Abstract 489: Identification And In-silico Analysis Of Pathogenic Non-synonymous Snps Of Human Sos1 Protein In Noonan Syndrome

2020 ◽  
Vol 127 (Suppl_1) ◽  
Author(s):  
Vinoth Sigamani ◽  
SHEEJA RAJASINGH ◽  
Narasimman Gurusamy ◽  
Shivaani Kirankumar ◽  
Jayavardini Vasanthan ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Noonan Syndrome ◽  
In Silico ◽  
Genetic Disorder ◽  
In Silico Analysis ◽  
Nucleotide Polymorphisms ◽  
Post Translational Modification ◽  
Single Nucleotide ◽  
Skeletal Malformation ◽  
Structural And Functional Properties

Introduction: Noonan syndrome is a genetic disorder (autosomal dominant) characterized by short stature, congenital heart disease, bleeding problems, developmental delays, and skeletal malformation. It is mainly caused by a single nucleotide alteration in four genes PTPN11, SOS1, RAF1, and KRAS . In this study, we computationally analyzed the SOS1 gene to identify the pathogenic non-synonymous single nucleotide polymorphisms (nsSNPs), which is known to cause Noonan syndrome. Hypothesis: We hypothesize that in-silico analysis of human SOS1 mutations in Noonan syndrome would be a promising predictor to study the post-translational modifications. Methods and Results: The variant information of SOS1 was collected from the dbSNP database and the literature review on Noonan syndrome. They were further analyzed by in-silico tools such as I-Mutant, iPTREE-STAB, and MutPred for their structural and functional properties. We found that 11 nsSNPs are more pathogenic for Noonan syndrome. The 3D comparative protein of 11 nsSNPs with its wild-type SOS1 was modeled by using I-Tasser and validated via ERRAT and RAMPAGE. The protein-protein interactions of SOS1, GATA4, TNNT2, and ACTN2 were analyzed using STRING, which showed that HRAS was intermediate between SOS1 and ACTN2 (Fig. 1) . Conclusion: This is the first in-silico study of the SOS1 variant with Noonan syndrome. We proposed that this 11 nsSNPs are the most pathogenic variant of SOS1 , which helps to screen the Noonan patient. Furthermore, our results are promising to study the gain/loss of post-translational modification (PTM) by mutation in cardiac genes and helps to explore the novel molecular pathways.$graphic_{DB5B0E7D-4DA6-4569-A16F-E05B2C9C4D2F}$$

In-Silico and In-vitro Analysis of Human SOS1 Protein Causing Noonan Syndrome – A Novel Approach to Explore the Molecular Pathways

2021 ◽  
Vol 22 ◽  
Author(s):  
Vinoth Sigamani ◽  
Sheeja Rajasingh ◽  
Narasimman Gurusamy ◽  
Arunima Panda ◽  
Johnson Rajasingh
Keyword(s):  
Noonan Syndrome ◽  
In Silico ◽  
Genetic Disorder ◽  
In Vitro Study ◽  
Human Skin Fibroblast ◽  
Nucleotide Polymorphisms ◽  
Gene Expressions ◽  
Single Nucleotide ◽  
Nucleotide Mutation

Aims: Noonan syndrome (NS) is an autosomal dominant genetic disorder caused by single nucleotide mutation in PTPN11, SOS1, RAF1, and KRAS genes. Background: We hypothesize that in-silico analysis of human SOS1 mutations would be a promising predictor in identifying the pathogenic effect of NS. Methods: Here, we computationally analyzed the SOS1 gene to identify the pathogenic non-synonymous single nucleotide polymorphisms (nsSNPs) to cause NS. The variant information of SOS1 was collected from the SNP database (dbSNP). The variants were further analyzed by in-silico tools I-Mutant, iPTREE-STAB, and MutPred to elucidate their structural and functional characteristics. Results: We found that 11 nsSNPs of SOS1 were more pathogenic to cause NS. The 3D modeling of the wild-type and the 11 nsSNPs were performed using I-TASSER and validated via ERRAT and RAMPAGE. SOS1 interacting proteins were analysed through STRING, which showed that SOS1 interacted with cardiac proteins GATA4, TNNT2, and ACTN2. During these interactions, GRB2 and HRAS act as an intermediate molecules between SOS1 and cardiac proteins. These in-silico analyses were validated using induced cardiomyocytes (iCMCs) derived from NS patients carrying SOS1 gene variant c.1654A>G (NS-iCMCs) and compared with control human skin fibroblast-derived iCMCs (C-iCMCs). Our in vitro data further confirmed that the SOS1, GRB2 and HRAS gene expressions as well as the activated ERK protein, were significantly decreased in NS-iCMCs compared to C-iCMCs. Conclusion: This is the first in-silico and in vitro study demonstrating that 11 nsSNPs of SOS1 were playing a deleterious pathogenic role in causing NS.

scholarly journals Characterization and risk association of polymorphisms in Aurora kinases A, B and C with genetic susceptibility to gastric cancer development

BMC Cancer ◽  
2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Aner Mesic ◽  
Marija Rogar ◽  
Petra Hudler ◽  
Nurija Bilalovic ◽  
Izet Eminovic ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Transcription Factors ◽  
In Silico ◽  
In Silico Analysis ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Mitotic Kinases ◽  
Genes Encoding ◽  
Silico Analysis ◽  
Control Study

Abstract Background Single nucleotide polymorphisms (SNPs) in genes encoding mitotic kinases could influence development and progression of gastric cancer (GC). Methods Case-control study of nine SNPs in mitotic genes was conducted using qPCR. The study included 116 GC patients and 203 controls. In silico analysis was performed to evaluate the effects of polymorphisms on transcription factors binding sites. Results The AURKA rs1047972 genotypes (CT vs. CC: OR, 1.96; 95% CI, 1.05–3.65; p = 0.033; CC + TT vs. CT: OR, 1.94; 95% CI, 1.04–3.60; p = 0.036) and rs911160 (CC vs. GG: OR, 5.56; 95% CI, 1.24–24.81; p = 0.025; GG + CG vs. CC: OR, 5.26; 95% CI, 1.19–23.22; p = 0.028), were associated with increased GC risk, whereas certain rs8173 genotypes (CG vs. CC: OR, 0.60; 95% CI, 0.36–0.99; p = 0.049; GG vs. CC: OR, 0.38; 95% CI, 0.18–0.79; p = 0.010; CC + CG vs. GG: OR, 0.49; 95% CI, 0.25–0.98; p = 0.043) were protective. Association with increased GC risk was demonstrated for AURKB rs2241909 (GG + AG vs. AA: OR, 1.61; 95% CI, 1.01–2.56; p = 0.041) and rs2289590 (AC vs. AA: OR, 2.41; 95% CI, 1.47–3.98; p = 0.001; CC vs. AA: OR, 6.77; 95% CI, 2.24–20.47; p = 0.001; AA+AC vs. CC: OR, 4.23; 95% CI, 1.44–12.40; p = 0.009). Furthermore, AURKC rs11084490 (GG + CG vs. CC: OR, 1.71; 95% CI, 1.04–2.81; p = 0.033) was associated with increased GC risk. A combined analysis of five SNPs, associated with an increased GC risk, detected polymorphism profiles where all the combinations contribute to the higher GC risk, with an OR increased 1.51-fold for the rs1047972(CT)/rs11084490(CG + GG) to 2.29-fold for the rs1047972(CT)/rs911160(CC) combinations. In silico analysis for rs911160 and rs2289590 demonstrated that different transcription factors preferentially bind to polymorphic sites, indicating that AURKA and AURKB could be regulated differently depending on the presence of particular allele. Conclusions Our results revealed that AURKA (rs1047972 and rs911160), AURKB (rs2241909 and rs2289590) and AURKC (rs11084490) are associated with a higher risk of GC susceptibility. Our findings also showed that the combined effect of these SNPs may influence GC risk, thus indicating the significance of assessing multiple polymorphisms, jointly. The study was conducted on a less numerous but ethnically homogeneous Bosnian population, therefore further investigations in larger and multiethnic groups and the assessment of functional impact of the results are needed to strengthen the findings.

scholarly journals A Comprehensive In Silico Analysis of the Functional and Structural Impact of Nonsynonymous SNPs in the ABCA1 Transporter Gene

Cholesterol ◽  
2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Francisco R. Marín-Martín ◽  
Cristina Soler-Rivas ◽  
Roberto Martín-Hernández ◽  
Arantxa Rodriguez-Casado
Keyword(s):  
In Silico ◽  
Rare Variants ◽  
Experimental Studies ◽  
In Silico Analysis ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Experimental Approaches ◽  
And Function

Disease phenotypes and defects in function can be traced to nonsynonymous single nucleotide polymorphisms (nsSNPs), which are important indicators of action sites and effective potential therapeutic approaches. Identification of deleterious nsSNPs is crucial to characterize the genetic basis of diseases, assess individual susceptibility to disease, determinate molecular and therapeutic targets, and predict clinical phenotypes. In this study using PolyPhen2 and MutPred in silico algorithms, we analyzed the genetic variations that can alter the expression and function of the ABCA1 gene that causes the allelic disorders familial hypoalphalipoproteinemia and Tangier disease. Predictions were validated with published results from in vitro, in vivo, and human studies. Out of a total of 233 nsSNPs, 80 (34.33%) were found deleterious by both methods. Among these 80 deleterious nsSNPs found, 29 (12.44%) rare variants resulted highly deleterious with a probability >0.8. We have observed that mostly variants with verified functional effect in experimental studies are correctly predicted as damage variants by MutPred and PolyPhen2 tools. Still, the controversial results of experimental approaches correspond to nsSNPs predicted as neutral by both methods, or contradictory predictions are obtained for them. A total of seventeen nsSNPs were predicted as deleterious by PolyPhen2, which resulted neutral by MutPred. Otherwise, forty two nsSNPs were predicted as deleterious by MutPred, which resulted neutral by PolyPhen2.

scholarly journals In Silico Analysis of Single Nucleotide Polymorphisms (SNPs) in Human <i>MPL</i> Gene

2019 ◽  
Vol 7 (4) ◽  
pp. 124
Author(s):  
Mohamed Mubarak Babeker ◽  
Afra Mohamed Suliman Albakry ◽  
Mohammed Nagm Eldin Elsamani ◽  
Gihan Mossalami ◽  
Hind Abdelaziz Elnasri ◽  
...  
Keyword(s):  
Single Nucleotide Polymorphisms ◽  
In Silico ◽  
In Silico Analysis ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Silico Analysis
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