Smoking and SARS-CoV-2 Impair Dendritic Cells and Regulate DC-SIGN Expression in Tissues
Bibliographic record
Abstract
Summary The current spreading novel coronavirus SARS-CoV-2 is highly infectious and pathogenic. In this study, we screened the gene expression of three SARS-CoV-2 host receptors (ACE2, DC-SIGN and L-SIGN) and DC status in bulk and single cell transcriptomic datasets of upper airway, lung or blood of smokers, non-smokers and COVID-19 patients. We found smoking increased DC-SIGN gene expression and inhibited DC maturation and its ability of T cell stimulation. In COVID-19, DC-SIGN gene expression was interestingly decreased in lung DCs but increased in blood DCs. Strikingly, DCs shifted from cDCs to pDCs in COVID-19, but the shift was trapped in an immature stage (CD22+ or ANXA1+ DC) with MHCII downregulation in severe cases. This observation indicates that DCs in severe cases stimulate innate immune responses but fail to specifically recognize SARS-CoV-2. Our study provides insights into smoking effect on COVID-19 risk and the profound modulation of DC function in severe COVID-19. Graphical Abstract Highlights Smoking upregulates the expression of ACE2 and CD209 and inhibits DC maturation in lungs. SARS-CoV-2 modulates the DCs proportion and CD209 expression differently in lung and blood. Severe infection is characterized by DCs less capable of maturation, antigen presentation and MHCII expression. DCs shift from cDCs to pDCs with SARS-CoV-2 infection but are trapped in an immature stage in severe cases.
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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 | 0.000 |
| 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) | 0.002 | 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".