One Health (Jun 2024)

A species-independent lateral flow microarray immunoassay to detect WNV and USUV NS1-specific antibodies in serum

  • Bijan Godarzi,
  • Felicity Chandler,
  • Anne van der Linden,
  • Reina S. Sikkema,
  • Erwin de Bruin,
  • Edwin Veldhuizen,
  • Aart van Amerongen,
  • Andrea Gröne

Journal volume & issue
Vol. 18
p. 100668

Abstract

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Arboviruses such as West Nile Virus (WNV) and Usutu Virus (USUV) are emerging pathogens that circulate between mosquitoes and birds, occasionally spilling over into humans and horses. Current serological screening methods require access to a well-equipped laboratory and are not currently available for on-site analysis. As a proof of concept, we propose here a species-independent lateral flow microarray immunoassay (LMIA) able to quickly detect and distinguish between WNV Non-Structural 1 (NS1) and USUV NS1-specific antibodies. A double antigen approach was used to test sera collected from humans, horses, European jackdaws (Corvus monedula), and common blackbirds (Turdus merula). Optimization of the concentration of capture antigen spotted on the LMIA membrane and the amount of detection antigen conjugated to detector particles indicated that maximizing both parameters increased assay sensitivity. Upon screening of a larger serum panel, the optimized LMIA showed significantly higher spot intensity for a homologous binding event. Using a Receiver Operating Characteristics (ROC) curve, WNV NS1 LMIA results in humans, horses, and C. monedula showed good correlation when compared to “gold standard” WNV FRNT90. The most optimal derived sensitivity and specificity of the WNV NS1 LMIA relative to corresponding WNV FRNT90-confirmed sera were determined to be 96% and 86%, respectively. While further optimization is required, this study demonstrates the feasibility of developing a species-independent LMIA for on-site analysis of WNV, USUV, and other arboviruses. Such a tool would be useful for the on-site screening and monitoring of relevant species in more remote or low-income regions.

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