Discovery of reversing enzymes for RNA ADP-ribosylation reveals a possible defence module against toxic attack
Bibliographic record
Abstract
Nucleic acid ADP-ribosylation and its associated enzymes involved in catalysis and hydrolysis are widespread among all kingdoms of life. Yet, its roles in mammalian and bacterial physiology including inter-/intraspecies conflicts are currently underexplored. Recently, several examples of enzymatic systems for RNA ADP-ribosylation have been identified, showing that all major types of RNA species, including messenger RNA, ribosomal RNA, and transfer RNA, can be targeted by ADP-ribosyltransferases (ARTs) which attach ADP-ribose modifications either to nucleobases, the backbone ribose, or phosphate ends. Yet little is known about the reversibility of RNA ADP-ribosylation by ADP-ribosylhydrolases belonging to the macrodomain, ARH, or NADAR superfamilies. Here, we characterize the hydrolytic activity of ADP-ribosylhydrolases on RNA species ADP-ribosylated by mammalian and bacterial ARTs. We demonstrate that NADAR ADP-ribosylhydrolases are the only hydrolase family able to reverse guanosine RNA base ADP-ribosylation while they are inactive on phosphate-end RNA ADP-ribosylation. Furthermore, we reveal that macrodomain-containing PARG enzymes are the only hydrolase type with the ability for specific and efficient reversal of 2'-hydroxyl group RNA ADP-ribosylation catalysed by Pseudomonas aeruginosa effector toxin RhsP2. Moreover, using the RhsP2/bacterial PARG system as an example, we demonstrate that PARG enzymes can act as protective immunity enzymes against antibacterial RNA-targeting ART toxins.
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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 | 0.000 |
| 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".