Metalloproteinases are enriched in microglia compared with leukocytes and they regulate cytokine levels in activated microglia
Bibliographic record
Abstract
Microglia are resident immune cells within the central nervous system (CNS). They become activated following neurological insults and increase their expression of cytokines. Also elevated in CNS injuries are proteases, including matrix metalloproteinases (MMPs) and A disintegrin and metalloproteinases (ADAMs). The spectrum of metalloproteinase members expressed by microglia and by the systemic leukocytes that infiltrate the injured CNS is unknown, as are their functions. We determined the levels of transcripts encoding all 24 MMPs, nine ADAMs, and their four physiological antagonists, tissue inhibitor of metalloproteinases (TIMPs), in human microglia, B and T cells, monocytes, and neutrophils. We found a distinct pattern for each immune subset and an enrichment of metalloproteinases in microglia compared with leukocytes. When microglia were activated, there was an upregulation of transcripts for nine metalloproteinases, and reduction of TIMP3. Activation of microglia also resulted in increased levels of tumor necrosis factor (TNF)-alpha, interleukin (IL)-1beta, and IL-10 protein in the conditioned media of cells. The amount of secreted TNF-alpha, but not IL-1beta or IL-10, was suppressed by BB94, a broad spectrum metalloproteinase inhibitor, and by TIMP3 but not TIMP1 or TIMP2. This inhibitory profile suggests the involvement of an ADAM member in TNF-alpha secretion. We conclude that microglia bear a metalloproteinase signature distinct from systemic cells, and that following activation, microglia upregulate TNF-alpha protein levels through a combination of elevated cytokine transcripts, increased metalloproteinase level and activity, and through the decrease of TIMP3. The results have implications for the regulation of neuroinflammation and its outcomes following CNS injuries.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".