Effects of etizolam and ethyl loflazepate on the P300 event-related potential in healthy subjects

<p>Abstract</p> <p>Background</p> <p>Benzodiazepines carry the risk of inducing cognitive impairments, which may go unnoticed while profoundly disturbing social activity. Furthermore, these impairments are partly associated with the elimination half-life (EH) of the substance from the body. The object of the present study was to examine the effects of etizolam and ethyl loflazepate, with EHs of 6 h and 122 h, respectively, on information processing in healthy subjects.</p> <p>Methods</p> <p>Healthy people were administered etizolam and ethyl loflazepate acutely and subchronically (14 days). The auditory P300 event-related potential and the neuropsychological batteries described below were employed to assess the effects of drugs on cognition. The P300 event-related potential was recorded before and after drug treatments. The digit symbol test, trail making test, digit span test and verbal paired associates test were administered to examine mental slowing and memory functioning.</p> <p>Results</p> <p>Acute administration of drugs caused prolongation in P300 latency and reduction in P300 amplitude. Etizolam caused a statistically significant prolongation in P300 latency compared to ethyl loflazepate. Furthermore, subchronic administration of etizolam, but not ethyl loflazepate, still caused a weak prolongation in P300 latency. In contrast, neuropsychological tests showed no difference.</p> <p>Conclusions</p> <p>The results indicate that acute administration of ethyl loflazepate induces less effect on P300 latency than etizolam.</p

The D4Z4 Macrosatellite Repeat Acts as a CTCF and A-Type Lamins-Dependent Insulator in Facio-Scapulo-Humeral Dystrophy

Both genetic and epigenetic alterations contribute to Facio-Scapulo-Humeral Dystrophy (FSHD), which is linked to the shortening of the array of D4Z4 repeats at the 4q35 locus. The consequence of this rearrangement remains enigmatic, but deletion of this 3.3-kb macrosatellite element might affect the expression of the FSHD-associated gene(s) through position effect mechanisms. We investigated this hypothesis by creating a large collection of constructs carrying 1 to >11 D4Z4 repeats integrated into the human genome, either at random sites or proximal to a telomere, mimicking thereby the organization of the 4q35 locus. We show that D4Z4 acts as an insulator that interferes with enhancer–promoter communication and protects transgenes from position effect. This last property depends on both CTCF and A-type Lamins. We further demonstrate that both anti-silencing activity of D4Z4 and CTCF binding are lost upon multimerization of the repeat in cells from FSHD patients compared to control myoblasts from healthy individuals, suggesting that FSHD corresponds to a gain-of-function of CTCF at the residual D4Z4 repeats. We propose that contraction of the D4Z4 array contributes to FSHD physio-pathology by acting as a CTCF-dependent insulator in patients

Predicting genome-wide DNA methylation using methylation marks, genomic position, and DNA regulatory elements

Background: Recent assays for individual-specific genome-wide DNA methylation profiles have enabled epigenome-wide association studies to identify specific CpG sites associated with a phenotype. Computational prediction of CpG site-specific methylation levels is important, but current approaches tackle average methylation within a genomic locus and are often limited to specific genomic regions. Results: We characterize genome-wide DNA methylation patterns, and show that correlation among CpG sites decays rapidly, making predictions solely based on neighboring sites challenging. We built a random forest classifier to predict CpG site methylation levels using as features neighboring CpG site methylation levels and genomic distance, and co-localization with coding regions, CGIs, and regulatory elements from the ENCODE project, among others. Our approach achieves 91% -- 94% prediction accuracy of genome-wide methylation levels at single CpG site precision. The accuracy increases to 98% when restricted to CpG sites within CGIs. Our classifier outperforms state-of-the-art methylation classifiers and identifies features that contribute to prediction accuracy: neighboring CpG site methylation status, CpG island status, co-localized DNase I hypersensitive sites, and specific transcription factor binding sites were found to be most predictive of methylation levels. Conclusions: Our observations of DNA methylation patterns led us to develop a classifier to predict site-specific methylation levels that achieves the best DNA methylation predictive accuracy to date. Furthermore, our method identified genomic features that interact with DNA methylation, elucidating mechanisms involved in DNA methylation modification and regulation, and linking different epigenetic processes