Skin and gill microbiome profiles and network structures in farmed tilapia (Oreochromis niloticus) and their relationships with health conditions.

backgroundTilapia is one of the most popular finfish in aquaculture, but various emerging infectious diseases are limiting the growth of the tilapia aquaculture industry globally. The external mucosal microbiomes of fish act as a first line of defence for maintaining host health. However, how skin and gill microbiomes differ between healthy and naturally infected tilapia remains poorly understood. Here, we employed 16S rRNA and 18S rRNA high-throughput metabarcoding to characterise the microbiome of tilapia skin, gills, and water from ponds reported with diseased and non-diseased conditions, and to investigate signatures of microbial dysbiosis related to health conditions.

resultsMicrobial diversity varied significantly across different sample types (gill, skin and pond water) and geographical locations. Skin and gill microbiomes from reported non-diseased conditions differed in the presence of the commensal genus Cetobacterium, while diseased gill-skin were enriched with pathogenic genera including Flavobacterium, Aeromonas, Vibrio, Vogesella, and Klebsiella. Additionally, the relative abundance of diatom Cyclotella in pond water under diseased conditions appeared to be almost double that of non-diseased pond water, albeit this was statistically non-significant. Cetobacterium formed a core component of the bacterial genera in the non-diseased gill and skin microbiome. In contrast, Aeromonas formed a core component of the core microbiome in the diseased gill and skin microbiomes. Analysis of the microbial co-occurrence network in the diseased skin and gill found it to be relatively less complex compared with these tissues in the non-diseased state.

conclusionsThe findings show that the tilapia microbiome differs across the skin and gill tissue surfaces, and from the pond waters in which they are cultured. In reported diseased cases, these microbiomes show enrichment of potential pathogenic genera and less complex microbial co-occurrence networks, which may be used as an indicator of microbial dysbiosis in aquaculture systems. Understanding how these alterations may be used to predict potential disease outbreaks requires an understanding of the functional impacts of the changes in the microbial assemblages, allowing for timely interventions to mitigate the impacts of disease in the aquaculture system.