Ong, Jun Kiat Edwin, Nair, Thiviya, Ng, Tze Hann, Domingos, Jose A., and Gomes, Giana Bastos (2022) Identification of scale drop disease virus based on environment DNA in an aquaculture facility of Singapore. Aquaculture, 563. 738993.

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Abstract

Scale drop disease virus (SDDV) is an emerging virus in Asian countries that infects barramundi, resulting in huge economic losses in aquaculture industries. Current methods of diagnosing viral diseases in aquaculture are time-consuming and rely on infected sick fish to confirm results. Delays in the management of outbreaks often occur due to the inefficiencies of conventional pathogen detection methods and difficulty in visually inspecting animals in aquaculture facilities. Environment DNA (eDNA) approaches on the other hand investigate the provenance of the genetic material in the environment, thus allowing the detection of specific pathogens even before animals become infected. SDDV was recently detected in the mucus and fin clips of infected fish, therefore it presents itself as an ideal viral candidate pathogen to test the viability of eDNA as an early monitoring tool for its detection and quantification from water samples. This study aimed to test for the first time, the viability of detecting SDDV using eDNA approach in an aquaculture research facility in Singapore with past reports of SDDV infected barramundi. Quantitative PCR (qPCR) and digital droplet PCR (ddPCR) targeting SDDV in water samples from several tanks over a period of 6 months were conducted to understand the association of abiotic and biotic variables with SDDV abundance. Both PCR techniques detected SDDV eDNA, with ddPCR outperforming qPCR as it was more sensitive for low virus copy numbers. SDDV was also detected in fingerling fin clips from tanks where higher levels of its genome were found. SDDV abundance was significantly associated with periods of higher rainfall and lower salinity (p < 0.05). Although SDDV abundance was more strongly associated to these cooler periods, temperature itself was not significantly associated with SDDV (p > 0.05). These findings demonstrated ddPCR as a more efficient technique to detect low levels of SDDV in aquatic environments. The eDNA approach offers an innovative way to identify viral diseases in aquaculture as generally very few options are available for farmers to pre-empt outbreaks. It also warrants future studies to better understand SDDV dynamics throughout the year and its association with the water physicochemical parameters and host microbiome.

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