Gli3 utilizes Hand2 to synergistically regulate tissue-specific transcriptional networks

Abstract

Despite a common understanding that Gli TFs are utilized to reiterate a Hh morphogen gradient, genetic analyses suggest craniofacial development does not completely fit this paradigm. Using the mouse model (Mus musculus), we demonstrated that rather than being driven by a Hh threshold, robust Gli3 transcriptional activity during skeletal and glossal development required interaction with the basic helix-loop-helix TF Hand2. Not only did genetic and expression data support a co-factorial relationship, but genomic analysis revealed that Gli3 and Hand2 were enriched at regulatory elements for genes essential for mandibular patterning and development. Interestingly, motif analysis at sites co-occupied by Gli3 and Hand2 uncovered mandibular-specific, low-affinity, 'divergent' Gli binding motifs (<strong>d</strong>GBMs). Functional validation revealed these <strong>d</strong>GBMs conveyed synergistic activation of Gli targets essential for mandibular patterning and development. In summary, this work elucidates a novel, sequence-dependent mechanism for Gli transcriptional activity within the craniofacial complex that is independent of a graded Hh signal.

Frem1 activity regulated by Sonic Hedgehog signaling in the cranial neural crest mesenchyme guides midfacial morphogenesis

“…We found that SHH ligand-stimulated upregulation of Frem1 can be blocked by SMO inhibition and that genetic or pharmacologic SMO activation is sufficient to induce Frem1 expression. Leveraging a previously published GLI3 ChIP-seq data set [9], we also found evidence of GLI transcription factor binding at the Frem1 promoter in vivo. Taken together, these findings suggest that Frem1 expression is directly regulated by canonical Shh-Gli signaling during midfacial morphogenesis.…”

“…Three zinc finger GLI proteins, GLI1, GLI2, and GLI3, regulate transcription of Shh target genes by binding to consensus GLI DNA sequence motifs [20; 55]. To identify GLI binding sites in the developing face, we utilized published mouse GLI3 ChIP-seq data sets generated from GD11.5 whole face (including MNP and MxP) and isolated MdP tissue [9]. In addition to Frem1 and Gli1 , we examined Ccnd2 , an established transcriptional target of Shh-Gli [25; 36; 55].…”

“…To identify GLI binding sites in the developing face, published FLAG ChIP-seq data from Gli3 3XFlag knock-in mice were accessed from GEO record GSE146961 [9; 32]. Raw sequencing reads were aligned to the mm10 genome using Bowtie2 and SAMtools [27] and filtered for a minimum mapping quality of 10 (-q 10).…”

Differential regulation of SHH signaling and the developmental control of species-specific jaw size through neural crest-mediated Gas1 expression

“…Accordingly, we can speculate that the axes of growth that generate species-specific size and shape may be differentially affected by Ptch1- versus Gli -mediated activation of the SHH pathway. Other studies have shown that various aspects of the SHH pathway can affect tissue size and shape such as changes in Shh enhancers (Kvon et al, 2016), variation in levels of SHH ligand, (Young et al, 2010; Xu et al, 2015), differential regulation of SHH receptors (Lopez-Rios et al, 2014; Xavier et al, 2016; Echevarría-Andino and Allen, 2020), transcriptional activity of target genes (Chang et al, 2016; Uygur et al, 2016), and variation in interacting co-factors (Elliott et al, 2020; Swartz et al, 2021).…”

Single Cell Epigenetics Reveal Cell-Cell Communication Networks in Normal and Abnormal Cardiac Morphogenesis

“…A second neural crest-specific model used validated branchial arch and facial mesenchyme enhancers as neural crest positives and non-neural crest tissue enhancers as negatives. All enhancers were featurized using intersections (bedtools) with publicly available data including ENCODE mouse E10.5 reference epigenomes, Akerberg et al 2019 E12.5 cardiomyocyte ATAC- and ChIP-seq 15 , Cesario et al 2015 E11.5 maxillary arch ChIP-seq 16 , Elliott et al 2020 E11.5 mandibular prominence and whole face ChIP-seq 17 , Haro et al 2017 E12.5 limb ChIP-seq 18 , Infante et al 2013 E11.5 hindlimb ChIP-seq 19 , Lex et al 2020 E10.5 forelimb ChIP-seq, 20 Luna-Zurita et al 2016 mES cardiomyocyte ChIP-exo 21 , and Zhou et al 2017 E12.5 forebrain and heart ChIP-seq 22 , as well as average sequence conservation over the region (phyloP/PhastCons 100-way) 23 . The trained classifiers scored all scATAC-seq peaks between 0 and 1, estimating their similarity to either validated VISTA heart enhancers (model 1) or neural crest enhancers (model 2).…”