WITHDRAWN: Overlapping Targets of Shh and Gata3 during Craniofacial Development

doi:10.21203/rs.3.rs-2973064/v1

Background

Congenital birth defects are among the leading causes of infant mortality. Phenotypic variation in these defects results from a combination of genetic and environmental factors. One example of this is the modulation of palate phenotypes caused by mutation of the transcription factor Gata3 via the Sonic hedgehog (Shh) pathway. We exposed zebrafish to a subteratogenic dose of the Shh antagonist cyclopamine, and another group to both cyclopamine and gata3 knockdown. We performed RNA-seq on these zebrafish to characterize the overlap of Shh and Gata3 targets.

Results

We examined those genes whose expression patterns mirrored the biological effect of exacerbated misregulation. These genes were not significantly misregulated by the subteratogenic dose of ethanol but more misregulated by combinatorial disruption of Shh and Gata3 relative to Gata3 alone. Using gene-disease association discovery, we were able to refine this gene list to 11 that have published links to clinical outcomes similar to the gata3 phenotype or with craniofacial malformations. We also used weighted gene co-expression network analysis to identify a module of genes that correlate strongly with co-regulation by Shh and Gata3. This module is enriched in genes related to Wnt signaling.

Conclusions

We found numerous differentially expressed genes in response to cyclopamine treatment, and even more from double treatment. Most notably, we found a group of genes whose expression profile mirrored the biological effect of Shh/Gata3 interaction. Pathway analysis implicated the importance of Wnt signaling in Gata3/Shh interactions in palate development.

Craniofacial

development

zebrafish

genomics

WGNCA

gata3

sonic hedgehog

Congenital birth defects are among the leading causes of infant mortality in the United States, according to the Centers for Disease Control. Defects of the craniofacial skeleton are among the most common birth defects. For example, the prevalence of cleft lip with or without cleft palate (CL/P) is 10.2 per 10,000 births in the United States [1]. In addition, phenotypic variation is common within birth defects. The causes of these defects and their variability is not well understood, but is thought to result from a complex combination of genetic and environmental factors.

Proper formation of the craniofacial skeleton depends on precise and coordinated events controlled by multiple signaling pathways. These networks regulate the generation, migration, differentiation, and proliferation of cranial neural crest cells (CNCC), which serve as precursors for skeletal and connective tissues in the face [2]. All neural crest, including CNCC, originate from the dorsolateral edge of the neural plate during gastrulation, before undergoing an epithelial–mesenchymal transition. The cells then follow distinct migratory pathways to arrive at their final destination [3]. CNCCs that migrate into the frontonasal prominence and first pharyngeal arch are progenitors for the palatal skeleton, among other structures [4, 5]. In zebrafish, the anterior neurocranium is analogous to the palatal skeleton and consists of an ethmoid plate connected posteriorly to bilateral trabeculae [6–8].

Numerous signaling pathways regulate craniofacial development. One key player is the Sonic hedgehog (Shh) pathway. Shh often acts as a morphogen and plays important roles in patterning during embryonic development. Shh signaling from the facial ectoderm and the pharyngeal endoderm is paramount for growth and survival of cranial neural crest cells required for craniofacial morphogenesis [5, 7, 9, 10]. Both elevation and reduction of Shh signaling leads to abnormal craniofacial development, including CL/P [11, 12].

GATA3 is a zinc finger transcription factor that is expressed in numerous cell populations, including the neural crest [13, 14]. Mutation of human GATA3 causes hypoparathyroidism, sensorineural deafness and renal dysplasia (HDR) syndrome, and is associated with craniofacial microsomia [15–17]. Human GATA3 mutant phenotypes are highly variable, suggesting a complex etiology. In zebrafish, gata3 mutants display similarly variable phenotypes, including a range of palate defects. These abnormalities span from mild trabecular cell rearrangements to loss of the trabeculae [14, 18].

Previous work by our lab shows that the Shh pathway modulates the severity of gata3 mutant phenotypes. Decreasing Shh signaling worsens the mutant phenotypes, while increasing signaling lessens them. Notably, reduction of Shh signaling in zebrafish embryos heterozygous for gata3 mutation led to palate defects, resulting in haploinsufficiency as seen in human HDR patients [18]. These data are indicative of genetic interactions between the Gata3 and Shh pathways in craniofacial development, yet the shared downstream targets have not been elucidated. Here, we use transcriptomics to identify target genes and associated biological pathways of Gata3 and Shh.

We previously identified targets of Gata3 in CNCCs of 28 hpf zebrafish embryos using FACS to isolate cells positive for both fli1:EGFP and sox10:mRFP. We performed RNA-seq using cells isolated from gata3 morpholino-injected embryos, embryos transgenically overexpressing GATA3, and control embryos [19]. In this study, we used a similar approach to determine targets of Shh and genes co-regulated by Shh and Gata3. At 24 hpf, one group of zebrafish embryos was treated with a subteratogenic dose of the Shh antagonist cyclopamine, another was injected with a gata3 morpholino in addition to cyclopamine treatment, and a third group received only vehicle treatment as a control group. FACS and RNA-seq was performed on these embryos at 28 hpf.

To determine the effect of treatment and batch, we performed principal component analysis (PCA) across all six groups (Fig. 1). Embryos with loss-of-function treatments separated from overexpression and control embryos along PC1 (which explained 36.7% of the variance). Embryos from the overexpression group separated from controls along PC 2 (13.5% of the variance). PC 2 also separated the Shh loss-of-function and Gata3 loss-of-function groups, with the double loss-of-function group being intermediate (Fig. 1). Together, these results show that treatment was a major driver of variation in the dataset, while also indicating similarity in effects by disruption of Shh and Gata3 signaling. Finally, the lack of clustering across RNA-seq experiments and the tight clustering of the two separate control groups demonstrate that batch was not a major source of variation.

Despite the low dose, RNA-seq on cyclopamine-treated embryos identified 704 significantly differentially expressed genes (Fig. 2A), of which 351 were upregulated and 353 were downregulated. Gene Ontology (GO) analysis showed multiple important enriched signaling pathways (Fig. 2B). Hedgehog signaling was the most heavily affected, as expected. Notably, the Wnt signaling pathway was the next most highly enriched (Fig. 2B), similar to our previous findings with gata3 [19]. This suggests potential co-regulation of this pathway by Gata3 and Shh.

The transcriptome of embryos injected with gata3 morpholino and treated with cyclopamine consists of 429 significantly differentially expressed genes (Fig. 2C), of which 187 genes were upregulated and 242 were downregulated. Unlike the single-treatment experiments, the enriched pathways were primarily related to lipids. Connective tissue development was also enriched in our data (Fig. 2D). We performed RT-qPCR to validate our RNA-seq on the five most up- and down-regulated genes using the cyclopamine dataset. Eight out of ten showed significant differential expression in the expected direction, recapitulating our results (Fig. 3).

To elucidate the shared targets of both Gata3 and Shh, we looked at differentially regulated genes that were common to multiple experimental groups (Fig. 4). Our dose of cyclopamine does not result in craniofacial defects on its own, only when combined with gata3 loss of function. Therefore, we were particularly interested in the genes differentially regulated in both the gata3 and double-treatment groups (184 genes). Within that subset, 96 genes had a magnitude of expression change that was greater when exposed to both treatments compared to the single gata3 morpholino knockdown alone. Once again, we find Wnt signaling to be the most enriched term (Fig. 5). This gene set is enriched in pathways relating to cartilage and bone morphogenesis (Fig. 5), suggesting a possible mechanism for the phenotype.

To determine targets likely to be biologically relevant, we used the discovery platform DisGeNET (https://www.disgenet.org/home/) to identify those with published gene-disease associations [20]. We found ten genes with disease associations related to cleft palate, general craniofacial defects, and outcomes similar or related to HDR syndrome (Table 1), which serve as prime candidates for future characterization.

Table 1: Gene-disease associations of genes that originated from the “biological mirroring” group (same direction of differential expression, greater magnitude in double-treatment group).

Zebrafish gene	Human disease association	Evidence (PMID)	Log2FC (gata3)	Log2FC (double)
scfd1	“Profound craniofacial abnormality”	27851892	−0.26	−2.51
flrt3	Hypogonadotropic hypogonadism	23643382	0.41	0.83
f3b	Nephrotic Syndrome	17469034	0.49	0.98
srpx	Hypogonadotropic hypogonadism	11397871	0.56	1.12
tbx1	DiGeorge Syndrome Cleft palate	23996541 30121012	0.58	1.16
lhfpl2	IgA glomerulonephritis	25133636	0.27	2.08
sh3pxd2b	Ter Haar Syndrome Hearing impairment Craniofacial abnormalities	30962481 21818352 19669234	0.29	1.44
twist2	Barber-Say Syndrome, macrostomia	29329175	0.75	1.50
tmco1	Cerebrofaciothoracic dysplasia	30556256	0.28	1.56
smarcb1b	Coffin-Siris Syndrome	25169651	0.27	2.97
col9a1	Stickler Syndrome, cleft palate	21421862	2.30	4.60

Table 2: Gene-disease associations of genes that were only differentially expressed in fish exposed to both treatments (gata3 MO and cyclopamine).

Zebrafish gene	Human disease association	Evidence (PMID)	Log2FC (double)
tgfb3	Loeys-Dietz syndrome	26184463	2.15
slitrk6	Sensorineural hearing loss	29551497 23543054	0.77
polr1a	Acrofacial dystosis	25913037	0.80
bbs10	Bardet-Biedl syndrome syndrome Severe renal disease	27659767	1.07
tfap2b	Char syndrome	30579973	1.14
alx1	Frontonasal dysplasia Severe micropthalmia	23059813	1.87
fgf3	Labyrinthine aplasia, microtia, and microdontia Oto-dental syndrome	21752681 17656375	1.18

Another group of interest comprises genes that were only differentially expressed in fish receiving both treatments (147 genes, Fig. 4). As they require disruption in both pathways for misregulation, these genes are prime candidates for synergism between Gata3 and Shh. GO analysis did not reveal any pathways that immediately suggested a mechanism of action for the phenotypic interactions. We used the same gene-disease association approach for the double-treatment group. Looking at only the top 100 genes by gene-disease association score on DisGeNET, we found seven genes (Table 2).

To find modules of co-regulated genes connected to Shh and Gata3, we performed weighted gene co-expression network analysis (WGCNA) on our 24 datasets, which yielded 10 modules (Fig. 6). The number of genes per module ranged from 32 to 1,858 genes with an average size of 571 genes. Moreover, there were 91 genes that did not share similar co-expression with the other genes in the network and were assigned to the gray module.

We next mapped treatment back to the modules to identify those related to combined loss of Shh signaling and Gata3 function. Using the Pearson correlation coefficient between module eigengenes and sample traits (treatment type and batch), we found one module of particular interest, Module 3, containing 37 genes. It had a coefficient of 0.87 in the Shh/Gata3 module, which would make it an especially promising module to identify co-targets and hub genes of these two pathways. GO analysis of this module shows multiple highly enriched biological processes and pathways, again including Wnt signaling. In this module, eight genes are concordantly upregulated in both the gata3 experiment and the Shh experiment, while five are concordantly downregulated in these groups (Table 3).

Table 3: Module 3 genes with concordant differential expression in Gata3 and Shh treatment groups.

Gene	Gata3 Log2FC	CyA Log2FC
Concordantly upregulated
fzd3a	1.08	0.46
fzd3b	1.34	0.29
prkcg	0.56	0.8
grem2b	0.34	1.63
nkx2.2a	0.58	1.28
gpc1a	3.31	0.26
fosl1a	1.77	0.33
fosl2l	0.98	0.59
Concordantly downregulated
roraa	−0.36	−0.64
daam1a	−0.35	−0.53
foxa2	−0.34	−0.47
sfrp2	−0.35	−1.41
celsr1a	−0.25	−0.9

We used two complementary bioinformatics-based approaches to determine shared targets of Gata3 and Shh in palate development. We looked at overlapping differentially expressed genes identified via RNA-seq to identify co-targets. We also used WGCNA across these experiments to look for novel pathways in addition to common targets.

Our PCA analysis demonstrates the similarity of the transcriptomic profiles of fish with Shh disrupted with those of Gata3 disrupted. Despite this, we see that the actual misregulated genes between the two experiments are largely separate, as they share only 84 differentially expressed genes, while hundreds are unique to each treatment. The relatedness may stem from transcriptional forces exerted on different members of a single signaling pathway. GO analysis from our gata3 study and cyclopamine treatment revealed that Wnt signaling was highly enriched in these datasets [19]. Examination of individual genes that were differentially regulated in both groups reveal several Wnt signaling pathway members, including wnt5b, sfrp2, fzd7a, and fzd8a. These data suggest that Wnt signaling is a common thread in the interaction between Gata3 and Shh to modulate palate formation.

The combination of Gata3 and Shh disruption in the same system produces a larger proportion of unique differentially expressed genes. Despite clustering with the single-treatment groups in our PCA analysis, the enrichment profile was quite different, relating primarily to lipids. Lipids play multiple key roles in hedgehog signaling, including as ligands and as covalent modifiers of effector molecules [21, 22]. The role of lipids in Gata3-mediated developmental processes is less clear. Lipid metabolic defects have been shown to rescue cleft palate [23], but further study in cranial neural crest will be required to determine if there is an undiscovered link specifically between lipidation and the interaction between Gata3 and Shh.

When looking at genes in a subgroup that mirrored our biological effect (differentially regulated in both gata3 and double-treatment groups, but not cyclopamine), our GO analysis offered a strong link between these targets and bone/cartilage development. Individual genes from this subset link the systems to the phenotype. For example, a human ortholog of col9a1a (2.30 Log2FC in the gata3 group and 4.60 Log2FC in the double-treatment group) has been linked to Stickler syndrome, which presents multiple symptoms that are also associated with GATA3 disruption, including sensorineural hearing loss and cleft palate. Crucially, the penetrance and severity of these defects are variable, which is a hallmark of the GATA3 phenotype [14, 24]. Another gene from this subgroup, cd151l, also associates with hearing loss. Thus, genes with demonstrated clinical relevance are prime candidates for understanding how Shh signaling modulates the gata3 loss-of-function phenotype.

Using DisGeNET, we were able to reduce our high-priority group of 96 genes to 11 genes. All of these genes are associated with craniofacial defects or HDR-like defects. This makes this group of genes highly likely to modulate the phenotypes of Gata3 and Shh loss of function.

Through transcriptomic analysis, we investigated the interactions between Shh and Gata3 in craniofacial development. The similarity of the Shh- and Gata3-sensitive transcriptomes further the evidence of their potential interactions in CNCC-derived cells. We found numerous differentially expressed genes in response to cyclopamine treatment, and even more from double treatment. Most notably, we found a group of genes whose expression profile mirrored the biological effect of Shh/Gata3 interaction. We also found 11 genes that are associated with clinical outcomes related to the gata3 phenotype. We used WGCNA to identify highly enriched modules that might reveal novel targets of Gata3 and Shh. One module of genes correlated highly with double treatment of Gata3 and Shh knockdown. Pathway analysis showed that Wnt signaling was enriched in this cluster, further implicating this pathway’s importance in Gata3/Shh interactions in palate development. Altogether, we were able to demonstrate a pipeline for more precisely delineating target genes from interacting genetic pathways.

Animal care and use

Zebrafish were raised according to IACUC-approved protocols at the University of Texas at Austin and were staged as previously described [25]. The following transgenic lines were used: Tg(fli1:EGFP)^v1 and Tg(sox10:mRFP)^vu2 [26, 27]. These are referred to as fli1:EGFP and sox10:mRFP for clarity, respectively.

Chemical treatment

As previously described, fli1:EGFP;sox10:mRFP embryos were treated with a subteratogenic dose of 12.5 µM cyclopamine (Cayman Chemical) at 24 hpf [18]. DMSO was used as the vehicle in control groups.

Morpholino injection

An incross of fli1:EGFP;sox10:mRFP was performed to generate embryos for injection. At the one-cell stage, embryos were injected with a characterized translation-blocking gata3 morpholino 5′-CCGGACTTACTTCCATCGTTTATTT-3′ (Gene Tools, Philomath, OR, USA) [28]. A 3 nL bolus of a 5 ng/µL morpholino solution was injected, a concentration that phenocopies the gata3 mutant.

Sample preparation and FACS

For each experimental group, 150–200 embryos were dechorionated in a 2 mg/mL pronase solution at 28 hpf (Sigma-Aldrich, St. Louis, MO, USA). Embryos were washed in fresh embryo medium after the chorions were removed. The embryos were briefly washed in cold calcium-free Ringer’s solution and deyolked through up and down pipetting. The deyolked embryos were collected by 300× g centrifugation, washed in embryo media, and placed on ice. They were then pelleted through centrifugation. The embryos were washed with FACSmax cell dissociation solution (Genlantis, San Diego, CA, USA) and filtered through a 40-micron cell strainer into a 35 mm Petri dish. Dissociated cells were collected in a fresh Eppendorf tube and placed on ice. Just before sorting, the cells were filtered in a polystyrene Falcon tube with a cell-strainer cap (Fisher Scientific, Hampton, NH, USA). Cell sorting was performed with a BD FACSAria Fusion SORP Cell Sorter with DIVA 8 software (BD Biosciences, San Jose, CA, USA), using a 100-micron nozzle. Cells were first analyzed for forward scatter and side scatter to be selected for live singlets; these cells were then sorted for fluorescence. A 488 nm laser was used to identify GFP-expressing cells and a 561 nm laser for RFP-expressing cells. For each sample, approximately 100,000 cells were sorted for fluorescence. Cells from wild-type (AB) embryos of the same embryonic stage were used as a negative control. For two-color sorts, cells from embryos expressing only green or only red fluorescent markers were used as compensation controls. Double-positive cells are cranial neural crest cells and were collected into 1 mL of TRIzol (Invitrogen, Waltham, MA, USA) at 4°C.

cDNA library preparation and RNA sequencing

Total RNA was extracted according to the TRIzol RNA isolation protocol. Samples were purified with the RNA Clean & Concentrate kit (Zymo, Irvine, CA, USA). A Nanodrop spectrophotomer was used to determine the concentration of each sample, followed by RNA quality analysis with the Agilent BioAnalyzer (Agilent Technologies, Santa Clara, CA, USA). Total RNA from each sample ranged from 5 to 12 ng/µL. Samples were processed by the University of Texas at Austin Genomic Sequencing and Analysis Facility (GSAF). Sequencing was performed on the Illumina NextSeq 500 platform, with 75-bp paired end reads. Reads were trimmed for quality and adapters with Cutadapt v1.18 and aligned to the Genome Reference Consortium Zebrafish Build 11 (GRCz11), downloaded from Ensembl, using TopHat 2.1.1 [29–31]. Within each experimental group, four biological replicates were used for RNA-sequencing.

Differential expression and GO analysis

Normalization and gene expression analysis were performed with the R package DESeq2 [32]. An FDR-corrected p-value of 0.05 was used as the cut-off to identify differentially expressed transcripts. The web tool ShinyGO 0.77 was used to identify enriched Gene Ontology (GO) terms in gene lists using a cut-off of 0.05 FDR corrected p-value [33].

WGCNA

Weighted gene co-expression network analysis (WGCNA) was performed with the corresponding R package [34]. We took differentially expressed genes from 24 samples based on RNA-seq data. We used a soft thresholding power of 16 to calculate adjacency for construction of an unsigned weighted correlation network. To prune the clustering dendrograms, we used the cutreeDynamic function (mergeCutHeight = 0.25) to create co-expression modules. We calculated correlation using the Pearson correlation coefficient to identify significant modules.

Quantitative Reverse Transcription PCR (RT-qPCR)

For RT-qPCR we used Invitrogen’s SuperScript First-Strand Synthesis System for RT-PCR with oligo-d(T) primers. We performed RT-qPCR with Power Sybr Green PCR master mix (Thermo Fisher Scientific, Waltham, MA, USA, 4367659) on the Applied Biosystems QuantStudio 3 Real-Time PCR System (Thermo Fisher, A28567). QuantStudio Real-Time PCR Software was used for data analysis using the 2^–∆∆Ct method of relative gene expression analysis. We used the gene csnk2b (casein kinase 2, beta polypeptide) as an endogenous control based on its relatively unchanging expression across all experimental groups. All primers used for qPCR can be found in Table 4. Experiments were performed in triplicate.

Table 4

Primers used for qPCR experiments.
Gene	Purpose	Forward (5’ -> 3’)	Reverse (5’ -> 3’)
csnk2b	qPCR housekpeeing	GGCTTATGCACCGTAAAG	TCCTCCGCCGGCCTCCCC
dlx3b	qPCR	TCGGTCTGCCTTGGAAATAAA	GATATGGAGGGCTCTGGTAGTA
dlx4b	qPCR	CAGACCATCATCGGGCTTTAT	CTCGACCCATGCTTCATTATCT
emx2	qPCR	GGTCTTGGGTGTGGAGTATTT	TGTCAGTACACAGCATCCTTATC
grem2b	qPCR	CTGAAGAGCGACTGGTGTAAG	GTCTTTGTGGTCCAGCATTTG
hmga1a	qPCR	AGAGGAGGAAGAGGAACAGTAA	GTTTCGAGGACGATGTCGTATAG
alpl	qPCR	CTCGGGCTCTTCTTCATACTTC	CCACTTGAGGCTGGGTAAAT
bmp2b	qPCR	CCGGAGCGAACTGATACAAA	GGCCTGAACACCTCGTAAAT
col21a1	qPCR	GATGGGTGTGCCAGGATTTA	CTTGACCAGACTCTCCACTTTC
col2a1a	qPCR	GAAAGGAGAACCAGGCGATATT	GACCAGGCAGACCATCATTAC
arf4a	qPCR	GACGGGTTTATACGAGGGATTAG	GTACCGTGCCTCACCATAAA

Ethics approval and consent to participate

Animal use was approved by the University of Texas at Austin in accordance with the Institutional Animal Care and Use Committee protocols (protocol number AUP-2020-00290). All animal methods and experiments were carried out in accordance with the relevant guidelines and regulations. All methods are reported in accordance with ARRIVE guidelines (https://arriveguidelines.org/) for the reporting of animal experiments.

Consent for publication

Not applicable.

Availability of data and materials

The sequencing data supporting the conclusions of this article are available in the NCBI repository (Accession: PRJNA975174) https://www.ncbi.nlm.nih.gov/bioproject/975174.

Acknowledgments

We thank Vishy Iyer for his valuable input on bioinformatic analysis of the transcriptomic data. We thank all members of the Eberhart lab for their manuscript and visualization suggestions. We also thank Richard Salinas for his instruction in flow cytometry. We would also like to thank Mary Swartz (lab manager), Nika Sarraf, and Adriana van Zanten (fish facility technicians) for support of this research.

Funding

This work was supported by the NIH/NIDCR under Award Number R35DE029086.

Author Contributions

Conceptualization, R.D.K. and J.K.E.; Formal analysis, R.D.K.; Funding acquisition, J.K.E.; Investigation, R.D.K.; Methodology, R.D.K. and J.K.E.; Project administration, J.K.E.; Resources, J.K.E.; Supervision, J.K.E.; Validation, R.D.K.; Visualization, R.D.K.; Writing—original draft, R.D.K.; Writing—review and editing, R.D.K. and J.K.E. All authors have read and agreed to the published version of the manuscript.

Competing interests

The authors declare that they have no competing interests.

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No competing interests reported.

WITHDRAWN: Overlapping Targets of Shh and Gata3 during Craniofacial Development

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Abstract

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Background

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