Stable expression of α1-antitrypsin Portland in MDA-MB-231 cells increased MT1-MMP and MMP-9 levels, but reduced tumour progression.
Bibliographic record
Abstract
The membrane bound matrix metalloproteinase MT1-MMP plays roles in modulating cell movement, independent of its abilities to remodel the extracellular matrix. Unlike many MMPs, MT1-MMP is activated in the Golgi prior to secretion by a pro-protein convertase, primarily furin. Regulation of the activation of pro-MT1-MMP has been methodically investigated, as altering the level of the active protein has broad implications in both activating other pro-MMPs, including pro-MMP-2, and many subsequent remodelling events. Our previous work in MCF-7 cells has demonstrated that modest, and not extremely high, levels of active MT1-MMP manifests into altered cell morphology and movement. At this low but optimal amount of MT1-MMP protein, changes to MT1-MMP levels are always mirrored by MMP-9 and pERK levels, and always opposite to MMP-2 levels. In this study, stable expression of the furin inhibitor α1-antitrypsin Portland (α1-PDX) in MDA-MB-231 cells increased overall MT1-MMP levels, but cells maintained a 21% proportion of pro-MT1-MMP. The increase in MT1-MMP was mirrored by increases in MMP-9 and pERK, but a decrease in MMP-2. These changes were associated with increased NF-κB transcription. In vitro analysis showed that α1-PDX decreased cell protrusions and migration, and this manifested as decreased tumourigenesis when examined in vivo using a chick CAM assay.
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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) | 0.001 | 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 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".