Landfills as Hotspots of Multidrug Resistance Genes: Profiles, Drivers, and Hosts
Bibliographic record
Abstract
The United Nations has identified municipal solid waste (MSW) landfills as significant reservoirs of antibiotic resistance genes (ARGs). Although ARG profiles, their primary drivers, and associated hosts have been well characterized in landfill leachate, such information remains limited for MSW landfills, which are the original source of the resistome. This knowledge gap impedes effective ARG monitoring at the source and poses challenges for public health management. Herein, we investigated the profiles of ARGs, their potential drivers, and associated hosts in refuse samples collected from a large-scale landfill using metagenomic sequencing and quantitative polymerase chain reaction analysis. Our findings revealed that landfills harbor diverse ARGs, with multidrug resistance genes (MDRGs) emerging as the dominant class, accounting for 39.78% of all ARGs detected. Notably, MDRGs exhibited high mobility potential (associated with plasmids, phages, and mobile genetic elements (MGEs)) and were frequently colocated with virulence factors. Pseudomonas, Acinetobacter, and Brevundimonas were identified as key MDRG hosts. Partial least-squares path modeling analysis indicated that MDRG variation was driven by multiple factors (i.e., MGEs, metal resistance genes (MRGs), hosts, and environmental factors). Additionally, metagenome-assembled genomes were found to carry multiple MDRGs. Collectively, these results underscore the role of landfills as critical hotspots for MDRGs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".