Identification of Myxomaviral Serpin Reactive Site Loop Sequences That Regulate Innate Immune Responses
Bibliographic record
Abstract
The thrombolytic serine protease cascade is intricately involved in activation of innate immune responses. The urokinase-type plasminogen activator and receptor form complexes that aid inflammatory cell invasion at sites of arterial injury. Plasminogen activator inhibitor-1 is a mammalian serpin that binds and regulates the urokinase receptor complex. Serp-1, a myxomaviral serpin, also targets the urokinase receptor, displaying profound anti-inflammatory and anti-atherogenic activity in a wide range of animal models. Serp-1 reactive center site mutations, mimicking known mammalian and viral serpins, were constructed in order to define sequences responsible for regulation of inflammation. Thrombosis, inflammation, and plaque growth were assessed after treatment with Serp-1, Serp-1 chimeras, plasminogen activator inhibitor-1, or unrelated viral serpins in plasminogen activator inhibitor or urokinase receptor-deficient mouse aortic transplants. Altering the P1-P1' Arg-Asn sequence compromised Serp-1 protease-inhibitory activity and anti-inflammatory activity in animal models; P1-P1' Ala-Ala mutants were inactive, P1 Met increased remodeling, and P1' Thr increased thrombosis. Substitution of Serp-1 P2-P7 with Ala6 allowed for inhibition of urokinase but lost plasmin inhibition, unexpectedly inducing a diametrically opposed, proinflammatory response with mononuclear cell activation, thrombosis, and aneurysm formation (p < 0.03). Other serpins did not reproduce Serp-1 activity; plasminogen activator inhibitor-1 increased thrombosis (p < 0.0001), and unrelated viral serpin, CrmA, increased inflammation. Deficiency of urokinase receptor in mouse transplants blocked Serp-1 and chimera activity, in some cases increasing inflammation. In summary, 1) Serp-1 anti-inflammatory activity is highly dependent upon the reactive center loop sequence, and 2) plasmin inhibition is central to anti-inflammatory activity.
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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".