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An all-in-one metabarcoding approach to mosquito and arbovirus xenosurveillance
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  • Brian Johnson,
  • Melissa Graham,
  • Elina Panahi,
  • Carla Vieira,
  • Nisa S. Nath,
  • Paul Mason,
  • Jamie Gleadhill,
  • Darran Thomas,
  • Michael Onn,
  • Martin Shivas,
  • Damien Shearman,
  • Jonathan Darbro,
  • Gregor Devine
Brian Johnson
Queensland Institute of Medical Research - QIMR

Corresponding Author:brian.johnson@qimrberghofer.edu.au

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Melissa Graham
Queensland Institute of Medical Research - QIMR
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Elina Panahi
Queensland Institute of Medical Research - QIMR
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Carla Vieira
Queensland Institute of Medical Research - QIMR
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Nisa S. Nath
Queensland Institute of Medical Research - QIMR
Paul Mason
Gold Coast City Council
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Jamie Gleadhill
Gold Coast City Council
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Darran Thomas
Gold Coast City Council
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Michael Onn
Brisbane City Council
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Martin Shivas
Brisbane City Council
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Damien Shearman
Queensland Health
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Jonathan Darbro
Queensland Health
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Gregor Devine
Queensland Institute of Medical Research - QIMR
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Abstract

Next-generation sequencing (NGS) has the potential to transform mosquito-borne disease surveillance but remains under-utilized. This study introduces a comprehensive multi-loci metabarcoding-based MX (molecular xenomonitoring) approach to mosquito and arbovirus surveillance, enabling parallel identification of mosquito vectors, circulating arboviruses, and vertebrate hosts from bulk mosquito collections. The feasibility of this approach was demonstrated through its application to a large set (n=134) of bulk field collections. This set was complemented by a number (n=28) of single-species mosquito pools that had previously been screened for viruses using quantitative reverse transcription PCR (RT-qPCR) and metatranscriptomics. Universal alphavirus and flavivirus primer sets were used to screen for arboviruses in the resulting metabarcoding library. Viral amplicons were then indexed and combined with mosquito-specific (ITS2), universal invertebrate (COI), and vertebrate (Cyt b) barcode amplicons prior to sequencing. This approach confirmed the presence of all previously identified mosquito species, as well as those commonly misidentified morphologically, and enabled a degree of quantification regarding their relative physical abundance in each collection. Additionally, the developed approach identified a diverse vertebrate host community (18 species), demonstrating its potential for defining host preferences and, in tandem with the viral screens and associated vector data, understanding disease transmission pathways. Importantly, metabarcoding detected a diversity of regionally prevalent arboviruses and insect-specific viruses, with all three viral diagnostics demonstrating a similar sensitivity and specificity in detecting Ross River virus and Barmah Forest virus, Australia’s commonest arboviruses. In summary, multi-loci metabarcoding is an affordable and efficient MX tool that enables complete mosquito-borne disease surveillance.
11 Oct 2024Submitted to Molecular Ecology Resources
14 Oct 2024Submission Checks Completed
14 Oct 2024Assigned to Editor
14 Oct 2024Review(s) Completed, Editorial Evaluation Pending
25 Oct 2024Reviewer(s) Assigned