COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.

Funder: Bundesministerium für Bildung und ForschungFunder: Bundesministerium für Bildung und Forschung (BMBF)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

Performance of epistasis detection methods in semi-simulated GWAS

International audienceBackground: Part 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. Results: GBOOST, 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 6.10 5 SNPs and 15,000 samples in a couple of hours using a GPU. Conclusion: This 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

Near-infrared light-responsive UCST-nanogels using an efficient nickel-bis(dithiolene) photothermal crosslinker

International audienceA new kind of near-infrared (NIR) light-responsive polymer nanogel is demonstrated. The micellar aggregates of an ABA-type triblock copolymer, whose core is a thermosensitive polymer displaying an upper critical solution temperature (UCST), are crosslinked using a photothermal nickel-bis(dithiolene) complex that absorbs NIR light and converts efficiently optical energy into heat. We show that when the nanogel aqueous solution is exposed to NIR light, even at a low power density of 0.16 W cm(-2)and a low nickel-bis(dithiolene) complex concentration of 61.4 mu g mL(-1), the photothermally induced heating is sufficient to allow the nanogel particles to undergo a volume phase transition. The induced volume increase due to the positive thermosensitivity of the polymer leads to the release of loaded hydrophobic dye molecules. Using an energy balance model, the photothermal conversion efficiency of the nickel-bis(dithiolene) complex in the nanogel was evaluated through solution temperature and transmittance measurements under NIR laser irradiation at various light power densities as well as different nanoparticle concentrations and solvents. The photothermal conversion efficiency can reach about 64%, which positions the nickel-bis(dithiolene) complex among the most efficient photothermal agents in the NIR spectral region around 1000 nm

A Mechanistic Cellular Atlas of the Rheumatic Joint

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AttOmics: attention-based architecture for diagnosis and prognosis from omics data

International audienceMotivation The increasing availability of high-throughput omics data allows for considering a new medicine centered on individual patients. Precision medicine relies on exploiting these high-throughput data with machine-learning models, especially the ones based on deep-learning approaches, to improve diagnosis. Due to the high-dimensional small-sample nature of omics data, current deep-learning models end up with many parameters and have to be fitted with a limited training set. Furthermore, interactions between molecular entities inside an omics profile are not patient specific but are the same for all patients. Results In this article, we propose AttOmics, a new deep-learning architecture based on the self-attention mechanism. First, we decompose each omics profile into a set of groups, where each group contains related features. Then, by applying the self-attention mechanism to the set of groups, we can capture the different interactions specific to a patient. The results of different experiments carried out in this article show that our model can accurately predict the phenotype of a patient with fewer parameters than deep neural networks. Visualizing the attention maps can provide new insights into the essential groups for a particular phenotype. Availability and implementation The code and data are available at https://forge.ibisc.univ-evry.fr/abeaude/AttOmics. TCGA data can be downloaded from the Genomic Data Commons Data Portal

Large-scale computational modelling of the M1 and M2 synovial macrophages in rheumatoid arthritis

Abstract Macrophages play an essential role in rheumatoid arthritis. Depending on their phenotype (M1 or M2), they can play a role in the initiation or resolution of inflammation. The M1/M2 ratio in rheumatoid arthritis is higher than in healthy controls. Despite this, no treatment targeting specifically macrophages is currently used in clinics. Thus, devising strategies to selectively deplete proinflammatory macrophages and promote anti-inflammatory macrophages could be a promising therapeutic approach. State-of-the-art molecular interaction maps of M1 and M2 macrophages in rheumatoid arthritis are available and represent a dense source of knowledge; however, these maps remain limited by their static nature. Discrete dynamic modelling can be employed to study the emergent behaviours of these systems. Nevertheless, handling such large-scale models is challenging. Due to their massive size, it is computationally demanding to identify biologically relevant states in a cell- and disease-specific context. In this work, we developed an efficient computational framework that converts molecular interaction maps into Boolean models using the CaSQ tool. Next, we used a newly developed version of the BMA tool deployed to a high-performance computing cluster to identify the models’ steady states. The identified attractors are then validated using gene expression data sets and prior knowledge. We successfully applied our framework to generate and calibrate the M1 and M2 macrophage Boolean models for rheumatoid arthritis. Using KO simulations, we identified NFkB, JAK1/JAK2, and ERK1/Notch1 as potential targets that could selectively suppress proinflammatory macrophages and GSK3B as a promising target that could promote anti-inflammatory macrophages in rheumatoid arthritis

The Samapleu mafic-ultramafic intrusion and its Ni-Cu-PGE mineralization: an Eburnean (2.09 Ga) feeder dyke to the Yacouba layered complex (Man Archean craton, western Ivory Coast)

International audienceThe Yacouba layered complex intrudes the Archean (3.5–2.7 Ga) Kenema-Man craton in the Samapleu-Yorodougou area, western Ivory Coast. In Samapleu area, the complex was recognized in drill holes at three locations: Samapleu Main (SM); Samapleu Extension 1 (E1) and Yorodougou (Yo). It comprises websterites, peridotites and gabbro-norites arranged symmetrically with mafic layers at the center and ultramafic layers at both margins. The complex is inclined at 70–80° to the SE. The thickness of individual layers varies from 2 to 60 m and the total thickness is 120 to 200 m. At the E1 site, the complex extends to depths > 500 m. Contacts with the country rock gneiss are characterized by a hybrid zone that is a few meters thick and composed of plagioclase-orthopyroxene bearing metabasites, and locally (E1 site) a metamorphic assemblage of sapphirine-cordierite-sillimanite-spinel ± rutile. This assemblage is attributed to contact metamorphism during intrusion of the complex in the lower crust at a depth of about 25 km. Zircons in country rock gneisses and granulites, as well as in the hybrid facies, yield Archean ages of ~ 2.78 Ga, similar to ages reported in the Man craton. Rutiles in the hybrid zone give a U-Pb age of 2.09 Ga, which is interpreted as the age of contact metamorphism and emplacement of the intrusion. The Samapleu Main and Samapleu Extension 1 sites contain Ni and Cu sulfide deposit with reserves estimated as more than 40 million tons grading 0.25% Ni and 0.22% Cu (Sama Nickel-CI, August 2013). The Ni-Cu mineralization is composed of pentlandite, chalcopyrite, pyrrhotite and rare pyrite, which is disseminated mainly in pyroxenite or occurs as subvertical and semi-massive to massive sulfide veins. The sulfide textures range from matrix ore, net-textured, droplets or breccia textures. Zones enriched in PGM, particularly Pd, are associated with the sulfides and several chromite bands are also present. These observations suggest that an immiscible sulfide liquid formed from a parental silicate liquid and percolated through the crystal pile. The parental melt composition, determined using the Chai and Naldrett [1992] method, has a SiO2-rich mafic composition with 53% SiO2 and 10% MgO. This result, the presence of the hybrid zone, and the trace-element signature determined using the Bedard [1994] method, suggest a mantle-derived basaltic parental magma that had assimilated abundant continental crust. These observations indicate that Samapleu intrusion corresponds to a magmatic conduit of the Yacouba complex as at Jinchuan (China), Voisey’s bay (Canada), Kabanga (Tanzania) or Nkomati (South Africa)