Comparative genomic analyses of β-lactamase (<i>bla<sub>CMY-42</sub></i>)-encoding plasmids isolated from wastewater treatment plants in Canada
Bibliographic record
Abstract
Wastewater treatment plants (WWTPs) are useful environments for investigating the occurrence, diversity, and evolution of plasmids encoding clinically relevant antibiotic resistance genes (ARGs). Our objective was to isolate and sequence plasmids encoding meropenem resistance from bacterial hosts within Canadian WWTPs. We used two enrichment culture approaches for primary plasmid isolation, followed by screening for antibiotic resistance, conjugative mobility, and stability in enteric bacteria. Isolated plasmids were sequenced using Illumina MiSeq and Sanger sequencing methods. Bioinformatics analyses resolved a multi-resistance IncF/MOB F12 plasmid, pFEMG (209 357 bp), harbouring resistance genes to β-lactam (bla CMY-42 , bla TEM-1β , and bla NDM-5 ), macrolide (mphA-mrx-mphR), tetracycline (tetR-tetB-tetC-tetD), trimethoprim (dfrA12), aminoglycoside (aadA2), and sulfonamide (sul1) antibiotic classes. We also isolated an IncI1/MOB P12 plasmid pPIMR (172 280 bp) carrying similar β-lactamase and a small multi-drug efflux resistance gene cluster (bla CMY-42 -blc-sugE) to pFEMG. The co-occurrence of different ARGs within a single 24 552 bp cluster in pFEMG — interspersed with transposons, insertion sequence elements, and a class 1 integron — may be of significant interest to human and veterinary medicine. Additionally, the presence of conjugative and plasmid maintenance genes in the studied plasmids corresponded to observed high conjugative transfer frequencies and stable maintenance. Extensive investigation is required to further understand the fitness trade-offs of plasmids with different types of conjugative transfer and maintenance modules.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".