Multiple insecticide resistance in Anopheles gambiae from Tanzania : a major concern for malaria vector control

Malaria vector control in Tanzania is based on use of long-lasting insecticide treated nets (LLINs) and indoor residual spraying (IRS), which both rely on the use of chemical insecticides. The effectiveness of these control tools is endangered by the development of insecticide resistance in the major malaria vectors. This study was carried out to monitor the susceptibility status of major malaria vectors to insecticides used for IRS and LLINs in mainland Tanzania.; Mosquito larvae were collected in 20 sites of Tanzania mainland in 2015. Phenotypic resistance was determined using standard WHO susceptibility tests. Molecular assay were used to determine distribution of Anopheles gambiae sub-species. A microplate assay approach was used for identifying enzyme levels on single mosquitoes from each sites compared with a susceptible reference strain, An. gambiae sensu stricto (s.s.) Kisumu strain.; Anopheles arabiensis was the dominant malaria specie in the country, accounting for 52% of the sibling species identified, while An. gambiae s.s. represented 48%. In Arumeru site, the dominant species was An. arabiensis, which was resistant to both pyrethroids (permethrin and deltamethrin), and pirimiphos-methyl, and had significant elevated levels of GSTs, non-specific esterases, and oxidase enzymes. An. arabiensis was also a dominant species in Kilombero and Kondoa sites, both were resistant to permethrin and deltamethrin with significant activity levels of oxidase enzymes. Resistance to bendiocarb was recorded in Ngara site where specie composition is evenly distributed between An. gambiae s.s. and An.arabiensis. Also bendiocarb resistance was recorded in Mbozi site, where An. gambiae s.s. is the dominant species.; Overall, this study confirmed resistance to all four insecticide classes in An. gambiae sensu lato in selected locations in Tanzania. Results are discussed in relation to resistance mechanisms and the optimization of resistance management strategies.