Metalloproteinase inhibitor TIMP‐1 affects hepatocyte cell cycle via HGF activation in murine liver regeneration†
Bibliographic record
Abstract
Liver regeneration depends on timely restoration of cellular mass while orchestrating structural matrix remodeling. Matrix metalloproteinases (MMPs) and their endogenous inhibitors (TIMPs) are known to regulate the extracellular matrix (ECM) turnover and, more recently, the processing of growth factors and cytokines. We have previously demonstrated that TIMP-1 inhibits preneoplastic hepatocyte proliferation by attenuating growth factor bioavailability. In the present study, we examined the role of TIMP-1 in de novo hepatocyte cell division during liver regeneration. Comprehensive real-time reverse-transcriptase polymerase chain reaction analyses of regenerating livers revealed significant inductions in the messenger RNA of TIMP-1, TIMP-3, TIMP-4, MMP-2, MMP-9, MMP-13, MMP-14, and MMP-24, while MMP-15 expression was significantly reduced. Induction of TIMP-1 occurred during the peak of hepatocyte DNA synthesis. Studies using genetically altered mice revealed that TIMP-1 loss of function accelerated hepatocyte cell cycle progression. This finding was demonstrated by earlier expression of cyclin D1, proliferating cell nuclear antigen, and phosphorylated histone H3, which mark the G(1)-S, S, and M phase, respectively. Conversely, TIMP-1 gain of function delayed cell cycle progression. MMP activity was increased in the absence of Timp-1. Examination of hepatocyte growth factor (HGF), and its receptor Met, both of which provide a mitogenic signal for hepatocyte division, showed increased HGF activity in Timp-1(-/-)-regenerating livers. HGF is released from the ECM and is proteolytically processed to its active form. Active HGF was elevated in Timp-1(-/-) mice, leading to increased immunostaining of phosphorylated Met as well as activation of a downstream effector, p38. In conclusion, TIMP-1 is a novel negative regulator of HGF activity during liver regeneration.
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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 | 0.001 | 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 | 0.001 |
| 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".