scholarly journals Cloning and in silico analysis revealed a genetic variation in osmotin-encoding genes in an Indonesian local cacao cultivar

2020 ◽  
Vol 25 (2) ◽  
pp. 84
Author(s):  
Imam Bagus Nugroho ◽  
Fahrurrozi Fahrurrozi
Keyword(s):  
Structural Alignment ◽  
Protein Structures ◽  
In Silico Analysis ◽  
Fundamental Study ◽  
Single Nucleotide ◽  
Single Nucleotide Change ◽  
Overlapping Peaks ◽  
Encoding Gene ◽  
Encoding Genes ◽  
Silico Analysis

Theobroma cacao L. is an important Indonesian estate crop, which suffers from biotic and abiotic stresses. TcOSM, which encodes osmotin as a response to pathogens and environmental stresses, is, therefore, a focus of interest in this research, aiming to characterize TcOSM in an Indonesian local cacao cultivar. Bioinformatics queries for putative TcOSM were performed against the reference genome of a Criollo-type cacao cultivar. Based on nucleotide sequence determination, our results revealed two genes, TcOSM1 and TcOSM2, which have the highest similarity (≥ 90\%) to the cacao reference genes. Heterozygosity was detected in the TcOSM1-encoding gene, which contained two overlapping peaks in Sanger-sequencing chromatograms. One of the alleles resulted from a single nucleotide change (G to A), leading to a same-sense mutation that did not substitute corresponding alanine residue. Homology modeling using Phyre2 and structural alignment (superimposition) was conducted to examine the influence of genetic variations in TcOSM sequences upon the global protein structures. The result showed no significant changes (RMSD ≤ 0.206 Å, TM-score > 0.5) in tertiary protein structures. Altogether, this research succeeded in characterizing TcOSM while providing a fundamental study for future cacao biotechnology endeavors. 

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.

In silico analysis of non-synonymous single nucleotide polymorphism in a human KLK-2 gene associated with prostate cancer

Meta Gene ◽  
2019 ◽  
Vol 21 ◽  
pp. 100578
Author(s):  
Tooba Yousefi ◽  
Seyed Mostafa Mir ◽  
Jahanbakhsh Asadi ◽  
Mehdi Tourani ◽  
Ansar Karimian ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Single Nucleotide Polymorphism ◽  
In Silico ◽  
In Silico Analysis ◽  
Nucleotide Polymorphism ◽  
Single Nucleotide ◽  
Silico Analysis

scholarly journals In silico analysis of single nucleotide polymorphism (SNPs) in human [BMPR2] gene: دراسة وتحليل الجين (BMPR2) عن طريق (SNPs) باستخدام برمجيات الحاسب الآلي

2019 ◽  
Vol 3 (4) ◽  
Author(s):  
Mohamed Saeed Elhassan Mohamed, Hind AbdelAziz Elnasri, Afra
Keyword(s):  
Pulmonary Arterial Hypertension ◽  
Arterial Hypertension ◽  
In Silico Analysis ◽  
Structural Effect ◽  
Nucleotide Polymorphism ◽  
Single Nucleotide ◽  
Prediction Tools ◽  
Project Hope ◽  
Pulmonary Arterial ◽  
Silico Analysis

Background: (BMPR2) gene is encoded gene and cause pulmonary arterial hypertension. Also, it has major role in regulating the growth the maturation of cells. A (BMPR2) gene contains only 25 SNPs as deleterious SNPs and was analyzed in this study. Material and methods: 25 SNPs investigated using the NCBI database (htt: / / www.ncbi.nlm.nih.gov/)and the SNPs were analyzed using six prediction tools: SIFT, Polyphen- 2, I- Mutant, PROVEAN, PhD- SNP and Project Hope. 76% SNPs predict Probably Damaging by POLYPHEN software. the protein stability checked by I- MUTANT and 72% SNPs trend to decrease effected. when used SNPs & GO 52% SNPs were diseased.64% SNPs were deleterious by PROVEAN. There are 20 associated genes, 14 genes share the same protein domains and 13 genes similar in their expression when predicted by GENE MANIA software. Using PROJEC HOPE software to predict the structural effect in function. Result: eight SNPs of 25 SNPs were sharing the same and significant results, so that leads to confirm this result. Conclusions: eight SNPs, rs137852744, rs137852745, rs137852746, rs137852749, rs137852750, rs137852750, rs143740797and rs374694591 were shown to have highly damaging and cause the pulmonary arterial hypertension disease.

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