scholarly journals In Silico Analysis of the Genes Encoding Proteins that Are Involved in the Biosynthesis of the RMS/MAX/D Pathway Revealed New Roles of Strigolactones in Plants

2015 ◽  
Vol 16 (12) ◽  
pp. 6757-6782 ◽  
Author(s):  
Marek Marzec ◽  
Aleksandra Muszynska
Keyword(s):  
In Silico ◽  
In Silico Analysis ◽  
Genes Encoding ◽  
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 Co-existence of virulence factors and antibiotic resistance in new Klebsiella pneumoniae clones emerging in south of Italy

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Teresa Fasciana ◽  
Bernardina Gentile ◽  
Maria Aquilina ◽  
Andrea Ciammaruconi ◽  
Chiara Mascarella ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Klebsiella Pneumoniae ◽  
Virulence Factors ◽  
In Silico ◽  
Geographical Area ◽  
In Silico Analysis ◽  
University Hospital ◽  
Uptake System ◽  
Genes Encoding ◽  
Silico Analysis

Abstract Background Endemic presence of Klebsiella pneumoniae resistant to carbapenem in Italy has been due principally to the clonal expansion of CC258 isolates; however, recent studies suggest an ongoing epidemiological change in this geographical area. Methods 50 K. pneumoniae strains, 25 carbapenem-resistant (CR-Kp) and 25 susceptible (CS-Kp), collected from march 2014 to march 2016 at the Laboratory of Bacteriology of the Paolo Giaccone Polyclinic University hospital of Palermo, Italy, were characterized for antibiotic susceptibility and fully sequenced by next generation sequencing (NGS) for the in silico analysis of resistome, virulome, multi-locus sequence typing (MLST) and core single nucleotide polymorphism (SNP) genotypes Results MLST in silico analysis of CR-Kp showed that 52% of isolates belonged to CC258, followed by ST395 (12%), ST307 (12%), ST392 (8%), ST348 (8%), ST405 (4%) and ST101 (4%). In the CS-Kp group, the most represented isolate was ST405 (20%), followed by ST392 and ST15 (12%), ST395, ST307 and ST1727 (8%). The in silico β-lactamase analysis of the CR-Kp group showed that the most detected gene was blaSHV (100%), followed by blaTEM (92%), blaKPC (88%), blaOXA (88%) and blaCTX-M (32%). The virulome analysis detected mrk operon in all studied isolates, and wzi-2 was found in three CR-Kp isolates (12%). Furthermore, the distribution of virulence genes encoding for the yersiniabactin system, its receptor fyuA and the aerobactin system did not show significant distribution differences between CR-Kp and CS-Kp, whereas the Klebsiella ferrous iron uptake system (kfuA, kfuB and kfuC genes), the two-component system kvgAS and the microcin E495 were significantly (p < 0.05) prevalent in the CS-Kp group compared to the CR-Kp group. Core SNP genotyping, correlating with the MLST data, allowed greater strain tracking and discrimination than MLST analysis. Conclusions Our data support the idea that an epidemiological change is ongoing in the Palermo area (Sicily, Italy). In addition, our analysis revealed the co-existence of antibiotic resistance and virulence factors in CR-Kp isolates; this characteristic should be considered for future genomic surveillance studies.

scholarly journals The role of mobile genetic elements in evolution of cyanobacteria

2011 ◽  
Vol 9 (4) ◽  
pp. 52-62
Author(s):  
Lidia E Mikheeva ◽  
Elena A Karbysheva ◽  
Sergey V Shestakov
Keyword(s):  
Gene Regulation ◽  
Significant Role ◽  
In Silico ◽  
In Silico Analysis ◽  
Whole Genome ◽  
Number Of Genes ◽  
Genes Encoding ◽  
Adaptation Processes ◽  
Silico Analysis

Possible pathways of cyanobacterial evolution are discussed on the basis of in silico analysis of fully sequenced genomes of 45 species/strains of cyanobacteria. The information on quantity and functions of different mobile elements (IS, MITE elements and group II introns) was reviewed. Positive correlation between whole genome sizes and number of genes, encoding transposases has been revealed. It is suggested that transpositions play significant role in genome rearrangements taking part in gene regulation and adaptation processes determining the directions of microevolution processes in cyanobacterial populations.

A systematic review with in silico analysis on transcriptomic profile of gallbladder carcinoma

2020 ◽  
Vol 47 (6) ◽  
pp. 398-408
Author(s):  
Sonam Tulsyan ◽  
Showket Hussain ◽  
Balraj Mittal ◽  
Sundeep Singh Saluja ◽  
Pranay Tanwar ◽  
...  
Keyword(s):  
Systematic Review ◽  
Gallbladder Carcinoma ◽  
In Silico ◽  
In Silico Analysis ◽  
Transcriptomic Profile ◽  
Silico Analysis

Enalos Suite of Tools: Enhancing Cheminformatics and Nanoinfor - matics through KNIME

2020 ◽  
Vol 27 (38) ◽  
pp. 6523-6535 ◽  
Author(s):  
Antreas Afantitis ◽  
Andreas Tsoumanis ◽  
Georgia Melagraki
Keyword(s):  
Data Analysis ◽  
Virtual Screening ◽  
In Silico ◽  
Model Development ◽  
In Silico Analysis ◽  
Material Design ◽  
Efficient Solutions ◽  
Biological Data ◽  
Cost Efficient ◽  
Silico Analysis

Drug discovery as well as (nano)material design projects demand the in silico analysis of large datasets of compounds with their corresponding properties/activities, as well as the retrieval and virtual screening of more structures in an effort to identify new potent hits. This is a demanding procedure for which various tools must be combined with different input and output formats. To automate the data analysis required we have developed the necessary tools to facilitate a variety of important tasks to construct workflows that will simplify the handling, processing and modeling of cheminformatics data and will provide time and cost efficient solutions, reproducible and easier to maintain. We therefore develop and present a toolbox of >25 processing modules, Enalos+ nodes, that provide very useful operations within KNIME platform for users interested in the nanoinformatics and cheminformatics analysis of chemical and biological data. With a user-friendly interface, Enalos+ Nodes provide a broad range of important functionalities including data mining and retrieval from large available databases and tools for robust and predictive model development and validation. Enalos+ Nodes are available through KNIME as add-ins and offer valuable tools for extracting useful information and analyzing experimental and virtual screening results in a chem- or nano- informatics framework. On top of that, in an effort to: (i) allow big data analysis through Enalos+ KNIME nodes, (ii) accelerate time demanding computations performed within Enalos+ KNIME nodes and (iii) propose new time and cost efficient nodes integrated within Enalos+ toolbox we have investigated and verified the advantage of GPU calculations within the Enalos+ nodes. Demonstration data sets, tutorial and educational videos allow the user to easily apprehend the functions of the nodes that can be applied for in silico analysis of data.

In-Silico Analysis of Chromone Containing Sulfonamide Derivatives as Human Carbonic Anhydrase Inhibitors

2013 ◽  
Vol 9 (4) ◽  
pp. 608-616 ◽  
Author(s):  
Zaheer Ul-Haq ◽  
Saman Usmani ◽  
Uzma Mahmood ◽  
Mariya al-Rashida ◽  
Ghulam Abbas
Keyword(s):  
Carbonic Anhydrase ◽  
In Silico ◽  
In Silico Analysis ◽  
Carbonic Anhydrase Inhibitors ◽  
Human Carbonic Anhydrase ◽  
Silico Analysis
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