Resistance Gene Dynamics, Biogeochemical Coupling, and Ecological Risks in Sediments of Anthropogenically Impacted Lake Wetlands in China
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Antibiotic resistance is a growing global threat to both public health and ecosystem stability. While the “One Health” framework emphasizes the need to monitor antibiotic resistance genes (ARGs) across diverse environments worldwide, the risks posed by ARGs in lakes affected by human activities, particularly in lake sediments that serve as natural reservoirs of ARGs, remain poorly understood. Metagenomics enables culture-independent analysis of microbial communities and resistance genes, providing essential insights into ARG dynamics. This study investigates microbial communities, ARGs, metal resistance genes (MRGs), and mobile genetic elements (MGEs) in sediments from Lake Donghu and Lake Weishan in China, two contrasting lake ecosystems subject to urbanization and agricultural activities for over four decades, using high-throughput metagenomic sequencing and assembly. ARGs and MRGs were more strongly influenced by deterministic environmental factors, particularly heavy metals (Cd, Pb, Cu), whereas microbial community structures were predominantly shaped by stochastic processes. Metagenomic binning yielded 293 metagenome-assembled genomes (MAGs), 125 of which were identified as potential ARG hosts, with Proteobacteria and Desulfobacterota being the most common. These hosts frequently cocarried MGEs, virulence factor genes (VFGs), and MRGs and exhibited metabolic pathways linked to carbon, nitrogen, and greenhouse gas (CO 2 and N 2 O) cycling. Dissolved organic carbon (DOC) was determined as a key factor influencing microbial metabolism and promoting resistance gene dissemination. Our findings highlight a tight coupling between ARG dissemination, microbial ecological functions, and biogeochemical processes, underscoring ecosystem-level risks associated with resistance proliferation in human-impacted wetlands of China and elsewhere.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".