Bacterial persistence increases as environmental fitness decreases
Bibliographic record
Abstract
Since persister cells cause chronic infections and since Escherichia coli toxin MqsR increases persisters, we used protein engineering to increase the toxicity of MqsR to gain insights into persister cell formation. Through two amino acid replacements that increased the stability of MqsR, toxicity and persistence were increased. A whole-transcriptome study revealed that the MqsR variant increased persistence by repressing genes for acid resistance, multidrug resistance and osmotic resistance. Corroborating these microarray results, deletion of rpoS, as well as the genes that the master stress response regulator RpoS controls, increased persister formation dramatically to the extent that nearly the whole population became persistent. Furthermore, wild-type cells stressed by prior treatment to acid or hydrogen peroxide increased persistence 12 000-fold. Whole-transcriptome analyses of persister cells generated by two different methods (wild-type cells pretreated with hydrogen peroxide and the rpoS deletion) corroborated the importance of suppressing RpoS in persister cell formation. Therefore, the more toxic MqsR increases persistence by decreasing the ability of the cell to respond to antibiotic stress through its RpoS-based regulation of acid resistance, multidrug resistance and osmotic resistance 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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".