Inhaled corticosteroids downregulate SARS-CoV-2-related gene expression in COPD: results from a RCT
Bibliographic record
Abstract
Abstract Rationale Chronic obstructive pulmonary disease (COPD) is a risk factor for severe COVID-19. Inhaled corticosteroids (ICS) are commonly prescribed for the prevention of acute exacerbations in people with COPD, but their use is associated with increased risk of respiratory infections. The effects of ICS on SARS-CoV-2 susceptibility or COVID-19 severity are currently unknown. Objectives To determine the effects of ICS treatment on the bronchial epithelial cell expression of key SARS-CoV-2-related genes in volunteers with COPD. Methods We performed a randomized, open-label, parallel treatment trial of 12 weeks treatment with ICS in combination with long-acting beta-agonist (formoterol/budesonide 12/400 µg twice daily or salmeterol/fluticasone propionate 25/250 µg twice daily), or treatment with LABA only (formoterol 12 µg twice daily), in volunteers with mild to very severe COPD. We obtained bronchial epithelial cell samples via bronchoscopy before and after treatment, and determined transcriptome-wide gene expression by RNA sequencing. Main Results 63 volunteers were randomized to receive treatment. Compared to formoterol alone, formoterol/budesonide treatment decreased the expression of the SARS-CoV-2 receptor gene ACE2 and the host cell protease gene ADAM17 . These genes were highly co-expressed with innate immune response genes, particularly those of the type I interferon and anti-viral response pathways, which also tended to decrease following ICS treatment. Conclusions This is the first randomized controlled trial to show that ICS affect the expression of key SARS-CoV-2-related genes in COPD. Their relation to important anti-viral response genes may have critical implications for SARS-CoV-2 susceptibility or COVID-19 severity in this vulnerable population.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".