scholarly journals In Silico Analysis of Possible Interaction between Host Genomic Transcription Factors (TFs) and Zika Virus (ZikaSPH2015) Strain with Combinatorial Gene Regulation; Virus Versus Host—The Game Reloaded

Pathogens ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 69
Author(s):  
Massimiliano Chetta ◽  
Marina Tarsitano ◽  
Laura Vicari ◽  
Annalisa Saracino ◽  
Nenad Bukvic
Keyword(s):  
Transcription Factors ◽  
Zika Virus ◽  
In Silico ◽  
In Silico Analysis ◽  
Entire Genome ◽  
Severe Condition ◽  
Binding Motifs ◽  
Virus Sequence ◽  
Virus Versus ◽  
Silico Analysis

In silico analysis is a promising approach for understanding biological events in complex diseases. Herein we report on the innovative computational workflow allowed to highlight new direct interactions between human transcription factors (TFs) and an entire genome of virus ZikaSPH2015 strain in order to identify the occurrence of specific motifs on a genomic Zika Virus sequence that is able to bind and, therefore, sequester host’s TFs. The analysis pipeline was performed using different bioinformatics tools available online (free of charge). According to obtained results of this in silico analysis, it is possible to hypothesize that these TFs binding motifs might be able to explain the complex and heterogeneous phenotype presentation in Zika-virus-affected fetuses/newborns, as well as the less severe condition in adults. Moreover, the proposed in silico protocol identified thirty-three different TFs identical to the distribution of TFBSs (Transcription Factor Binding Sites) on ZikaSPH2015 strain, potentially able to influence genes and pathways with biological functions confirming that this approach could find potential answers on disease pathogenesis.

scholarly journals In Silico Analysis of Possible Interaction between Host Genomic Transcription Factors and Zika Virus (ZikaSPH2015) Strain with Combinatorial Gene Regulation; Virus Versus Host - The Game Reloaded

2020 ◽  
Author(s):  
Massimiliano Chetta ◽  
Marina Tarsitano ◽  
Laura Vicari ◽  
Annalisa Saracino ◽  
Nenad Bukvic
Keyword(s):  
Transcription Factors ◽  
Zika Virus ◽  
In Silico ◽  
Genomic Sequence ◽  
In Silico Analysis ◽  
Entire Genome ◽  
Severe Condition ◽  
Binding Motifs ◽  
Virus Versus ◽  
Silico Analysis

In silico analysis is a promising approach for understanding biological events in complex diseases. Herein, we report an in silico analysis of the entire genome of virus ZikaSPH2015 strain, which was performed in order to identify the occurrence of specific motifs on genomic sequence of Zika Virus that is able to bind and therefore, sequester host’s Transcription Factors (TFs) as well as subsequently predict a possible interactions within host genome. Accordingly to obtained results of this original computational work-flow it is possible to hypothesize that these TFs Binding Motifs might be able to explain the complex and heterogeneous phenotype presentation in Zika Virus affected fe-tus/newborns, as well as the less severe condition in adults. Moreover, the proposed in silico protocol identified thirty three different TFs same as the distribution of TFBSs (Transcription Factor Binding Sites) on ZikaSPH2015 strain, potentially able to influence genes and pathways with biological functions confirming that this approach could find potential answers on disease pathogenesis.

In silico analysis and prediction of transcription factors of the proteins interacting with astrocyte elevated gene-1

2021 ◽  
Vol 92 ◽  
pp. 107478
Author(s):  
Sushmitha Sriramulu ◽  
Suman K. Nandy ◽  
Harsha Ganesan ◽  
Antara Banerjee ◽  
Surajit Pathak
Keyword(s):  
Transcription Factors ◽  
In Silico ◽  
In Silico Analysis ◽  
Silico Analysis

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 clinical and in-silico analysis of hsa-miR-21 and Growth Differentiation factor-15 expression in Diabetic Nephropathy

2021 ◽  
Author(s):  
Riddhi Girdhar Agarwal ◽  
Purvi Purohit ◽  
Manoj Khokhar ◽  
Anupama Modi ◽  
Nitin Kumar Bajpai ◽  
...  
Keyword(s):  
Diabetic Nephropathy ◽  
Transcription Factors ◽  
In Silico ◽  
Clinical Chemistry ◽  
In Silico Analysis ◽  
Healthy Controls ◽  
Ppi Network ◽  
Renal Disorder ◽  
Stage Renal Disease ◽  
Silico Analysis

Abstract Background: Diabetic Nephropathy (DN), a microvascular complication, is a major cause of end-stage renal disease (ESRD). GDF-15 and hsa-miR-21 are closely associated with endothelial dysfunction and inflammation.Methods: In-silico analysis was used to identify GDF-15 and insulin related protein-protein interaction (PPI) network and a common set of GDF-15 regulating transcription factors. Common targeting miRNA of GDF-15 regulating transcription factors were investigated in miRNet and TargetScan. Further, 30 type 2 DN patients and 30 healthy controls were included for clinical chemistry analysis, to analyze serum GDF-15 levels by ELISA and to evaluate the fold change expression (FCE) of circulating hsa-miR-21 by RT-PCR.Results: In the PPI network of IRS1, IRS2, INSR, IGF1R, INS, AKT1, PPARG, CEBPB, EGR1, TP53, KLF4, ATF3, GDF15, TWIST2, the common nodes between insulin and GDF-15 were identified. MicroRNA-21 was bioinformatically observed to directly target GDF-15 downregulating transcription factors KLF4, TP-53, and CEBPB. Serum GDF-15 was nearly ten (10) folds higher in DN patients (p˂0.0001) as compared to healthy controls. A positive and significant correlation of serum GDF-15 was found with HbA1c, HOMA-IR, serum urea and serum creatinine. The FCE of hsa-miR-21 was 9.18 folds higher in DN patients. Conclusion: Raised serum GDF-15 and circulating hsa-miR-21 can serve as clinically important therapeutic targets and biomarkers of progressive renal disorder.

In silico analysis revealed Zika virus miRNAs associated with viral pathogenesis through alteration of host genes involved in immune response and neurological functions

2019 ◽  
Vol 91 (9) ◽  
pp. 1584-1594 ◽  
Author(s):  
Md. Sajedul Islam ◽  
Md. Abdullah‐Al‐Kamran Khan ◽  
Md. Wahid Murad ◽  
Marwah Karim ◽  
Abul Bashar Mir Md. Khademul Islam
Keyword(s):  
Immune Response ◽  
Zika Virus ◽  
In Silico ◽  
In Silico Analysis ◽  
Viral Pathogenesis ◽  
Silico Analysis ◽  
Host Genes

scholarly journals In silico Analysis of Transcription Factor Repertoire and Prediction of Stress Responsive Transcription Factors in Soybean

DNA Research ◽  
2009 ◽  
Vol 16 (6) ◽  
pp. 353-369 ◽  
Author(s):  
K. Mochida ◽  
T. Yoshida ◽  
T. Sakurai ◽  
K. Yamaguchi-Shinozaki ◽  
K. Shinozaki ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
In Silico ◽  
In Silico Analysis ◽  
Silico Analysis

scholarly journals In Silico Analysis of Transcription Factor Repertoires and Prediction of Stress-Responsive Transcription Factors from Six Major Gramineae Plants

DNA Research ◽  
2011 ◽  
Vol 18 (5) ◽  
pp. 321-332 ◽  
Author(s):  
K. Mochida ◽  
T. Yoshida ◽  
T. Sakurai ◽  
K. Yamaguchi-Shinozaki ◽  
K. Shinozaki ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
In Silico ◽  
In Silico Analysis ◽  
Silico Analysis

In silico analysis of HOX Associated Transcription Factors as Potential Regulators of Oral Cancer

2021 ◽  
Author(s):  
Kanaka Sai Ram Padam ◽  
Sanjiban Chakrabarty ◽  
Shama Prasada Kabekkodu ◽  
Bobby Paul ◽  
Keith Hunter ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Oral Cancer ◽  
In Silico ◽  
In Silico Analysis ◽  
Silico Analysis
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