Background

The microbial community in residence in the mosquito midgut plays a key role in determining the outcome of the pathogen infection of mosquitoes [1,2,3,4,5,6,7,8]. Midgut microbiota interferes with pathogen infection by activating the basal immunity of the mosquito [3, 4, 9] as well as releasing metabolites that contain antimicrobial properties [10]. Indeed, due to their potential as candidates for the development of transmission-blocking agents, investigators have studied the interaction of gut microbial taxa and arboviruses of medical importance extensively [2,3,4, 11,12,13,14,15]. At the field application level, Wolbachia, a maternally inherited intracellular bacteria, has demonstrated significant success in containing the spread of dengue and Zika on massive scales [16, 17]. Evidence shows the activation of mosquito immune responses by Wolbachia subsequently limits viral replication [18].

Midgut microbiota is also present in the arthropod saliva and can be egested at the skin bite site [19,20,21]. Indeed, past findings have demonstrated that the sandfly midgut microbiome is egested into the host skin together with Leishmania parasites [19]. The midgut microbiota egested on the host skin triggers the inflammasome to produce Interleukin-1 beta (IL-1β), a cytokine whose main role is inflammation and immune-amplifying effects, resulting in sustained recruitment of neutrophils. The neutrophils shield the Leishmania parasites thus enhancing parasite visceralization and disease severity [19]. The same study demonstrated that the reduction of midgut microbiota by pretreatment of Leishmania-infected sandflies with antibiotics results in a reduction in neutrophil recruitment and impairment of parasite visceralization [19]. Taken together, the egestion of arthropod-borne microbiota at the bite site may be a conserved mechanism [19], yet there is a lack of knowledge on how the mosquito saliva microbiome interacts with arthropod-borne viruses contained in the saliva. This knowledge is important as it can lead to an improved understanding of the initial stages of virus transmission from the mosquito saliva to the human host and the identification of potential targets for transmission barrier development. However, before these studies can be conducted, we need to gain a greater understanding of the bacterial taxa composition of the mosquito saliva.

Indeed, mosquito saliva is important in aiding the replication and dissemination of viruses in vertebrate hosts. In a separate study, Ae. aegypti mosquito bites followed by injection of a known viral titer of Semliki Forest virus (SFV) and Bunyamwera virus (BUNV) inoculums resulted in a significantly higher virus RNA copy number in the skin at the inoculation site compared with unbitten mice that were injected with similar virus titer [22]. The study concluded that mosquito saliva augmented virus replication in the myeloid cells and dissemination leading to a more severe outcome. The myeloid cells were attracted to the bite site by a neutrophil-driven inflammasome-dependent skin bite site edema inflammation. Neutrophil depletion resulted in repressed inflammation and the inability of the mosquito bite to enhance viral replication [22]. Furthermore, recent reports demonstrate that Anopheline mosquito saliva contains bacteria that are transferred to a mammalian host through blood feeding [20]. The report demonstrated a successful dual colonization of mammalian host tissues by bacteria and Plasmodium berghei transmitted through the Anopheles spp. mosquito saliva [20]. In addition, An. gambiae infected with Rickettsia felis causes a systemic infection in mice [21].

Ae. albopictus (Skuse) (Subgenus: Stegomyia; Diptera: Culicidae) is a medically important vector of viruses that causes diseases of major global public health concerns and economic burdens such as dengue virus (DENV), chikungunya virus (CHIKV) and Zika virus (ZIKV) [23, 24]. It is quite invasive, globally spread to over 129 countries [25]. Ae. albopictus demonstrates high ecological plasticity, is evenly distributed in suburban, rural, and sylvatic habitats, and can utilize both clean and stagnant water to hatch its eggs [26]. Other than the pathogens that they transmit, mosquitoes harbor other microorganisms such as bacteria [26,27,28]. Consumption of water, nectar, or other environmental food sources acts as a source of microbiota associated with Ae. albopictus [29,30,31]. The microbial composition of Ae. albopictus differs across tissues, life stages, individuals, and geographical regions [32, 33]. In addition, past studies demonstrate that viral infection of the mosquito midgut impacts its dynamic bacterial community. ZIKV infection, for instance, reduces the Ae. albopictus midgut microbial diversity[34], while infection with La Crosse orthobunyavirus lowers the midgut microbial richness of Ae. japonicus and Ae. triseriatus [35]. Lastly, exposure of Culex pipiens to West Nile Virus infection results in a lower bacterial diversity [36]. Despite past findings on the significant impact of viral infection on the microbiome composition of the mosquito midgut, the effect of viral infection on the mosquito saliva microbiome community is poorly studied. In the present study, we hypothesized that there are conserved bacterial taxa in the saliva and midgut of Ae. albopictus and that ZIKV infection may alter the microbial composition of Ae. albopictus saliva and midgut. We tested our hypotheses by analyzing the microbial profile of Ae. albopictus midgut and saliva exposed to a ZIKV-infected and uninfected blood meal. In the future, our findings can be built upon to understand the interaction of Ae. albopictus saliva microbiome and ZIKV. Further, our findings can be utilized as an avenue for an innovative approach to develop a transmission-blocking tool.

Methods

Virus

The ZIKV strain utilized in this study was HND (2016-19563, GenBank accession no. KX906952). The Zika virus isolate used in this study was obtained from an infected patient serum. In the lab, virus stocks were created by passaging three times on Vero cell culture (ATCC) and once on C636 cell culture (ATCC). Stocks were frozen at −70 °C before use.

Insect sampling and sample preparation

Ae. albopictus mosquitoes were collected from Suffolk County, New York State (NYS) in 2015 (kindly provided by I. Rochlin) and colonized at the New York State Department of Health (NYSDOH) Arbovirus Laboratory. The F15 generation Ae. albopictus eggs were vacuum hatched. The larvae that emerged from the hatched eggs were maintained in plastic rectangular flat containers (35.6 cm length × 27.9 cm width × 8.3 cm height; Sterilite, catalog no. 1963) at a density of one larva per 5 ml of dechlorinated water and reared at 60% relative humidity and a light–dark (LD) photoperiod of 14:10 h. The larvae were fed 1.25 mg/larvae of Tetra Pond Koi growth feed for first and second instar larvae and 2.5 mg/larvae for third and fourth instar larvae [37]. Upon emergence, the male and female adults were transferred to 3.8-l containers and housed together for 8 days at 28 °C at a relative humidity of 60% while being provided with sugar and water ad libitum [37]. To stimulate blood feeding, females were starved of water and sugar for 12 h before infectious blood meal according to our insectary laboratory standard operating procedures. The blood meal consisted of either 1:1 dilution of defibrinated sheep blood plus 2.5% sucrose, sodium bicarbonate (to adjust pH to 8.0) and virus, or a non-infectious blood meal containing a final concentration of 2.5% sucrose solution. Infectious blood meals contained 8.3 log10 PFU/ml ZIKV HND [38]. The female mosquitoes were exposed to blood meals in a 37 °C preheated Hemotek membrane feeding system (Discovery Workshops, Accrington, UK) with a porcine sausage casing membrane. After an hour, the mosquitoes were anesthetized with CO2 and immobilized on a prechilled tray connected to 100% CO2 Engorged females were separated and placed in separate 0.6 l cardboard cartons (30 individuals per carton). Blood-fed females were maintained on a 10% sucrose solution provided ad libitum.

At 14 days postinfection (dpi), the female mosquitoes were immobilized using triethylamine (Sigma Aldrich, St. Louis, MO, USA). The mosquitoes were surface sterilized by dipping twice in 70% ethanol. To examine for ZIKV dissemination, the legs were removed from the mosquitoes and placed in individual tubes. Saliva was collected under sterile conditions by inserting the proboscis of the surface-sterilized female into a capillary tube containing ~ 20 µl filter sterilized fetal bovine serum plus 50% sucrose 1:1 for 30 min before subsequently ejecting the mixture into filter-sterilized 125 µl Mosquito Diluent [MD; 20% heat-inactivated fetal bovine serum in Dulbecco phosphate-buffered saline plus 50 µg/ml penicillin/streptomycin, 50 µg/ml gentamicin and 2 µg/ml Fungizone (Sigma Aldrich, St. Louis, MO, USA)]. As described in [39], briefly, to dissect the salivary glands, the tip of the abdomen was removed with a sharp cut using a dissecting probe. Placing a probe in the thorax, the head was removed gently by pulling away from the thorax. The salivary glands were then detached from the head using the probe, rinsed twice in sterilized phosphate-buffered Saline (PBS) (Sigma Aldrich, St. Louis, MO, USA), and transferred to a sterile microcentrifuge tube and stored at −80 °C. The midgut was pulled from the tip of the abdomen and rinsed twice in sterile PBS before being transferred to sterile microcentrifuge tubes and stored at −80 °C until tested.

The individual mosquito carcasses were then transferred into individual tubes sterile microcentrifuge tubes. All samples were held at −80 °C until assayed.

Sequencing and analysis of microbiome of Aedes mosquito saliva and midguts

A ZIKV-specific quantitative PCR assay that targets the NS1 region [40] was utilized to obtain viral titer from the legs and saliva as described by [38]. The individuals whose midguts and saliva samples were infected with the virus were identified. ZIKV-infected saliva and midgut samples as well as those exposed to a naïve blood meal were used for downstream analysis.

We experienced challenges in obtaining enough genomic DNA material from singleton saliva samples and hence to increase the amount of genomic material for microbiome study, we adopted a whole transcriptome amplification approach. RNA was isolated from the saliva and midgut samples using Trizol (Thermo Fisher Scientific, Waltham, MA, USA). The saliva and midgut RNA samples were reverse transcribed, followed by whole transcriptome amplification according to the REPLI-g whole transcriptome amplification (WTA) single-cell kit protocol (Qiagen, Hilden, Germany). The resulting amplified complementary DNA (cDNA) was diluted 1:100. Thereafter, the 16S V3–V4 hypervariable region was amplified using an Illumina barcoded 16S primer set [16S_341F (TCG TCG GCA GCG TCA GAT GTG TAT AAG AGA CAG CCT ACG GGN GGC WGC AG) and 16S_805R(GTC TCG TGG GCT CGG AGA TCT GTA TAA GAG ACA GGA CTA CHV GGG TAT CTA ATC C)] [36]. PCR reactions were carried out in a total volume of 50 µl, 5 µM each of 16S ribosomal RNA (rRNA) primer pair, 2 µl WTA cDNA, 8 µl deionized filter-sterilized water, and 36 µl AccuStart II PCR supermix (Quantabio, Beverly, MA, USA). We included two negative controls at the PCR amplification step: water and nontemplate control. In addition, a positive spike at the sequencing step. A fragment size of  ~  460 bp of each sample was submitted to the Wadsworth Center Applied Genomics Core for sequencing together with the negative controls. Automated cluster generation and paired-end sequencing (250-bp reads) was performed on the Illumina MiSeq 500 cycle.

A total of 30 samples [five infected midguts (INF MG), six noninfected midguts (NINF MG), ten infected saliva (INF SAL), six noninfected saliva (NINF SAL), one negative control (NC), one nontemplate control (NTC), and one positive spike (PC)] were included in this study. Analysis of the data was carried out on QIITA (https://qiita.ucsd.edu/) and MicrobiomeAnalyst [41]. In summary, the command split libraries FASTQ was used to convert the de-multiplexed FASTQ files to the format used by QIITA. This was followed by clustering of the sequences into operational taxonomic units (OTUs), which was done by the utilization of a closed-reference OTU picking based on the GreenGenes 16S reference database. The choice of closed reference was due to its fully parallelizable process, which makes it a computationally efficient process. We neither elected to utilize open reference nor de novo OTU picking due to the computationally complex strategies that make them unsuitable for large data sets. Open-reference OTU picking reduces read loss by de novo clustering reads that do not match the reference sequences; some of the steps of the workflow run serially and hence slows down the process. De novo OTU picking, on the other hand, clusters reads against one another without an external reference collection, is computationally complex, results in large data set loss, and can reduce quantification accuracy while reducing the potential to detect rare taxa [42].

A 97% sequence identity threshold was utilized to assign taxonomy to the sequence. A BIOM-formatted OTU table was subsequently generated. To estimate alpha diversity metrics, we adjusted for differences in library sizes across samples using rarefaction and generated rarefaction curves to assess whether the sequencing depth was sufficient. Rarefaction involves picking a specified number of samples equal to or less than the number of samples in the smallest sample followed by a random removal of reads from larger samples until the number of remaining samples is equal to this threshold [43]. The reads have been deposited in NCBI GenBank Short Read Archives.

We anticipated the presence of rare taxa in our microbial community. To accommodate the rare taxa while estimating diversity, we utilized Chao1 diversity measure at the family taxonomic level to assess species richness of INF and NINF saliva and INF and NINF MG. Statistical significance was tested using a t-test assuming a normal distribution of values. Chao1 index was used to estimate alpha diversity because in estimating the diversity it makes inferences about rare or difficult-to-detect species [44]. We compared these results with other ecological indices used to test for alpha diversity such as the Shannon diversity and Simpson index. The Shannon diversity index considers both the richness and evenness of the microbial species present. It calculates this by utilizing the proportion of species i relative to the total number of species (pi) and multiplying by the natural logarithm of the proportion (lnpi) [45]. The Simpson Index tests for species dominance by considering the total number of species present as well as the relative abundance of each species. In this diversity index, species richness and evenness are directly proportional to diversity [46].

To compare the microbial community composition between the global INF and NINF saliva and INF and NINF MG, beta diversity was measured utilizing the Bray–Curtis dissimilarity statistic at the family taxonomic level [47]. The beta diversity measurement utilized the permutational analysis of variance (PERMANOVA) test to estimate the differences in microbial communities. PERMANOVA is a nonparametric multivariate statistical permutational test measured through a geometric partitioning of multivariate variation of a dissimilarity measure according to a given ANOVA design [48]. The distance matrix generated after comparing each sample to the other was visualized for the dissimilarity between samples using the principal coordinate analysis ordination method.

Biomarker analysis

Random forest algorithm [49] within Microbiome Analyst [41] was utilized to identify taxa associated with the different infection statuses of saliva and midgut. Random forest algorithm [49] is a directed classification algorithm of trees generated by bootstrapping samples while training data and random feature selection in tree induction. It is an ensemble of unpruned classification or regression trees trained with the bagging method [50]. The default setting of the number of trees to grow and the number of predictors to try was applied (500 and 7, respectively) with the randomness setting left on. Further, we tried to adjust the parameters to different settings, but we did not observe any improvement in the model performance. We assessed the Out-of-Bag (OOB) error rate of the model.

Results

Ae. albopictus saliva contains a more diverse microbial profile than its midgut

Illumina MiSeq 16S rRNA sequencing effort resulted in 2,187,947 reads. A total of 936 OTUs were identified in this study (Supplementary File 1). The minimum number of reads observed was 31,362 and the maximum was 360,372 (Supplementary File 1). To adjust for differences in read depth, we rarefied our samples to a read depth of 31,632. The rarefaction curve analysis was performed, and the samples analyzed attained the saturation plateau, indicating that sequencing depth was sufficient (Supplementary Fig. 1).

The core phyla of microbes identified in the midgut were bacteria of the phyla Bacteroidetes and Proteobacteria while that of saliva were Proteobacteria, Bacteroidetes, and Cyanobacteria (Fig. 1A, B). The Ae. albopictus saliva displayed a significant species richness compared with the midguts (Chao1; P = 0.0167; t-test statistic: −2.8268) (Fig. 2A). We extended our query of species richness to evenness, and this also demonstrated a diverse saliva microbial community (Shannon Index, P = 0.0013, t-test statistic: −3.7183; Simpson Index, P = 0.0012, t-test statistic: −3.7439) (Supplementary Figs. 2, 3). Both the saliva and midgut of the Ae. albopictus contained unique clusters of bacterial genera not shared between them, yet a few bacterial genera–Elizabethkingia, Pseudomonas, Sphingomonas, and Wolbachia were present in both the midgut and saliva samples (PERMANOVA; F-value: 11.163; R2 = 0.3087; P < 0.001; Fig. 2B; Supplementary File 2).

Fig. 1
figure 1

A The core phyla identified in the Ae. albopictus midgut were bacteria of the phyla Bacteroidetes and proteobacteria. B The Ae. albopictus saliva core phyla consisted of proteobacteria, Bacteroidetes, and Cyanobacteria

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Fig. 2
figure 2

A Each box and whisker plot represents the variation in the microbial taxa in the midgut and saliva samples. The saliva samples have a richer microbial composition compared to the midgut (Chao 1; P = 0.0167; t-test statistic: −2.8268). B Principal component analyses were completed to assess the relationships among and between bacterial taxa identified in midgut and saliva. Despite several shared individual bacterial taxa, significant separation by tissue type was identified (PERMANOVA; F-value: 11.163; R2 = 0.3087; P < 0.001). The percentage of total variation explained by each axis is indicated

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ZIKV infection does not impact the midgut and saliva microbial diversity

A total of ten infected Ae. albopictus saliva and five infected midguts were analyzed in this study. An average viral RNA copies number range of log10 1.05 to log10 1.76 RNA copies/ml with a standard deviation of 0.22 were associated with the infected saliva samples, while an average viral RNA copies number range of log10 1.47 to log10 1.49 RNA copies/ml with a standard deviation of 0.008 were representative of the infected midgut samples.

At a tissue-specific level, we observed that ZIKV infection neither altered the microbial diversity of the saliva (Chao1; P = 0.12749; [ANOVA]: F = 1.6755) nor the midgut (Chao1; P = 0.86761; [ANOVA]: F = −0.17159) (Fig. 3). Bacteria belonging to the Elizabethkingia spp. were significantly enriched in INF MG (average of 10 902 hits), NINF MG and NINF saliva had an average of 5696 and 1319 hits respectively while INF saliva had diminished levels (average of 22 hits) (ANOVA Test, P = 0.0003) (Supplementary File 2).

Fig. 3
figure 3

ZIKV infection resulted in a globally homogenous microbiome community among the INF and NINF saliva (Chao 1; P = 0.12749; ANOVA: F = 1.6755) and INF and NINF MG (Chao 1; P = 0.86761; ANOVA: F = −0.17159)

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The prediction error of the machine learning model of the random forest algorithm resulted in an Out-of-bag (OOB) error of 0.636. Elizabethkingia was most significantly associated with INF MG while Wolbachia was the most abundant taxa identified in the NINF MG. Bacteria belonging to the family Chitinophagaceae were found to be enriched in the INF SAL samples while the NINF SAL had several taxa associated with them (Fig. 4).

Fig. 4
figure 4

The Random Forest classifier was used to determine the association of bacterial taxa with the infection status of Ae. albopictus midgut and saliva. Mean decrease accuracy is reported for each of the taxa. This measure is obtained by removing the relationship of taxa and measuring the increase in error. The taxa with the highest mean decrease in accuracy is considered to have the highest association with the state. The highest mean decrease in accuracy was identified for Elizabethkingia associated with INF MG

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Discussion

While microbes have been identified in mosquito salivary glands [51,52,53], there exist significant gaps in the study of mosquito saliva microbes. This is despite a few studies reporting the presence of microbes in the saliva of mosquitoes [20, 21] with the capacity to infect vertebrate host tissues and establish systemic infections. The current study aimed to compare the microbiome profile of Ae. albopictus saliva to its midgut. In addition, we studied the impact of ZIKV exposure on the saliva and midgut microbiome profile.

In this study, we observed that the core microbes in both saliva and midguts of Ae. albopictus consisted of Bacteroidetes and Proteobacteria. Our findings coincide with the findings of a study examining the microbial profile of saliva, salivary gland, and midgut tissues of Anopheles gambiae and An. stephensi. This study observed that similar taxa phyla were shared between the saliva, salivary glands, and midguts of An. stephensi with Proteobacteria as the main phylum. An. gambiae had two dominant populations in saliva and midguts, which were Proteobacteria and Bacteroidetes [20]. Our current study and the mentioned study utilized laboratory-reared mosquito strains, yet lab-reared mosquitoes are known to have an altered microbiome profile compared to wild populations [54]. Future studies utilizing freshly collected field samples will be important to compare with both findings [55].

Further, in this study, we observed a richer Ae. albopictus saliva microbiome compared with the midgut, yet several taxa were common to both the saliva and midguts. Previous study findings noted a more diverse bacterial taxon in the An. gambiae saliva compared with the midgut [20], as well as a richer and more complex microbial taxa in the salivary gland of naïve sugar-fed An. culicifacies compared with their gut [53]. We did not observe any alteration in richness in the microbiome community of the Ae. albopictus saliva exposed to ZIKV infectious blood meals. This corroborates the findings of studies of the impact of P. berghei infection on the An. gambiae and An. stephensi saliva microbial community [20]. In the future, it is important to study how the saliva microbiome interacts with arthropod-borne viruses present in the saliva. This is important as it will provide fresh insights into early activities at the skin bite site and may lead to novel approaches in designing transmission barrier tools [56].

Additionally, we did not measure a difference in richness in the midguts of individuals exposed to ZIKV infectious blood meal. These results differ from our previous findings which showed a significant reduction in Ae. albopictus midgut microbial diversity upon exposure to a ZIKV blood meal [34]. In addition, our findings are different from findings that demonstrate that La Crosse orthobunyavirus viral infection diminishes the midgut microbial diversity of Ae. japonicus and Ae. triseriatus [35]. The difference between our findings and the previous findings may be due to the differences in the experimental approach. Our current study included a genome amplification step in order to overcome limited starting genomic material of saliva, and this may have caused the differences observed. Future experimental approaches that overcome the barrier of limited genomic material will be important to ensure comparisons with past findings.

The Bray–Curtis dissimilarity statistic may have captured a portion of the variation in the microbial community; however, other factors such as differences in species richness, genetic diversity of the microbial taxa, and gene mutations may also explain the differences we observed. In the future, an all-encompassing study that included these other factors could provide a deeper understanding of microbial species diversity in mosquitoes.

We observed a significant increase in Elizabethkingia spp. levels in midguts associated with ZIKV-infected blood meal. In our previous findings, we have demonstrated that E. anophelis possesses an antiviral activity and accumulates in the midguts of Ae. albopictus associated with ZIKV infectious blood meals [34]. Further, our unpublished findings demonstrate that coinfection of ZIKV and E. anophelis in vitro results in a decrease in metabolites important for viral replication and progression of viral infection. Further, we observed the presence of Elizabethkingia spp. in the saliva of Ae. albopictus that were exposed to a naïve blood meal. The findings of this study corroborate a study that identified Elizabethkingia bacterial species in both the salivary glands and midguts of An. Culicifacies [53]. As well as the observation of Elizabethkingia spp. in the saliva of An. gambiae and An. stephensi after a naïve blood meal [20]. E. anophelis has been identified as a cause of sepsis as well as for neonatal meningitis [55]. By 2016, E. anophelis had caused outbreaks in the Midwestern USA that resulted in morbidity and mortality of  ~ 30% [55,56,57]. The Midwestern outbreak occurred primarily in community settings [56] and despite extensive investigations, no point source of infection has been identified [55, 57]. While the role of mosquitoes in the transmission of E. anophelis is unclear, our identification and previous study findings of Elizabethkingia spps. in mosquito saliva warrants the need for further studies to establish whether mosquitoes can transmit E. anophelis in the saliva. The findings of such a study will improve E. anophelis epidemiology and inform public health strategies.

One of the significant limitations of this study is the utilization of amplified RNA to overcome the significant challenge of minute amounts of starting genomic material. This approach may result in overamplification of the dominant bacterial species and underrepresentation of the rare bacterial taxa resulting in skewed results that may not be representative of the taxa diversity. Despite this possible limitation, we observed similar results as past findings of similar studies. In addition, by increasing the amount of starting material, we have been able to study microbial communities at an individual level. Future studies should consider pooling saliva samples to preclude any bias when studying saliva microbiome. Further, in this study, we worked with a small sample size, and our results may not provide a complete representation of the microbial composition of the midgut and saliva of Ae. albopictus. In the future, there is a need for a larger sample size and utility of wild Aedes spp. populations over lab-reared strains. Together, these findings demonstrate a need for further studies of aspects of mosquito saliva components that have not been studied in the past. This is important due to mosquito saliva’s primary role in blood acquisition and pathogen transmission.

Conclusions

This study provides insights into the microbial community of the Ae. albopictus saliva and the impact of ZIKV infection on saliva and midgut microbial profiles. We observed a richer and more diverse saliva microbial community compared with the midgut. We identified Elizabethkingia spp., an emerging pathogen causing life-threatening infections in humans, in the saliva of Ae. albopictus. The results of this study lay a foundation for future exploratory studies on interactions of mosquito saliva microbiome and ZIKV to understand the role of mosquito saliva microbiome in ZIKV transmission. These findings can be utilized as an avenue for an innovative approach to develop a transmission-blocking tool. Lastly, the identification of Elizabethkingia spp., an emerging global threat causing infections with high mortality rates, is important and there is a need for a vector competence study to assess the capacity of Ae. albopictus to transmit the pathogen [58].