Reduction of thrombus size in murine models of thrombosis following administration of recombinant α1-proteinase inhibitor mutant proteins
Bibliographic record
Abstract
The variant serpin α1-PI M358R inhibits thrombin and other proteases such as activated protein C (APC) and factor XIa. We previously described recombinant proteins HAPI M358R (α1-PI M358R containing an N-terminal extension corresponding to residues 1-75 of heparin cofactor II) and HAPI RCL5 (HAPI M358R with F352-I356 and I360 substituted for the corresponding residues of antithrombin), with enhanced selectivity for thrombin over APC inhibition. We tested the hypotheses that these recombinant proteins would limit thrombosis in three mouse models, and that the HAPI chimeric proteins would be more effective than α1-PI M358R. Recombinant serpins were purified from Escherichia coli by nickel chelate and ion exchange affinity chromatography, and administered to mice intravenously. HAPI RCL5 reduced incorporation of radiolabelled fibrin(ogen) into thrombi in the ferric chloride-injured vena cava in a dose-dependent manner; HAPI M358R was less effective and α1-PI M358R was without effect. In a model of murine endotoxaemia, HAPI RCL5 was more effective than α1-PI M358R in reducing radiolabelled fibrin(ogen) deposition in heart and kidneys; immunohistochemistry of tissue sections showed lesser staining with anti-fibrin(ogen) antibodies with both treatments. In the ferric chloride-injured murine carotid artery, administration of both recombinant serpins was equally effective in lengthening the vessel's time to occlusion. Our results show that the antithrombotic efficacy of the recombinant serpins correlates with their potency as thrombin inhibitors, since HAPI RCL5 inhibits thrombin, but not factors Xa, XIa, XIIa, or neutrophil elastase, more rapidly than α1-PI M358R.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".