1. Computational and Systems Biology
  2. Genetics and Genomics

Leveraging the mendelian disorders of the epigenetic machinery to systematically map functional epigenetic variation

  1. Teresa R Luperchio
  2. Leandros Boukas
  3. Li Zhang
  4. Genay Opal Pilarowski
  5. Jenny Jiang
  6. Allison Kalinousky
  7. Kasper Daniel Hansen
  8. Hans T Bjornsson  Is a corresponding author
  1. Johns Hopkins University, United States
  2. Stanford University, United States
  3. Bloomberg School of Public Health, United States
https://doi.org/10.7554/eLife.65884
Share this article
Cite this article
  1. Teresa R Luperchio
  2. Leandros Boukas
  3. Li Zhang
  4. Genay Opal Pilarowski
  5. Jenny Jiang
  6. Allison Kalinousky
  7. Kasper Daniel Hansen
  8. Hans T Bjornsson
(2021)
Leveraging the mendelian disorders of the epigenetic machinery to systematically map functional epigenetic variation
eLife 10:e65884.
https://doi.org/10.7554/eLife.65884
  2. Download BibTeX
  3. Download .RIS

Abstract

Although each Mendelian Disorder of the Epigenetic Machinery (MDEM) has a different causative gene, there are shared disease manifestations. We hypothesize that this phenotypic convergence is a consequence of shared epigenetic alterations. To identify such shared alterations we interrogate chromatin (ATAC-Seq) and expression (RNA-Seq) states in B cells from three MDEM mouse models (Kabuki (KS) types 1&2 and Rubinstein-Taybi (RT1) syndromes). We develop a new approach for the overlap analysis and find extensive overlap primarily localized in gene promoters. We show that disruption of chromatin accessibility at promoters often disrupts downstream gene expression, and identify 587 loci and 264 genes with shared disruption across all three MDEMs. Subtle expression alterations of multiple, IgA-relevant genes, collectively contribute to IgA deficiency in KS1 and RT1, but not in KS2. We propose that the joint study of MDEMs offers a principled approach for systematically mapping functional epigenetic variation in mammals.

Data availability

Sequencing data have been deposited in GEO under accession code GSE162181.

The following data sets were generated

Article and author information

Author details

  1. Teresa R Luperchio

    Department of Biological Chemistry, Johns Hopkins University, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.

  2. Leandros Boukas

    Predoctoral Training Program in Human Genetics, McKusick-Nathans Institute of Genetic Medicine, Department of Biostatistics, Johns Hopkins University, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.

  3. Li Zhang

    McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.

  4. Genay Opal Pilarowski

    Pathology, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.

  5. Jenny Jiang

    Pediatrics and Genetics, Johns Hopkins University, Stafford, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8310-298X

  6. Allison Kalinousky

    Pediatrics and Genetics, Johns Hopkins University, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7292-8500

  7. Kasper Daniel Hansen

    Biostatistics, Bloomberg School of Public Health, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.

  8. Hans T Bjornsson

    Pediatrics and Genetics, Johns Hopkins University, Baltimore, United States
    For correspondence
    hbjorns1@jhmi.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6635-6753

Funding

National Institutes of Health (R01GM121459)

  • Kasper Daniel Hansen

Icelandic Centre for Research (195835-051)

  • Hans T Bjornsson

Icelandic Centre for Research (206806-051)

  • Hans T Bjornsson

Icelandic Centre for Research (2010588-0611)

  • Hans T Bjornsson

Louma G Private Foundation (KS grant)

  • Teresa R Luperchio
  • Hans T Bjornsson

Johns Hopkins University (Discovery grant)

  • Leandros Boukas
  • Kasper Daniel Hansen
  • Hans T Bjornsson

Burroughs Wellcome Fund (MD-GEM grant)

  • Leandros Boukas

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

Ethics

Animal experimentation: We performed all mouse experiments in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals and all were approved by the Animal Care and Use Committee of the Johns Hopkins University. (protocol number: MO18M112).

Copyright

© 2021, Luperchio 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

  • 1,686
    views
  • 244
    downloads
  • 18
    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. Teresa R Luperchio
  2. Leandros Boukas
  3. Li Zhang
  4. Genay Opal Pilarowski
  5. Jenny Jiang
  6. Allison Kalinousky
  7. Kasper Daniel Hansen
  8. Hans T Bjornsson
(2021)
Leveraging the mendelian disorders of the epigenetic machinery to systematically map functional epigenetic variation
eLife 10:e65884.
https://doi.org/10.7554/eLife.65884

Share this article

https://doi.org/10.7554/eLife.65884

Categories and tags

Research organism

Further reading

    1. Cancer Biology
    2. Computational and Systems Biology

    Luminal epithelial cells integrate variable responses to aging into stereotypical changes that underlie breast cancer susceptibility

    Rosalyn W Sayaman, Masaru Miyano ... Mark LaBarge
    Research Article

    Effects from aging in single cells are heterogenous, whereas at the organ- and tissue-levels aging phenotypes tend to appear as stereotypical changes. The mammary epithelium is a bilayer of two major phenotypically and functionally distinct cell lineages: luminal epithelial and myoepithelial cells. Mammary luminal epithelia exhibit substantial stereotypical changes with age that merit attention because these cells are the putative cells-of-origin for breast cancers. We hypothesize that effects from aging that impinge upon maintenance of lineage fidelity increase susceptibility to cancer initiation. We generated and analyzed transcriptomes from primary luminal epithelial and myoepithelial cells from younger <30 (y)ears old and older >55y women. In addition to age-dependent directional changes in gene expression, we observed increased transcriptional variance with age that contributed to genome-wide loss of lineage fidelity. Age-dependent variant responses were common to both lineages, whereas directional changes were almost exclusively detected in luminal epithelia and involved altered regulation of chromatin and genome organizers such as SATB1. Epithelial expression of gap junction protein GJB6 increased with age, and modulation of GJB6 expression in heterochronous co-cultures revealed that it provided a communication conduit from myoepithelial cells that drove directional change in luminal cells. Age-dependent luminal transcriptomes comprised a prominent signal that could be detected in bulk tissue during aging and transition into cancers. A machine learning classifier based on luminal-specific aging distinguished normal from cancer tissue and was highly predictive of breast cancer subtype. We speculate that luminal epithelia are the ultimate site of integration of the variant responses to aging in their surrounding tissue, and that their emergent phenotype both endows cells with the ability to become cancer-cells-of-origin and represents a biosensor that presages cancer susceptibility.

    1. Computational and Systems Biology
    2. Microbiology and Infectious Disease

    An antimicrobial drug recommender system using MALDI-TOF MS and dual-branch neural networks

    Gaetan De Waele, Gerben Menschaert, Willem Waegeman
    Research Article

    Timely and effective use of antimicrobial drugs can improve patient outcomes, as well as help safeguard against resistance development. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is currently routinely used in clinical diagnostics for rapid species identification. Mining additional data from said spectra in the form of antimicrobial resistance (AMR) profiles is, therefore, highly promising. Such AMR profiles could serve as a drop-in solution for drastically improving treatment efficiency, effectiveness, and costs. This study endeavors to develop the first machine learning models capable of predicting AMR profiles for the whole repertoire of species and drugs encountered in clinical microbiology. The resulting models can be interpreted as drug recommender systems for infectious diseases. We find that our dual-branch method delivers considerably higher performance compared to previous approaches. In addition, experiments show that the models can be efficiently fine-tuned to data from other clinical laboratories. MALDI-TOF-based AMR recommender systems can, hence, greatly extend the value of MALDI-TOF MS for clinical diagnostics. All code supporting this study is distributed on PyPI and is packaged at https://github.com/gdewael/maldi-nn.

    1. Computational and Systems Biology
    2. Genetics and Genomics

    Functional characteristics and computational model of abundant hyperactive loci in the human genome

    Sanjarbek Hudaiberdiev, Ivan Ovcharenko
    Research Article

    Enhancers and promoters are classically considered to be bound by a small set of transcription factors (TFs) in a sequence-specific manner. This assumption has come under increasing skepticism as the datasets of ChIP-seq assays of TFs have expanded. In particular, high-occupancy target (HOT) loci attract hundreds of TFs with often no detectable correlation between ChIP-seq peaks and DNA-binding motif presence. Here, we used a set of 1003 TF ChIP-seq datasets (HepG2, K562, H1) to analyze the patterns of ChIP-seq peak co-occurrence in combination with functional genomics datasets. We identified 43,891 HOT loci forming at the promoter (53%) and enhancer (47%) regions. HOT promoters regulate housekeeping genes, whereas HOT enhancers are involved in tissue-specific process regulation. HOT loci form the foundation of human super-enhancers and evolve under strong negative selection, with some of these loci being located in ultraconserved regions. Sequence-based classification analysis of HOT loci suggested that their formation is driven by the sequence features, and the density of mapped ChIP-seq peaks across TF-bound loci correlates with sequence features and the expression level of flanking genes. Based on the affinities to bind to promoters and enhancers we detected five distinct clusters of TFs that form the core of the HOT loci. We report an abundance of HOT loci in the human genome and a commitment of 51% of all TF ChIP-seq binding events to HOT locus formation thus challenging the classical model of enhancer activity and propose a model of HOT locus formation based on the existence of large transcriptional condensates.