Logo-jcvtr
Submitted: 20 Apr 2021
Revised: 05 Sep 2021
Accepted: 24 Sep 2021
First published online: 01 Nov 2021
EndNote EndNote

(Enw Format - Win & Mac)

BibTeX BibTeX

(Bib Format - Win & Mac)

Bookends Bookends

(Ris Format - Mac only)

EasyBib EasyBib

(Ris Format - Win & Mac)

Medlars Medlars

(Txt Format - Win & Mac)

Mendeley Web Mendeley Web
Mendeley Mendeley

(Ris Format - Win & Mac)

Papers Papers

(Ris Format - Win & Mac)

ProCite ProCite

(Ris Format - Win & Mac)

Reference Manager Reference Manager

(Ris Format - Win only)

Refworks Refworks

(Refworks Format - Win & Mac)

Zotero Zotero

(Ris Format - FireFox Plugin)

Abstract View: 447
PDF Download: 396
Full Text View: 91

J Cardiovasc Thorac Res. 13(4):336-354. doi: 10.34172/jcvtr.2021.45

Original Article

In silico analysis of GATA4 variants demonstrates main contribution to congenital heart disease

Shiva Abbasi 1, *ORCID logo, Neda Mohsen-Pour 2, *, Niloofar Naderi 1, Shahin Rahimi 3, Majid Maleki 1, Samira Kalayinia 1, *ORCID logo
1Cardiogenetic Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
2Zanjan Pharmaceutical Biotechnology Research Center, Zanjan University of Medical Sciences, Zanjan, Iran
3Department of Cardiology, Rajaie Cardiovascular Medical and Research Centre, Iran University of Medical Sciences, Tehran, Iran
*Corresponding Author: Samira Kalayinia, Email: samira.kalayi@yahoo.com #Both authors contributed equally to this work.

Abstract

Introduction: Congenital heart disease (CHD) is the most common congenital abnormality and the main cause of infant mortality worldwide. Some of the mutations that occur in the GATA4 gene region may result in different types of CHD. Here, we report our in silico analysis of gene variants to determine the effects of the GATA4 gene on the development of CHD.

Methods: Online 1000 Genomes Project, ExAC, gnomAD, GO-ESP, TOPMed, Iranome, GME, ClinVar, and HGMD databases were drawn upon to collect information on all the reported GATA4 variations.The functional importance of the genetic variants was assessed by using SIFT, MutationTaster, CADD,PolyPhen-2, PROVEAN, and GERP prediction tools. Thereafter, network analysis of the GATA4protein via STRING, normal/mutant protein structure prediction via HOPE and I-TASSER, and phylogenetic assessment of the GATA4 sequence alignment via ClustalW were performed.

Results: The most frequent variant was c.874T>C (45.58%), which was reported in Germany.Ventricular septal defect was the most frequent type of CHD. Out of all the reported variants of GATA4,38 variants were pathogenic. A high level of pathogenicity was shown for p.Gly221Arg (CADD score=31), which was further analyzed.

Conclusion: The GATA4 gene plays a significant role in CHD; we, therefore, suggest that it be accorded priority in CHD genetic screening.

Keywords: Congenital Heart Disease, GATA4, In Silico Analysis, Transcription Factor

Copyright

© 2021 The Author(s)
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Introduction

Congenital heart disease (CHD) is the most common congenital malformation and a significant cause of childhood mortality with an estimated prevalence of 1% of infants born each year. 1,2 Cardiovascular abnormalities are reported in approximately 29% of dead infants. CHD can be caused by variants in different genes whose roles have evolved. The number of genes and variants thereof involved in the CHD pathogenesis has increased, and an accurate determination of the molecular mechanisms of CHD remains particularly challenging due to genetic heterogeneity and incomplete penetrance. 3 Also extremely complex is the differential diagnosis of CHD in that it is a multifactorial disease encompassing both genetic predisposition and environmental components. 4 Thus, it is vitally important to identify disease-causing genetic variants. 5 Some CHD-associated genes encode transcription factors such as GATA4, NKX2-5, and TBX5, and a number of gene variants identified in these genes have been associated with cardiac structure and functional impairment. 1 GATA-binding factor 4 (GATA4) (OMIM: 600576) is one of the 6-member GATA family of transcription factors: GATA1, GATA2, GATA3, GATA4, GATA5, and GATA6. Amongst GATA-binding proteins, GATA1–3 are expressed in hematopoietic stem cells as significant regulators, whereas GATA4–6 are expressed in different mesoderm- and endoderm-derived tissues such as the heart, the lung, the gonad, the gut, and the liver. 6 Variants in the GATA4, GATA5, and GATA6 genes have been found in patients with various types of CHD. 7-9 GATA proteins comprise 2 conserved zinc finger domains (ZNI and ZNII), which cover various aspects of functions including DNA attachment, GATA4 preservation, and protein-protein and the target DNA sequence interactions. The GATA4 gene consists of 7 exons located on chromosome 8p23.1-p22. The gene encodes one of the earliest-expressed transcription factors with 442 amino acids and is imperative for normal cardiogenesis. GATA4is significantly expressed in embryonic development, with the expression continuing in the adult myocardium. 10-12 A rise has been reported in the number of patients with CHD who reach adulthood. 13 This transcription factor contains 2 transcriptional activation domains (TAD1 and TAD2); 2 zinc finger domains: 1 at the c-terminal region (CZF) and the other at the n-terminal region (NZF); and 1 nuclear localization signal domain (NLS). 14 Variants in the GATA4 gene are highly associated with different types of CHD, 15 including tetralogy of Fallot, ventricular septal defect, atrial septal defect, atrioventricular septal defect, patent ductus arteriosus, dilated cardiomyopathy, and pulmonary valve stenosis. 14,16-21

The current literature lacks in silico analysis on the variants of the GATA4 transcription factor and their critical role in the different levels of cardiovascular development. Accordingly, for the first time, we aimed to conduct a comprehensive in silico analysis of the effects of GATA4 alterations associated with CHD.


Materials and Methods

For the detection of genetic variants in the GATA4gene, the following methodology was utilized in the present study:

Data Collection

The amino acid sequence of the human GATA4 gene was obtained from the National Center for Biotechnology Information (NCBI; https://www.ncbi.nlm.nih.gov/), based on the human genome assembly GRCh37. Accordingly, the Human Gene Mutation Database (HGMD; http://www.hgmd.cf.ac.uk/ac/index.php), as a strongly reliable database, was employed to identify alterations in the GATA4gene. 22 Concurrently, all pathogenic/likely pathogenic alterations reported in public access databases were identified. The databases were ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/), 23 dbSNP (the NCBI database of genetic variation; https://www.ncbi.nlm.nih.gov/snp/), GeneCards (the human gene database; https://www.genecards.org/), 24 ExAC (the exome aggregation consortium; http://exac.broadinstitute.org/), 25 the 1000 Genomes Project (https://www.internationalgenome.org/), 26 gnomAD (the Genome Aggregation Database; http://gnomad.broadinstitute.org/), 27 GO-ESP (NHLBI “Grand Opportunity” Exome Sequencing Project; http://evs.gs.washington.edu/EVS/), 28 TOPMed (Trans-Omics for Precision Medicine; https://www.nhlbiwgs.org/), 29 Iranome (http://www.iranome.ir/), 30 and the Greater Middle East (GME) Variome Project (http://igm.ucsd.edu/gme/). 31 Moreover, extensive research was carried out through computerized search of PubMed, Scopus, Google Scholar, ScienceDirect, MalaCards (the human disease database), and ResearchGate databases by using the following terms: GATA4 variants, the clinical importance of the GATA4 gene, GATA4-related disorders, CHD, the pathophysiology of CHD, and the incidence of CHD.

Frequency

The frequencies of the selected variants were determined using the aforementioned databases. Furthermore, the number of participants and individuals having variations in the studied populations was reported.

Computational Methods

Given its increasing importance and use to determine the possible effects of genetic variants, computational analysis was employed in the present study. The variants of the GATA4 gene and their correlations with the molecular pathogenesis of CHD were further explored by predicting the pathogenicity/tolerance of the variants through the following bioinformatics tools: SIFT (Sorting Intolerant from Tolerant; https://sift.bii.a-star.edu.sg/www/SIFT_seq_submit2.html), 32 PolyPhen-2 (Polymorphism Phenotyping, version 2; http://genetics.bwh.harvard.edu/pph2/), 33 PROVEAN (Protein Variation Effect Analyzer, version 1.1.3; http://provean.jcvi.org/seq_submit.php), 34 CADD (Combined Annotation-Dependent Depletion; https://cadd.gs.washington.edu/), 35 MutationTaster (http://www.mutationtaster.org/), 36 and GERP (Genomic Evolutionary Rate Profiling; http://mendel.stanford.edu/SidowLab/downloads/gerp/). 37 All these bioinformatics tools are capable of distinguishing pathogenic from nonpathogenic alterations. Protein sequences in the FASTA format (NM_002052.5), the positions and substitutions of amino acids, and the positions of chromosomes were used as input data. A SIFT score of 0.05 or less is regarded as deleterious, and a SIFT score of greater than 0.05 is considered to signify a tolerated variant. 32 PolyPhen-2 results are shown with qualitative levels as benign, possibly damaging, and probably damaging. PolyPhen-2 prediction outputs have a numerical score range of 0 to 1. The cutoff score considered for PolyPhen-2 is 0.5, and variants with scores equal to or greater than 0.5 are predicted to be deleterious. 33,38 The cutoff score for PROVEAN is −2.5, and variants equal to or greater than −2.5 are assigned as deleterious. 34 Also calculated in the current investigation was the CADD score. All genomic features used to calculate the CADD score via a machine-learning model are summarized into a Phred score with a cutoff point of 20. Disease-causing variants display a high Phred score ( > 20), whereas a low score (<20) signifies less pathogenicity. 35,39 MutationTaster, which was applied for all the detected variants in the present study, considers an alteration to be a polymorphism if it is reported as a single-nucleotide polymorphism (SNP) in the HapMap data and the 1000 Genomes Project. Thus, any alteration that could result in premature termination codon and ultimately lead to nonsense-mediated mRNA decay is considered a disease-causing variant. GERP is an evolutionary measurement tool whose results are based on multi-species sequence alignment by comparison with neutral expectation. GERP scores show a reduction in the number of substitutions. Positive scores indicate a substitution deficit, while negative scores show that a site is probably evolving neutrally. 40

GATA4 Network Analysis

The functional association between 2 proteins is the primary purpose of the STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) database. This web-based tool expresses the interaction of proteins in a particular biological function. 41 STRING (version 11.0; https://string-db.org/) is used to recognize the known and predicted interactions between the GATA4 protein and other related proteins in a cell. 42

Prediction of Normal and Mutant Protein Structures

Structural and functional differences between wild-type and mutated GATA4 were anticipated by using HOPE (Have [y]Our Protein Explained; https://www3.cmbi.umcn.nl/hope/input/) and 43 I-TASSER (Iterative Threading ASSEmbly Refinement; https://zhanglab.ccmb.med.umich.edu/I-TASSER/). 44-46 The objective was to analyze a pathogenic variant with a high CADD score. HOPE shows the 3D structural and functional effects of a point mutation in human proteins. The input for this tool is the amino acid sequence of the GATA4 protein and the specific amino acid alteration of the variant. 43 The I-TASSER server predicts secondary structures and 3D models through various alignment methods. The accuracy of the formed models is evaluated based on a confidence score (C-score). Predicted models with a C-score of greater than −1.5 are considered to possess a correct topology. I-TASSER predicts the template modeling score (Tm-score) and the root mean square deviation (RMSD). The TM-score ranges between 0 and 1, with higher values specifying better structural models. 47

Phylogenetic Analysis

GATA4 protein sequences from 5 different organisms, namely Homo sapiens (humans), Canis lupus familiaris (dogs), Rattus norvegicus (rats), Gallus gallus domesticus (chickens), and Xenopus laevis (African clawed frogs), were retrieved from UniProt (the Universal Protein Resource; https://www.uniprot.org/). Afterward, all the GATA4 protein sequences were aligned via the multiple sequence alignment program ClustalW (version 1.83; https://www.genome.jp/tools-bin/clustalw). Thereafter, a phylogenetic tree was built by using ClustalW via the neighbor-joining method. As a result of the multiple sequence alignment, the tree showed scores that represented a sequence distance measure. These values determine the length of the branches, with the length showing the distance between the sequences.


Results

Literature Analysis

Using online databases and publications, we succeeded in finding 110 reported variations in the GATA4 gene. We also determined the frequency of the gene variants from online resources. The data are depicted in Table 1. The distributions of the reported variants in the different regions of the GATA4 gene are presented in .

Table 1. Reported frequency of the variants in online databases
DNA Change Genomic Placement on Chromosome 8 1000Genome ExAC GnomAD GO-ESP TOPMED Iranome GME
c.17C > T115658380.00020.000020.00003----
c.46G > T11565860.0002-0.00003-0.000016--
c.62G > T 115658830.00020.000040.00002588-0.000024--
c.82C > G11565903----0.000008--
c.82C > T11565903----0.000008--
c.106C > T11565927-------
c.112T > G11565933-------
c.115G > T11565936-0.0000075410.000007541----
c.127C > T11565948-00.00006-0.000056--
c.136-138delTCC11565957------
c.151C > G11565972-------
c.155C > T11565976-------
c.164A > G11565985-------
c.191G > A11566012----0.000008--
c.196G > A11566017----0.000032--
c.206G > A11566027-------
c.209G > C11566030-------
c.221C > A11566042-0.000036250.00003625-0.000032--
c.244A > G11566065--0.00006-0.000032--
c.259C > T11566080-------
c.270C > A11566091-------
c.278G > C11566099----0.000016--
c.284A > G11566105-------
c.286G > A11566107-------
c.307C > G11566128-------
c.357_359CGC11566175-11566176-------
c.392C > G11566213--0.00003-0.000303--
c.431C > T11566252--0.0001441-0.000016--
c.448G > T11566269 ------
c.479G > C11566300-------
c.487C > T115663080.00020.00020.00003-0.000175--
c.488C > G11566309 0.00020.00003-0.000008--
c.569A > G11566390-------
c.578C > A11566399------
c.590A > G11566411-------
c.620C > T115617280.2017-0.16213-0.174583--
c.622T > C11606433------
c.628G > A11606439-0.00003-0.000080.000008--
c.631T > C11606442------
c.640G > A11606451-------
c.640G > A11606451-------
c.648C > G 11606459-------
c.661G > A11606472-------
c.668 T > C11606479-------
c.677C > A11606488-------
c.687G > T11606498-------
c.700G > A11606511-------
c.715A > G11606526-------
c.716A > G11606527-------
c.731A > G11606542-------
c.740T > C11606551-------
c.743A > G11606554-0.0000160.000008----
c.749T > A11606560-------
c.754C > T11606565-------
c.764T > C11606575-------
c.779G > A11606590--0.00003-0.000008--
c.782T > C11606593------
c.783T > G11606594-------
c.788C > G11607624-------
c.796C > T11607632------
c.799G > A116076350.00040.0003060.00029-0.000231--
c.812G > C11607648-------
c.818A > G11607654--0.00003----
c.819C > A11607655-------
c.822C > T116076580.00180.0022430.002580.003080.003297--
c.830 C > T11607666--0.000004-0.000008--
c.835A > T11607671-------
c.839C > T11607675-------
c.848G > A11607684-------
c.851G > A11607687-------
c.854A > G11607690-------
c.855T > C11607691-------
c.871G > C11607707 ------
c.874T > C11607710-------
c.881C > T11607717-------
c.886G > C11607722-------
c.886G > A11607722-------
c.886G > T11607722-------
c.899A > C11607735-------
c.905A > G11607741------
c.928A > G11612573----0.000008--
c.931C > T11612576-------
c.946C > G11612591-0.000008-----
c.955A > G11612600-------
c.958C > T11612603----0.000008--
c.989C > G11612634-------
c.1017C > A11614463-------
c.1037C > T116144830.000600.001780.001240.002380.00149--
c.1060G > A11614503-------
c.1074delC11614520-------
c.1075delG11614521-------
c.1078G > C11614524-0.000074-0.000080.000135--
c.1079A > G11614525-- ----
c.1081A > G11614527------
c.1129A > G116145750.042930.096210.106320.100570.08186-0.133198
c.1180C > A116158350.00640.0025220.00003-0.0000800.00250.003524
c.1196T > G11615851-------
c.1207C > A11615862-------
c.1211A > G11615866-------
c.1220C > A116158750.0012-0.000100.001130.0002470.0006250.00503

c.1273G > A
116159280.00340.0021170.00003-0.0002390.011880.002517
c.1286G > C11615941-------
c.1288C > G11615943-------
c.1295T > C11615950-------
c.1306C > T11615961-------
c.1310G > C11615965-------
c.1324G > A11615979----0.000008--
c.1325C > T11615980---0.000080.000104--
jcvtr-13-336-g001
Figure 1. The image shows the distribution of the variations of the GATA4 gene. The figure demonstrates the 3 main parts of the GATA4 protein, the GATA-type transcription activator, and the zinc finger regions. The numbers beneath the protein structure represent the number of amino acids in the GATA4 amino acid sequence. The reported variations are categorized based on their locations.

Frequency of the Variants

A wide range of GATA4 variants has been reported in different countries such as Japan, Australia, the United States, Brazil, Egypt, India, Germany, Lebanon, France, Iran, Italy, and especially China. Precise data on the reported variations and the phenotype condition of the individuals studied in different countries are depicted in Table 2. Genetic alterations in c.1129A > G were reported in 3 countries: China (0.33%), Germany (23%), and Australia (19.04%), with the highest frequency in Germany. Additionally, c.874T > C (45.58%), which was reported in Germany, represented the highest frequency among all the reported variations.

Table 2. Frequency of the variants in different populations
DNA Change Condition 1 Population (Frequency) CHD Type References
c.17C > T-China (0.2%)VSD 48
c.46G > T-China (0.62%)AF 49
c.62G > TUncertain significanceChina (1%)ASD 50,51
c.82C > G-China (0.62%)AF 49
c.82C > T-China (2%)VSD 52
c.106C > T-China (0.45%)ASD 53
c.112T > G-China (0.66%)AF 10
c.115G > T-China (0.45%)DCM 11
c.127C > TUncertain significanceChina (0.62%)VSD 54
c.136-138delTCC-China (0.2%)VSD 48
c.151C > G-China (1.92%)TOF 55
c.155C > TPathogenicJapan (6.25%)ASD 56
c.164A > G-China (0.43%)VSD 57
c.191G > A-China (0.38%)VSD, CTD 58,59
c.196G > A-China (0.29%)VSD, PDA, TOF 59-61
c.206G > A-Australia (0.28%)VSD 62
c.209G > C-China (0.76%)AF 63
c.221C > A-China (0.26%)PS 61
c.259C > T-China (0.55%)ASD 50
c.270C > A-China (0.83%)CHD 64
c.278G > C-America (0.15%)ASD 65
c.284A > G-China (0.83%)CHD 64
c.286G > A-China (0.43%)VSD 57
c.307C > G-China (0.66%)AF 10
c.357-359CGCPathogenicChina (0.2%)VSD 48
c.392C > GUncertain significanceBrazil (3.12%)AVSD 66
c.431C > T-Japan (0.9%)PA, ASD 67
C.448G > T-China (0.26%)TOF 61
c.479G > C-China (0.76%)AF 63
c.487C > TPathogenic; Uncertain significance China (0.31%)
America (0.93%)
AVSD, VSD, SA+SV, TOF, TGA
VSD, PS, TOF, VSD
48,59,61,68,69
c.488C > GUncertain significanceAustralia (0.28%)VSD 62
c.569A > G- China (0.45%)ASD 53
c.578C > A-Egypt (9.09%) VSD,VSD, ASD 70
c.590A > G-China (0.43%)VSD 57
c.620C > T-India (3%)ASD,VSD 71
c.622T > C-Germany (1.47%)VSD 72
c.628G > AUncertain significanceChina (0.26%)AVSD 61
c.631T > C-Germany (2.9%)VSD, AVSD 72
c.640G > A-Germany (1.47%)VSD 72
c.640G > A-India (1%)ASD 71
c.648C > G -Lebanon (1.66%)TOF 73
c.661G > APathogenicFrance (family-based)CHD 74
c.668T > C-Germany (1.47%)VSD 72
c.677C > A-China (0.45%)DCM 11
c.687G > T-Germany (4.41%)VSD 72
c.700G > A-Germany (1.47%)AVSD 72
c.715A<G-Germany (1.47%)VSD 72
c.716A > G-Germany (1.47%)VSD 72
c.731A > G-Germany (2.94%)VSD 72
c.740T > CUncertain significanceGermany (1.04%)AF 75
c.743A > G-Germany (2.94%)ASD, AVSD 72
c.749T > A- China (0.26%)VSD 61
c.754C > T-Germany (1.47%)AVSD 72
c.764T > C-Germany (1.47%)ASD 72
c.779G > A-Germany (1.47%)VSD 72
c.782T > C-Germany (2.94%)VSD, ASD 72
c.783T > G-China (0.45%)ASD 53
c.788C > G-China (0.44%)VSD 76
c.796C > T-Germany (2.94%)ASD, AVSD 72
c.799G > A-China (0.58%)ASD, CTD 59,77
c.812G > C-China (0.9%)DCM 78
c.818 A > G-Germany (1.47%)AVSD 72
c.819C > A-Iran (1)VSD, ASD 79
c.822C > TBenign; Likely benign; Uncertain significance Germany (0.97%)
Australia (0.28%)
ASD, ASD, DCM, TOF, VSD 62,80
c.830C > T-Germany (1.47%)AVSD 72
c.835A > T-China (0.45%)DCM 11
c.839C > TUncertain significanceChina (13.33%)AVSD, ASD 81
c.848G > A-Germany (1.47%)AVSD 72
c.851G > A-France (0.3%)ASD 82
c.854A > G-China (1.92%)TOF 55
c.855T > C-Germany (1.47%)AVSD 72
c.871G > C-China (0.66%)DCM 82
c.874T > C-Germany (45.58%)ASD,VSD, AVSD 72
c.881C > T- Germany (1.47%)
Iran(1)
ASD, CHD 72,79
c.886G > CPathogenicChina (0.47%)VSD 83
c.886G > APathogenic America(Family-based)
Italy(family-based)
ASD, PVS 84,85
c.886G > TPathogenicAmerica (0.93%)ASD 68
c.899A > C-China (Family-based)ASD 14
c.905A > G-Germany (1.47%)AVSD 72
c.928A > GPathogenicChina (Family-based)ASD 86
c.931C > T-China(Family-based)TOF, VSD, ASD, PDA 87
c.946C > GPathogenicAmerica (0.31%)ASD 65
c.958C > TLikely pathogenic; Uncertain significanceItaly (family-based)ASD 88
c.989C > GUncertain significantJapan (0.39%)PTA,ASD 89
c.1017C > A-Japan (0.39%)PA,VSD 89
c.1037C > T-America (0.93%)ASD 68
c.1060G > A-China (1.17%)ASD 77
c.1074delC-Japan(family-based)ASD 68
c.1075G > APathogenicChina (0.41%)VSD 48
c.1075delGPathogenicJapan(family-based)ASD 56
c.1079A > G-China (0.26%)VSD 61
c.1081A > G-Germany (1.47%)VSD 90
c.1129A > G- China (0.33%)
Germany (23.9%)
Australia (19.04%)
Iran (45%)
ASD, VSD, AVSD, TOF, PA 17,59,62,80,91
c.1180C > A-India (1.62%)AVSD,VSD 92
c.1196T > G-China (0.45%)VSD 53
c.1207C > A-America (0.93%)ASD 68
c.1211A > G-China (0.43%)VSD 57
c.1220C > ABenign; Uncertain significance China (0.59%)
Iran (Family-based)
ASD, AVSD, VSD, TOF, VSD 17,48,59,69,77,93
c.1273G > AUncertain significance, PathogenicAmerica (0.16%)PA, PS, ASD, TOF, AVSD 65
c.1286G > C-China (0.2%)VSD 48
c.1288C > G-Germany (2.94%)ASD 90
c.1295T > C-India (0.32%)PDA 92
c.1306C > T-China (8%)CSDs 60
c.1310G > C-America (1.28%)BAV 94
c.1324G > A-Germany (1.47%)VSD 90
c.1325C > TPathogenicChina (0.34%)VSD 48

Abbreviation: AVSD, atrioventricular septal defect; ASD, atrial septal defects; CDH, congenital diaphragmatic hernia; CTD, conotruncal heart defects; CHD,congenital heart disease; CSDS, cardiac septal defects; DCM, dilated cardiomyopathy; DORV, double-outlet right ventricle; DILV, double-inlet left ventricle; LVHT, left ventricular hypertrabeculation; LVNC, left ventricular noncompaction; PA, pulmonary atresia; PA + IVS, pulmonary atresia with interventricular septum; PVS, pulmonary valve stenosis; SA+SV, single atrium with single ventricle; TGA, transposition of the great arteries; TOF, tetralogy of Fallot; TGA, transposition of the great arteries; VSD, ventricular septal defect; PTA, persistent truncus arteriosus; BAV, bicuspid aortic valve; AF, atrial fibrillation; PDA,patent ductus arteriosus; PS, pulmonary stenosis

1According to ClinVar

Bioinformatics

The results of the identification and analysis of the variations via online prediction tools are shown in Table 3. Out of the 110 substitutions identified, PROVEAN predicted 55 variations to be deleterious and 50variations to beneutral. (Five variations were not available.) SIFT predicted 62 alterations to be damaging and 33 variations to be tolerated. (Fourteen variations were not available.) PolyPhen-2 defined 25 variations as benign, 18 as possibly damaging, and 59 as probably damaging. (Eight variations were not available.) MutationTaster predicted 82 disease-causing variations and 11 polymorphisms. (Seventeen variations were not available.) The maximum CADD score (Phred score = 53) was shown by c.796C > T R266X, indicating high pathogenicity, while c.196G > A, A66T showed the lowest CADD score (Phred score = 0.009). As a result, among the 110 substitutions, 38 were predicted to be deleterious by PROVEAN, SIFT, PolyPhen-2, and MutationTaster.

In this study, c.1075G > A indicated the highest GERP score (5.83), which represents 4.83 fewer substitutions than was expected. No negative GERP scores were reported for these variations.

Table 3. In silico analysis of GATA4 variations
DNA Change Protein Change Variant Type dbSNP HGMD CADD 1 MutationTaster PolyPhen 2 (Score) PROVEAN 3 SIFT 4 (Score) GERP
c.17C > TA6VMissensers199922907CM08682124.48DCPRD (0.986)NEDE (0.01)NA
c.46G > TG16CMissensers533331682CM11780223POLYMORPHISMPRD (1.000)NETO (0.1)NA
c.62G > TG21VMissensers202213149CM10759624.4DCPRD (0.972)NEDE (0.02)NA
c.82C > GH28DMissensers1406275331CM11780325DCPRD (0.993)DEDE (0)NA
c.82C > TH28YMissensers1406275331CM091017824DCPRD (0.993)DEDE (0)NA
c.106C > TP36CMissense-CM131374625.2DCPRD (1)DEDE (0)NA
c.112T > GY38DMissense-CM12351326DCPRD (0.998)DEDE(0)NA
c.115G > TV39LMissensers1139241CM14737724DCPRD (0.958)NEDE (0)NA
c.127C > TR43WMissensers387906770CM11951925DCPRD (1)DEDE (0)NA
c.136 e138delTCC46delSdeletion-----NE-NA
c.151C > GL51VMissense-CM131206423.6DCPRD  (0.977)NEDE (0.01)NA
c.155C > TS52FMissensers104894074CM131206425.6DCPRD (0.975)DEDE (0)NA
c.164A > GQ55RMissense CM12506222.9DCPOD (0.586)NEDE (0.01)NA
c.191G > AG64EMissensers1249347695CM10723711.21POLYMORPHISMBENIGN (0.392)NETO (0.99)NA
c.196G > AA66TMissensers1139244CM10102690.009DCBENIGN (0)NETO (0.58)NA
c.206G > AG69DMissense CM109056-POLYMORPHISMBENIGN (0.157)NETO (0.46)NA
c.209G > CS70TMissense-CM11516510.71DCBENIGN (0.001)NETO (0.46)NA
c.221C > AA74DMissensers1258064099CM101026519.96POLYMORPHISMPRD (0.997)NETO (0.14)NA
c.244A > GT82AMissensers961114777-12.10POLYMORPHISMBENIGN (0)-TO (0.38)NA
c.259C > TP87SMissense-CM107597--PRD (0.977)NETO (0.14)NA
c.270C > AS90RMissense-CM104917--BENIGN (0.440)NEDE (0.04)NA
c.278G > CG93AMissensers56191129CM07620619.85DCPOD (0.943)NETO(0.09)NA
c.284A > GD95GMissense-CM104918--BENIGN (0)NETO (0.06)NA
c.286G > AG96RMissense-CM1213107-POLYMORPHISMBENIGN (0.012)NETO (0.06)NA
c.307C > GP103AMissense-CM12351419.50DCBENIGN (0.001)NETO (0.7)NA
c.357_359CGCA126dupDuplicationrs1182566703----NE-NA
c.392C > GA131GMissensers1013984246-18.4POLYMORPHISMBENIGN (0.002)NETO (0.66)NA
c.431C > TA144VMissensers1308945507CM16197414.22POLYMORPHISMPOD (0.727)NETO (0.16)NA
c.448G > TG150WMissensers1024075653CM101026626.0DCPRD (0.997)DEDE (0)NA
c.479G > CS160TMissensers1358565879CM11516623.5DCPOD (0.891)NETO (0.35)NA
c.487C > TP163SMissensers387906769CM07620122.1DCPOD (0.669)NENA NA
c.488C > GP163RMissensers540578824CM10905725.4DCPRD (0.973)DETO (0.42)NA
c.569A > GH190RMissense-CM131374724DCPRD (0.988)DEDE (0)NA
c.578C > AP193HMissense--24.2DCPOD (0.921)NEDE (0.05)NA
c.590A > GN197SMissense-CM12506315.18POLYMORPHISMBENIGN (0.009)NETO (0.65)NA
c.620C > T-5′ UTRrs61277615-8.145----NA
c.622T > CF208LMissense---20.6DCBENIGN (0.071)NETO (1)NA
c.628G > AD210NMissensers377673676CM101026732DCPRD (0.996)DENA5.08
c.631T > CF211LMissense- 22.4DCBENIGN (0.005)NETO (0.47)NA
c.640G > AG214GSynonymous-CM05148824.3--NETO (1)NA
c.640G > AG214SMissense- 24.3DCPOD (0.921)DETO (0.442)NA
c.648C > G E216DMissense-CM061008--PRD (0.999)DEDE (0)NA
c.661G > AG221RMissensers398122402CM11056232DCPRD (0.999)DEDE (0)NA
c.668T > CM223TMissense--23.6DCBENIGN (0.126)DETO (0.32)NA
c.677C > AP226QMissense-CM14737825.3DCPRD (1)DEDE (0)NA
c.687G > TR229SMissense--24.2DCPRD (0.998)DEDE (0)3.83
c.700G > AG234SMissense--28.1DCPRD (1)DEDE (0)5.61
c.715A > GN239DMissense--27.3DCPRD (0.995)DENANA
c.716A > GN239SMissense--25.8DCPRD (1)DEDE (0)NA
c.731A > GY244CMissense--28.9DCPRD (1)DEDE (0)NA
c.740T > CM247TMissensers1131691325CM10421925.5DCPOD (0.890)DEDE (0)NA
c.743A > GN248SMissensers749360828-25.9DCPRD (0.994)DEDE (0)NA
c.749T > AI250NMissense-CM101026827.8DCPRD (0.994)DEDE (0.01)NA
c.754C > TR252WMissense-CM13131831DCPRD (1)DEDE (0)NA
c.764T > CI255TMissense--25DCPOD (0.748)DEDE (0)NA
c.779G > AR260QMissensers1245034279-27.9DCPRD (0.979)DEDE (0.01)NA
c.782T > CL261PMissense--26.2DCPOD (0.653)DEDE (0)NA
c.783T > GS262AMissense-CM1313748--BENIGN (0.255)NETO (0.2)NA
c.788C > GA263GMissense-CM12840624.5DCBENIGN (0.449)NEDE (0.04)NA
c.796C > TR266XNonsense ---53DC-NANANA
c.799G > AV267MMissensers116781972CM06834324.9DCBENIGN (0.401)NETO (0.09)NA
c.812G > CC271SMissense--27.7-PRD (1)DEDE (0)NA
c.818A > GN273SMissensers1340083717-25.9DCPRD (1)DEDE (0)NA
c.819C > AN273KMissense--25.3-PRD (1)DEDE (0)NA
c.822C > TCys274 = synonymousrs55980825-11.33DC-NE-4.51
c.830C > TT277IMissensers1236909953-27DCPOD (0.770)DEDE (0)NA
c.835A > TT279SMissense--26.3-PRD (0.999)DEDE (0)NA
c.839C > TT280MMissensers387906771CM104913.29DCPRD (1)DEDE (0)NA
c.848G > AR283HMissensers180765750-31DCPRD (1)DEDE (0)NA
c.851G > AR284HMissense-CM16038531DCPRD (1)DEDE (0)NA
c.854A > GN285SMissense-CM131206524.4DCPOD (0.858)DEDE (0.02)NA
c.855T > CN285KMissense-CM0515040.667DCPRD (0.999)DEDE (0)NA
c.871G > CV291LMissense-CM14146925.4DCPRD (0.997)DEDE (0)NA
c.874T > CC292RMissense-CM05150527DCPRD (1)DEDE (0)NA
c.881C > TA294VMissense-CM05150628.2DCPRD (1)DEDE (0)NA
c.886G > CG296RMissensers104894073CM11466627.9DCPRD (1)DENANA
c.G886AG296SMissensers104894073CM03168527.8DCPRD (1)DENANA
c.886G > TG296CMissensers104894073CM07620329.2DCPRD (1)DEDE (0)NA
c.899A > CK300TMissense-CM16000624.6DCPRD (1)DEDE (0)NA
c.905A > GH302RMissense-CM05150724.6DCPRD (0.962)DEDE (0)NA
c.928A > GM310VMissensers387906772CM10209526.4DCPOD (0.934)DEDE (0)NA
c.931C > TR311WMissense---DCPRD (0.999)DEDE(0)NA
c.946C > GQ316EMissensers56298569CM07620026.6DCPRD (0.996)DEDE (0)NA
c.955A > GK319EMissense-CM14018429.2DCPRD (0.991)DEDE (0.01)NA
c.958C > TR319WMissensers1282433424CM10684433DCPRD (1)DEDE (0)NA
c.989C > GT330RMissense-CM12328623.5DCBENIGN (0.048)DETO (0.14)NA
c.1017C > AS339RMissensers1042942931-20.9DCBENIGN (0.93)NEDE (0.03)NA
c.1037C > TA346VMissensers115372595CM07620514.12POLYMORPHISMBENIGN (0.112)NETO (0.28)3.93
c.1060G > AT354AMissense-CM107551--BENIGN (0)NETO (0.21)NA
c.1074delCS358XNonsense-------NA
c.1075G > AE359KMissensers368489876CM08682025.2DCPRD (1)NENA5.83
c.1075delGE359fsDeletionrs1585703301------NA
c.1078G > CE360QMissensers141808522-24.6DCPRD (0.985)NENA5.83
c.1079A > GE360GMissense-CM101026425.9DCPRD (0.985)NETO (0.37)NA
c.1081A > GM361VMissense--17.29DCPOD (0.664)NETO (0.49)NA
c.1129A > GS377GMissensers3729856CM164458 -BENIGN (0)NETO (0.56)0.906
c.1180C > AP394AMissensers200319078CM11935517.05POLYMORPHISMBENIGN (0)NETO (0.39)2.670
c.1196T > GV399GMissense-CM131374920.7DCBENIGN (0.131)NETO (0.53)NA
c.1207C > AL403MMissense-CM07620225DCPRD (0.997)NETO (0.06)NA
c.1211A > GK404RMissense-CM12506426.4DCPRD (0.996)NEDE (0)NA
c.1220C > AP407QMissensers115099192CM08681925.8DCPOD (0.675)DEDE (0.04)4.780
c.1273G > AD425NMissensers56208331CM07620729DCPRD (0.970)DEDE (0.01)5.180
c.1286G > CS429TMissense-CM08681823.18DCPOD (0.646)NEDE (0.04)NA
c.1288C > GL430VMissense--24.8DCPRD (0.990)NEDE (0)NA
c.1295T > CL432SMissense-CM11935427.98DCPRD (0.998)DEDE (0)NA
c.1306C > TH436YMissense-CM09570726.08DCPOD (0.851)NEDE (0)NA
c.1310G > CG437AMissense-CM14908123.5DCPOD (0.787)NEDE (0)NA
c.1324G > AA442TMissensers1270266865-26.8DCPRD (0.996)NEDE (0)NA
c.1325C > TA442VMissensers146017816-27.1DCPRD (0.999)NEDE (0)5.18

All GATA4 variants are reported based on the NCBI nucleotide (NM_002052.5) and protein (NP_002043.2) sequences (NG_008177.2).

1 CADD, Phred ≤ 20: Neutral; Phred > 20: Damaging; 2 PolyPhen-2, score = 0-0.15: Benign; score = 0.15-0.85: Possibly damaging; score = 0.85-1: Probably damaging; 3 PROVEAN, score ≤ -2.5: Deleterious; score > -2.5: Neutral; 4 SIFT, score ≤ 0.05: Deleterious; score > 0.05: Tolerable; TO: Tolerable; DE: Deleterious; NE: natural, DC: Disease-causing; NA: Not available. PRD: Probably damaging; POD: Possibly damaging

Protein-Protein Interaction Network Analysis

As is illustrated in , STRING, version 11.0, demonstrated that 11 proteins (GATA4, NKX2-5, MEF2C, ZFPM2, TBX5, BMP4, SRF, BMP2, HAND2, NPPA, and HEY2) and 41 edges (protein-protein associations) grouped to create a protein network.

jcvtr-13-336-g002
Figure 2. The image presents the STRING protein-protein interaction analysis. The network contains 11 nudes and 41 edges. The edges are represented with various colors, with each color indicating protein-protein associations. The GATA4 protein and its functional interactions with 11 other proteins display possible effects on each other. NKX2-5: Homeobox protein NKX2-5, MEF2C: Myocyte-specific enhancer factor 2C, ZFPM2: Zinc finger protein ZFPM2, TBX5: T-box transcription factor, BMP4: Bone morphogenetic protein 4, SRF: Serum response factor, BMP2: Bone morphogenetic protein 2, HAND2: Heart- and neural crest derivatives-expressed protein 2, NPPA: Natriuretic peptides A, HEY2: Hairy/enhancer-of-split related with YRPW motif protein 2.

Differences Between the Wild-Type GATA4 Protein and the Mutant Model

In this study, the effects of the predicted disease-causing p.Gly221Arg variant in GATA4 with the CADD Phred score of 31 were further analyzed. The variant, p.Gly221Arg, with a high level of pathogenicity is a heterozygous missense variant in the conserved N-terminal zinc finger of GATA4. 74 HOPE results showed the alteration of glycine to arginine at position 221 (G221R, CADD Phred = 31). The size, charge, and hydrophobicity value of the 2 residues, as well as the differences between them, are presented in . The mutant residue showed a larger size, with a positive charge, while the wild-type protein charge was neutral. Furthermore, arginine was more hydrophobic than was glycine. These differences in amino acid features could affect the zinc finger site of the protein and its function. Accordingly, this change in the GATA4 sequence might result in the conformation of the protein and exert negative influences on the structure of the protein in this specific residue ().I-TASSER produced 3D structures of GATA4 in 5 models with different C-scores. A model with a C-score of −0.5, an estimated TM-score of 0.65, and an estimated RMSD of 8.2 Å was selected. Hence, the findings proved that the solubility of the mutant protein was similar to that of the wild-type one, with a score of 3 ().

jcvtr-13-336-g003
Figure 3. A) The image presents the schematic structure of a normal amino acid on the left (glycine) and a mutant one on the right (arginine) at position 221 of the GATA4 protein. The red parts show the similar parts of the amino acids (the backbone), and the black part shows the unique part of the amino acids (the side chain). This picture illustrates the structural differences between the 2 amino acids. The G221R alteration is shown by HOPE. B) A photograph generated by HOPE shows that the G221R variation affects the structure of the GATA4 protein. The green color shows the wild-type residue (glycine), and the red color represents the mutant residue (arginine). C) I-TASSER shows the secondary and 3D structure, as well as the predicted solvent accessibility, of the normal (left) and G221R mutant (right) of the GATA4 protein.

GATA4 Protein Sequence Alignment and the Phylogenetic Tree

According to the phylogenetic tree generated by ClustalW, the human GATA4 protein had the closest homology with that of Canis lupus familiaris (dogs). Further, the most distant orthologue was Xenopus laevis (African clawed frogs) (). The results of the multiple-alignment sequencing of the species are illustrated in .

jcvtr-13-336-g004
Figure 4. A) The phylogenetic tree is constructed by CLUSTALW implementing the neighbor-joining method for GATA4 in Homo sapiens (humans), Rattus norvegicus (rats), Canis lupus familiaris (Dogs), Xenopus laevis (African clawed frogs), and Gallus gallusdomesticus (chickens). The length of the horizontal lines shows the evolutionary distance between each organism based on the GATA4 protein sequence. (B) The results of the CLUSTALW multiple-sequence-alignment program show the conservation of the GATA4 G221 position among the different organisms. The identical (*), conserved (:), and semiconserved (.) residues are specified. This position is highly conserved among the different species.


Discussion

CHD is the most frequent congenital abnormality and the major cause of infant mortality the world over. GATA4, a transcription factor with 2 zinc finger domains, has been reported to play an essential role in embryogenesis and cardiac development. 90 The GATA4 gene is reported to modulate heart hypertrophy in adults. 95 The number of studies seeking to explicate the correlation between GATA4 variants and CHD occurrence is on the rise. Indeed, recent studies have identified several novel variants in the GATA4 gene with potential roles in CHD development. 17

CHD is very heterogeneous, and the etiology of the majority of cases remains greatly unknown. Both genetic and environmental factors contribute to CHD. 96 Therefore, the elucidation of the pathogenesis and differential diagnosis of the disease requires the identification of not only the disease-causing or susceptibility genes but also new genetic variants associated with the different types of CHD. Research has linked several genes to CHD, with NKX2-5, TBX5, and GATA4 comprising the most studied transcription factor genes. 15 These genes interact during embryonic development, and they are involved in the regulation of cardiogenesis and embryonic heart development. 97 Protein-protein interactions between transcription factors play a vital role in biological systems. The results concerning GATA4 protein interactions, generated by STRING, showed that 11 proteins (GATA4, NKX2-5, MEF2C, ZFPM2, TBX5, BMP4, SRF, BMP2, HAND2, NPPA, and HEY2) grouped in a network. GATA4 and NKX2-5 transcription factors are critical to cardiomyocyte hypertrophy; thus, single-point variants could create an imbalance in the interaction between these proteins. 12 GATA4 has been shown to interact with HAND2 to modulate the transcription of the downstream gene by binding to the conserved GATA-binding sites of the HAND2 promoter. 98 NKX2-5, as a central regulator of many aspects of heart development, interacts with SRF and GATA4 to promote the expression of the cardiac sarcomeric protein gene. 99 Mutations in the ZFPM2 gene, which encodes the FOG2 protein (a transcription regulator of the GATA family members), disrupt the interaction with GATA4 or the nucleosome remodeling and deacetylation (NuRD) complex and, thus, lead to CHD. 100-103 Loss-of-function mutation in the MEF2C gene, which encodes a transcription factor required for normal cardiovascular development, is associated with increased vulnerability to CHD in humans. 104 MEF2C, TBX5, and GATA4 can induce cardiomyocyte differentiation and directly reprogram endogenous cardiac fibroblasts into functional cardiomyocytes. 105 Remarkably, BMP2 and BMP4 are vital for cardiogenesis in that they induce the expression of NKX2-5 and GATA4 transcription factors. These 2 genes play a significant role during the initial induction of cardiogenesis. Nevertheless, no association between BMP2 and BMP4 genetic variations (rs1049007, rs235768, and rs17563) and the risk of CHD was reported by Li FF et al. 106 Variations in the NPPA gene, which encodes the ANP precursor, are correlated with hypertension, stroke, coronary artery disease, and heart failure. 107 The HEY2 transcription factor plays an important function in mammalian heart development. Three non-synonymous variations, namely c.286A > G (p.Thr96Ala), c.293A > C (p.Asp98Ala), and c.299T > C (p.Leu100Ser), were reported to affect the second helix of HEY2 in the diseased cardiac tissues of 2 cases with atrioventricular septal defect, suggesting its possible function in the regulation of ventricular septation in humans. 108 Somatic mutations were identified in NKX2-5 and its molecular partners, TBX5 and GATA4, as well as the transcription factor HEY2, in formalin-fixed tissues taken from a collection of hearts with atrial septal defect, 109 ventricular septal defect, and atrioventricular canal defect. 90,108,110-112

The GATA4 missense variation (p.G221R), on which we focused in the present study, was identified in three 46, XY DSD patients from a family of French origin. The in vitro assays in that investigation demonstrated the failure of the p.G221R mutant protein to bind to FOG2, which is required for gonad formation. Furthermore, the mutant protein failed to transactivate the anti-Müllerian hormone promoter. 74

Some variants of GATA4 investigated in the present study have been previously analyzed for genotype-phenotype correlations. These investigations evaluated families manifesting those variations associated with different CHD types.

Lourenço D et al 74 reported the G221R variant in 5 members of a family with cardiac anomalies including atrial septal defect, tetralogy of Fallot, and congenital cyanotic heart disease.

In a study conducted by Garg V et al, 84 the c.886G > A (G296S) variation of GATA4 was stated in 13 affected members with atrial septal defect in a family with 5 generations. The authors also reported the E359del variation of GATA4 in 5 members of another family with the autosomal dominant transmission of atrial septal defect in 4 generations, indicating GATA4 as a genetic cause of atrial septal defect.

Sarkozy et al 85 detected the G296S variation of GATA4 in 2 members of 1 family and 3 members of another family diagnosed with atrial septal defect.

Chen J et al 14 recognized the GATA4 c.899A > C (K300T) substitution in 10 members of a family: 8 affected members with severe symptoms (7 patients with atrial septal defect and 1 patient with ventricular septal defect) and 2 unaffected members. The K300T substitution lessens the transcription of the GATA4 target gene by harming the DNA-binding activity of GATA4.

Yu Chen et al 86 identified the c.928A > G (M310V) variant located in the NLS region of GATA4 in all patients of a 3-generation family with atrial septal defect. The variant reduces the transcriptional activity of the GATA4 protein and may disturb the interaction between GATA4 and TBX or NKX2-5.

A genetic investigation conducted by E. D’Amato et al 88 reported the R319W variation in 3 members of a family: the proband and the proband’s sister, both diagnosed with atrial septal defect, and the proband’s father, who was considered not affected.

Rajagopal et al 68 studied 107 probands with cardiac abnormalities and identified the c.886G > T (G296C) variant in a proband with atrial septal defect and pulmonary stenosis. They also reported the substitution in the proband’s father with persistent left superior vena cava to the coronary sinus. The G296S variation resulted in a reduction in GATA4 DNA-binding activity and disrupted binding to the transcription factor TBX5. Also in their study, the c.1207C > A (L403M) variant was identified in a proband with a hypoplastic right ventricle and sinus venosus atrial septal defect. Their results also demonstrated the c.487C > T (P163S) and c.1037C > T (A346V) variants in probands with endocardial cushion defect. Additionally, a missense variation, c.931C > T (R311W), in GATA4 was identified in a pedigree spanning 3 generations with 7 members diagnosed with CHD. All the affected members presented different cardiac phenotypes, including tetralogy of Fallot, ventricular septal defect, atrial septal defect, and patent ductus arteriosus, indicating that the same genetic alteration could lead to different subtypes of CHD. 87

In the present study, we filtered the literature and online databases for the pathogenic variants of the GATA4 gene. Our search yielded 210 variants; nonetheless, we excluded 100 of these variants due to a dearth of information and continued the study with 110 variations. After analyzing the frequency distributions of all the variants, we employed computational tools with different algorithms to predict the pathogenicity of the variants. As is shown in Table 3, our in silico analysis using MutationTaster, PolyPhen, PROVEAN, and SIFT revealed 38 pathogenic genetic variations. Our findings may broaden the spectrum of the known GATA4 genetic variations associated with different types of CHD.


Conclusions

Several gene deficiencies could contribute to the pathogenesis of CHD. In this study, we drew upon different in silico predictive tools for the analysis of the variants of the GATA4 gene. The most frequent variant was c.874T > C (45.58%), and the most frequent type of CHD was ventricular septal defect. Out of all the reported variants of GATA4, 38 variants were pathogenic. The p.Gly221Arg variant (CADD score = 31) showed a high level of pathogenicity. All the identified pathogenic variations in GATA4 could assist in the rapid identification and better understanding of the mechanisms underlying CHD.


Acknowledgments

The authors wish to thank the Rajaie Cardiovascular Medical and Research Center, Tehran, Iran, and Zanjan University of Medical Sciences, Zanjan, Iran.


Competing Interests

None declared.


Ethical Approval

Not applicable.


Funding

This research was funded by Rajaie Cardiovascular Medical and Research Center, Tehran, Iran, and Zanjan University of Medical Sciences, Zanjan, Iran.


References

  1. Williams K, Carson J, Lo C. Genetics of congenital heart disease. Biomolecules 2019; 9(12):879. doi: 10.3390/biom9120879 [Crossref]
  2. Abdul Samad F, Suliman BA, Basha SH, Manivasagam T, Essa MM. A comprehensive in silico analysis on the structural and functional impact of SNPs in the congenital heart defects associated with NKX2-5 gene-a molecular dynamic simulation approach. PLoS One 2016; 11(5):e0153999. doi: 10.1371/journal.pone.0153999 [Crossref]
  3. Edwards JJ, Gelb BD. Genetics of congenital heart disease. Curr Opin Cardiol 2016; 31(3):235-241. doi: 10.1097/hco.0000000000000274 [Crossref]
  4. Ferencz C, Loffredo CA, Rubin JD, Magee CA. Perspectives in Pediatric Cardiology. Armonk, New York: Futura Publishing Company; 1997.
  5. Shabana NA, Shahid SU, Irfan U. Genetic contribution to congenital heart disease (CHD). Pediatr Cardiol 2020; 41(1):12-23. doi: 10.1007/s00246-019-02271-4 [Crossref]
  6. Martinez de LaPiscina I, de Mingo C, Riedl S, Rodriguez A, Pandey AV, Fernández-Cancio M. GATA4 variants in individuals with a 46,XY disorder of sex development (DSD) may or may not be associated with cardiac defects depending on second hits in other DSD genes. Front Endocrinol (Lausanne) 2018; 9:142. doi: 10.3389/fendo.2018.00142 [Crossref]
  7. Kodo K, Nishizawa T, Furutani M, Arai S, Yamamura E, Joo K. GATA6 mutations cause human cardiac outflow tract defects by disrupting semaphorin-plexin signaling. Proc Natl Acad Sci U S A 2009; 106(33):13933-13938. doi: 10.1073/pnas.0904744106 [Crossref]
  8. Jiang JQ, Li RG, Wang J, Liu XY, Xu YJ, Fang WY. Prevalence and spectrum of GATA5 mutations associated with congenital heart disease. Int J Cardiol 2013; 165(3):570-573. doi: 10.1016/j.ijcard.2012.09.039 [Crossref]
  9. Granados-Riveron JT, Pope M, Bu’lock FA, Thornborough C, Eason J, Setchfield K. Combined mutation screening of NKX2-5, GATA4, and TBX5 in congenital heart disease: multiple heterozygosity and novel mutations. Congenit Heart Dis 2012; 7(2):151-159. doi: 10.1111/j.1747-0803.2011.00573.x [Crossref]
  10. Wang J, Sun YM, Yang YQ. Mutation spectrum of the GATA4 gene in patients with idiopathic atrial fibrillation. Mol Biol Rep 2012; 39(8):8127-8135. doi: 10.1007/s11033-012-1660-6 [Crossref]
  11. Li J, Liu WD, Yang ZL, Yuan F, Xu L, Li RG. Prevalence and spectrum of GATA4 mutations associated with sporadic dilated cardiomyopathy. Gene 2014; 548(2):174-181. doi: 10.1016/j.gene.2014.07.022 [Crossref]
  12. Jumppanen M, Kinnunen SM, Välimäki MJ, Talman V, Auno S, Bruun T. Synthesis, identification, and structure-activity relationship analysis of GATA4 and NKX2-5 protein-protein interaction modulators. J Med Chem 2019; 62(17):8284-8310. doi: 10.1021/acs.jmedchem.9b01086 [Crossref]
  13. Wang T, Chen L, Yang T, Huang P, Wang L, Zhao L. Congenital heart disease and risk of cardiovascular disease: a meta-analysis of cohort studies. J Am Heart Assoc 2019; 8(10):e012030. doi: 10.1161/jaha.119.012030 [Crossref]
  14. Chen J, Qi B, Zhao J, Liu W, Duan R, Zhang M. A novel mutation of GATA4 (K300T) associated with familial atrial septal defect. Gene 2016; 575(2 Pt 2):473-477. doi: 10.1016/j.gene.2015.09.021 [Crossref]
  15. Suluba E, Shuwei L, Xia Q, Mwanga A. Congenital heart diseases: genetics, non-inherited risk factors, and signaling pathways. Egypt J Med Hum Genet 2020; 21(1):11. doi: 10.1186/s43042-020-0050-1 [Crossref]
  16. Su W, Zhu P, Wang R, Wu Q, Wang M, Zhang X. Congenital heart diseases and their association with the variant distribution features on susceptibility genes. Clin Genet 2017; 91(3):349-354. doi: 10.1111/cge.12835 [Crossref]
  17. Kalayinia S, Maleki M, Rokni-Zadeh H, Changi-Ashtiani M, Ahangar H, Biglari A. GATA4 screening in Iranian patients of various ethnicities affected with congenital heart disease: co-occurrence of a novel de novo translocation ( 5;7) and a likely pathogenic heterozygous GATA4 mutation in a family with autosomal dominant congenital heart disease. J Clin Lab Anal 2019; 33(7):e22923. doi: 10.1002/jcla.22923 [Crossref]
  18. Clark KL, Yutzey KE, Benson DW. Transcription factors and congenital heart defects. Annu Rev Physiol 2006; 68:97-121. doi: 10.1146/annurev.physiol.68.040104.113828 [Crossref]
  19. El Bouchikhi I, Bouguenouch L, Moufid FZ, Belhassan K, Samri I, Chaouti A. Absence of GATA4 mutations in Moroccan patients with atrial septal defect (ASD) provides further evidence of limited involvement of GATA4 in major congenital heart defects. Eurasian J Med 2020; 52(3):283-287. doi: 10.5152/eurasianjmed.2020.19237 [Crossref]
  20. Misra C, Sachan N, McNally CR, Koenig SN, Nichols HA, Guggilam A. Congenital heart disease-causing GATA4 mutation displays functional deficits in vivo. PLoS Genet 2012; 8(5):e1002690. doi: 10.1371/journal.pgen.1002690 [Crossref]
  21. Maitra M, Schluterman MK, Nichols HA, Richardson JA, Lo CW, Srivastava D. Interaction of GATA4 and GATA6 with Tbx5 is critical for normal cardiac development. Dev Biol 2009; 326(2):368-377. doi: 10.1016/j.ydbio.2008.11.004 [Crossref]
  22. Stenson PD, Ball EV, Mort M, Phillips AD, Shiel JA, Thomas NS. Human Gene Mutation Database (HGMD): 2003 update. Hum Mutat 2003; 21(6):577-581. doi: 10.1002/humu.10212 [Crossref]
  23. Landrum MJ, Lee JM, Benson M, Brown GR, Chao C, Chitipiralla S. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res 2018; 46(D1):D1062-D1067. doi: 10.1093/nar/gkx1153 [Crossref]
  24. Stelzer G, Rosen N, Plaschkes I, Zimmerman S, Twik M, Fishilevich S. The GeneCards suite: from gene data mining to disease genome sequence analyses. Curr Protoc Bioinformatics 2016; 54:1.30.31-31. doi: 10.1002/cpbi.5 [Crossref]
  25. Karczewski KJ, Weisburd B, Thomas B, Solomonson M, Ruderfer DM, Kavanagh D. The ExAC browser: displaying reference data information from over 60 000 exomes. Nucleic Acids Res 2017; 45(D1):D840-D845. doi: 10.1093/nar/gkw971 [Crossref]
  26. Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, Korbel JO. A global reference for human genetic variation. Nature 2015; 526(7571):68-74. doi: 10.1038/nature15393 [Crossref]
  27. Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 2020; 581(7809):434-443. doi: 10.1038/s41586-020-2308-7 [Crossref]
  28. Auer PL, Reiner AP, Wang G, Kang HM, Abecasis GR, Altshuler D. Guidelines for large-scale sequence-based complex trait association studies: lessons learned from the NHLBI exome sequencing project. Am J Hum Genet 2016; 99(4):791-801. doi: 10.1016/j.ajhg.2016.08.012 [Crossref]
  29. Brody JA, Morrison AC, Bis JC, O’Connell JR, Brown MR, Huffman JE. Analysis commons, a team approach to discovery in a big-data environment for genetic epidemiology. Nat Genet 2017; 49(11):1560-1563. doi: 10.1038/ng.3968 [Crossref]
  30. Fattahi Z, Beheshtian M, Mohseni M, Poustchi H, Sellars E, Nezhadi SH. Iranome: a catalog of genomic variations in the Iranian population. Hum Mutat 2019; 40(11):1968-1984. doi: 10.1002/humu.23880 [Crossref]
  31. Scott EM, Halees A, Itan Y, Spencer EG, He Y, Azab MA. Characterization of Greater Middle Eastern genetic variation for enhanced disease gene discovery. Nat Genet 2016; 48(9):1071-1076. doi: 10.1038/ng.3592 [Crossref]
  32. Sim NL, Kumar P, Hu J, Henikoff S, Schneider G, Ng PC. SIFT web server: predicting effects of amino acid substitutions on proteins. Nucleic Acids Res 2012; 40(Web Server issue):W452-457. doi: 10.1093/nar/gks539 [Crossref]
  33. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P. A method and server for predicting damaging missense mutations. Nat Methods 2010; 7(4):248-249. doi: 10.1038/nmeth0410-248 [Crossref]
  34. Choi Y, Chan AP. PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics 2015; 31(16):2745-2747. doi: 10.1093/bioinformatics/btv195 [Crossref]
  35. Rentzsch P, Witten D, Cooper GM, Shendure J, Kircher M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res 2019; 47(D1):D886-D894. doi: 10.1093/nar/gky1016 [Crossref]
  36. Schwarz JM, Cooper DN, Schuelke M, Seelow D. MutationTaster2: mutation prediction for the deep-sequencing age. Nat Methods 2014; 11(4):361-362. doi: 10.1038/nmeth.2890 [Crossref]
  37. Cooper GM, Stone EA, Asimenos G, Green ED, Batzoglou S, Sidow A. Distribution and intensity of constraint in mammalian genomic sequence. Genome Res 2005; 15(7):901-913. doi: 10.1101/gr.3577405 [Crossref]
  38. Adzhubei I, Jordan DM, Sunyaev SR. Predicting functional effect of human missense mutations using PolyPhen-2. Curr Protoc Hum Genet 2013;Chapter 7:Unit7.20. 10.1002/0471142905.hg0720s76
  39. Niroula A, Vihinen M. How good are pathogenicity predictors in detecting benign variants?. PLoS Comput Biol 2019; 15(2):e1006481. doi: 10.1371/journal.pcbi.1006481 [Crossref]
  40. Huber CD, Kim BY, Lohmueller KE. Population genetic models of GERP scores suggest pervasive turnover of constrained sites across mammalian evolution. PLoS Genet 2020; 16(5):e1008827. doi: 10.1371/journal.pgen.1008827 [Crossref]
  41. Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P. The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res 2011; 39(Database issue):D561-568. doi: 10.1093/nar/gkq973 [Crossref]
  42. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2019; 47(D1):D607-D613. doi: 10.1093/nar/gky1131 [Crossref]
  43. Venselaar H, Te Beek TA, Kuipers RK, Hekkelman ML, Vriend G. Protein structure analysis of mutations causing inheritable diseases An e-Science approach with life scientist friendly interfaces. BMC Bioinformatics 2010; 11:548. doi: 10.1186/1471-2105-11-548 [Crossref]
  44. Roy A, Kucukural A, Zhang Y. I-TASSER: a unified platform for automated protein structure and function prediction. Nat Protoc 2010; 5(4):725-738. doi: 10.1038/nprot.2010.5 [Crossref]
  45. Yang J, Yan R, Roy A, Xu D, Poisson J, Zhang Y. The I-TASSER Suite: protein structure and function prediction. Nat Methods 2015; 12(1):7-8. doi: 10.1038/nmeth.3213 [Crossref]
  46. Yang J, Zhang Y. I-TASSER server: new development for protein structure and function predictions. Nucleic Acids Res 2015; 43(W1):W174-181. doi: 10.1093/nar/gkv342 [Crossref]
  47. Zhang Y. I-TASSER server for protein 3D structure prediction. BMC Bioinformatics 2008; 9:40. doi: 10.1186/1471-2105-9-40 [Crossref]
  48. Zhang W, Li X, Shen A, Jiao W, Guan X, Li Z. GATA4 mutations in 486 Chinese patients with congenital heart disease. Eur J Med Genet 2008; 51(6):527-535. doi: 10.1016/j.ejmg.2008.06.005 [Crossref]
  49. Jiang JQ, Shen FF, Fang WY, Liu X, Yang YQ. Novel GATA4 mutations in lone atrial fibrillation. Int J Mol Med 2011; 28(6):1025-1032. doi: 10.3892/ijmm.2011.783 [Crossref]
  50. Liu XY, Yang YQ, Ma J, Lin XP, Zheng JH, Bai K. [Novel GATA4 mutations identified in patients with congenital atrial septal defects]. Zhonghua Xin Xue Guan Bing Za Zhi 2010; 38(8):724-727.
  51. Liu XY, Wang J, Zheng JH, Bai K, Liu ZM, Wang XZ. Involvement of a novel GATA4 mutation in atrial septal defects. Int J Mol Med 2011; 28(1):17-23. doi: 10.3892/ijmm.2011.638 [Crossref]
  52. Chen MW, Pang YS, Guo Y, Liu BL, Shen J, Song HD. [Association between GATA-4 mutations and congenital cardiac septal defects in Han Chinese patients]. Zhonghua Xin Xue Guan Bing Za Zhi 2009; 37(5):409-412.
  53. Yang YQ, Wang J, Liu XY, Chen XZ, Zhang W, Wang XZ. Mutation spectrum of GATA4 associated with congenital atrial septal defects. Arch Med Sci 2013; 9(6):976-983. doi: 10.5114/aoms.2013.39788 [Crossref]
  54. Yang YQ, Li L, Wang J, Liu XY, Chen XZ, Zhang W. A novel GATA4 loss-of-function mutation associated with congenital ventricular septal defect. Pediatr Cardiol 2012; 33(4):539-546. doi: 10.1007/s00246-011-0146-y [Crossref]
  55. Yang YQ, Gharibeh L, Li RG, Xin YF, Wang J, Liu ZM. GATA4 loss-of-function mutations underlie familial tetralogy of Fallot. Hum Mutat 2013; 34(12):1662-1671. doi: 10.1002/humu.22434 [Crossref]
  56. Hirayama-Yamada K, Kamisago M, Akimoto K, Aotsuka H, Nakamura Y, Tomita H. Phenotypes with GATA4 or NKX25 mutations in familial atrial septal defect. Am J Med Genet A 2005; 135(1):47-52. doi: 10.1002/ajmg.a.30684 [Crossref]
  57. Yang YQ, Wang J, Liu XY, Chen XZ, Zhang W, Wang XZ. Novel GATA4 mutations in patients with congenital ventricular septal defects. Med Sci Monit 2012; 18(6):CR344-350. doi: 10.12659/msm.882877 [Crossref]
  58. Yang YQ, Tang YQ, Liu XY, Lin XP, Chen YH. [A novel GATA4 mutation leading to congenital ventricular septal defect]. Zhonghua Yi Xue Yi Chuan Xue Za Zhi 2010; 27(5):512-516. doi: 10.3760/cma.j.issn.1003-9406.2010.05.008 [Crossref]
  59. Liu Y, Li B, Xu Y, Sun K. Mutation screening of GATA4 gene in CTD patients within Chinese Han population. Pediatr Cardiol 2017; 38(3):506-512. doi: 10.1007/s00246-016-1542-0 [Crossref]
  60. Chen MW, Pang YS, Guo Y, Pan JH, Liu BL, Shen J. GATA4 mutations in Chinese patients with congenital cardiac septal defects. Pediatr Cardiol 2010; 31(1):85-89. doi: 10.1007/s00246-009-9576-1 [Crossref]
  61. Wang E, Sun S, Qiao B, Duan W, Huang G, An Y. Identification of functional mutations in GATA4 in patients with congenital heart disease. PLoS One 2013; 8(4):e62138. doi: 10.1371/journal.pone.0062138 [Crossref]
  62. Butler TL, Esposito G, Blue GM, Cole AD, Costa MW, Waddell LB. GATA4 mutations in 357 unrelated patients with congenital heart malformation. Genet Test Mol Biomarkers 2010; 14(6):797-802. doi: 10.1089/gtmb.2010.0028 [Crossref]
  63. Yang YQ, Wang MY, Zhang XL, Tan HW, Shi HF, Jiang WF. GATA4 loss-of-function mutations in familial atrial fibrillation. Clin Chim Acta 2011; 412(19-20):1825-1830. doi: 10.1016/j.cca.2011.06.017 [Crossref]
  64. Wang J, Hu DY, Li XM, Xin YF, Zhou H, Wang LJ. [Novel GATA4 mutations identified in patients with congenital heart disease]. Zhonghua Yi Xue Za Zhi 2010; 90(10):667-671.
  65. Tomita-Mitchell A, Maslen CL, Morris CD, Garg V, Goldmuntz E. GATA4 sequence variants in patients with congenital heart disease. J Med Genet 2007; 44(12):779-783. doi: 10.1136/jmg.2007.052183 [Crossref]
  66. Porto MP, Vergani N, Carvalho AC, Cernach MC, Brunoni D, Perez AB. Novel mutations in the TBX5 gene in patients with Holt-Oram syndrome. Genet Mol Biol 2010; 33(2):232-236. doi: 10.1590/s1415-47572010005000051 [Crossref]
  67. Yoshida A, Morisaki H, Nakaji M, Kitano M, Kim KS, Sagawa K. Genetic mutation analysis in Japanese patients with non-syndromic congenital heart disease. J Hum Genet 2016; 61(2):157-162. doi: 10.1038/jhg.2015.126 [Crossref]
  68. Rajagopal SK, Ma Q, Obler D, Shen J, Manichaikul A, Tomita-Mitchell A. Spectrum of heart disease associated with murine and human GATA4 mutation. J Mol Cell Cardiol 2007; 43(6):677-685. doi: 10.1016/j.yjmcc.2007.06.004 [Crossref]
  69. Peng T, Wang L, Zhou SF, Li X. Mutations of the GATA4 and NKX25 genes in Chinese pediatric patients with non-familial congenital heart disease. Genetica 2010; 138(11-12):1231-1240. doi: 10.1007/s10709-010-9522-4 [Crossref]
  70. Shaker O, Omran S, Sharaf E, G AH, Mashaly M, N EAG. A novel mutation in exon 1 of GATA4 in Egyptian patients with congenital heart disease. Turk J Med Sci 2017; 47(1):217-221. doi: 10.3906/sag-1605-166 [Crossref]
  71. Mattapally S, Nizamuddin S, Murthy KS, Thangaraj K, Banerjee SK. c620C > T mutation in GATA4 is associated with congenital heart disease in South India. BMC Med Genet 2015; 16:7. doi: 10.1186/s12881-015-0152-7 [Crossref]
  72. Reamon-Buettner SM, Borlak J. GATA4 zinc finger mutations as a molecular rationale for septation defects of the human heart. J Med Genet 2005; 42(5):e32. doi: 10.1136/jmg.2004.025395 [Crossref]
  73. Nemer G, Fadlalah F, Usta J, Nemer M, Dbaibo G, Obeid M. A novel mutation in the GATA4 gene in patients with tetralogy of Fallot. Hum Mutat 2006; 27(3):293-294. doi: 10.1002/humu.9410 [Crossref]
  74. Lourenço D, Brauner R, Rybczynska M, Nihoul-Fékété C, McElreavey K, Bashamboo A. Loss-of-function mutation in GATA4 causes anomalies of human testicular development. Proc Natl Acad Sci U S A 2011; 108(4):1597-1602. doi: 10.1073/pnas.1010257108 [Crossref]
  75. Posch MG, Boldt LH, Polotzki M, Richter S, Rolf S, Perrot A. Mutations in the cardiac transcription factor GATA4 in patients with lone atrial fibrillation. Eur J Med Genet 2010; 53(4):201-203. doi: 10.1016/j.ejmg.2010.03.008 [Crossref]
  76. Xiong F, Li Q, Zhang C, Chen Y, Li P, Wei X. Analyses of GATA4, NKX25, and TFAP2B genes in subjects from southern China with sporadic congenital heart disease. Cardiovasc Pathol 2013; 22(2):141-145. doi: 10.1016/j.carpath.2012.07.001 [Crossref]
  77. Wang J, Li XM, Xin YF, Wang LJ, Xu WJ, Hu DY. [Genetic screening for novel GATA4 mutations associated with congenital atrial septal defect]. Zhonghua Xin Xue Guan Bing Za Zhi 2010; 38(5):429-434.
  78. Li RG, Li L, Qiu XB, Yuan F, Xu L, Li X. GATA4 loss-of-function mutation underlies familial dilated cardiomyopathy. Biochem Biophys Res Commun 2013; 439(4):591-596. doi: 10.1016/j.bbrc.2013.09.023 [Crossref]
  79. Soheili F, Jalili Z, Rahbar M, Khatooni Z, Mashayekhi A, Jafari H. Novel mutation of GATA4 gene in Kurdish population of Iran with nonsyndromic congenital heart septals defects. Congenit Heart Dis 2018; 13(2):295-304. doi: 10.1111/chd.12571 [Crossref]
  80. Posch MG, Perrot A, Schmitt K, Mittelhaus S, Esenwein EM, Stiller B. Mutations in GATA4, NKX25, CRELD1, and BMP4 are infrequently found in patients with congenital cardiac septal defects. Am J Med Genet A 2008; 146A(2):251-253. doi: 10.1002/ajmg.a.32042 [Crossref]
  81. Chen Y, Mao J, Sun Y, Zhang Q, Cheng HB, Yan WH. A novel mutation of GATA4 in a familial atrial septal defect. Clin Chim Acta 2010; 411(21-22):1741-1745. doi: 10.1016/j.cca.2010.07.021 [Crossref]
  82. El Malti R, Liu H, Doray B, Thauvin C, Maltret A, Dauphin C. A systematic variant screening in familial cases of congenital heart defects demonstrates the usefulness of molecular genetics in this field. Eur J Hum Genet 2016; 24(2):228-236. doi: 10.1038/ejhg.2015.105 [Crossref]
  83. Wang J, Fang M, Liu XY, Xin YF, Liu ZM, Chen XZ. A novel GATA4 mutation responsible for congenital ventricular septal defects. Int J Mol Med 2011; 28(4):557-564. doi: 10.3892/ijmm.2011.715 [Crossref]
  84. Garg V, Kathiriya IS, Barnes R, Schluterman MK, King IN, Butler CA. GATA4 mutations cause human congenital heart defects and reveal an interaction with TBX5. Nature 2003; 424(6947):443-447. doi: 10.1038/nature01827 [Crossref]
  85. Sarkozy A, Conti E, Neri C, D’Agostino R, Digilio MC, Esposito G. Spectrum of atrial septal defects associated with mutations of NKX25 and GATA4 transcription factors. J Med Genet 2005; 42(2):e16. doi: 10.1136/jmg.2004.026740 [Crossref]
  86. Chen Y, Han ZQ, Yan WD, Tang CZ, Xie JY, Chen H. A novel mutation in GATA4 gene associated with dominant inherited familial atrial septal defect. J Thorac Cardiovasc Surg 2010; 140(3):684-687. doi: 10.1016/j.jtcvs.2010.01.013 [Crossref]
  87. Zhang X, Wang J, Wang B, Chen S, Fu Q, Sun K. A novel missense mutation of GATA4 in a Chinese family with congenital heart disease. PLoS One 2016; 11(7):e0158904. doi: 10.1371/journal.pone.0158904 [Crossref]
  88. D’Amato E, Giacopelli F, Giannattasio A, D’Annunzio G, Bocciardi R, Musso M. Genetic investigation in an Italian child with an unusual association of atrial septal defect, attributable to a new familial GATA4 gene mutation, and neonatal diabetes due to pancreatic agenesis. Diabet Med 2010; 27(10):1195-1200. doi: 10.1111/j.1464-5491.2010.03046.x [Crossref]
  89. Kodo K, Nishizawa T, Furutani M, Arai S, Ishihara K, Oda M. Genetic analysis of essential cardiac transcription factors in 256 patients with non-syndromic congenital heart defects. Circ J 2012; 76(7):1703-1711. doi: 10.1253/circj.cj-11-1389 [Crossref]
  90. Reamon-Buettner SM, Cho SH, Borlak J. Mutations in the 3’-untranslated region of GATA4 as molecular hotspots for congenital heart disease (CHD). BMC Med Genet 2007; 8:38. doi: 10.1186/1471-2350-8-38 [Crossref]
  91. Al-Azzouny MA, El Ruby MO, Issa HA, Behiry EG, Elsayed NR, Fayez AG. Detection and putative effect of GATA4 gene variants in patients with congenital cardiac septal defects. Cell Mol Biol (Noisy-le-grand) 2016; 62(3):10-14.
  92. Dinesh SM, Lingaiah K, Savitha MR, Krishnamurthy B, Narayanappa D, Ramachandra NB. GATA4 specific nonsynonymous single-nucleotide polymorphisms in congenital heart disease patients of Mysore, India. Genet Test Mol Biomarkers 2011; 15(10):715-720. doi: 10.1089/gtmb.2010.0278 [Crossref]
  93. Wang J, Lu Y, Chen H, Yin M, Yu T, Fu Q. Investigation of somatic NKX2-5, GATA4 and HAND1 mutations in patients with tetralogy of Fallot. Pathology 2011; 43(4):322-326. doi: 10.1097/PAT.0b013e32834635a9 [Crossref]
  94. Bonachea EM, Zender G, White P, Corsmeier D, Newsom D, Fitzgerald-Butt S. Use of a targeted, combinatorial next-generation sequencing approach for the study of bicuspid aortic valve. BMC Med Genomics 2014; 7:56. doi: 10.1186/1755-8794-7-56 [Crossref]
  95. Suzuki YJ. Cell signaling pathways for the regulation of GATA4 transcription factor: implications for cell growth and apoptosis. Cell Signal 2011; 23(7):1094-1099. doi: 10.1016/j.cellsig.2011.02.007 [Crossref]
  96. Naghavi-Behzad M, Alizadeh M, Azami S, Foroughifar S, Ghasempour-Dabbaghi K, Karzad N. Risk factors of congenital heart diseases: a case-control study in Northwest Iran. J Cardiovasc Thorac Res 2013; 5(1):5-9. doi: 10.5681/jcvtr.2013.002 [Crossref]
  97. Zhou P, He A, Pu WT. Regulation of GATA4 transcriptional activity in cardiovascular development and disease. Curr Top Dev Biol 2012; 100:143-169. doi: 10.1016/b978-0-12-387786-4.00005-1 [Crossref]
  98. Fang T, Zhu Y, Xu A, Zhang Y, Wu Q, Huang G. Functional analysis of the congenital heart disease-associated GATA4 H436Y mutation in vitro. Mol Med Rep 2019; 20(3):2325-2331. doi: 10.3892/mmr.2019.10481 [Crossref]
  99. Sepulveda JL, Vlahopoulos S, Iyer D, Belaguli N, Schwartz RJ. Combinatorial expression of GATA4, Nkx2-5, and serum response factor directs early cardiac gene activity. J Biol Chem 2002; 277(28):25775-25782. doi: 10.1074/jbc.M203122200 [Crossref]
  100. Garnatz AS, Gao Z, Broman M, Martens S, Earley JU, Svensson EC. FOG-2 mediated recruitment of the NuRD complex regulates cardiomyocyte proliferation during heart development. Dev Biol 2014; 395(1):50-61. doi: 10.1016/j.ydbio.2014.08.030 [Crossref]
  101. Pizzuti A, Sarkozy A, Newton AL, Conti E, Flex E, Digilio MC. Mutations of ZFPM2/FOG2 gene in sporadic cases of tetralogy of Fallot. Hum Mutat 2003; 22(5):372-377. doi: 10.1002/humu.10261 [Crossref]
  102. De Luca A, Sarkozy A, Ferese R, Consoli F, Lepri F, Dentici ML. New mutations in ZFPM2/FOG2 gene in tetralogy of Fallot and double outlet right ventricle. Clin Genet 2011; 80(2):184-190. doi: 10.1111/j.1399-0004.2010.01523.x [Crossref]
  103. Tan ZP, Huang C, Xu ZB, Yang JF, Yang YF. Novel ZFPM2/FOG2 variants in patients with double outlet right ventricle. Clin Genet 2012; 82(5):466-471. doi: 10.1111/j.1399-0004.2011.01787.x [Crossref]
  104. Qiao XH, Wang F, Zhang XL, Huang RT, Xue S, Wang J. MEF2C loss-of-function mutation contributes to congenital heart defects. Int J Med Sci 2017; 14(11):1143-1153. doi: 10.7150/ijms.21353 [Crossref]
  105. Ieda M, Fu JD, Delgado-Olguin P, Vedantham V, Hayashi Y, Bruneau BG. Direct reprogramming of fibroblasts into functional cardiomyocytes by defined factors. Cell 2010; 142(3):375-386. doi: 10.1016/j.cell.2010.07.002 [Crossref]
  106. Li FF, Deng X, Zhou J, Yan P, Zhao EY, Liu SL. Characterization of human bone morphogenetic protein gene variants for possible roles in congenital heart disease. Mol Med Rep 2016; 14(2):1459-1464. doi: 10.3892/mmr.2016.5428 [Crossref]
  107. Song W, Wang H, Wu Q. Atrial natriuretic peptide in cardiovascular biology and disease (NPPA). Gene 2015; 569(1):1-6. doi: 10.1016/j.gene.2015.06.029 [Crossref]
  108. Reamon-Buettner SM, Borlak J. HEY2 mutations in malformed hearts. Hum Mutat 2006; 27(1):118. doi: 10.1002/humu.9390 [Crossref]
  109. Garcia-Blanco MA, Baraniak AP, Lasda EL. Alternative splicing in disease and therapy. Nat Biotechnol 2004; 22(5):535-546. doi: 10.1038/nbt964 [Crossref]
  110. Reamon-Buettner SM, Borlak J. TBX5 mutations in non-Holt-Oram syndrome (HOS) malformed hearts. Hum Mutat 2004; 24(1):104. doi: 10.1002/humu.9255 [Crossref]
  111. Reamon-Buettner SM, Hecker H, Spanel-Borowski K, Craatz S, Kuenzel E, Borlak J. Novel NKX2-5 mutations in diseased heart tissues of patients with cardiac malformations. Am J Pathol 2004; 164(6):2117-2125. doi: 10.1016/s0002-9440(10)63770-4 [Crossref]
  112. Reamon-Buettner SM, Borlak J. Somatic NKX2-5 mutations as a novel mechanism of disease in complex congenital heart disease. J Med Genet 2004; 41(9):684-690. doi: 10.1136/jmg.2003.017483 [Crossref]