Comparison of three sequencing methods for identifying and quantifying antibiotic resistance genes (ARGs) in sewage
Bibliographic record
Abstract
Abstract Background Globally, antimicrobial resistance (AMR) poses a critical threat, requiring robust surveillance methodologies to tackle the growing challenge of drug-resistant microbes. AMR is a huge challenge in India due to high disease burden, lack of etiology-based diagnostic tests and over the counter availability of antibiotics and inadequate treatment of wastewaters are important drivers of AMR in India. There is lack of effective surveillance platforms that monitor health-associated infections. This include developing an understanding on background levels of AMR in the environment and comparison of AMR monitoring methods. Objectives This study evaluated the performance of three AMR sequencing methods, Illumina AmpliSeq AMR panel, QIAseq xHYB AMR panel and shotgun sequencing method for the detection of antimicrobial resistance genes (ARGs) in urban sewage. Our goal is to provide insights into the application and robustness of each sequencing method. Methods We compare the prevalence, diversity, and composition of ARGs across sequencing method and by sample type (inlet vs outlet) in four sewage treatment plants (STP). Results Regardless of the sequencing method used the dominant ARGs remained consistent, and their differential analysis showed consistent trends in detection of epidemiologically relevant ARGs. The cost-effectiveness analysis revealed comparable per-sample costs, with amplicon-based sequencing offering specificity for targeted genes, and shotgun sequencing uses a whole-genome sequencing approach that provides high-resolution taxonomic information for the characterisation of pathogens. This methodology can only detect ARGs that have been annotated in the reference database (e.g., CARD). Therefore, some novel types of ARGs present in the samples may be missed since the analysis is based on a similarity search. Differential abundance analysis to understand change in abundance in dominant ARGs from inlet to outlet of STP showed consistent trends across methods. However, with caution raised regarding potential artifacts introduced by enrichment steps in QIAseq xHYB AMR panel. Conclusion The choice of panel used would be governed by the context of the study. Nonetheless, our exploratory study shows that the data gathered using different sequencing pipelines helps in quantifying the ARG burden in the environment. This information is crucial in understanding the spatio-temporal distribution of ARGs in different environment and could help in developing PCR-based approaches for targeted surveillance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".