Determinants of SARS-CoV-2 receptor gene expression in upper and lower airways.

The recent outbreak of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), has led to a worldwide pandemic. One week after initial symptoms develop, a subset of patients progresses to severe disease, with high mortality and limited treatment options. To design novel interventions aimed at preventing spread of the virus and reducing progression to severe disease, detailed knowledge of the cell types and regulating factors driving cellular entry is urgently needed. Here we assess the expression patterns in genes required for COVID-19 entry into cells and replication, and their regulation by genetic, epigenetic and environmental factors, throughout the respiratory tract using samples collected from the upper (nasal) and lower airways (bronchi). Matched samples from the upper and lower airways show a clear increased expression of these genes in the nose compared to the bronchi and parenchyma. Cellular deconvolution indicates a clear association of these genes with the proportion of secretory epithelial cells. Smoking status was found to increase the majority of COVID-19 related genes including ACE2 and TMPRSS2 but only in the lower airways, which was associated with a significant increase in the predicted proportion of goblet cells in bronchial samples of current smokers. Both acute and second hand smoke were found to increase ACE2 expression in the bronchus. Inhaled corticosteroids decrease ACE2 expression in the lower airways. No significant effect of genetics on ACE2 expression was observed, but a strong association of DNA- methylation with ACE2 and TMPRSS2- mRNA expression was identified in the bronchus.

Is the humoral immunity dispensable for the pathogenesis of psoriasis?

BACKGROUND: Imbalances of T-cell subsets are hallmarks of disease-specific inflammation in psoriasis. However, the relevance of B cells for psoriasis remains poorly investigated. OBJECTIVE: To analyse the role of B cells and immunoglobulins for the disease-specific immunology of psoriasis. METHODS: We characterized B-cell subsets and immunoglobulin levels in untreated psoriasis patients (n = 37) and compared them to healthy controls (n = 20) as well as to psoriasis patients under disease-controlling systemic treatment (n = 28). B-cell subsets were analysed following the flow cytometric gating strategy based on the surface markers CD24, CD38 and CD138. Moreover, immunofluorescence stainings were used to detect IgA in psoriatic skin. RESULTS: We found significantly increased levels of IgA in the serum of treatment-naïve psoriasis patients correlating with disease score. However, IgA was only observed in dermal vessels of skin sections. Concerning B-cell subsets, we only found a moderately positive correlation of CD138+ plasma cells with IgA levels and disease score in treatment-naïve psoriasis patients. Confirming our hypothesis that psoriasis can develop in the absence of functional humoral immunity, we investigated a patient who suffered concomitantly from both psoriasis and a hereditary common variable immune defect (CVID) characterized by a lack of B cells and immunoglobulins. We detected variants in three of the 13 described genes of CVID and a so far undescribed variant in the ligand of the TNFRSF13B receptor leading to disturbed B-cell maturation and antibody production. However, this patient showed typical psoriasis regarding clinical presentation, histology or T-cell infiltrate. Finally, in a group of psoriasis patients under systemic treatment, neither did IgA levels drop nor did plasma cells correlate with IgA levels and disease score. CONCLUSION: B-cell alterations might rather be an epiphenomenal finding in psoriasis with a clear dominance of T cells over shifts in B-cell subsets.

How to extract marker genes from microarray data sets.

In this study we focus on classification tasks and apply matrix factorization techniques like principal component analysis (PCA), independent component analysis (ICA) and non-negative matrix factorization (NMF) to a microarray data set. The latter monitors the gene expression levels (GEL) of mononcytes and macrophages during and after differentiation. We show that these tools are able to identify relevant signatures in the deduced matrices and extract marker genes from these gene expression profiles (GEPs) without the need for extensive data bank search for appropriate functional annotations. With these marker genes corresponding test data sets can then easily be classified into related diagnostic categories.