A standardized protocol for the detection of arboviruses in different Aedes mosquito species in North Borneo Sabah, Malaysia

TL;DR

This study presents a standardized protocol for detecting arboviruses in various Aedes mosquito species in Sabah, Malaysia, improving virus detection and surveillance.

Contribution

The study introduces a standardized RNA extraction protocol validated across six Aedes species, enabling broader arbovirus surveillance.

Findings

A standardized RNA extraction protocol was validated for six Aedes species in Sabah.

Multiplex real-time PCR detection showed consistent cycle threshold values across species.

The protocol improves virus detection efficiency in ecologically diverse environments.

Abstract

Arboviruses, including dengue, Zika and chikungunya viruses, are mainly transmitted by Aedes mosquitoes and pose a threat to public health. The viruses are transmitted by the primary vector, Aedes aegypti, which is more commonly found in urban environments. However, with increasing urbanization, the overlap of rural and forested areas where different Aedes species are found could also contribute to transmission. Nevertheless, most extraction methods focus on human blood samples or on Ae. aegypti, which limits standardization of virus detection in a variety of less common Aedes populations, especially sylvatic species.

In this study, we demonstrated a standardized protocol for extracting sufficient amounts of RNA for detection from a single mosquito sample. We validated the protocol by extracting arboviruses from six different Aedes species collected in the field in Sabah, Malaysia: Ae. aegypti, Ae. albopictus, Ae. poecilus, Ae. butleri, Ae. niveus, and Ae. vexans. Multiplex real-time PCR detection yielded consistent cycle threshold (Ct) values across species (range: 18.9–40.3), with a positivity cut-off of Ct < 41. Our results show that this protocol improves current practice by extending the target sample to different Aedes mosquito species, ultimately contributing to more efficient virus detection and supporting more comprehensive surveillance, even in ecologically diverse environments.

(include separately in a different file – TIFF format)

Image, graphical abstract

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