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.

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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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