Comparative Genomic Analysis of Three Salmonid Species Identifies Functional Candidate Genes Involved in Resistance to the Intracellular Bacterium Piscirickettsia salmonis.

José M Yáñez,Liane N Bassini,Rodrigo Pulgar,Grazyella M Yoshida,Maria E López,Jean P Lhorente,Roberto Carvalheiro,Ángel Parra,Agustín Barría,Kris A Christensen,Katharina Correa

doi:10.3389/fgene.2019.00665

Abstract

Piscirickettsia salmonis is the etiologic agent of salmon rickettsial syndrome (SRS) and is responsible for considerable economic losses in salmon aquaculture. The bacterium affects coho salmon (CS; Oncorhynchus kisutch), Atlantic salmon (AS; Salmo salar), and rainbow trout (RT; Oncorhynchus mykiss) in several countries, including Norway, Canada, Scotland, Ireland, and Chile. We used Bayesian genome-wide association study analyses to investigate the genetic architecture of resistance to P. salmonis in farmed populations of these species. Resistance to SRS was defined as the number of days to death and as binary survival (BS). A total of 828 CS, 2130 RT, and 2601 AS individuals were phenotyped and then genotyped using double-digest restriction site-associated DNA sequencing and 57K and 50K Affymetrix® Axiom® single nucleotide polymorphism (SNP) panels, respectively. Both traits of SRS resistance in CS and RT appeared to be under oligogenic control. In AS, there was evidence of polygenic control of SRS resistance. To identify candidate genes associated with resistance, we applied a comparative genomics approach in which we systematically explored the complete set of genes adjacent to SNPs, which explained more than 1% of the genetic variance of resistance in each salmonid species (533 genes in total). Thus, genes were classified based on the following criteria: i) shared function of their protein domains among species, ii) shared orthology among species, iii) proximity to the SNP explaining the highest proportion of the genetic variance, and iv) presence in more than one genomic region explaining more than 1% of the genetic variance within species. Our results allowed us to identify 120 candidate genes belonging to at least one of the four criteria described above. Of these, 21 of them were part of at least two of the criteria defined above and are suggested to be strong functional candidates influencing P. salmonis resistance. These genes are related to diverse biological processes, such as kinase activity, GTP hydrolysis, helicase activity, lipid metabolism, cytoskeletal dynamics, inflammation, and innate immune response, which seem essential in the host response against P. salmonis infection. These results provide fundamental knowledge on the potential functional genes underpinning resistance against P. salmonis in three salmonid species.

Highlights

Infectious diseases are responsible for large economic losses in salmon farming
The aims of this study were i) to dissect the genetic architecture of resistance to P. salmonis in coho salmon (CS), Atlantic salmon (AS), and rainbow trout (RT) using single nucleotide polymorphism (SNP) and phenotype data modeled together using Bayesian genomewide association study (GWAS) approach, ii) to identify genomic regions involved in P. salmonis resistance among the three salmonid species, and iii) to identify candidate genes associated with P. salmonis resistance through a comparative genomics analysis
We hypothesize that variations in the sequences of these genes could play important roles in the host response to P. salmonis infection, which could be tested through new genetic approaches such as gene editing using CRISPR-Cas9 and used through genomic selection (GS) or more traditional selection practices

Summary

Introduction

Infectious diseases are responsible for large economic losses in salmon farming. Current control protocols and treatments are based on antibiotics and vaccines. The effectiveness of both strategies in field conditions is not optimal (Rozas and Enríquez, 2014). From the total mortalities ascribed to infectious diseases in Chile, SRS is responsible for 18.3%, 92.6%, and 67.9% in CS, RT, and AS, respectively (Sernapesca, 2018). These mortality rates, together with other factors such as antibiotic treatments and vaccinations, have generated economic losses up to USD $450 million per year (Camusetti et al, 2015)

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