Integrating data from multiple African countries to identify and validate novel insecticide resistance candidates in the malaria vector An. gambiae sl

Vector control strategies are key to malaria eradication efforts; an estimated 80 % of the 663 million malaria cases prevented since the year 2000 were due to insecticide treated nets (ITNs and indoor residual spraying). As a result of this scale up in vector control, resistance to public health insecticides is now wide spread. All ITNs are treated with pyrethroids and yet 80% of African countries reporting resistance to this class. The precise impact of this resistance on the protective efficacy of ITNs is still being debated but there is consensus that there is an urgent need for informed field strategies to prevent failure of these crucial interventions and hence; the resurgence of malaria associated morbidity and mortality in the world's poorest regions.

Concerns over the potential impact of vector control failure have stimulated the generation of a wide variety of field data available on malaria vectors in sub-Saharan Africa, including extensive population genetics resources and continent-wide gene expression data. These data represent an incredibly diverse and rich set of information that have previously been examined in isolation. Integrating these multi-species, multi-country datasets in an objective way, is expected to identify previously unrecognised genes and pathways associated with insecticide resistance. These in silico results will be validated in a laboratory setting prior to the development of diagnostics which will be evaluated under field settings. The availability of a comprehensive set of resistance makers will also enable more detailed studies of the impact of resistance on the fitness of the mosquito and its ability to transmit the malaria parasite. The data generated will be of value to control programmes to help mitigate the impact of resistance, and industry partners, developing new insecticides and vector control tools.

I will use big data integration techniques in R to combine noisy field -omics and populations genetics data with the aim of identifying and characterising novel insecticide resistant candidate transcripts or pathways in Anopheles gambiae sl.

Knockdown (KD) of identified transcripts through dsRNA injection and the phenotypes after exposure to a panel of insecticides will determine whether the candidate is involved in insecticide resistance and hence carried forward to characterisation.

If the candidate transcript is hypothesised to be part of a pathway, KD of pathway partners and qPCR on these for each of the KDs will be used to verify the pathway. Once the upstream candidate is identified microarray experiments comparing the KD mosquitoes with GFP-injected controls will identify larger-scale changes.

Characterisation of the transcript will depend upon its hypothesised function. To identify these putative functions, a variety of resources are available, including literature trawling, enrichment analysis and homology-based methods. An array of techniques can be employed to determine the function of the candidate including insecticide binding assays, behavioural assays, localisation studies, proteomics, structural resolving, up-regulation through transgenic lines and complete knock out via CRISPR.

Population genetics tools and heritability analysis can then be employed to determine whether there is scope and value to use the transcript as an in-field diagnostic. By identifying SNPs or other variable genetic regions in-field resistance diagnostics via PCR, or restriction digest PCR can be developed.

A further output from this fellowship will be to inform industry partners such as IVCC of potential candidate targets for new insecticides. To this end, Plasmodium falciparum competence of the mosquito will be assessed to ensure that disruption of the transcript will not affect this in a significant way.