bioRxivDevelopmental BiologyPreprint
The role of Kabuki Syndrome genes KMT2D and KDM6A in development: Analysis in Human sequencing data and compared to mice and zebrafish
Rwik SeniD, Ezra S. LenceriD, Elizabeth A. Geiger, Kenneth L. JonesiD, Tamim H. ShaikhiD, Kristin ArtingeriD
Abstract
KMT2D and KDM6A are epigenetic regulators that have been implicated in Kabuki Syndrome, a rare congenital birth defect with multiple tissue and organ abnormalities, including craniofacial and heart defects. Our previous study identified human families with mutations in the epigenetic modifiers KMT2D and KDM6A, which is implicated in 32% and 10% of Kabuki Syndrome patients respectively. To understand the connection to Kabuki syndrome patients, and the transcriptional targets of KMT2D and KDM6A in humans, we performed RNA sequencing (seq) of lymphoblastoid cells from Kabuki Syndrome patients carrying mutations in KMT2D and KDM6A. We identified 1995 significant changes in transcriptional targets for KMT2D and 1917 for KDM6A, as compared to control. When compared with RNA-seq datasets obtained from other mouse and zebrafish studies, our analysis revealed KMT2D mutations affect the expression of 76 orthologous genes across all three datasets. Similarliy, KDM6A afftects the expprssion of 7 orthologous genes across three datasets. Despite the differences in cell types, stages, and species in the comparison between the transcriptomic datasets, there are common gene expression changes associated with KMT2D and KDM6A mutations. qPCR on novel zebrafish mutants confirmed the differentially expression in KMT2D or KDM6A mutant backgrounds. Taken together, our results show that KMT2D and KDM6A regulate common and unique genes across humans, mice, and zebrafish for early craniofacial and cardiac development and this information contributes to the understanding of epigenetic dysregulation during development of Kabuki syndrome.
- organism: human
- organism: mouse
- organism: zebrafish
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 5
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