Calmodulin inhibitors trigger the proteolytic processing of membrane type-1 matrix metalloproteinase, but not its shedding in glioblastoma cells
Bibliographic record
Abstract
Most transmembrane proteins are subjected to limited proteolysis by cellular proteases, and stimulation of cleavage of membrane proteins by calmodulin (CaM) inhibitors was recently shown. The present study investigated the ability of several CaM inhibitors to induce the proteolytic cleavage of the membrane type-1 matrix metalloproteinase (MT1-MMP) from the cell surface of highly invasive U-87 glioblastoma cells. Although no shedding of a soluble MT1-MMP form was induced by CaM inhibitors in the conditioned media, we showed that these inhibitors induced MT1-MMP proteolytic processing to the 43 kDa membrane-bound inactive form that was not correlated with an increase in proMMP-2 activation but rather with an increase in tissue inhibitor of MMPs (TIMP)-2 expression levels. Moreover, this proteolytic processing was sensitive to marimastat suggesting the involvement of MMPs. Interestingly, CaM inhibitors antagonized concanavalin A- and cytochalasin D-induced proMMP-2 activation, and affected the cytoskeletal actin organization resulting in the loss of migratory potential of U-87 glioblastoma cells. Cytoplasmic tail-truncated MT1-MMP constructs expressed in COS-7 cells were also affected by CaM inhibitors suggesting that these inhibitors stimulated MT1-MMP proteolytic processing by mechanisms independent of the CaM-substrate interaction. We also propose that TIMP-2 acts as a negative regulator of MT1-MMP-dependent activities promoted by the action of CaM inhibitors in U-87 glioblastoma cells.
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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) | 0.001 | 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".