A Transcriptional Regulatory Role for the Membrane Type-1 Matrix Metalloproteinase in Carcinogen-Induced Inflammasome Gene Expression
Bibliographic record
Abstract
Signal-transducing functions driven by the cytoplasmic domain of membrane type-1 matrix metalloproteinase (MT1-MMP) are believed to regulate many inflammation-associated cancer cell functions including migration, proliferation, and survival. Aside from upregulation of the inflammation biomarker cyclooxygenase-2 (COX-2) expression, MT1-MMP's role in relaying intracellular signals triggered by extracellular pro-inflammatory cues remains poorly understood. Here, we triggered inflammation in HT1080 fibrosarcoma cells with phorbol-12-myristate-13-acetate (PMA), an inducer of COX-2 and of MT1-MMP. To assess the global transcriptional regulatory role that MT1-MMP may exert on inflammation biomarkers, we combined gene array screens with a transient MT1-MMP gene silencing strategy. Expression of MT1-MMP was found to exert both stimulatory and repressive transcriptional control of several inflammasome-related biomarkers such as interleukin (IL)-1B, IL-6, IL-12A, and IL-33, as well as of transcription factors such as EGR1, ELK1, and ETS1/2 in PMA-treated cells. Among the signal-transducing pathways explored, the silencing of MT1-MMP prevented PMA from phosphorylating extracellular signal-regulated kinase, inhibitor of κB, and p105 nuclear factor κB (NF-κB) intermediates. We also found a signaling axis linking MT1-MMP to MMP-9 transcriptional regulation. Altogether, our data indicate a significant involvement of MT1-MMP in the transcriptional regulation of inflammatory biomarkers consolidating its contribution to signal transduction functions in addition to its classical hydrolytic activity.
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) | 0.000 | 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 | 0.000 |
| 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".