Analyses of all matrix metalloproteinase members in leukocytes emphasize monocytes as major inflammatory mediators in multiple sclerosis
Bibliographic record
Abstract
Matrix metalloproteinases (MMPs) are implicated in multiple sclerosis where one of their roles may be to facilitate the transmigration of circulating leukocytes into the CNS. Studies have focused on only a few MMPs, and much remains unknown of which of the 23 MMP family members is/are critical to the multiple sclerosis disease process. Using quantitative real time polymerase chain reactions, we have systematically analysed the expression of all 23 MMP members in subsets of leukocytes isolated from the blood of normal individuals. We found a distinctive pattern of MMP expression in different cellular populations: MMP-11, MMP-26 and MMP-27 were enriched in B cells, while MMP-15, MMP-16, MMP-24 and MMP-28 were prominent in T lymphocytes. Of interest is the enrichment of a majority of MMP members in monocytes: MMP-1, MMP-3, MMP-9, MMP-10, MMP-14, MMP-19 and MMP-25. MMP-2 and MMP-17 were also significantly represented in monocytes, although B cells had significant amounts of these MMPs. In correspondence with their strong expression of many MMP members, monocytes migrated more rapidly across a model of the blood-brain barrier in culture than T or B lymphocytes. Finally, we found higher levels of two of the monocyte-expressed MMPs in multiple sclerosis patients compared with normal individuals: MMP-2 and MMP-14. Tissue inhibitor of metalloproteinases (TIMP)-2 was also elevated in monocytes from multiple sclerosis patients, providing a mechanism for the reported activation of MMP-2 by MMP-14 and TIMP-2. These results emphasize that monocytes are prominent contributors of the neuroinflammation in multiple sclerosis through a mechanism that involves their high MMP expression and that they identify specific MMP members as targets for novel therapeutics in the disease.
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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 | 0.001 | 0.001 |
| 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".