P-Selectin promotes SARS-CoV-2 interactions with platelets and the endothelium
Bibliographic record
Abstract
Abstract COVID-19 causes a clinical spectrum of acute and chronic illness and host / virus interactions are not completely understood 1,2 . To identify host factors that can influence SARS-CoV-2 infection, we screened the human genome for genes that, when upregulated, alter the outcome of authentic SARS-CoV-2 infection. From this, we identify 34 new genes that can alter the course of infection, including the innate immune receptor P-selectin, which we show is a novel SARS-CoV-2 spike receptor. At the cellular level expression of P-selectin does not confer tropism for SARS-CoV-2, instead it acts to suppress infection. More broadly, P-selectin can also promote binding to SARS-CoV-2 variants, SARS-CoV-1 and MERS, acting as a general spike receptor for highly pathogenic coronaviruses. P-selectin is expressed on platelets and endothelium 3 , and we confirm SARS-CoV-2 spike interactions with these cells are P-selectin-dependent and can occur under shear flow conditions. In vivo , authentic SARS-CoV-2 uses P-selectin to home to airway capillary beds where the virus interacts with the endothelium and platelets, and blocking this interaction can clear vascular-associated SARS-CoV-2 from the lung. Together we show for the first time that coronaviruses can use the leukocyte recruitment system to control tissue localization, and this fundamental insight may help us understand and control highly pathogenic coronavirus disease progression.
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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".