eLifeGeneticsPeer-reviewed journal article
Leveraging the Mendelian Disorders of the Epigenetic Machinery to Systematically Map Functional Epigenetic Variation
Teresa Romeo LuperchioiD, Leandros BoukasiD, Li Zhang, Genay O.W. PilarowskiiD, Jenny JiangiD, Allison J. KalinouskyiD, Kasper D. HanseniD, Hans Tomas BjornssoniD
Abstract
The Mendelian Disorders of the Epigenetic Machinery (MDEMs) have emerged as a class of Mendelian disorders caused by loss-of-function variants in epigenetic regulators. Although each MDEM has a different causative gene, they exhibit several overlapping disease manifestations. Here, we hypothesize that this phenotypic convergence is a consequence of common abnormalities at the epigenomic level, which directly or indirectly lead to downstream convergence at the transcriptomic level. Therefore, we seek to identify abnormalities shared across multiple MDEMs, in order to pinpoint locations where epigenetic variation is causally related to disease phenotypes. To this end, we perform a comprehensive interrogation of chromatin (ATAC-Seq) and expression (RNA-Seq) states in B cells from mouse models of three MDEMs (Kabuki types 1&2 and Rubinstein-Taybi syndromes). We build on recent work in covariate-powered multiple testing to develop a new approach for the overlap analysis, which enables us to find extensive overlap primarily localized in gene promoters. We show that disruption of chromatin accessibility at promoters often leads to disruption of downstream gene expression, and identify 463 loci and 249 genes with shared disruption across all three MDEMs. As an example of how widespread dysregulation leads to specific phenotypes, we show that subtle expression alterations of multiple, IgA-relevant genes, collectively contribute to IgA deficiency in KS1 and RT1. In contrast, we predict that KS2 does not have IgA deficiency, and confirm this observation in vivo. We propose that the joint study of MDEMs offers a principled approach for systematically mapping functional epigenetic variation in mammals.
- organism: mouse
- assay: ATAC-seq
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 26
- FWCI
- 1.41
- Field percentile
- top 18%
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