bioRxivGeneticsPreprint
Truncating ASXL1 mutations in Bohring-Opitz Syndrome dysregulate canonical and non-canonical Wnt Signaling
Isabella LiniD, Angela WeiiD, Zain AwamlehiD, Meghna SinghiD, Aileen Ning, Analeyla Herrera, Bianca E. RusselliD, Rosanna WeksbergiD, Valerie A. ArboledaiD
Abstract
ASXL1 (Additional sex combs-like 1) plays key roles in epigenetic regulation of early developmental gene expression. De novo truncating mutations in ASXL1 cause Bohring-Opitz syndrome (BOS, OMIM #605039), a rare neurodevelopmental condition characterized by severe intellectual disabilities, characteristic facial features, hypertrichosis, increased risk of Wilms tumor, and variable congenital anomalies including heart defects and severe skeletal defects giving rise to a typical BOS posture. These BOS-causing ASXL1 variants are also high-prevalence somatic driver mutations in acute myeloid leukemia (AML). We use primary cells from BOS individuals (n = 18) and controls (n = 49) to dissect gene regulatory changes caused by ASXL1 mutations using comprehensive multi-omics assays for chromatin accessibility (ATAC-seq), DNA methylation, histone methylation binding, and transcriptome in peripheral blood and skin fibroblasts. Our data shows that regardless of cell type, ASXL1 mutations drive strong cross-tissue effects that disrupt multiple layers of the epigenome. The data showed a broad activation of canonical Wnt signaling at the transcriptional and protein levels and upregulation of VANGL2, a planar cell polarity pathway protein that acts through non-canonical Wnt signaling to direct tissue patterning and cell migration. This multi-omics approach identifies the core impact of ASXL1 mutations and therapeutic targets for BOS and myeloid leukemias. Brief summaryGermline ASXL1 mutations that cause Bohring Optiz syndrome disrupt the epigenome and dysregulate gene expression resulting in activation of canonical and non-canonical Wnt signaling pathways.
- organism: human
- assay: ATAC-seq
- assay: DNA methylation
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 3
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