Mitogen-Activated Protein Kinase-Activated Protein Kinase 2 Regulates Tumor Necrosis Factor mRNA Stability and Translation Mainly by Altering Tristetraprolin Expression, Stability, and Binding to Adenine/Uridine-Rich Element
Bibliographic record
Abstract
The mitogen-activated protein kinase (MAPK) p38/MAPK-activated protein kinase 2 (MK2) signaling pathway plays an important role in the posttranscriptional regulation of tumor necrosis factor (TNF), which is dependent on the adenine/uridine-rich element (ARE) in the 3' untranslated region of TNF mRNA. After lipopolysaccharide (LPS) stimulation, MK2-deficient macrophages show a 90% reduction in TNF production compared to the wild type. Tristetraprolin (TTP), a protein induced by LPS, binds ARE and destabilizes TNF mRNA. Accordingly, macrophages lacking TTP produce large amounts of TNF. Here, we generated MK2/TTP double knockout mice and show that, after LPS stimulation, bone marrow-derived macrophages produce TNF mRNA and protein levels comparable to those of TTP knockout cells, indicating that in the regulation of TNF biosynthesis TTP is genetically downstream of MK2. In addition, we show that MK2 is essential for the stabilization of TTP mRNA, and phosphorylation by MK2 leads to increased TTP protein stability but reduced ARE affinity. These data suggest that MK2 inhibits the mRNA destabilizing activity of TTP and, in parallel, codegradation of TTP together, with the target mRNA resulting in increased cellular levels of TTP.
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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 | 0.000 |
| 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".