Mosquitoes top the list of vectors for arthropod-borne diseases, being implicated in the transmission of many human pathogens responsible for arboviral diseases, malaria, and lymphatic filariasis (WHO, 2017). Mosquito-borne viruses circulate in sylvatic (between wild animals) or urban (between humans) transmission cycles driven by different mosquito species with their own distinct host preferences. Although urban mosquito species are chiefly responsible for amplifying epidemics in dense human populations, sylvatic mosquitoes maintain the transmission of these viruses among forest-dwelling animal reservoir hosts and are involved in spillover events when humans enter their ecological niches (Valentine et al., 2019). Given that mosquito-borne virus emergence is preceded by such spillover events, continuous surveillance and virus discovery in sylvatic mosquitoes is integral to designing effective public health measures to pre-empt or respond to mosquito-borne viral epidemics.

Metagenomics on field specimens is a powerful method in our toolkit to understand mosquito-borne disease ecology through the One Health lens (Webster et al., 2016). With next-generation sequencing becoming more accessible, such studies have provided unprecedented insights into the interfaces among mosquitoes, their environment, and their animal and human hosts. As mosquito-associated viruses are mostly RNA viruses, RNA sequencing (RNA-seq) is especially informative for surveillance and virus discovery. However, working with lesser studied mosquito species poses several problems.

First, metagenomics studies based on RNA-seq are bedevilled by overabundant ribosomal RNAs (rRNAs). These non-coding RNA molecules comprise at least 80% of the total cellular RNA population (Gale and Crampton, 1989). Due to their length and their abundance, they are a sink for precious next-generation sequencing reads, decreasing the sensitivity of pathogen detection unless depleted during library preparation. Yet the most common rRNA depletion protocols require prior knowledge of rRNA sequences of the species of interest as they involve hybridizing antisense oligos to the rRNA molecules prior to removal by ribonucleases (Fauver et al., 2019; Phelps et al., 2021) or by bead capture (Kukutla et al., 2013). Presently, reference sequences for rRNAs are limited to only a handful of species from three genera: Aedes, Culex, and Anopheles (Ruzzante et al., 2019). The lack of reliable rRNA depletion methods could deter mosquito metagenomics studies from expanding their sampling diversity, resulting in a gap in our knowledge of mosquito vector ecology. The inclusion of lesser studied yet medically relevant sylvatic species is therefore imperative.

Second, species identification based on morphology is notoriously complicated for members of certain species subgroups. This is especially the case among Culex subgroups. Sister species are often sympatric and show at least some competence for a number of viruses, such as Japanese encephalitis virus, St Louis encephalitic virus, and Usutu virus (Nchoutpouen et al., 2019). Although they share many morphological traits, each of these species have distinct ecologies and host preferences, thus the challenge of correctly identifying vector species can affect epidemiological risk estimation for these diseases (Farajollahi et al., 2011). DNA molecular markers are often employed to a limited degree of success to distinguish between sister species (Batovska et al., 2017; Zittra et al., 2016).

To address the lack of full-length rRNA sequences in public databases, we sought to determine the 28S and 18S rRNA sequences of a diverse set of Old and New World sylvatic mosquito species from four countries representing three continents: Cambodia, the Central African Republic, Madagascar, and French Guiana. These countries, due to their proximity to the equator, contain high mosquito biodiversity (Foley et al., 2007) and have had long histories of mosquito-borne virus circulation (Desdouits et al., 2015; Halstead, 2019; Héraud et al., 2022; Jacobi and Serie, 1972; Ratsitorahina et al., 2008; Saluzzo et al., 2017; Zeller et al., 2016). Increased and continued surveillance of local mosquito species could lead to valuable insights on mosquito virus biogeography. Using a unique score-based read filtration strategy to remove interfering non-mosquito rRNA reads for accurate de novo assembly, we produced a dataset of 234 novel full-length 28S and 18S rRNA sequences from 33 mosquito species, 30 of which have never been recorded before.

We also explored the functionality of 28S and 18S rRNA sequences as molecular markers by comparing their performance to that of the mitochondrial cytochrome c oxidase subunit I (COI) gene for molecular taxonomic and phylogenetic investigations. The COI gene is the most widely used DNA marker for molecular species identification and forms the basis of the Barcode of Life Data System (BOLD) (Hebert et al., 2003; Ratnasingham and Hebert, 2007). Presently, full-length rRNA sequences are much less represented compared to other molecular markers. However, given the availability of relevant reference sequences, 28S and concatenated 28S+18S rRNA sequences can be the better approach for molecular taxonomy and phylogenetic studies. We hope that our sequence dataset, with its species diversity and eco-geographical breadth, and the assembly strategy we describe would further facilitate the use of rRNA as markers. In addition, this dataset enables the design of species-specific oligos for cost-effective rRNA depletion for a broader range of mosquito species and streamlined molecular species identification during RNA-seq.

RNA-seq metagenomics on field-captured sylvatic mosquitoes is a valuable tool for tracking mosquito viruses through surveillance and virus discovery. However, the lack of reference rRNA sequences hinders good oligo-based depletion and efficient clean-up of RNA-seq data. Additionally, de novo assembly of rRNA sequences is complicated due to regions that are highly conserved across all distantly related organisms that could be present in a single specimen, that is, microbiota, parasites, or vertebrate blood meal. Hence, we established a method to bioinformatically filter out non-host rRNA reads for the accurate assembly of novel 28S and 18S rRNA reference sequences.

We found that phylogenetic reconstructions based on 28S sequences or concatenated 28S+18S rRNA sequences were able to correctly cluster mosquito taxa according to species and corroborate current mosquito classification. This demonstrates that our bioinformatics methodology reliably generates bona fide 28S and 18S rRNA sequences, even in specimens parasitized by water mites or engorged with vertebrate blood. Further, we were able to use 28S+18S rRNA sequence taxonomy for molecular species identification when COI sequences were unavailable or ambiguous, thus supporting the use of rRNA sequences as a molecular marker. In RNA-seq metagenomics applications, they have the advantage of circumventing the need to additionally isolate and sequence DNA from specimens, as RNA-seq reads can be directly mapped against reference sequences. In our hands, there are sufficient numbers of remaining reads post-depletion (5–10% of reads per sample) to assemble complete rRNA contigs (unpublished data).

Phylogenetic inferences based on 28S or 18S rRNA sequences alone do not recover the same interspecific relationships (Figure 4—figure supplements 1 and 2). Relative to 28S sequences, we observed more instances where multiple specimens have near-identical 18S rRNA sequences. This can occur for specimens belonging to the same species, but also for conspecifics sampled from different geographic locations, such as An. coustani, An. gambiae, or Ae. albopictus. More rarely, specimens from the same species subgroup, such as Cx. pseudovishnui and Cx. tritaeniorhynchus, also shared 18S rRNA sequences. This was surprising given that the 18S rRNA sequences in our dataset is 1,900 bp long. Concatenation of 28S and 18S rRNA sequences resolved this issue, enabling species delineation even among sister species of Culex subgroups, where morphological identification meets its limits.

In Cambodia and other parts of Asia, the Cx. vishnui subgroup includes Cx. tritaeniorhynchus, Cx. vishnui, and Cx. pseudovishnui, which are important vectors of JEV (Maquart and Boyer, 2022). The former two were morphologically identified in our study but later revealed by COI sequencing to be a sister species. Discerning sister species of the Cx. pipiens subgroup is further complicated by interspecific breeding, with some populations showing genetic introgression to varying extents (Cornel et al., 2003). The seven sister species of this subgroup are practically indistinguishable based on morphology and require molecular methods to discern (Farajollahi et al., 2011; Zittra et al., 2016). Indeed, the 621 bp COI sequence amplified in our study did not contain enough nucleotide divergence to allow clear identification, given that the COI sequence of Cx. quinquefasciatus specimens differed from that of Cx. pipiens by a single nucleotide. Batovska et al., 2017, found that even the Internal Transcribed Spacer 2 (ITS2) rDNA region, another common molecular marker, could not differentiate the two species. Other DNA molecular markers such as nuclear Ace-2 or CQ11 genes (Aspen and Savage, 2003; Zittra et al., 2016) or Wolbachia pipientis infection status (Cornel et al., 2003) are typically employed in tandem. In our study, 28S rRNA sequence-based phylogeny validated the identity of specimen ‘Cx quinquefasciatus MG S74’ (Figure 3, in coral) and suggested that specimen ‘Cx quinquefasciatus MG S75’ might have been a pipiens-quinquefasciatus hybrid. These examples demonstrate how 28S rRNA sequences, concatenated with 18S rRNA sequences or alone, contain enough resolution to differentiate between Cx. pipiens and Cx. quinquefasciatus. rRNA-based phylogeny thus allows for more accurate species identification and ecological observations in the context of disease transmission. Additionally, tracing the genetic flow across hybrid populations within the Cx. pipiens subgroup can inform estimates of vectorial capacity for each species. As only one or two members from the Cx. pipiens and Cx. vishnui subgroups were represented in our taxonomic assemblage, an explicit investigation including all member species of these subgroups in greater sample numbers is warranted to further test the degree of accuracy with which 28S and 18S rRNA sequences can delineate sister species.

Our study included French Guianese Culex species Cx. spissipes (group Spissipes), Cx. pedroi (group Pedroi), and Cx. portesi (group Vomerifer). These species belong to the New World subgenus Melanoconion, section Spissipes, with well-documented distribution in North and South Americas (Sirivanakarn, 1982) and are vectors of encephalitic alphaviruses EEEV and VEEV among others (Talaga et al., 2021; Turell et al., 2008; Weaver et al., 2004). Indeed, our rooted rRNA and COI trees showed the divergence of the three Melanoconion species from the major Culex clade comprising species broadly found across Africa and Asia (Auerswald et al., 2021; Farajollahi et al., 2011; Nchoutpouen et al., 2019; Takhampunya et al., 2011). The topology of the concatenated 28S+18S rRNA tree places the Cx. portesi and Cx. pedroi species-clades as sister groups (92% bootstrap support), with Cx. spissipes as a basal group within the Melanoconion clade (100% bootstrap support) (Figure 4, in coral). This corroborates the systematics elucidated by Navarro and Weaver, 2004, using the ITS2 marker, and those by Sirivanakarn, 1982 and Sallum and Forattini, 1996 based on morphology. Curiously, in the COI tree, Cx. spissipes sequences were clustered with unknown species Cx. sp.1, forming a clade sister to another containing other Culex (Culex) and Culex (Oculeomyia) species, albeit with very low bootstrap support (Figure 5, in coral). Previous phylogenetic studies based on the COI gene have consistently placed Cx. spissipes or the Spissipes group basal to other groups within the Melanoconion subgenus (Torres-Gutierrez et al., 2016; Torres-Gutierrez et al., 2018). However, these studies contain only Culex (Melanoconion) species in their assemblage, apart from Cx. quinquefasciatus to act as an outgroup. This clustering of Cx. spissipes with non-Melanoconion species in our COI phylogeny could be an artefact of a much more diversified assemblage rather than a true phylogenetic link.

Taking advantage of our multi-country sampling, we examined whether rRNA or COI phylogeny can be used to distinguish conspecifics originating from different geographies. Our assemblage contains five of such species: An. coustani, An. funestus, An. gambiae, Ae. albopictus, and Ma. uniformis. Among the rRNA trees, the concatenated 28S+18S and 28S rRNA trees were able to discriminate between Ma. uniformis specimens from Madagascar, Cambodia, and the Central African Republic (in dark green), and between An. coustani specimens from Madagascar and the Central African Republic (in purple) (100% bootstrap support). In the COI tree, only Ma. uniformis was resolved into geographical clades comprising specimens from Madagascar and specimens from Cambodia (in dark green) (72% bootstrap support). No COI sequence was obtained from one Ma. uniformis specimen from the Central African Republic. The 28S+18S rRNA sequences ostensibly provided more population-level genetic information than COI sequences alone with better support. The use of rRNA sequences in investigating the biodiversity of mosquitoes should therefore be explored with a more comprehensive taxonomic assemblage.

The phylogenetic reconstructions based on rRNA or COI sequences in our study are hardly congruent (Table 2), but two principal differences stand out. First, the COI phylogeny does not recapitulate the early divergence of Anophelinae from Culicinae (Figure 5). This is at odds with other studies estimating mosquito divergence times based on mitochondrial genes (Logue et al., 2013; Lorenz et al., 2021) or nuclear genes (Reidenbach et al., 2009). The second notable feature in the rRNA trees is the remarkably large interspecies and intersubgeneric evolutionary distances within genus Anopheles relative to other genera in the Culicinae subfamily (Figure 3, Figure 3—figure supplement 1, Figure 4, Figure 4—figure supplements 1 and 2; Anopheles in purple) but this is not apparent in the COI tree. The hyperdiversity among Anopheles taxa may be attributed to the earlier diversification of the Anophelinae subfamily in the early Cretaceous period compared to that of the Culicinae subfamily—a difference of at least 40 million years (Lorenz et al., 2021). The differences in rRNA and COI tree topologies indicate a limitation in using COI alone to determine evolutionary relationships. Importantly, drawing phylogenetic conclusions from short DNA markers such as COI has been cautioned against due to its weak phylogenetic signal (Hajibabaei et al., 2006). The relatively short length of our COI sequences (621–699 bp) combined with the 100-fold higher nuclear substitution rate of mitochondrial genomes relative to nuclear genomes (Arctander, 1995) could result in homoplasy (Danforth et al., 2005), making it difficult to clearly discern ancestral sequences and correctly assign branches into lineages, as evidenced by the poor nodal bootstrap support at genus-level branches. Indeed, in the study by Lorenz et al., 2021, a phylogenetic tree constructed using a concatenation of all 13 protein-coding genes of the mitochondrial genome was able to resolve ancient divergence events. This affirms that while COI sequences can be used to reveal recent speciation events, longer or multi-gene molecular markers are necessary for studies into deeper evolutionary relationships (Danforth et al., 2005).

In contrast to Anophelines where 28S rRNA phylogenies illustrated higher interspecies divergence compared to COI phylogeny, two specimens of an unknown Mansonia species, ‘Ma sp.4 GF S103’ and ‘Ma sp.4 GF S104’, provided an example where interspecies relatedness based on their COI sequences is greater than that based on their rRNA sequences in relation to ‘Ma titillans GF S105’. While all rRNA trees placed ‘Ma titillans GF S105’ as a sister taxon with 100% bootstrap support, the COI tree placed M sp.4 basal to all other species except Ur. geometrica (Figure 5; Mansonia in dark green, Uranotaenia in pink). This may hint at a historical selective sweep in the mitochondrial genome, whether arising from geographical separation, mutations, or linkage disequilibrium with inherited symbionts (Hurst and Jiggins, 2005), resulting in the disparate mitochondrial haplogroups found in French Guyanese Ma sp.4 and Ma. titillans. In addition, both haplogroups are distant from those associated with members of subgenus Mansonoides. To note, the COI sequences of ‘M sp.4 GF S103’ and ‘M sp.4 GF S104’ share 87.12% and 87.39% nucleotide similarity, respectively, to that of ‘Ma titillans GF S105’. Interestingly, the endosymbiont Wo. pipientis has been detected in Ma. titillans sampled from Brazil (de Oliveira et al., 2015), which may contribute to the divergence of ‘Ma titillans GF S105’ COI sequence away from those of Ma sp.4. This highlights other caveats of using a mitochondrial DNA marker in determining evolutionary relationships (Hurst and Jiggins, 2005), which nuclear markers such as 28S and 18S rRNA sequences may be immune to.

Multiple sequence alignment files are included as source data files. All sequences generated in this study have been deposited in GenBank under the accession numbers OM350214–OM350327 for 18S rRNA sequences, OM542339–OM542460 for 28S rRNA sequences, and OM630610–OM630715 for COI sequences.

1. 1. Altschul SF
   2. Gish W
   3. Miller W
   4. Myers EW
   5. Lipman DJ

   (1990) Basic local alignment search tool

   Journal of Molecular Biology 215:403–410.

   https://doi.org/10.1016/S0022-2836(05)80360-2

   • PubMed
   • Google Scholar
2. 1. Arctander P

   (1995) Comparison of a mitochondrial gene and a corresponding nuclear pseudogene

   Proceedings. Biological Sciences 262:13–19.

   https://doi.org/10.1098/rspb.1995.0170

   • PubMed
   • Google Scholar
3. 1. Arunachalam N
   2. Samuel PP
   3. Hiriyan J
   4. Thenmozhi V
   5. Gajanana A

   (2004) Japanese encephalitis in Kerala, south India: can Mansonia (Diptera: Culicidae) play a supplemental role in transmission?

   Journal of Medical Entomology 41:456–461.

   https://doi.org/10.1603/0022-2585-41.3.456

   • PubMed
   • Google Scholar
4. 1. Aspen S
   2. Savage HM

   (2003)

   Polymerase chain reaction assay identifies North American members of the Culex pipiens complex based on nucleotide sequence differences in the acetylcholinesterase gene Ace.2

   Journal of the American Mosquito Control Association 19:323–328.

   • PubMed
   • Google Scholar
5. 1. Auerswald H
   2. Maquart PO
   3. Chevalier V
   4. Boyer S

   (2021) Mosquito vector competence for Japanese encephalitis virus

   Viruses 13:1154.

   https://doi.org/10.3390/v13061154

   • PubMed
   • Google Scholar
6. 1. Bankevich A
   2. Nurk S
   3. Antipov D
   4. Gurevich AA
   5. Dvorkin M
   6. Kulikov AS
   7. Lesin VM
   8. Nikolenko SI
   9. Pham S
   10. Prjibelski AD
   11. Pyshkin AV
   12. Sirotkin AV
   13. Vyahhi N
   14. Tesler G
   15. Alekseyev MA
   16. Pevzner PA

   (2012) SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing

   Journal of Computational Biology 19:455–477.

   https://doi.org/10.1089/cmb.2012.0021

   • PubMed
   • Google Scholar
7. 1. Barrio-Nuevo KM
   2. Cunha MS
   3. Luchs A
   4. Fernandes A
   5. Rocco IM
   6. Mucci LF
   7. de Souza RP
   8. Medeiros-Sousa AR
   9. Ceretti-Junior W
   10. Marrelli MT

   (2020) Detection of Zika and dengue viruses in wild-caught mosquitoes collected during field surveillance in an environmental protection area in São Paulo, brazil

   PLOS ONE 15:e0227239.

   https://doi.org/10.1371/journal.pone.0227239

   • PubMed
   • Google Scholar
8. 1. Batovska J
   2. Cogan NOI
   3. Lynch SE
   4. Blacket MJ

   (2017) Using next-generation sequencing for DNA barcoding: capturing allelic variation in ITS2

   G3: Genes, Genomes, Genetics 7:19–29.

   https://doi.org/10.1534/g3.116.036145

   • PubMed
   • Google Scholar
9. 1. Beebe NW

   (2018) DNA barcoding mosquitoes: advice for potential prospectors

   Parasitology 145:622–633.

   https://doi.org/10.1017/S0031182018000343

   • PubMed
   • Google Scholar
10. 1. Behura SK

    (2006) Molecular marker systems in insects: current trends and future avenues

    Molecular Ecology 15:3087–3113.

    https://doi.org/10.1111/j.1365-294X.2006.03014.x

    • PubMed
    • Google Scholar
11. 1. Belda E
    2. Nanfack-Minkeu F
    3. Eiglmeier K
    4. Carissimo G
    5. Holm I
    6. Diallo M
    7. Diallo D
    8. Vantaux A
    9. Kim S
    10. Sharakhov IV
    11. Vernick KD

    (2019) De novo profiling of RNA viruses in Anopheles malaria vector mosquitoes from forest ecological zones in Senegal and Cambodia

    BMC Genomics 20:664.

    https://doi.org/10.1186/s12864-019-6034-1

    • PubMed
    • Google Scholar
12. 1. Bhattacharya S
    2. Basu P

    (2016)

    The Southern House Mosquito, Culex quinquefasciatus: profile of a smart vector

    Journal of Entomology and Zoology Studies JEZS 4:73–81.

    • Google Scholar
13. 1. Bishop-Lilly KA
    2. Turell MJ
    3. Willner KM
    4. Butani A
    5. Nolan NME
    6. Lentz SM
    7. Akmal A
    8. Mateczun A
    9. Brahmbhatt TN
    10. Sozhamannan S
    11. Whitehouse CA
    12. Read TD

    (2010) Arbovirus detection in insect vectors by rapid, high-throughput pyrosequencing

    PLOS Neglected Tropical Diseases 4:e878.

    https://doi.org/10.1371/journal.pntd.0000878

    • PubMed
    • Google Scholar
14. 1. Brault AC
    2. Foy BD
    3. Myles KM
    4. Kelly CLH
    5. Higgs S
    6. Weaver SC
    7. Olson KE
    8. Miller BR
    9. Powers AM

    (2004) Infection patterns of o’nyong nyong virus in the malaria-transmitting mosquito, Anopheles gambiae

    Insect Molecular Biology 13:625–635.

    https://doi.org/10.1111/j.0962-1075.2004.00521.x

    • PubMed
    • Google Scholar
15. 1. Cardoso J
    2. de Almeida MAB
    3. dos Santos E
    4. da Fonseca DF
    5. Sallum MAM
    6. Noll CA
    7. Monteiro H
    8. Cruz ACR
    9. Carvalho VL
    10. Pinto EV
    11. Castro FC
    12. Nunes Neto JP
    13. Segura MNO
    14. Vasconcelos PFC

    (2010) Yellow fever virus in Haemagogus leucocelaenus and Aedes serratus mosquitoes, Southern Brazil, 2008

    Emerging Infectious Diseases 16:1918–1924.

    https://doi.org/10.3201/eid1612.100608

    • PubMed
    • Google Scholar
16. 1. Chandler JA
    2. Liu RM
    3. Bennett SN

    (2015) RNA shotgun metagenomic sequencing of northern California (USA) mosquitoes uncovers viruses, bacteria, and fungi

    Frontiers in Microbiology 6:185.

    https://doi.org/10.3389/fmicb.2015.00185

    • PubMed
    • Google Scholar
17. 1. Cornel AJ
    2. McAbee RD
    3. Rasgon J
    4. Stanich MA
    5. Scott TW
    6. Coetzee M

    (2003) Differences in extent of genetic introgression between sympatric Culex pipiens and Culex quinquefasciatus (Diptera: Culicidae) in California and South Africa

    Journal of Medical Entomology 40:36–51.

    https://doi.org/10.1603/0022-2585-40.1.36

    • PubMed
    • Google Scholar
18. 1. Danforth BN
    2. Lin CP
    3. Fang J

    (2005) How do insect nuclear ribosomal genes compare to protein-coding genes in phylogenetic utility and nucleotide substitution patterns?

    Systematic Entomology 30:549–562.

    https://doi.org/10.1111/j.1365-3113.2005.00305.x

    • Google Scholar
19. 1. de Oliveira CD
    2. Gonçalves DS
    3. Baton LA
    4. Shimabukuro PHF
    5. Carvalho FD
    6. Moreira LA

    (2015) Broader prevalence of Wolbachia in insects including potential human disease vectors

    Bulletin of Entomological Research 105:305–315.

    https://doi.org/10.1017/S0007485315000085

    • PubMed
    • Google Scholar
20. 1. Desdouits M
    2. Kamgang B
    3. Berthet N
    4. Tricou V
    5. Ngoagouni C
    6. Gessain A
    7. Manuguerra JC
    8. Nakouné E
    9. Kazanji M

    (2015) Genetic characterization of Chikungunya virus in the Central African Republic

    Infection, Genetics and Evolution 33:25–31.

    https://doi.org/10.1016/j.meegid.2015.04.006

    • PubMed
    • Google Scholar
21. 1. Diallo D
    2. Fall G
    3. Diagne CT
    4. Gaye A
    5. Ba Y
    6. Dia I
    7. Faye O
    8. Diallo M

    (2020) Concurrent amplification of Zika, chikungunya, and yellow fever virus in a sylvatic focus of arboviruses in Southeastern Senegal, 2015

    BMC Microbiology 20:181.

    https://doi.org/10.1186/s12866-020-01866-9

    • PubMed
    • Google Scholar
22. 1. Edgar RC

    (2004) Muscle: a multiple sequence alignment method with reduced time and space complexity

    BMC Bioinformatics 5:113.

    https://doi.org/10.1186/1471-2105-5-113

    • PubMed
    • Google Scholar
23. Book

    1. Edwards FW

    (1941)

    Mosquitoes of the Ethiopian Region: III

    Culicine Adults and Pupae. Order of the Trustees.

    • Google Scholar
24. 1. Farajollahi A
    2. Fonseca DM
    3. Kramer LD
    4. Marm Kilpatrick A

    (2011) “ Bird biting ” mosquitoes and human disease: a review of the role of Culex pipiens complex mosquitoes in epidemiology

    Infection, Genetics and Evolution 11:1577–1585.

    https://doi.org/10.1016/j.meegid.2011.08.013

    • PubMed
    • Google Scholar
25. 1. Fauver JR
    2. Akter S
    3. Morales AIO
    4. Black WC
    5. Rodriguez AD
    6. Stenglein MD
    7. Ebel GD
    8. Weger-Lucarelli J

    (2019) A reverse-transcription/RNase H based protocol for depletion of mosquito ribosomal RNA facilitates viral intrahost evolution analysis, transcriptomics and pathogen discovery

    Virology 528:181–197.

    https://doi.org/10.1016/j.virol.2018.12.020

    • PubMed
    • Google Scholar
26. 1. Foley DH
    2. Rueda LM
    3. Wilkerson RC

    (2007) Insight into global mosquito biogeography from country species records

    Journal of Medical Entomology 44:554–567.

    https://doi.org/10.1603/0022-2585(2007)44[554:iigmbf]2.0.co;2

    • PubMed
    • Google Scholar
27. 1. Folmer O
    2. Black M
    3. Hoeh W
    4. Lutz R
    5. Vrijenhoek R

    (1994)

    DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates

    Molecular Marine Biology and Biotechnology 3:294–299.

    • PubMed
    • Google Scholar
28. 1. Gale K
    2. Crampton J

    (1989) The ribosomal genes of the mosquito, Aedes aegypti

    European Journal of Biochemistry 185:311–317.

    https://doi.org/10.1111/j.1432-1033.1989.tb15117.x

    • PubMed
    • Google Scholar
29. Book

    1. Grjebine A

    (1966)

    Insectes Diptères Culicidae Anophelinae

    ORSTOM / CNRS.

    • Google Scholar
30. 1. Hajibabaei M
    2. Singer GAC
    3. Hickey DA

    (2006) Benchmarking DNA barcodes: an assessment using available primate sequences

    Genome 49:851–854.

    https://doi.org/10.1139/g06-025

    • PubMed
    • Google Scholar
31. 1. Halstead SB

    (2019) Travelling arboviruses: a historical perspective

    Travel Medicine and Infectious Disease 31:101471.

    https://doi.org/10.1016/j.tmaid.2019.101471

    • PubMed
    • Google Scholar
32. 1. Harbach RE

    (2007) The Culicidae (Diptera): A review of taxonomy, classification and phylogeny

    Zootaxa 1668:591–638.

    https://doi.org/10.11646/zootaxa.1668.1.28

    • Google Scholar
33. 1. Harbach RE
    2. Kitching IJ

    (2016) The phylogeny of Anophelinae revisited: inferences about the origin and classification of Anopheles (Diptera: Culicidae)

    Zoologica Scripta 45:34–47.

    https://doi.org/10.1111/zsc.12137

    • Google Scholar
34. 1. Harbach RE
    2. Culverwell CL
    3. Kitching IJ

    (2017) Phylogeny of the nominotypical subgenus of Culex (Diptera: Culicidae): insights from analyses of anatomical data into interspecific relationships and species groups in an unresolved tree

    Systematics and Biodiversity 15:296–306.

    https://doi.org/10.1080/14772000.2016.1252439

    • Google Scholar
35. 1. Hayes CG
    2. Basit A
    3. Bagar S
    4. Akhter R

    (1980) Vector competence of Culex tritaeniorhynchus (Diptera: Culicidae) for West nile virus

    Journal of Medical Entomology 17:172–177.

    https://doi.org/10.1093/jmedent/17.2.172

    • PubMed
    • Google Scholar
36. 1. Hebert PDN
    2. Cywinska A
    3. Ball SL
    4. deWaard JR

    (2003) Biological identifications through DNA barcodes

    Proceedings. Biological Sciences 270:313–321.

    https://doi.org/10.1098/rspb.2002.2218

    • PubMed
    • Google Scholar
37. Book

    1. Héraud JM
    2. Andriamandimby SF
    3. Olive MM
    4. Guis H
    5. Miatrana Rasamoelina V
    6. Tantely L

    (2022)

    Arthropod-borne viruses of Madagascar

    In: Goodman SM, editors. The New Natural History of Madagascar. Princeton University Press. pp. 285–291.

    • Google Scholar
38. 1. Hoyos-López R
    2. Soto SU
    3. Rúa-Uribe G
    4. Gallego-Gómez JC

    (2015) Molecular identification of Saint Louis encephalitis virus genotype IV in Colombia

    Memorias Do Instituto Oswaldo Cruz 110:719–725.

    https://doi.org/10.1590/0074-02760280040

    • PubMed
    • Google Scholar
39. Book

    1. Huang Y
    2. Ward RA

    (1981)

    A Pictorial Key for the Identification of the Mosquitoes Associated with Yellow Fever in Africa

    Mosquito Systematics.

    • Google Scholar
40. 1. Hurst GDD
    2. Jiggins FM

    (2005) Problems with mitochondrial DNA as a marker in population, phylogeographic and phylogenetic studies: the effects of inherited symbionts

    Proceedings. Biological Sciences 272:1525–1534.

    https://doi.org/10.1098/rspb.2005.3056

    • PubMed
    • Google Scholar
41. 1. Jacobi JC
    2. Serie C

    (1972)

    Prevalence of group B arbovirus infections in French Guiana in 1967-69

    Medecine d’Afrique Noire 19:225–226.

    • Google Scholar
42. 1. Jupp PG
    2. Kemp A
    3. Grobbelaar A
    4. Lema P
    5. Burt FJ
    6. Alahmed AM
    7. Al Mujalli D
    8. Al Khamees M
    9. Swanepoel R

    (2002) The 2000 epidemic of Rift Valley fever in Saudi Arabia: mosquito vector studies

    Medical and Veterinary Entomology 16:245–252.

    https://doi.org/10.1046/j.1365-2915.2002.00371.x

    • PubMed
    • Google Scholar
43. 1. Kim H
    2. Cha GW
    3. Jeong YE
    4. Lee WG
    5. Chang KS
    6. Roh JY
    7. Yang SC
    8. Park MY
    9. Park C
    10. Shin EH

    (2015) Detection of Japanese encephalitis virus genotype V in Culex orientalis and Culex pipiens (Diptera: Culicidae) in Korea

    PLOS ONE 10:e0116547.

    https://doi.org/10.1371/journal.pone.0116547

    • PubMed
    • Google Scholar
44. 1. Kraemer MUG
    2. Reiner RC
    3. Brady OJ
    4. Messina JP
    5. Gilbert M
    6. Pigott DM
    7. Yi D
    8. Johnson K
    9. Earl L
    10. Marczak LB
    11. Shirude S
    12. Davis Weaver N
    13. Bisanzio D
    14. Perkins TA
    15. Lai S
    16. Lu X
    17. Jones P
    18. Coelho GE
    19. Carvalho RG
    20. Van Bortel W
    21. Marsboom C
    22. Hendrickx G
    23. Schaffner F
    24. Moore CG
    25. Nax HH
    26. Bengtsson L
    27. Wetter E
    28. Tatem AJ
    29. Brownstein JS
    30. Smith DL
    31. Lambrechts L
    32. Cauchemez S
    33. Linard C
    34. Faria NR
    35. Pybus OG
    36. Scott TW
    37. Liu Q
    38. Yu H
    39. Wint GRW
    40. Hay SI
    41. Golding N

    (2019) Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus

    Nature Microbiology 4:854–863.

    https://doi.org/10.1038/s41564-019-0376-y

    • PubMed
    • Google Scholar
45. 1. Kukutla P
    2. Steritz M
    3. Xu J

    (2013) Depletion of ribosomal RNA for mosquito gut metagenomic RNA-seq

    Journal of Visualized Experiments 74:50093.

    https://doi.org/10.3791/50093

    • PubMed
    • Google Scholar
46. 1. Kumar N
    2. Creasy T
    3. Sun Y
    4. Flowers M
    5. Tallon LJ
    6. Dunning Hotopp JC

    (2012) Efficient subtraction of insect rRNA prior to transcriptome analysis of Wolbachia-Drosophila lateral gene transfer

    BMC Research Notes 5:230.

    https://doi.org/10.1186/1756-0500-5-230

    • PubMed
    • Google Scholar
47. 1. Kumar S
    2. Stecher G
    3. Li M
    4. Knyaz C
    5. Tamura K

    (2018) MEGA X: molecular evolutionary genetics analysis across computing platforms

    Molecular Biology and Evolution 35:1547–1549.

    https://doi.org/10.1093/molbev/msy096

    • PubMed
    • Google Scholar
48. 1. Logue K
    2. Chan ER
    3. Phipps T
    4. Small ST
    5. Reimer L
    6. Henry-Halldin C
    7. Sattabongkot J
    8. Siba PM
    9. Zimmerman PA
    10. Serre D

    (2013) Mitochondrial genome sequences reveal deep divergences among Anopheles punctulatus sibling species in Papua New Guinea

    Malaria Journal 12:1–11.

    https://doi.org/10.1186/1475-2875-12-64

    • Google Scholar
49. 1. Lorenz C
    2. Alves JMP
    3. Foster PG
    4. Suesdek L
    5. Sallum MAM

    (2021) Phylogeny and temporal diversification of mosquitoes (Diptera: Culicidae) with an emphasis on the neotropical fauna

    Systematic Entomology 46:798–811.

    https://doi.org/10.1111/syen.12489

    • Google Scholar
50. 1. Lutomiah J
    2. Bast J
    3. Clark J
    4. Richardson J
    5. Yalwala S
    6. Oullo D
    7. Mutisya J
    8. Mulwa F
    9. Musila L
    10. Khamadi S
    11. Schnabel D
    12. Wurapa E
    13. Sang R

    (2013) Abundance, diversity, and distribution of mosquito vectors in selected ecological regions of Kenya: public health implications

    Journal of Vector Ecology 38:134–142.

    https://doi.org/10.1111/j.1948-7134.2013.12019.x

    • PubMed
    • Google Scholar
51. 1. Madeira F
    2. Park YM
    3. Lee J
    4. Buso N
    5. Gur T
    6. Madhusoodanan N
    7. Basutkar P
    8. Tivey ARN
    9. Potter SC
    10. Finn RD
    11. Lopez R

    (2019) The EMBL-EBI search and sequence analysis tools apis in 2019

    Nucleic Acids Research 47:W636–W641.

    https://doi.org/10.1093/nar/gkz268

    • PubMed
    • Google Scholar
52. 1. Maquart PO
    2. Sokha C
    3. Boyer S

    (2021) Mosquito diversity (Diptera: Culicidae) and medical importance, in a bird sanctuary inside the flooded forest of Prek Toal, Cambodia

    Journal of Asia-Pacific Entomology 24:1221–1227.

    https://doi.org/10.1016/j.aspen.2021.08.001

    • Google Scholar
53. 1. Maquart PO
    2. Boyer S

    (2022) Culex vishnui

    Trends in Parasitology 38:491–492.

    https://doi.org/10.1016/j.pt.2022.01.003

    • PubMed
    • Google Scholar
54. 1. Mitchell CJ
    2. Forattini OP
    3. Miller BR

    (1986) Vector competence experiments with Rocio virus and three mosquito species from the epidemic zone in Brazil

    Revista de Saude Publica 20:171–177.

    https://doi.org/10.1590/s0034-89101986000300001

    • PubMed
    • Google Scholar
55. 1. Morlan JD
    2. Qu K
    3. Sinicropi DV

    (2012) Selective depletion of rRNA enables whole transcriptome profiling of archival fixed tissue

    PLOS ONE 7:e42882.

    https://doi.org/10.1371/journal.pone.0042882

    • PubMed
    • Google Scholar
56. 1. Mukwaya LG
    2. Kayondo JK
    3. Crabtree MB
    4. Savage HM
    5. Biggerstaff BJ
    6. Miller BR

    (2000) Genetic differentiation in the yellow fever virus vector, Aedes simpsoni complex, in Africa: sequence variation in the ribosomal DNA internal transcribed spacers of anthropophilic and non-anthropophilic populations

    Insect Molecular Biology 9:85–91.

    https://doi.org/10.1046/j.1365-2583.2000.00161.x

    • PubMed
    • Google Scholar
57. 1. Mwangangi JM
    2. Muturi EJ
    3. Muriu SM
    4. Nzovu J
    5. Midega JT
    6. Mbogo C

    (2013) The role of Anopheles arabiensis and Anopheles coustani in indoor and outdoor malaria transmission in Taveta District, Kenya

    Parasites & Vectors 6:114.

    https://doi.org/10.1186/1756-3305-6-114

    • PubMed
    • Google Scholar
58. 1. Navarro JC
    2. Weaver SC

    (2004) Molecular phylogeny of the Vomerifer and Pedroi groups in the Spissipes Section of the subgenus Culex (Melanoconion)

    Journal of Medical Entomology 41:575–581.

    https://doi.org/10.1603/0022-2585-41.4.575

    • PubMed
    • Google Scholar
59. 1. Nchoutpouen E
    2. Talipouo A
    3. Djiappi-Tchamen B
    4. Djamouko-Djonkam L
    5. Kopya E
    6. Ngadjeu CS
    7. Doumbe-Belisse P
    8. Awono-Ambene P
    9. Kekeunou S
    10. Wondji CS
    11. Antonio-Nkondjio C

    (2019) Culex species diversity, susceptibility to insecticides and role as potential vector of Lymphatic filariasis in the city of Yaoundé, Cameroon

    PLOS Neglected Tropical Diseases 13:e0007229.

    https://doi.org/10.1371/journal.pntd.0007229

    • PubMed
    • Google Scholar
60. 1. Ndiaye EH
    2. Fall G
    3. Gaye A
    4. Bob NS
    5. Talla C
    6. Diagne CT
    7. Diallo D
    8. B A Y
    9. Dia I
    10. Kohl A
    11. Sall AA
    12. Diallo M

    (2016) Vector competence of Aedes vexans (Meigen), Culex poicilipes (Theobald) and Cx. quinquefasciatus say from Senegal for West and East African lineages of Rift Valley fever virus

    Parasites & Vectors 9:94.

    https://doi.org/10.1186/s13071-016-1383-y

    • PubMed
    • Google Scholar
61. 1. Nepomichene T
    2. Raharimalala FN
    3. Andriamandimby SF
    4. Ravalohery JP
    5. Failloux AB
    6. Heraud JM
    7. Boyer S

    (2018) Vector competence of Culex antennatus and Anopheles coustani mosquitoes for Rift Valley fever virus in Madagascar

    Medical and Veterinary Entomology 32:259–262.

    https://doi.org/10.1111/mve.12291

    • PubMed
    • Google Scholar
62. 1. Nikolay B
    2. Diallo M
    3. Boye CSB
    4. Sall AA

    (2011) Usutu virus in Africa

    Vector Borne and Zoonotic Diseases 11:1417–1423.

    https://doi.org/10.1089/vbz.2011.0631

    • PubMed
    • Google Scholar
63. 1. Oo TT
    2. Kaiser A
    3. Becker N

    (2006) Illustrated keys to the anopheline mosquitoes of Myanmar

    Journal of Vector Ecology 31:9–16.

    https://doi.org/10.3376/1081-1710(2006)31[9:ikttam]2.0.co;2

    • PubMed
    • Google Scholar
64. 1. Phelps WA
    2. Carlson AE
    3. Lee MT

    (2021) Optimized design of antisense oligomers for targeted rRNA depletion

    Nucleic Acids Research 49:e5.

    https://doi.org/10.1093/nar/gkaa1072

    • PubMed
    • Google Scholar
65. 1. Quast C
    2. Pruesse E
    3. Yilmaz P
    4. Gerken J
    5. Schweer T
    6. Yarza P
    7. Peplies J
    8. Glöckner FO

    (2013) The silva ribosomal RNA gene database project: improved data processing and web-based tools

    Nucleic Acids Research 41:D590–D596.

    https://doi.org/10.1093/nar/gks1219

    • PubMed
    • Google Scholar
66. 1. Ratnasingham S
    2. Hebert PDN

    (2007) BOLD: the barcode of life data system: barcoding

    Molecular Ecology Notes 7:355–364.

    https://doi.org/10.1111/j.1471-8286.2007.01678.x

    • Google Scholar
67. 1. Ratovonjato J
    2. Olive MM
    3. Tantely LM
    4. Andrianaivolambo L
    5. Tata E
    6. Razainirina J
    7. Jeanmaire E
    8. Reynes JM
    9. Elissa N

    (2011) Detection, isolation, and genetic characterization of Rift Valley fever virus from Anopheles (Anopheles) coustani, Anopheles (Anopheles) squamosus, and Culex (Culex) antennatus of the Haute Matsiatra region, Madagascar

    Vector Borne and Zoonotic Diseases 11:753–759.

    https://doi.org/10.1089/vbz.2010.0031

    • PubMed
    • Google Scholar
68. 1. Ratsitorahina M
    2. Harisoa J
    3. Ratovonjato J
    4. Biacabe S
    5. Reynes JM
    6. Zeller H
    7. Raoelina Y
    8. Talarmin A
    9. Richard V
    10. Louis Soares J

    (2008) Outbreak of dengue and chikungunya fevers, Toamasina, Madagascar, 2006

    Emerging Infectious Diseases 14:1135–1137.

    https://doi.org/10.3201/eid1407.071521

    • PubMed
    • Google Scholar
69. 1. Rattanarithikul R
    2. Harbach RE
    3. Harrison BA
    4. Panthusiri P
    5. Jones JW
    6. Coleman RE

    (2005a)

    Illustrated keys to the mosquitoes of Thailand

    II Genera Culex and Lutzia: The Southeast Asian Journal of Tropical Medicine and Public Health.

    • Google Scholar
70. 1. Rattanarithikul R
    2. Harrison BA
    3. Panthusiri P
    4. Coleman RE

    (2005b)

    Illustrated keys to the mosquitoes of Thailand I. Background; geographic distribution; lists of genera, subgenera, and species; and a key to the genera

    The Southeast Asian Journal of Tropical Medicine and Public Health 36 Suppl 1:1–80.

    • PubMed
    • Google Scholar
71. 1. Rattanarithikul R
    2. Harrison BA
    3. Harbach RE
    4. Panthusiri P
    5. Coleman RE
    6. Panthusiri P

    (2006a)

    Illustrated keys to the mosquitoes of Thailand. IV. anopheles

    The Southeast Asian Journal of Tropical Medicine and Public Health 37 Suppl 2:1–128.

    • PubMed
    • Google Scholar
72. 1. Rattanarithikul R
    2. Harrison BA
    3. Panthusiri P
    4. Peyton EL

    (2006b)

    Illustrated keys to the mosquitoes of Thailand: III. Genera Aedeomyia, Ficalbia, Mimomyia, Hodgesia, Coquillettidia, Mansonia, and Uranotaenia

    Southeast Asian Journal of Tropical Medicine and Public Health 37:1–10.

    • Google Scholar
73. 1. Rattanarithikul R
    2. Harbach RE
    3. Harrison BA
    4. Panthusiri P
    5. Coleman RE

    (2007)

    Illustrated keys to the mosquitoes of Thailand V. Genera Orthopodomyia, Kimia, Malaya, Topomyia, Tripteroides, and Toxorhynchites

    Suppl 38:1–65.

    • Google Scholar
74. 1. Rattanarithikul R
    2. Harbach RE
    3. Harrison BA
    4. Panthusiri P
    5. Coleman RE
    6. Richardson JH

    (2010)

    Illustrated keys to the mosquitoes of Thailand. VI. Tribe Aedini

    The Southeast Asian Journal of Tropical Medicine and Public Health 41 Suppl 1:1–225.

    • PubMed
    • Google Scholar
75. 1. Rausch T
    2. Hsi-Yang Fritz M
    3. Korbel JO
    4. Benes V

    (2019) Alfred: interactive multi-sample bam alignment statistics, feature counting and feature annotation for long- and short-read sequencing

    Bioinformatics 35:2489–2491.

    https://doi.org/10.1093/bioinformatics/bty1007

    • PubMed
    • Google Scholar
76. 1. Rausch T
    2. Fritz MHY
    3. Untergasser A
    4. Benes V

    (2020) Tracy: basecalling, alignment, assembly and deconvolution of sanger chromatogram trace files

    BMC Genomics 21:230.

    https://doi.org/10.1186/s12864-020-6635-8

    • PubMed
    • Google Scholar
77. 1. Reidenbach KR
    2. Cook S
    3. Bertone MA
    4. Harbach RE
    5. Wiegmann BM
    6. Besansky NJ

    (2009) Phylogenetic analysis and temporal diversification of mosquitoes (diptera: culicidae) based on nuclear genes and morphology

    BMC Evolutionary Biology 9:1–14.

    https://doi.org/10.1186/1471-2148-9-298

    • PubMed
    • Google Scholar
78. 1. Romero-Alvarez D
    2. Escobar LE

    (2018) Oropouche fever, an emergent disease from the Americas

    Microbes and Infection 20:135–146.

    https://doi.org/10.1016/j.micinf.2017.11.013

    • PubMed
    • Google Scholar
79. 1. Rueda LM

    (2004) Pictorial keys for the identification of mosquitoes (Diptera: Culicidae) associated with dengue virus transmission

    Zootaxa 589:1.

    https://doi.org/10.11646/zootaxa.589.1.1

    • Google Scholar
80. 1. Ruzzante L
    2. Reijnders M
    3. Waterhouse RM

    (2019) Of genes and genomes: mosquito evolution and diversity

    Trends in Parasitology 35:32–51.

    https://doi.org/10.1016/j.pt.2018.10.003

    • PubMed
    • Google Scholar
81. 1. Sallum MAM
    2. Forattini OP

    (1996)

    Revision of the spissipes section of Culex (Melanoconion) (Diptera:culicidae)

    Journal of the American Mosquito Control Association 12:517–600.

    • PubMed
    • Google Scholar
82. 1. Saluzzo JF
    2. Evidera TV
    3. Veas F
    4. Gonzalez JPJ

    (2017)

    Arbovirus discovery in Central African Republic (1973-1993): Zika, Bozo, Bouboui, and more

    Annals of Infectious Disease and Epidemiology 2:.

    • Google Scholar
83. 1. Serra OP
    2. Cardoso BF
    3. Ribeiro ALM
    4. Santos FD
    5. Slhessarenko RD

    (2016) Mayaro virus and dengue virus 1 and 4 natural infection in culicids from Cuiabá, state of Mato Grosso, Brazil

    Memorias Do Instituto Oswaldo Cruz 111:20–29.

    https://doi.org/10.1590/0074-02760150270

    • PubMed
    • Google Scholar
84. 1. Sirivanakarn S

    (1982)

    A review of the systematics and a proposed scheme of internal classification of the New World subgenus Melanoconion of Culex (Diptera, Culicidae)

    Mosquito Systematics 14:265–333.

    • Google Scholar
85. 1. Stevenson JC
    2. Simubali L
    3. Mbambara S
    4. Musonda M
    5. Mweetwa S
    6. Mudenda T
    7. Pringle JC
    8. Jones CM
    9. Norris DE

    (2016) Detection of Plasmodium falciparum infection in Anopheles squamosus (Diptera: Culicidae) in an area targeted for malaria elimination, Southern Zambia

    Journal of Medical Entomology 53:1482–1487.

    https://doi.org/10.1093/jme/tjw091

    • PubMed
    • Google Scholar
86. 1. Sun L
    2. Li TJ
    3. Fu WB
    4. Yan ZT
    5. Si FL
    6. Zhang YJ
    7. Mao QM
    8. Demari-Silva B
    9. Chen B

    (2019) The complete mt genomes of Lutzia halifaxia, lt. fuscanus and Culex pallidothorax (Diptera: Culicidae) and comparative analysis of 16 Culex and Lutzia mt genome sequences

    Parasites & Vectors 12:368.

    https://doi.org/10.1186/s13071-019-3625-2

    • PubMed
    • Google Scholar
87. 1. Tabue RN
    2. Awono-Ambene P
    3. Etang J
    4. Atangana J
    5. Toto JC
    6. Patchoke S
    7. Leke RGF
    8. Fondjo E
    9. Mnzava AP
    10. Knox TB
    11. Tougordi A
    12. Donnelly MJ
    13. Bigoga JD

    (2017) Role of Anopheles (Cellia) rufipes (Gough, 1910) and other local anophelines in human malaria transmission in the northern savannah of Cameroon: a cross-sectional survey

    Parasites & Vectors 10:22.

    https://doi.org/10.1186/s13071-016-1933-3

    • PubMed
    • Google Scholar
88. 1. Takhampunya R
    2. Kim HC
    3. Tippayachai B
    4. Kengluecha A
    5. Klein TA
    6. Lee WJ
    7. Grieco J
    8. Evans BP

    (2011) Emergence of Japanese encephalitis virus genotype V in the Republic of Korea

    Virology Journal 8:449.

    https://doi.org/10.1186/1743-422X-8-449

    • PubMed
    • Google Scholar
89. 1. Talaga S
    2. Duchemin JB
    3. Girod R
    4. Dusfour I

    (2021) The Culex mosquitoes (Diptera: Culicidae) of French Guiana: a comprehensive review with the description of three new species

    Journal of Medical Entomology 58:182–221.

    https://doi.org/10.1093/jme/tjaa205

    • PubMed
    • Google Scholar
90. 1. Torres-Gutierrez C
    2. Bergo ES
    3. Emerson KJ
    4. de Oliveira TMP
    5. Greni S
    6. Sallum MAM

    (2016) Mitochondrial COI gene as a tool in the taxonomy of mosquitoes Culex subgenus Melanoconion

    Acta Tropica 164:137–149.

    https://doi.org/10.1016/j.actatropica.2016.09.007

    • PubMed
    • Google Scholar
91. 1. Torres-Gutierrez C
    2. de Oliveira TMP
    3. Emerson KJ
    4. Sterlino Bergo E
    5. Mureb Sallum MA

    (2018) Molecular phylogeny of Culex subgenus Melanoconion (Diptera: Culicidae) based on nuclear and mitochondrial protein-coding genes

    Royal Society Open Science 5:171900.

    https://doi.org/10.1098/rsos.171900

    • PubMed
    • Google Scholar
92. 1. Travassos da Rosa JF
    2. de Souza WM
    3. Pinheiro FDP
    4. Figueiredo ML
    5. Cardoso JF
    6. Acrani GO
    7. Nunes MRT

    (2017) Oropouche virus: clinical, epidemiological, and molecular aspects of a neglected orthobunyavirus

    The American Journal of Tropical Medicine and Hygiene 96:1019–1030.

    https://doi.org/10.4269/ajtmh.16-0672

    • PubMed
    • Google Scholar
93. 1. Turell MJ

    (1999) Vector competence of three Venezuelan mosquitoes (Diptera: Culicidae) for an epizootic IC strain of Venezuelan equine encephalitis virus

    Journal of Medical Entomology 36:407–409.

    https://doi.org/10.1093/jmedent/36.4.407

    • PubMed
    • Google Scholar
94. 1. Turell MJ
    2. O’Guinn ML
    3. Dohm D
    4. Zyzak M
    5. Watts D
    6. Fernandez R
    7. Calampa C
    8. Klein TA
    9. Jones JW

    (2008) Susceptibility of Peruvian mosquitoes to eastern equine encephalitis virus

    Journal of Medical Entomology 45:720–725.

    https://doi.org/10.1603/0022-2585(2008)45[720:sopmte]2.0.co;2

    • PubMed
    • Google Scholar
95. 1. Ughasi J
    2. Bekard HE
    3. Coulibaly M
    4. Adabie-Gomez D
    5. Gyapong J
    6. Appawu M
    7. Wilson MD
    8. Boakye DA

    (2012) Mansonia africana and Mansonia uniformis are vectors in the transmission of Wuchereria bancrofti lymphatic filariasis in Ghana

    Parasites & Vectors 5:1–5.

    https://doi.org/10.1186/1756-3305-5-89

    • Google Scholar
96. 1. Valentine MJ
    2. Murdock CC
    3. Kelly PJ

    (2019) Sylvatic cycles of arboviruses in non-human primates

    Parasites & Vectors 12:463.

    https://doi.org/10.1186/s13071-019-3732-0

    • PubMed
    • Google Scholar
97. 1. Vasconcelos PF
    2. Costa ZG
    3. Travassos Da Rosa ES
    4. Luna E
    5. Rodrigues SG
    6. Barros VL
    7. Dias JP
    8. Monteiro HA
    9. Oliva OF
    10. Vasconcelos HB
    11. Oliveira RC
    12. Sousa MR
    13. Barbosa Da Silva J
    14. Cruz AC
    15. Martins EC
    16. Travassos Da Rosa JF

    (2001) Epidemic of jungle yellow fever in Brazil, 2000: implications of climatic alterations in disease spread

    Journal of Medical Virology 65:598–604.

    https://doi.org/10.1002/jmv.2078.abs

    • PubMed
    • Google Scholar
98. 1. Vezenegho SB
    2. Issaly J
    3. Carinci R
    4. Gaborit P
    5. Girod R
    6. Dusfour I
    7. Briolant S
    8. Sallum MA

    (2022) Discrimination of 15 Amazonian Anopheline mosquito species by polymerase chain reaction—restriction fragment length polymorphism

    Journal of Medical Entomology 59:1060–1064.

    https://doi.org/10.1093/jme/tjac008

    • Google Scholar
99. 1. Weaver SC
    2. Ferro C
    3. Barrera R
    4. Boshell J
    5. Navarro JC

    (2004) Venezuelan equine encephalitis

    Annual Review of Entomology 49:141–174.

    https://doi.org/10.1146/annurev.ento.49.061802.123422

    • PubMed
    • Google Scholar
100. 1. Webster JP
     2. Gower CM
     3. Knowles SCL
     4. Molyneux DH
     5. Fenton A

     (2016) One health-an ecological and evolutionary framework for tackling neglected zoonotic diseases

     Evolutionary Applications 9:313–333.

     https://doi.org/10.1111/eva.12341

     • PubMed
     • Google Scholar
101. 1. Weedall GD
     2. Irving H
     3. Hughes MA
     4. Wondji CS

     (2015) Molecular tools for studying the major malaria vector Anopheles funestus: improving the utility of the genome using a comparative poly (a) and Ribo-Zero RNAseq analysis

     BMC Genomics 16:931.

     https://doi.org/10.1186/s12864-015-2114-z

     • PubMed
     • Google Scholar
102. Report

     1. WHO

     (2017)

     Global vector control response 2017–2030

     In World Health Organization.

     • Google Scholar
103. 1. Zakrzewski M
     2. Rašić G
     3. Darbro J
     4. Krause L
     5. Poo YS
     6. Filipović I
     7. Parry R
     8. Asgari S
     9. Devine G
     10. Suhrbier A

     (2018) Mapping the virome in wild-caught Aedes aegypti from Cairns and Bangkok

     Scientific Reports 8:4690.

     https://doi.org/10.1038/s41598-018-22945-y

     • PubMed
     • Google Scholar
104. 1. Zeller H
     2. Van Bortel W
     3. Sudre B

     (2016) Chikungunya: its history in Africa and Asia and its spread to new regions in 2013-2014

     The Journal of Infectious Diseases 214:S436–S440.

     https://doi.org/10.1093/infdis/jiw391

     • PubMed
     • Google Scholar
105. 1. Zittra C
     2. Flechl E
     3. Kothmayer M
     4. Vitecek S
     5. Rossiter H
     6. Zechmeister T
     7. Fuehrer HP

     (2016) Ecological characterization and molecular differentiation of Culex pipiens complex taxa and Culex torrentium in eastern Austria

     Parasites & Vectors 9:197.

     https://doi.org/10.1186/s13071-016-1495-4

     • PubMed
     • Google Scholar

Author details

1. Cassandra Koh

   Institut Pasteur, Université Paris Cité, CNRS UMR3569, Viruses and RNA Interference Unit, F-75015, Paris, France

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   cassandra.koh@pasteur.fr

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2466-6731

2. Lionel Frangeul

   Institut Pasteur, Université Paris Cité, CNRS UMR3569, Viruses and RNA Interference Unit, F-75015, Paris, France

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

3. Hervé Blanc

   Institut Pasteur, Université Paris Cité, CNRS UMR3569, Viruses and RNA Interference Unit, F-75015, Paris, France

   Contribution

   Conceptualization, Data curation, Investigation, Methodology

   Competing interests

   No competing interests declared

4. Carine Ngoagouni

   Institut Pasteur de Bangui, Medical Entomology Laboratory, Bangui, Central African Republic

   Contribution

   Resources, Writing – review and editing

   Competing interests

   No competing interests declared

5. Sébastien Boyer

   Institut Pasteur du Cambodge, Medical and Veterinary Entomology Unit, Phnom Penh, Cambodia

   Contribution

   Resources, Formal analysis, Writing – review and editing

   Competing interests

   No competing interests declared

6. Philippe Dussart

   Institut Pasteur du Cambodge, Virology Unit, Phnom Penh, Cambodia

   Contribution

   Resources, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1931-3037

7. Nina Grau

   Institut Pasteur de Madagascar, Medical Entomology Unit, Antananarivo, Madagascar

   Contribution

   Resources, Writing – review and editing

   Competing interests

   No competing interests declared

8. Romain Girod

   Institut Pasteur de Madagascar, Medical Entomology Unit, Antananarivo, Madagascar

   Contribution

   Resources, Writing – review and editing

   Competing interests

   No competing interests declared

9. Jean-Bernard Duchemin

   Institut Pasteur de la Guyane, Vectopôle Amazonien Emile Abonnenc, Cayenne, French Guiana

   Contribution

   Resources, Formal analysis, Writing – review and editing

   Competing interests

   No competing interests declared

10. Maria-Carla Saleh

    Institut Pasteur, Université Paris Cité, CNRS UMR3569, Viruses and RNA Interference Unit, F-75015, Paris, France

    Contribution

    Conceptualization, Supervision, Funding acquisition, Investigation, Project administration, Writing – review and editing

    For correspondence

    carla.saleh@pasteur.fr

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-8593-4117

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