P selectin promotes SARS-CoV-2 interactions with platelets and the endothelium
Bibliographic record
Abstract
The physiology of SARS-CoV-2 virus/host interactions is not well understood. To better understand host/virus interactions, we performed a CRISPR activation screen to identify host genes that confer resistance to authentic SARS-CoV-2. This highlighted 34 new candidate genes that may alter the course of infection. We validated that 7 of these genes can suppress authentic SARS-CoV-2 infection, including the innate immune receptor P selectin, which increases SARS-CoV-2 spike-dependent binding to cells, while protecting from infection. P selectin also promotes binding to SARS-CoV-2 variants, SARS-CoV-1, and Middle East respiratory syndrome spike proteins, suggesting a general role for P selectin in highly pathogenic coronavirus infections. Importantly, P selectin protein expression driven by synthetic mRNA can block SARS-CoV-2 infection. Naturally, P selectin is expressed on platelets, and we show that it promotes spike-mediated platelet aggregation. P selectin is also expressed on the endothelium, where SARS-CoV-2 spike interactions are also P selectin dependent. In vivo, SARS-CoV-2 uses P selectin to home to capillary beds where the virus interacts with platelets and endothelium, and blocking this interaction can clear vascular-associated pulmonary SARS-CoV-2.
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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) | 0.001 | 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".