Chromobacterium Csp_P Reduces Malaria and Dengue Infection in Vector Mosquitoes and Has Entomopathogenic and In Vitro Anti-pathogen Activities

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Abstract

Plasmodium and dengue virus, the causative agents of the two most devastating vector-borne diseases, malaria and dengue, are transmitted by the two most important mosquito vectors, Anopheles gambiae and Aedes aegypti, respectively. Insect-bacteria associations have been shown to influence vector competence for human pathogens through multi-faceted actions that include the elicitation of the insect immune system, pathogen sequestration by microbes, and bacteria-produced anti-pathogenic factors. These influences make the mosquito microbiota highly interesting from a disease control perspective. Here we present a bacterium of the genus Chromobacterium ( Csp_P), which was isolated from the midgut of field-caught Aedes aegypti. Csp_P can effectively colonize the mosquito midgut when introduced through an artificial nectar meal, and it also inhibits the growth of other members of the midgut microbiota. Csp_P colonization of the midgut tissue activates mosquito immune responses, and Csp_P exposure dramatically reduces the survival of both the larval and adult stages. Ingestion of Csp_P by the mosquito significantly reduces its susceptibility to Plasmodium falciparum and dengue virus infection, thereby compromising the mosquito's vector competence. This bacterium also exerts in vitro anti- Plasmodium and anti-dengue activities, which appear to be mediated through Csp_P -produced stable bioactive factors with transmission-blocking and therapeutic potential. The anti-pathogen and entomopathogenic properties of Csp_P render it a potential candidate for the development of malaria and dengue control strategies.

Author Summary

The infectious agents that cause malaria and dengue are transmitted by Anopheles and Aedes mosquitoes, respectively. Bacteria found in the mosquito midgut have the potential to dramatically affect the susceptibility of the mosquito vector to the malaria parasite and dengue virus. In this work, we investigate one such microbe, Chromobacterium sp. (Csp_P), a bacterium isolated from a field-caught Aedes aegypti mosquito. We show that Csp_P can effectively colonize the midguts of Anopheles gambiae and Aedes aegypti mosquitoes and can, when ingested by the mosquito, significantly reduce the mosquito's susceptibility to infection with the malaria parasite and dengue virus. We also show that exposure to, and ingestion of, Csp_P can reduce the lifespan of larval and adult mosquitoes, respectively. We show that Csp_P has anti- Plasmodium and anti-dengue activity independent of the mosquito, suggesting that the bacterium secretes metabolites that could potentially be exploited to prevent disease transmission or to treat infection.

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Human malaria parasites in continuous culture.

W Trager, Jens-Erik B Jensen, Louise Jensen (1976)

Plasmodium falciparum can now be maintained in continuous culture in human erythrocytes incubated at 38 degrees C in RPMI 1640 medium with human serum under an atmosphere with 7 percent carbon dioxide and low oxygen (1 or 5 percent). The original parasite material, derived from an infected Aotus trivirgatus monkey, was diluted more than 100 million times by the addition of human erythrocytes at 3- or 4-day intervals. The parasites continued to reproduce in their normal asexual cycle of approximately 48 hours but were no longer highly synchronous. The have remained infective to Aotus.

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The Aedes aegypti Toll Pathway Controls Dengue Virus Infection

Zhiyong Xi, JOSÉ RAMÍREZ, George Dimopoulos (2008)

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 (http://www.jove.com/index/Details.stp?ID=230). 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 (http://www.jove.com/index/Details.stp?ID=220): 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.

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

Yuemei Dong, Fabio Manfredini, George Dimopoulos (2009)

Introduction The malaria parasite has to go through series of complex developmental transitions within the mosquito vector before it can be transmitted to the human host. The major bottleneck for Plasmodium's development occurs during the ookinete invasion of the midgut epithelium, prior to the development of oocysts on the basal lamina [1]. The factors that are believed to contribute to parasite losses at this stage are digestive enzymes, the mosquito's immune defenses and the intestinal microbial flora [2]–[4]. Large communities of diverse microorganisms reside in insects with a major concentration in the intestinal sections [5]. While much research has been focused on the microbiota of the mammalian intestine and its role in defense against pathogenic microorganisms [6], studies of insect gut microbiota have mainly concentrated on the contribution of microbial endosymbionts to the host's nutritional homeostasis [5]. However, the microbiota of the insect gut has also been shown to play a pivotal role of preventing development of pathogens. Studies have reported the wide spread of various species of Gram-negative bacteria in the midguts of both laboratory-reared and field derived mosquitoes, and some of this flora has been associated with an inhibitory activity on the sporogonic development of the Plasmodium parasites in the mosquito midgut [7]–[11]. However, these studies have not identified the causal mechanisms through which the presence of bacteria negatively impacts on malaria parasite development. Bacteria within the midgut lumen may directly interact with, and adversely affect, the different malaria parasite stages within the bloodmeal through the production of various enzymes and toxins or physical barriers that hinder the interaction between Plasmodium ookinetes and the midgut epithelium (reviewed in [12]). Alternatively, the effect of bacteria on parasite development may occur indirectly through alterations in the physiology of the mosquito host itself, possibly through induction of immune responses that are cross-reactive between bacteria and malaria parasites, and/or changes of host metabolism that would affect the composition of mosquito derived molecules that are essential for Plasmodium development. Some studies have indicated that some of the mosquitoes' immune factors induced by bacterial challenge are involved in the killing of parasites at the pre-oocysts development stages [13]–[15]. Indeed, a great overlap, at the functional level, between antibacterial and anti-Plasmodium immune responses has been observed and suggests that mosquitoes lack highly specific mechanisms for defense against malaria parasites, but are using their anti-bacterial mechanisms to limit Plasmodium infection [14],[16],[17]. A reasonable hypothesis is that the presence of bacteria activates the mosquito's antimicrobial immune responses and the synthesized antimicrobial peptides and other immune factors will act against co-infecting Plasmodium parasites. Indeed, a complex interplay between the mammalian immune system and the intestinal microbiota is essential for protection from infectious pathogenic microorganisms [18]. Some intestinal microbial species induce innate immune effector molecules which can kill competing bacterial species, including pathogens (reviewed in [19]). The composition of mosquito midgut microbiota is much less complicated than that of mammalian intestine microbiota which makes it as a good model for dissecting the dynamics between the host innate immune system, natural bacterial flora, and the pathogenic microorganisms. Besides, mosquitoes transmit a broad range of human parasitic and viral diseases, within which malaria is still one of today's most devastating infectious diseases. A better understanding of the roles of microbiota in the exploiting host immunity in defending against pathogens could potentially lead to the development of new malaria control strategies. We have examined the influence of the mosquito's midgut microbial flora and the derived antibacterial immune responses on malaria parasite infection through a series of infection assays in conjunction with functional genomics analyses. Results/Discussion Composition of microbiota in A. gambiae mosquitoes To gain a better understanding of the potential fluctuations in microbial load and species composition between laboratory reared mosquitoes of different generations and within the same generation, we monitored the bacterial loads and species composition in individual five-day-old female A. gambiae mosquitoes of five consecutive generations. In accordance to previous studies our results showed a great variability in both parameters [8], [9], [20]–[22]. Interestingly, these variations were also observed between mosquitoes originating from the same generation and cage (Figure 1). This intriguing pattern may in some way relate to the equally broad distribution of Plasmodium infection intensities among mosquitoes that have fed on the same gametocyte culture. On average, individual mosquitoes carried around 40,000 colony forming units (CFU). Similarly to previous studies, the majority of the isolated bacteria were Gram-negative suggesting that the midguts of mosquitoes have more optimal growth conditions for this type, especially those from the Enterobacteriaceae family. This strong bias is also likely to have been attributed, to some degree, to the LB agar– based aerobic culturing method that was used for these assays. Sequence analyses of the 16s ribosomal genes from morphologically distinct bacteria colonies identified the following five different species as dominant in all assayed generations: Enterobacter asburiae (98%), Microbacterium sp. (98%), Sphingomonas sp. E-(s)-e-D-4(2) (100%), Serratia sp. (99%) and Chryseobacterium meningosepticum (100%). The C. meningosepticum and Serratia sp. species were dominant within all five generations and the former was the most abundant, especially within the second generation. Other bacteria identified from different generations were: Asaia bogorensis (99%), Bacillus subtilis (99%), Enterobacter aerogenes (98%), Escherichia coli (91%), Herbaspirillum sp. (99%), Pantoea agglomerans (98%), Pseudomonas fluorescens (99%), Pseudomonas straminea (99%), Phytobacter diazotrophicus (97%) and Serratia marcescens (99%). Interestingly, when C. meningosepticum became the dominant bacterium of the midgut flora, the growth of other bacterial species, that could be cultured on LB agar, was usually limited suggesting that this species may possess some competitive advantages in the gut environment. 10.1371/journal.ppat.1000423.g001 Figure 1 Distribution of bacterial loads and major species composition of midguts microbiota in 5 individual laboratory-reared 5-d-old female A. gambiae mosquitoes from 5 consecutive generations. The bacteria species were determined to be closely related to Enterobacter asburiae, Microbacterium sp., Sphingomonas sp., Serratia sp., and Chryseobacterium meningosepticum. G1 to G5 denotes generation 1st to 5th. Our LB agar –based culture assays have some limitations in providing the complete picture of the composition of the mosquito midgut microbiota since a large fraction of bacteria are likely to be un-culturable, similarly to the human intestinal microbiota [23]. Future high throughput sequencing -based metagenomics approaches are likely to provide comprehensive information on the composition of the midgut microbiota. Nevertheless, as a proof of principle, our approach shows the great variations in both load and composition of the microbiota between different individuals and generations of insectary-reared mosquitoes. The mosquitoes' natural microbiota can influence their permissiveness to Plasmodium infection We assessed the impact of the mosquito's natural microbial flora on P. falciparum's capacity to establish infection through the removal of bacteria with antibiotic treatment, according to the established methodology [24],[25]. Provision of antibiotic through the sugar meal effectively eliminated all detectable bacteria from mosquitoes fed on either sugar or human blood (Figure 2A). The average bacterial load of sugar fed mosquito midguts was 104 CFU, and those fed on blood contained as many as 106 CFU (Figure 2A). After antibiotic treatment mosquitoes became aseptic and are referred as aseptic mosquitoes, while untreated mosquitoes are referred as septic. Aseptic mosquitoes were significantly more susceptible to P. falciparum infection, as a measure of oocysts numbers on the midgut, compared to the septic mosquitoes (p 0.05), compared to the PBS injected controls (Figure 3B). This result further supports that the anti-Plasmodium activity of bacteria is indirect and involves a response by the mosquito vector since the injected bacteria are unlikely to directly interact with the parasites that are confined within the midgut epithelium or under the basal lamina. It is more likely that the systemic infection will induce a battery of defense molecules in the hemolymph, from where they can attack the midgut-stage parasites on the basal side of the gut, or even within the epithelium by diffusion through the basal labyrinth. Indeed our previously published studies showed that injected bacteria induced a battery of anti-Plasmodium immune factors [14]. The stronger anti-Plasmodium effect of either injected or co-fed live bacteria, compared to heat inactivated bacteria, suggest that a factor which is more specific for live bacteria may be responsible for the inhibitory effect. Alternatively, the stronger effect of live bacteria may simply reflect their proliferative capacity which resulted in multiplication of their numbers to induce a much stronger immune response from the mosquito host. Mosquito genome-wide responses to microbial exposure Mosquitoes, as all other higher organisms, are continuously exposed to a variety of microbes. And we have shown that this exposure, whether it originates from the midgut lumen or the hemolymph, can influence the mosquito's permissiveness to P. falciparum infection. We have also shown that this effect is likely to be mediated through a mosquito response to the bacterial exposure. To better understand this response we have performed a series of genome-wide expression analyses to assess the regulation of the mosquito transcriptome upon microbial exposure. We used a microarray-based genome-wide gene expression strategy to compare transcript abundance between septic and aseptic adult female mosquitoes that had been fed on either sugar or non-infected blood (Figure 5 and Tables S2, S3). The presence of the endogenous bacteria flora in sugar fed mosquitoes resulted in the differential regulation of some 185 genes; 121 genes were up-regulated and 64 genes were down-regulated compared to antibiotic treated aseptic mosquitoes. A similar number of 195 genes were regulated by the presence of the endogenous microbial flora after feeding on non-infected blood; 137 genes were up-regulated and 58 genes were down-regulated (Figure 5A). The relatively small number of genes that were regulated as a consequence of the presence of the endogenous microbial flora most likely indicates a symbiotic relationship that has led to the adaptation of the mosquito to this flora. This hypothesis is strengthened by subsequent experiments that investigated the effect of ingested non-natural bacteria on the mosquito's transcriptome (see below). The mosquitoes' responses to natural microbiota when fed with either sugar or non-infected blood were quite divergent with only limited overlap in gene expression (Figure 5A), that comprised 21 induced and 1 repressed gene, corresponding to approximately 6.5% of the total regulated genes. However, one third of the commonly induced genes belonged to the immunity class. 10.1371/journal.ppat.1000423.g005 Figure 5 Global gene regulation at the different conditions of infection. (A) Comparison of transcript abundance between whole septic and aseptic mosquitoes after feeding on sugar (SF) or uninfected blood (BF), and in the midguts (Bac-Gut) or carcass tissues (Bac-Carc.) of mosquitoes 12 hrs post ingestion of uninfected blood supplemented with E. coli and S. aureus (substitution of bacteria with PBS as control). Colored arrows indicate genes that are up- or down- regulated in the corresponding treatment group. (B) Proportions and numbers of genes belonging to distinct functional groups which were up- or down- regulated in the corresponding treatment group. SF Whole: sugar-fed whole septic mosquitoes compared to aseptic ones; BF Whole: uninfected blood-fed whole septic mosquitoes compared to aseptic ones; Bac-Gut: mosquitoes midgut tissues 12 hrs post ingestion of experimental bacteria; Bac-Carc.: mosquitoes carcass tissues 12 hrs post ingestion of experimental bacteria (E. coli and S. aureus); I/A: putative immunity and apoptosis; R/S/M: oxidoreductive, stress-related and mitochondrial; C/S: cytoskeletal, structural; MET: metabolism; R/T/T: replication, transcription, translation; P/D: proteolysis, digestion; TRP: transport; DIV: diverse; UKN: unknown functions; gene functions were predicted based on Gene Ontology data and manual sequence homology searches. (C) Same as in (B), but also including genes of diverse functions (DIV) and unknown functions (UKN). The regulated genes represented a variety of functional classes with a general strong bias and over-representation of innate immunity genes (Figure 5B and 5C). Several of these immune genes have been previously shown to be transcriptionally-induced during malaria parasite infection, and to mediate anti-Plasmodium activity (Tables S2, S3). The septic mosquitoes displayed elevated expression of genes code for the antimicrobial peptides Cecropins 1 (Cec1) and 3 (Cec3), Defensin 1 (Def1) and Gambicin; the signal transducing serine proteases SP5, ClipA9, ClipA7 and ClipB8, and various pattern recognition receptors including AgMDL8, CTLMA4, FREP7 and FBN51, Tep4 and Tep5, Galectin 8, and PGRP-LB, PGRP-LC2 and PGRP-S3 [14], [27]–[31]. Surprisingly, the expression of the anti-Plasmodium factors FBNs 6, 9, and 36 were decreased in the septic mosquitoes (Tables S2, S3). The immune responsive Lysozyme c-1 (LYSC1) which previously has been linked to melanization reactions [32]–[34], was up-regulated in septic sugar-fed mosquitoes; lysozymes are key antibacterial factors. These results suggest that the natural microbiota play an important role in stimulating a basal immune activity which in turn is likely to contribute towards the determination of the mosquito's susceptibility to various pathogens, and hence their vectorial capacity. In fact a recent study has established that Plasmodium development is significantly more influenced by the mosquito's basal level immunity rather than the induction of immune responses upon parasite infection [35]. Of particular interest was the elevated expression of the peritrophic matrix protein gene Ag-Aper1 in septic mosquitoes that had fed on either sugar or uninfected blood, and several other genes encoding proteins with peritrophin-like, laminin-EGF-like and chitin-binding like domains (Tables S2, S3) [36]. Ag-APer1 and proteins containing chitin-binding domains may function as structural components of the insect cuticle, the peritrophic matrix and/or as pattern recognition receptors. The elevated expression of Ag-Aper1 in septic mosquitoes may indicate a role of the peritrophic matrix in protecting the epithelium from the infection of midgut flora bacteria. The natural microbial flora also stimulated expression of several metabolic genes involved in glycolysis, gluconeogenesis and sugar transport and this may relate to digestion of midgut bacteria that function as a food source for the mosquitoes [37] (Tables S2, S3). The genes exhibiting the greatest fold-differences in expression between septic and aseptic mosquitoes were of unknown function (Figure 5C). To investigate the mosquito's global transcriptional response to exposure to non-natural midgut flora we compared transcript abundance between mosquitoes that had fed on blood supplemented with a mixture of both Gram-negative (E. coli) and Gram-positive (Staphylococcus aureus) bacteria and control mosquitoes that had fed on uninfected blood with PBS. These treatments resulted in a much broader response. The ingestion of these bacteria triggered the regulation of as many as 656 and 520 genes in the midgut and carcass, respectively (Figure 5 and Tables S4, S5). In the midgut, 458 genes were up-regulated and 198 genes were down-regulated. As expected, fewer genes were regulated in the carcass compared to midgut tissue which was in direct contact with the ingested bacteria; 224 genes were up-regulated and 296 were repressed. Among the immune genes exhibiting differential expression between sterile-blood-fed and bacteria-blood-fed mosquitoes were several that have previously been shown to mediate anti-Plasmodium immune responses and to be transcriptionally up-regulated during Plasmodium parasite infection (Tables S4, S5). The ingestion of bacteria stimulated an elevated expression of genes code for the antimicrobial peptide IRSP1, the signal transducing serine proteases ClipB16, and inhibitor SRPN6 and SRPN7, and various pattern recognition receptors including AgMDLs 4, 6, and 7, CTL, CTLGA1, CTLGA3, and CTLMA6, FBNs 9, 20, 21, and 51, LRRD8, PGRP-LB, PGRP-LC2 and PGRP-S3, Tep11 and Toll6 [14], [38]–[42]. Only four immune genes, SP5, TPX4, DCCE2, and FBN51 were induced by both the natural flora and the ingested non-natural bacteria, while Tep-like, PGRP-LD, and FBN9 displayed an opposite pattern of regulation (Figure 5A and Tables S4, S5). As mentioned above, these differences are likely to reflect an adaptation of the mosquito to its natural microbial flora. Potential differences in the dosage of bacterial exposure may however also have influenced the quite different outcome. The natural microbiota stimulates basal immune activity that controls its proliferation Depletion of several immune factors through RNAi-mediated gene silencing has been shown to result in a proliferation of bacteria in the hemolymph as a result of a compromised immune system [43],[44]. To test whether the immune genes that are induced by the natural flora are indeed implicated in defending against opportunistic bacterial infections, we assayed the proliferation of the mosquito midgut flora upon their silencing. We subjected 12 genes to this test of which Cec1, Cec3, Def1, ClipA9, Gambicin, PGRP-LB, and FBN9 were induced by the presence of the natural bacterial flora, and the remaining LRRD7, LRRD19, TEP1, Rel1 and Rel2 genes represented anti-Plasmodium pattern recognition receptors or immune signaling pathway factors [35], [45]–[47]. Depletion of Cec3, Gambicin, PGRP-LB, LRRD7, TEP1, and Rel2 resulted in the significant proliferation of the natural bacterial flora in the mosquitoes' midguts. Gene silencing of Cec1, Def1, ClipA9, FBN9, and LRRD19 also resulted in some increase of bacterial loads in the midgut; however these effects were not statistically significant (Figure 6). The lack of significant bacterial proliferation in these knock-down mosquitoes could also be explained by the lower efficacy of gene silencing in the midgut tissue compared to the abdominal and thoracic compartment (Figure S3, Table S1). These results show that the mosquito's innate immune system is actively involved in controlling the bacterial load in the midgut lumen in a constitutive fashion, and that exposure to increased bacteria will result in increased production of some of these anti-Plasmodium factors. We believe that this is the mechanistic basis of how the mosquito's endogenous flora is important in priming an anti-Plasmodium defense. 10.1371/journal.ppat.1000423.g006 Figure 6 Immune gene-silencing influenced the bacterial loads of the mosquito midguts. Bars represent the mean values of total CFUs (log10 transformed) from 10 midguts, and standard error bars are included. *, p<0.05; **, p<0.01. The anti-Plasmodium action of immune genes can be modulated by the presence of the mosquito's endogenous microbiota The dual role of anti-Plasmodium factors in defending against both the parasite and bacteria, and the influence of bacteria on Plasmodium development, suggests the existence of complex interactions and relationships between the parasite, the microbiota and the mosquito's innate immune system. For example, the anti-Plasmodium activities of certain genes might be modulated by their parallel activities against bacteria. To assess such complexities and interactions, we studied the effect of various immune genes on P. falciparum's capacity to establish infection in the midgut tissue of both septic and aseptic mosquitoes through RNAi gene silencing approach (Figure 7). 10.1371/journal.ppat.1000423.g007 Figure 7 The depletion of PGRP-LB, Cec3, and ClipA9 through RNAi gene silencing resulted in the changes of P. falciparum oocyst intensity in the septic (untreated) and aseptic (antibiotic-treated) mosquitoes. Points indicate the absolute value of oocysts counts in individual mosquitoes, and horizontal black bars in each column represent the median value of oocysts from three replicates where the narrow black bars above or below the median values indicate the standard errors. p-values were calculated through a Kruskal-Wallis test. (A) P. falciparum oocyst intensity increased in aseptic mosquitoes (Antibiotic) when Cec3 or PGRP-LB was silenced. dsGFP injected mosquitoes (GFP) were used as controls. (B) P. falciparum oocyst intensity decreased in septic mosquitoes (Untreated) when ClipA9 was silenced. RNAi-mediated depletion of the antimicrobial peptides Cec1, Def1, and Gambicin had no statistically significant effect on the levels of P. falciparum oocyst infection in either mosquito groups (data not shown), while gene silencing of Cec3 and PGRP-LB resulted in an increased susceptibility to P. falciparum only in the aseptic mosquitoes (Figure 7A). This result may suggest that the depletion of these two immune genes in septic mosquitoes resulted in a proliferation of the microbial flora which in turn may have counteracted, or masked, the potential decrease of anti-Plasmodium immune responses. Another striking example of how important the microbial flora is in regulating anti-Plasmodium activity of immune genes is represented by the serine protease ClipA9. When this factor was depleted in septic conditions, the mosquitoes became significantly less susceptible to P. falciparum infection (p<0.05). In contrast, when ClipA9 was silenced in aseptic mosquitoes it had no significant effect on susceptibility to the parasite (Figure 7B). This observation suggests that the ClipA9-mediated anti-Plasmodium defense is exerted through the microbial flora and not directly against the parasite. ClipA9 is likely to be more specific for antibacterial defense and its depletion, under septic conditions, will hence result in the proliferation of bacteria which will exert strong anti-Plasmodium activity. Alternatively, it may mediate some direct Plasmodium protective activity which is abolished in the absence of bacteria. Interestingly the malaria parasite infection phenotype of ClipA9 gene silencing is opposite to that observed for the serine protease inhibitor SRPN6, suggesting that SRPN6 may function in the same cascade as an inhibitor of ClipA9 [30],[39]. In conclusion, similarly to humans, the mosquito intestine harbors a natural microbiota which is necessary for maintaining normal physiological functions including host metabolism and immune homeostasis. Accordingly, we have shown that the mosquito's natural bacterial flora show great variability between mosquitoes originating from the same colony and that it is an important regulator of mosquito permissiveness to Plasmodium. The mosquito's natural microbiota and artificially introduced non-natural bacteria negatively affected malaria parasite development through a mechanism that appears to implicate in the innate immune system, and not a direct killing of Plasmodia by the bacteria. The natural bacterial flora is essential in inducing a basal level immunity that in turn enhances the mosquito's ability in defending against the infection from the malaria parasites [35]. Interestingly, the effect of certain immune genes on Plasmodium infection is dependent on the presence of the microbial flora, suggesting that their mode of action is complex. This finding suggests that future studies on gene specific anti-Plasmodium action should also consider the complex interplay between the microbiota and the mosquito's immune defenses against the Plasmodium parasite. This relationship is further corroborated by observations from Dr. Barillas-Mury's group, where RNAi gene silencing of one immune gene facilitated the proliferation of microbial flora but reduced the Plasmodium infection. The natural bacterial flora has also been shown to be involved in the suppression of other pathogenic organisms in other mosquito species. Tetracycline treatment of Culex bitaeniorhynchus rendered this mosquito more susceptible to the Japanese encephalitis virus [48] and the Aedes aegypti mosquito microbial flora has been shown to stimulate a basal-level immunity which suppresses dengue virus infection [25]. Materials and Methods Ethics statement All animals were handled in strict accordance with good animal practice as defined by the relevant national and/or local animal welfare bodies, and all animal work was approved by the appropriate committee. Mosquito rearing, antibiotic treatments, and RNA sample preparation A. gambiae Keele strain mosquitoes were maintained on a 10% sugar solution in laboratory culture at 27°C and 70% humidity with a 12 hrs light/dark cycle according to standard rearing procedures [49]. A single cohort of adult female mosquitoes were collected immediately after eclosion, and either maintained under normal, non-sterile insectary conditions or placed into a sterile environment. Following, adult female mosquitoes were daily given fresh filtered sterilized 10% sucrose solution containing 15 µg gentamicin sulphate (Sigma) and 10 units/10 µg of penicillin-streptomycin (Invitrogen) per ml, respectively. Each cohort of mosquitoes was simultaneously membrane-fed freshly washed human erythrocytes resuspended to 40% haematocrit using human serum. As far as possible, every care was taken to maintain the sterility of the blood and membrane-feeding apparatus used to feed the mosquitoes, in order to prevent the antibiotic-treated mosquitoes acquiring bacterial infection during the process of membrane-feeding. The mosquitoes were starved for 8 hrs before feeding to encourage engorgement, and sugar solution was replaced once blood feeding had finished. At 24 hrs after blood feeding, 20 mosquitoes from each replicate of each cohort was collected and dissected on ice. RNA was extracted from dissected tissues at the assayed time points using the RNeasy kit (Qiagen). The quantification of RNA concentrations was performed using a Spectrophotometer (Eppendorf). Microarray hybridization and data mining Probe sequence design and microarray construction were kept the same as described in [14]. Probe preparation and microarray hybridizations were performed essentially as previously described with some modifications [14]. Briefly, Cy3-labeled control cRNA probes and Cy5-labeled treatment cRNA probes were synthesized from 2–3 µg of RNA using the Agilent Technologies low-input linear amplification RNA labeling kit according to the manufacturer's instructions. Hybridizations were performed with the Agilent Technologies in situ hybridization kit according to the manufacturer's instructions with 2 µg of cRNA probes and 16 hrs after hybridization the microarray slides were washed and dried with compressed air. Microarrays were scanned with an Axon GenePix 4200AL scanner using a 10 µm pixel size (Axon Instruments, Union City, California, United States). Laser power was set to 60%, and the photomultiplier tube (PMT) voltage was adjusted to maximize effective dynamic range and minimize the saturation of pixels. Scanned images were analyzed by using GenePix software, and Cy5 and Cy3 signal and ratio values were obtained and subjected to statistical analysis with TIGR MIDAS and MeV software [50]. The minimum signal intensity was set to 100 fluorescent units, and the signal to background ratio cutoff was set to 2.0 for both Cy5 and Cy3 channels. Three or four biological replicates were performed for each experimental set. The background-subtracted median fluorescent values for good spots (no bad, missing, absent, or not-found flags) were normalized according to a LOWESS normalization method, and Cy5/Cy3 ratios from replicate assays were subjected to t tests at a significance level of p<0.05 using cutoff value for the significance of gene regulation of 0.7 and 0.8 in log2 scale, for septic mosquitoes and mosquitoes co-fed with experimental bacteria respectively, according to previously established methodology [51]. Microarray-assayed gene expression of 6 genes was further validated with quantitative RT-PCR and showed a high degree of correlation with the Pearson correlation coefficient (p = 0.84), the best-fit linear-regression analysis (R2 = 0.70), and the slope of the regression line (m = 1.247) demonstrated a high degree of correlation of the magnitude of regulation between the two assays (Figure S2). Primers design and qRT–PCR Primers' sequences for validation of microarray hybridization data were as described in [14]. And new primers for RNAi gene silencing and verification were designed with Primer 3 Program on a web-based server (http://frodo.wi.mit.edu/). All the primer sequences were listed in Table S1. Real-time quantitative PCR (qRT–PCR) to check the efficiency of gene silencing were done essentially according to [14]. The quantification was performed using the QuantiTect SYBR Green PCR Kit (Qiagen) and ABI Detection System ABI Prism 7300. All PCR reactions were performed in triplicate. Specificity of the PCR reactions was assessed by analysis of melting curves for each data point. The ribosomal protein S7 gene was served as internal control for normalization of cDNA templates. RNAi gene silencing and P. falciparum infection assays Sense and antisense RNAs were synthesized from PCR-amplified gene fragments using the T7 Megascript kit (Ambion). The sequences of the primers are listed in Table S1. dsRNA mediated gene silencing was done according to [14],[28]. About 80 4-d-old female mosquitoes were injected, in parallel, with GFP dsRNA as a control group or with target gene–specific dsRNA for the experimental group. Gene silencing in the whole mosquitoes was verified 3 to 4 d after dsRNA injection by qRT-PCR, done in triplicate, with the A. gambiae ribosomal S7 gene as the internal control for normalization. Gene silencing efficiency were listed in Table S1 with standard errors shown (KD%±SE). RNAi gene silencing in the midguts was verified by RT-PCR, 10 midguts were used for each replicate and at least two replicates were included with only one replicate shown (Figure S3). At least 50 control (GFP dsRNA–injected) and 50 experimental (gene dsRNA–injected) mosquitoes were fed on the same P. falciparum NF54 gametocytes culture at 3–4 d after the dsRNA injection. 24 hrs post blood feeding (pbf), the unfed mosquitoes were removed and the fed-mosquitoes were dissected at 7–8 d after feeding and midguts were stained with 0.2% mercurochrome [43]. Oocyst numbers per midgut were determined using a light-contrast microscope (Olympus). The median number of oocysts per midgut was calculated for each tested gene and for GFP dsRNA–injected control mosquitoes. The results for equal numbers of midguts from all three independent biological replicates were pooled. The dot plots of the oocysts number in each midgut within each treatment were presented by MedCalc software with the median value of the oocysts indicated. The Kruskal-Wallis (KW) test and Mann-Whitney test were used to determine the significance of oocysts numbers (p<0.05). Co-feeding and pre-injection of bacteria and P. falciparum infection assays About 80 4-day-old mosquitoes were first injected with PBS as control, or a mixture of live bacteria with approximately 30,000 E. coli and 60,000 S. aureus, or a mixture of heat-inactivated bacteria with the same number as the live ones. 24 hrs or 48 hrs after injection, mosquitoes were fed with P. falciparum NF54 gametocytes culture which were carried out according to our establish protocols [14]. For the co-feeding assay, the same sets of control PBS or bacteria were mixed in the blood meal to result in the same amount of either bacterium in the mosquito midguts. Unfed mosquitoes were removed, and the rest were kept in 26°C for 8 days before the oocysts counts. The infection phenotypes were determined as described above. Endogenous bacteria enumeration from mosquitoes' midguts Isolation and colony forming units (CFU) enumeration of bacteria from midguts of untreated control, antibiotic-treated mosquitoes and gene-silenced mosquitoes were done essentially according to [43] with modifications. The midguts from surface sterilized mosquitoes were dissected with sterilized PBS 4 d after dsRNA injection, and CFU were determined by plating the homogenate of the midguts with series dilutions on LB agar plates and incubating the plates at 27°C for 2 days. Each assay was performed with one midgut and at least 10 independent replicates were included for each gene. The species of the isolated bacteria were determined by amplifying a region of the 16s rDNA as described by using primers 27f and 1492r [52]. PCR products were sequenced and blasted against Nucleotide collection (nr/nt) database to verify the species. Immuno-fluorescent microscopy of ookinetes from bloodmeal and oocysts from midgut epithelium The early stages of P. falciparum development within untreated, antibiotic-treated and bacteria co-feeding mosquito midguts were compared by using the immuno-staining of ookinetes with anti-Pfs25 antibody (MRA-28, provided by MR4). Preparation of samples for immuno-fluorescence microscopy of malaria parasite within the bloodmeal was carried out based on [53] with substantial modifications. Sterile 0.5 ml “non-stick” low retention hydrophobic tubes (Alpha Laboratory Supplies) and sterile “non-stick” low retention hydrophobic pipette tips (Alpha Laboratory Supplies) were used to minimize malaria parasite loss during sample preparation due to their adhesion to plastic surfaces. The midguts including the entire bloodmeal contents were individually homogenized and diluted in 280 µl of PBS. 10 µl was then spotted, in duplicate, onto Teflon®-printed microwell glass slides (VWR International) previously coated with 3-aminopropyltriethoxysilane (APES) according to the supplier's instructions (Sigma). The sample slides were then air-dried, fixed in ice cold acetone for 2 mins and subjected to blocking in 10% goat serum for 1 hr, followed by the incubation with primary antibodies at 1∶400 dilutions for 2 hrs. After three PBS washes, sample slides were incubated with secondary antibodies (Molecular Probes, 1∶1000) for 2 hrs with Alexa Fluor 488-conjugated (green) goat anti-mouse antibody (1∶500 dilution). After another three PBS washes, sample slides were analyzed under a Nikon E800 upright microscope with epi-fluorescence. The total number of round forms, retort-forms and mature ookinetes in each spotted sample was counted. Average values for the densities of each malaria parasite stage present within each midgut examined were calculated from the three replicates. For checking the ookinetes and early oocysts in the midgut epithelium cells, at 24–30 hrs or 8 d after blood feeding, the midguts were dissected in 1% paraformaldehyde and washed with 3 times of PBS to remove the blood content and were subjected to the fixation in 4% paraformaldehyde (in PBS) for 1 hr and followed with 2 PBS washes. The midguts were then subjected to blocking and immune staining with primary antibody and secondary antibody as mentioned above. Midguts stained with pre-immune of anti-Pfs25 antibody were used as control. Midgut samples were mounted using the ProLong Antifade Kit (Molecular Probes) with DAPI staining of the cell nuclei and analyzed with same microscopy set as described above. Supporting Information Figure S1 Survival rates of A. gambiae Keele mosquitoes after P. falciparum infection. At least 40 mosquitoes were in each replicate, and three replicates were included with standard errors shown. Non-treated: septic mosquitoes harbor natural microbiota; Antibiotic-treated: mosquitoes treated with antibiotics, referred as aseptic mosquitoes; Rechallenged: aseptic mosquitoes co-fed with bacteria and P. falciparum infected blood. (0.02 MB PDF) Click here for additional data file. Figure S2 Validation of microarray-assayed gene expression with qRT-PCR. The values for the expression data obtained by microarray analysis (log2 ratio) for six genes were plotted against the corresponding expression values obtained with qRT-PCR (also log2 transformed) from two biological replicates of each experiment. Only the comparisons between the whole septic and aseptic mosquitoes which fed on sugar or uninfected blood were shown here. (0.01 MB PDF) Click here for additional data file. Figure S3 Verification of gene silencing in the mosquito midgut tissue 4-d post dsRNA injection. dsGFP-injected mosquito midguts were used as controls, and 10 midguts were included in each replicate and at least two replicates were done with only one replicate shown here. Def1: defensin 1, Gam: gambicin; Cec: cecropin. (0.09 MB PDF) Click here for additional data file. Table S1 Primers used for dsRNA synthesis, qRT-PCR validation of RNAi-mediated gene silencing and the efficiency of gene silencing. (0.01 MB PDF) Click here for additional data file. Table S2 List of genes identified from microarray analysis exhibiting significant differential expression between untreated septic and antibiotic-treated aseptic adult female A. gambiae Keele mosquitoes fed with sugar (7-day-old whole mosquitoes minus head). (0.06 MB XLS) Click here for additional data file. Table S3 List of genes identified from microarray analysis exhibiting significant differential expression between septic and aseptic adult female A. gambiae Keele mosquitoes fed on uninfected blood (7-day-old whole mosquitoes minus head). (0.06 MB XLS) Click here for additional data file. Table S4 List of genes identified from microarray analysis exhibiting significant differential expression in the midguts of 7-day-old female A. gambiae Keele mosquitoes 12 hrs after feeding on uninfected blood supplemented with E. coli and S. aureus, PBS substitution of bacteria as control. (0.17 MB XLS) Click here for additional data file. Table S5 Table S5. List of genes identified from microarray analysis exhibiting significant differential expression in the carcass of 7-day-old female A. gambiae Keele mosquitoes 12 hrs post feeding on uninfected blood supplemented with E. coli and S. aureus, PBS substitution of bacteria as control. (0.15 MB XLS) Click here for additional data file.

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Elena Levashina: Role: Editor

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Journal ID (nlm-ta): PLoS Pathog

Journal ID (iso-abbrev): PLoS Pathog

Journal ID (publisher-id): plos

Journal ID (pmc): plospath

Title: PLoS Pathogens

Publisher: Public Library of Science (San Francisco, USA )

ISSN (Print): 1553-7366

ISSN (Electronic): 1553-7374

Publication date Collection: October 2014

Publication date (Electronic): 23 October 2014

Volume: 10

Issue: 10

Electronic Location Identifier: e1004398

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[1]W. Harry Feinstone Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America

Max Planck Institute for Infection Biology, Germany

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* E-mail: gdimopou@ 123456jhsph.edu

The authors have declared that no competing interests exist.

Conceived and designed the experiments: JLR SMS GD. Performed the experiments: JLR SMS ACB RGS YD SK AT GM. Analyzed the data: JLR SMS ACB RGS YD SK AT GM. Contributed reagents/materials/analysis tools: GD. Wrote the paper: JLR SMS GD.

[¤a]

Current address: Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Maryland, United States of America

[¤b]

Current address: Instituto de Bioquímica Médica, Programa de Biologia Molecular e Biotecnologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil

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Publisher ID: PPATHOGENS-D-14-00451

DOI: 10.1371/journal.ppat.1004398

PMC ID: 4207801

PubMed ID: 25340821

SO-VID: ea569a85-ece1-4476-8c18-2c3ab95c934e

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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.

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Date received : 20 February 2014

Date accepted : 14 August 2014

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This work has been supported by National Institutes of Health/National Institute for Allergy and Infectious Disease grants 1R01AI061576-01A1, RO1 AI059492, and RO1AI078997; the Johns Hopkins Malaria Research Institute and the Bloomberg Family Foundation. JLR was supported by an individual F31 NRSA training grant from NIH/NIAID (1F31AI080161-01A1) and by the American Society for Microbiology Robert D. Watkins Graduate Research Fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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