Comprehensive Analysis of rsSNPs Associated with Hypertension Using In-Silico Bioinformatics Tools

Gassoum, Alsadig; Abdelraheem, Nahla E.; Elsadig, Nehad

doi:10.4236/oalib.1102839

Cited by 8 publications

(7 citation statements)

References 33 publications

(33 reference statements)

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“…The methods used in this study revealed the importance of using various algorithms with different prediction capacities to estimate the effect of variations on structural and functional levels. Using a single bioinformatic tool to predict potentially pathogenic nsSNPs may not be significant [45]. Hence, the present study was based on multiple computational tools including SIFT, Poly-Phen1/2, MAAP, PhD-SNP, PredictSNP, SNAP, ConSurf, ModPred, I-Mutant2.0, and MUpro in order to identify the most deleterious nsSNPs in RETN gene.…”

Section: Discussionmentioning

confidence: 99%

In Silico Analysis of Coding/Noncoding SNPs of Human RETN Gene and Characterization of Their Impact on Resistin Stability and Structure

Elkhattabi

Morjane

Charoute

et al. 2019

Journal of Diabetes Research

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Resistin (RETN) is a gene coding for proinflammatory adipokine called resistin secreted by macrophages in humans. Single nucleotide polymorphisms (SNPs) in RETN are linked to obesity and insulin resistance in various populations. Using dbSNP, 78 nonsynonymous SNPs (nsSNPs) were retrieved and tested on a PredictSNP 1.0 megaserver. Among these, 15 nsSNPs were predicted as highly deleterious and thus subjected to further analyses, such as conservation, posttranscriptional modifications, and stability. The 3D structure of human resistin was generated by homology modeling using Swiss model. Root-mean-square deviation (RMSD), hydrogen bonds (h-bonds), and interactions were estimated. Furthermore, UTRscan served to identify UTR functional SNPs. Among the 15 most deleterious nsSNPs, 13 were predicted to be highly conserved including variants in posttranslational modification sites. Stability analysis predicted 9 nsSNPs (I32S, C51Y, G58E, G58R, C78S, G79C, W98C, C103G, and C104Y) which can decrease protein stability with at least three out of the four algorithms used in this study. These nsSNPs were chosen for structural analysis. Both variants C51Y and C104Y showed the highest RMS deviations (1.137 Å and 1.308 Å, respectively) which were confirmed by the important decrease in total h-bonds. The analysis of hydrophobic and hydrophilic interactions showed important differences between the native protein and the 9 mutants, particularly I32S, G79C, and C104Y. Six SNPs in the 3′UTR (rs920569876, rs74176247, rs1447199134, rs943234785, rs76346269, and rs78048640) were predicted to be implicated in polyadenylation signal. This study revealed 9 highly deleterious SNPs located in the human RETN gene coding region and 6 SNPs within the 3′UTR that may alter the protein structure. Interestingly, these SNPs are worth to be analyzed in functional studies to further elucidate their effect on metabolic phenotype occurrence.

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Section: Discussionmentioning

confidence: 99%

In Silico Analysis of Coding/Noncoding SNPs of Human RETN Gene and Characterization of Their Impact on Resistin Stability and Structure

Elkhattabi

Morjane

Charoute

et al. 2019

Journal of Diabetes Research

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“…However, not all SNPs are associated with human diseases. Coding region SNPs are known as nsSNPs or non-synonymous SNPs which have greater impact on the protein structure, function, stability, and solubility through amino acid replacement in protein sequence 5,6 . These nsSNPs can be categorized as either 'pathogenic/deleterious' causing disease phenotypes, or 'tolerated/neutral' causing no effect on protein structure, function or stability 7,8 .…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Characterization of the Coding and Non-coding Single Nucleotide Polymorphisms in the Tumor Protein p63 (TP63) Gene Using In Silico Tools.

Akter

Hossain

Hosen

et al. 2021

Preprint

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Single Nucleotide Polymorphisms (SNPs) help to understand the phenotypic variations in humans. Numerous studies have examined the association of SNPs with various complex diseases. Researchers have identified the association of SNPs of genes through Genome-wide association study (GWAS). A number of GWAS have identified a loci located in the TP63 gene to be significantly associated with the risk of urinary bladder cancer. However, there is not any study characterizing the SNPs located at the TP63 gene for their functional and structural significance. Hence, the study aimed to comprehensively characterize SNPs in the human TP63 gene for their functional and structural significance. We investigated and evaluated the genomic variations affecting the expression, structure, and function of the TP63 protein. The study systematically retrieved nsSNPs information for the TP63 gene from the dbSNP database. We screened and analyzed both nsSNPs and non-coding SNPs in TP63 protein using a wide variety of computational tools to find the risk of pathogenicity. A total of 17 nsSNPs were identified using the 13 bioinformatics tools (i.e., SIFT, CADD, PROVEAN, PolyPhen2, PANTHER, PhD-SNP, SNP&GO, I-Mutant 2.0, ClinVar, Mutpred2, ConSurf, HOPE, and Mutation 3D) along with domain analysis. These pathogenic mutations cause a decrease in protein stability according to I-Mutant2.0. HOPE predicted 17 SNPs to have significant effect on TP63 protein structure and function. 12 nsSNPs were found in highly conserved position in TP63 inferring the damaging effect on the structure and function of the protein. Swiss PDB Viewer showed loss of hydrogen bonds and increased energy due to the SNPs. Molecular docking showed the reduction of the binding affinity of proteins for DNA and loss of hydrogen bonds. Six non-coding SNPs were found in miRNA binding sites in gene showing the effect on protein regulation using PolymiRTS and five non-coding SNPs were identified in single tissue expression quantitative trait loci (eQTL) in lung tissue, heart tissue (LV), and cerebral hemisphere (Brain) according to GTEx portal. The characterization of nsSNPs and non-coding SNPs will support researchers to focus on TP63 gene loci and ascertain their association with certain diseases.

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“…The difference of a single nucleotide, i.e., A, T, C, or G at a specific position in the genome is defined as an SNP. Human genes contain about 93% SNPs [ 11 ]. Coding, non-coding, or intergenic regions of genes have SNPs [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Characterization of the Coding and Non-Coding Single Nucleotide Polymorphisms in the Tumor Protein p63 (TP63) Gene Using In Silico Tools

et al. 2021

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Single nucleotide polymorphisms (SNPs) help to understand the phenotypic variations in humans. Genome-wide association studies (GWAS) have identified SNPs located in the tumor protein 63 (TP63) locus to be associated with the genetic susceptibility of cancers. However, there is a lack of in-depth characterization of the structural and functional impacts of the SNPs located at the TP63 gene. The current study was designed for the comprehensive characterization of the coding and non-coding SNPs in the human TP63 gene for their functional and structural significance. The functional and structural effects of the SNPs were investigated using a wide variety of computational tools and approaches, including molecular dynamics (MD) simulation. The deleterious impact of eight nonsynonymous SNPs (nsSNPs) affecting protein stability, structure, and functions was measured by using 13 bioinformatics tools. These eight nsSNPs are in highly conserved positions in protein and were predicted to decrease protein stability and have a deleterious impact on the TP63 protein function. Molecular docking analysis showed five nsSNPs to reduce the binding affinity of TP63 protein to DNA with significant results for three SNPs (R319H, G349E, and C347F). Further, MD simulations revealed the possible disruption of TP63 and DNA binding, hampering the essential protein function. PolymiRTS study found five non-coding SNPs in miRNA binding sites, and the GTEx portal recognized five eQTLs SNPs in single tissue of the lung, heart (LV), and cerebral hemisphere (brain). Characterized nsSNPs and non-coding SNPs will help researchers to focus on TP63 gene loci and ascertain their association with certain diseases.

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Comprehensive Analysis of rsSNPs Associated with Hypertension Using In-Silico Bioinformatics Tools

Cited by 8 publications

References 33 publications

In Silico Analysis of Coding/Noncoding SNPs of Human RETN Gene and Characterization of Their Impact on Resistin Stability and Structure

In Silico Analysis of Coding/Noncoding SNPs of Human RETN Gene and Characterization of Their Impact on Resistin Stability and Structure

Comprehensive Characterization of the Coding and Non-coding Single Nucleotide Polymorphisms in the Tumor Protein p63 (TP63) Gene Using In Silico Tools.

Comprehensive Characterization of the Coding and Non-Coding Single Nucleotide Polymorphisms in the Tumor Protein p63 (TP63) Gene Using In Silico Tools

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