66 research outputs found

    N-acetyltransferase 2 (NAT2) gene polymorphisms in Parkinson's disease

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    BACKGROUND: Parkinson's disease (PD) is a movement disorder caused by the degeneration of dopaminergic neurons in the substantia nigra of the midbrain. The molecular basis of this neural death is unknown, but genetic predisposition and environmental factors may cause the disease. Sequence variations in N-acetyltransferase 2 (NAT2) gene leading to slow acetylation process have been associated with PD, but results are contradictory. METHODS: We analyzed three NAT2 genetic variations, c.481C>T, c.590G>A (p.R197Q) and c.857G>A (p.G286E), which are known to result in a slow acetylator phenotype. Using validated PCR-RFLP assays, we genotyped 243 healthy unrelated Caucasian control subjects and 124 PD patients for these genetic variations. Further, we have undertaken a systematic review of NAT2 studies on PD and we incorporated our results in a meta-analysis consisting of 10 studies, 1,206 PD patients and 1,619 control subjects. RESULTS: Overall, we did not find significant differences in polymorphic acetylation genotypes in PD and control subjects. In the meta-analysis of slow acetylators from 10 studies and representing 604/1206 PD vs. 732/1619 control subjects, a marginally significant odds ratio (OR) of 1.32 (95% CI 1.12–1.54, p < 0.05) was obtained. Re-analysis of the data to exclude the only two studies showing positive association of slow acetylators to PD, resulted in a non-significant OR (1.07, 95% CI 0.9–1.28). Furthermore, meta-analysis of studies for c.590G>A, where both allele and genotype frequencies in PD vs. control subjects were analyzed, did not give significant summary odds ratios as well. CONCLUSION: We found little evidence for differences in polymorphic acetylation genotypes in PD and control subjects. Results of the meta-analyses did not also provide conclusive evidence for an overall association of NAT2 slow acetylator genotypes to PD

    RNAi-Mediated Knock-Down of Arylamine N-acetyltransferase-1 Expression Induces E-cadherin Up-Regulation and Cell-Cell Contact Growth Inhibition

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    Arylamine N-acetyltransferase-1 (NAT1) is an enzyme that catalyzes the biotransformation of arylamine and hydrazine substrates. It also has a role in the catabolism of the folate metabolite p-aminobenzoyl glutamate. Recent bioinformatics studies have correlated NAT1 expression with various cancer subtypes. However, a direct role for NAT1 in cell biology has not been established. In this study, we have knocked down NAT1 in the colon adenocarcinoma cell-line HT-29 and found a marked change in cell morphology that was accompanied by an increase in cell-cell contact growth inhibition and a loss of cell viability at confluence. NAT1 knock-down also led to attenuation in anchorage independent growth in soft agar. Loss of NAT1 led to the up-regulation of E-cadherin mRNA and protein levels. This change in E-cadherin was not attributed to RNAi off-target effects and was also observed in the prostate cancer cell-line 22Rv1. In vivo, NAT1 knock-down cells grew with a longer doubling time compared to cells stably transfected with a scrambled RNAi or to parental HT-29 cells. This study has shown that NAT1 affects cell growth and morphology. In addition, it suggests that NAT1 may be a novel drug target for cancer therapeutics

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

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    <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

    Mode of Effective Connectivity within a Putative Neural Network Differentiates Moral Cognitions Related to Care and Justice Ethics

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    BACKGROUND: Moral sensitivity refers to the interpretive awareness of moral conflict and can be justice or care oriented. Justice ethics is associated primarily with human rights and the application of moral rules, whereas care ethics is related to human needs and a situational approach involving social emotions. Among the core brain regions involved in moral issue processing are: medial prefrontal cortex, anterior (ACC) and posterior (PCC) cingulate cortex, posterior superior temporal sulcus (pSTS), insula and amygdala. This study sought to inform the long standing debate of whether care and justice moral ethics represent one or two different forms of cognition. METHODOLOGY/PRINCIPAL FINDINGS: Model-free and model-based connectivity analysis were used to identify functional neural networks underlying care and justice ethics for a moral sensitivity task. In addition to modest differences in patterns of associated neural activity, distinct modes of functional and effective connectivity were observed for moral sensitivity for care and justice issues that were modulated by individual variation in moral ability. CONCLUSIONS/SIGNIFICANCE: These results support a neurobiological differentiation between care and justice ethics and suggest that human moral behavior reflects the outcome of integrating opposing rule-based, self-other perspectives, and emotional responses

    Interaction among apoptosis-associated sequence variants and joint effects on aggressive prostate cancer

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    <p>Abstract</p> <p>Background</p> <p>Molecular and epidemiological evidence demonstrate that altered gene expression and single nucleotide polymorphisms in the apoptotic pathway are linked to many cancers. Yet, few studies emphasize the interaction of variant apoptotic genes and their joint modifying effects on prostate cancer (PCA) outcomes. An exhaustive assessment of all the possible two-, three- and four-way gene-gene interactions is computationally burdensome. This statistical conundrum stems from the prohibitive amount of data needed to account for multiple hypothesis testing.</p> <p>Methods</p> <p>To address this issue, we systematically prioritized and evaluated individual effects and complex interactions among 172 apoptotic SNPs in relation to PCA risk and aggressive disease (i.e., Gleason score ≥ 7 and tumor stages III/IV). Single and joint modifying effects on PCA outcomes among European-American men were analyzed using statistical epistasis networks coupled with multi-factor dimensionality reduction (SEN-guided MDR). The case-control study design included 1,175 incident PCA cases and 1,111 controls from the prostate, lung, colo-rectal, and ovarian (PLCO) cancer screening trial. Moreover, a subset analysis of PCA cases consisted of 688 aggressive and 488 non-aggressive PCA cases. SNP profiles were obtained using the NCI Cancer Genetic Markers of Susceptibility (CGEMS) data portal. Main effects were assessed using logistic regression (LR) models. Prior to modeling interactions, SEN was used to pre-process our genetic data. SEN used network science to reduce our analysis from > 36 million to < 13,000 SNP interactions. Interactions were visualized, evaluated, and validated using entropy-based MDR. All parametric and non-parametric models were adjusted for age, family history of PCA, and multiple hypothesis testing.</p> <p>Results</p> <p>Following LR modeling, eleven and thirteen sequence variants were associated with PCA risk and aggressive disease, respectively. However, none of these markers remained significant after we adjusted for multiple comparisons. Nevertheless, we detected a modest synergistic interaction between <it>AKT3 rs2125230-PRKCQ rs571715 </it>and disease aggressiveness using SEN-guided MDR (p = 0.011).</p> <p>Conclusions</p> <p>In summary, entropy-based SEN-guided MDR facilitated the logical prioritization and evaluation of apoptotic SNPs in relation to aggressive PCA. The suggestive interaction between <it>AKT3-PRKCQ </it>and aggressive PCA requires further validation using independent observational studies.</p
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