Examination of polymorphic glutathione S-transferase (GST) genes, tobacco smoking and prostate cancer risk among Men of African Descent: A case-control study

<p>Abstract</p> <p>Background</p> <p>Polymorphisms in <it>glutathione S-transferase </it>(GST) genes may influence response to oxidative stress and modify prostate cancer (PCA) susceptibility. These enzymes generally detoxify endogenous and exogenous agents, but also participate in the activation and inactivation of oxidative metabolites that may contribute to PCA development. Genetic variations within selected <it>GST </it>genes may influence PCA risk following exposure to carcinogen compounds found in cigarette smoke and decreased the ability to detoxify them. Thus, we evaluated the effects of polymorphic <it>GSTs </it>(<it>M1</it>, <it>T1</it>, and <it>P1</it>) alone and combined with cigarette smoking on PCA susceptibility.</p> <p>Methods</p> <p>In order to evaluate the effects of <it>GST </it>polymorphisms in relation to PCA risk, we used TaqMan allelic discrimination assays along with a multi-faceted statistical strategy involving conventional and advanced statistical methodologies (e.g., Multifactor Dimensionality Reduction and Interaction Graphs). Genetic profiles collected from 873 men of African-descent (208 cases and 665 controls) were utilized to systematically evaluate the single and joint modifying effects of <it>GSTM1 </it>and <it>GSTT1 </it>gene deletions, <it>GSTP1 </it>105 Val and cigarette smoking on PCA risk.</p> <p>Results</p> <p>We observed a moderately significant association between risk among men possessing at least one variant <it>GSTP1 </it>105 Val allele (OR = 1.56; 95%CI = 0.95-2.58; p = 0.049), which was confirmed by MDR permutation testing (p = 0.001). We did not observe any significant single gene effects among <it>GSTM1 </it>(OR = 1.08; 95%CI = 0.65-1.82; p = 0.718) and <it>GSTT1 </it>(OR = 1.15; 95%CI = 0.66-2.02; p = 0.622) on PCA risk among all subjects. Although the <it>GSTM1</it>-<it>GSTP1 </it>pairwise combination was selected as the best two factor LR and MDR models (p = 0.01), assessment of the hierarchical entropy graph suggested that the observed synergistic effect was primarily driven by the <it>GSTP1 </it>Val marker. Notably, the <it>GSTM1</it>-<it>GSTP1 </it>axis did not provide additional information gain when compared to either loci alone based on a hierarchical entropy algorithm and graph. Smoking status did not significantly modify the relationship between the <it>GST </it>SNPs and PCA.</p> <p>Conclusion</p> <p>A moderately significant association was observed between PCA risk and men possessing at least one variant <it>GSTP1 </it>105 Val allele (p = 0.049) among men of African descent. We also observed a 2.1-fold increase in PCA risk associated with men possessing the <it>GSTP1 </it>(Val/Val) and <it>GSTM1 </it>(*1/*1 + *1/*0) alleles. MDR analysis validated these findings; detecting <it>GSTP1 </it>105 Val (p = 0.001) as the best single factor for predicting PCA risk. Our findings emphasize the importance of utilizing a combination of traditional and advanced statistical tools to identify and validate single gene and multi-locus interactions in relation to cancer susceptibility.</p

Multiple Pathway-Based Genetic Variations Associated with Tobacco Related Multiple Primary Neoplasms

BACKGROUND: In order to elucidate a combination of genetic alterations that drive tobacco carcinogenesis we have explored a unique model system and analytical method for an unbiased qualitative and quantitative assessment of gene-gene and gene-environment interactions. The objective of this case control study was to assess genetic predisposition in a biologically enriched clinical model system of tobacco related cancers (TRC), occurring as Multiple Primary Neoplasms (MPN). METHODS: Genotyping of 21 candidate Single Nucleotide Polymorphisms (SNP) from major metabolic pathways was performed in a cohort of 151 MPN cases and 210 cancer-free controls. Statistical analysis using logistic regression and Multifactor Dimensionality Reduction (MDR) analysis was performed for studying higher order interactions among various SNPs and tobacco habit. RESULTS: Increased risk association was observed for patients with at least one TRC in the upper aero digestive tract (UADT) for variations in SULT1A1 Arg²¹³His, mEH Tyr¹¹³His, hOGG1 Ser³²⁶Cys, XRCC1 Arg²⁸⁰His and BRCA2 Asn³⁷²His. Gene-environment interactions were assessed using MDR analysis. The overall best model by MDR was tobacco habit/p53(Arg/Arg)/XRCC1(Arg³⁹⁹His)/mEH(Tyr¹¹³His) that had highest Cross Validation Consistency (8.3) and test accuracy (0.69). This model also showed significant association using logistic regression analysis. CONCLUSION: This is the first Indian study on a multipathway based approach to study genetic susceptibility to cancer in tobacco associated MPN. This approach could assist in planning additional studies for comprehensive understanding of tobacco carcinogenesis

Microclimate monitoring in commercial tomato (Solanum Lycopersicum L.) greenhouse production and its effect on plant growth, yield and fruit quality dataset

This data repository contains data from greenhouse and tomato growth monitoring from Proefcentrum Hoogstraaten (Voort 71, 2328 Hoogstraten, Belgium) collected during GROW! project (Interreg Vl-Nl, https://www.grensregio.eu/projecten/grow). The repository consists of:•environmental measurements (T&amp;RH folder): temperature, relative humidity;•tomato measurements (PlantData folder): truss weight, truss number, firmness, color, soluble solids content, ethylene emission rate, stem length, stem diameter. Each folder contains table Info explaining recorded variables.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Fully automated attractor analysis of cyanobacteria models

© 2018 IEEE. Complex dynamics arising in biological systems can be characterised by various kinds of attractors. To that end, the task of determining attractors becomes important in modern systems analysis. Biological systems are typically formalised as highly parametrised continuous-time ODE models. Such models can be abstracted in the form of parametrised graphs. In such abstractions, attractors are observed in the form of terminal strongly connected components (tSCCs). In this paper, we demonstrate a novel method for detecting tSCCs in parametrised graphs on several models of cyanobacteria taken from the domain-specific online platform e-cyanobacterium.org