Asthma Associated Cytokines Regulate the Expression of SARS-CoV-2 Receptor ACE2 in the Lung Tissue of Asthmatic Patients
Bibliographic record
Abstract
It is still controversial whether chronic lung inflammation increases the risk for COVID-19. One of the risk factors for acquiring COVID-19 is the level of expression of SARS-CoV-2 entry receptors, ACE2 and TMPRSS2, in lung tissue. It is, however, not clear how lung tissue inflammation affects expression levels of these receptors. We hence aimed to determine the level of SARS-CoV-2 receptors in lung tissue of asthmatic relative to age, gender, and asthma severity, and to investigate the factors regulating that. Therefore, gene expression data sets of well-known asthmatic cohorts (SARP and U-BIOPRED) were used to evaluate the association of ACE2 and TMPRSS2 with age, gender of the asthmatic patients, and also the type of the underlying lung tissue inflammatory cytokines. Notably, ACE2 and to less extent TMPRSS2 expression were upregulated in the lung tissue of asthmatics compared to healthy controls. Although a differential expression of ACE2, but not TMPRSS2 was observed relative to age within the moderate and severe asthma groups, our data suggest that age may not be a key regulatory factor of its expression. The type of tissue inflammation, however, associated significantly with ACE2 and TMPRSS2 expression levels following adjusting with age, gender and oral corticosteroids use of the patient. Type I cytokine (IFN-γ), IL-8, and IL-19 were associated with increased expression, while Type II cytokines (IL-4 and IL-13) with lower expression of ACE2 in lung tissue (airway epithelium and/or lung biopsies) of moderate and severe asthmatic patients. Of note, IL-19 was associated with ACE2 expression while IL-17 was associated with TMPRSS2 expression in sputum of asthmatic subjects. In vitro treatment of bronchial fibroblasts with IL-17 and IL-19 cytokines confirmed the regulatory effect of these cytokines on SARS-CoV-2 entry receptors. Our results suggest that the type of inflammation may regulate ACE2 and TMPRSS2 expression in the lung tissue of asthmatics and may hence affect susceptibility to SARS-CoV-2 infection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".