Comparison of Methods to Identify Pathogens and Associated Virulence Functional Genes in Biosolids from Two Different Wastewater Treatment Facilities in Canada
Bibliographic record
Abstract
The use of treated municipal wastewater residues (biosolids) as fertilizers is an attractive, inexpensive option for growers and farmers. Various regulatory bodies typically employ indicator organisms (fecal coliforms, E. coli and Salmonella) to assess the adequacy and efficiency of the wastewater treatment process in reducing pathogen loads in the final product. Molecular detection approaches can offer some advantages over culture-based methods as they can simultaneously detect a wider microbial species range, including non-cultivable microorganisms. However, they cannot directly assess the viability of the pathogens. Here, we used bacterial enumeration methods together with molecular methods including qPCR, 16S rRNA and cpn60 gene amplicon sequencing and shotgun metagenomic sequencing to compare pre- and post-treatment biosolids from two Canadian wastewater treatment plants (WWTPs). Our results show that an anaerobic digestion WWTP was unsuccessful at reducing the live indicator organism load (coliforms, generic E. coli and Salmonella) below acceptable regulatory criteria, while biosolids from a dewatering/pelletization WWTP met these criteria. DNA from other pathogens was detected by the molecular methods, but these species were considered less abundant. Clostridium DNA increased significantly following anaerobic digestion treatments. In addition to pathogen DNA, genes related to virulence and antibiotic resistance were identified in treated biosolids. Shotgun metagenomics revealed the widest range of pathogen DNA and, among the approaches used here, was the only approach that could access functional gene information in treated biosolids. Overall, our results highlight the potential usefulness of amplicon sequencing and shotgun metagenomics as complementary screening methods that could be used in parallel with culture-based methods, although more detailed comparisons across a wider range of sites would be needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".