Severity of SARS-CoV-2 infection is associated with high numbers of alveolar mast cells and their degranulation.

Background: The systemic inflammatory response post-SARS-CoV-2 infection increases pro-inflammatory cytokine production, multi-organ damage, and mortality rates. Mast cells (MC) modulate thrombo-inflammatory disease progression (e.g., deep vein thrombosis) and the inflammatory response post-infection. Objective: To enhance our understanding of the contribution of MC and their proteases in SARS-CoV-2 infection and the pathogenesis of the disease, which might help to identify novel therapeutic targets. Methods: MC proteases chymase (CMA1), carboxypeptidase A3 (CPA3), and tryptase beta 2 (TPSB2), as well as cytokine levels, were measured in the serum of 60 patients with SARS-CoV-2 infection (30 moderate and 30 severe; severity of the disease assessed by chest CT) and 17 healthy controls by ELISA. MC number and degranulation were quantified by immunofluorescent staining for tryptase in lung autopsies of patients deceased from either SARS-CoV-2 infection or unrelated reasons (control). Immortalized human FcεR1+c-Kit+ LUVA MC were infected with SARS-CoV-2, or treated with its viral proteins, to assess direct MC activation by flow cytometry. Results: The levels of all three proteases were increased in the serum of patients with COVID-19, and strongly correlated with clinical severity. The density of degranulated MC in COVID-19 lung autopsies was increased compared to control lungs. The total number of released granules and the number of granules per each MC were elevated and positively correlated with von Willebrand factor levels in the lung. SARS-CoV-2 or its viral proteins spike and nucleocapsid did not induce activation or degranulation of LUVA MC in vitro. Conclusion: In this study, we demonstrate that SARS-CoV-2 is strongly associated with activation of MC, which likely occurs indirectly, driven by the inflammatory response. The results suggest that plasma MC protease levels could predict the disease course, and that severe COVID-19 patients might benefit from including MC-stabilizing drugs in the treatment scheme.

16S rRNA gene sequencing data of the upper respiratory tract microbiome in the SARS-CoV-2 infected patients.

The SARS-CoV-2 pandemic is a big challenge for humanity. The COVID-19 severity differs significantly from patient to patient, and it is important to study the factors protecting from severe forms of the disease. Respiratory microbiota may influence the patient's susceptibility to infection and disease severity due to its ability to modulate the immune system response of the host organism. This data article describes the microbiome dataset from the upper respiratory tract of SARS-CoV-2 positive patients from Russia. This dataset reports the microbial community profile of 335 human nasopharyngeal swabs collected between 2020-05 and 2021-03 during the first and the second epidemic waves. Samples were collected from both inpatients and outpatients in 4 cities of the Russian Federation (Moscow, Kazan, Irkutsk, Nizhny Novgorod) and sequenced using the 16S rRNA gene amplicon sequencing of V3-V4 region. Data contains information about the patient such as age, sex, hospitalization status, percent of damaged lung tissue, oxygen saturation (SpO2), respiratory rate, need for supplemental oxygen, chest computer tomography severity score, SARS-CoV-2 lineage, and also information about smoking and comorbidities. The amplicon sequencing data were deposited at NCBI SRA as BioProject PRJNA751478.

Artificial Intelligence Predicts Severity of COVID-19 Based on Correlation of Exaggerated Monocyte Activation, Excessive Organ Damage and Hyperinflammatory Syndrome: A Prospective Clinical Study.

Background: Prediction of the severity of COVID-19 at its onset is important for providing adequate and timely management to reduce mortality. Objective: To study the prognostic value of damage parameters and cytokines as predictors of severity of COVID-19 using an extensive immunologic profiling and unbiased artificial intelligence methods. Methods: Sixty hospitalized COVID-19 patients (30 moderate and 30 severe) and 17 healthy controls were included in the study. The damage indicators high mobility group box 1 (HMGB1), lactate dehydrogenase (LDH), aspartate aminotransferase (AST), alanine aminotransferase (ALT), extensive biochemical analyses, a panel of 47 cytokines and chemokines were analyzed at weeks 1, 2 and 7 along with clinical complaints and CT scans of the lungs. Unbiased artificial intelligence (AI) methods (logistic regression and Support Vector Machine and Random Forest algorithms) were applied to investigate the contribution of each parameter to prediction of the severity of the disease. Results: On admission, the severely ill patients had significantly higher levels of LDH, IL-6, monokine induced by gamma interferon (MIG), D-dimer, fibrinogen, glucose than the patients with moderate disease. The levels of macrophage derived cytokine (MDC) were lower in severely ill patients. Based on artificial intelligence analysis, eight parameters (creatinine, glucose, monocyte number, fibrinogen, MDC, MIG, C-reactive protein (CRP) and IL-6 have been identified that could predict with an accuracy of 83-87% whether the patient will develop severe disease. Conclusion: This study identifies the prognostic factors and provides a methodology for making prediction for COVID-19 patients based on widely accepted biomarkers that can be measured in most conventional clinical laboratories worldwide.