Mechanisms of transcriptional regulation in Anopheles gambiae revealed by allele-specific expression
Year: 2024
Type: article
Abstract: Malaria control relies on insecticides targeting the mosquito vector, but this is increasingly compromised by insecticide resistance, which can be achieved by elevated expression of detoxifying enzymes that metabolize the insecticide. In diploid organisms, gene expression is regulated both in cis, by regulatory sequences on the same chromosome, and by trans acting factors, affecting both alleles equally. Differing levels of transcription can be caused by mutations in cis-regulatory modules (CRM), but few of these have been identified in mosquitoes. We crossed bendiocarb-resistant and susceptible Anopheles gambiae strains to identify cis-regulated genes that might be responsible for the resistant phenotype using RNAseq, and CRM sequences controlling gene expression in insecticide resistance relevant tissues were predicted using machine learning. We found 115 genes showing allele-specific expression (ASE) in hybrids of insecticide susceptible and resistant strains, suggesting cis-regulation is an important mechanism of gene expression regulation in A. gambiae. The genes showing ASE included a higher proportion of Anopheles-specific genes on average younger than genes with balanced allelic expression.
Language: English
Topic: Malaria Research and Control
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: G127891921, G1279806978, Using spatial statistics and genomics to develop epidemiologically relevant definitions of insecticide resistance in African Malaria Vectors, Structural and biophysical mechanism of microRNA nuclear export, G2347075150, Pan-African Mosquito Control Association (PAMCA) - to support bioinformatics training for vector biologists in Africa, G2655491414, G3611179903, Translational & Quantitative Skills Doctoral Training Programme (DTP) in Global Health : Industrial CASE Studentships, New advances in insecticide resistance genomics: using Machine Learning to predict resistance phenotype from large-scale genomic data., G597897128, Wellcome Sanger Institute - to support the establishment of a working system for genomic surveillance of Plasmodium parasites and Anopheles mosquitoes that will overcome criti..., G6664751172, G7063735130
OpenAlex ID: W7074094622
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