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Journal of Virology logoLink to Journal of Virology
. 2017 Aug 10;91(17):e00680-17. doi: 10.1128/JVI.00680-17

High-Resolution Metatranscriptomics Reveals the Ecological Dynamics of Mosquito-Associated RNA Viruses in Western Australia

Mang Shi a, Peter Neville b,c, Jay Nicholson b,c,d, John-Sebastian Eden a,e, Allison Imrie c,✉, Edward C Holmes a,✉
Editor: Douglas S Lylesf
PMCID: PMC5553174  PMID: 28637756

ABSTRACT

Mosquitoes harbor a high diversity of RNA viruses, including many that impact human health. Despite a growing effort to describe the extent and nature of the mosquito virome, little is known about how these viruses persist, spread, and interact with both their hosts and other microbes. To address this issue we performed a metatranscriptomics analysis of 12 Western Australian mosquito populations structured by species and geographic location. Our results identified the complete genomes of 24 species of RNA viruses from a diverse range of viral families and orders, among which 19 are newly described. Comparisons of viromes revealed a striking difference between the two mosquito genera, with viromes of mosquitoes of the Aedes genus exhibiting substantially less diversity and lower abundances than those of mosquitoes of the Culex genus, within which the viral abundance reached 16.87% of the total non-rRNA. In addition, there was little overlap in viral diversity between the two genera, although the viromes were very similar among the three Culex species studied, suggesting that the host taxon plays a major role in structuring virus diversity. In contrast, we found no evidence that geographic location played a major role in shaping RNA virus diversity, and several viruses discovered here exhibited high similarity (95 to 98% nucleotide identity) to those from Indonesia and China. Finally, using abundance-level and phylogenetic relationships, we were able to distinguish potential mosquito viruses from those present in coinfecting bacteria, fungi, and protists. In sum, our metatranscriptomics approach provides important insights into the ecology of mosquito RNA viruses.

IMPORTANCE Studies of virus ecology have generally focused on individual viral species. However, recent advances in bulk RNA sequencing make it possible to utilize metatranscriptomic approaches to reveal both complete virus diversity and the relative abundance of these viruses. We used such a metatranscriptomic approach to determine key aspects of the ecology of mosquito viruses in Western Australia. Our results show that RNA viruses are some of the most important components of the mosquito transcriptome, and we identified 19 new virus species from a diverse set of virus families. A key result was that host genetic background plays a more important role in shaping virus diversity than sampling location, with Culex species harboring more viruses at higher abundance than those from Aedes mosquitoes.

KEYWORDS: Australia, ecology, evolution, mosquito, phylogeny, transcriptomics, virome

INTRODUCTION

Mosquitoes (Diptera: Culicidae) act as vectors for a number of disease agents that infect humans and domestic animals, including malaria, dengue virus, Chikungunya virus, and Zika virus. However, in addition to their role as transmission vectors, mosquitoes harbor a far larger virome, including many viruses that are confined to these insects, such that they are “insect specific” (1, 2). Although these insect-specific viruses have no direct impact on public health, they may modulate the transmission of viruses that are pathogenic to vertebrates (3). The development of metagenomic sequencing approaches has therefore led to a reevaluation of the mosquito virome, including the recent discovery of viruses of the families Bunyaviridae (4–8), Rhabdoviridae (6, 9–11), Orthomyxoviridae (6, 12), Flaviviridae (13–15), Mesoniviridae (16), and Reoviridae (8, 17) as well as of the unclassified Chuvirus (6) and Negevirus (18) groups. In addition, metagenomics surveys have discovered viruses in families not previously known to infect mosquitoes, such as the Iflaviridae, Dicistroviridae, Totiviridae, Chrysoviridae, and Narnaviridae (8, 19–22). Although these viruses have not been isolated or characterized in vivo, their host association is supported by the presence of related endogenous viruses in the genomes of various mosquito species (8). Hence, it is clear that mosquitoes harbor substantial viral diversity, the majority of which may not be associated with vertebrates (1, 2).

Despite our expanding knowledge of the mosquito virome, there have been fewer studies of ecological aspects of these viruses within their hosts (1). It has been suggested that most of these newly discovered viruses share features that distinguish them from “classic” human pathogens, including (i) the inability to infect vertebrates or vertebrate cell lines, (ii) high prevalence, (iii) prolonged host infection, and (iv) vertical transmission (1, 2, 23). Based on these features, these mosquito viruses have been referred to as “commensal” microbes (3). In reality, however, little is known about their natural infection status (e.g., abundance and frequency of superinfection), host specificity in relation to different mosquito species, geographic distribution and movement, and interactions with hosts and other microbes that may be present within a specific host.

To reveal more of the natural ecology of mosquito RNA viruses, we employed a metatranscriptomics approach to characterize the entire RNA environment, excluding rRNA, within a mosquito sample. Metatranscriptomics has several advantages over approaches such as cell culture, consensus PCR, and metagenomics methods based on viral particle purification (24, 25) and has proven successful in characterizing the RNA viromes of diverse invertebrates (6, 8, 14, 20). Specifically, (i) it reveals the entire RNA virome, with sufficient coverage to reconstruct complete viral genomes, including those from coinfecting parasites; (ii) it provides reliable quantification and assessment of both viral and host RNAs; and (iii) it is relatively simple, requiring minimal sample processing. Most importantly, metatranscriptomics provides more information than what is provided by the genome sequence alone, allowing a straightforward characterization of viral diversity and ecology.

To infer aspects of virome ecology among mosquito species sampled from different geographic locations, we characterized the total transcriptomes of 12 mosquito populations, comprising five species collected from four locations in Western Australia. In particular, we determined the number, type, and abundance of each virus within the context of the host transcriptome and those of other microbial symbionts/parasites and addressed whether these parameters varied by species and/or sampling location.

RESULTS

The mosquito virome.

We characterized the total transcriptome of 12 mosquito pools, representing five species of mosquitoes sampled from four geographic locations in Western Australia (Fig. 1). RNA sequencing of rRNA-depleted libraries resulted in 40 million to 47 million reads per pool, which were assembled de novo into 159,861 to 225,352 contigs. Subsequent BLAST analyses revealed the complete genomes of 24 species of RNA viruses, 19 of which are newly described here. These virus species fell into a wide range of RNA virus groups, including those that fell within existing families and orders, namely, the Bunyaviridae, Mononegavirales, Orthomyxoviridae, Narnaviridae, Mesoniviridae, Partitiviridae, Reoviridae, Totiviridae, and Chrysoviridae, as well as those in several newly described groups: Qinvirus (a highly divergent group of negative-sense RNA viruses [8]), the Partiti-like viruses, the Luteo-like viruses, and the Negev-like viruses (Table 1). Importantly, these viruses were unlikely to represent endogenous viral elements (EVEs), as they were present as complete genomes without any interruption by frameshifts, nonsense mutations, repeat sequences, reverse transcriptases, or other features that are common to EVEs.

FIG 1.

FIG 1

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Information on the hosts and geographic locations (southwestern Australia) of the mosquito samples collected in this study. (Top) Maximum likelihood phylogeny of the cytochrome c oxidase (cox1) gene from mosquito samples collected in this study. The name of each sequence contains information on the sampling location and host species identification in the field. (Bottom) Locations of four sampling sites, marked by sold black dots.

TABLE 1.

Presence and abundance of viruses from different mosquito species and locations (percentage of total reads)

Virusd Classification Abundance of virus (% of total reads)
Aedes camptorhynchus
 Aedes alboannulatus, LocD Culex globocoxitus
 Culex australicus
 Culex quinquefasciatus, LocD
LocA LocB LocC LocD LocA LocC LocD LocA LocC LocD
CPLV Bunyaviridae 0 0 0 0 0 3.881 4.113 3.547 3.908 2.659 3.952 1.632
CMLV1 Mononegavirales 0 0 0 0 0 0.193 0.068 0.191 0 0.059 0.063 0
CMLV2 Mononegavirales 0 0 0 0 0 0.011 0.022 0 0.021 0.034 0.016 0.009
CRLV Rhabdoviridae 0 0 0 0 0 0 0 0.217 0.138 0 0 0.169
WHMV6a Orthomyxoviridae 0 0 0 0 0 1.035 1.494 3.340 1.353 1.756 1.358 1.380
AAOLV Orthomyxoviridae 0 0 0 0 0.217 0 0 0 0 0 0 0
WQLV Qinvirus (new negative-sense virus) 0 0.008 0 0.014 0 0.074 0 0 0 0 0 0
WOLV Ophioviridae 0 0 0 0.003 0 0 0 0 0 0 0 0
CNLV1 Negev virus-related 0 0 0 0 0 0.286 0.236 0 0 0 0.501 0
CNLV2 Negev virus-related 0 0 0 0 0 0 0 0 2.645 0 0 0
CNLV3 Negev virus-related 0 0 0 0 0 0 0 0 4.092 0 0 0
ACNLV Negev virus-related 0 0 0.389 0 0 0 0 0 0 0 0 0
CLLV Luteoviridae-related 0 0 0 0 0 0 0.031 0.050 0 0 0.044 0
PDNLV Narnaviridae 0 0 0 0 0 0.036 0 0 0 0 0 0
ZJMV3b Narnaviridae 0 0 0 0 0 0.840 0.449 1.510 0.342 0 0.080 2.181
WNLV1 Narnaviridae 0 0 0.002 0.009 0 0 0 0 0 0 0 0
WNLV2 Narnaviridae 0 0 0 0.013 0 0 0 0 0 0 0 0
Ngewotan virusc Mesoniviridae 0 0 0 0 0 0 0 0 4.326 0 0 0
WPLV1 Partitiviridae-related 0 0 0 0.002 0 0 0 0 0 0 0 0
WPLV2 Partitiviridae-related 0 0 0 0.005 0 0.002 0 0 0 0 0 0
LPLV Partitiviridae-related 0 0.006 0 0 0 0 0 0 0 0 0 0
ACRLV Reoviridae 0 0.132 0 0 0 0 0 0 0 0 0 0
ACTLV Totiviridae 0.013 0 0 0.001 0 0 0 0 0 0 0 0
HBCLV1b Chrysoviridae 0 0 0 0 0 0.108 0.142 0.131 0.044 0 0.027 0
SCLV1b Chrysoviridae 0 0 0 0 0 0 0 0 0 0 0 0.141
All viruses 0.013 0.146 0.391 0.047 0.217 6.464 6.555 8.987 16.870 4.508 6.095 5.513
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a

See reference 6.

b

See reference 8.

c

See reference 18.

d

PDNLV, Point Douro narna-like virus; LPLV, Leschenault Partiti-like virus; ACRLV, Aedes camptorhynchus reo-like virus.

For each library, the number of virus species varied from 1 to 10 (Table 1). The abundance (i.e., frequency) of each virus also varied from 0.013% to 16.87% of total non-rRNA reads within the pool (Table 1). In comparison, the host ribosomal protein L32 (RPL32) gene, which is often used as a reference gene in quantitative PCR assays, showed consistent abundance levels across all libraries (from 0.034 to 0.065%) (Table 2). This suggests that the huge variation in viral numbers and abundances is unlikely to be an artifact of sample processing or nucleic acid extraction. Indeed, for individual viral species, the abundance levels were comparable across libraries, including both highly abundant viruses such as Culex phasma-like virus (CPLV) (1.632 to 4.113%) and those of lower abundance such as Culex mononega-like virus 2 (CMLV2) (0.011 to 0.034%). Overall, for all the Culex pools, the total abundance levels of viral RNA were above 4% of the total non-rRNA, suggesting that RNA viruses can make up a substantial part of the RNA environment in mosquitoes.

TABLE 2.

The most abundant genes from mosquitoes and other microbial organisms present in mosquitoes

Organism Gene Abundance of gene (% of total)
Aedes camptorhynchus
 Aedes alboannulatus, LocD Culex globocoxitus
 Culex australicus
 Culex quinquefasciatus, LocD
LocA LocB LocC LocD LocA LocC LocD LocA LocC LocD
Mosquito (principal host) cox1 0.455 0.669 0.335 0.346 0.437 1.114 0.851 0.606 0.587 0.830 0.866 0.499
Mosquito (principal host) RPL32 0.041 0.040 0.034 0.039 0.045 0.043 0.057 0.053 0.051 0.054 0.065 0.069
Fungi
    Unknown species 1 cox1 0 0 0 0 0 0.032 0 0 0 0 0 0
    Unknown species 1 RPL32 0 0 0 0 0 0.00028 0 0 0 0 0 0
    Unknown species 2 cox1 0 0 0 0.026 0 0 0 0 0 0 0 0
    Unknown species 2 RPL32 0 0 0 0.00065 0 0 0 0 0 0 0 0
    Unknown species 3 cox1 0 0.125 0 0 0 0 0 0 0 0 0 0
    Unknown species 3 RPL32 0 0.00126 0 0 0 0 0 0 0 0 0 0
    Microsporidium sp. RPL32 0 0 0.00008 0.00033 0 0 0 0 0 0 0 0
Protists
    Leishmania sp. cox1 0 0 0 0 0 0 0 0 0.00022 0 0 0.00006
    Leishmania sp. RPL32 0 0 0 0 0 0 0 0 0.00027 0 0 0
    Trypanosoma sp. RPL32 0 0 0.00005 0 0 0 0 0 0 0 0 0
Nematodes
    Mermithidae sp. cox1 0 0.00153 0 0 0 0 0 0 0 0 0 0
    Onchocercidae sp. cox1 0 0 0.00138 0 0 0 0 0 0 0 0 0
Bacteria
    Zymobacter palmae gyrB 0.00018 0 0 0 0 0 0 0 0 0 0 0
    Zymobacter palmae recA 0.00049 0 0 0 0 0 0 0 0 0 0 0
    Wolbachia wPip gyrB 0 0 0 0 0 0 0 0 0 0 0 0.00028
    Wolbachia wPip recA 0 0 0 0 0 0 0 0 0 0 0 0.00069
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Also of note was that some of the viruses were highly prevalent. In particular, CPLV and Wuhan mosquito virus 6 (WHMV6) appeared in all of the Culex pools, while CMLV1 and -2, Zhejiang mosquito virus 3 (ZJMV3), and Hubei chryso-like virus 1 (HBCLV1) appeared in most of the Culex pools. WHMV6, ZJMV3, and HBCLV1 were also prevalent in the Culex species from China (6). Importantly, each of these viruses had consistent abundance levels across different libraries and were absent from the Aedes pools, suggesting that they are unlikely to result from contamination. This observation highlights the persistence of some viral infections in Culex mosquitoes, to the extent that infections are the norm rather than the exception.

Virome ecology.

Our analysis revealed substantial differences between the Aedes and Culex genera in terms of virus composition and abundance. Generally, Aedes mosquitoes contained fewer viruses than did Culex mosquitoes (Fig. 2). Although the Aedes camptorhynchus pool from South Guildford contained seven viral species, all were of low abundance and with an uncertain host association (see below). More striking was that the total viral abundance was much lower in the Aedes pools (0.013 to 0.391%) than in the Culex pools (4.508 to 16.87%), an observation that was consistent across sampling locations.

FIG 2.

FIG 2

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Overview of the diversity and abundance of the RNA viruses discovered. From top to bottom, we show four column graphs depicting the number of viruses, the composition of viral families, the abundance of the total virome, and the abundance of the host RPL32 gene in each of the 12 pools sequenced here. The mosquito species and location information for each pool are shown at the top.

The differences between the two mosquito genera were also reflected in the types of viruses that they harbored (Fig. 3A). Of the 24 viral species discovered, only two, Wilkie qin-like virus (WQLV) and Wilkie narna-like virus 1 (WNLV1), were shared between the Aedes and Culex pools (Table 1). However, that these viruses were of low abundances and coappeared with a group of related fungal pathogens rendered them more likely to be associated with fungi than with mosquitoes (see below). The lack of similarity between the Aedes and Culex viromes was in marked contrast to the number of common viral species found among the three Culex species (Fig. 3 and Table 1). Notably, Culex quinquefasciatus shared five of the six viruses with the other two Culex species despite the substantial genetic distance between these hosts (Fig. 1). Conversely, no viruses were shared between A. camptorhynchus and Aedes alboannulatus, although only one virus was discovered in A. alboannulatus.

FIG 3.

FIG 3

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Similarity of viromes between host species (A) and geographic locations (B). The size of the circle is proportional to the total number of viruses discovered in each mosquito species (A) or geographic location (B). Within the circle, information on the host species or geographic location and the number of viruses (in parentheses) is provided. The thickness of the line connecting the circles reflects the number of viruses shared between species or geographic locations. The number of shared viruses is shown next to the line.

Also of note was that there was a significant overlap between the viromes from the three locations that harbored Culex species (Fig. 3B). The fourth location, Leschenault Peninsula, contained only A. camptorhynchus mosquitoes, whose virome was very limited. Hence, there is seemingly a lack of geographic structure to the RNA virome at the scale of this study. Indeed, the geographic distribution of each of these viral species may be much broader and involve locations outside Australia. In particular, several of the viruses that we identified shared high genetic identity with those found in disparate geographic locations, including Wuhan mosquito virus 6 (98% nucleotide identity), Zhejiang mosquito virus 3 (96%), Hubei chryso-like virus 1 (97%), and Shuangao chryso-like virus 1 (SCLV1) (97%), which were also identified in China (6), as well as Ngewotan virus (99%) from Indonesia (18).

Evolutionary history of the newly identified RNA viruses.

While the majority of the viruses identified in this study exhibited relatively close relationships to viruses previously described for either mosquitoes, dipteran insects, or other related arthropods, six clustered with fungal viruses (Fig. 4 to 6 and see below). The clustering of mosquito-associated viruses from different countries or mosquito species was apparent at many places within the phylogenies, and sometimes, these monophyletic groups contained substantial genetic diversity suggestive of a long-term association between the viruses and their mosquito hosts. Notably, the mosquito-associated clusters often contained multiple viral lineages associated with single or multiple host species/genera (Fig. 4 to 6), with no clear pattern of virus-host codivergence, although this needs to be examined with a much larger sample size.

FIG 4.

FIG 4

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Evolutionary history and genomic features of the negative-sense RNA viruses discovered. The maximum likelihood phylogenetic trees show the positions of newly discovered viruses (solid black circles) in the context of representatives of their closest relatives. The names of mosquito viruses identified in previous studies are marked in red and contain information on the mosquito species from which they were sampled (square brackets). The genome structures of these newly discovered viruses are shown next to their corresponding phylogenies. Predicted ORFs of these genomes are labeled with information on the potential protein or protein domain that they encode.

FIG 6.

FIG 6

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Evolutionary history and genomic features of the double-stranded RNA (dsRNA) viruses discovered. The legend is the same as that for Fig. 4.

Negative-sense RNA viruses.

We discovered eight putative negative-sense RNA viruses, representing all the major taxonomic categories (Table 1). Among these, six were related to previously described mosquito viruses, while the remaining two viruses either grouped with a fungal virus (Wilkie ophio-like virus 1 [WOLV1]; Ophioviridae) or had an uncertain host association (WQLV and Qinvirus) (Fig. 4). In the RNA-dependent RNA polymerase (RdRp) phylogeny, CPLV clustered within the recently proposed phasmavirus group (family Bunyaviridae) (26), whose host range is currently limited to arthropods (6, 8). Its closest relative was Wuhan mosquito virus 2 identified from Culex mosquitoes in China. CPLV showed a genome structure typical of phasmaviruses, which have substantially shorter glycoprotein-encoding segments than those of other bunyaviruses.

Culex rhabdo-like virus (CRLV), CMLV1, and CMLV2 were related to viruses of the order Mononegavirales. CRLV1 was from the Dimarhabdovirus group and related to North Creek virus that was isolated from Culex sitiens (Wiedemann) sampled on the east coast of Australia (10) (Fig. 4), while CMLV1 and -2 grouped with Xincheng mosquito virus in a currently unclassified clade. Interestingly, CMLV1 had a bisegment genome arrangement that occurs only rarely in the Mononegavirales (27) (Fig. 4), although the most closely related viruses, CMLV2 and Xincheng mosquito virus, had unsegmented genomes.

Aedes alboannulatus orthomyxo-like virus (AAOLV) and WHMV6 belonged to two separate mosquito-associated clusters within the family Orthomyxoviridae. WHMV6 was initially identified in Culex mosquitoes from China (6, 8), and we were able to reveal 2 more genome segments, containing a glycoprotein gene and an unknown protein gene, in addition to those described previously, making a total of 6 segments. Although the Aedes alboannulatus orthomyxo-like virus was discovered in only one pool, it was of moderately high abundance (0.217%) and clustered with viruses identified from the other mosquito hosts in China (Fig. 4), which suggested a potential association with A. alboannulatus.

Positive-sense RNA viruses.

The positive-sense RNA viruses discovered in this study fell within the Narnaviridae, Mesoniviridae (Nidovirales), Negev-like viruses, and Luteoviridae-related viruses (Fig. 5). The Negev-like viruses were initially identified in mosquitoes (18) and have now been expanded to include a number of other arthropod species. Based on the RdRp, the Negev-like viruses form part of a larger group referred to as the alpha-like supergroup (28) or the Hepe-Virga-like group (8), which includes the Togaviridae, Virgaviridae, Hepeviridae, and Tymovirales, among others. We identified four divergent viruses within the Negev-like virus group. Among these, Culex Negev-like virus 2 (CNLV2) and CNLV3 were closely related to viruses identified in mosquitoes and had a genome structure similar to that of the prototype Negev virus (Fig. 5). In contrast, CNLV1 was distantly related to a virus identified from nematodes (Fig. 5). However, since CNLV1 was of moderately high abundance and appeared in three Culex pools that contained no traces of nematode genes, its host association was more likely mosquitoes. Aedes camptorhynchus Negev-like virus (ACNLV) showed a distant relationship with Muthill virus and Marsac virus identified from flies (Fig. 5). Its genome had several unique features, including a permuted RdRp domain, a potential stop codon readthrough site between the helicase and RdRp domains, and a distinctive (and longer) set of genes downstream of the replicase.

FIG 5.

FIG 5

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Evolutionary history and genomic features of the positive-sense RNA viruses discovered. The legend is the same as that for Fig. 4.

We also identified four viruses of the Narnaviridae (Fig. 5). Of these, ZJMV3 was highly abundant and prevalent across all the Culex pools, while the other three viruses were of low abundance and clustered with fungal pathogens. Viruses closely related to ZJMV3 have been identified in China (8), France (19), and the United States (20) and can be distinguished from other narnaviruses because of their dual-coding genome structure, characterized by two open reading frames (ORFs) that cover the complete length of both the sense and antisense genomes (Fig. 5). One of the ORFs encodes the RdRp, while the other had no homology to any gene. Importantly, this feature was conserved across a divergent phylogenetic group, including more distantly related viruses such as Hubei narna-like virus 20 (Fig. 5).

We also identified a virus related to the Luteo-Sobemo-like group whose host range has recently expanded from plants to include arthropods, nematodes, molluscs, and protists (8). Specifically, we identified a single member of this group, Culex Luteo-like virus (CLLV), that was related to Hubei Sobemo-like virus 41 previously identified in mosquitoes from China. Despite its relatively low abundance, it was identified in the three Culex pools that did not contain any abundant cellular parasites (Table 2), suggesting that it is most likely associated with Culex mosquitoes. The genome of CLLV contained two segments, encoding the replicase and the capsid (identified by structural BLAST analysis). The replicase segment contained a ribosomal frameshift site before the coding regions of the RdRp, typical of the members of the Luteo-Sobemo-like group (8).

Double-stranded RNA viruses.

We identified seven double-stranded RNA viruses belonging to Chrysoviridae (n = 2), Totiviridae (n = 1), Reoviridae (n = 1), and Partitiviridae (n = 3). With the exception of the three viruses from the Partitiviridae, all these viruses were related to those identified from mosquito or other arthropod hosts (Fig. 6). HBCLV1 and SCLV1, initially identified from mosquitoes in China, were now found to be prevalent in Culex mosquitoes from Western Australia. Their complete genomes, as revealed in this study, contained four segments, similar to the prototype genome of the Chrysoviridae (Fig. 6). In the case of the Totiviridae, we identified Aedes camptorhynchus toti-like virus (ACTLV), which, like the other totiviruses, has an unsegmented genome comprising two major ORFs. Finally, the only reovirus identified here, Aedes camptorhynchus reo-like virus, was related to Hubei reo-like virus 11 from dragonflies, which in turn formed a distant sister clade to viruses of the genus Phytoreovirus.

Revealing host associations.

The total transcriptomes described here not only contained virus transcripts but also abundantly expressed host genes and those from other intrahost microbes such as bacteria, archaea, fungi, and protists. To reveal the presence and diversity of these microbes, we searched within the assembled transcripts for the presence of abundantly expressed marker genes of cellular organisms. In this way, we were able to identify several dominant microbes within the mosquito host: some were related to parasites known to cause infections in humans (e.g., Leishmania), whereas others included intracellular symbiotic bacteria such as Wolbachia sp. strain wPip (Table 2). Generally, the abundance levels of genes from the (nonviral) microbes were orders of magnitude lower than those of the mosquito hosts (Table 2). In addition, we identified a group of related fungi, which we termed “unknown species 1, 2, and 3,” in three of the pools, including both A. camptorhynchus and Culex globocoxitus. The abundance levels of these fungi were relatively high: in one of the A. camptorhynchus pools, the abundance of the fungal cox1 gene reached 0.125%, compared to 0.669% for that of mosquitoes. Interestingly, two viruses (WQLV and Wilkie Partiti-like virus 2 [WPLV2]) found in both A. camptorhynchus and C. globocoxitus coappeared with these fungi (Tables 1 and 2), and the viruses and fungi had matching evolutionary histories (Fig. 7). Furthermore, both WQLV and WPLV2 grouped with fungal viruses rather than mosquito or arthropod viruses (Fig. 3 and 5). Collectively, these results provide strong evidence that these viruses were more likely to be associated with fungi than mosquitoes.

FIG 7.

FIG 7

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Matching tree topologies of the Wilkie qin-like viruses and a group of fungi (cox1 gene) discovered in three mosquito pools. Pool information is given in the middle of the two phylogenies, both of which are midpoint rooted for clarity only.

Finally, to provide a summary of potential host associations for all the viruses discovered here, we considered several key attributes that are relevant to host association: abundance level, prevalence, host association of close relatives, and coappearance with other cellular microbes within the hosts (Table 3). Among the 24 viruses identified here, 16 were likely to be associated with mosquitoes according these criteria, whereas 8 were more likely to be associated with other hosts, although this clearly requires additional confirmation.

TABLE 3.

Criteria used to identify viruses likely associated with mosquitoes

Virus Criterion
 Positive association with mosquitoes
Relatively high abundance level (>0.1% of total RNA in the library) Found in >2 libraries Close relatives are mosquito or insect viruses
Culex phasma-like virus Yes Yes Yes Strong
Culex mononega-like virus 1 Yes Yes Yes Strong
Culex mononega-like virus 2 Yes Yes Strong
Culex rhabdo-like 1 Yes Yes Yes Strong
Wuhan mosquito virus 6 Yes Yes Yes Strong
Aedes alboannulatus orthomyxo-like virus Yes Yes Strong
Wilkie qin-like virus Yes Weak; viruses coappear with fungi
Wilkie ophio-like virus 1 Weak
Culex Negev-like virus 1 Yes Yes Yes Strong
Culex Negev-like virus 2 Yes Yes Strong
Culex Negev-like virus 3 Yes Yes Strong
Culex Negev-like virus 4 Yes Yes Strong
Culex Luteo-like virus Yes Yes Strong
Point Douro narna-like virus Weak
Culex narna-like virus Yes Yes Yes Strong
Wilkie narna-like virus 1 Weak
Wilkie narna-like virus 2 Weak
Nam Dinh virus Yes Yes Strong
Wilkie Partiti-like virus 1 Weak
Wilkie Partiti-like virus 2 Yes Weak; viruses coappear with fungi
Leschenault Partiti-like virus Weak
Aedes alboannulatus reo-like virus Yes Yes Strong
Aedes alboannulatus toti-like virus Yes Strong
Culex chryso-like virus Yes Yes Yes Strong
Culex quinquefasciatus chryso-like virus Yes Yes Strong
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DISCUSSION

We have used a metagenomics approach to reveal key aspects of the ecology of RNA viruses in mosquitoes from Western Australia. Of particular interest was the high diversity, high prevalence, and relatively high abundance of a number of the RNA viruses from multiple virus groups. Hence, these results highlight the capacity of Culex mosquitoes to tolerate high levels of viral RNA, as was described previously for other invertebrates (6, 8). Indeed, given the very high prevalence of these viruses, it seems intuitively unlikely that these viruses are associated with severe disease in their hosts, and we propose that the most likely status for these viruses is either sublethal infection or commensal. This is supported by the observation that viruses have been detected in both laboratory mosquito colonies and insect cell lines that show little loss of fitness (29, 30), although this clearly requires additional study.

Our results also revealed a striking difference between the viral diversities harbored by the Aedes and Culex genera of mosquitoes: infections in the former group are sporadic, and there is little resemblance between different populations, although clearly, this needs to be examined with more data. Similarly, among the previously described vector-borne viruses, there is little overlap in the viruses carried by Aedes and Culex mosquitoes, such that the diversity of mosquito-borne flaviviruses can be further subdivided into Culex- or Aedes-associated phylogenetic groups (31, 32). The three Culex species studied here (C. quinquefasciatus, C. australicus, and C. globocoxitus) all form part of the Culex pipiens complex and are closely related in their cox1 gene sequences (33, 34). Hence, the similarity in viromes among the three Culex species may in part reflect their close evolutionary relationships, which may in turn dictate similarities in the cellular environment, immunological response, and perhaps ecological niche (35). The two mosquito genera also exhibit a large discrepancy in virus numbers and abundances, which is robust across all comparisons despite the relatively small sample size (Fig. 2).

In contrast to the difference in viromes between genera, the Culex virome was relatively homogenous among the species and across the regions sampled. Furthermore, a number of the viruses discovered here were found not only in Western Australia but also in regional countries like China and Indonesia, indicating that they infect hosts over a wide geographical area. As the viruses present in these different countries are very similar (95% to ∼98% nucleotide identities), such limited genetic distance tentatively suggests that these viruses were introduced by windblown mosquitoes (36, 37) or by cyclones from neighboring regions (38) or were inadvertently spread by humans, rather than the result of ancient mosquito dispersal. Conversely, based on current data, there appears to be relatively little overlap between the mosquito viromes sampled from Western Australia and those sampled from other parts of Australia (3, 10), which may reflect the different mosquito species present in these localities. In addition, a previous survey of viruses in Eastern Australia was performed after the viruses were passaged in cell culture, which may eliminate some of the viruses present in the original sample (10). A more complete characterization of virome ecology in Australia evidently requires larger-scale sampling covering more geographic locations and mosquito species. Similarly, the present study relied on the collection of mosquitoes with encephalitis virus surveillance (EVS) CO2 traps, which are likely to be biased in the species of mosquitoes collected. A broader sampling of mosquito fauna to determine the overall diversity of viruses will evidently require the use of a variety of trapping techniques that reflect specific mosquito habits or attraction to collection traps.

Although our study was directed toward mosquitoes (20), it was striking that we identified a number of RNA viruses that were likely associated with hosts other than mosquitoes. Specifically, potential nonmosquito viruses were revealed through phylogenetic analysis (i.e., they clustered with viruses from fungi rather than from mosquitoes), evidence of codivergence with their microbial hosts, and their low abundance (Table 3 and Fig. 7). The abundances of these confirmed and suspected microbial viruses were generally below 0.001% of the total non-rRNA reads (i.e., so low that they are unlikely to be associated with mosquitoes), although the highest abundance (WQLV) reached 0.074%. This in turn suggests that the viral abundance level is a useful indicator of host association, although it should be examined in the context of the type and quantity of the dominant microbes within a sample.

Finally, it is important to note that among the various virus species discovered here, none fell into the category of “vector-borne” viruses that are known to infect humans or other mammalian hosts. Indeed, in a previous metagenomics survey of mosquitoes and ticks, most of the viruses discovered either clustered with “arthropod-specific” viruses or were uncharacterized (1, 2), and sequencing of nearly 200 mosquitoes revealed only two known vector-borne viruses (6, 8). This suggests that human and vertebrate pathogens represent only a tiny fraction of the mosquito virome, although it is possible that they exist at very low copy numbers if they exhibit low levels of replication. Whatever the cause, the observation that vector-borne viruses are rare further indicates how the characterization of the mosquito virome provides important insight into the ecology and evolution of insect viruses.

MATERIALS AND METHODS

Sample collection.

A total of 519 adult mosquitoes were collected in 2015 from four locations in Western Australia considered to be of significant public health risk in relation to mosquito-borne diseases, including Ross River virus (RRV) and Barmah Forest virus (BFV). The four locations comprised (i) South Guildford, an eastern suburb of the Perth metropolitan region located on the Swan River; (ii) Leschenault Peninsula, near Australind, and (iii) Point Douro, Bunbury, both of which are tidally driven inlet sites and approximately 160 km and 175 km southwest of Perth, respectively; and (iv) Siesta Park, in Dunsborough, approximately 250 km southwest of Perth (Fig. 1). Mosquitoes were collected by using EVS CO2 traps that were set at each location for approximately 12 h. Each trap was baited with dry ice to attract mosquitoes. Upon trap collection, the mosquitoes were euthanized by placing each collection on dry ice to kill and preserve the mosquitoes and RNA. Mosquitoes were then placed in labeled vials and left on dry ice until they were returned to the laboratory, where the samples were placed in a −80°C freezer.

Mosquito species identification was initially carried out by experienced field biologists using taxonomic keys (39) and dissecting microscopes on cold tables and was later verified by analysis of the cytochrome c oxidase subunit I (COI) gene (cox1) (Fig. 1). The majority of the mosquitoes collected in this study were from five species: A. camptorhynchus (Thomson), A. alboannulatus (Macquart), Culex globocoxitus (Dobrotworsky), C. australicus (Dobrotworsky and Drummond), and C. quinquefasciatus (Say). As C. globocoxitus and C. australicus cannot be distinguished by COI gene sequences (Fig. 1), they were identified by using two main morphological diagnostic features: the tergal banding patterns and median patches of dark scales on the sternites. Specifically, C. globocoxitus has tergal banding without lateral constrictions and no dark patches of scales on the sternites, while C. australicus has lateral constrictions on tergal bands and prominent patches of dark scales on the sternites (39). All mosquito samples were then categorized by species and geographic location and stored at −80°C before RNA extraction.

Sample processing and sequencing.

RNA extraction and sequencing were carried out on 12 pools of mosquitoes, with each pool containing 5 to 10 representative female mosquitoes from the same geographic region and species (Table 1). Prior to homogenization, each mosquito pool was washed three times with 1 ml of a sterile RNA- and DNA-free phosphate-buffered saline (PBS) solution (Gibco) to remove external microbes. The samples were then homogenized in 600 μl of lysis buffer by using a TissueRuptor instrument (Qiagen). Total RNA was extracted by using an RNeasy Plus minikit according to the manufacturer's instructions. The quality of the extracted RNA was evaluated by using an Agilent 2100 bioanalyzer (Agilent Technologies). All extractions performed in this study had an RNA integrity number (RIN) of >8.7. Sequencing libraries were constructed by using a TruSeq total RNA library preparation kit (Illumina) with the host rRNA removed by using a Ribo-Zero-Gold (Human-Mouse-Rat) kit (Illumina). Paired-end (100-bp) sequencing of each library was then performed on the Hiseq2500 platform (Illumina). All library preparation and sequencing procedures were carried out by the Australian Genome Research Facility (AGRF).

RNA virus discovery.

Sequencing reads were demultiplexed and trimmed for quality with Trimmomatic (40) before de novo assembly using Trinity (41). The resulting contigs were first compared against the database of all reference RNA virus proteins downloaded from GenBank by using BLASTX with an E value cutoff at 1E−5. Potential viral contigs were then compared to the entire nonredundant nucleotide (nt) and protein (nr) database to remove false-positive results. The quality-filtered virus contigs with unassembled overlaps were then merged by using the SeqMan program implemented in the Lasergene software package v7.1 (DNAStar). To confirm the assembly results, reads were mapped back to the virus genomes with Bowtie2 (42) and inspected by using Integrated Genomics Viewer (IGV) (43) for any assembly errors. The final sequences of the virus genomes were obtained from the majority consensus of the mapping assembly.

Virus genome annotation.

The potential ORFs of the newly identified virus genomes were predicted based on those from the closest reference virus genomes. To characterize the functional domains within each ORF, we performed a domain-based BLAST search against the Conserved Domain Database (CDD) with an expected value threshold of 1E−5. The potential functions of the remaining ORFs were predicted by homology with other known viral proteins. A potential viral glycoprotein from families of negative-sense RNA viruses was identified based on the presence of (i) an N-terminal signal domain, (ii) a C-terminal or midpoint transmembrane domain, and (iii) putative glycosylation sites.

For those viruses with the multiple segments, non-RdRp segments were usually identified by homology to the proteins of related reference viruses. Other potential segments of no homology were identified by using an in silico approach that utilizes information on RNA quantity, protein structure, and/or conserved genome termini. To determine whether these segments belonged to the same virus, we checked (i) the sequencing depth of the segments, (ii) the presence of conserved genome termini, (iii) coappearance with the RdRp segments, and (iv) the phylogenetic positions of related viral proteins.

Identification of other microbes within mosquitoes.

To identify abundant bacteria, fungi, and protists within the mosquito populations sampled, we searched the assembled transcriptome for a collection of key marker genes that are abundantly and stably expressed in eukaryotes and prokaryotes. Specifically, we looked for the cox1 and RPL32 genes to identify eukaryotes (including the mosquito host) and the DNA gyrase subunit B (gyrB) and recombinase A protein (recA) genes to identify prokaryotes. The contigs discovered were then confirmed with (i) a BLASTX search against the nr database and (ii) read mapping. The quality-screened contigs were then trimmed to contain only coding regions for quantification (see below).

RNA quantification.

To help determine the abundance of RNA transcripts, we estimated the percentage of total reads that mapped to target genomes/genes. The sequences used for mapping involved viral genomes as well as the mosquito and microbial marker genes mentioned above. Mapping was performed by using Bowtie2 (42). The mapping results were manually checked for potential assembly errors with IGV (43).

Phylogenetic analyses.

We used the amino acid sequences of the viral replicase (i.e., RNA-dependent RNA polymerase) to determine the evolutionary history of the newly discovered viruses. For comparison, we included previously reported viral protein sequences representative of each of the relevant phylogenetic groups (e.g., virus family). This also included all the previously described mosquito viruses within these groups. Within each group, the replicase proteins were aligned by using the E-INS-i algorithm in MAFFT (version 7) (44). Ambiguously aligned regions were subsequently removed by using TrimAl (45). Based on the sequence alignment, the best-fit model of amino acid substitutions was determined by using ProtTest 3.4 (46). Phylogenetic trees were then estimated by using the maximum likelihood (ML) method implemented in PhyML version 3.0 (47), utilizing the best-fit substitution model and the Subtree Pruning and Regrafting (SPR) branch-swapping algorithm. Support for individual nodes on the phylogenetic tree was assessed by using an approximate likelihood ratio test (aLRT) with the Shimodaira-Hasegawa-like procedure as implemented in PhyML.

Accession number(s).

The raw sequence reads generated in this study are available at the NCBI Sequence Read Archive (SRA) database under BioProject accession number PRJNA388696. All virus genome sequences generated in this study have been deposited in GenBank under the accession numbers MF176241 to MF176391.

ACKNOWLEDGMENTS

We thank the staff of the Environmental Health Directorate of the Department of Health, Western Australia, for the collection of mosquitoes from the southwest of Western Australia. In addition, we thank the staff at the City of Swan (especially Neil Harries and James McCallum) for the collection of mosquitoes from the east of Perth. We also acknowledge The University of Sydney HPC service for providing high-performance computing resources that have contributed to the research results reported in this paper.

J.-S.E. is supported by an NHMRC early career fellowship (GNT1073466), and E.C.H. is supported by an NHMRC Australia fellowship (GNT1037231).

REFERENCES

  • 1.Bolling BG, Weaver SC, Tesh RB, Vasilakis N. 2015. Insect-specific virus discovery: significance for the arbovirus community. Viruses 7:4911–4928. doi: 10.3390/v7092851. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Vasilakis N, Tesh RB. 2015. Insect-specific viruses and their potential impact on arbovirus transmission. Curr Opin Virol 15:69–74. doi: 10.1016/j.coviro.2015.08.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Hall RA, Bielefeldt-Ohmann H, McLean BJ, O'Brien CA, Colmant AM, Piyasena TB, Harrison JJ, Newton ND, Barnard RT, Prow NA, Deerain JM, Mah MG, Hobson-Peters J. 2017. Commensal viruses of mosquitoes: host restriction, transmission, and interaction with arboviral pathogens. Evol Bioinform Online 12:35–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Marklewitz M, Handrick S, Grasse W, Kurth A, Lukashev A, Drosten C, Ellerbrok H, Leendertz FH, Pauli G, Junglen S. 2011. Gouleako virus isolated from West African mosquitoes constitutes a proposed novel genus in the family Bunyaviridae. J Virol 85:9227–9234. doi: 10.1128/JVI.00230-11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Marklewitz M, Zirkel F, Rwego IB, Heidemann H, Trippner P, Kurth A, Kallies R, Briese T, Lipkin WI, Drosten C, Gillespie TR, Junglen S. 2013. Discovery of a unique novel clade of mosquito-associated bunyaviruses. J Virol 87:12850–12865. doi: 10.1128/JVI.01862-13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Li CX, Shi M, Tian JH, Lin XD, Kang YJ, Chen LJ, Qin XC, Xu J, Holmes EC, Zhang YZ. 2015. Unprecedented genomic diversity of RNA viruses in arthropods reveals the ancestry of negative-sense RNA viruses. eLife 4:e05378. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Marklewitz M, Zirkel F, Kurth A, Drosten C, Junglen S. 2015. Evolutionary and phenotypic analysis of live virus isolates suggests arthropod origin of a pathogenic RNA virus family. Proc Natl Acad Sci U S A 112:7536–7541. doi: 10.1073/pnas.1502036112. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Shi M, Lin XD, Tian JH, Chen LJ, Chen X, Li CX, Qin XC, Li J, Cao JP, Eden JS, Buchmann J, Wang W, Xu J, Holmes EC, Zhang YZ. 2016. Redefining the invertebrate RNA virosphere. Nature 540:539–543. doi: 10.1038/nature20167. [DOI] [PubMed] [Google Scholar]
  • 9.Kuwata R, Isawa H, Hoshino K, Tsuda Y, Yanase T, Sasaki T, Kobayashi M, Sawabe K. 2011. RNA splicing in a new rhabdovirus from Culex mosquitoes. J Virol 85:6185–6196. doi: 10.1128/JVI.00040-11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Coffey LL, Page BL, Greninger AL, Herring BL, Russell RC, Doggett SL, Haniotis J, Wang C, Deng X, Delwart EL. 2014. Enhanced arbovirus surveillance with deep sequencing: identification of novel rhabdoviruses and bunyaviruses in Australian mosquitoes. Virology 448:146–158. doi: 10.1016/j.virol.2013.09.026. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Walker PJ, Firth C, Widen SG, Blasdell KR, Guzman H, Wood TG, Paradkar PN, Holmes EC, Tesh RB, Vasilakis N. 2015. Evolution of genome size and complexity in the Rhabdoviridae. PLoS Pathog 11:e1004664. doi: 10.1371/journal.ppat.1004664. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Presti RM, Zhao G, Beatty WL, Mihindukulasuriya KA, da Rosa AP, Popov VL, Tesh RB, Virgin HW, Wang D. 2009. Quaranfil, Johnston Atoll, and Lake Chad viruses are novel members of the family Orthomyxoviridae. J Virol 83:11599–11606. doi: 10.1128/JVI.00677-09. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Qin XC, Shi M, Tian JH, Lin XD, Gao DY, He JR, Wang JB, Li CX, Kang YJ, Yu B, Zhou DJ, Xu J, Plyusnin A, Holmes EC, Zhang YZ. 2014. A tick-borne segmented RNA virus contains genome segments derived from unsegmented viral ancestors. Proc Natl Acad Sci U S A 111:6744–6749. doi: 10.1073/pnas.1324194111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Shi M, Lin XD, Vasilakis N, Tian JH, Li CX, Chen LJ, Eastwood G, Diao XN, Chen MH, Chen X, Qin XC, Widen SG, Wood TG, Tesh RB, Xu J, Holmes EC, Zhang YZ. 2015. Divergent viruses discovered in arthropods and vertebrates revise the evolutionary history of the Flaviviridae and related viruses. J Virol 90:659–669. doi: 10.1128/JVI.02036-15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Ladner JT, Wiley MR, Beitzel B, Auguste AJ, Dupuis AP II, Lindquist ME, Sibley SD, Kota KP, Fetterer D, Eastwood G, Kimmel D, Prieto K, Guzman H, Aliota MT, Reyes D, Brueggemann EE, St John L, Hyeroba D, Lauck M, Friedrich TC, O'Connor DH, Gestole MC, Cazares LH, Popov VL, Castro-Llanos F, Kochel TJ, Kenny T, White B, Ward MD, Loaiza JR, Goldberg TL, Weaver SC, Kramer LD, Tesh RB, Palacios G. 2016. A multicomponent animal virus isolated from mosquitoes. Cell Host Microbe 20:357–367. doi: 10.1016/j.chom.2016.07.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Nga PT, Parquet MDC, Lauber C, Parida M, Nabeshima T, Yu F, Thuy NT, Inoue S, Ito T, Okamoto K, Ichinose A, Snijder EJ, Morita K, Gorbalenya AE. 2011. Discovery of the first insect nidovirus, a missing evolutionary link in the emergence of the largest RNA virus genomes. PLoS Pathog 7:e1002215. doi: 10.1371/journal.ppat.1002215. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Attoui H, Jaafar FM, Belhouchet M, Tao SJ, Chen BQ, Liang GD, Tesh RB, de Micco P, de Lamballerie X. 2006. Liao ning virus, a new Chinese seadornavirus that replicates in transformed and embryonic mammalian cells. J Gen Virol 87:199–208. doi: 10.1099/vir.0.81294-0. [DOI] [PubMed] [Google Scholar]
  • 18.Vasilakis N, Forrester NL, Palacios G, Nasar F, Savji N, Rossi SL, Guzman H, Wood TG, Popov V, Gorchakov R, Gonzalez AV, Haddow AD, Watts DM, da Rosa AP, Weaver SC, Lipkin WI, Tesh RB. 2013. Negevirus: a proposed new taxon of insect-specific viruses with wide geographic distribution. J Virol 87:2475–2488. doi: 10.1128/JVI.00776-12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Cook S, Chung BY, Bass D, Moureau G, Tang S, McAlister E, Culverwell CL, Glucksman E, Wang H, Brown TD, Gould EA, Harbach RE, de Lamballerie X, Firth AE. 2013. Novel virus discovery and genome reconstruction from field RNA samples reveals highly divergent viruses in dipteran hosts. PLoS One 8:e80720. doi: 10.1371/journal.pone.0080720. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Chandler JA, Liu RM, Bennett SN. 2015. RNA shotgun metagenomic sequencing of northern California (USA) mosquitoes uncovers viruses, bacteria, and fungi. Front Microbiol 6:185. doi: 10.3389/fmicb.2015.00185. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Cholleti H, Hayer J, Abilio AP, Mulandane FC, Verner-Carlsson J, Falk KI, Fafetine JM, Berg M, Blomstrom AL. 2016. Discovery of novel viruses in mosquitoes from the Zambezi valley of Mozambique. PLoS One 11:e0162751. doi: 10.1371/journal.pone.0162751. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Frey KG, Biser T, Hamilton T, Santos CJ, Pimentel G, Mokashi VP, Bishop-Lilly KA. 2016. Bioinformatic characterization of mosquito viromes within the eastern United States and Puerto Rico: discovery of novel viruses. Evol Bioinform Online 12:1–12. doi: 10.4137/EBO.S38518. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Junglen S, Drosten C. 2013. Virus discovery and recent insights into virus diversity in arthropods. Curr Opin Microbiol 16:507–513. doi: 10.1016/j.mib.2013.06.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Mokili JL, Rohwer F, Dutilh BE. 2012. Metagenomics and future perspectives in virus discovery. Curr Opin Virol 2:63–77. doi: 10.1016/j.coviro.2011.12.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Radford AD, Chapman D, Dixon L, Chantrey J, Darby AC, Hall N. 2012. Application of next-generation sequencing technologies in virology. J Gen Virol 93:1853–1868. doi: 10.1099/vir.0.043182-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Ballinger MJ, Bruenn JA, Hay J, Czechowski D, Taylor DJ. 2014. Discovery and evolution of bunyavirids in arctic phantom midges and ancient bunyavirid-like sequences in insect genomes. J Virol 88:8783–8794. doi: 10.1128/JVI.00531-14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Dietzgen RG, Kondo H, Goodin MM, Kurath G, Vasilakis N. 2017. The family Rhabdoviridae: mono- and bipartite negative-sense RNA viruses with diverse genome organization and common evolutionary origins. Virus Res 227:158–170. doi: 10.1016/j.virusres.2016.10.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Koonin EV. 1991. The phylogeny of RNA-dependent RNA polymerases of positive-strand RNA viruses. J Gen Virol 72:2197–2206. doi: 10.1099/0022-1317-72-9-2197. [DOI] [PubMed] [Google Scholar]
  • 29.Stollar V, Thomas VL. 1975. An agent in the Aedes aegypti cell line (Peleg) which causes fusion of Aedes albopictus cells. Virology 64:367–377. doi: 10.1016/0042-6822(75)90113-0. [DOI] [PubMed] [Google Scholar]
  • 30.Bolling BG, Vasilakis N, Guzman H, Widen SG, Wood TG, Popov VL. 2015. Insect-specific viruses detected in laboratory mosquito colonies and their potential implications for experiments evaluating arbovirus vector competence. Am J Trop Med Hyg 92:422–428. doi: 10.4269/ajtmh.14-0330. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Coffey LL, Forrester N, Tsetsarkin K, Vasilakis N, Weaver SC. 2013. Factors shaping the adaptive landscape for arboviruses: implications for the emergence of disease. Future Microbiol 8:155–176. doi: 10.2217/fmb.12.139. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Huang YJ, Higgs S, Horne KM, Vanlandingham DL. 2014. Flavivirus-mosquito interactions. Viruses 6:4703–4730. doi: 10.3390/v6114703. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Batovska J, Blacket MJ, Brown K, Lynch SE. 2016. Molecular identification of mosquitoes (Diptera: Culicidae) in southeastern Australia. Ecol Evol 6:3001–3011. doi: 10.1002/ece3.2095. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Smith JL, Fonseca DM. 2004. Rapid assays for identification of members of the Culex (Clex) pipiens complex, their hybrids, and other sibling species (Diptera: Culicidae). Am J Trop Med Hyg 70:339–345. [PubMed] [Google Scholar]
  • 35.Streicker DG, Turmelle AS, Vonhof MJ, Kuzmin IV, McCracken GF, Rupprecht CE. 2010. Host phylogeny constrains cross-species emergence and establishment of rabies virus in bats. Science 329:676–679. doi: 10.1126/science.1188836. [DOI] [PubMed] [Google Scholar]
  • 36.Johansen CA, Van den Hurk AF, Ritchie SA, Zborowski P, Nisbet DJ, Paru R, Bockarie MJ, MacDonald J, Drew AC, Khromykh TI, MacKenzie JS. 2000. Isolation of Japanese encephalitis virus from mosquitoes (Diptera: Culicidae) collected in the Western Province of Papua New Guinea, 1997-1998. Am J Trop Med Hyg 62:631–638. doi: 10.4269/ajtmh.2000.62.631. [DOI] [PubMed] [Google Scholar]
  • 37.Ritchie S, Rochester W. 2001. Wind blown mosquitoes and introduction of Japanese encephalitis into Australia. Emerg Infect Dis 5:900–903. doi: 10.3201/eid0705.017524. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Inglis TJJ, O'Rielly L, Merritt AJ, Levy A, Heath C. 2011. Review: the aftermath of the western Australian melioidosis outbreak. Am J Trop Med Hyg 84:851–857. doi: 10.4269/ajtmh.2011.10-0480. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Liehne PFS. 1991. An atlas of the mosquitoes of Western Australia. Health Department of Western Australia, Perth, Australia. [Google Scholar]
  • 40.Bolger AM, Lohse M, Usadel B. 2014. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30:2114–2120. doi: 10.1093/bioinformatics/btu170. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R, Zeng Q, Chen Z, Mauceli E, Hacohen N, Gnirke A, Rhind N, di Palma F, Birren BW, Nusbaum C, Lindblad-Toh K, Friedman N, Regev A. 2011. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol 29:644–652. doi: 10.1038/nbt.1883. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Langmead B, Salzberg SL. 2012. Fast gapped-read alignment with Bowtie 2. Nat Methods 9:357–359. doi: 10.1038/nmeth.1923. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Thorvaldsdottir H, Robinson JT, Mesirov JP. 2013. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform 14:178–192. doi: 10.1093/bib/bbs017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Katoh K, Standley DM. 2013. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol 30:772–780. doi: 10.1093/molbev/mst010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Capella-Gutierrez S, Silla-Martinez JM, Gabaldon T. 2009. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25:1972–1973. doi: 10.1093/bioinformatics/btp348. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Darriba D, Taboada GL, Doallo R, Posada D. 2011. ProtTest 3: fast selection of best-fit models of protein evolution. Bioinformatics 27:1164–1165. doi: 10.1093/bioinformatics/btr088. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Guindon S, Gascuel O. 2003. A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst Biol 52:696–704. doi: 10.1080/10635150390235520. [DOI] [PubMed] [Google Scholar]

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