Computational prediction and experimental validation of novel Hedgehog-responsive enhancers linked to genes of the Hedgehog pathway

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• 作者：Katherine Gurdziel ; Kyle R. Vogt ; Gary Schneider…
• 关键词：Hedgehog signaling ; Enhancers ; Machine learning ; GLI
• 刊名：BMC Developmental Biology
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：16
• 期：1
• 全文大小：1,627 KB
• 刊物主题：Developmental Biology; Animal Models; Life Sciences, general;
• 出版者：BioMed Central
• ISSN：1471-213X

文摘

Background The Hedgehog (Hh) signaling pathway, acting through three homologous transcription factors (GLI1, GLI2, GLI3) in vertebrates, plays multiple roles in embryonic organ development and adult tissue homeostasis. At the level of the genome, GLI factors bind to specific motifs in enhancers, some of which are hundreds of kilobases removed from the gene promoter. These enhancers integrate the Hh signal in a context-specific manner to control the spatiotemporal pattern of target gene expression. Importantly, a number of genes that encode Hh pathway molecules are themselves targets of Hh signaling, allowing pathway regulation by an intricate balance of feed-back activation and inhibition. However, surprisingly few of the critical enhancer elements that control these pathway target genes have been identified despite the fact that such elements are central determinants of Hh signaling activity. Recently, ChIP studies have been carried out in multiple tissue contexts using mouse models carrying FLAG-tagged GLI proteins (GLIFLAG). Using these datasets, we tested whether a meta-analysis of GLI binding sites, coupled with a machine learning approach, could reveal genomic features that could be used to empirically identify Hh-regulated enhancers linked to loci of the Hh signaling pathway.