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Monoacylglycerol lipase (MAGL) represents a primary degradation enzyme of the endogenous cannabinoid (eCB), 2-arachidonoyglycerol (2-AG). This study reports a potent covalent MAGL inhibitor, SAR127303. The compound behaves as a selective and competitive inhibitor of mouse and human MAGL, which potently elevates hippocampal levels of 2-AG in mice. In vivo, SAR127303 produces antinociceptive effects in assays of inflammatory and visceral pain. In addition, the drug alters learning performance in several assays related to episodic, working and spatial memory. Moreover, long term potentiation (LTP) of CA1 synaptic transmission and acetylcholine release in the hippocampus, two hallmarks of memory function, are both decreased by SAR127303. Although inactive in acute seizure tests, repeated administration of SAR127303 delays the acquisition and decreases kindled seizures in mice, indicating that the drug slows down epileptogenesis, a finding deserving further investigation to evaluate the potential of MAGL inhibitors as antiepileptics. However, the observation that 2-AG hydrolysis blockade alters learning and memory performance, suggests that such drugs may have limited value as therapeutic agents.

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

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Rheumatoid Arthritis (RA) is an autoimmune disease of unknown aetiology involving complex interactions between environmental and genetic factors. Its pathogenesis is suspected to arise from intricate interplays between signalling, gene regulation and metabolism, leading to synovial inflammation, bone erosion and cartilage destruction in the patients’ joints. In addition, the resident synoviocytes of macrophage and fibroblast types can interact with innate and adaptive immune cells and contribute to the disease’s debilitating symptoms. Therefore, a detailed, mechanistic mapping of the molecular pathways and cellular crosstalks is essential to understand the complex biological processes and different disease manifestations. In this regard, we present the RA-Atlas, an SBGN-standardized, interactive, manually curated representation of existing knowledge related to the onset and progression of RA. This state-of-the-art RA-Atlas includes an updated version of the global RA-map covering relevant metabolic pathways and cell-specific molecular interaction maps for CD4+ Th1 cells, fibroblasts, and M1 and M2 macrophages. The molecular interaction maps were built using information extracted from published literature and pathway databases and enriched using omic data. The RA-Atlas is freely accessible on the webserver MINERVA (https://ramap.uni.lu/minerva/), allowing easy navigation using semantic zoom, cell-specific or experimental data overlay, gene set enrichment analysis, pathway export or drug query.

BackgroundPart of the missing heritability in Genome Wide Association Studies (GWAS) is expected to be explained by interactions between genetic variants, also called epistasis. Various statistical methods have been developed to detect epistasis in case-control GWAS. These methods face major statistical challenges due to the number of tests required, the complexity of the Linkage Disequilibrium (LD) structure, and the lack of consensus regarding the definition of epistasis. Their limited impact in terms of uncovering new biological knowledge might be explained in part by the limited amount of experimental data available to validate their statistical performances in a realistic GWAS context. In this paper, we introduce a simulation pipeline for generating real scale GWAS data, including epistasis and realistic LD structure. We evaluate five exhaustive bivariate interaction methods, fastepi, GBOOST, SHEsisEpi, DSS, and IndOR. Two hundred thirty four different disease scenarios are considered in extensive simulations. We report the performances of each method in terms of false positive rate control, power, area under the ROC curve (AUC), and computation time using a GPU. Finally we compare the result of each methods on a real GWAS of type 2 diabetes from the Welcome Trust Case Control Consortium.ResultsGBOOST, SHEsisEpi and DSS allow a satisfactory control of the false positive rate. fastepi and IndOR present an increase in false positive rate in presence of LD between causal SNPs, with our definition of epistasis. DSS performs best in terms of power and AUC in most scenarios with no or weak LD between causal SNPs. All methods can exhaustively analyze a GWAS with 6.105 SNPs and 15,000 samples in a couple of hours using a GPU.ConclusionThis study confirms that computation time is no longer a limiting factor for performing an exhaustive search of epistasis in large GWAS. For this task, using DSS on SNP pairs with limited LD seems to be a good strategy to achieve the best statistical performance. A combination approach using both DSS and GBOOST is supported by the simulation results and the analysis of the WTCCC dataset demonstrated that this approach can detect distinct genes in epistasis. Finally, weak epistasis between common variants will be detectable with existing methods when GWAS of a few tens of thousands cases and controls are available.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2229-8) contains supplementary material, which is available to authorized users.

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Phosphoribosyltransferases catalyze the displacement of a PRPP α-1'-pyrophosphate to a nitrogen-containing nucleobase. How they control the balance of substrates/products binding and activities is poorly understood. Here, we investigated the human adenine phosphoribosyltransferase (hAPRT) that produces AMP in the purine salvage pathway. We show that a single oxygen atom from the Tyr105 side chain is responsible for selecting the active conformation of the 12 amino acid long catalytic loop. Using in vitro, cellular, and in crystallo approaches, we demonstrated that Tyr105 is key for the fine-tuning of the kinetic activity efficiencies of the forward and reverse reactions. Together, our results reveal an evolutionary pressure on the strictly conserved Tyr105 and on the dynamic motion of the flexible loop in phosphoribosyltransferases that is essential for purine biosynthesis in cells. These data also provide the framework for designing novel adenine derivatives that could modulate, through hAPRT, diseases-involved cellular pathways.