One HealthGenomicsPeer-reviewed journal article
Viral metagenomics of synanthropic urban bats: a surveillance strategy for uncovering potentially zoonotic viruses
Juliana Amorim ConselheiroiD, Filipe Romero Rebello MoreiraiD, Gisely Toledo BaroneiD, Adriana Araújo Reis-MenezesiD, Adriana Rückert da RosaiD, Debora Cardoso de Oliveira, Bárbara Aparecida ChavesiD, Vanderson de Souza SampaioiD, Felipe RochaiD, Marco Antonio Natal VigilatoiD, Rodrigo G. StábeliiD, Rodrigo Fabiano do Carmo SaidiD, Paulo Eduardo BrandãoiD, Gabriel Luz WallauiD, Anderson Fernandes BritoiD
Abstract
Bats are natural reservoirs for diverse viruses, including coronaviruses, filoviruses, and paramyxoviruses, many leading to zoonotic spillover events and demanding an integrated surveillance. Here, we present a framework that leverages Brazils rabies passive surveillance program to detect bat-borne viruses. Using an algorithm to select representative specimens from 2,422 bats collected across Sao Paulo state, we submitted 150 paired lung and intestine samples to nanopore metagenomic sequencing. We detected 98 viral contigs from 12 families of public health relevance, including Arenaviridae, Coronaviridae, and Paramyxoviridae. Notably, the approach identified a previously unknown filovirus in bats in the Americas, validating the frameworks capacity for epidemic preparedness. These findings reveal an undetected viral diversity and demonstrate how existing animal surveillance can monitor pathogen threats. Crucially, in a workshop involving multisectoral One Health experts in Brazil, this framework was validated as a scalable model for national expansion, adapted for low- and middle-income countries (LMICs). One-sentence summary lineThe study introduces a viral surveillance method using metagenomics in bats, offering a scalable, cost-effective model for operationalizing One Health surveillance, especially relevant to low- and middle-income countries.
- organism: bacteria (other)
- assay: metagenomics
- research focus: benchmarking
- approach: statistical model
- Citations
- 4
- FWCI
- 25.90
- Field percentile
- top 1%
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