et al., A. (2025). Mapping Tool of the Potential Restriction Sites for Field Identification of Major Mosquito Vectors in Aquatic Ecosystems. Egyptian Journal of Aquatic Biology and Fisheries, 29(4), 3459-3474. doi: 10.21608/ejabf.2025.450438
Ahmed et al.. "Mapping Tool of the Potential Restriction Sites for Field Identification of Major Mosquito Vectors in Aquatic Ecosystems". Egyptian Journal of Aquatic Biology and Fisheries, 29, 4, 2025, 3459-3474. doi: 10.21608/ejabf.2025.450438
et al., A. (2025). 'Mapping Tool of the Potential Restriction Sites for Field Identification of Major Mosquito Vectors in Aquatic Ecosystems', Egyptian Journal of Aquatic Biology and Fisheries, 29(4), pp. 3459-3474. doi: 10.21608/ejabf.2025.450438
et al., A. Mapping Tool of the Potential Restriction Sites for Field Identification of Major Mosquito Vectors in Aquatic Ecosystems. Egyptian Journal of Aquatic Biology and Fisheries, 2025; 29(4): 3459-3474. doi: 10.21608/ejabf.2025.450438
Mapping Tool of the Potential Restriction Sites for Field Identification of Major Mosquito Vectors in Aquatic Ecosystems
Advances in genomic technologies have driven the development of molecular tools for mosquito species identification—an essential part of disease vector surveillance—especially during their aquatic life stages, where morphologically similar species can differ greatly in their role as vectors. Traditional genomic approaches, though accurate, are often labor-intensive, time-consuming, and costly, posing challenges for large-scale surveillance in resource-limited settings. This study proposes an automated, restriction enzyme–based method to distinguish three medically significant mosquito species: Anopheles gambiae, Aedes aegypti, and Culex quinquefasciatus. This focus is critically important as GST-mediated metabolic resistance to insecticides has been linked to increased Plasmodium infection in Anopheles mosquitoes, raising serious concerns for malaria epidemiology in sub-Saharan Africa. By targeting polymorphic regions within three glutathione S-transferase genes (Gste1, Gste3, Gstt2), species-specific restriction profiles are generated using three selected enzymes. Experimental validation demonstrated a classification accuracy exceeding 99%. The method enables simultaneous discrimination among species and improves reliability by minimizing false negatives that could arise from missing sequences. Unlike sequencing, this workflow is scalable, cost-effective, and suitable for rapid, field-friendly deployment. While this study focuses on three species, the method is adaptable to others with known GST polymorphisms. Overall, restriction enzyme mapping offers a robust, low-cost alternative to sequencing, with the potential to transform mosquito vector identification and enhance public health interventions.
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