Information content differentiates enhancers from silencers in mouse photoreceptors

  1. Ryan Z Friedman
  2. David M Granas
  3. Connie A Myers
  4. Joseph C Corbo
  5. Barak A Cohen
  6. Michael A White  Is a corresponding author
  1. Washington University School of Medicine, United States

Abstract

Enhancers and silencers often depend on the same transcription factors (TFs) and are conflated in genomic assays of TF binding or chromatin state. To identify sequence features that distinguish enhancers and silencers, we assayed massively parallel reporter libraries of genomic sequences targeted by the photoreceptor TF CRX in mouse retinas. Both enhancers and silencers contain more TF motifs than inactive sequences, but relative to silencers, enhancers contain motifs from a more diverse collection of TFs. We developed a measure of information content that describes the number and diversity of motifs in a sequence and found that, while both enhancers and silencers depend on CRX motifs, enhancers have higher information content. The ability of information content to distinguish enhancers and silencers targeted by the same TF illustrates how motif context determines the activity of cis-regulatory sequences.

Data availability

The pJK01 and pJK03 plasmids have been deposited with AddGene (IDs 173489, 173490). Raw sequencing data and barcode counts have been uploaded to the NCBI GEO database under accession GSE165812. All processed activity data, predicted occupancy, and information content values are available in the supplementary material. All code for data processing, analysis, and visualization is available on Github at https://github.com/barakcohenlab/CRX-Information-Content.

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Ryan Z Friedman

    Edison Family Center for Genome Sciences and Systems Biology, and Department of Genetics, Washington University School of Medicine, St. Louis, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9013-8676
  2. David M Granas

    Edison Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Connie A Myers

    Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Joseph C Corbo

    Department of Pathology and Immunology, Washington University School of Medicine, St Louis, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9323-7140
  5. Barak A Cohen

    Edison Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3350-2715
  6. Michael A White

    Edison Family Center for Genome Sciences and Systems Biology, and Department of Genetics, Washington University School of Medicine, St. Louis, United States
    For correspondence
    mawhite@wustl.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8511-6026

Funding

National Institutes of Health (F31HG011431)

  • Ryan Z Friedman

National Institutes of Health (R01GM121755)

  • Michael A White

National Institutes of Health (R01EY027784)

  • Barak A Cohen

National Institutes of Health (EY025196)

  • Joseph C Corbo

National Institutes of Health (EY03075)

  • Joseph C Corbo

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

Reviewing Editor

  1. Naama Barkai, Weizmann Institute of Science, Israel

Ethics

Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to protocol # A-3381-01 approved by the Institutional Animal Care and Use Committee of Washington University in St. Louis. Euthanasia of mice was performed according to the recommendations of the American Veterinary Medical Association Guidelines on Euthanasia. Appropriate measures are taken to minimize pain and discomfort to the animals during experimental procedures.

Version history

  1. Preprint posted: February 7, 2021 (view preprint)
  2. Received: February 9, 2021
  3. Accepted: September 3, 2021
  4. Accepted Manuscript published: September 6, 2021 (version 1)
  5. Version of Record published: October 5, 2021 (version 2)

Copyright

© 2021, Friedman 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.

Metrics

  • 3,233
    views
  • 264
    downloads
  • 19
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Ryan Z Friedman
  2. David M Granas
  3. Connie A Myers
  4. Joseph C Corbo
  5. Barak A Cohen
  6. Michael A White
(2021)
Information content differentiates enhancers from silencers in mouse photoreceptors
eLife 10:e67403.
https://doi.org/10.7554/eLife.67403

Share this article

https://doi.org/10.7554/eLife.67403

Further reading

    1. Computational and Systems Biology
    2. Evolutionary Biology
    Ryan T Bell, Harutyun Sahakyan ... Eugene V Koonin
    Research Article

    A comprehensive census of McrBC systems, among the most common forms of prokaryotic Type IV restriction systems, followed by phylogenetic analysis, reveals their enormous abundance in diverse prokaryotes and a plethora of genomic associations. We focus on a previously uncharacterized branch, which we denote coiled-coil nuclease tandems (CoCoNuTs) for their salient features: the presence of extensive coiled-coil structures and tandem nucleases. The CoCoNuTs alone show extraordinary variety, with three distinct types and multiple subtypes. All CoCoNuTs contain domains predicted to interact with translation system components, such as OB-folds resembling the SmpB protein that binds bacterial transfer-messenger RNA (tmRNA), YTH-like domains that might recognize methylated tmRNA, tRNA, or rRNA, and RNA-binding Hsp70 chaperone homologs, along with RNases, such as HEPN domains, all suggesting that the CoCoNuTs target RNA. Many CoCoNuTs might additionally target DNA, via McrC nuclease homologs. Additional restriction systems, such as Type I RM, BREX, and Druantia Type III, are frequently encoded in the same predicted superoperons. In many of these superoperons, CoCoNuTs are likely regulated by cyclic nucleotides, possibly, RNA fragments with cyclic termini, that bind associated CARF (CRISPR-Associated Rossmann Fold) domains. We hypothesize that the CoCoNuTs, together with the ancillary restriction factors, employ an echeloned defense strategy analogous to that of Type III CRISPR-Cas systems, in which an immune response eliminating virus DNA and/or RNA is launched first, but then, if it fails, an abortive infection response leading to PCD/dormancy via host RNA cleavage takes over.

    1. Computational and Systems Biology
    Skander Kazdaghli, Iordanis Kerenidis ... Philip Teare
    Research Article

    Imputing data is a critical issue for machine learning practitioners, including in the life sciences domain, where missing clinical data is a typical situation and the reliability of the imputation is of great importance. Currently, there is no canonical approach for imputation of clinical data and widely used algorithms introduce variance in the downstream classification. Here we propose novel imputation methods based on determinantal point processes (DPP) that enhance popular techniques such as the multivariate imputation by chained equations and MissForest. Their advantages are twofold: improving the quality of the imputed data demonstrated by increased accuracy of the downstream classification and providing deterministic and reliable imputations that remove the variance from the classification results. We experimentally demonstrate the advantages of our methods by performing extensive imputations on synthetic and real clinical data. We also perform quantum hardware experiments by applying the quantum circuits for DPP sampling since such quantum algorithms provide a computational advantage with respect to classical ones. We demonstrate competitive results with up to 10 qubits for small-scale imputation tasks on a state-of-the-art IBM quantum processor. Our classical and quantum methods improve the effectiveness and robustness of clinical data prediction modeling by providing better and more reliable data imputations. These improvements can add significant value in settings demanding high precision, such as in pharmaceutical drug trials where our approach can provide higher confidence in the predictions made.