Negative Feed-forward Control of Tumor Necrosis Factor (TNF) by Tristetraprolin (ZFP36) Is Limited by the Mitogen-activated Protein Kinase Phosphatase, Dual-specificity Phosphatase 1 (DUSP1)
Bibliographic record
Abstract
TNF is central to inflammation and may play a role in the pathogenesis of asthma. The 3'-untranslated region of the TNF transcript contains AU-rich elements (AREs) that are targeted by the RNA-binding protein, tristetraprolin (also known as zinc finger protein 36 (ZFP36)), which is itself up-regulated by inflammatory stimuli, to promote mRNA degradation. Using primary human bronchial epithelial and pulmonary epithelial A549 cells, we confirm that interleukin-1β (IL1B) induces expression of dual-specificity phosphatase 1 (DUSP1), ZFP36, and TNF. Whereas IL1B-induced DUSP1 is involved in feedback control of MAPK pathways, ZFP36 exerts negative (incoherent) feed-forward control of TNF mRNA and protein expression. DUSP1 silencing increased IL1B-induced ZFP36 expression at 2 h and profoundly repressed TNF mRNA at 6 h. This was partly due to increased TNF mRNA degradation, an effect that was reduced by ZFP36 silencing. This confirms a regulatory network, whereby DUSP1-dependent negative feedback control reduces feed-forward control by ZFP36. Conversely, whereas DUSP1 overexpression and inhibition of MAPKs prevented IL1B-induced expression of ZFP36, this was associated with increased TNF mRNA expression at 6 h, an effect that was predominantly due to elevated transcription. This points to MAPK-dependent feed-forward control of TNF involving ZFP36-dependent and -independent mechanisms. In terms of repression by dexamethasone, neither silencing of DUSP1, silencing of ZFP36, nor silencing of both together prevented the repression of IL1B-induced TNF expression, thereby demonstrating the need for further repressive mechanisms by anti-inflammatory glucocorticoids. In summary, these data illustrate why understanding the competing effects of feedback and feed-forward control is relevant to the development of novel anti-inflammatory therapies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".