Background: Morbidity and mortality from COVID-19 caused by novel coronavirus SARS-CoV-2 is accelerating worldwide and novel clinical presentations of COVID-19 are often reported. The range of human cells and tissues targeted by SARS-CoV-2, its potential receptors and associated regulating factors are still largely unknown. The aim of our study was to analyze the expression of known and potential SARS-CoV-2 receptors and related molecules in the extensive collection of primary human cells and tissues from healthy subjects of different age and from patients with risk factors and known comorbidities of COVID-19. Methods: We performed RNA sequencing and explored available RNA-Seq databases to study gene expression and co-expression of ACE2, CD147 ( BSG ), CD26 ( DPP4 ) and their direct and indirect molecular partners in primary human bronchial epithelial cells, bronchial and skin biopsies, bronchoalveolar lavage fluid, whole blood, peripheral blood mononuclear cells (PBMCs), monocytes, neutrophils, DCs, NK cells, ILC1, ILC2, ILC3, CD4 ⁺ and CD8 ⁺ T cells, B cells and plasmablasts. We analyzed the material from healthy children and adults, and from adults in relation to their disease or COVID-19 risk factor status. Results: ACE2 and TMPRSS2 were coexpressed at the epithelial sites of the lung and skin, whereas CD147 ( BSG ), cyclophilins ( PPIA and PPIB ), CD26 ( DPP4 ) and related molecules were expressed in both, epithelium and in immune cells. We also observed a distinct age-related expression profile of these genes in the PBMCs and T cells from healthy children and adults. Asthma, COPD, hypertension, smoking, obesity, and male gender status generally led to the higher expression of ACE2- and CD147-related genes in the bronchial biopsy, BAL or blood. Additionally, CD147-related genes correlated positively with age and BMI. Interestingly, we also observed higher expression of ACE2- and CD147-related genes in the lesional skin of patients with atopic dermatitis. Conclusions: Our data suggest different receptor repertoire potentially involved in the SARS-CoV-2 infection at the epithelial barriers and in the immune cells. Altered expression of these receptors related with age, gender, obesity and smoking, as well as with the disease status might contribute to COVID-19 morbidity and severity patterns.

Introduction In the current COVID-19 pandemic, clinicians require a manageable set of decisive parameters that can be used to (i) rapidly identify SARS-CoV-2 positive patients, (ii) identify patients with a high risk of a fatal outcome on hospital admission, and (iii) recognize longitudinal warning signs of a possible fatal outcome. Methods This comparative study was performed in 515 patients in the Maria Skłodowska-Curie Specialty Voivodeship Hospital in Zgierz, Poland. The study groups comprised 314 patients with COVID-like symptoms who tested negative and 201 patients who tested positive for SARS-CoV-2 infection; of the latter, 72 patients with COVID-19 died and 129 were released from hospital. Data on which we trained several machine learning (ML) models included clinical findings on admission and during hospitalization, symptoms, epidemiological risk, and reported comorbidities and medications. Results We identified a set of eight on-admission parameters: white blood cells, antibody-synthesizing lymphocytes, ratios of basophils/lymphocytes, platelets/neutrophils, and monocytes/lymphocytes, procalcitonin, creatinine, and C-reactive protein. The medical decision tree built using these parameters differentiated between SARS-CoV-2 positive and negative patients with up to 90–100% accuracy. Patients with COVID-19 who on hospital admission were older, had higher procalcitonin, C-reactive protein, and troponin I levels together with lower hemoglobin and platelets/neutrophils ratio were found to be at highest risk of death from COVID-19. Furthermore, we identified longitudinal patterns in C-reactive protein, white blood cells, and D dimer that predicted the disease outcome. Conclusions Our study provides sets of easily obtainable parameters that allow one to assess the status of a patient with SARS-CoV-2 infection, and the risk of a fatal disease outcome on hospital admission and during the course of the disease.

The sequencing of SARS-CoV-2 RNA in wastewater is an unbiased method to detect the spread of emerging variants and to track regional infection dynamics, which is especially useful in case of limited testing and clinical sequencing. To test how major international events influence the spread of new variants we have sequenced SARS-CoV-2 RNA in the wastewater samples of Davos, Landquart, Lostallo, and St. Moritz in the Swiss canton of Grisons in the time around the international sports competitions in Davos and St. Moritz in December 2021, and additionally in May 2022 and January 2023 in Davos and St. Moritz during the World Economic Forum (WEF) in Davos. The prevalence of the variants identified from the wastewater sequencing data showed that the Omicron variant BA.1 had spread in Davos and St. Moritz during the international sporting events hosted there in December 2021. This spread was associated with an increase in case numbers, while it was not observed in Landquart and Lostallo. Another instance of new variant spread occurred during the WEF in January 2023, when the Omicron variant BA.2.75 arrived in Davos but not in St. Moritz. We can therefore conclude that major international events promote the spread of new variants in the respective host region, which has important implications for the protective measures that should be taken.

Rhinoviruses (RV) and inhaled allergens, such as house dust mite (HDM) are the major agents responsible for asthma onset, exacerbations and progression to the severe disease, but the mechanisms of these pathogenic reciprocal virus-allergen interactions are not well understood. To address this, we analyzed mechanisms of airway epithelial sensing and response to RV infection using controlled experimental in vivo RV infection in healthy controls and patients with asthma and in vitro models of HDM exposure and RV infection in primary airway epithelial cells. We found that intranasal RV infection in patients with asthma led to the highly augmented inflammasome-mediated lower airway inflammation detected in bronchial brushes, biopsies and bronchoalveolar lavage fluid. Mechanistically, RV infection in bronchial airway epithelium led to retinoic acid-inducible gene I (RIG-I), but not via NLR family pyrin domain containing 3 (NLRP3) inflammasome activation, which was highly augmented in patients with asthma, especially upon pre-exposure to HDM. This excessive activation of RIG-I inflammasomes was responsible for the impairment of antiviral type I/III interferons (IFN), prolonged viral clearance and unresolved inflammation in asthma in vivo and in vitro. Pre-exposure to HDM amplifies RV-induced epithelial injury in patients with asthma via enhancement of pro-IL1 β expression and release, additional inhibition of type I/III IFNs and activation of auxiliary proinflammatory and pro-remodeling proteins. Finally, in order to determine whether RV-induced activation of RIG-I inflammasome may play a role in the susceptibility to severe acute respiratory syndrome coronavirus (SARS-CoV-2) infection in asthma, we analyzed the effects of HDM exposure and RV/SARS-CoV-2 coinfection. We found that prior infection with RV restricted SARS-CoV-2 replication, but co-infection augmented RIG-I inflammasome activation and epithelial inflammation in patients with asthma, especially in the presence of HDM. Timely inhibition of epithelial RIG-I inflammasome activation may lead to more efficient viral clearance and lower the burden of RV and SARS-CoV-2 infections.

Motivation: High-dimensional Bulk RNA sequencing (RNAseq) datasets pose a considerable challenge in identifying biologically relevant features for downstream analyses and data mining efforts. The standard approach involves differential gene expression (DGE) analysis, but its effectiveness can be limited depending on the data due to its univariate nature. In complex datasets, an alternative approach involves employing a variety of machine learning (ML) tools, which attempt to understand non-linear relationships between features and focus on generalizability rather than statistical significance. This approach will result in the generation of multiple feature lists, which might exhibit similarities in terms of classification performance metrics. Therefore, there is an urgent need for a cohesive workflow that seamlessly integrates robust feature selection using diverse ML methods while also evaluating the biological relevance of the resulting feature lists. This combined approach would enable the prioritization of the best-performing list, considering both sets of criteria. Results: We introduce GeneSelectR, an open-source R package that innovatively combines ML and bioinformatic data mining approaches for enhanced feature selection. With GeneSelectR, features can be selected from a normalized RNAseq dataset with a variety of ML methods and user-defined parameters. This is followed by an assessment of their biological relevance with Gene Ontology (GO) enrichment analysis, along with a semantic similarity analysis of the resulting GO terms. Additionally, similarity coefficients and fractions of the GO terms of interest are calculated. With this, GeneSelectR optimizes ML performance and rigorously assesses the biological relevance of the various lists, offering a means to prioritize feature lists with regard to the biological question. When applied to the TCGA-BRCA dataset, the GeneSelectR workflow generated several feature lists using different ML methods and a DGE analysis. By leveraging the various functions in GeneSelectR, the different lists could be evaluated based on both ML performance and biological relevance. This comprehensive evaluation facilitated the selection of the best-performing list, which exhibited both strong machine learning performance and high relevance to the biological question while maintaining a manageable number of highly specific features. Availability: The package is available on CRAN. To install it, run: install.packages(‘GeneSelectR’) Contact: dzhakparov@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.