Moving towards improved surveillance and earlier diagnosis of aquatic pathogens: From traditional methods to emerging technologies

Abstract

Early and accurate diagnosis is key to mitigating the impact of infectious diseases, along with efficient surveillance. This however is particularly challenging in aquatic environments due to hidden biodiversity and physical constraints. Traditional diagnostics, such as visual diagnosis and histopathology, are still widely used, but increasingly technological advances such as portable next generation sequencing (NGS) and artificial intelligence (AI) are being tested for early diagnosis. The most straightforward methodologies, based on visual diagnosis, rely on specialist knowledge and experience but provide a foundation for surveillance. Future computational remote sensing methods, such as AI image diagnosis and drone surveillance, will ultimately reduce labour costs whilst not compromising on sensitivity, but they require capital and infrastructural investment. Molecular techniques have advanced rapidly in the last 30 years, from standard PCR through loop-mediated isothermal amplification (LAMP) to NGS approaches, providing a range of technologies that support the currently popular eDNA diagnosis. There is now vast potential for transformative change driven by developments in human diagnostics. Here we compare current surveillance and diagnostic technologies with those that could be used or developed for use in the aquatic environment, against three gold standard ideals of high sensitivity, specificity, rapid diagnosis, and cost-effectiveness.

Keywords: aquatic diagnostics; aquatic disease; disease surveillance; eDNA; molecular diagnostics; visual diagnosis.

FIGURE 1

Visual diagnostic techniques and example of their application to specific aquatic pathogens. Images sourced as follows: Saprolegnia parasitica—Environment Agency, Trichodina spp.—KoiQuestion (https://www.flickr.com/photos/koiquest10/26357384027), T. bryosalmonae—AquaTT (https://commons.wikimedia.org/wiki/File:T._bryosalmonae_parasites_in_rainbow_trout_kidney._Tissue_section_stained_with_haematoxylin_and_eosin.jpg), L. salmonis—Thomas Bjørkan (https://commons.wikimedia.org/wiki/File:Salmonlouse.jpg), A. invadans—adapted from Majeed et al. (https://commons.wikimedia.org/wiki/File:Dwarf_gourami_infected_by_Aphanomyces_invadans.png)

FIGURE 2

Diseases of fish which can be diagnosed through visual observation. (a) Juvenile Argulus foliaceus on the caudal fin of a three‐spined stickleback (Gastrosteus aculeatus). [Photograph by R. Hunt]. (b) Puffy skin disease in a rainbow trout (Oncorhynchus mykiss) [Photo by Environment Agency]. (c) Red vent syndrome in an Atlantic salmon (Salmo salar). [Photograph by Environment Agency]. (d) An Atlantic salmon suffering from Saprolegniasis caused by Saprolegnia parasitica [Photograph by Environment Agency]

FIGURE 3

IHC staining for Tetracapsuloides bryosalmonae in kidney tissue of farmed rainbow trout (Onchorhynchus mykiss). (a) positive control, T. bryosalmonae indicated by arrows. (b, c) negative kidney tissue

FIGURE 4

Molecular diagnostic techniques, and examples of their application to specific aquatic pathogens. Images sourced as follows: A. salmonicida—Robert Durborow (https://commons.wikimedia.org/wiki/File:Furunculosis_on_Brown_Trout_F12‐50.JPG), Chilodonella hexasticha protist—Picturepest (https://pxhere.com/en/photo/363624), salmonid alphavirus Salmo salar—Hans‐Petter Fjeld (https://commons.wikimedia.org/wiki/File:Salmo_salar‐Atlantic_SalmonAtlanterhavsparken_Norway_(cropped).JPG)