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The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3,4,5,6,7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease.
Background The inhibitor telaprevir (VX-950) of the hepatitis C virus (HCV) protease NS3-4A has been tested in a recent phase 1b clinical trial in patients infected with HCV genotype 1. This trial revealed residue mutations that confer varying degrees of drug resistance. In particular, two protease positions with the mutations V36A/G/L/M and T54A/S were associated with low to medium levels of drug resistance during viral breakthrough, together with only an intermediate reduction of viral replication fitness. These mutations are located in the protein interior and far away from the ligand binding pocket. Results Based on the available experimental structures of NS3-4A, we analyze the binding mode of different ligands. We also investigate the binding mode of VX-950 by protein-ligand docking. A network of non-covalent interactions between amino acids of the protease structure and the interacting ligands is analyzed to discover possible mechanisms of drug resistance. We describe the potential impact of V36 and T54 mutants on the side chain and backbone conformations and on the non-covalent residue interactions. We propose possible explanations for their effects on the antiviral efficacy of drugs and viral fitness. Molecular dynamics simulations of T54A/S mutants and rotamer analysis of V36A/G/L/M side chains support our interpretations. Experimental data using an HCV V36G replicon assay corroborate our findings. Conclusion T54 mutants are expected to interfere with the catalytic triad and with the ligand binding site of the protease. Thus, the T54 mutants are assumed to affect the viral replication efficacy to a larger degree than V36 mutants. Mutations at V36 and/or T54 result in impaired interaction of the protease residues with the VX-950 cyclopropyl group, which explains the development of viral breakthrough variants.
This paper reports on Monte Carlo simulation results for future measurements of the moduli of time-like proton electromagnetic form factors, |GE | and |GM|, using the ¯pp → μ+μ− reaction at PANDA (FAIR). The electromagnetic form factors are fundamental quantities parameterizing the electric and magnetic structure of hadrons. This work estimates the statistical and total accuracy with which the form factors can be measured at PANDA, using an analysis of simulated data within the PandaRoot software framework. The most crucial background channel is ¯pp → π+π−,due to the very similar behavior of muons and pions in the detector. The suppression factors are evaluated for this and all other relevant background channels at different values of antiproton beam momentum. The signal/background separation is based on a multivariate analysis, using the Boosted Decision Trees method. An expected background subtraction is included in this study, based on realistic angular distribuations of the background contribution. Systematic uncertainties are considered and the relative total uncertainties of the form factor measurements are presented.