Regulation of stress granule maturation and dynamics by poly(ADP-ribose) interaction with PARP13
Bibliographic record
Abstract
Non-covalent interactions of poly(ADP-ribose) (PAR) facilitate condensate formation, yet the impact of these interactions on condensate properties remains unclear. Here, we demonstrate that PAR-mediated interactions through PARP13, specifically the PARP13.2 isoform, are essential for modulating the dynamics of stress granules—a class of cytoplasmic condensates that form upon stress, including types frequently observed in cancers. Single amino acid mutations in PARP13, which reduce its PAR-binding activity, lead to the formation of smaller yet more numerous stress granules than observed in the wild-type. This fragmented stress granule phenotype is also apparent in PARP13 variants with cancer-associated single-nucleotide polymorphisms (SNPs) that disrupt PAR binding. Notably, this fragmented phenotype is conserved across a variety of stresses that trigger stress granule formation via diverse pathways. Furthermore, this PAR-binding mutant diminishes condensate dynamics and impedes fusion. Overall, our study uncovers the important role of PAR-protein interactions in stress granule dynamics and maturation, mediated through PARP13. Stress granules, cellular structures essential for stress response, require poly(ADP-ribose) as a multivalent scaffold. Here, the authors show that disrupting poly(ADP-ribose) binding to PARP13 alters granule size, dynamics, and maturation, despite PARP13 lacking ADPribosyltransferase activity.
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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 | 0.000 |
| 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".