Reverse transcription recombinase polymerase amplification-lateral flow assay for detection of pathogenic orthoflaviviruses in mosquito vectors

Abstract

Background. The genus Orthoflavivirus primarily consists of arthropod-borne viruses capable of infecting vertebrate hosts and causing serious human diseases such as dengue fever, Zika fever, Japanese encephalitis, West Nile fever, and yellow fever. This study describes the development of a simple and field-deployable detection system for multiple pathogenic orthoflavivirus species using the recombinase polymerase amplification (RPA) technique. Methods. Several previously published broad-specific primers targeting the genus Orthoflavivirus were evaluated. A new primer pair, FlaviPath-F and FlaviPath-R, was designed and tested for its applicability in an RPA assay. The RPA protocol was experimentally optimized, with a focus on determining the assay's sensitivity and assessing the primers' specificity against pathogenic orthoflaviviruses. Results. The primer FlaviPath-F and FlaviPath-R targeted 36% of the selected pathogenic orthoflavivirus species without cross-reacting with non-pathogenic strains based on in silico analysis. The RPA assay successfully amplified DNA oligonucleotides from dengue virus, Japanese encephalitis virus, Zika virus, and West Nile virus. Furthermore, positive amplification was observed in RNA samples extracted from mosquitoes infected with dengue and Zika viruses. The RPA assay demonstrated high sensitivity, with the potential to detect as few as a single viral RNA copy, although confirmation is needed for concentrations below the detection limit of 104 RNA copies. Discussion. This is the first study to develop an RPA-based method for the detection of multiple orthoflavivirus pathogens in mosquito vectors. The reverse transcription recombinase polymerase amplification assays with lateral flow dipsticks (RT-RPA-LFD) platform offers a rapid, cost-effective tool for identifying regions at risk of arboviral transmission, supporting the targeting of individual viral diseases. This technique holds promise as an early warning system for emerging arboviral threats in public health.