PLoS GeneticsGeneticsPeer-reviewed journal article
Epigenome and transcriptome changes in KMT2D-related Kabuki syndrome Type 1 iPSCs, neuronal progenitors and cortical neurons
Sara CuvertinoiD, Evgenii MartirosianiD, Kedar BhosaleiD, Peiwen ChengiD, Terence GarneriD, Ian J. DonaldsoniD, Adam JacksoniD, Adam StevensiD, Andrew D SharrocksiD, Susan J. KimberiD, Siddharth BankaiD
Abstract
Kabuki syndrome type 1 (KS1) is a neurodevelopmental disorder caused by loss-of-function variants in KMT2D which encodes a H3K4 methyltransferase. The mechanisms underlying neurodevelopmental problems in KS1 are still largely unknown. Here, we track the epigenome and transcriptome across three stages of neuronal differentiation using patient-derived induced pluripotent stem cells (iPSCs) to gain insights into the disease mechanism of KS1. In KS1 iPSCs we detected significantly lower levels of functional KMT2D transcript and KMT2D protein, and lower global H3K4me1 and H3K4me2 levels. We identify loss of thousands of H3K4me1 peaks in iPSCs, neuronal progenitors (NPs) and early cortical neurons (CNs) in KS1. We show that the number of lost peaks increase as differentiation progresses. We also identify hundreds of differentially expressed genes (DEGs) in iPSCs, NPs and CNs in KS1. In contrast with the epigenomic changes, the number of DEGs decrease as differentiation progresses. Our analysis reveals significant enrichment of differentially downregulated genes in areas containing putative enhancer regions with H3K4me1 loss. We also identify a set of distinct transcription factor binding sites in differentially methylated regions and a set of DEGs related to KS1 phenotypes. We find that genes regulated by SUZ12, a subunit of Polycomb Repressive complex 2, are over-represented in KS1 DEGs at early stages of differentiation. In conclusion, we present a disease-relevant human cellular model for KS1 that provides mechanistic insights for the disorder and could be used for high throughput drug screening for KS1.
- organism: human
- assay: ATAC-seq
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 7
- FWCI
- 4.09
- Field percentile
- top 5%
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