Identification of Insecticide Resistance Markers Using Weighted Gene Co-Expression Networks and Understanding Underlying Resistance Mechanisms in Anopheles gambiae and Anopheles arabiensis Mosquitoes

Abstract

Malaria remains a burden in sub-Saharan Africa with increase in malaria cases and deaths yearly according to the WHO malaria reports. Intense efforts are set to aid in malaria elimination and eradication worldwide by several governmental and non-governmental institutions i.e. National Malaria Control Programs, The Global Fund, and WHO Global Malaria Program etc. These efforts have stagnated for several reasons including vector resistance to insecticides, resistance of malaria parasites to antimalarial drugs, vector behavioral changes hence change in residual transmission, emergence of new vector species i.e. An. stephensi etc. The primary malaria vectors in Africa include An funestus, An. gambiae and An. arabiensis. The primary methods for controlling vector populations and preventing malaria are indoor residual spraying (IRS) and insecticide-treated nets (ITNs). The development of vector resistance to insecticides hinders the effectiveness of these methods and, consequently, the control of malaria. Research on the genetic development that results in insecticide resistance may help in identifying molecular markers for insecticide resistance (IR), enhance surveillance efforts, and deepen our understanding of the molecular mechanisms underlying IR. Through the conduction of the research identification of genes with co-expression patterns (modules) that are identical and hub genes that may serve as molecular markers for surveillance of insecticide resistance in Kenya and Benin., using the weighted gene co-expression network analysis (WGCNA) algorithm, a systems biology method. An. gambiae and An. arabiensis were collected from Benin and Kenya and phenotyped to different insecticides (Deltamethrin, pirimiphosmethyl and alphacypermethrin). The RNA was extracted and sequenced using Illumina sequencing platform. This RNASeq data was utilized for WGCNA analysis. After WGCNA analysis, the identified markers/targets primers designed, RNA extracted, converted RNA to cDNA and used in real-time PCR to validate their role in insecticide resistance. WGCNA average linkage hierarchical clustering was utilized in an. arabiensis and an. gambiae datasets which led to identification of 20 and 26 gene co-expression modules, respectively, along with their hub genes (highly connected genes) found within each module. Among the top hub genes identified in the two species, E3 ubiquitin-protein ligase, cuticular protein RR2, leucine-rich immune protein, and serine protease came out as possible markers and targets for tracking IR in these malaria vectors. Along with the identified markers, the molecular mechanisms were investigated which uncovered a complex process involved in development of IR in anopheles including neuronal signaling, odorant binding, cellular responses, cellular metabolism, gene regulation and immune modulation. Validation of these identified hub genes with real-time PCR confirmed their differential expression nature in resistant Anopheles arabiensis. In conclusion, these targets could be used in timely detection of resistance in different regions in Kenya and Benin.The integration of these factors (molecular mechanisms associated with IR) contributes to the development of new insecticides and in monitoring for IR could enhance the sustainability and effectiveness of interventions used.