scholarly journals ACE2 polymorphisms and individual susceptibility to SARS-CoV-2 infection: insights from an in silico study

2020 ◽  
Author(s):  
Matteo Calcagnile ◽  
Patricia Forgez ◽  
Antonio Iannelli ◽  
Cecilia Bucci ◽  
Marco Alifano ◽  
...  
Keyword(s):  
In Silico ◽  
Alveolar Epithelial Cells ◽  
Nucleotide Polymorphisms ◽  
Related Factors ◽  
Single Nucleotide ◽  
Angiotensin Converting Enzyme 2 ◽  
Alveolar Epithelial ◽  
African People ◽  
In Silico Study ◽  
Ace2 Gene

AbstractThe current SARS covid-19 epidemic spread appears to be influenced by ethnical, geographical and sex-related factors that may involve genetic susceptibility to diseases. Similar to SARS-CoV, SARS-CoV-2 exploits angiotensin-converting enzyme 2 (ACE2) as a receptor to invade cells, notably type II alveolar epithelial cells. Importantly, ACE2 gene is highly polymorphic. Here we have used in silico tools to analyze the possible impact of ACE2 single-nucleotide polymorphisms (SNPs) on the interaction with SARS-CoV-2 spike glycoprotein. We found that S19P (common in African people) and K26R (common in European people) were, among the most diffused SNPs worldwide, the only two SNPs that were able to potentially affect the interaction of ACE2 with SARS-CoV-2 spike. FireDock simulations demonstrated that while S19P may decrease, K26R might increase the ACE2 affinity for SARS-CoV-2 Spike. This finding suggests that the S19P may genetically protect, and K26R may predispose to more severe SARS-CoV-2 disease.

An Updated Review on the Role of Single Nucleotide Polymorphisms in COVID-19 Disease Severity: A Global Aspect

2022 ◽  
Vol 23 ◽  
Author(s):  
Jun Wei Ng ◽  
Eric Tzyy Jiann Chong ◽  
Ping-Chin Lee
Keyword(s):  
Single Nucleotide Polymorphisms ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Angiotensin Converting Enzyme 2 ◽  
Global Pandemic ◽  
Global Aspect ◽  
Common Risk Factors ◽  
Severity Of The Disease ◽  
Ace2 Gene

Abstract: Coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and recently has become a serious global pandemic. Age, gender, and comorbidities are known to be common risk factors for severe COVID-19 but are not enough to fully explain the magnitude of their effect on the risk of severity of the disease. Single nucleotide polymorphisms (SNPs) in several genes have been reported as a genetic factor contributing to COVID-19 severity. This comprehensive review focuses on the association between SNPs in four important genes and COVID-19 severity in a global aspect. We discuss a total of 39 SNPs in this review: five SNPs in the ABO gene, nine SNPs in the angiotensin-converting enzyme 2 (ACE2) gene, 19 SNPs in the transmembrane protease serine 2 (TMPRSS2) gene, and six SNPs in the toll-like receptor 7 (TLR7) gene. These SNPs data could assist in monitoring an individual's risk of severe COVID-19 disease, and therefore personalized management and pharmaceutical treatment could be planned in COVID-19 patients.

In Silico Identification of Novel Acute Myeloid Leukemia Associated Missense SNPs in Human CEBPA Gene

2020 ◽  
pp. 10-24
Keyword(s):  
Acute Myeloid Leukemia ◽  
In Silico ◽  
Myeloid Leukemia ◽  
Gene Interaction ◽  
Nucleotide Polymorphisms ◽  
Future Perspectives ◽  
Single Nucleotide ◽  
In Silico Identification ◽  
And Function ◽  
Acute Myeloid

Single nucleotide polymorphisms (SNPs) in CEBPA gene have been found to be associated with cancer especially Acute Myeloid Leukemia (AML). Therefore, the identification of functional and structural polymorphisms in CEBPA is important to study and discover therapeutics targets and potential malfunctioning. For this purpose, several bioinformatics tools were used for the identification of disease-associated nsSNPs, which might be vital for the structure and function of CEBPA, making them extremely important. In silico tools used in this study included SIFT, PROVEAN, PolyPhen2, SNP&GO and PhD-SNP, followed by ConSurf and I-Mutant. Protein 3D modelling was carried out using I-TASSER and MODELLER v9.22, while GeneMANIA and string were used for the prediction of gene-gene interaction in this regard. From our study, we found that the L345P, R333C, R339Q, V328G, R327W, L317Q, N292S, E284A, R156W, Y108N and F82L mutations were the most crucial SNPs. Additionally, the gene-gene interaction showed the genes having correlation with CEBPA’s co-expressions and importance in several pathways. In future, these 11 mutations should be investigated while studying diseases related to CEBPA, especially for AML. Being the first of its kind, future perspectives are proposed in this study, which will help in precision medicine. Animal models are of great significance in finding out CEBPA effects in disease.

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 In Silico Karyotyping of Chromosomally Polymorphic Malaria Mosquitoes in the Anopheles gambiae Complex

2019 ◽  
Vol 9 (10) ◽  
pp. 3249-3262 ◽  
Author(s):  
R. Rebecca Love ◽  
Seth N. Redmond ◽  
Marco Pombi ◽  
Beniamino Caputo ◽  
Vincenzo Petrarca ◽  
...  
Keyword(s):  
Anopheles Gambiae ◽  
In Silico ◽  
Mosquito Species ◽  
Chromosomal Rearrangements ◽  
Small Subset ◽  
Anopheles Coluzzii ◽  
Strongly Correlated ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Malaria Mosquitoes

Chromosomal inversion polymorphisms play an important role in adaptation to environmental heterogeneities. For mosquito species in the Anopheles gambiae complex that are significant vectors of human malaria, paracentric inversion polymorphisms are abundant and are associated with ecologically and epidemiologically important phenotypes. Improved understanding of these traits relies on determining mosquito karyotype, which currently depends upon laborious cytogenetic methods whose application is limited both by the requirement for specialized expertise and for properly preserved adult females at specific gonotrophic stages. To overcome this limitation, we developed sets of tag single nucleotide polymorphisms (SNPs) inside inversions whose biallelic genotype is strongly correlated with inversion genotype. We leveraged 1,347 fully sequenced An. gambiae and Anopheles coluzzii genomes in the Ag1000G database of natural variation. Beginning with principal components analysis (PCA) of population samples, applied to windows of the genome containing individual chromosomal rearrangements, we classified samples into three inversion genotypes, distinguishing homozygous inverted and homozygous uninverted groups by inclusion of the small subset of specimens in Ag1000G that are associated with cytogenetic metadata. We then assessed the correlation between candidate tag SNP genotypes and PCA-based inversion genotypes in our training sets, selecting those candidates with >80% agreement. Our initial tests both in held-back validation samples from Ag1000G and in data independent of Ag1000G suggest that when used for in silico inversion genotyping of sequenced mosquitoes, these tags perform better than traditional cytogenetics, even for specimens where only a small subset of the tag SNPs can be successfully ascertained.

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