RNA-seq analyses of blood-induced changes in gene expression in the mosquito vector species, Aedes aegypti
- Mariangela Bonizzoni†1, 2,
- W Augustine Dunn†2, 3,
- Corey L Campbell4,
- Ken E Olson5,
- Michelle T Dimon6, 7,
- Osvaldo Marinotti2 and
- Anthony A James2, 8Email author
© Bonizzoni et al; licensee BioMed Central Ltd. 2011
Received: 29 October 2010
Accepted: 28 January 2011
Published: 28 January 2011
Hematophagy is a common trait of insect vectors of disease. Extensive genome-wide transcriptional changes occur in mosquitoes after blood meals, and these are related to digestive and reproductive processes, among others. Studies of these changes are expected to reveal molecular targets for novel vector control and pathogen transmission-blocking strategies. The mosquito Aedes aegypti (Diptera, Culicidae), a vector of Dengue viruses, Yellow Fever Virus (YFV) and Chikungunya virus (CV), is the subject of this study to look at genome-wide changes in gene expression following a blood meal.
Transcriptional changes that follow a blood meal in Ae. aegypti females were explored using RNA-seq technology. Over 30% of more than 18,000 investigated transcripts accumulate differentially in mosquitoes at five hours after a blood meal when compared to those fed only on sugar. Forty transcripts accumulate only in blood-fed mosquitoes. The list of regulated transcripts correlates with an enhancement of digestive activity and a suppression of environmental stimuli perception and innate immunity. The alignment of more than 65 million high-quality short reads to the Ae. aegypti reference genome permitted the refinement of the current annotation of transcript boundaries, as well as the discovery of novel transcripts, exons and splicing variants. Cis-regulatory elements (CRE) and cis-regulatory modules (CRM) enriched significantly at the 5'end flanking sequences of blood meal-regulated genes were identified.
This study provides the first global view of the changes in transcript accumulation elicited by a blood meal in Ae. aegypti females. This information permitted the identification of classes of potentially co-regulated genes and a description of biochemical and physiological events that occur immediately after blood feeding. The data presented here serve as a basis for novel vector control and pathogen transmission-blocking strategies including those in which the vectors are modified genetically to express anti-pathogen effector molecules.
Insect vector-borne pathogens cause some of the most widespread infectious diseases worldwide, including dengue fever, yellow fever, malaria, encephalitis, filariasis, leishmaniasis and trypanosomiasis [1, 2]. The corresponding vectors are hematophagous insects that become infected by ingesting pathogens during blood feeding. Transmission of the pathogen to a subsequent vertebrate host occurs during the acquisition of another blood meal.
Hematophagy is a behavior exhibited by more than 14,000 species of insects [3–5], but genome-wide information regarding blood meal-regulated gene expression is available for only a few of these. Remarkable differences in the levels of accumulation of specific transcription products following a blood meal were reported in the malaria vector mosquito, Anopheles gambiae[6, 7] and as many as 50% of all transcripts varied significantly during a gonotrophic cycle. Our study investigates blood meal-induced changes in transcript accumulation in the dengue vector mosquito, Aedes aegypti, that last shared a common ancestor with the Anophelines some 120-150 million years ago . Elucidating transcriptional changes in mosquitoes following a blood meal can reveal novel molecular targets and strategies for control of vector populations and pathogen transmission. Alternative control strategies are required for dengue due to the continuous rise of cases worldwide [9, 10], the current lack of an effective vaccine and the fact that vector control strategies aimed at reducing human contact with Ae. aegypti, the principal vector for all the four serotypes of Dengue viruses (DENV 1-4), have largely failed [11–13].
Previous studies analyzing the effects of blood meals on Ae. aegypti females were limited to the midgut , muscle mitochondria  or to specific gene sets [16, 17]. Transcriptome sequencing, or RNA-seq, has emerged recently as a powerful tool to gain a holistic picture of the expression profile of an organism, tissue or cells [18, 19]. Using next-generation sequencing technologies (Roche 454 GS FLX Genome Sequencer, Solexa/Illumina Genome Analyzer, ABI/SOLiD gene Sequencer and Helicos Genetic Analyses System), millions of cDNA reads of a length dependent on the platform chosen are generated and can be used either to create a de novo transcriptome assembly  or can be mapped to a reference genome to derive a genome-scale transcriptional map that consists of the structures of transcriptional units and their expression levels [21–23]. Sequencing-based methods provide absolute rather than relative gene expression measurements avoiding many inherent limitations of microarray technologies [24, 25]. Additionally, RNA-seq data can be analyzed to assess differential-splicing activity, discover novel regions of transcription and locate precise transcription product boundaries [19, 26].
We used the Illumina RNA-seq technology to compare the accumulation of transcription products in nonblood-fed female Ae. aegypti and mosquitoes at five hours post blood meal (PBM). This time point was chosen so that we may evaluate early genome-wide transcriptional responses to a blood meal. Results from our analyses assisted in refining the current annotation of the Ae. aegypti genome, improved our understanding of the biochemical pathways and biological processes elicited shortly after a blood meal and identified promoters and/or putative cis-regulatory elements correlated with changes in accumulation of specific gene products occurring as a consequence of ingestion of a blood meal.
Results and Discussion
Basic sequencing data
Differential transcript accumulation between nonblood-fed and blood-fed Ae. aegypti females
qRT-PCR validation of RNA-seq data on a random selection of thirteen genes
Actual number of reads1
Normalized expression value3
(Number of paralogous genes)
Normalized fold changes2
Average fold changes4
Serine-type endopeptidase AaLT (27)
Lipid binding (2)
Catalytic activity (0)
Protein binding (0)
Molecular function (0)
Structural constituent of Cytoskeleton (16)
Catalytic activity (0)
Catalytic activity (4)
Electron Transporter (0)
Serine-type endopeptidase (28)
Iron Ion Binding (0)
The functions of proteins encoded by Ae. aegypti transcripts are predominantly theoretical and based on sequence similarities to those of other organisms. Acknowledging this limitation, functional parent attributions were assigned  for over 90% of the Ae. aegypti conceptual translation products allowing a description of the biochemical and physiological changes occurring following a blood meal (Figure 2). Blood feeding induced an accumulation of transcripts involved in lipid metabolism (acyl-CoA dehydrogenase, and aldehyde dehydrogenase), protein degradation (cathepsin, trypsins and serine proteases), ammonia/nitrogen metabolisms (glutamine synthetase and aspartate ammonia lyase) and egg maturation (vitellogenin). Based on the PFAM protein family database , the 21 transcripts whose abundance was increased ≥50 times in B versus S mosquitoes included those encoding two vitellogenins (AAEL010434-RA and AAEL006138-RA), 15 digestive enzymes, a member of the cytochrome P450 family (AAEL007812-RA), a sugar transporter (AAEL005533-RA) and one transcript (AAEL010435-RA) encoding an orthologue of the G12 gene of An. gambiae (AGAP006187). The G12 proteins in mosquitoes, thought to be secreted into the midgut lumen or maintained on the surface of microvilli, are encoded by transcripts that accumulate quickly in female midguts within one hour of blood feeding, reaching a maximum level of expression at about 12 hours PBM . The same pattern of G12 expression is seen in Ae. aegypti females after feeding on blood infected with Plasmodium gallinaceum.
Transcript levels of genes whose products are involved in redox metabolism, such as dehydrogenases and members of the cytochrome P450 family, as well as those implicated in iron ion binding, increase between 5- and 2-fold, but several genes whose products are involved in similar physiology are decreased up to 10-fold. Furthermore, transcripts whose levels increased more than 5-fold are involved mainly in lipid and protein metabolism; levels of the majority of transcripts involved in trafficking/transport increased only slightly (less than 5-fold), if not decreased (Figure 2; Additional File 2, Supplemental Table 1). These observations are consistent with the conclusion that 5 hours PBM represents a time when Ae. aegypti females are beginning to respond actively to a blood meal through differential transcription. Additionally, the pattern of expression detected at the whole-body level 5 hours PBM reflects what is seen in Ae. aegypti midguts between 3 and 6 hours PBM , which is consistent with the conclusion that the blood meal is the event that signals the start of the metabolic activity. Transcripts involved in stimuli perception, such as those encoding odorant-binding proteins, were decreased, a finding that correlates with what is seen in An. gambiae females at 3 hours PBM . Interestingly, transcripts associated with genes whose products are involved in transcription and translation also decreased at 5 hours PBM (Figure 2). The apparent contrast between the enhancement of digestive activity, which is centered in the midgut, and the decrease in transcripts linked to transcription and translation may reflect changes in transcript abundance occurring at the whole-body level.
Transcripts found exclusively in blood-fed mosquitoes
Forty transcripts were found only in blood-fed mosquitoes, with the highest read-counts reaching ~1000/transcript, after normalizing for different library sizes (Additional File 2 Supplemental Table 1). Functional parent attribution for these transcripts is consistent with a role in digestion and in the progression of the gonotrophic cycle. Specifically, two transcripts, Aa5G1 (AAE013712-RA) and AaSPVI (AAE010196-RA), correspond to the midgut serine proteases shown previously to be elicited by a blood meal in the midgut of Ae. aegypti females . Seven other transcripts encode enzymes (i.e. decarboxylase, cathepsin b and trypsins), and two are implicated in trafficking. Transcripts AAE014815-RA and AAE005950-RB correspond to the vacuolar protein sorting 13B from yeast and the chloride channel protein 2, respectively. Ten transcripts are paralogous to the G12 gene of An. gambiae and share the Insect Allergen Repeat motif. This motif is hypothesized to be a novel, insect-specific detoxifying domain implicated in the co-evolution of herbivorous insects and their plant hosts and also has been linked to nitrile-specific detoxification . Transcripts AAEL006126-RB and AAEL008921-RC are predicted orthologues of the Culex quinquefasciatus vitellogenin-A1 gene and the Drosophila melanogaster spaghetti squash (sqh) gene, respectively. The sqh gene product encodes the regulatory light-chain of non-muscle myosin II, which is required for cytoplasmic transport in nurse cells during oogenesis and also has been implicated in germline RNA interference (RNAi) processes .
Transcripts found exclusively in sugar-fed mosquitoes
Twenty-eight transcripts were found to accumulate significantly only in sugar-fed mosquitoes. Parent attribution is consistent with roles in basal metabolism and stimuli perception. In particular, six of the 28 transcripts encode proteins with catalytic activity (peptidase and protease), three belong to the cytochrome P450 protein family (AAEL014684-RA, AAEL013555-RA, AAEL000320-RA), and five (AAEL000350-RA, AAEL003311-RA, AAEL000318-RA, AAEL006108-RA, AAEL009597-RA) are conserved hypothetical proteins that share the Insect pheromone/odorant binding protein (PhBP) domain . Two of the 28 correspond to putative cuticle proteins (AAEL000879-RA, AAEL013520-RA), and one transcript (AAEL013434-RA) encodes a protein similar to the product of Spätzle 1A, which is required for the Toll-dependent antimicrobial response in both adult and larval vinegar flies [34, 35]. Two transcripts (AAEL8931-RA and AAEL10995-RA) encode proteins with predicted transporter activity. The functions of the proteins encoded by the remaining nine transcripts are unknown.
Transcripts related to pathogen interaction
Blood feeding is the primary port of entry into mosquitoes for viral, protozoan and metazoan pathogens that cause diseases in vertebrates. While blood is a source of nutritive resources for mosquitoes, it also is potentially harmful to them, and a balance between these factors determines their fitness . Two mechanistically different innate immune defense mechanisms have been described in Ae. aegypti: one relies on gene expression control and degradation of mRNA through the small RNA regulatory pathways (SRRPs) [37, 38] and the other induces the production of antimicrobial peptides and/or promotes phagocytosis, encapsulation and melanization of pathogens through the Toll, Imd and JAK-STAT signaling pathways [39–41]. The activities of the genes in these pathways have been analyzed in Ae. aegypti challenged by injection with various pathogens including bacteria [39, 42], the filarial worm Brugia malayi, Sindbis and dengue viruses [37, 40, 44–47]. Transcriptional activation of innate immunity genes occurs within minutes after infection and the response lacks immunologic memory . Additionally, it has been hypothesized that the natural bacterial flora in mosquitoes maintains a basal level of immune response [44, 48] and that immunity processes share bio-products, such as reactive oxygen species (ROS), with digestion . As a consequence, analyzing the basal expression of immunity genes shortly after a blood meal could help identify elements that govern vector competence and clarify the level of synergy among immunity and digestive processes. Early transcriptional responses to a blood meal are relevant particularly with respect to dengue infection as viruses can be internalized within 5-7 minutes of contact between the virions and the mosquito midgut epithelial cells , and viral replication is evident in the midgut two days post infection .
Fold-changes for the 79 transcripts showing increased accumulation vary between 1.16 and 29.32, the former corresponding to transcript AAEL008073-RA, a SRRP member, and the latter to transcript AAEL015136-RA, belonging to the MD2-like protein (MLs) group. MD2-like genes encode secreted proteins containing a lipid recognition domain that acts as intermediate in the immune response. The observed expansion of the mosquito MD-2 gene family may indicate a specialized function of their products in the defense against pathogens ingested with blood meals . Three other MD2-like transcripts (AAEL003325-RA; AAEL004120-RA; AAEL009531-RA) increase in abundance at 5 hours PBM, although not more than 2.3 fold. In addition to AAEL015136-RA, only two other transcripts (AAEL000859-RA and AAEL003255-RA), not classified in any of the canonical immunity gene categories [46, 51, 52], accumulate more than 5-fold (Additional file 3 Table 2). The majority of transcripts (52 out of 79) accumulated less than 2-fold higher in blood- versus sugar-fed mosquitoes. The negative regulators of the Toll and IMD pathways, Cactus (AAEL000709-RA) and Caspar (AAEL0014734-RA), were 1.52-and 4.72-fold, respectively, more abundant.
A number of genes involved in autophagy, SRRP members and inhibitors of apoptosis had transcripts whose accumulation increased significantly following a blood meal (Figure 3B; Additional file 2 supplemental Table 1). The maximum increase observed, 3.10 fold, was detected for the inhibitor of apoptosis IAP2 (AAEL006633-RA). Autophagy is a tightly-regulated catabolic process whereby cells degrade intracellular components via the lysosomal machinery and it plays an important role in homeostasis maintenance, cell development, growth and immunity [46, 52, 53]. The increase in accumulation of autophagy genes and of members of the inhibitors of apoptosis is not surprising considering the time-point, 5 h PBM, sample here. Among the 17 SRRP members showing increased transcript accumulation, four, Dicer 2 (AAEL006794-RA), TSN (AAEL000293-RA), Dicer1 (AAEL001612-RA) and PIWI4 (AAEL007698-RA), were at least 2-fold more abundant following a blood meal. Dicer2 and TSN are essential components of the RNA interference (RNAi) effector multi-component RNA-induced Silencing Complex (RISC) [38, 47], and Dicer1 has been shown to control gene expression of 'housekeeping' genes . PIWI4 is a member of the PIWI small RNA (piRNA) pathway proposed to be involved in anti-viral defense .
Cis-regulatory element discovery
Tightly-regulated and blood meal-induced expression profiles are of particular interest for designing transgenic mosquito-based control strategies to reduce transmission of dengue fever. Cis regulatory sequences derived from blood meal-induced/up-regulated mosquito genes allow potentiating swift induction and effective levels of transcription of an associated effector gene, while likely inflicting the least fitness cost [54, 55]. We interpret the different levels of mRNA accumulation seen in this study to reflect changes in transcriptional activity of the corresponding genes, although it is possible that some levels may vary as a function of changing transcript stability or rates of turnover. With this in mind, we used SCOPE  to predict putative CREs that may provide the basis for rational identification and selection of new candidate promoter regions and for modification of the transcriptional profiles of current transgene constructs. We examined the 2000 base pairs (bp) flanking the 5'-boundaries of the 40 transcripts that were undetected in libraries from sugar-fed mosquitoes but detected at significant levels in the RNA-seq libraries from blood-fed mosquitoes and identified a redundant list of 22 motifs that are enriched significantly in these sequences (Additional File 4 Figure 2). A possible cis-regulatory module (CRM) constructed with the discovered CREs is represented by the motif consensus sequences, cnatcnkcwgtt, gyactyvar, and tgakamga, and is associated with Ae. aegypti paralogues of the G12 gene of An. gambiae (AGAP006187) (Additional File 4 Figure 2). Aedes aegypti has 17 G12 genes, many more relative to other insects, which have 4.5 on average (according to OrthoDB; group EOG95TCTG) . The transcripts of nine of the G12 paralogues are present in this co-regulated gene set (representing ~25% of the 40).
Another putative CRM contains the consensus sequence tgakamga, cnatcnkcwgtt, asttrccc and aarcttbd (Additional File 4 Figure 2). This CRM groups with the cathepsin b genes, AAEL015312-RA and AAEL007585-RA. Verification of these CRMs will require empirical testing, however, the top 10 matches for tgakamga, which is present in both putative CRMs, align well to members of the mosquito-conserved GATA motifs correlated to transcriptional responses to blood feeding in An. gambiae.
RNA-seq identifies annotation corrections
Predicted novel junctions within varying distances from annotated transcripts1
Distance from annotation (±bp)
We provide a detailed examination of the changes in transcripts accumulation occurring at the whole-body level of Ae. aegypti females 5 hours PBM. The observed changes are consistent with the beginning of an intense physiological response to a blood meal. The majority of immunity-related transcripts tended to accumulate at lower levels in blood fed mosquitoes. This finding supports the hypothesis that there may be a gap in immunity following a blood meal. Reduced expression of immune genes in blood fed mosquitoes could favor the establishment of infections, especially considering that pathogens such as dengue viruses infect the midgut epithelial cells within minutes after the contact . However, changes in transcript abundance observed at the whole-body level may mask changes in accumulation occurring primarily in the midgut. Different levels of activation of immunity genes after a blood feeding may be one of the factors contributing to the variability in vector competence for dengue viruses observed in different geographic populations of Ae. aegypti[62, 63]. The quantity and quality of data generated by RNA-seq technology makes this an ideal approach for comparative analyses of the transcriptome of Ae. aegypti strains with different vector competence and vectorial capacity.
Our analyses of the expression profiles of S and B mosquitoes allowed the identification of co-regulated genes and putative cis-regulatory elements and modules from the Ae. aegypti genome. Further knowledge of the mechanisms involved in regulation of gene expression in vector species is critical to the development of control strategies whereby the vector is modified genetically to express anti-pathogen effector molecules in tissue-specific and time-regulated manners . Promoter and other cis-acting regulatory DNA fragments are needed to regulate restricted expression of selected anti-pathogen effector molecules. Moreover, we described several examples of how the RNA-seq data generated can help improve the current annotation of the Ae. aegypti genome.
Mosquito strains and rearing
The Ae. aegypti Liverpool strain (LTV) used in this study originated from West Africa where it was selected for susceptibility to the filarial worm parasite, Brugia malayi, and has been maintained at the Liverpool School of Tropical Medicine since 1936. DNA from mosquitoes of this strain, derived after twelve consecutive generation of single-pair inbreeding, was used to generate the currently available Ae. aegypti genome sequence . Mosquitoes were maintained at 28°C, 70-80% relative humidity, with 12-12 h light-dark photoperiod at Colorado State University (Fort Collins, Colorado). Larvae were fed on a finely-ground fish food (Tetramin, Tetra Werke, Germany). Males and females were kept together in a cage with unlimited access to water and sugar (raisin) until blood feeding. Mosquitoes aged 3-5 days after eclosion were allowed to feed on immobilized mice. The study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. Female mosquitoes were flash-frozen in dry ice and promptly stored (-80°C) five hours after blood feeding and shipped to the University of California, Irvine for RNA extraction.
RNA extraction and Illumina library preparation
Total RNA was extracted with TRIZOL (Invitrogen) from pools of three females (3-5 days old) either exclusively kept on a sugar diet (S) or five hours after blood feeding (B). After checking for the quality of RNA with an Agilent 2100 bioanalyzer, two samples of S and B were pooled to reach the 20 micrograms necessary for the preparation of two single-read Illumina libraries . Illumina libraries were prepared and run for 40 cycles by the Expression Analysis Core at the UC Davis Genome Center . Libraries were run at a concentration of 4-5 pM.
Processing of Illumina sequencing data
Sequencing data were retrieved from the UC Davis Genome Center through r-sync. Sequencing data have been deposited at the Short Read Archive (NCBI) under accession number GSE24872. Data from the two technical replicates were combined to gain sequencing depth after having verified the technical reproducibility of the two libraries generated for each condition (B and S). Bowtie  was used to align the Illumina reads against the Ae. aegypti genome (version AaegL1) , allowing a maximum of two mismatches and with the -m option, which returns only reads with a single best match in the genome. Reads mapping to ribosomal RNA genes were filtered out from the Bowtie output using a custom Python script. The percentage of covered transcriptome was determined using BEDTools . Differential expression between conditions was assessed by the likelihood ratio test as implemented in the program DEGseq , after accounting for the different total gene counts of each library, at a p value of 0.001 and with a false discovery rate (FDR) of 0.1% . Transcript description was based on the Ae. aegypti protein database AegyXcel .
Real-time quantitative RT-PCR validation of RNA-seq data
A total of 13 genes identified by RNA-seq to be expressed differentially between S and B mosquitoes were chosen for real-time quantitative PCR analysis (Additional File 5 Table S3). Total RNA was extracted by TRIZOL (Invitrogen) from a pool of eight females kept exclusively on a sugar diet or a similar pool collected five hours after blood feeding. Following DNAse I (Invitrogen) treatment, a total of 10 μg of RNA were used for cDNA synthesis with superscript III (Invitrogen) and random primers. Real-time quantitative PCR reactions of 20 μl were performed in triplicate with SYBR Green Supermix (Biorad) and 0.3 μM of each primer on three sequential five-fold dilutions each of the original cDNA. Real-time quantitative PCR reactions were run on an iQ3 system (Biorad). No primer dimer was detected when inspecting the melting curves and primer pairs were chosen that displayed greater than 90% amplification efficiency, in all cases except AAEL002565, where efficiency was 89.313 ± 5.384 (Additional File 5 Table S3). Fold-changes in gene expression between S and B mosquitoes were derived by the comparative CT method , using the constitutive gene rp49 (GenBank Acc. No.:AY539746; AAEL003396) as the reference and four samples each for S and B mosquitoes. Correlation between the expression values detected by RNA-seq and qRT-PCR for the 13 genes tested was estimated by calculating Spearman's Rho correlation in the JMP501 statistical software (SAS Institute INC., Cary, NC). The paired t-test in Excel was used to compare the expression values for each transcript in the two methods. The significance of the qRT-PCR-based difference in expression values between B and S mosquitoes based on four samples each for B and S were calculated using a standard t-test.
The program HMMsplicer  followed by custom Python scripts was used to assess transcriptome plasticity. Initial HMMsplicer runs were performed separately for sugar-fed and blood-fed samples using all RNA-seq reads that passed Illumina's quality filtering, regardless of whether they aligned to the genome. Junctions were predicted initially for single reads and then combined with perfectly matching junctions and junctions within 3 bp of each other. The combined junction inherits the location of the highest scoring junction and the combined score is adjusted appropriately. Only junctions predicting canonical splice sites after this combination were retained. Predictions for sugar-fed and blood-fed samples were combined and scores adjusted similar to above to improve the predictive power, but perfectly matching junctions were required for junctions to be combined. Finally, only junctions with more than one supporting RNA-seq read and an HMMsplicer score of 600 or greater were considered here.
SCOPE  uses an ensemble method to combine the results of three specialized motif finders that separately concentrate on non-degenerate motifs, degenerate motifs and motifs that contain two separate "half-sites". It generates significance scores by combining overrepresentation, positional bias and the proportion of the co-regulated promoters to contain at least one instance of the motif. It is resistant to the common problem of extraneous or "non-informative" promoter regions included in the co-regulated set. SCOPE was run using the 2000 bp upstream of the start codon for each transcript with SCOPE's OccurrenceKSScorer to generate the significance values.
We thank LMO for help in preparing the manuscript and Joseph DeRisi for providing the use of the HMMSplicer tool. The project described was supported by Award Number U54AI065359 from the National Institute of Allergy And Infectious Diseases. WAD is supported in part by T15LM07443 from the National Library of Medicine, National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Allergy And Infectious Diseases or the National Institutes of Health.
- Gubler DJ: Vector-borne diseases. Rev Sci Tech. 2009, 28: 583-588.PubMedGoogle Scholar
- Gubler DJ: Resurgent vector-borne diseases as a global health problem. Emerg Infect Dis. 1998, 4: 442-450. 10.3201/eid0403.980326.View ArticlePubMedPubMed CentralGoogle Scholar
- Adams TS: Hematophagy and Hormone Release. Ann Entomol Soc Am. 1999, 92: 1-13.View ArticleGoogle Scholar
- Ribeiro JM: Blood-feeding arthropods: live syringes or invertebrate pharmacologists?. Infect Agents Dis. 1995, 4: 143-152.PubMedGoogle Scholar
- Yuval B: Mating systems of blood-feeding flies. Annu Rev Entomol. 2006, 51: 413-440. 10.1146/annurev.ento.51.110104.151058.View ArticlePubMedGoogle Scholar
- Dana AN, Hong YS, Kern MK, Hillenmeyer ME, Harker BW, Lobo NF, Hogan JR, Romans P, Collins FH: Gene expression patterns associated with blood-feeding in the malaria mosquito Anopheles gambiae. BMC Genomics. 2005, 6: 5-10.1186/1471-2164-6-5.View ArticlePubMedPubMed CentralGoogle Scholar
- Marinotti O, Calvo E, Nguyen QK, Dissanayake S, Ribeiro JMC, James AA: Genome-wide analyses of gene expression in adult Anopheles gambiae. Insect Mol Biol. 2006, 15: 1-12. 10.1111/j.1365-2583.2006.00610.x.View ArticlePubMedGoogle Scholar
- Krywinski J, Grushko OG, Besansky NJ: Analyses of the complete mitochondrial DNA from Anopheles funestus: an improved dipteran mitochondrial genome annotation and a temporal dimension of mosquito evolution. Mol Phylogenet Evol. 2006, 39: 417-423. 10.1016/j.ympev.2006.01.006.View ArticleGoogle Scholar
- San Martín JL, Brathwaite O, Zambrano B, Solórzano JO, Bouckenooghe A, Dayan GH, Guzmán MG: The epidemiology of dengue in the americas over the last three decades: a worrisome reality. Am J Trop Med Hyg. 2010, 82: 128-135.View ArticlePubMedPubMed CentralGoogle Scholar
- Franco L, Di Caro A, Carletti F, Vapalahti O, Renaudat C, Zeller H, Tenorio A: Recent expansion of dengue virus serotype 3 in West Africa. Euro Surveill. 2010, 15: 19490-PubMedGoogle Scholar
- Monath TP: Dengue: the risk to developed and developing countries. Proc Natl Acad Sci USA. 1994, 91: 2395-2400. 10.1073/pnas.91.7.2395.View ArticlePubMedPubMed CentralGoogle Scholar
- Monath TP: Dengue and yellow fever-challenges for the development and use of vaccines. N Engl J Med. 2007, 357: 2222-2225. 10.1056/NEJMp0707161.View ArticlePubMedGoogle Scholar
- Hombach J, Barrett AD, Cardosa MJ, Deubel V, Guzman M, Kurane I, Roehrig JT, Sabchareon A, Kieny MP: Review on flavivirus vaccine development. Proceedings of a meeting jointly organised by the World Health Organization and the Thai Ministry of Public Health, 26-27 April 2004, Bangkok, Thailand. Vaccine. 2005, 23: 2689-2695. 10.1016/j.vaccine.2004.11.040.View ArticlePubMedGoogle Scholar
- Sanders H, Evans AM, Ross LS, Gill SS: Blood meal induces global changes in midgut gene expression in the disease vector, Aedes aegypti. Insect Biochem Mol Biol. 2003, 33: 1105-1122. 10.1016/S0965-1748(03)00124-3.View ArticlePubMedGoogle Scholar
- Goncalves RLS, Machafo ACL, Paiva-Silva GO, Sorgine MHF, Oliveira JHM, Vanniet-Santos MA, Galina A, Oluveira PL, Oliveira MF: Blood-feeding induces reversible functional changes in flight muscle mitochondria of Aedes aegypti mosquitoes. PLoS ONE. 2009, 4 (11): e7854-10.1371/journal.pone.0007854.View ArticlePubMedPubMed CentralGoogle Scholar
- Evans AM, Aimanova KG, Gill SS: Characterization of a blood-meal-responsive proton-dependent amino acid transporter in the disease vector, Aedes aegypti. J Exp Biol. 2009, 212: 3263-3271. 10.1242/jeb.029553.View ArticlePubMedPubMed CentralGoogle Scholar
- Brackney DE, Isoe J, Black WCIV, Zamora J, Foy BD, Miesfeld RL, Olson KE: Expression profiling and comparative analyses of seven midgut serine proteases from the yellow fever, Aedes aegypti. J Insect Physiol. 2010, 56: 736-744. 10.1016/j.jinsphys.2010.01.003.View ArticlePubMedPubMed CentralGoogle Scholar
- Ozsolak F, Platt AR, Jones DR, Reifenberger JG, Sass LE, McInerney P, Thompson JF, Bowers J, Jarosz M, Milos PM: Direct RNA sequencing. Nature. 2009, 461: 814-818. 10.1038/nature08390.View ArticlePubMedGoogle Scholar
- Nagalakshmi U, Waern K, Snyder M: RNA-Seq: a method for comprehensive transcriptome analysis. Curr Protoc Mol Biol. 2010, 1-13. Chapter 4: Unit 4.11Google Scholar
- Diguistini S, Liao NY, Platt D, Robertson G, Seidel M, Chan SK, Docking TR, Birol I, Holt RA, Hirst M, Mardis E, Marra MA, Hamelin RC, Bohlmann J, Breuil C, Jones SJ: De novo genome sequence assembly of a filamentous fungus using Sanger, 454 and Illumina sequence data. Genome Biol. 2009, 10: R94-10.1186/gb-2009-10-9-r94.View ArticlePubMedPubMed CentralGoogle Scholar
- Mardis ER: Next-generation DNA sequencing methods. Annu Rev Genomics Hum Genet. 2008, 9: 387-402. 10.1146/annurev.genom.9.081307.164359.View ArticlePubMedGoogle Scholar
- Morozova O, Marra MA: Applications of next-generation sequencing technologies in functional genomics. Genomics. 2008, 92: 255-264. 10.1016/j.ygeno.2008.07.001.View ArticlePubMedGoogle Scholar
- Harris TD, Buzby PR, Babcock H, Beer E, Bowers J, Braslavsky I, Causey M, Colonell J, Dimeo J, Efcavitch JW, Giladi E, Gill J, Healy J, Jarosz M, Lapen D, Moulton K, Quake SR, Steinmann K, Thayer E, Tyurina A, Ward R, Weiss H, Xie Z: Single-molecule DNA sequencing of a viral genome. Science. 2008, 320: 106-109. 10.1126/science.1150427.View ArticlePubMedGoogle Scholar
- Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y: RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Research. 2008, 18: 1509-1517. 10.1101/gr.079558.108.View ArticlePubMedPubMed CentralGoogle Scholar
- Wilhelm BT, Landry JR: RNA-Seq-quantitative measurement of expression through massively parallel RNA-sequencing. Methods. 2009, 48: 249-257. 10.1016/j.ymeth.2009.03.016.View ArticlePubMedGoogle Scholar
- Cloonan N, Grimmond SM: Transcriptome content and dynamics at single-nucleotide resolution. Genome Biol. 2008, 9: 234-10.1186/gb-2008-9-9-234.View ArticlePubMedPubMed CentralGoogle Scholar
- AegyXcel: an Aedes aegypti protein database. [http://exon.niaid.nih.gov/transcriptome.html#aegyxcel]
- Finn RD, Tate J, Mistry J, Coggill PC, Sammut SJ, Hotz HR, Ceric G, Forslund K, Eddy SR, Sonnhammer EL, Bateman A: The Pfam protein families database. Nucleic Acids Res. 2008, 36: D281-288. 10.1093/nar/gkm960.View ArticlePubMedGoogle Scholar
- Shao L, Devenport M, Fujioka H, Ghosh A, Jacobs-Lorena M: Identification and characterization of a novel peritrophic matrix protein, Ae-Aper50, and the microvillar membrane protein, AEG12, from the mosquito, Aedes aegypti. Insect Biochem Mol Biol. 2005, 35 (9): 947-59. 10.1016/j.ibmb.2005.03.012.View ArticlePubMedGoogle Scholar
- Morlais I, Mori A, Schneider JR, Severson DW: A targeted approach to the identification of candidate genes determining susceptibility to Plasmodium gallinaceum in Aedes aegypti. Mol Genet Genomics. 2003, 269 (6): 753-64. 10.1007/s00438-003-0882-7.View ArticlePubMedGoogle Scholar
- Fischer HM, Wheat CW, Heckel DG, Vogel H: Evolutionary origins of a novel host plant detoxification gene in butterflies. Mol Biol Evol. 2008, 25: 809-820. 10.1093/molbev/msn014.View ArticlePubMedGoogle Scholar
- Pane A, Wehr K, Schupbach T: zucchini and squash encode two putative nucleases required for rasiRNA production in the Drosophila germline. Dev Cell. 2007, 12: 851-862. 10.1016/j.devcel.2007.03.022.View ArticlePubMedPubMed CentralGoogle Scholar
- Lawson D, Arensburger P, Atkinson P, Besansky NJ, Bruggner RV, Butler R, Campbell KS, Christophides GK, Christley S, Dialynas E, Hammond M, Hill CA, Konopinski N, Lobo NF, MacCallum RM, Madey G, Megy K, Meyer J, Redmond S, Severson DW, Stinson EO, Topalis P, Birney E, Gelbart WM, Kafatos FC, Louis C, Collins FH: VectorBase: a data resource for invertebrate vector genomics. Nucleic Acids Res. 2009, D583-7. 10.1093/nar/gkn857. 37 DatabaseGoogle Scholar
- Hu X, Yagi Y, Tanji T, Zhou S, Ip TY: Multimerization and interaction of Toll and Spätzle in Drosophila. Proc Natl Acad Sci USA. 2004, 101: 9369-9374. 10.1073/pnas.0307062101.View ArticlePubMedPubMed CentralGoogle Scholar
- Shia AKH, Glittenberg M, Thomson G, Weber AN, Reichhart JM, Ligoxygakis P: Toll-dependent antimicrobial responses in Drosophila larval fat body require Spätzle secreted by haemocytes. J Cell Science. 2009, 122: 4505-10.1242/jcs.049155.View ArticlePubMedPubMed CentralGoogle Scholar
- Bize P, Jeannert C, Klopfenstein A, Roulin A: What makes a host profitable? Parasite balance host nutritive resources against immunity. Am Nat. 2008, 171: 107-118. 10.1086/523943.View ArticlePubMedGoogle Scholar
- Sánchez-Vargas I, Scott JC, Poole-Smith KB, Franz AWE, Barbosa-Solomieu V, Wilusz J, Olson KE, Blair CD: Dengue Virus Type 2 Infections of Aedes aegypti are modulated by the mosquito's RNA interference pathway. PLOS Pathogens. 2009, 5 (2): e1000299-View ArticlePubMedPubMed CentralGoogle Scholar
- Campbell C, Black IVWC, Hess AM, Foy BD: Comparative genomics of small RNA regulatory pathway components in vector mosquitoes. BMC Genomics. 2008, 9: 425-10.1186/1471-2164-9-425.View ArticlePubMedPubMed CentralGoogle Scholar
- Lowenberger C: Innate immune response of Aedes aegypti. Insect Biochem Mol Biol. 2001, 31: 219-229. 10.1016/S0965-1748(00)00141-7.View ArticlePubMedGoogle Scholar
- Ramirez JL, Dimopoulos G: The Toll immune signaling pathway control conserved anti-dengue defenses across diverse Ae. aegypti strains and against multiple dengue virus serotypes. Dev Comp Immunol. 2010, 34: 625-629. 10.1016/j.dci.2010.01.006.View ArticlePubMedPubMed CentralGoogle Scholar
- Zou Z, Shin SW, Alvarez KS, Kokoza V, Raikhel AS: Distinct melanization pathways in the mosquito Aedes aegypti. Immunity. 2010, 32: 41-53. 10.1016/j.immuni.2009.11.011.View ArticlePubMedGoogle Scholar
- Bartholomay LC, Fuchs JF, Cheng LL, Beck ET, Vizioli J, Lowenberger C, Christensen BM: Reassessing the role of defensin in the innate immune response of the mosquito, Aedes aegypti. Insect Mol Biol. 2004, 13: 125-132. 10.1111/j.0962-1075.2004.00467.x.View ArticlePubMedGoogle Scholar
- Erickson SM, Xi Z, Mayhew GF, Ramirez JL, Aliota MT, Christensen BM, Dimopoulos G: Mosquito infection responses to developing filarial worms. Plos Negl Trop Dis. 2009, e529-10.1371/journal.pntd.0000529.Google Scholar
- Xi Z, Ramirez JL, Dimopoulos G: The Aedes aegypti Toll Pathway Controls Dengue Virus Infection. PLOS Pathogens. 2008, 4 (7): e1000098-10.1371/journal.ppat.1000098.View ArticlePubMedPubMed CentralGoogle Scholar
- Khoo CC, Piper J, Sanchez-Vargas I, Olson KE, Franz AW: The RNA interference pathway affects midgut infection- and escape barriers for Sindbis virus in Aedes aegypti. BMC Microbiology. 2010, 10: 130-10.1186/1471-2180-10-130.View ArticlePubMedPubMed CentralGoogle Scholar
- Bartholomay LC, Waterhouse RM, Mayhew GF, Campbell CL, Michel K, Zou Z, Ramirez JL, Das S, Alvarez K, Arensburger P, et al: Pathogenomics of Culex quiquefasciatus and meta-analysis of infection responses to diverse pathogens. Science. 2010, 330: 88-10.1126/science.1193162.View ArticlePubMedPubMed CentralGoogle Scholar
- Campbell CL, Keene KM, Brackney DE, Olson KE, Blair CD, Wilusz J, Foy BD: Aedes aegypti uses RNA interference in defense against Sindbis virus infection. BMC Microbiology. 2008, 8: 47-10.1186/1471-2180-8-47.View ArticlePubMedPubMed CentralGoogle Scholar
- Dong Y, Manfredini F, Dimopoulos G: Implication of the mosquito midgut microbiota in the defense against malaria parasite. Plos Pathogens. 2009, 5 (5): e1000423-10.1371/journal.ppat.1000423.View ArticlePubMedPubMed CentralGoogle Scholar
- Molina-Cruz A, DeJong RJ, Charles B, Gupta L, Kumar S, Jaramillo-Gutlerrez G, Barillas-Mury C: Reactive oxygen species modulate Anopehels gambiae immunity against Bacteria and Plasmodium. J Biol Chem. 2008, 283: 3217-3223. 10.1074/jbc.M705873200.View ArticlePubMedGoogle Scholar
- Salazar MI, Richardson JH, Sánchez-Vargas I, Olson KE, Beaty BJ: Dengue virus type 2: replication and tropisms in orally infected Aedes aegypti mosquitoes. BMC Microbiol. 2007, 30: 7-9.Google Scholar
- Waterhouse RM, Kriventseva EV, Meister S, Xi Z, Alvarez KS, Bartholomay LC, Barillas-Mury C, Bian G, Blandin S, Christensen BM, et al: Evolutionary dynamics of immune-related genes and pathways in disease-vector mosquitoes. Science. 2007, 316: 1738-1743. 10.1126/science.1139862.View ArticlePubMedPubMed CentralGoogle Scholar
- Insect Immune-Related Genes and Gene Famillies. [http://cegg.unige.ch/Insecta/immunodb]
- Chang YY, Neufeld TP: Autophagy takes flight in Drosophila. FEBS Lett. 2010, 584: 1342-1349. 10.1016/j.febslet.2010.01.006.View ArticlePubMedPubMed CentralGoogle Scholar
- Marelli MT, Moreira CK, Kelly D, Alphey L, Jacobs-Lorena M: Mosquito transgenesis: what fitness cost?. Trends Parasitol. 2006, 22: 197-202. 10.1016/j.pt.2006.03.004.View ArticleGoogle Scholar
- Amenya DA, Bonizzoni M, Isaacs AT, Jasinskiene N, Chen H, Marinotti O, Yan G, James AA: Comparative fitness assessment of Anopheles stephensi transgenic lines receptive to site-specific integration. Insect Mol Biol. 2010, 19: 263-269. 10.1111/j.1365-2583.2009.00986.x.View ArticlePubMedPubMed CentralGoogle Scholar
- Carlson JM, Chakravarty A, DeZiel C, Gross RH: SCOPE: a web server for practical de novo motif discovery. Nucl Acids Res. 2007, 35: W259-W264. 10.1093/nar/gkm310.View ArticlePubMedPubMed CentralGoogle Scholar
- Kriventseva EV, Rahman N, Espinosa O, Zdobnov EM: OrthoDB: the hierarchical catalog of eukaryotic orthologs. Nucleic Acids Research. 2008, D271-5. 36 DatabaseGoogle Scholar
- Sieglaff DH, Dunn WA, Xie XS, Megy K, Marinotti O, James AA: Comparative genomics allows the discovery of cis-regulatory elements in mosquitoes. Proc Natl Acad Sci USA. 2009, 106: 3053-3058. 10.1073/pnas.0813264106.View ArticlePubMedPubMed CentralGoogle Scholar
- Dimon MT, Sorber K, DeRisi JL: HMMSplicer: a tool for efficient and sensitive discovery of known and novel splice junctions in RNA-seq data. PLoS ONE. 2010, 5 (11): e13875-10.1371/journal.pone.0013875.View ArticlePubMedPubMed CentralGoogle Scholar
- Hubbard T, Barker D, Birney E, Cameron G, Chen Y, Clark L, Cox T, Cuff J, Curwen V, Down T, et al: The Ensembl genome database project. Nucleic Acids Res. 2002, 30: 38-41. 10.1093/nar/30.1.38.View ArticlePubMedPubMed CentralGoogle Scholar
- VectorBase Aedes aegypti Liverpool annotation, AaegL1.2. [http://www.vectorbase.org]
- Bennet KE, Olson KE, Munoz ML, Fernandez-Salas I, Farfan-Ale JA, Higgs S, Black WC, Beaty BJ: Variation in vector competence for dengue 2 virus among 24 collections of Ae. aegypti from Mexico and the United States. Am J Trop Med Hyg. 2002, 67: 85-92.Google Scholar
- Black WC, Bennett KE, Gorrochotegui-Escalante N, Barillas-Mury CV, Fernandez-Salas I, Munoz ML, Farfan-Ale JA, Olson KE, Beaty BJ: Flavivirus susceptibility in Ae. aegypti. Arch Med Res. 2002, 33: 379-388. 10.1016/S0188-4409(02)00373-9.View ArticlePubMedGoogle Scholar
- Terenius O, Marinotti O, Sieglaff D, James AA: Molecular genetic manipulation of vector mosquitoes. Cell Host Microbe. 2008, 13: 417-423. 10.1016/j.chom.2008.09.002.View ArticleGoogle Scholar
- Macdonald WW: Further studies on a strain of Aedes aegypti susceptible to infection with sub-periodic Brugia malayi. Ann Trop Med Parasitol. 1963, 57: 452-460.View ArticlePubMedGoogle Scholar
- Nene V, Wortman JR, Lawson D, Haas B, Kodira C, Tu ZJ, Loftus B, Xi Z, Megy K, Grabherr M, Ren Q, et al: Genome sequence of Aedes aegypti, a major arbovirus vector. Science. 2007, 316: 1718-1723. 10.1126/science.1138878.View ArticlePubMedGoogle Scholar
- mRNA Sequencing Sample Preparation Guide. [http://www.illumina.com/support/documentation.ilmn]
- UC Davis Genome Center- Expression Analysis Core. [http://genomecenter.ucdavis.edu/expression_analysis/]
- Langmead B, Trapnell C, Pop M, Salzberg SL: Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biology. 2009, 10: R25-10.1186/gb-2009-10-3-r25.View ArticlePubMedPubMed CentralGoogle Scholar
- Quinlan AR, Hall IM: BEDTools: A flexible suite of utilities for comparing genomic features. Bioinformatics. 2010, 26: 841-842. 10.1093/bioinformatics/btq033.View ArticlePubMedPubMed CentralGoogle Scholar
- Wang L, Feng Z, Wang X, Wang X, Zhang X: DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics. 2010, 26: 136-138. 10.1093/bioinformatics/btp612.View ArticlePubMedGoogle Scholar
- Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B. 1995, 57: 289-300.Google Scholar
- Schmittgen TD, Livak KJ: Analyzing real-time PCR data by the comparative C(T) method. Nat Protoc. 2008, 3: 1101-1118. 10.1038/nprot.2008.73.View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.