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Record W4417159155 · doi:10.1128/mbio.02654-25

Clinical and environmental wastewater-based bacteriophage surveillance for high-impact diarrheal diseases, including cholera, in Bangladesh

2025· article· en· W4417159155 on OpenAlexaff
Marjahan Akhtar, Subah Nuzhat Hussain, Nazia Nazrul Nafsi, Nasrin Parvin, Farhana Khanam, Md Taufiqul Islam, Md Amirul Islam Bhuiyan, Rafia Afroz, Md Golam Firoj, Fahima Chowdhury, Ashraful Islam Khan, Mohammad Jubair, Edward T. Ryan, B. Jesse Shapiro, Nicholas R. Thomson, Eric J. Nelson, Mustafizur Rahman, Yasmin Ara Begum, Taufiqur Rahman Bhuiyan, Firdausi Qadri

Bibliographic record

VenuemBio · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsMcGill University
FundersNational Institute of Allergy and Infectious DiseasesFogarty International CenterNational Institutes of HealthInternational Centre for Diarrhoeal Disease Research, BangladeshWellcome Trust
KeywordsBacteriophageNorovirusCholeraWastewaterTransmission (telecommunications)Diarrhea

Abstract

fetched live from OpenAlex

ABSTRACT Bacteriophages (phages) likely play a critical role in modulating transmission dynamics of diarrheal pathogens. This study investigated the role of phages in modulating the prevalence and seasonal patterns of major diarrheal pathogens, Vibrio cholerae O1 (VCO1), enterotoxigenic Escherichia coli (ETEC), Shigella spp., and Salmonella spp. in diarrheal patients and environmental wastewater specimens collected from six different sites in Dhaka, Bangladesh, in 2024. VCO1, ETEC, Shigella , and Salmonella were detected in 10.1%, 7.8%, 1.7%, and 2.4% of diarrheal specimens, respectively. In contrast, phages targeting these pathogens were more frequently isolated, with detection rates of 20% for VCO1, 30% for ETEC, 57% for Shigella , and 9.2% for Salmonella -specific phages. Adults showed a significantly higher burden of VCO1 and corresponding phages compared with children <5 years ( P < 0.001). Seasonal analysis revealed significant correlations between VCO1 (37.3%) and corresponding phages (57.6%) peaking in late September in both clinical ( r = 0.53, P < 0.0001) and environmental wastewater specimens ( r = 0.65, P < 0.001). The highest correlation (r = 0.68) was found between the increased rate of wastewater phages in the preceding week and a rise in cholera cases in the following week. ETEC and ETEC phages isolated from wastewater also showed strong correlations ( r = 0.65, P < 0.001). Cross-specificity analysis demonstrated that VCO1 phages were highly specific to their targets, whereas ETEC and Shigella phages exhibited broader host ranges, with some Shigella phages capable of infecting ETEC and Salmonella spp. Overall, these findings support the hypothesis that Vibrio phages could serve as an alternative or complementary tool for cholera surveillance. IMPORTANCE Understanding the dynamics between phages and their bacterial hosts is critical for elucidating disease burden; however, their potential for surveillance remains underexplored. To our knowledge, this is the first study that longitudinally investigated major diarrheal pathogens and their phages in both clinical and environmental sources to assess the potential of bacteriophages as a tool to improve diarrheal surveillance. The high frequency of phages compared to the host bacterial counterparts suggests a valuable, yet underutilized, role for phages in surveillance systems. Strong seasonal alignment between V. cholerae O1 and its phages, both peaking in late September, suggests that phage dynamics may reflect pathogen transmission. These preliminary observations raise the possibility that wastewater-derived Vibrio phages could function as early indicators of cholera burden. Future research should aim to explore the complex and poorly understood interactions between phages and their bacterial hosts, particularly how these dynamics shape pathogen populations in endemic settings.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.012
GPT teacher head0.292
Teacher spread0.281 · 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 teacher head, 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
Published2025
Admission routes1
Has abstractyes

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