A functional genomics approach reveals suggestive quantitative trait loci associated with combined TLR4 and BCP crystal-induced inflammation and osteoarthritis

Objective: Basic calcium phosphate (BCP) crystals can activate the NLRP3 inflammasome and are potentially involved in the pathogenesis of osteoarthritis (OA). In order to elucidate relevant inflammatory mechanisms in OA, we used a functional genomics approach to assess genetic variation influencing BCP crystal-induced cytokine production. Method: Peripheral blood mononuclear cells (PBMCs) were isolated from healthy volunteers who were previously genotyped and stimulated with BCP crystals and/or lipopolysaccharide (LPS) after which cytokines release was assessed. Cytokine quantitative trait locus (cQTL) mapping was performed. For in vitro validation of the cQTL located in anoctamin 3 (ANO3), PBMCs were incubated with Tamoxifen and Benzbromarone prior to stimulation. Additionally, we performed co-localisation analysis of our top cQTLs with the most recent OA meta-analysis of genome-wide association studies (GWAS). Results: We observed that BCP crystals and LPS synergistically induce IL-1β in human PBMCs. cQTL analysis revealed several suggestive loci influencing cytokine release upon stimulation, among which are quantitative trait locus annotated to ANO3 and GLIS3. As functional validation, anoctamin inhibitors reduced IL-1β release in PBMCs after stimulation. Co-localisation analysis showed that the GLIS3 locus was shared between LPS/BCP crystal-induced IL-1β and genetic association with Knee OA. Conclusions: We identified and functionally validated a new locus, ANO3, associated with LPS/BCP crystal-induced inflammation in PBMCs. Moreover, the cQTL in the GLIS3 locus co-localises with the previously found locus associated with Knee OA, suggesting that this Knee OA locus might be explained through an inflammatory mechanism. These results form a basis for further exploration of inflammatory mechanisms in OA.</p

A Combination of Dopamine Genes Predicts Success by Professional Wall Street Traders

What determines success on Wall Street? This study examined if genes affecting dopamine levels of professional traders were associated with their career tenure. Sixty professional Wall Street traders were genotyped and compared to a control group who did not trade stocks. We found that distinct alleles of the dopamine receptor 4 promoter (DRD4P) and catecholamine-O-methyltransferase (COMT) that affect synaptic dopamine were predominant in traders. These alleles are associated with moderate, rather than very high or very low, levels of synaptic dopamine. The activity of these alleles correlated positively with years spent trading stocks on Wall Street. Differences in personality and trading behavior were also correlated with allelic variants. This evidence suggests there may be a genetic basis for the traits that make one a successful trader

Editorial introduction: decision making, reasoning, context and perspective

This edition includes articles based on presentations to an international conference in March 2019 at the University of West London (UWL) in the United Kingdom, on the subject of “Shared Decision-Making, Person-Centred Care & The Values Agenda”—a conference organized by UWL's European Institute for Person Centred Health and Social Care, in collaboration with the European Society for Person Centred Healthcare and the Collaborating Centre for Valuesbased Practice at St. Catherine's College, Oxford, U

Towards interactive Machine Learning (iML): Applying Ant Colony Algorithms to Solve the Traveling Salesman Problem with the Human-in-the-Loop Approach

Part 1: The International Cross Domain Conference (CD-ARES 2016)International audienceMost Machine Learning (ML) researchers focus on automatic Machine Learning (aML) where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from the availability of “big data”. However, sometimes, for example in health informatics, we are confronted not a small number of data sets or rare events, and with complex problems where aML-approaches fail or deliver unsatisfactory results. Here, interactive Machine Learning (iML) may be of help and the “human-in-the-loop” approach may be beneficial in solving computationally hard problems, where human expertise can help to reduce an exponential search space through heuristics.In this paper, experiments are discussed which help to evaluate the effectiveness of the iML-“human-in-the-loop” approach, particularly in opening the “black box”, thereby enabling a human to directly and indirectly manipulating and interacting with an algorithm. For this purpose, we selected the Ant Colony Optimization (ACO) framework, and use it on the Traveling Salesman Problem (TSP) which is of high importance in solving many practical problems in health informatics, e.g. in the study of proteins