Dynamics of integron structures across a wastewater network – Implications to resistance gene transfer
Bibliographic record
Abstract
Class 1 and other integrons are common in wastewater networks, often being associated with antibiotic resistance genes (ARGs). However, the importance of different integron structures in ARG transfer within wastewater systems has only been implied, especially between community and hospital sources, among wastewater treatment plant compartments, and in receiving waters. This uncertainty is partly because current clinical class 1 integron qPCR assays (i.e., that target human-impacted structures, i.e., clintI1) poorly delineate clintI1 from non-impacted class 1 integron structures. They also say nothing about their ARG content. To fill these technical gaps, new real-time qPCR assays were developed for "impacted" class 1 structures (called aint1; i.e., anthropogenic class 1 integrons) and empty aint1 structures (i.e., carry no ARGs; called eaint1). The new assays and other integron assays then were used to examine integron dynamics across a wastewater network. 16S metagenomic sequencing also was performed to characterise associated microbiomes. aint1 abundances per bacterial cell were about 10 times greater in hospital wastewaters compared with other compartments, suggesting aint1 enrichment with ARGs in hospital sources. Conversely, the relative abundance of eaint1 structures were over double in recycled activated sludge compared with other compartments, except receiving waters (RAS; ∼30% of RAS class 1 structures did not carry ARGs). Microbiome analysis showed that human-associated bacterial taxa with mobile integrons also differed in RAS and river sediments. Further, class 1 integrons in RAS bacteria appear to have released ARGs, whereas hospital bacteria have accumulated ARGs. Results show that quantifying integron dynamics can help explain where ARG transfer occurs in wastewater networks, and should be considered in future studies on antibiotic resistance in the environment.
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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 | 0.000 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".