scRNA-seq reveals persistent aberrant differentiation of nasal epithelium driven by TNFα and TGFβ in post-COVID syndrome (preprint)

Post-COVID syndrome (PCS) currently affects approximately 3-17% of people following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and has the potential to become a significant global health burden. PCS presents with various symptoms, and methods for improved PCS assessment are presently developed to guide therapy. Nevertheless, there are few mechanistic insights and treatment options. Here, we performed single-cell RNA transcriptomics on nasal biopsies from 33 patients suffering from PCS with mild, moderate, or severe symptoms. We identified 17 different cell clusters representing 12 unique cell populations, including all major epithelial cell types of the conducting airways and basal, secretory, and ciliated cells. Severe PCS was associated with decreased numbers of ciliated cells and the presence of immune cells. Ensuing inflammatory signaling upregulated TGF{beta} and induced an epithelial-mesenchymal transition, which led to the high abundance of basal cells and a mis-stratified epithelium. We confirmed the results in vitro using an air-liquid interface culture and validated TNF as the causal inflammatory cytokine. In summary, our results show that one mechanism for sustained PCS is not through continued viral load, but through the presence of immune cells in nasal tissue leading to impaired mucosal barrier function and repeated infections. These findings could be further explored as a therapeutic option akin to other chronic inflammatory diseases by inhibiting the TNF-TGF{beta} axis, restoring the nasal epithelium, and reducing respiratory tract-related infections.

Increased circulating fibronectin, depletion of natural IgM and heightened EBV, HSV-1 reactivation in ME/CFS and long COVID (preprint)

Myalgic Encephalomyelitis/ Chronic Fatigue syndrome (ME/CFS) is a complex, debilitating, long-term illness without a diagnostic biomarker. ME/CFS patients share overlapping symptoms with long COVID patients, an observation which has strengthened the infectious origin hypothesis of ME/CFS. However, the exact sequence of events leading to disease development is largely unknown for both clinical conditions. Here we show antibody response to herpesvirus dUTPases, particularly to that of Epstein-Barr virus (EBV) and HSV-1, increased circulating fibronectin (FN1) levels in serum and depletion of natural IgM against fibronectin ((n)IgM-FN1) are common factors for both severe ME/CFS and long COVID. We provide evidence for herpesvirus dUTPases-mediated alterations in host cell cytoskeleton, mitochondrial dysfunction and OXPHOS. Our data show altered active immune complexes, immunoglobulin-mediated mitochondrial fragmentation as well as adaptive IgM production in ME/CFS patients. Our findings provide mechanistic insight into both ME/CFS and long COVID development. Finding of increased circulating FN1 and depletion of (n)IgM-FN1 as a biomarker for the severity of both ME/CFS and long COVID has an immediate implication in diagnostics and development of treatment modalities.

Consistency as a Data Quality Measure for German Corona Consensus items mapped from National Pandemic Cohort Network data collections (preprint)

Background As a national effort to better understand the current pandemic, three cohorts collect sociodemographic and clinical data from COVID-19 patients from different target populations within the German National Pandemic Cohort Network (NAPKON). Furthermore, the German Corona Consensus Dataset (GECCO) was introduced as a harmonized basic information model for COVID-19 patients in clinical routine. To compare the cohort data with other GECCO-based studies, data items are mapped to GECCO. As mapping from one information model to another is complex, an additional consistency evaluation of the mapped items is recommended to detect possible mapping issues or source data inconsistencies.Objectives The goal of this work is to assure high consistency of research data mapped to the GECCO data model. In particular, it aims at identifying contradictions within interdependent GECCO data items of the German national COVID-19 cohorts to allow investigation of possible reasons for identified contradictions. We furthermore aim at enabling other researchers to easily perform data quality evaluation on GECCO-based datasets and adapt to similar data models.Methods All suitable data items from each of the three NAPKON cohorts are mapped to the GECCO items. A consistency assessment tool (dqGecco) is implemented, following the design of an existing quality assessment framework, retaining their-defined consistency taxonomies, including logical and empirical contradictions. Results of the assessment are verified independently on the primary data source.Results Our consistency assessment tool helped in correcting the mapping procedure and reveals remaining contradictory value combinations within COVID-19 symptoms, vital-signs, and COVID-19 severity. Consistency rates differ between the different indicators and cohorts ranging from 95.84% up to 100%.Conclusion An efficient and portable tool capable to discover inconsistencies in the COVID-19 domain has been developed and applied to three different cohorts. As the GECCO dataset is employed in different platforms and studies, the tool can be directly applied there or adapted to similar information models.

Multiple effects of TNFα inhibitors on the development of the adaptive immune response after SARS-CoV-2 vaccination (preprint)

Objectives The humoral immune response to SARS-CoV-2 vaccination in patients with chronic inflammatory disease (CID) declines more rapidly with TNFα inhibition. Furthermore, the efficacy of current vaccines against Omicron variants of concern (VOC) including BA.2 is limited. Alterations within immune cell populations, changes in IgG affinity and the ability to neutralise a pre-VOC strain and the BA.2 virus were investigated in these at-risk patients. Methods Serum levels of anti-SARS-CoV-2 IgG, IgG avidity and neutralising antibodies (NA) were determined in anti-TNFα patients (n=10) and controls (n=24 healthy individuals; n=12 patients under other disease-modifying anti-rheumatic drugs, oDMARD) before and after the second and third vaccination by ELISA, immunoblot and live virus neutralisation assay. SARS-CoV-2-specific B-and T cell subsets were analysed by multicolour flow cytometry. Results IgG avidity and anti-pre-VOC NA titres decreased faster in anti-TNFα recipients than in controls 6 months after the second vaccination (healthy individuals: avidity: p≤0.0001; NA: p=0.0347; oDMARDs: avidity: p=0.0012; NA: p=0.0293). Total plasma cell counts were increased in anti-TNFα patients (Healthy individuals: p=0.0344; oDMARDs: p=0.0254), whereas absolute numbers of SARS-CoV-2-specific cells were comparable 7 days after vaccination. These patients had lower BA.2 NA titres compared to both other groups, even after the third vaccination. Conclusions We show a reduced SARS-CoV-2 neutralising capacity in patients under TNFα blockade. In this cohort, the plasma cell response appears to be less specific and show stronger bystander activation. While these effects were observable after the first two vaccinations and with older VOC, the differences in responses to BA.2 were magnified. What is already known on this topic Patients with chronic inflammatory diseases treated with TNFα inhibitors show a greater decrease in SARS-CoV-2 IgG 6 months after the second vaccination than patients taking oDMARDs and healthy individuals. What this study adds Antibodies from patients taking TNFα blockers have a lower SARS-CoV-2 neutralising capacity and maturity. Plasma cells from these patients exhibit less specific immune reaction. SARS-CoV-2-specific T cells are less activated. Neutralisation against BA.2 is drastically reduced even after the third vaccination. How this study might affect research, practice or policy This study emphasizes the need to protect vulnerable groups such as patients using TNF inhibitors. They could benefit from Omicron-adapted vaccination, but most likely they need to be protected by additional means other than vaccination.

The German National Pandemic Cohort Network (NAPKON): rationale, study design and baseline characteristics. (preprint)

The German government initiated the Network University Medicine (NUM) in early 2020 to improve national research activities on the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. To this end, 36 German Academic Medical Centers started to collaborate on 13 projects, with the largest being the National Pandemic Cohort Network (NAPKON). The NAPKON’s goal is creating the most comprehensive Coronavirus Disease 2019 (COVID-19) cohort in Germany. Within NAPKON, adult and pediatric patients are observed in three complementary cohort platforms (Cross-Sectoral, High-Resolution and Population-Based) from the initial infection until up to three years of follow-up. Study procedures comprise comprehensive clinical and imaging diagnostics, quality-of-life assessment, patient-reported outcomes and biosampling. The three cohort platforms build on four infrastructure core units (Interaction, Biosampling, Epidemiology, and Integration) and collaborations with NUM projects. Key components of the data capture, regulatory, and data privacy are based on the German Centre for Cardiovascular Research. By December 01, 2021, 34 university and 34 non-university hospitals have enrolled 4,241 patients with local data quality reviews performed on 2,812 (66%). 47% were female, the median age was 53 (IQR: 38-63)) and 3 pediatric cases were included. 30% of patients were hospitalized, 11% admitted to an intensive care unit, and 4% of patients deceased while enrolled. 7,143 visits with biosampling in 3,595 patients were conducted by November 29, 2021. In this overview article, we summarize NAPKON’s design, relevant milestones including first study population characteristics, and outline the potential of NAPKON for German and international research activities.Trial registration:· https://clinicaltrials.gov/ct2/show/NCT04768998· https://clinicaltrials.gov/ct2/show/NCT04747366· https://clinicaltrials.gov/ct2/show/NCT04679584

The relationship between autoantibodies targeting GPCRs and the renin-angiotensin system associates with COVID-19 severity (preprint)

The coronavirus disease 2019 (COVID-19) can evolve to clinical manifestations resembling systemic autoimmune diseases, with the presence of autoantibodies that are still poorly characterized. To address this issue, we performed a cross-sectional study of 246 individuals to determine whether autoantibodies targeting G protein-coupled receptors (GPCRs) and renin-angiotensin system (RAS)-related molecules were associated with COVID-19-related clinical outcomes. Moderate and severe patients exhibited the highest autoantibody levels, relative to both healthy controls and patients with mild COVID-19 symptoms. Random Forest, a machine learning model, ranked anti-GPCR autoantibodies targeting downstream molecules in the RAS signaling pathway such as the angiotensin II type 1 and Mas receptor, and the chemokine receptor CXCR3 as the three strongest predictors of severe disease. Moreover, while the autoantibody network signatures were relatively conserved in patients with mild COVID-19 compared to healthy controls, they were disrupted in moderate and most perturbed in severe patients. Our data indicate that the relationship between autoantibodies targeting GPCRs and RAS-related molecules associates with the clinical severity of COVID-19, suggesting novel molecular pathways for therapeutic interventions.

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

Due to the highly variable clinical phenotype of Coronavirus disease 2019 (COVID-19), deepening the host genetic contribution to severe COVID-19 may further improve our understanding about underlying disease mechanisms. Here, we describe an extended GWAS meta-analysis of 3,260 COVID-19 patients with respiratory failure and 12,483 population controls from Italy, Spain, Norway and Germany, as well as hypothesis-driven targeted analysis of the human leukocyte antigen (HLA) region and chromosome Y haplotypes. We include detailed stratified analyses based on age, sex and disease severity. In addition to already established risk loci, our data identify and replicate two genome-wide significant loci at 17q21.31 and 19q13.33 associated with severe COVID-19 with respiratory failure. These associations implicate a highly pleiotropic ~0.9-Mb 17q21.31 inversion polymorphism, which affects lung function and immune and blood cell counts, and the NAPSA gene, involved in lung surfactant protein production, in COVID-19 pathogenesis.

Longitudinal multi-omics analysis identifies responses of megakaryocytes, erythroid cells and plasmablasts as hallmarks of severe COVID-19 trajectories (preprint)

The pandemic spread of the potentially life-threatening disease COVID-19 requires a thorough understanding of the longitudinal dynamics of host responses. Temporal resolution of cellular features associated with a severe disease trajectory will be a pre-requisite for finding disease outcome predictors. Here, we performed a longitudinal multi-omics study using a two-centre German cohort of 13 patients (from Cologne and Kiel, cohort 1). We analysed the bulk transcriptome, bulk DNA methylome, and single-cell transcriptome (>358,000 cells, including BCR profiles) of peripheral blood samples harvested from up to 5 time points. The results from single-cell and bulk transcriptome analyses were validated in two independent cohorts of COVID-19 patients from Bonn (18 patients, cohort 2) and Nijmegen (40 patients, cohort 3), respectively. We observed an increase of proliferating, activated plasmablasts in severe COVID-19, and show a distinct expression pattern related to a hyperactive cellular metabolism of these cells. We further identified a notable expansion of type I IFN-activated circulating megakaryocytes and their progenitors, indicative of emergency megakaryopoiesis, which was confirmed in cohort 2. These changes were accompanied by increased erythropoiesis in the critical phase of the disease with features of hypoxic signalling. Finally, projecting megakaryocyte- and erythroid cell-derived co-expression modules to longitudinal blood transcriptome samples from cohort 3 confirmed an association of early temporal changes of these features with fatal COVID-19 disease outcome. In sum, our longitudinal multi-omics study demonstrates distinct cellular and gene expression dynamics upon SARS-CoV-2 infection, which point to metabolic shifts of circulating immune cells, and reveals changes in megakaryocytes and increased erythropoiesis as important outcome indicators in severe COVID-19 patients.

Autoantibody-negative insulin-dependent Diabetes after COVID-19 (preprint)

Here we report the manifestation of insulin dependent diabetes after a COVID-19 infection in the absence of typical autoantibodies for type 1 diabetes. A 19-year-old Caucasian male subject presented to our emergency department with diabetic ketoacidosis (DKA). C-peptide levels accounted to 0.62µg/L in the presence of blood glucose concentrations of 30.6 mmol/L (552 mg/dL). The patient´s case history revealed a COVID-19 disease 6-8 weeks prior to admission. This is of interest, since COVID-19 internalization into host cells is mediated via Angiotensin-converting enzyme 2 (ACE2) [1], a transmembrane glycoprotein which amongst others is crucial for β-cell homeostasis and function [2,3,4]. Detailed laboratory testing was performed, revealing no serum-antibodies against islet-cells (ICA), glutamic acid decarboxylase (GAD65-AA), tyrosine phosphatase (IA-2-AA), insulin (IAA) and zinc-transport-8 (ZnT8-AA), but against COVID-19. Hence, this is a presentation of an insulin-dependent diabetes mellitus in the absence of markers of autoimmunity, which might suggest direct cytolytic effects of COVID-19 on pancreatic β-cells presumably mediated via ACE2.