1. Developmental Biology
  2. Stem Cells and Regenerative Medicine
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GATA6 mutations in hiPSCs inform mechanisms for maldevelopment of the heart, pancreas, and diaphragm

  1. Arun Sharma
  2. Lauren K Wasson
  3. Jon AL Willcox
  4. Sarah U Morton
  5. Joshua M Gorham
  6. Daniel M DeLaughter
  7. Meraj Neyazi
  8. Manuel Schmid
  9. Radhika Agarwal
  10. Min Young Jang
  11. Christopher N Toepfer
  12. Tarsha Ward
  13. Yuri Kim
  14. Alexandre C Pereira
  15. Steven R DePalma
  16. Angela Tai
  17. Seongwon Kim
  18. David Conner
  19. Daniel Bernstein
  20. Bruce D Gelb
  21. Wendy K Chung
  22. Elizabeth Goldmuntz
  23. George Porter
  24. Martin Tristani-Firouzi
  25. Deepak Srivastava
  26. Jonathan G Seidman
  27. Christine E Seidman  Is a corresponding author
  28. Pediatric Cardiac Genomics Consortium
  1. Department of Genetics, Harvard Medical School, United States
  2. Smidt Heart Institute, Cedars-Sinai Medical Center, United States
  3. Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, United States
  4. Howard Hughes Medical Institute, Harvard Medical School, United States
  5. Division of Newborn Medicine, Boston Children's Hospital, United States
  6. Hannover Medical School, Germany
  7. Deutsches Herzzentrum München, Technische Universität München, Germany
  8. Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, United Kingdom
  9. Wellcome Centre for Human Genetics, University of Oxford, United Kingdom
  10. Laboratory of Genetics and Molecular Cardiology, Heart Institute, Medical School of University of Sao Paulo, Brazil
  11. Department of Pediatrics, Stanford University School of Medicine, United States
  12. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, United States
  13. Department of Medicine, Columbia University Medical Center, United States
  14. Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, United States
  15. Department of Pediatrics, University of Rochester Medical Center, United States
  16. Division of Pediatric Cardiology, University of Utah School of Medicine, United States
  17. Gladstone Institutes, United States
  18. Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, United States
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Cite this article as: eLife 2020;9:e53278 doi: 10.7554/eLife.53278

Abstract

Damaging GATA6 variants cause cardiac outflow tract defects, sometimes with pancreatic and diaphragmic malformations. To define molecular mechanisms for these diverse developmental defects, we studied transcriptional and epigenetic responses to GATA6 loss of function (LoF) and missense variants during cardiomyocyte differentiation of isogenic human induced pluripotent stem cells. We show that GATA6 is a pioneer factor in cardiac development, regulating SMYD1 that activates HAND2, and KDR that with HAND2 orchestrates outflow tract formation. LoF variants perturbed cardiac genes and also endoderm lineage genes that direct PDX1 expression and pancreatic development. Remarkably, an exon 4 GATA6 missense variant, highly associated with extra-cardiac malformations, caused ectopic pioneer activities, profoundly diminishing GATA4, FOXA1/2, and PDX1 expression and increasing normal retinoic acid signaling that promotes diaphragm development. These aberrant epigenetic and transcriptional signatures illuminate the molecular mechanisms for cardiovascular malformations, pancreas and diaphragm dysgenesis that arise in patients with distinct GATA6 variants.

Introduction

Congenital heart disease (CHD), the leading birth defect worldwide that occurs in approximately 1% of newborns (van der Linde et al., 2011), comprises a range of structural malformations arising during embryonic development (Zaidi and Brueckner, 2017). Contemporary treatments for CHD enables survival into adulthood, but many patients have ongoing medical problems related to extra-cardiac malformations and/or neurodevelopmental disabilities (Brickner et al., 2000; Marino et al., 2012). Understanding the developmental mechanisms that cause co-occurrence of these congenital anomalies is expected to provide better patient care, improve predictive risk assessments, and potentially uncover therapeutic targets.

Heart development is an intricately regulated process driven by genetic, epigenetic, and biomechanical events that form a complex, multi-chambered, muscular organ containing cardiomyocytes and other cell lineages (Litviňuková et al., 2020). Early in cardiogenesis, the first heart field creates the left ventricle and portions of the atria, while the second heart field gives rise to the right ventricle and outflow tract, with contributions from neural crest cells (Buckingham et al., 2005). Multiple transcription factors orchestrate particular components of these developmental processes (Srivastava and Olson, 2000), as evidenced by regional defects in CHD patients with damaging mutations in particular genes (Fahed et al., 2013; Jin et al., 2017; Sifrim et al., 2016). For example, dominant human loss of function (LoF) variants in TBX5, NKX2-5, and GATA4 that are highly expressed in the first and second heart fields cause atrial and ventricular septal defects (Basson et al., 1997; Benson et al., 1999; Garg et al., 2003). GATA6, while expressed in the first heart field (Morrisey et al., 1996), plays particularly critical roles in the developing second heart field (Molkentin, 2000) and in recruitment of cardiac neural crest lineages (Lepore et al., 2006) that together shape the cardiac outflow tract. Consistent with these developmental functions, CHD patients with GATA6 mutations have a striking preponderance of outflow tract malformations (Gharibeh et al., 2018; Kodo et al., 2009; Maitra et al., 2010). GATA6 also is critical for endodermal development (Fisher et al., 2017), and some CHD patients with damaging GATA6 variants also have pancreatic agenesis, congenital diaphragmatic hernia (Yu et al., 2014) or other abdominal malformations (Chao et al., 2015; De Franco et al., 2013; Shi et al., 2017).

Developmental transcription factors work in concert to exert network-level effects on organogenesis (Luna-Zurita et al., 2016): physical interactions of NKX2-5, TBX5, and GATA4 proteins modulate the expression of other cardiac genes (Garg et al., 2003; Hiroi et al., 2001; Bruneau et al., 2001; Maitra et al., 2009) while GATA6 and GATA4 proteins together promote pancreatic development (Chao et al., 2015; De Franco et al., 2013; Shi et al., 2017). Notably, members of the GATA family of transcription factors serve as endodermal pioneer factors that participate with FOXA, also a pioneer factor, to engage and open chromatin and recruit additional transcriptional activators (Fisher et al., 2017; Zaret and Carroll, 2011). Thus, we expect that understanding the molecular networks in which GATA6 participates will help to elucidate the mechanisms by which human mutations cause defects in morphogenesis of the heart cardiac other organs.

A traditional approach for studying developmental mechanisms relies on gene disruption in model organisms. While often informative, prior studies of mice with one inactivated Gata6 allele have either subtle dysmorphic aortic valves (Gharibeh et al., 2018) or no CHD (Lepore et al., 2006) and notably lack pancreatic agenesis or congenital diaphragmatic hernia that occur in human CHD patients with damaging GATA6 variants (Chao et al., 2015; De Franco et al., 2013; Shi et al., 2017). Mice with biallelic Gata6 inactivation have deficits in early visceral endoderm formation resulting in nonviable embryos (Morrisey et al., 1996; Zhao et al., 2005), which may explain the absence of homozygous GATA6 null alleles in humans, but provide limited insights into organ morphogenesis. The introduction of human CHD mutations using CRISPR/Cas9 (De Franco et al., 2013) into isogenic human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) provides an alternative model system suited for analyzing developmental consequences, as differentiation of these cells activate transcriptional networks that regulate early in vivo human cardiogenesis (DeLaughter et al., 2016; Li et al., 2016). Moreover, isogenic hiPSC-CMs containing distinct variants can illuminate variant-specific transcriptional patterns.

We employed this approach to study human de novo GATA6 variants, identified by whole-exome sequencing (WES) of CHD patients. We demonstrate the graded transcriptional effects of GATA6 heterozygous and homozygous LoF variants during hiPSC-CM differentiation. We also examine the transcriptional requirements for a specific arginine residue encoded by GATA6 exon four that is recurrently mutated in unrelated CHD patients. Combining these data with Assay for transposase-accessible chromatin using sequencing (ATAC-seq) (Buenrostro et al., 2015; Corces et al., 2017) and GATA6 ChIP-seq analyses, we demonstrate that GATA6 is a pioneer factor for cardiac development. We define direct and indirect transcriptional responses to GATA6 variants. Integrating these datasets with clinical phenotypes observed in CHD patients with pathogenic GATA6 variants, we demonstrate how disrupted molecular programs cause aberrant development of the cardiac outflow tract, pancreas, and diaphragm.

Results

Identification of CHD patients with GATA6 LoF and missense variants

Among >4000 CHD patients enrolled and studied by WES through National Heart, Lung, and Blood Institute’s Pediatric Cardiac Genomics Consortium (PCGC) (Homsy et al., 2015; Jin et al., 2017), we identified nine heterozygous de novo variants: four LoF (LoF) and five damaging missense variants in GATA6 (Figure 1A). The congenital anomalies in these patients were consistent with previously recognized roles for GATA6 in developing the cardiac outflow tract, arterial-ventricular valves, and posterior brachial arches that form aorta and pulmonary vessels (Gharibeh et al., 2018; Laforest and Nemer, 2011; Losa et al., 2017). Among PCGC CHD patients and 61 previously reported CHD patients with pathogenic GATA6 variants (Figure 1B) there were a preponderance of outflow tract malformations, including persistent truncus arteriosus, double-outlet right ventricle, tetralogy of Fallot, as well as aortic and pulmonary valve and septation defects (Kelly, 2012). Some but not all of these patients also had extra-cardiac phenotypes including pancreatic agenesis, congenital diaphragmatic hernia, and neurodevelopmental deficits.

Genetic information and clinical phenotypes of individuals with GATA6 variants.

A) Schematic of GATA6 gene and locations of PCGC GATA6 variants (LoF-purple, missense-orange). (B) Previously described (see Supplementary file 1) GATA6 variants (R456 and N466 variants highlighted in red), and GATA6 protein domains. TAD: Topologically associating domain, ZF: zinc finger, NLS: nuclear localization signal. TGA: Transposition of the Great Arteries, ToF: Tetralogy of Fallot, DORV: Double-Outlet Right Ventricle, DOLV: Double-Outlet Left Ventricle, DXC: Dextrocardia, VSD: Ventricular Septal Defect, HLV: Hypoplastic Left Ventricle, PA: Pulmonary Atresia, ASD: Atrial Septal Defect, PTA: Persistent Truncus Arteriosus, SPS: Supravalvular Pulmonary Stenosis, HLPA: Hypoplastic Left Pulmonary Artery, PS: Pulmonary Stenosis, HPA: Hypoplastic Pulmonary Artery, PANC: Pancreatic Agenesis (C) Model of GATA6 DNA-binding domain bound to major groove of DNA indicating the location of amino acid residue 456. Left panel: GATA6 residues R456 and N466 normally interact with each other via hydrogen bonding (dashed lines) and with target G base and second A base in the GATA motif via hydrogen bonding, respectively (dashed lines). Right panel: Replacing the arginine (R) residue at position 456 with a glycine (G) residue alters normal molecular interactions by disrupting the hydrogen bonds.

We considered whether the distribution of these 70 damaging GATA6 variants (61 published, 9 PCGC) across the 595 encoded GATA6 amino acids correlated with clinical phenotypes (Figure 1; Supplementary file 1A, B). All variants (43 missense, 27 LoF, including eight recurrent variants) caused CHD. Extra-cardiac phenotypes occurred in 29/70 (41%) patients, and more often with LoF (18/27) than missense (11/43) variants (p<0.001). Neurocognitive dysfunction occurred in 13 patients (18.5%). Pancreatic agenesis/hypoplasia or congenital diaphragmatic hernia occurred in 20/70 patients (28.5%) and more frequently with GATA6 LoF (14/27) than missense (9/43) variants (p=0.001).

Among 43 GATA6 missense variants, 11 variants altered residues in the DNA-binding zinc finger (ZF) domain encoded by exon 4 (amino acids 435–477), significantly more than expected by chance (p=0.0004). Nine of these 11 patients with exon four missense variants had pancreatic agenesis or congenital diaphragmatic hernia, but none of 32 patients with missense variants located elsewhere (p=1.1e-6). Within exon 4, recurrent missense mutations altered the basic arginine residue 456. Computational modeling of this domain (PyMOL software) positioned residue 456 alongside a polar residue (asparagine 466) in close proximity to DNA (Bates et al., 2008Figure 1C). Substitution of a non-polar glycine at residue 456 (R456G) is predicted to disrupt these interactions, and potentially alter GATA6 binding to DNA.

Generation of GATA6 LoF and GATA6R456G/R456G hiPSCs using CRISPR/Cas9

We created GATA6 LoF hiPSCs using two independent guide RNAs (gRNAs) targeting exon 2 (Figure 2—figure supplement 1A) that were transfected with Cas9 endonuclease into an early passage healthy hiPSC line PGP1 (Lee et al., 2009). Targeted hiPSCs were subcloned and GATA6 variants were confirmed by next-generation and Sanger sequencing of PCR-amplified fragments (METHODS). Four independent mutant hiPSC lines were obtained: two carry a heterozygous 1 bp insertion (GATA6+/-, chr18:19,752,124–19,752,124, A:TA) and two have a homozygous 1 bp deletion (GATA6-/-, chr18:19,752,123–19,752,124, TA:T). Using similar strategies, we transfected a gRNA targeting exon four with a single-stranded DNA oligonucleotide to serve as a template for homology-directed repair and generated two hiPSC lines with homozygous missense variant R456G (GATA6R456G/R456G; Figure 2—figure supplement 1B). No lines with a heterozygous R456G variant were obtained. In parallel we produced GATA6+/-, GATA6-/-, and GATA6R456G/R456G variants in a PGP1 hiPSC line that carried expressed green fluorescent protein (GFP) fused to endogenous cardiac troponin T alleles (TNNT2-GFP; Figure 2—figure supplement 1C).

Differentiation of GATA6 mutant hiPSCs into hiPSC-CMs

Wildtype (WT), GATA6+/-, GATA6-/-, and GATA6R456G/R456G hiPSCs were processed for differentiation into cardiomyocytes (hiPSC-CMs) by modulation of the Wnt signaling pathway and subsequent metabolic selection via glucose deprivation (Sharma et al., 2018b). This protocol yields hiPSC-CMs that express first and second heart field genes (Zhang et al., 2019). As previous single-cell RNA- sequencing (RNA-Seq) of cardiomyocytes isolated from developing mouse hearts (DeLaughter et al., 2016) identified peak Gata6 expression in cardiac progenitors and early cardiomyocytes, we studied hiPSC-CMs at differentiation days 4 and 8, which approximate these in vivo developmental stages. GATA6 protein expression and nuclear localization were reduced in GATA6+/- compared to isogenic WT lines and absent in GATA6-/- lines (Figure 2—figure supplement 1D,E). GATA6+/- hiPSCs, unlike WT hiPSCs, exhibited mono-allelic GATA6 expression, suggesting nonsense-mediated decay of RNAs transcribed from targeted alleles.

We assessed sarcomere production as a measure of cardiomyocyte differentiation in the TNNT2-GFP lines. GATA6+/- hiPSC-CMs had weaker fluorescent signal than WT cells, while GATA6-/- lines expressed no fluorescence at baseline or during the differentiation protocol (Figure 2A,B). Consistent with these data, at differentiation day eight when contracting sarcomeres were present in WT cells, fewer independent differentiation rounds of GATA6+/- hiPSC-CMs contained beating sarcomeres and no GATA6-/- cells showed sarcomeres or spontaneous beating (Figure 2C). Analogous studies of GATA6R456G/R456G hiPSC-CMs (Figure 2D–F) showed a fluorescence signal comparable to that of GATA6+/- hiPSC-CMs and spontaneous beating cells in approximately ~60% of differentiation rounds.

Figure 2 with 1 supplement see all
GATA6 mutant hiPSCs exhibit hiPSC-CM differentiation defects.

(A) GATA6 variants in a TNNT2-GFP reporter line showed reduced (GATA6+/-) or absent (GATA6-/-) GFP-tagged sarcomeres in comparison to WT cells. (B) Distribution of dissociated GFP-TNNT2-GATA6 mutant cells assessed using the Countess system with a GFP filter cube (METHODS). (C) Number of GATA6 LoF mutant differentiation cultures (n = 25 per genotype) with beating hiPSC-CMs. (D) The GATA6R456G/R456G variant has reduced expression of GFP-tagged sarcomeres. (E) Fluorescence distribution of differentiated GFP-TNNT2 GATA6R456G/R456G cells assessed using the Countess system with a GFP filter cube (see Materials and methods). (F) Number of GFP-TNNT2 GATA6R456G/R456G differentiation cultures (n = 25) with beating day eight hiPSC-CMs. All lines were studied at differentiation day 8. Significance was assessed using Student’s t-test.

Transcriptional analysis of GATA6 LoF hiPSC-CMs

We assessed transcriptional responses to altered GATA6 levels throughout hiPSC-CM differentiation by bulk RNA-Seq analyses of cultures at days 0, 4, 8, 12, and 30 (Figure 3, Figure 2—figure supplement 1F, Figure 3—figure supplement 1, Supplementary file 2), and by single-cell RNA-Seq on days 4 and 8 (Figure 4) that precedes metabolic selection and enriches cultures for cardiomyocytes. All cells were differentiated, processed, and harvested in parallel. RNA-Seq data was aligned and processed to limit potential batch effects, and clustered using methods implemented in DESEQ2 (bulk) or Seurat (single cell) (METHODS). We observed consistency between bulk and single-cell expression data. In addition, principal component analyses (PCA) (Figure 3A, Supplementary file 3) of independent, genotype-identical lines demonstrated close clustering of RNA expression, indicating that GATA6 genotype and differentiation stage largely accounted for differences in gene expression.

Figure 3 with 1 supplement see all
GATA6 mutant cells exhibit downregulation of second heart field-related genes during hiPSC-CM differentiation.

A) Gene expression principal component analysis (PCA) of day 0–30 WT (GATA6+/+), GATA6+/-, GATA6-/-, and GATA6R456G/R456G hiPSC-CMs. RNA-Seq samples were harvested in duplicate for all time points. (B) Venn Diagrams (left) and heat map (right) of day 4 GATA6+/- and GATA6-/- cells. In the heatmap, red indicates upregulated genes whereas blue represents downregulated genes. Samples are in duplicate. Selected second heart field genes are shown. (C) Expression data in normalized counts for second heart field-related genes (top row), cardiac developmental transcription factors (middle row), sarcomere, and other selected genes (bottom row) during differentiation of GATA6 mutant hiPSC-CMs. Data represented as mean ± SD. Note that SMYD1 and HAND2 graphs are plotted with logarithmic scale. (D) Gene expression scatterplot illustrating downregulation of expression of HAND2 upstream and downstream gene network in day 4 GATA6+/- and GATA6-/- cells. X-axis, log2 fold-change of gene expression in GATA6-/- cells relative to WT. Y-axis, log2-fold-change of gene expression in GATA6+/- cells relative to WT. Canonical cardiac development and the second heart field genes are bolded.

Figure 4 with 1 supplement see all
Single-cell transcriptional analysis of GATA6 mutant hiPSC-CMs during differentiation.

(A) tSNE of single-cell RNA-Seq of day 4 GATA6 mutant hiPSC-CMs identified four clusters. Labels reflect marker gene expression: SC, Stem cell; CPC, cardiac progenitor cells; E-CPC, cardiac progenitors enriched with endoderm markers; ECM, endodermal-like cells enriched for extracellular matrix proteins. (B) Examples of marker gene expression in clustered hiPSC-derived cells. CPC cells express mesodermal factors (MESP1, EOMES) as well as cardiac transcription factors (GATA6, GATA4, ISL1, NKX2.5, HAND1, and HAND2). SCs expressed OCT4 (POU5F1). E-CPCs expressed GATA6, SOX17, HNF4A, and FOXA2. (C) tSNE clustering of single-cell RNA-Seq of day 8 GATA6 mutant hiPSC-CMs identified five clusters. Labels reflect marker gene expression: CM, cardiomyocytes; CM (‘RA’), cardiomyocytes with increased RA-signaling pathway genes; SC, Stem Cell; CM (‘Endo’), cardiomyocytes with enrichment in endothelial genes; FB; fibroblast-like cells (D) Examples of marker gene expression in clustered cells. SC expressed OCT4 (POU5F1). CMs expressed sarcomere protein genes (MYH6, MYH7), SMYD1, a CM-specific histone methyl-transferase, and HAND2, a second heart field transcription factor. CMs (RA) also expressed retinoic acid pathway genes (SOX6, HOXB1). CMs (Endo) have upregulated endothelial cell gene expression (MEF2C, BMPER), while FB cells expressed ECM markers (IGFBP7).

At day 4 of differentiation, RNA-Seq of WT cells showed expression of both pluripotent stem cell markers (e.g., POU5F1 encoding OCT4) and transcriptional modulators associated with early cardiomyocyte differentiation (Figures 3B,C and 4A,B). These primordial cardiomyocytes expressed SMYD1 (SET and MYND domain-containing protein-1), a nuclear histone methyl-transferase involved in remodeling chromatin and sarcomere assembly (Li et al., 2011), Tbox transcription factors TBX5 and TBX20, two of the earliest markers of cardiac development (Bruneau et al., 2001; Takeuchi et al., 2005), and MEF2C, an essential transcription factor for sarcomere assembly and function (Lin et al., 1997). From day eight onward, the expression of stem cell marker genes was extinguished while expression of cardiomyocyte differentiation transcripts increased (Figures 3C and 4C,D).

Our protocol for cardiomyocyte differentiation also yielded subpopulations of cells expressing endodermal genes. Day 4 WT cells expressed hepatocyte nuclear factor family members (HNF1, HNF4A, HNF4B) that are depleted in Gata6-null mice (Morrisey et al., 1998), FOXA1 and FOXA2, transcription factors that activate expression of the pancreas/duodenum homeobox-1 gene PDX1, which is essential for pancreas development and β islet cell differentiation (Gao et al., 2008; Gerrish et al., 2001; Lee et al., 2019; Zhou et al., 2008) and other endodermal markers such as SOX17 (Wang et al., 2011Figure 4A,B and Supplementary file 2). PDX1 was expressed through differentiation day 12 in WT lines, but not after metabolic enrichment for cardiomyocytes.

GATA6+/- cells at day 4 of differentiation had lower transcript levels of primordial cardiomyocyte genes compared to WT cells (Figure 3B,C) and gene ontology (GO) analyses inferred that pathways involved in cardiac muscle contraction, development, and chamber organization were reduced. Notably, the expression of SMYD1, which regulates the expression of HAND2, a critical transcriptional regulator of the second heart field (Gottlieb et al., 2002; Laurent et al., 2017) that gives rise to the outflow tract, was strikingly diminished (Figure 3B–D, Supplementary file 2). Similarly, expression of multiple HAND2 network genes was also lower than in WT cells (Figure 3C,D), including GATA gene family members, ISL1, MEF2C, and prototypic cardiac transcription factors MEIS-1, NKX2-5, TBX5, and TBX20.

Prompted by the transcriptional signatures in GATA6+/- cells, we compared the heart malformations in 54 PCGC patients with damaging variants in 11 HAND2-network and second heart field genes (Supplementary file 1C) with patients who have GATA6 mutations. Forty of these 54 patients also had outflow tract malformations, including all (n = 5) CHD patients with damaging de novo variants in TBX20.

GATA6+/- cells also identified dysregulation of other gene programs involved in forming the outflow tract. The transient peak expression in WT cells of HOXA1, HOXB1, and MSX1, which regulate the induction and expression of critical molecules involved in specifying neural crest cell development (Makki and Capecchi, 2011; Simões-Costa and Bronner, 2015; Tvrdik and Capecchi, 2006), remained low throughout differentiation of GATA6+/- cells. Notably, HOXB1 also participates in specifying endoderm destined for pancreatic and other abdominal cell lineages (Huang et al., 2002). Expression was also diminished of key molecules that couple outflow tract myocardium with developing vascular beds, including KDR (encoding vascular endothelial growth factor (VEGF) receptor-2) and the VEGF co-receptor, NRPI (Supplementary file 2). Human mutations in HOXA1 (Tischfield et al., 2005) and KDR (Reuter et al., 2019) alleles cause cardiac outflow tract malformations. Our data infers a critical role for GATA6 in orchestrating SMYD1-HAND2, VEGF, and neural crest network interactions during outflow tract morphogenesis.

Day 8 GATA6+/- cells expressed many cardiomyocyte transcripts at levels found in WT lines, but single-cell RNA-Seq analyses demonstrated some persistent differences including reduced expression of MYH7, a defining transcript of mature cardiomyocytes (Figure 3C, Figure 4C,D and Figure 2—figure supplement 1F). Genes encoding other sarcomere proteins were reduced through day 12 in GATA6+/- cells and abnormalities remained after metabolic enrichment for cardiomyocytes (Supplementary file 2). Compared to WT, day 30 GATA6+/- cardiomyocytes had 10-fold higher expression levels of ISL1 and 4-fold higher levels of fetal myosin transcripts (MYH6), suggesting developmental immaturity of GATA6 mutant cardiomyocytes.

Endodermal lineage genes were variably misexpressed in differentiating GATA6+/- cells. In comparison to WT cells, transcript levels were lower for GATA4, HNF1, and HNF4A but normal for FOXA1 and FOXA2 (Supplementary file 2). The day 12 peak expression of PDX1 in WT cells was absent in GATA6+/- cells.

GATA6+/- cells also misexpressed transcription factors genes involved in diaphragm development. In day 8 cells transcripts encoding NR2F2 (encoding a retinoic acid (RA) responsive transcription factor) were 2 to 3-fold higher while expression of ZFPM2 (encoding a family member of the Friend of GATA (FOG) transcription factors) was half of WT levels. As damaging variants in these genes cause congenital diaphragmatic hernia (Kardon et al., 2017), these data imply that GATA6 haploinsufficiency causes diaphragmatic dysgenesis by disrupting NR2F2 and ZFPM2 gene programs (Supplementary file 2).

GATA6-/- hiPSC had profound deficits in cardiomyocyte differentiation (Figure 3, Figure 2—figure supplement 1F, Supplementary file 2). Transcripts associated with early mesoderm specification including MESP1 and transcription factors associated with primordial cardiomyocytes were more depressed than in GATA6+/- cells. These findings support prior observations that shRNA knockdown of GATA6 reduces hiPSC-CM differentiation capacity (Yoon et al., 2018). Given the absence of emerging cardiomyocytes, metabolic selection to further enrich for this lineage was not performed.

The expression of endodermal genes in GATA6-/- hiPSCs was also abnormal. Throughout differentiation GATA4 transcripts were 10-fold reduced, FOXA1 and FOXA2 were transiently expressed only at day 4, and HNF4A and PDX1 expression were extinguished (Supplementary file 2). Dysregulated expression of NR2F2 (increased) and ZFPM2 (decreased) was also observed.

Single-cell RNA-Seq (Figure 4C,D) identified two GATA6-/- populations. One population maintained high expression of the stem cell (SC) marker POU5F1 throughout differentiation day 8, while the other (FB) had increased expression of fibroblast markers (COL3A1, IGFBP7), epithelial to mesenchymal transition markers (SNAIL1/2, MMP2, VIM) (Figure 3—figure supplement 1A), and neural differentiation genes (SHH, ZEB1/2, NCAM2) (Figure 3—figure supplement 1A,B). Together these data inferred that GATA6-/- cells, lacking the normal signals involved in specifying cardiomyocyte and endoderm lineages, adopted alternative differentiation programs.

Transcriptional analysis of GATA6R456G/R456G hiPSC-CMs

Parallel analyses of isogenic GATA6R456G/R456G lines showed shared and distinct transcriptional profiles from GATA6+/- or GATA6-/- cells. At days 4–12, primordial cardiomyocyte transcripts (GATA4, HAND2, and SMYD1) in GATA6R456G/R456G lines were expressed at levels midway between GATA6+/- and GATA6-/- cells, but markedly below levels in WT cells (Figures 3C and 4, and Supplementary file 2).

Distinctive transcription profiles in differentiating GATA6R456G/R456G cells (days 4–12) suggested aberrant RA signaling (Figure 4C,D, Supplementary file 2). ALDH1A2 transcripts (encoding the enzyme that converts RA from retinaldehyde) were 50-fold higher than WT cells and the expression of RARA, RARB (RA receptors A and B), and STRA6 (receptor for retinol uptake) were increased 2 to 3-fold. Transcripts encoding targets of RA signaling were also increased. HOXB1, which contains two RA-responsive elements, including one 6.5 kb 3’ of coding sequences that is critical for foregut expression (Huang et al., 2002), was 100-fold increased at differentiation day 4 – a marked contrast to the depressed levels observed in GATA6+/- cells.

RA signaling modulates neural crest cell development (Martínez-Morales et al., 2011) and at day 4 GATA6R456G/R456G cells had increased PAX3 and PAX7 expression (100-fold and 3-fold, respectively; Supplementary file 2, Figure 3—figure supplement 1B). HOXA1 (6-fold increased) and HOXB contribute to activating expression of ZIC1 (8-fold increased) (Jaurena et al., 2015; Makki and Capecchi, 2011; Tvrdik and Capecchi, 2006) that with PAX3/7 induce and specify a neural crest gene program. PAX3 also promotes neural crest migration into the cardiac outflow tract development (Conway et al., 1997) and like STRA6, (Coles and Ackerman, 2013) and contributes to diaphragm development (Stuelsatz et al., 2012) by specifying paraxial mesoderm (Magli et al., 2019). Aberrant RA signaling may contributed to dysregulation of other developmental regulators of diaphragm formation, including NR2F2 (~8-fold increased) and ZFPM2 (5- to 12-fold decreased).

We further assessed activation of RA signaling by comparing RNA-Seq analyses of day 4 differentiating WT and GATA6R456G/R456G cells that were cultured with or without an inhibitor of the ALDH1A2 enzyme (METHODS). Treated WT cells (Figure 4—figure supplement 1A) had significantly altered expression (p<0.05, 1.5-fold), decreasing the expression of transcripts assigned to Gene Ontology terms for heart and muscle cell differentiation and increasing endodermal fate specification and Wnt signaling transcripts. Treated GATA6R456G/R456G cells (Figure 4—figure supplement 1B) had increased expression of transcripts assigned to Gene Ontology terms for myoblast differentiation, circulatory system development and anatomical structures. Notably, the expression of 25% of genes in treated GATA6R456G/R456G cells (Figure 4—figure supplement 1C–F) had levels found in untreated WT cells (HOXB1 and FOXH1) or normalized levels, approaching those in untreated WT cells (HOXA1, HAND2, RARB, TBX20).

GATA6R456G/R456G cells had aberrant expression of other endoderm genes, including markedly lower transcripts of HNF gene family members and striking loss (≥100-fold below WT) of FOXA1 and FOXA2 expression (Supplementary file 2). Biallelic deletion of FOXA2 in human stem cells prevents pancreatic specification (Lee et al., 2019). GATA6R456G/R456G cells also had increased expression of SOX6, which represses PDX1-dependent transcriptional activation of the insulin gene, INS (Iguchi et al., 2005). The cumulative effect of these deficits could account for the extinguished expression of PDX1 and contribute to the high association of pancreatic agenesis with heterozygous GATA6R456G mutations (Figure 1).

Despite multiple transcriptional abnormalities, metabolic enrichment yielded GATA6R456G/R456G cardiomyocytes (day 30) with prototypic gene expression. However, like GATA6+/- hiPSC-CMs, GATA6R456G/R456G hiPSC-CMs exhibited a higher MYH6:MYH7 transcript ratio than WT, indicating immaturity.

GATA6 is a pioneer factor for cardiac development

We studied potential mechanisms by which GATA6 regulated gene transcription by performing Assays for Transposase-Accessible Chromatin sequencing (ATAC-seq), GATA6 chromatin immunoprecipitation sequencing (ChIP-seq) and Hypergeometric Optimization of Motif EnRichment (HOMER) analyses at day 4 (Figure 5, Figure 6) and day 8 (Supplementary file 4). ChIP-seq of GATA6-/- cells yielded very few peaks above background (data not shown), confirming antibody specificity.

Figure 5 with 1 supplement see all
GATA6 is a pioneer factor for cardiac development.

ATAC-seq and GATA6 ChIP-seq were performed in WT and mutant day 4 hiPSCs and overlapped to assess GATA6 direct binding to open vs. closed chromatin. (A) Approximately 12% of GATA6 ChIP-seq peaks overlapped with an ATAC-seq peak and were characterized as ‘Open’. The genomic location of these ChIP-seq peaks in open chromatin were characterized with respect to gene bodies, and DNA-binding motif enrichment was performed using HOMER analysis (METHODS). When GATA6 binds to open regions of chromatin, peaks are enriched for the GATA motif. (B) The remaining 88% of GATA6 ChIP-seq peaks were characterized as ‘Closed’. The genomic location of these ChIP-seq peaks were characterized with respect to gene bodies, and DNA-binding motif enrichment was performed using HOMER analysis. When GATA6 binds to closed regions of chromatin, peaks are enriched for the MEF2 motif. (C) Of the 20932 WT GATA6 ChIP-seq peaks, 39.3% were reduced in GATA6R456G/R456G cells (adjusted p<1e-4, two fold). These peaks were enriched for the GATA and HAND2 binding motifs by HOMER analysis. (D) Of the 67652 GATA6R456G/R456G ChIP-seq peaks, 64.8% were upregulated in GATA6R456G/R456G cells (adjusted p<1e-4, two fold). These peaks were enriched for the REST and SOX binding motifs by HOMER analysis. (E) Peaks diminished in GATA6R456G/R456G cells were overlapped with WT ATAC-seq data to establish chromatin accessibility. Almost 21% of peaks diminished in GATA6R456G/R456G cells were in open chromatin regions; these peaks were enriched for the GATA and HAND motifs. (F) Peaks upregulated in GATA6R456G/R456G cells were overlapped with GATA6R456G/R456G ATAC-seq data to establish chromatin accessibility. Four percent of peaks enriched in GATA6R456G/R456G cells were in open chromatin regions; these peaks were enriched for the CTCF and SP1 motifs.

Figure 6 with 2 supplements see all
Epigenetic abnormalities in GATA6+/- cells.

(A) Of the 20932 WT GATA6 ChIP-seq peaks, 39.3% were reduced in GATA6+/- cells (adjusted p<1e-4, two fold). These peaks were enriched for the GATA and EWS binding motifs by HOMER analysis. (B) Of the 17624 GATA6+/- ChIP-seq peaks, 47% were upregulated in GATA6+/- cells (adjusted p<1e-4, two fold). These peaks were enriched for the USF2 and GATA-binding motifs by HOMER analysis. (C) Peaks diminished in GATA6+/-cells were overlapped with WT ATAC-seq data to establish chromatin accessibility. Only 10.7% of peaks diminished in GATA6+/- cells were in open chromatin regions; these peaks were enriched for the GATA and SP1 motifs. (F) Peaks upregulated in GATA6R456G/R456G cells were overlapped with GATA6+/- ATAC-seq data to establish chromatin accessibility. Only 2.4% of peaks enriched in GATA6+/- cells were in open chromatin regions; these peaks were enriched for the SP1 and GATA motifs.

At day 4, GATA6 bound ~21,000 open chromatin peaks genome-wide, of which ~ 21% are near promoters. Notably, only 12% of GATA6-bound open chromatin peaks overlapped with ATAC peaks and were markedly enriched for a GATA-binding motif (Figure 5A). ChIP-seq data from day 8 cells were similar (Supplementary file 4). These data inferred that GATA6 directly participates in regulating transcriptional activation of EOMES, GATA4, MEIS1/2, NKX2.5, TBX5, and TBX20 (Supplementary file 4).

By contrast, 88% of GATA-bound peaks occurred in closed chromatin (Figure 5B). Sequences within these peaks were not enriched for the GATA motif but were highly enriched for the MEF2 binding motif. MEF2 proteins contain a DNA-binding domain (MADS) and transcriptional activating domain (TAD) (Backs and Olson, 2006) that promote differentiation gene programs for cardiac, skeletal, and smooth muscle myocytes (Potthoff and Olson, 2007). During cardiomyocyte differentiation of WT hiPSCs MEF2 transcripts increased 120-fold between days 4–8 (Supplementary file 2). In comparison, at day 8–12, MEF2A and MEF2C levels were decreased in GATA6+/- cells. As together, these data implied that GATA6 functioned as a cardiac pioneer factor, which has been proposed for other GATA proteins, we examined the correlation between GATA6 bound to closed chromatin at day 4 and gene expression at day 5 in WT cells. There were 2878 genes differentially expressed between day 4 and day 5 WT cells. Of those, 1049 were associated with GATA6-bound closed chromatin (36.4%). Among these, 583 (56%) genes had increased expression at day 5, including key cardiac developmental transcription factors, GATA4, SMYD1, KDR, and TBX5 (Figure 5—figure supplement 1, Supplementary file 5). Based on these data, we propose that GATA6 is a cardiac development pioneer factor.

Epigenetic abnormalities in GATA6 mutant iPSCs

In comparison to WT, day 4 GATA6+/- cells had~6500 diminished GATA6 ChIP-seq peaks and ~5800 diminished ATAC peaks (Figure 6A and Figure 6—figure supplement 1). Diminished ATAC peaks in proximity to promoters were enriched for GATA, HAND, HNF, and SOX motifs (Figure 6—figure supplement 1, Figure 3, Figure 4 and Supplementary file 2), indicating that attenuated expression of these transcription factor family members could have direct functional consequences. Notably however, few diminished GATA-bound peaks resided within open chromatin (10.7%), of which only 35% were enriched for GATA motifs (Figure 5—figure supplement 1B, Figure 6C). Instead, and consistent with properties of a pioneer factor, GATA6+/- cells had prominent loss (89%) and gain (97%) of GATA-bound peaks within closed chromatin (Figure 6B–D). From these data we infer that GATA interactions with closed chromatin are highly sensitive to protein dosage, while interactions with open chromatin at promoter regions remain relatively intact despite reduced GATA6 protein levels.

By contrast, day 4 GATA6-/- cells contained~29,000 diminished ATAC-seq peaks of which ~ 31% resided in proximity to promoters (Figure 6—figure supplement 1). Profiles at day 8 cells were similar (Figure 6—figure supplement 2). Notably, diminished ATAC peaks showed enrichment in the binding motif for NFY that encodes the ubiquitous promoter element binding factor of CCAAT-boxes. These data implied that extinguishing GATA6-/- expression caused widespread deficits in promoter activation, which likely accounted for failed mesoderm specification and cardiomyocyte differentiation.

ChIP-seq analyses of GATA6R456G/R456G cells (Figure 5C–F, Figure 5—figure supplement 1, Supplementary file 4) identified ~8000 diminished peaks. In comparison to other mutant lines, GATA6R456G/R456G cells had a significantly higher proportion of diminished peaks (21%) in open chromatin that contained a GATA or HAND binding motif and 19% of these diminished peaks were associated with differential gene expression (Figure 5—figure supplement 1C). Additionally, missense cells had a remarkable number (~44,000) of augmented and ectopic peaks. We suggest that these aberrant chromatin interactions reflected direct promiscuous GATA6R456G binding activity or perhaps occurred in response to aberrant RA signaling.

Among the upregulated peaks,~4% of these peaks had GATA-bound to open chromatin, with enrichment of the CTCF binding motif. Furthermore,~12% were associated with differential gene expression, including MSX1, FOXA1, MEIS2, HCN4, and HOXA2 (Supplementary file 4). However, the vast majority of increased or ectopic peaks (96%) resided within closed chromatin and lacked the GATA6 binding motif. Even so, 14% of the differential closed peaks were associated with differential gene expression, including peaks in TBX20, FOXA1, and FOXH1. Our data suggest that the GATA6R456G variant impairs binding to the GATA motif, reducing normal GATA6 function, and also promotes promiscuous binding with either repressive or activating transcriptional effects.

Phenotypes associated with epigenetic abnormalities in GATA6 mutant iPSCs

We examined ATAC-seq and GATA6-ChIP-seq data to identify potential mechanisms for altered gene transcription with relevance to clinical phenotypes in CHD patients with pathogenic GATA6 variants. GATA6-bound peaks were universally decreased in GATA6+/- cells, including those associated many cardiac developmental transcription factors genes (e.g., HAND2, KDR, and TBX5 Figure 7A–C) and these changes were associated with normal or modest reduction in transcript levels (Supplementary file 2). While many of these GATA6-bound peaks were also diminished in GATA6R456G/R456G cells, the missense cells also had many enhanced and augmented peaks which were associated greater differential (decreased or increased) gene expression. For example, only GATA6R456G/R456G cells had diminished GATA6-bound peaks identified in WT cells associated with GATA4 (Figure 7D) and MEF2A and also new GATA6-bound peaks in MEF2C, ZIC1 and ZIC3 (Figure 7E). The distinct epigenetic profiles in GATA6R456G/R456G may contribute to the greater dysregulation of cardiac gene expression and cardiomyocyte maturation in comparison to GATA6+/-cells (Figures 2 and 3 A-C). However regardless of whether epigenetic changes in GATA6R456G/R456G cells decreased normal gene expression or erroneously activated the expression of other genes, the associated cardiac consequence of altered transcription was similar to that of GATA6+/-cells – disruption of cardiac outflow tract development (Figure 1).

Figure 7 with 2 supplements see all
ATAC-seq and GATA6 ChIP-seq analysis of GATA6 variant hiPSC-CMs reveals aberrant binding to congenital heart disease genes.

ATAC peaks (upper), GATA6 ChIP peaks (lower) and DNA-binding motifs (upper, boxed) found in day 4 GATA6 LoF and GATA6R456G/R456G cells, visualized using the Integrative Genomics Viewer (IGV) (A) Lost GATA6 ChIP-seq peak in the HAND2 locus, with differential chromatin accessibility (ATAC-seq). (B) Lost GATA6 ChIP-seq peak at the KDR promoter, leading to reduced chromatin accessibility. (C) Lost GATA6 ChIP-seq peak in TBX5. (D) GATA6R456G does not bind the GATA4 locus in regions of open chromatin. (E) GATA6R456G ectopically binds the ZIC3 promoter.

By contrast, altered GATA6 binding and chromatin accessibility with accompanying dysregulated transcripts likely contributed to the extra-cardiac phenotypes that disproportionately affect patients with heterozygous GATA6 R456G and other exon four missense variants. Pancreatic development requires cooperative GATA4 -GATA6 interactions and compound Gata4-null and Gata6-haploinsufficient mice have reduced Pdx1 expression, pancreatic agenesis or profound hypoplasia (Carrasco et al., 2012). In addition to reduced occupancy of GATA6-motifs in GATA4 with striking attenuated expression (Figure 7D, Supplementary file 2, Supplementary file 4), GATA6 R456G also aberrantly bound chromatin associated with FOXA2, DEANR1 (LINC00261), and PDX1 and diminished transcription of these genes (Figure 7—figure supplement 1). DEANR1 encodes a long noncoding RNA that specifies endoderm and pancreatic lineages by regulating the expression of FOXA2 (Jiang et al., 2015) which in turn regulates PDX1 expression (Gao et al., 2008).

Aberrant RA signaling may have contributed to the abnormal epigenetic profiles in GATA6R456G/R456G cells. In missense cells, prominent ATAC peaks were identified in association with binding motifs for GATA, SOX6, HOX, and RARa in the promoter and exon 1 of ALDH1A2 and transcript levels were increased. SOX6 also had augmented ATAC peaks with RARa and GATA motifs, and GATA6R456G bound an intron one motif (Figure 7—figure supplement 2A–B). GATA6R456G/R456G cells also showed increased ATAC peak heights with RARa binding motifs in close proximity to the reported RA-responsive element in HOXB1 that promotes foregut expression. We also observe GATA6R456G protein aberrantly bound to the STRA6 locus, potentially affecting expression of this gene (Figure 7—figure supplement 2C) These data suggest multiple direct and indirect epigenetic mechanisms by which the GATA6R456G variant distinctly perturbed gene programs and resulted in maldevelopment of the diaphragm, pancreas and other abdominal organs in addition to cause cardiovascular malformations.

Discussion

By combining the strengths of WES, CRISPR/Cas9 genome editing, and hiPSCs, we demonstrate molecular and developmental mechanisms by which damaging variants in GATA6 cause cardiac and extra-cardiac congenital anomalies. Transcriptional and epigenetic analyses of differentiating cardiomyocytes from isogenic WT and mutant hiPSCs provide evidence that GATA6 functions as a pioneer factor that modifies chromatin accessibility and promotes the expression of gene networks involving HAND2, VEGFR and neural crest cells that enable second heart field development and patterning of the outflow tract and atria. Our data also supports critical roles for GATA6 in endodermal and retinoic acid signaling gene networks that gives rise to the pancreas and diaphragm. We find that LoF GATA6 variants repress epigenetic modification and transcriptional activity, while an exon 4 GATA6 missense variant alters normal and also causes ectopic epigenetic effects that enhanced retinoic acid signaling resulting in profound deleterious consequences on gene expression. The selective transcriptional deficits evoked by these variants provides mechanistic insights into the high prevalence of particular heart malformation and associated pancreatic dysgenesis and congenital diaphragmatic hernias that occur in CHD patients with pathogenic GATA6 variants (Figure 8).

Model for GATA6 transcriptional regulation of cardiac and pancreatic gene expression (see Discussion).

ChIP-seq and ATAC-seq data of WT hiPSCs identified GATA6 bound to closed chromatin in intergenic regions without a GATA-binding motif. Moreover, GATA6 binding was associated with temporal activation of transcription in nearby genes that activate cardiomyocyte and endoderm gene network. These findings indicate that GATA6 engages chromatin and fosters a competent state for transcription factor binding and transcriptional activation, supporting the conclusion that GATA6 is a pioneer factor, as is suggested for other GATA proteins (Fisher et al., 2017). Notably, nonsense-mediated decay of GATA6+/- transcripts reduced GATA6 protein levels, altered chromatin accessibility and decreased gene transcription, implying that intergenic sites are sensitive to GATA6 dosage. In addition, GATA6 functions as a traditional transcription factor, binding GATA motifs in promoters and activating transcription. These functions were relatively insensitive to half normal GATA6 levels.

Our analyses demonstrate that some GATA6 expression is required for lineage specification of human cardiomyocytes. When cultured to promote cardiomyocyte differentiation GATA6-/- cells had markedly reduced promoter activation with NFY transcripts that encode the binding factor of CCAAT-box, a nearly ubiquitous promoter element. GATA6-/- cells were viable but showed very limited transcriptional evidence of mesoderm specification and extinguished cardiomyocyte gene expression. Instead GATA6-/- cells adopted a gene program suggestive of nonspecific cells with expression of neuroectodermal and fibroblast genes.

These observations are consistent with prior analyses of Gata6 ablation in mice that exhibit early embryonic lethality due to defects in extraembryonic endoderm formation (Gottlieb et al., 2002; Morrisey et al., 1998; Zhao et al., 2005). While a tetraploid complementation system reported circumvention of this defect (Zhao et al., 2005), only diminutive Gata6-null embryos were recovered with poor tissue integrity and embryonic resorption after E10.5. Although reverse-transcription-PCR of ‘rescued’ E9.5 Gata6-null embryos identified transcripts associated with early cardiomyocyte lineages, similar transcripts were absent in GATA6-/- hiPSCs from which no cardiomyocytes emerged. Perhaps these differences reflect diffusible factors from other cell lineages not present in hiPSCs cultured in vitro, species-specific dosage requirements, or other mechanisms.

GATA6 haploinsufficiency influenced cardiomyocyte differentiation. ATAC and GATA6 ChIP-seq peaks were depleted in multiple developmental cardiac genes including SMYD1, NKX2-5, and ISL1 in GATA6+/- lines. As SMYD1 regulates HAND2 expression (Gottlieb et al., 2002), depression of SMYD1 transcripts likely resulted in a cascade effect, depleting HAND2-network genes. Notably, transcriptional deficits did not persist at differentiation day 30 GATA6+/- hiPSC-CMs when many cardiac transcription factors (SMYD1, HAND2, NKX2-5, GATA4, and MEIS1) were expressed at levels found in WT cells. We presume that a compensatory transcriptional mechanism restored sufficient expression of many gene programs. However, ISL1 transcripts remained abnormally high, and expression profiles of fetal and adult myosins (high MYH6 and low MYH7, respectively) indicated immaturity of GATA6+/- hiPSC-CMs. Persistence of this developmental deficit in vivo could contribute to longitudinal adverse cardiac outcomes in patients with pathogenic GATA6 variants.

Our cardiomyocyte differentiation protocol also activated endodermal gene programs and uncovered misexpression in mutant lines. Aberrant but variable epigenetic profiles were identified in GATA6+/- cells and in GATA6R456G/R456G cells that led to misexpression of HNF and FOXA gene family-members occurred in both mutant lines. For example, both mutant lines had diminished GATA6 binding of open chromatin binding to HNF1 with reduced transcript levels. By contrast, only GATA6R456G/R456G cells lacked GATA6 binding to open chromatin near DEANR1 a regulator of FOXA2 expression (Gao et al., 2008). GATA6R456G/R456G cell also reduced expression of GATA4. While these data illustrate multiple mechanisms for depleting PDX1 expression and account for the prominent association of CHD and pancreatic agenesis in patients with GATA6 LoF and exon four missense variants, the pathways leading to this shared consequence were strikingly different. Epigenetic changes observed in GATA6+/- cells depleted normal GATA6 binding to chromatin with attenuated gene transcription. Epigenetic changes observed in GATA6R456G/R456G cells were far more sweeping, repressing many more genes and erroneously activating others.

We found that GATA6R456G/R456G cells strikingly increased activation of RA signaling. Indeed, early inhibition of excessive RA signaling normalized some of the many aberrant in GATA6R456G/R456G cells. Transcriptional data showed increased expression of the RA biosynthetic enzyme encoded by ALDH1A2 and of STRA6, which encodes the integral membrane receptor that triggers release and uptake of retinol from circulating retinol-binding protein (Chen et al., 2016). As RARa motifs occur in ATAC peaks associated with HOXA2, HOXB1, and SOX6 genes and others, we deduced that GATA6R456G/R456G likely activates transcription of these genes by increasing RA signaling. Moreover, as ATAC peaks in the ALDH2A2 contain SOX6-binding motifs, enhanced SOX6 expression would increase RA biosynthesis that in turn would further increase STRA6 expression (Bouillet et al., 1997) to further amplify RA signaling.

Human congenital anomalies arise from inadequate and excessive expression of key developmental signaling genes. Our analyses support the conclusion that CHD, pancreatic malformations and congenital diaphragmatic hernia that occur in GATA6 exon four missense mutants reflect both loss of physiologic levels of GATA6 and excessively activated RA signaling. Development of the second heart field and neural crest cell migration into the nascent outflow tract is regulated by RA signaling (Zaffran and Kelly, 2012), and when inappropriate, cardiac morphogenesis is perturbed (Perl and Waxman, 2019). RA signaling also regulates transcriptional signals to develop the diaphragm (Kardon et al., 2017), particularly formation of the central tendon (Coles and Ackerman, 2013) around which diaphragmatic myocytes are patterned. As RA signals inhibit the expression of myogenic specification genes, including PAX3 (El Haddad et al., 2017; Magli et al., 2019), excessive RA signaling could impair diaphragm formation by impairing tendon development in addition to altering differentiation and maturation of diaphragmatic myocytes. Increased RA signaling also augments SOX6 expression, which attenuates PDX1 activation of insulin signaling. When combined with deficient expression in critical endodermal genes (FOXa1 FOXa2), specification and differentiation of pancreatic progenitors are likely to be impaired. Consistent with these RA-mediated mechanisms, we note that human mutations in STRA6 cause syndromic congenital diaphragmatic hernia with both CHD and pancreatic anomalies (Golzio et al., 2007; Pasutto et al., 2007).

Based on the heightened risk for congenital diaphragmatic hernia and pancreatic agenesis only among patients with missense variants within exon 4, we suggest that the encoded ZF domain in GATA6 has critical interactions with chromatin and DNA sequences that evoke the epigenetic and transcriptional consequences. Exon four missense residues perturb these functions- culminating in augmented RA signaling and extinguished PDX1 expression. We suggest that missense variants residing outside of exon four do participate in these molecular processes, nor convey similar risks for developmental anomalies. Conformation of this hypothesis would improve clinical interpretation of GATA6 genotypes.

Our studies also demonstrate that in vitro differentiation of hiPSCs evoke developmental gene expression profiles, independent of the many three-dimensional morphological cues that occur in vivo. This system has the potential to illuminate the consequences of lethal mutations that difficult to study in model organisms. While we recognize the limitations of cell models, by interpreting our molecular data with prior mouse studies and clinical studies of human patients, we believe that these iPSC studies provide valid molecular mechanism by which human GATA6 mutations cause cardiac outflow tract defects, pancreatic agenesis, and congenital diaphragmatic hernias.

In conclusion, we show that genetic perturbation of a pioneer factor, GATA6, altered network-level transcriptional pathways that are critically involved in development of the heart (GATA4, HAND2), endodermal lineages (HNF, FOXA1, FOXA2), and pancreas (PDX1, SOX6) and diaphragm (NR2F2, STRA6, ZFPM2). Development of these organs is highly sensitive to changes in GATA6 gene dosage from LoF variants and from missense variants in the second zinc-finger domain that alter RA signaling.

The combination of WES, genome sequencing data, CRISPR/Cas9 genome editing, and molecular analyses of developmentally immature human iPSC-derived lineages represents a valuable paradigm for mechanistic studies of human development. We expect that this platform will continue to illuminate molecular understandings of organogenesis that may improve clinical use of genetic data for personalized medicine.

Materials and methods

Key resources table
Reagent type
(species) or resource
DesignationSource or referenceIdentifiersAdditional information
Cell line (human)PGP1Lee et al., 2009GM23338Male; mycoplasma-free
Cell line (human)TNNT2-GFPThis paperDerived from PGP1 cell line GM23338, mycoplasma-free
Commercial assay or kitZero Blunt TOPO PCR Cloning KitThermoFisherK280002
Commercial assay or kitHuman Stem Cell Nucleofector KitLonzaVPH-5022
Commercial assay or kitNextera XT Sample Preparation KitIlluminaFC-131–1096
Commercial assay or kitNextera DNA Sample Kit (ATAC-seq)IlluminaFC-121–1030
Commercial assay or kitTru ChIP Chromatin Shearing HitCovaris520154
Commercial assay or kitChromium i7 Multiplex Kit10X Genomics1000073
Commercial assay or kitChromium Chip B Single-Cell Kit10X Genomics1000075
Sequence-based reagentGuide RNAsThis paperGATA6 Exon 2 Guide 1: GAGCCCCTACTCGCCCTACG
GATA6 Exon 2 Guide 2:
GCCCCTACTCGCCCTACGTG
GATA6 Exon 4 Guide 1:
GGCGTTTCTGCGCCATAAGG
Sequence-based reagentGATA6 sequencing primersThis paperPCR PrimersGATA6 Exon 2 Sequencing Primer Left/Forward
GACGTACCACCACCACCA
GATA6 Exon 2 Sequencing Primer Right/Reverse
CTTACCTGCACTGGGACCC
GATA6 Exon 4 Sequencing Primer Left/Forward
TGAATTCACGGAGACAGGCT
GATA6 Exon 4 Sequencing Primer Right/Reverse
TACAAGTGAGCAGAATACATGGCA
Sequence-based reagentATAC-seq amplification oligosBuenrostro et al., 2015
Recombinant DNA reagentCas9 plasmidAddgenePX459v2
Chemical compound, drugWIN 18446Tocris
Antibodyrabbit mono-clonal Gata6Cell Signaling Technology5851S10 ug/ChIP
Antibodyrabbit mono-clonal Gata6AbcamAb1759271:1000 dilution
Software, algorithmRNA-seq pipeline: bcbio-nextgenChapman et al., 2020v.1.2.3Hg19
Software, algorithmR Package: DESEQ2Love et al., 2014v. 2.1.18.1
Software, algorithmR Package: SeuratStuart et al., 2019v.3.0.0
Software, algorithmR Package: ChIP-seekerYu et al., 2015v.1.14.1
Software, algorithmHOMERHeinz et al., 2010v4.10.3

Human subjects

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CHD subjects were recruited to the Congenital Heart Disease Network Study of the Pediatric Cardiac Genomics Consortium (CHD GENES: ClinicalTrials.gov identifier NCT01196182) after approval from Institutional Review Boards as previously described (Jin et al., 2017; Gelb et al., 2013). Written informed consent was received from subjects or their parents prior to inclusion in the study. Any subject with CHD, regardless of age, race, or ethnicity was eligible for enrollment. Subjects with variants in GATA6 and 2nd heart field genes including CHD diagnoses, and extra-cardiac phenotypes are provided in Supplementary file 1.

Exome sequencing and analyses

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DNA was extracted peripheral blood samples and captured using the Nimblegen v.2 exome capture reagent (Roche) or Nimblegen SeqxCap EZ MedExome Target Enrichment Kit (Roche) followed by Illumina DNA sequencing as previously described (Jin et al., 2017). Reads were mapped to the reference genome (hg19), and further processed using the GATK Best Practices workflows as previously described (Jin et al., 2017; McKenna et al., 2010). Single nucleotide variants (SNVs) and small indels were called with GATK HaplotypeCaller and filtered for rarity (ExAC allele frequency ≤10–5) and quality as previously described (Jin et al., 2017). The MetaSVM algorithm was used to predict deleteriousness of de novo missense mutations (annotated as ‘D-Mis’) using software defaults (Dong et al., 2015; Sulahian et al., 2014).

TNNT2-GFP, GATA6+/-, GATA6-/-, and GATA6R456G/R456G hiPSCs and iPSC-CMs

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All hiPSCs and hiPSC-CMs used here are derived from the male parent iPSC line PGP1 (Akerman et al., 2017; RRID-GM23338) and are mycoplasma-free. The TNNT2-GFP iPSC line was generated by homology-directed repair, using a plasmid with the endogenous TNNT2 sequence connected to a GSSSS linker region, attached to the GFP gene, and the entire construct was flanked by 500 bp homology arms. PGP1 iPSCs were transfected as described (Sharma et al., 2018b) to obtain homozygous integration of the GFP tag in both TNNT2 alleles. Edited clones were selected using puromycin and expanded and genotyped as previously described (Sharma et al., 2018b) and differentiated into cardiomyocytes as described (Sharma et al., 2018a).

GATA6+/- and GATA6-/- hiPSCs were generated from TNNT2-GFP and PGP1 iPSCs by non-homologous end joining using a 2 µg plasmid expressing Cas9 (PX459v2 from Addgene) that was co-transfected with 2 µg plasmid expressing guide RNA (provided in Key Resource Table) using a stem cell nucleofector kit (Amaxa). Edited clones were selected using puromycin and expanded and genotyped as previously described (Sharma et al., 2018b) and differentiated into cardiomyocytes as described (Sharma et al., 2018a).

GATA6R456G/R456G hiPSCs were generated by homology-directed repair using a 2 µg plasmid expressing Cas9 (PX459v2 from Addgene) that was co-transfected with 2 µg plasmid expressing guide RNA (provided in Key Resource Table) and 2 µg single-stranded oligonucleotide (HDR template) using a stem cell nucleofector kit (Amaxa). Edited clones were selected using puromycin and expanded and genotyped as previously described (Sharma et al., 2018b) and differentiated into cardiomyocytes as described (Sharma et al., 2018a).

Confirmation of editing in subcloned iPSC lines

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All edited lines sub-cloned, by dissociating 1000 genome-edited hiPSCs cells, filtering through a 60 µm strainer, and evenly distributing them onto a 15 cm dish containing Matrigel and mTeSR+ rho kinase inhibitor. Individual monoclonal hiPSC colonies were picked when colony size reached approximately 200 cells and placed into individual separate wells of a 96 well plate. Clones were allowed to grow to 80% confluency, at which time a sample was obtained for PCR amplification to verify the GATA6 variant or GFP-tagged TNNT2 and to assess zygosity. PCR-amplified fragments (primers provided in Key Resource Table) containing putative variants were submitted for Sanger sequencing and next-generation sequencing. Sanger sequencing traces were deconvoluted using TIDE software to confirm zygosity. Clones carrying GATA6 mutations were further expanded for subsequent culture and differentiation.

hiPSC-CM differentiation

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The hiPSC-CMs were generated from GATA6 mutant hiPSCs using a small-molecule mediated differentiation approach that modulates Wnt signaling (Sharma et al., 2018a). Cells began beating at approximately day 7 post-differentiation. Cardiomyocytes were metabolically selected from other differentiated cells by using glucose deprivation as previously described (Sharma et al., 2018a). hiPSC-CM Western blots: GATA6 protein expression and nuclear localization were performed using ab175927.

RNA-sequencing and analysis

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RNA-seq experiments were performed on at least two independent cultured cell samples per time point, with the exception of GATA6R256G/R256G DMSO control in the RA-signaling experiment. Trizol (Thermo Fisher) was used to harvest differentiating GATA6 mutant hiPSC-CMs (days 0, 4, 8, 12, 30 of differentiation) designated for RNA-sequencing analysis. Samples harvested in Trizol were stored at −80°C until RNA was extracted. All RNA samples had an RNA integrity number of ≥8. Library preparation was conducted using the Nextera library preparation method. RNA-Seq library samples were pooled and run on four lanes (one flow cell) using the Illumina NextSeq500 platform. All data was combined into a single fastq file. Samples typically had 30–50M reads each. The raw reads were aligned by HISAT2 (v.2.1.0) to human genome (hg38). The aligned reads were quantified by FeatureCounts, counts were normalized, and differentially expressed genes were identified using DESeq2 (v1.24.0). DESeq2 data was analyzed and visualized in R using the ggplot2 (v3.1.0), pheatmap (v1.0.12), gProfiler (v0.6.7), and VennDiagram (v1.6.20) packages. Single-cell RNA-seq analysis for hiPSC-CMs was conducted using a 10x Genomics Chromium platform and analyzed in R using the Seurat (v. 3.5.1) pipeline. Seurat was used to regress out cell-cell variation in cell complexity, gene expression driven by batch, cell alignment rate, the number of detected molecules, and mitochondrial gene expression. Starting with Seurat v2.0, Seurat implements this regression as part of the data scaling process.

ATAC-seq and Hi-C chromatin analysis

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ATAC-seq was performed as previously described (Buenrostro et al., 2015; Corces et al., 2017). Briefly, 50,000 cells were harvested and lysed to isolate nuclei. Nuclei were treated with Tn5 transposase (Nextera DNA Sample Prep Kit, Illumina) and DNA was isolated. Fragmented DNA was amplified using bar-coded PCR primers (defined in Buenrostro et al., 2015) and libraries were pooled. Pooled libraries were sequenced (Illumina Next-Seq) to a depth of 100 million reads per sample. Reads were aligned to the hg19 reference genome using BWA-MEM and peaks were called using HOMER (v4.10.3) (http://homer.ucsd.edu/homer/index.html). Functional analysis of ATAC-seq peaks was performed using ChIP-Seeker (v.1.14.1). Motif enrichment was performed using HOMER (v4.10.3). Differential peaks were called using HOMER (v4.10.3).

ChIP-seq

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Cells (6-well plates) were grown to approximately 80% confluency and then fixed with 1% fresh formaldehyde diluted in media. Cells were quenched with glycine, washed, and harvested. Cell pellets were stored at −80°C. Cells were thawed and nuclei were prepared using the Covaris Tru ChIP Chromatin Shearing Kit (Covaris, MA, USA). Chromatin was sheared using the Covaris E210 to an average fragment size of 200–700 bp and an input sample was purified. Chromatin (5 µg) was incubated with GATA6 antibody (RRID:AB_5851S) at 4°C O/N and then added to Protein G beads for 2 hr at 4°C. Beads were washed and bound chromatin was eluted at 65°C for 30 min on a heated vortex. DNA was then purified and quantified. ChIP-seq libraries were prepared using Nextera XT DNA sample prep (Illumina). Sequences were aligned to hg19 using BWA-MEM. Peaks were called using MACS2 using default parameters with a q value of 0.001. Differential peaks were called using HOMERv4.10.3.

Retinoic acid inhibitor treatment assay

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hiPSCs were seeded and differentiated to day 4, and then treated for 24 hr with either DMSO or 1 µM WIN 18446 (Tocris), an inhibitor of the ALDH1A2 enzyme. Cells were harvested and processed for RNA-seq.

Statistical analyses

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The distribution of damaging human variants across the GATA6 gene was analyzed using the binominal test, implemented in R. The association of GATA6+/- and GATA6R456G/+ variants and pancreatic agenesis or congenital diaphragmatic hernia was analyzed using the Fisher Exact test, implemented in R. Transcriptional responses of iPSC-CMs with or without a GATA6 variant was compared using the Student’s t-test was used for comparison. All error bars refer to standard deviation, unless otherwise specified. A p value of < 0.05 was considered significant.

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Decision letter

  1. Edward E Morrisey
    Senior and Reviewing Editor; University of Pennsylvania, United States
  2. Stephen Duncan
    Reviewer; Medical University of South Carolina, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

Your study clearly defines new roles for GATA6 in the development of multiple organs. Your study also provides new molecular targets of GATA6, generating possible therapeutics targets for cardiac, pancreatic, and diaphragmatic congenital diseases.

Decision letter after peer review:

Thank you for submitting your article "GATA6 mutations in induced pluripotent stem cells inform developmental mechanisms for heart, pancreas, and diaphragm" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by Edward Morrisey as the Senior and Reviewing Editor. The following individual involved in review of your submission has agreed to reveal their identity: Stephen Duncan (Reviewer #2).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

As you will see below, all of the reviewers found your study to have significant merit. In your revisions, please pay particular attention to the request for additional data regarding the role of RA signaling and a needed focus on how GATA6 regulates cardiac differentiation.

Reviewer #1:

In this manuscript, Sharma et al. performed extensive RNAseq, scRNAseq and ATACseq on early iPS-derived cardiomyocytes with mutation in exon 4 of GATA6 or deletion of GATA6. In addition, they provide an in depth analysis of GATA6 mutations and their clinical correlates in published data and in the Pediatric Cardiac Genomics consortium. This resulted in an impressive set of data that will be of major use to field and will help in understanding how GATA6 mutations cause congenital heart disease and pancreas agenesis, among others. This is a major strength of the manuscript. Perhaps the most interesting and unexpected outcome of this work are the data on the GATA6R456G/R456G mutant, which was generated based on clinical data and in silico modeling indicating an essential role for this amino acid in GATA6 transcriptional regulation and its role in heart and pancreas development. These data convincingly show enhanced retinoic acid signaling, which is very interesting. This is also the only weakness of the manuscript. With these exciting data in hand, it would perhaps not be too difficult and translationally highly relevant to examine to what extent inhibiting retinoic acid signaling might correct or at least improve the strong in vitro phenotypes observed. No matter the outcome of such experiments, this would be important information for the field.

Reviewer #2:

Review of: GATA6 Mutations in Induced Pluripotent Stem Cells Inform Developmental Mechanisms for Heart, Pancreas and Diaphragm Malformations – Sharma et al

The authors present an investigation into the perturbed pathways that manifest in some of the most common pathophysiologies associated with GATA6 haploinsufficiency. The study uses iPSCs to introduce clinically relevant or loss of function mutations into the GATA6 gene and study the developmental consequences of these using a cardiac differentiation protocol. This study is well-written and offers an interesting and novel insight into the role GATA6 plays during cardiac development. There is considerable enthusiasm for the significance of the study, but there is concern that the authors do not provide enough evidence to irrefutably justify a subset of the conclusions that they have drawn. This is particularly true of the findings regarding development of pancreas and diaphragm. It would seem that the manuscript would benefit from a greater focus on the cardiac impact of GATA6 mutations if supported by additional mechanistic studies.

1) GATA6 ChIP-seq analysis is used from previously published datasets from various endoderm populations and cell lines. However, throughout the manuscript the authors refer to GATA binding motifs, common sites which can be bound to varying degrees by GATA1-6. Overlaying the GATA6 ChIP-seq with the ATAC-seq data will give greater confidence that GATA6 does indeed bind to the identified GATA motif. A list is said to be provided in the supplementary data; however, it did not appear to be present in the review package. The binding sites should also be identified in the figures showing individual examples of ATAC-seq genome alignment. However, using ChIP-seq data from a different cell type presents its own concerns. Transcription factors, including GATA4, have been shown to have significantly differential binding patterns between different cell types and also have transient binding sites (PMID: 29358654, PMID: 31350899). Therefore drawing conclusions of direct GATA6 function using the presence of binding motifs or even ChIP-seq derived from different developmental lineages is problematic. Therefore, ChIP-seq data from the cell type/stage of interest, derived previously, or by the authors themselves, would alleviate this concern.

2) Pancreatic observations are obtained from a cardiac differentiation protocol, using single-cell RNA-seq to identify an endoderm subpopulation. While interesting, these are cells that are being subjected strong exogenous differentiation cues, in a protocol not designed to induce endoderm lineage commitments. Therefore, endodermal differentiation cues are likely being derived via paracrine signaling from mesodermal cells – an undefined variable that may vary between the different GATA6 genotypes as they follow the developmental trajectories identified. Importantly, GATA6 haploinsufficiency during pancreatic development using iPSCs has been well-published in the last few years, limiting the novelty of this part of the study (Shi et al., 2017, PMID: 30629940, PMID: 28196690). Indeed, in all three of these studies, FOXA2 has been shown to be down-regulated in GATA6+/- and -/- cells compared to +/+, whereas it is increased in all but the R456G mutant lines in this study. Given the caveats of this differentiation model, and the conserved findings of the published investigations, it would suggest that the model is not optimized for studying endodermal lineages. Consequently, the observations require follow-up using a specific pancreatic differentiation protocol that more tightly controls the factors required for pancreatic development.

3) The following points all relate to the major perceived weakness of this study – several points of interest are identified, but no further investigation of them is attempted. This leaves the paper unfocussed. It would seem that this concern could be addressed by focusing on the cardiac elements of the study if the work were supplemented with mechanistic analyses, rather than trying to generate and partially address such a diversity of hypotheses focusing on other lineages. iPSCs represent a fantastic tool for mechanistic studies, so it is disappointing that the model has not been utilized to its full potential.

4) The authors propose that the GATA6R456G/R456G line has differential transcriptional activity due to altered capacity for DNA binding. The changes in transcriptional and accessibility profile support this hypothesis, but do not confirm it. GATA6 ChIP-seq should be performed to elucidate the differential GATA6 binding that occurs in GATA6+/+ and GATA6R456G cells. If ChIP-seq is not possible, CHIP-PCR at a number of sites with differential accessibility highlighted throughout the manuscript is required.

5) Previously derived Hi-C analysis is used to establish potential interactions between distal lncRNA and the HAND2 gene, with GATA-binding motifs found at sites of interaction, and reduced chromatin accessibility observed in the GATA6 mutant cells. Localized 3-C or equivalent analysis in the GATA6+/+ and mutant cells should be used to confirm whether the interactions are perturbed, as hypothesized.

6) Reduced chromatin accessibility around PLUT1 is identified as a potential cause of the reduced PDX1 expression seen in the endodermal sub-population. The expression of PLUT1 was undetectable using the RNA-seq datasets. Therefore, RT-qPCR should be used to confirm PLUT1 expression is altered in GATA6 mutant cells. This investigation would preferably be completed in a specific pancreatic differentiation model, for the reasons outlined above.

7) Increased retinoic acid (RA) signaling is a proposed mechanism by which GATA6R456G mutant cells induce aberrant changes to accessibility changes to induce malformation of the diaphragm. Attempts to rescue this phenotype should be made using RA inhibitors, or phenocopy the observed changes in GATA6 (+/+) cells by supplementing with RA.

Reviewer #3:

Overall this is an excellent set of studies from labs that have an established expertise in the definition of the mechanisms that contribute to CHD. These studies clearly link the clinical genotypic and phenotypic data with mechanisms that are further explored using hiPSC-derived CMs. The manuscript is well written with excellent figures and controls (comparing Gata6 WT, Het, and Variants). The following major issues are intended to help make an already robust study better.

1) Figure 4: The scRNA-esq analysis focused on a few (limited selected) markers. The authors need to use unbiased pathway analysis to characterize the molecular programs of markers specifically expressed in four clusters identified in D4 and five clusters from D8. A comparison with published scRNA-seq of in vivo heart development (e.g. de Soysa et al., 2019) would be a better way to comprehensively characterize the impact of Gata6 mutations on the developmental program of second heart field.

2) Figure 5: The ATAC-seq analysis was also limited on HAND2 and SMYD1. It would be interesting to see the global changes of chromatin accessibility landscape among the four GATA6 conditions, by using tools such chromVAR.

As the reviewers were unable to identify CMs in the absence of Gata6, is it possible that in this context Gata6 is functioning as a pioneer factor? Based on the ATAC-seq dataset and the ChIP-seq dataset does Gata6 bind nucleosomal DNA?

https://doi.org/10.7554/eLife.53278.sa1

Author response

Reviewer #1:

In this manuscript, Sharma et al. performed extensive RNAseq, scRNAseq and ATACseq on early iPS-derived cardiomyocytes with mutation in exon 4 of GATA6 or deletion of GATA6. In addition, they provide an in depth analysis of GATA6 mutations and their clinical correlates in published data and in the Pediatric Cardiac Genomics consortium. This resulted in an impressive set of data that will be of major use to field and will help in understanding how GATA6 mutations cause congenital heart disease and pancreas agenesis, among others. This is a major strength of the manuscript. Perhaps the most interesting and unexpected outcome of this work are the data on the GATA6R456G/R456G mutant, which was generated based on clinical data and in silico modeling indicating an essential role for this amino acid in GATA6 transcriptional regulation and its role in heart and pancreas development. These data convincingly show enhanced retinoic acid signaling, which is very interesting. This is also the only weakness of the manuscript. With these exciting data in hand, it would perhaps not be too difficult and translationally highly relevant to examine to what extent inhibiting retinoic acid signaling might correct or at least improve the strong in vitro phenotypes observed. No matter the outcome of such experiments, this would be important information for the field.

Thank you for this positive impression of our manuscript. We were delayed due to the COVID sequestration but have now performed retinoic acid inhibition experiments in wild type and GATA6R456G/R456G cells at day 4 of differentiation. We treated cells for 24 hours with DMSO or WIN 18446 (Tocris), an inhibitor of ALDH1A2 that catalyzes the synthesis of retinoic acid from retinaldehyde. From RNA-seq analyses of treated WT hiPSCs we observed downregulation of transcripts associated with heart and muscle cell differentiation and increased expression of transcripts associated with endodermal fate specification. By contrast, treated GATA6R456G/R456G hiPSCs had increased expression of cardiovascular genes, including HAND2. Overall, 25% of transcripts in treated GATA6R456G/R456G hiPSCs were normal or normalized toward levels found in untreated WT cells, including retinoic acid signaling genes (HOXA1, HOXB1). These data are included in revised Figure 4—figure supplement 1.

Reviewer #2:

Review of: GATA6 Mutations in Induced Pluripotent Stem Cells Inform Developmental Mechanisms for Heart, Pancreas and Diaphragm Malformations – Sharma et al

The authors present an investigation into the perturbed pathways that manifest in some of the most common pathophysiologies associated with GATA6 haploinsufficiency. The study uses iPSCs to introduce clinically relevant or loss of function mutations into the GATA6 gene and study the developmental consequences of these using a cardiac differentiation protocol. This study is well-written and offers an interesting and novel insight into the role GATA6 plays during cardiac development. There is considerable enthusiasm for the significance of the study, but there is concern that the authors do not provide enough evidence to irrefutably justify a subset of the conclusions that they have drawn. This is particularly true of the findings regarding development of pancreas and diaphragm. It would seem that the manuscript would benefit from a greater focus on the cardiac impact of GATA6 mutations if supported by additional mechanistic studies.

We appreciate the reviewer’s positive impression of our manuscript. We apologize for the delayed response which reflects both COVID sequestration and requests for many additional experiments that we have performed and incorporated into the revised manuscript. In addition we have substantially reorganized and edited the text to focus on mechanisms for cardiac and extra-cardiac phenotypes.

1) GATA6 ChIP-seq analysis is used from previously published datasets from various endoderm populations and cell lines. However, throughout the manuscript the authors refer to GATA binding motifs, common sites which can be bound to varying degrees by GATA1-6. Overlaying the GATA6 ChIP-seq with the ATAC-seq data will give greater confidence that GATA6 does indeed bind to the identified GATA motif. A list is said to be provided in the supplementary data; however, it did not appear to be present in the review package. The binding sites should also be identified in the figures showing individual examples of ATAC-seq genome alignment. However, using ChIP-seq data from a different cell type presents its own concerns. Transcription factors, including GATA4, have been shown to have significantly differential binding patterns between different cell types and also have transient binding sites (PMID: 29358654, PMID: 31350899). Therefore drawing conclusions of direct GATA6 function using the presence of binding motifs or even ChIP-seq derived from different developmental lineages is problematic. Therefore, ChIP-seq data from the cell type/stage of interest, derived previously, or by the authors themselves, would alleviate this concern.

We agree that ATAC-seq can only provide provisional information regarding transcription factors that regulate gene expression. To more directly define the binding of GATA6 at GATA motifs we performed GATA6 ChIP-seq on WT and mutant hiPSCs and re-interpreted ATAC-seq data in the context of these ChIP-seq data. Our new findings are incorporated into the main Figures 5-7 and in supplements associated with each of these figures.

The integration of ChIP-seq and ATAC-seq data led to several new conclusions:

We now show that GATA6 binds GATA motifs at regions of open chromatin (defined by an ATAC-seq peak) as highlighted in the initial manuscript, and that mutant lines have differential expression of the genes. In addition, we demonstrate that GATA6 also binds closed chromatin that lacks a GATA binding motif. Further, we describe temporal activation of expression for 36% of genes associated with GATA6 bound to closed chromatin. Notably, many of these genes encode cardiac transcription factors. With these data we deduced that GATA6 is as a pioneer factor in cardiac development (Figure 5-6).

Additionally, analyses of GATA6 ChIP-seq in GATA6R456G/R456G hiPSCs demonstrate reduced binding to GATA motifs in open chromatin as well as over 3-fold increased binding to closed chromatin (without a GATA motif). Based on these data, we conclude that the missense allele has both loss of function (failed binding of the GATA motif) and gain of function (increased binding of closed chromatin, and aberrant gene expression) activities. Together these data help to explain the profound shift in gene expression observed in missense cells.

2) Pancreatic observations are obtained from a cardiac differentiation protocol, using single cell RNA-seq to identify an endoderm subpopulation. While interesting, these are cells that are being subjected strong exogenous differentiation cues, in a protocol not designed to induce endoderm lineage commitments. Therefore, endodermal differentiation cues are likely being derived via paracrine signaling from mesodermal cells – an undefined variable that may vary between the different GATA6 genotypes as they follow the developmental trajectories identified. Importantly, GATA6 haploinsufficiency during pancreatic development using iPSCs has been well-published in the last few years, limiting the novelty of this part of the study (Shi et al., 2017, PMID: 30629940, PMID: 28196690). Indeed, in all three of these studies, FOXA2 has been shown to be down-regulated in GATA6 +/- and -/- cells compared to +/+, whereas it is increased in all but the R456G mutant lines in this study. Given the caveats of this differentiation model, and the conserved findings of the published investigations, it would suggest that the model is not optimized for studying endodermal lineages. Consequently, the observations require follow-up using a specific pancreatic differentiation protocol that more tightly controls the factors required for pancreatic development.

Thank you for these thoughtful comments. We fully agree that considerable information has emerged about the how haploinsufficiency and ablation of GATA6 perturbs endoderm formation. We also agree with the reviewer that the cardiomyocyte differentiation protocol employed here might result in findings than would not occur with endodermal differentiation in vivo. However, we respectfully disagree that the important and novelty of our data is limited due to these considerations. First, to the best of our knowledge, this is the first demonstration of the profound association between GATA6 exon 4/5 missense alleles and maldevelopment of the pancreas and diaphragm (Figure 1). Missense mutations in other exons do not cause these phenotypes. Second, our data shows that these missense alleles profoundly activate retinoic acid (RA) signaling, and RA signaling is well recognized (see references in the text) to participate in endoderm formation (e.g., RA activation of HOXA1/HOXB1) and diaphragm development (RA activation of STRA6 and PAX3). Together these data provide a molecular explanation for the striking occurrence of these malformations with GATA6 missense mutations in exon 4/5 – erroneous activation of RA signaling. The inclusion of RA inhibition studies confirms these data. The addition of ChIP-seq data provide further support for these conclusions and provide evidence for widespread epigenetic changes when a mutation both depletes GATA6 and activates RA signaling, as occurs in the missense lines. Together our data demonstrate the critical importance of the zinc finger-2 binding domain in GATA6. Interactions by this domain are essential for cardiac, pancreas and diaphragm development. Finally, understanding why some but not all missense alleles cause these extra-cardiac malformations is clinically important for appropriate recognition of the potential developmental outcomes associated with GATA6 mutations.

3) The following points all relate to the major perceived weakness of this study – several points of interest are identified, but no further investigation of them is attempted. This leaves the paper unfocussed. It would seem that this concern could be addressed by focusing on the cardiac elements of the study if the work were supplemented with mechanistic analyses, rather than trying to generate and partially address such a diversity of hypotheses focusing on other lineages. iPSCs represent a fantastic tool for mechanistic studies, so it is disappointing that the model has not been utilized to its full potential.

4) The authors propose that the GATA6 R456G/R456G line has differential transcriptional activity due to altered capacity for DNA binding. The changes in transcriptional and accessibility profile support this hypothesis, but do not confirm it. GATA6 ChIP-seq should be performed to elucidate the differential GATA6 binding that occurs in GATA6 +/+ and GATA6 R456G cells. If ChIP-seq is not possible, CHIP-PCR at a number of sites with differential accessibility highlighted throughout the manuscript is required.

Thank you for this important point. In addition to adding new experimental data and interpretation, we substantially revised the manuscript to focus on the epigenetic and transcriptional responses to GATA6 mutations that result in maldevelopment of the heart, pancreas and diaphragm. As detailed above we now show that GATA6 functions as a cardiac pioneer factor that primes chromatin for subsequent transcriptional activation as well as a traditional transcription factor that regulates gene expression. Heterozygous mutations predominantly perturb its function as a pioneer factor, while homozygous deletion also abrogates functions related to transcriptional activation.

The comparative GATA6 ChIP-seq data from WT and mutant cells are illustrative of how molecular changes elicit clinical phenotypes. We observe ~17,000 ChIP-seq peaks in GATA6+/- cells, with ~9,000 downregulated and ~6,000 upregulated when compared to WT. The vast majority of associated transcriptional changes indicate decreased gene expression. By contrast there are 21,000 GATA6 ChIP-seq peaks in WT but over 65,000 ChIP-seq peaks in GATA6R456G/R456G cells, with most peaks residing in areas of closed chromatin. These are associated with widespread abnormal transcriptional profiles (increased and decreased in comparison to WT) in GATA6R456G/R456G cells that are not found in GATA6+/- cells. These data has been incorporated into a new set of figures (e.g. Figures 5-6 with associated supplementary figures and files).

5) Previously derived Hi-C analysis is used to establish potential interactions between distal lncRNA and the HAND2 gene, with GATA binding motifs found at sites of interaction, and reduced chromatin accessibility observed in the GATA6 mutant cells. Localized 3-C or equivalent analysis in the GATA6 +/+ and mutant cells should be used to confirm whether the interactions are perturbed, as hypothesized.

Our GATA6 ChIP-seq results identified some GATA6 binding to regions discovered by Hi-C. However, these reads are not statistically enriched over input, implying indicate that the Hi-C interactions are unlikely to have major regulatory effects on HAND2 expression. We have therefore removed this data from the revised manuscript. However, the GATA6 ChIP-seq does identify direct GATA6 binding near the HAND2 locus. As these closed chromatin binding sites are altered in GATA6 mutants, we are still confident that GATA6 participates in regulating HAND2 gene, through its functions as a pioneer factor (see Figure 7A).

6) Reduced chromatin accessibility around PLUT1 is identified as a potential cause of the reduced PDX1 expression seen in the endodermal sub-population. The expression of PLUT1 was undetectable using the RNA-seq datasets. Therefore, RT-qPCR should be used to confirm PLUT1 expression is altered in GATA6 mutant cells. This investigation would preferably be completed in a specific pancreatic differentiation model, for the reasons outlined above.

While our original hypothesis suggested that PLUT1 was a potential cause of reduced PDX1 expression at day 4, our GATA6 ChIP-seq analysis found that GATA6R456G/R456G was aberrantly bound to the PDX1 locus. WT cells do not show GATA6 bound to this locus. At Day 8, GATA6 is bound at the PDX1 locus, and the ChIP-seq peak is not present in GATA6+/- or GATA6-/- cells, providing a potential PLUT1-independent mechanism of PDX1 regulation. This data is shown in Figure 7—figure supplement 1.

7) Increased retinoic acid (RA) signaling is a proposed mechanism by which GATA6 R456G mutant cells induce aberrant changes to accessibility changes to induce malformation of the diaphragm. Attempts to rescue this phenotype should be made using RA inhibitors, or phenocopy the observed changes in GATA6 (+/+) cells by supplementing with RA.

Reviewer 1 also proposed this idea. To address these comments we treated WT and missense cells for 24 hours with DMSO or WIN 18446 (Tocris), an inhibitor of ALDH1A2, which catalyzes the synthesis of retinoic acid from retinaldehyde. From RNA-seq analyses of WT hiPSCs we observed downregulation of transcripts associated with heart and muscle cell differentiation and increase expression of transcripts associated with endodermal fate specification. By contrast, treated GATA6R456G/R456G hiPSCs had increased expression of cardiovascular genes, including HAND2. Overall, 25% of transcripts in treated GATA6R456G/R456G hiPSCs were normal or normalized toward levels found in untreated WT cells, including retinoic acid signaling genes (HOXA1, HOXB1). These data are included in revised Figure 3—figure supplement 2. Please also see Figure 4—figure supplement 1.

Reviewer #3:

Overall this is an excellent set of studies from labs that have an established expertise in the definition of the mechanisms that contribute to CHD. These studies clearly link the clinical genotypic and phenotypic data with mechanisms that are further explored using hiPSC-derived CMs. The manuscript is well written with excellent figures and controls (comparing Gata6 WT, Het, and Variants). The following major issues are intended to help make an already robust study better.

We appreciate the reviewer’s positive impression of our manuscript. We apologize for the delayed response which reflects both COVID sequestration and requests for many additional experiments that we have performed and incorporated into the revised manuscript.

1) Figure 4: The scRNA-esq analysis focused on a few (limited selected) markers. The authors need to use unbiased pathway analysis to characterize the molecular programs of markers specifically expressed in four clusters identified in D4 and five clusters from D8. A comparison with published scRNA-seq of in vivo heart development (e.g. de Soysa et al., 2019) would be a better way to comprehensively characterize the impact of Gata6 mutations on the developmental program of second heart field.

We appreciate this suggestion. We were very aware of the elegant study by de Soysa and colleagues, although we concluded that these mouse data (starting as E7.75) may not be directly relevant to our study that is focused on human development. We also note that there is very limited single cell RNAseq data derived from few captured human cells at gestational age of 7 weeks, which corresponds to E10.5 of mouse hearts (Cui et al., Cell Reports 2019). Our analyses of iPSCs as they begin lineage commitment, identified transcriptional changes by day 4, a time that likely precedes developmental stages characterized in the published single cell mouse and human studies. Additionally as pointed out in the Introduction, GATA6 mutations engineered into mice do not recapitulate phenotypes found in humans. We also hope that the reader recognizes that we did perform unbiased pathway analyses, which led to the Figure 3 in the manuscript. We provide these for the reviewer’s interest in Author response image 1. They are not incorporated into the text due to the considerable information already provided and because of concern by other reviewers that study was insufficiently focused.

Author response image 1
Gene ontology analysis of altered gene networks in day 8 GATA6 LoF and R456G missense cells.

Gene ontology analysis comparing wild type to GATA6 LoF or R456G cells. Downregulated cardiac gene networks of interest highlighted in red. Upregulated neurodevelopmental gene networks of interest highlighted in blue. Upregulated EMT gene networks of interest highlighted in green. Analysis conducted using gProfileR package in R and uploaded to REVIGO online software (http://revigo.irb.hr/) to generate plots of top 20 GO pathway terms by p value.

2) Figure 5: The ATAC-seq analysis was also limited on HAND2 and SMYD1. It would be interesting to see the global changes of chromatin accessibility landscape among the four GATA6 conditions, by using tools such chromVAR.

A broader analysis of the ATAC-seq data is provided in Figure 6—figure supplements 1 and 2. Analysis was performed using HOMER (peak calling and motif analysis) and ChIP-Seeker (genomic localization and annotation). In our opinion, chromVar is a tool that is best suited for single cell ATAC-seq analysis or sparse accessibility data, which does not apply to our dataset.

As the reviewers were unable to identify CMs in the absence of Gata6, is it possible that in this context Gata6 is functioning as a pioneer factor? Based on the ATAC-seq dataset and the ChIP-seq dataset does Gata6 bind nucleosomal DNA?

Thank you for this very important comment. As the reviewer likely recognizes, other GATA proteins have been demonstrated to be pioneer factors. We have now performed extensive analyses of GATA6 ChIP-seq in WT and mutant lines and analyzed these data in the context of ATAC-seq data and temporal expression of genes. We find that in WT cells GATA6 primarily binds closed chromatin (nucleosomal DNA), as only ~10% of the ChIP-seq peaks overlap with ATAC-seq data. We identified temporal increases in gene expression among 36.4% of genes associated with GATA6-bound closed chromatin. Among these differentially expressed genes, 56% encode key cardiac developmental transcription factors, GATA4, SMYD1, KDR, and TBX5 (See Figure 5—figure supplement 1 and Supplementary file 5). Based on these data, we propose that GATA6 is a cardiac development pioneer factor. Moreover, we find that the pioneer factor functions of GATA6 are dysregulated in GATA6-/+ cells and even more so in GATA6R456G/R456G cells These new data are incorporated into Figures 5-7.

https://doi.org/10.7554/eLife.53278.sa2

Article and author information

Author details

  1. Arun Sharma

    1. Department of Genetics, Harvard Medical School, Boston, United States
    2. Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, United States
    3. Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, United States
    Contribution
    Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation
    Contributed equally with
    Lauren K Wasson
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0607-5455
  2. Lauren K Wasson

    1. Department of Genetics, Harvard Medical School, Boston, United States
    2. Howard Hughes Medical Institute, Harvard Medical School, Boston, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation
    Contributed equally with
    Arun Sharma
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5193-5215
  3. Jon AL Willcox

    Department of Genetics, Harvard Medical School, Boston, United States
    Contribution
    Data curation, Investigation
    Competing interests
    No competing interests declared
  4. Sarah U Morton

    1. Department of Genetics, Harvard Medical School, Boston, United States
    2. Division of Newborn Medicine, Boston Children's Hospital, Boston, United States
    Contribution
    Data curation, Investigation
    Competing interests
    No competing interests declared
  5. Joshua M Gorham

    Department of Genetics, Harvard Medical School, Boston, United States
    Contribution
    Data curation, Investigation
    Competing interests
    No competing interests declared
  6. Daniel M DeLaughter

    Department of Genetics, Harvard Medical School, Boston, United States
    Contribution
    Data curation, Investigation
    Competing interests
    No competing interests declared
  7. Meraj Neyazi

    1. Department of Genetics, Harvard Medical School, Boston, United States
    2. Hannover Medical School, Hannover, Germany
    Contribution
    Data curation, Investigation
    Competing interests
    No competing interests declared
  8. Manuel Schmid

    1. Department of Genetics, Harvard Medical School, Boston, United States
    2. Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
    Contribution
    Data curation, Investigation
    Competing interests
    No competing interests declared
  9. Radhika Agarwal

    Department of Genetics, Harvard Medical School, Boston, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  10. Min Young Jang

    Department of Genetics, Harvard Medical School, Boston, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  11. Christopher N Toepfer

    1. Department of Genetics, Harvard Medical School, Boston, United States
    2. Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
    3. Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  12. Tarsha Ward

    Department of Genetics, Harvard Medical School, Boston, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  13. Yuri Kim

    Department of Genetics, Harvard Medical School, Boston, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  14. Alexandre C Pereira

    1. Department of Genetics, Harvard Medical School, Boston, United States
    2. Laboratory of Genetics and Molecular Cardiology, Heart Institute, Medical School of University of Sao Paulo, Sao Paulo, Brazil
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  15. Steven R DePalma

    Department of Genetics, Harvard Medical School, Boston, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  16. Angela Tai

    Department of Genetics, Harvard Medical School, Boston, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  17. Seongwon Kim

    Department of Genetics, Harvard Medical School, Boston, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  18. David Conner

    Department of Genetics, Harvard Medical School, Boston, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  19. Daniel Bernstein

    Department of Pediatrics, Stanford University School of Medicine, Stanford, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  20. Bruce D Gelb

    Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  21. Wendy K Chung

    Department of Medicine, Columbia University Medical Center, New York, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  22. Elizabeth Goldmuntz

    Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2936-4396
  23. George Porter

    Department of Pediatrics, University of Rochester Medical Center, Rochester, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  24. Martin Tristani-Firouzi

    Division of Pediatric Cardiology, University of Utah School of Medicine, Salt Lake City, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  25. Deepak Srivastava

    Gladstone Institutes, San Francisco, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3480-5953
  26. Jonathan G Seidman

    Department of Genetics, Harvard Medical School, Boston, United States
    Contribution
    Conceptualization, Funding acquisition, Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9082-3566
  27. Christine E Seidman

    1. Department of Genetics, Harvard Medical School, Boston, United States
    2. Howard Hughes Medical Institute, Harvard Medical School, Boston, United States
    3. Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Boston, United States
    Contribution
    Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Investigation
    For correspondence
    cseidman@genetics.med.harvard.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6380-1209
  28. Pediatric Cardiac Genomics Consortium

    Contribution
    Resources
    Competing interests
    No competing interests declared

Funding

National Institutes of Health (UM1HL128711)

  • George Porter
  • Martin Tristani-Firouzi
  • Deepak Srivastava
  • Jonathan G Seidman
  • Christine E Seidman

National Institutes of Health (UM1HL128761)

  • Christine E Seidman

National Institutes of Health (UM1HL098147)

  • Daniel M DeLaughter

National Institutes of Health (U01-HL098153)

  • Jonathan G Seidman
  • Christine E Seidman

National Institutes of Health (U01-HL098163)

  • Jonathan G Seidman
  • Christine E Seidman

National Institutes of Health (U01-HL098123)

  • Jonathan G Seidman
  • Christine E Seidman

National Institutes of Health (U01-HL098162)

  • Jonathan G Seidman
  • Christine E Seidman

National Science Foundation (EEC-1647837)

  • Jonathan G Seidman
  • Christine E Seidman

National Institutes of Health (T32HL116273)

  • Arun Sharma

Howard Hughes Medical Institute

  • Lauren K Wasson

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We gratefully acknowledge the NHLBI Pediatric Cardiac Genomics Consortium (PCGC) and Cardiovascular Development Consortium (CVDC) investigators for their support and expertise regarding the mechanisms driving congenital heart disease. Funding support for this study was provided in part by the NHLBI CVDC (U01HL098166) PCGC grants (U01-HL098188, U01-HL098147, U01-HL098153, U01-HL098163, U01-HL098123 and U01-HL098162), Fondation Leducq, the Engineering Research Centers Program of the National Science Foundation (NSF Cooperative Agreement No. EEC-1647837), NIH T32 HL116273, the Wellcome Trust (Sir Henry Wellcome fellowship), the German Academic Scholarship Foundation, and the Howard Hughes Medical Institute.

Ethics

Human subjects: CHD subjects were recruited to the Congenital Heart Disease Network Study of the Pediatric Cardiac Genomics Consortium (CHD GENES: ClinicalTrials.gov identifier NCT01196182) after approval from Institutional Review Boards as previously described (Pediatric Cardiac Genomics et al., 2013; Jin et al., 2017). Written informed consent was received from subjects or their parents prior to inclusion in the study. Clinical diagnoses were standardized based on review of medical data and family interviews.

Senior and Reviewing Editor

  1. Edward E Morrisey, University of Pennsylvania, United States

Reviewer

  1. Stephen Duncan, Medical University of South Carolina, United States

Publication history

  1. Received: November 1, 2019
  2. Accepted: October 14, 2020
  3. Accepted Manuscript published: October 15, 2020 (version 1)
  4. Version of Record published: October 28, 2020 (version 2)

Copyright

© 2020, Sharma et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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