Prevalence and Characterization of Plasmid-Mediated Quinolone Resistance in Various Aquatic Sources
Bibliographic record
Abstract
Bacterial isolates found in aquatic ecosystems often carry antibiotic resistance genes (ARGs). These ARGs are often found on plasmids and transposons, which allows them to be proliferate throughout bacterial communities via horizontal gene transfer (HGT) causing dissemination of multidrug resistance. The increase in antibiotic resistance has raised concerns about the ability to continue to use these drugs to fight infectious diseases. Novel synthetic antibiotics like ciprofloxacin that are not naturally found in the environment were developed to prevent resistances. However, ciprofloxacin resistance has occurred through chromosomal gene mutations of type 2 topoisomerases or by the acquisition of plasmid-mediated quinolone resistances (PMQR). A particular PMQR, qnr genes, encoding for pentapeptide repeat proteins that confer low levels of quinolone resistance and protect DNA gyrase and topoisomerase IV from antibacterial activity. These qnr genes have been identified globally in both clinical and environmental isolates. The aim of this study was to determine the prevalence of ciprofloxacin-resistant bacteria in aquatic environments in the Greater Toronto Area and the potential dissemination of ciprofloxacin resistance. With the selective pressure of ciprofloxacin, we hypothesize that ciprofloxacin-resistant bacteria (CipR) in the environment may carry PMQR mechanisms while the sensitive population (CipS) would not carry PMQR genes. Isolates were tested for resistance to an additional 12 different antibiotics and identified using Sanger sequencing PCR products of the 16S rRNA gene. To determine which genes are responsible for ciprofloxacin resistance, multiplex PCR of associated qnr genes, qnrA, qnrB, and qnrS, was carried out on 202 environmental isolates. Our data demonstrate a similar prevalence of qnr genes was found in CipR (19%) and CipS (14%) populations suggesting that the presence of these genes was not necessarily correlated with the phenotypic resistance to the antibiotic. Furthermore, ciprofloxacinresistant bacteria were found in all locations at similar frequencies suggesting that resistance genes are widespread and could possibly arise through HGT events. Overall, determining the underlying cause and prevalence of ciprofloxacin resistance could help re-establish the effectiveness of these antimicrobial compounds.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".