Gene expression and in situ protein profiling of candidate SARS-CoV-2 receptors in human airway epithelial cells and lung tissue
Bibliographic record
Abstract
In December 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)emerged, causing the coronavirus disease 2019 (COVID-19) pandemic. SARS-CoV, the agent responsible for the 2003 SARS outbreak, utilises angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) host molecules for viral entry. ACE2 and TMPRSS2 have recently been implicated in SARS-CoV-2 viral infection. Additional host molecules including ADAM17, cathepsin L, CD147 and GRP78 may also function as receptors for SARS-CoV-2.To determine the expression and in situ localisation of candidate SARS-CoV-2 receptors in the respiratory mucosa, we analysed gene expression datasets from airway epithelial cells of 515 healthy subjects, gene promoter activity analysis using the FANTOM5 dataset containing 120 distinct sample types, single cell RNA sequencing (scRNAseq) of 10 healthy subjects, proteomic datasets, immunoblots on multiple airway epithelial cell types, and immunohistochemistry on 98 human lung samples.We demonstrate absent to lowACE2promoter activity in a variety of lung epithelial cell samples andlowACE2gene expression in both microarray and scRNAseq datasets of epithelial cell populations.Consistent with gene expression, rare ACE2 protein expression was observed in the airway epithelium and alveoli of human lung, confirmed with proteomics. We present confirmatory evidence for the presence ofTMPRSS2, CD147 and GRP78 protein in vitro in airway epithelial cells and confirm broad in situ protein expression of CD147 and GRP78 in the respiratory mucosa. Collectively, our data suggest the presence of a mechanism dynamically regulating ACE2 expression inhuman lung, perhaps in periods of SARS-CoV-2 infection, and also suggest that alternative receptors forSARS-CoV-2 exist to facilitate initial host cell infection.
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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 | 0.000 | 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) | 0.000 | 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".