Characterizing antimicrobial resistance and virulence genes in Calgary wastewater Escherichia coli isolates
Bibliographic record
Abstract
Antibiotic resistance is becoming a global health issue. The number of infection cases caused by multi-drug resistant pathogens is increasing, causing loss of lives and burdening healthcare costs. As antibiotic resistance is a problem that affects the health of humans, animals, and the environment, solutions require multidisciplinary approaches. One area of focus in research is characterizing antibiotic resistance genes in the aquatic environment, and in particular, wastewater treatment plants (WWTPs). Not only do WWTPS serve to reduce the impact of human activity on the environment, but they also play an important role as a hotspot for horizontal gene transfer, which could accelerate the spread of antibiotic resistance genes. The goal of this project was to characterize antibiotic resistance and virulence genes from a library of multi-drug resistant minicipal wastewater Escherichia coli (E. coli) isolates located in Calgary, Alberta, Canada. We performed preliminary screening for antibiotic resistance of wastewater E. coli isolates collected from 2014 to 2017 (n=9242). Multi-drug resistant isolates were selected to build a sub-library of 400 isolates which were subjected to Dr. Neumann’s group at the University of Alberta for further antibiotic resistance profiling and biochemical testing. From the detailed resistance profile, a sub-library of 82 E. coli isolates with multi-drug resistance was selected for this study. Genomic DNA was extracted from the isolates, then their DNA libraries were prepared before being sent for whole-genome sequencing. Bioinformatics tools were used to analyse their genomic contents and detect resistance and virulence genes in silico. The 82 multi-drug resistant isolates were found to have an open pangenome model, with a large reservoir of accessory genes for adaptation. Several isolates, including ones that were collected after the ultraviolet treatment, clustered closely to known pathogenic strains on the phylogenetic tree and carry typical characteristic virulence genes for various E. coli pathotypes. The results from this study indicate the presence of potential pathogenic E. coli with multi-drug resistance in the environment and highlight the potential of using whole-genome sequencing as a robust tool for diagnostic tests.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".