<scp>tRNA</scp> methylation: An unexpected link to bacterial resistance and persistence to antibiotics and beyond
Bibliographic record
Abstract
Abstract A major threat to public health is the resistance and persistence of Gram‐negative bacteria to multiple drugs during antibiotic treatment. The resistance is due to the ability of these bacteria to block antibiotics from permeating into and accumulating inside the cell, while the persistence is due to the ability of these bacteria to enter into a nonreplicating state that shuts down major metabolic pathways but remains active in drug efflux. Resistance and persistence are permitted by the unique cell envelope structure of Gram‐negative bacteria, which consists of both an outer and an inner membrane (OM and IM, respectively) that lay above and below the cell wall. Unexpectedly, recent work reveals that m1G37 methylation of tRNA, at the N1 of guanosine at position 37 on the 3′‐side of the tRNA anticodon, controls biosynthesis of both membranes and determines the integrity of cell envelope structure, thus providing a novel link to the development of bacterial resistance and persistence to antibiotics. The impact of m1G37‐tRNA methylation on Gram‐negative bacteria can reach further, by determining the ability of these bacteria to exit from the persistence state when the antibiotic treatment is removed. These conceptual advances raise the possibility that successful targeting of m1G37‐tRNA methylation can provide new approaches for treating acute and chronic infections caused by Gram‐negative bacteria. This article is categorized under: Translation > Translation Regulation RNA Processing > RNA Editing and Modification RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".