Metagenomic analysis after selective culture enrichment of hospital and community wastewater enhances antimicrobial resistance gene detection
Bibliographic record
Abstract
Antimicrobial resistance is an accelerating threat to global health. Wastewater-based surveillance (WBS) enables objective, inclusive, and comprehensive assessments of population-level antimicrobial resistance; however, it is limited in its ability to detect rare antibiotic resistance genes (ARG). We compared traditional high-depth metagenomic sequencing of raw wastewater with lower-depth sequencing following semi-selective culture enrichment for gram negatives for rare ARGs in wastewater from two tertiary-care hospitals and two nearby urban neighborhoods. In total, 26 antibiotic resistance gene types (1,225 subtypes) were identified, with beta-lactamase genes being the most prevalent. Resistomes differed between raw and culture-enriched wastewater metagenomes and clustered based on sample type (hospitals versus neighborhoods). Hospital wastewater had higher diversity and a greater abundance of ARGs relative to both raw and culture-enriched neighborhood wastewater metagenomes. Lower coverage sequencing following culture enrichment proved superior to deeper sequencing for identifying rare, clinically relevant targets, including carbapenemase genes. In particular, enrichment with meropenem proved the most sensitive to identifying clinically relevant genes and enabled significant cost savings. ARG WBS has enormous potential for augmenting hospital-based infection prevention and control and antimicrobial stewardship programs.IMPORTANCEAntimicrobial resistance (AMR) poses a considerable burden to healthcare systems and contributes to increased morbidity and mortality. This is expected to further increase with time. AMR surveillance programs are key to understanding and controlling this progressive threat. Wastewater-based surveillance (WBS) is an emerging tool that can be adapted to this end. This study explores the role of metagenomic analysis of WBS with/and without culture enrichment to detect rare antibiotic resistance genes (ARG) of clinically important pathogens across a range of scales. We were able to demonstrate that the resistome of hospitals significantly differs from communities having a greater abundance, and more heterogeneous ARGs. Culture enrichment, particularly with meropenem, improved the detection of clinically relevant ARGs even at lower sequencing depths. WBS is an important tool with the capacity to augment hospital-based infection control and antimicrobial stewardship programs, providing real-time, cost-effective information on the population within.
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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) | 0.000 | 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 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".