Prevalence, source and risk of antibiotic resistance genes in the sediments of Lake Tai (China) deciphered by metagenomic assembly: A comparison with other global lakes
Bibliographic record
Abstract
Lakes are one of the natural reservoirs of antibiotic resistance genes (ARGs) in environments. Long retention times in lakes potentially allow ARGs to persist and may create increased opportunities for the emergence of resistant pathogens. In this study, we investigated the prevalence, source and dissemination risk of ARGs in the sediments of a typical urban lake, Lake Tai (China) which has been a drastic example of water pollution with eutrophication in the world due to its proliferated cyanobacterial blooms. High-throughput profilings of ARGs in the sediments of Lake Tai were characterized with metagenomic assembly, and were compared with those in other global lakes from Australia, Canada, Indonesia, Rwanda and the United States of America. The hosts of ARGs in the sediments of Lake Tai were explored based on the taxonomic annotation of ARG-carrying contigs and network analysis, and a novel recently-discovered crAssphage was employed for source tracking of resistance bacteria. Meanwhile, the potential resistome risk was identified by projecting the co-occurrence of acquired ARGs, mobile genetic elements (MGEs) and human bacterial pathogens into a three-dimensional exposure space. Results showed 321 ARG subtypes belonging to 21 ARG types were detected in the sediments of Lake Tai, dominated by multidrug, macrolide-lincosamide-streptogramin, bacitracin, quinolone, mupirocin and trimethoprim resistance genes. Relatively, the ARG levels in the sediments of Lake Tai were significantly higher than those in other global lakes. Source tracking showed the coverages of detected crAssphage in the sediments of Lake Tai were positively correlated with the total ARG coverage, suggesting the contribution of human fecal contamination to the prevalence of ARGs in this lake. It should be noted that the co-occurrence ratio of ARGs, MGEs and human pathogens in the sediments of Lake Tai was higher than that in other global lakes, likely indicating a higher risk for the resistance dissemination in the China's third largest freshwater lake.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".