5‐Aza‐2′‐deoxycytidine and interleukin‐1 cooperate to regulate matrix metalloproteinase‐3 gene expression
Bibliographic record
Abstract
Members of the matrix metalloproteinase (MMP) family of enzymes play a critical role in extracellular matrix remodeling in a number of normal and pathologic processes. Accordingly, activation of MMP gene expression is tightly regulated at the level of transcription by specific transcription factors, most notably following exposure to inflammatory cytokines. Recent studies with 5-aza-2'-deoxycytidine (5-aza-dC), a specific DNA methylase inhibitor, also suggest that epigenetic processes contribute to the regulation of MMP expression. Although inflammation-related aberrant patterns of DNA methylation have been described, a mechanistic link between inflammation and epigenetic alterations in the control of MMP expression remains unclear. Here, we provide evidence that increased MMP-3 expression by 5-aza-dC is modulated by interleukin-1 (IL-1). More specifically, we found that stimulation with IL-1, but not with IL-6 or TNFα, significantly increased the hypomethylation status of the MMP-3 promoter to a level similar to that found in dnmt1/dnmt3b-deficient HCT116 (DKO) cells. Furthermore, we showed that increased MMP-3 expression by 5-aza-dC was associated with increased expression and activity of specific transcription factors known to regulate MMP-3 expression. In fact, treatment with 5-aza-dC was obligatory for some transcription factors to trigger an increase in MMP-3 expression, such as Ap-1. In contrast, CCAAT enhancer-binding proteins and E-twenty six were capable of inducing MMP-3 alone. Overall, these findings provide a novel perspective of the collaborative role of 5-aza-dC and inflammatory cytokines with specific transcription factors that are normally involved in MMP-3 expression.
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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".