Individual Timp Deficiencies Differentially Impact Pro-MMP-2 Activation
Bibliographic record
Abstract
Membrane-type matrix metalloproteinases (MT-MMPs) have emerged as key enzymes in tumor cell biology. The importance of MT1-MMP, in particular, is highlighted by its ability to activate pro-MMP-2 at the cell surface through the formation of a trimolecular complex comprised of MT1-MMP/tissue inhibitor of metalloproteinase-2 (TIMP-2)/pro-MMP-2. TIMPs 1-4 are physiological MMP inhibitors with distinct roles in the regulation of pro-MMP-2 processing. Here, we have shown that individual Timp deficiencies differentially affect MMP-2 processing using primary mouse embryonic fibroblasts (MEFs). Timp-3 deficiency accelerated pro-MMP-2 activation in response to both cytochalasin D and concanavalin A. Exogenous TIMP-2 and N-TIMP-3 inhibited this activation, whereas TIMP-3 containing matrix from wild-type MEFs did not rescue the enhanced MMP-2 activation in Timp-3(-/-) cells. Increased processing of MMP-2 did not arise from increased expression of MT1-MMP, MT2-MMP, or MT3-MMP or altered expression of TIMP-2 and MMP-2. To test whether increased MMP-2 processing in Timp-3(-/-) MEFs is dependent on TIMP-2, double deficient Timp-2(-/-)/-3(-/-) MEFs were used. In these double deficient cells, the cleavage of pro-MMP-2 to its intermediate form was substantially increased, but the subsequent cleavage of intermediate-MMP-2 to fully active form, although absent in Timp-2(-/-) MEFs, was detectable with combined Timp-2(-/-)/-3(-/-) deficiency. TIMP-4 associates with MMP-2 and MT1-MMP in a manner similar to TIMP-3, but its deletion had no effect on pro-MMP-2 processing. Thus, TIMP-3 provides an inherent regulation over the kinetics of pro-MMP-2 processing, serving at a level distinct from that of TIMP-2 and TIMP-4.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".