Neural crest cell-derived DKK1 and NEDD4 modulate Wnt signalling in the second heart field to orchestrate outflow tract development

Wiszniak, Sophie; Alankarage, Dimuthu; Lohraseb, Iman; Marchant, Ceilidh; Secker, Genevieve; Domingo, Deepti; Hartmann, Jasmine; Zhang, Tianyang; Parker, Wendy; Toubia, John; White, Melissa; Piltz, Sandra; Tondl, Markus; Giannoulatou, Eleni; Winlaw, David; Blue, Gillian M.; Tam, Patrick P. L.; Thomas, Paul; Harvey, Natasha; Dunwoodie, Sally L.; Schwarz, Quenten

doi:10.1038/s41467-026-68459-4

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Published: 22 January 2026

Neural crest cell-derived DKK1 and NEDD4 modulate Wnt signalling in the second heart field to orchestrate outflow tract development

Nature Communications volume 17, Article number: 1751 (2026) Cite this article

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Abstract

Cardiac outflow tract morphogenesis requires coordinated interactions between multiple cell populations and is dependent on the contribution of cardiac progenitors from the second heart field. While neural crest cells have been proposed to impact second heart field development, how they regulate progenitor behaviour remains unclear. Here, we discover neural crest cells are a primary source of Dickkopf-1 (DKK1) which modulates Wnt signalling activity in the second heart field to influence the balance between cardiac progenitor maintenance and differentiation. We show that the ubiquitin ligase NEDD4 regulates DKK1, with disruption of Nedd4 leading to outflow tract defects. We further identify a new NEDD4 variant underlying human congenital heart disease. Our findings uncover an unexpected role for neural crest cells as a rheostat of Wnt signalling in cardiac progenitors, identifying a new molecular pathway promoting outflow tract morphogenesis, and a new causative factor of congenital heart disease.

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Introduction

The cardiac outflow tract of the heart provides the pathway for blood to leave the ventricles and circulate to the body via the aorta, or to the lungs via the pulmonary artery. Outflow tract development is complex and requires the contribution of cell types from multiple embryological origins, including second heart field cardiac progenitor cells, cardiomyocytes, smooth muscle, endocardium, and neural crest cells¹. During early development, the primitive heart tube expands through the progressive addition of cardiac progenitors from the anterior second heart field, which give rise to the myocardium of the outflow tract and right ventricle. As the outflow tract lengthens, it repositions to overlie the future interventricular septum. Inadequate addition of second heart field cells to the outflow tract leads to insufficient lengthening and underpins conotruncal defects².

In the developing heart, neural crest cells form components of the outflow tract valves, arteries, and conotruncal septum. Classical defects associated with disrupted cardiac neural crest cell development include persistent truncus arteriosus, where the outflow tract fails to septate into the aorta and pulmonary arteries³. Prior to entering the outflow tract, neural crest cells migrate in close apposition to cardiac progenitors of the second heart field, and are suggested to play a role in modulating the growth dynamics of second heart field derivatives². However, prior models in which neural crest cells have been surgically or genetically ablated^4,5,6,7 have not been amenable to elucidate how neural crest cells normally interact with the second heart field to control its growth, differentiation, and morphogenesis.

NEDD4 is an E3 ubiquitin ligase that targets specific protein substrates for ubiquitination to modulate protein function and/or turnover⁸. Prior studies have determined a necessary role for Nedd4 in heart development, with full knockout mouse models exhibiting DORV and endocardial cushion defects⁹. However, the cell-type-specific mechanisms by which Nedd4 regulates cardiac development remained unexplored.

In this work, we determine an essential tissue-specific role for Nedd4 in neural crest cells for correct outflow tract development. Wnt1-Cre; Nedd4^fl/fl embryos exhibit outflow tract defects typically associated with failed addition of second heart field cells to the outflow tract, suggesting a novel role for Nedd4 and cardiac neural crest cells in regulating second heart field development. Furthermore, we identify DKK1 as a previously unknown substrate of NEDD4-mediated ubiquitination, and find that this functionality is impaired in a human variant of NEDD4 associated with the outflow tract defect Tetralogy of Fallot. We propose NEDD4 controls DKK1 protein levels in neural crest cells, which in turn modulates Wnt signalling in the second heart field to balance cardiac progenitor maintenance versus myocardial differentiation. This provides new insight into the mechanisms underpinning normal outflow tract morphogenesis, and defines a potential pathogenic mechanism underlying congenital heart disease.

Results

Nedd4 is required in neural crest cells for outflow tract development

Prior studies have reported heart defects in Nedd4^−/− embryos⁹. We specifically assessed outflow tract defects in Nedd4^−/− embryos, revealing defects at full penetrance with variable phenotypes, most commonly persistent truncus arteriosus (PTA) and transposition of the great arteries (TGA) (Fig. 1A, E, I–L, Q). Nedd4 is broadly expressed throughout all embryonic tissues that contribute to early heart development (Supplementary Fig. 1). To determine the cell types in which Nedd4 activity is critically required, we conditionally removed Nedd4 using tissue-specific Cre-drivers for cell lineages important in outflow tract development. Ablation of Nedd4 in the second heart field (Mef2cAHF-Cre; Nedd4^fl/fl) and endothelial/endocardial cells (Tie2-Cre; Nedd4^fl/fl) had no discernible impact on the development of the outflow tract (Fig. 1C, D, G, H). In contrast, ablation of Nedd4 in the neural crest cells (Wnt1-Cre; Nedd4^fl/fl) led to fully penetrant outflow tract defects such as double-outlet right ventricle (DORV) and overriding aorta (Fig. 1B, F, M–P, Q). Importantly, the outflow tract defects identified in Nedd4^−/− and Wnt1-Cre; Nedd4^fl/fl embryos are of the same class of outflow tract defect, that being artery-ventricle alignment defects, that present on a variable spectrum of severity. These findings point to a critical cell-type-specific role for Nedd4 in neural crest cells to orchestrate outflow tract development, consistent with previous demonstration of essential roles for Nedd4 in neural crest cells in craniofacial morphogenesis and peripheral nervous system development^10,11,12.

Fig. 1: Nedd4 activity in neural crest cells is required for cardiac outflow tract development. — **Fig. 1: *Nedd4* activity in neural crest cells is required for cardiac outflow tract development.**

Loss of Nedd4 in neural crest cells induces premature second heart field differentiation

The myocardial component of the outflow tract is derived from cardiac progenitors of the second heart field, with growth of the outflow tract dependent on the continued addition of cardiac progenitors to lengthen the outflow tract, allowing for heart looping and outflow tract rotation to establish correct artery-ventricle alignment^1,2. Underpinning the artery-ventricle alignment defects in Wnt1-Cre; Nedd4^fl/fl embryos, defective outflow tract lengthening at E10.5 (Fig. 2A and Supplementary Fig. 2) led to incomplete clockwise rotation of the outflow tract at E12.5, and mispositioning of the aorta over the right ventricle (Fig. 2B). Semilunar valve morphology was also altered (Fig. 2B). Such artery-ventricle alignment defects typically stem from failed addition of second heart field cells to the lengthening outflow tract. Since the removal of Nedd4 in second heart field derivatives (Mef2cAHF-Cre; Nedd4^fl/fl) did not lead to such outflow tract defects (Fig. 1C, G), this suggests a novel role for Nedd4 and cardiac neural crest cells in regulating second heart field development.

Fig. 2: Hearts of Wnt1-Cre; Nedd4fl/fl embryos exhibit shortened outflow tract and incomplete outflow tract rotation. — **Fig. 2: Hearts of *Wnt1-Cre; Nedd4*^fl/fl embryos exhibit shortened outflow tract and incomplete outflow tract rotation.**

Lineage tracing of neural crest cells using an EGFP reporter revealed that prior to entering the outflow tract, cardiac neural crest cells migrate in close proximity to the second heart field in E9.5 embryos (Fig. 3A, B). This spatial relationship presents an appropriate environment for neural crest cells to interact with the second heart field during development. It is noted that Wnt1-Cre; Nedd4^fl/fl embryos did not exhibit any deficiency in cardiac neural crest cells (Fig. 3C), nor were changes in cell proliferation (Fig. 3D) or rate of cell death (Fig. 3E) observed in the tissue domain populated by the neural crest cells and second heart field tissue. Furthermore, expression of the second heart field marker Isl1 was unaffected (Fig. 3F and Supplementary Fig. 3C), as were additional markers of the second heart field and other cardiac components (Supplementary Fig. 3), suggesting the development of other cardiogenic tissues was not affected by loss of Nedd4 function in neural crest cells. Taken together, this further supports a role for disruption of Nedd4 function in neural crest cells, specifically impacting the morphogenesis of the outflow tract, and not due to loss or misspecification of either the neural crest or second heart field tissues.

Fig. 3: Wnt1-Cre; Nedd4fl/fl embryos display precocious differentiation and defective deployment of anterior second heart field derivatives. — **Fig. 3: *Wnt1-Cre; Nedd4*^fl/fl embryos display precocious differentiation and defective deployment of anterior second heart field derivatives.**

Myocardial differentiation is initiated as second heart field cardiac progenitors enter the developing outflow tract, where they upregulate expression of various myocardial markers such as myosin heavy and light chains and other myogenic factors¹³. MF20, which recognises myosin heavy chain, is widely used to assess in vivo differentiation dynamics at the transition zone between the second heart field and outflow tract myocardium^13,14,15,16. While in wildtype embryos the second heart field maintains minimal expression of MF20, Wnt1-Cre; Nedd4^fl/fl embryos exhibited precocious MF20 expression in the second heart field (Fig. 3G and Supplementary Fig. 4C, D). The enhanced MF20 expression was also observed in ex vivo second heart field explant cultures (Fig. 3H), supporting the inference that loss of Nedd4 in neural crest cells causes premature differentiation of second heart field cardiac progenitors. Precocious myocardial differentiation is reputed to be one mechanism underpinning defective outflow tract lengthening, due to reducing the pool of cardiac progenitors available for contribution to the growing outflow tract². To assess the effects of premature differentiation on second heart field deployment into the outflow tract, we used EdU to label the highly proliferative second heart field (relative to the lowly-proliferative outflow tract myocardium) at E9.5, and tracked the extent of deployment of EdU +ve cells into the outflow tract 24 h after labelling^17,18. After a 1 h pulse, EdU labelling of the second heart field was equivalent between genotypes (Fig. 3I). EdU incorporation was terminated 1 h after the EdU pulse by injection of unlabelled thymidine, and EdU+ve cells were chased for 24 h to assess their movement from the second heart field into the outflow tract. After a 24 h chase at E10.5, EdU+ve cells had moved a shorter distance into the outflow tract of Wnt1-Cre; Nedd4^fl/fl embryos (Fig. 3J and Supplementary Fig. 5), consistent with impeded deployment of second heart field cells into the outflow tract underpinning outflow tract lengthening defects.

Neural crest cells have been implicated in modulating BMP and FGF signalling in the second heart field to impact outflow tract development^19,20. Expression of Bmp4 and Fgf8, as well as downstream signalling components phospho-SMAD1/5/9 and phospho-ERK1/2, was not affected in Wnt1-Cre; Nedd4^fl/fl embryos (Supplementary Figs. 3E, F and 6). Hence, we hypothesised that neural crest cells may be regulating the differentiation dynamics of the second heart field via other molecular mechanisms or signalling pathways.

Neural crest-derived DKK1 modulates Wnt signalling in the second heart field

To investigate how loss of Nedd4 in neural crest cells causes premature second heart field differentiation, we used laser capture microdissection to isolate cells from a defined region of the anterior second field, as well as neural crest cells and pharyngeal endoderm in the immediate vicinity, from wild-type and Wnt1-Cre; Nedd4^fl/fl embryos for transcriptome profiling (Fig. 4A and Supplementary Dataset 1). 897 differentially expressed genes (DEGs) with P < 0.05 were identified (Fig. 4B). Gene ontology enrichment analysis of the top 30 DEGs (Fig. 4C) revealed Wnt signalling as the top signalling pathway with differential gene expression (Supplementary Fig. 7A, B). Dkk1, the gene encoding a secreted Wnt signalling antagonist, was the top DEG identified (Fig. 4C). We next performed immunostaining and in situ hybridisation to investigate cell-type specificity of gene expression. Immunostaining revealed DKK1 expression was localised exclusively to neural crest cells (that co-express the neural crest marker AP2α), with no expression in other cell lineages such as the second heart field or pharyngeal endoderm (Fig. 4D). Importantly, DKK1 was only expressed in cardiac neural crest cells that had reached the peri-second heart field region in their migration trajectory, and not in earlier post-delaminating or migrating neural crest cells (Supplementary Fig. 8). In situ hybridisation, in parallel with immunostaining, validated the enhanced Dkk1 expression and enriched DKK1 abundance in cardiac neural crest cells in Wnt1-Cre; Nedd4^fl/fl embryos (Fig. 4E–G). Given the function of DKK1 as a canonical Wnt signalling inhibitor, and the proximity of cardiac neural crest cells to the anterior second heart field, we hypothesised that neural crest-derived DKK1 may antagonise Wnt signalling in the adjacent second heart field. Employing β-catenin protein stabilisation and nuclear translocation as a readout of canonical Wnt signalling activity²¹, immunostaining for active forms of β-catenin (non-pS45, ABC (non-pS37/T41) and pY489^22,23) revealed a specific reduction in nuclear localised staining intensity in the anterior second heart field of Wnt1-Cre; Nedd4^fl/fl embryos (Fig. 4H and Supplementary Fig. 9A–E). Furthermore, we observed a reduction in Pitx2 expression, a canonical Wnt signalling target gene²⁴ in the second heart field of Wnt1-Cre; Nedd4^fl/fl embryos (Supplementary Fig. 9G, H). As well as signalling functions, β-catenin is also involved in regulating cell-adhesion via interactions with cadherins and the actin cytoskeleton²⁵. Recent work has demonstrated requisite roles for cell-adhesion, polarity, and epithelial-like properties of the second heart field in outflow tract development^13,26. Immunostaining for markers of cell-adhesion, polarity, and ECM components did not reveal any changes of these cellular attributes in the second heart field region of Wnt1-Cre; Nedd4^fl/fl embryos (Supplementary Fig. 10). Taken together, our data support a role for neural crest-derived DKK1 in modulating canonical Wnt signalling activity in the second heart field.

Fig. 4: Laser capture mRNA sequencing reveals disrupted Wnt signalling in Wnt1-Cre; Nedd4fl/fl embryos. — **Fig. 4: Laser capture mRNA sequencing reveals disrupted Wnt signalling in *Wnt1-Cre; Nedd4*^fl/fl embryos.**

To assess canonical Wnt signalling activity in Wnt1-Cre; Nedd4^fl/fl embryos, we intersected our laser-capture tissue mRNAseq dataset with a β-catenin ChIPseq dataset²⁷ to analyse expression of canonical Wnt target genes. We identified 179 differentially expressed Wnt target genes, with 49 upregulated and 130 downregulated (Fig. 4I). To validate and determine cell-type specificity of expression, we performed in situ hybridisation for ten candidate genes on tissue sections of the outflow tract region from E9.5 wildtype and Wnt1-Cre; Nedd4^fl/fl embryos. Downregulated Wnt target genes, e.g., Axin2, SP5, Pitx2c, Fam49a, Mical2, were localised to the second heart field (Fig. 4K and Supplementary Fig. 11), consistent with the reduction in nuclear β-catenin in the second heart field. Of note, upregulated expression of Wnt target genes was observed in the cardiac neural crest cells (Fig. 4J), suggesting there may be a cell-autonomous role for Nedd4 in regulating Wnt activity in neural crest cells.

Disruption of Wnt signalling activity underpins outflow defects

To ascertain that disruption of Wnt signalling activity causes outflow tract defects in Wnt1-Cre; Nedd4^fl/fl embryos, we manipulated canonical Wnt signalling in vivo by in utero treatment with small molecule activators (CHIR99021) and inhibitors (iCRT3) of Wnt signalling over the developmental time-period when neural crest cells interact with the second heart field (Fig. 5A). Treatment with Wnt agonist CHIR99021, which may enhance Wnt signalling activity, did not rescue the outflow tract defects in Wnt1-Cre; Nedd4^fl/fl embryos by E13.5 (Fig. 5B, C and Supplementary Fig. 12B), suggesting that escalating Wnt signalling activity could not counteract the effect of elevated DKK1 activity. However, when Wnt signalling was blocked with the chemical inhibitor iCRT3, Wnt1-Cre; Nedd4^fl/+ heterozygous embryos, which do not normally exhibit structural heart defects, now exhibited outflow tract rotation defects (Fig. 5B, C and Supplementary Fig. 12C). Thus, loss of one copy of Nedd4 in neural crest cells sensitises the phenotypic response to disruption of Wnt signalling, and points to genetic interaction between Nedd4 and the Wnt signalling pathway.

**Fig. 5: Reduced canonical Wnt signalling underpins precocious second heart field differentiation.**

To determine if the outflow tract rotation deficiency in iCRT3-treated Wnt1-Cre; Nedd4^fl/+ heterozygous embryos is caused by premature second heart field differentiation, we examined the differentiation of second heart field tissue in E9.5 + 6 h iCRT3-treated embryos by MF20 immunostaining (Fig. 5D). iCRT3-treated Wnt1-Cre; Nedd4^fl/+ heterozygous embryos exhibited precocious second heart field differentiation, like that in Wnt1-Cre; Nedd4^fl/fl knockout embryos (Fig. 5E, F and Supplementary Fig. 4E, F). Furthermore, examination of Wnt signalling activity using β-catenin pY489 and non-pS45 immunostaining revealed reduced nuclear-localised β-catenin in the second heart field of iCRT3-treated Wnt1-Cre; Nedd4^fl/+ heterozygous embryos, indistinguishable from that of Wnt1-Cre; Nedd4^fl/fl knockout embryos (Supplementary Fig. 13). These findings suggest that a reduced level of canonical Wnt signalling underpins premature second heart field differentiation, and is causative of outflow tract rotation deficiency.

To determine if ectopic Dkk1 overexpression is a contributing factor to outflow tract defects, we generated compound Wnt1-Cre; Nedd4^fl/fl; Dkk1^+/− embryos in which Dkk1 expression was reduced in the Wnt1-Cre; Nedd4^fl/fl background, to test for genetic rescue of outflow tract defects. Compared to Wnt1-Cre; Nedd4^fl/fl embryos, Wnt1-Cre; Nedd4^fl/fl; Dkk1^+/− embryos exhibited improvement in outflow tract development at E13.5 (Fig. 5G, H and Supplementary Fig. 12D), including restoration of aortic valve diameter (Fig. 5I and Supplementary Fig. 14A) partially rescued pulmonary valve diameter (Fig. 5J and Supplementary Fig. 14B) and partial rescue of outflow tract tissue mass (Fig. 5K and Supplementary Fig. 14C). While artery-ventricle alignment was not completely rescued in Wnt1-Cre; Nedd4^fl/fl; Dkk1^+/- embryos, there was a greater extent of outflow tract rotation (Fig. 5L and Supplementary Fig. 12D). Taken together, the enhanced outflow tract development and rotation suggest that ectopic Dkk1 overexpression is one major factor underpinning defective outflow tract development in Wnt1-Cre; Nedd4^fl/fl embryos.

DKK1 is a substrate for NEDD4-mediated ubiquitination

Expression of DKK1, at both transcript and protein levels, was elevated in neural crest cells with loss of Nedd4 function (Fig. 4). Given NEDD4 is an E3 ubiquitin ligase with important roles in regulating protein abundance, we investigated if DKK1 may be a ubiquitinated substrate of NEDD4. Computational analysis on the UbiBrowser bioinformatic platform²⁸ predicts DKK1 can be ubiquitinated, with NEDD4 predicted to be the targeting E3 ligase. Using in vitro ubiquitination assays, we demonstrated that wildtype NEDD4 indeed ubiquitinates DKK1, while an inactive version of NEDD4 in which the catalytic cysteine is mutated (NEDD4 CS) is unable to ubiquitinate DKK1 (Fig. 6A and Supplementary Fig. 15A). Moreover, NEDD4 specifically ubiquitinates DKK1, but not other DKK family members such as DKK2 (Fig. 6B). This ubiquitination likely targets DKK1 for protein degradation, as overexpression of NEDD4 WT in HEK293T cells reduces the steady-state levels of DKK1-Myc, whereas overexpression of NEDD4 CS did not affect DKK1-Myc levels (Fig. 6C). Overexpression of DKK1-GFP and FLAG-NEDD4 in HeLa cells revealed an inverse correlation of fluorescence intensity that measured protein abundance between DKK1 and NEDD4 protein, but correlation was observed between DKK1 and NEDD4 CS (Fig. 6D and Supplementary Fig. 16A). Expression of a GFP-only construct with NEDD4 WT and NEDD4 CS revealed no inverse correlation (Fig. 6E), suggesting that DKK1 is the substrate targeted by NEDD4. Furthermore, high-magnification imaging of cardiac neural crest cells in vivo revealed co-localisation of NEDD4 and DKK1 in wild-type neural crest cells, with greatly increased DKK1 fluorescent intensity and abundance in Wnt1-Cre; Nedd4^fl/fl cardiac neural crest cells (Fig. 6F), consistent with the inverse correlation of fluorescence intensity that was observed in the in vitro HeLa cell assay. Interestingly, NEDD4 exhibited a particularly enhanced expression and punctate subcellular localisation specifically in cardiac neural crest cells (Supplementary Fig. 1E and Fig. 6F), suggesting a potentially unique function for NEDD4 in this cell type. Taken together, the increased levels of DKK1 in the neural crest cells of Wnt1-Cre; Nedd4^fl/fl embryos can be accounted for in part by the absence of NEDD4-mediated ubiquitination and consequent degradation of DKK1.

**Fig. 6: NEDD4 ubiquitinates DKK1 to regulate protein levels.**

NEDD4 is clinically implicated in congenital heart defects

We identified a homozygous missense variant in NEDD4 in a child with Tetralogy of Fallot. The child and parents were recruited and assessed as part of an Australian congenital heart disease genome sequencing study²⁹ (now extended to n = 363 trios and singletons). Analysis of genome sequencing data from the trio revealed the recessive inheritance of a NEDD4 variant by the proband from parents carrying one allele each (Fig. 7A). The variant NEDD4:c.2297 A > G:p.K766R (GenBank:NM_006154.4) changes the reference lysine to an arginine, which is predicted to be damaging by in silico metrics CADD and PolyPhen (CADD: 25, PolyPhen 0.995). No predicted damaging variants were identified in other genes presently known to cause congenital heart disease, making the NEDD4 variant a compelling candidate for causing disease in this family. The parents were not consanguineous. While the family also reported congenital heart disease in distant relatives on the maternal side of the family, no clinical data were available. Furthermore, an additional NEDD4 missense variant was recently documented in another individual with Tetralogy of Fallot in an independent study³⁰, strengthening disease links to this gene.

Fig. 7: A NEDD4 variant identified in an individual with congenital heart disease has impaired ubiquitination of DKK1. — **Fig. 7: A *NEDD4* variant identified in an individual with congenital heart disease has impaired ubiquitination of DKK1.**

The K766R amino acid change is located in the HECT domain of NEDD4, which is responsible for ubiquitin ligase activity (Fig. 7B). Given that NEDD4 can ubiquitinate DKK1, in vitro assays to assess the effect of K766R amino acid substitution on NEDD4 function revealed NEDD4 K766R had reduced capacity to ubiquitinate DKK1 (Fig. 7C and Supplementary Fig. 15B). Furthermore, analysis of fluorescence intensity revealed no correlation between DKK1 and NEDD4 K766R protein levels (Fig. 7D and Supplementary Fig. 16B), confirming NEDD4 K766R is a loss-of-function variant for ubiquitination of DKK1.

To investigate the in vivo consequences of NEDD4 K766R substitution, we generated a CRISPR-engineered Nedd4^K766R/K766R mouse model. Homozygous embryos of this mutant mouse strain were present at expected mendelian ratios at all developmental stages and phenotypically normal (Fig. 7E). Examination of hearts in E15.5 to E17.5 Nedd4^K766R/K766R homozygous embryos revealed a range of developmental cardiac defects (Fig. 7F). While artery-ventricle alignment defects such as overriding aorta, which is a clinical feature of Tetralogy of Fallot, were not present, there were defects consistent with deficiency of right ventricular development. This included a thin ventricular wall which was restricted to the right ventricle, often with a rugged appearance (Fig. 7G, H) suggestive of defective right ventricular compaction, a disorganised interventricular septum, and ventricular septal defects (Fig. 7G). Hence, Nedd4^K766R/K766R embryos exhibit heart defects, including some features of Tetralogy of Fallot, such as right ventricular wall and interventricular septal defects, implicating the NEDD4 K766R variant as a cause of congenital heart disease.

Taken together, NEDD4 regulation of DKK1 in neural crest cells unveils a novel mechanistic paradigm in heart development, which when disrupted leads to heart defects in mice and humans.

Discussion

Wnt signalling plays critical roles in multiple aspects of heart development, where it has stage-dependent positive and negative effects on progenitor cell specification, maintenance, and differentiation (reviewed in refs. ^31,32). Canonical Wnt/β-catenin signalling is required for initial mesoderm induction in the gastrulating embryo; however, it is then downregulated to promote cardiac precursor specification, which concomitantly is supported by activation of non-canonical Wnt signalling^33,34. As heart development progresses, cells of the second heart field require canonical Wnt/β-catenin signalling to maintain cells in a proliferative and undifferentiated state. As second heart field progenitors move into the outflow tract, canonical Wnt signalling is gradually downregulated, arresting cell proliferation and initiating myocardial differentiation, in which non-canonical Wnt signalling plays an important role. Hence, the temporal balance of activation and inhibition of Wnt signalling is essential for orchestrating the proliferation and differentiation dynamics of cardiac progenitors during heart development.

Conditional knockout and constitutive activation of β-catenin in specific cardiac cell lineages support a role for the dynamic regulation of canonical Wnt signalling in heart development. Knockout of β-catenin in the Islet1-Cre or SM22α-Cre lineages results in failure of the right ventricle to form, while overactivation of β-catenin causes right ventricular enlargement^35,36. In other lineages (Mef2cAHF-Cre and MesP1-Cre), both loss- and gain-of-function of β-catenin are detrimental to right ventricle formation^37,38. Hence, the precise dosage and timing of Wnt/β-catenin signalling is critical. Inactivation of β-catenin target genes in the second heart field also supports a role for canonical Wnt signalling in outflow tract development. For example, conditional knockout of Pitx2 in Mef2cAHF-Cre and Islet1-Cre lineages causes artery-ventricle alignment defects including double outlet right ventricle and transposition of the great arteries³⁹, and is consistent with the phenotype of Wnt1-Cre; Nedd4^fl/fl embryos which exhibit downregulation of Pitx2c in the second heart field.

While Wnt signalling is known for its role in second heart field development, the source of Wnt ligands has remained unclear. A recent study suggests that Wnt2 secreted from the first heart field can promote second heart field proliferation at early stages of heart development (E8.5)⁴⁰. Whether Wnt2 is the key Wnt ligand responsible for maintaining the second heart field in a proliferative state at later stages (E9.5) during the critical time period of outflow tract lengthening remains to be determined. Nevertheless, as anterior second heart field cells move into the outflow tract, canonical Wnt signalling is downregulated to promote myocardial differentiation. This reduction in Wnt signalling may be a consequence of these cells moving away from a localised source of Wnt morphogen as they enter the outflow tract. Alternatively, we propose that cardiac neural crest cells can act as a critical rheostat of Wnt signalling as second heart field cells transition into the outflow tract. Neural crest cells are spatially positioned in this transition zone; providing a source of DKK1 to fine-tune Wnt signals and promote coordinated second heart field differentiation.

Neural crest cells have previously been implicated in second heart field development. Early studies in avian models demonstrated that surgical ablation of the premigratory cardiac neural crest caused an array of conotruncal heart defects⁴, underpinned by failed addition of second heart field cells to the lengthening outflow tract^6,7. Furthermore, these studies showed loss of cardiac neural crest cells led to increased FGF signalling and excessive proliferation in the second heart field, suggesting cardiac neural crest act to modulate FGF signalling to promote appropriate differentiation of the outflow tract myocardium^5,41,42. BMP signalling antagonises the pro-proliferative effects of FGF signalling to promote myocardial differentiation, and neural crest cells have been shown to be essential for mediating the effects of BMP on cardiomyocyte differentiation^19,20. Disruption of downstream BMP signalling components in neural crest cells also causes abnormal second heart field differentiation and outflow tract defects^43,44. How neural crest cells mediate BMP-induced second heart field differentiation is unknown, however an attractive hypothesis is that BMP signalling may induce neural crest cells to secrete another factor which in turn suppresses FGF signalling and/or promotes differentiation. Wnt/β-catenin signalling also feeds into these complex signalling interactions, with Wnt gain- and loss-of-function also affecting BMP and FGF signalling outcomes in the second heart field^36,37,38. As no disruption of FGF and BMP signalling, or changes in proliferation of second heart field progenitors were observed in Wnt1-Cre; Nedd4^fl/fl embryos, this may suggest neural crest-derived factors act downstream of these signalling interactions in this model to regulate myocardial differentiation of the second heart field. Hence, FGF and BMP signals may be essential to prime the second heart field to appropriately respond to differentiation signals, with neural crest cells acting as a critical source of DKK1 to modulate Wnt signalling and actively initiate myocardial differentiation.

DKK1 is ideally located to act as a rheostat of Wnt signalling, providing precise spatial and temporal control of canonical Wnt signalling activity in the second heart field. This dynamic regulation is essential for maintaining a critical balance of second heart field progenitor maintenance versus differentiation, to foster appropriate outflow tract elongation. Our study has unveiled the expression of DKK1 specifically in cardiac neural crest cells, providing new mechanistic data to explain how neural crest cells regulate second heart field myocardial differentiation. Enhanced DKK1 activity induces cardiac differentiation in embryonic stem cell models^35,45, and in Xenopus embryos⁴⁶, which is consistent with the increased myocardial differentiation observed in the second heart field of Wnt1-Cre; Nedd4^fl/fl embryos. While Dkk1 knockout mouse embryos have no reported heart defects⁴⁷, Dkk1 and Dkk2 double knockout embryos exhibit mild heart defects, including ventricular septal defects and myocardial/epicardial hyperplasia⁴⁸. Findings of our present study are supportive of ectopic gain-of-function of DKK1, rather than loss-of-function, as being causative of developmental defects of the heart. The impact of widespread ectopic gain-of-function of Dkk1 on heart development remains to be investigated.

Our study reports the ubiquitin modification of the DKK1 protein as an additional level of post-translational control to modify DKK1 protein abundance, and hence further regulates the precise level of Wnt signalling activity. Our finding provides a molecular mechanism linking the loss of the ubiquitin ligase NEDD4 in neural crest cells to changes in DKK1 abundance. While we have focused on post-translational regulation of DKK1 protein, we also observe an increase in Dkk1 mRNA in neural crest cells of Wnt1-Cre; Nedd4^fl/fl embryos. Indeed, many of the differentially expressed genes that were upregulated in Wnt1-Cre; Nedd4^fl/fl embryos are localised to the cardiac neural crest. This likely points to cell-autonomous roles for Nedd4 loss-of-function in cardiac neural crest cells, which may also underpin cardiac defects. This would be a subject of future studies in heart development.

The clinical significance of our findings is highlighted by the outcome of the functional study of a deleterious homozygous missense variant of NEDD4 in an individual with Tetralogy of Fallot. This NEDD4 variant results in a non-synonymous amino acid change in the protein domain responsible for ubiquitin ligase activity, and we show that this variant has a greatly reduced ability to ubiquitinate DKK1, as well as exhibiting heart defects in a CRISPR-engineered in vivo model. While the heart defects exhibited in Nedd4^K766R/K766R mice do not fully phenocopy the defects observed in Wnt1-Cre; Nedd4^fl/fl mice, this is likely due to functional differences between hypomorphic NEDD4 K766R protein expression in the whole embryo versus complete removal of NEDD4 protein in neural crest cells of Wnt1-Cre; Nedd4^fl/fl mice. The clinical Tetralogy of Fallot phenotype in the NEDD4 p.K766R individual overlaps with the heart defect phenotype of Wnt1-Cre; Nedd4^fl/fl mice, implicating an association of neural crest-derived NEDD4 and congenital heart disease. Mis-regulation of DKK1 activity may represent a new pathogenic mechanism underpinning congenital heart disease. Neural crest cells have never been considered as paracrine modifiers of Wnt signalling activity. We here define a new developmental paradigm in heart morphogenesis, and implicate neural crest cell modulation of Wnt signalling as a key mechanistic attribute controlling heart development.

Methods

Mice

All experiments were carried out in accordance with the ethical guidelines of the University of South Australia Animal Ethics Committee. Mice are housed with a 12 h dark/light cycle, with temperature maintained between 19-23 °C and 40-70% relative humidity. Mice used for timed matings were between 6-12 weeks of age. All mouse lines were maintained on a mixed C57BL/6 background. Nedd4^-/- mice and Nedd4^fl/fl have been described previously^49,50. To remove Nedd4 in specific tissues, we crossed Nedd4^fl/fl females to Wnt1-Cre;Nedd4^fl/+⁵¹, Mef2cAHF-Cre;Nedd4^fl/+⁵² or Tie2-Cre;Nedd4^fl/+⁵³ males. Wnt1-Cre; Nedd4^fl/fl mice were intercrossed with Dkk1^+/−⁴⁷ mice (Wnt1-Cre; Nedd4^fl/+ males crossed to Nedd4fl/fl; Dkk1^+/− females) to generate Wnt1-Cre; Nedd4^fl/fl; Dkk1^+/- embryos (as well as littermate controls) for assessing the rescue effect of DKK1 dosage compensation. The following reporter lines were also intercrossed with Wnt1-Cre;Nedd4^fl/+ mice; Mef2cAHF-LacZ⁵⁴ and Z/EG⁵⁵. Nedd4^K766R mice were generated using CRISPR targeting. As embryos that contained the variant on both chromosomes (homozygotes) or in combination with a frameshifting indel (compound heterozygotes) were likely to have compromised cardiac function, we used a dual template competition strategy. SaCas9 KKH mRNA was microinjected with two single-stranded DNA oligonucleotide donor templates. The first oligonucleotide contained silent mutations at residues K766, I765 & L764 and retained WT function when integrated (sequence: TTCCGACGCGTTTCCTTTCCTTCTAGGGATTTTTTGAACTGATACCACAGGATttgATtAAaATATTTGATGAAAATGAGCTAGAGGTAAGAACTATTTCTGCATGTGCTTGGGAATGTAG). The second oligonucleotide encoded the K766R variant and a silent CTC(L764) > TTG(L764) mutation to abolish the PAM site and prevent re-cutting of the targeted DNA by the nuclease (sequence: TTCCGACGCGTTTCCTTTCCTTCTAGGGATTTTTTGAACTGATACCACAGGATTTGATCAGGATATTTGATGAAAATGAGCTAGAGGTAAGAACTATTTCTGCATGTGCTTGGGAATGTAG). The K766R oligonucleotide also introduced a BclI recognition site for subsequent genotyping. Both oligonucleotides had 60 bp homology arms on either side of the targeted mutations.

To obtain mouse embryos of defined gestational ages, mice were mated in the evening, and the morning of vaginal plug formation was counted as E0.5. In all experiments, stage-matched embryo littermates were selected for comparison using stage-appropriate parameters such as somite number, crown-rump length, and forelimb/digit maturity. For embryonic analyses, we did not genotype for sex.

Drug treatments of pregnant dams

Pregnant dams were intraperitoneally injected with 16.6 mg/kg CHIR99021 (Selleck Chem) or 10 mg/kg iCRT3 (Selleck Chem) at the indicated time points in 15% DMSO, 30% PEG 300, PBS vehicle. Embryos were harvested at E13.5 or E9.5, and processed for immunostaining.

Histology and immunostaining

Embryos were fixed in 4% paraformaldehyde in PBS. Cryosections were stained with Hematoxylin and Eosin to classify heart defects at E15.5-E17.5. For immunolabelling, cryosections, whole embryos, or fixed cells were blocked in 10% DAKO block, 0.2% BSA, 0.2% Triton X-100 in PBS, and stained with the indicated primary antibodies. Antibodies used were rabbit anti-NEDD4 (Abcam 14592) 1:300; rabbit anti-NEDD4 (purified serum, gift from S. Kumar) 1:300; mouse anti-alpha smooth muscle actin (Sigma A2547) 1:2000; rat anti-CD31 (Biolegend 102502) 1:150; goat anti-SOX10 (R&D Systems AF2864) 1:200; chicken anti-GFP (Abcam ab13970) 1:1000; mouse anti-Isl1 (DSHB 39.4D5 or 40.3A4) 1:50; mouse anti-MF20 (DSHB) 1:100; goat anti-DKK1 (R&D Systems AF1765) 1:200; mouse anti-AP2a (DSHB 3B5) 1:20; rabbit anti-beta-catenin non-pS45 (Cell Signaling Technology 19807) 1:200; mouse anti-beta-catenin ABC (8E7) (Millipore 05-665) 1:100; mouse anti-beta-catenin pY489 (DSHB) 1:50; mouse anti-FLAG (Sigma F3165) 1:1000; mouse anti-GM130 (BD 610822) 1:100; rabbit anti-phospho-Histone H3 (Millipore 06-570) 1:500; rabbit anti-cleaved-Caspase-3 (Cell Signaling Technology 9661) 1:500; rabbit anti-phospho-SMAD1/5/9 (Cell Signaling Technology 13820) 1:200; rabbit anti-phospho-ERK1/2 (Cell Signaling Technology 4370) 1:100; goat anti-Scribble (Santa Cruz Biotechnology sc-11048) 1:50; rabbit anti-Laminin (Sigma L9393) 1:1000; rabbit anti-Fibronectin (DakoCytomation A0245) 1:1000; mouse anti-N-cadherin (Cell Signaling Technology 14215) 1:100; AlexaFluor 647 conjugated Phalloidin (Invitrogen) 1:200. EdU staining was performed following manufacturers recommendations (Invitrogen Click-iT EdU AlexaFluor 555 Cell Proliferation Kit). Slides were mounted in Fluoro-mount G with DAPI (ProSciTech). Histology and whole embryo/heart images were captured using Olympus DP2-AOU1.1 software or Improvision OpenLab 5 software. Confocal images were acquired with a Zeiss LSM 800 microscope and ZEN 2.6 (Blue edition) software.

Fluorescence intensity and image quantitation

Mean fluorescence intensity (MFI) was measured using ZEN 3.4 (Blue edition) software (Zeiss) and represents the total fluorescence intensity of the region of interest, normalised to the area of the region of interest. For MF20 measurements in the second heart field at E9.5, a line of 100 μm was drawn downwards perpendicular to the outflow tract, and the single layer of cells of the second heart field (pericardial wall) was traced around only to the end of the 100 μm line. Hence, an equivalent area of MF20 fluorescent intensity was measured across different embryo samples. A minimum of 2 and up to 4 sagittal sections per embryo (at the medial position relative to the left and right side of the second heart field) were used to calculate MFI as described above, and averaged. Individual points on graphs represent the mean value calculated from 1 embryo. A minimum of 5 and up to 12 embryos per genotype were used for MF20 quantitation. In addition, MF20 MFI in the second heart field was normalised to MFI measurements taken of the adjacent unaffected atria within the same acquired microscopy image, and presented in the Supplementary Figs. MFI was measured from a 100 μm length of MF20+ve atria tissue, with the normalised value representing MFI in the second heart field/MFI in atria.

For MF20 MFI measurements of SHF explants, the entire DAPI+ve region was selected, and MFI was measured in this region of interest. Individual values represent MFI for independent explants (i.e., biological replicates) of the indicated genotype.

For DKK1 MFI measurements, a minimum of three and up to six sagittal sections per embryo were measured and averaged. An area encompassing the cardiac neural crest cells was drawn using the boundaries of the basal sides of the pharyngeal endoderm and dorsal pericardial wall for width, and ~100 μm for height (area ~2000–3000 μm²). Given DKK1 is only expressed in cardiac neural crest cells in the pericardiac region, it was not possible to assess any other DKK1+ve areas of immunostaining for the purposes of normalisation to an unaffected structure.

For β-catenin immunostaining, high-magnification images were assessed, and co-localisation of β-catenin immunostaining with DAPI fluorescence was counted as nuclear β-catenin + ve. A 100 μm length of second heart field nuclei were counted, which ~20–25 nuclei per section. β-catenin + ve nuclei were represented as a percentage of total nuclei counted. A minimum of 3 and up to 8 sagittal sections were quantified per embryo and averaged. Individual points on graphs represent the mean value calculated from 1 embryo. A minimum of 4 and up to 9 embryos per genotype were used for β-catenin quantitation.

For EdU quantitation in SHF after 1 h pulse, a 100 μm length of second heart field nuclei were counted, which ~20–25 nuclei per section. EdU +ve nuclei were represented as a percentage of total nuclei counted. Five sagittal sections were quantified per embryo and averaged. Individual points on graphs represent the mean value calculated from 1 embryo, with 6 embryos assessed per genotype. For EdU migration front distance at E10.5 after 24 h chase, the EdU +ve migration front in the OFT was measured as the distance (in μm) of the last EdU +ve cell in the MF20+ve OFT from the pericardial wall. Three to four sagittal sections were quantified per embryo and averaged. Individual points on graphs represent the mean value calculated from 1 embryo. A minimum of 5 and up to 6 embryos per genotype were used for quantitation. EdU +ve migration front distance was also normalised to atrio-ventricular valve primordium thickness (an unaffected structure present in the same microscopy images) as a means to account for any variation in embryo size, and is presented in the Supplementary Figs.

For HeLa cell assays, the entire cell outline was traced to define the region of interest, and MFI was measured for NEDD4 and DKK1 or GFP-only within this region of interest. Approximately eight images were acquired at random per transfection condition, from n = 3 experiments. Data points on graphs represent individual cells, with a minimum of 40 cells measured per transfection condition in total.

Semi-quantitative optical density measurements of whole-mount in situ hybridisation were performed in ImageJ. Images were subjected to colour deconvolution to generate 8-bit grayscale images of BCIP/NBT substrate staining. Mean grey values were calculated from a region of interest corresponding to the second heart field/dorsal pericardial wall. Optical density was calculated using the formula OD = Log₁₀(255/mean grey value). A minimum of three and up to seven whole-mount embryos per genotype were assessed.

In situ hybridisation

Whole embryos or cryosections were hybridised with digoxigenin-labelled antisense probes for the following genes Dkk1, Dlx2, Prrx1, Msx1, Msx2, Col1a1, Axin2, SP5, Pitx2c, Fam49a, Mical2, Tbx1, Tbx20, Isl1, HoxB1, Fgf8, Bmp4. Signal was detected using anti-DIG-AP conjugated Fab fragments (Roche), and staining with NBT/BCIP (Roche). Whole embryos were imaged with Olympus CZX10, and tissue sections were imaged with Olympus IX73.

β-galactosidase staining

Fixed embryos were incubated in staining solution: 19 mM sodium dihydrogen phosphate, 81 mM Disodium hydrogen phosphate, 2 mM MgCl₂, 5 mM EGTA, 0.01% sodium deoxycholate, 0.02% NP-40, 5 mM potassium ferricyanide, 5 mM potassium ferrocyanide, and 1 mg/ml X-gal substrate, at 37 °C until blue staining was sufficient.

SHF explant

The second heart field region was dissected from E9.5 embryos by removing the head above pharyngeal arch 2, cutting off the outflow tract and heart, cutting down the lateral sides of the pharynx to create a tissue flap, and then separating this second heart field tissue flap at the posterior end from the rest of the embryo. This was transferred to tissue culture dishes coated with 50 μg/ml Collagen and 50 μg/ml Fibronectin, and cultured for 5 days in 5% FCS + DMEM. Explants were fixed in 4% PFA.

SHF deployment assay

In methods similar to refs. ^17,18, pregnant dams were intraperitoneally injected with 100 mg/kg EdU (5-ethynyl-2’-deoxyuridine) at E9.5 to pulse-label proliferating cells. Embryos were harvested after 1 h to assess the extent of EdU labelling. To chase EdU-labelled proliferating cells, pregnant dams were injected with 500 mg/kg of thymidine 1 h after the EdU pulsing period to compete for remaining EdU label, and were harvested 24 h later at E10.5. Embryos or tissue sections were stained with Click-iT EdU Kit AlexaFluor-555 conjugated (Life Technologies) following staining with primary antibodies.

Laser capture and RNA preparation

Tissue preparation and laser capture were based on ref. ⁵⁶. Briefly, mouse embryos were fresh-frozen in OCT, cryosectioned, and collected on PEN membrane slides. Slides were stained with 1% Cresyl Violet to visualise tissue sections, air-dried, and then laser dissection was performed promptly (Leica LMD) to specifically capture the central anterior second heart field region (with associated neural crest and pharyngeal endoderm tissue). RNA was prepared from tissue by lysing in 4 M guanidine isothiocyanate solution (Invitrogen) and precipitating with sodium acetate and ethanol. After 75% ethanol wash, the RNA pellet was resuspended in 12 μl H₂O, with 1 μl reserved for Bioanalyser quality control of RNA, and 9.5μl used for subsequent cDNA synthesis.

RNA sequencing and bioinformatics analyses

Isolated RNA samples were submitted to the ACRF Genomics Facility for library preparation and sequencing. Extracted RNA was quantified using the Bioanalyser Pico chip, and 9.5 μl (containing 5–17 ng of RNA) was input into SMART-Seq v4 Ultra Low Input RNA Kit for Sequencing (Takara Bio) for generation and amplification of double-stranded full-length cDNA (using nine cycles of LD-PCR). Prior to library preparation, cDNA was sheared to 200–500 bp fragments using the Covaris system. NGS libraries were prepared using the ThruPLEX DNA-seq Kit (Takara Bio) together with the DNA Unique Dual Index Kit (Takara Bio), using 11 cycles of PCR for library amplification. Indexed libraries from three biological replicates for each wildtype and Wnt1-Cre; Nedd4^fl/fl samples were pooled equally for sequencing using 2 × 75bp reads on an Illumina NextSeq High Output kit. Raw data, averaging 47 million reads per sample, were analysed and quality checked using the FastQC program (http://www.bioinformatics.babraham.ac.uk/projects/fastqc). Reads were mapped against the mouse reference genome (mm10) using the STAR spliced alignment algorithm⁵⁷ (version 2.6.1 d with default parameters and --chimSegmentMin 20, --quantMode GeneCounts), returning an average unique alignment rate of 81%. The resulting mapped reads were found to possess a high rate of duplicates ( ≈ 64%). These were marked and subsequently removed using Picard Tools (version 2.16.0)⁵⁸ leaving, on average 18 million deduplicated reads per sample. Differential expression analysis was evaluated from TMM normalised gene counts using R (version 3.2.3), edgeR (version 3.3)⁵⁹ and Degust⁶⁰ following protocols as described⁶¹. Alignments were visualised and interrogated using the Integrative Genomics Viewer v2.3.80⁶². Graphical representations of differentially expressed genes were generated using Glimma⁶³, Degust⁶⁰, and MaGIC Volcano Plot Tool⁶⁴. Gene ontology analysis was performed using DAVID^65,66.

Ubiquitination assays

For cell-based ubiquitination assays, HEK293T cells originally obtained from ATCC (CRL-3216) were grown in DMEM supplemented with 10% foetal calf serum. Plasmid transfections were carried out with Lipofectamine 2000 (Thermo Fisher). HEK293T cells were co-transfected with an equal amount of pcDNA3-FLAG-tagged NEDD4 WT, NEDD4 CS, or NEDD4 K766R, pCDNA3-HA-tagged ubiquitin (gift from Sharad Kumar), and GFP-tagged DKK1 or DKK2. The DKK1-GFP and DKK2-GFP constructs were generated by cloning a PCR fragment of the open reading frames from pCS2-DKK1-FLAG (Addgene plasmid #16690) or pCS2-DKK2-FLAG (Addgene plasmid #15495) in frame to EGFPN3 using EcoRI and BamHI restriction sites. Cells were grown for 24–48 h at 37 °C with 5% CO₂ in a humidified atmosphere. Transfected cells were treated with 10 μM MG132 and 100 μM of Chloroquine for 3 h prior to protein extraction.

Immunoprecipitation and western blot

Cells were lysed in NP-40 buffer (137 mM NaCl, 10 mM Tris pH 7.4 (108319 Merck), 10% glycerol, 1% NP-40, 2 mM sodium fluoride, and 2 mM sodium vanadate) containing Complete Protease Inhibitors. For purification of GFP-fusion proteins, GFP-Trap beads were used following the manufacturer’s protocols (gta-10 Chromotek). Protein samples were separated by SDS-PAGE and transferred to a PVDF membrane (88585 Thermo Fisher). Membranes were blocked in 5% skim milk powder in tris-buffered saline (TBS, 50 mM Tris pH 7.5, 150 mM NaCl) with 0.1% Tween 20 (P1379 Merck). The following primary antibodies were used: mouse anti-HA (6E2, Cell Signaling Technologies 2367) 1:1000; mouse anti-Myc (9B11, Cell Signaling Technologies 2276) 1:1000; rabbit anti-GFP (ClonTech 632592) 1:1000; Goat anti-DKK1 (R&D Systems AF1765) 1:500; mouse anti-beta-Actin (Sigma A5441) 1:1000; rabbit anti-14-3-3zeta C-16 (sc1019 Santa Cruz) 1:1000. Western blot images were captured using BioRad Image Lab Touch Software or FUJIFILM ImageQuant LAS 4000 software. Densitometry of all western blot data was performed with ImageJ.

HeLa protein abundance correlation assays

HeLa cells (kindly provided by the laboratory of Prof. Stuart Pitson (Centre for Cancer Biology, Adelaide) were grown in DMEM supplemented with 10% foetal calf serum. Plasmid transfections were carried out with X-tremeGENE HP (Roche) in eight-well glass-bottom imaging slides (Eppendorf). Cells were co-transfected with an equal amount of FLAG-tagged NEDD4 WT, NEDD4 CS, or NEDD4 K766R, and DKK1-GFP. Cells were grown for 24–48 h at 37 °C with 5% CO₂ in a humidified atmosphere. Cells were fixed with 4% paraformaldehyde in PBS for 20 min, and immunostained following the protocol described above. Mean fluorescent intensity for each cell was calculated using ZEN 3.4 software (Zeiss). Approximately 15 cells were quantified for each condition per experiment, for n = 3 individual experiments. Graphs represent combined data for all experiments. Simple linear regression analysis was performed in GraphPad Prism.

Human patient recruitment

Ethics approval was obtained from the Sydney Children’s Hospital Network Human Research Ethics Committee (approval number HREC/16/SCHN/73). The family was recruited through the Kids Heart Research DNA Bank at the Children’s Hospital at Westmead, Sydney, Australia. Informed, written consent was obtained from the family. Heart defects in the proband were confirmed by echocardiography.

Patient sequencing and genome data analysis

Genomic DNA was extracted, as described previously⁶⁷. DNA sample libraries were prepared using the Illumina TruSeq Nano DNA HT Library Prep Kit and genome sequenced (Illumina HiSeq X Ten, 150 base paired-end reads) at Genome.One, Garvan Institute of Medical Research, Sydney, Australia. Calling and annotation of sequencing data were performed as per ref. ²⁹. In short, sequence reads were mapped to the human reference genome hg38 using Burrows-Wheeler Aligner (BWA-mem v0.7.12)⁶⁸. Single-nucleotide variants (SNVs) and small insertions or deletions were called using Platypus v0.8.1⁶⁹. The variant call files (VCFs) were annotated using ANNOVAR v 2016Feb01⁷⁰. Variants were prioritised as described in ref. ²⁹ by screening for a list of curated CHD genes, followed by an unbiased comprehensive analysis for all genes. Variant frequencies were in reference to the gnomAD database (v2.1.1)⁷¹. A variant of interest identified in the family, NEDD4:c.2297 A > G:p.K766R (GenBank:NM_006154.4), had an allele frequency of 0.004438.

Statistics

In all graphical representations, individual values represent independent biological replicates. Unless otherwise specified, P values were calculated using unpaired two-tailed t test. For grouped analysis, significance was assessed using 1 or two-way ANOVA multiple comparisons. All graphs and statistical analyses were performed using GraphPad Prism 10.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Laser capture mRNA sequencing data is available publicly from NCBI Gene Expression Omnibus under accession number GSE309818. All other data supporting the findings of this study are available within the article or Supplementary Information files or from the corresponding authors upon request. Source data are provided with this paper.

References

Rosenthal, N. & Harvey, R. Heart Development and Regeneration, first edn (Academic Press, 2010).
Rochais, F., Mesbah, K. & Kelly, R. G. Signaling pathways controlling second heart field development. Circ. Res. 104, 933–942 (2009).
CAS PubMed Google Scholar
Keyte, A. & Hutson, M. R. The neural crest in cardiac congenital anomalies. Differentiation 84, 25–40 (2012).
CAS PubMed PubMed Central Google Scholar
Kirby, M. L., Turnage, K. L. 3rd & Hays, B. M. Characterization of conotruncal malformations following ablation of “cardiac” neural crest. Anat. Rec. 213, 87–93 (1985).
CAS PubMed Google Scholar
Waldo, K. et al. A novel role for cardiac neural crest in heart development. J. Clin. Invest. 103, 1499–1507 (1999).
CAS PubMed PubMed Central Google Scholar
Waldo, K. L. et al. Cardiac neural crest is necessary for normal addition of the myocardium to the arterial pole from the secondary heart field. Dev. Biol. 281, 66–77 (2005).
CAS PubMed Google Scholar
Yelbuz, T. M. et al. Shortened outflow tract leads to altered cardiac looping after neural crest ablation. Circulation 106, 504–510 (2002).
PubMed Google Scholar
Huang, X. et al. The many substrates and functions of NEDD4-1. Cell Death Dis. 10, 904 (2019).
ADS CAS PubMed PubMed Central Google Scholar
Fouladkou, F. et al. The ubiquitin ligase Nedd4-1 is required for heart development and is a suppressor of thrombospondin-1. J. Biol. Chem. 285, 6770–6780 (2010).
CAS PubMed Google Scholar
Wiszniak, S. et al. The ubiquitin ligase Nedd4 regulates craniofacial development by promoting cranial neural crest cell survival and stem-cell like properties. Dev. Biol. 383, 186–200 (2013).
CAS PubMed Google Scholar
Wiszniak, S., Harvey, N. & Schwarz, Q. Cell autonomous roles of Nedd4 in craniofacial bone formation. Dev. Biol. 410, 98–107 (2016).
CAS PubMed Google Scholar
Lohraseb, I. et al. Global ubiquitinome profiling identifies NEDD4 as a regulator of Profilin 1 and actin remodelling in neural crest cells. Nat. Commun. 13, 2018 (2022).
ADS CAS PubMed PubMed Central Google Scholar
Astrof, S. et al. Aberrant differentiation of second heart field mesoderm prefigures cellular defects in the outflow tract in response to loss of FGF8. Dev. Biol. 499, 10–21 (2023).
CAS PubMed PubMed Central Google Scholar
Zhang, J. et al. FRS2alpha-mediated FGF signals suppress premature differentiation of cardiac stem cells through regulating autophagy activity. Circ. Res. 110, e29–e39 (2012).
CAS PubMed Google Scholar
Roux, M., Laforest, B., Capecchi, M., Bertrand, N. & Zaffran, S. Hoxb1 regulates proliferation and differentiation of second heart field progenitors in pharyngeal mesoderm and genetically interacts with Hoxa1 during cardiac outflow tract development. Dev. Biol. 406, 247–258 (2015).
CAS PubMed Google Scholar
Ramsbottom, S. A. et al. Vangl2-regulated polarisation of second heart field-derived cells is required for outflow tract lengthening during cardiac development. PLoS Genet. 10, e1004871 (2014).
PubMed PubMed Central Google Scholar
Shenje, L. T. et al. Precardiac deletion of Numb and Numblike reveals renewal of cardiac progenitors. eLife 3, e02164 (2014).
PubMed PubMed Central Google Scholar
Li, D. et al. Spatial regulation of cell cohesion by Wnt5a during second heart field progenitor deployment. Dev. Biol. 412, 18–31 (2016).
CAS PubMed PubMed Central Google Scholar
Tirosh-Finkel, L. et al. BMP-mediated inhibition of FGF signaling promotes cardiomyocyte differentiation of anterior heart field progenitors. Development 137, 2989–3000 (2010).
CAS PubMed Google Scholar
Hutson, M. R. et al. Arterial pole progenitors interpret opposing FGF/BMP signals to proliferate or differentiate. Development 137, 3001–3011 (2010).
CAS PubMed PubMed Central Google Scholar
MacDonald, B. T., Tamai, K. & He, X. Wnt/beta-catenin signaling: components, mechanisms, and diseases. Dev. Cell 17, 9–26 (2009).
CAS PubMed PubMed Central Google Scholar
Liu, C. et al. Control of beta-catenin phosphorylation/degradation by a dual-kinase mechanism. Cell 108, 837–847 (2002).
ADS CAS PubMed Google Scholar
Rhee, J., Buchan, T., Zukerberg, L., Lilien, J. & Balsamo, J. Cables links Robo-bound Abl kinase to N-cadherin-bound beta-catenin to mediate Slit-induced modulation of adhesion and transcription. Nat. Cell Biol. 9, 883–892 (2007).
CAS PubMed Google Scholar
Kioussi, C. et al. Identification of a Wnt/Dvl/beta-Catenin –> Pitx2 pathway mediating cell-type-specific proliferation during development. Cell 111, 673−685 (2002).
Valenta, T., Hausmann, G. & Basler, K. The many faces and functions of beta-catenin. EMBO J. 31, 2714–2736 (2012).
ADS CAS PubMed PubMed Central Google Scholar
Francou, A., De Bono, C. & Kelly, R. G. Epithelial tension in the second heart field promotes mouse heart tube elongation. Nat. Commun. 8, 14770 (2017).
ADS CAS PubMed PubMed Central Google Scholar
Nakamura, Y., de Paiva Alves, E., Veenstra, G. J. & Hoppler, S. Tissue- and stage-specific Wnt target gene expression is controlled subsequent to beta-catenin recruitment to cis-regulatory modules. Development 143, 1914–1925 (2016).
CAS PubMed PubMed Central Google Scholar
Li, Y. et al. An integrated bioinformatics platform for investigating the human E3 ubiquitin ligase-substrate interaction network. Nat. Commun. 8, 347 (2017).
ADS PubMed PubMed Central Google Scholar
Alankarage, D. et al. Identification of clinically actionable variants from genome sequencing of families with congenital heart disease. Genet Med. 21, 1111–1120 (2019).
PubMed Google Scholar
Sierant, M. C. et al. Genomic analysis of 11,555 probands identifies 60 dominant congenital heart disease genes. Proc. Natl. Acad. Sci. USA 122, e2420343122 (2025).
MathSciNet CAS PubMed PubMed Central Google Scholar
Gessert, S. & Kuhl, M. The multiple phases and faces of wnt signaling during cardiac differentiation and development. Circ. Res. 107, 186–199 (2010).
CAS PubMed Google Scholar
Ruiz-Villalba, A., Hoppler, S. & van den Hoff, M. J. Wnt signaling in the heart fields: variations on a common theme. Dev. Dyn. 245, 294–306 (2016).
PubMed Google Scholar
Palpant, N. J. et al. Transmembrane protein 88: a Wnt regulatory protein that specifies cardiomyocyte development. Development 140, 3799–3808 (2013).
CAS PubMed PubMed Central Google Scholar
Shen, S. et al. Atlas of multilineage stem cell differentiation reveals TMEM88 as a developmental regulator of blood pressure. Nat. Commun. 16, 1356 (2025).
ADS CAS PubMed PubMed Central Google Scholar
Kwon, C. et al. Canonical Wnt signaling is a positive regulator of mammalian cardiac progenitors. Proc. Natl. Acad. Sci. USA 104, 10894–10899 (2007).
ADS CAS PubMed PubMed Central Google Scholar
Cohen, E. D. et al. Wnt/beta-catenin signaling promotes expansion of Isl-1-positive cardiac progenitor cells through regulation of FGF signaling. J. Clin. Invest. 117, 1794–1804 (2007).
CAS PubMed PubMed Central Google Scholar
Ai, D. et al. Canonical Wnt signaling functions in second heart field to promote right ventricular growth. Proc. Natl. Acad. Sci. USA 104, 9319–9324 (2007).
ADS CAS PubMed PubMed Central Google Scholar
Klaus, A., Saga, Y., Taketo, M. M., Tzahor, E. & Birchmeier, W. Distinct roles of Wnt/beta-catenin and Bmp signaling during early cardiogenesis. Proc. Natl. Acad. Sci. USA 104, 18531–18536 (2007).
ADS CAS PubMed PubMed Central Google Scholar
Ai, D. et al. Pitx2 regulates cardiac left-right asymmetry by patterning second cardiac lineage-derived myocardium. Dev. Biol. 296, 437–449 (2006).
CAS PubMed PubMed Central Google Scholar
Miyamoto, M. et al. Cardiac progenitors instruct second heart field fate through Wnts. Proc. Natl. Acad. Sci. USA 120, e2217687120 (2023).
CAS PubMed PubMed Central Google Scholar
Farrell, M. J. et al. FGF-8 in the ventral pharynx alters development of myocardial calcium transients after neural crest ablation. J. Clin. Invest. 107, 1509–1517 (2001).
CAS PubMed PubMed Central Google Scholar
Hutson, M. R. et al. Cardiac arterial pole alignment is sensitive to FGF8 signaling in the pharynx. Dev. Biol. 295, 486–497 (2006).
CAS PubMed Google Scholar
Jia, Q. et al. Smad signaling in the neural crest regulates cardiac outflow tract remodeling through cell autonomous and non-cell autonomous effects. Dev. Biol. 311, 172–184 (2007).
CAS PubMed PubMed Central Google Scholar
Kaartinen, V. et al. Cardiac outflow tract defects in mice lacking ALK2 in neural crest cells. Development 131, 3481–3490 (2004).
CAS PubMed Google Scholar
Rai, M., Walthall, J. M., Hu, J. & Hatzopoulos, A. K. Continuous antagonism by Dkk1 counter activates canonical Wnt signaling and promotes cardiomyocyte differentiation of embryonic stem cells. Stem Cells Dev. 21, 54–66 (2012).
CAS PubMed Google Scholar
Schneider, V. A. & Mercola, M. Wnt antagonism initiates cardiogenesis in Xenopus laevis. Genes Dev. 15, 304–315 (2001).
CAS PubMed PubMed Central Google Scholar
Mukhopadhyay, M. et al. Dickkopf1 is required for embryonic head induction and limb morphogenesis in the mouse. Dev. Cell 1, 423–434 (2001).
CAS PubMed Google Scholar
Phillips, M. D., Mukhopadhyay, M., Poscablo, C. & Westphal, H. Dkk1 and Dkk2 regulate epicardial specification during mouse heart development. Int. J. Cardiol. 150, 186–192 (2011).
PubMed Google Scholar
Cao, X. R. et al. Nedd4 controls animal growth by regulating IGF-1 signaling. Sci. Signal 1, ra5 (2008).
PubMed PubMed Central Google Scholar
Kawabe, H. et al. Regulation of Rap2A by the ubiquitin ligase Nedd4-1 controls neurite development. Neuron 65, 358–372 (2010).
CAS PubMed PubMed Central Google Scholar
Danielian, P. S., Muccino, D., Rowitch, D. H., Michael, S. K. & McMahon, A. P. Modification of gene activity in mouse embryos in utero by a tamoxifen-inducible form of Cre recombinase. Curr. Biol. 8, 1323–1326 (1998).
CAS PubMed Google Scholar
Verzi, M. P., McCulley, D. J., De Val, S., Dodou, E. & Black, B. L. The right ventricle, outflow tract, and ventricular septum comprise a restricted expression domain within the secondary/anterior heart field. Dev. Biol. 287, 134–145 (2005).
CAS PubMed Google Scholar
Kisanuki, Y. Y. et al. Tie2-Cre transgenic mice: a new model for endothelial cell-lineage analysis in vivo. Dev. Biol. 230, 230–242 (2001).
CAS PubMed Google Scholar
Dodou, E., Verzi, M. P., Anderson, J. P., Xu, S. M. & Black, B. L. Mef2c is a direct transcriptional target of ISL1 and GATA factors in the anterior heart field during mouse embryonic development. Development 131, 3931–3942 (2004).
CAS PubMed Google Scholar
Novak, A., Guo, C., Yang, W., Nagy, A. & Lobe, C. G. Z/EG, a double reporter mouse line that expresses enhanced green fluorescent protein upon Cre-mediated excision. Genesis 28, 147–155 (2000).
CAS PubMed Google Scholar
Chen, J. et al. Spatial transcriptomic analysis of cryosectioned tissue samples with Geo-seq. Nat. Protoc. 12, 566–580 (2017).
CAS PubMed Google Scholar
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
CAS PubMed Google Scholar
Broad Institute. Picard Toolkit. Broad Institute, GitHub Repository, https://broadinstitute.github.io/picard/ (2019).
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
CAS PubMed Google Scholar
Powell, D. R. Degust: interactive RNA-seq analysis. https://degust.erc.monash.edu (2019).
Lun, A. T., Chen, Y. & Smyth, G. K. It’s DE-licious: a recipe for differential expression analyses of RNA-seq experiments using quasi-likelihood methods in edgeR. Methods Mol. Biol. 1418, 391–416 (2016).
PubMed Google Scholar
Thorvaldsdottir, H., Robinson, J. T. & Mesirov, J. P. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief. Bioinform. 14, 178–192 (2013).
CAS PubMed Google Scholar
Su, S. et al. Glimma: interactive graphics for gene expression analysis. Bioinformatics 33, 2050–2052 (2017).
CAS PubMed PubMed Central Google Scholar
MaGIC. MaGIC volcano plot tool. https://volcano.bioinformagic.tools (2020).
Sherman, B. T. et al. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 50, W216–W221 (2022).
ADS CAS PubMed PubMed Central Google Scholar
Huang da, W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).
PubMed Google Scholar
Blue, G. M. et al. Targeted next-generation sequencing identifies pathogenic variants in familial congenital heart disease. J. Am. Coll. Cardiol. 64, 2498–2506 (2014).
CAS PubMed Google Scholar
Li, H. Exploring single-sample SNP and INDEL calling with whole-genome de novo assembly. Bioinformatics 28, 1838–1844 (2012).
CAS PubMed PubMed Central Google Scholar
Rimmer, A. et al. Integrating mapping-, assembly- and haplotype-based approaches for calling variants in clinical sequencing applications. Nat. Genet. 46, 912–918 (2014).
CAS PubMed PubMed Central Google Scholar
Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).
PubMed PubMed Central Google Scholar
Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020).
ADS CAS PubMed PubMed Central Google Scholar

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Acknowledgements

This research was supported by funds from the National Health and Medical Research Council (NHMRC): Project Grant (ID1144004) (Q.S. and S.W.); Ideas Grant (ID2030340) (S.W. and Q.S.); Principal Research Fellowship (ID1135886) (S.L.D.); Leadership Level 3 Fellowship (ID2007896) (S.L.D.); Project Grant (ID1162878) (S.L.D., D.W., and E.G.); Synergy Grant (ID1181325) (S.L.D., D.W., and E.G.). Channel 7 Children’s Research Foundation (S.W.). Royal Adelaide Hospital Mary Overton Postdoctoral Fellowship (S.W.). National Heart Foundation Future Leader Fellowship (Q.S.). NSW Health Cardiovascular Research Capacity Program Senior Researcher Grant (S.L.D.). We thank Paul Riley for the critical reading of the manuscript.

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Authors and Affiliations

Centre for Cancer Biology, University of South Australia and SA Pathology, Adelaide, SA, Australia
Sophie Wiszniak, Iman Lohraseb, Ceilidh Marchant, Genevieve Secker, Deepti Domingo, Jasmine Hartmann, Tianyang Zhang, Wendy Parker, John Toubia, Markus Tondl, Natasha Harvey & Quenten Schwarz
College of Health, Adelaide University, Adelaide, SA, Australia
Sophie Wiszniak, Ceilidh Marchant, Genevieve Secker, Deepti Domingo, Jasmine Hartmann, Natasha Harvey & Quenten Schwarz
Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia
Dimuthu Alankarage, Eleni Giannoulatou, Gavin Chapman & Sally L. Dunwoodie
University of New South Wales, Kensington, NSW, Australia
Dimuthu Alankarage
ACRF Cancer Genomics Facility, SA Pathology, Adelaide, SA, Australia
Wendy Parker & John Toubia
School of Biomedicine and Robinson Research Institute, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA, Australia
Melissa White, Sandra Piltz & Paul Thomas
South Australian Genome Editing Facility, South Australian Health & Medical Research Institute, Adelaide, SA, Australia
Melissa White, Sandra Piltz & Paul Thomas
School of Clinical Medicine, Faculty of Medicine and Health, UNSW, Sydney, NSW, Australia
Eleni Giannoulatou & Sally L. Dunwoodie
Heart Center, Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL, USA
David Winlaw
Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
David Winlaw
Heart Centre for Children, The Children’s Hospital at Westmead, Westmead, NSW, Australia
Gillian M. Blue & Gary Sholler
Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
Gillian M. Blue & Gary Sholler
Children’s Medical Research Institute, University of Sydney, Westmead, NSW, Australia
Patrick P. L. Tam
School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
Patrick P. L. Tam
School of Clinical Medicine, University of New South Wales, Kensington, NSW, Australia
Edwin Kirk & Gavin Chapman
Centre for Clinical Genetics, Sydney Children’s Hospital, Randwick, NSW, Australia
Edwin Kirk
Randwick Genomics Laboratory, NSW Health Pathology, Randwick, NSW, Australia
Edwin Kirk
Children’s Hospital Westmead Clinical School, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
Natasha Nassar & Samantha Lain

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Sophie Wiszniak
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Dimuthu Alankarage
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Iman Lohraseb
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Ceilidh Marchant
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Genevieve Secker
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Deepti Domingo
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Jasmine Hartmann
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Tianyang Zhang
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John Toubia
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Melissa White
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Sandra Piltz
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Markus Tondl
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Eleni Giannoulatou
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David Winlaw
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Gillian M. Blue
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Paul Thomas
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Natasha Harvey
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Sally L. Dunwoodie
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Quenten Schwarz
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Consortia

Congenital Heart Disease Synergy Group

Sally L. Dunwoodie
, David Winlaw
, Eleni Giannoulatou
, Edwin Kirk
, Gavin Chapman
, Natasha Nassar
, Gillian M. Blue
, Gary Sholler
& Samantha Lain

Contributions

S.W. and Q.S. designed the study and experiments. S.W. and Q.S. wrote the manuscript. S.W., I.L., C.M., G.S., D.D., J.H., T.Z., M.T., and Q.S. performed all wet laboratory experiments. G.M.B. and D.W. recruited the patient and family with congenital heart disease. D.A., E.G., and S.L.D. performed and analysed whole-genome sequencing data and identified the NEDD4 variant. W.P. prepared libraries for laser capture mRNA sequencing. J.T. performed bioinformatic analysis of laser capture mRNAseq data. M.W., S.P., and P.Th. generated NEDD4K766R mice. PPLT provided Dkk1^−/− mice. NH provided Tie2-Cre; Nedd4^fl/fl mice. All authors intellectually contributed to and approved the final manuscript.

Corresponding authors

Correspondence to Sophie Wiszniak or Quenten Schwarz.

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Wiszniak, S., Alankarage, D., Lohraseb, I. et al. Neural crest cell-derived DKK1 and NEDD4 modulate Wnt signalling in the second heart field to orchestrate outflow tract development. Nat Commun 17, 1751 (2026). https://doi.org/10.1038/s41467-026-68459-4

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Received: 25 May 2025
Accepted: 07 January 2026
Published: 22 January 2026
Version of record: 17 February 2026
DOI: https://doi.org/10.1038/s41467-026-68459-4