Poly (ADP-ribose) polymerase 1 promotes HuR/ELAVL1 cytoplasmic localization and inflammatory gene expression by regulating p38 MAPK activity
Bibliographic record
Abstract
Post-transcriptional regulation of cytokine/chemokine mRNA turnover is critical for immune processes and contributes to the mammalian cellular response to diverse inflammatory stimuli. The ubiquitous RNA-binding protein human antigen R (HuR) is an integral regulator of inflammation-associated mRNA fate. HuR function is regulated by various post-translational modifications that alter its subcellular localization and ability to stabilize target mRNAs. Both poly (ADP-ribose) polymerase 1 (PARP1) and p38 mitogen-activated protein kinases (MAPKs) have been reported to regulate the biological function of HuR, but their specific regulatory and crosstalk mechanisms remain unclear. In this study, we show that PARP1 acts via p38 to synergistically promote cytoplasmic accumulation of HuR and stabilization of inflammation-associated mRNAs in cells under inflammatory conditions. Specifically, p38 binds to auto-poly ADP-ribosylated (PARylated) PARP1 resulting in the covalent PARylation of p38 by PARP1, thereby promoting the retention and activity of p38 in the nucleus. In addition, PARylation of HuR facilitates the phosphorylation of HuR at the serine 197 site mediated by p38, which then increases the translocation of HuR to the cytoplasm, ultimately stabilizing the inflammation-associated mRNA expression at the post-transcriptional level.
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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 | 0.000 |
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".