Sex influences the mosquito virome in a host specific way

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Alexandre Kaboré, Antoni Exbrayat, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6701286/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Mosquitoes ( Diptera : Culicidae ) harbor a large diversity of eukaryotic viruses. These viral communities, or viromes, probably influence mosquito physiology, including pathogen transmission. However, the factors that structure the virome remain largely unstudied. This lack of data limits our understanding of the influence of the virome on mosquito biology. Here, we assess the influence of sex on the eukaryotic virome of mosquitoes. Differences in the ecology of males and females, like female-specific blood feeding, may lead to differences in exposure to viral diversity. Consequently, mosquito viromes may differ between sexes. Results To explore this hypothesis, we analyzed the influence of sex in sympatric populations of two mosquito species , Culex quinquefasciatus and Aedes aegypti. These mosquitoes are among the most important vectors of human pathogens worldwide. Males and females of both mosquito species were sampled simultaneously in large numbers (5 743 individuals in total) in different habitats in Burkina Faso (West Africa) over a two-year period. A total of 47 viral taxonomic units (VTUs) from 28 viral families were identified in 115 mosquito pools using shotgun sequencing. The viromes differed between the two mosquito species, thus allowing the analysis of sex influence on viromes within each of these species. Significant differences in beta diversity were observed between sexes in both mosquito species. However, significant differences in alpha diversity were only detected in C. quinquefasciatus . Moreover, five indicators viruses were identified associated to sex. Indicator viruses were taxonomically diverse, including two novel species. Contrary to expectations, most of the indicator VTU were found in both sexes but with different infection rates. Their low infection rates suggest that sex-based differences are not present in all individuals and can only be detected at the population level. Conclusions Our findings unveil that sex can influence the mosquito virome, and that this influence depends on mosquito species. Moreover, our results provide a first model of the virome structure as a function of sex in two mosquito species of public health significance. Mosquito virome metagenomics community ecology Culex quinquefasciatus Aedes aegypti Burkina Faso Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Mosquitoes are vectors of numerous pathogens, including viruses ( i.e ., arboviruses), which have a significant impact on human health worldwide [ 1 ] . This impact is likely to increase as a result of growing demographics and urbanization, human mobility and trade, as well as climatic change [ 2 , 3 ] . These phenomena facilitate the spread and establishment of mosquito vectors and arboviruses, which in turn lead to a resurgence and expansion of related viral diseases [ 3 ] . Despite the increasing risk, the arsenal of tools to control arboviruses is currently limited. Thus, different research avenues are being explored with the aim of developing new control tools. One such avenue is to gain a better understanding of the influence of the mosquito virome on arbovirus transmission [ 4 , 5 ] . Before the advent of next-generation sequencing, our knowledge on the viral diversity in mosquitoes was mainly limited to arboviruses and a few viruses that do not infect vertebrates (known as insect-specific viruses or ISVs) and with a potential as bioinsecticides [ 6 , 7 ] . A wealth of studies has demonstrated that the mosquito virome encompasses a very high taxonomic diversity, with the vast majority of viruses being RNA viruses [ 8 ] . This diversity is likely to influence various aspects of mosquito physiology, as observed in bacteriome-mosquito interactions [ 9 ] . For example, a pioneering study has recently demonstrated that the virome of the mosquito Aedes aegypti influences the epidemiology of dengue virus in nature [ 5 ] . However, there is still a lack of data on the ecology of the mosquito virome. This paucity of data on the virome is also observed in most pluricellular organisms [ 10 , 11 ] . There is a clear need to explore which factors influence the mosquito virome. The resulting knowledge will be key to design hypothesis-based tests on the influence of the virome on mosquito biology. One of these factors is mosquito sex. Female mosquitoes are the only sex that transmit viruses to vertebrates. Thus, studying the influence of sex may unveil specific female-virome interactions with an applied interest. Beyond the transmission of pathogens to vertebrates, males and females in mosquitoes differ in several aspects of their biology. These differences may specifically influence the virome, as has already been observed in the mosquito bacteriome [ 12 , 13 ] . One of the main differences is feeding behavior. Both sexes feed on sugar ( e.g. , nectar and fruit juice) but females also feed on vertebrate blood [ 14 , 15 ] . Blood feeding exposes females to the viruses present in vertebrate blood and allows arbovirus infection. Moreover, the immune system differs between mosquito sexes [ 16 ] . This could lead to differences in susceptibility to viruses and, in turn, to distinct viromes. Males tend to disperse and live less than females [ 17 , 18 ] , and should therefore be less likely to encounter viruses along their life. Finally, behavioral differences between sexes have also been observed in larvae [ 19 , 20 ] , further enlarging the period allowing for differences in virus exposure. Based on these differences, one could imagine that sex would lead to differences in the mosquito virome. However, viruses are transmitted between the sexes and, thus, these virus exchanges may result in similar viromes in both sexes. For example, venereal transmission has been observed between the sexes, sometimes in both directions [ 21 – 23 ] . In addition, viruses can be transmitted vertically from females to their offspring, including males [ 24 – 26 ] . Finally, examples of virus transmission through food or habitat sharing have also been described [ 6 , 27 – 29 ] . In line with a “no influence of sex” scenario, the only robust study on the influence of sex found no differences in the virome of a population of the mosquito Culex pipiens [ 30 ] . Here, we have explored the potential influence of sex on the eukaryotic virome of two mosquito species. Contrary to previous work, this study includes two variables that have been shown to influence the mosquito virome. These variables are mosquito species and habitat [ 31 – 36 ] . The virome of two mosquito species, Aedes aegypti and Culex quinquefasciatus , was analyzed in parallel in different habitats. These mosquitoes are among the main vectors of arboviruses worldwide [ 37 – 39 ] . Adult mosquitoes were collected from sympatric populations of the two mosquitoes in Burkina Faso, an African country where a wide range of arboviruses has been detected [ 40 – 42 ] . To explore the influence of habitat, populations from six zones representative of two levels of urbanization were sampled over a two-year period. A metagenomic analysis of 115 libraries including almost 6 000 individuals revealed that sex influenced the virome of both mosquito species. Furthermore, this influence differed between the two mosquito species. Methods Mosquito sampling Sampling sites have been previously described in detail [ 43 ] . Briefly, mosquitoes were collected from 19 sites distributed over six zones (Fig. 1 ). The zones were situated in two regions of Burkina Faso, the Hauts-Bassins and South‐West regions. Sites in both regions were grouped in two urbanization levels, urban and rural. Urbanization level was based on information from the general population and housing census conducted by the Ministry of Demography and Territorial Administration. A diversity of habitats was present in the rural sites, including wooded savannah, forested areas, and croplands (mainly rice, banana and papaya). Urban sites were characterized by the presence of neighborhoods with modern or semi-modern housing, horticulture, and a high population density (> 7 000 inhabitants per site). Three zones were sampled in the Hauts‐Bassins region: one urban (Urban 1) and two rural zones (Rural 1 and Rural 2). Urban 1 zone was located in the city of Bobo‐Dioulasso and included three sites. Rural 1 zone included four sites situated 30 km north of Bobo-Dioulasso and dominated by rice fields. Rural 2 zone comprised two forested areas, Nasso and Dinderesso, situated 18 km west of Bobo-Dioulasso. Sampling in the South‐West region was conducted in two urban and one rural zones (Urban 2, Urban 3 and Rural 3 zones). Urban 2 and Urban 3 zones were located in the towns of Diébougou and Gaoua, respectively. Rural 3 zone included four sites along the road connecting these two cities (Fig. 1 , Table S1 ). The mosquito collection was conducted over three years (2019, 2020 and 2021), during the main mosquito season (mainly May to September). Sampling was carried out on two consecutive days at each site using Biogents (BG) sentinel traps (Biogents, Germany), Prokopack aspirators [ 44 ] , and double-net tents [ 45 ] . Mosquito species and sex were morphologically identified using taxonomic keys [ 46 , 47 ] . Species identification was carried out on an ice-cold bench to limit degradation of viral genomes. Mosquitoes were then stored dry at − 80℃ for further analyses. Blood-engorged females were not included in the analysis to avoid the detection of vertebrate viruses present in the blood meal in the stomach, which do not infect the mosquitoes. A total of 1356 females and 945 males of A. aegypti , and 1629 females and 2547 males of C. quinquefasciatus were obtained (Figure S1 ). Individuals were pooled based on mosquito species, date, collection zone, and sex, with 6 to 30 individuals per pool (137 pools in total, Table S2 ). Library preparation and sequencing Each of the 137 pools was processed to obtain a library for Illumina sequencing. In addition, six laboratory controls were generated and processed in parallel with the pools. These controls included negative controls of the RNA extraction (template was water instead of mosquito homogenate; three controls) and of the library construction (template was water instead of RNA suspension; three controls). First, nucleic acid isolation enriched in nuclease-protected molecules was carried out as previously described [ 48 ] . Mosquitoes in each pool were homogenized in 500 µl ice-cold 1X-PBS (Phosphate Buffered Saline) buffer with two ice-cold steel bearing balls (3 mm diameter, LOUDET) using a TissueLyser II (Qiagen). Samples were incubated at 37°C for 1 h with a mixture of DNases and RNases to enrich samples in virion-protected nucleic acids. Briefly, 150µl aliquots of clarified homogenates were digested with a cocktail of nucleases consisting of 20 U/L of exonuclease I (Thermofisher), 5 U/L of RNase I (Thermofisher), 25 U/L of benzonase (Merck Chemical), and 20 U/L of Turbo DNase (Ambion). Digestion took place at 37°C for 60 min in 1X Turbo DNase buffer (Ambion). Nucleic acids were then isolated using the NucleoMag VET kit (Macherey Nagel) according to the manufacturer’s instructions. A second aliquot of the mosquito homogenate was used for nucleic-acid isolation as described above but without the nuclease treatment. This total-RNA sample was used as a backup and for virus detection with PCR. Libraries for Illumina sequencing were constructed according to the protocol by Gil and coworkers [ 48 ] . Briefly, cDNA was generated using the RevertAid First Strand cDNA synthesis kit (ThermoScientific) and the 454-E-8N primers. Double-stranded DNA (dsDNA) was then generated using the Klenow fragment polymerase (Fisher Scientific) and the 454-E-8N primers. The resulting dsDNA was amplified in a PCR reaction with the 454-E primer and the Phusion High-Fidelity DNA Polymerase kit (Fisher Scientific). Amplicons were purified using the NucleoSpin gel and PCR Clean-up kit (Macherey-Nagel). Adapters were then ligated to the amplicons in a PCR reaction using the P5 and P7-bearing adapter primers and the Phusion High-Fidelity DNA Polymerase kit (Thermo Scientific). Size selection and purification of amplicons were carried out using AmpurXP magnetic bead capture (Agencourt). Library size (expected 500–600 bp) was validated using capillary electrophoresis (Agilent 2100 Bioanalyzer, Agilent Technologies). Library concentration was estimated using the Library Quantification kit (Takara Bio) following the manufacturer’s protocol. The 137 libraries were pooled together in similar concentrations and sequenced using an Illumina HiSeq2500 sequencer to an expected depth of approximately 500 million reads (paired-end 250-bp reads). Sequencing was performed by Macrogen (Korea) using specific sequencing primers [ 48 ] . All reads have been deposited in the Sequence Read Archive (SRA) database under the Bioproject accession PRJNA1099472. Bioinformatics analysis for virus detection The bioinformatic analysis of reads was conducted using the Snakevir pipeline [ 48 ] . Snakevir and its accompanying documentation can be freely accessed at https://github.com/FlorianCHA/snakevir . Briefly, Cutadapt 1.6 [ 49 ] was used to remove adapter and low-quality sequences from reads. Subsequently, rRNA-derived reads were filtered out from the dataset by mapping using BWA 0.7.15 [ 50 ] against rRNA sequences sourced from SILVA databases (SILVA bacterial bases: SSURefNr99 and LSURef, 18/01/2017; SILVA dipteran base, release 132) [ 51 ] . Reads from all libraries were pooled and subjected to de-novo assembly using Megahit v1.1.2 [ 52 ] to generate a non-redundant set of contigs. The contigs, along with non-assembled reads, underwent a second de-novo assembly using CAP3 [ 53 ] . The resulting metagenome was then screened for virus-derived contigs via a homology search using Diamond v.2.1.8. [ 54 ] against the NCBI nr database (release 243 - april 15, 2021; e-value cutoff = 10 − 3). Potential host and virus taxonomies of each best-hit species were automatically collected using the taxonomy retrieval tool of Snakevir and manually verified. The number of reads per virus-like contig and library was quantified by read mapping on the virus-like contigs using BWA 0.7.15 [ 50 ] . Duplicate reads had been previously removed using the markdup tool in Samtools [ 55 ] . Contigs shorter than 500 bp, and the associated reads, were removed from the dataset. Contigs were clustered into viral taxonomic units (VTUs) using the “ViralOtuProphet” tool included in Snakevir. Briefly, ViralOtuProphet uses the first five BLAST hits of each contig and generates a network of contigs and best hits employing label propagation. ViralOtuProphet is freely available at https://gitlab.cirad.fr/astre/viralotuprophet . The taxonomic affiliation of VTUs to families was frequently impeded by the absence of a family rank in the taxonomy of the best hits found using Diamond. Consequently, VTUs were grouped into a family-like level, designated here as "cluster", as previously described [ 32 ] . This family-like cluster was defined as the probable family of the best-hit species, based either on GenBank data or on the taxonomy of the closest relatives of the best-hit species identified through BLASTn searches. Consolidation of the dataset followed several filtering steps. First, to avoid an unbalanced experimental design, the libraries from 2019 were not included in the final dataset due to their limited number (14 libraries, 10% of all libraries), resulting in a dataset with 123 libraries. Then, libraries containing less viral reads than the control library with the largest number of viral reads (1 300 reads) were removed. VTUs from virus families that are not known to infect Arthropoda were eliminated to exclude viruses likely not to infect mosquitoes (11 VTUs representing 0.1% of the viral reads) (Figure S1 ). The VTUs found in the control libraries were used in a filter step to limit VTU detection due to potential contamination during library preparation. The maximum number of reads found in the control libraries for each VTU was subtracted from the number of reads of the corresponding VTU in all other libraries (Figure S2 ). Two filters were also applied to limit potential read spillover between libraries during sequencing. The first filter focused on the VTUs more likely to have led to spillover, that is those with the highest number of reads. First, the most abundant VTUs were defined as those with a total read number greater than the third quartile of the distribution (VTUs with more than 220 000 reads). For each of these VTUs, we subtracted 1% of the reads of the VTU in the library with more reads to the reads of the VTU in each library (Figure S2 ). The second filter focused on VTUs with less than 220 000 reads in total. This filter involved two steps. First, a VTU was considered detected only if the sum of reads in all libraries was at least 2 000 reads. This threshold was arbitrarily chosen to represent 0.1% of the most abundant VTU in the dataset (around 2 million reads). The second step was applied to each library separately. In this step, the read number was set to zero for all VTUs with less than 10 reads in a given library (Figure S2 ). The final list of VTUs and the corresponding reads counts are provided in Table S3 . Analyses of alpha and beta diversities We used generalized linear mixed models to assess whether species richness (number of VTUs) and diversity (Shannon and inverse Simpson indices) differed in relation to the main variables in the study. The fixed terms in the models were mosquito species, sex, habitat, year, and the interactions mosquito species/sex and habitat/year. Analyses of the datasets of each mosquito species were carried out setting sex, habitat, year, and the interaction habitat/year as fixed terms. Replicate pools (i.e. pools from the same site and date) were included as a random term in all models. The best model for each index was selected based on all variable combinations using the Akaike information criterion or AIC. All linear models were implemented with the “glmmTMB” package. Post-hoc analyses with pairwise comparisons were performed using the “emmeans” function (Tukey HSD test) to assess differences between groups. Permutational multivariate analyses of variance (PERMANOVA) with 9 999 iterations were performed using the “vegan” package to assess whether community structure differed in relation to host species, sex, habitat, and year with the full dataset. Bray-Curtis distance matrices were used as measures of community structure in the PERMANOVAs. The variables in the PERMANOVAs applied to the datasets of each mosquito species were sex, habitat, and year. Non-metric multidimensional scaling (NMDS) plots were created with the same distance matrices to visualize differences between viromes. Detection and infection rates of indicator species We identified potential indicator species of sex (i.e. a species representative of an environment [ 56 , 57 ] , here sex). Indicator species were identified using the IndVal index in the “indicspecies” package [ 58 ] . This index takes into account the abundance and occurrence of species in a given group [ 59 ] . Infection rates of the indicator species were estimated based on a maximum likelihood approach implemented in the “binGroup” package [ 60 ] . This approach estimates infection rates and their confidence intervals using the results of detection tests on pools of individuals. Phylogenetic analyses of new viral species VTUs showing less than 90% identity at the amino acid level compared to the first hit provided by Diamond were considered as potential new species. For each of the potential new species, a search was manually conducted for contigs containing hallmark genes, that is either an RNA-dependent RNA polymerase (RdRP) or a capsid protein. Then, contigs were considered to belong to a new viral species if their RdRP or capsid sequence had a percentage identity at the amino acid level with the best hit of less than 90%. New viral species in the study were named using names provided by public attending Science awareness and communication events organized by our group. We estimated the phylogenies of new species. The amino-acid sequences encoding the RdRP or the capsid protein of the new viruses were aligned against sequences from related viral species. Alignment was performed using the L-INS-i algorithm implemented in MAFFT v.7.515 [ 61 ] . Poorly aligned regions (i.e. all columns with gaps in more than 50% of the sequences) were then removed using TrimAL v.1.4.1 [ 62 ] . Phylogenetic trees were constructed using the maximum likelihood method implemented in PhyML v.3.3.20190909 [ 63 ] with 100 bootstrap replicates, using the FreeRates model and the Subtree Pruning and Regrafting (SPR) branch-swapping algorithm. Statistical analysis and figure production Unless otherwise stated, all statistical analyses were performed using Rstudio (v.4.1.1). Most graphics were created using the “ggplot2” package. Venn diagrams were created using the “ggvenn” package. Pie Charts were produced using “moonBook” and “webr” packages. Heatmaps were generated using the “ComplexHeatmap” package. The “tidyverse” package was used to organize the dataset. Results Sequencing output Sequencing produced a total of 420 million reads, with an average of 3.7 million reads per library (range: 1 to 8.7 million reads per library). Viral reads accounted for 13 million reads. The ratio of viral reads (3% of the total reads) was in the same range to those in similar studies [ 64 – 66 ] . The number of viral reads varied considerably between libraries (range: 17 to 753 625 viral reads per library). Libraries from C. quinquefasciatus and A. aegypti had 8 million and 5 million viral reads in total, respectively (Table S2 , Table S3 ). The number of viral reads per library did not significantly differ between mosquito species (Wilcoxon rank sum exact test: W = 1 617, p-value = 0.16). A significant difference in the number of viral reads was observed between males and females (Wilcoxon rank sum exact test: W = 445, p-value < 3.05e-16), with a total of 10.5 million reads in males compared to 2.5 million reads in females (Figures S3 A, S3B). No significant correlation was found between the number of mosquitoes and the number of viral reads per library, either in the overall dataset or in the datasets for each mosquito species or sex (Figure S3 C, D, E, F). Bioinformatics analysis of reads generated 723 viral contigs associated with 99 viral taxonomic units (VTUs). Rarefaction analysis supported the adequacy of sampling efforts in terms of viral reads, no matter mosquito sex or species (Figure S4 ). Viromes were taxonomically diverse and structured The consolidated dataset included 115 libraries. This dataset contained 47 VTUs, representing 537 contigs (mean/min/max contig length = 1.9/0.5/28.4 Kb) and 95% of the viral reads (Figure S2 ). These VTUs encompassed a wide taxonomic diversity (Figure S5 ). More precisely, VTUs were categorized into 28 family-like clusters belonging to 19 orders. Most VTUs (92%) were associated with RNA viruses as typically found in mosquito viromes [ 8 , 32 , 67 , 68 ] (Figure S5 ). The distribution of reads among VTUs was often highly skewed within each library, with a few taxa contributing most of the reads (Fig. 2), as previously observed [ 31 , 35 ] . Five VTUs provided 50% of the viral reads (Fig. 2, Table S3 ). Most of these VTUs, but VTU39_Parvo, were relatively prevalent in one of the mosquito species ( i.e . detected in more than 45% of the libraries; Fig. 2, Figure S6 - S7 ). Thirteen out of the 47 VTUs did not contain any contig with a percentage of identity at the amino acid level greater than 90% with their Diamond best hit, strongly suggesting that these VTUs included putative novel species (Table S3 ). Figure 2. Relative abundance of viral taxonomic units (VTUs) among libraries. Library names are indicated on the top of the heatmap (see Table S2 for name explanation), and ordered following a hierarchical clustering. The three upper rows in the heatmap show the variables habitat, mosquito species and sex, respectively. Variable names are shown on the right of each row. Tiles in these rows are colored as follows: habitat (green: rural, grey: urban), species (blue: Culex, orange: Aedes) and sex (pink: male, yellow: female). Read abundances per library for each VTU are shown in the rows below the upper three rows. VTU names are provided on the left of the heatmap. The VTUs are ranked according to total read abundance. Tile color stands for relative read abundance of each VTU in a given library as shown in the legend at the bottom. Influence of mosquito species on the virome Figure 2 and Figure S6 A show that viromes mostly clustered according to mosquito species. More VTUs were detected in C. quinquefasciatus than in A. aegypti libraries (40 versus 20; Fig. 2, Figure S6 ). Thirteen out of 47 VTUs (27.7%) were shared by both mosquitoes (Figure S6 ). We assessed the influence of mosquito species, habitat and year on VTU richness (number of VTUs), Shannon and Inverse Simpson (“InvSimpson”) indexes using Generalized Linear Mixed Models (GLMM). The viromes of Culex mosquitoes were significantly different from those of Aedes mosquitoes in terms of VTU richness (Chisq = 113.8, p-value < 2.2e-16), Shannon index (Chisq = 82.6, p-value < 2.2e-16) and InvSimpson index (Chisq = 39.6, p-value = 3.1e-10) (Table S4 ). Multiple pairwise comparisons showed that these indices were significantly higher in Culex mosquitoes compared to Aedes mosquitoes (Fig. 3 , Table S4 ). Beta diversity analyses further supported that the two mosquitoes had distinct viromes. The virome differed between mosquito species (PERMANOVA on Bray-Curtis dissimilarities: F = 112.2, p-value < 0.001). Mosquito species explained 47.5% of the total variance (Table S4 ). Sex, year, and habitat also influenced virome structure, explaining 1.6%, 2.5%, and 1.8% of the total variance, respectively (Table S4 ). Non-parametric Multidimensional Scaling (NMDS) with Bray-Curtis dissimilarities matrix showed a clustering of viromes according to mosquito species (Fig. 3 ). Virome diversity and structure in each mosquito species We analyzed alpha and beta diversities of the virome in each mosquito species separately to avoid a confounding effect of mosquito species. Below, we first present the results on C. quinquefasciatus and then on A. aegypti. Sex significantly influenced several alpha diversity indexes of the virome of C. quinquefasciatus (Fig. 4 A). More precisely, males had significantly more VTUs than females (Chisq = 48.9, p-value = 2.6e-12) (Table S4 ). Males also had significantly higher Shannon and InvSimpson indices (Shannon: Chisq = 4.1, p-value = 0.04; InvSimpson: Chisq = 4.4, p-value = 0.04) (Table S4 ). Beta diversity analyses of the virome in Culex mosquitoes also showed an influence of sex (PERMANOVA on Bray-Curtis dissimilarities: F = 4.4, P < 0.001) (Table S5 ). Sex was the variable explaining the largest fraction of the variance among those included in the PERMANOVA (5.6% of the variance). Additionally, year, habitat, and the interaction between year and habitat influenced virome structure, each explaining 4.1%, 3.8%, and 2.4% of the variance, respectively (Table S5 ). An NMDS based on Bray-Curtis dissimilarities matrix showed a limited clustering based on sex (Fig. 4 B). Habitat also significantly influenced alpha diversity indexes in the virome of C. quinquefasciatus . Viromes from urban sites had more VTUs than those from rural sites (habitat-year interaction: Chisq = 9.3, p-value = 0.002). The Shannon index was also significantly higher in urban sites compared to rural sites (habitat: Chisq = 7.6, p-value = 0.006), but only for the year 2020 (t(64) = 2.8, p-value = 0.04). Habitat had no significant effect on the InvSimpson index for C. quinquefasciatus (Figure S8 , Table S4 ). The results of the alpha and beta diversities in A. aegypti did not always follow those for C. quinquefasciatus. Contrary to C. quinquefasciatus , VTU richness, Shannon, and Inverse Simpson indexes did not differ according to sex in the virome of A. aegypti (Fig. 4 C, Table S4 ). These indexes were significantly higher in urban compared to rural habitats in the viromes of A. aegypti only in 2020, as was observed in C. quinquefasciatus (habitat-year interaction: VTU richness: Chisq = 5.6, p-value = 0.018; Shannon: Chisq = 6.8, p-value = 0.009; habitat: Inverse Simpson: Chisq = 9, p-value = 0.003) (Figure S8 , Table S4 ). PERMANOVA showed that the virome of Aedes mosquitoes was influenced by sex, habitat and year (Table S5 ). Year was the variable explaining the largest fraction of the variance (19.3%), followed by habitat and sex (11.1% and 6.7% respectively). An NMDS based on Bray-Curtis dissimilarities matrix showed no clear clustering (Fig. 4 D). Indicator species of sex in the mosquito virome We searched for indicator viral species associated with host sex in the viromes of the Culex and Aedes mosquitoes. Indicator species are species whose abundance reflects a specific environmental condition [ 56 , 57 ] (here sex of the host). Using the IndVal index, which considers both species occurrence and abundance in communities [ 59 ] , we identified five indicator VTUs (VTU16_Spinareo, VTU3_Spinareo, VTU11_Nege, VTU41_Tombus, VTU15_Toli_unclass) (Table S6 ). All the indicator VTUs were significantly associated with males. One indicator VTU, VTU3_Spinareo, was found in both mosquito species. The other four VTUs were only detected in Culex viromes. Indicator VTUs were taxonomically diverse, including single and double-stranded RNA viruses, and comprised two novel virus species (VTU16_Spinareo and VTU15_Toli_unclass). We then analyzed the distribution of indicator VTUs among sexes and their infection rates (Fig. 5 A, Table S7 ). Four out of the five indicator VTUs were detected in both males and females. VTU16_Spinareo, VTU11_Nege and VTU41_Tombus were detected in both sexes in Culex mosquitoes and VTU3_Spinareo in both sexes in Aedes mosquitoes. The infection rates of the three indicator VTUs present in both sexes in C. quinquefasciatus were significantly higher in male populations (Kruskall wallis: χ2 = 3.86, p-value = 0.049) (Fig. 5 A). The infection rates of VTU3_Spinareo in males and females of A. aegypti exhibited overlapping confidence intervals (infection rates in males/females = 2.2%/0.8%) (Fig. 5 A). The IndVal index is not very sensitive in the case of species present in only one category (i.e. either males or females) [ 56 ] . We thus also searched for VTUs unique to males or females, regardless of their detection as indicator species. These VTUs might be indicator species for sex, given the relatively large number of individuals screened per sex and per mosquito species. Six VTUs were detected in a single sex. Four VTUs were only detected in males, all in Culex libraries (VTU4_Mesoni, VTU15_Toli_unclass, VTU20_Martelli_unclass, VTU24_Noda) (Fig. 5 B). Three of these VTUs were new viral species (VTU15_Toli_unclass, VTU20_Martelli_unclass and VTU24_Noda). Moreover, two VTUs were found only in females. One female-specific VTU was found in both Culex and Aedes viromes (VTU12_Permutotetra), and the other one was only found in Aedes viromes (VTU31_Partiti) (Fig. 5 B). Phylogeny of new viruses We found four new viruses among indicator VTUs detected in only one sex. The four viruses associated with sex were three single-stranded RNA viruses (VTU15_Toli_unclass, VTU24_Noda, and VTU20_Martelli_unclass) and a double-stranded RNA (dsRNA) virus (VTU16_Spinareo). We conducted phylogenetic analyses to further characterize these viruses. Moreover, phylogenetic analyses of all other new viruses detected in this study were also carried out and are presented in Figures S9-S16. VTU15_Toli_unclass represented a new virus related to the Tombusviridae family and was named Bejuita virus. The genome of Bejuita virus was similar to those of certain Tombusviridae . The genome comprised two segments (lengths = 3.8 kb/2.2 kb), each carrying either the RNA-dependent RNA polymerase (RdRp) ORF or a putative capsid protein. Bejuita virus RdRp clustered within a branch that includes Dansoman virus, Drosophila Midmar tombus-like virus, and Hubei tombus-like virus 42 (Fig. 6 A). These viruses have been mainly found in insects [ 69 , 70 ] . The RdRp segment of Bejuita virus (accession number: PV485319) exhibited approximately 48% amino acid similarity to these viruses. We characterized the phylogeny of a new Nodavirus associated to VTU24_Noda, that was named Nika virus (Fig. 6 B). Nika virus was closely related to the Macrobrachium rosenbergii nodavirus (MrNV). MrNV was previously identified in shrimp [ 71 ] and associated with White tail disease in various shrimp species. The clade including Nika virus also included Nodamura virus (NoV) and Penaeus vannamei nodavirus (PvNV). PvNV is known to cause muscle necrosis in certain shrimp species, while NoV is the only known member of the Alphanodavirus genus capable of infecting insects, fish, and mammals [ 72 ] . The genome of Nika virus shared a similar structure to that of MrNV, its closest relative, and its RdRp shared 45% identity at the amino acid level. The contig of Nika virus included ORFs coding for the RdRp and the B2 proteins (length = 3.3 kb, accession number: PV485317). We did not found a second segment coding for the capsid protein, as usually found in the Nodaviridae . VTU20_Martelli_unclass included a virus related to the Virgaviridae that was named Joly virus (Fig. 6 C). Several viruses closely related to Virgaviridae have recently been discovered in mosquitoes but they yet lack taxonomical classification [ 30 , 70 ] . The genome of Joly virus was similar to that of some members of this group, such as Hubei virga-like virus 21. The genome of Joly virus contained one segment (length = 9.9 kb, accession number: PV485315). The RdRp of the Joly virus exhibited approximately 60% amino-acid similarity to Hubei virga-like virus 21. We identified Atona virus, a dsRNA virus associated to VTU16_Spinareo, and probably belonging to the Spinareoviridae family (Fig. 6 D). We detected eight segments of Atona virus (lengths = 0.4kb – 4.1kb). Atona virus probably has more segments, yet undetected, since we did not find an RdRP-bearing segment and the majority of spinareoviruses have 10 to 12 segments [ 73 ] . We constructed the phylogeny of the Atona virus using the putative capsid protein (accession number: PV485309). The capsid protein region shared 44% amino acid identity with that of Elemess virus. These two viruses were placed close to Dinovernaviruses . Elemess virus and other Dinovernaviruses have also been identified in mosquitoes [ 74 – 76 ] . Discussion We investigated the potential influence of host sex on the eukaryotic virome of mosquitoes in two species of major interest in public health, C. quinquefasciatus and A. aegypti . Beyond sex, the study design explicitly included two of the few variables known to influence virome structure in mosquitoes. These variables are mosquito species [ 31 , 35 , 36 ] and habitat [ 32 , 34 , 77 , 78 ] . Mosquito sampling was thus designed to avoid their potential confounding effect on the influence of sex. Our results show that both mosquito species and habitat had a significant influence on the viromes analyzed. This study is among the few to provide evidence on the role of these variables using a design with replicated sites and years, as well as a large number of mosquitoes. Importantly, large mosquito numbers have been shown to be required to unveil deterministic patterns in the mosquito virome [ 32 ] . The need for large numbers may be due to the large heterogeneity in virome composition between individuals observed in several mosquito species [ 32 , 36 , 76 ] . Our work unveiled that host sex influenced the eukaryotic virome of mosquitoes. This result was found in two mosquito species belonging to different genera. Several aspects of the biology of mosquitoes and their viruses could lead to both differences and similarities between the viromes of males and females. Previously, the question on the influence of sex remained largely open due to the paucity of studies. Only one study had explored the question with a relatively large number of mosquitoes [ 30 ] . That study found no differences between sexes in several alpha diversity indexes in the virome of Culex pipiens , a twin species of C. quinquefasciatus found in temperate regions. This observation was obtained after comparing the viromes of six pools of individuals of each sex from the same site and the same year (approximately 300 individuals of each sex in total). The divergent results between this study and ours on a closely-related mosquito, C. quinquefasciatus , could be explained by differences in virus exposure between the sexes depending on mosquito species and/or habitat. In line with the explanation proposed above, our analysis revealed that the influence of sex depended on mosquito species. This novel result was observed in sympatric populations of two mosquito species, and using a relatively large and similar numbers of individuals and libraries. More precisely, the influence of sex was more pronounced in C. quinquefasciatus than in A. aegypti . For example, males had significantly more VTUs than females in C. quinquefasciatus , whereas no difference in VTU richness was observed between the sexes in A. aegypti . The two mosquito species have distinct ecologies and physiologies [ 15 , 79 ] that may contribute to the observed differences in virome assembly. However, due to the current limitations of our knowledge of virome assembly in mosquitoes, we cannot provide a precise mechanism to explain these differences. We further investigated differences in the virome between sexes with an analysis of indicator species. Five indicator species were found, all related to males. One indicator species was found in both mosquito species whereas the other four were only detected in the virome of C. quinquefasciatus . No clear trend in taxonomic diversity was found among the indicator species and none was known to be an arbovirus. Moreover, four out of the five indicator species were detected in both sexes. Indicator species shared by both sexes often had lower infection rates in females. Thus, most indicator species seemed able to infect both sexes, albeit with different probabilities. These results suggest that indicator viruses interact differently with mosquitoes depending on sex. Whether these differences are due to physiological or ecological factors ( e.g. , the immune system or feeding behavior) remains to be elucidated. The infection rate analysis unveiled that indicator species were not found in all individuals of a given sex. In fact, their infection rates were relatively low and ranged between 1% and 2%. These results imply that the influence of sex on the mosquito virome would often not be detected when comparing a pair of individuals of different sex. The analysis of relatively large numbers of individuals seems required to detect sex-based differences. That is, specific differences in the virome in mosquitoes could be predicted between groups of individuals, but not between pairs of individuals of different sex. This pattern is similar to that observed for the influence of geography on the virome of C. pipiens [ 32 ] . These observations, in conjunction with studies on the virome of individual mosquitoes, suggest that the scale of investigation is important to understand the ecological processes involved in virome assembly in mosquitoes. At a fine scale (individual level), stochastic processes seem to exert a dominant influence on virome assembly. Conversely, on a larger scale (population level), deterministic processes seem more prominent potentially leading, for example, to the patterns observed in virome assembly between the sexes. Interestingly, the relative importance of random and deterministic processes as a function of population scale has been observed in communities of other organisms, including bacteria [ 80 , 81 ] , bryophytes [ 82 ] , and tadpoles [ 83 ] . There are limitations of our study that require further work to assess the generality of our results. Firstly, the variation in virome structure explained by sex was relatively small (i.e. around 6% in both mosquito species). Most of the variation in virome structure in each mosquito species remained unexplained. This result could be due to the influence of stochastic events or environmental, host or viral factors that were not included in this study. For example, mosquito age is a host factor that was not taken into account and could influence the virome of each sex differently ( e.g ., emerging females that had not yet taken a blood meal were not exposed to blood-borne viruses). Secondly, given the global distribution of C. quinquefasciatus and A. aegypti , the geographical scope of the study can be considered limited [ 84 ] . The generality of our results in populations of these mosquitoes from other regions needs to be tested. Thirdly, the number of mosquito species and their taxonomical diversity were low compared to the large diversity of Culicidae . Further work with other mosquito species, comparing sympatric species from both the same and different genera, could unveil whether the influence of sex follows specific patterns depending on mosquito taxonomy. Another result that requires further validation is the potential detection of sex-specific viruses. Certain VTUs were only detected in a single sex, but metagenomics may not be sensitive enough to guarantee the absence of false negatives. Future studies focusing on these VTUs and using highly sensitive diagnostic methods will make it possible to determine whether they are true sex-specific viruses. Finally, the analysis of pools of individuals is blind to co-infection patterns or the relative abundances of viruses at the individual mosquito level. Thus, whether indicator species have specific co-infection patterns or within-host abundances remain open questions. Conclusions Overall, our work provides a first model of virome assembly depending on mosquito sex. This model postulates that mosquito populations of different sexes carry different viromes. These differences appear to be mostly associated with variations in the infection rates of viruses infecting both sexes. Finally, sex-based differences would depend on the mosquito species being considered. This model is yet preliminary and requires further validation. Nevertheless, this model provides a basis to generate hypotheses to be tested. Finally, the indicator species identified in this study may have specific influences on mosquito physiology depending on host sex. Future studies could explore whether these sex-specific interactions exist and their potential influence on pathogen transmission. Declarations Ethics approval and consent to participate Not applicable Consent for publication: Not applicable. Availability of data and material Supplementary file 1 contains the R codes used in our analyses. The bioinformatic pipeline Snakevir and its accompanying documentation can be accessed freely at https://github.com/FlorianCHA/snakevir. The ViralOtuProphet tool, included in Snakevir, is also available at https://gitlab.cirad.fr/astre/viralotuprophet. The Supplemental Figures file (docx format) contains supplemental figures (Figures S1-S16) with their associated legends. The the datasets used for analysis, metadata and statistical results are provided in the supplementary tables (Tables S1-S7, csv format). The titles of these tables have been added to the end of the Supplemental Figures file. All reads have been deposited in the Sequence Read Archive (SRA) database under the Bioproject accession PRJNA1099472. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Montpellier University of Excellence programme, 2018 (ArboSud project; authors receiving the grant: P.V.T., R.D., T.B., S.G.). C.M. was the recipient of a PhD fellowship from the French Occitanie Region. D.A.P.K. was the recipient of a funding by European Union’s Horizon 2020 research and innovation program (Infravec2 project, grant agreement 731060). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author contributions C.M.: formal analysis, investigation, methodology, interpretation of data, writing—original draft, writing – review & editing P.G.: formal analysis, investigation, methodology, interpretation of data, writing—original draft, writing – review & editing D.P.A.K.: investigation, methodology, writing – review & editing A.E.: data curation, software, writing – review & editing F.C.: data curation, software, writing – review & editing D.D.S.: investigation, methodology, writing – review & editing S.P.S.: investigation, methodology, writing – review & editing G.A.O.: investigation, methodology, writing – review & editing P.V.d.P.: Funding acquisition, conceptualization, writing – review & editing T.B.: Funding acquisition, conceptualization, supervision, writing – review & editing R.K.D.: Funding acquisition, conceptualization, writing – review & editing S.G.: conceptualization, funding acquisition, methodology, project administration, resources, supervision, validation, writing—review and editing. All authors read and approved the final manuscript. Acknowledgments This work is part of the ArboSud project funded by the 2018 call of the Montpellier University of Excellence (MUSE) program. C. Morel acknowledges funding from the French Occitanie Region. D. Kabore acknowledges funding from the European Union’s Horizon 378 2020 research and innovation program (Infravec2 project, grant agreement 731060). References Öhlund P, Lundén H, Blomström A-L. Insect-specific virus evolution and potential effects on vector competence. Virus Genes 2019;55:127–37. [DOI: 10.1007/s11262-018-01629–9] Ogden NH, Lindsay LR. Effects of Climate and Climate Change on Vectors and Vector-Borne Diseases: Ticks Are Different. Trends in Parasitology 2016;32:646–56. [DOI: 10.1016/j.pt.2016.04.015] Cuthbert RN, Darriet F, Chabrerie O, Lenoir J, Courchamp F, Claeys C, et al. Invasive hematophagous arthropods and associated diseases in a changing world. Parasit Vectors 2023;16:291. 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Roguet A, Laigle GS, Therial C, Bressy A, Soulignac F, Catherine A, et al. Neutral community model explains the bacterial community assembly in freshwater lakes. FEMS Microbiology Ecology 2015;91:fiv125. [DOI: 10.1093/femsec/fiv125] Shi Y, Li Y, Xiang X, Sun R, Yang T, He D, et al. Spatial scale affects the relative role of stochasticity versus determinism in soil bacterial communities in wheat fields across the North China Plain. Microbiome 2018;6:27. [DOI: 10.1186/s40168-018-0409–4] Monteiro J, Vieira C, Branquinho C. Bryophyte assembly rules across scales. Journal of Ecology 2023;111:1531–44. [DOI: 10.1111/1365–2745.14117] Fava FG, Alves-Ferreira G, da Paixão IBF, Mello M, Nomura F. Spatial scale affects the importance of deterministic and stochastic factors in the structuring of tadpole assemblages in Brazilian Cerrado. Can J Zool 2023;101:848–58. [DOI: 10.1139/cjz–2022–0181] Alaniz AJ, Carvajal MA, Bacigalupo A, Cattan PE. Global spatial assessment of Aedes aegypti and Culex quinquefasciatus: a scenario of Zika virus exposure. Epidemiol Infect 2018;147:e52. [PMID: 30474578 DOI: 10.1017/S0950268818003102] Additional Declarations No competing interests reported. Supplementary Files TableS1.csv TableS2.csv TableS3.csv TableS4.csv TableS5.csv TableS6.csv TableS7.csv SupplementalFiguresTableslegendsVF.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6701286","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":459039536,"identity":"bbc92ec2-6480-47cb-9de4-5d085b95e58c","order_by":0,"name":"Côme Morel","email":"","orcid":"","institution":"ASTRE, Univ Montpellier","correspondingAuthor":false,"prefix":"","firstName":"Côme","middleName":"","lastName":"Morel","suffix":""},{"id":459039537,"identity":"6bafdf10-98f7-4631-ae34-6447d721b834","order_by":1,"name":"Patricia Gil","email":"","orcid":"","institution":"ASTRE, Univ Montpellier","correspondingAuthor":false,"prefix":"","firstName":"Patricia","middleName":"","lastName":"Gil","suffix":""},{"id":459039538,"identity":"68af201b-fd79-4d14-9f93-160df6ca33f0","order_by":2,"name":"Didier P. Alexandre Kaboré","email":"","orcid":"","institution":"Institut de Recherche en Sciences de la Santé","correspondingAuthor":false,"prefix":"","firstName":"Didier","middleName":"P. Alexandre","lastName":"Kaboré","suffix":""},{"id":459039539,"identity":"d49fd841-71cd-470c-87d3-7a365fb47e3f","order_by":3,"name":"Antoni Exbrayat","email":"","orcid":"","institution":"ASTRE, Univ Montpellier","correspondingAuthor":false,"prefix":"","firstName":"Antoni","middleName":"","lastName":"Exbrayat","suffix":""},{"id":459039540,"identity":"4647015d-43bf-49a0-8e23-210b8d47bd41","order_by":4,"name":"Florian Charriat","email":"","orcid":"","institution":"ASTRE, Univ Montpellier","correspondingAuthor":false,"prefix":"","firstName":"Florian","middleName":"","lastName":"Charriat","suffix":""},{"id":459039541,"identity":"39a92bd8-e709-48bc-9cb3-d26db5130a26","order_by":5,"name":"Dieudonné Diloma Soma","email":"","orcid":"","institution":"Institut de Recherche en Sciences de la Santé","correspondingAuthor":false,"prefix":"","firstName":"Dieudonné","middleName":"Diloma","lastName":"Soma","suffix":""},{"id":459039542,"identity":"e21216a8-c0cd-49ed-8bfc-dfc9cf6c811e","order_by":6,"name":"Simon P. Sawadogo","email":"","orcid":"","institution":"Institut de Recherche en Sciences de la Santé","correspondingAuthor":false,"prefix":"","firstName":"Simon","middleName":"P.","lastName":"Sawadogo","suffix":""},{"id":459039543,"identity":"90821968-72e4-4931-a077-c56f7c34e314","order_by":7,"name":"Georges Anicet Ouédraogo","email":"","orcid":"","institution":"Nazi Boni University","correspondingAuthor":false,"prefix":"","firstName":"Georges","middleName":"Anicet","lastName":"Ouédraogo","suffix":""},{"id":459039544,"identity":"d4d464de-fd06-465c-beed-83191a2812da","order_by":8,"name":"Philippe Van de Perre","email":"","orcid":"","institution":"Pathogenesis and Control of Chronic and Emerging Infections, INSERM, University of Montpellier","correspondingAuthor":false,"prefix":"","firstName":"Philippe","middleName":"Van","lastName":"de Perre","suffix":""},{"id":459039545,"identity":"0ad9fdf0-e3c0-4005-b779-7e82cdcc437e","order_by":9,"name":"Thierry Baldet","email":"","orcid":"","institution":"ASTRE, Univ Montpellier","correspondingAuthor":false,"prefix":"","firstName":"Thierry","middleName":"","lastName":"Baldet","suffix":""},{"id":459039546,"identity":"2fe957ca-800d-44b2-b6fa-87839e5f9f4a","order_by":10,"name":"Roch K. Dabiré","email":"","orcid":"","institution":"Institut de Recherche en Sciences de la Santé","correspondingAuthor":false,"prefix":"","firstName":"Roch","middleName":"K.","lastName":"Dabiré","suffix":""},{"id":459039547,"identity":"bf61fae3-d4ca-4612-a9d5-46b14e6e92f8","order_by":11,"name":"Serafin Gutierrez","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIie3PMUvDQBTA8SeBuDycTwK5r3Chi9ChX8PxSiFdBAWXDoIpQrNU54iDX8ThHQd1uQ+QQYQSyJwu0qGISa8u0qur4P2HXBLux70D8Pn+ahNhV9o+j7PuJehWd2ZLgh1BsgTpEAFLbEzaLxfh+f2yoas34OeXilYv7zF/rJZ6ctMHjPYbYV57jEQNSTkCVdTXvaOnVCizGAOeyP2EpRA1QkNSjEAjyeFddAEqCzUM0DHYcx2s6ZtsSN7OTk1LPjWgg0AZhqwjnLUESEpkCGo6cxNh0vCsJSiwEmpOMimwvcv0YYwuwvNFUNJGxzwfVs2aJOe5rlbZRz92DrYLBf38cxh0x2W/bvH5fL7/2he5BmBiNqA8BAAAAABJRU5ErkJggg==","orcid":"","institution":"ASTRE, Univ Montpellier","correspondingAuthor":true,"prefix":"","firstName":"Serafin","middleName":"","lastName":"Gutierrez","suffix":""}],"badges":[],"createdAt":"2025-05-19 18:08:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6701286/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6701286/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83443912,"identity":"80152ba7-1ebb-48f8-bb08-c674b22b6b46","added_by":"auto","created_at":"2025-05-26 10:24:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":160554,"visible":true,"origin":"","legend":"\u003cp\u003eMosquito collection sites. A map of Burkina Faso (in gray) with all bordering countries is displayed in the upper right-hand quadrant. This map shows the location of the country's capital, Ouagadougou (star) and the city of Bobo-Dioulasso (dot). The main map presents an enlarged view of the sampling regions (Hauts Bassins and Sud-Ouest), colored in yellow. Sampling sites are represented by dots, with the color of the dots corresponding to the sampling zone. Sampling sites in rural zones (R) (R1, R2, and R3) are indicated by shades of green, while sampling sites in urban zones (U) (U1, U2, and U3) are indicated by shades of blue.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6701286/v1/c5bfc904d060157857d161ea.png"},{"id":83443192,"identity":"ca8caeb7-01d8-4a3b-888a-6034daea8f60","added_by":"auto","created_at":"2025-05-26 10:16:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":360854,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance of viral taxonomic units (VTUs) among libraries. Library names are indicated on the top of the heatmap (see Table S2 for name explanation), and ordered following a hierarchical clustering. The three upper rows in the heatmap show the variables habitat, mosquito species and sex, respectively. Variable names are shown on the right of each row. Tiles in these rows are colored as follows: habitat (green: rural, grey: urban), species (blue: Culex, orange: Aedes) and sex (pink: male, yellow: female). Read abundances per library for each VTU are shown in the rows below the upper three rows. VTU names are provided on the left of the heatmap. The VTUs are ranked according to total read abundance. Tile color stands for relative read abundance of each VTU in a given library as shown in the legend at the bottom.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6701286/v1/33f3b6e5873416eaaeaa44d5.png"},{"id":83443189,"identity":"aa937089-1558-46bf-a85f-b185c24c9499","added_by":"auto","created_at":"2025-05-26 10:16:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":317977,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical representation of the results of the alpha (top) and beta (bottom) diversity analyses of mosquito viromes. \u003cu\u003eTop panel:\u003c/u\u003e boxplots showing, from left to right, values for Species richness, Shannon index and Inverse Simpson index for the virome of each mosquito species (yellow: \u003cem\u003eAedes aegypti\u003c/em\u003e; blue: \u003cem\u003eCulex quinquefasciatus\u003c/em\u003e). Dots indicate values for each library, and dot shape indicates the sex (circle: female; triangle: male). Bars above the boxplots show the model statistics between the two groups (Table S4). Significance codes: 0 '***', 0.001 '**', 0.01 '*', 0.05 '.', \u0026gt;0.1 ' '. \u003cu\u003eBottom panel\u003c/u\u003e: Non-metric Multidimensional Scale (NMDS) with Bray-Curtis dissimilarities obtained from the viromes of the two mosquito species. The color of the dots and ellipses indicates mosquito species (yellow: \u003cem\u003eAedes aegypti\u003c/em\u003e; blue: \u003cem\u003eCulex quinquefasciatus\u003c/em\u003e). Dot shape indicates the sex (round: female; triangle: male). Ellipses were determined using the Student distribution with a confidence level of 95%.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6701286/v1/5f2cd2096c281ba905203e85.png"},{"id":83443984,"identity":"542e6d04-ee92-465d-b463-8168e1766b51","added_by":"auto","created_at":"2025-05-26 10:32:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":584181,"visible":true,"origin":"","legend":"\u003cp\u003eAlpha (A, C) and beta (B, D) diversity analyses of the \u003cem\u003eCulex\u003c/em\u003elibraries (A, B) and the \u003cem\u003eAedes\u003c/em\u003elibraries (C, D). \u003cu\u003e(A, C):\u003c/u\u003e boxplots showing, from left to right, the values of Species richness, Shannon index and Inverse Simpson index for each sex in \u003cem\u003eCulex quinquefasciatus\u003c/em\u003e (A) and \u003cem\u003eAedes aegypti\u003c/em\u003e (C). Boxplot color indicates the sex of each mosquito species, as indicated on the x-axis. Dots indicate the index values for each library. Dot shape represents habitat (circle: rural, triangle: urban). The degree of significance between the two groups is provided on the bar above the boxplots. Significance codes: 0 '***', 0.001 '**', 0.01 '*', 0.05 '.', \u0026gt;0.1 ' '. \u003cu\u003e(B, D)\u003c/u\u003e: Non-metric multidimensional scaling (NMDS) with Bray-Curtis dissimilarities of the viromes of \u003cem\u003eCulex quinquefasciatus\u003c/em\u003e (B) and \u003cem\u003eAedes aegypti\u003c/em\u003e (D). Dot color indicates the sex of the mosquito, as shown in the legend. Dot shape represents the habitat (circle: rural, triangle: urban).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6701286/v1/6d36c40deb775e3ce8b90f88.png"},{"id":83443914,"identity":"019daa64-e15a-42bd-b2ba-d9481f6a75ce","added_by":"auto","created_at":"2025-05-26 10:24:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":788098,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003eInfection rates of the different indicator species of sex detected in \u003cem\u003eCulex\u003c/em\u003e and \u003cem\u003eAedes\u003c/em\u003e mosquitoes (right and left panels, respectively). As reference, dotted horizontal lines represent the mean infection rate of all VTUs in each sex and mosquito species. Line and dot color identifies the sex and species of mosquito according to the legends at the bottom \u003cstrong\u003eB.\u003c/strong\u003e Distribution of VTUs between mosquito species and sex. Numbers between brackets stand for the proportion of each group to the total number of VTUs. VTUs found in only one sex are encircled, and their names are identified in boxes at the periphery of the Venn diagram. Names in red correspond to potential new viral species (percentage of identity with their best-hit at the amino-acid level below 90%). In both panels, color represents the the mosquito sex/species pair (blue: \u003cem\u003eCulex\u003c/em\u003e females, green: \u003cem\u003eCulex\u003c/em\u003emales; yellow: \u003cem\u003eAedes\u003c/em\u003e females; red: \u003cem\u003eAedes\u003c/em\u003e males).\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6701286/v1/4160507f48de95f00c023028.png"},{"id":83443202,"identity":"3b8631e0-376d-42d9-9291-7272223665b1","added_by":"auto","created_at":"2025-05-26 10:16:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":342192,"visible":true,"origin":"","legend":"\u003cp\u003eMaximum-likelihood phylogenetic trees of several viruses related to the \u003cem\u003eTombusviridae\u003c/em\u003e (\u003cstrong\u003eA\u003c/strong\u003e), \u003cem\u003eNodaviridae\u003c/em\u003e (\u003cstrong\u003eB\u003c/strong\u003e), \u003cem\u003eVirga-like \u003c/em\u003efamily (\u003cstrong\u003eC\u003c/strong\u003e) and \u003cem\u003eSpinareoviridae\u003c/em\u003e (\u003cstrong\u003eD\u003c/strong\u003e). Phylogenies of viruses related to the \u003cem\u003eTombusviridae\u003c/em\u003e, \u003cem\u003eNodaviridae\u003c/em\u003e and \u003cem\u003eVirga-like \u003c/em\u003efamily are based on an analysis of the RNA-dependent RNA polymerase (RdRP) domain. Phylogeny of the \u003cem\u003eSpinareoviridae\u003c/em\u003eis based on an analysis of the capsid protein because the RdRP ORF of Atona virus was not found in our dataset. The new viruses identified in this study are shown with names in red. The main clades of each family are provided on the right of the virus names. The colored silhouettes represent the host taxonomy of the known viruses included in the analysis (yellow: \u003cem\u003eHexapoda\u003c/em\u003e; red: \u003cem\u003eMalacostraca\u003c/em\u003e; blue: \u003cem\u003eActinopterygii\u003c/em\u003e; purple: \u003cem\u003eNematoda\u003c/em\u003e; light green: \u003cem\u003eViridiplantae\u003c/em\u003e; grey: \u003cem\u003eVertebrata\u003c/em\u003e). Several host silhouettes are provided for viral taxa identified in hosts belonging to different groups (\u003cem\u003ee.g\u003c/em\u003e., arboviruses). The values on the branches represent the bootstrap values of the internal nodes.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6701286/v1/9b78afc4c110bd6d40db21f1.png"},{"id":86051124,"identity":"c05acc54-109c-4f7e-9cbe-27c999cb3c10","added_by":"auto","created_at":"2025-07-05 02:31:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3399830,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6701286/v1/06cf2e1a-315c-4287-8f43-dad06693e2e4.pdf"},{"id":83443193,"identity":"2bf6c70c-6da1-4423-892c-e4a2f6c60912","added_by":"auto","created_at":"2025-05-26 10:16:59","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1929,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.csv","url":"https://assets-eu.researchsquare.com/files/rs-6701286/v1/73b87c6080507db12ecf6579.csv"},{"id":83443188,"identity":"f2387e8c-487e-446f-914f-0c0e286ea98e","added_by":"auto","created_at":"2025-05-26 10:16:59","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15050,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.csv","url":"https://assets-eu.researchsquare.com/files/rs-6701286/v1/c012095fce1353054010afc8.csv"},{"id":83443196,"identity":"e67c0de8-088e-4c6a-9421-ed6ca671264c","added_by":"auto","created_at":"2025-05-26 10:16:59","extension":"csv","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":26381,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.csv","url":"https://assets-eu.researchsquare.com/files/rs-6701286/v1/c745b5bbf33ca08627495bfd.csv"},{"id":83443197,"identity":"5986fc3c-46b2-4a77-85d5-12950bc9579c","added_by":"auto","created_at":"2025-05-26 10:16:59","extension":"csv","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":4625,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.csv","url":"https://assets-eu.researchsquare.com/files/rs-6701286/v1/23cde48c309d5b924c8c62ee.csv"},{"id":83443201,"identity":"e56ae7df-5fff-4376-95c8-084508e1ab8b","added_by":"auto","created_at":"2025-05-26 10:16:59","extension":"csv","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1027,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.csv","url":"https://assets-eu.researchsquare.com/files/rs-6701286/v1/90a2442752d1627561b1e9ea.csv"},{"id":83443915,"identity":"eb446e4c-6f81-42e7-997d-ed62cb35968a","added_by":"auto","created_at":"2025-05-26 10:24:59","extension":"csv","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":924,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6.csv","url":"https://assets-eu.researchsquare.com/files/rs-6701286/v1/8b0b9f4a360d9a3abadee274.csv"},{"id":83443200,"identity":"10263b24-e240-4eb4-9b96-4ae79ebc2f18","added_by":"auto","created_at":"2025-05-26 10:16:59","extension":"csv","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":11682,"visible":true,"origin":"","legend":"","description":"","filename":"TableS7.csv","url":"https://assets-eu.researchsquare.com/files/rs-6701286/v1/d4739f2342c2489d21ce30d6.csv"},{"id":83443204,"identity":"1876c4a2-184d-4a09-889d-624e335c567c","added_by":"auto","created_at":"2025-05-26 10:17:00","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":9076053,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFiguresTableslegendsVF.docx","url":"https://assets-eu.researchsquare.com/files/rs-6701286/v1/46437a04ff3701973a3b8a40.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sex influences the mosquito virome in a host specific way","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMosquitoes are vectors of numerous pathogens, including viruses (\u003cem\u003ei.e\u003c/em\u003e., arboviruses), which have a significant impact on human health worldwide\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. This impact is likely to increase as a result of growing demographics and urbanization, human mobility and trade, as well as climatic change\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. These phenomena facilitate the spread and establishment of mosquito vectors and arboviruses, which in turn lead to a resurgence and expansion of related viral diseases\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Despite the increasing risk, the arsenal of tools to control arboviruses is currently limited. Thus, different research avenues are being explored with the aim of developing new control tools. One such avenue is to gain a better understanding of the influence of the mosquito virome on arbovirus transmission\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBefore the advent of next-generation sequencing, our knowledge on the viral diversity in mosquitoes was mainly limited to arboviruses and a few viruses that do not infect vertebrates (known as insect-specific viruses or ISVs) and with a potential as bioinsecticides\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. A wealth of studies has demonstrated that the mosquito virome encompasses a very high taxonomic diversity, with the vast majority of viruses being RNA viruses\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. This diversity is likely to influence various aspects of mosquito physiology, as observed in bacteriome-mosquito interactions\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. For example, a pioneering study has recently demonstrated that the virome of the mosquito \u003cem\u003eAedes aegypti\u003c/em\u003e influences the epidemiology of dengue virus in nature\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. However, there is still a lack of data on the ecology of the mosquito virome. This paucity of data on the virome is also observed in most pluricellular organisms\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. There is a clear need to explore which factors influence the mosquito virome. The resulting knowledge will be key to design hypothesis-based tests on the influence of the virome on mosquito biology.\u003c/p\u003e \u003cp\u003eOne of these factors is mosquito sex. Female mosquitoes are the only sex that transmit viruses to vertebrates. Thus, studying the influence of sex may unveil specific female-virome interactions with an applied interest. Beyond the transmission of pathogens to vertebrates, males and females in mosquitoes differ in several aspects of their biology. These differences may specifically influence the virome, as has already been observed in the mosquito bacteriome\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. One of the main differences is feeding behavior. Both sexes feed on sugar (\u003cem\u003ee.g.\u003c/em\u003e, nectar and fruit juice) but females also feed on vertebrate blood\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Blood feeding exposes females to the viruses present in vertebrate blood and allows arbovirus infection. Moreover, the immune system differs between mosquito sexes\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. This could lead to differences in susceptibility to viruses and, in turn, to distinct viromes. Males tend to disperse and live less than females\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, and should therefore be less likely to encounter viruses along their life. Finally, behavioral differences between sexes have also been observed in larvae\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, further enlarging the period allowing for differences in virus exposure.\u003c/p\u003e \u003cp\u003eBased on these differences, one could imagine that sex would lead to differences in the mosquito virome. However, viruses are transmitted between the sexes and, thus, these virus exchanges may result in similar viromes in both sexes. For example, venereal transmission has been observed between the sexes, sometimes in both directions\u003csup\u003e[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e–\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. In addition, viruses can be transmitted vertically from females to their offspring, including males\u003csup\u003e[\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e–\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Finally, examples of virus transmission through food or habitat sharing have also been described \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e–\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. In line with a “no influence of sex” scenario, the only robust study on the influence of sex found no differences in the virome of a population of the mosquito \u003cem\u003eCulex pipiens\u003c/em\u003e \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHere, we have explored the potential influence of sex on the eukaryotic virome of two mosquito species. Contrary to previous work, this study includes two variables that have been shown to influence the mosquito virome. These variables are mosquito species and habitat\u003csup\u003e[\u003cspan additionalcitationids=\"CR32 CR33 CR34 CR35\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e–\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. The virome of two mosquito species, \u003cem\u003eAedes aegypti\u003c/em\u003e and \u003cem\u003eCulex quinquefasciatus\u003c/em\u003e, was analyzed in parallel in different habitats. These mosquitoes are among the main vectors of arboviruses worldwide\u003csup\u003e[\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e–\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Adult mosquitoes were collected from sympatric populations of the two mosquitoes in Burkina Faso, an African country where a wide range of arboviruses has been detected\u003csup\u003e[\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e–\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. To explore the influence of habitat, populations from six zones representative of two levels of urbanization were sampled over a two-year period. A metagenomic analysis of 115 libraries including almost 6 000 individuals revealed that sex influenced the virome of both mosquito species. Furthermore, this influence differed between the two mosquito species.\u003c/p\u003e "},{"header":"Methods","content":"\u003ch3\u003eMosquito sampling\u003c/h3\u003e\u003cp\u003eSampling sites have been previously described in detail\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. Briefly, mosquitoes were collected from 19 sites distributed over six zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The zones were situated in two regions of Burkina Faso, the Hauts-Bassins and South‐West regions. Sites in both regions were grouped in two urbanization levels, urban and rural. Urbanization level was based on information from the general population and housing census conducted by the Ministry of Demography and Territorial Administration. A diversity of habitats was present in the rural sites, including wooded savannah, forested areas, and croplands (mainly rice, banana and papaya). Urban sites were characterized by the presence of neighborhoods with modern or semi-modern housing, horticulture, and a high population density (\u0026gt; 7 000 inhabitants per site). Three zones were sampled in the Hauts‐Bassins region: one urban (Urban 1) and two rural zones (Rural 1 and Rural 2). Urban 1 zone was located in the city of Bobo‐Dioulasso and included three sites. Rural 1 zone included four sites situated 30 km north of Bobo-Dioulasso and dominated by rice fields. Rural 2 zone comprised two forested areas, Nasso and Dinderesso, situated 18 km west of Bobo-Dioulasso. Sampling in the South‐West region was conducted in two urban and one rural zones (Urban 2, Urban 3 and Rural 3 zones). Urban 2 and Urban 3 zones were located in the towns of Diébougou and Gaoua, respectively. Rural 3 zone included four sites along the road connecting these two cities (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe mosquito collection was conducted over three years (2019, 2020 and 2021), during the main mosquito season (mainly May to September). Sampling was carried out on two consecutive days at each site using Biogents (BG) sentinel traps (Biogents, Germany), Prokopack aspirators\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e, and double-net tents\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. Mosquito species and sex were morphologically identified using taxonomic keys\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. Species identification was carried out on an ice-cold bench to limit degradation of viral genomes. Mosquitoes were then stored dry at − 80℃ for further analyses. Blood-engorged females were not included in the analysis to avoid the detection of vertebrate viruses present in the blood meal in the stomach, which do not infect the mosquitoes.\u003c/p\u003e\u003cp\u003eA total of 1356 females and 945 males of \u003cem\u003eA. aegypti\u003c/em\u003e, and 1629 females and 2547 males of \u003cem\u003eC. quinquefasciatus\u003c/em\u003e were obtained (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Individuals were pooled based on mosquito species, date, collection zone, and sex, with 6 to 30 individuals per pool (137 pools in total, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eLibrary preparation and sequencing\u003c/h2\u003e\u003cp\u003eEach of the 137 pools was processed to obtain a library for Illumina sequencing. In addition, six laboratory controls were generated and processed in parallel with the pools. These controls included negative controls of the RNA extraction (template was water instead of mosquito homogenate; three controls) and of the library construction (template was water instead of RNA suspension; three controls).\u003c/p\u003e\u003cp\u003eFirst, nucleic acid isolation enriched in nuclease-protected molecules was carried out as previously described\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. Mosquitoes in each pool were homogenized in 500 µl ice-cold 1X-PBS (Phosphate Buffered Saline) buffer with two ice-cold steel bearing balls (3 mm diameter, LOUDET) using a TissueLyser II (Qiagen). Samples were incubated at 37°C for 1 h with a mixture of DNases and RNases to enrich samples in virion-protected nucleic acids. Briefly, 150µl aliquots of clarified homogenates were digested with a cocktail of nucleases consisting of 20 U/L of exonuclease I (Thermofisher), 5 U/L of RNase I (Thermofisher), 25 U/L of benzonase (Merck Chemical), and 20 U/L of Turbo DNase (Ambion). Digestion took place at 37°C for 60 min in 1X Turbo DNase buffer (Ambion). Nucleic acids were then isolated using the NucleoMag VET kit (Macherey Nagel) according to the manufacturer’s instructions. A second aliquot of the mosquito homogenate was used for nucleic-acid isolation as described above but without the nuclease treatment. This total-RNA sample was used as a backup and for virus detection with PCR.\u003c/p\u003e\u003cp\u003eLibraries for Illumina sequencing were constructed according to the protocol by Gil and coworkers\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. Briefly, cDNA was generated using the RevertAid First Strand cDNA synthesis kit (ThermoScientific) and the 454-E-8N primers. Double-stranded DNA (dsDNA) was then generated using the Klenow fragment polymerase (Fisher Scientific) and the 454-E-8N primers. The resulting dsDNA was amplified in a PCR reaction with the 454-E primer and the Phusion High-Fidelity DNA Polymerase kit (Fisher Scientific). Amplicons were purified using the NucleoSpin gel and PCR Clean-up kit (Macherey-Nagel). Adapters were then ligated to the amplicons in a PCR reaction using the P5 and P7-bearing adapter primers and the Phusion High-Fidelity DNA Polymerase kit (Thermo Scientific). Size selection and purification of amplicons were carried out using AmpurXP magnetic bead capture (Agencourt). Library size (expected 500–600 bp) was validated using capillary electrophoresis (Agilent 2100 Bioanalyzer, Agilent Technologies). Library concentration was estimated using the Library Quantification kit (Takara Bio) following the manufacturer’s protocol.\u003c/p\u003e\u003cp\u003eThe 137 libraries were pooled together in similar concentrations and sequenced using an Illumina HiSeq2500 sequencer to an expected depth of approximately 500\u0026nbsp;million reads (paired-end 250-bp reads). Sequencing was performed by Macrogen (Korea) using specific sequencing primers\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAll reads have been deposited in the Sequence Read Archive (SRA) database under the Bioproject accession PRJNA1099472.\u003c/p\u003e\u003ch3\u003eBioinformatics analysis for virus detection\u003c/h3\u003e\u003cp\u003eThe bioinformatic analysis of reads was conducted using the Snakevir pipeline\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. Snakevir and its accompanying documentation can be freely accessed at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/FlorianCHA/snakevir\u003c/span\u003e\u003cspan address=\"https://github.com/FlorianCHA/snakevir\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Briefly, Cutadapt 1.6\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e was used to remove adapter and low-quality sequences from reads. Subsequently, rRNA-derived reads were filtered out from the dataset by mapping using BWA 0.7.15\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e against rRNA sequences sourced from SILVA databases (SILVA bacterial bases: SSURefNr99 and LSURef, 18/01/2017; SILVA dipteran base, release 132)\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. Reads from all libraries were pooled and subjected to de-novo assembly using Megahit v1.1.2\u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e to generate a non-redundant set of contigs. The contigs, along with non-assembled reads, underwent a second de-novo assembly using CAP3\u003csup\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e. The resulting metagenome was then screened for virus-derived contigs via a homology search using Diamond v.2.1.8.\u003csup\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e against the NCBI nr database (release 243 - april 15, 2021; e-value cutoff = 10 − 3). Potential host and virus taxonomies of each best-hit species were automatically collected using the taxonomy retrieval tool of Snakevir and manually verified. The number of reads per virus-like contig and library was quantified by read mapping on the virus-like contigs using BWA 0.7.15\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e. Duplicate reads had been previously removed using the markdup tool in Samtools\u003csup\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e. Contigs shorter than 500 bp, and the associated reads, were removed from the dataset. Contigs were clustered into viral taxonomic units (VTUs) using the “ViralOtuProphet” tool included in Snakevir. Briefly, ViralOtuProphet uses the first five BLAST hits of each contig and generates a network of contigs and best hits employing label propagation. ViralOtuProphet is freely available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gitlab.cirad.fr/astre/viralotuprophet\u003c/span\u003e\u003cspan address=\"https://gitlab.cirad.fr/astre/viralotuprophet\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The taxonomic affiliation of VTUs to families was frequently impeded by the absence of a family rank in the taxonomy of the best hits found using Diamond. Consequently, VTUs were grouped into a family-like level, designated here as \"cluster\", as previously described\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. This family-like cluster was defined as the probable family of the best-hit species, based either on GenBank data or on the taxonomy of the closest relatives of the best-hit species identified through BLASTn searches.\u003c/p\u003e\u003cp\u003eConsolidation of the dataset followed several filtering steps. First, to avoid an unbalanced experimental design, the libraries from 2019 were not included in the final dataset due to their limited number (14 libraries, 10% of all libraries), resulting in a dataset with 123 libraries. Then, libraries containing less viral reads than the control library with the largest number of viral reads (1 300 reads) were removed. VTUs from virus families that are not known to infect Arthropoda were eliminated to exclude viruses likely not to infect mosquitoes (11 VTUs representing 0.1% of the viral reads) (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The VTUs found in the control libraries were used in a filter step to limit VTU detection due to potential contamination during library preparation. The maximum number of reads found in the control libraries for each VTU was subtracted from the number of reads of the corresponding VTU in all other libraries (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTwo filters were also applied to limit potential read spillover between libraries during sequencing. The first filter focused on the VTUs more likely to have led to spillover, that is those with the highest number of reads. First, the most abundant VTUs were defined as those with a total read number greater than the third quartile of the distribution (VTUs with more than 220 000 reads). For each of these VTUs, we subtracted 1% of the reads of the VTU in the library with more reads to the reads of the VTU in each library (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The second filter focused on VTUs with less than 220 000 reads in total. This filter involved two steps. First, a VTU was considered detected only if the sum of reads in all libraries was at least 2 000 reads. This threshold was arbitrarily chosen to represent 0.1% of the most abundant VTU in the dataset (around 2\u0026nbsp;million reads). The second step was applied to each library separately. In this step, the read number was set to zero for all VTUs with less than 10 reads in a given library (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The final list of VTUs and the corresponding reads counts are provided in Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e.\u003c/p\u003e\u003ch3\u003eAnalyses of alpha and beta diversities\u003c/h3\u003e\u003cp\u003eWe used generalized linear mixed models to assess whether species richness (number of VTUs) and diversity (Shannon and inverse Simpson indices) differed in relation to the main variables in the study. The fixed terms in the models were mosquito species, sex, habitat, year, and the interactions mosquito species/sex and habitat/year. Analyses of the datasets of each mosquito species were carried out setting sex, habitat, year, and the interaction habitat/year as fixed terms. Replicate pools (i.e. pools from the same site and date) were included as a random term in all models. The best model for each index was selected based on all variable combinations using the Akaike information criterion or AIC. All linear models were implemented with the “glmmTMB” package. Post-hoc analyses with pairwise comparisons were performed using the “emmeans” function (Tukey HSD test) to assess differences between groups. Permutational multivariate analyses of variance (PERMANOVA) with 9 999 iterations were performed using the “vegan” package to assess whether community structure differed in relation to host species, sex, habitat, and year with the full dataset. Bray-Curtis distance matrices were used as measures of community structure in the PERMANOVAs. The variables in the PERMANOVAs applied to the datasets of each mosquito species were sex, habitat, and year. Non-metric multidimensional scaling (NMDS) plots were created with the same distance matrices to visualize differences between viromes.\u003c/p\u003e\u003ch3\u003eDetection and infection rates of indicator species\u003c/h3\u003e\u003cp\u003eWe identified potential indicator species of sex (i.e. a species representative of an environment\u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/sup\u003e, here sex). Indicator species were identified using the IndVal index in the “indicspecies” package\u003csup\u003e[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/sup\u003e. This index takes into account the abundance and occurrence of species in a given group\u003csup\u003e[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]\u003c/sup\u003e. Infection rates of the indicator species were estimated based on a maximum likelihood approach implemented in the “binGroup” package\u003csup\u003e[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/sup\u003e. This approach estimates infection rates and their confidence intervals using the results of detection tests on pools of individuals.\u003c/p\u003e\u003ch3\u003ePhylogenetic analyses of new viral species\u003c/h3\u003e\u003cp\u003eVTUs showing less than 90% identity at the amino acid level compared to the first hit provided by Diamond were considered as potential new species. For each of the potential new species, a search was manually conducted for contigs containing hallmark genes, that is either an RNA-dependent RNA polymerase (RdRP) or a capsid protein. Then, contigs were considered to belong to a new viral species if their RdRP or capsid sequence had a percentage identity at the amino acid level with the best hit of less than 90%. New viral species in the study were named using names provided by public attending Science awareness and communication events organized by our group.\u003c/p\u003e\u003cp\u003eWe estimated the phylogenies of new species. The amino-acid sequences encoding the RdRP or the capsid protein of the new viruses were aligned against sequences from related viral species. Alignment was performed using the L-INS-i algorithm implemented in MAFFT v.7.515\u003csup\u003e[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]\u003c/sup\u003e. Poorly aligned regions (i.e. all columns with gaps in more than 50% of the sequences) were then removed using TrimAL v.1.4.1\u003csup\u003e[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]\u003c/sup\u003e. Phylogenetic trees were constructed using the maximum likelihood method implemented in PhyML v.3.3.20190909\u003csup\u003e[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]\u003c/sup\u003e with 100 bootstrap replicates, using the FreeRates model and the Subtree Pruning and Regrafting (SPR) branch-swapping algorithm.\u003c/p\u003e\u003ch2\u003eStatistical analysis and figure production\u003c/h2\u003e\u003cp\u003eUnless otherwise stated, all statistical analyses were performed using Rstudio (v.4.1.1). Most graphics were created using the “ggplot2” package. Venn diagrams were created using the “ggvenn” package. Pie Charts were produced using “moonBook” and “webr” packages. Heatmaps were generated using the “ComplexHeatmap” package. The “tidyverse” package was used to organize the dataset.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSequencing output\u003c/h2\u003e \u003cp\u003eSequencing produced a total of 420\u0026nbsp;million reads, with an average of 3.7\u0026nbsp;million reads per library (range: 1 to 8.7\u0026nbsp;million reads per library). Viral reads accounted for 13\u0026nbsp;million reads. The ratio of viral reads (3% of the total reads) was in the same range to those in similar studies\u003csup\u003e[\u003cspan additionalcitationids=\"CR65\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]\u003c/sup\u003e. The number of viral reads varied considerably between libraries (range: 17 to 753 625 viral reads per library).\u003c/p\u003e \u003cp\u003eLibraries from \u003cem\u003eC. quinquefasciatus\u003c/em\u003e and \u003cem\u003eA. aegypti\u003c/em\u003e had 8\u0026nbsp;million and 5\u0026nbsp;million viral reads in total, respectively (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). The number of viral reads per library did not significantly differ between mosquito species (Wilcoxon rank sum exact test: W\u0026thinsp;=\u0026thinsp;1 617, p-value\u0026thinsp;=\u0026thinsp;0.16). A significant difference in the number of viral reads was observed between males and females (Wilcoxon rank sum exact test: W\u0026thinsp;=\u0026thinsp;445, p-value\u0026thinsp;\u0026lt;\u0026thinsp;3.05e-16), with a total of 10.5\u0026nbsp;million reads in males compared to 2.5\u0026nbsp;million reads in females (Figures \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA, S3B). No significant correlation was found between the number of mosquitoes and the number of viral reads per library, either in the overall dataset or in the datasets for each mosquito species or sex (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eC, D, E, F). Bioinformatics analysis of reads generated 723 viral contigs associated with 99 viral taxonomic units (VTUs). Rarefaction analysis supported the adequacy of sampling efforts in terms of viral reads, no matter mosquito sex or species (Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eViromes were taxonomically diverse and structured\u003c/h2\u003e \u003cp\u003eThe consolidated dataset included 115 libraries. This dataset contained 47 VTUs, representing 537 contigs (mean/min/max contig length\u0026thinsp;=\u0026thinsp;1.9/0.5/28.4 Kb) and 95% of the viral reads (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). These VTUs encompassed a wide taxonomic diversity (Figure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). More precisely, VTUs were categorized into 28 family-like clusters belonging to 19 orders. Most VTUs (92%) were associated with RNA viruses as typically found in mosquito viromes\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]\u003c/sup\u003e (Figure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe distribution of reads among VTUs was often highly skewed within each library, with a few taxa contributing most of the reads (Fig.\u0026nbsp;2), as previously observed\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Five VTUs provided 50% of the viral reads (Fig.\u0026nbsp;2, Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Most of these VTUs, but VTU39_Parvo, were relatively prevalent in one of the mosquito species (\u003cem\u003ei.e\u003c/em\u003e. detected in more than 45% of the libraries; Fig.\u0026nbsp;2, Figure \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e-\u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThirteen out of the 47 VTUs did not contain any contig with a percentage of identity at the amino acid level greater than 90% with their Diamond best hit, strongly suggesting that these VTUs included putative novel species (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eFigure 2.\u003c/span\u003e Relative abundance of viral taxonomic units (VTUs) among libraries. Library names are indicated on the top of the heatmap (see Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e for name explanation), and ordered following a hierarchical clustering. The three upper rows in the heatmap show the variables habitat, mosquito species and sex, respectively. Variable names are shown on the right of each row. Tiles in these rows are colored as follows: habitat (green: rural, grey: urban), species (blue: Culex, orange: Aedes) and sex (pink: male, yellow: female). Read abundances per library for each VTU are shown in the rows below the upper three rows. VTU names are provided on the left of the heatmap. The VTUs are ranked according to total read abundance. Tile color stands for relative read abundance of each VTU in a given library as shown in the legend at the bottom.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eInfluence of mosquito species on the virome\u003c/h2\u003e \u003cp\u003eFigure 2 and Figure \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eA show that viromes mostly clustered according to mosquito species. More VTUs were detected in \u003cem\u003eC. quinquefasciatus\u003c/em\u003e than in \u003cem\u003eA. aegypti\u003c/em\u003e libraries (40 versus 20; Fig.\u0026nbsp;2, Figure \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e). Thirteen out of 47 VTUs (27.7%) were shared by both mosquitoes (Figure \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe assessed the influence of mosquito species, habitat and year on VTU richness (number of VTUs), Shannon and Inverse Simpson (\u0026ldquo;InvSimpson\u0026rdquo;) indexes using Generalized Linear Mixed Models (GLMM). The viromes of \u003cem\u003eCulex\u003c/em\u003e mosquitoes were significantly different from those of \u003cem\u003eAedes\u003c/em\u003e mosquitoes in terms of VTU richness (Chisq\u0026thinsp;=\u0026thinsp;113.8, p-value\u0026thinsp;\u0026lt;\u0026thinsp;2.2e-16), Shannon index (Chisq\u0026thinsp;=\u0026thinsp;82.6, p-value\u0026thinsp;\u0026lt;\u0026thinsp;2.2e-16) and InvSimpson index (Chisq\u0026thinsp;=\u0026thinsp;39.6, p-value\u0026thinsp;=\u0026thinsp;3.1e-10) (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Multiple pairwise comparisons showed that these indices were significantly higher in \u003cem\u003eCulex\u003c/em\u003e mosquitoes compared to \u003cem\u003eAedes\u003c/em\u003e mosquitoes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeta diversity analyses further supported that the two mosquitoes had distinct viromes. The virome differed between mosquito species (PERMANOVA on Bray-Curtis dissimilarities: F\u0026thinsp;=\u0026thinsp;112.2, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Mosquito species explained 47.5% of the total variance (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Sex, year, and habitat also influenced virome structure, explaining 1.6%, 2.5%, and 1.8% of the total variance, respectively (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Non-parametric Multidimensional Scaling (NMDS) with Bray-Curtis dissimilarities matrix showed a clustering of viromes according to mosquito species (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eVirome diversity and structure in each mosquito species\u003c/h2\u003e \u003cp\u003eWe analyzed alpha and beta diversities of the virome in each mosquito species separately to avoid a confounding effect of mosquito species. Below, we first present the results on \u003cem\u003eC. quinquefasciatus\u003c/em\u003e and then on \u003cem\u003eA. aegypti.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eSex significantly influenced several alpha diversity indexes of the virome of \u003cem\u003eC. quinquefasciatus\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). More precisely, males had significantly more VTUs than females (Chisq\u0026thinsp;=\u0026thinsp;48.9, p-value\u0026thinsp;=\u0026thinsp;2.6e-12) (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Males also had significantly higher Shannon and InvSimpson indices (Shannon: Chisq\u0026thinsp;=\u0026thinsp;4.1, p-value\u0026thinsp;=\u0026thinsp;0.04; InvSimpson: Chisq\u0026thinsp;=\u0026thinsp;4.4, p-value\u0026thinsp;=\u0026thinsp;0.04) (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeta diversity analyses of the virome in \u003cem\u003eCulex\u003c/em\u003e mosquitoes also showed an influence of sex (PERMANOVA on Bray-Curtis dissimilarities: F\u0026thinsp;=\u0026thinsp;4.4, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). Sex was the variable explaining the largest fraction of the variance among those included in the PERMANOVA (5.6% of the variance). Additionally, year, habitat, and the interaction between year and habitat influenced virome structure, each explaining 4.1%, 3.8%, and 2.4% of the variance, respectively (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). An NMDS based on Bray-Curtis dissimilarities matrix showed a limited clustering based on sex (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eHabitat also significantly influenced alpha diversity indexes in the virome of \u003cem\u003eC. quinquefasciatus\u003c/em\u003e. Viromes from urban sites had more VTUs than those from rural sites (habitat-year interaction: Chisq\u0026thinsp;=\u0026thinsp;9.3, p-value\u0026thinsp;=\u0026thinsp;0.002). The Shannon index was also significantly higher in urban sites compared to rural sites (habitat: Chisq\u0026thinsp;=\u0026thinsp;7.6, p-value\u0026thinsp;=\u0026thinsp;0.006), but only for the year 2020 (t(64)\u0026thinsp;=\u0026thinsp;2.8, p-value\u0026thinsp;=\u0026thinsp;0.04). Habitat had no significant effect on the InvSimpson index for \u003cem\u003eC. quinquefasciatus\u003c/em\u003e (Figure \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e, Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results of the alpha and beta diversities in \u003cem\u003eA. aegypti\u003c/em\u003e did not always follow those for \u003cem\u003eC. quinquefasciatus.\u003c/em\u003e Contrary to \u003cem\u003eC. quinquefasciatus\u003c/em\u003e, VTU richness, Shannon, and Inverse Simpson indexes did not differ according to sex in the virome of \u003cem\u003eA. aegypti\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). These indexes were significantly higher in urban compared to rural habitats in the viromes of \u003cem\u003eA. aegypti\u003c/em\u003e only in 2020, as was observed in \u003cem\u003eC. quinquefasciatus\u003c/em\u003e (habitat-year interaction: VTU richness: Chisq\u0026thinsp;=\u0026thinsp;5.6, p-value\u0026thinsp;=\u0026thinsp;0.018; Shannon: Chisq\u0026thinsp;=\u0026thinsp;6.8, p-value\u0026thinsp;=\u0026thinsp;0.009; habitat: Inverse Simpson: Chisq\u0026thinsp;=\u0026thinsp;9, p-value\u0026thinsp;=\u0026thinsp;0.003) (Figure \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e, Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePERMANOVA showed that the virome of \u003cem\u003eAedes\u003c/em\u003e mosquitoes was influenced by sex, habitat and year (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). Year was the variable explaining the largest fraction of the variance (19.3%), followed by habitat and sex (11.1% and 6.7% respectively). An NMDS based on Bray-Curtis dissimilarities matrix showed no clear clustering (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIndicator species of sex in the mosquito virome\u003c/h2\u003e \u003cp\u003eWe searched for indicator viral species associated with host sex in the viromes of the \u003cem\u003eCulex\u003c/em\u003e and \u003cem\u003eAedes\u003c/em\u003e mosquitoes. Indicator species are species whose abundance reflects a specific environmental condition\u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/sup\u003e (here sex of the host). Using the IndVal index, which considers both species occurrence and abundance in communities\u003csup\u003e[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]\u003c/sup\u003e, we identified five indicator VTUs (VTU16_Spinareo, VTU3_Spinareo, VTU11_Nege, VTU41_Tombus, VTU15_Toli_unclass) (Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e). All the indicator VTUs were significantly associated with males. One indicator VTU, VTU3_Spinareo, was found in both mosquito species. The other four VTUs were only detected in \u003cem\u003eCulex\u003c/em\u003e viromes. Indicator VTUs were taxonomically diverse, including single and double-stranded RNA viruses, and comprised two novel virus species (VTU16_Spinareo and VTU15_Toli_unclass).\u003c/p\u003e \u003cp\u003eWe then analyzed the distribution of indicator VTUs among sexes and their infection rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e). Four out of the five indicator VTUs were detected in both males and females. VTU16_Spinareo, VTU11_Nege and VTU41_Tombus were detected in both sexes in \u003cem\u003eCulex\u003c/em\u003e mosquitoes and VTU3_Spinareo in both sexes in \u003cem\u003eAedes\u003c/em\u003e mosquitoes. The infection rates of the three indicator VTUs present in both sexes in \u003cem\u003eC. quinquefasciatus\u003c/em\u003e were significantly higher in male populations (Kruskall wallis: χ2\u0026thinsp;=\u0026thinsp;3.86, p-value\u0026thinsp;=\u0026thinsp;0.049) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The infection rates of VTU3_Spinareo in males and females of \u003cem\u003eA. aegypti\u003c/em\u003e exhibited overlapping confidence intervals (infection rates in males/females\u0026thinsp;=\u0026thinsp;2.2%/0.8%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eThe IndVal index is not very sensitive in the case of species present in only one category (i.e. either males or females)\u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/sup\u003e. We thus also searched for VTUs unique to males or females, regardless of their detection as indicator species. These VTUs might be indicator species for sex, given the relatively large number of individuals screened per sex and per mosquito species. Six VTUs were detected in a single sex. Four VTUs were only detected in males, all in \u003cem\u003eCulex\u003c/em\u003e libraries (VTU4_Mesoni, VTU15_Toli_unclass, VTU20_Martelli_unclass, VTU24_Noda) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Three of these VTUs were new viral species (VTU15_Toli_unclass, VTU20_Martelli_unclass and VTU24_Noda). Moreover, two VTUs were found only in females. One female-specific VTU was found in both \u003cem\u003eCulex\u003c/em\u003e and \u003cem\u003eAedes\u003c/em\u003e viromes (VTU12_Permutotetra), and the other one was only found in \u003cem\u003eAedes\u003c/em\u003e viromes (VTU31_Partiti) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePhylogeny of new viruses\u003c/h2\u003e \u003cp\u003eWe found four new viruses among indicator VTUs detected in only one sex. The four viruses associated with sex were three single-stranded RNA viruses (VTU15_Toli_unclass, VTU24_Noda, and VTU20_Martelli_unclass) and a double-stranded RNA (dsRNA) virus (VTU16_Spinareo). We conducted phylogenetic analyses to further characterize these viruses. Moreover, phylogenetic analyses of all other new viruses detected in this study were also carried out and are presented in Figures S9-S16.\u003c/p\u003e \u003cp\u003eVTU15_Toli_unclass represented a new virus related to the \u003cem\u003eTombusviridae\u003c/em\u003e family and was named Bejuita virus. The genome of Bejuita virus was similar to those of certain \u003cem\u003eTombusviridae\u003c/em\u003e. The genome comprised two segments (lengths\u0026thinsp;=\u0026thinsp;3.8 kb/2.2 kb), each carrying either the RNA-dependent RNA polymerase (RdRp) ORF or a putative capsid protein. Bejuita virus RdRp clustered within a branch that includes Dansoman virus, Drosophila Midmar tombus-like virus, and Hubei tombus-like virus 42 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). These viruses have been mainly found in insects\u003csup\u003e[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]\u003c/sup\u003e. The RdRp segment of Bejuita virus (accession number: PV485319) exhibited approximately 48% amino acid similarity to these viruses.\u003c/p\u003e \u003cp\u003eWe characterized the phylogeny of a new \u003cem\u003eNodavirus\u003c/em\u003e associated to VTU24_Noda, that was named Nika virus (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Nika virus was closely related to the Macrobrachium rosenbergii nodavirus (MrNV). MrNV was previously identified in shrimp\u003csup\u003e[\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]\u003c/sup\u003e and associated with White tail disease in various shrimp species. The clade including Nika virus also included Nodamura virus (NoV) and Penaeus vannamei nodavirus (PvNV). PvNV is known to cause muscle necrosis in certain shrimp species, while NoV is the only known member of the \u003cem\u003eAlphanodavirus\u003c/em\u003e genus capable of infecting insects, fish, and mammals\u003csup\u003e[\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]\u003c/sup\u003e. The genome of Nika virus shared a similar structure to that of MrNV, its closest relative, and its RdRp shared 45% identity at the amino acid level. The contig of Nika virus included ORFs coding for the RdRp and the B2 proteins (length\u0026thinsp;=\u0026thinsp;3.3 kb, accession number: PV485317). We did not found a second segment coding for the capsid protein, as usually found in the \u003cem\u003eNodaviridae\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eVTU20_Martelli_unclass included a virus related to the \u003cem\u003eVirgaviridae\u003c/em\u003e that was named Joly virus (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Several viruses closely related to \u003cem\u003eVirgaviridae\u003c/em\u003e have recently been discovered in mosquitoes but they yet lack taxonomical classification\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]\u003c/sup\u003e. The genome of Joly virus was similar to that of some members of this group, such as Hubei virga-like virus 21. The genome of Joly virus contained one segment (length\u0026thinsp;=\u0026thinsp;9.9 kb, accession number: PV485315). The RdRp of the Joly virus exhibited approximately 60% amino-acid similarity to Hubei virga-like virus 21.\u003c/p\u003e \u003cp\u003eWe identified Atona virus, a dsRNA virus associated to VTU16_Spinareo, and probably belonging to the \u003cem\u003eSpinareoviridae\u003c/em\u003e family (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). We detected eight segments of Atona virus (lengths\u0026thinsp;=\u0026thinsp;0.4kb \u0026ndash; 4.1kb). Atona virus probably has more segments, yet undetected, since we did not find an RdRP-bearing segment and the majority of spinareoviruses have 10 to 12 segments\u003csup\u003e[\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]\u003c/sup\u003e. We constructed the phylogeny of the Atona virus using the putative capsid protein (accession number: PV485309). The capsid protein region shared 44% amino acid identity with that of Elemess virus. These two viruses were placed close to \u003cem\u003eDinovernaviruses\u003c/em\u003e. Elemess virus and other \u003cem\u003eDinovernaviruses\u003c/em\u003e have also been identified in mosquitoes\u003csup\u003e[\u003cspan additionalcitationids=\"CR75\" citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe investigated the potential influence of host sex on the eukaryotic virome of mosquitoes in two species of major interest in public health, \u003cem\u003eC. quinquefasciatus\u003c/em\u003e and \u003cem\u003eA. aegypti\u003c/em\u003e. Beyond sex, the study design explicitly included two of the few variables known to influence virome structure in mosquitoes. These variables are mosquito species\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e and habitat\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]\u003c/sup\u003e. Mosquito sampling was thus designed to avoid their potential confounding effect on the influence of sex. Our results show that both mosquito species and habitat had a significant influence on the viromes analyzed. This study is among the few to provide evidence on the role of these variables using a design with replicated sites and years, as well as a large number of mosquitoes. Importantly, large mosquito numbers have been shown to be required to unveil deterministic patterns in the mosquito virome\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. The need for large numbers may be due to the large heterogeneity in virome composition between individuals observed in several mosquito species\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur work unveiled that host sex influenced the eukaryotic virome of mosquitoes. This result was found in two mosquito species belonging to different genera. Several aspects of the biology of mosquitoes and their viruses could lead to both differences and similarities between the viromes of males and females. Previously, the question on the influence of sex remained largely open due to the paucity of studies. Only one study had explored the question with a relatively large number of mosquitoes\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. That study found no differences between sexes in several alpha diversity indexes in the virome of \u003cem\u003eCulex pipiens\u003c/em\u003e, a twin species of \u003cem\u003eC. quinquefasciatus\u003c/em\u003e found in temperate regions. This observation was obtained after comparing the viromes of six pools of individuals of each sex from the same site and the same year (approximately 300 individuals of each sex in total). The divergent results between this study and ours on a closely-related mosquito, \u003cem\u003eC. quinquefasciatus\u003c/em\u003e, could be explained by differences in virus exposure between the sexes depending on mosquito species and/or habitat.\u003c/p\u003e \u003cp\u003eIn line with the explanation proposed above, our analysis revealed that the influence of sex depended on mosquito species. This novel result was observed in sympatric populations of two mosquito species, and using a relatively large and similar numbers of individuals and libraries. More precisely, the influence of sex was more pronounced in \u003cem\u003eC. quinquefasciatus\u003c/em\u003e than in \u003cem\u003eA. aegypti\u003c/em\u003e. For example, males had significantly more VTUs than females in \u003cem\u003eC. quinquefasciatus\u003c/em\u003e, whereas no difference in VTU richness was observed between the sexes in \u003cem\u003eA. aegypti\u003c/em\u003e. The two mosquito species have distinct ecologies and physiologies\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]\u003c/sup\u003e that may contribute to the observed differences in virome assembly. However, due to the current limitations of our knowledge of virome assembly in mosquitoes, we cannot provide a precise mechanism to explain these differences.\u003c/p\u003e \u003cp\u003eWe further investigated differences in the virome between sexes with an analysis of indicator species. Five indicator species were found, all related to males. One indicator species was found in both mosquito species whereas the other four were only detected in the virome of \u003cem\u003eC. quinquefasciatus\u003c/em\u003e. No clear trend in taxonomic diversity was found among the indicator species and none was known to be an arbovirus. Moreover, four out of the five indicator species were detected in both sexes. Indicator species shared by both sexes often had lower infection rates in females. Thus, most indicator species seemed able to infect both sexes, albeit with different probabilities. These results suggest that indicator viruses interact differently with mosquitoes depending on sex. Whether these differences are due to physiological or ecological factors (\u003cem\u003ee.g.\u003c/em\u003e, the immune system or feeding behavior) remains to be elucidated.\u003c/p\u003e \u003cp\u003eThe infection rate analysis unveiled that indicator species were not found in all individuals of a given sex. In fact, their infection rates were relatively low and ranged between 1% and 2%. These results imply that the influence of sex on the mosquito virome would often not be detected when comparing a pair of individuals of different sex. The analysis of relatively large numbers of individuals seems required to detect sex-based differences. That is, specific differences in the virome in mosquitoes could be predicted between groups of individuals, but not between pairs of individuals of different sex. This pattern is similar to that observed for the influence of geography on the virome of \u003cem\u003eC. pipiens\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. These observations, in conjunction with studies on the virome of individual mosquitoes, suggest that the scale of investigation is important to understand the ecological processes involved in virome assembly in mosquitoes. At a fine scale (individual level), stochastic processes seem to exert a dominant influence on virome assembly. Conversely, on a larger scale (population level), deterministic processes seem more prominent potentially leading, for example, to the patterns observed in virome assembly between the sexes. Interestingly, the relative importance of random and deterministic processes as a function of population scale has been observed in communities of other organisms, including bacteria\u003csup\u003e[\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]\u003c/sup\u003e, bryophytes\u003csup\u003e[\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]\u003c/sup\u003e, and tadpoles\u003csup\u003e[\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThere are limitations of our study that require further work to assess the generality of our results. Firstly, the variation in virome structure explained by sex was relatively small (i.e. around 6% in both mosquito species). Most of the variation in virome structure in each mosquito species remained unexplained. This result could be due to the influence of stochastic events or environmental, host or viral factors that were not included in this study. For example, mosquito age is a host factor that was not taken into account and could influence the virome of each sex differently (\u003cem\u003ee.g\u003c/em\u003e., emerging females that had not yet taken a blood meal were not exposed to blood-borne viruses). Secondly, given the global distribution of \u003cem\u003eC. quinquefasciatus\u003c/em\u003e and \u003cem\u003eA. aegypti\u003c/em\u003e, the geographical scope of the study can be considered limited \u003csup\u003e[\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]\u003c/sup\u003e. The generality of our results in populations of these mosquitoes from other regions needs to be tested. Thirdly, the number of mosquito species and their taxonomical diversity were low compared to the large diversity of \u003cem\u003eCulicidae\u003c/em\u003e. Further work with other mosquito species, comparing sympatric species from both the same and different genera, could unveil whether the influence of sex follows specific patterns depending on mosquito taxonomy. Another result that requires further validation is the potential detection of sex-specific viruses. Certain VTUs were only detected in a single sex, but metagenomics may not be sensitive enough to guarantee the absence of false negatives. Future studies focusing on these VTUs and using highly sensitive diagnostic methods will make it possible to determine whether they are true sex-specific viruses. Finally, the analysis of pools of individuals is blind to co-infection patterns or the relative abundances of viruses at the individual mosquito level. Thus, whether indicator species have specific co-infection patterns or within-host abundances remain open questions.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOverall, our work provides a first model of virome assembly depending on mosquito sex. This model postulates that mosquito populations of different sexes carry different viromes. These differences appear to be mostly associated with variations in the infection rates of viruses infecting both sexes. Finally, sex-based differences would depend on the mosquito species being considered. This model is yet preliminary and requires further validation. Nevertheless, this model provides a basis to generate hypotheses to be tested. Finally, the indicator species identified in this study may have specific influences on mosquito physiology depending on host sex. Future studies could explore whether these sex-specific interactions exist and their potential influence on pathogen transmission.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary file 1 contains the R codes used in our analyses. The bioinformatic pipeline Snakevir and its accompanying documentation can be accessed freely at https://github.com/FlorianCHA/snakevir. The ViralOtuProphet tool, included in Snakevir, is also available at https://gitlab.cirad.fr/astre/viralotuprophet. The Supplemental Figures file (docx format) contains supplemental figures (Figures S1-S16) with their associated legends. The the datasets used for analysis, metadata and statistical results are provided in the supplementary tables (Tables S1-S7, csv format). The titles of these tables have been added to the end of the Supplemental Figures file.\u003c/p\u003e\n\u003cp\u003eAll reads have been deposited in the Sequence Read Archive (SRA) database under the Bioproject accession PRJNA1099472.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Montpellier University of Excellence programme, 2018 (ArboSud project; authors receiving the grant: P.V.T., R.D., T.B., S.G.). C.M. was the recipient of a PhD fellowship from the French Occitanie Region. D.A.P.K. was the recipient of a funding by European Union\u0026rsquo;s Horizon 2020 research and innovation program (Infravec2 project, grant agreement 731060). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC.M.: formal analysis, investigation, methodology, interpretation of data, writing\u0026mdash;original draft, writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eP.G.: formal analysis, investigation, methodology, interpretation of data, writing\u0026mdash;original draft, writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eD.P.A.K.: investigation, methodology, writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eA.E.: data curation, software, writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eF.C.: data curation, software, writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eD.D.S.: investigation, methodology, writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eS.P.S.: investigation, methodology, writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eG.A.O.: investigation, methodology, writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eP.V.d.P.: Funding acquisition, conceptualization, writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eT.B.: Funding acquisition, conceptualization, supervision, writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eR.K.D.: Funding acquisition, conceptualization, writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eS.G.: conceptualization, funding acquisition, methodology, project administration, resources, supervision, validation, writing\u0026mdash;review and editing.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is part of the ArboSud project funded by the 2018 call of the Montpellier University of Excellence (MUSE) program. C. Morel acknowledges funding from the French Occitanie Region. D. Kabore acknowledges funding from the European Union\u0026rsquo;s Horizon 378 2020 research and innovation program (Infravec2 project, grant agreement 731060).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e\u0026Ouml;hlund P, Lund\u0026eacute;n H, Blomstr\u0026ouml;m A-L. Insect-specific virus evolution and potential effects on vector competence. Virus Genes 2019;55:127\u0026ndash;37. [DOI: 10.1007/s11262-018-01629\u0026ndash;9]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOgden NH, Lindsay LR. Effects of Climate and Climate Change on Vectors and Vector-Borne Diseases: Ticks Are Different. Trends in Parasitology 2016;32:646\u0026ndash;56. 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[DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40168-018-0409\u0026ndash;4]\u003c/span\u003e\u003cspan address=\"10.1186/s40168-018-0409\u0026ndash;4]\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonteiro J, Vieira C, Branquinho C. Bryophyte assembly rules across scales. Journal of Ecology 2023;111:1531\u0026ndash;44. [DOI: 10.1111/1365\u0026ndash;2745.14117]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFava FG, Alves-Ferreira G, da Paix\u0026atilde;o IBF, Mello M, Nomura F. Spatial scale affects the importance of deterministic and stochastic factors in the structuring of tadpole assemblages in Brazilian Cerrado. Can J Zool 2023;101:848\u0026ndash;58. [DOI: 10.1139/cjz\u0026ndash;2022\u0026ndash;0181]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlaniz AJ, Carvajal MA, Bacigalupo A, Cattan PE. Global spatial assessment of Aedes aegypti and Culex quinquefasciatus: a scenario of Zika virus exposure. Epidemiol Infect 2018;147:e52. [PMID: 30474578 DOI: 10.1017/S0950268818003102]\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mosquito virome, metagenomics, community ecology, Culex quinquefasciatus, Aedes aegypti, Burkina Faso","lastPublishedDoi":"10.21203/rs.3.rs-6701286/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6701286/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eMosquitoes (\u003c/span\u003e \u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003eDiptera\u003c/span\u003e: \u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003eCulicidae\u003c/span\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e) harbor a large diversity of eukaryotic viruses. These viral communities, or viromes, probably influence mosquito physiology, including pathogen transmission. However, the factors that structure the virome remain largely unstudied. This lack of data limits our understanding of the influence of the virome on mosquito biology. Here, we assess the influence of sex on the eukaryotic virome of mosquitoes. Differences in the ecology of males and females, like female-specific blood feeding, may lead to differences in exposure to viral diversity. Consequently, mosquito viromes may differ between sexes.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eTo explore this hypothesis, we analyzed the influence of sex in sympatric populations of two mosquito species\u003c/span\u003e, \u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003eCulex quinquefasciatus\u003c/span\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eand\u003c/span\u003e \u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003eAedes aegypti.\u003c/span\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eThese mosquitoes are among the most\u003c/span\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eimportant vectors of human pathogens worldwide. Males and females of both mosquito species were sampled simultaneously in large numbers (5 743 individuals in total) in different habitats in Burkina Faso (West Africa) over a two-year period. A total of 47 viral taxonomic units (VTUs) from 28 viral families were identified in 115 mosquito pools using shotgun sequencing. The viromes differed between the two mosquito species, thus allowing the analysis of sex influence on viromes within each of these species. Significant differences in beta diversity were observed between sexes in both mosquito species. However, significant differences in alpha diversity were only detected in\u003c/span\u003e \u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003eC. quinquefasciatus\u003c/span\u003e. \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eMoreover, five indicators viruses were identified associated to sex. Indicator viruses were taxonomically diverse, including two novel species. Contrary to expectations, most of the indicator VTU were found in both sexes but with different infection rates. Their low infection rates suggest that sex-based differences are not present in all individuals and can only be detected at the population level.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOur findings unveil that sex can influence the mosquito virome, and that this influence depends on mosquito species. Moreover, our results provide a first model of the virome structure as a function of sex in two mosquito species of public health significance.\u003c/p\u003e","manuscriptTitle":"Sex influences the mosquito virome in a host specific way","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-26 10:16:54","doi":"10.21203/rs.3.rs-6701286/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"450e9025-6e93-4d48-beb6-737e54ac7c21","owner":[],"postedDate":"May 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-05T02:23:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-26 10:16:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6701286","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6701286","identity":"rs-6701286","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}