ACE-2 Expression in the Small Airway Epithelia of Smokers and COPD Patients: Implications for COVID-19
Bibliographic record
Abstract
Abstract Introduction Coronavirus disease 2019 (COVID-19) is a respiratory infection caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). This virus uses the angiotensin converting enzyme II (ACE-2) as the cellular entry receptor to infect the lower respiratory tract. Because individuals with chronic obstructive pulmonary disease (COPD) are at increased risk of severe COVID-19, we determined whether ACE-2 expression in the lower airways was related to COPD and cigarette smoking. Methods Using RNA-seq, we determined gene expression levels in bronchial epithelia obtained from cytologic brushings of 6 th to 8 th generation airways in individuals with and without COPD. We eternally validated these results from two additional independent cohorts, which used microarray technologies to measure gene expression levels from 6 th to 12 th generation airways. Results In the discovery cohort (n=42 participants), we found that ACE-2 expression levels were increased by 48% in the airways of COPD compared with non-COPD subjects (COPD=2.52±0.66 log2 counts per million reads (CPM) versus non-COPD= 1.70±0.51 CPM, p=7.62×10 −4 ). There was a significant inverse relationship between ACE-2 gene expression and FEV1% of predicted (r=-0.24; p=0.035). Current smoking also significantly increased ACE-2 expression levels compared with never smokers (never current smokers=2.77±0.91 CPM versus smokers=1.78±0.39 CPM, p=0.024). These findings were replicated in the two eternal cohorts. Conclusions ACE-2 expression in lower airways is increased in patients with COPD and with current smoking. These data suggest that these two subgroups are at increased risk of serious COVID-19 infection and highlight the importance of smoking cessation in reducing the risk.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".