Neuronal activity‐dependent increase of net matrix metalloproteinase activity is associated with MMP‐9 neurotoxicity after kainate
Bibliographic record
Abstract
Matrix metalloproteinases (MMPs) and the tissue inhibitors of MMPs (TIMPs) are emerging as important modulators of brain physiopathology. Dramatic changes in the expression of MMPs and TIMPs occur during excitotoxic/neuroinflammatory processes. However, only the measurement of net protease activity is relevant physiologically, and the functional consequences of MMP/TIMP ratio modifications in the brain remain elusive. In order to assess MMP activity and effects in brain tissue, we combined in vivo and organotypic culture models of kainate (KA)-induced excitotoxicity to provoke selective neuronal death and neuroinflammation in the hippocampus. Using in situ zymography, we show that KA-induced excitotoxic seizures in rats increase net MMP activity in hippocampal neurons 8 h after seizures, before their death, and that this increase is neuronal activity-dependent. Three days after KA, proteolytic activity increases in blood vessels and reactive glial cells of vulnerable areas, in relation with neuroinflammation. At 7 and 15 days, proteolysis remains high in blood vessels whereas it is reduced in glia. In organotypic hippocampal cultures, which lack blood cell-mediated inflammation and extrinsic connections, a broad-spectrum inhibitor of MMPs (MMPI), but also a selective MMP-9 inhibitor, protect hippocampal neurons against KA-induced excitotoxicity. Moreover, recombinant MMP-9, but not MMP-2, induces selective pyramidal cell death in these cultures and KA-induced neuronal activity exacerbates the neuronal death promoting effects of MMP-9. These data strongly implicate MMPs, and MMP-9 in particular, in both excitotoxic neuronal damage and subsequent neuroinflammatory processes, and suggest that selective MMPIs could be therapeutically relevant in related neurological disorders.
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) | 0.001 | 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".