In Silico Analysis and Modeling of Deleterious Single Nucleotide Polymorphism (SNPs) in Human GATA4 Gene

Research Paper (postgraduate) , 2016 , 40 Pages

Medicine - Human Genetics

Congenital heart disease (CHD) is an abnormality in the structure and function of the heart and great vessels caused by embryonic development disorders, it is highly complex and is not fully understood yet.

This study performs a computational analysis of the nsSNPs in the GATA4 gene, to identify the possible mutations and propose a modeled structure for the mutant protein that potentially affects its function.

The nsSNPs were analyzed using 5 prediction tools: SIFT, Polyphen-2, I-Mutant 3.0, PhD-SNP and Project Hope. The SNPs on 3’UTR and 5’UTR regions were analyzed using PolymRTS and SNP Function Prediction softwares, respectively. Twenty nine nsSNPs were found to be deleterious and damaging by SIFT and 22 nsSNPs by PolyPhen server; 22 nsSNPs were found to be common in both SIFT and PolyPhen server. Also, 6 nsSNPs were observed to be highly deleterious and damaging as per SIFT and PolyPhen server. Moreover, the PolymiRTS results showed 34 SNPs in the 3’UTR region and only one SNP in 5’ UTR by SNP Function Prediction to be functionally significant.

Hence, we hope our results will provide useful information that needed to help researchers to do further study in heart disease in children especially in our country

Table of Contents

INTRODUCTION
Materials and Methods
Dataset
Functional analysis Prediction

RESULTS
Prediction Programs

DISCUSSION
nsSNPs in coding region:
SNPs in 3’ UTR region:
SNP in 5’ UTR region:

CONCLUSION

ACKNOWLEDGMENTS

References

ABSTRACT

Congenital heart disease (CHD) presents as the abnormality in the structure and function of heart and great vessels caused by embryonic development disorders, it is highly complex and is not fully understood yet. This study aimed to perform a computational analysis of the nsSNPs in the GATA4 gene, to identify the possible mutations and propose a modeled structure for the mutant protein that potentially affects its function. The nsSNPs were analyzed using 5 prediction tools: SIFT, Polyphen-2, I-Mutant 3.0, PhD-SNP and Project Hope. While the SNPs on 3’UTR and 5’UTR regions were analyzed using PolymRTS and SNP Function Prediction softwares, respectively. Twenty nine nsSNPs were found to be deleterious and damaging by SIFT and 22 nsSNPs by PolyPhen server; 22 nsSNPs were found to be common in both SIFT and PolyPhen server. Also, 6 nsSNPs were observed to be highly deleterious and damaging as per SIFT and PolyPhen server. Moreover the PolymiRTS results showed 34 SNPs in the 3’UTR region and only one SNP in 5’ UTR by SNP Function Prediction to be functionally significant. Hence, we hope our results will provide useful information that needed to help researchers to do further study in heart disease in children especially in our country.

Keywords: Congenital heart disease (CHD), SNP, GATA4 and SNP Function Prediction.

INTRODUCTION

Congenital heart disease (CHD) affects approximately 1.33 million of the newborns in United States and worldwide, Defects of atrial and ventricular septation are the most frequent form of congenital heart disease, accounting for almost 50% of all cases. CHD presents as the abnormality in the structure and function of heart and great vessels caused by embryonic development disorders, it is highly complex and is not fully understood yet [[1]-[6]].

A single nucleotide polymorphism (SNP) is a single base mutation in DNA, as an alternative form of sequence variation for gene identification and mapping studies also can consider genetic markers, SNPs can be used to follow the inheritance patterns of chromosomal regions from generation to generation and are powerful tools in the study of genetic factors associated with human diseases among these non-synonymous single nucleotide polymorphisms (nsSNPs) that lead to an amino acid change in the protein product are of particular interest for their close relevance to human inherited diseases Functional impacts of nsSNPs generally fall into two classes: disease-associated (deleterious) and benign (no observable phenotypic effect). [[7]-[9]]

The human a zinc finger protein GATA4 gene maps to chromosome 8p23.1-p22, consists of seven exons, and encodes a protein of 442 amino acids (MIM n600576), is consider one of the hypertrophy-responsive transcription factors is expressed in adult vertebrate heart, gut epithelium, and gonads. During fetal development GATA4 is expressed in yolk sac endoderm and cells involved in heart formation. GATA-4 forms a functional protein complex with an intrinsic histone acetyltransferase, p300 and regulates pathological cardiac hypertrophy. Disruption of this complex result in the inhibition of cardiac hypertrophy and heart failure in vivo . GATA-4 play an important role in embrognatic heart development also may regulate a set of cardiac-specific genes and play a crucial role in cardiogenesis such as develop of right ventricle, numerous cardiac gene, including myosin heavy chin (±- MHC), cardiac troponin-C(C-TNC) and a trial natriuretic factor have been shown to be direct transcription target GATA4 a rounding more than 1,700 gene have been reported to be involved in the development. also is the most important gene change causes the phenotype atrial Septal defect, (ASD) which accounts for about 33% of all congenital cardiovascular deformities, affecting over 3 out of 1,000 live births. [[10]-[21]]

Furthermore severe forms of congenital heart disease, including septation defects, outflow tract alignment defects, dextrocardia, pulmonary stenosis and chamber hypoplasia observed in patients with GATA4 mutation or deletion in which indicate GATA4 is an important regulator of cardiomyocyte proliferation through direct transcriptional activation of cell cycle regulators, including cyclin D2 and cdk4.[[22]-[24]]

In addition reduction in dosage of GATA4 leads to abnormal cardiac development with a common atrioventricular canal, double outlet right ventricle, and hypoplasia of the ventricular myocardium.[[13]]

In the present study we aimed to perform a computational analysis of the nsSNPs in the GATA4 gene, to identify the possible mutations and propose a modeled structure for the mutant protein that potentially affects its function.

Materials and Methods

Dataset

Human GATA4 gene data were obtained from OMIM (#600576 - http://www.ncbi.nlm.nih.gov/omim) and Entered on the National Center for Biotechnology Information (NCBI) website in December 2015, including Protein accession number (NP_002043) and mRNA accession number (NM_002052). The Uniprot accession number (P43694) was obtained in the Swissprot database (http://expasy.org). The information of SNPs in human GATA4 was collected from dbSNP (http://www.ncbi.nlm.nih.gov/snp). Gene’s functions and other genes that related to GATA4 gene were obtained from GeneMANIA (http://www.genemania.org/) .

Functional analysis Prediction

The nsSNPs were analyzed using 5 prediction tools: SIFT, Polyphen-2, I-Mutant 3.0, PANTHER, SNPs3D, PhD-SNP and Project Hope while the SNPs on 3’UTR and 5’UTR regions were analyzed using PolymRTS and SNP Function Prediction softwares, respectively, Figure (1). The data for amino acid sequence of the human MC1R gene (ref. Seq. NP_002043), Uniprot accession number (P43694), position in the protein, and wild and mutated residue of the nsSNPs were used according to the program requirements.

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Figure (1): Softwares used in our investigation

SIFT software (http://sift.bii.a-star.edu.sg/) . This is a sequences homology-based tool that presumes that important amino acids will be conserved in the protein family. Hence, changes at well-conserved positions tend to be predicted as deleterious [[25]]. The cutoff value in the SIFT program is a tolerance index of ≥0.05. The higher the tolerance index, the less functional impact a particular amino acid substitution is likely to have.

PolyPhen-2 (P olymorphism P henotyping v2 ). This server is available at http://genetics.bwh.harvard.edu/pph2/ has been used to analyze the structural damage due to coding nsSNPs which can affect protein functionality. The server is able to calculate a score on the basis of the characterization of the substitution site to a known protein three-dimensional structure. A PSIC score has been calculate for each variant of each site and the difference between them reported. The higher the PSIC score difference is, the higher is the functional impact a particular amino acid substitution is likely to have. PolyPhen scores were assigned probably damaging (2.00 or more), possibly damaging (1.40–1.90), potentially damaging (1.0–1.50), benign (0.00–0.90). Basically PolyPhen accepts input in form of SNPs or protein sequences [[26]].

I Mutant 3.0 is a support vector machine (SVM) tool for the prediction of protein stability free-energy change (ΔΔG or DDG) on a specific nsSNP. It predicts the free energy changes starting from either the protein structure or the protein sequence. A negative DDG value means that the mutation decreases the stability of the protein, while a positive DDG value indicates an increase in stability. I-Mutant 3.0 also implements a prediction of disease-associated SNPs from a sequence analysis based on a decision tree with the SVM-based classifier (SVM-Sequence) coupled to the SVM-Profile trained on sequence profile information. The nsSNPs are then classified as disease-related or neutral polymorphisms [[27]].

PhD-SNP (P redictor of H uman D eleterious S ingle N ucleotide P olymorphisms) is a SVM-based classifier that uses protein sequence information to predict whether an nsSNP is disease-associated, based on a supervised training algorithm. The output is obtained from the frequencies of the wild and mutant residues, the number of aligned sequences, and the conservation index alculated for the position involved, and provides a prediction of disease-related (disease) or neutral polymorphism [[28]].

Project Hope software (http://www.cmbi.ru.nl/hope/input) is an online web service where the user can submit a sequence and mutation. This software collects structural information from a series of sources, including calculations on the 3D protein structure, sequence annotations in UniProt and predictions from DAS-servers. It combines this information to give analyze the effect of a certain mutation on the protein structure and will show the effect of that mutation in such a way that even those without a bioinformatics background can understand it [[29]].

PolymiRTS is a database of naturally occurring DNA variations in microRNA seed regions and microRNA target sites. Integrated data from CLASH (cross linking, ligation and sequencing of hybrids) experiments, PolymiRTS database provides more complete and accurate microRNA–mRNA interactions [[31]]. The polymorphic microRNA target sites are assigned into four classes: ‘D’ (the derived allele disrupts a conserved microRNA site), ‘N’ (the derived allele disrupts a nonconserved microRNA site), ‘C’ (the derived allele creates a new microRNA site) and ‘O’ (other cases when the ancestral allele cannot be determined unambiguously). The class ‘C’ may cause abnormal gene repression and class ‘D’ may cause loss of normal repression control. So these two classes of PolymiRTS are most likely to have functional impacts [[30]-[31]]. PolymiRTS is available at ( http://compbio.uthsc.edu/miRSNP/).

SNP Function Prediction (https://snpinfo.niehs.nih.gov/cgibin/snpinfo/snpfunc.cgi) SNP function prediction (FuncPred) checked if the SNP variants could alter transcriptional regulation by affecting transcription factor binding sites (TFBS) activity or changing of splicing pattern or efficiency by disrupting splice site, exonic splicing enhancers (ESE) or silencers (ESS).

GeneMANIA (http://www.genemania.org/) is an online database that helps you predict the function of your favorite genes and gene sets. GeneMANIA finds other genes that are related to a set of input genes, using a very large set of functional association data. Association data include protein and genetic interactions, pathways, co-expression, co-localization and protein domain similarity. You can use GeneMANIA to find new members of a pathway or complex, find additional genes you may have missed in your screen or find new genes with a specific function, such as protein kinases. Your question is defined by the set of genes you input [[32]].

RESULTS

According to NCBI database (http://www.ncbi.nlm.nih.gov/projects/SNP); The GATA4 gene contained a total of 18598 SNPs at the time of the study, out of which 5982 were Homo sapiens, 120 occurred in coding synonymous SNPs, 192 occurred in nsSNPs, 147 occurred in the miRNA 3′ UTR, 334 occurred in 5′ UTR region, 5 occurred in Frame Shift and 5486 occurred in intronic regions. We selected (missense & nonsense) nsSNPs, 3′ UTR and 5′ UTR SNPs for our investigation.

Prediction Programs

A total of 192 nsSNPs from the NCBI dbSNP database were analyzed to identify the deleterious mutations by SIFT software. Of these, 26 (59 mutatios) were found to be damaging (score < 0.05), with 12 assigned a score of 0.

In Polyphen-2, a total of 22 nsSNPs (in 51 mutations) were predicted as damaging (PSIC > 0.5); 6 of these nsSNPs were predicted to be highly deleterious, with a PSIC score of 1.

The DDG predicted by I-Mutant 3.0 classified 18 (41 mutations) of the nsSNPs as decreasing the stability of the mutated protein (DDG <0) and 5(10 mutatios) as increasing it (DDG=00.4 to -0.25).

The PhD-SNP 2.0 tool classified the mutation as a disease-related or neutral polymorphism. Of the set of nsSNPs in the GATA4 gene analyzed, 11 (22 mutations) were predicted to be disease-related by PhD-SNP 2.0 and the 11 SNPs (22 mutations) predicted to be Neutral. The prediction results of the 5 tools are summarized in Table (1).

Highest deleterious nature among these damaging nsSNPs of GATA4 gene obtained from previously described software and addition to I mutant /Phd-SNP server presented to Project Hope (http://www.cmbi.ru.nl/hope/input) revealed the 3D structure for the truncated proteins with its new candidates; in addition, it described the reaction and physiochemical properties of these candidates. Here we present the results upon each candidate and discus the conformational variations and interactions with the neighboring amino acids, Figure (2) illuminates these six highest SNPs. The sequences of The Transcription factor (GATA-4) protein (and its 2 isofroms) were obtained from ExPASy Database (www.expasy.org/).

In PolymiRTS software, a total of 167 SNPs at 3’UTR were analyzed and only 34 were predicted by this software, Table (2).

In SNP Function Prediction; the output showed that among 334 SNPs at 5′UTR region of GATA-4 gene, only one SNPs was predicted, namely rs61277615, Table (3).

The Transcription factor (GATA-4) protein had many vital functions. Gene’s functions and the genes co-expressed with, share similar protein domain, or participate to achieve similar function are illustrated by using GeneMANIA and shown in Table (4), Table (5).

Table (1): list of nonsynonymous SNPs with SIFT, POLYPHEN-2, I MUTAT AD PHD-SNP results.

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PolyPhen-2 result: POROBABLY DAMAGING (more confident prediction) / POSSIBLY DAMAGING (less confident prediction), PSIC SD: Position-Specific Independent Counts software if the score is ≥ 0.5, Tolerance Index: Ranges from 0 to 1. The amino acid substitution is predicted damaging if the score is ≤ 0.05, and tolerated if the score is > 0.05. RI: Reliability Index DDG: ΔΔ G sign SVM: support vector machine DDG value: DG (New Protein)-DG (Wild Type) in Kcal/mole, SVM2 value: DDG < 0: decrease stability, DDG >0 increase stability.

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Figure (2): 3D model by Project Hope for GATA4 proteins

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Table (2): SNPs and INDELs in miRNA target sites at 3’UTR by PolymiRTS Software

‘D: the derived allele disrupts a conserved microRNA site, ‘N’: the derived allele disrupts a nonconserved microRNA site, ‘C’: the derived allele creates a new microRNA site.

Table (3): SNP in Transcriptional factor binding sites and splicing site on GATA4 gene at 5′UTR by SNP Function Prediction

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TFBS: Transcription factor-binding site ESE: exonic splicing enhancer ESS: exonic splicing silencer

Table (4): the GATA4 functions and its appearance in network and genome

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FDR: False discovery rate is greater than or equal to the probability that thus is false positive.

Table (5): The genes co-expressed and share a domain with GATA 4 (7)

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DISCUSSION

nsSNPs in coding region:

We compared between wild and mutant residue which is differ on size, charge, domain and hydrophobicity value in six highest deleterious SNPs:

The wild residue of rs56298569 SNP is neutral, the mutant residue is negatively charged. This can cause repulsion between the mutant residue and neighboring residues, the wild-type residue is much conserved, but a few other residue types have been observed at this position too. Mutant residue is located near a highly conserved position. Based on conservation scores this mutation is probably damaging to the protein. It should be noticed that this residue is also part of an interpro domain named Zinc Finger, Nhr/gata-Type ( IPR013088). More broadly speaking, these GO annotations indicate the domain has a function in Ion Binding ( GO: 0043167). The mutated residue is located in a domain that is important for binding of other molecules. The mutated residue is in contact with residues in another domain. It is possible that the mutation disturbs these contacts.

The mutant residue of rs138404762 SNP is bigger than the wild-type residue. The wild-type residue is positively charged, the mutant residue is neutral. The mutant residue is more hydrophobic than the wild-type residue. The residue is located on the surface of the protein; mutation of this residue can disturb interactions with other molecules or other parts of the protein. In addition mutant residue is located near a highly conserved position. But the wild-type residue is not conserved at this position. The other residue type is not similar to mutant residue. Therefore, the mutation is possibly damaging. Also this residue is part of an interpro domain named Transcription Factor Gata-4/gata-Binding Factor A ( IPR028436).The mutated residue is located in a domain that is important for binding of other molecules and in contact with residues in a domain that is also important for binding. The mutation might disturb the interaction between these two domains and as such affect the function of the protein.

The mutant residue of rs180765750 SNP is smaller than the wild-type residue. The wild-type residue is positively charged, the mutant residue is neutral. The wild-type residue is much conserved, but a few other residue types have been observed at this position too. Mutant residue was among the residues at this position observed in other sequences. This means that homologous proteins exist with the same residue type as mutant at this position and this mutation is possibly not damaging to the protein.

The mutant residue of rs267601735 SNP is smaller than the wild-type residue. The wild-type residue is much conserved, but a few other residue types have been observed at this position too. Based on conservation scores this mutation is probably damaging to the protein. In addition mutant residue is located near a highly conserved position. This will cause a possible loss of external interactions.

The mutant residue of rs377222076 SNP is bigger than the wild-type residue. The wild-type residue was neutral, the mutant residue is positively charged. The wild-type residue is more hydrophobic than the mutant residue. The wild-type residue occurs often at this position in the sequence, but other residues have also been observed here. On other hand mutant residue is among the other residue types that have been observed at this position in homologous sequences. This means that this mutation can occur at this position and is probably not damaging to the protein.

The mutant residue of rs387906771 SNP is bigger than the wild-type residue. The mutant residue is more hydrophobic than the wild-type residue. The wild-type residue is much conserved, but a few other residue types have been observed at this position too. Neither your mutant residue nor another residue type with similar properties was observed at this position in other homologous sequences. Based on conservation scores this mutation is probably damaging to the protein and mutant residue is located near a highly conserved position.

SNPs in 3’ UTR region:

We found 164 functional classes in 34 SNPs at 3’ UTR region; 55 were ‘D’ allele D’ allele that disrupts a conserved miRNA site and 99 were ‘C’ allele as target binging site can be disrupts a conserved miRNA. RS1062221 contained 14 ‘C’ allele as target binging site can be disrupts a conserved miRNA and one ‘D’ allele while rs146304341 had 12 ‘D’ allele that disrupts a conserved miRNA site and 7 ‘C’ allele.

SNP in 5’ UTR region:

We found one SNP namely rs61277615 was located at a transcription factor-binding site (TFBS) of a gene may affect the level, location, or timing of gene expression and also not have exonic splicing enhancer (ESE), or exonic splicing silencer (ESS) to disrupt splicing activity and cause alternative splicing.

CONCLUSION

In conclusion, our results suggest that the application of computational tools like SIFT, PolyPhen-2, I mutant-3. Phd-SNP, polymRTS, SNP Function Prediction and Project Hope may provide an alternative approach for selecting target SNPs. The GATA-4 gene responsible for causing congenital heart defect especially in newborns was investigated through computational methods and the influence of functional SNPs were evaluated. In a total of 18598 SNPs, 192 were found to be nonsynonymous. Out of the 192 nsSNPs, 29 nsSNPs were found to be deleterious and damaging by SIFT and 22 nsSNPs by PolyPhen server. Twenty two nsSNPs were found to be common in both SIFT and PolyPhen server. Also, 6 nsSNPs were observed to be highly deleterious and damaging as per SIFT and PolyPhen server. Moreover the PolymiRTS results showed 34 SNPs in the 3’UTR region and only one SNP in 5’ UTR by SNP Function Prediction to be functionally significant. Hence, we hope our results will provide useful information that needed to help researchers to do further study in heart disease in children especially in our country.

ACKNOWLEDGMENTS

Authors express their deep gratitude to African City of Technology members for their assistance and help.

Competing interests

The authors declare that they have no competing interests.

References

1. Garg V. Insights into the genetic basis of congenital heart disease. Cellular and Molecular Life Sciences CMLS. 2006; 63(10):1141-8.

2. Ransom J, Srivastava D. The genetics of cardiac birth defects. Semin Cell Dev Biol. 2007;18(1):132-9.

3. Marelli AJ, Mackie AS, Ionescu-Ittu R, Rahme E, Pilote L. Congenital heart disease in the general populationchanging prevalence and age distribution. Circulation. 2007; 115(2):163-72.

4. Misra C, Sachan N, McNally CR, Koenig SN, Nichols HA, Guggilam A . et.al. Congenital heart disease-causing Gata4 mutation displays functional deficits in vivo. PLoS Genet. 2012; 8(5):e1002690.

5. Frey N, Katus HA. Dilated cardiomyopathy as a genetic disease: molecular and clinical aspects. Internist (Berl). 2008, 49:43-50.

6. Humbert M, Nunes H, Sitbon O. et. al. Risk factors for pulmonary arterial hypertension. Clin Chest Med. 2001; 22(3):459 -75.

7. Schork NJ, Fallin D, Lanchbury S. Single nucleotide polymorphisms and the future of genetic epidemiology. Clin Genet. 2000 ; 58(4): 250–64.

8. Kwok PY, Chen X. Detection of Single Nucleotide Polymorphisms. Curr. Issues Mol. Biol.2003; 5: 43-60.

9. Bao L, Cui Y. Functional impacts of non-synonymous single nucleotide polymorphisms: Selective constraint and structural environments. FEBS Letters 2006; 580(5): 1231–34.

10 Arceci RJ, King A, Simon MC, Orkin SH, Wilson DB. Mouse GATA-4: a retinoic acid-inducible GATA-binding transcription factor expressed in endodermally derived tissues and heart. Molec. Cell. Biol.1993; 13(4): 2235-46.

11. Sunagawa Y, Katanasaka Y, Wada H, Hasegawa K, Morimoto T. Functional Analysis of GATA4 Complex, a Cardiac Hypertrophy-response Transcriptional Factor, Using a Proteomics Approach. Yakugaku Zasshi. 2015; 136(2):151-6.

12. Liu XY, Wang J, Zheng JH, Bai K, Liu ZM, Wang XZ, et al. Involvement of a novel GATA4 mutation in atrial septal defects. Int J Mol Med. 2011; 28(1):17–23.

13. Pu WT, Ishiwata T, Juraszek AL, Ma Q and Izumo S. GATA4 is a dosage-sensitive regulator of cardiac morphogenesis. Dev Biol. 2004; 275(1): 235-244.

14. Zeisberg EM, Ma Q, Juraszek AL, Moses K, Schwartz RJ, Izumo S, Pu WT. Morphogenesis of the right ventricle requires myocardial expression of Gata4. J Clin Invest . 2005; 115(6):1522-31.

15. Xin M, Davis CA, Molkentin JD, Lien CL, Duncan SA, Richardson JA and Olson EN (2006). A threshold of GATA4 and GATA6 expression is required for cardiovascular development. Proc Natl Acad Sci. 2006; 103(30): 11189-94.

16. Rivera-Feliciano J, Lee KH, Kong SW, Rajagopal S, Ma Q, Springer Z, Izumo S, Tabin CJ, Pu WT. Development of heart valves requires Gata4 expression in endothelial-derived cells. Development. 2006; 133(18):3607-18.

17. Maitra M, Schluterman MK, Nichols HA, Richardson JA, Lo CW, Srivastava D, Garg V. Interaction of Gata4 and Gata6 with Tbx5 is critical for normal cardiac development. Dev Biol . 2009; 326(2):368-77.

18. Bentham J, Bhattacharya S.Genetic mechanisms controlling cardiovascular development. Ann N Y Acad Sci. 2008; 1123(1):10-9.

19. Watt AJ, Battle MA, Li J, Duncan SA.GATA4 is essential for formation of the proepicardium and regulates cardiogenesis. Proc Natl Acad Sci. 2004; 101(34):12573-8.

20. Huang WY, Cukerman E, Liew CC. Identification of a GATA motif in the cardiac α-myosin heavy-chain-encoding gene and isolation of a human GATA-4 cDNA. Gene. 1995; 155(2):219-23.

21. Garg V, Kathiriya IS, Barnes R, Schluterman MK, King IN, Butler CA, Rothrock CR, Eapen RS, Hirayama-Yamada K, Joo K, Matsuoka R. GATA4 mutations cause human congenital heart defects and reveal an interaction with TBX5. Nature. 2003; 424(6947):443-7.

22. Rojas A, Kong SW, Agarwal P, Gilliss B, Pu WT, Black BL. GATA4 is a direct transcriptional activator of cyclin D2 and Cdk4 and is required for cardiomyocyte proliferation in anterior heart field-derived myocardium. J Mol Cell Biol. 2008;28(17):5420-31.

23. Rajagopal SK, Ma Q, Obler D, Shen J, Manichaikul A, Tomita-Mitchell A. et. al. Spectrum of heart disease associated with murine and human GATA4 mutation. J Mol Cell Cardiol. 2007; 43(6):677-85.

24. Sarkozy A, Conti E, Neri C, d’Agostino R, Digilio MC, Esposito G, et. al. Spectrum of atrial septal defects associated with mutations of NKX2. 5 and GATA4 transcription factors J Med Genet. 2005; 42(2):e16-.

25. Ng PC, Henikoff S. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res.. 2003;31(13):3812-4.

26. Ramensky V, Bork P, Sunyaev S. Human non‐synonymous SNPs: server and survey. Nucleic Acids Res. 2002;30(17):3894-900.

27. Capriotti E, Fariselli P, Rossi I, Casadio R. A three-state prediction of single point mutations on protein stability changes. BMC bioinformatics. 2008;9(2):1. DOI: 10.1186/1471-2105-9-S12-S6

28. Capriotti E, Calabrese R, Casadio R. Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information. Bioinformatics. 2006;22(22):2729-34.

29. 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(1):1. DOI: 10.1186/1471-2105-11-548. PubMed: 21059217.

30 Jia M, Yang B, Li Z, Shen H, Song X, Gu W. Computational analysis of functional single nucleotide polymorphisms associated with the CYP11B2 gene. PloS one. 2014;9(8):e104311. DOI:10.1371/journal.pone.0104311

31. Osman MM, Khalifa AS, Mutasim AEY, Massaad SO, Gasemelseed MM, Abdagader MA, et al. In Silico Analysis of Single Nucleotide Polymorphisms (Snps) in Human FTO Gene . J Bioinform, Genomics, Proteomics.2016; 1(1): 1003.

32. Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, et. al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 2010; 38(suppl 2):W214-20.

• Authors: Marwa Osman (Author), et al. (Author)
• Publication Year: 2016
• Pages: 40
• Catalog Number: V341493
• ISBN (eBook): 9783668335202
• ISBN (Book): 9783668335219
• Language: English
• Tags: silico, analysis, modeling, deleterious, single, nucleotide, polymorphism, snps, human, gata4, gene