Mosquito bacterial communities show stage-specific patterns relevant for vector ecology and AMR surveillance

Mosquito bacterial communities show stage-specific patterns relevant for vector ecology and AMR surveillance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mosquito bacterial communities show stage-specific patterns relevant for vector ecology and AMR surveillance Saria Otani, Federica Lucati, Ragna Eberhardt, Frederik Duus Møller, and 20 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7544775/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 Mosquito-associated microbiota are influenced by a number of factors, e.g., geography, host species, and developmental stage. Understanding these microbiotas is crucial for assessing their role as vectors and in pathogen dissemination and most studies have largely focused on a few model species, while others like Aedes japonicus remain poorly characterized. Here, we compared the bacterial communities of Aedes albopictus and Aedes japonicus across eight countries: six in Europe, plus the USA and Japan, from both adults and larval stages when possible, using 16S rRNA amplicon sequencing. We found large differences in microbiota composition between mosquito species, with Ae. albopictus exhibiting lower bacterial diversity than Ae. japonicus . Geographic variation in bacterial diversity was also evident, with mosquitoes from Japan and the Netherlands harbouring the most diverse bacterial communities, while Austrian populations displayed the lowest diversity. Developmental stage (adults and larvae) had the strongest influence on bacterial composition, with aquatic-associated genera such as Limnohabitans and Aeromonas dominating larvae, whereas adult mosquitoes harboured higher abundances of Acinetobacter and Methylobacterium . No association was found between Aedes species genetic distance, determined by relatedness, and the bacterial community compositions. A number of bacterial genera with known pathogenic potential, including Pseudomonas , Serratia , Klebsiella , and Acinetobacter , were detected across multiple locations, suggesting that mosquitoes could serve as environmental reservoirs for opportunistic and antimicrobial-resistant bacteria. We identified Wolbachia in Ae. albopictus from Spain and Italy and at low abundances in Ae. japonicus from the USA and Japan, marking one of the first reports of Wolbachia in this species. This is, to our knowledge, the most comprehensive study on Ae. japonicus microbiotas. Our findings provide insights into the ecological and epidemiological implications of mosquito microbiota and emphasize the need for further investigation into their role in pathogen transmission and antimicrobial resistance dissemination. Mosquitoes bacterial communities host variation geographic variation Figures Figure 1 Figure 2 Figure 3 Introduction Mosquitoes are major vectors of infectious diseases [1–7], transmitting pathogens that can cause malaria, dengue, chikungunya, Zika, and other serious illnesses. The primary mosquito vector species belong to the genera Aedes , Anopheles , and Culex [8–13]. Aedes albopictus has emerged as a key vector species, while Aedes japonicus is recognised as a major invasive species, and both are characterised by their rapid global spread. Ae. albopictus , originally from Southeast Asia, has spread worldwide and has reached Europe (Albania) in 1979 [14, 15]. Ae. japonicus , native to East Asia, has more recently expanded into Europe, where it was first detected in 2000 in northern France [16, 17]. Despite their increasing presence, little is known about the bacterial community composition of Ae. japonicus compared to Ae. albopictus , particularly in relation to geographic distribution and host-related factors. Insect-associated microbiomes are known to play significant roles in the host physiology including reproduction, growth and development, and vector density, all influencing vector competence [18]. Mosquitoes acquire their microbiome through vertical transmission of endosymbionts, ingestion of water and organic matter in larval habitats, and blood or sugar feeding in adulthood [19]. Previous studies have identified substantial microbiome variation across mosquito species, developmental stages, and geographic regions [20–22]. While Ae. albopictus populations show reduced microbial diversity compared to other Aedes species and in areas of recent invasion [8, 22, 23], the microbiome of Ae. japonicus remains largely unexplored [24–26]. Furthermore, studies on Ae. albopictus have primarily focused on the role of Wolbachia in limiting pathogen transmission (where it has been mainly reported), with less attention given to broader microbial community dynamics [27]. This suggests a mosquito-specific microbiome where certain bacterial taxa might be more persistent in a mosquito species but not in another, resulting in host-specific community profiles [28, 29]. A clear variation has been observed for different geographical areas [30–33] that was unrelated to temperature, rainfall and urbanisation [32, 33], as well as between host developmental stages and sexes [30, 34]. Several studies suggest that mosquitoes, and other invertebrate vectors, may also act as reservoirs for bacterial pathogens and antimicrobial resistance (AMR) genes [35–38]. Several mosquito-associated bacteria, such as Acinetobacter , Pseudomonas , and Enterobacter are known opportunistic pathogens or AMR-carrying bacteria. However, the extent to which these pathogenic bacteria are influenced by host species, geography, and mosquito phylogenetic relatedness remains unclear. Generally, whether mosquito microbiota structure is primarily shaped by genetic factors or environmental conditions remains to be explored more extensively. However, besides the observed link between low Ae. albopictus genomic diversity and reduced bacterial diversity [28], the possible association between Ae. albopictus phylogeny and bacterial diversity, especially the abundances of specific bacterial genera, has not been studied. This is also true for the other host species Ae. japonicus . In this study, we aimed to investigate how bacterial communities are shaped in Aedes mosquitoes, especially the understudied Ae. japonicus , across eight countries, and which pathogenic bacteria they harbour. We performed a comparative analysis of the bacterial communities of Aedes across six European countries, the USA, and Japan in two host species when possible. We assessed microbiota diversity, geographic and developmental stage-specific variation, and the potential role of mosquitoes as reservoirs of opportunistic bacteria and AMR-associated taxa. Furthermore, we examined whether host genetic relatedness influences the bacterial composition in both mosquito species: Ae. albopictus and Ae. japonicus . Material and methods Sample collection and species identification : DNA extraction and amplicon sequencing : Mosquitoes were stored separately and individually, without distinguishing their sexes as they were part of another invertebrate project, in 100% EtOH at -20°C until DNA extraction. Individual mosquitoes were homogenized, and genomic DNA was extracted using the DNeasy Blood and Tissue Kit (Qiagen) following the manufacturer's instructions. DNA was further purified using a clean-up procedure with Phenol:Chloroform:Isoamyl Alcohol (25:24:1) and quantified using a Qubit Fluorometer (Thermo Fisher Scientific) following the manufacturer's instructions. 16S rRNA gene sequencing was performed using primers 341F (5′- CCTAYGGGRBGCASCAG − 3′) and 806R (5′- GGACTACNNGGGTATCTAAT − 3′) flanking the hypervariable V3 – V4 regions [ 48 , 49 ]. The PCR reaction was set up in a total volume of 20 µl, consisting of 12.4 µl sterile distilled water, 0.4 µl dNTPs (10 µM), 4 µl 5× HF buffer, 1 µl of each primer (10 µM), 1 µl template DNA, and 0.2 µl Phusion High-Fidelity DNA Polymerase (Thermo Scientific, Germany). The thermal cycling conditions included an initial denaturation at 98°C for 30 s, followed by 15 cycles of 98°C for 5 s, 56°C for 20 s, and 72°C for 20 s, with a final extension at 72°C for 5 min. Libraries were prepared following the manufacturer’s instructions (Illumina) and run on Illumina MiSeq platform (2 × 250 bp). We aimed for a minimum of 0.1M raw reads per sample during sequencing. After quality filtering, chimera removal, and taxonomic assignment, the retained read counts per sample ranged from 2,567 to71,570. DNA extraction negative controls were included to detect potential contamination, however, all yielded DNA concentrations below the sequencing threshold and were thus not processed further. Table 1 Number of samples per species and country included in this study. The numbers in parentheses represent the counts of collected adults and larvae, respectively. Country Ae. albopictus Ae. japonicus Spain 73 (35, 38) 162 (152, 10) Italy - 48 (20, 28) Austria - 28 (0, 28) Netherlands - 20 (20, 0) Hungary - 52 (17, 35) France - 20 (10, 10) USA - 33 (33, 0) Japan - 64 (64, 0) Total 73 427 Bioinformatic analysis: Raw reads were processed using Qiime2 (v2021.4) [ 40 ], with denoising performed via DADA2 plugin in Qiime2 [ 41 ]. Taxonomic classification was conducted and amplicon sequence variants (ASVs) were inferred using DADA2 without clustering, and taxonomy was assigned against SILVA v138.1 (full-length, unclustered) [ 42 ] at 97% identity thresholds. Sequences from non-target true bacterial taxa (Mitochondria, Chloroplast, Archaea, and Eukarya) were removed. Bacterial diversity analysis: To calculate diversity indices using a standardised sequencing depth, data were rarefied to the lowest sample read counts; 5,816 reads per sample. Alpha diversity indices (Chao1, Shannon and Evenness) were calculated using R, and beta diversity was assessed with Bray Curtis and visualised using Principal Coordinates Analysis (PCoA) using R software and the microbiome package [ 43 ]. In order to assess the observed taxa assignment in the entire dataset, a rarefaction curve was constructed. The number of reads per sample was taken into account. The rarefaction curve was generated using R, vegan package [ 43 ]. Statistical analysis of community structure and Indicator species analysis : To quantitatively assess the influence of mosquito species, developmental stage, and sampling location on the bacterial community structure, we conducted a permutational multivariate analysis of variance (PERMANOVA) based on BrayCurtis dissimilarity. Prior to analysis, the abundance matrix was filtered to remove samples with no detectable bacterial genera. The PERMANOVA was run using the adonis2 function in the vegan R package (v. 2.6-4) with 999 permutations, testing the effects of Species, Stage, and Location on microbial beta-diversity. BrayCurtis dissimilarities were calculated on normalized abundance data, with missing values treated as zeros. Additionally, we stratified the adult mosquito dataset by rearing method (wild-caught vs. lab-reared) to explore the potential influence of geographic location within each rearing group, acknowledging the confounding between rearing conditions between the countries in our dataset. Separate PERMANOVAs were conducted for wild-caught adults and lab-reared adults using Bray-Curtis dissimilarities and testing for the effect of country within each subset, Bray-Curtis dissimilarities were visualised in PCoA plots for each subset (Figure S5 ) among wild-caught and lab-reared adults across different countries. To identify bacterial genera associated with mosquito species, developmental stages, and geographic locations, we also conducted indicator species analysis using the multipatt function in the indicspecies R package (v.1.7.12). Analyses were based on the same filtered abundance table used for PERMANOVA (see above), with NAs replaced by zero and samples with total zero abundance removed. The association index used was the group-equalized IndVal.g statistic, with statistical significance assessed using 999 permutations. Significant indicator taxa (p < 0.05) were identified separately for mosquito species, developmental stage (larva vs. adult), and sampling location (country), and interpreted in the context of observed microbiota structure. Correlation analysis between host relatedness and gut bacterial communities: To establish if there was any statistical correlation between the host relatedness and their corresponding bacterial communities, two matrices were compared per mosquito species: Ae. albopictus genetic relatedness matrix and a microbiota dissimilarity matrix (70 Ae. albopictus ), and another with Ae. japonicus (136 Ae. japonicus ) genetic relatedness matrix and a microbiota dissimilarity matrix. The relatedness matrix quantifies genetic kinship between host individuals based on microsatellite markers. As for Ae. albopictus , samples were screened for 19 microsatellite loci following conditions detailed in [ 44 , 45 ]. As for Ae. japonicus , samples were screened for 18 microsatellites (7 specific for Ae. japonicus and 11 specific for Ae. albopictus ) spread in 3 multiplexes. The first multiplex comprised 7 Ae. japonicus loci as described in [ 46 ], whereas the second and third multiplexes consisted of a total of 11 loci developed for use on Ae. albopictus [ 47 , 48 ]. PCR conditions followed [ 46 ] as for the first multiplex, and [ 44 ] for the second and third multiplexes. The program ML-RELATE [ 49 ] was employed to calculate maximum likelihood estimates of relatedness between individuals, separately for each mosquito species. Relatedness coefficients between pairs of individuals go from 0 (totally unrelated) to 1 (clones or identical individuals). The microbiota dissimilarity matrix is based on our Bray Curtis analysis. To assess the relationship between host genetic relatedness and microbiota dissimilarity, we performed three statistical analyses: Mantel test, Procrustes analysis, and Redundancy Analysis (RDA) using Python (v3.11) and the following libraries: pandas (v1.5.3), numpy (v1.24.3), scipy (v1.10.1), and scikit-learn (v1.2.2). Mantel test was performed using Spearman correlation with 9,999 permutations to assess statistical correlation between host genetic relatedness and microbiota dissimilarity. Procrustes analysis was performed to evaluate structural similarity, using 10 principal components (PCA) before analysis. Lastly, to model microbiota dissimilarity as a function of host genetic relatedness, we performed RDA using Partial Least Squares Regression (PLS-R) with two components. Results and Discussion Sequencing output and taxonomic composition of the entire mosquito bacterial communities: A total of 12,404,833 reads were generated across all samples of both mosquito species. Our rarefaction analysis showed sufficient coverage of bacterial communities (Fig. S1 ). At a 97% sequence similarity threshold, 24,807 amplicon sequence variants (ASVs) were identified. Following filtering to keep only high-quality reads, 20,257 ASVs remained, with per-sample reads ranging from 2,567 to 71,570. A subset of 4,093 ASVs remained unassigned, and 388 taxa were identified as chloroplasts, 61 as mitochondria, seven as archaea, and one as eukaryotic. The remaining ASVs were all assigned to a bacterial taxon. Forty-two bacterial phyla were identified across all mosquitoes (Table S1 ), with the five most abundant being Proteobacteria (64,00%), Bacteridota (14,5%), Actinobacteriota (8,3%), Firmicutes (4,5%), and Cyanobacteria (2,1%) (Figure S2 ; Table S1 ). At the genus level, 992 bacterial genera were detected (Table S1 ). All bacterial genera were more similar in the adults compared to the larvae, regardless of mosquito origin (Fig. 1 ). However, when examining Ae. japonicus , the only species sampled across multiple countries, we observed a clear signal of geographical separation in the larval microbiota (Fig. 1 ). This spatial structuring was still detectable in the adult microbiotas, though more diluted (Fig. 1 ). The top 5 most abundant genera were: Wolbachia (6%), Acinetobacter (4,7%), Flavobacterium (3,7%), Aeromonas (3,1%) and Limnobacter (2,8%) (Figure S2 ; Table S1 ). All of which are known to occupy Aedes bacterial communities, especially in the mosquito guts and reproductive organs [ 8 , 22 ]. Wolbachia was detected in 50 of 73 Ae. albopictus samples, and in 16 of 427 Ae. japonicus samples. In Ae. japonicus , most of the detections came from the Spanish population, while only minor occurrences were observed in Italy, Japan, and the USA. This indicates that although Wolbachia is present in those populations, it is consistently dominant in Ae. albopictus , with a patchier and generally low-prevalence distribution in Ae. japonicus . Bacterial genera richness, using Chao1 index, was the highest in the Spanish population, followed by Japan, Hungary, The Netherlands, USA, Italy, France and the lowest in the Austrian one (Table S2 ). While Spain had the highest number of bacterial genera, and Austria the least, Chao1 index and other richness indices are largely influenced by the sample size [ 50 , 51 ] and the sequencing depth, and our Spanish population was the largest in the collection (235 mosquitoes). However, this correlation is not linear, for example The Netherlands and France had the same sample size (each had 20 mosquitoes in the dataset) yet the Netherlands had much higher bacterial richness compared to France (336 and 205 Chao1 index, respectively, Table S2 ). As for bacterial genera diversity, Japan and The Netherlands had the highest bacterial diversity using Shannon index (Table S2 ) and the Austrian mosquito microbiotas were the least diverse. Community evenness, which measures how equally disturbed the bacterial abundances are, was assessed using the Evenness index representing the relative abundance of different species within the communities. Our evenness outputs showed similar trends, with the Netherlands and Japan having the most even bacterial community, while Spain and the USA were the least even (Table S2 ). Lower evenness in Spain and the USA suggests dominance by taxa like Wolbachia in Spain, while higher evenness in the Netherlands and Japan indicates a more balanced bacterial community. Finally, across eight countries, 219 indicator taxa were identified, with clear biogeographic patterns: Curvibacter and Polynucleobacter were strongly linked to Austria, Janthinobacterium to France, and Methylobacterium and Erwinia to the Netherlands. These analyses confirm that mosquito-associated microbiotas are structured by host species, life stage, and local environmental conditions, offering robust support for the observed beta-diversity patterns. Developmental stage effects on mosquito bacterial communities: Comparisons between larvae and adult mosquitoes revealed clear stage-specific bacterial profiles (Fig. 1 ; Fig. 2 ; Figure S2 ; Table S1 ). Venn diagram comparisons showed that France and Italy exhibited the largest overlap in bacterial genera between larvae and adults, whereas Hungary displayed a more divergent composition (Figure S4 ; Table S3 ). For example, in Italy, larvae and adults shared many bacterial genera, including Acinetobacter , Pseudomonas , Hydrogenophaga , and Novosphingobium (Figure S2 ; Table S1 ). However, relative abundances differed markedly between stages: adults were more frequently dominated by Acinetobacter and Wolbachia , whereas larvae harboured higher levels of environmental taxa such as Limnohabitans and Flavobacterium . This indicates that while a core set of shared genera is maintained, developmental stage strongly influences the relative community composition. In the French population, Ae. japonicus larvae showed higher relative abundances of genera such as Chryseobacterium , Rhizobacter , Limnohabitans , Novosphingobium and Hydrogenophaga (Fig. 2 ; Figure S2 ; Table S1 ), some of which have been described in mosquito microbiome before [ 26 , 52 ] and others ( Limnohabitans , Novosphingobium and Hydrogenophaga ) are common in aquatic environments [ 26 , 53 ], consistent with their exposure to waterborne bacteria and a detritus‐rich diet. While Ae. japonicus adults from the same French population were enriched in bacteria such as Nevskia and Methylobacterium (Fig. 2 ; Figure S2 ; Table S1 ), which are known to inhabit Aedes species microbiomes [ 22 , 54 ], and commonly found in soil and environment bacterial communities [ 55 ]. However, their role in hematophagous insects remains unknown, or maybe they are better adapted to the gut conditions associated with sugar feeding and blood ingestion in adult mosquitoes. Additionally, those adult-abundant genera are often found in environmental samples, raising the question of whether these bacteria could be externally adhered to the mosquitoes and not actually innate to the mosquito guts. Pectobacterium was found only in one adult Ae. japonicus from this population where it dominated the entire gut (74% of that gut bacterial community - Table S1 ). Pectobacterium has been reported before in Anopheles mosquitoes [ 56 ] and its role remains to be characterised in mosquito guts. A similar pattern of distinct larval and adult bacterial communities was also observed in the other populations included here: Hungary and Spain, where both stages were collected and from both mosquito species ( Ae. japonicus and Ae. albopictus ) (details in Table S1 ; Figure S2 ; Figure S3 ). These findings support previous studies that have reported large shifts in microbiota composition during metamorphosis (e.g., [ 20 , 21 ]), suggesting that the transition from an aquatic to a terrestrial niche strongly selects for different bacterial assemblages, and that environmental flux of bacteria are probably the main driver to such separation in bacterial composition between larvae and adult mosquitoes. Geographic and species-level variation in Ae. japonicus: The identified bacterial genera across Ae. japonicus mosquitoes (only Ae. japonicus was collected form all the included countries) from Austria, Spain, France, Hungary, Italy, Japan, the USA, and the Netherlands revealed signals of geographic heterogeneity. However, those signals were diluted in parts, i.e., communities showed higher similarities from different countries. This is likely due to several taxa that are relatively stable in their relative abundances across samples from different countries. For example, Pseudomonas and Acinetobacter were widely present at similar proportions across most countries, including Austria, Spain, Italy, Hungary, Japan, and the USA (Table S1 ). Other genera with broad geographic distributions included Flavobacterium , Aeromonas , and Novosphingobium , which were consistently detected across multiple regions, particularly in larvae. Intra-country and micro–geographical variations were also observed. For example, in Hungary, even within a remarkably confined geographic area (with collection sites separated by at most 4.3 km), apparent differences in Ae. japonicus -associated bacterial communities were observed. Larval samples collected on the same day from cemeteries in Szalafő, Keserűszer, and Őriszentpéter exhibited distinct bacterial profiles (Fig. 3 ; Table S1 ). For example, Xanthobacter , that is previously described in mosquito larvae [ 57 ], and water-abundant Actinobacteria PeM15 [ 58 ] were more abundant in Szalafő (Fig. 3 ; Table S1 ). Moreover, adult mosquitoes collected in Kovácsszénája displayed considerable variability among themselves; e.g. , some individuals harboured notably higher relative abundances of genera such as Aeromonas and Acinetobacter compared to others from the same area. This is suggesting that subtle microhabitat differences, perhaps variations in water chemistry, soil composition, or organic matter inputs, can substantially influence the composition of the mosquito microbiotas. It is worth mentioning that these differences could stem either from the differences between larval and adult microbiotas or from local environmental factors. In several locations in Hungary, only one developmental stage was represented (Fig. 3 ), which makes it difficult to attribute the observed variation solely to geographic differences. Variation in water quality, nutrient availability, and organic matter may favour distinct bacterial taxa in those breeding sites. Aquatic-associated genera (e.g., Limnohabitans , Hydrogenophaga ) were more prominent in some larval samples, while bacteria that tolerate the gut environment of adults (e.g., Acinetobacter ) became enriched after metamorphosis. Similar patterns of distinct bacterial communities between within-county locations were also observed in other countries, e.g., USA, Austria, Japan, Italy and Spain ( Figure S3 ; Table S1 ).For example, in the USA, adult mosquito microbiotas formed two dissimilar clusters between the two collection sites in the USA: St. Louis, Missouri, and College Park, Maryland, which are geographically distant (~ 1,100 km) and represent ecologically different environments, suburban woodland edges versus urban/residential neighbourhoods (Figure S3 ; Table S1 ). In Austria, all larvae were collected from a single locality (Althofen), but clustering patterns in the ordination plot likely reflect intra-site variation due to microhabitat heterogeneity (Figure S3 ; Table S1 ). Similarly, in Clauzetto, Italy, larvae were collected from two nearby but separate sites ~ 1.7 km apart, possibly reflecting small-scale environmental differences such as water chemistry or organic matter (Figure S3 ; Table S1 ). In Japan, Ae. japonicus adult microbiotas showed two dissimilar clusters: those from Saga (collected in 2018) differed from those collected in Ishikawa and Sapporo in 2021, suggesting temporal effects may have contributed to the observed variation (Figure S3 ; Table S1 ). These findings suggest that both large- and small-scale geographic factors, including habitat type and collection time, influence the mosquito microbiota. However, also in some of those countries, variations were observed due to the different developmental stages: e.g., Italy and Spain (Figure S2 ; Figure S3 ; Table S1 ). This suggests that developmental stage plays a substantial role in shaping the microbiota, yet because different locations were often associated with different stages, we cannot fully disentangle the effects of stage versus location without further controlled sampling. Wolbachia and host–symbiont dynamics : Wolbachia is one of the most intensively studied endosymbionts in mosquitoes because of its capacity to e.g. manipulate reproduction (e.g., cytoplasmic incompatibility) and reduce vector competence for pathogens [ 59 , 60 ]. In our dataset, Wolbachia was often dominant in Ae. albopictus , being detected in 50 of 73 individuals with relative abundances up to ~ 98.5%. In contrast, Wolbachia was much less common in Ae. japonicus , detected in only 16 of 427 individuals, with the highest abundances observed in Spain and only sporadic, low-level detections in Italy, Japan, and the USA (Table S1 ). To our knowledge, this represents the first report of Wolbachia in Ae. japonicus . Previous studies have also reported that Wolbachia dominates Ae. albopictus adults [ 61 , 62 ]. As an intracellular, maternally transmitted symbiont, Wolbachia may be present at very low titers in early developmental stages and then increase as the host matures, which may explain its consistent presence in adults but very scarce presence in larvae in our study.. The high Wolbachia abundance observed in Ae. albopictus adults from Spain aligns with previous studies showing that this species frequently harbours stable Wolbachia infections [ 61 , 62 ]. In contrast, Wolbachia was never detected in Ae. japonicus before, and the patchy positive detections in our dataset may reflect transient infections or environmental contamination, though this requires further validation. However, the presence of native Wolbachia in both Ae. albopictus and Ae. japonicus here requires further investigation, unlike transinfections used in vector control programs, native strains may have limited or no impact on vector survival and arbovirus transmission. As such, the functional relevance of these infections remains to be experimentally validated. Comparison of bacterial genera abundance between Ae. albopictus and Ae. japonicus from Spain : A comparative analysis of the bacterial communities in the two species showed that Ae. japonicus has higher bacterial diversity and more even compared to Ae. albopictus from the Spanish population, where only the two species were collected (Table S2 ), similar to previous studies where Ae. albopictus microbiota was compared to other Aedes species [ 8 , 22 , 23 , 28 ]. Ae. japonicus exhibited distinct bacterial signatures compared to Ae. albopictus , consistent across several countries. Given the limited number of studies on Ae. japonicus , these findings add largely to the sparse literature and underscore the species-specific structuring of its microbiota. Microbiota comparison showed that several bacterial genera exhibit strong, species-specific associations (Figure S2 ; Figure S3 ). Consistent with the indicator taxa analysis, several genera differentiated the two species within the Spanish populations. Wolbachia and Comamonas were strongly associated with Ae. albopictus , with Wolbachia dominating many individuals, whereas Aeromonas and Limnohabitans were significantly associated with Ae. japonicus , particularly in larval samples (Table S1 ). Cutibacterium was also detected at low abundance in Ae. japonicus but was largely absent from Ae. albopictus . These taxa exemplify species-specific structuring of the microbiota in Spain. Such contrasting patterns suggest that Wolbachia as an example, as an obligate intracellular symbiont, is maintained by vertical transmission and thus strongly tied to Ae. albopictus biology, whereas taxa such as Aeromonas , Limnohabitans , Cutibacterium , and Comamonas are environmental or host-associated bacteria that may be selectively enriched in Ae. japonicus through differences in mosquito habitats, cuticle-associated communities, or host physiology. In contrast, some genera appear in both species, indicating the presence of a shared bacterial community. Nevertheless, the species‐specific enrichment of taxa may help drive the overall divergence in microbial communities between the two mosquito species, even though the extent of variation in some taxa also reflects environmental and developmental influences that complicate a straightforward interpretation. While host species identity contributes to defining the microbiotas, the variability observed within species is likely driven, at least in part, by spatial heterogeneity at the local scale within each species (Figure S3 ; Table S1 ), as shown by these Spanish mosquitoes, which were collected from 23 different locations. Correlation between host relatedness and bacterial community composition: Mantel test (Spearman correlation, 9,999 permutations) yielded a weak, non-significant correlation for Ae. albopictus (r = -0.0614, p = 0.9988), suggesting no strong relationship between host genetic relatedness and bacterial community composition. Similarly, Procrustes analysis showed a high disparity score (0.8599), indicating weak structural correspondence between the two datasets. However, redundancy analysis suggested that host genetics might influence microbiota composition to some extent, with the first two constrained components explaining 60.8% and 39.2% of the variance, respectively. For Ae. japonicus , the Mantel test also showed a weak yet statistically significant negative correlation (r = -0.043, p = 0.0003), suggesting a minor inverse relationship between host genetic relatedness and microbiota dissimilarity. Procrustes analysis yielded a similarly high disparity score (0.94), supporting weak structural alignment. Redundancy analysis indicated a possible influence of host genetics on microbiota composition, with the first two components explaining 45.3% and 54.7% of the variance. These findings suggest that while host genetics may contribute to microbiota structuring, it does not appear to be a dominant factor shaping mosquito bacterial communities in either species. Mosquito as reservoir for opportunistic and pathogenic bacteria: The mosquitoes harboured a diverse bacterial community, including genera with known pathogenic potential, which may contribute to their role as disease vectors. In our dataset, genera such as Aeromonas , Pseudomonas , Serratia , Elizabethkingia , Klebsiella , Enterobacter , and Acinetobacter are present, all of which have been implicated in human and animal infections. For instance, Aeromonas species are known to be water-derived species and also linked to fish and human infection [ 67 – 69 ]. Pseudomonas includes opportunistic pathogens such as P. aeruginosa associated with respiratory infections and sepsis [ 70 ]. Serratia species has been reported in hospital-acquired infections, particularly in immunocompromised patients [ 71 ]. Elizabethkingia was found in the Spanish Ae. japonicus from one location only (Alava) and Japan (Table S1 ). This genus has been reported in Anopheles gambiae reproductive organs and suggested to aid in sugar degradation [ 72 ], one bacterial species in this genus was originally isolated from Anopheles mosquitoes, has emerged as a cause of neonatal meningitis and bloodstream infections [ 73 ]. Enteric bacteria such as Klebsiella and Enterobacter species, which have been detected in mosquitoes, are particularly concerning due to their ability to acquire and disseminate antimicrobial resistance (AMR) genes [ 74 ]. Acinetobacter was widespread in our mosquito microbiotas and can include A. baumannii , a major nosocomial pathogen and carrier of several antimicrobial resistance genes, making it a serious public health threat [ 75 ]. More specifically, one sample from the Netherlands contained Bartonella with high relative abundance (2.87%, Table S1 ). Bartonella is known to cause infections in several mammalian hosts. In humans, it causes symptoms such as fever, rashes, and headaches but it can also lead to serious endocarditis [ 76 ]. One Ae. albopictus from Spain showed a high relative abundance (64.47%) of Bordetella . Bordetella is a genus that includes B. pertussis , the causative agent of whooping cough [ 77 ]. No prior records were found that mosquitos are associated with the transmission of this pathogen though. Mycobacterium was found in 71 mosquitoes with various abundances (Table S1 ), and it was more abundant in mosquitoes from France and Japan. Species of Mycobacterium in mosquitoes were linked to outbreaks of Buruli ulcer in Australia [ 4 ]. Arthropod-derived pathogenic bacteria, such as Borrelia and Francisella or Anaplasma and Ehrlichia , which can cause Anaplasmosis and Ehrlichiosis respectively, that are usually transmitted by other arthropods like ticks [ 78 ], were also not identified in our mosquitoes. While the direct role of mosquitoes in the transmission of these bacteria remains to be fully elucidated, possible pathways include mechanical transfer during feeding, regurgitation, and faecal deposition, which may facilitate the environmental spread of these bacteria and associated AMR genes. The presence of these bacteria in both larval and adult stages, with stage-specific patterns regardless of the rearing conditions, suggests that developmental stage may influence the potential of mosquitoes to act as reservoirs or vectors for these bacteria, which is relevant for vector ecology and AMR surveillance efforts. Given the increasing interest in the use of mosquito-based surveillance for monitoring environmental AMR and pathogen circulation, our findings support the potential of mosquitoes to serve as bioindicators of environmental bacterial diversity and AMR gene presence across diverse geographic regions. Future studies employing metagenomic sequencing will be essential to determine the functional potential of these mosquito-associated microbiomes and to clarify their role in pathogen transmission and AMR gene dissemination. Conclusion While the majority of the bacterial taxa in the two mosquito species were commensal, other reports suggested that most of which are acquired from the surrounding environment to the vertebrate hosts [ 79 ]. Yet distinct structuring occurs across developmental stages, geography, and local sites, with species identity having a lesser impact on the microbiota. Mosquitoes therefore act as ecological filters, selecting bacterial taxa that perhaps best support their physiological needs at different life stages. For example, during the larval stage, mosquito microbiomes are dominated by aquatic and detritus-associated bacteria, such as Flavobacterium , Limnohabitans , and Hydrogenophaga . These taxa are known for their roles in nutrient cycling in aquatic environments, as mentioned above. Flavobacterium spp., in particular, have been linked to nutrient acquisition and digestion of complex polysaccharides [ 80 ], which may aid larvae in extracting energy from nutrient-poor breeding sites. Upon metamorphosis into adults, the microbiota shifts to taxa better suited for a terrestrial and sugar-rich diet. Genera such as Acinetobacter and Methylobacterium , both commonly found in soil, become dominant, reflecting the transition from aquatic feeding to sugar and blood feeding. Acinetobacter species are known for their ability to metabolize sugars and hydrocarbons [ 81 ], which may support energy metabolism in nectar-feeding adult mosquitoes. This shift aligns with the hypothesis that mosquito bacterial communities are not static but dynamically selected based on functional needs. In addition, this is the first report, to our knowledge, to detect Wolbachia in Ae. japonicus in seven mosquitoes from the USA. Further investigations are required to describe the extent of such pattern. The geographic structuring of bacterial communities, among mosquitoes of the same species, suggests that local environmental conditions strongly influence the bacterial composition. This makes mosquito microbiota a potential bioindicator of environmental bacterial diversity, pollution levels, and even AMR gene circulation in different ecosystems. We retained the lab-reared adult samples in our analysis to maximise geographic and host phylogenetic coverage, despite the acknowledged confounding with country of origin. Including these samples allows for robust comparisons across countries within the same host taxon, which is essential for understanding microbiome structuring across broad geographic scales. The presence of potentially pathogenic bacteria in our mosquitoes, reported also in previous studies [ 61 – 72 ], suggests that mosquitoes can act as reservoirs or carriers of bacterial pathogens, including AMR genes. The transmission of these bacteria could occur through direct contact, regurgitation, or faecal deposition during feeding, potentially facilitating the spread of AMR genes between hosts and environments. Understanding the composition of mosquito-associated bacterial communities is essential for assessing their potential role in the dissemination of bacterial infections and AMR genes. future research should also aim to have a more balanced sample size per country as the mosquitoes here were exploited and available as part of a previous project on mosquito population dynamics. As this research aimed to investigate bacterial community presence and structure, future research should focus on how these microbial associations impact mosquito vector competence and the transmission dynamics of both symbiotic and importantly pathogenic bacteria, using metagenomics approach to investigate the gene content of such communities. Declarations Competing Interests: The authors have no relevant financial or non-financial interests to disclose. Consent to Publish declaration: Not applicable. Funding: This work was supported mainly by Horizon 2020 grant VEO (874735). The collection of mosquito specimens in Hungary was supported by the National Research, Development and Innovation Office, grant numbers FK-138563 and RRF-2.3.1-21-2022-00010 “National Laboratory of Virology”. The collection of mosquito specimens in Spain was supported by the Human-Mosquito Interaction Project, funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 853271). Author Contribution S.O., F.L., M.V., F.B., and F.M.A., contributed to the concept and design of the study. K.BL., F.M., K.M.W., M.A.G., Z.S., P.T.L., K.K., N.T., J.R.B.P., F.S., S.DE., M.M., R.E., I.RA., J.F.B., and A.C. collected the mosquito samples from all the countries included in the study. J.C., and F.L. performed laboratory work and microsatellite analyses. S.O., R.E., and F.D.M., performed the sequencing and the bioinformatic and statistical analyses. S.O., and R.E., made the figures. S.O., and F.M.A., wrote the first draft of the manuscript. All authors contributed to the writing and read and approved the submitted manuscript. Acknowledgement We are thankful to Hanne Mordhorst for the assistance in the laboratory. Data Availability All 16S rRNA data are available at the European Nucleotide Archive (ENA) with the project accession number: ERX13771555-ERX13772064. References Weaver SC, Charlier C, Vasilakis N, Lecuit M (2018) Zika, Chikungunya, and Other Emerging Vector-Borne Viral Diseases. Annu Rev Med 69:395–408. https://doi.org/10.1146/annurev-med-050715-105122 Braack L, Gouveia De Almeida AP, Cornel AJ, et al (2018) Mosquito-borne arboviruses of African origin: review of key viruses and vectors. Parasites Vectors 11:29. https://doi.org/10.1186/s13071-017-2559-9 Singh H, Singh OP, Akhtar N, et al (2019) First report on the transmission of Zika virus by Aedes (Stegomyia) aegypti (L.) (Diptera: Culicidae) during the 2018 Zika outbreak in India. Acta Tropica 199:105114. https://doi.org/10.1016/j.actatropica.2019.105114 Johnson PDR, Azuolas J, Lavender CJ, et al (2007) Mycobacterium ulcerans in Mosquitoes Captured during Outbreak of Buruli Ulcer, Southeastern Australia. Emerg Infect Dis 13:1653–1660. https://doi.org/10.3201/eid1311.061369 Dieme C, Bechah Y, Socolovschi C, et al (2015) Transmission potential of Rickettsia felis infection by Anopheles gambiae mosquitoes. Proc Natl Acad Sci USA 112:8088–8093. https://doi.org/10.1073/pnas.1413835112 Tchoumbou MA, Mayi MPA, Malange ENF, et al (2020) Effect of deforestation on prevalence of avian haemosporidian parasites and mosquito abundance in a tropical rainforest of Cameroon. International Journal for Parasitology 50:63–73. https://doi.org/10.1016/j.ijpara.2019.10.006 Antonio-nkondjio C, Kerah CH, Simard F, et al (2006) Complexity of the Malaria Vectorial System in Cameroon: Contribution of Secondary Vectors to Malaria Transmission. J Med Entomol 43:1215–1221. https://doi.org/10.1093/jmedent/43.6.1215 Gómez M, Martinez D, Muñoz M, Ramírez JD (2022) Aedes aegypti and Ae. albopictus microbiome/virome: new strategies for controlling arboviral transmission? Parasites Vectors 15:287. https://doi.org/10.1186/s13071-022-05401-9 Izri A, Bitam I, Charrel RN (2011) First entomological documentation of Aedes (Stegomyia) albopictus (Skuse, 1894) in Algeria. Clinical Microbiology and Infection 17:1116–1118. https://doi.org/10.1111/j.1469-0691.2010.03443.x Benallal KE, Allal-Ikhlef A, Benhamouda K, et al (2016) First report of Aedes (Stegomyia) albopictus (Diptera: Culicidae) in Oran, West of Algeria. Acta Tropica 164:411–413. https://doi.org/10.1016/j.actatropica.2016.09.027 Shoukry NM, Elwan MA, Morsy TA (2012) Aedes aegypti (Linnaeus) re-emerging in southern Egypt. J Egypt Soc Parasitol 42:41–50. https://doi.org/10.12816/0006293 Beji M, Rhim A, Roiz D, Bouattour A (2017) Ecophysiological characterization and molecular differentiation of Culex pipiens forms (Diptera: Culicidae) in Tunisia. Parasit Vectors 10:327. https://doi.org/10.1186/s13071-017-2265-7 Mikhail MW, Al-Bursheed KM, Abd El-Halim AS, Morsy TA (2009) Studies on mosquito borne dieases in Egypt and Qatar. J Egypt Soc Parasitol 39:745–756 Gratz NG (2004) Critical review of the vector status of Aedes albopictus . Medical Vet Entomology 18:215–227. https://doi.org/10.1111/j.0269-283X.2004.00513.x Adhami J, Reiter P (1998) Introduction and establishment of Aedes (Stegomyia) albopictus skuse (Diptera: Culicidae) in Albania. J Am Mosq Control Assoc 14:340–343 Koban MB, Kampen H, Scheuch DE, et al (2019) The Asian bush mosquito Aedes japonicus japonicus (Diptera: Culicidae) in Europe, 17 years after its first detection, with a focus on monitoring methods. Parasites Vectors 12:109. https://doi.org/10.1186/s13071-019-3349-3 Schaffner F, Chouin S, Guilloteau J (2003) First record of Ochlerotatus (Finlaya) japonicus japonicus (Theobald, 1901) in metropolitan France. J Am Mosq Control Assoc 19:1–5 Cansado-Utrilla C, Zhao SY, McCall PJ, et al (2021) The microbiome and mosquito vectorial capacity: rich potential for discovery and translation. Microbiome 9:111. https://doi.org/10.1186/s40168-021-01073-2 David MR, Santos LMBD, Vicente ACP, Maciel-de-Freitas R (2016) Effects of environment, dietary regime and ageing on the dengue vector microbiota: evidence of a core microbiota throughout Aedes aegypti lifespan. Mem Inst Oswaldo Cruz 111:577–587. https://doi.org/10.1590/0074-02760160238 Muturi EJ, Ramirez JL, Rooney AP, Kim C-H (2017) Comparative analysis of gut microbiota of mosquito communities in central Illinois. PLoS Negl Trop Dis 11:e0005377. https://doi.org/10.1371/journal.pntd.0005377 Rodpai R, Boonroumkaew P, Sadaow L, et al (2023) Microbiome Composition and Microbial Community Structure in Mosquito Vectors Aedes aegypti and Aedes albopictus in Northeastern Thailand, a Dengue-Endemic Area. Insects 14:184. https://doi.org/10.3390/insects14020184 Melo T, Sousa CA, Delacour-Estrella S, et al (2024) Characterization of the microbiome of Aedes albopictus populations in different habitats from Spain and São Tomé. Sci Rep 14:20545. https://doi.org/10.1038/s41598-024-71507-y Scolari F, Casiraghi M, Bonizzoni M (2019) Aedes spp. and Their Microbiota: A Review. Front Microbiol 10:2036. https://doi.org/10.3389/fmicb.2019.02036 Muturi EJ, Bara JJ, Rooney AP, Hansen AK (2016) Midgut fungal and bacterial microbiota of Aedes triseriatus and Aedes japonicus shift in response to La Crosse virus infection. Mol Ecol 25:4075–4090. https://doi.org/10.1111/mec.13741 Zotzmann S, Steinbrink A, Schleich K, et al (2017) Bacterial diversity of cosmopolitan Culex pipiens and invasive Aedes japonicus from Germany. Parasitol Res 116:1899–1906. https://doi.org/10.1007/s00436-017-5466-2 Juma EO, Allan BF, Kim C-H, et al (2021) The larval environment strongly influences the bacterial communities of Aedes triseriatus and Aedes japonicus (Diptera: Culicidae). Sci Rep 11:7910. https://doi.org/10.1038/s41598-021-87017-0 Ferreira QR, Lemos FFB, Moura MN, et al (2023) Role of the Microbiome in Aedes spp. Vector Competence: What Do We Know? Viruses 15:779. https://doi.org/10.3390/v15030779 Minard G, Tran FH, Van VT, et al (2015) French invasive Asian tiger mosquito populations harbor reduced bacterial microbiota and genetic diversity compared to Vietnamese autochthonous relatives. Front Microbiol 6:. https://doi.org/10.3389/fmicb.2015.00970 Rosso F, Tagliapietra V, Albanese D, et al (2018) Reduced diversity of gut microbiota in two Aedes mosquitoes species in areas of recent invasion. Sci Rep 8:16091. https://doi.org/10.1038/s41598-018-34640-z Lee JM, Yek SH, Wilson RF, Rahman S (2020) Characterization of the Aedes albopictus (Diptera: Culicidae) holobiome: bacterial composition across land use type and mosquito sex in Malaysia. Acta Tropica 212:105683. https://doi.org/10.1016/j.actatropica.2020.105683 Kang X, Wang Y, Li S, et al (2020) Comparative Analysis of the Gut Microbiota of Adult Mosquitoes From Eight Locations in Hainan, China. Front Cell Infect Microbiol 10:596750. https://doi.org/10.3389/fcimb.2020.596750 Seabourn P, Spafford H, Yoneishi N, Medeiros M (2020) The Aedes albopictus (Diptera: Culicidae) microbiome varies spatially and with Ascogregarine infection. PLoS Negl Trop Dis 14:e0008615. https://doi.org/10.1371/journal.pntd.0008615 Medeiros MCI, Seabourn PS, Rollins RL, Yoneishi NM (2022) Mosquito Microbiome Diversity Varies Along a Landscape-Scale Moisture Gradient. Microb Ecol 84:893–900. https://doi.org/10.1007/s00248-021-01865-x Juma EO, Allan BF, Kim C-H, et al (2020) Effect of life stage and pesticide exposure on the gut microbiota of Aedes albopictus and Culex pipiens L. Sci Rep 10:9489. https://doi.org/10.1038/s41598-020-66452-5 Arif W, Thangaraj G, Srinivasan P, et al (2024) Culex quinquefasciatus Mosquitoes Resist Acquisition of Methicillin-Resistant Staphylococcus aureus: Insights from Field and Laboratory Studies. Antibiotics (Basel) 13:618. https://doi.org/10.3390/antibiotics13070618 Abdolmaleki Z, Mashak Z, Safarpoor Dehkordi F (2019) Phenotypic and genotypic characterization of antibiotic resistance in the methicillin-resistant Staphylococcus aureus strains isolated from hospital cockroaches. Antimicrob Resist Infect Control 8:54. https://doi.org/10.1186/s13756-019-0505-7 Lowe CF, Romney MG (2011) Bedbugs as vectors for drug-resistant bacteria. Emerg Infect Dis 17:1132–1134. https://doi.org/10.3201/eid/1706.101978 Huang W, Wang S, Jacobs-Lorena M (2020) Use of Microbiota to Fight Mosquito-Borne Disease. Front Genet 11:196. https://doi.org/10.3389/fgene.2020.00196 Mogi M, Armbruster PA, Tuno N (2020) Differences in Responses to Urbanization Between Invasive Mosquitoes, Aedes japonicus japonicus (Diptera: Culicidae) and Aedes albopictus, in Their Native Range, Japan. J Med Entomol 57:104–112. https://doi.org/10.1093/jme/tjz145 ECDC (2012) Guidelines for the surveillance of invasive mosquitoes in Europe. https://www.ecdc.europa.eu/en/publications-data/guidelines-surveillance-invasive-mosquitoes-europe Becker (2020) Mosquitoes. Cham: Springer International Publishing; 2020. Günay F, Picard M, Robert V. (2020) MosKeyTool, an interactive identification key for mosquitoes of Euro-Mediterranean. Version 2.2. Available at https://www.medilabsecure.com/entomology-tools-0/moskeytool#How%20to%20install%20Moskeytool%20? . Tanaka, K., K. Mizusawa, and E. S. Saugstad. (1979) A revision of the adult and larval mosquitoes of Japan (including the Ryukyu Archipelago and the Ogasawara Islands) and Korea (Diptera: Culicidae). Contrib. Am. Entomol. Inst. 16: 1–987. Ree H (2003) Taxonomic Review and Revised Keys of the Korean Mosquitoes (Diptera: Culicidae). Entomological Research 33:39–52. https://doi.org/10.1111/j.1748-5967.2003.tb00047.x Schaffner, F., Angel, G., Geoffroy, B., Hervy, J.-P., Rhaiem, A., and Brunhes, J. (2001) Les moustiques d’Europe: logiciel d’identification et d’enseignement = The mosquitoes of Europe: an identification and training programme. IRD Editions & EID Méditerranée: Montpellier, France. Severini, F., Toma, L., & di Luca, M. (2022) Mosquitoes in Italy: collection, identification and preservation of the most common species. ISTISAN Reports 22/3, Rome: Istituto Superiore di Sanità. Darsie, RF and Ward, RA. (2005) Identification and geographical distribution of the mosquitoes of North America, north of Mexico, 2nd Edition. University Press of Florida, USA. Otani S, Mikaelyan A, Nobre T, et al (2014) Identifying the core microbial community in the gut of fungus-growing termites. Mol Ecol 23:4631–4644. https://doi.org/10.1111/mec.12874 Yu Y, Lee C, Kim J, Hwang S (2005) Group-specific primer and probe sets to detect methanogenic communities using quantitative real‐time polymerase chain reaction. Biotech & Bioengineering 89:670–679. https://doi.org/10.1002/bit.20347 Bolyen E, Rideout JR, Dillon MR, et al (2019) Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 37:852–857. https://doi.org/10.1038/s41587-019-0209-9 Callahan BJ, McMurdie PJ, Rosen MJ, et al (2016) DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13:581–583. https://doi.org/10.1038/nmeth.3869 Quast C, Pruesse E, Yilmaz P, et al (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41:D590-596. https://doi.org/10.1093/nar/gks1219 R Core Team (2024) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria https://wwwR-project.org/ Albó Timor A, Lucati F, Bartumeus F, et al (2023) A fast and inexpensive genotyping system for the simultaneous analysis of human and Aedes albopictus short tandem repeats. Parasites Vectors 16:347. https://doi.org/10.1186/s13071-023-05977-w Ontiveros V, Lucati F, Caner J, et al (2025) Fine-scale tiger mosquito population dynamics in urban and densely populated landscapes Widdel AK, McCuiston LJ, Crans WJ, et al (2005) Finding needles in the haystack: single copy microsatellite loci for Aedes japonicus (Diptera: Culicidae). Am J Trop Med Hyg 73:744–748 Beebe NW, Ambrose L, Hill LA, et al (2013) Tracing the tiger: population genetics provides valuable insights into the Aedes (Stegomyia) albopictus invasion of the Australasian Region. PLoS Negl Trop Dis 7:e2361. https://doi.org/10.1371/journal.pntd.0002361 Manni M, Gomulski LM, Aketarawong N, et al (2015) Molecular markers for analyses of intraspecific genetic diversity in the Asian Tiger mosquito, Aedes albopictus. Parasit Vectors 8:188. https://doi.org/10.1186/s13071-015-0794-5 Kalinowski ST, Wagner AP, Taper ML (2006) ml - relate: a computer program for maximum likelihood estimation of relatedness and relationship. Molecular Ecology Notes 6:576–579. https://doi.org/10.1111/j.1471-8286.2006.01256.x Chao A, Jost L (2012) Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size. Ecology 93:2533–2547. https://doi.org/10.1890/11-1952.1 Chiu C (2023) Sample coverage estimation, rarefaction, and extrapolation based on sample-based abundance data. Ecology 104:e4099. https://doi.org/10.1002/ecy.4099 Schrieke H, Maignien L, Constancias F, et al (2022) The mosquito microbiome includes habitat-specific but rare symbionts. Comput Struct Biotechnol J 20:410–420. https://doi.org/10.1016/j.csbj.2021.12.019 Feichtinger S, Lazar AA, Luebbe MA, et al (2023) Case report: isolation of Hydrogenophaga from septic blood culture following near-death drowning in lakewater. Access Microbiol 5:000533.v4. https://doi.org/10.1099/acmi.0.000533.v4 Minard G, Tran FH, Raharimalala FN, et al (2013) Prevalence, genomic and metabolic profiles of Acinetobacter and Asaia associated with field-caught Aedes albopictus from Madagascar. FEMS Microbiol Ecol 83:63–73. https://doi.org/10.1111/j.1574-6941.2012.01455.x Yan L, Zhang W, Duan W, et al (2021) Temporal Bacterial Community Diversity in the Nicotiana tabacum Rhizosphere Over Years of Continuous Monocropping. Front Microbiol 12:641643. https://doi.org/10.3389/fmicb.2021.641643 Tainchum K, Dupont C, Chareonviriyaphap T, et al (2020) Bacterial Microbiome in Wild-Caught Anopheles Mosquitoes in Western Thailand. Front Microbiol 11:965. https://doi.org/10.3389/fmicb.2020.00965 Flores GAM, Lopez RP, Cerrudo CS, et al (2023) Wolbachia dominance influences the Culex quinquefasciatus microbiota. Sci Rep 13:18980. https://doi.org/10.1038/s41598-023-46067-2 Meng L-J, Zhang Y, Li X-X, et al (2021) Comparative analysis of bacterial communities of water and intestines of silver carp (Hypophthalmichthys molitrix) and bighead carp (H. nobilis) reared in aquaculture pond systems. Aquaculture 534:736334. https://doi.org/10.1016/j.aquaculture.2020.736334 Jiggins FM (2017) The spread of Wolbachia through mosquito populations. PLoS Biol 15:e2002780. https://doi.org/10.1371/journal.pbio.2002780 Johnson K (2015) The Impact of Wolbachia on Virus Infection in Mosquitoes. Viruses 7:5705–5717. https://doi.org/10.3390/v7112903 Mancini MV, Damiani C, Accoti A, et al (2018) Estimating bacteria diversity in different organs of nine species of mosquito by next generation sequencing. BMC Microbiol 18:126. https://doi.org/10.1186/s12866-018-1266-9 Lucati F, Delacour S, Palmer JRB, et al (2022) Multiple invasions, Wolbachia and human-aided transport drive the genetic variability of Aedes albopictus in the Iberian Peninsula. Sci Rep 12:20682. https://doi.org/10.1038/s41598-022-24963-3 Moreira LA, Iturbe-Ormaetxe I, Jeffery JA, et al (2009) A Wolbachia Symbiont in Aedes aegypti Limits Infection with Dengue, Chikungunya, and Plasmodium. Cell 139:1268–1278. https://doi.org/10.1016/j.cell.2009.11.042 Hoffmann AA, Montgomery BL, Popovici J, et al (2011) Successful establishment of Wolbachia in Aedes populations to suppress dengue transmission. Nature 476:454–457. https://doi.org/10.1038/nature10356 Utarini A, Indriani C, Ahmad RA, et al (2021) Efficacy of Wolbachia-Infected Mosquito Deployments for the Control of Dengue. N Engl J Med 384:2177–2186. https://doi.org/10.1056/NEJMoa2030243 Sasaki T, Moi ML, Saito K, et al (2022) Aedes albopictus Strain and Dengue Virus Serotype in the Dengue Fever Outbreaks in Japan: Implications of Wolbachia Infection. Jpn J Infect Dis 75:140–143. https://doi.org/10.7883/yoken.JJID.2021.376 Austin, B., and C. Adams. (1996) Fish pathogens, p. 197–244. In B. Austin, M. Altwegg, P. J. Gosling, and S. Joseph (ed.), The genus Aeromonas. John Wiley & Sons Ltd., Chichester, England. Figueras MJ, Suarez-Franquet A, Chacón MR, et al (2005) First record of the rare species Aeromonas culicicola from a drinking water supply. Appl Environ Microbiol 71:538–541. https://doi.org/10.1128/AEM.71.1.538-541.2005 Altwegg, M. (1999) Aeromonas and Plesiomonas, p. 507–516. In P. R. Murray, E. J. Baron, M. A. Pfaller, F. C. Tenover, and R. H. Yolken (ed.), Manual of clinical microbiology. American Society for Microbiology, Washington, D.C. Janda JM, Abbott SL (2010) The genus Aeromonas: taxonomy, pathogenicity, and infection. Clin Microbiol Rev 23:35–73. https://doi.org/10.1128/CMR.00039-09 Enciso-Moreno JA, Pernas-Buitrón N, Ortiz-Herrera M, Coria-Jiménez R (2004) Identification of Serratia marcescens populations of nosocomial origin by RAPD-PCR. Archives of Medical Research 35:12–17. https://doi.org/10.1016/j.arcmed.2003.07.005 Boissière A, Tchioffo MT, Bachar D, et al (2012) Midgut Microbiota of the Malaria Mosquito Vector Anopheles gambiae and Interactions with Plasmodium falciparum Infection. PLoS Pathog 8:e1002742. https://doi.org/10.1371/journal.ppat.1002742 Bernardet J.-F., Hugo C., Bruun B. 2006; Bernardet J.-F., Hugo C., Bruun B. (2006) The genera Chryseobacterium and Elizabethkingia. In The Prokaryotes: a Handbook on the Biology of Bacteria, 3rd edn. vol. 7 pp. 638–676 Edited by Dworkin M., Falkow S., Rosenberg E., Schleifer K. H., Stackebrandt E. New York: Springer Chavshin AR, Oshaghi MA, Vatandoost H, et al (2012) Identification of bacterial microflora in the midgut of the larvae and adult of wild caught Anopheles stephensi: A step toward finding suitable paratransgenesis candidates. Acta Tropica 121:129–134. https://doi.org/10.1016/j.actatropica.2011.10.015 Fariba Akrami, Amirmorteza Ebrahimzadeh Namvar (2019) Acinetobacter baumannii as Nosocomial Pathogenic Bacteria. Mol Genet Microbiol Virol 34:84–96. https://doi.org/10.3103/S0891416819020046 Rudolf I, Blažejová H, Mendel J, et al (2020) Bartonella species in medically important mosquitoes, Central Europe. Parasitol Res 119:2713–2717. https://doi.org/10.1007/s00436-020-06732-1 Nieves DJ, Heininger U (2016) Bordetella pertussis. In: Scheld WM, Hughes JM, Whitley RJ (eds) Emerging Infections 10. ASM Press, Washington, DC, USA, pp 311–339 Guo W-P, Tian J-H, Lin X-D, et al (2016) Extensive genetic diversity of Rickettsiales bacteria in multiple mosquito species. Sci Rep 6:38770. https://doi.org/10.1038/srep38770 Kikuchi Y, Hosokawa T, Fukatsu T (2007) Insect-Microbe Mutualism without Vertical Transmission: a Stinkbug Acquires a Beneficial Gut Symbiont from the Environment Every Generation. Appl Environ Microbiol 73:4308–4316. https://doi.org/10.1128/AEM.00067-07 Lian X-D, Guan Y, Jiang Y, et al (2025) Discovery of two novel Flavobacterium species with potential for complex polysaccharide degradation. Sci Rep 15:3494. https://doi.org/10.1038/s41598-025-87876-x Papade SE, Mohapatra B, Phale PS (2024) Pseudomonas and Acinetobacter spp. capable of metabolizing aromatics displays multifarious plant growth promoting traits: Insights on strategizing consortium-based application to agro-ecosystems. Environmental Technology & Innovation 36:103786. https://doi.org/10.1016/j.eti.2024.103786 Additional Declarations No competing interests reported. Supplementary Files OtanietalFigureS1.png Figure S1: Rarefaction curves of sequence depth of the mosquito microbiotas included in this study (using R Core Team 2013). The curves represent the mosquito gut samples, and each curve shows the number of genus-level taxa as a function of the read numbers. OtanietalFigureS2.pdf Figure S2 : Relative abundances of the top 30 most abundant bacterial genera in all the mosquitoes included in this study. Others are all the remaining assigned genera; full list in Table S1. A: All countries without Spain with one species Ae. japonicus . B: Only the Spanish population with the two species; Ae. albopictus and Ae. japonicus . Full list of all the genera and the samples is in Table S1. Relative abundances of Wolbachia in red, for Spain (Ae. albopictus) and USA (Ae. japonicus) and r elative abundances of the bacterial phyla in all the mosquitoes included in this study (A-N-P-R: Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium). OtanietalFigureS3.pdf Figure S3 : Principal coordinates analysis (PCoA) visualising bacterial community (genera) similarities across the mosquito stages, and species when different species were collected only in Spain, from the included countries and their locations within, which were analysed using either BrayCurtis distances in R. Each dot represents one gut community. PCO1 and PCO2 are shown with the percentage variation explained for each axis. First sheet shows the separation in each country based on the within-country collection site and developmental stage (a-h). Second sheet shows the separation in each country based on the mosquito species and developmental stage (i-p). Third sheet shows the separation in Spain where only the two mosquito species were collected and between the developmental stages. OtanietalFigureS4.png Figure S4: Venn diagrams of shared bacterial genera between the two developmental stages in the French and Hungarian mosquitoes. Detailed taxa are presented in Table S3. OtanietalFigureS5.png Principal coordinates analysis (PCoA) plots showing Bray-Curtis dissimilarities of mosquito bacterial communities in adult mosquitoes stratified by rearing method: (A) lab-reared adults (USA, France, Italy, Japan), (B) wild-caught adults (Spain, Hungary, Netherlands). Points represent individual mosquitoes coloured by country of origin. 95% confidence ellipses are shown for each country to visualize clustering patterns within each subset, demonstrating the geographic structuring within wild-caught and lab-reared adults separately. OtanietalTableS1.xlsx Table S1: Relative abundances of the bacterial communities of all the mosquitoes included in this study, with their metadata. Both at the bacterial genus level (sheet 1) and phyla level (sheet 2). Full metadata details, including sampling dates and sampling coordinates, are available in Sheet 3. OtanietalTableS2.xlsx Table S2: Diversity indices output of all the bacterial communities included in this study: between countries, between the two mosquito species Ae. japonicus and Ae. albopictus , and between all larval and adult samples (from all countries where different stages were collected). OtanietalTableS3.xlsx Table S3: Bacterial taxa (genera) that are shared between or unique to a developmental stage between larvae or adult misquotes in the French and Hungarian population. Presented in Figure S4. OtanietalTableS4.xlsx Table S4: Results of PERMANOVA analyses on Bray–Curtis dissimilarities of mosquito bacterial communities. The global model tested the marginal effects of mosquito species, developmental stage, and sampling location. Reported are degrees of freedom (Df), sums of squares, mean squares, F statistic, effect size (R²), and permutation p-values (Pr(>F)). 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7544775","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":526218893,"identity":"e6be5b42-f6eb-4964-8a2b-b49f9a56886b","order_by":0,"name":"Saria Otani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYLCCDzZA4gCDAZBkNiBGA2PjjDSwFsMGorU085CkRb798PHHNgk2cnwHmLc/5mGwNiaoxeBMWmJzTkKaseQBtsJmHoZ0M8JaGHIMm3N/HE7ccIDHEKjlsA1hh/W/MWy2SCBFC8MNoC0MSFqIcNiNZ4kze0B+OcxWOHOOQTph78v3Jx/48AMUYsebN3x4U2ENCmliATPYUuLVj4JRMApGwSjAAwDwRzvadTuVbwAAAABJRU5ErkJggg==","orcid":"","institution":"National Food Institute, Technical University of Denmark","correspondingAuthor":true,"prefix":"","firstName":"Saria","middleName":"","lastName":"Otani","suffix":""},{"id":526218894,"identity":"4e122a13-42c4-47ef-b46f-1c1acc4dd1e9","order_by":1,"name":"Federica Lucati","email":"","orcid":"","institution":"Pompeu Fabra University (UPF)","correspondingAuthor":false,"prefix":"","firstName":"Federica","middleName":"","lastName":"Lucati","suffix":""},{"id":526218895,"identity":"0b81d4af-c82b-4cf0-b98d-7bdeb9a7a638","order_by":2,"name":"Ragna Eberhardt","email":"","orcid":"","institution":"National Food Institute, Technical University of Denmark","correspondingAuthor":false,"prefix":"","firstName":"Ragna","middleName":"","lastName":"Eberhardt","suffix":""},{"id":526218896,"identity":"bd7dc710-7db2-4021-8dd0-1bba7be44f62","order_by":3,"name":"Frederik Duus Møller","email":"","orcid":"","institution":"National Food Institute, Technical University of Denmark","correspondingAuthor":false,"prefix":"","firstName":"Frederik","middleName":"Duus","lastName":"Møller","suffix":""},{"id":526218897,"identity":"c7c6f8d0-4a2c-48a1-9a25-6154dfc86b2c","order_by":4,"name":"Jenny Caner","email":"","orcid":"","institution":"Centre for Advanced Studies of Blanes (CEAB-CSIC)","correspondingAuthor":false,"prefix":"","firstName":"Jenny","middleName":"","lastName":"Caner","suffix":""},{"id":526218898,"identity":"a4a6ba4c-e99f-4ecb-bd26-c3571edd0f7c","order_by":5,"name":"Karin Bakran-Lebl","email":"","orcid":"","institution":"AGES - Austrian Agency for Health and Food Safety","correspondingAuthor":false,"prefix":"","firstName":"Karin","middleName":"","lastName":"Bakran-Lebl","suffix":""},{"id":526218899,"identity":"328f0d51-3f70-43c6-8079-77beceab7ff8","order_by":6,"name":"Fabrizio Montarsi","email":"","orcid":"","institution":"Istituto Zooprofilattico Sperimentale delle Venezie","correspondingAuthor":false,"prefix":"","firstName":"Fabrizio","middleName":"","lastName":"Montarsi","suffix":""},{"id":526218900,"identity":"027f6997-3e52-4880-b903-37b50f02e109","order_by":7,"name":"Katie M. Westby","email":"","orcid":"","institution":"Washington University in Saint Louis","correspondingAuthor":false,"prefix":"","firstName":"Katie","middleName":"M.","lastName":"Westby","suffix":""},{"id":526218901,"identity":"e835389a-6dfa-4a1b-b05c-d299d71640b7","order_by":8,"name":"Mikel A. González","email":"","orcid":"","institution":"Conservation Biology and Global Change. Doñana Biological Station (EBD-CSIC)","correspondingAuthor":false,"prefix":"","firstName":"Mikel","middleName":"A.","lastName":"González","suffix":""},{"id":526218902,"identity":"6beef0af-d853-4ef4-b8d9-5c6db5ef8a8c","order_by":9,"name":"Zoltán Soltész","email":"","orcid":"","institution":"HUN-REN Centre for Ecological Research","correspondingAuthor":false,"prefix":"","firstName":"Zoltán","middleName":"","lastName":"Soltész","suffix":""},{"id":526218903,"identity":"1d83d741-dd75-4074-b078-b12f51a69d50","order_by":10,"name":"Paul T. Leisnham","email":"","orcid":"","institution":"University of Maryland","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"T.","lastName":"Leisnham","suffix":""},{"id":526218904,"identity":"ab4718b7-099e-4058-ba4e-d06505d7c300","order_by":11,"name":"Kornélia Kurucz","email":"","orcid":"","institution":"National Laboratory of Virology, University of Pécs","correspondingAuthor":false,"prefix":"","firstName":"Kornélia","middleName":"","lastName":"Kurucz","suffix":""},{"id":526218905,"identity":"58667558-cd6d-409e-8a2f-0d21597a24a7","order_by":12,"name":"Nobuko Tuno","email":"","orcid":"","institution":"Kanazawa University","correspondingAuthor":false,"prefix":"","firstName":"Nobuko","middleName":"","lastName":"Tuno","suffix":""},{"id":526218907,"identity":"0f95aa49-89f5-49c8-8724-ec31146de730","order_by":13,"name":"John R.B. Palmer","email":"","orcid":"","institution":"Pompeu Fabra University (UPF)","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"R.B.","lastName":"Palmer","suffix":""},{"id":526218908,"identity":"74f4a4fb-2b4e-4917-b7e0-058628b37582","order_by":14,"name":"Francis Schaffner","email":"","orcid":"","institution":"Francis Schaffner EI","correspondingAuthor":false,"prefix":"","firstName":"Francis","middleName":"","lastName":"Schaffner","suffix":""},{"id":526218911,"identity":"af61cae5-4921-44e1-b56b-d53915fe3434","order_by":15,"name":"Sarah Delacour-Estrella","email":"","orcid":"","institution":"Instituto Agroalimentario de Aragón-IA2 (Universidad de Zaragoza-CITA","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Delacour-Estrella","suffix":""},{"id":526218913,"identity":"34a30bb1-f939-4705-b425-671676fbd421","order_by":16,"name":"Motoyoshi Mogi","email":"","orcid":"","institution":"Saga University","correspondingAuthor":false,"prefix":"","firstName":"Motoyoshi","middleName":"","lastName":"Mogi","suffix":""},{"id":526218915,"identity":"319c2a37-f7fe-478f-a44a-114af451206a","order_by":17,"name":"Roger Eritja","email":"","orcid":"","institution":"Centre for Advanced Studies of Blanes (CEAB-CSIC)","correspondingAuthor":false,"prefix":"","firstName":"Roger","middleName":"","lastName":"Eritja","suffix":""},{"id":526218917,"identity":"324fbfbe-a46c-4e12-943f-c292440d50a9","order_by":18,"name":"Ignacio Ruiz-Arrondo","email":"","orcid":"","institution":"Instituto Agroalimentario de Aragón-IA2 (Universidad de Zaragoza-CITA","correspondingAuthor":false,"prefix":"","firstName":"Ignacio","middleName":"","lastName":"Ruiz-Arrondo","suffix":""},{"id":526218919,"identity":"22f8898d-a99d-4d16-ae83-d2196420e604","order_by":19,"name":"Jesús F. Barandika","email":"","orcid":"","institution":"NEIKER-Basque Institute for Agricultural Research and Development. Basque Research and Technology Alliance (BRTA)","correspondingAuthor":false,"prefix":"","firstName":"Jesús","middleName":"F.","lastName":"Barandika","suffix":""},{"id":526218921,"identity":"5fa0dc83-bcba-4a7e-90d1-99563c40676a","order_by":20,"name":"Aitor Cevidanes","email":"","orcid":"","institution":"NEIKER-Basque Institute for Agricultural Research and Development. Basque Research and Technology Alliance (BRTA)","correspondingAuthor":false,"prefix":"","firstName":"Aitor","middleName":"","lastName":"Cevidanes","suffix":""},{"id":526218924,"identity":"09d39d65-0ce4-4416-a90e-06025835b2a2","order_by":21,"name":"Marc Ventura","email":"","orcid":"","institution":"Centre for Advanced Studies of Blanes (CEAB-CSIC)","correspondingAuthor":false,"prefix":"","firstName":"Marc","middleName":"","lastName":"Ventura","suffix":""},{"id":526218926,"identity":"626aef0e-3103-48ae-9a83-336ee03afb07","order_by":22,"name":"Frederic Bartumeus","email":"","orcid":"","institution":"Centre for Advanced Studies of Blanes (CEAB-CSIC)","correspondingAuthor":false,"prefix":"","firstName":"Frederic","middleName":"","lastName":"Bartumeus","suffix":""},{"id":526218928,"identity":"5e221bb9-ff63-4a98-8c01-46835f7faa78","order_by":23,"name":"Frank M. Aarestrup","email":"","orcid":"","institution":"National Food Institute, Technical University of Denmark","correspondingAuthor":false,"prefix":"","firstName":"Frank","middleName":"M.","lastName":"Aarestrup","suffix":""}],"badges":[],"createdAt":"2025-09-05 13:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7544775/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7544775/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93136369,"identity":"72fcc66e-e429-4e24-8a96-5672bbf443ad","added_by":"auto","created_at":"2025-10-09 12:18:33","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3341933,"visible":true,"origin":"","legend":"","description":"","filename":"OtanietalMainText31850620d4d94b67bb761e3ca991867cParasitesVectorsclean.docx","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/3905d510357ffa0621469072.docx"},{"id":93136359,"identity":"43cdc74f-ad08-4904-a238-527c76040f22","added_by":"auto","created_at":"2025-10-09 12:18:33","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":818676,"visible":true,"origin":"","legend":"","description":"","filename":"OtanietalFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/485c8fb4564c9e163e840ebe.png"},{"id":93137550,"identity":"e5ba33af-7d49-4d2d-81ed-c46b14d402f9","added_by":"auto","created_at":"2025-10-09 12:26:33","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":208518,"visible":true,"origin":"","legend":"","description":"","filename":"OtanietalFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/c6d3bcf002402f44aa3ac3c3.png"},{"id":93136367,"identity":"0d1b26ed-d802-4495-8ac4-d391a9967ee7","added_by":"auto","created_at":"2025-10-09 12:18:33","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":182883,"visible":true,"origin":"","legend":"","description":"","filename":"OtanietalFigure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/1de676d73efc5b267c1c8a92.jpg"},{"id":93136362,"identity":"bd072801-6a16-496b-97bb-716a35fbbf62","added_by":"auto","created_at":"2025-10-09 12:18:33","extension":"json","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":21880,"visible":true,"origin":"","legend":"","description":"","filename":"9b2b886892e5468598c29c8b2ec32b9e.json","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/4f1fbcf09faee58dd7f1dd0a.json"},{"id":93137551,"identity":"ed216138-2478-4283-b4f5-c6601bcb3012","added_by":"auto","created_at":"2025-10-09 12:26:33","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":23737,"visible":true,"origin":"","legend":"","description":"","filename":"OtanietalFigureS1.png","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/0f4866ebe35462ee60ca90e9.png"},{"id":93136387,"identity":"bce56511-69dc-4e8e-98fc-225b6536e455","added_by":"auto","created_at":"2025-10-09 12:18:33","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2188679,"visible":true,"origin":"","legend":"","description":"","filename":"OtanietalFigureS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/94bbbadaccb18c9cd5592805.pdf"},{"id":93136397,"identity":"1a2263e1-00bf-4809-8fc8-1b3673d6282d","added_by":"auto","created_at":"2025-10-09 12:18:34","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1054098,"visible":true,"origin":"","legend":"","description":"","filename":"OtanietalFigureS3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/0c9c7856b1d6a0913cc1575a.pdf"},{"id":93137964,"identity":"ddaa389e-60e0-4732-991a-b1cdbb73c917","added_by":"auto","created_at":"2025-10-09 12:34:33","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":125063,"visible":true,"origin":"","legend":"","description":"","filename":"OtanietalFigureS4.png","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/2dd940f5d8a7962d8e146e6e.png"},{"id":93137554,"identity":"205f517d-75d9-4b81-8cec-f3c58b8451bc","added_by":"auto","created_at":"2025-10-09 12:26:33","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":330864,"visible":true,"origin":"","legend":"","description":"","filename":"OtanietalFigureS5.png","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/5e804258e75294983b962f60.png"},{"id":93137564,"identity":"6c7b2ed5-d6f4-462f-b208-4dd5f7901046","added_by":"auto","created_at":"2025-10-09 12:26:33","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3347320,"visible":true,"origin":"","legend":"","description":"","filename":"OtanietalMainText31850620d4d94b67bb761e3ca991867cParasitesVectorstrackedchanged1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/e5c075f1de5df207eb8535d0.docx"},{"id":93136375,"identity":"6b566524-aa66-4afe-8d31-4cd72d41ac7b","added_by":"auto","created_at":"2025-10-09 12:18:33","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1852140,"visible":true,"origin":"","legend":"","description":"","filename":"OtanietalTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/d6a5c328ad4fb5e2fa7b835e.xlsx"},{"id":93136371,"identity":"c9dd7bdc-7ce9-4dc3-ba4c-86c5b97c42c5","added_by":"auto","created_at":"2025-10-09 12:18:33","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10145,"visible":true,"origin":"","legend":"","description":"","filename":"OtanietalTableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/71681e6ce6225e4842444b64.xlsx"},{"id":93136390,"identity":"03ebbd89-28fd-4dce-bbff-d96c8480839d","added_by":"auto","created_at":"2025-10-09 12:18:33","extension":"xlsx","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16229,"visible":true,"origin":"","legend":"","description":"","filename":"OtanietalTableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/c4870aff258f0d1ef65e7afb.xlsx"},{"id":93136381,"identity":"61ef9107-931a-4cdf-9b25-3e93a4874242","added_by":"auto","created_at":"2025-10-09 12:18:33","extension":"xlsx","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9558,"visible":true,"origin":"","legend":"","description":"","filename":"OtanietalTableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/4f608b102a6da4d171ac024b.xlsx"},{"id":93137563,"identity":"63eb8c22-814d-4565-8d6b-ee7fb5397987","added_by":"auto","created_at":"2025-10-09 12:26:33","extension":"docx","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":34660,"visible":true,"origin":"","legend":"","description":"","filename":"Responsetoreviewers31850620d4d94b67bb761e3ca991867cd1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/e301a42841e7e5291ad3d4e7.docx"},{"id":93137562,"identity":"f7065bcd-3510-41b4-9790-d8764cea07a8","added_by":"auto","created_at":"2025-10-09 12:26:33","extension":"xml","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":201314,"visible":true,"origin":"","legend":"","description":"","filename":"9b2b886892e5468598c29c8b2ec32b9e1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/6706c3c12e13a1530e561cf9.xml"},{"id":93136384,"identity":"48a0018d-32be-4a31-bbc7-5f59918da942","added_by":"auto","created_at":"2025-10-09 12:18:33","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":818676,"visible":true,"origin":"","legend":"","description":"","filename":"OtanietalFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/41bba30af0ad3dcc6cfcb8b3.png"},{"id":93136379,"identity":"809958eb-4154-4af3-9f5d-7876946ebf31","added_by":"auto","created_at":"2025-10-09 12:18:33","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":208518,"visible":true,"origin":"","legend":"","description":"","filename":"OtanietalFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/1af01c1f48564d5376e39c8b.png"},{"id":93136382,"identity":"f2028c9f-a3a2-4e09-afb9-e07b5020a137","added_by":"auto","created_at":"2025-10-09 12:18:33","extension":"jpg","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":182883,"visible":true,"origin":"","legend":"","description":"","filename":"OtanietalFigure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/89862dfc31b36972f5f97d78.jpg"},{"id":93137561,"identity":"4f847ae5-f4b4-4629-9a1a-5fa565f801d1","added_by":"auto","created_at":"2025-10-09 12:26:33","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":360229,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/eec40183b68cdc9ff86c1e07.png"},{"id":93136395,"identity":"2a87065f-96f8-42d7-9e02-70d8adb0e084","added_by":"auto","created_at":"2025-10-09 12:18:33","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":106710,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/cb2a7eb01d5285cc99e1a67a.png"},{"id":93136396,"identity":"163b82db-e705-4845-8edb-560a27583e4b","added_by":"auto","created_at":"2025-10-09 12:18:34","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":248187,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/0ae7d9e1fd880fb5235e5600.png"},{"id":93137560,"identity":"7ce1514a-8eda-49c7-8301-cad8e79009ef","added_by":"auto","created_at":"2025-10-09 12:26:33","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":132751,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineOtanietalFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/aad7d75f3af2ebfc3e79efc1.png"},{"id":93136391,"identity":"5d5017d7-44c6-4e2c-b2b4-d2cc85a559e0","added_by":"auto","created_at":"2025-10-09 12:18:33","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":43982,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineOtanietalFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/a253b4b2b33b548a49f766ff.png"},{"id":93136401,"identity":"1fb1cac4-50e4-4c49-8d4b-24cac7b44766","added_by":"auto","created_at":"2025-10-09 12:18:34","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":75846,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineOtanietalFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/820a37fe4b41056a12b168d5.png"},{"id":93136393,"identity":"ff9fb918-1154-4a2a-80f2-13dd10120814","added_by":"auto","created_at":"2025-10-09 12:18:33","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":59022,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/2fc625cd7b90a52d78d62fc1.png"},{"id":93136388,"identity":"015fe2b4-3177-4f11-833c-0a158fcf41fc","added_by":"auto","created_at":"2025-10-09 12:18:33","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":19318,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/543e0e9e8f5e4a7a4ff12972.png"},{"id":93137557,"identity":"5c64bac1-dae8-47d3-98a7-58cd7775b4f2","added_by":"auto","created_at":"2025-10-09 12:26:33","extension":"png","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":45855,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/1621994acb1dee9fc53dc0c6.png"},{"id":93136399,"identity":"6e859d77-0c8c-428f-8e90-f6b4288a843c","added_by":"auto","created_at":"2025-10-09 12:18:34","extension":"xml","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":196819,"visible":true,"origin":"","legend":"","description":"","filename":"9b2b886892e5468598c29c8b2ec32b9e1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/d75d2d4e6f1d6f961750a9a1.xml"},{"id":93136402,"identity":"cf3c3681-fbb5-4e2b-8a54-ba935b119184","added_by":"auto","created_at":"2025-10-09 12:18:34","extension":"html","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":221399,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/3a19719a1dbaf5e3e09fdb92.html"},{"id":93136356,"identity":"0a9f2b17-a74e-475a-8987-58e62ffc7b8c","added_by":"auto","created_at":"2025-10-09 12:18:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":247234,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal \u0026nbsp;coordinates analysis (PCoA) dissimilarity analysis of all mosquito bacterial \u0026nbsp;communities (top) and the two developmental stages (bottom) where one species \u0026nbsp;was collected from all included countries (\u003cem\u003eAe. japonicus\u003c/em\u003e), visualised \u0026nbsp;via Bray-Curtis distances across samples.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/13ae51536491b26914b521ef.png"},{"id":93136357,"identity":"f2a5bad8-0d9e-48d9-8111-df8c1ea480f7","added_by":"auto","created_at":"2025-10-09 12:18:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":112667,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundances of the most abundant bacterial genera in both developmental stages in the French population where only \u003cem\u003eAe. japonicus \u003c/em\u003emosquitoes were sampled. Relative abundances of all the countries are in Figure S2. Full list of all the genera and the samples is in Table S1.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/e746ccebe6c20f150f03a365.png"},{"id":93136361,"identity":"81062458-02c9-44a2-b8f7-ac8d68977fb6","added_by":"auto","created_at":"2025-10-09 12:18:33","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":182883,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundances of the most abundant bacterial genera in all local sites within Hungary (\u003cem\u003eAe. japonicus\u003c/em\u003e) (A-N-P-R: Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium). Full list of all the genera and the samples is in Table S1.\u003c/p\u003e","description":"","filename":"OtanietalFigure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/063680fce8323c2eb2cdc301.jpg"},{"id":96492569,"identity":"ceba6fd5-e43b-4497-bd37-88be3a60b6e9","added_by":"auto","created_at":"2025-11-21 18:08:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1504090,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/413983fc-fa66-40c2-b737-93c0d055a97c.pdf"},{"id":93137548,"identity":"e42cf376-3cde-43e0-8a5e-583ee24787b9","added_by":"auto","created_at":"2025-10-09 12:26:33","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23737,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1: Rarefaction curves of sequence depth of the \u0026nbsp;\u0026nbsp;mosquito microbiotas included in this study (using R Core Team 2013). The \u0026nbsp;\u0026nbsp;curves represent the mosquito gut samples, and each curve shows the number of \u0026nbsp;\u0026nbsp;genus-level taxa as a function of the read numbers.\u003c/p\u003e","description":"","filename":"OtanietalFigureS1.png","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/e5d3185d6569e5663cd4c375.png"},{"id":93137549,"identity":"537f3679-a0fc-481e-80c6-a501449d7da1","added_by":"auto","created_at":"2025-10-09 12:26:33","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2188679,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S2 : Relative \u0026nbsp;abundances of the top 30 most abundant bacterial genera in all the mosquitoes \u0026nbsp;included in this study. Others are all the remaining assigned genera; full \u0026nbsp;list in Table S1. A: All countries without Spain with one species \u003cem\u003eAe. \u0026nbsp;japonicus\u003c/em\u003e. B: Only the Spanish population with the two species; \u003cem\u003eAe. \u0026nbsp;albopictus\u003c/em\u003e and \u003cem\u003eAe. japonicus\u003c/em\u003e. Full list of all the genera and the \u0026nbsp;samples is in Table S1. Relative abundances of Wolbachia in red, for Spain \u0026nbsp;(Ae. albopictus) and USA (Ae. japonicus) and r elative abundances of the bacterial phyla in all the mosquitoes included in this study (A-N-P-R: Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium).\u003c/p\u003e","description":"","filename":"OtanietalFigureS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/8255ef4bbbfd3697e062087d.pdf"},{"id":93136364,"identity":"061cc543-5dee-4dac-8a5f-3213467e0338","added_by":"auto","created_at":"2025-10-09 12:18:33","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1054098,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S3 : Principal coordinates analysis (PCoA) visualising bacterial community (genera) similarities across the mosquito stages, and species when different species were collected only in Spain, from the included countries and their locations within, which were analysed using either BrayCurtis distances in R. Each dot represents one gut community. PCO1 and PCO2 are shown with the percentage variation explained for each axis. First sheet shows the separation in each country based on the within-country collection site and developmental stage (a-h). Second sheet shows the separation in each country based on the mosquito species and developmental stage (i-p). Third sheet shows the separation in Spain where only the two mosquito species were collected and between the developmental stages.\u003c/p\u003e","description":"","filename":"OtanietalFigureS3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/9568966231d48f4372dda661.pdf"},{"id":93137963,"identity":"ca8d24d9-ae35-4d77-adad-539960630909","added_by":"auto","created_at":"2025-10-09 12:34:33","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":125063,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S4: Venn diagrams of shared bacterial genera between the two developmental stages in the French and Hungarian mosquitoes. Detailed taxa are presented in Table S3.\u003c/p\u003e","description":"","filename":"OtanietalFigureS4.png","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/672830cb58982ff093485b5a.png"},{"id":93137558,"identity":"6986cdb3-2494-49cb-9233-ce98dc0d7af3","added_by":"auto","created_at":"2025-10-09 12:26:33","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":330864,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal coordinates analysis (PCoA) plots showing Bray-Curtis dissimilarities of mosquito bacterial communities in adult mosquitoes stratified by rearing method: (A) lab-reared adults (USA, France, Italy, Japan), (B) wild-caught adults (Spain, Hungary, Netherlands). Points represent individual mosquitoes coloured by country of origin. 95% confidence ellipses are shown for each country to visualize clustering patterns within each subset, demonstrating the geographic structuring within wild-caught and lab-reared adults separately.\u003c/p\u003e","description":"","filename":"OtanietalFigureS5.png","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/3e30daf0c7e3ba5609b672dd.png"},{"id":93136372,"identity":"041405cc-bd3c-4a6d-b4bc-9f8caca10d33","added_by":"auto","created_at":"2025-10-09 12:18:33","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1852140,"visible":true,"origin":"","legend":"\u003cp\u003eTable S1: Relative abundances of the bacterial communities of all the mosquitoes included in this study, with their metadata. Both at the bacterial genus level (sheet 1) and phyla level (sheet 2). Full \u0026nbsp;metadata details, including sampling dates and sampling coordinates, are \u0026nbsp;available in Sheet 3.\u003c/p\u003e","description":"","filename":"OtanietalTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/1a67039f5b29cca3fd515929.xlsx"},{"id":93137553,"identity":"2e14dc4b-8195-48d2-9f17-c2c96fd66aa2","added_by":"auto","created_at":"2025-10-09 12:26:33","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":10145,"visible":true,"origin":"","legend":"\u003cp\u003eTable S2: Diversity indices output of all the bacterial communities included in this study: between countries, between the two mosquito species \u003cem\u003eAe. japonicus \u003c/em\u003eand \u003cem\u003eAe. albopictus\u003c/em\u003e, and between all larval and adult samples (from all countries where different stages were collected).\u003c/p\u003e","description":"","filename":"OtanietalTableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/d5c13368d6f36a9225e52867.xlsx"},{"id":93136370,"identity":"043bc436-f6b8-41e3-80e3-dbeb14343cab","added_by":"auto","created_at":"2025-10-09 12:18:33","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":16229,"visible":true,"origin":"","legend":"\u003cp\u003eTable S3: Bacterial taxa (genera) that are shared between or unique to a developmental stage between larvae or adult misquotes in the French and Hungarian population. Presented in Figure S4.\u003c/p\u003e","description":"","filename":"OtanietalTableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/fca0a33557f6a93352ef9b62.xlsx"},{"id":93136378,"identity":"2acc3d69-85c6-4165-b498-86e39d129dd3","added_by":"auto","created_at":"2025-10-09 12:18:33","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":9558,"visible":true,"origin":"","legend":"\u003cp\u003eTable S4: Results of PERMANOVA analyses on Bray–Curtis dissimilarities of mosquito bacterial communities. The global model tested the marginal effects of mosquito species, developmental stage, and sampling location. Reported are degrees of freedom (Df), sums of squares, mean squares, F statistic, effect size (R²), and permutation p-values (Pr(\u0026gt;F)).\u003c/p\u003e","description":"","filename":"OtanietalTableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7544775/v1/260cb1eacd1e8d160c5d293a.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mosquito bacterial communities show stage-specific patterns relevant for vector ecology and AMR surveillance","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMosquitoes are major vectors of infectious diseases [1\u0026ndash;7], transmitting pathogens that can cause malaria, dengue, chikungunya, Zika, and other serious illnesses. The primary mosquito vector species belong to the genera \u003cem\u003eAedes\u003c/em\u003e, \u003cem\u003eAnopheles\u003c/em\u003e, and \u003cem\u003eCulex\u0026nbsp;\u003c/em\u003e[8\u0026ndash;13]. \u003cem\u003eAedes albopictus\u003c/em\u003e has emerged as a key vector species, while \u003cem\u003eAedes japonicus\u0026nbsp;\u003c/em\u003eis recognised as a major invasive species, and both are characterised by their rapid global spread. \u003cem\u003eAe. albopictus\u003c/em\u003e, originally from Southeast Asia, has spread worldwide and has reached Europe (Albania) in 1979 [14, 15]. \u003cem\u003eAe. japonicus\u003c/em\u003e, native to East Asia, has more recently expanded into Europe, where it was first detected in 2000 in northern France [16, 17]. Despite their increasing presence, little is known about the bacterial community composition of \u003cem\u003eAe. japonicus\u003c/em\u003e compared to \u003cem\u003eAe. albopictus\u003c/em\u003e, particularly in relation to geographic distribution and host-related factors.\u003c/p\u003e\n\u003cp\u003eInsect-associated microbiomes are known to play significant roles in the host physiology including reproduction, growth and development, and vector density, all influencing vector competence [18]. Mosquitoes acquire their microbiome through vertical transmission of endosymbionts, ingestion of water and organic matter in larval habitats, and blood or sugar feeding in adulthood [19].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrevious studies have identified substantial microbiome variation across mosquito species, developmental stages, and geographic regions [20\u0026ndash;22]. While \u003cem\u003eAe. albopictus\u003c/em\u003e populations show reduced microbial diversity compared to other \u003cem\u003eAedes\u003c/em\u003e species and in areas of recent invasion [8, 22, 23], the microbiome of \u003cem\u003eAe. japonicus\u003c/em\u003e remains largely unexplored [24\u0026ndash;26]. Furthermore, studies on \u003cem\u003eAe. albopictus\u003c/em\u003e have primarily focused on the role of \u003cem\u003eWolbachia\u003c/em\u003e in limiting pathogen transmission (where it has been mainly reported), with less attention given to broader microbial community dynamics [27]. This suggests a mosquito-specific microbiome\u0026nbsp;where certain bacterial taxa might be more persistent in a mosquito species but not in another, resulting in host-specific community profiles [28, 29]. A clear variation has been observed for different geographical areas [30\u0026ndash;33] that was unrelated to temperature, rainfall and urbanisation [32, 33], as well as between host developmental stages and sexes [30, 34].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSeveral studies suggest that mosquitoes, and other invertebrate vectors, may also act as reservoirs for bacterial pathogens and antimicrobial resistance (AMR) genes [35\u0026ndash;38]. Several mosquito-associated bacteria, such as \u003cem\u003eAcinetobacter\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, and \u003cem\u003eEnterobacter\u003c/em\u003e are known opportunistic pathogens or AMR-carrying bacteria. However, the extent to which these pathogenic bacteria are influenced by host species, geography, and mosquito phylogenetic relatedness remains unclear. Generally, whether mosquito microbiota structure is primarily shaped by genetic factors or environmental conditions remains to be explored more extensively.\u003c/p\u003e\n\u003cp\u003eHowever, besides the observed link between low \u003cem\u003eAe. albopictus\u003c/em\u003e genomic diversity and reduced bacterial diversity [28], the possible association between \u003cem\u003eAe. albopictus\u0026nbsp;\u003c/em\u003ephylogeny and bacterial diversity, especially the abundances of specific bacterial genera, has not been studied. This is also true for the other host species \u003cem\u003eAe. japonicus\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eIn this study, we aimed to investigate how bacterial communities are shaped in \u003cem\u003eAedes\u003c/em\u003e mosquitoes, especially the understudied \u003cem\u003eAe. japonicus\u003c/em\u003e,\u0026nbsp;across eight countries, and which pathogenic bacteria they harbour. We performed a comparative analysis of the bacterial communities of \u003cem\u003eAedes\u0026nbsp;\u003c/em\u003eacross six European countries, the USA, and Japan in two host species when possible. We assessed microbiota diversity, geographic and developmental stage-specific variation, and the potential role of mosquitoes as reservoirs of opportunistic bacteria and AMR-associated taxa. Furthermore, we examined whether host genetic relatedness influences the bacterial composition in both mosquito species: \u003cem\u003eAe. albopictus\u003c/em\u003e and \u003cem\u003eAe. japonicus\u003c/em\u003e.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003e\u003cem\u003eSample collection and species identification\u003c/em\u003e:\u003c/p\u003e\u003cp\u003e\u003cem\u003eDNA extraction and amplicon sequencing\u003c/em\u003e:\u003c/p\u003e\u003cp\u003eMosquitoes were stored separately and individually, without distinguishing their sexes as they were part of another invertebrate project, in 100% EtOH at -20\u0026deg;C until DNA extraction. Individual mosquitoes were homogenized, and genomic DNA was extracted using the DNeasy Blood and Tissue Kit (Qiagen) following the manufacturer's instructions. DNA was further purified using a clean-up procedure with Phenol:Chloroform:Isoamyl Alcohol (25:24:1) and quantified using a Qubit Fluorometer (Thermo Fisher Scientific) following the manufacturer's instructions.\u003c/p\u003e\u003cp\u003e16S rRNA gene sequencing was performed using primers 341F (5\u0026prime;- CCTAYGGGRBGCASCAG \u0026minus;\u0026thinsp;3\u0026prime;) and 806R (5\u0026prime;- GGACTACNNGGGTATCTAAT \u0026minus;\u0026thinsp;3\u0026prime;) flanking the hypervariable V3 \u0026ndash; V4 regions [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The PCR reaction was set up in a total volume of 20 \u0026micro;l, consisting of 12.4 \u0026micro;l sterile distilled water, 0.4 \u0026micro;l dNTPs (10 \u0026micro;M), 4 \u0026micro;l 5\u0026times; HF buffer, 1 \u0026micro;l of each primer (10 \u0026micro;M), 1 \u0026micro;l template DNA, and 0.2 \u0026micro;l Phusion High-Fidelity DNA Polymerase (Thermo Scientific, Germany). The thermal cycling conditions included an initial denaturation at 98\u0026deg;C for 30 s, followed by 15 cycles of 98\u0026deg;C for 5 s, 56\u0026deg;C for 20 s, and 72\u0026deg;C for 20 s, with a final extension at 72\u0026deg;C for 5 min. Libraries were prepared following the manufacturer\u0026rsquo;s instructions (Illumina) and run on Illumina MiSeq platform (2 \u0026times; 250 bp). We aimed for a minimum of 0.1M raw reads per sample during sequencing. After quality filtering, chimera removal, and taxonomic assignment, the retained read counts per sample ranged from 2,567 to71,570. DNA extraction negative controls were included to detect potential contamination, however, all yielded DNA concentrations below the sequencing threshold and were thus not processed further.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNumber of samples per species and country included in this study. The numbers in parentheses represent the counts of collected adults and larvae, respectively.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCountry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAe. albopictus\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eAe. japonicus\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73 (35, 38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e162 (152, 10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItaly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48 (20, 28)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAustria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 (0, 28)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNetherlands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (20, 0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHungary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52 (17, 35)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (10, 10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (33, 0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJapan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64 (64, 0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e427\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eBioinformatic analysis:\u003c/h3\u003e\n\u003cp\u003eRaw reads were processed using Qiime2 (v2021.4) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], with denoising performed via DADA2 plugin in Qiime2 [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Taxonomic classification was conducted and amplicon sequence variants (ASVs) were inferred using DADA2 without clustering, and taxonomy was assigned against SILVA v138.1 (full-length, unclustered) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] at 97% identity thresholds. Sequences from non-target true bacterial taxa (Mitochondria, Chloroplast, Archaea, and Eukarya) were removed.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eBacterial diversity analysis:\u003c/h2\u003e\u003cp\u003eTo calculate diversity indices using a standardised sequencing depth, data were rarefied to the lowest sample read counts; 5,816 reads per sample. Alpha diversity indices (Chao1, Shannon and Evenness) were calculated using R, and beta diversity was assessed with Bray Curtis and visualised using Principal Coordinates Analysis (PCoA) using R software and the microbiome package [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn order to assess the observed taxa assignment in the entire dataset, a rarefaction curve was constructed. The number of reads per sample was taken into account. The rarefaction curve was generated using R, vegan package [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cem\u003eStatistical analysis of community structure and Indicator species analysis\u003c/em\u003e:\u003c/p\u003e\u003cp\u003eTo quantitatively assess the influence of mosquito species, developmental stage, and sampling location on the bacterial community structure, we conducted a permutational multivariate analysis of variance (PERMANOVA) based on BrayCurtis dissimilarity. Prior to analysis, the abundance matrix was filtered to remove samples with no detectable bacterial genera. The PERMANOVA was run using the adonis2 function in the vegan R package (v. 2.6-4) with 999 permutations, testing the effects of Species, Stage, and Location on microbial beta-diversity. BrayCurtis dissimilarities were calculated on normalized abundance data, with missing values treated as zeros.\u003c/p\u003e\u003cp\u003eAdditionally, we stratified the adult mosquito dataset by rearing method (wild-caught vs. lab-reared) to explore the potential influence of geographic location within each rearing group, acknowledging the confounding between rearing conditions between the countries in our dataset. Separate PERMANOVAs were conducted for wild-caught adults and lab-reared adults using Bray-Curtis dissimilarities and testing for the effect of country within each subset, Bray-Curtis dissimilarities were visualised in PCoA plots for each subset (Figure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e) among wild-caught and lab-reared adults across different countries.\u003c/p\u003e\u003cp\u003eTo identify bacterial genera associated with mosquito species, developmental stages, and geographic locations, we also conducted indicator species analysis using the multipatt function in the indicspecies R package (v.1.7.12). Analyses were based on the same filtered abundance table used for PERMANOVA (see above), with NAs replaced by zero and samples with total zero abundance removed. The association index used was the group-equalized IndVal.g statistic, with statistical significance assessed using 999 permutations. Significant indicator taxa (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were identified separately for mosquito species, developmental stage (larva vs. adult), and sampling location (country), and interpreted in the context of observed microbiota structure.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCorrelation analysis between host relatedness and gut bacterial communities:\u003c/h3\u003e\n\u003cp\u003eTo establish if there was any statistical correlation between the host relatedness and their corresponding bacterial communities, two matrices were compared per mosquito species: \u003cem\u003eAe. albopictus\u003c/em\u003e genetic relatedness matrix and a microbiota dissimilarity matrix (70 \u003cem\u003eAe. albopictus\u003c/em\u003e), and another with \u003cem\u003eAe. japonicus\u003c/em\u003e (136 \u003cem\u003eAe. japonicus\u003c/em\u003e) genetic relatedness matrix and a microbiota dissimilarity matrix. The relatedness matrix quantifies genetic kinship between host individuals based on microsatellite markers. As for \u003cem\u003eAe. albopictus\u003c/em\u003e, samples were screened for 19 microsatellite loci following conditions detailed in [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. As for \u003cem\u003eAe. japonicus\u003c/em\u003e, samples were screened for 18 microsatellites (7 specific for \u003cem\u003eAe. japonicus\u003c/em\u003e and 11 specific for \u003cem\u003eAe. albopictus\u003c/em\u003e) spread in 3 multiplexes. The first multiplex comprised 7 \u003cem\u003eAe. japonicus\u003c/em\u003e loci as described in [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], whereas the second and third multiplexes consisted of a total of 11 loci developed for use on \u003cem\u003eAe. albopictus\u003c/em\u003e [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. PCR conditions followed [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] as for the first multiplex, and [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] for the second and third multiplexes. The program ML-RELATE [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] was employed to calculate maximum likelihood estimates of relatedness between individuals, separately for each mosquito species. Relatedness coefficients between pairs of individuals go from 0 (totally unrelated) to 1 (clones or identical individuals). The microbiota dissimilarity matrix is based on our Bray Curtis analysis.\u003c/p\u003e\u003cp\u003eTo assess the relationship between host genetic relatedness and microbiota dissimilarity, we performed three statistical analyses: Mantel test, Procrustes analysis, and Redundancy Analysis (RDA) using Python (v3.11) and the following libraries: pandas (v1.5.3), numpy (v1.24.3), scipy (v1.10.1), and scikit-learn (v1.2.2). Mantel test was performed using Spearman correlation with 9,999 permutations to assess statistical correlation between host genetic relatedness and microbiota dissimilarity. Procrustes analysis was performed to evaluate structural similarity, using 10 principal components (PCA) before analysis. Lastly, to model microbiota dissimilarity as a function of host genetic relatedness, we performed RDA using Partial Least Squares Regression (PLS-R) with two components.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eSequencing output and taxonomic composition of the entire mosquito bacterial communities:\u003c/h2\u003e\u003cp\u003eA total of 12,404,833 reads were generated across all samples of both mosquito species. Our rarefaction analysis showed sufficient coverage of bacterial communities (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). At a 97% sequence similarity threshold, 24,807 amplicon sequence variants (ASVs) were identified. Following filtering to keep only high-quality reads, 20,257 ASVs remained, with per-sample reads ranging from 2,567 to 71,570. A subset of 4,093 ASVs remained unassigned, and 388 taxa were identified as chloroplasts, 61 as mitochondria, seven as archaea, and one as eukaryotic. The remaining ASVs were all assigned to a bacterial taxon.\u003c/p\u003e\u003cp\u003eForty-two bacterial phyla were identified across all mosquitoes (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), with the five most abundant being Proteobacteria (64,00%), Bacteridota (14,5%), Actinobacteriota (8,3%), Firmicutes (4,5%), and Cyanobacteria (2,1%) (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). At the genus level, 992 bacterial genera were detected (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). All bacterial genera were more similar in the adults compared to the larvae, regardless of mosquito origin (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, when examining \u003cem\u003eAe. japonicus\u003c/em\u003e, the only species sampled across multiple countries, we observed a clear signal of geographical separation in the larval microbiota (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This spatial structuring was still detectable in the adult microbiotas, though more diluted (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The top 5 most abundant genera were: \u003cem\u003eWolbachia\u003c/em\u003e (6%), \u003cem\u003eAcinetobacter\u003c/em\u003e (4,7%), \u003cem\u003eFlavobacterium\u003c/em\u003e (3,7%), \u003cem\u003eAeromonas\u003c/em\u003e (3,1%) and \u003cem\u003eLimnobacter\u003c/em\u003e (2,8%) (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). All of which are known to occupy \u003cem\u003eAedes\u003c/em\u003e bacterial communities, especially in the mosquito guts and reproductive organs [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. \u003cem\u003eWolbachia\u003c/em\u003e was detected in 50 of 73 \u003cem\u003eAe. albopictus\u003c/em\u003e samples, and in 16 of 427 \u003cem\u003eAe. japonicus\u003c/em\u003e samples. In \u003cem\u003eAe. japonicus\u003c/em\u003e, most of the detections came from the Spanish population, while only minor occurrences were observed in Italy, Japan, and the USA. This indicates that although \u003cem\u003eWolbachia\u003c/em\u003e is present in those populations, it is consistently dominant in \u003cem\u003eAe. albopictus\u003c/em\u003e, with a patchier and generally low-prevalence distribution in \u003cem\u003eAe. japonicus\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eBacterial genera richness, using Chao1 index, was the highest in the Spanish population, followed by Japan, Hungary, The Netherlands, USA, Italy, France and the lowest in the Austrian one (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). While Spain had the highest number of bacterial genera, and Austria the least, Chao1 index and other richness indices are largely influenced by the sample size [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] and the sequencing depth, and our Spanish population was the largest in the collection (235 mosquitoes). However, this correlation is not linear, for example The Netherlands and France had the same sample size (each had 20 mosquitoes in the dataset) yet the Netherlands had much higher bacterial richness compared to France (336 and 205 Chao1 index, respectively, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). As for bacterial genera diversity, Japan and The Netherlands had the highest bacterial diversity using Shannon index (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e) and the Austrian mosquito microbiotas were the least diverse. Community evenness, which measures how equally disturbed the bacterial abundances are, was assessed using the Evenness index representing the relative abundance of different species within the communities. Our evenness outputs showed similar trends, with the Netherlands and Japan having the most even bacterial community, while Spain and the USA were the least even (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Lower evenness in Spain and the USA suggests dominance by taxa like \u003cem\u003eWolbachia\u003c/em\u003e in Spain, while higher evenness in the Netherlands and Japan indicates a more balanced bacterial community.\u003c/p\u003e\u003cp\u003eFinally, across eight countries, 219 indicator taxa were identified, with clear biogeographic patterns: \u003cem\u003eCurvibacter\u003c/em\u003e and \u003cem\u003ePolynucleobacter\u003c/em\u003e were strongly linked to Austria, \u003cem\u003eJanthinobacterium\u003c/em\u003e to France, and \u003cem\u003eMethylobacterium\u003c/em\u003e and \u003cem\u003eErwinia\u003c/em\u003e to the Netherlands. These analyses confirm that mosquito-associated microbiotas are structured by host species, life stage, and local environmental conditions, offering robust support for the observed beta-diversity patterns.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDevelopmental stage effects on mosquito bacterial communities:\u003c/h3\u003e\n\u003cp\u003eComparisons between larvae and adult mosquitoes revealed clear stage-specific bacterial profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Venn diagram comparisons showed that France and Italy exhibited the largest overlap in bacterial genera between larvae and adults, whereas Hungary displayed a more divergent composition (Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e; Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). For example, in Italy, larvae and adults shared many bacterial genera, including \u003cem\u003eAcinetobacter\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eHydrogenophaga\u003c/em\u003e, and \u003cem\u003eNovosphingobium\u003c/em\u003e (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). However, relative abundances differed markedly between stages: adults were more frequently dominated by \u003cem\u003eAcinetobacter\u003c/em\u003e and \u003cem\u003eWolbachia\u003c/em\u003e, whereas larvae harboured higher levels of environmental taxa such as \u003cem\u003eLimnohabitans\u003c/em\u003e and \u003cem\u003eFlavobacterium\u003c/em\u003e. This indicates that while a core set of shared genera is maintained, developmental stage strongly influences the relative community composition. In the French population, \u003cem\u003eAe. japonicus\u003c/em\u003e larvae showed higher relative abundances of genera such as \u003cem\u003eChryseobacterium\u003c/em\u003e, \u003cem\u003eRhizobacter\u003c/em\u003e, \u003cem\u003eLimnohabitans\u003c/em\u003e, \u003cem\u003eNovosphingobium\u003c/em\u003e and \u003cem\u003eHydrogenophaga\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), some of which have been described in mosquito microbiome before [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] and others (\u003cem\u003eLimnohabitans\u003c/em\u003e, \u003cem\u003eNovosphingobium\u003c/em\u003e and \u003cem\u003eHydrogenophaga\u003c/em\u003e) are common in aquatic environments [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], consistent with their exposure to waterborne bacteria and a detritus‐rich diet. While \u003cem\u003eAe. japonicus\u003c/em\u003e adults from the same French population were enriched in bacteria such as \u003cem\u003eNevskia\u003c/em\u003e and \u003cem\u003eMethylobacterium\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), which are known to inhabit \u003cem\u003eAedes\u003c/em\u003e species microbiomes [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], and commonly found in soil and environment bacterial communities [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. However, their role in hematophagous insects remains unknown, or maybe they are better adapted to the gut conditions associated with sugar feeding and blood ingestion in adult mosquitoes. Additionally, those adult-abundant genera are often found in environmental samples, raising the question of whether these bacteria could be externally adhered to the mosquitoes and not actually innate to the mosquito guts. \u003cem\u003ePectobacterium\u003c/em\u003e was found only in one adult \u003cem\u003eAe. japonicus\u003c/em\u003e from this population where it dominated the entire gut (74% of that gut bacterial community - Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). \u003cem\u003ePectobacterium\u003c/em\u003e has been reported before in \u003cem\u003eAnopheles\u003c/em\u003e mosquitoes [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] and its role remains to be characterised in mosquito guts.\u003c/p\u003e\u003cp\u003eA similar pattern of distinct larval and adult bacterial communities was also observed in the other populations included here: Hungary and Spain, where both stages were collected and from both mosquito species (\u003cem\u003eAe. japonicus\u003c/em\u003e and \u003cem\u003eAe. albopictus\u003c/em\u003e) (details in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e; Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e; Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese findings support previous studies that have reported large shifts in microbiota composition during metamorphosis (e.g., [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]), suggesting that the transition from an aquatic to a terrestrial niche strongly selects for different bacterial assemblages, and that environmental flux of bacteria are probably the main driver to such separation in bacterial composition between larvae and adult mosquitoes.\u003c/p\u003e\u003cp\u003e\u003cem\u003eGeographic and species-level variation in\u003c/em\u003e Ae. japonicus:\u003c/p\u003e\u003cp\u003eThe identified bacterial genera across \u003cem\u003eAe. japonicus\u003c/em\u003e mosquitoes (only \u003cem\u003eAe. japonicus\u003c/em\u003e was collected form all the included countries) from Austria, Spain, France, Hungary, Italy, Japan, the USA, and the Netherlands revealed signals of geographic heterogeneity. However, those signals were diluted in parts, i.e., communities showed higher similarities from different countries. This is likely due to several taxa that are relatively stable in their relative abundances across samples from different countries. For example, \u003cem\u003ePseudomonas\u003c/em\u003e and \u003cem\u003eAcinetobacter\u003c/em\u003e were widely present at similar proportions across most countries, including Austria, Spain, Italy, Hungary, Japan, and the USA (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Other genera with broad geographic distributions included \u003cem\u003eFlavobacterium\u003c/em\u003e, \u003cem\u003eAeromonas\u003c/em\u003e, and \u003cem\u003eNovosphingobium\u003c/em\u003e, which were consistently detected across multiple regions, particularly in larvae.\u003c/p\u003e\u003cp\u003eIntra-country and micro\u0026ndash;geographical variations were also observed. For example, in Hungary, even within a remarkably confined geographic area (with collection sites separated by at most 4.3 km), apparent differences in \u003cem\u003eAe. japonicus\u003c/em\u003e-associated bacterial communities were observed. Larval samples collected on the same day from cemeteries in Szalafő, Keserűszer, and Őriszentp\u0026eacute;ter exhibited distinct bacterial profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). For example, \u003cem\u003eXanthobacter\u003c/em\u003e, that is previously described in mosquito larvae [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], and water-abundant Actinobacteria PeM15 [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] were more abundant in Szalafő (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Moreover, adult mosquitoes collected in Kov\u0026aacute;cssz\u0026eacute;n\u0026aacute;ja displayed considerable variability among themselves; \u003cem\u003ee.g.\u003c/em\u003e, some individuals harboured notably higher relative abundances of genera such as \u003cem\u003eAeromonas\u003c/em\u003e and \u003cem\u003eAcinetobacter\u003c/em\u003e compared to others from the same area. This is suggesting that subtle microhabitat differences, perhaps variations in water chemistry, soil composition, or organic matter inputs, can substantially influence the composition of the mosquito microbiotas. It is worth mentioning that these differences could stem either from the differences between larval and adult microbiotas or from local environmental factors. In several locations in Hungary, only one developmental stage was represented (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), which makes it difficult to attribute the observed variation solely to geographic differences. Variation in water quality, nutrient availability, and organic matter may favour distinct bacterial taxa in those breeding sites. Aquatic-associated genera (e.g., \u003cem\u003eLimnohabitans\u003c/em\u003e, \u003cem\u003eHydrogenophaga\u003c/em\u003e) were more prominent in some larval samples, while bacteria that tolerate the gut environment of adults (e.g., \u003cem\u003eAcinetobacter\u003c/em\u003e) became enriched after metamorphosis.\u003c/p\u003e\u003cp\u003eSimilar patterns of distinct bacterial communities between within-county locations were also observed in other countries, e.g., USA, Austria, Japan, Italy and Spain ( Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e ).For example, in the USA, adult mosquito microbiotas formed two dissimilar clusters between the two collection sites in the USA: St. Louis, Missouri, and College Park, Maryland, which are geographically distant (~\u0026thinsp;1,100 km) and represent ecologically different environments, suburban woodland edges versus urban/residential neighbourhoods (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In Austria, all larvae were collected from a single locality (Althofen), but clustering patterns in the ordination plot likely reflect intra-site variation due to microhabitat heterogeneity (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Similarly, in Clauzetto, Italy, larvae were collected from two nearby but separate sites\u0026thinsp;~\u0026thinsp;1.7 km apart, possibly reflecting small-scale environmental differences such as water chemistry or organic matter (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In Japan, \u003cem\u003eAe. japonicus\u003c/em\u003e adult microbiotas showed two dissimilar clusters: those from Saga (collected in 2018) differed from those collected in Ishikawa and Sapporo in 2021, suggesting temporal effects may have contributed to the observed variation (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). These findings suggest that both large- and small-scale geographic factors, including habitat type and collection time, influence the mosquito microbiota.\u003c/p\u003e\u003cp\u003eHowever, also in some of those countries, variations were observed due to the different developmental stages: e.g., Italy and Spain (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e; Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This suggests that developmental stage plays a substantial role in shaping the microbiota, yet because different locations were often associated with different stages, we cannot fully disentangle the effects of stage versus location without further controlled sampling.\u003c/p\u003e\u003cp\u003eWolbachia \u003cem\u003eand host\u0026ndash;symbiont dynamics\u003c/em\u003e:\u003c/p\u003e\u003cp\u003e\u003cem\u003eWolbachia\u003c/em\u003e is one of the most intensively studied endosymbionts in mosquitoes because of its capacity to e.g. manipulate reproduction (e.g., cytoplasmic incompatibility) and reduce vector competence for pathogens [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. In our dataset, \u003cem\u003eWolbachia\u003c/em\u003e was often dominant in \u003cem\u003eAe. albopictus\u003c/em\u003e, being detected in 50 of 73 individuals with relative abundances up to ~\u0026thinsp;98.5%. In contrast, \u003cem\u003eWolbachia\u003c/em\u003e was much less common in \u003cem\u003eAe. japonicus\u003c/em\u003e, detected in only 16 of 427 individuals, with the highest abundances observed in Spain and only sporadic, low-level detections in Italy, Japan, and the USA (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). To our knowledge, this represents the first report of \u003cem\u003eWolbachia\u003c/em\u003e in \u003cem\u003eAe. japonicus\u003c/em\u003e.\u003c/p\u003e\u003cp\u003ePrevious studies have also reported that \u003cem\u003eWolbachia\u003c/em\u003e dominates \u003cem\u003eAe. albopictus\u003c/em\u003e adults [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. As an intracellular, maternally transmitted symbiont, \u003cem\u003eWolbachia\u003c/em\u003e may be present at very low titers in early developmental stages and then increase as the host matures, which may explain its consistent presence in adults but very scarce presence in larvae in our study.. The high \u003cem\u003eWolbachia\u003c/em\u003e abundance observed in \u003cem\u003eAe. albopictus\u003c/em\u003e adults from Spain aligns with previous studies showing that this species frequently harbours stable \u003cem\u003eWolbachia\u003c/em\u003e infections [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. In contrast, \u003cem\u003eWolbachia\u003c/em\u003e was never detected in \u003cem\u003eAe. japonicus\u003c/em\u003ebefore, and the patchy positive detections in our dataset may reflect transient infections or environmental contamination, though this requires further validation.\u003c/p\u003e\u003cp\u003eHowever, the presence of native \u003cem\u003eWolbachia\u003c/em\u003e in both \u003cem\u003eAe. albopictus\u003c/em\u003e and \u003cem\u003eAe. japonicus\u003c/em\u003e here requires further investigation, unlike transinfections used in vector control programs, native strains may have limited or no impact on vector survival and arbovirus transmission. As such, the functional relevance of these infections remains to be experimentally validated.\u003c/p\u003e\u003cp\u003e\u003cem\u003eComparison of bacterial genera abundance between\u003c/em\u003e Ae. albopictus \u003cem\u003eand\u003c/em\u003e Ae. japonicus \u003cem\u003efrom Spain\u003c/em\u003e:\u003c/p\u003e\u003cp\u003eA comparative analysis of the bacterial communities in the two species showed that \u003cem\u003eAe. japonicus\u003c/em\u003e has higher bacterial diversity and more even compared to \u003cem\u003eAe. albopictus\u003c/em\u003e from the Spanish population, where only the two species were collected (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), similar to previous studies where \u003cem\u003eAe. albopictus\u003c/em\u003e microbiota was compared to other \u003cem\u003eAedes\u003c/em\u003e species [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. \u003cem\u003eAe. japonicus\u003c/em\u003e exhibited distinct bacterial signatures compared to \u003cem\u003eAe. albopictus\u003c/em\u003e, consistent across several countries. Given the limited number of studies on \u003cem\u003eAe. japonicus\u003c/em\u003e, these findings add largely to the sparse literature and underscore the species-specific structuring of its microbiota. Microbiota comparison showed that several bacterial genera exhibit strong, species-specific associations (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e ; Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e ). Consistent with the indicator taxa analysis, several genera differentiated the two species within the Spanish populations. \u003cem\u003eWolbachia\u003c/em\u003e and \u003cem\u003eComamonas\u003c/em\u003e were strongly associated with \u003cem\u003eAe. albopictus\u003c/em\u003e, with \u003cem\u003eWolbachia\u003c/em\u003e dominating many individuals, whereas \u003cem\u003eAeromonas\u003c/em\u003e and \u003cem\u003eLimnohabitans\u003c/em\u003e were significantly associated with \u003cem\u003eAe. japonicus\u003c/em\u003e, particularly in larval samples (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). \u003cem\u003eCutibacterium\u003c/em\u003e was also detected at low abundance in \u003cem\u003eAe. japonicus\u003c/em\u003e but was largely absent from \u003cem\u003eAe. albopictus\u003c/em\u003e. These taxa exemplify species-specific structuring of the microbiota in Spain. Such contrasting patterns suggest that \u003cem\u003eWolbachia\u003c/em\u003e as an example, as an obligate intracellular symbiont, is maintained by vertical transmission and thus strongly tied to \u003cem\u003eAe. albopictus\u003c/em\u003e biology, whereas taxa such as \u003cem\u003eAeromonas\u003c/em\u003e, \u003cem\u003eLimnohabitans\u003c/em\u003e, \u003cem\u003eCutibacterium\u003c/em\u003e, and \u003cem\u003eComamonas\u003c/em\u003e are environmental or host-associated bacteria that may be selectively enriched in \u003cem\u003eAe. japonicus\u003c/em\u003e through differences in mosquito habitats, cuticle-associated communities, or host physiology. In contrast, some genera appear in both species, indicating the presence of a shared bacterial community. Nevertheless, the species‐specific enrichment of taxa may help drive the overall divergence in microbial communities between the two mosquito species, even though the extent of variation in some taxa also reflects environmental and developmental influences that complicate a straightforward interpretation.\u003c/p\u003e\u003cp\u003eWhile host species identity contributes to defining the microbiotas, the variability observed within species is likely driven, at least in part, by spatial heterogeneity at the local scale within each species (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), as shown by these Spanish mosquitoes, which were collected from 23 different locations.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eCorrelation between host relatedness and bacterial community composition:\u003c/h2\u003e\u003cp\u003eMantel test (Spearman correlation, 9,999 permutations) yielded a weak, non-significant correlation for \u003cem\u003eAe. albopictus\u003c/em\u003e (r = -0.0614, p\u0026thinsp;=\u0026thinsp;0.9988), suggesting no strong relationship between host genetic relatedness and bacterial community composition. Similarly, Procrustes analysis showed a high disparity score (0.8599), indicating weak structural correspondence between the two datasets. However, redundancy analysis suggested that host genetics might influence microbiota composition to some extent, with the first two constrained components explaining 60.8% and 39.2% of the variance, respectively.\u003c/p\u003e\u003cp\u003eFor \u003cem\u003eAe. japonicus\u003c/em\u003e, the Mantel test also showed a weak yet statistically significant negative correlation (r = -0.043, p\u0026thinsp;=\u0026thinsp;0.0003), suggesting a minor inverse relationship between host genetic relatedness and microbiota dissimilarity. Procrustes analysis yielded a similarly high disparity score (0.94), supporting weak structural alignment. Redundancy analysis indicated a possible influence of host genetics on microbiota composition, with the first two components explaining 45.3% and 54.7% of the variance. These findings suggest that while host genetics may contribute to microbiota structuring, it does not appear to be a dominant factor shaping mosquito bacterial communities in either species.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMosquito as reservoir for opportunistic and pathogenic bacteria:\u003c/h3\u003e\n\u003cp\u003eThe mosquitoes harboured a diverse bacterial community, including genera with known pathogenic potential, which may contribute to their role as disease vectors. In our dataset, genera such as \u003cem\u003eAeromonas\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eSerratia\u003c/em\u003e, \u003cem\u003eElizabethkingia\u003c/em\u003e, \u003cem\u003eKlebsiella\u003c/em\u003e, \u003cem\u003eEnterobacter\u003c/em\u003e, and \u003cem\u003eAcinetobacter\u003c/em\u003e are present, all of which have been implicated in human and animal infections. For instance, \u003cem\u003eAeromonas\u003c/em\u003e species are known to be water-derived species and also linked to fish and human infection [\u003cspan additionalcitationids=\"CR68\" citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. \u003cem\u003ePseudomonas\u003c/em\u003e includes opportunistic pathogens such as \u003cem\u003eP. aeruginosa\u003c/em\u003e associated with respiratory infections and sepsis [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. \u003cem\u003eSerratia\u003c/em\u003e species has been reported in hospital-acquired infections, particularly in immunocompromised patients [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. \u003cem\u003eElizabethkingia\u003c/em\u003e was found in the Spanish \u003cem\u003eAe. japonicus\u003c/em\u003e from one location only (Alava) and Japan (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This genus has been reported in \u003cem\u003eAnopheles gambiae\u003c/em\u003e reproductive organs and suggested to aid in sugar degradation [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e], one bacterial species in this genus was originally isolated from \u003cem\u003eAnopheles\u003c/em\u003e mosquitoes, has emerged as a cause of neonatal meningitis and bloodstream infections [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Enteric bacteria such as \u003cem\u003eKlebsiella\u003c/em\u003e and \u003cem\u003eEnterobacter\u003c/em\u003e species, which have been detected in mosquitoes, are particularly concerning due to their ability to acquire and disseminate antimicrobial resistance (AMR) genes [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. \u003cem\u003eAcinetobacter\u003c/em\u003e was widespread in our mosquito microbiotas and can include \u003cem\u003eA. baumannii\u003c/em\u003e, a major nosocomial pathogen and carrier of several antimicrobial resistance genes, making it a serious public health threat [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. More specifically, one sample from the Netherlands contained \u003cem\u003eBartonella\u003c/em\u003e with high relative abundance (2.87%, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). \u003cem\u003eBartonella\u003c/em\u003e is known to cause infections in several mammalian hosts. In humans, it causes symptoms such as fever, rashes, and headaches but it can also lead to serious endocarditis [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. One \u003cem\u003eAe. albopictus\u003c/em\u003e from Spain showed a high relative abundance (64.47%) of \u003cem\u003eBordetella\u003c/em\u003e. \u003cem\u003eBordetella\u003c/em\u003e is a genus that includes \u003cem\u003eB. pertussis\u003c/em\u003e, the causative agent of whooping cough [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. No prior records were found that mosquitos are associated with the transmission of this pathogen though. \u003cem\u003eMycobacterium\u003c/em\u003e was found in 71 mosquitoes with various abundances (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), and it was more abundant in mosquitoes from France and Japan. Species of \u003cem\u003eMycobacterium\u003c/em\u003e in mosquitoes were linked to outbreaks of Buruli ulcer in Australia [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Arthropod-derived pathogenic bacteria, such as \u003cem\u003eBorrelia\u003c/em\u003e and \u003cem\u003eFrancisella\u003c/em\u003e or \u003cem\u003eAnaplasma\u003c/em\u003e and \u003cem\u003eEhrlichia\u003c/em\u003e, which can cause Anaplasmosis and Ehrlichiosis respectively, that are usually transmitted by other arthropods like ticks [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e], were also not identified in our mosquitoes.\u003c/p\u003e\u003cp\u003eWhile the direct role of mosquitoes in the transmission of these bacteria remains to be fully elucidated, possible pathways include mechanical transfer during feeding, regurgitation, and faecal deposition, which may facilitate the environmental spread of these bacteria and associated AMR genes. The presence of these bacteria in both larval and adult stages, with stage-specific patterns regardless of the rearing conditions, suggests that developmental stage may influence the potential of mosquitoes to act as reservoirs or vectors for these bacteria, which is relevant for vector ecology and AMR surveillance efforts.\u003c/p\u003e\u003cp\u003eGiven the increasing interest in the use of mosquito-based surveillance for monitoring environmental AMR and pathogen circulation, our findings support the potential of mosquitoes to serve as bioindicators of environmental bacterial diversity and AMR gene presence across diverse geographic regions. Future studies employing metagenomic sequencing will be essential to determine the functional potential of these mosquito-associated microbiomes and to clarify their role in pathogen transmission and AMR gene dissemination.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWhile the majority of the bacterial taxa in the two mosquito species were commensal, other reports suggested that most of which are acquired from the surrounding environment to the vertebrate hosts [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Yet distinct structuring occurs across developmental stages, geography, and local sites, with species identity having a lesser impact on the microbiota. Mosquitoes therefore act as ecological filters, selecting bacterial taxa that perhaps best support their physiological needs at different life stages. For example, during the larval stage, mosquito microbiomes are dominated by aquatic and detritus-associated bacteria, such as \u003cem\u003eFlavobacterium\u003c/em\u003e, \u003cem\u003eLimnohabitans\u003c/em\u003e, and \u003cem\u003eHydrogenophaga\u003c/em\u003e. These taxa are known for their roles in nutrient cycling in aquatic environments, as mentioned above. \u003cem\u003eFlavobacterium\u003c/em\u003e spp., in particular, have been linked to nutrient acquisition and digestion of complex polysaccharides [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e], which may aid larvae in extracting energy from nutrient-poor breeding sites.\u003c/p\u003e\u003cp\u003eUpon metamorphosis into adults, the microbiota shifts to taxa better suited for a terrestrial and sugar-rich diet. Genera such as \u003cem\u003eAcinetobacter\u003c/em\u003e and \u003cem\u003eMethylobacterium\u003c/em\u003e, both commonly found in soil, become dominant, reflecting the transition from aquatic feeding to sugar and blood feeding. \u003cem\u003eAcinetobacter\u003c/em\u003e species are known for their ability to metabolize sugars and hydrocarbons [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e], which may support energy metabolism in nectar-feeding adult mosquitoes. This shift aligns with the hypothesis that mosquito bacterial communities are not static but dynamically selected based on functional needs. In addition, this is the first report, to our knowledge, to detect \u003cem\u003eWolbachia\u003c/em\u003e in \u003cem\u003eAe. japonicus\u003c/em\u003e in seven mosquitoes from the USA. Further investigations are required to describe the extent of such pattern.\u003c/p\u003e\u003cp\u003eThe geographic structuring of bacterial communities, among mosquitoes of the same species, suggests that local environmental conditions strongly influence the bacterial composition. This makes mosquito microbiota a potential bioindicator of environmental bacterial diversity, pollution levels, and even AMR gene circulation in different ecosystems.\u003c/p\u003e\u003cp\u003eWe retained the lab-reared adult samples in our analysis to maximise geographic and host phylogenetic coverage, despite the acknowledged confounding with country of origin. Including these samples allows for robust comparisons across countries within the same host taxon, which is essential for understanding microbiome structuring across broad geographic scales.\u003c/p\u003e\u003cp\u003eThe presence of potentially pathogenic bacteria in our mosquitoes, reported also in previous studies [\u003cspan additionalcitationids=\"CR62 CR63 CR64 CR65 CR66 CR67 CR68 CR69 CR70 CR71\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e], suggests that mosquitoes can act as reservoirs or carriers of bacterial pathogens, including AMR genes. The transmission of these bacteria could occur through direct contact, regurgitation, or faecal deposition during feeding, potentially facilitating the spread of AMR genes between hosts and environments. Understanding the composition of mosquito-associated bacterial communities is essential for assessing their potential role in the dissemination of bacterial infections and AMR genes.\u003c/p\u003e\u003cp\u003efuture research should also aim to have a more balanced sample size per country as the mosquitoes here were exploited and available as part of a previous project on mosquito population dynamics. As this research aimed to investigate bacterial community presence and structure, future research should focus on how these microbial associations impact mosquito vector competence and the transmission dynamics of both symbiotic and importantly pathogenic bacteria, using metagenomics approach to investigate the gene content of such communities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interests:\u003c/h2\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u0026nbsp;\u003c/strong\u003edeclaration: Not applicable.\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eThis work was supported mainly by Horizon 2020 grant VEO (874735). The collection of mosquito specimens in Hungary was supported by the National Research, Development and Innovation Office, grant numbers FK-138563 and RRF-2.3.1-21-2022-00010 \u0026ldquo;National Laboratory of Virology\u0026rdquo;. The collection of mosquito specimens in Spain was supported by the Human-Mosquito Interaction Project, funded by the European Research Council (ERC) under the European Union\u0026rsquo;s Horizon 2020 research and innovation programme (Grant agreement No. 853271).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eS.O., F.L., M.V., F.B., and F.M.A., contributed to the concept and design of the study. K.BL., F.M., K.M.W., M.A.G., Z.S., P.T.L., K.K., N.T., J.R.B.P., F.S., S.DE., M.M., R.E., I.RA., J.F.B., and A.C. collected the mosquito samples from all the countries included in the study. J.C., and F.L. performed laboratory work and microsatellite analyses. S.O., R.E., and F.D.M., performed the sequencing and the bioinformatic and statistical analyses. S.O., and R.E., made the figures. S.O., and F.M.A., wrote the first draft of the manuscript. All authors contributed to the writing and read and approved the submitted manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe are thankful to Hanne Mordhorst for the assistance in the laboratory.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eAll 16S rRNA data are available at the European Nucleotide Archive (ENA) with the project accession number: ERX13771555-ERX13772064.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWeaver SC, Charlier C, Vasilakis N, Lecuit M (2018) Zika, Chikungunya, and Other Emerging Vector-Borne Viral Diseases. Annu Rev Med 69:395\u0026ndash;408. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev-med-050715-105122\u003c/span\u003e\u003cspan address=\"10.1146/annurev-med-050715-105122\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBraack L, Gouveia De Almeida AP, Cornel AJ, et al (2018) Mosquito-borne arboviruses of African origin: review of key viruses and vectors. Parasites Vectors 11:29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13071-017-2559-9\u003c/span\u003e\u003cspan address=\"10.1186/s13071-017-2559-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSingh H, Singh OP, Akhtar N, et al (2019) First report on the transmission of Zika virus by Aedes (Stegomyia) aegypti (L.) (Diptera: Culicidae) during the 2018 Zika outbreak in India. Acta Tropica 199:105114. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.actatropica.2019.105114\u003c/span\u003e\u003cspan address=\"10.1016/j.actatropica.2019.105114\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJohnson PDR, Azuolas J, Lavender CJ, et al (2007) \u003cem\u003eMycobacterium ulcerans\u003c/em\u003e in Mosquitoes Captured during Outbreak of Buruli Ulcer, Southeastern Australia. Emerg Infect Dis 13:1653\u0026ndash;1660. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3201/eid1311.061369\u003c/span\u003e\u003cspan address=\"10.3201/eid1311.061369\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDieme C, Bechah Y, Socolovschi C, et al (2015) Transmission potential of \u003cem\u003eRickettsia felis\u003c/em\u003e infection by \u003cem\u003eAnopheles gambiae\u003c/em\u003e mosquitoes. Proc Natl Acad Sci USA 112:8088\u0026ndash;8093. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1413835112\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1413835112\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTchoumbou MA, Mayi MPA, Malange ENF, et al (2020) Effect of deforestation on prevalence of avian haemosporidian parasites and mosquito abundance in a tropical rainforest of Cameroon. International Journal for Parasitology 50:63\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijpara.2019.10.006\u003c/span\u003e\u003cspan address=\"10.1016/j.ijpara.2019.10.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAntonio-nkondjio C, Kerah CH, Simard F, et al (2006) Complexity of the Malaria Vectorial System in Cameroon: Contribution of Secondary Vectors to Malaria Transmission. J Med Entomol 43:1215\u0026ndash;1221. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jmedent/43.6.1215\u003c/span\u003e\u003cspan address=\"10.1093/jmedent/43.6.1215\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eG\u0026oacute;mez M, Martinez D, Mu\u0026ntilde;oz M, Ram\u0026iacute;rez JD (2022) Aedes aegypti and Ae. albopictus microbiome/virome: new strategies for controlling arboviral transmission? Parasites Vectors 15:287. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13071-022-05401-9\u003c/span\u003e\u003cspan address=\"10.1186/s13071-022-05401-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIzri A, Bitam I, Charrel RN (2011) First entomological documentation of Aedes (Stegomyia) albopictus (Skuse, 1894) in Algeria. Clinical Microbiology and Infection 17:1116\u0026ndash;1118. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1469-0691.2010.03443.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1469-0691.2010.03443.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBenallal KE, Allal-Ikhlef A, Benhamouda K, et al (2016) First report of Aedes (Stegomyia) albopictus (Diptera: Culicidae) in Oran, West of Algeria. Acta Tropica 164:411\u0026ndash;413. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.actatropica.2016.09.027\u003c/span\u003e\u003cspan address=\"10.1016/j.actatropica.2016.09.027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShoukry NM, Elwan MA, Morsy TA (2012) Aedes aegypti (Linnaeus) re-emerging in southern Egypt. J Egypt Soc Parasitol 42:41\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.12816/0006293\u003c/span\u003e\u003cspan address=\"10.12816/0006293\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeji M, Rhim A, Roiz D, Bouattour A (2017) Ecophysiological characterization and molecular differentiation of Culex pipiens forms (Diptera: Culicidae) in Tunisia. Parasit Vectors 10:327. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13071-017-2265-7\u003c/span\u003e\u003cspan address=\"10.1186/s13071-017-2265-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMikhail MW, Al-Bursheed KM, Abd El-Halim AS, Morsy TA (2009) Studies on mosquito borne dieases in Egypt and Qatar. J Egypt Soc Parasitol 39:745\u0026ndash;756\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGratz NG (2004) Critical review of the vector status of \u003cem\u003eAedes albopictus\u003c/em\u003e. Medical Vet Entomology 18:215\u0026ndash;227. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.0269-283X.2004.00513.x\u003c/span\u003e\u003cspan address=\"10.1111/j.0269-283X.2004.00513.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdhami J, Reiter P (1998) Introduction and establishment of Aedes (Stegomyia) albopictus skuse (Diptera: Culicidae) in Albania. J Am Mosq Control Assoc 14:340\u0026ndash;343\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKoban MB, Kampen H, Scheuch DE, et al (2019) The Asian bush mosquito Aedes japonicus japonicus (Diptera: Culicidae) in Europe, 17 years after its first detection, with a focus on monitoring methods. Parasites Vectors 12:109. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13071-019-3349-3\u003c/span\u003e\u003cspan address=\"10.1186/s13071-019-3349-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchaffner F, Chouin S, Guilloteau J (2003) First record of Ochlerotatus (Finlaya) japonicus japonicus (Theobald, 1901) in metropolitan France. J Am Mosq Control Assoc 19:1\u0026ndash;5\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCansado-Utrilla C, Zhao SY, McCall PJ, et al (2021) The microbiome and mosquito vectorial capacity: rich potential for discovery and translation. Microbiome 9:111. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40168-021-01073-2\u003c/span\u003e\u003cspan address=\"10.1186/s40168-021-01073-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDavid MR, Santos LMBD, Vicente ACP, Maciel-de-Freitas R (2016) Effects of environment, dietary regime and ageing on the dengue vector microbiota: evidence of a core microbiota throughout Aedes aegypti lifespan. Mem Inst Oswaldo Cruz 111:577\u0026ndash;587. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1590/0074-02760160238\u003c/span\u003e\u003cspan address=\"10.1590/0074-02760160238\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMuturi EJ, Ramirez JL, Rooney AP, Kim C-H (2017) Comparative analysis of gut microbiota of mosquito communities in central Illinois. PLoS Negl Trop Dis 11:e0005377. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pntd.0005377\u003c/span\u003e\u003cspan address=\"10.1371/journal.pntd.0005377\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRodpai R, Boonroumkaew P, Sadaow L, et al (2023) Microbiome Composition and Microbial Community Structure in Mosquito Vectors Aedes aegypti and Aedes albopictus in Northeastern Thailand, a Dengue-Endemic Area. Insects 14:184. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/insects14020184\u003c/span\u003e\u003cspan address=\"10.3390/insects14020184\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMelo T, Sousa CA, Delacour-Estrella S, et al (2024) Characterization of the microbiome of Aedes albopictus populations in different habitats from Spain and S\u0026atilde;o Tom\u0026eacute;. Sci Rep 14:20545. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-024-71507-y\u003c/span\u003e\u003cspan address=\"10.1038/s41598-024-71507-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eScolari F, Casiraghi M, Bonizzoni M (2019) Aedes spp. and Their Microbiota: A Review. Front Microbiol 10:2036. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmicb.2019.02036\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2019.02036\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMuturi EJ, Bara JJ, Rooney AP, Hansen AK (2016) Midgut fungal and bacterial microbiota of Aedes triseriatus and Aedes japonicus shift in response to La Crosse virus infection. Mol Ecol 25:4075\u0026ndash;4090. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/mec.13741\u003c/span\u003e\u003cspan address=\"10.1111/mec.13741\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZotzmann S, Steinbrink A, Schleich K, et al (2017) Bacterial diversity of cosmopolitan Culex pipiens and invasive Aedes japonicus from Germany. Parasitol Res 116:1899\u0026ndash;1906. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00436-017-5466-2\u003c/span\u003e\u003cspan address=\"10.1007/s00436-017-5466-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJuma EO, Allan BF, Kim C-H, et al (2021) The larval environment strongly influences the bacterial communities of Aedes triseriatus and Aedes japonicus (Diptera: Culicidae). Sci Rep 11:7910. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-021-87017-0\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-87017-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFerreira QR, Lemos FFB, Moura MN, et al (2023) Role of the Microbiome in Aedes spp. Vector Competence: What Do We Know? Viruses 15:779. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/v15030779\u003c/span\u003e\u003cspan address=\"10.3390/v15030779\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMinard G, Tran FH, Van VT, et al (2015) French invasive Asian tiger mosquito populations harbor reduced bacterial microbiota and genetic diversity compared to Vietnamese autochthonous relatives. Front Microbiol 6:. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmicb.2015.00970\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2015.00970\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRosso F, Tagliapietra V, Albanese D, et al (2018) Reduced diversity of gut microbiota in two Aedes mosquitoes species in areas of recent invasion. Sci Rep 8:16091. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-018-34640-z\u003c/span\u003e\u003cspan address=\"10.1038/s41598-018-34640-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee JM, Yek SH, Wilson RF, Rahman S (2020) Characterization of the Aedes albopictus (Diptera: Culicidae) holobiome: bacterial composition across land use type and mosquito sex in Malaysia. Acta Tropica 212:105683. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.actatropica.2020.105683\u003c/span\u003e\u003cspan address=\"10.1016/j.actatropica.2020.105683\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKang X, Wang Y, Li S, et al (2020) Comparative Analysis of the Gut Microbiota of Adult Mosquitoes From Eight Locations in Hainan, China. Front Cell Infect Microbiol 10:596750. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fcimb.2020.596750\u003c/span\u003e\u003cspan address=\"10.3389/fcimb.2020.596750\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSeabourn P, Spafford H, Yoneishi N, Medeiros M (2020) The Aedes albopictus (Diptera: Culicidae) microbiome varies spatially and with Ascogregarine infection. PLoS Negl Trop Dis 14:e0008615. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pntd.0008615\u003c/span\u003e\u003cspan address=\"10.1371/journal.pntd.0008615\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMedeiros MCI, Seabourn PS, Rollins RL, Yoneishi NM (2022) Mosquito Microbiome Diversity Varies Along a Landscape-Scale Moisture Gradient. Microb Ecol 84:893\u0026ndash;900. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00248-021-01865-x\u003c/span\u003e\u003cspan address=\"10.1007/s00248-021-01865-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJuma EO, Allan BF, Kim C-H, et al (2020) Effect of life stage and pesticide exposure on the gut microbiota of Aedes albopictus and Culex pipiens L. Sci Rep 10:9489. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-020-66452-5\u003c/span\u003e\u003cspan address=\"10.1038/s41598-020-66452-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArif W, Thangaraj G, Srinivasan P, et al (2024) Culex quinquefasciatus Mosquitoes Resist Acquisition of Methicillin-Resistant Staphylococcus aureus: Insights from Field and Laboratory Studies. Antibiotics (Basel) 13:618. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/antibiotics13070618\u003c/span\u003e\u003cspan address=\"10.3390/antibiotics13070618\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbdolmaleki Z, Mashak Z, Safarpoor Dehkordi F (2019) Phenotypic and genotypic characterization of antibiotic resistance in the methicillin-resistant Staphylococcus aureus strains isolated from hospital cockroaches. Antimicrob Resist Infect Control 8:54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13756-019-0505-7\u003c/span\u003e\u003cspan address=\"10.1186/s13756-019-0505-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLowe CF, Romney MG (2011) Bedbugs as vectors for drug-resistant bacteria. Emerg Infect Dis 17:1132\u0026ndash;1134. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3201/eid/1706.101978\u003c/span\u003e\u003cspan address=\"10.3201/eid/1706.101978\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang W, Wang S, Jacobs-Lorena M (2020) Use of Microbiota to Fight Mosquito-Borne Disease. Front Genet 11:196. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fgene.2020.00196\u003c/span\u003e\u003cspan address=\"10.3389/fgene.2020.00196\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMogi M, Armbruster PA, Tuno N (2020) Differences in Responses to Urbanization Between Invasive Mosquitoes, Aedes japonicus japonicus (Diptera: Culicidae) and Aedes albopictus, in Their Native Range, Japan. J Med Entomol 57:104\u0026ndash;112. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jme/tjz145\u003c/span\u003e\u003cspan address=\"10.1093/jme/tjz145\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eECDC (2012) Guidelines for the surveillance of invasive mosquitoes in Europe. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ecdc.europa.eu/en/publications-data/guidelines-surveillance-invasive-mosquitoes-europe\u003c/span\u003e\u003cspan address=\"https://www.ecdc.europa.eu/en/publications-data/guidelines-surveillance-invasive-mosquitoes-europe\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBecker (2020) Mosquitoes. Cham: Springer International Publishing; 2020.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eG\u0026uuml;nay F, Picard M, Robert V. (2020) MosKeyTool, an interactive identification key for mosquitoes of Euro-Mediterranean. Version 2.2. Available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.medilabsecure.com/entomology-tools-0/moskeytool#How%20to%20install%20Moskeytool%20?\u003c/span\u003e\u003cspan address=\"https://www.medilabsecure.com/entomology-tools-0/moskeytool#How%20to%20install%20Moskeytool%20?\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTanaka, K., K. Mizusawa, and E. S. Saugstad. (1979) A revision of the adult and larval mosquitoes of Japan (including the Ryukyu Archipelago and the Ogasawara Islands) and Korea (Diptera: Culicidae). Contrib. Am. Entomol. Inst. 16: 1\u0026ndash;987.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRee H (2003) Taxonomic Review and Revised Keys of the Korean Mosquitoes (Diptera: Culicidae). Entomological Research 33:39\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1748-5967.2003.tb00047.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1748-5967.2003.tb00047.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchaffner, F., Angel, G., Geoffroy, B., Hervy, J.-P., Rhaiem, A., and Brunhes, J. (2001) Les moustiques d\u0026rsquo;Europe: logiciel d\u0026rsquo;identification et d\u0026rsquo;enseignement\u0026thinsp;=\u0026thinsp;The mosquitoes of Europe: an identification and training programme. IRD Editions \u0026amp; EID M\u0026eacute;diterran\u0026eacute;e: Montpellier, France.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSeverini, F., Toma, L., \u0026amp; di Luca, M. (2022) Mosquitoes in Italy: collection, identification and preservation of the most common species. ISTISAN Reports 22/3, Rome: Istituto Superiore di Sanit\u0026agrave;.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDarsie, RF and Ward, RA. (2005) Identification and geographical distribution of the mosquitoes of North America, north of Mexico, 2nd Edition. University Press of Florida, USA.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOtani S, Mikaelyan A, Nobre T, et al (2014) Identifying the core microbial community in the gut of fungus-growing termites. Mol Ecol 23:4631\u0026ndash;4644. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/mec.12874\u003c/span\u003e\u003cspan address=\"10.1111/mec.12874\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu Y, Lee C, Kim J, Hwang S (2005) Group-specific primer and probe sets to detect methanogenic communities using quantitative real‐time polymerase chain reaction. Biotech \u0026amp; Bioengineering 89:670\u0026ndash;679. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/bit.20347\u003c/span\u003e\u003cspan address=\"10.1002/bit.20347\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBolyen E, Rideout JR, Dillon MR, et al (2019) Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 37:852\u0026ndash;857. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41587-019-0209-9\u003c/span\u003e\u003cspan address=\"10.1038/s41587-019-0209-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCallahan BJ, McMurdie PJ, Rosen MJ, et al (2016) DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13:581\u0026ndash;583. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nmeth.3869\u003c/span\u003e\u003cspan address=\"10.1038/nmeth.3869\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQuast C, Pruesse E, Yilmaz P, et al (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41:D590-596. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gks1219\u003c/span\u003e\u003cspan address=\"10.1093/nar/gks1219\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eR Core Team (2024) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria https://wwwR-project.org/\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlb\u0026oacute; Timor A, Lucati F, Bartumeus F, et al (2023) A fast and inexpensive genotyping system for the simultaneous analysis of human and Aedes albopictus short tandem repeats. Parasites Vectors 16:347. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13071-023-05977-w\u003c/span\u003e\u003cspan address=\"10.1186/s13071-023-05977-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOntiveros V, Lucati F, Caner J, et al (2025) Fine-scale tiger mosquito population dynamics in urban and densely populated landscapes\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWiddel AK, McCuiston LJ, Crans WJ, et al (2005) Finding needles in the haystack: single copy microsatellite loci for Aedes japonicus (Diptera: Culicidae). Am J Trop Med Hyg 73:744\u0026ndash;748\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeebe NW, Ambrose L, Hill LA, et al (2013) Tracing the tiger: population genetics provides valuable insights into the Aedes (Stegomyia) albopictus invasion of the Australasian Region. PLoS Negl Trop Dis 7:e2361. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pntd.0002361\u003c/span\u003e\u003cspan address=\"10.1371/journal.pntd.0002361\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eManni M, Gomulski LM, Aketarawong N, et al (2015) Molecular markers for analyses of intraspecific genetic diversity in the Asian Tiger mosquito, Aedes albopictus. Parasit Vectors 8:188. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13071-015-0794-5\u003c/span\u003e\u003cspan address=\"10.1186/s13071-015-0794-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKalinowski ST, Wagner AP, Taper ML (2006) ml - relate: a computer program for maximum likelihood estimation of relatedness and relationship. Molecular Ecology Notes 6:576\u0026ndash;579. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1471-8286.2006.01256.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1471-8286.2006.01256.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChao A, Jost L (2012) Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size. Ecology 93:2533\u0026ndash;2547. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/11-1952.1\u003c/span\u003e\u003cspan address=\"10.1890/11-1952.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChiu C (2023) Sample coverage estimation, rarefaction, and extrapolation based on sample-based abundance data. Ecology 104:e4099. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ecy.4099\u003c/span\u003e\u003cspan address=\"10.1002/ecy.4099\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchrieke H, Maignien L, Constancias F, et al (2022) The mosquito microbiome includes habitat-specific but rare symbionts. Comput Struct Biotechnol J 20:410\u0026ndash;420. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.csbj.2021.12.019\u003c/span\u003e\u003cspan address=\"10.1016/j.csbj.2021.12.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFeichtinger S, Lazar AA, Luebbe MA, et al (2023) Case report: isolation of Hydrogenophaga from septic blood culture following near-death drowning in lakewater. Access Microbiol 5:000533.v4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1099/acmi.0.000533.v4\u003c/span\u003e\u003cspan address=\"10.1099/acmi.0.000533.v4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMinard G, Tran FH, Raharimalala FN, et al (2013) Prevalence, genomic and metabolic profiles of \u003cem\u003eAcinetobacter\u003c/em\u003e and \u003cem\u003eAsaia\u003c/em\u003e associated with field-caught \u003cem\u003eAedes albopictus\u003c/em\u003e from Madagascar. FEMS Microbiol Ecol 83:63\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1574-6941.2012.01455.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1574-6941.2012.01455.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYan L, Zhang W, Duan W, et al (2021) Temporal Bacterial Community Diversity in the Nicotiana tabacum Rhizosphere Over Years of Continuous Monocropping. Front Microbiol 12:641643. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmicb.2021.641643\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2021.641643\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTainchum K, Dupont C, Chareonviriyaphap T, et al (2020) Bacterial Microbiome in Wild-Caught Anopheles Mosquitoes in Western Thailand. Front Microbiol 11:965. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmicb.2020.00965\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2020.00965\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFlores GAM, Lopez RP, Cerrudo CS, et al (2023) Wolbachia dominance influences the Culex quinquefasciatus microbiota. Sci Rep 13:18980. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-023-46067-2\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-46067-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMeng L-J, Zhang Y, Li X-X, et al (2021) Comparative analysis of bacterial communities of water and intestines of silver carp (Hypophthalmichthys molitrix) and bighead carp (H. nobilis) reared in aquaculture pond systems. Aquaculture 534:736334. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.aquaculture.2020.736334\u003c/span\u003e\u003cspan address=\"10.1016/j.aquaculture.2020.736334\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiggins FM (2017) The spread of Wolbachia through mosquito populations. PLoS Biol 15:e2002780. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pbio.2002780\u003c/span\u003e\u003cspan address=\"10.1371/journal.pbio.2002780\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJohnson K (2015) The Impact of Wolbachia on Virus Infection in Mosquitoes. Viruses 7:5705\u0026ndash;5717. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/v7112903\u003c/span\u003e\u003cspan address=\"10.3390/v7112903\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMancini MV, Damiani C, Accoti A, et al (2018) Estimating bacteria diversity in different organs of nine species of mosquito by next generation sequencing. BMC Microbiol 18:126. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12866-018-1266-9\u003c/span\u003e\u003cspan address=\"10.1186/s12866-018-1266-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLucati F, Delacour S, Palmer JRB, et al (2022) Multiple invasions, Wolbachia and human-aided transport drive the genetic variability of Aedes albopictus in the Iberian Peninsula. Sci Rep 12:20682. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-022-24963-3\u003c/span\u003e\u003cspan address=\"10.1038/s41598-022-24963-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoreira LA, Iturbe-Ormaetxe I, Jeffery JA, et al (2009) A Wolbachia Symbiont in Aedes aegypti Limits Infection with Dengue, Chikungunya, and Plasmodium. Cell 139:1268\u0026ndash;1278. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cell.2009.11.042\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2009.11.042\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHoffmann AA, Montgomery BL, Popovici J, et al (2011) Successful establishment of Wolbachia in Aedes populations to suppress dengue transmission. Nature 476:454\u0026ndash;457. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nature10356\u003c/span\u003e\u003cspan address=\"10.1038/nature10356\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUtarini A, Indriani C, Ahmad RA, et al (2021) Efficacy of Wolbachia-Infected Mosquito Deployments for the Control of Dengue. N Engl J Med 384:2177\u0026ndash;2186. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1056/NEJMoa2030243\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa2030243\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSasaki T, Moi ML, Saito K, et al (2022) Aedes albopictus Strain and Dengue Virus Serotype in the Dengue Fever Outbreaks in Japan: Implications of Wolbachia Infection. Jpn J Infect Dis 75:140\u0026ndash;143. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7883/yoken.JJID.2021.376\u003c/span\u003e\u003cspan address=\"10.7883/yoken.JJID.2021.376\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAustin, B., and C. Adams. (1996) Fish pathogens, p. 197\u0026ndash;244. In B. Austin, M. Altwegg, P. J. Gosling, and S. Joseph (ed.), The genus Aeromonas. John Wiley \u0026amp; Sons Ltd., Chichester, England.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFigueras MJ, Suarez-Franquet A, Chac\u0026oacute;n MR, et al (2005) First record of the rare species Aeromonas culicicola from a drinking water supply. Appl Environ Microbiol 71:538\u0026ndash;541. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/AEM.71.1.538-541.2005\u003c/span\u003e\u003cspan address=\"10.1128/AEM.71.1.538-541.2005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAltwegg, M. (1999) Aeromonas and Plesiomonas, p. 507\u0026ndash;516. In P. R. Murray, E. J. Baron, M. A. Pfaller, F. C. Tenover, and R. H. Yolken (ed.), Manual of clinical microbiology. American Society for Microbiology, Washington, D.C.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJanda JM, Abbott SL (2010) The genus Aeromonas: taxonomy, pathogenicity, and infection. Clin Microbiol Rev 23:35\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/CMR.00039-09\u003c/span\u003e\u003cspan address=\"10.1128/CMR.00039-09\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEnciso-Moreno JA, Pernas-Buitr\u0026oacute;n N, Ortiz-Herrera M, Coria-Jim\u0026eacute;nez R (2004) Identification of Serratia marcescens populations of nosocomial origin by RAPD-PCR. Archives of Medical Research 35:12\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.arcmed.2003.07.005\u003c/span\u003e\u003cspan address=\"10.1016/j.arcmed.2003.07.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBoissi\u0026egrave;re A, Tchioffo MT, Bachar D, et al (2012) Midgut Microbiota of the Malaria Mosquito Vector Anopheles gambiae and Interactions with Plasmodium falciparum Infection. PLoS Pathog 8:e1002742. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.ppat.1002742\u003c/span\u003e\u003cspan address=\"10.1371/journal.ppat.1002742\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBernardet J.-F., Hugo C., Bruun B. 2006; Bernardet J.-F., Hugo C., Bruun B. (2006) The genera Chryseobacterium and Elizabethkingia. In The Prokaryotes: a Handbook on the Biology of Bacteria, 3rd edn. vol. 7 pp. 638\u0026ndash;676 Edited by Dworkin M., Falkow S., Rosenberg E., Schleifer K. H., Stackebrandt E. New York: Springer\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChavshin AR, Oshaghi MA, Vatandoost H, et al (2012) Identification of bacterial microflora in the midgut of the larvae and adult of wild caught Anopheles stephensi: A step toward finding suitable paratransgenesis candidates. Acta Tropica 121:129\u0026ndash;134. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.actatropica.2011.10.015\u003c/span\u003e\u003cspan address=\"10.1016/j.actatropica.2011.10.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFariba Akrami, Amirmorteza Ebrahimzadeh Namvar (2019) Acinetobacter baumannii as Nosocomial Pathogenic Bacteria. Mol Genet Microbiol Virol 34:84\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3103/S0891416819020046\u003c/span\u003e\u003cspan address=\"10.3103/S0891416819020046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRudolf I, Blažejov\u0026aacute; H, Mendel J, et al (2020) Bartonella species in medically important mosquitoes, Central Europe. Parasitol Res 119:2713\u0026ndash;2717. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00436-020-06732-1\u003c/span\u003e\u003cspan address=\"10.1007/s00436-020-06732-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNieves DJ, Heininger U (2016) Bordetella pertussis. In: Scheld WM, Hughes JM, Whitley RJ (eds) Emerging Infections 10. ASM Press, Washington, DC, USA, pp 311\u0026ndash;339\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuo W-P, Tian J-H, Lin X-D, et al (2016) Extensive genetic diversity of Rickettsiales bacteria in multiple mosquito species. Sci Rep 6:38770. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/srep38770\u003c/span\u003e\u003cspan address=\"10.1038/srep38770\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKikuchi Y, Hosokawa T, Fukatsu T (2007) Insect-Microbe Mutualism without Vertical Transmission: a Stinkbug Acquires a Beneficial Gut Symbiont from the Environment Every Generation. Appl Environ Microbiol 73:4308\u0026ndash;4316. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/AEM.00067-07\u003c/span\u003e\u003cspan address=\"10.1128/AEM.00067-07\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLian X-D, Guan Y, Jiang Y, et al (2025) Discovery of two novel Flavobacterium species with potential for complex polysaccharide degradation. Sci Rep 15:3494. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-025-87876-x\u003c/span\u003e\u003cspan address=\"10.1038/s41598-025-87876-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePapade SE, Mohapatra B, Phale PS (2024) Pseudomonas and Acinetobacter spp. capable of metabolizing aromatics displays multifarious plant growth promoting traits: Insights on strategizing consortium-based application to agro-ecosystems. Environmental Technology \u0026amp; Innovation 36:103786. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.eti.2024.103786\u003c/span\u003e\u003cspan address=\"10.1016/j.eti.2024.103786\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Mosquitoes, bacterial communities, host variation, geographic variation","lastPublishedDoi":"10.21203/rs.3.rs-7544775/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7544775/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMosquito-associated microbiota are influenced by a number of factors, e.g., geography, host species, and developmental stage. Understanding these microbiotas is crucial for assessing their role as vectors and in pathogen dissemination and most studies have largely focused on a few model species, while others like \u003cem\u003eAedes japonicus\u003c/em\u003e remain poorly characterized. Here, we compared the bacterial communities of \u003cem\u003eAedes albopictus\u003c/em\u003e and \u003cem\u003eAedes japonicus\u003c/em\u003e across eight countries: six in Europe, plus the USA and Japan, from both adults and larval stages when possible, using 16S rRNA amplicon sequencing. We found large differences in microbiota composition between mosquito species, with \u003cem\u003eAe. albopictus\u003c/em\u003e exhibiting lower bacterial diversity than \u003cem\u003eAe. japonicus\u003c/em\u003e. Geographic variation in bacterial diversity was also evident, with mosquitoes from Japan and the Netherlands harbouring the most diverse bacterial communities, while Austrian populations displayed the lowest diversity. Developmental stage (adults and larvae) had the strongest influence on bacterial composition, with aquatic-associated genera such as \u003cem\u003eLimnohabitans\u003c/em\u003e and \u003cem\u003eAeromonas\u003c/em\u003e dominating larvae, whereas adult mosquitoes harboured higher abundances of \u003cem\u003eAcinetobacter\u003c/em\u003e and \u003cem\u003eMethylobacterium\u003c/em\u003e. No association was found between \u003cem\u003eAedes\u003c/em\u003e species genetic distance, determined by relatedness, and the bacterial community compositions. A number of bacterial genera with known pathogenic potential, including \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eSerratia\u003c/em\u003e, \u003cem\u003eKlebsiella\u003c/em\u003e, and \u003cem\u003eAcinetobacter\u003c/em\u003e, were detected across multiple locations, suggesting that mosquitoes could serve as environmental reservoirs for opportunistic and antimicrobial-resistant bacteria. We identified \u003cem\u003eWolbachia\u003c/em\u003e in \u003cem\u003eAe. albopictus\u003c/em\u003e from Spain and Italy and at low abundances in \u003cem\u003eAe. japonicus\u003c/em\u003e from the USA and Japan, marking one of the first reports of \u003cem\u003eWolbachia\u003c/em\u003e in this species. This is, to our knowledge, the most comprehensive study on \u003cem\u003eAe. japonicus\u003c/em\u003e microbiotas. Our findings provide insights into the ecological and epidemiological implications of mosquito microbiota and emphasize the need for further investigation into their role in pathogen transmission and antimicrobial resistance dissemination.\u003c/p\u003e","manuscriptTitle":"Mosquito bacterial communities show stage-specific patterns relevant for vector ecology and AMR surveillance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-09 12:18:28","doi":"10.21203/rs.3.rs-7544775/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":"5972a223-207d-4238-873b-42cf61d32027","owner":[],"postedDate":"October 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-21T18:08:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-09 12:18:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7544775","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7544775","identity":"rs-7544775","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}