Microbiome monitoring for risk assessment in recirculating aquaculture systems

Aquaculture is a key technology for achieving food security, as it is a source of high-quality protein that can be produced locally with a reduced environmental impact.

Recirculating Aquaculture Systems (RAS) are a promising development for achieving sustainable production, but experience high densities of fish and organic matter, which can favor the growth of opportunistic harmful bacteria.

Traditional pathogen control methods, such as disinfection or the use of antibiotics, are costly and can pose environmental risks, resulting in a less sustainable operation.

Instead, monitoring the microbiome associated to aquaculture facilities can help us develop strategies to prevent disease and promote beneficial bacteria.

However, our knowledge of microbial dynamics in aquaculture systems is still limited.In this project, we will use genome-resolved metagenomics to identify microbial species from a pilot RAS facility and classify them into different threat levels based on their genomic features. We use amplicon sequencing to track their dynamics when subjected to different amounts and qualities of organic matter.

Microbiological and chemical data will be integrated with machine learning in order to identify risk factors leading to the overgrowth of potentially harmful bacteria.

We will finally translate this approach to commercial partners by collaborating with a Swedish company, using our newly developed method to assess the role of microorganisms in their daily operations.