Uropathogenic Escherichia coli in a Diabetic Dog with Recurrent UTIs: Genomic Insights and the Impact of Glucose and Antibiotics on Biofilm Formation
Bibliographic record
Abstract
Recurrent urinary tract infections (UTIs) pose a significant clinical challenge in both human and veterinary medicine, due to antibiotic-resistant and biofilm-forming bacteria. We hypothesized that high glucose levels in diabetic animals enhance biofilm formation and reduce antibiotic efficacy, promoting infection persistence. This study analyzed Escherichia coli from a diabetic female Labrador Retriever with recurrent UTIs over 18 months, focusing on antimicrobial resistance, biofilm-forming capacity, and genomic characterization. Most isolates (9/11) were resistant to ampicillin and fluoroquinolones. Whole genome sequencing of six selected isolates revealed that they belonged to the multidrug-resistant ST1193 lineage, a globally emerging clone associated with persistent infections. Phylogenetic analysis revealed clonal continuity across six UTI episodes, with two distinct clones identified: one during a coinfection in the second episode and another in the last episode. High-glucose conditions significantly enhanced biofilm production and dramatically reduced antibiotic susceptibility, as evidenced by a marked increase in minimum biofilm inhibitory concentrations (MBICs), which were at least 256-fold higher than the corresponding minimum inhibitory concentration (MIC). Sulfamethoxazole–trimethoprim demonstrated the strongest antibiofilm activity, though this was attenuated in glucose-supplemented environments. This research highlights the clinical relevance of glucosuria in diabetic patients and emphasizes the need for therapeutic strategies targeting biofilm-mediated antibiotic tolerance to improve the management of recurrent UTIs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".