Subinhibitory Antibiotic Therapy Alters Recurrent Urinary Tract Infection Pathogenesis through Modulation of Bacterial Virulence and Host Immunity
Bibliographic record
Abstract
UNLABELLED: The capacity of subinhibitory levels of antibiotics to modulate bacterial virulence in vitro has recently been brought to light, raising concerns over the appropriateness of low-dose therapies, including antibiotic prophylaxis for recurrent urinary tract infection management. However, the mechanisms involved and their relevance in influencing pathogenesis have not been investigated. We characterized the ability of antibiotics to modulate virulence in the uropathogens Staphylococcus saprophyticus and Escherichia coli. Several antibiotics were able to induce the expression of adhesins critical to urothelial colonization, resulting in increased biofilm formation, colonization of murine bladders and kidneys, and promotion of intracellular niche formation. Mice receiving subinhibitory ciprofloxacin treatment were also more susceptible to severe infections and frequent recurrences. A ciprofloxacin prophylaxis model revealed this strategy to be ineffective in reducing recurrences and worsened infection by creating larger intracellular reservoirs at higher frequencies. Our study indicates that certain agents used for antibiotic prophylaxis have the potential to complicate infections. IMPORTANCE: Antibiotics are the mainstay treatment for bacterial infections; however, evidence is emerging that argues these agents may have off-target effects if sublethal concentrations are present. Most studies have focused on changes occurring in vitro, leaving questions regarding the clinical relevance in vivo. We utilized a murine urinary tract infection model to explore the potential impact of low-dose antibiotics on pathogenesis. Using this model, we showed that subinhibitory antibiotics prime uropathogens for adherence and invasion of murine urothelial tissues. These changes in initial colonization promoted the establishment of chronic infection. Furthermore, treatment of chronically infected mice with subtherapeutic ciprofloxacin served to exacerbate infection. A part of these changes was thought to be due to suppression of mucosal immunity, as demonstrated through reductions in cytokine secretion and migration of leukocytes into the urinary tract. This work identifies novel risk factors associated with antibiotic therapy when dosing strategies fall below subtherapeutic levels.
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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".