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Increasing insecticide resistance in malaria-transmitting vectors represents a public health threat, but underlying mechanisms are poorly understood. Here, a data integration approach is used to analyse transcriptomic data from comparisons of insecticide resistant and susceptible Anopheles populations from disparate geographical regions across the African continent. An unbiased, integrated analysis of this data confirms previously described resistance candidates but also identifies multiple novel genes involving alternative resistance mechanisms, including sequestration, and transcription factors regulating multiple downstream effector genes, which are validated by gene silencing. The integrated datasets can be interrogated with a bespoke Shiny R script, deployed as an interactive web-based application, that maps the expression of resistance candidates and identifies co-regulated transcripts that may give clues to the function of novel resistance-associated genes.
Ingham et al. (Wed,) studied this question.