An all-in-one metabarcoding approach to mosquito and arbovirus xenosurveillance

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Data may be preliminary. 3 September 2025 V2 Latest version Share on An all-in-one metabarcoding approach to mosquito and arbovirus xenosurveillance Authors : Brian Johnson 0000-0002-0545-4912 [email protected] , Melissa Graham , Elina Panahi , Carla Vieira , Nisa S. Nath , Paul Mason , Jamie Gleadhill , … Show All … , Darran Thomas , Michael Onn , Martin Shivas , Damien Shearman , Jonathan Darbro , and Gregor Devine Show Fewer Authors Info & Affiliations https://doi.org/10.22541/au.172888779.92930171/v2 Published Molecular Ecology Resources Version of record Peer review timeline 995 views 536 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share 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. An all-in-one metabarcoding approach to mosquito and arbovirus xenosurveillance Brian J. Johnson 1, *, Melissa C. Graham 1 , Elina Panahi 1 , Carla Julia S. P. Vieira 1,2 , Paul Mason 3 , Jamie Gleadhill 3 , Darran Thomas 3 , Michael B. Onn 4 , Martin A. Shivas 4 , Damien Shearman 5 , Jonathan M. Darbro 5 , and Gregor J. Devine 1 1 Mosquito Control Laboratory, QIMR-Berghofer Medical Research Institute, Brisbane, Queensland, Australia; [email protected] ; [email protected] ; [email protected] ; [email protected] . 2 Centre for Immunology and Infection Control, School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD4006, Australia 3 Vector and Biosecurity Services Unit, City of Gold Coast, Gold Coast, QLD 9726, Australia; [email protected] ; [email protected] ; [email protected] . 4 Entomology Laboratory, Public Space Operations, Brisbane City Council, Brisbane, QLD 4009, Australia; [email protected] ; [email protected] . 5 Metro North Public Health Unit, Queensland Health, Brisbane, QLD 4030, Australia [email protected] : [email protected] . * Corresponding author at: [email protected] Short title: Metabarcoding-based xenosurveillance 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. Keywords: metabarcoding, mosquito, arbovirus, xenosurveillance, mosquito-borne disease, zoonotic 1. Introduction Next-generation sequencing (NGS) is a high-throughput technology that can revolutionize mosquito-borne disease surveillance (Batovska et al. 2018, Batovska et al. 2022). When NGS methodologies are combined with DNA and RNA-based barcoding, they are termed ‘metabarcoding’ (Taberlet et al. 2012, Cristescu 2014). Metabarcoding offers significant advantages to traditional morphological and targeted arboviral diagnostic techniques due to its high throughput and ability to target and amplify well-chosen molecular markers (barcodes) using universal primer sets to identify the different actors of arboviral transmission (e.g., vector diversity, vertebrate host identity, and viral diversity) (Li et al. 2018, Estrada-Franco et al. 2020, Hernández-Andrade et al. 2020, Loh et al. 2024). Specifically, metabarcoding can detect and identify new, rare, or cryptic vector species and viruses that might otherwise be missed by traditional direct diagnostic methods, such as quantitative reverse transcription PCR (RT-qPCR), and can reduce the cost of surveillance by being applied to hundreds to thousands of specimens across many individual collections (Yu et al. 2012, Mechai et al. 2021). However, the full potential of metabarcoding to mosquito and arbovirus surveillance has yet to be fully realized due to a lack of field-tested protocols. Unlike traditional barcoding, metabarcoding allows for the simultaneous identification of many taxa within a single sample, providing an efficient and reliable means of determining community composition across many individual collections. Metabarcoding also offers faster and more accurate species-level identification at reduced costs (Yu et al. 2012, Cristescu 2014, Elbrecht et al. 2017). Recent advancements in metabarcoding have included the development of multi-loci methodologies, which utilize multiple genetic markers to enhance the precision and accuracy of species identification across multiple taxa (Richardson et al. 2015, Arulandhu et al. 2017, Da Lio et al. 2018, Carroll et al. 2019). Multi-loci metabarcoding has since become a valuable biodiversity assessment tool in fields such as ecology and conservation biology (Arulandhu et al. 2017, Andres et al. 2023, Wang et al. 2023) and has shown promise in combined mosquito and arbovirus surveillance (Batovska et al. 2018). While current investigations have been limited to well-curated mosquito pools spiked with individual viruses, the benefits of multi-loci metabarcoding to mosquito and arbovirus surveillance are evident, especially in the absence of taxonomic expertise. The operational advantages of metabarcoding have particular relevance to arbovirus surveillance, wherein the use of molecular tools to detect a particular pathogen is commonly referred to as molecular xenomonitoring (MX; Cameron and Ramesh 2021). Although current gold standard MX screening methods such as probe-based RT-qPCR assays are both affordable and highly sensitive, they are limited in their capacity to screen for only a small number of viruses at a time through multiplexed assays (Chao et al. 2007, Ramírez et al. 2018). In contrast, metabarcoding of RNA (either directly as RNA or as cDNA) offers the ability to detect a broad spectrum of viruses, including all known, novel, and unexpected viruses, by using universal primer sets that target specific viral groups or entire families (Li et al. 2018, Grubaugh et al. 2019). The scalability and throughput of the two technologies also differ significantly. While RT-qPCR is generally faster, more species-specific, and less expensive for small sample sizes (Harper et al. 2018), it becomes cumbersome as sample sizes grow and when screening requirements exceed the limitations of a single multiplexed assay. Conversely, metabarcoding becomes increasingly cost-effective as sample size increases (Bohmann et al. 2014, Cristescu 2014, Lafferty 2024) , providing a more affordable and comprehensive MX solution for large-scale surveillance operations, especially when the targeted arboviral community is diverse or poorly uncharacterised. The benefits of metabarcoding further extend to vector and vertebrate host surveillance, yet the greatest public health benefits will be achieved if both are fully integrated with viral surveillance. By combining these three critical aspects of virus transmission—mosquito vectors, vertebrate hosts, and viruses—researchers can conduct broad-scale transmission pathway or network analyses (Estrada-Franco et al. 2020, Hernández-Andrade et al. 2020). Such an approach would circumvent current bottlenecks in pathway analysis, such as reliance on limited taxonomic expertise and mosquito blood-meal typing (Hernández-Triana et al. 2021). This is especially valuable for studying zoonotic diseases like Japanese Encephalitis virus (van den Hurk et al. 2009) that exhibit complex transmission routes involving multiple hosts, vectors, and environmental factors (Loh et al. 2015, Fournet et al. 2018). Data limitations, including insufficient vector and animal host surveillance and incomplete information on animal movements add to the difficulty (Childs and Gordon 2009). Addressing these challenges requires an interdisciplinary approach, which can be facilitated by NGS-based solutions like multi-loci metabarcoding that enable efficient screening of taxa diverse collections (Arulandhu et al. 2017). Whilst alternative NGS-based solutions such as single-mosquito RNA-based metatranscriptomics have shown promise in simultaneously identifying vectors, vertebrate hosts, and viruses (Batson et al. 2021), such methods are not yet economically viable for zoonotic viruses that persist at low levels and where entomological isolations are rare (Engler et al. 2013, Yap et al. 2020). Moreover, the holistic (non-amplification) approach of metatranscriptomics may exhibit poorer viral diagnostic sensitivity relative to traditional RT-qPCR and PCR-based metabarcoding when applied to abundant and complex entomological collections (Batovska et al. 2022). In this report, we introduce and validate a novel multi-loci metabarcoding approach (Fig. 1) for comprehensive mosquito and arbovirus surveillance, applying it to a large set of bulk mosquito collections from Southeast Queensland, Australia. This innovative method enables the simultaneous detection of both DNA and RNA (as cDNA) amplicons from pooled mosquito samples, enabling the identification of regionally significant alpha- and flaviviruses. Importantly, the method provides complementary data on mosquito vectors and vertebrate host species from the same pools, enhancing the capacity for enzootic transmission network analysis. The results highlight the potential of multi-loci metabarcoding to revolutionize mosquito and arbovirus surveillance, offering a powerful new tool for more effective monitoring and management of zoonotic disease transmission. 2. Methods 2.1 Study area Entomological collections originated from local government vector control and surveillance programs operating in the Gold Coast and Brisbane metropolitan areas of Southeast, Queensland, Australia, between March 2021 and May 2023. Brisbane is the third most populous Australian city and the largest state capital in Australia by geographic area, whereas the City of Gold Coast (CoGC) is Queensland’s most populous regional area. Combined, they comprise >70% of Southeast Queensland’s population (ca. 2.9 million residents). Southeast Queensland has a sub-tropical climate and contains a diversity of mosquito habitats including freshwater, estuarine wetlands, saltmarshes, mangroves, bushlands and subtropical rainforests. 2.2 Mosquito collection and identification The primary mosquito collections used in this study consisted of 138 individual CO 2 -baited light trap collections. 110 of these collections were sampled across 24 individual surveillance sites during April – June 2022 as part of routine surveillance operations undertaken by the City of Gold Coast. These pools were complemented with a previously virus-validated (Table 1) subset of single-species mosquito pools (n=28) sourced from CO 2 -baited light trap collections originating from the greater Brisbane metropolitan area during March 2021- May 2023. A detailed description of these samples and the diagnostic methods employed is provided by Vieira et al. (2024a). All captured mosquitoes were cold-anesthetized and identified by council staff using dichotomous keys (Marks 1967, Russell 1996). Identified mosquitoes were pooled (recombined) and collections containing ≤200 mosquitoes were labeled and stored at −20°C until further analysis. In addition to field collected mosquitoes, virus isolates of Barmah Forest virus (BFV; strain BFVtully.2017; n=2), chikungunya virus (CHIKV; strain LR2006_OPY1; n=1), dengue (serotype 2) virus (DENV; strain Thailand/NGS-C/1944; n=4), Ross River virus (RRV; strain QML l; n=4), and Zika virus (ZIKV; ArD 41525; n=1) were included as internal controls for the pan-alpha and flavivirus primer sets used in the final library. These virus controls were extracted independently of mosquito pool extractions but processed in parallel. 2.3 Sample preparation and nucleic acid extraction Mosquito pools were homogenised in 2.0 ml Eppendorf Safelock microcentrifuge tubes using DNA/RNA Shield storage buffer (Zymo Research, Irvine, USA) and 2.3 mm zirconium silica beads (Daintree Scientific, St Helens, Australia). Mosquito pools were then mechanically homogenised for two 3-minute cycles at 1,500 rpm using a Mini Beadbeater-96 (BioSpecProducts, Bartlesville, USA) and centrifuged for two 5-minute cycles at 14,000 rpm, at 4°C. Nucleic acid was extracted from mosquito pools and control samples using the ZymoBIOMICS™ Quick-DNA/RNA Viral MagBead kit and workflow (Zymo Research Corporation, California, USA). In short, mosquito pools were homogenized in 2.0 ml deep well plates (96-well) with samples placed in alternating columns to minimize cross-contamination. Five ceramic beads (2.3 mm) and 500ul DNA/RNA shield/well (Zymo Research Corporation, California, USA) was added to each well prior to homogenization. Pools were homogenized for 3 min at full speed (2400 RPM) and then centrifuged for 10 min (3600 RPM). 200 ul of the centrifuged supernatant from each well was then added to a clean 96-well plate (non-treated). DNA/RNA was then extracted following the manufacturer’s instructions including Proteinase K digestion during sample lysis. The crude homogenate and remaining supernatant were stored at -20 C. A final elution volume of 50 ul was used for all samples. 2.4 cDNA synthesis metabarcoding amplicon library preparation Viral cDNA synthesis was performed using random hexamer primers and SuperScript™ III Reverse Transcriptase (ThermoFisher Scientific). Each cDNA synthesis reaction was performed with 6.5 μl of undiluted nucleic acid extract. All manufacturer’s instructions were followed; however, RNaseOUT was omitted from the protocol. Following cDNA synthesis, a set of five DNA amplicons was generated for each sample (Table 2). All primers were ordered with either the Illumina Nextera Read 1 (forward primers) adapter sequence (5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG-3′) or Illumina Nextera Read 2 (reverse primers) adapter sequence (5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG-3′) at the 5′ end prior to use. In short, mosquito vector species and other potential biting insects (Culicoides spp.) were targeted using the short mitochondrial cytochrome oxidase subunit 1 (COI) barcode described by Leray et al. (2013). This general invertebrate barcode was complemented by a mosquito-specific barcode targeting the second internal transcribed spacer (ITS2) of nuclear ribosomal as described by Batovska et al. (2017). Vertebrate blood meal hosts were targeted by amplifying a fragment of the vertebrate cytochrome b (cyt b) gene using the primer described by Townzen et al. (2008) and used successfully in Australia to characterize mosquito blood meals (Flies et al. 2016, Gyawali et al. 2019, Vieira et al. 2024b). Universal (pan) primer sets targeting circulating flaviviruses and alphaviruses were employed following the protocols of Vina-Rodriguez et al. (2017) and Hermanns et al. (2017), respectively. All amplicons were generated using Phire Hot Start II DNA Polymerase (ThermoFisher Scientific) and associated 5X reaction buffer. 200 µM of each dNTP and 0.5 µM of each primer was added to each individual reaction in a total reaction volume of 25 µl. PCR products were verified on a 2% w/v agarose gel before inclusion in the final NGS library. 2.5 Library preparation and sequencing Size-verified PCR products generated for each individual sample were pooled together by adding 5 ul of each PCR product to a clean well on a new 96-well plate. Pooled PCR products were then cleaned and normalized (~2.5 ng/ul) using SequalPrep plates (Thermo Fischer Scientific) following the manufacturer’s recommendations. Unique 8 bp dual-barcodes with Illumina P5 and P7 adapters were then added onto the cleaned and normalized PCR product via PCR (× 12 cycles) using the Nextera XT Index Kit v2 (Sets A-D, Illumina, San Diego, CA). A second round of cleaning and product normalization was then performed. 5 ul of each cleaned and normalized indexed PCR product was then added to a clean microcentrifuge tube to produce a single equimolar library (~10 nM) for sequencing. Bead-based size selection (>150 bp) using magnetic beads (AMPure XP, Beackman Coulter) at a ratio of 0.8X was used to remove adapter dimers and other non-target fragments. Final library size and quality was checked using a D1000 Tapestation (Agilent Technologies, Santa Clara, CA). The quality checked library was then sequenced on a MiSeq platform using version 3 chemistry (3 x 300 bp; Illumina, San Diego, CA). 2.6 Data analysis De-multiplexed sequence files were imported into the Geneious Prime software (Biomatters, Inc., Boston, MA) and trimmed to remove adapters, primers and low-quality sequence reads using the BBDuk Trimmer plugin from BBTools (Bushnell et al.). Reads were removed if they had a median Q score of < 20 or a read length of < 100 nt. Overlapping paired reads were merged using the BBMerge plugin (Bushnell et al. 2017) using standard settings. Sequences were then aligned and contigs assembled using the de novo assembly tool. Resulting contigs (alleles) were used to generate a classification database based on the resulting BLAST hits (≥98% sequence similarity). All individual de-multiplexed sequences (forward and reverse) were then classified using the Sequence Classifier tool using standard settings with a minimum overlap of 220 bp and a >95% overlap identify for species classification. The classification table was then exported as a .csv file and a summary pivot table grouped by each unique dual-barcode pair was generated using the pivottabler package in R (R Core Team, 2024). Pools with <500 classified reads and mosquito species accounting for <0.5% of total read counts were omitted from per pool relative abundance estimates. 3. Results 3.1 Mosquito species identification A total of 138 mosquito pools containing 10,939 mosquitoes were included in the final library. A total of 27 mosquito species within 10 genera were identified (Fig. 2). All species suspected to be present based on verified morphological identifications were confirmed (n=22), with the lone exception being Aedes alboscutellatus which misidentified and correctly assigned to Aedes procax. The presence of a select number of additional and easily misidentified species not identified morphologically were revealed by metabarcoding, including Culex globocoxitus, Culex orbostiensis, Culex palpalis, and Culex squamosus. The presence of regionally important biting midges (Culicoides spp.) were also detected by COI amplification, including Culicoides longior, Culicoides marmoratus, and Culicoides molestus (Reye 1964, Hagan and Kettle 1990). Similarly, the presence of select non-target invertebrates was also revealed, including Drosophila serrata (fruit fly), Clogmia albipunctata (drain moth), Pheidole megacephala (coastal brown ant), and Chironomus sp. (non-biting midge). 3.2 Performance of COI and ITS2 in predicting the physical presence or absence of individual mosquito species We were able to compare the amplification performance of COI and ITS2 barcodes across 14 mosquito species. The remaining 12 identified mosquito species had only COI or ITS2 reference sequences available at the time of analysis. Clear differences in the amplification and sequencing performance of each barcode were observed, with across library performance (represented as % differences in final read counts between barcodes) almost equally split across species (Fig. 3 A & B). Analysis of pools with verified morphological records (CoGC light trap collections; n=84) revealed that the number of known (morphologically confirmed) positive pools for each species was best estimated by combining COI and ITS2 barcode reads rather than relying on a single individual barcode (Fig. 4), but variations remained. Particularly high (>95%) agreement was observed for Culex annulirostris, Aedes notoscriptus, Anopheles annulipes, and for Culex cylindricus when considering the combined presence of Cx. cylindricus and Cx. orbostiensis as determined by metabarcoding. In contrast, the number of pools positive for Aedes vigilax and Verrallina funerea was notably underreported. 3.3 Estimates of mosquito species community composition Metabarcoding estimates of community composition, or site-specific species relative abundance, generally agreed with those determined from verified morphological records (Fig. 5 A & B), but distinct differences were noted. For example, there were instances when metabarcoding was able to identify and separate morphologically cryptic species (e.g., site 11; Culex quinquefasciatus detected in a pool identified as Culex australicus) (Russell 2012), resulting in a distinct differences in community composition. Other times select species such as Mansonia uniformis and An. annulipes were overrepresented due to inflated read counts for individual barcodes (COI- Ma. uniformis; ITS2- An. annulipes), the likely by-product of high amplification and sequencing efficiency. The highly prevalent and abundant Cx. annulirostris was also generally overrepresented. Simplifying estimates to the level of genus improved estimates of community composition (Fig. 5 C & D), but the overrepresentation of select genera remained. 3.4 Vertebrate Blood-Meal Host Identification A total of 34 individual vertebrate blood-meal host detections representing 19 species across 15 genera were observed (Fig. 6A). Vertebrate hosts were identified in 29 individual pools (20.2% of the total number of pools analyzed). A single vertebrate host was identified in 24 pools, while multiple vertebrate hosts were detected in another five pools. Vertebrate signals were generally low, accounting for 0.84% of total number of classified reads. Read counts for individual detections ranged from 10 to 7,980 (mean = 996.2; 95% CI 278.4-1714). Marsupials represented the largest number observations (n=17; 12.3% of processed pools), followed by placental mammals (n=9; 6.5% of processed pools), birds (n=7; 5.1% of processed pools), and reptiles (n=1; 0.7% of processed pools). Of these, the common brushtail possum (Trichosurus vulpecula) accounted for the largest number of individual observations (n=7), followed by the Eastern grey kangaroo (Macropus giganteus; n=4), the common ringtail possum (Pseudocheirus peregrinus), cows ( Bos taurus), and humans (Homo sapiens) (all n=3). Notably, Panthera tigris (tiger), a species held in only a few Australian zoological parks, was detected. Upon review, the positive pool originated from a trap placed in close proximity (<500 m) from a local wildlife attraction housing both Bengal and Sumatran tigers (Tiger World, Dreamworld Amusement Park, Gold Coast, Queensland), confirming the validity and sensitivity of our approach. Vertebrate host signals were observed in pools dominated (i.e., accounting for the largest proportion of the collection) by seven different vector species (Fig. 6B). The largest number of vertebrate host detections were observed in pools in which the highly abundant and prevalent Cx. annulirostris (n=12) and Ae. notoscriptus (n=8) were the dominate species. Four vertebrate host detections were made from single species mosquito pools, including detections of an Eastern grey kangaroo (Ae. procax), common brushtail possum (Cx. annulirostris), cow (Ae. procax), and cane toad (Mimomyia elegans). 3.5 Arbovirus detections and comparison to alternative diagnostics Metabarcoding produced the largest number of virus detections (n=27) identified with ≥98% pair-wise identity, followed by metatranscriptomics ( RNA-Seq; n=23) and qRT-PCR (n=21) (Table 1, Fig. 7). Metabarcoding produced additional detections of both RRV and BFV relative to RNA-Seq and RT-qPCR as well as additional detections of Paramatta River virus (PaRV) and Yada Yada virus (YYV), both insect specific viruses (Batovska et al. 2020, McLean et al. 2021). Both RT-qPCR and RNA-Seq produced five detections of Stratford virus (STRV) whereas metabarcoding confirmed three of these results with an additional unconfirmed (low read count) detection. The correlation between metabarcoding (r) and RT-qPCR and RNA-Seq detections across the five confirmed viruses observed was r=0.85 and r=0.90, respectively. Both RT-qPCR and metabarcoding detected the presence of insect specific viruses related to Phlebotomus-associated flavivirus and Quang Binh virus (QBV), observations of which could not be aligned to known reference sequences with >80% pair-wise identity. All viral culture controls (n=12) were detected with a per pool average of 15,325 classified merged reads (95% CI 6,137-24,512 reads). 4. Discussion We present a validated proof-of-principle for a novel multi-loci metabarcoding-based MX approach to mosquito and arbovirus surveillance that enables the parallel identification of mosquito vectors, circulating arboviruses, and vertebrate hosts from bulk mosquito collections. By simultaneously determining vector and vertebrate host identities along with virus presence, this approach streamlines the identification and establishment of critical transmission pathways, reducing both the time and resources traditionally required for such analyses. When integrated into carefully designed, longitudinal surveillance programs, this approach can significantly enhance vector and vertebrate host distribution maps, leading to better risk prediction and more effective pre-emptive mosquito control measures. With modification, the benefits of multi-loci metabarcoding are likely to extend to other vector-borne diseases, such as lymphatic filariasis, where MX has become an essential component of global elimination campaigns (Farid et al. 2007, Pilotte et al. 2017). The emergence of large paired-end sequencing (2 x 300 bp) on production-scale platforms (e.g., NextSeq 1000/2000; >500 Gb data generation) and continued development of metabarcoding workflows utilizing affordable sequencing platforms such as Oxford Nanopore Technologies (Herbert et al. 2024) will further serve to improve the operational benefits of multi-loci metabarcoding by enhancing read depth and providing progressive increases in throughput and per-sample cost reductions (Piper et al. 2019). 4.1 Vector species detection and quantification The ability to detect individual vector species and assemble vector community profiles is a critical first step in transmission pathway analysis. The successes, challenges and limitations of insect species delimitation and abundance estimation using the cytochrome oxidase subunit I ( COI ) gene and other markers have been well studied (Virgilio et al. 2010, Beebe 2018, Zhang and Ling 2018, Yates et al. 2019, Andres et al. 2023). The results of this study strongly support the use of universal invertebrate COI barcodes in identifying Australian mosquito species, including morphologically cryptic species such as Cx. orbostiensis and Cx. cylindricus (Dobrotworsky 1958). There are clear limitations in determining individual species abundance across diverse vector communities as a result of species-specific variations in primer efficiency (Elbrecht and Leese 2015). The parallel use of a mosquito-specific ITS2 barcode (Batovska et al. 2017), a notable nuclear marker for mosquito identification (Beebe 2018), greatly improved presence-absence and community abundance estimates, but species-specific variations remained. These considerations support the adoption of a dual-barcode approach to mosquito species identification, community composition, and abundance estimates (Beebe 2018). 4.2 Vertebrate host associations Zoonotic mosquito-borne diseases form a significant fraction of the global infectious disease burden (Taylor et al., 2001; van Doorn, 2014). It is therefore critical to identify the role that different vertebrate host species play in maintaining transmission and facilitating spillover to human populations. Conventionally, this requires traditional blood-meal typing on obviously blood-fed mosquitoes (Martínez-de la Puente et al. 2013). This method is resource intensive and often requires the use of alternative collections methods (e.g., aspiration) than those used in routine surveillance operations, which may result in sampling bias (Thiemann and Reisen 2012, Keven et al. 2021). Here, we validate the advantages of NGS-based techniques in characterizing vector-host associations (Brinkmann et al. 2016, Logue et al. 2016) in the absence of visible blood-meal scoring. Despite the inability to link individual vector species to each vertebrate host detection when processing mixed species collections, analyzing spatiotemporal trends in vector and vertebrate host assemblages can help establish those associations (Roiz et al. 2012, Lutomiah et al. 2014, Musa et al. 2020, Sloyer et al. 2022, Vieira et al. 2024b). Of the detections observed in this study, all observations fit the known blood-feeding behaviors of the dominate vector species observed in each pool (Stephenson et al. 2019). In particular, the findings support the general feeding preference of Ae. notoscriptus on urban marsupials such as the common brushtail possum (Kay et al. 2007, Jansen et al. 2009) and the cosmopolitan feeding behavior of Cx. annulirostris which contributes to its important role in the transmission of RRV and other endemic mosquito-borne diseases (Kay et al. 1985, Johansen et al. 2009, Hall-Mendelin et al. 2012, Gyawali et al. 2019). Our results further confirm the relatively high proportion of mosquito feeding on abundant macropods suspected of being significant enzootic reservoirs for RRV (Harley et al. 2001, Russell 2002). These include the agile wallaby, common brushtail and ringtail possums, and eastern grey kangaroo. The results also confirm the preference of Mimomyia elegans to feed on amphibians (Gould and Valdez 2024), in this instance the highly abundant and widely distributed cane toad (Urban et al. 2008). 4.3 Comparative performance of metabarcoding to alternative arbovirus detection methods Understanding virus diversity and prevalence in parallel with vector and vertebrate host data has rarely been attempted due to the many resource and logistical challenges in quantifying each component individually. Still, it is of ultimate importance in facilitating the accurate prediction of virus outbreaks and the implementation of pre-emptive mosquito control interventions (Simpson et al. 2012). Therefore, maintaining diagnostic sensitivity relative to current gold-standard methodologies is critical to the feasibility of our metabarcoding approach. In terms of arbovirus surveillance, metabarcoding outperformed both RT-qPCR and metatranscriptomics in the number of confirmed virus detections (27 vs. 21 and 23, respectively), including both arboviruses and insect-specific viruses. Critically, all three diagnostic methods demonstrated similar sensitivity and specificity in detecting RRV and BFV, Australia’s commonest arboviruses (Harley et al. 2001, Russell and Kay 2004). All methods also confirmed the presence of additional causative agents of human disease, including SINV and STRV. SINV and SIN-like viruses are the most widely distributed of all known alphaviruses, but clinically confirmed cases in humans in Australia are rare (Michie et al. 2023). However, higher rates of subclinical infections are suspected (Sammels et al. 1999). Similarly, STRV is a flavivirus in the Kokobera subgroup whose future clinical importance in Australia may increase in response to urbanization and climate change (Toi et al. 2017). Although all methods confirmed the presence of STRV in the analyzed dataset, metabarcoding failed to confirm two separate STRV detections confirmed by RT-qPCR and metatranscriptomics. These results suggest that virus-specific differences in PCR efficiency may have occurred, or that differences in the age of the RNA extracts at the time of metabarcoding (> 1 year older) affected assay sensitivity. Although RNA extracts were stored at -80C, extracted RNA stored in aqueous solutions, even at temperatures below -60 °C, is prone to degradation (Ginocchio et al. 1997). 4.4. Limitations As this was a proof-of-principle study, we did not attempt to address the many inherent and extrinsic sources of bias that may be introduced during DNA extraction and PCR amplification that can impede accurate species identification and quantification through metabarcoding (Batovska et al. 2018, Liu et al. 2020). Species-specific variations in PCR amplification performance and efficiency were observed for the chosen COI (Leray et al. 2013) and ITS2 (Batovska et al. 2017) primer sets across the majority of reported mosquito species. Their parallel use helped alleviate some of their individual biases, but the misrepresentation of select species remained. PCR cycle number optimization and inclusion of pool replicates can further reduce amplification biases and reduce false-negative species detections (Alberdi et al. 2018, Batovska et al. 2018). Future investigations should therefore consider the optimization of PCR conditions and sampling strategies for regionally important disease vectors. Careful consideration of sample collection and curation procedures in future studies is also needed to reduce cross-contamination. For standard mosquito collections, the greatest potential sources of cross-contamination are the re-use and poor decontamination of traps, collection chambers, and identification tools, surfaces, and resources. The use of single-use equipment is the easiest means of eliminating contamination (Liu et al. 2020), but is not feasible for the majority of routine mosquito surveillance operations. Therefore, a simple stepwise wash of surveillance equipment with soapy water followed by a 10% bleach soak for 10 minutes and a final rinse in distilled water is recommended to reduce cross-contamination in future studies (Erdozain et al. 2019, Jeunen et al. 2019). 5. Concluding Remarks In summary, multi-loci metabarcoding is an efficient and affordable MX-based surveillance tool that may overcome many of the barriers to the implementation of conventional entomological-based arbovirus surveillance (Killeen et al. 2018, Lourenço et al. 2019). With modification, there are clear extensions to other programs seeking greater sensitivity and high-throughput, such as those adopting MX as a tool for assessing progress toward elimination of lymphatic filariasis and malaria (Lau et al. 2016, Pilotte et al. 2016, Nascimento et al. 2018, Subramanian et al. 2020, Cameron and Ramesh 2021). Data Accessibility and Benefit-Sharing section: The raw sequence reads produced in this study are available in the NCBI Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra) under BioProject PRJNA1171139. Author Contributions: BJJ: Conceptualization; methodology; investigation; formal analysis; writing – original draft (lead); writing – review and editing (equal); visualization; data curation. MG: methodology; validation; investigation. EP: Investigation; validation. CJV: Resources; investigation. NSN: Methodology, investigation. PM, DS, DT, MBO, MAS, and JMD: Resources. GJD: Supervision; conceptualization; writing – review and editing (equal). 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Collection Molecular Ecology Resources Keywords arbovirus metabarcoding mosquito mosquito-borne disease xenosurveillance zoonotic Authors Affiliations Brian Johnson 0000-0002-0545-4912 [email protected] Queensland Institute of Medical Research - QIMR View all articles by this author Melissa Graham Queensland Institute of Medical Research - QIMR View all articles by this author Elina Panahi Queensland Institute of Medical Research - QIMR View all articles by this author Carla Vieira Queensland Institute of Medical Research - QIMR View all articles by this author Nisa S. Nath Queensland Institute of Medical Research - QIMR View all articles by this author Paul Mason Gold Coast City Council View all articles by this author Jamie Gleadhill Gold Coast City Council View all articles by this author Darran Thomas Gold Coast City Council View all articles by this author Michael Onn Brisbane City Council View all articles by this author Martin Shivas Brisbane City Council View all articles by this author Damien Shearman Queensland Health View all articles by this author Jonathan Darbro Queensland Health View all articles by this author Gregor Devine Queensland Institute of Medical Research - QIMR View all articles by this author Metrics & Citations Metrics Article Usage 995 views 536 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Brian Johnson, Melissa Graham, Elina Panahi, et al. An all-in-one metabarcoding approach to mosquito and arbovirus xenosurveillance. Authorea . 03 September 2025. 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