AMR Surveillance and Discovery Using Functional Metagenomics in Ontario Wastewater
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is an increasing challenge in health care. Wastewater sampling provides a chance to survey regional AMR in a non-invasive way for the contributing population. In this study, functional metagenomic libraries were constructed from total DNA using the mosaic ends tagmentation approach (METa), and the cosmid library construction method, yielding average insert sizes of 2kb and 20-30kb respectively. Shotgun metagenomics is performed in parallel on the wastewater samples to screen for known AMR genes. This approach seeks to determine the current antimicrobial resistance gene (ARG) landscape in the populations reflected by the regional wastewater and, in combination with functional metagenomics, to potentially discover new ARGs. Our research looks at ARG resistance towards some common antibiotics such as Kanamycin, Ampicillin, and Tetracycline while also targeting two carbapenems, Meropenem (MP) and Imipenem (IP). Beta-Lactam antibiotics are a well prescribed and diverse family of antibiotics. IP and MP specifically are used as final interventions for Gram-negative bacteria with other beta-lactam resistance that cause pulmonary infections in cystic fibrosis patients, meningitis, sepsis, and others. Therefore, the initial hosts of choice for these libraries were E. coli and P. putida. In further studies, using other hosts of interest, namely Moraxella catarrhalis and Bacillus subtilis, different ARGs not expressed in P. putida and E. coli can be identified. Clones which contain no known resistance genes will be sub-cloned using transposon mutagenesis to determine the location of the resistance gene and ORFs will be predicted as a first step in investigating potentially novel ARGs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".