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      Bacterial Communities Associated with Culex Mosquito Larvae and Two Emergent Aquatic Plants of Bioremediation Importance

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          Abstract

          Microbes are important for mosquito nutrition, growth, reproduction and control. In this study, we examined bacterial communities associated with larval mosquitoes and their habitats. Specifically, we characterized bacterial communities associated with late larval instars of the western encephalitis mosquito ( Culex tarsalis ), the submerged portions of two emergent macrophytes (California bulrush, Schoenoplectus californicus and alkali bulrush, Schoenoplectus maritimus ), and the associated water columns to investigate potential differential use of resources by mosquitoes in different wetland habitats. Using next-generation sequence data from 16S rRNA gene hypervariable regions, the alpha diversity of mosquito gut microbial communities did not differ between pond mesocosms containing distinct monotypic plants. Proteobacteria, dominated by the genus Thorsellia ( Enterobacteriaceae ), was the most abundant phylum recovered from C . tarsalis larvae. Approximately 49% of bacterial OTUs found in larval mosquitoes were identical to OTUs recovered from the water column and submerged portions of the two bulrushes. Plant and water samples were similar to one another, both being dominated by Actinobacteria, Bacteroidetes, Cyanobacteria, Proteobacteria and Verrucomicrobia phyla. Overall, the bacterial communities within C . tarsalis larvae were conserved and did not change across sampling dates and between two distinct plant habitats. Although Thorsellia spp. dominated mosquito gut communities, overlap of mosquito gut, plant and water-column OTUs likely reveal the effects of larval feeding. Future research will investigate the role of the key indicator groups of bacteria across the different developmental stages of this mosquito species.

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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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            Dynamic Gut Microbiome across Life History of the Malaria Mosquito Anopheles gambiae in Kenya

            Ying Wang, Thomas M Gilbreath, Phanidhar Kukutla … (2011)
            The mosquito gut represents an ecosystem that accommodates a complex, intimately associated microbiome. It is increasingly clear that the gut microbiome influences a wide variety of host traits, such as fitness and immunity. Understanding the microbial community structure and its dynamics across mosquito life is a prerequisite for comprehending the symbiotic relationship between the mosquito and its gut microbial residents. Here we characterized gut bacterial communities across larvae, pupae and adults of Anopheles gambiae reared in semi-natural habitats in Kenya by pyrosequencing bacterial 16S rRNA fragments. Immatures and adults showed distinctive gut community structures. Photosynthetic Cyanobacteria were predominant in the larval and pupal guts while Proteobacteria and Bacteroidetes dominated the adult guts, with core taxa of Enterobacteriaceae and Flavobacteriaceae. At the adult stage, diet regime (sugar meal and blood meal) significantly affects the microbial structure. Intriguingly, blood meals drastically reduced the community diversity and favored enteric bacteria. Comparative genomic analysis revealed that the enriched enteric bacteria possess large genetic redox capacity of coping with oxidative and nitrosative stresses that are associated with the catabolism of blood meal, suggesting a beneficial role in maintaining gut redox homeostasis. Interestingly, gut community structure was similar in the adult stage between the field and laboratory mosquitoes, indicating that mosquito gut is a selective eco-environment for its microbiome. This comprehensive gut metatgenomic profile suggests a concerted symbiotic genetic association between gut inhabitants and host.
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              Midgut Microbiota of the Malaria Mosquito Vector Anopheles gambiae and Interactions with Plasmodium falciparum Infection

              Anne Boissière, Majoline Tchioffo, Dipankar Bachar … (2012)
              Introduction Understanding how Plasmodium-Anopheles interactions contribute to the mosquito vector competence has received great attention lately, and the increasing knowledge promises to contribute to the development of new malaria control strategies [1]–[3]. Malaria still remains a serious health problem in developing African countries, causing more than 1 million deaths annually. Almost all these deaths are caused by the parasite Plasmodium falciparum whose major vector in Africa is Anopheles gambiae, which is widely distributed throughout the afro-tropical belt. A. gambiae s.s. is divided into two morphologically indistinguishable molecular forms, known as M and S, which are regarded as incipient species [4]–[6]. The M and S molecular forms exhibit ecological preferences [7], [8], but their respective epidemiological importance in malaria transmission has been poorly documented so far [9], [10]. The susceptibility of Anopheles mosquitoes to Plasmodium infection is under genetic control [11]–[13], but the large variability in oocyst number among closely related mosquitoes indicates that environmental factors also play a role. Multiple lines of evidence suggest that mosquito bacterial communities influence vector competence [14]–[17]. A protective role of Anopheles midgut bacteria against malaria infections was demonstrated by using antibiotic treatment to clear the gut microbiota, which resulted in enhanced Plasmodium infections [15], [18]. Consistently, coinfections of bacteria with Plasmodium reduced the number of developing oocysts in the mosquito midgut, both in laboratory and field conditions [15], [19], [20]–[24]. Interestingly, Cirimotich et al. [24] recently described an Enterobacter bacterium isolated from wild mosquitoes in Zambia that confers refractoriness to P. falciparum infection. Mechanisms mediating this refractory phenotype remain elusive. Instead of eliciting immune responses leading to reduced levels of parasite burden, the experiments conducted by Cirimotich et al. [24] revealed that the inhibition of Plasmodium development by commensal microbiota occurs through production of reactive oxygen species by the Enterobacter bacteria that directly target Plasmodium parasites in the midgut [17], [24]. Bacterial diversity in the Anopheles species is thought to be particularly low at the adult stage because of gut renewal during metamorphosis from pupae to adults. Nevertheless several bacterial species have been identified in the adult mosquito midgut using different conventional culture-mediated techniques [16], [23], [24]. These bacteria were acquired from the aquatic environment during immature stage development [25], [26], although vertical transmission (from mother to offspring) also has been documented [14], [16], [25], [27]. Generally, knowledge on mosquito midgut bacterial communities remains largely unknown, mostly because of the limitations of isolating techniques based on culturing and to the low resolution of fingerprint analysis. However, the recent deployment of next generation DNA sequencing technologies has provided new opportunities to explore microbial diversity of complex environments [28]–[31] as well as to further investigate disease susceptibilities and host-bacteria-pathogen interactions [32]–[34]. In this study, we performed a meta-taxogenomic analysis of microbial communities in the midguts of adult mosquitoes originating from natural larval habitats in Cameroon. We further investigated correlations between midgut microbiota and the mosquito malaria infection status. Previous investigations of bacteria-Plasmodium interactions in the mosquito vector have considered laboratory-reared mosquitoes challenged with cultured bacteria and infected with a cultured P. falciparum line. Here, we challenged wild female mosquitoes with a natural isolate of P. falciparum, thereby offering an opportunity to examine natural bacteria/parasite associations. Our analysis revealed that the midgut bacterial diversity represents an important force shaping the mosquito vector competence, where bacteria of the Enterobacteriaceae genera benefit P. falciparum development. Results Mosquito susceptibility to P. falciparum and bacterial flora A total of 92 A. gambiae mosquitoes collected at the larval stage and reared to adults in the insectary were successfully fed through membrane feeders on gametocyte containing blood from a single individual. The origin of mosquitoes and their genetic characteristics (molecular form and infection status) are summarized in Table S1. Mosquitoes of M molecular form were significantly more infected than those of S form (48.5% versus 27.1%; OR 0.39; 95% CI: 0.15–1.06; P = 0.044). However, the comparison of infection prevalence (number of infected mosquitoes) between the different localities revealed a “sampling site effect” (Fisher's exact test P 2% of the overall), the S1 domain reaches a lower percentage, indicating that this 16S library capable of identifying a greater number of minor clades (Figure 1). In addition, the S1 domain had better resolution, allowing more precise assigning of sequence tags (see Figure 1, mosquito NKD97). We then performed further analyses on the S1 domain, for all 30 samples that were successful for pyrosequencing. 10.1371/journal.ppat.1002742.g001 Figure 1 Comparison of bacterial diversity for the three 16S libraries at the genus level. Tag abundance was compared for three 16S libraries, and the graph shows the bacterial flora of six mosquito midguts. The three 16S libraries were obtained using primer sets targeting different 16S domains, as described in the Materials and Methods section. Only the most abundant categories (>2%) were considered. The S1 library only reached 95%, showing this domain allowed identifications for a greater number of minor clades. For mosquito NKD97, S2 and S3 primer sets only allowed the identification at the Enterobacteriaceae family level, whereas S1 reached the assignment at the genus level, Serratia. The bacterial communities of the mosquito midgut belonged to 26 different phyla, among which, 5 represented more than 99% of the total microbiota: Proteobacteria, Bacteroidetes, Actinobacteria, Firmicutes, and Fusobacteria. We examined the relative abundance of the major classes, that is, detected in more than 30% of the samples and having an average abundance of >0.1% (Figure 2). The gut microbiota presented a large inter-individual variability and was dominated by few taxa. The first striking result came from laboratory-reared mosquitoes that exhibited a drastically different composition of midgut bacteria from field mosquitoes. More than 96% of tags corresponding to bacteria in the midguts of mosquitoes from the Ngousso colony were assigned to Flavobacteria, although this class accounted for only 0.38% (±0.24) of the sequence tags in field mosquitoes. Similarity searches against the SSU SILVA database (release 108) identified Flavobacteria tags as belonging to Elizabethkingia sp., which already has been isolated from A. gambiae midguts from different insectaries [15], [35], [36]. The guts of Ngousso mosquitoes also contained, to a lower extent, Gammaproteobacteria (Pseudomonas sp.) and Alphaproteobacteria (Asaia sp.). 10.1371/journal.ppat.1002742.g002 Figure 2 Relative abundance of the different bacterial classes within each mosquito midgut sample. Mvan and NKD (Nkolondon) indicate the geographical origin of mosquitoes, NG being mosquitoes of the laboratory colony Ngousso. Pf+ and Pf− designate the P. falciparum infection status of the challenged mosquitoes, positive and negative, respectively. Only class rank groups that represented >1% of the total reads, and identified in at least 30% of mosquitoes, are shown. “Unclass” represents tags that could not be assigned to the class level, and were grouped into a higher taxonomical rank. In mosquitoes from natural habitats, midguts were mainly colonized by Proteobacteria (94%), and the most prominent classes were Gammaproteobacteria, Alphaproteobacteria and Betaproteobacteria (Figure 2). Figure 1 clearly shows the difference in bacterial composition between mosquitoes from the 2 breeding sites used in this study. In mosquitoes originating from Nkolondom, the intestinal bacterial flora is dominated by Alphaproteobacteria (68.65±7.38%), mainly of the genus Asaia sp. By contrast, the three major classes are almost equally represented in the midgut of mosquitoes from Mvan, although with large individual variability. The main taxa in this locality were Asaia, Sphingomonas, Burkholderia, Ralstonia, and Enterobacteriaceae. Enterobacteriaceae could not be assigned to more precise taxonomic ranks. Midgut bacteria were unevenly distributed among individual mosquitoes and between the different sampled localities. Several genera were found in all, or at least in a large majority, of the mosquitoes possibly representing the “mosquito midgut core microbiota” (Table S3). They included members of the genera Asaia, Burkholderia, Serratia, Ralstonia, Acinetobacter, Pseudomonas, Sphingomonas, Staphylococcus, Streptococcus, and Escherichia/Shigella. Of note, Asaia sp. was found in all samples, and its relative abundance showed great variation from one midgut to another, ranging from 1.49 to 98.95% in mosquitoes from Nkolondom and from 0.04 to 49.66% in those from Mvan. The group of unassigned Enterobacteriaceae also was identified in all field samples, with relative abundance ranging from 0.01 to 1.03% and from 0.04 to 71.51% in Nkolondom and Mvan, respectively. Other specific members of Enterobacteriaceae were frequent and represent a large proportion of the midgut bacterial communities: Serratia spp. accounted for 96.93% of the midgut bacteria in a mosquito (NKD97) from Nkolondom, and Cedecea spp. encompassed 12% of the gut bacterial content in 2 mosquitoes from Mvan. Escherichia/Shigella was found in more than 85% of the mosquitoes, at low densities. The sequence of the Esp_Z Enterobacter (JF690924), despite its presence in our reference database, was absent from the analyzed reads. Of note, the midgut bacterial flora was mainly composed of Gram-negative communities. No Gram-positive bacterium was identified in the laboratory mosquitoes, whereas they represented 5% of the total microbiota of the field mosquitoes. Gram-positive bacteria belonged to the classes Bacilli and Actinobacteria. To determine whether all phylotypes present in the mosquito midgut microbiota were detected in this study, we performed a rarefaction analysis for each sample on tags from the S1 domain; rarefaction curves are shown in Figure S1. The rarefaction curves decrease rapidly at approximately 2,000 sequences per sample and reach saturation at 3,000, indicating that our sequencing effort was sufficient to catch the overall bacterial diversity in the mosquito midgut. The rarefaction curves show the large variability in bacterial complexity among samples, varying from 13 to 340 operational taxonomic units (OTUs). In addition, they illustrate the paucity of clusters in the midgut of laboratory mosquitoes. They also revealed greater bacterial diversity in the samples from Mvan compared with those from Nkolondom (185±51 and 110±30, respectively; t-test t = 2.385, P = 0.025). Microbial diversity in the mosquito intestinal microbiota The Chao1 estimator, which gauges the number of unseen “species,” predicted that we covered, on average, 81% of the species diversity across all samples. To confirm this result, we computed the ACE and Jackknife estimator indexes; both had higher values than the observed richness, indicating an underestimation of the gut microbial diversity (see Table S2). We characterized the species diversity in our set of mosquito midguts using the species richness, the Shannon diversity index (H) and the Simpson's diversity index (D); data are shown in Table S2. No significant differences in the richness index were found comparing mosquito locality and/or P. falciparum prevalence. Significant differences of the diversity indexes were found when comparing mosquito locality (Shannon, P = 0.0091 and Simpson, P = 0.0097) but none when comparing the infection prevalence of mosquitoes. Thus, at the genus level, the microbiota of mosquitoes from Mvan was more diverse than that from Nkolondom, but Plasmodium-infected and non-infected mosquitoes did not exhibit differences in their microbial diversity. The relationship between the class taxonomic rank of bacteria and the origin of the mosquitoes (locality) was evaluated using redundancy analyses (RDA) (Figure 3). The Eigen values of the first four axes were recorded at 0.304, 0.214, 0.331, and 0.144, respectively. The first two constrained axes explained around 50% of the total variance in the bacterial community and 100% of the species-environment relationship. The unrestricted Monte Carlo permutation test (n = 499) indicated that all environmental variables were significant (Nkolondom variable: F = 10.99, P = 0.002; Mvan: F = 13.26, P = 0.004; Mvan and laboratory variables fit collinearly). Thus the different classes of bacteria were not randomly distributed but linked to the breeding site where mosquitoes grew up. As seen in Figure 2, Flavobacteria were related to laboratory mosquitoes, whereas Alphaproteobacteria were less diverse and related to the Nkolondom locality. The remaining classes cluster along the Mvan locality. These results confirm the higher diversity of bacterial taxa in mosquitoes collected in Mvan as compared with Nkolondom, and the paucity of the gut microbiota in laboratory-reared mosquitoes as compared with mosquitoes from the wild. 10.1371/journal.ppat.1002742.g003 Figure 3 Redundancy analysis for gut bacterial communities (taxonomic rank = class) in field and laboratory mosquitoes. The length of arrows indicates the strength of correlation between the variable and the ordination scores. Blue arrow: bacterial classes, green arrow: environmental variables. The Monte Carlo permutation test was used to test the statistical significance of the relationship between environmental variables and the bacterial classes. The “Flavo” (Flavobacteriaceae) segregates with “labo” environmental variable, “Alpha” (Alphabacteriaceae) with the “NKD” environmental variable (P 90%) felt into few taxa and only 21 bacterial families, and 28 genera had an abundance of >1% in at least one mosquito midgut. Thus, the mosquito midgut is colonized by few dominant bacteria species, probably involved in metabolic functions. We used three different pairs of primers (S1, S2, and S3) to amplify and analyze each midgut sample. The comparison of the bacterial diversity for the three libraries revealed similar patterns; however, the S1 library allowed more detailed identification of the mosquito microbiota. These data strengthen the previous observation that the SSU rRNA gene clone libraries are biased by the choice of the set of primers used for amplification and thereby distort the revealed biodiversity [45]. To our knowledge, this is the first 454 sequencing analysis where different couples of primers have been used to identify the bacterial diversity in biological samples. The analysis ensured that the primer sets used produced the most accurate view possible of the bacterial content of the mosquito midgut. Proteobacteria represented more than 90% of the bacterial gut content in the mosquitoes from the wild, whereas in laboratory-reared mosquitoes, more than 95% of sequence tags belonged to the Flavobacteria Elizabethkingia spp. The remaining tags from laboratory mosquitoes were assigned to the members of Gammaproteobacteria (Acinetobacter, Pseudomonas), Firmicutes (Staphylococcus, Streptococcus), and the Alphaproteobacterium Asaia sp. Bacterial richness and diversity seem to be particularly poor in the laboratory mosquitoes. We identified the Elizabethkingia spp. in 68% (19/28) of the field-collected mosquitoes, at low densities ( 50%. In insects, the gut microbiota differs according to the food source, and in blood-sucking insects, bacterial content is higher after a blood meal [15], [23], [59], [60]. Here, because adult mosquitoes were fed the same diet, the high variability of taxa abundance results from individual variation, and the most abundant lineages represent the mosquito “core gut microbiota.” The existence of a core gut microbiota, by which different bacteria species are sharing metabolic functions and maintain the gut homeostasis, is now emerging [34], [61], [62]. Because alteration of the microbiota composition has been related to the development of diseases or health disorders, the next challenge is to define members of the microbial community and/or the metabolic interdependencies essential to preserve optimal gut homeostasis [34], [63]. The characterization of the mosquito core microbiota during the time course of Plasmodium infection will be a next step toward understanding the impact of gut bacteria on parasite development within the mosquito midgut. P. falciparum traverses the intestinal epithelium within 24 h after blood meal, at the peak of the digestion process; and whether parasites take advantage of intense competitive interactions for nutrient resources between bacteria to thwart the immune surveillance has to be investigated. Indeed, the gut microbiota is known to play an important role in protecting the host from potentially pathogenic microbes [64], [65]. Protection occurs through different processes: stimulation of the mosquito immune response, competition for binding sites or nutrients and production of toxins [65]–[67]. However, despite the beneficial role of the microbiota, pathogens, such as helminthes and viruses have developed strategies for exploiting the gut microbiota to promote their transmission [68], [69], [70]. For the mosquito vector, our understanding is still at an early stage for how the natural resident microbial flora of the mosquito midgut contributes to its resistance to the Plasmodium [15], [17], [18], [24]. In this study, we found that the abundance of Enterobacteriaceae is higher in P. falciparum-infected mosquitoes, suggesting that some microbe-parasite interactions may contribute to the successful development of the malaria parasite. However, whether Enterobacteriaceae have an effect on parasite survival or whether the increased level of Enterobacteriaceae is a consequence of Plasmodium development remains elusive. Alternatively, genetic factors, such as allelic polymorphism of immune genes, could regulate the variable levels of permissiveness of the mosquitoes as has been previously shown [71]. In contrast to our findings, previous studies reported the deleterious effect of bacterial infections on Plasmodium development in the mosquito [19], [23]–[25]. Of interest in this context, several Enterobacteriaceae strains were able to inhibit the development of Plasmodium species in the mosquito midgut, among them Cedecea spp., Serratia spp., and Enterobacter spp. isolated from A. albimanus, A. stephensi, or A. arabiensis [19], [22]–[24]. The Esp_Z Enterobacter strain isolated from A. arabiensis caught in Zambia [24] was not identified in any of the reads we analyzed. The possibility that this Enterobacter strain would have been absent from the PCR products because of competition with a different clade is unlikely as we used three different sets of primers. Therefore, we expect that in the gut of A. gambiae mosquitoes in Cameroon, the Esp_Z Enterobacter strain was below the 0.1% abundance threshold or absent. This Enterobacter strain was isolated in Zambia from wild-caught A. arabiensis mosquitoes, and differences in the mosquito species, as well as differences between the study areas, may explain why we did not find this bacterium in our material. Cirimotich et al. [24] recovered the Esp_Z on LB media, and culturing methods can lead to the artificial amplification of a bacterial strain present in minute amounts in an environmental sample. Therefore, it would be of interest to examine the presence/abundance of Esp_Z in wild-caught Zambian A. arabiensis using the methodologies described here. In our study, we analyzed the gut resident microbiota and revealed a positive correlation between commensal Enterobacteriaceae and Plasmodium infection, indicating that the P. falciparum infection phenotype under natural conditions results from more complex interactions than previously thought. Our data suggest a possible protective role of the Enterobacteriaceae on natural P. falciparum infection. Interestingly, it has been shown that commensal Enterobacteriaceae may promote intestinal homeostasis by enhancing immune receptors in the human colon [72]. For the mosquito, as described for the insect model Drosophila [73], gut homeostasis could be maintained through the renewal of the intestinal epithelial layer that can be altered upon bacterial killing or through immune regulation. A major challenge now will be to correlate our data with quantitative phenotyping of the immune system of the gut. Previous reports on the susceptibility of the M and S molecular forms to P. falciparum infections relay contrasting findings [9], [10]. In Cameroon, mosquitoes of the two molecular forms collected in a sympatric area exhibited similar susceptibility to P. falciparum infection [10], whereas in Senegal, mosquitoes of the S form, derived from progenies of field-collected individuals, were more susceptible to P. falciparum than those of the M form [9]. In the present study with the mosquitoes collected in natural breeding sites and infected on the same blood donor, we found that the M form was more infected than the S form. However, a marked difference in the P. falciparum prevalence was observed according to the sampling site, and larger sample sizes of sympatric M and S populations of A. gambiae will be needed to further explore any difference of Plasmodium susceptibility between the two cryptic species. We propose that the composition of the gut microbiota may influence parasite transmission, which would explain the difference in infection levels between mosquito populations from diverse environments [9], [74]. The mosquito susceptibility to Plasmodium infection is under host genetic control, and several candidate genes have a recognized role in the establishment of the pathogen in the mosquito midgut [11]–[13]. However, how the mosquito gut microbiota influences Plasmodium transmission has to be unraveled. The mosquito gut ecosystem remains poorly understood, and elucidating the precise role of the symbiotic and commensal flora on the regulation of the insect immune response and on the infection course of pathogens, such as Plasmodium parasites, will be of great interest. Pathogens and microbes likely depend on similar mechanisms for interacting with their hosts, and a better knowledge of the mosquito-microbiota interactions would open new avenues for vector disease control through manipulation of gut microbial communities. Furthermore, unraveling these strategies mounted by the parasites to cooperate with the resident microbiota will allow a better understanding of co-evolution of host-pathogen interactions. Materials and Methods Ethics statement All procedures involving human subjects used in this study were approved by the Cameroonian national ethical committee (statement 099/CNE/SE/09). The gametocyte carrier used in this study was enrolled as a volunteer after his parents had signed a written informed consent. Mosquito collection and sample characterization A. gambiae mosquitoes were sampled in aquatic habitats at the L4 and pupae stages in four localities in Cameroon using standard dipping technique [75]. In each locality, breeding sites were inspected visually for presence of larval stages. At each breeding site, 10 dips were taken with a standard dipper (300 ml) and kept in a 5-liter container for transportation to the insectary at OCEAC. Anopheline larvae were identified morphologically; non-anopheline larvae and predators were removed. Larvae were kept in their original habitat water in a 3-liter plastic bucket and resulting pupae were collected daily for 2 days. Pupae were transferred to a plastic cup containing 20 ml of water from the breeding site, and the cup was placed in a 30×30 cm cage for emergence. The remaining larval collection was discarded after 2 days to avoid bias because of putative modifications of the biotic content of the aquatic habitats. Adult mosquitoes were maintained in standard insectary conditions (27±2°C, 85±5% RH, and 12 h light/dark) and provided with 8% sterile sucrose solution. Female mosquitoes were fed on a single P. falciparum gametocyte carrier to avoid infection rate variability because of the blood donor. Infectious feeding was performed as previously described [71], [76]. Females, 3 to 5 days old, were starved for 24 h and allowed to feed on the P. falciparum gametocyte containing blood for 35 minutes through membrane feeders. Unfed and partially fed mosquitoes were removed by aspiration and discarded. Fully engorged females were kept in the insectary until dissections 8 days after the infectious blood meal. Mosquitoes were surface sterilized in 70% ethanol for 5 minutes, then rinsed twice in sterile PBS solution, and midguts were dissected and stored individually at −20°C until processing. DNA was extracted using the DNeasy Blood &Tissue Kit from Qiagen (Valencia, CA) and quantified (Nanodrop ND-1000, NanoDrop Technologies, Montchanin, DE, USA). A 20-ng aliquot of DNAs was subjected to whole-genome amplification using the GenomiPhi V2 DNA Amplification Kit (GE HealthCare, Uppsala, Sweden), and the GenomiPhi templates served to characterize molecular forms of A. gambiae and the P. falciparum infection status. Molecular forms were determined according to Fanello et al. [77] and the identification of mosquitoes that successfully developed malaria infection using a P. falciparum specific PCR amplifying a Cox gene fragment [78]. Sample selection for the metagenomic analysis A total of 32 individual midguts were subjected to the 454-sequencing analysis. We included 2 samples of midguts dissected from mosquitoes of our local colony of A. gambiae, Ngousso. The Ngousso colony was established in January 2006 from larvae collected in breeding sites of an urbanized district of Yaounde, “Ngousso.” Larval collections were conducted during a 2-month period; mosquitoes were blood fed for oviposition and then PCR screened for molecular form of A. gambiae. Ngousso mosquitoes belong to the M molecular and Forest chromosomal forms. Since then, the colony has been routinely maintained at the OCEAC insectary. The Ngousso samples served to provide an overview of the bacterial content of laboratory mosquitoes reared under standard insectary conditions and as an experimental control in this study. The 30 remaining samples were chosen among field mosquitoes fed on blood from the same gametocyte carrier. We selected both P. falciparum positive and P. falciparum negative midguts to assess putative differences of microbiota between non-infected and infected individuals in our P. falciparum-challenged mosquitoes. 454 sequencing For each individual midgut DNA sample, we generated three PCR amplicon libraries. We targeted 3 different hypervariable regions of the 16S ribosomal RNA to allow accurate detection of the bacterial communities in each sample. Indeed, previous analyses showed that the set of primers used for amplification can have a strong impact on the biodiversity revealed; some abundant clades in a given sample could be foreseen, depending on the primers used [45]. The S1 library targeting the V4 hypervariable region was obtained using the forward primer 535F (5′-GTGCCAGCAGCCGCGGTAATA-3′) and the reverse primer 789R (5′-GCGTGGACTACCAGGGTATCT-3′), the S2 library for the V5–6 region using the 326F (5′-CAAACAGGATTAGATACCCTG-3′) and the 1082R (5′-CGTTRCGGGACTTAACCCAACA-3′) primers, and the S3 library targeting the V5–6 region with the 1065F (5′-CAGGTGCTGCATGGCYGTCGT-3′) and the 1336R (5′-CGATTACTAGCGATTCC-3′) primers. Amplified DNA was purified and quantified using Picogreen fluorescent dye (Molecular Probes, Eurogen, OR). Individual libraries were processed for 454 sequencing by ligating the 454 adapters coupled to MID tags, allowing the multiplexing of samples. The amplicon libraries were pooled in two separate batches and sequenced. The MIDs and 454 linkers were ligated after the PCR amplification and the pyrotags were sequenced unidirectionally. Pyrosequencing was performed at Genoscreen (Lille, France) using a Genome Sequencer FLX Titanium (GS-FLX) system (Roche, Basel, Switzerland). In total, we recovered 663,651 sequence reads (tags) that were subjected to quality controls. All 454 sequences were deposited in Genebank (SRS281724.1 and SRS281725.1). Data processing and taxonomic assignment Tags were extracted only if they contained the combination linker-MID-primer and the complement primer sequence at the 3′end. Tags were sorted in appropriate files according to their MID barcode and converted to the forward strand when necessary. A strict dereplication step was then applied that discarded tags with unidentified nucleotides (Ns) and those longer than 350 bp or shorter than 200 bp. Dereplicated tags were sorted by decreasing number of occurrences and clustered at k = 3 number of differences as described in Stoeck et al. [79]; this pipeline resulted in determining unique sequences. We next processed taxonomic assignments by implementing a new approach that clearly optimizes the successive assignments. We first extracted from the reference sequences of SILVA (release 106) domains corresponding to the various possible couples of the primers. Extraction was first performed requiring a perfect match between each primer and a sequence and, when no match was found, 1, 2 and 3 differences between each primer and a sequence were used successively. This pipeline then gave three reference databases, one per amplified 16S rDNA region, containing all reference amplicons putatively matching our tags. The S1, S2 and S3 tagged databases contained 424.634, 359.198 and 394.370 reference amplicons, respectively. In a second step, all unique tags were assigned a taxon using a global alignment method. Each amplicon of the reference database was considered if it had at least 70% similarity with a tag. The list of reference amplicons was sorted by decreasing percentage of similarity and rounded to an integer. For taxonomic assignments, the reference sequence with the highest percentage was used, and taxonomy to a given level was obtained by the consensus of these taxonomies when more than one result emerged. For example, a tag with 98% similarity to the class Gammaproteobacteria and Alphaproteobacteria was only assigned to the phylum Proteobacteria. When similarity was <80%, sequences were not assigned. Tags were clustered into OTUs according to their consensus taxonomy. For each mosquito sample and each amplified 16S rDNA region, OTU abundances represent relative abundances, the number of reads for the given OTU divided by the total of tags in the SSU region of that sample. Rarefaction curves were produced by plotting the number of unique sequence tags as a function of the number of randomly sampled tags. To generate rarefaction curves, we retained OTUs containing at least 2 sequence tags and encompassing the abundance threshold of 0.04% because rare sequences likely represent random sequencing errors and overestimate the overall diversity. Ecological indexes and statistical analysis Ecological indexes such as richness and diversity indexes (Simpson, Shannon), were computed using the Vegan [80] and BiodiversityR [81] packages under the R software (available at http://www.R-project.org) [82]. Chao1, ACE1 and Jackknife richness estimators were calculated using the SPADE software [83]. Indexes were calculated using values from the genus taxonomic rank, the lowest rank obtained with the 454 technology. Association between microbiota and environmental variables was assessed using a multivariate ordination test. We defined the different taxa present in the data set as “species variables” and the origin of the mosquitoes and the P. falciparum infection status as “environmental variables” for each individual. A detrended canonical correspondence analysis (DCA) was performed to determine the ordination method suitable for our data. The longest gradient we obtained was shorter than 3.0, indicating that the constrained form of linear ordination method, the RDA, was the most appropriate test [84]. Redundancy analysis was performed using Canoco v. 4.5 Software [85]. The environmental variables were set as dummy variables (0 or 1 values). RDA and associated Monte Carlo permutation tests (n = 499) were used to identify the measured environmental variable that contributed most significantly to the variation in the bacterial community data. The Monte Carlo test returns a p value associated with the effect of the environmental variable on the microbiota composition of the samples. Results were visualized on a biplot ordination diagram using CanoDraw extension. Supporting Information Figure S1 Rarefaction analyses for each mosquito midgut sample. Saturation curves were generated by plotting the number of unique sequence tags as a function of the number of randomly sampled tags. Tags were clustered at k = 3 differences, and OTUs were set when containing at least 2 sequence tags and encompassing the abundance threshold of 0.04%. (TIF) Click here for additional data file. Figure S2 Redundancy analysis for gut bacterial communities (taxonomic rank = family) in field mosquitoes. The length of arrows indicates the strength of correlation between the variable and the ordination scores. Blue arrow: bacterial classes, green arrow: environmental variables. The Monte Carlo permutation test was used to test the statistical significance of the relationship between environmental variables and the bacterial classes. “Family” and “Locality” variables segregate along the first axis, but “Entero” (Enterobacteriaceae) and “Infection” gathers along the second axis (P<0.05). (TIF) Click here for additional data file. Table S1 Molecular form identification and infection status of female A. gambiae mosquitoes collected at larval stage in different localities and challenged after emergence to a single P. falciparum donor. (DOC) Click here for additional data file. Table S2 Mosquito characteristics and diversity indexes for each individual analyzed upon 454 sequencing of the S1 library. (DOC) Click here for additional data file. Table S3 Main genera that composed the natural A. gambiae gut microbiota. (DOC) Click here for additional data file.
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                Contributors
                Olle Terenius: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, USA )
                ISSN (Electronic): 1932-6203
                Publication date Collection: 2013
                Publication date (Electronic): 15 August 2013
                Volume: 8
                Issue: 8
                Electronic Location Identifier: e72522
                Affiliations
                [1 ]Department of Entomology, University of California Riverside, California, United States of America
                [2 ]Department of Entomology, Michigan State University, East Lansing, Michigan, United States of America
                [3 ]Department of Biology, University of Waterloo, Waterloo, Ontario, Canada
                Swedish University of Agricultural Sciences, Sweden
                Author notes

                Competing Interests: JDN is a PLOS ONE Editorial Board member and this does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials.

                Conceived and designed the experiments: DD PRJ MGK RS WEW. Performed the experiments: DD PRJ. Analyzed the data: DD MWH JDN. Wrote the manuscript: DD PRJ MGK MWH JDN RS WEW.

                Article
                Publisher ID: PONE-D-13-21379
                DOI: 10.1371/journal.pone.0072522
                PMC ID: 3744470
                PubMed ID: 23967314
                SO-VID: a88efab3-0578-49f7-b7a7-153d97d69182
                Copyright statement: Copyright @ 2013
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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.

                History
                Date received : 24 May 2013
                Date accepted : 17 July 2013
                Funding
                WEW acknowledges funding from the Agricultural Experiment Station at University of California Riverside. DD was supported by an Ian and Helen Moore Scholarship and the Graduate Division Research Mentorship Program of University of California Riverside. MWH and JDN acknowledge funding from a Discovery Grant from the Natural Sciences and Engineering Research Council of Canada. MGK acknowledges National Institutes of Health award R37 A121884. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Subject: Research Article

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