Genes on the Move: Exploring the Resistome of Viable Isolates from a First Nation Community in Manitoba
Bibliographic record
Abstract
Abstract Despite wastewater treatment plants (WWTPs) and oxidation lagoons (OL) being designed for pollutant and pathogen removal, these systems can also serve as environments conducive to the exchange of mobile genetic elements (MGEs) and antibiotic resistance genes (ARGs) among microorganisms. This context facilitates the dissemination of antimicrobial resistance (AMR) among both pathogenic and non-pathogenic environmental bacteria, as well as phage populations, within these facilities. To enhance understanding of AMR dynamics in wastewater, our study examined the resistome of bacterial isolates recovered from a OL in a First Nation (FN) community in Manitoba, Canada. Thirty-five samples were collected from all OL stages, including raw sewage, lagoons, submerged attached growth reactor (SAGR), UV-treated effluent, and an upstream control site, from September 2022 to April 2023. Whole genome sequencing of 58 viable isolates was performed using Oxford Nanopore MinION, with genomes reconstructed via de novo assembly. The isolates corresponded to the following genera: Aeromonas 50.87% , Serratia 15.78%, Pantoea and Escherichia 7.01% each , Lelliottia 5.26% , Rahnella, Enterobacter and Buttiauxella 3.50% each, and finally Acinetobacter, Yersinia and Citrobacter 1.75%. Furthermore, a total of 32,559 elements were classified as ARGs. MGEs carrying ARGs were detected in 72.72 % of the isolates. ARGs conferring resistance to macrolides, tetracyclines, and fluoroquinolones demonstrated a notable presence within MGEs (plasmids and bacteriophages) identified throughout the facility’s stages. This research represents a non-invasive approach, enabling an in-depth exploration of AMR dynamics by discerning the resistome profiles of bacteria residing within a rural OL facility from a FN community. Importance OLs commonly found in rural and Indigenous communities in Canada, are crucial for mitigating environmental hazards and safeguarding public health. Paradoxically, these systems can also serve as hotspots for the dissemination of ARGs and MGEs, thereby exacerbating AMR. The persistence of ARGs within both pathogenic and non-pathogenic bacteria, often carried on MGEs including phages, in treated effluents raises significant concerns about the environmental impact and public health. This study offers fundamental insights into the unique dynamics of AMR within OLs serving a First Nation community in Manitoba. Understanding these specific dynamics is crucial for developing robust monitoring frameworks and implementing targeted, sustainable strategies to mitigate the public health and environmental risks posed by AMR in these often-underserved remote settings.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".