Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases

Despite available vaccinations COVID-19 case numbers around the world are still growing, and effective medications against severe cases are lacking. In this work, we developed a machine learning model which predicts mortality for COVID-19 patients using data from the multi-center Lean European Open Survey on SARS-CoV-2-infected patients (LEOSS) observational study (>100 active sites in Europe, primarily in Germany), resulting into an AUC of almost 80%. We showed that molecular mechanisms related to dementia, one of the relevant predictors in our model, intersect with those associated to COVID-19. Most notably, among these molecules was tyrosine kinase 2 (TYK2), a protein that has been patented as drug target in Alzheimers Disease but also genetically associated with severe COVID-19 outcomes. We experimentally verified that anti-cancer drugs Sorafenib and Regorafenib showed a clear anti-cytopathic effect in Caco2 and VERO-E6 cells and can thus be regarded as potential treatments against COVID-19. Altogether, our work demonstrates that interpretation of machine learning based risk models can point towards drug targets and new treatment options, which are strongly needed for COVID-19.

New susceptibility loci for severe COVID-19 by detailed GWAS analysis in European populations

Given the highly variable clinical phenotype of Coronavirus disease 2019 (COVID-19), a deeper analysis of the host genetic contribution to severe COVID-19 is important to improve our understanding of underlying disease mechanisms. Here, we describe an extended GWAS meta-analysis of a well-characterized cohort of 3,260 COVID-19 patients with respiratory failure and 12,483 population controls from Italy, Spain, Norway and Germany/Austria, including stratified analyses based on age, sex and disease severity, as well as targeted analyses of chromosome Y haplotypes, the human leukocyte antigen (HLA) region and the SARS-CoV-2 peptidome. By inversion imputation, we traced a reported association at 17q21.31 to a highly pleiotropic [~]0.9-Mb inversion polymorphism and characterized the potential effects of the inversion in detail. Our data, together with the 5th release of summary statistics from the COVID-19 Host Genetics Initiative, also identified a new locus at 19q13.33, including NAPSA, a gene which is expressed primarily in alveolar cells responsible for gas exchange in the lung.

Lack of antibodies against seasonal coronavirus OC43 nucleocapsid protein identifies patients at risk of critical COVID-19

Most COVID-19 patients experience a mild disease; a minority suffers from critical disease. We report about a biomarker validation study regarding 296 patients with confirmed SARS-CoV-2 infections from four tertiary care referral centers in Germany and France. Patients with critical disease had significantly less anti-HCoV OC43 nucleocapsid protein antibodies compared to other COVID-19 patients (p=0.007). In multivariate analysis, OC43 negative inpatients had an increased risk of critical disease, higher than the risk by increased age or BMI, and lower than the risk by male sex. A risk stratification based on sex and OC43 serostatus was derived from this analysis. Our results indicate that prior infections with seasonal human coronaviruses can protect against a severe course of COVID-19. Anti-OC43 antibodies should be measured for COVID-19 inpatients and considered as part of the risk assessment. We expect individuals tested negative for anti-OC43 antibodies to particularly benefit from vaccination, especially with other risk factors prevailing.

T cell anergy in COVID-19 reflects virus persistence and poor outcomes

Coronavirus disease 2019 (COVID-19) can lead to severe pneumonia and hyperinflammation. So far, insufficient or excessive T cell responses were described in patients. We applied novel approaches to analyze T cell reactivity and showed that T anergy is already present in non-ventilated COVID-19 patients, very pronounced in ventilated patients, strongly associated with virus persistence and reversible with clinical recovery. T cell activation was measured by downstream effects on responder cells like basophils, plasmacytoid dendritic cells, monocytes and neutrophils in whole blood and proved to be much more meaningful than classical readouts with PBMCs. Monocytes responded stronger in males than females and IL-2 partially reversed T cell anergy. Downstream markers of T cell anergy were also found in fresh blood samples of critically ill patients with severe T cell anergy. Based on our data we were able to develop a score to predict fatal outcomes and to identify patients that may benefit from strategies to overcome T cell anergy.

Metabolic stress and disease-stage specific basigin expression of peripheral blood immune cell subsets in COVID-19 patients

Coronavirus disease 2019 (COVID-19) is driven by dysregulated immune responses yet the role of immunometabolism in COVID-19 pathogenesis remains unclear. By investigating 47 patients with confirmed SARS-CoV-2 infection and 16 uninfected controls, we found an immunometabolic dysregulation specific for patients with progressed disease that was reversible in the recovery phase. Specifically, T cells and monocytes exhibited increased mitochondrial mass, accumulated intracellular ROS and these changes were accompanied by disrupted mitochondrial architecture. Basigin (CD147), but not established markers of T cell activation, was up-regulated on T cells from progressed COVID-19 patients and correlated with ROS accumulation, reflected in the transcriptome. During recovery, basigin and ROS decreased to match the uninfected controls. In vitro analyses confirmed the correlation and showed a down-regulation of ROS by dexamethasone treatment. Our findings provide evidence of a basigin-related and reversible immunometabolic dysregulation in COVID-19.