bioRxivMicrobiologyPreprint
COSMOS: integrated vector-pathogen metagenomics for mosquito-borne disease
Annie J ForsteriD, Adil A KhaniD, Emma Garrett, Cheryl Toi, Carl JE SusteriD, John Haniotis, Kai Dang, Jack Watson, Stephen Doggett, Sharon C-A CheniD, Matthew R Watts, Jen KokiD, Vitali SintchenkoiD, Michael PayneiD, Cameron WebbiD, Rebecca J RockettiD, Tanya GolubchikiD
Abstract
By 2050, almost half of the global population will be at risk of mosquito-borne disease. Effective response requires monitoring both pathogen transmission and vector abundance, yet these are typically assessed using separate assays with limited taxonomic breadth. Here we present COSMOS (COmprehensive Sequencing for MOSquito-borne disease), a targeted metagenomics workflow designed to reconstruct pathogen genomes and mosquito community composition directly from mixed field-collected samples. Over three annual rounds of a major Australian surveillance program, we evaluated >120,000 mosquitoes in 219 traps, with 98.7% agreement with surveillance data across seven monitored arboviruses, and additional detections of Sindbis virus and the state's first detection of encephalitic Little Sussex virus. Of 137 viral detections, 101 yielded near-complete genomes (>90% coverage) at median 95x depth. Using an accompanying computational workflow, COSMOS quantified mosquito composition, matching entomological estimates of reported vector species (CCC 0.78-0.89). Joint analysis of virus and vector composition identified structured mosquito communities associated with spatial and temporal environmental variation, allowing the ecological context of arbovirus detections to be characterised. Detections during the Murray Valley encephalitis virus outbreak in 2023 were concentrated within a narrowly defined mosquito-community space corresponding to rural communities dominated by the inland S-AUS sub-lineage of Culex annulirostris. Integrated vector-pathogen metagenomics provides ecological and evolutionary context for arbovirus activity within a single scalable workflow for surveillance and outbreak response.
- assay: metagenomics
- research focus: ecology & biodiversity
- approach: deep learning
- approach: statistical model
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