Seasonal patterns of viromes in urban aquatic environments of Manitoba
Bibliographic record
Abstract
ABSTRACT Although wastewater treatment plants harbor many pathogens, traditional methods that monitor the microbial quality of surface water and wastewater have not changed since the early 1900s and often disregard the presence of other types of significant waterborne pathogens such as viruses. We used metagenomics and quantitative PCR to assess the taxonomy, functional profiling, and seasonal patterns of DNA and RNA viruses, including the virome distribution in aquatic environments receiving wastewater discharges. Environmental water samples were collected at 11 locations in Winnipeg, Manitoba, along the Red and Assiniboine rivers during the Spring, Summer, and Fall 2021. Samples were filtered and underwent skimmed milk flocculation for viral concentration. The taxonomy of classified DNA was primarily bacteriophages such as Autographiviridae , Kyanoviridae , and Peduoviridae, which were abundant to a lesser extent. Phage-related functionalities such as portal proteins and bacteriophage T4 major head proteins accounted for approximately 20%–40% of aquatic samples across seasons, which may possibly correspond to the DNA phages that were previously identified. RNA viruses went unclassified in the study; however, similar to DNA viruses, many were found to be non-enveloped or “naked viruses” such as Partiviridae, Picobirnaviridae, Tombusviridae, and Picornaviridae, which accounted for 3%–30% of RNA viruses in the study locations across season. Interestingly, aquatic samples were revealed to maintain an abundance of RNA phage-related functionalities such as long tail fiber protein and putative tail proteins, which accounted for approximately 2%–5% of aquatic samples during the Fall of 2021. IMPORTANCE Municipal wastewater effluents discharged into the Red and Assiniboine rivers of Winnipeg, Manitoba, rely on traditional methods that monitor the microbial quality of effluents and receiving surface waters focus solely on the detection of coliforms, which are not necessarily good indicators of viruses or other pathogens. There is also a lack of current wastewater system effluent regulations at the federal and provincial level. Furthermore, previous literature has shown that when viral DNA and RNA sequences are blasted against current genomic databases, approximately 50% of the viral reads are classified as unknown. The significance of our research in characterizing the virome distribution in aquatic environments addresses a knowledge gap in the current effluent guidelines and a need for regulatory practices. In the long run, fecal indicator bacteria, combined with the detection of enteric viruses, may complement assessment of water quality in effluents discharged into rivers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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 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".