RNA-seq analyses of changes in the Anopheles gambiae transcriptome associated with resistance to pyrethroids in Kenya: identification of candidate-resistance genes and candidate-resistance SNPs

Abstract

Background: The extensive use of pyrethroids for control of malaria vectors, driven by their cost, efficacy and safety, has led to widespread resistance. To favor their sustainable use, the World Health Organization (WHO) formulated an insecticide resistance management plan, which includes the identification of the mechanisms of resistance and resistance surveillance. Recognized physiological mechanisms of resistance include target site mutations in the para voltage-gated sodium channel, metabolic detoxification and penetration resistance. Such understanding of resistance mechanisms has allowed the development of resistance monitoring tools, including genotyping of the kdr mutation L1014F/S in the para gene.

Methods: The sequence-based technique RNA-seq was applied to study changes in the transcriptome of deltamethrin-resistant and -susceptible Anopheles gambiae mosquitoes from the Western Province of Kenya. The resulting gene expression profiles were compared to data in the most recent literature to derive a list of candidate resistance genes. RNA-seq data were analyzed also to identify sequence polymorphisms linked to resistance.

Results: A total of five candidate-resistance genes (AGAP04177, AGAP004572, AGAP008840, AGAP007530 and AGAP013036) were identified with altered expression between resistant and susceptible mosquitoes from West and East Africa. A change from G to C at position 36043997 of chromosome 3R resulting in A101G of the sulfotransferase gene AGAP009551 was significantly associated with the resistance phenotype (odds ratio: 5.10). The kdr L1014S mutation was detected at similar frequencies in both phenotypically resistant and susceptible mosquitoes, suggesting it is no longer fully predictive of the resistant phenotype.

Conclusions: Overall, these results support the conclusion that resistance to pyrethroids is a complex and evolving phenotype, dependent on multiple gene functions including, but not limited to, metabolic detoxification. Functional convergence among metabolic detoxification genes may exist, with the role of each gene being modulated by the life history and selection pressure on mosquito populations. As a consequence, biochemical assays that quantify overall enzyme activity may be a more suitable method for predicting metabolic resistance than gene-based assays.

Fig. 1

Map of the Western Province of Kenya. Localities in the Western Province of Kenya around which multiple larvae collections occurred are shown with a red circle. Kagamega and Kisumu are the capitals of the Western and Nyanza Provinces, respectively. Nearby countries are shown with a square and in purple. Main roads are in yellow

Fig. 2

Quality control of RNA-seq data. a multidimensional scaling (MDS) plot showing variation among RNA-seq libraries. RNA-seq libraries from resistant, susceptible and Kisumu mosquitoes. b Results of qRT-PCR. The level of expression of 14 genes was measured by qRT-PCR from different resistant and susceptible mosquitoes than those used for RNA-seq. An axterix indicate genes significantly differentially expressed between resistant (green) and susceptible (pink) mosquitoes. c Pearson correlation between fold-changes in gene expression between resistant and susceptible mosquitoes as determined by qRT-PCR (X-axe) and RNA-seq (Y-axe)

Fig. 3

Functional classifications of candidate-resistance genes. Genes expressed significantly more (R > S) or less (R < S) in resistant versus susceptible mosquitoes were classified based on their functions. A percentage was attributed to each function based on the total number of genes considered. Functional abbreviation is as follows: UNK (Unknown); TR (transport); TT (transcription and translation); STD (signal transduction); RTS (response to stress); REDOX (oxido-reduction processes); PROT (proteolysis); OBP (odorant binding proteins); MCT (microtubule-associated movement); MET (metabolism); DNA_R (DNA repair); DIV (diverse functions); CHR (chromosome or chromatin-related functions); CUT (cuticule); CC (cell cycle); CA (catalytic activity)