Engineered Nanobodies for early and accurate diagnosis of dengue virus infection
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Background
Dengue virus (DENV), a mosquito-borne flavivirus responsible for dengue disease, has emerged as an escalating global health concern, with cases increasing sharply in recent decades. In Argentina, dengue has transitioned from a sporadic disease to a recurrent epidemic, now affecting 18 of 23 provinces and exposing gaps in diagnostic capacity.
Methodology and Principal Findings
The DENV genome encodes the non-structural protein 1 (NS1), a key biomarker for early infection detection. Considering the restrictions of the public health system in accessing commercially available diagnostic kits, we developed a combined ELISA system incorporating Nanobodies designed to target NS1 across all four DENV serotypes as detection antibodies. This system demonstrates excellent discriminative performance (AUC > 0.9), with a diagnostic sensitivity of 93.6% (95% CI: 86.6–97.6%) and a specificity of 81.1% (95% CI: 70.3–89.3%). The analytical sensitivity showed strong correlation between sera pool dilutions and detected signals, with a limit of detection aligning with reported NS1 concentrations in human samples. While the system exhibits limitations in detecting NS1 from DENV-4, it successfully identified cases in patients five days post-symptom onset who were initially considered epidemiologically negative for dengue infection.
Significance
Our results underscore the urgent need for accessible, high-precision diagnostic tools in regions facing a surge in dengue outbreaks. Additionally, they highlight the necessity of revising current diagnostic algorithms to enhance the detection of late-presenting cases.
Author summary
Dengue virus is a major public health concern in Latin America, with recurring outbreaks placing significant strain on healthcare systems. Early detection is crucial for effective patient management and controlling the spread of the virus. This study focuses on the development and optimization of a combined ELISA-based detection system for dengue virus, leveraging Nanobody technology to enhance sensitivity and specificity. Compared to traditional methods, this approach offers a cost-effective and scalable solution, making it particularly valuable for resource-limited settings. The findings presented here underscore the importance of robust diagnostic algorithms that can accurately identify both early and late-stage infections, addressing gaps in current detection strategies. By improving the precision and accessibility of dengue diagnostics, this research contributes to public health initiatives aimed at mitigating the impact of outbreaks across Latin America. The insights gained could support local and regional efforts in establishing stronger surveillance programs and informed policy decisions, ultimately reducing the burden of dengue in affected communities.
