Málstofa Lífvísindaseturs - Identification of GRHL1 as an orofacial cleft risk gene

Málstofa Lífvísindaseturs mánudaginn 24. ágúst kl. 15:00-16:00 í Læknagarði, stofu 201

Fyrirlesari: Rob Cornell, professor, Department of Oral Health Sciences, School of Dentistry, University of Washington, Seattle, USA

Titill: Conservation of transcriptional regulatory networks in zebrafish and human periderm facilitates identification of GRHL1 as an orofacial cleft risk gene

Ágrip: Most heritable risk for orofacial clefts (OFC) remains unassigned to specific genes or loci. IRF6 and GRHL3, two established OFC risk genes, encode transcription factors (TFs) essential for the differentiation of the periderm, a transient embryonic tissue required for secondary palate fusion. To identify novel risk candidates, we modeled the zebrafish periderm transcriptional regulatory network (TRN). Using single-cell multiome sequencing (RNA-seq and ATAC-seq) from embryos at the start of gastrulation, we inferred TF-to-target gene connections by integrating correlated gene expression with TF binding site predictions within chromatin elements open in periderm cells.  We generated sets of gold-standard edges by conducting RNA-seq on TF-depleted embryos and ChIP-seq/CUT&RUN on wild-type embryos and used them to benchmark model performance. Within the top-performing model, zebrafish periderm modules are strongly preserved in human embryonic periderm and orthologs of human OFC-associated genes have higher centrality and edge-sum scores than non-associated genes. Functional validation confirmed the network's predictive power: depleting high-centrality TFs, including grhl1, klf6a, tead3b, and klf17, disrupted periderm differentiation in sensitized embryos. Moreover, analysis of whole-genome sequencing data from 2,415 OFC trios identified 15 individuals with rare or de novo GRHL1 variants, four of which introduced premature stop codons. This study establishes GRHL1 as a novel OFC risk gene and highlights the power of cross-species gene regulatory network analysis to prioritize candidates for rare variants in complex structural birth defects.