Interleukin-1 is a key regulator of matrix metalloproteinase-9 expression in human neurons in culture and following mouse brain trauma in vivo
Bibliographic record
Abstract
An acute trauma to the CNS rapidly results in the upregulation of inflammatory cytokines that include interleukin-1 (IL-1). We report here that the levels of several matrix metalloproteinases (MMPs) are also elevated following a corticectomy trauma injury to the mouse CNS. The delayed upregulation of MMPs compared to that for IL-1 suggests the possibility that inflammatory cytokines regulate MMP production in CNS trauma. To resolve this, we developed a method to isolate and maintain highly enriched human fetal neurons or astrocytes in culture and examined the regulation by cytokines of the activity of a subgroup of MMPs, the gelatinases (MMP-2 and -9). While both neuronal and astrocytic cultures displayed comparable MMP-2 activity, as evidenced by gelatin zymography, levels of MMP-9 were proportionately higher in neurons compared to astrocytes. Of a variety of cytokines and growth factors tested in vitro, only IL-1beta was effective in increasing the neuronal expression of MMP-9. Finally, an IL-1 receptor antagonist attenuated the increase of neuronal MMP-9 in culture and abolished the injury-induced increase of MMP-9 in the mouse brain. These results implicate IL-1beta as a key regulator of neuronal MMP-9 in culture and of the elevation of MMP-9 that occurs following mouse CNS trauma.
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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) | 0.002 | 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".