Leveraging the mendelian disorders of the epigenetic machinery to systematically map functional epigenetic variation
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.
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ATAC-Seq dataNCBI Gene Expression Omnibus, GSE162181.
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RNA-Seq dataNCBI Gene Expression Omnibus, GSE162181.
Article and author information
Author details
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).
Reviewing Editor
- Job Dekker, University of Massachusetts Medical School, United States
Version history
- Preprint posted: November 8, 2020 (view preprint)
- Received: December 17, 2020
- Accepted: August 27, 2021
- Accepted Manuscript published: August 31, 2021 (version 1)
- Version of Record published: September 15, 2021 (version 2)
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.
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Further reading
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- Computational and Systems Biology
A deep analysis of multiple genomic datasets reveals which genetic pathways associated with atherosclerosis and coronary artery disease are shared between mice and humans.
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- Computational and Systems Biology
Mouse models have been used extensively to study human coronary artery disease (CAD) or atherosclerosis and to test therapeutic targets. However, whether mouse and human share similar genetic factors and pathogenic mechanisms of atherosclerosis has not been thoroughly investigated in a data-driven manner. We conducted a cross-species comparison study to better understand atherosclerosis pathogenesis between species by leveraging multiomics data. Specifically, we compared genetically driven and thus CAD-causal gene networks and pathways, by using human GWAS of CAD from the CARDIoGRAMplusC4D consortium and mouse GWAS of atherosclerosis from the Hybrid Mouse Diversity Panel (HMDP) followed by integration with functional multiomics human (STARNET and GTEx) and mouse (HMDP) databases. We found that mouse and human shared >75% of CAD causal pathways. Based on network topology, we then predicted key regulatory genes for both the shared pathways and species-specific pathways, which were further validated through the use of single cell data and the latest CAD GWAS. In sum, our results should serve as a much-needed guidance for which human CAD-causal pathways can or cannot be further evaluated for novel CAD therapies using mouse models.