Meta-Analysis of Dependent Transcriptional Networks: A Roadmap for Identifying Novel Conserved Genetic Pathways.

The () and vertebrate - () transcription factors are among the most critical genes for epithelial development, maintenance and homeostasis, and are remarkably well conserved from fungi to humans. Mutations affecting function lead to a myriad of developmental and adult onset epithelial disease, such as aberrant skin barrier formation, facial/palatal clefting, impaired neural tube closure, age-related hearing loss, ectodermal dysplasia, and importantly, cancers of epithelial origin. Recently, mutations in the family member have been shown to lead to both syndromic and non-syndromic facial and palatal clefting in humans, particularly the genetic disorder Van Der Woude Syndrome (VWS), as well as spina bifida, whereas mutations in mammalian lead to exencephaly and facial clefting. As transcription factors, Grhl proteins bind to and activate (or repress) a substantial number of target genes that regulate and drive a cascade of transcriptional networks. A multitude of large-scale datasets have been generated to explore the /-dependent transcriptome, following ablation or mis-regulation of /-function. Here, we have performed a meta-analysis of all 41 currently published and RNA-SEQ, and microarray datasets, in order to identify and characterise the transcriptional networks controlled by / genes across disparate biological contexts. Moreover, we have also cross-referenced our results with published ChIP and ChIP-SEQ datasets, in order to determine which of the critical effector genes are likely to be direct targets, based on genomic occupancy by / genes. Lastly, to interrogate the predictive strength of our approach, we experimentally validated the expression of the top 10 candidate target genes in epithelial development, in a zebrafish model lacking , and found that orthologues of seven of these (, , and ) were significantly down-regulated at 48 hours post-fertilisation. Therefore, our study provides a strong predictive resource for the identification of putative / effector target genes.