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Microbiota composition of Culex perexiguus mosquitoes during the West Nile virus outbreak in southern Spain

  • Marta Garrigós ,

    Roles Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing

    marta.garrigos@ebd.csic.es

    Affiliation Department of Conservation Biology and Global Change, Estación Biológica de Doñana (EBD), CSIC, Sevilla, Spain

  • Mario Garrido,

    Roles Visualization, Writing – original draft, Writing – review & editing

    Affiliation Department of Parasitology, Faculty of Pharmacy, University of Granada, Granada, Spain

  • María José Ruiz-López,

    Roles Formal analysis, Investigation, Methodology, Writing – review & editing

    Affiliations Department of Conservation Biology and Global Change, Estación Biológica de Doñana (EBD), CSIC, Sevilla, Spain, CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain

  • María José García-López,

    Roles Formal analysis, Writing – review & editing

    Affiliations Department of Endocrinology and Nutrition, Instituto de Investigación Biomédica de Málaga (IBIMA), Hospital Universitario Virgen de la Victoria, Málaga, Spain, Facultad de Medicina, Universidad de Málaga, Málaga, Spain

  • Jesús Veiga,

    Roles Formal analysis, Investigation, Methodology, Software, Writing – review & editing

    Affiliation Department of Conservation Biology and Global Change, Estación Biológica de Doñana (EBD), CSIC, Sevilla, Spain

  • Sergio Magallanes,

    Roles Investigation, Writing – review & editing

    Affiliations Department of Conservation Biology and Global Change, Estación Biológica de Doñana (EBD), CSIC, Sevilla, Spain, CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain

  • Ramón Soriguer,

    Roles Investigation, Writing – review & editing

    Affiliations Department of Conservation Biology and Global Change, Estación Biológica de Doñana (EBD), CSIC, Sevilla, Spain, CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain

  • Isabel Moreno-Indias,

    Roles Formal analysis, Investigation, Methodology, Writing – review & editing

    Affiliations Department of Endocrinology and Nutrition, Instituto de Investigación Biomédica de Málaga (IBIMA), Hospital Universitario Virgen de la Victoria, Málaga, Spain, CIBER de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Madrid, Spain

  • Jordi Figuerola,

    Roles Conceptualization, Funding acquisition, Investigation, Methodology, Validation, Writing – review & editing

    Affiliations Department of Conservation Biology and Global Change, Estación Biológica de Doñana (EBD), CSIC, Sevilla, Spain, CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain

  • Josué Martínez-de la Puente

    Roles Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – review & editing

    Affiliations Department of Conservation Biology and Global Change, Estación Biológica de Doñana (EBD), CSIC, Sevilla, Spain, CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain

Abstract

West Nile virus (WNV) is a flavivirus naturally circulating between mosquito vectors and birds, occasionally infecting horses and humans and causing epidemiologically relevant outbreaks. In Spain, the first big WNV outbreak was recorded in 2020, resulting in 77 people infected and 8 fatalities, most of them in southern Spain. Culex perexiguus was identified as the primary vector of WNV maintaining its enzootic circulation of the virus. Growing evidence highlights the role of mosquito microbiota as a key component determining the vectorial capacity of mosquitoes, largely contributing to disease epidemiology. Here, we develop, to our knowledge, the first identification of the microbiota composition of this mosquito vector under natural conditions and test for the potential relationship between mosquito microbiota composition and WNV infection. To do so, we collected mosquitoes in a natural area of southern Spain during the 2020 WNV outbreak and identified the microbiota composition of mosquitoes using a 16S rRNA gene metabarcoding approach. The microbiota of Cx. perexiguus was dominated by the phylum Proteobacteria. The most abundant families were Burkholderiaceae and Erwiniaceae, including the genera Burkholderia, Erwinia, and Pantoea. The genus Wolbachia, which use to dominate the microbiota of Cx. pipiens and negatively interact with WNV according to the literature, had a low prevalence and relative abundance in Cx. perexiguus and its abundance did not differ between WNV-positive and WNV-negative mosquito pools. The microbiota diversity and composition of Cx. perexiguus were not significantly related to the WNV infection status. These results provide the first identification of the mosquito microbiota in an endemic area of WNV circulation in Spain.

Introduction

West Nile virus (WNV; Flavivirus; Flaviviridae) is a significant global threat, causing disease in humans and animals worldwide. In nature, WNV is transmitted through mosquito vectors to birds, its main reservoirs, and occasionally to ‘dead-end’ hosts like horses and humans. WNV is underdiagnosed and associated with harmful health, social, and economic consequences [1]. During recent decades, WNV caused different outbreaks in Europe, with a large increase in autochthonous infections occurring in 2018 [2]. In Spain, the local circulation of WNV has been reported since 2003 [3, 4], and the first WNV-associated disease case in humans was reported in 2004. Despite endemic circulation supported by long-term studies using wild birds [5] and feral horses [3], human cases were sporadic in subsequent years (2 cases in 2010 and 3 in 2016). In 2020, a significant outbreak occurred with 77 human cases and 8 fatalities, most of them in Seville province [6], southern Spain. Human infections have also been documented during 2024 [7], supporting the necessity to investigate the factors affecting WNV epidemiology in the area.

Mosquitoes of the Culex (Cx.) genus are the primary WNV vectors, especially the species within the Culex pipiens complex [1, 8]. Yet, most evidence supports the role of other species in the natural circulation of the virus. This is the case of Culex perexiguus which is considered a major vector in southern Spain [9]. WNV has been repeatedly identify in Cx. perexiguus pools captured in the area [10, 11] and the abundance of Cx. perexiguus has been positively associated with the prevalence of WNV in wild bird populations [12]. During the 2020 outbreak, most WNV infections were detected in Cx. perexiguus (33 out of 419 mosquito pools tested using Real-Time RT–PCR; 7.88%), with a much lower incidence in Cx. pipiens (1 out of 152 mosquito pools tested using Real-Time RT–PCR; 0.66%) and a total absence in Cx. modestus (75 mosquito pools tested). Culex perexiguus was identified as the primary vector maintaining WNV enzootic circulation in natural and agricultural areas, while Cx. pipiens potentially aided in the WNV transmission to humans in urban areas [8, see also 9].

Growing evidence highlights mosquito microbiota’s significant role in affecting the different components of the vectorial capacity [13] and in disease transmission control [14, 15]. Mosquito microbiota of wild mosquitoes largely varies between species and areas [16, 17], and its composition may have profound effects on their vector competence and the dynamics of mosquito-borne pathogen transmission. Mosquito microbiota may directly affect the development of pathogens in mosquitoes by the competition with the pathogens for resources [18] and the secretion of anti-pathogen molecules [19], the hindrance of necessary interactions between the pathogen and vector epithelium [20] or the formation of the peritrophic matrix around the blood bolus after blood feeding, which is a barrier against pathogens [19], among other mechanisms. In addition, mosquito microbiota may modulate the immunological responses of mosquitoes finally determining pathogen development [14].

According to studies conducted under controlled conditions, WNV infection has been negatively correlated to Wolbachia relative abundance in Culex mosquitoes [21]. On the contrary, WNV infection positively correlated with the relative abundance of bacteria of the genera Enterobacter and Serratia and to bacterial diversity [21, 22]. However, despite the necessity to identify natural associations between mosquitoes and their microbiota and their effects on pathogen development, studies in the field using Culex mosquitoes are scarce and mainly focus on bacteria of the genus Wolbachia, showing no clear association patterns with WNV infections [21]. Furthermore, the microbiota of Cx. perexiguus and its relationship with pathogens such as WNV is virtually unknown.

Here, we characterize the bacterial community of the Cx. perexiguus microbiota and investigate the potential relationship between its bacterial diversity and composition and the WNV infection status. We used field-collected Cx. perexiguus mosquito females from a locality of the Seville province captured in August 2020, during the WNV outbreak in southern Spain, to identify the ecological interactions occurring under natural conditions.

Material and methods

Mosquito collection

Mosquitoes were collected on 27th of August 2020 in La Dehesa de Abajo (Seville, southern Spain; 6°14’W, 36°57’N), close to the area where the main WNV human outbreak occurred in 2020. La Dehesa de Abajo is a natural protected area surrounded by rice fields which include a lagoon hosting a diversity of bird species. Mosquitoes were captured using Biogents (BG)-sentinel-2 traps (Biogents, Regensbourg, Germany) supplemented with CO2. Trapping of mosquitoes was done with all the necessary permits from landowners and the local authorities (Consejería de Medio Ambiente, Junta de Andalucía). Trapped mosquitoes were sexed and identified at species level using available morphological keys [2325]. A total of 1,000 Cx. perexiguus females, the only hematophagous sex, with no signs of recent blood ingestion (to avoid virus amplification from bloodmeals) were grouped into 100 pools of 10 mosquitoes each and maintained frozen (-80°C) until molecular analyses.

Molecular procedures

RNA and DNA were simultaneously extracted from each mosquito pool with the Maxwell® extraction robot and the Viral Total Nucleic Acid Purification kit (Promega, Madison, Wisconsin, USA), following manufacturer instructions. This procedure allows the extraction of both RNA and DNA from the samples. RNA was subsequently used to screen for WNV infection and DNA for mosquito microbiota analyses. The surface of mosquitoes was not sterilized before the extraction of genetic material to avoid RNA degradation in the samples. Non-significant differences have been previously found in the bacterial community of surface sterilized and non-sterilized insects [26].

The WNV infection status was tested using a RT–PCR protocol that amplifies all the known WNV lineages [27]. WNV-positive samples (n = 19) and a comparable number of WNV-negative mosquito pools (n = 21) were used for the molecular characterization of the bacterial microbiota of mosquitoes. In these samples, DNA concentration and purity were estimated with a Nanodrop spectrophotometer (Nanodrop Technologies, Wilmington, Delaware, USA). For microbiota analyses, libraries from each mosquito pool were built with the Ion 16S Metagenomics kit (Thermofisher, Waltham, Massachusetts, USA), consisting of primer pools to amplify multiple variable regions (V2, 3, 4, 6–7, 8 and 9) of the 16S rRNA. After generating amplicons, the Ion PlusTM Fragment Library Kit (Thermofisher, Waltham, Massachusetts, USA) was used to ligate barcoded adapters and synthesize libraries. Barcoded libraries from all the samples were pooled and templated on the automated Ion Chef system (Thermofisher, Waltham, Massachusetts, USA) followed by a 400 bp sequencing on the Ion S5 (Thermofisher, Waltham, Massachusetts, USA).

The quality of the reads was checked using FastQC (ver. 0.12.1) [28] and MultiQC (ver. 1.17) [29]. Bacteria sequences obtained from mosquito pools were translated into amplicon sequence variants (ASVs) using DADA2 [30] with the microbiome analysis package QIIME2 (ver. 2023.5.1) [31]. QIIME2 was also used to assign each ASV to a taxonomic group with an identity of 99% based on the SILVA database (ver. 138.1) [32]. Subsequently, reads were filtered removing singletons, reads with low taxonomic resolution (below phylum), and non-bacterial, chloroplast, and mitochondrial ASVs. Furthermore, rarefaction curves were plotted to ensure that all samples had enough sequencing depth to capture the bacterial community diversity (See S1 File for the script of QIIME2 analysis).

Statistical analyses

Statistical analyses and graphical representations were carried out in R [33] with the Bioconductor packages qiime2R (ver. 0.99.6) [34], phyloseq (ver. 1.38.0) [35], microViz (ver.0.11.0) [36], and microbiome (ver. 1.16.0) [37]. WNV prevalence was calculated using Epitools (https://epitools.ausvet.com.au) considering mosquito pools of equal size and assuming 100% sensitivity and specificity. The most abundant taxa were described from phylum to genus levels. In addition, we assessed the prevalence and relative abundance of the genera Wolbachia, Serratia, and Enterobacter because of their previously reported correlation with WNV infection in Culex mosquitoes [16, 22]. Most statistical analyses were performed at the family level as it provided the greatest certainty about taxonomic identification. ASVs with a taxonomic resolution below family or ambiguous family annotation were not included in the analyses. Bacterial alpha diversity (within-sample diversity) was estimated through observed richness and Shannon index. The Shannon index takes into account both species richness and evenness within a sample, being higher when the number of taxa is higher and more evenly distributed. Beta-diversity (between-sample diversity) was explored by ordination analyses including a Principal Coordinate Analysis (PCoA) of both Bray-Curtis dissimilarity matrix for family abundances and Jaccard similarity matrix for family presence/absence. The relationship between mosquito microbiota and WNV infection was assessed using linear models (LMs) to test if the observed richness or the Shannon index varied according to the WNV infection status. To assess the association between mosquito microbiota beta diversity and WNV infection, we performed a PERmutational Multivariate ANalysis Of VAriance (PERMANOVA) to test whether PCoA ordination analyses for Bray-Curtis dissimilarity and Jaccard similarity matrices clustered samples by WNV infection status. Statistical significance of F-values obtained from PERMANOVA was determined by comparison to 999,999 permutations. Differences in the microbiota composition in mosquito pools according to the WNV infection were checked for all taxonomic levels from phylum to species when available. We first filtered taxa with a minimum prevalence of 10% (to avoid spurious ASVs) and then fitted a LM with a log2-transformed response including the relative abundance of all taxa found in the filtered dataset as the dependent variable and the WNV infection status as the independent variable. The Benjamini-Hochberg procedure was used to adjust the p-values for multiple testing. The level WNV-negative was set as the reference level in all LMs including the WNV infection status as the independent variable (the R script is shown in S2 File to R script).

Results

Overall, 1,000 Cx. perexiguus females grouped in 100 pools of 10 mosquitoes each were molecularly tested for the presence of WNV. Of them, 19 mosquito pools were positive (19%), representing an estimated prevalence of 0.02 (Confidence limits 2.5%: 0.01; 97.5%: 0.03; SE: 0.0047). These 19 WNV-positive mosquito pools together with 21 WNV-negative mosquito pools were used for the subsequent analyses.

Characterization of Cx. perexiguus microbiota

We analyzed the microbiota of 40 Cx. perexiguus pools obtaining a total of 6,453,096 reads, ranging from 46,513 to 419,497 (average = 161,327.4). The rarefaction curves reached saturation for all samples, indicating that our sampling effort captured most of the bacterial diversity (S1 Fig). We identified a relatively rich bacterial community in Cx. perexiguus pools with a total of 4,434 ASVs belonging to 18 phyla, 33 classes, 85 orders, 168 families, and 410 genera.

The microbiota of Cx. perexiguus mosquito pools was dominated by the phylum Proteobacteria. The Burkholderiaceae family was the most abundant (average relative abundance = 0.676, SD = 0.150) except in two samples. In these two samples, the Erwiniaceae family was the most abundant (average relative abundance = 0.085, SD = 0.137). Following these two families, the next most abundant families in mosquito pools were Rhodobacteraceae, Cellulomonadaceae, Micrococcaceae, Microbacteriaceae, and Enterobacteriaceae (Fig 1A; S2 Fig). Burkholderia (Burkholderiaceae) was the most abundant genus in mosquito pools (average relative abundance = 0.696, SD = 0.152), followed by Erwinia and Pantoea (Erwiniaceae) with a relative abundance of 0.050 (SD = 0.091) and 0.037 (SD = 0.093), respectively. The genera Wolbachia, Serratia, and Enterobacter were identified in 4, 4 and 33 mosquito pools, with a maximum relative abundance of <0.001, 0.002, and 0.004, respectively.

thumbnail
Fig 1.

A: Relative abundance of the 12 most abundant taxa at family level in negative and positive Cx. perexiguus mosquito pools for West Nile virus (WNV). B: Distribution of the Shannon diversity index (alpha-diversity) at the family level of Cx. perexiguus mosquito pools according to the infection status by WNV. The vertical lines go from the lower and upper quartiles to the minimum or maximum, respectively, and the horizontal line represents the median. C: Principal Co-ordinates Analysis (PCoA) for Bray-Curtis distance matrix (relative abundance) by WNV infection at family level. Percentages shown in MDS1 and MDS2 axes refer to the percentage of variation explained by each of the two selected main coordinate axes.

https://doi.org/10.1371/journal.pone.0314001.g001

Relationships between Cx. perexiguus microbiota and WNV infection

We did not find any significant difference in the alpha and beta diversity (Fig 1B and 1C; S3 and S4 Figs) of Cx. perexiguus pools according to the WNV infection status (all P > 0.05). Likewise, we did not detect significant differences in the relative abundances of any taxa, at any taxonomic level, between mosquito pools based on their infection status (all P > 0.05). This was also true for analyses performed for bacteria of the genera Wolbachia, Serratia, and Enterobacter.

Discussion

Mosquito microbiota has been highlighted as a major driver of vector competence by affecting the development of pathogens directly and indirectly, supporting the importance of studying their composition in natural mosquito populations. To our knowledge, here we characterized for the first time the microbiota composition of field-collected Cx. perexiguus mosquito females captured in an area of endemic circulation of vector-borne pathogens, including the zoonotic WNV.

Using a metabarcoding approach, we identified a relatively rich bacterial community in female Cx. perexiguus pools representing 410 different genera. Among them, bacteria of the genera Burkholderia (Burkholderiaceae), Erwinia and Pantoea (Erwiniaceae) dominated, in terms of relative abundance, the microbiota of Cx. perexiguus females. These genera are commonly found in Aedes spp. and Anopheles spp. mosquitoes, both in the wild and laboratory colonies [38]. However, they are rare in the microbiota of Culex mosquitoes [39]. In addition, Wolbachia showed a low prevalence in the studied mosquito population. While Cx. pipiens has a very high prevalence of Wolbachia in different populations [40], other related species of the Culex genus may show an opposite pattern [40, 41]. This is the case of species including Culex torrentum [41], Culex restuans [42] or Cx. perexiguus, as shown here. These results suggest that the microbiota composition of mosquitoes may largely differ between species, including those breeding in the same area [41, 42]. However, our results are limited by the fact that we only sampled mosquitoes a single population and time point, thus further studies are necessary to identify the natural variation of the microbiota composition of this species in a broader study area.

Mosquito microbiota has been highlighted as a major driver of vector competence by affecting the development of pathogens both directly and indirectly. Different field and laboratory studies on Culex spp. have associated WNV infection with changes in the microbiota community composition of mosquitoes, including changes in the abundance of the most prevalent microbiota genera [21]. For instance, Zink et al. [22] exposed Cx. pipiens mosquitoes to WNV and observed that WNV-exposed and infected mosquitoes showed lower relative abundance of Wolbachia but higher relative abundance of bacteria of the genera Enterobacter and Serratia and bacterial diversity. In this study, authors also observed an up-regulation of numerous genes related to the mosquito innate immune system, suggesting an indirect interaction between WNV and the mosquito microbiome through the immune response. On the other hand, Wolbachia is known to block replication of WNV and other viruses such as dengue, Chikungunya, and West Nile virus [18, 43, 44], while its absence may affect the susceptibility to pathogen infection by mosquitoes. For example, Wolbachia-free Cx. modestus mosquitoes are major vectors of WNV in endemic regions, relegating sympatric Cx. pipiens to a secondary role [41, 45]. Likewise, according to our results, Cx. perexiguus, the primary vector of WNV during the 2020 Spanish outbreak [9], exhibits a low prevalence of Wolbachia potentially explaining, at least in part, the role of this species in the transmission of WNV in the area. In addition, the low presence of this taxa in Cx. perexiguus could potentially explain the absence of differences in the microbiota composition of mosquitoes according to their WNV infection status. Further studies using Cx. perexiguus mosquitoes reared under controlled conditions could help to assess the link between mosquito microbiota and WNV-infection in wild mosquitoes in the future. These studies may consider using Wolbachia bacteria (strain wMel), which reduce the lifespan and partially block viral infections in other mosquito species such as Aedes aegypti [18, 46].

Conclusion

This study provides the first information about the microbiome composition of Cx. perexiguus, a major WNV vector in southern Spain, during the largest WNV outbreak ever recorded in the country. The low prevalence of Wolbachia in Cx. perexiguus mosquitoes could potentially explain, at least in part, the relevance of this mosquito species in the WNV transmission and makes necessary experimental tests to identify the impact of mosquito microbiota composition on Cx. perexiguus vector capacity. However, in order to conduct these studies, previous information about the composition of the microbiota of mosquitoes in the wild is essential.

Supporting information

S1 Fig. Rarefaction curves of the microbiota analyses of each Cx. perexiguus mosquito pool included in the study.

Rarefaction of the samples consists in randomly keep a specific number of sequencing reads from the sample, removing the rest of the reads. The rarefaction curve represents the number of ASVs present in each sample (y-axis) when rarified to different number of reads (x-axis). If a sample plateau, it indicates that its sequencing depth was sufficient to represent the bacterial diversity in that sample.

https://doi.org/10.1371/journal.pone.0314001.s001

(PNG)

S2 Fig. Heatmap of Centered Log Ratio (CLR) transformation of taxa relative abundance at family level.

The figure shows the 15 most abundant families. Higher CLR values are colored red and correspond to higher relative abundances, while lower CLR values are colored blue and correspond to lower relative abundances. In the legend above the graph blue corresponds to WNV-positive samples and light blue to WNV-negative samples. The tree below the graph shows the samples grouped according to the similarity of microbiota composition based on Euclidean distances.

https://doi.org/10.1371/journal.pone.0314001.s002

(PDF)

S3 Fig. Distribution of the microbiota richness at the family level of Cx. perexiguus mosquito pools according to the infection status by WNV.

The vertical lines go from the lower and upper quartiles to the minimum or maximum, respectively, and the horizontal line represents the median.

https://doi.org/10.1371/journal.pone.0314001.s003

(PDF)

S4 Fig. Principal Co-ordinates Analysis (PCoA) for Jaccard matrix of the microbiota Cx. perexiguus mosquito pools at family level according to the infection status by WNV.

The percentage of variation explained by each component (axis) is shown in square brackets.

https://doi.org/10.1371/journal.pone.0314001.s004

(PDF)

Acknowledgments

We thank the contribution of Álvaro Solís during the fieldwork. Two anonymous reviewers provided valuable comments on a previous version of the manuscript

References

  1. 1. Klingelhöfer D, Braun M, Kramer IM, Reuss F, Müller R, Groneberg DA, et al. A virus becomes a global concern: research activities on West-Nile virus. Emerg Microbes & Infect. 2023;12:2256424. pmid:37671854
  2. 2. ECDC: Epidemiological update: West Nile virus transmission season in Europe, 2018. 2018 Dec 14 [cited 2024 Apr 02]. In: European Centre for Disease Prevention and Control [Internet]. Available from: https://www.ecdc.europa.eu/en/news-events/epidemiological-update-west-nile-virus-transmission-season-europe-2018.
  3. 3. Magallanes S, Llorente F, Ruiz-López MJ, Martínez-de la Puente J, Soriguer R, Calderon J, et al. Long-term serological surveillance for West Nile and Usutu virus in horses in south-West Spain. One Health. 2023;17:100578. pmid:38024263
  4. 4. Figuerola J, Soriguer R, Rojo G, Tejedor CG, Jimenez-Clavero MA. Seroconversion in wild birds and local circulation of west Nile virus, Spain. Emerg Infect Dis. 2007;13:1915–1917. pmid:18258046
  5. 5. Magallanes S, Llorente F, Ruiz-López MJ, Martínez-de la Puente J, Ferraguti M, Gutiérrez-López R, et al. Warm winters are associated to more intense West Nile virus circulation in southern Spain. Emerg Microbes & Infect. 2024;13:2348510. pmid:38686545
  6. 6. García San Miguel Rodríguez-Alarcón L, Fernández-Martínez B, Sierra Moros MJ, Vázquez A, Julián Pachés P, García Villacieros E, et al. Unprecedented increase of West Nile virus neuroinvasive disease, Spain, summer 2020. Euro Surveill. 2021;26:2002010. pmid:33988123
  7. 7. ECDC: Weekly updates: 2024 West Nile virus transmission season. 2024 Aug 1 [cited 2024 Aug 10]. In: European Centre for Disease Prevention and Control [Internet]. Available from: https://www.ecdc.europa.eu/en/west-nile-fever/surveillance-and-disease-data/disease-data-ecdc.
  8. 8. Figuerola J, Jiménez-Clavero MÁ, Ruíz-López MJ, Llorente F, Ruiz S, Hoefer A, et al. A One Health view of the West Nile virus outbreak in Andalusia (Spain) in 2020. Emerg Microbes & Infect. 2022;11:2570–2578. pmid:36214518
  9. 9. Ferraguti M, Heesterbeek H, Martínez-de la Puente J, Jiménez-Clavero MÁ, Vázquez A, Ruiz S, et al. The role of different Culex mosquito species in the transmission of West Nile virus and avian malaria parasites in Mediterranean areas. Transbound. Emerg. Dis. 2021;68(2):920–930.
  10. 10. Vázquez A, Ruiz S, Herrero L, Moreno J, Molero F, Magallanes A, et al. West Nile and Usutu viruses in mosquitoes in Spain, 2008–2009. Am J Trop Med Hyg. 2011;85(1):178–181. pmid:21734145
  11. 11. Ruiz-López MJ, Muñoz-Chimeno M, Figuerola J, Gavilán AM, Varona S, Cuesta I, et al. Genomic analysis of West Nile virus lineage 1 detected in mosquitoes during the 2020–2021 outbreaks in Andalusia, Spain. Viruses. 2023;15(2):266. pmid:36851481
  12. 12. Martínez-de la Puente J, Ferraguti M, Ruiz S, Roiz D, Llorente F, Pérez-Ramírez E, et al. Mosquito community influences West Nile virus seroprevalence in wild birds: implications for the risk of spillover into human populations. Sci Rep. 2018:8(1):1–7.
  13. 13. Cansado-Utrilla C, Zhao SY, McCall PJ, Coon KL, Hughes GL. The microbiome and mosquito vectorial capacity: rich potential for discovery and translation. Microbiome. 2021;9(1):111. pmid:34006334
  14. 14. Caragata EP, Dutra HLC, Sucupira PHF, Ferreira AGA, Moreira LA. Wolbachia as translational science: controlling mosquito-borne pathogens. Trends Parasitol. 2021;37:1050–1067.
  15. 15. Gabrieli P, Caccia S, Varotto-Boccazzi I, Arnoldi I, Barbieri G, Comandatore F, et al. Mosquito trilogy: Microbiota, immunity and pathogens, and their implications for the control of disease transmission. Front Microbiol. 2021;12:630438. pmid:33889137
  16. 16. Novakova E, Woodhams DC, Rodríguez-Ruano SM, Brucker RM, Leff JW, Maharaj A, et al. Mosquito microbiome dynamics, a background for prevalence and seasonality of west Nile virus. Front Microbiol. 2017;8:00526. pmid:28421042
  17. 17. Muturi EJ, Lagos-Kutz D, Dunlap C, Ramirez JL, Rooney AP, Hartman GL, et al. Mosquito microbiota cluster by host sampling location. Parasit Vectors. 2018;11:468. pmid:30107817
  18. 18. Moreira LA, Iturbe-Ormaetxe I, Jeffery JA, Lu G, Pyke AT, Hedges LM, et al. A Wolbachia symbiont in Aedes aegypti limits infection with dengue, Chikungunya, and Plasmodium. Cell. 2009;139(7):1268–78.
  19. 19. Azambuja P, Garcia ES, Ratcliffe NA. Gut microbiota and parasite transmission by insect vectors. Trends Parasitol. 2005;21(12):568–72. pmid:16226491
  20. 20. Kumar S, Molina-Cruz A, Gupta L, Rodrigues J, Barillas-Mury C. A peroxidase/dual oxidase system modulates midgut epithelial immunity in Anopheles gambiae. Science. 2010;327(5973):1644–8.
  21. 21. Garrigós M, Garrido M, Panisse G, Veiga J, Martínez-de la Puente J. Interactions between West Nile Virus and the microbiota of Culex pipiens Vectors: A literature review. Pathogens. 2023;12:1287.
  22. 22. Zink S, Van Slyke G, Palumbo M, Kramer L, Ciota A. Exposure to west Nile virus increases bacterial diversity and immune gene expression in Culex pipiens. Viruses. 2015;7(10):5619–5631
  23. 23. Schaffner E, Angel G, Geoffroy B, Hervy G-P, Rhaiem A, Brunhes J. The Mosquitoes of Europe: An Identification and Training Programme. 2001. Paris (FRA); Montpellier: IRD; EID, 1 CD ROM. (Didactiques).
  24. 24. Gunay F, Picard M, Robert V. MosKeyTool, an interactive identification key for mosquitoes of Euro-Mediterranean. 2018. Version 2.1. Available from: http://www.medilabsecure.com/moskeytool.
  25. 25. Harbach. The identity of Culex perexiguus Theobald versus ex. univittatus Theobald in southern Europe. European Mosq Bull. 1999;4:7.
  26. 26. Hammer TJ, Dickerson C, Fierer N. Evidence-based recommendations on storing and handling specimens for analyses of insect microbiota. PeerJ. 2015:3:e1190. pmid:26311208
  27. 27. Vázquez A, Herrero L, Negredo A, Hernández L, Sánchez-Seco MP, Tenorio A. Real time PCR assay for detection of all known lineages of West Nile virus. J Virol Methods, 2016;236:266–270. pmid:27481597
  28. 28. Andrews S. FastQC: A quality control tool for high throughput sequence data. 2010. [Online].
  29. 29. Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32:3047–3048. pmid:27312411
  30. 30. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–583. pmid:27214047
  31. 31. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–857. pmid:31341288
  32. 32. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2012;41(D1):D590–D596. pmid:23193283
  33. 33. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2022. https://www.R-project.org/
  34. 34. Bisanz JE. qiime2R: Importing QIIME2 artifacts and associated data into R sessions. 2018. https://github.com/jbisanz/qiime2R.
  35. 35. McMurdie PJ, Holmes S. Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PloS One. 2013;8:e61217. pmid:23630581
  36. 36. Barnett D, Arts I, Penders J. microViz: an R package for microbiome data visualization and statistics. J Open Source Softw. 2021;6:3201.
  37. 37. Lahti L, Shetty S. (2012–2019). microbiome R package. http://microbiome.github.io
  38. 38. Scolari F, Casiraghi M, Bonizzoni M. Aedes spp. and their microbiota: A review. Front Microbiol. 2019;10:02036.
  39. 39. Minard G, Mavingui P, Moro CV. Diversity and function of bacterial microbiota in the mosquito holobiont. Parasit Vectors. 2013;6:146. pmid:23688194
  40. 40. Inácio da Silva LM, Dezordi FZ, Paiva MHS, Wallau GL. Systematic review of Wolbachia symbiont detection in mosquitoes: An entangled topic about methodological power and true symbiosis. Pathogens. 2021;10:39.
  41. 41. Bergman A, Hesson JC. Wolbachia prevalence in the vector species Culex pipiens and Culex torrentium in a Sindbis virus-endemic region of Sweden. Parasit Vectors. 2021;14:428.
  42. 42. Muturi EJ, Kim C-H, Bara J, Bach EM, Siddappaji MH. Culex pipiens and Culex restuans mosquitoes harbor distinct microbiota dominated by few bacterial taxa. Parasit Vectors. 2016;9:18
  43. 43. Alomar AA, Pérez-Ramos DW, Kim D, Kendziorski NL, Eastmond BH, Alto BW, et al. Native Wolbachia infection and larval competition stress shape fitness and West Nile virus infection in Culex quinquefasciatus mosquitoes. Front Microbiol. 2023;14:1138476.
  44. 44. Glaser RL, Meola MA. The native Wolbachia endosymbionts of Drosophila melanogaster and Culex quinquefasciatus increase host resistance to West Nile virus infection. PLoS One 2010; 5(8):e11977.
  45. 45. Soto A, Delang L. Culex modestus: the overlooked mosquito vector. Parasit Vectors. 2023;16:373.
  46. 46. McMeniman CJ, Lane RV, Cass BN, Fong AWC, Sidhu M, Wang Y-F, et al. Stable introduction of a life-shortening Wolbachia infection into the mosquito Aedes aegypti. Science 2009;323:141–144.