Influence of aquaculture and genomic surveillance techniques on antimicrobial resistance gene profiles and microbiota detected in marine and freshwater sediments
Bibliographic record
Abstract
Surveillance methods for antimicrobial resistance genes (ARGs) are needed to assess potential risk of antimicrobial resistance, especially in complex environmental samples with limited data on ARG distribution. This study employed target-enrichment metagenomics (bait-capture) and a Resistomap qPCR assay to assess the resistome in marine and freshwater sediments associated with active Canadian finfish aquaculture operations. Differences in resistome profiles were considered with distance to the net-pens, concentrations of three aquaculture-associated chemical residues, and microbial communities as detected with 16S rRNA gene amplicon sequencing. With bait-capture, a total of 194 ARGs and 41 replicon types were detected across the sediment samples. Differences due to aquaculture proximity were noted in the composition of the resistome, which was dominated by tetracycline resistance genes. With qPCR, 37 out of 51 ARGs targets were detected, and proximity to net-pens or region did not show changes in resistome composition. Co-occurrence networks revealed significant correlations among genera and the resistome detected with bait-capture, highlighting a potential influence of aquaculture on ARGs in the environment. This study demonstrates the utility of bait-capture and qPCR assays in detection of ARGs in both freshwater and marine sediments from aquaculture sites that will assist further ARG surveillance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".