1. Chromosomes and Gene Expression
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A promoter interaction map for cardiovascular disease genetics

  1. Lindsey E Montefiori  Is a corresponding author
  2. Debora R Sobreira
  3. Noboru J Sakabe
  4. Ivy Aneas
  5. Amelia C Joslin
  6. Grace T Hansen
  7. Grazyna Bozek
  8. Ivan P Moskowitz
  9. Elizabeth M McNally
  10. Marcelo A Nóbrega  Is a corresponding author
  1. University of Chicago, United States
  2. Northwestern University, United States
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Cite this article as: eLife 2018;7:e35788 doi: 10.7554/eLife.35788

Abstract

Over 500 genetic loci have been associated with risk of cardiovascular diseases (CVDs), however most loci are located in gene-distal non-coding regions and their target genes are not known. Here, we generated high-resolution promoter capture Hi-C (PCHi-C) maps in human induced pluripotent stem cells (iPSCs) and iPSC-derived cardiomyocytes (CMs) to provide a resource for identifying and prioritizing the functional targets of CVD associations. We validate these maps by demonstrating that promoters preferentially contact distal sequences enriched for tissue-specific transcription factor motifs and are enriched for chromatin marks that correlate with dynamic changes in gene expression. Using the CM PCHi-C map, we linked 1,999 CVD-associated SNPs to 347 target genes. Remarkably, more than 90% of SNP-target gene interactions did not involve the nearest gene, while 40% of SNPs interacted with at least two genes, demonstrating the importance of considering long-range chromatin interactions when interpreting functional targets of disease loci.

Article and author information

Author details

  1. Lindsey E Montefiori

    Department of Human Genetics, University of Chicago, Chicago, United States
    For correspondence
    lem@uchicago.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2342-6349
  2. Debora R Sobreira

    Department of Human Genetics, University of Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Noboru J Sakabe

    Department of Human Genetics, University of Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Ivy Aneas

    Department of Human Genetics, University of Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Amelia C Joslin

    Department of Human Genetics, University of Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Grace T Hansen

    Department of Human Genetics, University of Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Grazyna Bozek

    Department of Human Genetics, University of Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Ivan P Moskowitz

    Department of Human Genetics, University of Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0014-4963
  9. Elizabeth M McNally

    Center for Genetic Medicine, Northwestern University, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Marcelo A Nóbrega

    Department of Human Genetics, University of Chicago, Chicago, United States
    For correspondence
    nobrega@uchicago.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0451-7846

Funding

National Institutes of Health (HL123857)

  • Marcelo A Nóbrega

National Institutes of Health (HL119967)

  • Marcelo A Nóbrega

National Institutes of Health (HL118758)

  • Marcelo A Nóbrega

National Institutes of Health (HL128075)

  • Elizabeth M McNally
  • Marcelo A Nóbrega

National Institutes of Health (T32GMOO7197)

  • Lindsey E Montefiori

American Heart Association (17PRE33410726)

  • Lindsey E Montefiori

National Institutes of Health (HL137307-01)

  • Lindsey E Montefiori

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Job Dekker, University of Massachusetts Medical School, United States

Publication history

  1. Received: February 20, 2018
  2. Accepted: June 21, 2018
  3. Accepted Manuscript published: July 10, 2018 (version 1)
  4. Version of Record published: July 19, 2018 (version 2)

Copyright

© 2018, Montefiori et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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Further reading

    1. Chromosomes and Gene Expression
    2. Computational and Systems Biology
    Anna Nagy-Staron et al.
    Research Article

    Gene expression levels are influenced by multiple coexisting molecular mechanisms. Some of these interactions, such as those of transcription factors and promoters have been studied extensively. However, predicting phenotypes of gene regulatory networks remains a major challenge. Here, we use a well-defined synthetic gene regulatory network to study in Escherichia coli how network phenotypes depend on local genetic context, i.e. the genetic neighborhood of a transcription factor and its relative position. We show that one gene regulatory network with fixed topology can display not only quantitatively but also qualitatively different phenotypes, depending solely on the local genetic context of its components. Transcriptional read-through is the main molecular mechanism that places one transcriptional unit within two separate regulons without the need for complex regulatory sequences. We propose that relative order of individual transcriptional units, with its potential for combinatorial complexity, plays an important role in shaping phenotypes of gene regulatory networks.

    1. Chromosomes and Gene Expression
    2. Computational and Systems Biology
    Michael Roland Wolff et al.
    Research Article

    Chromatin dynamics are mediated by remodeling enzymes and play crucial roles in gene regulation, as established in a paradigmatic model, the S. cerevisiae PHO5 promoter. However, effective nucleosome dynamics, i.e. trajectories of promoter nucleosome configurations, remain elusive. Here, we infer such dynamics from the integration of published single-molecule data capturing multi-nucleosome configurations for repressed to fully active PHO5 promoter states with other existing histone turnover and new chromatin accessibility data. We devised and systematically investigated a new class of 'regulated on-off-slide' models simulating global and local nucleosome (dis)assembly and sliding. Only seven of 68145 models agreed well with all data. All seven models involve sliding and the known central role of the N-2 nucleosome, but regulate promoter state transitions by modulating just one assembly rather than disassembly process. This is consistent with but challenges common interpretations of previous observations at the PHO5 promoter and suggests chromatin opening by binding competition.