Tobacco Smoking Increases the Lung Gene Expression of ACE2, the Receptor of SARS-CoV-2
Bibliographic record
Abstract
declared the coronavirus disease (COVID-19) outbreak a pandemic.As of May 28, 2020, laboratories had confirmed 5,701,337 COVID-19 cases, and 357,668 deaths had been reported in 216 countries, areas, or territories (1).COVID-19 is caused by a new type of pathogenic coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is phylogenetically similar to SARS-CoV, with approximately 80% identity between the genomes (2).SARS viruses affect the respiratory tract and cause an acute respiratory response through the same cell-entry receptor, ACE2 (angiotensin-converting enzyme 2), which is the only experimentally confirmed SARS-CoV-2 receptor.SARS-CoV-2 infection also uses activation of the spike proteins found on the surface of the virus for cellular entry.The best candidates for priming spike proteins are two host cell enzymes called Furin and TMPRSS2 (2).In the current severe global emergency, to enable effective prevention and care, it is imperative to identify potential risk factors, such as cigarette smoking, which is a substantial risk factor for various important bacterial and viral infections.Some of the results of this study were previously published in preprint form (medRxiv, https://www.medrxiv.org/content/10.1101/2020.02.05.20020107v3). MethodsWe evaluated a comprehensive set of transcriptomic data sets to investigate the associations of smoking with ACE2, FURIN, and TMPRSS2 gene expression in lung tissues.Two data sets were generated using normal lung tissues from patients with lung adenocarcinoma: a Caucasian RNA-sequencing (RNA-seq) data set from The Cancer Genome Atlas (n = 48) (3) and an Asian RNA-seq data set from the Gene Expression Omnibus (GSE40419, n = 74) (4).We included three polyethnic microarray data sets of gene expression in healthy small airway epithelium samples (GSE63127, n = 230 [5]; GSE19667, n = 116 [6]; and GSE5058, n = 24 [7]) and large airway epithelium samples (GSE7895, n = 104 [8]).In addition, we analyzed three microarray data sets of samples derived from healthy subjects and patients with chronic obstructive pulmonary disease (COPD), including small airway epithelium samples from current smokers (from GSE5058, n = 26 [7]),
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".