Modulation of RECK levels in Xenopus A6 cells: effects on MT1-MMP, MMP-2 and pERK levels
Bibliographic record
Abstract
Abstract Background MT1-MMP is a cell-surface enzyme whose regulation of pro-MMP-2 and ERK activation position it as a key facilitator of ECM remodelling and cell migration. These processes are modulated by endogenous MMP inhibitors, such as RECK, a GPI-anchored protein which has been shown to inhibit both MT1-MMP and MMP-2 activity. Our previous studies have revealed a link between MT1-MMP levels, and pro-MMP-2 and ERK activation in mammalian cells, as well as MT1-MMP and RECK co-localization inXenopusembryos. We here investigated how modulation of RECK would impact MT1-MMP and MMP-2 levels, as well as ERK signalling inXenopusA6 cells. Results We used a Morpholino approach to knockdown RECK, plasmid transfection to overexpress RECK, and PI-PLC treatment to shed RECK from the cell surface ofXenopusA6 cells. RECK reduction did not alter pERK or MT1-MMP levels, nor MMP-2 activity as measured by zymography; thus RECK-knockdown cells maintained the ability to remodel the ECM. RECK overexpression and PI-PLC treatment both increased ECM remodelling potential through increased MT1-MMP protein and relative MMP-2 activation levels. Conclusions RECK changes that reduce the ability of the cell to remodel the ECM (overexpression and cell surface shedding) are compensated for by increases in MT1-MMP, and MMP-2 levels as seen by zymography.
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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) | 0.000 | 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 | 0.001 |
| 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".