Endothelial Induction of fgl2 Contributes to Thrombosis during Acute Vascular Xenograft Rejection
Bibliographic record
Abstract
Thrombosis is a prominent feature of acute vascular rejection (AVR), the current barrier to survival of pig-to-primate xenografts. Fibrinogen-like protein 2 (fgl2/fibroleukin) is an inducible prothrombinase that plays an important role in the pathogenesis of fibrin deposition during viral hepatitis and cytokine-induced fetal loss. We hypothesized that induction of fgl2 on the vascular endothelium of xenografts contributes to thrombosis associated with AVR. We first examined fgl2 as a source of procoagulant activity in the pig-to-primate combination. The porcine fgl2 (pfgl2) was cloned and its chromosomal locus was identified. Recombinant pfgl2 protein expressed in vitro was detected on the cell surface and generated thrombin from human prothrombin. Studies of pig-to-baboon kidney xenografts undergoing AVR in vivo revealed induction of pfgl2 expression on graft vascular endothelial cells (ECs). Cultured porcine ECs activated by human TNF-alpha in vitro demonstrated induction of pfgl2 expression and enhanced activation of human prothrombin. The availability of gene-targeted fgl2-deficient mice allowed the contribution of fgl2 to the pathogenesis of AVR to be directly examined in vivo. Hearts heterotopically transplanted from fgl2(+/+) and fgl2(+/-) mice into Lewis rats developed AVR with intravascular thrombosis associated with induction of fgl2 in graft vascular ECs. In contrast, xenografts from fgl2(-/-) mice were devoid of thrombosis. These observations collectively suggest that induction of fgl2 on the vascular endothelium plays a role in the pathogenesis of AVR-associated thrombosis. Manipulation of fgl2, in combination with other interventions, may yield novel strategies by which to overcome AVR and extend xenograft survival.
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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 | 0.001 |
| 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".