Age of Inoculum Strongly Influences Persister Frequency and Can Mask Effects of Mutations Implicated in Altered Persistence
Bibliographic record
Abstract
The majority of cells transferred from stationary-phase culture into fresh medium resume growth quickly, while a few remain in a nongrowing state for longer. These temporarily nonproliferating bacteria are tolerant of several bactericidal antibiotics and constitute a main source of persisters. Several genes have been shown to influence the frequency of persisters in Escherichia coli, although the exact mechanism underlying persister formation is unknown. This study demonstrates that the frequency of persisters is highly dependent on the age of the inoculum and the medium in which it has been grown. The hipA7 mutant had 1,000 times more persisters than the wild type when inocula were sampled from younger stationary-phase cultures. When started after a long stationary phase, the two displayed equal and elevated persister frequencies. The lower persister frequencies of glpD, dnaJ, and surA knockout strains were increased to the level of the wild type when inocula aged. The mqsR and phoU deletions showed decreased persister levels only when the inocula were from aged cultures, while sucB and ygfA deletions had decreased persister levels irrespective of the age of the inocula. A dependency on culture conditions underlines the notion that during screening for mutants with altered persister frequencies, the exact experimental details are of great importance. Unlike ampicillin and norfloxacin, which always leave a fraction of bacteria alive, amikacin killed all cells in the growth resumption experiment. It was concluded that the frequency of persisters depends on the conditions of inoculum cultivation, particularly its age, and the choice of antibiotic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".