Université de Sherbrooke

About: Université de Sherbrooke is a education organization based out in Sherbrooke, Quebec, Canada. It is known for research contribution in the topics: Population & Receptor. The organization has 14922 authors who have published 28783 publications receiving 792511 citations. The organization is also known as: Universite de Sherbrooke & Sherbrooke University.

Design, synthesis, and biological evaluation of the first selective nonpeptide AT2 receptor agonist.

Abstract: The first druglike selective angiotensin II AT(2) receptor agonist (21) with a K(i) value of 0.4 nM for the AT(2) receptor and a K(i) > 10 microM for the AT(1) receptor is reported. Compound 21, with a bioavailability of 20-30% after oral administration and a half-life estimated to 4 h in rat, induces outgrowth of neurite cells, stimulates p42/p44(mapk), enhances in vivo duodenal alkaline secretion in Sprague-Dawley rats, and lowers the mean arterial blood pressure in anesthetized, spontaneously hypertensive rats. Thus, the peptidomimetic 21 exerts a similar biological response as the endogenous peptide angiotensin II after selective activation of the AT(2) receptor. Compound 21, derived from the prototype nonselective AT(1)/AT(2) receptor agonist L-162,313 will serve as a valuable research tool, enabling studies of the function of the AT(2) receptor in more detail.

Overall uncertainty study of the hydrological impacts of climate change for a Canadian watershed

Abstract: [1] General circulation models (GCMs) and greenhouse gas emissions scenarios (GGES) are generally considered to be the two major sources of uncertainty in quantifying the climate change impacts on hydrology. Other sources of uncertainty have been given less attention. This study considers overall uncertainty by combining results from an ensemble of two GGES, six GCMs, five GCM initial conditions, four downscaling techniques, three hydrological model structures, and 10 sets of hydrological model parameters. Each climate projection is equally weighted to predict the hydrology on a Canadian watershed for the 2081–2100 horizon. The results show that the choice of GCM is consistently a major contributor to uncertainty. However, other sources of uncertainty, such as the choice of a downscaling method and the GCM initial conditions, also have a comparable or even larger uncertainty for some hydrological variables. Uncertainties linked to GGES and the hydrological model structure are somewhat less than those related to GCMs and downscaling techniques. Uncertainty due to the hydrological model parameter selection has the least important contribution among all the variables considered. Overall, this research underlines the importance of adequately covering all sources of uncertainty. A failure to do so may result in moderately to severely biased climate change impact studies. Results further indicate that the major contributors to uncertainty vary depending on the hydrological variables selected, and that the methodology presented in this paper is successful at identifying the key sources of uncertainty to consider for a climate change impact study.

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes

Abstract: We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P < 2.2 × 10−7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent ‘false leads’ with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition.