The role of TIMPs in regulation of extracellular matrix proteolysis
Bibliographic record
Abstract
Tissue inhibitors of metalloproteinases (TIMPs), which inhibit matrix metalloproteinases (MMPs) as well as the closely related, a disintegrin and metalloproteinases (ADAMs) and ADAMs with thrombospondin motifs (ADAMTSs), were traditionally thought to control extracellular matrix (ECM) proteolysis through direct inhibition of MMP-dependent ECM proteolysis. This classical role for TIMPs suggests that increased TIMP levels results in ECM accumulation (or fibrosis), whereas loss of TIMPs leads to enhanced matrix proteolysis. Mice lacking TIMP family members have provided support for such a role; however, studies with these TIMP deficient mice have also demonstrated that loss of TIMPs can often be associated with an accumulation of ECM. Collectively, these studies suggest that the divergent roles of TIMPs in matrix accumulation and proteolysis, which together can be referred to as ECM turnover, are dependent on the TIMP, specific tissue, and local tissue environment (i.e. health vs. injury/disease). Ultimately, these combined factors dictate the specific metalloproteinases being regulated by a given TIMP, and it is likely the diversity of metalloproteinases and their physiological substrates that determines whether TIMPs inhibit matrix proteolysis or accumulation. In this review, we discuss the evidence for the dichotomous roles of TIMPs in ECM turnover highlighting some of the common findings between different TIMP family members. Importantly, while we now have a better understanding of the role of TIMPs in regulating ECM turnover, much remains to be determined. Data on the specific metalloproteinases inhibited by different TIMPs in vivo remains limited and must be the focus of future studies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".