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Discovery of Mosquito Saliva MicroRNAs during CHIKV Infection
research-article
Author(s):
Payal D. Maharaj
1 ,
Steven G. Widen
2 ,
Jing Huang
1 ,
Thomas G. Wood
2 ,
Saravanan Thangamani
1
,
3
,
4
,
*
Publication date (Electronic):
22 January 2015
Journal:
PLoS Neglected Tropical Diseases
Publisher:
Public Library of Science
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Abstract
Mosquito borne pathogens are transmitted to humans via saliva during blood feeding. Mosquito saliva is a complex concoction of many secretory factors that modulate the feeding foci to enhance pathogen infection and establishment. Multiple salivary proteins/factors have been identified/characterized that enhance pathogen infection. Here, we describe, for the first time, the identification of exogenous microRNAs from mosquito saliva. MicroRNAs are short, 18–24 nucleotide, non-coding RNAs that regulate gene expression, and are generally intracellular. However, circulating miRNAs have been described from serum and saliva of humans. Exogenous miRNAs have not been reported from hematophagous arthropod saliva. We sought to identify miRNAs in the mosquito saliva and their role in Chikungunya virus (CHIKV) infection. Next generation sequencing was utilized to identify 103 exogenous miRNAs in mosquito saliva of which 31 miRNAs were previously unidentified and were designated novel. Several miRNAs that we have identified are expressed only in the CHIKV infected mosquitoes. Five of the saliva miRNAs were tested for their potential to regulated CHIKV infection, and our results demonstrate their functional role in the transmission and establishment of infection during blood feeding on the host.
Author Summary
Mosquito saliva contains a complex repertoire of bioactive factors that are secreted into blood feeding site, the skin. Infected mosquitoes transmit pathogens to the host during feeding via saliva. The bioactive factors in mosquito saliva are responsible for modulating host hemostasis, immune defenses and pain/itch responses, and have been implicated to enhance pathogen infection and establishment in the host. In our efforts to identify and characterize salivary immunomodulators that enhance Chikungunya virus (CHIKV) transmission, we have discovered, for the first time, exogenous microRNA in mosquito saliva. MicroRNAs (miRNAs) are short, 18–24 nucleotide, non-coding RNAs that regulate gene expression. Short non-coding RNAs were extracted from the saliva of Chikungunya virus (CHIKV) infected and uninfected Aedes aegypti and Aedes albopictus saliva, and subjected to Illumina next generation sequencing. Bioinformatic analysis revealed the presence of miRNAs in the mosquito saliva. We have also identified several novel miRNAs that are expressed only during CHIKV infection. Though the functional roles of these miRNAs are yet to be established, our in-vitro data from testing 5 miRNAs demonstrate their role in the regulation of CHIKV infection. These miRNAs may play an important role in regulating the establishment of CHIKV infection in the mammalian host during blood feeding.
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Most cited references35
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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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- Abstract: found
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The global emergence/resurgence of arboviral diseases as public health problems.
Duane J. Gubler (2002)
During the past 20 years there has been a dramatic resurgence or emergence of epidemic arboviral diseases affecting both humans and domestic animals. These epidemics have been caused primarily by viruses thought to be under control such as dengue, Japanese encephalitis, yellow fever, and Venezuelan equine encephalitis, or viruses that have expanded their geographic distribution such as West Nile and Rift Valley fever. Several of these viruses are presented as case studies to illustrate the changing epidemiology. The factors responsible for the dramatic resurgence of arboviral diseases in the waning years of the 20th century are discussed, as is the need for rebuilding the public health infrastructure to deal with epidemic vector-borne diseases in the 21st century.
- Record: found
- Abstract: found
- Article: found
C6/36 Aedes albopictus Cells Have a Dysfunctional Antiviral RNA Interference Response
Doug E Brackney, Jaclyn C. Scott, Fumihiko Sagawa … (2010)
Introduction RNA interference (RNAi) is a process by which intracellular long double stranded RNA (dsRNA) is cleaved into small RNA (sRNA) effector molecules that direct the silencing of complementary RNA sequences. Multiple pathways, including exo- and endo-small interfering RNA (siRNA), microRNA (miRNA) and PIWI-interacting RNA (piRNA), contribute to this process. The exo-siRNA pathway, in which silencing is triggered by exogenously derived dsRNA molecules, is thought to comprise the main antiviral response in mosquitoes [1]–[7]. This pathway is initiated when Dicer 2 (Dcr2) binds to and cleaves long dsRNA molecules that exist within cells as viral replicative intermediates and/or RNA secondary structures into 20–22 nucleotide siRNAs [8]–[11]. The resulting siRNAs are loaded by Dcr2 and R2D2 into the multi-protein RNA-induced silencing complex (RISC), which includes Argonaut 2 (Ago2), and unwound, after which the 3′ terminus of the retained guide strand is 2′-O-methylated [12], [13]. The siRNA-loaded RISC identifies single stranded RNAs complementary to the guide strand, which are cleaved by the endoribonucleolytic activity of Ago2 [14], [15]. The advent of high-throughput sequencing technologies allows researchers to characterize viral-derived siRNAs (viRNAs) and to quantitatively map the areas of the viral genome most often targeted [1], [5], [6], [16], [17]. Arthropod-borne viruses (arboviruses) are a diverse group of viruses maintained in nature by horizontal transmission between hematophagous, arthropod vectors and vertebrates. Arbovirus infections can cause an acute, pathogenic outcome in the vertebrate host, but establish a persistent, relatively non-pathogenic infection in the invertebrate vector, with some noted exceptions [18]–[20]. The reasons for this difference are not fully understood, but may be related to the highly inflammatory innate antiviral immune response in vertebrates (type-I interferon-mediated) compared to invertebrates, which rely on antiviral mechanisms such as RNAi and the Toll, JAK/STAT, and Imd/Jnk signaling pathways [7], [18], [21]–[23]. Of these, RNAi appears to be the primary means of limiting viral infections in the vector [2]–[4], [7], [16], [24]–[27]. High-throughput sequencing has identified viRNAs from Aedes aegypti and Culex pipiens quinquefasciatus mosquitoes infected with Sindbis virus (SINV, Togaviridae: Alphavirus) and West Nile virus (WNV, Flaviviridae: Flavivirus), respectively, and Drosophila infected with Flock House virus (FHV, Nodaviridae: Alphanodavirus) [1], [6], [16]. In each case, the viRNAs were predominantly 21 nt in length, asymmetrically distributed across the length of the genome and derived from both the positive and negative RNA strands. Aedes albopictus C6/36 and Drosophila melanogaster S2 cells are commonly used, immortalized, invertebrate cell lines that have become powerful and convenient tools for studying many host/virus interactions at the molecular level. Originally established from mosquito larvae homogenates, C6/36 cells are easy to maintain and highly permissive to numerous arboviruses. Likewise, S2 cells are easy to maintain and manipulate and critical specific reagents, such as antibodies and specific knockouts, are available commercially. However, immortalized cells may not accurately model the natural environment encountered by viruses in the whole organism. For example, Vero (African green monkey kidney) cells, which are used to study many human viruses because of their inherent permissiveness, lack a functional type-I interferon response [28]. A recent report on WNV infection in insect cell culture models raised the possibility that C6/36 cells may be similarly deficient in key components of their antiviral defense mechanisms, leading to their permissiveness for arbovirus infection [29]. Therefore, we investigated the RNAi response in C6/36 cells compared to S2 cells. Specifically, C6/36 and S2 cells were infected with representatives of three diverse arbovirus families and small RNA populations of infected cells were characterized by deep sequencing. Materials and Methods Viruses The viruses used in these studies are representative of each of three major arbovirus families that include many human pathogens; Flaviviridae, Togaviridae, and Bunyaviridae. WNV (Flaviviridae; Flavivirus) was generated from an infectious cDNA clone derived from the NY99 strain [30]. SINV (Togaviridae, Alphavirus) was generated from the SINV TE3′2J infectious clone [31]. The LACV (Bunyaviridae; Orthobunyavirus) used in these studies was the LACV/Human/1960 strain (GenBank accession nos. EF485032.1, EF485031.1, and EF485030.1). Originally, isolated from the brain of a LaCrosse encephalitis patient in 1965 the virus was subsequently passaged in suckling mice three times and baby hamster kidney cells (BHK-21) an additional six times. Stock virus was prepared in BHK-21 cells [32]. Cell Culture, Virus Infections and RNA Extractions The C6/36 Aedes albopictus cells were grown in Dulbecco's modified essential medium (DMEM) with 10% fetal bovine serum (FBS), 100 U/ml penicillin, 100 µg/ml streptomycin, L-glutamine, and sodium bicarbonate at 28°C with CO2. The S2 Drosophila melanogaster cells were grown in Schneider's Drosophila medium with 10% FBS, penicillin/streptomycin, and L-glutamine at 28°C without CO2. C6/36 and S2 cell cultures were infected in triplicate with each of the three viruses at a multiplicity of infection (MOI) of 0.1 and 1, respectively. Considering that Drosophila is not a natural host for any of these viruses, higher MOIs were required to ensure infection. Virus stocks were diluted in maintenance medium with a FBS concentration of 2%, inoculated onto confluent cell monolayers, and allowed to adsorb for one hour at room temperature. The virus inocula were removed and replaced with maintenance medium. Five days post infection, WNV and SINV infected C6/36 cells were harvested. LACV infection of C6/36 cells and all infections in S2 cells were maintained an additional two days before harvesting. Cells were pelleted by centrifugation and resuspended in mirVana lysis buffer. RNA from each sample was extracted using the mirVana miRNA Isolation Kit (Ambion, Austin, TX) according to the manufacturer's instructions. RNA quantity and integrity was determined on an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA). Preparation of Small RNA Libraries and High-Throughput Sequencing Equal amounts of RNA from the three replicates were pooled and ethanol precipitated. Approximately 10 µg of total RNA from each experimental group was size fractionated on a TBE/urea 15% polyacrylamide gel and small RNA (sRNA) populations (17–30 nt) recovered. 5′ and 3′ sequencing adapters (5′ adapter: 5′-GUUCAGAGUUCUACAGUCCGACGAUC-3′, 3′ adapter: 5′-P-UCGUAUGCCGUCUUCUGCUUGU-3′) (Oligonucleotide sequences © 2007-2009 Illumina, Inc. All rights reserved) were then ligated to sRNAs using T4 RNA ligase, of which the 3′ adapter was not pre-adenylated. The sRNAs were reverse transcribed and PCR amplified according to the manufacturer's instructions (Illumina, San Diego, CA). The resulting libraries were sequenced at the National Center for Genome Resources (Santa Fe, NM) using an Illumina Cluster Station and Genome Analyzer II or IIx. Assembly and Analysis of sRNA Libraries Reads from sRNA libraries were trimmed of adapter sequences and aligned to genome sequences of either the WNV NY99 infectious clone, the SINV TE3′2J infectious clone or the LACV/Human/1960 strain using the Short Oligonucleotide Alignment Package v.1 (SOAP) (http://soap.genomics.org.cn/). A seed size of eight and a maximum of two mismatches were permitted. Gaps and further trimming were not allowed. Quality scores represent an average of the confidence in each sequenced nucleotide in each sRNA. These are based on the Illumina scoring system where 1 is a minimum and 40 is the maximum. Additional analyses were performed using Microsoft Excel and GraphPad. Results Characterization of Small RNA Profiles To assess the antiviral RNAi response in two model invertebrate cell culture systems, we sequenced small RNAs (sRNAs) from C6/36 and S2 cells infected with three arboviruses, WNV, SINV and LACV (Bunyaviridae: Orthobunyavirus). These viruses were chosen as representative members of three major arbovirus families with both positive- (WNV, SINV) and negative-polarity (LACV) RNA genomes. The total number of sRNA reads obtained varied greatly between samples and most likely reflects differences in cell density at the experimental endpoint, since the majority of the sRNAs originate from the host in the form of miRNAs. In WNV infected cells, there were 3.4 and 9.9 million reads from C6/36 and S2 cells, respectively (Table 1). Of these, only 12,539 reads (0.37%; 6,151 unique) in C6/36 cells and 4,431 (0.045%; 3,127 unique) in S2 cells aligned to the WNV genome, where unique reads represent individual viRNAs that code for a specific nucleotide sequence. In both cell lines, greater than 87% of the reads perfectly aligned with the WNV genome. Analysis of the viRNA sizes revealed that over 76% of the viRNAs isolated from S2 cells were 20–22-mers (mean length 21.6 nt) with 54% constituting 21-mers. In contrast, 57% of the viRNAs from C6/36 cells were 20–22-mers (mean length 21.3 nt) and 17.2% were 21-mers (Figure 1A) with a high proportion of the WNV viRNAs being 17–18 mers (data not shown). 10.1371/journal.pntd.0000856.g001 Figure 1 Size and abundance of small RNA reads Mmapping to the viral genomes. The abundance of 19–30-mer sRNA reads mapping to the WNV (A), SINV (B) and LACV (C) genomes based on size. Abundance is represented as a percentage of the total viRNAs from each sample. The black bars correspond with samples collected from S2 cells and white bars from C6/36 cells. 10.1371/journal.pntd.0000856.t001 Table 1 Small RNA profiles from C6/36 and S2 infected cells. Reads Aligning To Viral Genome Reads With Mismatches n (%) Small RNA Library Total # of Reads (×106) Total # (unique #) Average Length Average Quality Score 0 1 2 WNV C6/36 3.4 12,539 (6,151) 21.3 39.8 11,399 (91) 964 (8) 176 (1) WNV S2 9.9 4,431 (3,127) 21.6 29.4 3,912 (88) 363 (8) 156 (4) SINV C6/36 14.4 1.6×106 (1.2×105) 25.2 30.8 1.5×106 (92) 1.1×105 (7) 19,411 (1) SINV S2 7.3 3.5×105 (46,103) 21.0 31.4 3.1×105 (91) 27,070 (8) 4,202 (1) LACV C6/36 11.2 9.4×105 (73,098) 25.2 31.2 8.7×105 (92) 65,265 (7) 9,672 (1) LACV S2 8.6 6,777 (2,201) 20.4 29.2 5,899 (87) 683 (10) 195 (3) Analysis of the sRNA populations in SINV infected cells revealed that there were 14.4 and 7.3 million reads in C6/36 and S2 cells, respectively (Table 1). Of these, 1.6×106 reads (11.11%; 1.2×105 unique) in C6/36 and 3.5×105 reads (4.79%; 46,103 unique) in S2 cells aligned to the SINV genome, of which greater than 90% perfectly aligned. As with the WNV infected cells, greater than 93% of the viRNAs isolated from S2 were 20–22-mers (mean length 21 nt) and 76% were 21-mers (Figure 1B). In C6/36 cells, only 9% of SINV viRNAs were 20-22 nt in length. Mean length was 25.2 nt, with the majority of the viRNAs (76%) being between 24–27 nt. Finally, there were 11.2 and 8.6 million reads in LACV infected C6/36 and S2 cells, respectively (Table 1). In LACV infected C6/36 cells, 9.4×105 reads (8.39%; 73,098 unique) aligned with the LACV genome, with greater than 90% of the reads having zero mismatches. In contrast to the C6/36 infected cells, only 6,777 reads (0.078%; 2,201 unique) from S2 cells aligned with the LACV genome; however, 87% of these reads matched perfectly to LACV RNA. Consistent with the results from WNV and SINV, the majority of the LACV viRNAs in S2 cells were 20–22-mers (80%), with 43% of these reads being 21 nt in length (mean length 20.4 nt) (Figure 1C). In C6/36 cells, only 9% of the total viRNAs were 20–22-mers. Mean length of LACV viRNAs from C6/36 cells was 25.2 nt, with the majority (79%) being between 24–28 nt in length. Distribution and Abundance of viRNAs To more closely examine the viRNA populations, viRNAs from each experimental sample were aligned to the input viral genome and viRNA coverage intensity determined per nucleotide across the length of the genome. Included in these analyses are all viRNA reads 19–30 nucleotides in length. Over 90% of the WNV genome was targeted by at least one viRNA in both C6/36 and S2 cells. Inspection of the intensity of viRNA coverage of the WNV genome revealed significant positional and regional differences between C6/36 and S2 cells (Figure 2). viRNAs isolated from infected C6/36 cells were asymmetrically distributed across the length of the genome and were derived almost exclusively from the positive sense viral genomic RNA strand (vRNA) (99.9%) (Figure 2A). The most highly targeted site was genome position 206 within the capsid coding sequence with 776 reads covering this position. Expansion of the data set to include 17–18-mers revealed that the first 17 nucleotides of the complementary, negative RNA strand were the most highly targeted (1,019 reads mapping to these sites) within the genome (data not shown). Alignment of viRNAs obtained from WNV infected S2 cells revealed that they were asymmetrically distributed and were derived from both the positive sense vRNA (84%) and complementary cRNA (16%) (Figure 2B). Nucleotide 125 located in the capsid coding sequence was the most targeted site within the genome (99 reads mapping to this site).These results are strikingly similar to those generated from WNV infected Cx. p. quinquefasciatus midguts [1]. 10.1371/journal.pntd.0000856.g002 Figure 2 viRNA coverage of the WNV genome in C6/36 and S2 cells. Complete genome of WNV (11,029 nt.) showing intensity at each nucleotide of the genome in C6/36 (A) and S2 (B) cells. Plotted are the 19–30-mer viRNA reads. Reads originating from the genomic, positive strand are represented in blue above the x-axis and those originating from the negative strand are represented in red below the x-axis. The viral genome coverage, viRNA frequency and distribution for the SINV infected C6/36 and S2 cells were very similar to those observed for WNV infected cells. In both cell lines 100% of the viral genome was targeted by at least one viRNA. However, the percent coverage in the C6/36 cells is deceiving as the majority of viRNAs were directed at only a few “hotspots”. The viRNAs from C6/36 cells were unevenly distributed across the genome with both positive sense vRNA (71%) and negative sense cRNA (29%) targeting (Figure 3A). The region just 3′ of the SINV subgenomic promoter was the most highly targeted with 201,104 reads mapping to nucleotide 8,013, located in the viral capsid coding region. The increased targeting of the subgenomic transcripts may reflect the overall abundance of these transcripts in comparison to the full length genome. This predilection for targeting the subgenomic transcript was not observed in the SINV infected S2 cells (Figure 3B) or SINV infected Ae. aegypti [6]. Further similarities between the S2 cell viRNA profile and that from Ae. aegypti included the asymmetry of distribution across the genome and the proportion of viRNAs derived from the positive sense vRNA (52% in S2 cells and 54% in Ae. aegypti) [6]. The most highly targeted site within the viral genome was nucleotide 4,601 (4,803 reads) in the viral nsp3 gene. 10.1371/journal.pntd.0000856.g003 Figure 3 viRNA coverage of the TE3′2J SINV genome in C6/36 and S2 cells. Complete genome of TE3′2J SINV (11,385 nt.) showing intensity at each nucleotide of the genome in C6/36 (A) and S2 (B) cells. Plotted are the 19–30-mer viRNA reads. Reads originating from the genomic, positive strand are represented in blue above the x-axis and those originating from the negative strand are represented in red below the x-axis. The green vertical line represents the location of the subgenomic promoter. Analysis of sRNAs revealed 100% viRNA coverage of the LACV genome in both C6/36 and S2 cells. viRNAs from both cell lines were asymmetrically distributed across the genome and were derived from both the vRNA (negative-sense) and cRNA (positive-sense) strands (Figure 4). The predominance of positive-polarity strand targeting observed for WNV and SINV in C6/36 cells was also observed in both cell types infected with the negative-sense RNA LACV. However, a difference in the proportions of negative- and positive-sense targeting was noted. In S2 cells, 84% of the viRNAs were derived from the cRNA (positive-sense) strand compared to 72.5% in C6/36 cells (Figure 4B, D, F and 4A, C, E, respectively). The intensity of viRNA targeting for each of the three segments of the tri-partite LACV genome (L segment 6,980 nt, M segment 4,526 nt and S segment 984 nt) was determined. Values are presented as the total number of viRNAs per kb per segment. The S segment was the most frequently targeted in both cell types with 4.3×105 and 3,829 viRNA reads per kb in C6/36 and S2 cells, respectively. As expected, the most frequently targeted sites within the genome were located in the S segment. The predominant S segment region targeted in LACV infected S2 cells was located between nt 904-923 at the 3′ end (∼3,500 hits), while nt 482 (89,871 hits) was the most targeted site in C6/36 cells (Figures 4E and 4F). In each cell line there was an approximately 10-fold reduction in the targeting of the M and L segments as compared to the S segment (M = 3.0×104 and L = 5.5×104 in C6/36 cells and M = 263 and L = 261 in S2 cells) (Figure 4A–D). The observed S segment targeting bias most likely reflects the abundance of S segment mRNA in comparison to M and L segment mRNA in bunyavirus-infected cells [33]. 10.1371/journal.pntd.0000856.g004 Figure 4 viRNA coverage of the LACV/Human/1960 strain genome in C6/36 and S2 cells. Complete genome of LACV/Human/1960 strain showing intensity at each nucleotide of the genome in C6/36 (A,C,E) and S2 (B,D,F) cells. A and B correspond with the L gene segment (6,980 nt), C and D the M gene segment (4,526 nt), and E and F to the S gene segment (984 nt). Plotted are the 19–30-mer viRNA reads across the length of each segment represented by the x-axis. Reads originating from the genomic, negative strand are represented in red below the x-axis and those originating from the positive strand are represented in blue above the x-axis. Discussion Cell culture systems have become invaluable tools in the study of host-virus interactions. However, they may not faithfully model certain molecular features of the host organism-virus interaction. As a result, interpreting data generated in these systems can have limitations and clearly defining the limitations of such systems is crucial to glean as much accurate information as possible from these studies. C6/36 Aedes albopictus cells are a mosquito cell line commonly used to study arbovirus-vector interactions [34]–[36]. Recently, it was demonstrated that WNV-specific siRNAs could not be detected by northern blot hybridization following infection of C6/36 cells, although siRNAs were found in Drosophila S2 cells [29]. The authors concluded that WNV actively evaded the antiviral RNAi response either through the activity of a, as yet unidentified, WNV encoded viral suppressor of RNAi or by sequestration of viral replicative complexes within protective membranous vesicles [37], [38]. However, it remains unclear why these observations were limited to C6/36, but not S2 cells. Therefore, we tested the hypothesis that C6/36 cells lack a fully functional antiviral RNAi response. To this end, C6/36 and S2 cells were infected with members of three taxonomically diverse arbovirus families and the RNAi response characterized by high-throughput sequencing of viRNAs. Examination of three unrelated arboviruses allowed us to determine if the observed results were specific to a particular virus and its associated strategies for evading host defense, or a general defect in the cells themselves. We prepared six sRNA libraries from WNV, SINV and LACV infected C6/36 and S2 cells. In general, the total number of sequence reads and the average base call quality scores were comparable, with the exception of the WNV infected C6/36 cell library, which had fewer total reads and higher quality scores (Table 1). This observed difference may be attributable to the library preparation or sequencing efficiency, as the WNV infected C6/36 cell library was prepared and sequenced independently of the other samples. Nevertheless, the results (average read lengths and sRNA reads perfectly aligning to the viral genomes) were consistent among samples and with previous studies and therefore provide confidence that informative comparisons can be made between libraries. Inspection of sRNA sequencing results from infected C6/36 and S2 cells revealed obvious differences between the proportions of total reads mapping to the viral genomes. Of the sRNAs from WNV-infected C6/36 cells, 0.37% of all reads matched the WNV genome, whereas only 0.045% from infected S2 cells were WNV-specific. Likewise, the LACV infected S2 cells (0.078% virus-specific) had a proportion ≥100-fold lower than observed in infected C6/36 cells (8.39%). In contrast to the WNV-infected samples, the proportions of sRNAs matching viral genome RNA were much higher in SINV-infected cells and the difference between cell types was considerably less (11% in C6/36 cells and 4.8% in S2 cells). These findings are consistent with the 13.9% of sRNAs matching the SINV genome observed in Ae. aegypti four days post SINV inoculation [6]. These results may reflect differences in the replication kinetics of the viruses in each of the two cell lines. For instance, WNV infectious titers in S2 cells seven days post infection are usually 2–3 logs lower than titers in C6/36 cells five days post infection (data not shown) [29]. A relatively small proportion of small RNAs in flavivirus-infected cultured mosquito cells and mosquitoes are virus-specific as compared to mosquito infections by members of other arbovirus families. Although this has not previously been shown for bunyaviruses, it was independently determined in previous studies of flaviviruses [1], [7] (Scott et. al., submitted) and alphaviruses [5], [6] and is confirmed by our current results. This could reflect a more effective mechanism of evasion of innate immunity by flaviviruses, such as sequestration of the viral replication complex in membrane-enclosed vesicles in mosquito cells as well as mammalian cells [38], [39]. Analysis of sRNA reads aligning to the various viral genomes revealed obvious differences that may be related to their biogenesis. The average length of viRNAs mapping to WNV, SINV and LACV genomes from infected S2 cells was approximately 21 nt with the majority being 20-22-mers (Figure 1). These observations are indicative of Dcr2 processing of viral RNA and are consistent with previous analyses of viRNAs from WNV infected Cx. p. quinquefasciatus midguts, SINV infected Ae. aegypti and O'nyong-nyong infected Anopheles gambiae [1], [5], [6]. viRNA populations generated in C6/36 cells were markedly different. The average length of sRNA reads mapping to the SINV and LACV genomes was 25.2 nt with a comparatively small proportion composed of 20–22-mers. These results are consistent with those from two Flaviviruses, dengue virus (DENV) and cell fusing agent virus (CFAV) [40]. The abundant of 24-28 nt long viRNAs may represent products of the piRNA pathway. piRNAs are typically derived from a positive-polarity strand in a Dcr1 and Dcr2 independent manner and are thought to control the development of reproductive tissues and the transcription of transposons [41]-[43]. Recently, what appear to be virus-derived piRNAs have been identified in Drosophila ovary somatic sheet cells [44]. While SINV and LACV sRNAs were not limited to a single strand, the size distributions suggest their biogenesis may have occurred through the piRNA pathway. Interestingly, the size distribution of viRNAs from WNV infected C6/36 cells was quite different from the other viruses (SINV and LACV) (Figure 1) and from DENV and CFAV [40], lacking the characteristic peak at ∼27 nt and casting doubt on the role of the piRNA pathway in their biogenesis. However, the WNV viRNAs were almost exclusively derived from the positive strand, a characteristic of piRNA biogenesis (Figure 2A). The reasons for this apparent paradox are not clear, but may be due to either technical problems or biological mechanisms. A technical explanation seems unlikely because all libraries were processed under the same conditions using identical protocols. Future experiments are required to fully examine this observation. Pre-adenylated 3′ adapters, which would have minimized our sampling of small RNA degradation products, were not utilized in these experiments [17]. Nevertheless, the paucity of 20–22 nt Dcr2-like sRNAs we observed strongly suggests that the majority of the sRNA reads mapping to the viral genomes from C6/36 cells were derived either from the piRNA pathway or cellular degradation pathways. Examination of the polarity (positive sense vs. negative sense) of the sRNA reads mapping to the viral genomes further highlighted the differences between C6/36 and S2 cells. Whereas 16% of the likely Dcr2 generated viRNAs in WNV infected S2 cells were derived from negative-sense strand, almost no viRNAs from C6/36 cells originated from the negative-sense strand (0.1%). This may explain why WNV-derived sRNAs were not detected in C6/36 cells by Chotkowski et. al. as the northern blot probes used in that study were homologous to the NS1 positive sense-strand [29], and would have been unlikely to detect the extremely small proportion of negative sense sRNAs from C6/36 cells mapping to this region. Similarly, there was a 20% excess of positive-sense (vRNA) targeting in SINV infected C6/36 cells compared to S2 cells. The ratio of vRNA- to cRNA-derived viRNAs in infected S2 cells more closely resembled the ratio found in SINV infected Ae. aegypti [6]. Likewise, notable differences were observed in strand polarity ratios between S2 and C6/36 cells infected with LACV. In both cell types, the positive-sense (cRNA) strand was targeted: 72.5% and 84% of the viRNAs in C6/36 and S2 cells, respectively. Although this propensity for positive-sense (cRNA) LACV targeting differed from the observed positive-sense (vRNA) targeting of WNV and SINV, this discrepancy might be due to the marked differences in viral replication and gene expression mechanisms between positive- and negative-sense RNA viruses. The genomes of positive-sense RNA viruses serve as both mRNA and templates for asymmetrical replication through a negative sense RNA intermediate; however, the negative-sense RNA genomes of bunyaviruses serve as templates for both full length complementary RNA replicative intermediates and transcription of highly abundant subgenomic mRNA [45]. Furthermore, since dsRNA is undetectable by staining with a specific antibody in either LACV-infected mammalian cells [46] or mosquito cells (K. Poole-Smith, personal communication) the trigger for initiation of RNAi is unknown. Together, our data for all three viruses suggest that both replicative intermediates containing both genome-sense and anti-sense RNAs, and intra-strand secondary structures within mRNA are targeted by RNAi or other cellular nucleases. The mRNA strand bias observed in all the samples most likely reflects the proportionate abundance of mRNAs as well as use of an alternative small RNA processing pathway in C6/36 cells [40]. For both WNV and SINV in S2 cells, our findings are consistent with small RNA processing by the exogenous siRNA pathway as seen in infected mosquitoes [1], [6]. On the other hand, the increased positive-sense RNA targeting in C6/36 cells suggests that an alternate mechanism may be acting upon viral RNAs. A deep sequencing sRNA dataset was generated from WNV infected DF-1 chicken cells. Upon comparison of the WNV derived sRNAs to the C6/36 cell WNV viRNAs, it was determined that the intensity of viRNA targeting of each nucleotide of the genome was significantly correlated (Spearman r = 0.8882; p<0.0001) (data not shown). However, no correlation was observed between the S2 samples and the C6/36 (p = 0.3126) or DF-1 (p = 0.8467) samples. Since the role of RNAi in vertebrate cellular innate immunity is currently unclear and mRNA turnover pathways are conserved among metazoans, including C6/36 cells, we propose that the WNV derived sRNA populations from C6/36 cells are most likely degradation products or virus-derived piRNAs [17], [44], [47]–[49]. Further, in SINV infected C6/36 cells, the region 3′ to the subgenomic promoter on the positive-sense vRNA was intensely targeted. Were this region highly susceptible to RNAi targeting, then a similar topography would have been observed in S2 cells and Ae. aegypti, but this was not the case (Figure 3) [6]. A more likely explanation is that the highly abundant subgenomic transcripts were not targeted by RNAi, but rather by RNA degradation pathways or the piRNA pathway [44]. Together these results suggest that arboviruses are targeted by the antiviral exogenous siRNA pathway in Drosophila S2 cells, but not C6/36 mosquito cells. The results presented in this study demonstrate that in C6/36 cells, the absence of typical siRNAs, the hallmark of RNAi mediated antiviral immunity, is not limited to WNV and is evident in infections by other diverse arboviruses, such as SINV and LACV. There are multiple steps within the antiviral RNAi response that may be responsible for the observed dysfunction. However, a recent study suggests that it may be related to lack of Dcr2 activity. Studies with cell-free lysates of C6/36 cells revealed that they are unable to process 500 bp dsRNA into 21-mers; however, complementation of C6/36 cell lysates with recombinant human Dcr restored normal dsRNA processing and the presence of detectable 21-mers [40]. Further, when C6/36 cells were co-transfected with an EGFP expression plasmid and either EGFP siRNA or EGFP long dsRNA only the siRNA was able to suppress EGFP expression (J Scott et. al., submitted). These combined with our results suggest that the observed dysfunction is indeed related to lack of dicing activity and more precisely Dcr2 itself. These findings suggest that C6/36 cells may fail to accurately model important aspects of mosquito-arbovirus interactions.
Author and article information
Contributors
Ken E. Olson: Role: Editor
Journal
Journal ID (nlm-ta): PLoS Negl Trop Dis
Journal ID (iso-abbrev): PLoS Negl Trop Dis
Journal ID (publisher-id): plos
Journal ID (pmc): plosntds
Title:
PLoS Neglected Tropical Diseases
Publisher:
Public Library of Science
(San Francisco, CA USA
)
ISSN
(Print):
1935-2727
ISSN
(Electronic):
1935-2735
Publication date Collection:
January
2015
Publication date
(Electronic):
22
January
2015
Volume: 9
Issue: 1
Electronic Location Identifier: e0003386
Affiliations
[1
]Department of Pathology, University of Texas Medical Branch, Galveston, Texas, United
States of America
[2
]Department of Biochemistry and Molecular Biology, University of Texas Medical Branch,
Galveston, Texas, United States of America
[3
]Institute for Human Infections and Immunity, University of Texas Medical Branch, Galveston,
Texas, United States of America
Author notes
Performed the experiments: PDM JH SGW ST. Analyzed the data: PDM SGW TGW ST. Contributed reagents/materials/analysis tools: PDM SGW TGW ST. Wrote the paper: PDM SGW ST. Conceived the idea: ST. Designed experiments: ST PDM.
* E-mail:
sathanga@
123456utmb.edu
Article
Publisher ID: PNTD-D-14-01337DOI: 10.1371/journal.pntd.0003386
PMC ID: 4303268
PubMed ID: 25612225
SO-VID: 791e89c6-15a1-49aa-aeaa-7d92f702e92a
Copyright statement:
Copyright @
2015
License:
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
History
Date
received
: 4
August
2014
Date
accepted
: 30
October
2014
Page count
Figures: 2,
Tables: 4,
Pages: 19
Funding
This work is supported by the departmental start-up fund, and funds provided by the
Institute for Human Infections and Immunity to ST. ST is partially supported by NIAID/NIH
# AI097675. PDM is supported by NIAID/NIH T32 fellowship (#5T32AI007536-15). The funders
had no role in study design, data collection and analysis, decision to publish, or
preparation of the manuscript.
Categories
Subject:
Research Article
Custom metadata
Data Availability All relevant data are within the paper and its Supporting Information files.
ScienceOpen disciplines: Infectious disease & Microbiology
Data availability:
ScienceOpen disciplines: Infectious disease & Microbiology