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      Implication of the Mosquito Midgut Microbiota in the Defense against Malaria Parasites

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      PLoS Pathogens

      Public Library of Science

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          Malaria-transmitting mosquitoes are continuously exposed to microbes, including their midgut microbiota. This naturally acquired microbial flora can modulate the mosquito's vectorial capacity by inhibiting the development of Plasmodium and other human pathogens through an unknown mechanism. We have undertaken a comprehensive functional genomic approach to elucidate the molecular interplay between the bacterial co-infection and the development of the human malaria parasite Plasmodium falciparum in its natural vector Anopheles gambiae. Global transcription profiling of septic and aseptic mosquitoes identified a significant subset of immune genes that were mostly up-regulated by the mosquito's microbial flora, including several anti- Plasmodium factors. Microbe-free aseptic mosquitoes displayed an increased susceptibility to Plasmodium infection while co-feeding mosquitoes with bacteria and P. falciparum gametocytes resulted in lower than normal infection levels. Infection analyses suggest the bacteria-mediated anti- Plasmodium effect is mediated by the mosquitoes' antimicrobial immune responses, plausibly through activation of basal immunity. We show that the microbiota can modulate the anti- Plasmodium effects of some immune genes. In sum, the microbiota plays an essential role in modulating the mosquito's capacity to sustain Plasmodium infection.

          Author Summary

          The Anopheles gambiae mosquito that transmits the malaria-causing parasite Plasmodium has an intestinal bacterial flora, or microbiota, which comprises a variety of species. Elimination of this microbiota with antibiotic treatment will render the Anopheles mosquito more susceptible to Plasmodium infection. In this study we show that these bacteria can inhibit the infection of the mosquito with the human malaria parasite Plasmodium falciparum through a mechanism that involves the mosquito's immune system. Our study suggests that the microbial flora of mosquitoes is stimulating a basal immune activity, which comprises several factors with known anti- Plasmodium activity. The same immune factors that are needed to control the mosquito's microbiota are also defending against the malaria parasite Plasmodium. This complex interplay among the mosquito's microbiota, the innate immune system, and the Plasmodium parasite may have significant implications for the transmission of malaria in the field where the bacterial exposure of mosquitoes may differ greatly between ecological niches.

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          Most cited references 54

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          The gut bacteria of insects: nonpathogenic interactions.

          The diversity of the Insecta is reflected in the large and varied microbial communities inhabiting the gut. Studies, particularly with termites and cockroaches, have focused on the nutritional contributions of gut bacteria in insects living on suboptimal diets. The indigenous gut bacteria, however, also play a role in withstanding the colonization of the gut by non-indigenous species including pathogens. Gut bacterial consortia adapt by the transfer of plasmids and transconjugation between bacterial strains, and some insect species provide ideal conditions for bacterial conjugation, which suggests that the gut is a "hot spot" for gene transfer. Genomic analysis provides new avenues for the study of the gut microbial community and will reveal the molecular foundations of the relationships between the insect and its microbiome. In this review the intestinal bacteria is discussed in the context of developing our understanding of symbiotic relationships, of multitrophic interactions between insects and plant or animal host, and in developing new strategies for controlling insect pests.
            • Record: found
            • Abstract: found
            • Article: not found

            Commensal host-bacterial relationships in the gut.

            One potential outcome of the adaptive coevolution of humans and bacteria is the development of commensal relationships, where neither partner is harmed, or symbiotic relationships, where unique metabolic traits or other benefits are provided. Our gastrointestinal tract is colonized by a vast community of symbionts and commensals that have important effects on immune function, nutrient processing, and a broad range of other host activities. The current genomic revolution offers an unprecedented opportunity to identify the molecular foundations of these relationships so that we can understand how they contribute to our normal physiology and how they can be exploited to develop new therapeutic strategies.
              • Record: found
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              The Aedes aegypti Toll Pathway Controls Dengue Virus Infection

              Introduction The dengue viruses, whose geographic distribution resembles that of malaria, has become the most important arboviral pathogen in recent years because of its increasing incidence in the tropics and subtropics as well as its high morbidity and mortality. The public health impact of dengue is enormous, given that 2.5 billion people live in dengue-endemic areas and are at daily risk of infection [1]. The dengue viruses are single-stranded positive RNA belonging to the family Flaviviridae, genus Flavivirus. They are transmitted between humans primarily by the mosquito Ae. aegypti and by Ae. albopictus as a secondary vector [2]. The four closely related dengue serotypes are antigenically distinct, each comprising several genotypes that exhibit differences in their infection characteristics in both the mosquito vector and the human host [3],[4]. The extrinsic incubation period of dengue viruses in the mosquito is 7–14 days and is dependent on the mosquito strain, virus genotype, and environmental factors such as humidity and temperature [5],[6]. When the mosquito ingests a dengue-infected blood meal, the virus first infects the midgut tissue, within which it replicates to produce more virus particles. It then spreads through the hemolymph to other tissues such as the trachea, fat body, and salivary glands, where it is further propagated through replication. Peak virus titers usually occur between 7 and 10 days post-infection in the midgut and between 7 and 17 days in the abdomen. Peak levels in the head and salivary gland occur later, at about 12–18 days after feeding [7]. This extrinsic incubation time varies for different virus-vector combinations, and the tropism of the virus is dependent on the mosquito's tissue- and cell-specific susceptibility to different genotypes [5],[7]. In arthropods, innate immunity plays an important role in limiting pathogen infection, both through the production of effector molecules such as antimicrobial peptides and through phagocytosis and encapsulation, secretion of physical barriers, and melanization [8]. Studies that were mainly conducted in the insect model D. melanogaster have shown that arthropod immune responses are largely regulated by two main pathways, the Toll and immune deficiency (Imd) pathways [9],[10]. Activation of the Toll pathway by microbes through pattern recognition receptors (PRRs) leads to a cascade of events that result in the degradation of the negative regulator Cactus, translocation to the nucleus of transcription factors such as Dif, and a rapid increase in antimicrobial compounds and other effectors [10]–[12]. The Imd pathway is involved in the defense against Gram-negative bacteria, and upon activation it follows a cascade of events similar to those in the Toll pathway, involving putative degradation of its negative regulator Caspar, translocation of the transcription factor Relish to the nucleus, and the production of effectors and antimicrobial compounds [13],[14]. In contrast to the relatively well-characterized Toll and Imd pathways, less is known about the Janus kinase signal transducers and activators of transcription (JAK/STAT) pathway, which comprises multiple factors and has been linked to immune responses in the fruit fly [15],16. Comparative genomics analyses have shown a striking degree of conservation of these immune signaling pathways in D. melanogaster, Anopheles gambiae and Ae. aegypti; in contrast, the upstream pattern recognition receptors and the downstream effectors have differentiated quite significantly among the three species, probably as a result of different microbial exposures [17]. The Rel family transcription factors, Dif and Relish in Drosophila or their corresponding Rel1 and Rel2 in mosquitoes, can be studied through RNA interference (RNAi)-mediated silencing of the negative regulators Cactus and Caspar, respectively [11],[13],[14]. This approach allows a transient simulation, to at least a partial degree, of the Toll and Imd pathways in the absence of a microbial elicitor. The activation of these pathways can be monitored through the transcriptional activation of some of the signal cascade factors, such as the up-regulation of the Rel family transcription factors and down-regulation of the negative regulator Cactus or Caspar for the Toll or Imd pathway, respectively [11],[13],[14]. At present, relatively little is known about the anti-viral defense systems in insects. In D. melanogaster, the RNAi-mediated defenses appear to be key players in the defense against a broad range of viruses [18],[19], while some of the classical innate immune pathways such as the Toll and JAK-STAT pathways have also been implicated in limiting virus infection [20],[21]. Specifically, D. melanogaster has been shown to use its RNAi machinery and the Toll pathway to limit Drosophila×virus infection (a member of the Dicistroviridae) [19],[21], and it uses its RNAi machinery and the JAK-Stat pathway to limit Drosophila C virus infection (a member of the Birnavirus family) [20],[22]. Another study has demonstrated the involvement of the D. melanogaster RNAi machinery in the defense against two diverse animal viruses: a flock house virus and a cricket paralysis virus [18]. With all the above knowledge, however, the molecular mechanisms that govern their activation after infection and their role in virus clearance are unknown. Links between the RNAi machinery and the innate immune signaling pathways have yet not been identified [18],[23]. Similarly, limited knowledge on the antiviral response in mosquitoes is available. In Ae. aegypti, Sindbis virus (Alphavirus; Togaviridae) infection has been shown to induce the Toll pathway-related Rel1 transcription factor and three transcripts of the ubiquitin-ligase pathway genes, which are known regulators of NFkB-like proteins [24]. The RNAi machinery has also been linked to the anti-dengue defense in Ae. aegypti [25] and anti- O'nyong-nyong virus (Alphavirus; Togaviridae) in An. gambiae [26]. In addition, the O'nyong-nyong virus has been shown to induce 18 genes in A. gambiae, including a 70-kDa heat shock protein factor that later was shown to influence the virus's ability to propagate in the vector [27]. The recently available Ae. aegypti genome sequence [28], in combination with high-throughput gene expression and reverse genetic methodology, have provided unprecedented opportunities to study the mosquito's responses and defenses against dengue virus infection. Here, we report the global transcriptional response of Ae. aegypti to the infection of dengue virus serotype 2 (DENV-2), and show that DENV-2 induces a set of genes corresponding to the Toll and JAK-STAT pathways. Activation of the Toll and Imd pathways in Ae. aegypti through RNAi-mediated silencing of Cactus and Caspar caused a reduction in dengue virus infection level that appeared to be controlled primarily by the Toll pathway. Repression of the Toll pathway through MYD88 gene silencing resulted in higher dengue virus infection levels. We also present compelling evidence for an inhibitory effect of the mosquito's natural microbiota on virus infection and discuss the implications of these findings and the potential role of the mosquito's microbial exposure and innate immune system in modulating dengue virus transmission. Results Global transcriptome responses to dengue infection at 10 days after an infected blood meal We first assessed the physiological response of the Ae. aegypti mosquito to systemic dengue infection at the gene-specific level in the midgut and remaining carcass by using a genome-wide transcriptional profiling approach. A comparison of the transcript abundance in the two body compartments of mosquitoes that were fed 10 days earlier on dengue-infected blood or naïve blood revealed broad responses to virus infection that entailed a variety of physiological systems (Fig 1). The carcass displayed a significantly larger number of regulated genes (240 up-regulated and 192 down-regulated) than did the smaller midgut tissue (28 up-regulated and 35 down-regulated). The magnitude of the gene regulation, as measured by the -fold change in transcript abundance, was also greater in the carcass, suggesting that tissues in the carcass are at this stage of infection more actively engaged in the response to infection, while the midgut tissue may have reached a steady-state/balance in its interaction with the virus (Tables S1 and S2). A fairly large proportion (33.5%) of the genes displayed a similar expression profile in the midgut and the carcass (Tables 1, S1, and S2). The most striking infection-responsive gene regulation was observed for genes with putative functions related to the mosquito's innate immune system; these genes represented 34.5% in the midgut and 27.5% in the carcass of all the regulated genes with predicted functions (Fig. 1). Other major functional gene groups that were affected by virus infection included metabolism, oxidoreductive processes, and stress responsive systems, and are discussed in greater detail in Text S1. 10.1371/journal.ppat.1000098.g001 Figure 1 Functional classification of differentially expressed genes in the dengue-infected midgut and carcass at 10 days after blood meal. The graph shows the functional class distributions in real numbers of genes that are regulated by virus infection (+ indicate induced and – indicate repressed). The virus infection responsive gene expression data are presented in Tables S1 and S2. Functional group abbreviations: IMM, immunity; R/S/M, redox, stress and mitochondrion; CSR, chemosensory reception; DIG, blood and sugar food digestive; PRT, proteolysis; C/S, cytoskeletal and structural; TRP, transport; R/T/T, replication, transcription, and translation; MET, metabolism; DIV, diverse functions; UNK, unknown functions. 10.1371/journal.ppat.1000098.t001 Table 1 Differentially expressed putative immune genes in the dengue-infected midgut and carcass and their overlap with those of Cactus- and Caspar-silenced mosquitoes. Gene ID Gene Name No Function group Logfold Carcass Midgut dsCact dsCaspar AAEL000709 CACT 62 Toll −0.842 −0.084 0.515 0.074 AAEL007696 REL1A 64 Toll 0.924 −0.096 1.005 0.157 AAEL001929 SPZ5 63 Toll 1.61 0.037 0.034 0.106 AAEL003507 TOLL1B 66 Toll 0.947 0.014 0.08 AAEL013441 TOLL9A 65 Toll 1.189 −0.036 −0.054 0.149 AAEL004223 CECB 5 Effector 0.544 0.81 −0.148 0.61 AAEL015515 CECG 6 Effector 1.052 0.131 1.394 −2.992 AAEL003832 DEFC 9 Effecttor −1.81 0.143 0.95 −1.665 AAEL003857 DEFD 8 Effector −0.127 1.076 0.999 −1.962 AAEL003849 DEFE 7 Effector 0.824 0.053 −0.811 1.697 AAEL004522 GAM 10 Effector 0.851 1.118 −1.406 0.85 AAEL015404 LYSC 11 Effector 1.007 0.935 1.105 0.082 AAEL006702 FREP 31 Pattern Recognition Receptor 1.143 0.031 −0.333 −0.009 AAEL006699 FREP 32 Pattern Recognition Receptor −1.129 −1.297 0.016 AAEL006704 FREP 33 Pattern Recognition Receptor 0.073 −0.896 −1.128 0.313 AAEL000652 GNBPA2 28 Pattern Recognition Receptor 0.805 0.928 0.041 −0.025 AAEL009178 GNBPB4 30 Pattern Recognition Receptor 0.92 −0.126 −0.065 0.061 AAEL007064 GNBPB6 29 Pattern Recognition Receptor 0.886 0.088 0.118 −1.077 AAEL003325 ML 34 Pattern Recognition Receptor −0.949 0.85 0.05 0.083 AAEL009531 ML 35 Pattern Recognition Receptor 1.427 −0.072 0.031 −0.83 AAEL006854 ML 36 Pattern Recognition Receptor 0.031 1.143 0.263 0.219 AAEL014989 PGPPLD, putative 38 Pattern Recognition Receptor 2.11 0.101 −0.155 −0.113 AAEL011608 PGRPLD 37 Pattern Recognition Receptor 1.962 0.011 −0.098 −0.099 AAEL012267 TEP13 41 Pattern Recognition Receptor 1.325 0.084 0.112 0.8 AAEL014755 TEP15 42 Pattern Recognition Receptor 1.19 −0.023 1.628 0.168 AAEL001794 TEP20 40 Pattern Recognition Receptor 0.896 0.191 1.518 0.313 AAEL000087 TEP22 39 Pattern Recognition Receptor 1.819 0.084 1.896 0.317 Aaeg:N19306 TEP24 44 Pattern Recognition Receptor 0.8943 Aaeg:N18111 TEP25 43 Pattern Recognition Receptor 1.2427 AAEL003253 CLIPB13B 45 Signal Modulation 1.038 0.209 1.638 0.003 AAEL005093 CLIPB46 48 Signal Modulation −0.913 1.121 0.344 AAEL005064 CLIPB5 46 Signal Modulation −0.852 0.059 1.548 0.315 AAEL007593 CLIPC2 47 Signal Modulation −0.815 0.15 1.379 0.155 AAEL014390 CTL 52 Signal Modulation 0.986 0.162 0.942 0.188 AAEL003119 CTL6 49 Signal Modulation 0.85 0.018 0.12 −0.009 AAEL011619 CTLGA8 51 Signal Modulation 0.986 0.085 1.129 0.281 AAEL011455 CTLMA12 50 Signal Modulation 1.095 0.134 2.473 0.216 AAEL000256 SCRB9 53 Signal Modulation 1.036 0.203 0.223 0.023 AAEL014079 SRPN1 59 Signal Modulation 0.915 −0.017 0.995 −0.027 AAEL007765 SRPN10A 61 Signal Modulation 0.166 −0.963 0.841 −0.009 AAEL014078 SRPN2 58 Signal Modulation 0.884 −0.048 −2.026 AAEL002730 SRPN21 54 Signal Modulation 1.426 0.128 0.41 0.148 AAEL002715 SRPN22 60 Signal Modulation 0.12 1.244 0.062 0.166 AAEL013936 SRPN4A 57 Signal Modulation 1.35 0.041 1.426 0.156 AAEL013934 SRPN4D 56 Signal Modulation 1.343 0.217 0.91 0.259 AAEL008364 SRPN9 55 Signal Modulation −0.951 −0.031 1.275 0.169 AAEL000393 Suppressors of cytokine signalling 13 JAK-STAT 0.909 0.058 0.186 0.103 AAEL009645 Hypothetical protein 14 JAK-STAT −0.846 −0.584 0.427 −0.012 AAEL009822 Metabotropic glutamate receptor 15 JAK-STAT 1.405 0.185 −0.086 0.028 AAEL012471 DOME 16 JAK-STAT 1.078 −0.06 1.561 −0.868 AAEL012510 IKK2 12 Imd −0.912 −1.042 0.043 −0.034 AAEL003439 CASPS18 1 Apoptosis 0.803 0.017 −0.821 −0.094 AAEL012143 CASPS7 2 Apoptosis −0.854 0.014 −0.068 −0.034 AAEL011562 CASPL2 3 Apoptosis −0.606 −0.839 0.183 0.076 AAEL014658 CASPS20 4 Apoptosis −0.898 −0.064 0.019 Aaeg:N41501 CAT1A 17 Oxidative defense enzymes −0.84617 AAEL004386 HPX8C 18 Oxidative defense enzymes −1.106 −0.031 −1.745 0.049 AAEL004388 HPX8A 19 Oxidative defense enzymes −1.685 0.047 −2.054 0.094 AAEL004390 HPX8B 20 Oxidative defense enzymes −1.034 0.063 −1.357 0.268 AAEL000274 CuSOD3, putative 21 Oxidative defense enzymes −0.911 −0.119 −1.184 0.078 AAEL006271 CuSOD2 22 Oxidative defense enzymes −0.841 −0.006 −1.099 −0.001 AAEL009436 SOD-Cu-Zn 23 Oxidative defense enzymes −0.955 −0.473 0.173 0.148 AAEL011498 CuSOD3 24 Oxidative defense enzymes −0.9 −0.16 −1.205 0.079 AAEL004112 TPX2 25 Oxidative defense enzymes −1.433 −0.265 −0.84 0.06 AAEL014548 TPX3 26 Oxidative defense enzymes −0.893 0.021 −0.17 −0.041 AAEL002309 TPX4 27 Oxidative defense enzymes −0.301 −1.486 0.13 0.153 Dengue-infected midguts and carcasses were dissected and collected from the mosquitoes at 10 day after the blood meal. Injection of dsRNA of Cactus and Caspar into mosquitoes was conducted at 2 days post-emergence, and samples were collected for microarray analysis at 4 days after injection. Immune responses to dengue infection The 53 and 18 putative immune genes that were regulated by virus infection in the carcass and midgut tissues, respectively, were associated with a variety of immune functions such as PRRs, signaling modulation and transduction, effector systems, and apoptosis (Table 1). The functional group representations of the infection-responsive genes and their direction of regulation in the carcass and midgut tissues were quite similar, suggesting that the anti-viral responses involved the same types of defense mechanisms in these two compartments. For example, specific genes that displayed a similar pattern of regulation were lysozyme C (LYSC, AAEL015404), gambicin (AAEL004522), Ikkg (AAEL012510) and the Gram-negative binding protein A2 (GNBPA2, AAEL000652). A closer investigation of immune gene regulation using in silico comparative genomics analysis [17] revealed a striking bias toward genes putatively linked with the Toll immune signaling pathway (Fig. 2) as well as the JAK-STAT pathway. Activation of the Toll pathway in the carcass was supported by the up-regulation of Spaetzle (Spz), Toll, and Rel1A, and the down-regulation of the negative regulator Cactus. Three members of the Gram-negative bacteria-binding protein (GNBP) family were up-regulated, together with a clip-domain serine protease (CLIP), while the other two CLIPs were down-regulated; several antimicrobial effector molecules were up-regulated, including the defensins (DEFs), cecropins (CECs) and a lysozyme (LYSC). Only one predicted gene of the Imd immune signaling pathway, Ikkg, was down-regulated. One of the key components of the JAK-STAT pathway, Domeless (Dome), was induced upon dengue virus infection as well as three other genes (AAEL009645, AAEL009822 and AAEL000393) which have JAK-STAT pathway related orthologs in D. melanogaster [29]. Six members of the thio-ester containing protein (TEPs) gene family were also regulated by dengue infection, while TEP1 has been demonstrated to be a down-stream effector molecule of JAK-STAT pathway in D. melanogaster [30]. 10.1371/journal.ppat.1000098.g002 Figure 2 Regulation of putative Toll signaling pathway genes by dengue virus infection. Red color indicates infection responsive up-regulation and green color indicate infection responsive down-regulation. Non-colored gene boxes indicate lack of infection responsive regulation. The pathway was built with GenMapp software based on the immunogenomics prediction by Waterhouse et al 2007. To establish further evidence that dengue infection activates the Toll immune signaling pathway, we designed experiments to assess the relationships between dengue infection-responsive gene regulation and Rel1- and Rel2-controlled gene regulation. Previous studies in D. melanogaster and An. gambiae have shown that the Rel1 and Rel2 transcription factors can be activated by depleting their negative regulators Cactus and Caspar, respectively [13],[14],[31]. To confirm that the Toll and Imd pathway had been activated, we depleted Cactus and Caspar using RNAi silencing and assayed the expression of the antimicrobial peptide genes DEF and CEC in gene-silenced mosquitoes and non-silenced controls (Fig. 3A). Gene silencing of either Cactus or Caspar induced the expression of these two genes. To link this activation to the Rel1 and Rel2 transcription factors, we performed double-knockdown assays in which both Cactus and Rel1 or Caspar and Rel2 were targeted simultaneously with RNAi and compared the effect of this double silencing on antimicrobial peptide gene expression to that of silencing the negative regulators alone. The double-knockdown treatments either compromised (in the case of Cactus and Rel1) or completely reversed (in the case of Caspar and Rel2) the effect induced by single-knockdown of Cactus or Caspar, respectively, indicating that these negative regulators could be used to activate these two transcription factors (Fig. 3A). The quantitative differences in the levels of de-activation of the Rel1- and Rel2-controlled transcription that were produced with this double-knockdown approach most likely reflect differences in the efficiency and kinetics of the RNAi-mediated depletion of different proteins. 10.1371/journal.ppat.1000098.g003 Figure 3 Comparative analysis of the dengue virus infection-responsive and Rel1 and Rel2 regulated transcriptomes. A. Expression analysis of defensin (DEF), cecropin (CEC), Cactus (CAC), and Rel1 in Cactus, and Cactus and Rel1 depleted mosquitoes (upper panel) and in Caspar, and Caspar and Rel2 depleted mosquitoes. Bar represents standard error. B. Venn diagram showing uniquely and commonly regulated genes in dengue infected and Cactus and Caspar depleted mosquitoes. C. Cluster analysis of 131 genes that were regulated in at least two of four treatments: dengue-infected midgut and carcass, and whole mosquitoes upon Cactus (CAC(-)) or Caspar (CSP(-)) depletion. The expression data of immune genes, indicated by the number beside the panel are presented in Table 1, and all genes presented in the hierarchical cluster matrix are listed in Table S6. The primary data for the real-time qPCR assays are presented in Table S3. We then determined the gene repertoires that were regulated by the Rel1 and Rel2 transcription factors, using a microarray-based approach in which we compared the transcript abundance in the Cactus and Caspar gene-silenced mosquitoes to that in GFP dsRNA-treated control mosquitoes. Our results indicated that differential gene regulation in the Cactus-depleted mosquitoes showed a strong bias toward the Toll pathway. For instance, we observed the up-regulation of Rel1 (AAEL007696), multiple Toll receptors (AAEL007619, AAEL000057, AAEL007613), Spätzle ligands (AAEL013434, AAEL008596), Gram-negative binding proteins (AAEL007626 and AAEL003889), and the antimicrobial peptides DEFD, CECA, D, E & G (AAEL003857, AAEL000627, AAEL000598, AAEL000611, AAEL015515). In total, Cactus gene silencing resulted in the up-regulation of 460 and down-regulation of 1423 genes belonging to different functional classes, with a predominant representation by immune genes (13.7% of all genes with predicted functions). The regulation of a variety of other functional gene groups by Rel1 is indicative of the multiple functional roles of the Toll pathway, including its contributions to immunity and development [32]. Differential gene regulation in Caspar-depleted mosquitoes was much less pronounced, with only 35 genes being induced and 137 being repressed. Those induced by Caspar silencing included TEP13 and the antimicrobial peptides DEFE and gambicin (AAEL004522 and AAEL003849). Rel1 and Rel2 are most likely regulating additional genes that were not detected because of the limited sensitivity of microarray-based gene expression assays. A comparison of the dengue infection-responsive gene repertoire to that of Cactus gene-silenced mosquitoes showed a significant overlap, with 41% (18 of 44) of the immune genes being up-regulated by both the virus infection and Cactus gene silencing (Fig. 3B). In contrast, only 9% (4 of 44) of the dengue-regulated immune genes were also regulated in Caspar gene-silenced mosquitoes (Fig. 3B). Hierarchical clustering of genes that were differentially expressed in at least two of the three situations (Cactus silencing, Caspar silencing, and dengue infection) revealed a close relationship between Cactus silencing- and dengue infection-related regulation (Fig. 3C). In particular, expression cluster V, which is highly enriched with immune genes, was affected by both the Cactus silencing and dengue infection treatments. Differential gene expression in Cactus-silenced and dengue-infected mosquitoes showed a strong correlation with regard to both the direction and magnitude of the regulation of this expression cluster (Fig. 3C, Cluster V). Further dissection of the expression cluster V defined three main groups: Toll pathway-, JAK-STAT pathway-, and signal modulation- related genes. The signal modulation cascade genes included four C-type lectins (CTLs) and six serine protease inhibitors (SRPNs). A plausible hypothesis is that both the Toll and JAK-STAT pathways may be regulated at least in part by the same signal modulation cascade that includes serine proteases and serpins. Consistent to this hypothesis, evidences suggest that the JAK-STAT pathway could be indirectly activated by the Toll cascade in D. melanogaster [30]. Interestingly, genes in this cluster showed similar regulation for the midgut and carcass and for Cactus-silenced mosquitoes, although the magnitude of the regulation was smaller in the midgut, further supporting the notion of a similar type of antiviral defense in the gut and carcass tissues. Expression cluster III was characterized by a repression of seven oxidative defense enzyme genes in both Cactus-silenced and dengue-infected mosquitoes (Fig3C, Cluster III). The genes that showed different profiles for Cactus silencing and dengue infection are listed in the remaining clusters (Fig 3C, Cluster I, II and IV). Several putative apoptotic genes, such as caspases, were also regulated by dengue infection. Similar results have also been observed in D. melanogaster in response to Drosophila C virus infection [20], suggesting a potential connection between virus infection and apoptosis. The Toll pathway is involved in the anti-dengue defense The prominent activation of the Toll pathway (Rel1)-regulated genes in response to dengue infection strongly suggested that this pathway is involved in the mosquito's anti-dengue defense. To investigate this hypothesis, we tested the effect of both Cactus and Caspar gene silencing on virus infection in the midgut and carcass at 7 days after an infectious blood meal. This cactus gene silencing reduced the extent of dengue infection in the midgut by 4.0-fold (P 0.05, the inconsistent replicates (with distance to the median of replicate ratios large than 0.8) were removed, and only the value from a gene with at least two replicates were further averaged. Toll and Imd signaling pathways were built on the basis of a recent bioinformatics prediction [17] with GeneMAPP2 software [39]. The latter was also used for the generation of the expression datasets. The gene database was created with the Ae. aegypti gene ontology by the GeneMapp development team. Three independent biological replicate assays were performed. Numeric microarray gene expression data are presented in Tables S1 and S2. Real-time qPCR assays Real-time qPCR assays were conducted as previous described [37]. Briefly, RNA samples were treated with Turbo DNase (Ambion, Austin, Texas, United States) and reverse-transcribed using Superscript III (Invitrogen, Carlsbad, California, United States) with random hexamers. Real-time quantification was performed using the QuantiTect SYBR Green PCR Kit (Qiagen) and ABI Detection System ABI Prism 7000 (Applied Biosystems, Foster City, California, United States). Three independent biological replicates were conducted and all PCR reactions were performed in triplicate. The ribosomal protein S7 gene was used for normalization of cDNA templates. Primer sequences are listed in Table S5. Numeric data for the real-time qPCR assays are presented in Table S3. Gene-silencing assays RNA interference (RNAi)-based gene-silencing assays were conducted according to standard methodology [34]: Approximately 69 ηl dsRNAs (3 µg/µl) in water was injected into the thorax of cold-anesthetized 4-day-old female mosquitoes using a nano-injector as previously described ( Three to four days after injection and validation of gene-specific silencing, mosquitoes were fed on a DENV-2-supplemented blood meal. Dissection of mosquito midguts, thoraxes, and heads were done on the seventh day PBM. Each tissue was homogenized separately in the same medium as used for C6/36 cells (MEM) and used for virus titration. Three independent biological replicate assays were performed for each gene. The following primers were used for the synthesis of Cactus, Caspar and MyD88 dsRNA using the T7 megascript kit (Ambion): Cactus_F: TAATACGACTCACTATAGGG CGAGTCAACAGAACCCGAGCAG, Cactus_R: TAATACGACTCACTATAGGG TGGCCCGTCAGCACCGAAAG, Caspar_F: TAATACGACTCACTATAGGG GGAAGCAGATCGAGCCAAGCAG, Caspar_R: TAATACGACTCACTATAGGG GCATTGAGCCGCCTGGTGTC, MyD88_F: TAATACGACTCACTATAGGGGGCGATTGGTGGTTGTTATT, MyD88_R: TAATACGACTCACTATAGGGTTGAGCGCATTGCTAACATC, DENV-2 virus titration Virus titers in the tissue homogenates were measured as previously reported ( The virus-containing homogenates were serially diluted and inoculated into C6/36 cells in 24-well plates. After incubation for 5 days at 32°C and 5% CO2, the plates were assayed for plaque formation by peroxidase immunostaining, using mouse hyperimmune ascitic fluid (MHIAF, specific for DENV-2) and a goat anti-mouse HRP conjugate as the primary and secondary antibody, respectively. Numeric PFU data are presented in Table S3. Mosquito antibiotic treatment After pupation, mosquitoes were transferred to a sterile cage and provided a sterile 10% sucrose solution with 15 mg/ml gentamicin, 10 units penicillin, and 10 µg streptomycin as a sugar source. The removal of microbes was confirmed by colony-forming unit assays prior to blood-feeding and after a surface sterilization that involved vortexing in 70% ethanol and subsequent rinsing in double-distilled sterile H2O. Each entire mosquito was then homogenized in 100 µl autoclaved PBS and plated on LB-agar, and the plates were checked for presence of bacterial growth at 48 h post-inoculation. Indirect immunofluorescence assays These assays were performed according to a modification of a previously established method [40]. The midguts from 7-day-old mosquitoes were dissected in 1.0% paraformaldehyde in PBS. After a 1-h incubation in 50 µl of 4.0% paraformaldehyde in a 96-well plate, the midguts were washed three times with 100 µl PBS for 1 min each; 100 µl of 10% goat serum was then added to the antibody dilution buffer (0.1% TritonX-100 and 0.2% BSA in PBS) and incubated overnight. The midguts were then incubated with FITC-conjugated monoclonal antibody 2H2 at 37°C for 1 h. The midguts were washed twice with PBS at room temperature for 1 h and then stained with Evans blue counter-stain (diluted 1: 100), placed onto slides, and covered with Bartel B 1029-45B mounting medium and a coverslip. Preparations were examined under a Nikon fluorescence microscope. Accession numbers The Entrez Gene ID for genes and proteins mentioned in the text are 5565922 (Cactus), 5569526 (REL1A), 5578608 (Caspar), 5569427 (REL2), 5579094 (DEF), 5579377 (CEC), 5578028 (Attacin), 5565542 (Diptericin), 5579192 (GNBPB1), 5564897 (PGRGLC), 5564993 (Gambicin), 5569574 (MyD88), 5579458 (LYSC), 5576410 (Ikkg), 5565422 (GNBPA2), 5580019 (AAEL009645), 5572476 (AAEL009822), 5576330 (AAEL000393), 5576380 (DOME), 5573010 (SPZ5), 5578273 (TOLL1B), 5577966 (TOLL9A), 5576030 (TEP13), 5565197 (TEP15), 5572428 (TEP20). 5563609 (TEP22), 5568254 (FREP), 5577659 (CLIPB13B). Supporting Information Figure S1 A. The bacteria flora in the mosquito lumen does not influence the viability of the dengue virus. Seven days old antibiotic treated aseptic or non-treated septic mosquitoes were fed with the same mixture of DENV-2 and blood. Two hour after the blood meal, midguts were dissected and their content was immediately diluted with 100 ul sterile PBS. Three replicates of five guts each were collected. After a brief homogenization and centrifugation, the supernatants were used to determine the virus titer with the standard plaque assay. B. In vitro exposure of dengue virus to midgut bacteria does not affect the virus viability. Incubation of the dengue virus with either sterile PBS, bacteria exposed supernatant or a bacteria suspension did not result in any significant difference in virus viability. Ten midguts from seven days old septic female mosquitoes were dissected and homogenized in 100 ul sterile PBS prior to plating on a LB agar plate for bacterial growth. Bacteria colonies were washed off the plate with PBS and collected into a 1.5 ml tube. After a 10 minutes centrifugation at 1,500 g the bacteria-free supernatant and the bacteria pellet were collected. The bacteria pellet was re-suspended into PBS to get the bacteria solution. Then, equal amount of virus were incubated for 3 hrs at room temperature with the bacteria, the bacteria free supernatant and the sterile PBS prior to titer determination with plaques assay. Three replicates were performed for each treatment. (0.07 MB JPG) Click here for additional data file. Table S1 The functional groups of the total 432 genes that were regulated by DENV-2 infection in the mosquito carcass at ten days after an infected blood meal, compared to that of non-infected blood fed control mosquitoes. Functional group abbreviations: IMM, immunity; RED/STE, redox and oxidoreductive stress; CSR, chemosensory reception; DIG, blood and sugar food digestive; PROT, proteolysis; CYT/STR, cytoskeletal and structural; TRP, transport; R/T/T, replication, transcription, and translation; MET, metabolism; DIV, diverse functions; UNK, unknown functions. (0.38 MB DOC) Click here for additional data file. Table S2 The functional groups of the total 63 genes that were regulated by DENV-2 infection in the mosquito midgut at ten days after an infected blood meal, compared to that of non-infected blood fed control mosquitoes. Functional group abbreviations: IMM, immunity; RED/STE, redox and oxidoreductive stress; CSR, chemosensory reception; DIG, blood and sugar food digestive; PROT, proteolysis; CYT/STR, cytoskeletal and structural; TRP, transport; R/T/T, replication, transcription, and translation; MET, metabolism; DIV, diverse functions; UNK, unknown functions. (0.08 MB DOC) Click here for additional data file. Table S3 Averaged data from three biological replicate real time qPCR assays of the expression of defensin, cecropin, Cactus, and Rel1in Cactus, and Cactus & Rel1 depleted mosquitoes (A) and in Caspar, and Caspar & Rel2 depleted mosquitoes (B). C. Fold change in the expression of selected immune genes in aseptic mosquitoes compared to septic mosquitoes. S.E., standard error. (0.06 MB DOC) Click here for additional data file. Table S4 A. Averaged data from three independent biological replicate plaque assays of the virus titer in the midguts of the Cactus, Caspar, MYD88 and GFP dsRNA treated mosquitoes. B. Results from three independent biological replicate plaque assays of the virus titer in the midgut of antibiotic treated aseptic and non-treated septic mosquitoes. S.E., standard error; S, significant; NS, Non-significant. (0.04 MB DOC) Click here for additional data file. Table S5 The prime sequences used for the real-time qPCR assays. (0.04 MB DOC) Click here for additional data file. Table S6 The expression data of all the genes that are shown in the hierarchical cluster matrix (Fig. 3C). (0.30 MB DOC) Click here for additional data file. Text S1 This section refers to other dengue infection responsive genes. (0.05 MB DOC) Click here for additional data file.

                Author and article information

                Role: Editor
                PLoS Pathog
                PLoS Pathogens
                Public Library of Science (San Francisco, USA )
                May 2009
                May 2009
                8 May 2009
                : 5
                : 5
                W. Harry Feinstone Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America
                Stanford University, United States of America
                Author notes

                Current address: Department of Evolutionary Biology, University of Siena, Siena, Italy

                Conceived and designed the experiments: YD FM GD. Performed the experiments: YD FM. Analyzed the data: YD FM GD. Wrote the paper: YD GD.

                Dong et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                Page count
                Pages: 10
                Research Article
                Computational Biology/Transcriptional Regulation
                Genetics and Genomics/Functional Genomics
                Immunology/Immune Response
                Immunology/Innate Immunity
                Infectious Diseases
                Microbiology/Immunity to Infections
                Microbiology/Innate Immunity

                Infectious disease & Microbiology


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