Abstract 2697: A role for the membrane type-1 matrix metalloproteinase in the transcriptional regulation of carcinogen-induced inflammasome components
Bibliographic record
Abstract
Abstract BACKGROUND : Signal transducing functions driven by the cytoplasmic domain of membrane type-1 matrix metalloproteinase (MT1-MMP) are believed to regulate many inflammation-mediated cancer cell functions including migration, proliferation, and survival. Besides the upregulation of the inflammation biomarker cyclooxygenase (COX)-2 expression, MT1-MMP’s role in relaying the signals triggered from pro-inflammatory cues remain poorly understood. METHODS : Here, we treated HT1080 fibrosarcoma cells with phorbol-12-myristate-13-acetate (PMA), a well-known carcinogen and inducer of COX-2 and of MT1-MMP. In order to assess the global transcriptional regulatory role that MT1-MMP may exert on inflammation biomarkers, we combined gene array screens to transiant MT1-MMP gene silencing strategy. RESULTS : We found that MT1-MMP expression exerted both stimulatory and repressive transcriptional control of several inflammasome-related biomarkers such as 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, silencing of MT1-MMP prevented PMA from phosphorylating Erk, IκB, and p105 KF-κB intermediates. We also highlight a signaling axis linking MT1-MMP to MMP-9 transcriptional regulation. CONCLUSIONS : Altogether, our data evidence an important involvement of MT1-MMP in the transcriptional regulation of inflammatory biomarkers consolidating its contribution in signal transducing functions, in addition to its classical hydrolytic activity. Citation Format: Samuel Sheehy, Borhane Annabi. A role for the membrane type-1 matrix metalloproteinase in the transcriptional regulation of carcinogen-induced inflammasome components [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2697. doi:10.1158/1538-7445.AM2017-2697
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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) | 0.004 | 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".