Mental health in Germany in the first weeks of the Russo-Ukrainian war.

BACKGROUND: In the connected world, although societies are not directly involved in a military conflict, they are exposed to media reports of violence. AIMS: We assessed the effects of such exposures on mental health in Germany during the military conflict in Ukraine. METHOD: We used the German population-based cohort for digital health research, DigiHero, launching a survey on the eighth day of the Russo-Ukrainian war. Of the 27 509 cohort participants from the general population, 19 444 (70.7%) responded within 17 days. We measured mental health and fear of the impact of war compared with other fears (natural disasters or health-related). RESULTS: In a subsample of 4441 participants assessed twice, anxiety in the population (measured by the Generalised Anxiety Disorder-7 screener) was higher in the first weeks of war than during the strongest COVID-19 restrictions. Anxiety was elevated across the whole age spectrum, and the mean was above the cut-off for mild anxiety. Over 95% of participants expressed various degrees of fear of the impact of war, whereas the percentage for other investigated fears was 0.47-0.82. A one-point difference in the fear of the impact of war was associated with a 2.5 point (95% CI 2.42-2.58) increase in anxiety (11.9% of the maximum anxiety score). For emotional distress, the increase was 0.67 points (0.66-0.68) (16.75% of the maximum score). CONCLUSIONS: The population in Germany reacted to the Russo-Ukrainian war with substantial distress, exceeding reactions during the strongest restrictions in the COVID-19 pandemic. Fear of the impact of war was associated with worse mental health.

Liquid biomarkers of macrophage dysregulation and circulating spike protein illustrate the biological heterogeneity in patients with post-acute sequelae of COVID-19.

Post-acute sequelae of COVID-19 (PASC) are long-term consequences of SARS-CoV-2 infection that can substantially impair quality of life. Underlying mechanisms ranging from persistent virus to innate and adaptive immune dysregulation have been discussed. Here, we profiled plasma of 181 individuals from the cohort study for digital health research in Germany (DigiHero) including individuals after mild to moderate COVID-19 with or without PASC and uninfected controls. We focused on soluble factors related to monocyte/macrophage biology and on circulating SARS-CoV-2 spike (S1) protein as potential biomarker for persistent viral reservoirs. At a median time of eight months after infection, we found pronounced dysregulation in almost all tested soluble factors including both pro-inflammatory and pro-fibrotic cytokines. These immunological perturbations were remarkably independent of ongoing PASC symptoms per se, but further correlation and regression analyses suggested PASC specific patterns involving CCL2/MCP-1 and IL-8 that either correlated with sCD162, sCD206/MMR, IFN-α2, IL-17A and IL-33, or IL-18 and IL-23. None of the analyzed factors correlated with the detectability or levels of circulating S1 indicating that this represents an independent subset of patients with PASC. This data confirms prior evidence of immune dysregulation and persistence of viral protein in PASC and illustrates its biological heterogeneity that still awaits correlation with clinically defined PASC subtypes. This article is protected by copyright. All rights reserved.

Rapid Hypermutation B Cell Trajectory Recruits Previously Primed B Cells Upon Third SARS-Cov-2 mRNA Vaccination.

The COVID-19 pandemic shows that vaccination strategies building on an ancestral viral strain need to be optimized for the control of potentially emerging viral variants. Therefore, aiming at strong B cell somatic hypermutation to increase antibody affinity to the ancestral strain - not only at high antibody titers - is a priority when utilizing vaccines that are not targeted at individual variants since high affinity may offer some flexibility to compensate for strain-individual mutations. Here, we developed a next-generation sequencing based SARS-CoV-2 B cell tracking protocol to rapidly determine the level of immunoglobulin somatic hypermutation at distinct points during the immunization period. The percentage of somatically hypermutated B cells in the SARS-CoV-2 specific repertoire was low after the primary vaccination series, evolved further over months and increased steeply after boosting. The third vaccination mobilized not only naïve, but also antigen-experienced B cell clones into further rapid somatic hypermutation trajectories indicating increased affinity. Together, the strongly mutated post-booster repertoires and antibodies deriving from this may explain why the third, but not the primary vaccination series, offers some protection against immune-escape variants such as Omicron B.1.1.529.

Landscape of T-cell repertoires with public COVID-19-associated T-cell receptors in pre-pandemic risk cohorts.

OBJECTIVES: T cells have an essential role in the antiviral defence. Public T-cell receptor (TCR) clonotypes are expanded in a substantial proportion of COVID-19 patients. We set out to exploit their potential use as read-out for COVID-19 T-cell immune responses. METHODS: We searched for COVID-19-associated T-cell clones with public TCRs, as defined by identical complementarity-determining region 3 (CDR3) beta chain amino acid sequence that can be reproducibly detected in the blood of COVID-19 patients. Of the different clonotype identification algorithms used in this study, deep sequencing of brain tissue of five patients with fatal COVID-19 delivered 68 TCR clonotypes with superior representation across 140 immune repertoires of unrelated COVID-19 patients. RESULTS: Mining of immune repertoires from subjects not previously exposed to the virus showed that these clonotypes can be found in almost 20% of pre-pandemic immune repertoires of healthy subjects, with lower representation in repertoires from risk groups like individuals above the age of 60 years or patients with cancer. CONCLUSION: Together, our data show that at least a proportion of the SARS-CoV-2 T-cell response is mediated by public TCRs that are present in repertoires of unexposed individuals. The lower representation of these clones in repertoires of risk groups or failure to expand such clones may contribute to more unfavorable clinical COVID-19 courses.

Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort.

BACKGROUND: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. METHODS: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. RESULTS: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict "survival". Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients' age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. CONCLUSIONS: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration "ClinicalTrials" (clinicaltrials.gov) under NCT04455451.

SARS-CoV-2-specific antibody rearrangements in prepandemic immune repertoires of risk cohorts and patients with COVID-19.

A considerable fraction of B cells recognize severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with germline-encoded elements of their B cell receptor, resulting in the production of neutralizing and nonneutralizing antibodies. We found that antibody sequences from different discovery cohorts shared biochemical properties and could be retrieved across validation cohorts, confirming the stereotyped character of this naive response in coronavirus disease 2019 (COVID-19). While neutralizing antibody sequences were found independently of disease severity, in line with serological data, individual nonneutralizing antibody sequences were associated with fatal clinical courses, suggesting detrimental effects of these antibodies. We mined 200 immune repertoires from healthy individuals and 500 repertoires from patients with blood or solid cancers - all acquired prior to the pandemic - for SARS-CoV-2 antibody sequences. While the largely unmutated B cell rearrangements occurred in a substantial fraction of immune repertoires from young and healthy individuals, these sequences were less likely to be found in individuals over 60 years of age and in those with cancer. This reflects B cell repertoire restriction in aging and cancer, and may to a certain extent explain the different clinical courses of COVID-19 observed in these risk groups. Future studies will have to address if this stereotyped B cell response to SARS-CoV-2 emerging from unmutated antibody rearrangements will create long-lived memory.

Next-Generation Sequencing of T and B Cell Receptor Repertoires from COVID-19 Patients Showed Signatures Associated with Severity of Disease.

We profiled adaptive immunity in COVID-19 patients with active infection or after recovery and created a repository of currently >14 million B and T cell receptor (BCR and TCR) sequences from the blood of these patients. The B cell response showed converging IGHV3-driven BCR clusters closely associated with SARS-CoV-2 antibodies. Clonality and skewing of TCR repertoires were associated with interferon type I and III responses, early CD4+ and CD8+ T cell activation, and counterregulation by the co-receptors BTLA, Tim-3, PD-1, TIGIT, and CD73. Tfh, Th17-like, and nonconventional (but not classical antiviral) Th1 cell polarizations were induced. SARS-CoV-2-specific T cell responses were driven by TCR clusters shared between patients with a characteristic trajectory of clonotypes and traceability over the disease course. Our data provide fundamental insight into adaptive immunity to SARS-CoV-2 with the actively updated repository providing a resource for the scientific community urgently needed to inform therapeutic concepts and vaccine development.