Mosquito-borne bacterial communities are shaped by their insect host species, geography and developmental stage

Mosquito-borne bacterial communities are shaped by their insect host species, geography and developmental stage | 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-borne bacterial communities are shaped by their insect host species, geography and developmental stage Saria Otani, Federica Lucati, Ragna Eberhardt, Frederik Duus Møller, and 20 more This is a preprint; it has not been peer reviewed by a journal.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-6119176/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 Mosquitoes harbour diverse bacterial communities that 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. 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 Hydrogenophaga 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. These 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 [ 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 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, DNA extraction and amplicon sequencing: Mosquitoes were collected between 2018 and 2021 from Italy (48 mosquitoes), the Netherlands (20), Austria (28), Spain (235), Hungary (52), France (20), the USA (33), and Japan (64). Of the 500 mosquitoes, 427 were Ae. japonicus and from all eight countries, while 73 Ae. albopictus samples exclusively from Spain (Table 1 ; Table S1 ). Across both species, 351 were adults and 149 were larvae (Table 1 , Table S1 ). Samples were collected from multiple locations within each country (details in Table S1 ). 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 [ 39 ]. 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). Each sample had a minimum of 0.1M paired-end reads. 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 until the genus bacterial level using a Naïve Bayes classifier trained on the SILVA 138 database [ 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 operational taxonomic units (OTUs) 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 ]. 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: a 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. At a 97% sequence similarity threshold, 24,807 operational taxonomic units (OTUs) were identified. Following filtering to keep only high-quality reads, 20,257 OTUs remained, with per-sample reads ranging from 2,567 to 71,570. A subset of 4,093 OTUs remained unassigned, and 388 taxa were identified as chloroplasts, 61 as mitochondria, seven as archaea, and one as eukaryotic. The remaining OTUs 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 S1 ; Table S1 ). At the genus level, 992 bacterial genera were detected (Table S1 ). The top 5 most abundant genera were: Wolbachia (6% exclusively in Spanish Ae. albopictus adults and never in the same species larvae, and in seven Ae. japonicus from the USA), Acinetobacter (4,7%), Flavobacterium (3,7%), Aeromonas (3,1%) and Limnobacter (2,8%) (Fig. 1 ; Table S1 ). All of which are known to occupy Aedes bacterial communities, especially in the mosquito guts and reproductive organs [ 8 , 22 ], with Acinetobacter and Wolbachia being the dominant taxa in the Spanish population [ 22 ]. 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. Developmental stage effects on mosquito bacterial communities: Comparisons between larvae and adult mosquitoes revealed clear stage-specific bacterial profiles (Fig. 2 ; Figure S2 ; Table S1 ). For example, in the French population, Ae. japonicus larvae showed higher relative abundances of genera such as Chryseobacterium , Rhizobacter , Limnohabitans , Novosphingobium and Hydrogenophaga (Fig. 2 ), 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 Acinetobacter , Nevskia , Blfdi19 and Methylobacterium (Fig. 2 ), 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 - Fig. 2 ;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 other populations where both stages were collected and from both mosquito species ( Ae. Japonicus and Ae. Albopictus ): Italy, Hungary, and Spain (details in Table S1 ;Figure S2 ). 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 Sphingomonas are widely present at similar proportions across most of the samples from Austria, Spain, Italy, Hungary, Japan, and USA (Table S1 ). 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., Japan, Italy and Spain (Fig. 3 ; Figure S2 ). However, also in some of those countries, variations were observed due to the different developmental stages: e.g., Italy and Spain (Figure S2 ). 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 ]. Our data revealed a clear “all or nothing” pattern in Wolbachia abundance. Wolbachia was only detected in Spanish Ae. albopictus adults and seven Ae. japonicus in the USA, and never any other mosquitoes in this study including the Spanish Ae. japonicus (Table S1 ). This is, to our knowledge, the first time Wolbachia to be reported 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 might explain its presence in only our adult mosquitoes. High Wolbachia abundances in certain populations (only in the Spanish Ae. albopictus here) suggests long–term establishment, possibly due to reproductive manipulation that favours its spread. In contrast, populations where Wolbachia is absent in adult mosquitoes (e.g., the Netherlands and Japan) or at low levels might represent either recent introductions or a failure to fix the infection. Given that Wolbachia has well–documented effects on reducing the replication of arboviruses in mosquitoes (e.g., dengue, Zika) [ 63 – 66 ], the observed geographic heterogeneity may have profound implications for vector competence and should be considered when designing biocontrol strategies. While environmental factors likely play a role, this heterogeneity may also be driven more by host species differences, and their distribution, than by geography. 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 ]. Microbiota comparison also showed that several bacterial genera exhibit strong, species-specific associations (Fig. 1 B; Figure S2 ). For example, apart from Wolbachia , the genus Hydrogenophaga is consistently abundant in samples from species Ae. albopictus while it is nearly absent in Ae. japonicus (Fig. 1 B; Table S1 ). Similarly, Acidovorax is predominantly present in Ae. albopictus and is either low or undetectable in Ae. japonicus (Fig. 1 B; Table S1 ). Such contrasting patterns suggest that these taxa, which are typically associated with soil and environmental surroundings, may be selectively enriched by Ae. albopictus due to differences in host physiology or microhabitat use. In contrast, some genera appear in both species, indicating the presence of a shared bacterial community. Nevertheless, the species‐specific enrichment of taxa like Hydrogenophaga and Acidovorax 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 S2 ; 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. Aeromonas species are known to be water-derived species and also linked to fish and human infection [ 67 – 69 ], while Pseudomonas (if P. aeruginosa ) is an opportunistic pathogen associated with respiratory infections and sepsis [ 70 ]. Serratia species S . marcescens 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 Anopeheles 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 baumannii , a major nosocomial pathogen, is also present in mosquitoes and is known as a 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 . One member of this genus, Bordetella pertussis , is known to cause Pertussis, also known as 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 causeAnaplasmosis and Ehrlichiosis respectively, that are usually transmitted by other arthropods like ticks [ 78 ], were also not identified in our mosquitoes. 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 investigation 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. 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 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 Data availability: All 16S rRNA data are available at the European Nucleotide Archive (ENA) with the project accession number: ERX13771555-ERX13772064. Acknowledgment: We are thankful to Hanne Mordhorst for the assistance in the laboratory. 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). Competing Interests: The authors have no relevant financial or non-financial interests to disclose. 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Supplementary Files OtanietalFigureS1d1.pdf Figure S1: Relative abundances of the bacterial phyla in all the mosquitoes included in this study (A-N-P-R: Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium). OtanietalFigureS2.pdf Figure S2: 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. Second sheet shows the separation in each country based on the mosquito species and developmental stage. Third sheet shows the separation in Spain where only the two mosquito species where collected and between the developmental stages. OtanietalTableS1d1.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). OtanietalTableS2d1.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). 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. 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Westby","email":"","orcid":"","institution":"Washington University in Saint Louis","correspondingAuthor":false,"prefix":"","firstName":"Katie","middleName":"M.","lastName":"Westby","suffix":""},{"id":424608558,"identity":"40a029ea-6a03-46a4-851e-c118eb1d1422","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":424608563,"identity":"0b266dcb-8510-4aff-adee-98a04c8a8db6","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":424608565,"identity":"7aa41837-1d1b-40a8-bc3f-3f51334f57eb","order_by":10,"name":"Paul T. Leisnham","email":"","orcid":"","institution":"University of Maryland","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"T.","lastName":"Leisnham","suffix":""},{"id":424608567,"identity":"b40be538-4d6a-4c05-bf0b-0b8c7f3352e9","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":424608570,"identity":"cc528c95-55b5-47d3-9db6-6de14bda62b0","order_by":12,"name":"Nobuko Tuno","email":"","orcid":"","institution":"Kanazawa University","correspondingAuthor":false,"prefix":"","firstName":"Nobuko","middleName":"","lastName":"Tuno","suffix":""},{"id":424608573,"identity":"41c8a522-f718-43b2-911b-6d639c0e8d89","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":424608577,"identity":"fda557df-cb3f-4c4c-81b8-1d62565d748b","order_by":14,"name":"Francis Schaffner","email":"","orcid":"","institution":"Francis Schaffner EI","correspondingAuthor":false,"prefix":"","firstName":"Francis","middleName":"","lastName":"Schaffner","suffix":""},{"id":424608580,"identity":"068ffa43-8933-4b49-aaf2-2221a3a5f73d","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":424608583,"identity":"31144fc0-695e-4069-8b75-4c0fe4e50fb6","order_by":16,"name":"Motoyoshi Mogi","email":"","orcid":"","institution":"Saga University","correspondingAuthor":false,"prefix":"","firstName":"Motoyoshi","middleName":"","lastName":"Mogi","suffix":""},{"id":424608588,"identity":"1a75e591-f463-4a22-b2d3-648b95e57c73","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":424608591,"identity":"16156fd5-d9fa-4b8f-8da5-62d53b97f986","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":424608592,"identity":"8c8f4e9c-d9ff-44c6-8cfd-5e29b244ead6","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":424608595,"identity":"9a20975c-6359-4172-a901-60dd1642efa7","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":424608597,"identity":"046d13ba-8dc7-4b4f-91d7-347ebfe15efb","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":424608600,"identity":"9c2b860f-f80c-4f8e-a202-d6b1bfef7e16","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":424608602,"identity":"c7eb71e4-4a3e-42d0-926e-aeed0e6579a0","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":"2025-02-27 08:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6119176/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6119176/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77891724,"identity":"51e3ddb7-b686-4f1d-8f91-5ce0e046464a","added_by":"auto","created_at":"2025-03-06 13:56:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":558515,"visible":true,"origin":"","legend":"\u003cp\u003eRelative 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 \u003cem\u003eAe. japonicus\u003c/em\u003e. B: Only the Spanish population with the two species; \u003cem\u003eAe. albopictus\u003c/em\u003e and \u003cem\u003eAe. japonicus\u003c/em\u003e. Full list of all the genera and the samples is in Table S1. Relative abundances of \u003cem\u003eWolbachia\u003c/em\u003e in red, for Spain (\u003cem\u003eAe. albopictus\u003c/em\u003e) and USA (\u003cem\u003eAe. japonicus\u003c/em\u003e)\u003c/p\u003e","description":"","filename":"floatimage118.png","url":"https://assets-eu.researchsquare.com/files/rs-6119176/v1/c08600c06aa6d3a9f87e2d5a.png"},{"id":77892184,"identity":"bd137541-1fa8-4e40-a322-78d31f3c2018","added_by":"auto","created_at":"2025-03-06 14:04:39","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":507363,"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. (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":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6119176/v1/7f291e05d14df62fffba19c6.jpeg"},{"id":77893612,"identity":"37f59163-8f75-4f48-aa00-a3bab79ce37f","added_by":"auto","created_at":"2025-03-06 14:12:39","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":680317,"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":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6119176/v1/f17dcadeab49c329255584ec.jpeg"},{"id":83796842,"identity":"0c61a579-521d-49da-b0a9-69f7d4c9b4d8","added_by":"auto","created_at":"2025-06-03 01:01:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2559031,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6119176/v1/71f0aebc-6e46-47c2-8c42-54a5f91ad1d4.pdf"},{"id":77893614,"identity":"77da3759-a8d3-4bad-aa5e-8c93edea3dc8","added_by":"auto","created_at":"2025-03-06 14:12:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":494089,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1: Relative 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":"OtanietalFigureS1d1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6119176/v1/b7ac0d2153c83dea029477a7.pdf"},{"id":77891732,"identity":"64ce2b38-b7f4-4470-a27b-4df4832232e6","added_by":"auto","created_at":"2025-03-06 13:56:39","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":980271,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S2: 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. Second sheet shows the separation in each country based on the mosquito species and developmental stage. Third sheet shows the separation in Spain where only the two mosquito species where collected and between the developmental stages.\u003c/p\u003e","description":"","filename":"OtanietalFigureS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6119176/v1/73b2cb95f5a5d836c4334889.pdf"},{"id":77892182,"identity":"4eb00880-3c3b-47c7-a84c-18c9ce5f37c1","added_by":"auto","created_at":"2025-03-06 14:04:39","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1805255,"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).\u003c/p\u003e","description":"","filename":"OtanietalTableS1d1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6119176/v1/9f01fa109fac30579256895e.xlsx"},{"id":77891726,"identity":"e1c71020-c2ad-438b-b069-81f50fc4ef64","added_by":"auto","created_at":"2025-03-06 13:56:38","extension":"xlsx","order_by":4,"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":"OtanietalTableS2d1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6119176/v1/9a4b895bbd599415dbc0bff8.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mosquito-borne bacterial communities are shaped by their insect host species, geography and developmental stage","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMosquitoes are major vectors of infectious diseases [\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], 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\u003c/em\u003e [\u003cspan additionalcitationids=\"CR9 CR10 CR11 CR12\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e–\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. \u003cem\u003eAedes albopictus\u003c/em\u003e has emerged as a key vector species, while \u003cem\u003eAedes japonicus\u003c/em\u003e is 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 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. \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 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. 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 \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 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies have identified substantial microbiome variation across mosquito species, developmental stages, and geographic regions [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e–\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. 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 [\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], the microbiome of \u003cem\u003eAe. japonicus\u003c/em\u003e remains largely unexplored [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e–\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This suggests a mosquito-specific microbiome [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A clear variation has been observed for different geographical areas [\u003cspan additionalcitationids=\"CR31 CR32\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e–\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] that was unrelated to temperature, rainfall and urbanisation [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], as well as between host developmental stages and sexes [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral studies suggest that mosquitoes, and other invertebrate vectors, may also act as reservoirs for bacterial pathogens and antimicrobial resistance (AMR) genes [\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e–\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. 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 \u003cp\u003eHowever, besides the observed link between low \u003cem\u003eAe. albopictus\u003c/em\u003e genomic diversity and reduced bacterial diversity [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], the possible association between \u003cem\u003eAe. albopictus\u003c/em\u003e phylogeny 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 \u003cp\u003eIn this study, we aimed to investigate how bacterial communities are shaped in \u003cem\u003eAedes\u003c/em\u003e mosquitoes across eight countries, and which pathogenic bacteria they harbour. We performed a comparative analysis of the bacterial communities of \u003cem\u003eAedes\u003c/em\u003e 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: \u003cem\u003eAe. albopictus\u003c/em\u003e and \u003cem\u003eAe. japonicus\u003c/em\u003e.\u003c/p\u003e "},{"header":"Material and methods","content":"\u003cp\u003eSample collection, DNA extraction and amplicon sequencing:\u003c/p\u003e\u003cp\u003eMosquitoes were collected between 2018 and 2021 from Italy (48 mosquitoes), the Netherlands (20), Austria (28), Spain (235), Hungary (52), France (20), the USA (33), and Japan (64). Of the 500 mosquitoes, 427 were \u003cem\u003eAe. japonicus\u003c/em\u003e and from all eight countries, while 73 \u003cem\u003eAe. albopictus\u003c/em\u003e samples exclusively from Spain (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Across both species, 351 were adults and 149 were larvae (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Samples were collected from multiple locations within each country (details in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). 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.\u003c/p\u003e\u003cp\u003e16S rRNA gene sequencing was performed using primers 341F (5′- CCTAYGGGRBGCASCAG − 3′) and 806R (5′- GGACTACNNGGGTATCTAAT − 3′) flanking the hypervariable V3 – V4 regions [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. 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). Each sample had a minimum of 0.1M paired-end reads.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\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\u003c/colgroup\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/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBioinformatic analysis:\u003c/p\u003e\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 until the genus bacterial level using a Naïve Bayes classifier trained on the SILVA 138 database [\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\u003cp\u003eBacterial diversity analysis:\u003c/p\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 operational taxonomic units (OTUs) 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\u003eCorrelation analysis between host relatedness and gut bacterial communities:\u003c/p\u003e\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: a \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":"\u003cp\u003eSequencing output and taxonomic composition of the entire mosquito bacterial communities:\u003c/p\u003e\u003cp\u003eA total of 12,404,833 reads were generated across all samples of both mosquito species. At a 97% sequence similarity threshold, 24,807 operational taxonomic units (OTUs) were identified. Following filtering to keep only high-quality reads, 20,257 OTUs remained, with per-sample reads ranging from 2,567 to 71,570. A subset of 4,093 OTUs remained unassigned, and 388 taxa were identified as chloroplasts, 61 as mitochondria, seven as archaea, and one as eukaryotic. The remaining OTUs 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=\"MOESM1\" class=\"InternalRef\"\u003eS1\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). The top 5 most abundant genera were: \u003cem\u003eWolbachia\u003c/em\u003e (6% exclusively in Spanish \u003cem\u003eAe. albopictus\u003c/em\u003e adults and never in the same species larvae, and in seven \u003cem\u003eAe. japonicus\u003c/em\u003e from the USA), \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%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\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], with \u003cem\u003eAcinetobacter\u003c/em\u003e and \u003cem\u003eWolbachia\u003c/em\u003e being the dominant taxa in the Spanish population [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\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\u003eDevelopmental stage effects on mosquito bacterial communities:\u003c/p\u003e\u003cp\u003eComparisons between larvae and adult mosquitoes revealed clear stage-specific bacterial profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" 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). For example, 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=\"Fig3\" class=\"InternalRef\"\u003e2\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\u003eAcinetobacter\u003c/em\u003e, \u003cem\u003eNevskia\u003c/em\u003e, \u003cem\u003eBlfdi19\u003c/em\u003e and \u003cem\u003eMethylobacterium\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\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 - Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e;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 other populations where both stages were collected and from both mosquito species (\u003cem\u003eAe. Japonicus\u003c/em\u003e and \u003cem\u003eAe. Albopictus\u003c/em\u003e): Italy, Hungary, and Spain (details in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e;Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\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\u003eGeographic and species-level variation in \u003cem\u003eAe. japonicus\u003c/em\u003e:\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\u003eSphingomonas\u003c/em\u003e are widely present at similar proportions across most of the samples from Austria, Spain, Italy, Hungary, Japan, and USA (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIntra-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 \u003cem\u003eAe. japonicus\u003c/em\u003e-associated bacterial communities were observed. Larval samples collected on the same day from cemeteries in Szalafő, Keserűszer, and Őriszentpéter exhibited distinct bacterial profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" 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=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Moreover, adult mosquitoes collected in Kovácsszénája 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=\"Fig1\" 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. Similar patterns of distinct bacterial communities between within-county locations were also observed in other countries, e.g., Japan, Italy and Spain (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). However, 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). 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\u003e \u003cem\u003eWolbachia\u003c/em\u003e and host–symbiont dynamics:\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]. Our data revealed a clear “all or nothing” pattern in \u003cem\u003eWolbachia\u003c/em\u003e abundance. \u003cem\u003eWolbachia\u003c/em\u003e was only detected in Spanish \u003cem\u003eAe. albopictus\u003c/em\u003e adults and seven \u003cem\u003eAe. japonicus\u003c/em\u003e in the USA, and never any other mosquitoes in this study including the Spanish \u003cem\u003eAe. japonicus\u003c/em\u003e (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This is, to our knowledge, the first time \u003cem\u003eWolbachia\u003c/em\u003e to be reported 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 might explain its presence in only our adult mosquitoes. High \u003cem\u003eWolbachia\u003c/em\u003e abundances in certain populations (only in the Spanish \u003cem\u003eAe. albopictus\u003c/em\u003e here) suggests long–term establishment, possibly due to reproductive manipulation that favours its spread. In contrast, populations where \u003cem\u003eWolbachia\u003c/em\u003e is absent in adult mosquitoes (e.g., the Netherlands and Japan) or at low levels might represent either recent introductions or a failure to fix the infection.\u003c/p\u003e\u003cp\u003eGiven that \u003cem\u003eWolbachia\u003c/em\u003e has well–documented effects on reducing the replication of arboviruses in mosquitoes (e.g., dengue, Zika) [\u003cspan additionalcitationids=\"CR64 CR65\" citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e–\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], the observed geographic heterogeneity may have profound implications for vector competence and should be considered when designing biocontrol strategies. While environmental factors likely play a role, this heterogeneity may also be driven more by host species differences, and their distribution, than by geography.\u003c/p\u003e\u003cp\u003eComparison of bacterial genera abundance between \u003cem\u003eAe. albopictus\u003c/em\u003e and \u003cem\u003eAe. japonicus\u003c/em\u003e from Spain:\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]. Microbiota comparison also showed that several bacterial genera exhibit strong, species-specific associations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eB; Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). For example, apart from \u003cem\u003eWolbachia\u003c/em\u003e, the genus \u003cem\u003eHydrogenophaga\u003c/em\u003e is consistently abundant in samples from species \u003cem\u003eAe. albopictus\u003c/em\u003e while it is nearly absent in \u003cem\u003eAe. japonicus\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eB; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Similarly, \u003cem\u003eAcidovorax\u003c/em\u003e is predominantly present in \u003cem\u003eAe. albopictus\u003c/em\u003e and is either low or undetectable in \u003cem\u003eAe. japonicus\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eB; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Such contrasting patterns suggest that these taxa, which are typically associated with soil and environmental surroundings, may be selectively enriched by \u003cem\u003eAe. albopictus\u003c/em\u003e due to differences in host physiology or microhabitat use. In contrast, some genera appear in both species, indicating the presence of a shared bacterial community. Nevertheless, the species‐specific enrichment of taxa like \u003cem\u003eHydrogenophaga\u003c/em\u003e and \u003cem\u003eAcidovorax\u003c/em\u003e 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=\"MOESM2\" class=\"InternalRef\"\u003eS2\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\u003cp\u003eCorrelation between host relatedness and bacterial community composition:\u003c/p\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 = 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 = 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\u003cp\u003eMosquito as reservoir for opportunistic and pathogenic bacteria:\u003c/p\u003e\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. \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–\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], while \u003cem\u003ePseudomonas\u003c/em\u003e (if \u003cem\u003eP. aeruginosa\u003c/em\u003e) is an opportunistic pathogen associated with respiratory infections and sepsis [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. \u003cem\u003eSerratia\u003c/em\u003e species \u003cem\u003eS\u003c/em\u003e. \u003cem\u003emarcescens\u003c/em\u003e 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\u003eAnopeheles 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 baumannii\u003c/em\u003e, a major nosocomial pathogen, is also present in mosquitoes and is known as a 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. One member of this genus, \u003cem\u003eBordetella pertussis\u003c/em\u003e, is known to cause Pertussis, also known as 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 causeAnaplasmosis 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"},{"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 investigation 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\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–\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 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":"\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll 16S rRNA data are available at the European Nucleotide Archive (ENA) with the project accession number: ERX13771555-ERX13772064.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are thankful to Hanne Mordhorst for the assistance in the laboratory.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003e\u003c/p\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\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\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.\u0026nbsp;\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. 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Environ Technol Innov 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-6119176/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6119176/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMosquitoes harbour diverse bacterial communities that 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. 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\u003eHydrogenophaga\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. These 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-borne bacterial communities are shaped by their insect host species, geography and developmental stage","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-06 13:56:34","doi":"10.21203/rs.3.rs-6119176/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":"March 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-03T00:53:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-06 13:56:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6119176","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6119176","identity":"rs-6119176","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}