Evidence map›Paper›PMID 42803564›Full record

ArticleApplied and environmental microbiology2026

Prospective metagenomic sequencing of wastewater across the United States yields robust viral enrichment and concordance with digital PCR measurements.

Marlene K Wolfe, Devin North, Alexander L Jaffe, Alessandro Zulli, Dorothea Duong, Bridgette Shelden, Miriam Goldman, Miles Richardson, Peter Thana, Vikram Chan-Herur and 6 more

Abstract read
In one paragraph

Article in Applied and environmental microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Marlene K Wolfe *Gangarosa Department of Environmental Health, Emory University, Atlanta, Georgia, USA.ORCID 0000-0002-6476-0450
Devin North *Gangarosa Department of Environmental Health, Emory University, Atlanta, Georgia, USA.ORCID 0000-0002-0671-3381
Alexander L Jaffe *Department of Civil and Environmental Engineering, Stanford University, Stanford, California, USA.ORCID 0000-0002-6903-1687
Alessandro ZulliDepartment of Civil and Environmental Engineering, Stanford University, Stanford, California, USA.
Dorothea DuongVerily Health, South San Francisco, California, USA.
Bridgette SheldenVerily Health, South San Francisco, California, USA.
Miriam GoldmanVerily Health, South San Francisco, California, USA.
Miles RichardsonVerily Health, South San Francisco, California, USA.
Peter ThanaVerily Health, South San Francisco, California, USA.
Vikram Chan-HerurVerily Health, South San Francisco, California, USA.
Pouya KheradpourVerily Health, South San Francisco, California, USA.
Amanda L BidwellDepartment of Civil and Environmental Engineering, Stanford University, Stanford, California, USA.
Stephen P HiltonGangarosa Department of Environmental Health, Emory University, Atlanta, Georgia, USA.
Sheena ConfortiDepartment of Civil and Environmental Engineering, Stanford University, Stanford, California, USA.
Abigail P PaulosGangarosa Department of Environmental Health, Emory University, Atlanta, Georgia, USA.
Alexandria B BoehmDepartment of Civil and Environmental Engineering, Stanford University, Stanford, California, USA.ORCID 0000-0002-8162-5090

Funding

Sergey Brin Family Foundation
6 · The paper itself

Abstract

Metagenomic sequencing is increasingly applied to wastewater to characterize the diversity, dynamics, and relative abundance of human and animal viruses. Among these sequencing approaches are those that enrich viral nucleic acids from the wastewater matrix, aiming to increase the viral read fraction for analysis. However, the feasibility of scaling targeted viral sequencing to diverse sewersheds across large geographic scales is currently unknown. In this study, we apply hybrid capture metagenomic sequencing to nearly 450 weekly wastewater samples collected during the respiratory virus season in the United States and evaluate sequencing performance for generating public health-relevant data. Analysis of data from 15 wastewater treatment plants demonstrates that our approach enabled efficient capture of pathogens of interest, achieving a median viral read fraction over 19%. Importantly, relative abundance estimates of common pathogens correlated with direct quantification of viral targets using reverse transcription digital droplet PCR. Together, our results demonstrate that hybrid capture sequencing of wastewater is a viable tool to monitor both common and rare pathogens across geographically diverse sewersheds.IMPORTANCEWastewater testing is commonly used to identify and quantify human pathogens at a community scale. However, the most commonly used approaches rely on targeted, PCR-based methods that are highly specific to a single virus. Metagenomic sequencing provides an opportunity to detect and quantify the relative abundance of a wide range of viruses that are important for human health from wastewater, and hybrid capture approaches work by first enriching samples for these extremely rare targets to increase sensitivity. We demonstrate that hybrid capture metagenomic sequencing successfully enriches wastewater samples from diverse locations across the United States and that data derived from sequences are associated with detections of 11 key viruses using standard RT-ddPCR methods. This work suggests that scaling hybrid capture metagenomics for viruses in wastewater is a feasible way to generate data critical for public health to support infectious disease outbreak response.

Indexed as

MetagenomicsPolymerase Chain ReactionVirusesWastewaterHumansUnited StatesWastewaterepidemiologyhybrid capturemetagenomicsviruswastewater

Identifiers

PMID42803564
PMCPMC13688950

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.