Effects of fish size and route of infection on virulence of a Danish Yersinia ruckeri O1 biotype 2 strain in rainbow trout (Oncorhynchus mykiss).

Abstract The comparative virulence of Yersinia ruckeri serotype O1 biotype 2 strain 07111224 from a Danish outbreak of enteric redmouth disease in rainbow trout (Oncorhynchus mykiss) was investigated in different sizes of rainbow trout (5, 19, 25 and 52 g) using a panel of challenge methods (bath, intraperitoneal (IP) injection, anal intubation and cohabitation). The results show that Y. ruckeri 07111224 is virulent enough to successfully cause mortality in 5–52 g rainbow trout through three different infection routes (bath, IP injection and anal intubation), and strongly suggests that susceptibility to Y. ruckeri infection is dependent on fish size. Bath challenge (108 CFU/ml) caused 79% mortality in 5 g fish, while the mortality in 19–52 g fish was approximately 26%. IP injection challenges (105–106 CFU/individual) resulted in high mortalites in fish of all sizes. Anal intubation challenges (109 CFU/individual) induced 82% mortality in 5 g fish and decreased with increasing fish weight. Finally, cohabitation challenges induced 45% mortality in 5 g test fish when test fish were cohabitated 1:1 with shedder fish, while mortalities in test fish of five other challenge groups were lower (5–25%). Statistical models were fit to the data from each challenge model. The model parameter predictions suggests statistically significant effects of fish weight for bath, IP and anal intubation challenges. Furthermore, they highlight the importance of the ratio of shedders to test fish in cohabitation challenges and of the challenge doses, in particular for the IP challenge model. Highlights • Comprehensive study comparing commonly used, as well as novel challenge models. • Each model is performed in multiple fish weight groups for comparison. • Fish weight and route of infection are major determinants in obtained results. • Detailed insights into each challenge model provided by statistical modeling. • Combining challenge data and model parameters facilitates comparison of models. [ABSTRACT FROM AUTHOR]