eDNA metabarcoding of amoebic gill disease (AGD) pathogens exposes potential vectors and reservoirs

Abstract

Aquaculture worldwide is under constant scrutiny and financial pressure to maintain fish health, while providing an essential food source to a growing populace. To assist in monitoring mariculture sites and preventing harmful diseases, we tested a recently proposed method of trace pathogen detection using eDNA metabarcoding. The target pathogen, Paramoeba perurans, is the etiological agent of AGD, a respiratory infection which causes significant mortalities and treatment costs, affecting a growing percentage of the salmon aquaculture industry each year. 14 reference sequences of the COI Leray gene fragment from the family, genus, and species level were curated in order to identify DNA extracted and amplified from environmental samples collected at an active commercial scale aquaculture facility in Arctic Norway. The 12 Paramoeba sp. assigned MOTUs that were detected did not directly match any references, but showed considerable genetic relatedness to P. pemaquidensis, a known co-infector, and displayed significant spatiotemporal trends within the sampled area and time series. Over a grid of 14 sampling points, distance from the farm, transect direction, and depth all effected the relative abundance of Paramoeba pathogens detected, and combined with known physical factors, provided evidence that incubation of these pathogens may have been occurring within the fish population despite no clinical signs being observed. Potential reservoirs for the pathogens in sediments and biofouling were evidenced by temporal changes in relative abundance coinciding with commercial activities at the start of production, and the existence of reads in the sediment prior to salmon placement. Continuous detection of Paramoeba throughout winter months indicate a lower temperature tolerance than was previously recorded for this genus, as low as 2.5°C. Phylogenetic assessment of detected MOTUs revealed 12 divergent haplotypes with varying degrees of relatedness to reference Paramoeba spp. and each other, exposing a novel and diverse assemblage of Paramoeba in this region. These discoveries highlight the capability of COI Leray metabarcoding to identify trace pathogens and assess spatiotemporal trends in their relative abundance and diversity, encouraging our continued monitoring of Skogshamn and other aquaculture facilities going forward.