Screening for latent tuberculosis before starting TNF-alpha inhibitors in a population with high BCG vaccination rates.

It is assumed that in candidates for TNF-alpha inhibitor (TNFi) treatment, tuberculin skin test (TST) may be unreliable, since BCG vaccination causes false positive and drugs cause false negative results, favoring the use of Quantiferon or T-spot assays. However, these tests may not be readily available in all parts of the world. We aimed to determine the reliability of TST with respect to BCG vaccination and drugs in candidates for TNFi treatment, and how isoniazid is tolerated, assuming that the use of TST would result in increased isoniazid use. We included 1031 adult patients who were prescribed a TNFi for the first time. We analysed the association of BCG and drugs with TST and Quantiferon results, the determinants of a positive TST, and evaluated the tolerability of isoniazid. BCG vaccination and male sex were associated with positive TST (OR 3.56, 95% CI 1.98-6.41 and OR 2.54, 95% CI 1.75-3.68, respectively), while prednisolone and azathioprine were associated with negative TST (OR 0.63, 95% CI 0.43-0.91 and OR 0.40, 95% CI 0.11-0.76). Isoniazid was prescribed to 684 (66.3%) patients and had to be discontinued in 12.2% of these before 9 months, most commonly due to hepatotoxicity (44%). One patient developed tuberculosis despite isoniazid use. BCG vaccination may be associated with false positive TST, despite a long time since vaccination in candidates for TNFi treatment. Prednisolone and azathioprine use were associated with negative TST. Despite the high frequency of isoniazid use associated with using TST instead of QTF, isoniazid was generally well tolerated.

Chromatin three-dimensional interactions mediate genetic effects on gene expression.

Studying the genetic basis of gene expression and chromatin organization is key to characterizing the effect of genetic variability on the function and structure of the human genome. Here we unravel how genetic variation perturbs gene regulation using a dataset combining activity of regulatory elements, gene expression, and genetic variants across 317 individuals and two cell types. We show that variability in regulatory activity is structured at the intra- and interchromosomal levels within 12,583 cis-regulatory domains and 30 trans-regulatory hubs that highly reflect the local (that is, topologically associating domains) and global (that is, open and closed chromatin compartments) nuclear chromatin organization. These structures delimit cell type-specific regulatory networks that control gene expression and coexpression and mediate the genetic effects of cis- and trans-acting regulatory variants on genes.

The predictive value of MDR1, CYP2C9, and CYP2C19 polymorphisms for phenytoin plasma levels.

Phenytoin, an anticonvulsant, exhibits nonlinear pharmacokinetics with large interindividual differences. Because of its small therapeutic range with the risk of therapeutic failure or adverse drug effects in susceptible persons, therapeutic drug monitoring is frequently applied. The interindividual differences in dose response can partially be explained by known genetic polymorphisms in the metabolic enzyme CYP2C9 but a large deal of individual variability remains still unexplained. Part of this variability might be accounted for by variable uptake of phenytoin, which is a substrate of p-glycoprotein, encoded by the human MDR1 gene. We evaluated, whether phenytoin plasma levels correlate with a polymorphism in the MDR1 gene, C3435T, which is associated with intestinal PGP activity. Genotyping and analyses of plasma levels of phenytoin and metabolites in 96 healthy Turkish volunteers showed that the MDR1C > T3435 polymorphism affects phenytoin plasma levels (P = 0.064) and the metabolic ratio of p-HPPH vs phenytoin (MDR1*TT genotype, P = 0.026). The MDR1*CC genotype is more common in volunteers with low phenytoin levels (P < or = 0.001, chi2 test). A combined analysis of variable alleles of CYP2C9, 2C19 and MDR1 revealed that the number of mutant CYP2C9 alleles is a major determinant, the number of MDR1*T alleles further contributes to the prediction of phenytoin plasma levels and CYP2C19*2 does not explain individual variability. The regression equation that fitted the data best included the number of mutant CYP2C9 and MDR*T alleles as predictory variables and explained 15.4% of the variability of phenytoin data (r2 = 0.154, P = 0.0002). Furthermore, analysis of CYP2C9 and MDR1 genotypes in 35 phenytoin-treated patients recruited from therapeutic drug monitoring showed that combined CYP2C9 and MDR1 analysis has some predictive value not only in the controlled settings of a clinical trial, but also in the daily clinical practice.