Impact of Minocycline on Extracellular Matrix Metalloproteinase Inducer, a Factor Implicated in Multiple Sclerosis Immunopathogenesis
Bibliographic record
Abstract
Extracellular matrix metalloproteinase inducer (EMMPRIN, CD147) is a transmembrane glycoprotein that is upregulated on leukocytes in active lesions in multiple sclerosis (MS) and its animal model, experimental autoimmune encephalomyelitis (EAE). Administration of anti-EMMPRIN Abs reduces the severity of EAE. Minocycline is a tetracycline antibiotic with immune-modulatory properties that decreases the severity of EAE; it was recently found to attenuate the conversion from a first demyelinating event to clinically definite MS in a phase III trial. We investigated whether and how minocycline affects the expression of EMMPRIN on T cells in culture and in mice afflicted with EAE. EMMPRIN expression in cultures of mouse splenocytes or human PBMCs was elevated upon polyclonal T cell activation, and this was reduced by minocycline correspondent with decreased P-Akt levels. An established MS medication, IFN-β, also diminished EMMPRIN levels on human cells whereas this was not readily observed for fingolimod or monomethylfumarate. In EAE-afflicted mice, minocycline treatment significantly reduced EMMPRIN levels on splenic lymphocytes at the presymptomatic (day 7) phase, and prevented the development of disease. Day 7 spleen transcripts from minocycline-treated EAE mice had a significantly lower MMP-9/TIMP-1 ratio, and significantly lower MCT-1 and CD98 levels, factors associated with EMMPRIN function. Day 16 (peak clinical severity) CNS samples from EAE mice had prominent representation of inflammatory perivascular cuffs, inflammatory molecules and EMMPRIN, and these were abrogated by minocycline. Overall, minocycline attenuated the activation-induced elevation of EMMPRIN on T cells in culture and in EAE mice, correspondent with reduced immune function and EAE CNS pathology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".