The Mycobacterium tuberculosis catalase-peroxidase is a multifunctional heme-dependent enzyme that activates the core anti-tuberculosis drug isoniazid. Numerous studies have been undertaken to elucidate the enzyme-dependent mechanism of isoniazid activation, and it is well documented that mutations that reduce activity or inactivate the catalase-peroxidase lead to increased levels of isoniazid resistance in M. tuberculosis. Interpretation of the catalytic activities and the effects of mutations upon the action of the enzyme to date have been limited due to the lack of a three-dimensional structure for this enzyme. In order to provide a more accurate model of the three-dimensional structure of the M. tuberculosis catalase-peroxidase, we have crystallized the enzyme and now report its crystal structure refined to 2.4-Å resolution. The structure reveals new information about dimer assembly and provides information about the location of residues that may play a role in catalysis including candidates for protein-based radical formation. Modeling and computational studies suggest that the binding site for isoniazid is located near the ␦-meso heme edge rather than in a surface loop structure as currently proposed. The availability of a crystal structure for the M. tuberculosis catalase-peroxidase also permits structural and functional effects of mutations implicated in causing elevated levels of isoniazid resistance in clinical isolates to be interpreted with improved confidence.
BackgroundThe prevalence of diabetes is increasing worldwide. It has been long known that increased rates of inflammatory diseases, such as obesity (OBS), hypertension (HT) and cardiovascular diseases (CVD) are highly associated with type 2 diabetes (T2D). T2D and/or OBS can develop independently, due to genetic, behavioral or lifestyle-related variables but both lead to oxidative stress generation. The underlying mechanisms by which theses complications arise and manifest together remain poorly understood. Protein-protein interactions regulate nearly every living process. Availability of high-throughput genomic data has enabled unprecedented views of gene and protein co-expression, co-regulations and interactions in cellular systems.MethodsThe present work, applied a systems biology approach to develop gene interaction network models, comprised of high throughput genomic and PPI data for T2D. The genes differentially regulated through T2D were 'mined' and their 'wirings' were studied to get a more complete understanding of the overall gene network topology and their role in disease progression.ResultsBy analyzing the genes related to T2D, HT and OBS, a highly regulated gene-disease integrated network model has been developed that provides useful functional linkages among groups of genes and thus addressing how different inflammatory diseases are connected and propagated at genetic level. Based on the investigations around the 'hubs' that provided more meaningful insights about the cross-talk within gene-disease networks in terms of disease phenotype association with oxidative stress and inflammation, a hypothetical co-regulation disease mechanism model been proposed. The results from this study revealed that the oxidative stress mediated regulation cascade is the common mechanistic link among the pathogenesis of T2D, HT and other inflammatory diseases such as OBS.ConclusionThe findings provide a novel comprehensive approach for understanding the pathogenesis of various co-associated chronic inflammatory diseases by combining the power of pathway analysis with gene regulatory network evaluation.
BackgroundHypoxia inducible factor 1-alpha (HIF1A) is a transcription factor that plays important role in regulating cascade of reactions. In this study, the effect of rs11549465 (1772 C/T) and rs11549467 (1790 G/A) polymorphisms of HIF1A gene and its association with cancers were investigated through meta-analysis.MethodsMeta-analysis of genome wide association studies of HIF1A 1772 C/T polymorphism were conducted on 22 case-control studies of sample size 19024 and for 1790 G/A polymorphism 19 case-control studies were included with sample size 10654. Genotype and allelic frequency compared between cases and controls together with further subgroup analyses were carried out by cancer type and ethnicity.ResultsMeta-analysis from this study indicated that HIF1A 1772 C/T polymorphism is significantly associated with overall cancer risk. T allele and genotype TT are significantly associated with increasing overall cancer risk; odds ratios (OR) dominant model [TT + CT vs. CC: OR 1.30, 95 % CI (1.06-1.59), p-value: 0.0115], and T allele vs. C allele: OR 1.32, 95 % CI (1.07-1.63), p-value: 0.0098. Also, HIF1A 1790 G/A polymorphism, analyses showed that A allele and genotype AA are significantly associated with increasing overall cancer risk; odds ratios (OR) homozygote comparison [AA vs. GG: OR 5.10, 95 % CI (3.12-8.33), p-value: <0.0001], heterozygote comparison [GA vs. GG: OR 1.74, 95 % CI (1.20-2.52), p-value: 0.0033], dominant model [AA + GA vs. GG: OR 1.82, 95 % CI (1.26-2.62), p-value: 0.0014], recessive model [AA vs. GA + GG: OR 3.79, 95 % CI (2.34-6.15), p-value: <0.0001] and A allele vs. G allele: OR 1.82, 95 % CI (1.31-2.52), p-value: 0.0003.ConclusionIn detail meta-analysis indicated that both the polymorphisms 1772 C/T and 1790 G/A are significantly associated with overall cancer risk. The subgroup analyses showed that lung cancer is significantly associated with both polymorphisms. Although the 1772 C/T polymorphism is significantly associated with decreasing risk of renal cell carcinoma but the 1790 G/A polymorphism has shown to significantly increase the cancer risk in both Caucasian and Asian population. Thus, HIF1A could be a useful prognostic marker for cancers early predisposition.Electronic supplementary materialThe online version of this article (doi:10.1186/s40364-015-0054-z) contains supplementary material, which is available to authorized users.
A good number of genome-wide association studies (GWAS), including meta-analyses, reported that single nucleotide polymorphisms (SNPs) of the IL-6 gene are significantly associated with various types of cancer risks, though some other studies reported insignificant association with cancers, in the literature. These contradictory results may be due to variations in sample sizes and/or deficiency of statistical modeling. Therefore, an attempt is made to provide a more comprehensive understanding of the association between the IL-6 gene SNPs (rs1800795, rs1800796, rs1800797) and different cancer risks, giving the weight on a large sample size, including different cancer types and appropriate statistical modeling with the meta-dataset. In order to attain a more reliable consensus decision about the association between the IL-6 gene polymorphisms and different cancer risks, in this study, we performed a multi-case statistical meta-analysis based on the collected information of 118 GWAS studies comprising of 50053 cases and 65204 control samples. Results from this Meta-analysis indicated a significant association (p-value < 0.05) of the IL-6 gene rs1800796 polymorphism with an overall increased cancer risk. The subgroup analysis data based on cancer types exhibited significant association (p-value < 0.05) of the rs1800795 polymorphism with an overall increased risk of cervical, liver and prostate cancers; the rs1800796 polymorphism with lung, prostate and stomach cancers; and the rs1800797 polymorphism with cervical cancer. The subgroup analysis of ethnicity data showed a significant association (p-value < 0.05) of an overall cancer risk with the rs1800795 polymorphism for the African and Asian populations, the rs1800796 polymorphism for the Asian only and the rs1800797 polymorphism in the African population. Comparative discussion showed that our multi-case meta-analyses received more support than any previously reported individual meta-analysis about the association between the IL-6 gene polymorphisms and cancer risks. Results from this study, more confidently showed that the IL-6 gene SNPs (rs1800795, rs1800796 and rs1800797) in humans are associated with increased cancer risks. Therefore, these three polymorphisms of the IL-6 gene have the potential to be evaluated as a population based rapid, low-cost PCR prognostic biomarkers for different types of cancers diagnosis and research.
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