Collagenase-2 Deficiency or Inhibition Impairs Experimental Autoimmune Encephalomyelitis in Mice
Bibliographic record
Abstract
Matrix metalloproteinases (MMPs) have been implicated in a variety of human diseases, including neuroimmunological disorders such as multiple sclerosis. However, the recent finding that some MMPs play paradoxical protective roles in these diseases has made necessary the detailed study of the specific function of each family member in their pathogenesis. To determine the relevance of collagenase-2 (MMP-8) in experimental autoimmune encephalomyelitis (EAE), an animal model for multiple sclerosis, we have performed two different analyses involving genetic and biochemical approaches. First, we have analyzed the development of EAE in mutant mouse deficient in MMP-8, with the finding that the absence of this proteolytic enzyme is associated with a marked reduction in the clinical symptoms of EAE. We have also found that MMP-8(-/-) mice exhibit a marked reduction in central nervous system-infiltrating cells and demyelinating lesions. As a second approach, we have carried out a pharmacological inhibition of MMP-8 with a selective inhibitor against this protease (IC(50) = 0.4 nM). These studies have revealed that the administration of the MMP-8 selective inhibitor to mice with EAE also reduces the severity of the disease. Based on these findings, we conclude that MMP-8 plays an important role in EAE development and propose that this enzyme may be a novel therapeutic target in human neuro-inflammatory diseases such as multiple sclerosis.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".