Identification of fibrinogen as a natural inhibitor of MMP-2
Bibliographic record
Abstract
Abstract Non-genetic MMP-2 insufficiency is a relatively unexplored condition which could be induced by pathological overexpression of endogenous MMP-2 inhibitors such as TIMPs and/or the acute phase reactant alpha-2-macroglobulin. Here, we investigate the hypothesis that human fibrinogen (FBG) – an acute phase reactant – inhibits human MMP-2. Following an unexpected observation where sera from human donors including arthritis patients with increased levels of serum FBG exhibited reduced binding of serum proMMP-2 to gelatin, we found that human FBG (0 to 3.6 mg/mL i.e., 0 to 10.6 μM) concentration-dependently inhibited human proMMP-2 and MMP2 from binding to gelatin. Moreover, at normal physiological concentrations, FBG (5.29–11.8 μM) concentration-dependently inhibited (40–70% inhibition) the cleavage of fluorescein-conjugated gelatin by MMP-2, but not MMP-9. Indicative of a mixed-type (combination of competitive and non-competitive) inhibition mechanism, FBG reduced the Vmax (24.9 ± 0.7 min−1 to 17.7 ± 0.9 min−1, P < 0.05) and increased the Michaelis-Menten constant KM (204 ± 6 nM to 478 ± 50 nM, P < 0.05) for the reaction of MMP-2 cleavage of fluorescein-conjugated gelatin. In silico analyses and studies of FBG neutralization with anti-FBG antibodies implicated the domains D and E of FBG in the inhibition of MMP-2. In conclusion, FBG is a natural selective MMP-2 inhibitor, whose pathological elevation could lead to MMP-2 insufficiency in humans.
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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".