Planktonic drivers of gill disease in farmed Atlantic salmon: from mechanisms to solutions

Lead Research Organisation: University of Aberdeen
Department Name: Inst of Biological and Environmental Sci

Abstract

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Technical Summary

Salmo salar mariculture is facing an intolerable burden of disease from gill damage caused by planktonic taxa. The resultant syndrome is termed 'Complex Gill Disease' (CGD). Cleaning of biofouling from the surface of net pen infrastructure may cause the release of further biological stressors into the water column. The identities of the various planktonic species that cause CGD are largely unknown. Furthermore, the pathophysiology of CGD is poorly understood. For example, the role of inflammatory processes in driving CGD are not clear, nor is it known how or whether the individual effects of different planktonic agents integrate at the immunological / physiological level to result in the pathology observed. Pilot data indicate that planktonic exposure profiles are highly variable between sites and unexpected taxa (e.g. tunicates) are even stronger correlates with CGD / mortality than those usually though to cause disease (e.g. hydrozoans, diatoms).

In this project, we will sample extensively (nine production sites over three years) to reveal the planktonic correlates of CGD via eDNA metabarcoding and microscopy. We will exploit transcriptomic markers of CGD that cover innate immune and inflammatory pathways to characterise gill damage and understand how responses to planktonic taxa integrate in the gill to drive disease. Indoor marine aquarium trials using post-smolts and danger-associated molecular pattern assays using salmon cell lines will be undertaken to understand the pathophysiology of acute gill challenge during net clearing activities. Mobile aquaria will then be deployed alongside aquaculture sites to simulate complex plankton exposure and enable a genome-wide association study for resilience to CGD and trial physical interventions against CGD. Data streams (planktonic, biomarker, genomic, environmental, phenotypic) will be integrated into a structural causal model to enable causal inference of CGD and inform best practise for future interventions.

Publications

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