178 resultados para Housekeeping genes


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Background: Genetic variation within interleukin genes has been reported to be associated with end-stage renal disease (ESRD). These findings have not been consistently replicated. No study has yet reported the comprehensive investigation of IL1A, IL1B, IL1RN, IL6 and IL10 genes. Methods: 664 kidney transplant recipients (cases) and 577 kidney donors (controls) were genotyped to establish if common variants in interleukin genes are associated with ESRD. Single nucleotide polymorphism (SNP) genotype data for each gene were downloaded for a northern and western European population from the International HapMap Project. Haploview was used to visualize linkage disequilibrium and select tag SNPs. Thirty SNPs were genotyped using MassARRAY (R) iPLEX Gold technology and data were analyzed using the chi(2) test for trend. Independent replication was conducted in 1,269 individuals with similar phenotypic characteristics. Results: Investigating all common variants in IL1A, IL1B, IL1RN, IL6 and IL10 genes revealed a statistically significant association (rs452204 p(empirical) = 0.02) with one IL1RN variant and ESRD. This IL1RN SNP tags three other variants, none of which have previously been reported to be associated with renal disease. Independent replication in a separate transplant population of comparable size did not confirm the original observation. Conclusions: Common variants in these five candidate interleukin genes are not major risk factors for ESRD in white Europeans. Copyright (C) 2010 S. Karger AG, Basel

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Background: Doxorubicin is one of the most effective anti-cancer drugs but its use is limited by cumulative cardiotoxicity that restricts lifetime dose. Redox damage is one of the most accepted mechanisms of toxicity, but not fully substantiated. Moreover doxorubicin is not an efficient redox cycling compound due to its low redox potential. Here we used genomic and chemical systems approaches in vivo to investigate the mechanisms of doxorubicin cardiotoxicity, and specifically test the hypothesis of redox cycling mediated cardiotoxicity.

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Several studies have provided compelling evidence implicating the Notch signalling pathway in diabetic nephropathy. Co-regulation of Notch signalling pathway genes with GREM1 has recently been demonstrated and several genes involved in the Notch pathway are differentially expressed in kidney biopsies from individuals with diabetic nephropathy. We assessed single-nucleotide polymorphisms (SNPs; n = 42) in four of these key genes (JAG1, HES1, NOTCH3 and ADAM10) for association with diabetic nephropathy using a case-control design.
Tag SNPs and potentially functional SNPs were genotyped using Sequenom or Taqman technologies in a total of 1371 individuals with type 1 diabetes (668 patients with nephropathy and 703 controls without nephropathy). Patients and controls were white and recruited from the UK and Ireland. Association analyses were performed using PLINK (http://pngu.mgh.harvard.edu/similar to purcell/plink/) and haplotype frequencies in patients and controls were compared. Adjustment for multiple testing was performed by permutation testing.
In analyses stratified by centre, we identified six SNPs, rs8708 and rs11699674 (JAG1), rs10423702 and rs1548555 (NOTCH3), rs2054096 and rs8027998 (ADAM10) as being associated with diabetic nephropathy before, but not after, adjustment for multiple testing. Haplotype and subgroup analysis according to duration of diabetes also failed to find an association with diabetic nephropathy.
Our results suggest that common variants in JAG1, HES1, NOTCH3 and ADAM10 are not strongly associated with diabetic nephropathy in type 1 diabetes among white individuals. Our findings, however, cannot entirely exclude these genes from involvement in the pathogenesis of diabetic nephropathy.

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We develop an approach utilizing randomized genotypes to rigorously infer causal regulatory relationships among genes at the transcriptional level, based on experiments in which genotyping and expression profiling are performed. This approach can be used to build transcriptional regulatory networks and to identify putative regulators of genes. We apply the method to an experiment in yeast, in which genes known to be in the same processes and functions are recovered in the resulting transcriptional regulatory network.