• Altmetrics
  • Sign-up for PNAS eTOC Alerts

Analysis of high-resolution 3D intrachromosomal interactions aided by Bayesian network modeling

  1. Arthur D. Riggsa,1
  1. aDepartment of Diabetes Complications and Metabolism, Diabetes and Metabolism Research Institute, City of Hope, Duarte, CA
  1. Contributed by Arthur D. Riggs, October 2, 2017 (sent for review December 16, 2016; reviewed by Peter N. Cockerill and Leonid A. Mirny)

Significance

We report here that a recently developed Bayesian network (BN) methodology and software platform yield useful information when applied to the analysis of intrachromosomal interaction datasets combined with Encyclopedia of DNA Elements publicly available datasets for the B-lymphocyte cell line GM12878. Of 106 variables analyzed, interaction strength between DNA segments was found to be directly dependent on only four types of variables: distance, Rad21 or SMC3 (cohesin components), transcription at transcription start sites, and the number of CCCTC-binding factor (CTCF)–cohesin complexes between interacting DNA segments. The importance of directionally oriented ctcf motifs was confirmed not only for loops but also for enhancer–promoter interactions. Purely data-driven BN analyses also identified known critical, lineage-determining transcription factors (TFs) as well as some potentially new dependencies between TFs.

Abstract

Long-range intrachromosomal interactions play an important role in 3D chromosome structure and function, but our understanding of how various factors contribute to the strength of these interactions remains poor. In this study we used a recently developed analysis framework for Bayesian network (BN) modeling to analyze publicly available datasets for intrachromosomal interactions. We investigated how 106 variables affect the pairwise interactions of over 10 million 5-kb DNA segments in the B-lymphocyte cell line GB12878. Strictly data-driven BN modeling indicates that the strength of intrachromosomal interactions (hic_strength) is directly influenced by only four types of factors: distance between segments, Rad21 or SMC3 (cohesin components),transcription at transcription start sites (TSS), and the number of CCCTC-binding factor (CTCF)–cohesin complexes between the interacting DNA segments. Subsequent studies confirmed that most high-intensity interactions have a CTCF–cohesin complex in at least one of the interacting segments. However, 46% have CTCF on only one side, and 32% are without CTCF. As expected, high-intensity interactions are strongly dependent on the orientation of the ctcf motif, and, moreover, we find that the interaction between enhancers and promoters is similarly dependent on ctcf motif orientation. Dependency relationships between transcription factors were also revealed, including known lineage-determining B-cell transcription factors (e.g., Ebf1) as well as potential novel relationships. Thus, BN analysis of large intrachromosomal interaction datasets is a useful tool for gaining insight into DNA–DNA, protein–DNA, and protein–protein interactions.

Footnotes

  • ?1To whom correspondence should be addressed. Email: ariggs{at}coh.org.
  • Author contributions: X.Z., S.B., G.G., A.S.R., and A.D.R. designed research; X.Z., S.B., G.G., and A.S.R. performed research; X.Z., S.B., G.G., A.S.R., and A.D.R. analyzed data; and X.Z., S.B., A.S.R., and A.D.R. wrote the paper.

  • Reviewers: P.N.C., University of Birmingham; and L.A.M., Massachusetts Institute of Technology.

  • The authors declare no conflict of interest.

  • This article contains supporting information online at www.danielhellerman.com/lookup/suppl/doi:10.1073/pnas.1620425114/-/DCSupplemental.

This is an open access article distributed under the PNAS license.

Online Impact

    <acronym id="UPyyYwe"></acronym>
    <rt id="UPyyYwe"></rt>
    <acronym id="UPyyYwe"></acronym>
    <acronym id="UPyyYwe"><optgroup id="UPyyYwe"></optgroup></acronym><acronym id="UPyyYwe"><small id="UPyyYwe"></small></acronym>
    <tr id="UPyyYwe"><optgroup id="UPyyYwe"></optgroup></tr>
    <tr id="UPyyYwe"><optgroup id="UPyyYwe"></optgroup></tr>
    <acronym id="UPyyYwe"></acronym>
  • 8189251275 2018-02-18
  • 6298941274 2018-02-18
  • 8345181273 2018-02-18
  • 207841272 2018-02-18
  • 2683681271 2018-02-18
  • 5067491270 2018-02-18
  • 2051721269 2018-02-18
  • 2999231268 2018-02-18
  • 183621267 2018-02-18
  • 5236401266 2018-02-18
  • 2592991265 2018-02-18
  • 9896941264 2018-02-18
  • 1171081263 2018-02-18
  • 983551262 2018-02-18
  • 3896031261 2018-02-18
  • 4643431260 2018-02-18
  • 4122621259 2018-02-18
  • 336531258 2018-02-17
  • 6455421257 2018-02-17
  • 5128821256 2018-02-17