- Research article
- Open Access
Transcriptome response to pollutants and insecticides in the dengue vector Aedes aegypti using next-generation sequencing technology
© David et al; licensee BioMed Central Ltd. 2010
- Received: 6 January 2010
- Accepted: 31 March 2010
- Published: 31 March 2010
The control of mosquitoes transmitting infectious diseases relies mainly on the use of chemical insecticides. However, mosquito control programs are now threatened by the emergence of insecticide resistance. Hitherto, most research efforts have been focused on elucidating the molecular basis of inherited resistance. Less attention has been paid to the short-term response of mosquitoes to insecticides and pollutants which could have a significant impact on insecticide efficacy. Here, a combination of LongSAGE and Solexa sequencing was used to perform a deep transcriptome analysis of larvae of the dengue vector Aedes aegypti exposed for 48 h to sub-lethal doses of three chemical insecticides and three anthropogenic pollutants.
Thirty millions 20 bp cDNA tags were sequenced, mapped to the mosquito genome and clustered, representing 6850 known genes and 4868 additional clusters not located within predicted genes. Mosquitoes exposed to insecticides or anthropogenic pollutants showed considerable modifications of their transcriptome. Genes encoding cuticular proteins, transporters, and enzymes involved in the mitochondrial respiratory chain and detoxification processes were particularly affected. Genes and molecular mechanisms potentially involved in xenobiotic response and insecticide tolerance were identified.
The method used in the present study appears as a powerful approach for investigating fine transcriptome variations in genome-sequenced organisms and can provide useful informations for the detection of novel transcripts. At the biological level, despite low concentrations and no apparent phenotypic effects, the significant impact of these xenobiotics on mosquito transcriptomes raise important questions about the 'hidden impact' of anthropogenic pollutants on ecosystems and consequences on vector control.
- Mosquito Larva
- Massive Parallel Signature Sequencing
- Cuticular Protein
During the past 60 years, the amount of anthropogenic xenobiotics released into natural ecosystems has dramatically increased. Although the effect of these chemicals on human health is intensively studied, their impact on other organisms remains poorly understood. Because pollutants often accumulate in fresh-water bodies and sediments , their impact on wetland fauna is of importance for these ecosystems. Among aquatic arthropods found in wetlands, mosquitoes are distributed worldwide and are often exposed to anthropogenic pollutants and insecticides during their aquatic larval stage. Indeed insecticides are often deliberately introduced into the mosquito habitat in the fight against the many human diseases they transmit (e.g. malaria, dengue fever, yellow fever and filariasis) . As a consequence mosquito control programs are now threatened by the selection of mosquito populations resistant to these chemical insecticides . Differential gene transcription in insecticide-resistant mosquitoes has been frequently used to identify genes putatively involved in inherited metabolic resistance mechanisms [4–7]. For that purpose most approaches used cDNA microarrays and were often focused on genes encoding enzymes potentially involved in the bio-transformation of insecticides molecules [8, 9], although recent findings suggest that the differential expression of other transcripts may also contribute to insecticide tolerance [4, 10]. Less attention has been paid to the short term transcriptome response of insects to xenobiotics, though this may lead to the discovery of novel molecular mechanisms contributing to insecticide tolerance [11–13]. We recently demonstrated that exposing mosquito larvae to low concentrations of pollutants for a few hours can increase their tolerance to chemical insecticides, possibly due to an alteration of the expression of detoxification enzymes [11, 12]. In this context, understanding cross responses of mosquitoes to insecticides and pollutants at the whole transcriptome level may ultimately lead to improvements in vector control strategies by optimizing insecticide treatments in polluted areas . Moreover, deciphering transcriptome response of mosquitoes to anthropogenic xenobiotics may identify genes involved in chemical stress response that were not detected by standard toxicological studies.
Today, quantitative transcriptomic methods are diversified and divided into two kind of technology: 'closed' and 'open' techniques depending on genome annotation constraints [14, 15]. In 'closed' technologies, gene expression microarrays are the standard method used for transcriptome analysis. However, this type of technology does not allow the characterization and analysis of new transcripts and suffers from various technical biases such as non-specific hybridization and insufficient signal for low expressed genes. In contrast, 'open' transcriptome analyses based on the sequencing of either ESTs or short cDNA tags, like Serial Analysis of Gene Expression (SAGE) , LongSAGE  and Massive Parallel Signature Sequencing (MPSS)  can measure the transcript level of both known and unknown genes . The short cDNA tags obtained by LongSAGE or MPSS can directly be mapped to the genome sequence, allowing the identification of new transcripts . Because these sequencing techniques do not target a defined portion of cDNAs, these approaches are not optimized for the deep analysis of transcriptome variations . Recently, a combination of LongSAGE and Solexa sequencing technology, leading to the production and sequencing of millions of tags on a defined region of cDNAs, has been used to characterize mouse hypothalamus transcriptome . To our knowledge, this new method, called Digital Gene Expression Tag Profiling (DGETP) has never been used to compare whole transcriptome variations of a non-mammalian organism in different environmental conditions.
Here, we used the DGETP approach to perform a deep transcriptome analysis of larvae of the mosquito Aedes aegypti exposed to different anthropogenic xenobiotics. We examined the effect of sublethal doses of three pollutants likely to be found in wetlands (the herbicide atrazine, the polycyclic aromatic hydrocarbon fluoranthene and the heavy metal copper) and three chemical insecticides used for mosquito control (the pyrethroid permethrin, the neonicotinoid imidacloprid and the carbamate propoxur). This approach was suitable for investigating deep transcriptome variations in mosquitoes and identified several loci with high transcription signal not previously identified in mosquito genome. At the biological level, the transcript levels of many genes were affected by xenobiotic exposure. Several genes and protein families responding to individual or multiple xenobiotics were identified, unraveling the complexity of xenobiotic-response in mosquitoes and identifying genes potentially involved in insecticide tolerance or biological interactions between insecticides and pollutants.
Sequencing, mapping and clustering of cDNA tags
Filtered from background
Mapped to genome
Mapped to genes
Clustering analysis of 20-mer cDNA tags successfully mapped to mosquito genome allowed us to identify a total of 13,118 distinct clusters including 8,250 clusters associated to predicted genes. Distribution of the total number of reads across genes, clusters and tags (Additional file 1: Suppl. Figure 1) spanned more than 4 orders of magnitude with most genes/clusters being represented by 25 to 5000 reads. Median total number of reads per gene, cluster, tag and cluster not mapped within predicted gene were 217, 124, 101 and 79 respectively.
Quantitative transcription data obtained from cDNA tags
Overall transcriptome variations across treatments
Genes and clusters differentially transcribed after xenobiotic exposure
Genes/clusters differentially transcribed
Total genes and additional clusters
Total additional clusters not within genes
Genes differentially transcribed across treatments
Impact of xenobiotics on transcripts encoding enzymes
Analyzing transcriptome variations using digital gene expression tag profiling
Following the genome sequencing of the dengue vector Ae. aegypti, 15,419 putative genes were identified and transcripts were detected for 12,350 genes by combining cDNA microarray, massive parallel signature sequencing (MPSS) or EST sequencing on several mosquito life stages . By using the DGETP method, we sequenced 29.4 millions 20-mer tags across 7 distinct cDNA libraries obtained from 4th-stage larvae. This approach allowed us to detect significant transcription signals for 6,850 predicted genes. Considering that several genes may not be transcribed in 4th-stage larvae and that transcripts assayed by the DGETP method require the presence of a DpnII restriction site, such transcriptome coverage appears satisfactory. Besides, sequence variations between the Ae. aegypti strain used in our study (Bora-Bora strain) and the one used for genome sequencing (Liverpool strain), led to the rejection of numerous reads. Within our mosquito strain, allelic variations were detected for numerous loci and also led to the rejection of a considerable proportion of reads as only alleles exactly matching to the reference genome sequence were considered in the analysis (see methods). However, we believe that such high mapping stringency is critical for generating accurate gene transcription data with short cDNA tags. Improving the number of reads by replicating sequencing libraries for each sample will allow a better assessment of biological and technical variations together with increasing transcriptome coverage. By sequencing 10 million random 36 bp cDNA fragments from two cDNA libraries of females Drosophila melanogaster, Sackton et al. detected 2,540 annotated genes . By targeting a defined region of cDNAs, the DGETP method can generate wider transcriptome coverage together with a higher number of cDNA tags per gene, leading to more precise gene transcription data. Provided a reference genome is available and the aim is to quantify transcript levels between different biological samples, we confirm that methods based on the combination of LongSAGE and next-generation sequencing technologies are perfectly suited for deep transcriptome analysis . Recent improvements in sequencing technologies (~30 million reads/lane on the illumina Genome Analyzer system) are now making sequencing-based approaches the methods of choice for whole transcriptome analyses.
Among the 15,253 20-mer cDNA tags successfully mapped to Ae. aegypti genome, 35% were not located within predicted gene boundaries extended by 300 bp at their 3' end (see methods). These tags could be gathered into 4,868 genomic clusters with more than 40% of them showing significant transcription signal (> 100 reads, Additional file 1: Suppl. Figure 1). These clusters may represent genes, exons or UTR extensions not predicted by automated annotation. Recent studies revealed that the genome of complex organisms produce large numbers of regulatory noncoding RNAs (ncRNAs) that can be antisense, intergenic, interleaved or overlapping with protein-coding genes [23, 24]. In that concern, it is likely that a significant proportion of transcript signatures detected outside predicted genes represent ncRNAs. The use of next-generation sequencing approaches specifically targeting insect ncRNAs will help decipher their role in mosquito gene regulation and in the capacity of insects to adapt to different environmental conditions.
Impact of xenobiotics on mosquito larvae transcriptome
Global analysis of transcriptome variations associated with a 48 h exposure of mosquito larvae to low doses of insecticides and pollutants revealed their ability to adjust to modifications of their chemical environment. The number of transcripts affected varies greatly depending on the xenobiotic used for exposure. When considering organic xenobiotics (all but copper), this number increased together with the molarity of the xenobiotics. Our results also revealed that the lipophilicity of the xenobiotics affects the number of differentially transcribed genes encoding cuticular proteins and transporters. It has been demonstrated that lipophilic xenobiotics accumulate in biological membranes or lipid reserves, modifying their distribution across tissues and cells [25, 26]. Although our experimental design did not allow segregating between the quantity of xenobiotic and their inherent chemical properties, it is likely that molarity and lipophilicity are key factors affecting the magnitude and the specificity of transcriptome variations observed here.
Our results demonstrated the similar strong transcriptome response of mosquito larvae exposed to the insecticides propoxur and imidacloprid. Despite belonging to two different chemical groups, the carbamate propoxur and the neonicotinoid imidacloprid both potentiate the functioning of nicotinic cholinergic receptors . Although genes encoding the primary targets of these insecticides (acetylcholinesterase or nicotinic receptors) were not found significantly differentially transcribed, the similar transcriptome responses to these two insecticides may be partly related to similar effects generated by the alteration of cholinergic neurons functioning [28, 29].
We previously demonstrated that exposing mosquito larvae to various pollutants for few hours can increase their tolerance to insecticides possibly through an induction of detoxification enzymes [11, 12, 30]. Among the different pollutants tested, polycyclic aromatic hydrocarbons were often the most potent for increasing insecticide tolerance, possibly due to their ability to induce detoxification enzymes . The present study detected a considerable number of genes encoding detoxification enzymes (89 cytochrome P450s, 22 GSTs and 27 carboxylesterases) including several genes showing transcription level variations. However, only a small proportion of them were found significantly affected by xenobiotic exposure, probably due to insufficient number of reads regarding our Fisher's t test Pvalue threshold. Among them, members of cytochrome P450 families frequently involved in resistance to insecticides and plant toxins [7–9, 32–34] were over transcribed following exposure to fluoranthene, propoxur or imidacloprid. By revealing that several other genes with a broad range of biological functions are similarly affected by insecticides and pollutants, our results suggest that the impact of pollutants on the ability of mosquitoes to better tolerate chemical insecticides might also be the consequence of the induction/repression of other proteins involved in a wide range of functions. In this concern, several cuticular proteins were found over transcribed in mosquito larvae exposed to insecticides or organic xenobiotics. It has been suggested that mosquito may protect themselves from insecticides by cuticular protein thickening leading to a reduction of insecticide penetration [4, 35]. Other studies demonstrated that cuticular component deposition is stimulated by environmental stress .
Our results also suggest that mosquito larvae exposed to xenobiotics undertake a metabolic stress associated with changes of their chemical environment. Global cellular stress response has been defined as all proteins over-produced due to environmental stress. This response initially named 'general adaptation syndrome' occurs together with increased mobilization of energy from storage tissues . Such stress response has been described for numerous stress factors including exposure to pollutants . In insect cells, response to environmental aggressions can involve various proteins including heat shock proteins , metallothioneins  or p-glycoprotein synthesis . Although differentiating between xenobiotic-specific and general stress responses is difficult, we also highlighted such protein families including chaperonins, heat shock proteins and ATP-binding cassette transporters (p-glycoprotein family). Moreover, numerous genes encoding enzymes involved in the production of energy or in cellular catabolism such as NADH dehydrogenase, ATP synthase, trypsin and lipases were found over transcribed in mosquito larvae exposed to xenobiotics, confirming a global stress response [37, 42].
Significant transcript level variations were observed in response to anthropogenic pollutants though those compounds were not toxic for mosquito larvae (see methods). Although we predicted the relatively important effect of the polycyclic aromatic hydrocarbon (PAH) fluoranthene on mosquito larvae due to known cellular effects on animals [11, 12, 31, 43], responses to atrazine and copper were unanticipated. In animals, the cellular impact of PAHs has been associated with the uncoupling of mitochondrial respiration, direct genotoxic damages and the formation of reactive oxygen species [31, 44–46]. The over transcription of NADH dehydrogenase and ATP synthase observed after exposing larvae to fluoranthene confirm that similar effects occur in mosquitoes. Although mosquitoes do not possess the protein targeted by the triazine herbicide atrazine (plastoquinone-binding protein in photosystem II)  and a very low concentration was used (10 μg/L), this chemical affected the transcription of several mosquito genes. In plants, atrazine disrupts the electron transport in chloroplasts . In mosquito larvae, several members of the oxidative phosphorylation pathway including NADH dehydrogenase and ATP synthase were induced by atrazine, suggesting a compensation for partial uncoupling of oxidative phosphorylation . Larvae exposed to copper sulfate exhibited a significant over transcription of 45 genes including a large proportion of enzymes while only 3 genes were under-transcribed. The induction of enzymes by copper might be the consequence of chemical interactions between Cu2+ ions and metalloenzymes together with other metalloproteins involved in electron transfers, hydrolysis and oxido-reductions [49–51]. The strong induction of the hemo-protein cytochrome b5 (co-factor of P450s for electron transfer) together with several serine proteases and oxidase/peroxidases support this hypothesis.
Overall, despite low concentrations, short exposure time and no apparent phenotypic modification, the significant effect of pollutants and insecticides on mosquito larvae transcriptome raise important questions about the 'hidden impact' of anthropogenic pollutants on ecosystems, including mammals. This concern may even be underestimated considering the complex and unknown cross-effects generated by pollutant mixtures often encountered in polluted ecosystems . In nematodes, it has been shown that by applying a realistic heat stress to both uncontaminated and polluted systems, the specimen from polluted environment showed a stronger response . Such effects are likely to occur in polluted mosquito breeding sites and are likely to affect the efficacy of chemical insecticides used for mosquito control [4, 5, 7, 11, 12, 53]. Although further experiments are required to fully characterize the molecular mechanisms by which pollutants affect insecticide tolerance in mosquitoes, the present study clearly demonstrate that similar response mechanisms are activated by pollutants and insecticides. Finally, the persistent contamination of wetlands by anthropogenic chemicals and the role of phenotypic plasticity in driving selection mechanisms  raise the question of the long-term impact of pollutants on the selection of insecticide resistance mechanisms. Additional experiments combining exposure of mosquitoes to pollutants and their subsequent selection with insecticides will provide valuable biological material to answer this question and may later allow improving mosquito control strategies.
Mosquitoes and xenobiotics
A laboratory strain of the dengue vector Ae. aegypti (Bora-Bora strain), susceptible to insecticides was reared in standard insectary conditions (26°C, 8 h/16 h light/dark period) and used for all experiments. Larvae were reared in tap water with controlled amount of larval food (ground hay pellets) for 4 days (3rd instar) before exposure for 48 h to 3 chemical insecticides and 3 pollutants belonging to various chemical classes: the pyrethroid insecticide permethrin (Chem Service, USA), the neonicotinoid insecticide imidacloprid (Sigma Aldrich, USA), the carbamate insecticide propoxur (Sigma Aldrich, USA), the herbicide atrazine (Cluzeau, France), the polycyclic aromatic hydrocarbon (PAH) fluoranthene (Aldrich, France) and the heavy metal copper (obtained from CuSO4, Prolabo, France). Atrazine is an herbicide heavily used worldwide and is likely to be found in mosquito breeding sites near cultivated areas (e.g. field drainpipes) [30, 55]. Similarly, copper is the major component of Bordeaux mixture and is widely used to control fungus on grapes and other berries . Finally, fluoranthene is one of the most ubiquitous PAH and is found at high concentrations in road sediments . Elevated doses of fluoranthene are likely to be found in urban mosquito breeding sites such as road trenches  or in oil spillage areas .
Exposures to all xenobiotics were performed in triplicate with larvae from different egg batches (3 biological replicates per treatment). One hundred larvae were exposed to each xenobiotic in 200 ml tap water containing 50 mg of larval food. Control larvae were obtained simultaneously in similar conditions without xenobiotics. Doses of xenobiotics used for larval exposure were chosen according to the doses likely to be found in highly polluted mosquito breeding sites (INERIS, http://www.ineris.fr). Preliminary experiments revealed that fluoranthene, atrazine or copper did not show any toxicity on mosquito larvae even at higher concentrations than those used in the present study. For insecticides, we chose a concentration resulting in less than 15% larval mortality after 48 h exposure. This low mortality threshold was chosen in order to minimize the effect of the artificial selection of particular genotypes more tolerant to the insecticide during exposure. Doses of xenobiotics used for exposures were 1.5 μg/L permethrin, 40 μg/L imidacloprid, 500 μg/L propoxur, 25 μg/L fluoranthene, 10 μg/L atrazine and 2 mg/L CuSO4. After 48 h, larvae were collected, rinsed twice in tap water and immediately used for RNA extractions.
Preparation of double stranded cDNA tag libraries
For each biological replicate, total RNA was extracted from 30 fresh larvae using the PicoPure™ RNA isolation kit (Arcturus Bioscience, Mountain View, USA) according to manufacturer's instructions. Total RNA quality and quantity were controlled on an Agilent 2100 Bioanalyzer (Agilent, USA). Total RNAs were then diluted to 750 ng/μL in nuclease-free water. For each treatment, total RNAs from the 3 biological replicates were then pooled together in equal proportions. Double-stranded cDNA tag libraries (Additional file 5: Suppl. Figure 4) were prepared by Illumina Corporation. Two μg total RNA were used to isolate mRNAs by using magnetic oligo(dT) beads before cDNA synthesis using superscript II (Invitrogen) at 42°C for 1 h. Second strand cDNAs were then synthesized and mRNAs were removed. Double stranded cDNAs were cleaved at DpnII restriction sites (5'-↓GATC-3') and fragments attached to the oligo(dT) beads on their 3' end were purified. Gene expression (GEX) adapters 1 were ligated to the DpnII cleavage sites using T4 DNA ligase (Invitrogen). Double stranded cDNAs containing both GEX adaptors 1 and oligo(dT) beads were then digested with MmeI for 1.5 h at 37°C to generate 20 bp double stranded cDNA tags. These tags were purified before ligating GEX adapters 2 at the MmeI cleavage site using T4 DNA ligase. The adapter-ligated cDNA tag library was then enriched by PCR with two primers annealing to the end of GeX adapters and Phusion DNA polymerase (Finnzymes Oy). PCR cycles were 30 s at 98°C followed by 15 cycles of 10 s at 98°C, 30 s at 60°C, 15 s at 72°C and a final elongation step of 10 min at 72°C. Sequences of primers used for library preparation are available at http://illumina.com. Enriched cDNA tag library was then gel-purified before quality control analysis on an Agilent 2100 Bioanalyzer.
Sequencing and mapping of cDNA tags to mosquito genome
Each cDNA tag library was sequenced as 20-mers on a genome analyzer I (illumina Corporation). Each cDNA tag library was sequenced on a separated flow cell lane. Sequenced cDNA tags were then filtered from background noise according to their total number of reads across all conditions. Only cDNA tags represented by more than 20 reads were kept for further analysis. Background-filtered cDNA tags were then mapped to the Ae. aegypti genome assembly (AaegL 1.1 annotation) using TagMatcher, a software developed in our laboratory and based on the short sequence mapping algorithm 'agrep' . TagMatcher allows matching tags to a reference genome with errors and multiple matching loci (available on request to firstname.lastname@example.org). After mapping to Ae. aegypti genome, only tags without ambiguous nucleotides and mapped without mismatch at a unique genomic location were kept for clustering and differential transcription analysis. To avoid possible bias due to incomplete 3' UTR annotation and because most cDNA tags were expected on the 3' side of genes (see Additional file 5: Suppl. Figure 4), cDNA tags were considered to be 'within' a gene if located between the 5' boundary of a gene and its 3' boundary extended by 300 bp.
Clustering and differential transcription analysis
In order to collect transcription data from distinct tags matching to a unique transcript or a unique genomic loci without a priori knowledge of genome annotation, we clustered tags previously mapped to Ae. aegypti genome. Two distinct tags were assigned to a single cluster if i) tags were found on the same DNA strand and genomic supercontig, ii) tags were separated by less than 500 bp and iii) the total number of reads across all conditions was higher for the tag located downstream (3' side) than for the tag located upstream (5' side). The later condition was adopted in order to take in account the effect of partial DpnII digestion of cDNAs during cDNA library preparation, leading to multiple tags located on a single transcript with decreasing number of reads toward the 5' direction (see Additional file 5: Suppl. Figure 4).
Differential analysis of transcription levels in mosquito larvae exposed to each xenobiotic was performed at the gene level for cDNA tags mapped within predicted genes (i.e. gathering all tags mapped within each gene) and at the cluster level for cDNA tags not mapped within predicted genes (i.e. gathering all tags mapped within each cluster). Transcription ratios (TR) were calculated by dividing the number of reads per million (RPM) in xenobiotic-exposed larvae by the number of RPM in control larvae following the formula: TR = [(RPMtreated + x)/(RPMcontrols + x)], where x is a pseudocount equal to 0.2 (approximately 1 read per million per condition). Then, the probability of each gene to be differentially transcribed more than 2-fold in either direction between treated and controls was computed for each condition from raw read counts, taking into account library size. This computation was performed using Fisher's noncentral hypergeometric distribution, which has the advantage over standard hypergeometric law to allow computation of Pvalue for a ratio different of one . Holm correction was then applied to multiple test procedure. Genes/clusters were considered differentially transcribed between xenobiotic-exposed larvae and controls if Pvalue < 10-3.
Differential effect of xenobiotics on mosquito larvae transcriptome
To compare the global effect of each xenobiotic on Ae. aegypti larvae transcriptome, a principal component analysis (PCA) based on Log10 TRs was performed on the 453 genes and 225 clusters not mapped within genes showing significant differential transcription following exposure to at least one xenobiotic. Representation of observations (genes and clusters) and conditions (xenobiotics used for exposure) on PCA axis was optimized by applying a Varimax rotation on the 5 axis best representing the variance . A comparative analysis of gene functions differentially transcribed was performed on the 453 genes showing significant differential transcription following exposure to at least one xenobiotic. Genes were classified in 9 different categories: enzymes, kinases, transport, DNA interaction, cuticle, cytoskeleton, ribosomes, others and hypothetical proteins. For each treatment, percentages of genes significantly over- and under-transcribed were compared. To investigate the role of enzymes in the response of mosquito larvae to xenobiotics, a hierarchical clustering analysis based on TRs was performed on the 115 enzymes showing a significant differential transcription. Clustering analysis was performed by loading Log10 transcription ratios into TM4 Multi experiment Viewer (MeV) software . Gene and condition trees were calculated using Pearson's uncentered distance metric and complete linkage method with optimization of genes order [63, 64].
Real-time quantitative RT-PCR validation
Transcription profiles of 14 genes were validated by reverse transcription followed by real-time quantitative PCR on same RNA samples used for cDNA library preparation. Four μg total RNAs were treated with DNAse I (Invitrogen) and used for cDNA synthesis with superscript III (Invitrogen) and oligo-dT20 primer according to manufacturer's instructions. Resulting cDNAs were diluted 100 times for PCR reactions. Real-time quantitative PCR reactions of 25 μL were performed in triplicate on an iQ5 system (BioRad) using iQ SYBR Green supermix (BioRad), 0.3 μM of each primer and 5 μL of diluted cDNAs according to manufacturer's instructions. Data analysis was performed according to the ΔΔCT method taking into account PCR efficiency  and using the two genes encoding the ribosomal protein L8 (GenBank accession no. DQ440262) and the ribosomal protein S7 (Genbank accession no. EAT38624.1) for normalisation. For each treatment, results were expressed as mean transcription ratios (± SE) between xenobiotic-exposed larvae and control larvae.
Detailed transcription data for the 6850 genes detected in the present study are presented in the Additional file 6 (supplementary Table 2).
All next-generation sequencing data and cDNA library informations associated to the present study have been deposited at the EMBL-EBI European Read Archive (ERA) under accession number ERA000115. Experiment metadata are freely accessible at ftp://ftp.era-xml.ebi.ac.uk/meta/xml/ and sequence data are freely accessible at ftp://ftp.era-xml.ebi.ac.uk/vol1/ERA000/ERA000115/. Expression data from the 453 genes found differentially transcribed after xenobiotic exposure are also accessible at http://funcgen.vectorbase.org/ExpressionData/.
All gene accession numbers mentioned in the present manuscript are compatible with Ensembl, NCBI-GenBank and Vectorbase http://aaegypti.vectorbase.org genome databases.
The present research project was funded by the French National Research Agency (ANR project 07SEST014 MOSQUITO-ENV). We are grateful to J. Patouraux and S. Veyrenc for technical help. We thank Dr. B. MacCallum from Vectorbase and Dr. C. Hunter and Dr. V. Zalunin from EMBL-EBI for help with sequence data deposition. We are grateful to Dr. J. Vontas, Dr. P. Taberlet, Dr. H. Ranson, Pr. P. Ravanel and anonymous reviewers for useful comments on the manuscript.
- Stegeman JJ, Hahn ME: Biochemistry and molecular biology of monooxygenase: current perspective on forms, functions, and regulation of cytochrome P450 in aquatic species. Aquatic toxicology; Molecular, Biochemical and Cellular Perspectives. Edited by: Malins DC, Ostrander GK. 1994, Boca Raton: Lewis Publisher, CRC Press, 87-206.Google Scholar
- Lounibos LP: Invasions by insect vectors of human disease. Annu Rev Entomol. 2002, 47: 233-266. 10.1146/annurev.ento.47.091201.145206.PubMedView ArticleGoogle Scholar
- Hemingway J, Field L, Vontas J: An overview of insecticide resistance. Science. 2002, 298 (5591): 96-97. 10.1126/science.1078052.PubMedView ArticleGoogle Scholar
- Djouaka RF, Bakare AA, Coulibaly ON, Akogbeto MC, Ranson H, Hemingway J, Strode C: Expression of the cytochrome P450s, CYP6P3 and CYP6M2 are significantly elevated in multiple pyrethroid resistant populations of Anopheles gambiae s.s. from Southern Benin and Nigeria. BMC Genomics. 2008, 9: 538-10.1186/1471-2164-9-538.PubMed CentralPubMedView ArticleGoogle Scholar
- Liu NN, Liu HQ, Zhu F, Zhang L: Differential expression of genes in pyrethroid resistant and susceptible mosquitoes, Culex quinquefasciatus (S.). Gene. 2007, 394 (1-2): 61-68. 10.1016/j.gene.2007.01.032.PubMedView ArticleGoogle Scholar
- Marcombe S, Poupardin R, Darriet F, Reynaud S, Bonnet J, Strode C, Brengues C, Yebakima A, Ranson H, Corbel V: Exploring the molecular basis of insecticide resistance in the dengue vector Aedes aegypti: a case study in Martinique Island (French West Indies). BMC Genomics. 2009, 10: 494-10.1186/1471-2164-10-494.PubMed CentralPubMedView ArticleGoogle Scholar
- Muller P, Donnelly MJ, Ranson H: Transcription profiling of a recently colonised pyrethroid resistant Anopheles gambiae strain from Ghana. BMC Genomics. 2007, 8: 36-10.1186/1471-2164-8-36.PubMed CentralPubMedView ArticleGoogle Scholar
- David JP, Strode C, Vontas J, Nikou D, Vaughan A, Pignatelli PM, Louis C, Hemingway J, Ranson H: The Anopheles gambiae detoxification chip: A highly specific microarray to study metabolic-based insecticide resistance in malaria vectors. Proc Natl Acad Sci USA. 2005, 102 (11): 4080-4084. 10.1073/pnas.0409348102.PubMed CentralPubMedView ArticleGoogle Scholar
- Strode C, Wondji CS, David JP, Hawkes NJ, Lumjuan N, Nelson DR, Drane DR, Karunaratne S, Hemingway J, Black WC: Genomic analysis of detoxification genes in the mosquito Aedes aegypti. Insect Biochem Mol Biol. 2008, 38 (1): 113-123. 10.1016/j.ibmb.2007.09.007.PubMedView ArticleGoogle Scholar
- Vontas J, Blass C, Koutsos AC, David JP, Kafatos FC, Louis C, Hemingway J, Christophides GK, Ranson H: Gene expression in insecticide resistant and susceptible Anopheles gambiae strains constitutively or after insecticide exposure. Insect Mol Biol. 2005, 14 (5): 509-521. 10.1111/j.1365-2583.2005.00582.x.PubMedView ArticleGoogle Scholar
- Poupardin R, Reynaud S, Strode C, Ranson H, Vontas J, David JP: Cross-induction of detoxification genes by environmental xenobiotics and insecticides in the mosquito Aedes aegypti: Impact on larval tolerance to chemical insecticides. Insect Biochem Mol Biol. 2008, 38 (5): 540-551. 10.1016/j.ibmb.2008.01.004.PubMedView ArticleGoogle Scholar
- Riaz MA, Poupardin R, Reynaud S, Strode C, Ranson H, David JP: Impact of glyphosate and benzo[a]pyrene on the tolerance of mosquito larvae to chemical insecticides. Role of detoxification genes in response to xenobiotics. Aquat Toxicol. 2009, 93 (1): 61-69. 10.1016/j.aquatox.2009.03.005.PubMedView ArticleGoogle Scholar
- Sasabe M, Wen ZM, Berenbaum MR, Schuler MA: Molecular analysis of CYP321A1, a novel cytochrome P450 involved in metabolism of plant allelochernicals (furanocoumarins) and insecticides (cypermethrin) in Helicoverpa zea. Gene. 2004, 338 (2): 163-175. 10.1016/j.gene.2004.04.028.PubMedView ArticleGoogle Scholar
- Green CD, Simons JF, Taillon BE, Lewin DA: Open systems: panoramic views of gene expression. J Immunol Methods. 2001, 250 (1-2): 67-79. 10.1016/S0022-1759(01)00306-4.PubMedView ArticleGoogle Scholar
- Hanriot L, Keime C, Gay N, Faure C, Dossat C, Wincker P, Scote-Blachon C, Peyron C, Gandrillon O: A combination of LongSAGE with Solexa sequencing is well suited to explore the depth and the complexity of transcriptome. BMC Genomics. 2008, 9: 418-10.1186/1471-2164-9-418.PubMed CentralPubMedView ArticleGoogle Scholar
- Velculescu VE, Zhang L, Vogelstein B, Kinzler KW: Serial analysis of gene-expression. Science. 1995, 270 (5235): 484-487. 10.1126/science.270.5235.484.PubMedView ArticleGoogle Scholar
- Saha S, Sparks AB, Rago C, Akmaev V, Wang CJ, Vogelstein B, Kinzler KW, Velculescu VE: Using the transcriptome to annotate the genome. Nat Biotechnol. 2002, 20 (5): 508-512. 10.1038/nbt0502-508.PubMedView ArticleGoogle Scholar
- Brenner S, Johnson M, Bridgham J, Golda G, Lloyd DH, Johnson D, Luo SJ, McCurdy S, Foy M, Ewan M: Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays. Nat Biotechnol. 2000, 18 (6): 630-634. 10.1038/76469.PubMedView ArticleGoogle Scholar
- Nielsen KL, Hogh AL, Emmersen J: DeepSAGE - digital transcriptomics with high sensitivity, simple experimental protocol and multiplexing of samples. Nucleic Acids Res. 2006, 34 (19): e133-10.1093/nar/gkl714.PubMed CentralPubMedView ArticleGoogle Scholar
- Eveland AL, McCarty DR, Koch KE: Transcript profiling by 3'-untranslated region sequencing resolves expression of gene families. Plant Physiol. 2008, 146 (1): 32-44. 10.1104/pp.107.108597.PubMed CentralPubMedView ArticleGoogle Scholar
- Nene V, Wortman JR, Lawson D, Haas B, Kodira C, Tu ZJ, Loftus B, Xi ZY, Megy K, Grabherr M: Genome sequence of Aedes aegypti, a major arbovirus vector. Science. 2007, 316 (5832): 1718-1723. 10.1126/science.1138878.PubMedView ArticleGoogle Scholar
- Sackton TB, Clark AG: Comparative profiling of the transcriptional response to infection in two species of Drosophila by short-read cDNA sequencing. BMC Genomics. 2009, 10: 259-10.1186/1471-2164-10-259.PubMed CentralPubMedView ArticleGoogle Scholar
- Kapranov P, Willingham AT, Gingeras TR: Genome-wide transcription and the implications for genomic organization. Nat Rev Genet. 2007, 8 (6): 413-423. 10.1038/nrg2083.PubMedView ArticleGoogle Scholar
- Mattick JS: The Genetic Signatures of Noncoding RNAs. PLoS Genet. 2009, 5 (4): e1000459-10.1371/journal.pgen.1000459.PubMed CentralPubMedView ArticleGoogle Scholar
- Roche H, Buet A, Ramade F: Accumulation of lipophilic microcontaminants and biochemical responses in eels from the Camargue Biosphere Reserve. Ecotoxicology. 2002, 11 (3): 155-164. 10.1023/A:1015418714492.PubMedView ArticleGoogle Scholar
- Sandermann H: Differential lipid affinity of xenobiotics and natural compounds. FEBS Lett. 2003, 554 (1-2): 165-168. 10.1016/S0014-5793(03)01143-8.PubMedView ArticleGoogle Scholar
- Tomizawa M, Casida JE: Neonicotinoid insecticide toxicology: Mechanisms of selective action. Annu Rev Pharmacol Toxicol. 2005, 45: 247-268. 10.1146/annurev.pharmtox.45.120403.095930.PubMedView ArticleGoogle Scholar
- Raymond-Delpech V, Matsuda K, Sattelle BM, Rauh JJ, Sattelle DB: Ion channels: molecular targets of neuroactive insecticides. Invert Neurosci. 2005, 5 (3-4): 119-133. 10.1007/s10158-005-0004-9.PubMedView ArticleGoogle Scholar
- Sherman JD: Organophosphates pesticides - Neurological and respiratory toxicity. Toxicol Ind Health. 1995, 11 (1): 33-39.PubMedView ArticleGoogle Scholar
- Boyer S, Serandour J, Lemperiere G, Raveton M, Ravanel P: Do herbicide treatments reduce the sensitivity of mosquito larvae to insecticides?. Chemosphere. 2006, 65 (4): 721-724. 10.1016/j.chemosphere.2006.02.032.PubMedView ArticleGoogle Scholar
- Reynaud S, Deschaux P: The effects of polycyclic aromatic hydrocarbons on the immune system of fish: A review. Aquat Toxicol. 2006, 77 (2): 229-238. 10.1016/j.aquatox.2005.10.018.PubMedView ArticleGoogle Scholar
- Chiu TL, Wen ZM, Rupasinghe SG, Schuler MA: Comparative molecular modeling of Anopheles gambiae CYP6Z1, a mosquito P450 capable of metabolizing DDT. Proc Natl Acad Sci USA. 2008, 105 (26): 8855-8860. 10.1073/pnas.0709249105.PubMed CentralPubMedView ArticleGoogle Scholar
- Daborn PJ, Yen JL, Bogwitz MR, Le Goff G, Feil E, Jeffers S, Tijet N, Perry T, Heckel D, Batterham P: A single P450 allele associated with insecticide resistance in Drosophila. Science. 2002, 297 (5590): 2253-2256. 10.1126/science.1074170.PubMedView ArticleGoogle Scholar
- Despres L, David JP, Gallet C: The evolutionary ecology of insect resistance to plant chemicals. Trends in Ecology & Evolution. 2007, 22 (6): 298-307. 10.1016/j.tree.2007.02.010.View ArticleGoogle Scholar
- Vontas J, David JP, Nikou D, Hemingway J, Christophides GK, Louis C, Ranson H: Transcriptional analysis of insecticide resistance in Anopheles stephensi using cross-species microarray hybridization. Insect Mol Biol. 2007, 16 (3): 315-324. 10.1111/j.1365-2583.2007.00728.x.PubMedView ArticleGoogle Scholar
- Zhang J, Goyer C, Pelletier Y: Environmental stresses induce the expression of putative glycine-rich insect cuticular protein genes in adult Leptinotarsa decemlineata (Say). Insect Mol Biol. 2008, 17 (3): 209-216. 10.1111/j.1365-2583.2008.00796.x.PubMedView ArticleGoogle Scholar
- Selye H: The stress of life, Revised Edition. Circuits and systems in stress: preclinical studies. Depression and Anxiety. Edited by: Vermetten E, Bremner D. 1976, New York: McGraw-Hill, 15: 126-147.Google Scholar
- Sanders BM, Martin LS: Stress protein as biomarkers of contaminant exposure in archived environmental-samples. Sci Total Environ. 1993, 140: 459-470. 10.1016/0048-9697(93)90043-6.View ArticleGoogle Scholar
- Lee SM, Lee SB, Park CH, Choi J: Expression of heat shock protein and hemoglobin genes in Chironomus tentans (Diptera, chironomidae) larvae exposed to various environmental pollutants: A potential biomarker of freshwater monitoring. Chemosphere. 2006, 65 (6): 1074-1081. 10.1016/j.chemosphere.2006.02.042.PubMedView ArticleGoogle Scholar
- Balamurugan K, Hua HQ, Georgiev O, Schaffner W: Mercury and cadmium trigger expression of the copper importer Ctr1B, which enables Drosophila to thrive on heavy metal-loaded food. Biol Chem. 2009, 390 (2): 109-113. 10.1515/BC.2009.020.PubMedView ArticleGoogle Scholar
- Vache C, Camares O, De Graeve F, Dastugue B, Meiniel A, Vaury C, Pellier S, Leoz-Garziandia E, Bamdad M: Drosophila melanogaster P-glycoprotein: A membrane detoxification system toward polycyclic aromatic hydrocarbon pollutants. Environ Toxicol Chem. 2006, 25 (2): 572-580. 10.1897/04-425.1.PubMedView ArticleGoogle Scholar
- Palmfeldt J, Vang S, Stenbroen V, Pedersen CB, Christensen JH, Bross P, Gregersen N: Mitochondrial proteomics on human fibroblasts for identification of metabolic imbalance and cellular stress. Proteome Science. 2009, 7: 20-10.1186/1477-5956-7-20.PubMed CentralPubMedView ArticleGoogle Scholar
- Holth TF, Nourizadeh-Lillabadi R, Blaesbjerg M, Grung M, Holbech H, Petersen GI, Alestrom P, Hylland K: Differential gene expression and biomarkers in zebrafish (Danio rerio) following exposure to produced water components. Aquat Toxicol. 2008, 90 (4): 277-291. 10.1016/j.aquatox.2008.08.020.PubMedView ArticleGoogle Scholar
- Owen J, Hedley BA, Svendsen C, Wren J, Jonker MJ, Hankard PK, Lister LJ, Sturzenbaum SR, Morgan AJ, Spurgeon DJ: Transcriptome profiling of developmental and xenobiotic responses in a keystone soil animal, the oligochaete annelid Lumbricus rubellus. BMC Genomics. 2008, 9: 266-10.1186/1471-2164-9-266.PubMed CentralPubMedView ArticleGoogle Scholar
- Reynaud S, Duchiron C, Deschaux P: 3-Methylcholanthrene induces lymphocyte and phagocyte apoptosis in common carp (Cyprinus carpio L) in vitro. Aquat Toxicol. 2004, 66 (3): 307-318. 10.1016/j.aquatox.2003.10.003.PubMedView ArticleGoogle Scholar
- Reynaud S, Raveton M, Ravanel P: Interactions between immune and biotransformation systems in fish: A review. Aquat Toxicol. 2008, 87 (3): 139-145. 10.1016/j.aquatox.2008.01.013.PubMedView ArticleGoogle Scholar
- Knauert S, Dawo U, Hollender J, Hommen U, Knauer K: Effects of photosystem II inhibitors and their mixture on freshwaterphytoplancton succession in outdoor mesocosms. Environ Toxicol Chem. 2009, 28 (4): 836-845. 10.1897/08-135R.1.PubMedView ArticleGoogle Scholar
- Knauert S, Escher B, Singer H, Hollender J, Knauer K: Mixture toxicity of three photosystem II inhibitors (atrazine, isoproturon, and diuron) toward photosynthesis of freshwater phytoplankton studied in outdoor mesocosms. Environ Sci Technol. 2008, 42 (17): 6424-6430. 10.1021/es072037q.PubMedView ArticleGoogle Scholar
- Harris ED: Cellular copper transport and metabolism. Annu Rev Nutr. 2000, 20: 291-310. 10.1146/annurev.nutr.20.1.291.PubMedView ArticleGoogle Scholar
- Krupanidhi S, Sreekumar A, Sanjeevi CB: Copper & biological health. Indian J Med Res. 2008, 128 (4): 448-461.PubMedGoogle Scholar
- Letelier ME, Martinez M, Gonzalez-Lira V, Faundez M, Aracena-Parks P: Inhibition of cytosolic glutathione S-transferase activity from rat liver by copper. Chem Biol Interact. 2006, 164 (1-2): 39-48. 10.1016/j.cbi.2006.08.013.PubMedView ArticleGoogle Scholar
- Posthuma L, Eijsacker HJP, Koelmans AA, Vijver MG: Ecological effects of diffuse mixed pollution are site-specific and require higher-tier risk assessment to improve site management decisions: A discussion paper. Sci Total Environ. 2008, 406 (3): 503-517. 10.1016/j.scitotenv.2008.06.065.View ArticleGoogle Scholar
- Wurff Van der AW, Kools SA, Boivin ME, Brink Van den PJ, Van Megen HH, Riksen JA, Doroszuk A, Kammenga JE: Type of disturbance and ecological history determine structural stability. Ecol Appl. 2007, 17 (1): 190-202. 10.1890/1051-0761(2007)017[0190:TODAEH]2.0.CO;2.PubMedView ArticleGoogle Scholar
- Ghalambor CK, McKay JK, Carroll SP, Reznick DN: Adaptive versus non-adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments. Functional Ecology. 2007, 21 (3): 394-407. 10.1111/j.1365-2435.2007.01283.x.View ArticleGoogle Scholar
- Gruessner B, Watzin MC: Response of aquatic communities from a Vermont stream to environmentally realistic atrazine exposure in laboratory microcosms. Symposium on Methods for Monitoring Effects of Pesticides, at the 15th Annual Meeting of the Society-of-Environmental-Toxicology-and-Chemistry: Oct 30-Nov 03 1994; Denver, Co. 1994, 410-419.Google Scholar
- Komarek M, Vanek A, Chrastny V, Szakova J, Kubova K, Drahota P, Balik J: Retention of copper originating from different fungicides in contrasting soil types. J Hazard Mater. 2009, 166 (2-3): 1395-1402. 10.1016/j.jhazmat.2008.12.061.PubMedView ArticleGoogle Scholar
- Durand C, Ruban V, Ambles A, Clozel B, Achard L: Characterisation of road sediments near Bordeaux with emphasis on phosphorus. J Environ Monit. 2003, 5 (3): 463-467. 10.1039/b300695f.PubMedView ArticleGoogle Scholar
- Zehetner F, Rosenfellner U, Mentler A, Gerzabek MH: Distribution of Road Salt Residues, Heavy Metals and Polycyclic Aromatic Hydrocarbons across a Highway-Forest Interface. Water Air and Soil Pollution. 2009, 198 (1-4): 125-132. 10.1007/s11270-008-9831-8.View ArticleGoogle Scholar
- Wu S, Manber U: Agrep - a fast approximate pattern-matching tool. Proceedings USENIX Winter 1992 Technical Conference: 1992; San Francisco, CA. 1992, 153-162.Google Scholar
- Fisher RA: The logic of inductive inference. Journal of the Royal Statistical Society Series A. 1935, 98: 39-54. 10.2307/2342435.View ArticleGoogle Scholar
- Kaiser HF: The varimax criterion for analytic rotation in factor analysis. Psychometrika. 1958, 23 (3): 187-200. 10.1007/BF02289233.View ArticleGoogle Scholar
- Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N, Braisted J, Klapa M, Currier T, Thiagarajan M: TM4: a free, open-source system for microarray data management and analysis. Biotechniques. 2003, 34 (2): 374-378.PubMedGoogle Scholar
- D'haeseleer P: How does gene expression clustering work?. Nat Biotechnol. 2005, 23 (12): 1499-1501. 10.1038/nbt1205-1499.PubMedView ArticleGoogle Scholar
- Eisen MB, Spellman PT, Brown PO, Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA. 1998, 95: 14863-14868. 10.1073/pnas.95.25.14863.PubMed CentralPubMedView ArticleGoogle Scholar
- Pfaffl MW: A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res. 2001, 29 (9): e45-10.1093/nar/29.9.e45.PubMed CentralPubMedView ArticleGoogle Scholar
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