Selective Target Inactivation Rather than Global Metabolic Dormancy Causes Antibiotic Tolerance in Uropathogens
Bibliographic record
Abstract
Persister cells represent a multidrug-tolerant (MDT), physiologically distinct subpopulation of bacteria. The ability of these organisms to survive lethal antibiotic doses raises concern over their potential role in chronic disease, such as recurrent urinary tract infection (RUTI). Persistence is believed to be conveyed through global metabolic dormancy, which yields organisms unresponsive to external stimuli. However, recent studies have contested this stance. Here, various antibiotics that target different cellular processes were used to dissect the activity of transcription, translation, and peptidoglycan turnover in persister cells. Differential susceptibility patterns were found in type I and type II persisters, and responses differed between Staphylococcus saprophyticus and Escherichia coli uropathogens. Further, SOS-deficient strains were sensitized to ciprofloxacin, suggesting DNA gyrase activity in persisters and indicating the importance of active DNA repair systems for ciprofloxacin tolerance. These results indicate that global dormancy per se cannot sufficiently account for antibiotic tolerance. Rather, the activity of individual cellular processes dictates multidrug tolerance in an antibiotic-specific fashion. Furthermore, the susceptibility patterns of persisters depended on their mechanisms of onset, with subinhibitory antibiotic pretreatments selectively shutting down cognate targets and increasing the persister fraction against the same agent. Interestingly, antibiotics targeting transcription and translation enhanced persistence against multiple agents indirectly related to these processes. Conducting these assays with uropathogenic E. coli isolated from RUTI patients revealed an enriched persister fraction compared to organisms cleared with standard antibiotic therapy. This finding suggests that persister traits are either selected for during prolonged antibiotic treatment or initially contribute to therapy failure.
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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 | 0.000 | 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".