Tissue inhibitors of metalloproteinases are proteolytic targets of matrix metalloproteinase 9
Bibliographic record
Abstract
Extracellular proteolysis and turnover are core processes of tissue homeostasis. The predominant matrix-degrading enzymes are members of the Matrix Metalloproteinase (MMP) family. MMPs extensively degrade core matrix components in addition to processing a range of other factors in the extracellular, plasma membrane, and intracellular compartments. The proteolytic activity of MMPs is modulated by the Tissue Inhibitors of Metalloproteinases (TIMPs), a family of four multi-functional matrisome proteins with extensively characterized MMP inhibitory functions. Thus, a well-regulated balance between MMP activity and TIMP levels has been described as critical for healthy tissue homeostasis, and this balance can be chronically disturbed in pathological processes. The relationship between MMPs and TIMPs is complex and lacks the constraints of a typical enzyme-inhibitor relationship due to secondary interactions between various MMPs (specifically gelatinases) and TIMP family members. We illustrate a new complexity in this system by describing how MMP9 can cleave members of the TIMP family when in molar excess. Proteolytic processing of TIMPs can generate functionally altered peptides with potentially novel attributes. We demonstrate here that all TIMPs are cleaved at their C-terminal tails by a molar excess of MMP9. This processing removes the N-glycosylation site for TIMP3 and prevents the TIMP2 interaction with latent proMMP2, a prerequisite for cell surface MMP14-mediated activation of proMMP2. TIMP2/4 are further cleaved producing ∼14 kDa N-terminal proteins linked to a smaller C-terminal domain through residual disulfide bridges. These cleaved TIMP2/4 complexes show perturbed MMP inhibitory activity, illustrating that MMP9 may bear a particularly prominent influence upon the TIMP:MMP balance in tissues.
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 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) | 0.001 | 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 | 0.000 |
| 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".