Tissue factor and glycoprotein C on herpes simplex virus type 1 are protease-activated receptor 2 cofactors that enhance infection
Bibliographic record
Abstract
The coagulation system provides physiologic host defense, but it can also be exploited by pathogens for infection. On the HSV1 surface, host-cell-derived tissue factor (TF) and virus-encoded glycoprotein C (gC) can stimulate protease activated receptor 1 (PAR1)-enhanced infection by triggering thrombin production. Using novel engineered HSV1 variants deficient in either TF and/or gC, in the present study, we show that activated coagulation factors X (FXa) or VII (FVIIa) directly affect HSV1 infection of human umbilical vein endothelial cells in a manner that is dependent on viral TF and gC. The combination of FXa and FVIIa maximally enhanced infection for TF(+)/gC(+) HSV1 and receptor desensitization and Ab inhibition demonstrated that both proteases act on PAR2. Inhibitory TF Abs showed that the required TF source was viral. Individually, TF or gC partly enhanced the effect of FXa, but not FVIIa, revealing gC as a novel PAR2 cofactor for FVIIa. In sharp contrast, thrombin enhanced infection via PAR1 independently of viral TF and gC. Thrombin combined with FXa/FVIIa enhanced infection, suggesting that PAR1 and PAR2 are independently involved in virus propagation. These results show that HSV1 surface cofactors promote cellular PAR2-mediated infection, indicating a novel mode by which pathogens exploit the initiation phase of the host hemostatic system.
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 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".