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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".