Tumor Necrosis Factor-α Induces Fibronectin Synthesis in Coronary Artery Smooth Muscle Cells by a Nitric Oxide–Dependent Posttranscriptional Mechanism
Bibliographic record
Abstract
Postcardiac transplant coronary arteriopathy is associated with tumor necrosis factor-alpha (TNF-alpha) induction of fibronectin-dependent smooth muscle cell (SMC) migration into the subendothelium, resulting in occlusive neointimal formation. Because expression of inducible nitric oxide synthase (iNOS) is elevated in neointimal formation after transplantation and upregulated in vascular SMCs by TNF-alpha, we investigated whether TNF-alpha induction of fibronectin synthesis in coronary artery (CA) SMCs is mediated by nitric oxide (NO). TNF-alpha caused a dose-dependent increase in reactive oxygen and nitrogen intermediates in CA SMCs (P<0.05). This correlated with increased NO production (P<0.05) and fibronectin synthesis (P<0.05). TNF-alpha induction of fibronectin synthesis was abrogated by the NOS inhibitor N(G)-monomethyl-L-arginine (L-NMMA) (P<0.05) or the flavonoid-containing enzyme inhibitor diphenyleneiodonium (DPI) (P<0.05) and reproduced with the NO donor S-nitroso-N-acetyl-penicillamine (SNAP) (P<0.05). Northern blotting showed no effect of TNF-alpha on steady-state fibronectin mRNA levels. TNF-alpha increased expression of light chain 3 (LC-3), a protein shown previously to facilitate fibronectin mRNA translation through its interaction with an adenosine-uracil rich element (ARE) in the 3'-untranslated region of fibronectin mRNA. RNA gel mobility shift and UV cross-linking assays using CA SMC lysates revealed protein binding complexes with radiolabeled oligonucleotide containing the ARE, similar to those generated with recombinant LC-3. One of these complexes increased after TNF-alpha treatment, an effect inhibited with L-NMMA or DPI. These data demonstrate a novel paradigm whereby cytokines regulate mRNA translation of extracellular matrix proteins through NO-dependent modulation of RNA binding protein interaction with mRNA.
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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 | 0.000 |
| 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".