Abstract:
Indoor residual spraying (IRS) and insecticide-treated nets (ITNs) are the main methods used to control mosquito pop
ulations for malaria prevention. The efficacy of these strategies is threatened by the spread of insecticide resistance
(IR), limiting the success of malaria control. Studies of the genetic evolution leading to insecticide resistance could
enable the identification of molecular markers that can be used for IR surveillance and an improved understanding
of the molecular mechanisms associated with IR. This study used a weighted gene co-expression network analysis
(WGCNA) algorithm, a systems biology approach, to identify genes with similar co-expression patterns (modules)
and hub genes that are potential molecular markers for insecticide resistance surveillance in Kenya and Benin. A total
of 20 and 26 gene co-expression modules were identified via average linkage hierarchical clustering from Anopheles
arabiensis and An. gambiae, respectively, and hub genes (highly connected genes) were identified within each mod
ule. Three specific genes stood out: serine protease, E3 ubiquitin-protein ligase, and cuticular proteins, which were top
hub genes in both species and could serve as potential markers and targets for monitoring IR in these malaria vectors.
In addition to the identified markers, we explored molecular mechanisms using enrichment maps that revealed
a complex process involving multiple steps, from odorant binding and neuronal signaling to cellular responses,
immune modulation, cellular metabolism, and gene regulation. Incorporation of these dynamics into the develop
ment of new insecticides and the tracking of insecticide resistance could improve the sustainable and cost-effective
deployment of interventions.
Keywords Insecticide resistance, Anopheles gambiae, Anopheles arabiensis, Hub genes, Molecular markers
