RNA toxins: mediators of stress adaptation and pathogen defense
Bibliographic record
Abstract
RNA toxins are a group of enzymes primarily synthesized by bacteria, fungi, and plants that either cleave or depurinate RNA molecules. These proteins may be divided according to their RNA substrates: ribotoxins are nucleases that cleave ribosomal RNA (rRNA), ribosome inactivating proteins are glycosidases that remove a base from rRNA, messenger RNA (mRNA) interferases are nucleases that cleave mRNAs, and anticodon nucleases cleave transfer RNAs (tRNAs). These modifications to the RNAs may substantially alter gene expression and translation rates. Given that some of these enzymes cause cell death, it has been suggested that they function mainly in defense, either to kill competing cells or to elicit suicide and thereby limit pathogen spread from infected cells. Although good correlations have been drawn between their enzymatic functions and toxicity, recent work has shown that some RNA toxins cause apoptosis in the absence of damage to RNA and that defense against pathogens can be achieved without host cell death. Moreover, a decrease in cellular translation rate, insufficient to cause cell death, allows some organisms to adapt to stress and environmental change. Although ascribing effects observed in vitro to the roles of these toxins in nature has been challenging, recent results have expanded our understanding of their modes of action, and emphasized the importance of these toxins in development, adaptation to stress and defense against pathogens.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".