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Ibrahim, S. S.

Publications and source records attributed to Ibrahim, S. S..

2 recordsLinked to original sources

Molecular drivers of insecticide resistance in the Sahelo-Sudanian populations of a major malaria vector

Information on common markers of metabolic resistance in malaria vectors from countries sharing similar eco-climatic characteristics can facilitate coordination of malaria control. Here, we characterized populations of the major malaria vector Anopheles coluzzii from Sahel region, spanning four sub-Saharan African countries: Nigeria, Niger, Chad and Cameroon. Genome-wide transcriptional analysis identified major genes previously implicated in pyrethroid and/or cross resistance to other insecticides, overexpressed across the Sahel, including CYP450s, glutathione S-transferases, carboxylesterases, and cuticular proteins. Several, well-known markers of insecticide resistance were found in high frequencies - including in the voltage-gated sodium channel (V402L, I940T, L995F, I1527T and N1570Y), the acetylcholinesterase-1 gene (G280S) and the CYP4J5-L43F (fixed). High frequencies of the epidemiologically important chromosomal inversions, 2La, 2Rb and 2Rc were observed ([~]80% for 2Rb and 2Rc). The 2La alternative arrangement is fixed across the Sahel. Low frequencies of these inversions (<10%) were observed in the fully insecticide susceptible laboratory colony of An. coluzzii (Ngoussou). Several of the most commonly overexpressed metabolic resistance genes sit in these three inversions. Two commonly overexpressed genes, GSTe2 and CYP6Z2 were functionally validated. Transgenic Drosophila melanogaster expressing GSTe2 exhibited extremely high DDT and permethrin resistance (mortalities < 10% in 24 h). Serial deletion of the 5 intergenic region, to identify putative nucleotide(s) associated with GSTe2 overexpression, revealed that simultaneous insertion of adenine nucleotide and a transition (T->C), between Fork-head box L1 and c-EST putative binding sites were responsible for the high overexpression of GSTe2 in the resistant mosquitoes. Transgenic flies expressing CYP6Z2 exhibited marginal resistance towards 3-phenoxybenzylalcohol (a primary product of pyrethroid hydrolysis by carboxylesterases) and a type II pyrethroid, -cypermethrin. However, significantly higher mortalities were observed in CYP6Z2 transgenic flies compared with controls, on exposure to the neonicotinoid, clothianidin. This suggests a possible bioactivation of clothianidin into a toxic intermediate, which if true make it an ideal insecticide against populations of An. coluzzii overexpressing this P450. These findings will facilitate regional collaborations within the Sahel region, and refine implementation strategies through re-focusing interventions, improving evidence-based, cross-border policy towards local and regional malaria pre-elimination.

genomics↗

Multi-omics analysis identifies a CYP9K1 haplotype conferring pyrethroid resistance in the malaria vector Anopheles funestus in East Africa

Metabolic resistance to pyrethroids is a menace to the continued effectiveness of malaria vector controls. Its molecular basis is complex and varies geographically across Africa. Here, we used a multi-omics approach, followed-up with functional validation to show that a directionally selected haplotype of a cytochrome P450, CYP9K1 is a major driver of resistance in Anopheles funestus. A PoolSeq GWAS using mosquitoes alive and dead after permethrin exposure, from Malawi and Cameroon, detected candidate genomic regions, but lacked consistency across replicates. Targeted deep sequencing of candidate resistance genes and genomic loci detected several SNPs associated with known pyrethroid resistance QTLs. The most significant SNP was in the cytochrome P450 CYP304B1 (Cameroon), CYP315A1 (Uganda) and the ABC transporter gene ABCG4 (Malawi). However, when comparing field resistant mosquitoes to laboratory susceptible, the pyrethroid resistance locus rp1 and SNPs around the ABC transporter ABCG4 were consistently significant, except for Uganda where CYP9K1 P450 was markedly significant. In vitro heterologous metabolism assays with recombinant CYP9K1 revealed that it metabolises type II pyrethroid (deltamethrin; 64% depletion) but not type I (permethrin; 0%), while moderately metabolising DDT (17%). CYP9K1 exhibited a drastic reduction of genetic diversity in Uganda, in contrast to other locations, highlighting an extensive selective sweep. Furthermore, a glycine to alanine (G454A) amino acid mutation located between the meander and cysteine pocket of CYP9K1 was detected in all Ugandan mosquitoes. This study sheds further light on the complex evolution of metabolic resistance in a major malaria vector, by adding further resistance genes and variants that can be used to design field applicable markers to better track this resistance Africa-wide. Author SummaryMetabolic resistance to pyrethroids is a menace to the continued effectiveness of malaria vector controls. Its molecular basis is complex and varies geographically across Africa. Here, we used several DNA based approach to associate genomic differences between resistant and susceptible mosquitoes from several field and laboratory populations of the malaria vector Anopheles funestus. We followed-up our genomic analyses with functional validation of a candidate resistance gene in East Africa. This gene (CYP9K1) is a member of the cytochrome P450 gene-family that helps to metabolise, and thereby detoxify, pyrethroid insecticides. We show that this gene is a major driver of resistance to a specific sub-class of pyrethroid insecticides only, with moderate to no effects on other insecticides used against Anopheles funestus. We were able to link resistance in this gene to a mutation that changes the amino acid glycine to alanine that may impact how the protein-product of this gene binds to target insecticides. In addition to demonstrating the biochemical specificity of an evolutionary response, we have broadened the available pool of genes can be used to monitor the spread of insecticide resistance in this species.

genomics↗