A differential expression of pyrethroid resistance genes in the malaria vector Anopheles funestus across Uganda is associated with patterns of gene flow

BACKGROUND: Insecticide resistance is challenging the effectiveness of insecticide-based control interventions to reduce malaria burden in Africa. Understanding the molecular basis of insecticides resistance and patterns of gene flow in major malaria vectors such as Anopheles funestus are important steps for designing effective resistance management strategies. Here, we investigated the association between patterns of genetic structure and expression profiles of genes involved in the pyrethroid resistance in An. funestus across Uganda and neighboring Kenya. METHODS: Blood-fed mosquitoes An. funestus were collected across the four localities in Uganda and neighboring Kenya. A Microarray-based genome-wide transcription analysis was performed to identify the set of genes associated with permethrin resistance. 17 microsatellites markers were genotyped and used to establish patterns of genetic differentiation. RESULTS: Microarray-based genome-wide transcription profiling of pyrethroid resistance in four locations across Uganda (Arua, Bulambuli, Lira, and Tororo) and Kenya (Kisumu) revealed that resistance was mainly driven by metabolic resistance. The most commonly up-regulated genes in pyrethroid resistance mosquitoes include cytochrome P450s (CYP9K1, CYP6M7, CYP4H18, CYP4H17, CYP4C36). However, expression levels of key genes vary geographically such as the P450 CYP6M7 [Fold-change (FC) = 115.8 (Arua) vs 24.05 (Tororo) and 16.9 (Kisumu)]. In addition, several genes from other families were also over-expressed including Glutathione S-transferases (GSTs), carboxylesterases, trypsin, glycogenin, and nucleotide binding protein which probably contribute to insecticide resistance across Uganda and Kenya. Genotyping of 17 microsatellite loci in the five locations provided evidence that a geographical shift in the resistance mechanisms could be associated with patterns of population structure throughout East Africa. Genetic and population structure analyses indicated significant genetic differentiation between Arua and other localities (F(ST)>0.03) and revealed a barrier to gene flow between Arua and other areas, possibly associated with Rift Valley. CONCLUSION: The correlation between patterns of genetic structure and variation in gene expression could be used to inform future interventions especially as new insecticides are gradually introduced.