Posttranscriptional Regulation of <i>PARG</i> mRNA by HuR Facilitates DNA Repair and Resistance to PARP Inhibitors
Bibliographic record
Abstract
Abstract The majority of pancreatic ductal adenocarcinomas (PDAC) rely on the mRNA stability factor HuR (ELAV-L1) to drive cancer growth and progression. Here, we show that CRISPR-Cas9–mediated silencing of the HuR locus increases the relative sensitivity of PDAC cells to PARP inhibitors (PARPi). PDAC cells treated with PARPi stimulated translocation of HuR from the nucleus to the cytoplasm, specifically promoting stabilization of a new target, poly (ADP-ribose) glycohydrolase (PARG) mRNA, by binding a unique sequence embedded in its 3′ untranslated region. HuR-dependent upregulation of PARG expression facilitated DNA repair via hydrolysis of polyADP-ribose on related repair proteins. Accordingly, strategies to inhibit HuR directly promoted DNA damage accumulation, inefficient PAR removal, and persistent PARP-1 residency on chromatin (PARP-1 trapping). Immunoprecipitation assays demonstrated that the PARP-1 protein binds and posttranslationally modifies HuR in PARPi-treated PDAC cells. In a mouse xenograft model of human PDAC, PARPi monotherapy combined with targeted silencing of HuR significantly reduced tumor growth compared with PARPi therapy alone. Our results highlight the HuR–PARG axis as an opportunity to enhance PARPi-based therapies. Cancer Res; 77(18); 5011–25. ©2017 AACR.
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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 | 0.001 | 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".