Persistence Increases in the Absence of the Alarmone Guanosine Tetraphosphate by Reducing Cell Growth
Bibliographic record
Abstract
Most bacterial cells are stressed, and as a result, some become tolerant to antibiotics by entering a dormant state known as persistence. The key intracellular metabolite that has been linked to this persister state is guanosine tetraphosphate (ppGpp), the alarmone that was first linked to nutrient stress. In Escherichia coli, ppGpp redirects protein production during nutrient stress by interacting with RNA polymerase directly and by inhibiting several proteins. Consistently, increased levels of ppGpp lead to increased persistence; but, the mechanism by which elevated ppGpp translates into persistence has not been determined. Hence, we explored persistence in the absence of ppGpp so that the underlying mechanism of persister cell formation could be explored. We found that persister cells still form, although at lower levels, in the absence of ppGpp. Additionally, the toxin/antitoxin systems that we investigated (MqsR, MazF, GhoT, and YafQ) remain able to increase persistence dramatically in the absence of ppGpp. By overproducing each E. coli protein from the 4287 plasmid vectors of the ASKA library and selecting for increased persistence in the absence of ppGpp (via a relA spoT mutant), we identified five new proteins, YihS, PntA, YqjE, FocA, and Zur, that increase persistence simply by reducing cell growth.
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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".