Vimentin autoantibodies induce platelet activation and formation of platelet-leukocyte conjugates via platelet-activating factor
Bibliographic record
Abstract
Anti-vimentin antibodies (AVA) are associated with autoimmunity and solid organ transplantation, conditions associated with vascular disease, but their contribution to disease pathogenesis is unknown. Here, we have examined interactions between AVA (mAb and serum from patients) and various leukocyte populations using whole blood and flow cytometry. Normal blood treated with patient sera containing high AVA-IgM titers or with a vimentin-specific monoclonal IgM led to activation of platelets and other leukocytes, as demonstrated by induced expression of P-selectin, fibrinogen, tissue factor, and formation of platelet:leukocyte (P:L) conjugates and a reduction in platelet counts. This activity was antigen (vimentin)-specific and was not mediated by irrelevant IgM antibodies. Flow cytometry demonstrated that AVA do not bind directly to resting platelets in whole blood, but they bind to approximately 10% of leukocytes. Supernatant, derived from AVA-treated leukocytes, induced platelet activation, as measured by the generation of platelet microparticles, when added to platelet-rich plasma. When AVA were added to whole blood in the presence of CV-6209, a platelet-activating factor (PAF) receptor inhibitor, platelet depletion was inhibited. This suggests that PAF is one of the mediators released from AVA-activated leukocytes that leads to P:L conjugation formation and platelet activation. In summary, AVA bind to leukocytes, resulting in release of a PAF and prothrombotic factor that exert a paracrine-activating effect on platelets. Overall, this proposed mechanism may explain the pathogenesis of thrombotic events in autoimmune diseases associated with AVA.
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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".