysics (11804)
  • Cancer Biology (9223)
  • Cell Biology (13336)
  • Clinical Trials (138)
  • Developmental Biology (7442)
  • Ecology (11425)
  • Epidemiology (2066)
  • Evolutionary Biology (15173)
  • Genetics (10452)
  • Genomics (14056)
  • Immunology (9187)
  • Microbiology (22198)
  • Molecular Biology (8823)
  • Neuroscience (47625)
  • Paleontology (351)
  • Pathology (1431)
  • Pharmacology and Toxicology (2493)
  • Physiology (3736)
  • Plant Biology (8090)
  • Scientific Communication and Education (1438)
  • Synthetic Biology (2224)
  • Systems Biology (6042)
  • Zoology (1254)