Nanopore-based pathogen surveillance allows complete metagenome-assembled genome reconstruction of low-abundance enteric pathogens in wastewater samples
Bibliographic record
Abstract
Abstract Background Pathogen surveillance often relies on culture-based methods and epidemiological traceback investigations that are both time- and cost-ineffective, especially in the case of enteropathogenic bacteria contaminating food supplies. Nevertheless, metagenomic sequencing of wastewater influent helps conduct continuous, culture-independent, and community-level monitoring of microbes shed from the human gut microbiome. However, even though amplicon-based microbiome census methods help identify taxa, they typically do not allow strain-level epidemiology or investigating virulence factors and antimicrobial resistance mechanisms involved in an outbreak. Conversely, whole metagenome shotgun sequencing allows both taxonomic surveys and genome reconstruction. Results Here we present a metagenomic “tracking and assembling” workflow, applied between September 2023 to January 2024, in which we tracked two low-abundance enteric pathogens (Shiga toxin-producing Escherichia coli and enteropathogenic non-typhoidal Salmonella enterica; 0.1-1% total reads) and reconstructed 95-99% complete genomes using a combined taxonomic read binning and reference-based assembly. Furthermore, for these two pathogens, a maximum abundance peak significantly above baseline levels, assuming 95% confidence, was detected and found to precede by a month two public food recalls, all within the same urban community where municipal wastewater sampling was conducted (Quebec City, Canada). Conclusions This present work suggests that a continuous “tracking and assembling” approach enhances the resolution of low-abundance pathogen monitoring to the strain level, while also providing information about the gene contents of low-abundance enteropathogens, even when relative abundance is too low to reconstruct genomes via a generic de novo assembly and contig binning approach.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".