Phylogenomic diversity of Vibrio species and other Gammaproteobacteria isolated from Pacific oysters (Crassostrea gigas) during a summer mortality outbreak

The Pacific oyster (PO), Crassostrea gigas, is an important commercial marine species but periodically experiences large stock losses due to disease events known as summer mortality. Summer mortality has been linked to environmental perturbations and numerous viral and bacterial agents, indicating this disease is multifactorial in nature. In 2013 and 2014, several summer mortality events occurred within the Port Stephens estuary (NSW, Australia). Extensive culture and molecular-based investigations were undertaken and several potentially pathogenic Vibrio species were identified. To improve species identification and genomically characterise isolates obtained from this outbreak, whole-genome sequencing (WGS) and subsequent genomic analyses were performed on 48 bacterial isolates, as well as a further nine isolates from other summer mortality studies using the same batch of juveniles. Average nucleotide identity (ANI) identified most isolates to the species level and included members of the Photobacterium, Pseudoalteromonas, Shewanella and Vibrio genera, with Vibrio species making up more than two-thirds of all species identified. Construction of a phylogenomic tree, ANI analysis, and pan-genome analysis of the 57 isolates represents the most comprehensive culture-based phylogenomic survey of Vibrios during a PO summer mortality event in Australian waters and revealed large genomic diversity in many of the identified species. Our analysis revealed limited and inconsistent associations between isolate species and their geographical origins, or host health status. Together with ANI and pan-genome results, these inconsistencies suggest that to determine the role that microbes may have in Pacific oyster summer mortality events, isolate identification must be at the taxonomic level of strain. Our WGS data (specifically, the accessory genomes) differentiated bacterial strains, and coupled with associated metadata, highlight the possibility of predicting a strain’s environm