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Record W4321184664 · doi:10.1101/2023.02.14.23285806

Detection of <i>Salmonella</i> Typhi bacteriophages in surface waters as a scalable approach to environmental surveillance

2023· preprint· en· W4321184664 on OpenAlexaff
Sneha Shrestha, Késia Esther da Silva, Jivan Shakya, Alexander T. Yu, Nishan Katuwal, Rajeev Shrestha, Mudita Shakya, Sabin Bikram Shahi, Shiva Ram Naga, Christopher LeBoa, Kristen Aiemjoy, Isaac I. Bogoch, Senjuti Saha, Dipesh Tamrakar, Jason R. Andrews

Bibliographic record

VenuemedRxiv · 2023
Typepreprint
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsUniversity of Toronto
FundersBill and Melinda Gates Foundation
KeywordsSalmonella typhiTyphoid feverBiologyPopulationMicrobiologySalmonellaSiphoviridaeVeterinary medicineVirologyBacteriaBacteriophageGeneEscherichia coliEnvironmental healthGeneticsMedicine

Abstract

fetched live from OpenAlex

Abstract Environmental surveillance, using detection of Salmonella Typhi DNA, has emerged as a potentially useful tool to identify typhoid-endemic settings; however, it is relatively costly and requires molecular diagnostic capacity. We sought to determine whether S . Typhi bacteriophages are abundant in water sources in a typhoid-endemic setting, using low-cost assays. We collected drinking and surface water samples from urban, peri-urban and rural areas in 4 regions of Nepal. We performed a double agar overlay with S . Typhi to assess the presence of bacteriophages. We isolated and tested phages against multiple strains to assess their host range. We performed whole genome sequencing of isolated phages, and generated phylogenies using conserved genes. S . Typhi-specific bacteriophages were detected in 54.9% (198/361) of river water samples and 6.3% (1/16) drinking water samples from the Kathmandu Valley and Kavrepalanchok. Water samples collected within or downstream of population-dense areas were more likely to be positive (72.6%, 193/266) than those collected upstream from population centers (5.3%, 5/95) (p=0.005). In urban Biratnagar and rural Dolakha, where typhoid incidence is low, only 6.7% (1/15, Biratnagar) and 0% (0/16, Dolakha) samples contained phages. All S . Typhi phages were unable to infect other Salmonella and non- Salmonella strains, nor a Vi-knockout S . Typhi strain. Representative strains from S . Typhi lineages were variably susceptible to the isolated phages. Phylogenetic analysis showed that S . Typhi phages belonged to two different viral families ( Autographiviridae and Siphoviridae ) and clustered in three distinct groups. S . Typhi bacteriophages were highly abundant in surface waters of typhoid-endemic communities but rarely detected in low typhoid burden communities. Bacteriophages recovered were specific for S . Typhi and required Vi polysaccharide for infection. Screening small volumes of water with simple, low-cost plaque assays enables detection of S . Typhi phages and should be further evaluated as a scalable tool for typhoid environmental surveillance. Highlights Typhoid phages are detectable in surface water using simple assays, in communities with high typhoid burden. Bacteriophages are highly specific for S . Typhi and required Vi polysaccharide for infection. S . Typhi phages have a broad lytic activity against the S . Typhi strains circulating in Nepal. Phage plaque assay can be used as a low-cost tool to identify communities where typhoid is endemic. The high abundance of phages in river water suggest that this could be an alternative to molecular methods for environmental surveillance for typhoid.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.014
GPT teacher head0.226
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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