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Chemical, computational and functional insights into the chemical stability of the Hedgehog pathway inhibitor GANT61.

ABSTRACT: This work aims at elucidating the mechanism and kinetics of hydrolysis of GANT61, the first and most-widely used inhibitor of the Hedgehog (Hh) signalling pathway that targets Glioma-associated oncogene homologue (Gli) proteins, and at confirming the chemical nature of its bioactive form. GANT61 is poorly stable under physiological conditions and rapidly hydrolyses into an aldehyde species (GANT61-A), which is devoid of the biological activity against Hh signalling, and a diamine derivative (GANT61-D), which has shown inhibition of Gli-mediated transcription. Here, we combined chemical synthesis, NMR spectroscopy, analytical studies, molecular modelling and functional cell assays to characterise the GANT61 hydrolysis pathway. Our results show that GANT61-D is the bioactive form of GANT61 in NIH3T3 Shh-Light II cells and SuFu-/- mouse embryonic fibroblasts, and clarify the structural requirements for GANT61-D binding to Gli1. This study paves the way to the design of GANT61 derivatives with improved potency and chemical stability.

SUBMITTER: Calcaterra A

PROVIDER: S-EPMC6009951 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Chemical, computational and functional insights into the chemical stability of the Hedgehog pathway inhibitor GANT61.

Calcaterra Andrea A Iovine Valentina V Botta Bruno B Quaglio Deborah D D'Acquarica Ilaria I Ciogli Alessia A Iazzetti Antonia A Alfonsi Romina R Lospinoso Severini Ludovica L Infante Paola P Di Marcotullio Lucia L Mori Mattia M Ghirga Francesca F

Journal of enzyme inhibition and medicinal chemistry 20181201 1

This work aims at elucidating the mechanism and kinetics of hydrolysis of GANT61, the first and most-widely used inhibitor of the Hedgehog (Hh) signalling pathway that targets Glioma-associated oncogene homologue (Gli) proteins, and at confirming the chemical nature of its bioactive form. GANT61 is poorly stable under physiological conditions and rapidly hydrolyses into an aldehyde species (GANT61-A), which is devoid of the biological activity against Hh signalling, and a diamine derivative (GAN ...[more]

PMID: 29338454

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Project description: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 (GLI(FLAG)). 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.A meta-analysis of four existing GLI(FLAG) datasets revealed a library of GLI binding motifs that was substantially more restricted than the potential sites predicted by previous in vitro binding studies. A machine learning method (kmer-SVM) was then applied to these datasets and enriched k-mers were identified that, when applied to the mouse genome, predicted as many as 37,000 potential Hh enhancers. For functional analysis, we selected nine regions which were annotated to putative Hh pathway molecules and found that seven exhibited GLI-dependent activity, indicating that they are directly regulated by Hh signaling (78% success rate).The results suggest that Hh enhancer regions share common sequence features. The kmer-SVM machine learning approach identifies those features and can successfully predict functional Hh regulatory regions in genomic DNA surrounding Hh pathway molecules and likely, other Hh targets. Additionally, the library of enriched GLI binding motifs that we have identified may allow improved identification of functional GLI binding sites.

Dataset Information

Chemical, computational and functional insights into the chemical stability of the Hedgehog pathway inhibitor GANT61.

Publications

Chemical, computational and functional insights into the chemical stability of the Hedgehog pathway inhibitor GANT61.

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