Protein Kinase C-ζ Regulates Transcription of the Matrix Metalloproteinase-9 Gene Induced by IL-1 and TNF-α in Glioma Cells via NF-κB
Bibliographic record
Abstract
The regulation of matrix metalloproteinase-9 (MMP-9) expression in glioma cells is one of the key processes in tumor invasion through the brain extracellular matrix. Although some studies have demonstrated the implication of classic protein kinase C (PKC) isoforms in the regulation of MMP-9 production by phorbol esters or lipopolysaccharide, the involvement of specific PKC isoforms in the signaling pathways leading to MMP-9 expression by inflammatory cytokines remains unclear. Here we report that the atypical PKC-zeta isoform participates in the induction of MMP-9 expression by interleukin-1 (IL-1) and tumor necrosis factor-alpha (TNF-alpha) in rat C6 glioma cells. Indeed, zymography and semi-quantitative reverse transcriptase-PCR analysis showed that pretreatment of C6 cells with PKC-zeta pseudosubstrate abolished MMP-9 activity and gene expression induced by IL-1 or TNF-alpha. Accordingly, IL-1 and TNF-alpha were able to induce PKC-zeta activity, as demonstrated by in vitro kinase assay using immunoprecipitated PKC-zeta. Furthermore, stable C6 clones overexpressing PKC-zeta, but not PKC-epsilon, displayed an up-regulation of MMP-9 constitutive expression as well as an increase of mmp-9 promoter activity. These processes were inhibited by an NF-kappaB-blocking peptide and completely prevented by NF-kappaB-binding site mutation in the mmp-9 promoter. Taken together, these results indicate that PKC-zeta plays a key role in the regulation of MMP-9 expression in C6 glioma cells through NF-kappaB.
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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) | 0.001 | 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".