TPL2 kinase expression is regulated by the p38γ/p38δ-dependent association of aconitase-1 with <i>TPL2</i> mRNA
Bibliographic record
Abstract
p38γ and p38δ (p38γ/p38δ) regulate inflammation, in part by controlling tumor progression locus 2 (TPL2) expression in myeloid cells. Here, we demonstrate that TPL2 protein levels are dramatically reduced in p38γ/p38δ-deficient (p38γ/δ −/− ) cells and tissues without affecting TPL2 messenger ribonucleic acid (mRNA) expression. We show that p38γ/p38δ posttranscriptionally regulates the TPL2 amount at two different levels. p38γ/p38δ interacts with the TPL2/A20 Binding Inhibitor of NF-κB2 (ABIN2)/Nuclear Factor κB1p105 (NF-κB1p105) complex, increasing TPL2 protein stability. Additionally, p38γ/p38δ regulates TPL2 mRNA translation by modulating the repressor function of TPL2 3′ Untranslated region (UTR) mediated by its association with aconitase-1 (ACO1). ACO1 overexpression in wild-type cells increases the translational repression induced by TPL2 3′UTR and severely decreases TPL2 protein levels. p38δ binds to ACO1, and p38δ expression in p38γ/δ −/− cells fully restores TPL2 protein to wild-type levels by reducing the translational repression of TPL2 mRNA. This study reveals a unique mechanism of posttranscriptional regulation of TPL2 expression, which given its central role in innate immune response, likely has great relevance in physiopathology.
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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 | 0.001 |
| 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".