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Record W3018257933 · doi:10.1164/rccm.202003-0693le

Tobacco Smoking Increases the Lung Gene Expression of ACE2, the Receptor of SARS-CoV-2

2020· letter· en· W3018257933 on OpenAlexaff
Guoshuai Cai, Yohan Bossé, Feifei Xiao, Farrah Kheradmand, Christopher I. Amos

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2020
Typeletter
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsUniversité Laval
FundersNational Cancer InstituteNational Institutes of HealthCancer Prevention and Research Institute of TexasNational Institute of Allergy and Infectious DiseasesNational Institute of Environmental Health SciencesU.S. Department of Veterans Affairs
KeywordsTMPRSS2FurinMedicineCoronavirusLungVirologyTranscriptomeOutbreakGenePandemicPneumoniaImmunologyDiseaseGene expressionBiologyCoronavirus disease 2019 (COVID-19)GeneticsInternal medicineEnzyme

Abstract

fetched live from OpenAlex

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]),

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.275
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.052
GPT teacher head0.371
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations344
Published2020
Admission routes1
Has abstractyes

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