Transcription profiling of a recently colonised pyrethroid resistant Anopheles gambiae strain from Ghana

Pie Müller; Martin J Donnelly; Hilary Ranson

doi:10.1186/1471-2164-8-36

Research article
Open Access

Transcription profiling of a recently colonised pyrethroid resistant Anopheles gambiae strain from Ghana

Pie Müller¹Email author,
Martin J Donnelly¹ and
Hilary Ranson¹

BMC Genomics20078:36

https://doi.org/10.1186/1471-2164-8-36

Received: 28 June 2006
Accepted: 29 January 2007
Published: 29 January 2007

Abstract

Background

Mosquito resistance to the pyrethroid insecticides used to treat bednets threatens the sustainability of malaria control in sub-Saharan Africa. While the impact of target site insensitivity alleles is being widely discussed the implications of insecticide detoxification – though equally important – remains elusive. The successful development of new tools for malaria intervention and management requires a comprehensive understanding of insecticide resistance, including metabolic resistance mechanisms. Although three enzyme families (cytochrome P450s, glutathione S-transferases and carboxylesterases) have been widely associated with insecticide detoxification the role of individual enzymes is largely unknown.

Results

Here, constitutive expression patterns of genes putatively involved in conferring pyrethroid resistance was investigated in a recently colonised pyrethroid resistant Anopheles gambiae strain from Odumasy, Southern Ghana. RNA from the resistant strain and a standard laboratory susceptible strain, of both sexes was extracted, reverse transcribed and labelled with either Cy3- or Cy5-dye. Labelled cDNA was co-hybridised to the detox chip, a custom-made microarray containing over 230 A. gambiae gene fragments predominantly from enzyme families associated with insecticide resistance. After hybridisation, Cy3- and Cy5-signal intensities were measured and compared gene by gene. In both females and males of the resistant strain the cytochrome P450s CYP6Z2 and CYP6M2 are highly over-expressed along with a member of the superoxide dismutase (SOD) gene family.

Conclusion

These genes differ from those found up-regulated in East African strains of pyrethroid resistant A. gambiae and constitute a novel set of candidate genes implicated in insecticide detoxification. These data suggest that metabolic resistance may have multiple origins in A. gambiae, which has strong implications for the management of resistance.

Keywords

Insecticide Resistance
Pyrethroid Resistance
Metabolic Resistance
Piperonyl Butoxide
Kisumu Strain

Background

Pyrethroid-treated bednets are the mainstay of malaria control programs in sub-Saharan Africa [1]. Pyrethroids are a class of fast acting and highly effective insecticides showing low mammalian toxicity levels. Currently no other insecticide class with similar efficacy and specificity is available for use on bednets. Therefore the emergence of pyrethroid resistance threatens to compromise the successful use of insecticide-treated materials [2–6].

Two mechanisms of insecticide resistance play an important role in mosquitoes, target site insensitivity and metabolic resistance [7]. Target site insensitivity to pyrethroids in Anopheles gambiae is associated with a single point mutation, commonly referred to as knock-down resistance (kdr). The kdr allele leads to a modification of the voltage-gated sodium channel protein resulting in reduced sensitivity to both DDT and pyrethroids [8, 9]. Metabolic resistance is generally associated with three enzyme families: cytochrome P450 monooxygenases (P450s), carboxylesterases (COEs), and glutathione-S-transferases (GSTs) [10]. In A. gambiae a total of 111 putative P450s, 51 COEs, and 31 GSTs genes have been identified [10]. Though the groups contain large numbers of genes, these enzyme families have multiple roles in the insect and only a small number are thought to be directly involved in insecticide metabolism. Enzymes from all three families have been implicated in conferring resistance to pyrethroids in other insect species (e.g. [11–15]). In A. gambiae increased levels have been observed for the GSTE2 transcript in the permethrin tolerant RSP strain from Western Kenya [16]. Increased expression of GSTE2 was also observed in the ZAN/U strain, from Zanzibar, resistant to DDT [17, 18]. GSTE2 transforms DDT into a non-toxic product by dehydrochlorination [17]. However, its role in detoxifying pyrethroids requires further investigations. In addition to elevated expression of GSTE2, over-expression of two P450 genes, CYP6Z1 and CYP325A3, was observed in adult mosquitoes of the RSP strain [16, 19].

Current data on gene expression levels related to metabolic resistance in A. gambiae is limited to strains from East Africa that were colonised over a decade ago. Despite the high levels of pyrethroid resistance found in malaria vectors in West Africa [2, 3] there have been no previous studies on gene expression patterns associated with metabolic resistance mechanisms operating in this region.

Noteworthy, studies on kdr revealed different alleles to be predominant in East and West African field populations suggesting that alternative mutations may evolve in response to insecticide exposure [8, 9]. Given the differences in selection pressures due to variations in the use and type of insecticide, and possible restrictions to gene flow across the African continent [20], we would predict similar differences in metabolic resistance mechanisms.

The presence of multiple resistance mechanisms within A. gambiae would have considerable implications for the success of vector intervention and the monitoring of ongoing control programs, demanding the development of appropriate tools to monitor resistance in field populations. The development of such tools requires the identification of candidate genes, and with this goal in mind, we used the detox chip, a custom-made DNA microarray chip, developed in our laboratories [16], to measure expression patterns in a recently colonised pyrethroid resistant strain from Southern Ghana.

Results

Gene expression associated with resistance status

The expression levels of genes putatively involved in insecticide resistance were compared by co-hybridising labelled RNA from the pyrethroid resistant Odumasy strain and the standard susceptible Kisumu strain to a custom made microarray [16]. As both sexes of the resistant Odumasy strain showed high resistance to permethrin hybridisations were performed for both females and males. Percentage survival after a one hour exposure to 0.75% permethrin, although statistically significant (χ²-test, p = 0.0045), differed only slightly between sexes with 74% (N = 297) for females and 62% (N = 224) for males. Out of 233 gene probes on the microarray, 40 genes were significantly (p < 0.001) differentially expressed among females, and 50 genes among males, between the two strains (see Additional file 1). Among these genes 14 are common to both sexes and showed the same expression patterns (correlation: r = 0.91, p < 0.001). Of particular interest are genes that show increased transcript levels in both sexes of the resistant strain. We set a cut-off value of two-fold differential expression to select biologically relevant candidate genes among those showing a significant difference in transcript levels, and focus on genes that are over-expressed in both sexes of the resistant strain.

In total, ten genes showed a greater than two-fold difference in expression between the strains (Figure 1 and Table 1). Six of these genes were expressed at higher levels in the Odumasy strain and four showed higher expression in the susceptible strain. The genes over-expressed in the resistant strain include three cytochrome P450s (CYP6M2, CYP6Z2 and CYP6Z3), a sigma class GST (GSTS1-2), a carboxylesterase, COEJHE5E, and a superoxide dismutase (SOD3B). GSTS1-2 was over-expressed in females while CYP6Z3 over-expression was detected in males only. In both males and females, the most pronounced differences were in the expression of two CYP6 P450s, CYP6M2 and CYP6Z2. CYP6Z3 was significantly over-expressed (3.0-fold) in males but the data for the female comparison was not significant. This may reflect the very low expression levels of this gene in adult mosquitoes rather than any sex specific differences in expression (CYP6Z3 expression was undetectable in adults via quantitative RT-PCR [19]). Indeed the female-male comparisons did not detect any differential expression of this gene (see Additional file 1). All three P450s over-expressed in the resistant strain are clustered in a group of 14 CYP6 genes on chromosome arm 3R [19].

Figure 1
**Microarray analysis comparing the permethrin resistant Odumasy strain with the standard susceptible Kisumu strain**. Data are shown for the differences in expression levels between the permethrin resistant Odumasy strain and the standard susceptible Kisumu strain for both females (A) and males (B). Each dot represents the mean estimates, fold (M) and p-value (adjusted for multiple testing), for one gene from the complete microarray experiment. Names are shown for genes that are at least two-fold differentially expressed. Underlined names show genes that are common to both the female-female and male-male comparison. Horizontal line represents the level of significance α = 0.001, and vertical lines indicate two-fold change threshold.

Table 1

Differential gene expression between the permethrin resistant Odumasy and the susceptible Kisumu strain

Gene	Function	Cytological position	Accession number	Females		Males
				Fold	p-Value	Fold	p-Value
Genes over-expressed in resistant Odumasy strain
CYP6M2	Cytochrome P450 monooxygenase	3R	[GenBank: AY193729]	5.2	7.94 × 10^-05	3.2	1.29 × 10^-11
CYP6Z2	Cytochrome P450 monooxygenase	3R	[GenBank: AF487780]	4.2	8.62 × 10^-07	8.0	2.27 × 10^-11
SOD3B	Cu-Zn superoxide dismutase	3L	[GenBank: AY745234, TIGR: TC54196]	3.5	8.11 × 10^-04	2.0	1.00 × 10^-04
GSTS1-2	Glutathione S-transferase	3L	GenBank: AF513639]	2.5	2.91 × 10^-04	-	n.s.
COEJHE5E	Esterase	2L	[TIGR: TC77639]	2.0	3.86 × 10^-09	1.8	1.67 × 10^-08
CYP6Z3	Cytochrome P450 monooxygenase	3R	[GenBank: AY193727]	-	n.s.	3.0	1.54 × 10^-⁰⁷
Genes over-expressed in the susceptible Kisumu strain
GSTE2	Glutathione S-transferase	3R	[GenBank: AF316636]	-	n.s.	2.7	4.61 × 10^-04
CYP6P1	Cytochrome P450 monooxygenase	2R	[GenBank: AY028785]	2.3	1.06 × 10^-06	1.6	7.32 × 10^-11
CYP6AJ1	Cytochrome P450 monooxygenase	3L	[GenBank: AY745226]	2.0	8.76 × 10^-06	-	n.s.
PX7	Peroxidase	3L	[GenBank: AY752901]	-	n.s.	7.5	6.45 × 10^-06

Only genes are listed which showed at least a two-fold difference in one or both sexes. p-values were corrected for multiple testing as described in the materials and methods section. n.s., p ≥ 0.001.

Two of the transcripts over-expressed in the resistant strain are derived from genes with alternative splicing. Two transcripts have been detected from the superoxide dismutase gene, SOD3 (Ranson, unpublished data), one of which (SOD3B) shows elevated expression in both males and females from the resistant strain. Both SOD3B and the alternative transcript SOD3A share a common N terminal but differ in their C termini. SOD3A transcripts are detectable in both male and female A. gambiae although in the Kisumu strain, the SOD3B transcript is consistently found at higher levels (Pignatelli and Ranson, unpublished data). The sigma class GST, GSTS1, also contains a common 5' exon that can be spliced to alternative 3' exons generating two distinct transcripts, GSTS1-1 and GSTS1-2 [21]. As for SOD3 both transcripts are detectable by quantitative RT-PCR in adults from both sexes [22]. The data from this study shows elevated expression of both transcripts in females from the resistant strain versus females from the susceptible strain (see Table 1 and Additional file 1) but no significant difference between the males.

Four genes were expressed at higher levels in the susceptible versus the resistant strain (Figure 1 and Table 1). Surprisingly these include GSTE2, the gene associated with both pyrethroid and DDT resistance in A. gambiae from East Africa [16–18]. One P450, CYP6P1 is a member of a cluster of nine P450 genes on chromosome arm 2R [23] while the second, CYP6AJ1 (previously named CYP6AK1), is located on chromosome arm 3L and has also been found under-expressed in the DDT resistant ZAN/U strain [16]. The fourth gene, PX7, is a peroxidase. As for CYP6Z3, PX7 is expressed at very low levels in both strains. Hence it is possible that small differences in absolute transcript levels, which could be an outcome of genetic drift, may have resulted in the strong differentiation in expression of this gene between the strains.

Gene expression associated with sex

In two strains of A. gambiae from East Africa, RSP and Kisumu, females were found to be intrinsically more tolerant to permethrin than males [24]. In the present study, the survival rates between the sexes in the resistant Odumasy strain differed only marginally. In order to assess whether this similarity in permethrin tolerance was correlated with expression of specific detoxification genes, labelled RNA from both sexes of the susceptible Kisumu strain was co-hybridised to the detox chip. Gene expression profiles between females and males of the pyrethroid resistant Odumasy strain were then indirectly estimated by comparing the data sets of all three microarray experiments, i.e. Odumasy females vs. Kisumu females, Kisumu females vs. Kisumu males, and Kisumu males vs. Odumasy males.

Out of 233 gene probes on the microarray 61 genes were differentially expressed between the sexes in the Kisumu strain and 31 in the Odumasy strain (see Additional file 1). Among these genes ten showed a difference over two-fold in the Kisumu strain and 18 in the Odumasy strain (Figure 2 and Table 2). If one or more of the three enzymes, CYP6Z2, CYP6M2 and SOD3B, played a major role in permethrin resistance we would expect similar levels of expression in both sexes of the resistant strain, given that the resistance phenotype between females and males differed only slightly. None of these candidate genes was differentially expressed between females and males of the resistant Odumasy strain (see Additional file 1).

Figure 2
**Microarray analysis comparing females with males within each strain**. Data are shown for the gene expression levels of susceptible Kisumu females vs. males (A) and the permethrin resistant Odumasy females vs. males (B). Each dot represents the mean estimates, fold (M) and p-value (adjusted for multiple testing), for one gene from the complete microarray experiment. Names are shown for genes that are at least two-fold differentially expressed. Underlined names show genes that are common to both the Kisumu female-male and the Odumasy female-male comparison. Horizontal line represents the level of significance α = 0.001, and vertical lines indicate two-fold change threshold. **CYP6Z1* was spotted on the *detox chip* as both cDNA and 70-mer oligonucleotide probe [16].

Table 2

Differential gene expression between females and males within each strain

Gene	Function	Cytological position	Accession number	Kisumu strain		Odumasy strain
				Fold	p-Value	Fold	p-Value
Genes over-expressed in males
CYP307A1	Cytochrome P450 monooxygenase	X	[GenBank: AY745209]	9.6	2.31 × 10^-08	11.7	1.20 × 10^-17
CYP314A1	Cytochrome P450 monooxygenase	2R	[GenBank: AY745210]	4.4	3.67 × 10^-07	5.7	9.29 × 10^-16
CYP12F3	Cytochrome P450 monooxygenase	3R	[GenBank: AY176049]	3.9	4.88 × 10^-04	4.0	9.04 × 10^-06
CYP306A1	Cytochrome P450 monooxygenase	2R	[GenBank: AB162964]	2.9	8.59 × 10^-05	3.0	6.76 × 10^-07
CYP6M2	Cytochrome P450 monooxygenase	3R	[GenBank: AY193729]	2.9	2.08 × 10^-06	-	n.s.
CYP6S2	Cytochrome P450 monooxygenase	3R	[GenBank: AY062207]	2.8	1.45 × 10^-05	3.7	2.31 × 10^-11
CYP6Z1*	Cytochrome P450 monooxygenase	3R	[GenBank: AF487535]	2.3	2.01 × 10^-05	-	n.s.
CYP325A1	Cytochrome P450 monooxygenase	2R	[GenBank: AY748842]	2.2	1.68 × 10^-09	2.7	1.66 × 10^-07
CYP6Z1*	Cytochrome P450 monooxygenase	3R	[GenBank: AF487535]	2.1	9.58 × 10^-09	2.1	3.55 × 10^-04
CYP6S1	Cytochrome P450 monooxygenase	3R	[GenBank: AY028784]	2.1	3.92 × 10^-06	3.4	6.83 × 10^-15
AGM1	Midgut maltase-like protein	3L	[GenBank: X87410]	1.6	7.58 × 10^-07	3.0	4.33 × 10^-07
CYP4K2	Cytochrome P450 monooxygenase	2R	[GenBank: AY748834]	1.5	4.59 × 10^-06	2.3	1.93 × 10^-07
PX8	Peroxidase	3L	[GenBank: AY752902]	-	n.s.	3.1	1.83 × 10^-04
CYP9M2	Cytochrome P450 monooxygenase	3R	[GenBank: AY748851]	-	n.s.	2.3	1.04 × 10^-08
Genes over-expressed in females
GSTD13	Glutathione S-transferase	X	[GenBank: AF515521]	1.9	1.99 × 10^-08	4.8	1.38 × 10^-11
PX5B	Peroxidase	3L	[GenBank: AY752899]	1.8	7.25 × 10^-04	2.1	1.47 × 10^-05
TPX1	Thioredoxin peroxidase	X	[GenBank: AY745234, TIGR: TC49541]	1.8	2.39 × 10^-07	2.5	5.02 × 10^-14
SOD2	Cu-Zn superoxide dismutase	2L	[GenBank: AY524130, TIGR: TC39286]	1.5	4.25 × 10^-07	3.5	2.11 × 10^-04
CYP4H24	Cytochrome P450 monooxygenase	X	[GenBank: AY062206]	-	n.s.	3.1	7.08 × 10^-14
GSTD1-5	Glutathione S-transferase	2R	[GenBank: Z81291]	-	n.s.	2.0	3.72 × 10^-04

Only genes are listed which showed at least a two-fold difference in one or both strains. p-values were corrected for multiple testing as described in the materials and methods section. n.s., p ≥ 0.001. *CYP6Z1 was spotted on the detox chip as both cDNA and 70-mer oligonucleotide probe [16].

Eight genes found to be over-expressed by two-fold are common to both strains only in males (Figure 2 and Table 2). Of these eight genes the P450s, CYP307A1 and CYP314A1, show particularly high male biased expression. Orthologs of these genes along with CYP306A1 have been implicated in ecdysteroid metabolism in other insects [25–27]. The expression of genes involved in ecdysone and 20-hydroxyecdysone synthesis is tightly regulated and it is unlikely that these P450s play a major role in xenobiotic detoxification. In contrary, the five additional genes CYP6S1, CYP6S2, CYP6Z1, CYP12F3 and CYP325A1 belong to P450 families in which individual genes are associated with permethrin resistance ([16, 19] and this study).

The over-expression of CYP307A1 in males is confirmed by semi-quantitative RT-PCR (Figure 3). Nikou et al. [19], using quantitative RT-PCR, found CYP6Z1 also being over-expressed in males over females in the susceptible Kisumu strain in accordance with the microarray results in this study. Over-expression of CYP6M2 in Kisumu males over females was slightly higher in the semi-quantitative RT-PCR but was statistically not significant (Figure 3). However, the semi-quantitative RT-PCR is limited to the detection of very large fold changes and may not be sensitive enough to detect the lower fold change of CYP6M2. We are confident in our microarray results as the quantitative RT-PCR [19] and the overall pattern of the semi-quantitative RT-PCR are consistent with the microarray measurements.

Figure 3
**Semi-quantitative RT-PCR validation of selected microarray data**. Gene expression levels of *CYP307A1* and *CYP6M2* between females and males of the susceptible Kisumu strain were compared. (A) Peak intensities of amplified cDNA products of the two target mRNAs were measured and normalised by the internal control, the ribosomal S7 product. Primers used to amplify cDNA are shown in Table 1. (B) Measurements were taken from four biological replicates for each sex (15 one-day adult mosquitoes per replicates). Dots represent normalised peak intensities for each replicate. Horizontal bars show mean values. *CYP307A1* shows a 3.4-fold over-expression in males (one-sided t-test, p < 0.001) whereas *CYP6M2* was 1.1-fold over-expressed in males but statistically not significant (one-sided t-test, p = 0.3).

Discussion

Monitoring of metabolic resistance to insecticides in field populations currently relies on biochemical assays. These assays measure the correlation between increased enzymatic activities towards model substrates in resistant mosquitoes and are a general indicator of the enzyme families putatively involved. However, the sensitivity of these assays is questionable. For example, recently Lumjuan et al. [28] have shown that the GST responsible for DDT resistance, GSTE2, has very low levels of activity against the model substrate CDNB used to detected GST-based resistance mechanisms. Synergists, such as piperonyl butoxide can also be used to indicate the enzyme class involved although these require large numbers of live insects and hence are not practical for routine monitoring of resistance in field populations. Molecular tools such as the allele specific PCR [8, 9] or the HOLA [29] assay developed to detect kdr mutations enable the frequency of resistance alleles to be measured directly. We still lack such tools to monitor metabolic resistance. Yet, this information is extremely valuable to mosquito control managers as it enables resistance to be detected when it first emerges in a population and before it may have a detectable effect on the resistance phenotype. However, the development of such molecular assays requires a detailed understanding of the mechanisms conferring resistance.

In the present study we identified six genes whose expression levels are associated with pyrethroid resistance in a recently colonised strain of A. gambiae from West Africa. Three of these six genes were over-expressed in both sexes of the resistant strain; two cytochrome P450s, CYP6Z2 and CYP6M2, and a member of the superoxide dismutase gene family, SOD3B. In females of the resistant strain the carboxylesterase COEJHE5E and a sigma class GST, GSTS1-2, were also found to be over-expressed while another P450, CYP6Z3, was over-expressed in the resistant males only.

Cytochrome P450-dependent monooxygenases are an important metabolic mechanism by which several insect species become resistant to insecticides [30]. Biochemical characterisation implicate P450s conferring pyrethroid resistance in the resistant East African A. gambiae RSP strain [31]. In A. gambiae P450 loci associated with pyrethroid resistance are mapped within a cluster in chromosome arm 3R [24]. This cluster also encompasses the three P450s, CYP6Z2, CYP6Z3 and CYP6M2, identified in this study among a subfamily of 14 CYP6 P450s. As survival rates after exposure to permethrin between females and males of the resistant strain vary only marginally, genes that are over-expressed in both sexes are more likely to be involved in conferring metabolic resistance. Arguably CYP6Z2 and CYP6M2 showing the highest transcript levels in both sexes are good candidate genes involved in metabolic detoxification of permethrin.

The CYP6 class of P450s is a rapidly evolving gene family [10] and there are no recognised orthologs for CYP6Z2 and CYP6M2 outside of the mosquito taxa. The most similar P450s to CYP6Z2 as retrieved by BLAST [32] found in other insect species are CYP6D3 in Musca domestica (44% amino sequence identity) and CYP6D4 in Drosophila melanogaster (43% amino sequence identity). Intriguingly CYP6D3 is over-expressed in several housefly strains resistant to permethrin [33] while deletion of CYP6D4 in D. melanogaster did not alter the toxicity of several insecticides including pyrethroids [34]. P450s most similar to CYP6M2 identified using BLAST [32] in A. gambiae are the CYP6N3 in Aedes albopictus (54% amino sequence identity), CYP6BB1 in the eastern saltmarsh mosquito, Ochlerotatus sollicitans, (49% amino sequence identity), and CYP6A5 in M. domestica (49% amino sequence identity). In D. melanogaster the protein showing the highest similarity with CYP6M2 is CYP6A23 (48% amino sequence identity). To our knowledge these putatively related genes have yet not been associated with pyrethroid resistance. Enzymes of the CYP6 family are, however, generally involved in detoxification of xenobiotics in insects [30]. Functional characterisation of these enzymes is required to demonstrate the biological role of CYP6Z2 and CYP6M2 in pyrethroid resistance in A. gambiae.

Along with the neurotoxic effect, pyrethroids may induce oxidative stress which increases the toxicity of the insecticide [15]. Elevated levels of the SOD3B might increase pyrethroid resistance as superoxide dismutase enzymes show antioxidant properties protecting organisms from the damaging effects of reactive oxygen species [35]. While SOD3B was up-regulated in the Odumasy strain superoxide dismutases were not found to be differentially expressed in the permethrin resistant RSP strain [16, 36]. Among the three candidate genes apparent in both sexes of the resistant strain, SOD3B shows the lowest expression levels and higher variability – indicated by its higher p-value – across measurements, assigning this gene a lower rank. Likewise, GSTS1-2 – over-expressed in females of the resistant strain – although unlikely to be directly involved in pyrethroid metabolism may confer increased pyrethroid tolerance as a result of its antioxidant properties. In D. melanogaster a sigma class GST, DmGSTS1, is thought to have a function in protection against oxidative injury [37] although it was initially subscribed a structural function in the indirect flight muscle [38]. Females of the permethrin resistant Odumasy strain also show higher transcript levels for COEJHE5E which is similar to the D. melanogaster gene product drome-CG8425. drome-CG8425 has been identified as a juvenile hormone esterase [39]. To our knowledge there is no implication that this gene is involved in pyrethroid detoxification and may rather reflect strain specific differences in the metabolism of the female reproductive system.

The comparison between females and males in each strain revealed a larger set of genes being differentially expressed than the strain comparison within each sex. This is not surprising as many traits such as reproduction, host seeking, blood feeding etc. differ between the sexes. In A. gambiae, whole genome arrays show sex-biased gene expression pattern [40], and a comprehensive microarray experiment looking at the contribution of sex, genotype and age to transcriptional variance in D. melanogaster found that most of the variation is attributed to sex [41]. While no gene has been found to be consistently above two-fold over-expressed in females among both the susceptible and resistant strain, eight P450 genes were over-expressed in males over females in both strains. Five P450s, CYP6S1, CYP6S2, CYP6Z1, CYP12F3 and CYP325A1, belong to families implicated in xenobiotic detoxification. The CYP6 members are implicated in pyrethroid resistance, CYP6M2 (this study) and CYP6Z1 [16, 19], while expression of CYP325A1 and CYP12F3 have not been correlated with insecticide resistance. However, expression of another member of the CYP325 family, CYP325A3, has been associated with insecticide resistance in the pyrethroid resistant RSP strain [16]. Putatively all five P450s may play a role in xenobiotic detoxification. Orthologs of the other three over-expressed P450s in males, CYP307A1, CYP314A1, and CYP306A1, are all involved in the ecdysteroid hormone biosynthesis in D. melanogaster [25–27]. Ecdysteroid hormones control a range of biological functions including molting, behaviour, the development of the nervous system, reproduction, and production of sex pheromones [42]. It is possible that the biosynthesis of ecdysteroid hormones such as those triggering spermatogenesis are increased in males over females during the early stages in adult anopheline mosquitoes.

Field studies in western Kenya revealed the presence of target site insensitivity and metabolic resistance in the same A. gambiae population where pyrethroid-treated bed nets are being widely used [6, 9, 31]. Measurements of transcript levels in the permethrin resistant RSP strain originating from the same region showed elevated levels of CYP6Z1 and GSTE2 [16, 19]. In the West African Odumasy strain neither of these genes is associated with permethrin resistance. In contrast GSTE2 levels are depressed in males from the resistant versus the susceptible strain. Here, two alternative P450s, CYP6Z2 and CYP6M2, are highly over-expressed in females and males.

The Odumasy strain also shows high frequency (84%) of the West African type kdr allele in field populations [43]. This kdr allele differs from the East African type by a different mutation at the same amino acid position conferring permethrin resistance [8, 9]. The present data implies that, as for target site insensitivity, metabolic resistance to the same insecticide class may be due to alternative mechanisms. In contrast to target site insensitivity – where only a few set of alternative alleles are possible as most alleles would alter the protein's overall structure – extensive gene duplication within the mosquito detoxification gene family may enable up-regulation of various enzymes that alternatively contribute to insecticide resistance. Analyses of expression patterns in a series of susceptible and resistant D. melanogaster strains revealed that P450-mediated metabolic resistance evolves via the differential selection of multiple P450s through selection to the same insecticides [44]. Similarly in house flies, Scott and Kasai [45] found different P450s being selected against permethrin in different populations. One of the factors causing evolutionary plasticity to the same insecticide could be differential selection pressures as a function of actual insecticide dosages encountered by mosquitoes in the field. It has been suggested if selection takes repeatedly place within the normal response of an insect population such doses of insecticide will select for polygenic resistance while those outside the normal response will select for monogenic resistance [46]. Selection pressures may be higher where insecticides are widely used in agriculture as opposed to pyrethroid-treated bed nets. While the intensity of application certainly plays an important role, temporal fluctuations in insecticide dosages may be equally important. For example, Diabate et al. [47] found seasonal variation of insecticide resistance in A. gambiae field populations in relation to the use of insecticides in agriculture. Given the overall heterogeneity in insecticide coverage it may well be that in some circumstances selection will favour either mono- or polygenic resistance.

Barriers in gene flow such as the Rift Valley between East and West Kenyan populations or geographical distance between populations may also favour multiple origins of insecticide resistance. Such barriers have been suggested to explain variation in the genetic diversity among African A. gambiae populations [20]. Noteworthy, the East African kdr allele type reported from the permethrin resistant A. gambiae in western Kenya is not found in coastal Kenya [6]. Likewise some of the differences in gene expression patterns found in this study may be attributable to geographic variation rather than the observed phenotype. Nevertheless, both loci, CYP6Z2 and CYP6M2, identified in this study are mapped within a cluster of P450s on chromosome 3R previously associated with pyrethroid resistance [24].

An unresolved issue is how much of the observed resistance phenotype is attributable to kdr. Target site and metabolic mechanisms may act synergistically [9] and the detection of both mechanisms is therefore crucial. The contribution of the two mechanisms in the Odumasy strain and other populations needs further research. Selection experiments on field collected mosquitoes where both kdr frequencies and gene expression levels are measured would provide the most complete evidence. Additionally, studies comparing selected and parental strains from various field sites across Africa are needed to shed more light on the origin and evolution of metabolic resistance in field populations of A. gambiae.

Conclusion

To date pyrethroids are the only highly effective class of commercially insecticides available to treat bednets. The present data suggest that there may be multiple metabolic resistance mechanisms found in A. gambiae populations which pose a severe challenge to the sustainability of malaria vector control programmes. New insecticides and a comprehensive screening tool for the detection and management of insecticide resistance are urgently required for use in malaria epidemic regions.

Methods

Mosquito strains and selection to insecticide

Two A. gambiae s.s. strains were compared; the permethrin resistant Odumasy strain vs. the insecticide susceptible Kisumu strain. Both strains belong to the molecular "S" form. The Odumasy strain originated from Odumasy, Southern Ghana, and was colonised in the laboratory from field collected specimens in 2002. The Odumasy strain shows high frequency (84%) of the West African type kdr allele in field populations [43]. In our lab colony the kdr allele is fixed (Amy Lynd and Nadine Randle, unpublished data). The field collected Odumasy strain has been repeatedly selected in the laboratory by exposure to filter paper impregnated with 0.75% permethrin according to WHO standard procedures [48]. Both sexes showed low mortality after a one-hour exposure (females: 26%, N = 224; males: 38%, N = 297) with females being slightly more resistant (χ²-test, p = 0.0045). To our knowledge this is the first resistant strain of A. gambiae s.s. "S" form originating from West Africa which has been successfully reared under laboratory conditions. The Kisumu strain originated from Kisumu, Western Kenya, and is susceptible to permethrin [3]. The susceptibility of the Kisumu lab strain has been repeatedly demonstrated using the same WHO protocol. No survivors were recorded in the Kisumu strain at this discriminatory dose. Both strains were reared under constant laboratory conditions (26°, 80% RH) and a 12:12 hours light:dark cycle. Larvae were fed with finely grounded fish food (TetraMin) ad libitum. Adult mosquitoes were provided with water and sugar and fed with human blood twice a week.

Target preparation and microarray hybridisations

Adult mosquitoes were collected the morning after emergence and frozen at -80°C. Total RNA was extracted using the RiboPure Kit (Ambion) from batches of 10 to 15 mosquitoes. A batch contained mosquitoes from the same generation collected on the same day. After extraction, RNA quantity was measured using a Nanodrop spectrophotometer (Nanodrop Technologies, Oxfordshire, UK). 8 μg of total RNA from each batch were first reverse transcribed to single-stranded cDNA using an oligo d(T)₂₄ primer (Sigma) and Superscript III reverse transcriptase (Invitrogen). Single-stranded cDNA was then converted into double-stranded cDNA. The double-stranded cDNA was used as a template to generate single-stranded antisense RNA (aRNA) in one amplification round using the MEGAscript T7 Kit (Ambion). Amplified aRNA was purified using the RNeasy Mini Kit (Qiagen). Prior to fluorescent labelling aRNA quantity and quality were assessed by a spectrophotometer (Nanodrop) and agarose gel electrophoresis. Final target samples were prepared from 8 μg of sample aRNA and 4 μg RNA control spike mix (Lucidea Universal Score Card, Amersham Biosciences). Fluorescent nucleotides, Cyanine 3-dUTP (Cy3) or Cyanine 5-dUTP (Cy5) (GeneBeam, Enzo) respectively, were incorporated into first-strand cDNA by reverse transcriptase using random hexamer primers (Life Technologies) and Superscript III reverse transcriptase (Invitrogen). After degradation of the aRNA template strand using 1 μl 1 M NaOH, 20 mM EDTA and incubating for 5 min at 70°C, cDNA targets were pooled together and purified using the CyScribe GFX Purification Kit (Amersham Biosciences). Dye incorporation efficiency was measured using a spectrophotometer (Nanodrop) and 5 μg of poly dA oligo (Amersham Biosciences) was added to prevent non-specific hybridisation. The labelled cDNA targets were vacuum dried and re-suspended in 15 μl formamide-based hybridisation buffer (Corning). Labelled targets were hybridised to the detox chip [16] for 16 h at 42°C following the preparation of the glass slides using the Universal Hybridisation Kit (Corning) according to the manufacturer's recommendations. The detox chip is a custom-made microarray chip containing probes from 233 A. gambiae genes associated with metabolic resistance; 103 cytochrome P450 monooxygenases (P450s), 31 carboxylesterases (COEs), 35 glutathione-S-transferases (GSTs), 41 redox genes, 5 ATP-binding-cassette transporters, and various control genes [16]. After incubation slides were washed according to manufacturer's instructions and intensity images were scanned by a Genepix Personal 4100a microarray scanner (Axon instruments) at a resolution of 10 μm.

Microarray experimental design

In a first set of microarray experiments transcription levels in resistant mosquitoes were compared to levels in susceptible mosquitoes. The experiments were carried out separately for females and males. In addition to the strain comparison transcript levels were then compared between the sexes in each strain. Females and males of the Kisumu strain were compared directly in an additional microarray experiment while transcript levels between females and males of the Odumasy strain were estimated indirectly by combining all above comparisons (see below). The Odumasy colony was difficult to maintain at a sustainable level to collect sufficient material for all comparisons.

Each set of microarray experiments comparing the two strains consisted of six hybridisations comprising three biological and two technical replicates. Each biological replicate was split into two aliquots which were co-hybridised to two arrays while the Cy3 and Cy5 labels were swapped between hybridisations. For the sex comparison within the susceptible Kisumu strain two independent biological replicates with two technical repeats were performed.

Microarray data analysis

After visual inspection spot and background intensities were calculated from the scanned array images using GenePix Pro 5.1 software (Axon Instruments). Subsequent data analysis was performed using the Limma 2.4.1 software package [49] for R 2.3.0, available from the CRAN repository [50]. Limma is part of the Bioconductor project [51]. For each spot background intensities were subtracted from the total spot intensities using the method normexp and an offset of 50 as recommended for GenePix data by the Limma user's guide. The detox chip contains 40 calibration spots that, when hybridised with labelled samples containing the mRNA spike mix, result in 1:1 ratios representing a concentration range from 1 pg to 30 ng per 2 μl of mRNA spike mix. Intensity values from the calibration spots were used in addition to the gene spot intensities for data normalisation. In a first step, background corrected intensities from the red, R (Cy5), and the green, G (Cy3), channel were transformed to intensity log-ratios, M = log₂ R/G, and their corresponding geometrical means, A = (log₂ R + log₂ G)/2. Then within each array M-values were normalised as a function of A using the loess scatter plot smoothing function. In a second normalisation step the M-values from each array were scaled to equalise the median absolute value across all arrays to account for technical biases between replicate hybridisations [52]. The between arrays normalisation was first performed for each experiment separately taking only those slides into account that were used in each experiment. For the comparison between Odumasy females and males where expression ratios were estimated indirectly by using the information from all experiments the within arrays normalisation was repeated across all arrays included in the analysis.

The association of gene expression with the phenotypic variable, i.e. strain or sex, was assessed by fitting linear models to estimate the gene expression for each gene on the array. To account for non-independent measurements between technical replicates estimates were first obtained from each biological replicate and then averaged between the biological replicates. Contrasts, linear combinations of the coefficients, were then tested for significance. For the comparison between Odumasy females and males the following linear combination was used: M₁ - M₂ + M₃ with M₁ = log₂(Odumasy females vs. Kisumu females), M₂ = log₂(Odumasy males vs. Kisumu males) and M₃ = log₂(Kisumu females vs. Kisumu males). As each spot is replicated four times on the detox chip, valuable information was available on the gene-wise within-array variability which was used to estimate the technical, spatial variation on the array. The correlation between the four spots was taken into account by fitting separate linear models to the expression data for each gene but with a common value for the between-replicate correlation [53]. Compared with simply averaging replicate spots, this method greatly improves the precision with which the gene wise variances are estimated. The significance was assessed with a moderated t-statistic which shows a robust behaviour for small numbers of arrays and allows for incomplete data from missing spots [54]. To account for multiple testing p-values were adjusted adopting the approach of Benjamini and Hochberg [55] to control for the false discovery rate as described in [49]. In order to define a set of candidate genes only those genes were considered which showed at least a two-fold change in expression, i.e. M = 1, and an adjusted p-value less than q = 0.001.

As a control a calibration experiment was performed in which two aliquots derived from the same sample were co-hybridised to two arrays with reverse-labelling. As expected, none of the gene probes came out significantly in the calibration experiment supporting the statistical approach chosen above (data not shown). In addition to the control experiment a semi-quantitative RT-PCR was performed to measure expression levels of two candidate genes (see below).

Semi-quantitative RT-PCR

To validate microarray data semi-quantitative RT-PCR was performed. RNA was first extracted from four replicate sets (15 mosquitoes per replicate) of one-day old adult males or females of the Kisumu strain. Total RNA was extracted using the methods described above followed by DNase treatment (RiboPure Kit, Ambion) to eliminate any genomic DNA. Extracted total RNA was reverse transcribed to cDNA using an oligo d(T)₁₄ primer and Superscript III reverse transcriptase (Promega). PCR primers (Table 3) were designed to amplify two P450 genes, CYP307A1 and CYP6M2, along with the ribosomal gene S7 [GenBank: AY380336] [56] which served as an internal standard to account for differences in initial cDNA and reaction efficiency. 0.5 μl cDNA was used as templates in the PCR reactions which contained 7.5 μl 2× Multiplex PCR Master Mix (Qiagen), 0.2 μM of each forward and reverse primer, CYP307A1 and CYP6M2, and 0.05 μM of each forward and reverse primer for S7. PCR reactions were carried out in a MJ Research PTC thermal cycler. An initial activation step of 15 minutes at 95°C was followed by 20 seconds at 94°C, 20 seconds at 55°C and 30 seconds at 72°C for 33 cycles. The conditions were chosen so that all of the cDNAs amplified were still within the dynamic range to avoid saturation of one of the templates at the end of the PCR protocol. To rule out genomic DNA contamination RNA was used as negative control. The PCR products were loaded onto ethidium bromide-stained 2% agarose gels in TAE buffer. Images of the gels were acquired with GeneSnap 4 (Syngene) and raw peak intensities analysed using GeneTools 3 (Syngene). Peak intensities of the CYP307A1 and CYP6M2 bands were normalised by the peak intensity of S7.

Table 3

Sequences of oligonucleotide primers used in semi-quantitative RT-PCR validation experiments

CYP6M2

[GenBank: AY193729]

CYP6M2_F

CYP6M2_R

GTATGATGCAGGCCCGTATAG

GCCATAATGAAACTCTCCTTCG

112 bp

CYP307A1

[GenBank: AY745209]

CYP307A1_F

CYP307A1_R

ACTACGAGCTGAACACGAGTGA

ATGCAGGTCCGTTTACCAATAC

253 bp

S7 ribosomal

[GenBank: AY380336]

SPC

SPD

GTGCCGGTGCCGAAACAGAA

AGCACAAACACTCCAATAATCAAG

472 bp

Only genes are listed which showed at least a two-fold difference in one or both strains. p-values were corrected for multiple testing as described in the materials and methods section. n.s., p = 0.001. *CYP6Z1 was spotted on the detox chip as both cDNA and 70-mer oligonucleotide probe [16].

Declarations

Acknowledgements

We are grateful to Alexander Egyir-Yawson for providing the parental Odumasy strain, to Clare Strode and Jean-Philippe David for training and support in microarray techniques, Margaret Hughes and Andrew Cossins for printing microarray slides, and to the rest of the Vector Group for taking care of the mosquito colonies. This work received financial support from the United Nations Development Program/World Bank/World Health Organization Special Program for Research and Training in Tropical Disease (Grant: A40190) and the United States National Institutes of Health (Grant: 1 U01 AI58542).

Electronic supplementary material

12864_2006_749_MOESM1_ESM.xls Additional file 1: Odumasy expression data ALL. This table contains a complete list of the current microarray analysis including fold expression and p-values for all genes represented on the detox chip. (XLS 134 KB)

Below are the links to the authors’ original submitted files for images.

12864_2006_749_MOESM2_ESM.eps Authors’ original file for figure 1

12864_2006_749_MOESM3_ESM.eps Authors’ original file for figure 2

12864_2006_749_MOESM4_ESM.png Authors’ original file for figure 3

12864_2006_749_MOESM5_ESM.png Authors’ original file for figure 4

Authors’ Affiliations

(1)

Vector Group, Liverpool School of Tropical Medicine, Liverpool, L3 5QA, UK

References

WHO: WHO Expert Committee on Malaria, 20th Report. 2000, Geneva, World Health Organization, 37-42.Google Scholar
Elissa N, Mouchet J, Riviere F, Meunier JY, Yao K: Resistance of Anopheles gambiae to pyrethroids in Cote d'Ivoire. Ann Soc Belg Med Trop. 1993, 73: 291-294.PubMedGoogle Scholar
Vulule JM, Beach RF, Ayieli FK, Roberts JM, Mount DL, Mwangi RW: Reduced susceptibility of Anopheles gambiae to permethrin associated with the use of permethrin-impregnated bednets and curtains in Kenya. Med Vet Entomol. 1994, 8: 71-75.PubMedView ArticleGoogle Scholar
Hargreaves K, Koekemoer LL, Brooke BD, Hunt RH, Mthembu J, Coetzee M: Anopheles funestus resistant to pyrethroid insecticides in South Africa. Med Vet Entomol. 2000, 14 (2): 181-189.PubMedView ArticleGoogle Scholar
Awolola TS, Brooke BD, Hunt RH, Coetzee M: Resistance of the malaria vector Anopheles gambiae s.s. to pyrethroid insecticides in south-western Nigeria. Ann Trop Med Parasitol. 2002, 96 (8): 849-852.PubMedView ArticleGoogle Scholar
Stump AD, Atieli FK, Vulule JM, Besansky NJ: Dynamics of the pyrethroid knockdown resistance allele in western Kenyan populations of Anopheles gambiae in response to insecticide-treated bed net trials. Am J Trop Med Hyg. 2004, 70 (6): 591-596.PubMedGoogle Scholar
Hemingway J, Hawkes NJ, McCarroll L, Ranson H: The molecular basis of insecticide resistance in mosquitoes. Insect Biochem Mol Biol. 2004, 34 (7): 653-665.PubMedView ArticleGoogle Scholar
Martinez-Torres D, Chandre F, Williamson MS, Darriet F, Berge JB, Devonshire AL, Guillet P, Pasteur N, Pauron D: Molecular characterization of pyrethroid knockdown resistance (kdr) in the major malaria vector Anopheles gambiae s.s. Insect Mol Biol. 1998, 7 (2): 179-184.PubMedView ArticleGoogle Scholar
Ranson H, Jensen B, Vulule JM, Wang X, Hemingway J, Collins FH: Identification of a point mutation in the voltage-gated sodium channel gene of Kenyan Anopheles gambiae associated with resistance to DDT and pyrethroids. Insect Mol Biol. 2000, 9 (5): 491-497.PubMedView ArticleGoogle Scholar
Ranson H, Claudianos C, Ortelli F, Abgrall C, Hemingway J, Sharakhova MV, Unger MF, Collins FH, Feyereisen R: Evolution of supergene families associated with insecticide resistance. Science. 2002, 298 (5591): 179-181.PubMedView ArticleGoogle Scholar
Berge JB, Feyereisen R, Amichot M: Cytochrome P450 monooxygenases and insecticide resistance in insects. Philos Trans R Soc Lond B Biol Sci. 1998, 353 (1376): 1701-1705.PubMed CentralPubMedView ArticleGoogle Scholar
Heidari R, Devonshire AL, Campbell BE, Dorrian SJ, Oakeshott JG, Russell RJ: Hydrolysis of pyrethroids by carboxylesterases from Lucilia cuprina and Drosophila melanogaster with active sites modified by in vitro mutagenesis. Insect Biochem Mol Biol. 2005, 35 (6): 597-609.PubMedView ArticleGoogle Scholar
Kostaropoulos I, Papadopoulos AI, Metaxakis A, Boukouvala E, Papadopoulou-Mourkidou E: Glutathione S-transferase in the defence against pyrethroids in insects. Insect Biochem Mol Biol. 2001, 31 (4-5): 313-319.PubMedView ArticleGoogle Scholar
Scott JG, Georghiou GP: The biochemical genetics of permethrin resistance in the Learn-PyR strain of house fly. Biochem Genet. 1986, 24 (1-2): 25-37.PubMedView ArticleGoogle Scholar
Vontas JG, Small GJ, Hemingway J: Glutathione S-transferases as antioxidant defence agents confer pyrethroid resistance in Nilaparvata lugens. Biochem J. 2001, 357 (Pt 1): 65-72.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 U S A. 2005, 102 (11): 4080-4084.PubMed CentralPubMedView ArticleGoogle Scholar
Ortelli F, Rossiter LC, Vontas J, Ranson H, Hemingway J: Heterologous expression of four glutathione transferase genes genetically linked to a major insecticide-resistance locus from the malaria vector Anopheles gambiae. Biochem J. 2003, 373: 957-963.PubMed CentralPubMedView ArticleGoogle Scholar
Ranson H, Rossiter L, Ortelli F, Jensen B, Wang X, Roth CW, Collins FH, Hemingway J: Identification of a novel class of insect glutathione S-transferases involved in resistance to DDT in the malaria vector Anopheles gambiae. Biochem J. 2001, 359 (Pt 2): 295-304.PubMed CentralPubMedView ArticleGoogle Scholar
Nikou D, Ranson H, Hemingway J: An adult-specific CYP6 P450 gene is overexpressed in a pyrethroid-resistant strain of the malaria vector, Anopheles gambiae. Gene. 2003, 318: 91-102.PubMedView ArticleGoogle Scholar
Lehmann T, Licht M, Elissa N, Maega BTA, Chimumbwa JM, Watsenga FT, Wondji CS, Simard F, Hawley WA: Population structure of Anopheles gambiae in Africa. J Hered. 2003, 94 (2): 133-147.PubMedView ArticleGoogle Scholar
Ding Y, Ortelli F, Rossiter LC, Hemingway J, Ranson H: The Anopheles gambiae glutathione transferase supergene family: annotation, phylogeny and expression profiles. BMC Genomics. 2003, 4 (1): 35-51.PubMed CentralPubMedView ArticleGoogle Scholar
Strode C, Steen K, Ortelli F, Ranson H: Differential expression of the detoxification genes in the different life stages of the malaria vector Anopheles gambiae. Insect Mol Biol. 2006, 15 (4): 523-530.PubMedView ArticleGoogle Scholar
Ranson H, Nikou D, Hutchinson M, Wang X, Roth CW, Hemingway J, Collins FH: Molecular analysis of multiple cytochrome P450 genes from the malaria vector, Anopheles gambiae. Insect Mol Biol. 2002, 11 (5): 409-418.PubMedView ArticleGoogle Scholar
Ranson H, Paton MG, Jensen B, McCarroll L, Vaughan A, Hogan JR, Hemingway J, Collins FH: Genetic mapping of genes conferring permethrin resistance in the malaria vector, Anopheles gambiae. Insect Mol Biol. 2004, 13 (4): 379-386.PubMedView ArticleGoogle Scholar
Niwa R, Matsuda T, Yoshiyama T, Namiki T, Mita K, Fujimoto Y, Kataoka H: CYP306A1, a cytochrome P450 enzyme, is essential for ecdysteroid biosynthesis in the prothoracic glands of Bombyx and Drosophila. J Biol Chem. 2004, 279 (34): 35942-35949.PubMedView ArticleGoogle Scholar
Namiki T, Niwa R, Sakudoh T, Shirai K, Takeuchi H, Kataoka H: Cytochrome P450 CYP307A1/Spook: a regulator for ecdysone synthesis in insects. Biochem Biophys Res Commun. 2005, 337 (1): 367-374.PubMedView ArticleGoogle Scholar
Petryk A, Warren JT, Marques G, Jarcho MP, Gilbert LI, Kahler J, Parvy JP, Li Y, Dauphin-Villemant C, O'Connor MB: Shade is the Drosophila P450 enzyme that mediates the hydroxylation of ecdysone to the steroid insect molting hormone 20-hydroxyecdysone. Proc Natl Acad Sci U S A. 2003, 100 (24): 13773-13778.PubMed CentralPubMedView ArticleGoogle Scholar
Lumjuan N, McCarroll L, Prapanthadara LA, Hemingway J, Ranson H: Elevated activity of an Epsilon class glutathione transferase confers DDT resistance in the dengue vector, Aedes aegypti. Insect Biochem Mol Biol. 2005, 35 (8): 861-871.PubMedView ArticleGoogle Scholar
Lynd A, Ranson H, McCall PJ, Randle NP, Black WC, Walker ED, Donnelly MJ: A simplified high-throughput method for pyrethroid knock-down resistance (kdr) detection in Anopheles gambiae. Malar J. 2005, 4 (1): 16-PubMed CentralPubMedView ArticleGoogle Scholar
Scott JG: Cytochromes P450 and insecticide resistance. Insect Biochem Mol Biol. 1999, 29 (9): 757-777.PubMedView ArticleGoogle Scholar
Vulule JM, Beach RF, Atieli FK, Mcallister JC, Brogdon WG, Roberts JM, Mwangi RW, Hawley WA: Elevated oxidase and esterase levels associated with permethrin tolerance in Anopheles gambiae from Kenyan villages using permethrin-impregnated nets. Med Vet Entomol. 1999, 13: 239-244.PubMedView ArticleGoogle Scholar
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignment search tool. J Mol Biol. 1990, 215 (3): 403-410.PubMedView ArticleGoogle Scholar
Kamiya E, Minoru Y, Shono T, Kono Y: Molecular cloning, nucleotide sequences and gene expression of new cytochrome P450s (CYP6A24, CYP6D3v2) from the pyrethroid resistant housfly, Musca domestica L. (Diptera: Muscidae). Appl Entomol Zool. 2001, 36 (2): 225-229.View ArticleGoogle Scholar
Hardstone MC, Baker SA, Gao J, Ewer J, Scott JG: Deletion of Cyp6d4 does not alter toxicity of insecticides to Drosophila melanogaster. Pestic Biochem Physiol. 2006, 84: 236-242.View ArticleGoogle Scholar
Fridovich I: The biology of oxygen radicals. Science. 1978, 201 (4359): 875-880.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.PubMedView ArticleGoogle Scholar
Singh SP, Coronella JA, Benes H, Cochrane BJ, Zimniak P: Catalytic function of Drosophila melanogaster glutathione S-transferase DmGSTS1-1 (GST-2) in conjugation of lipid peroxidation end products. Eur J Biochem. 2001, 268 (10): 2912-2923.PubMedView ArticleGoogle Scholar
Clayton JD, Cripps RM, Sparrow JC, Bullard B: Interaction of troponin-H and glutathione S-transferase-2 in the indirect flight muscles of Drosophila melanogaster. J Muscle Res Cell Motil. 1998, 19 (2): 117-127.PubMedView ArticleGoogle Scholar
Campbell PM, Harcourt RL, Crone EJ, Claudianos C, Hammock BD, Russell RJ, Oakeshott JG: Identification of a juvenile hormone esterase gene by matching its peptide mass fingerprint with a sequence from the Drosophila genome project. Insect Biochem Mol Biol. 2001, 31 (6-7): 513-520.PubMedView ArticleGoogle Scholar
Hahn MW, Lanzaro GC: Female-biased gene expression in the malaria mosquito Anopheles gambiae. Curr Biol. 2005, 16 (6): R192-R193.View ArticleGoogle Scholar
Jin W, Riley RM, Wolfinger RD, White KP, Passador-Gurgel G, Gibson G: The contribution of sex, genotype and age to transcriptional variance in Drosophila melanogaster. Nature Genet. 2001, 29: 389-395.PubMedView ArticleGoogle Scholar
Simonet G, Poels J, Claeys I, Van Loy T, Franssens V, De Loof A, Broeck JV: Neuroendocrinological and molecular aspects of insect reproduction. J Neuroendocrinol. 2004, 16 (8): 649-659.PubMedView ArticleGoogle Scholar
Yawson AE, McCall PJ, Wilson MD, Donnelly MJ: Species abundance and insecticide resistance of Anopheles gambiae in selected areas of Ghana and Burkina Faso. Med Vet Entomol. 2004, 18 (4): 372-377.PubMedView ArticleGoogle Scholar
Pedra JH, McIntyre LM, Scharf ME, Pittendrigh BR: Genome-wide transcription profile of field- and laboratory-selected dichlorodiphenyltrichloroethane (DDT)-resistant Drosophila. Proc Natl Acad Sci U S A. 2004, 101 (18): 7034-7039.PubMed CentralPubMedView ArticleGoogle Scholar
Scott JG, Kasai S: Evolutionary plasticity of monooxygenase-mediated resistance. Pestic Biochem Physiol. 2004, 78: 171-178.View ArticleGoogle Scholar
ffrench-Constant RH, Daborn JP, Le Goff G: The genetics and genomics of insecticide resistance. Trends Genet. 2004, 20 (3): 163-170.PubMedView ArticleGoogle Scholar
Diabate A, Baldet T, Chandre F, Akoobeto M, Guiguemde TR, Darriet F, Brengues C, Guillet P, Hemingway J, Small GJ, Hougard JM: The role of agricultural use of insecticides in resistance to pyrethroids in Anopheles gambiae s.l. in Burkina Faso. Am J Trop Med Hyg. 2002, 67 (6): 617-622.PubMedGoogle Scholar
WHO: Insecticide resistance and vector control, 17th Report. WHO Technical Report Series: 443. Edited by: WHO. 1970, Geneva, World Health Organization, 433: 47-56.Google Scholar
Smith GK: Limma: linear models for microarray data. Bioinformatics and Computational Biology Solutions using R and Bioconductor. Edited by: Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W. 2005, New York , Springer, 397-420.View ArticleGoogle Scholar
CRAN repository. [http://www.r-project.org]
Bioconductor. [http://www.bioconductor.org]
Smyth GK, Speed T: Normalization of cDNA microarray data. Methods. Edited by: Carter D. 2003, 31 (4): 265-273.PubMedView ArticleGoogle Scholar
Smyth GK, Michaud J, Scott HS: Use of within-array replicate spots for assessing differential expression in microarray experiments. Bioinformatics. 2005, 21 (9): 2067-2075.PubMedView ArticleGoogle Scholar
Smyth GK: Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Gen Mol Biol. 2004, 3: Article 3-Google Scholar
Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc B. 1995, 57: 289-300.Google Scholar
Salazar CE, Mills-Hamm D, Kumar V, Collins FH: Sequence of a cDNA from the mosquito Anopheles gambiae encoding a homologue of human ribosomal protein S7. Nucleic Acids Res. 1993, 21 (17): 4147-PubMed CentralPubMedView ArticleGoogle Scholar

Copyright

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.

Transcription profiling of a recently colonised pyrethroid resistant Anopheles gambiae strain from Ghana

Background

Results

Conclusion

Gene expression associated with resistance status

Gene expression associated with sex

Mosquito strains and selection to insecticide

Target preparation and microarray hybridisations

Microarray experimental design

Microarray data analysis

Semi-quantitative RT-PCR

Acknowledgements

BMC Genomics

Contact us