SALARECON connects the Atlantic salmon genome to growth and feed efficiency

PLoS Comput Biol. 2022 Jun 10;18(6):e1010194. doi: 10.1371/journal.pcbi.1010194. eCollection 2022 Jun.

Abstract

Atlantic salmon (Salmo salar) is the most valuable farmed fish globally and there is much interest in optimizing its genetics and rearing conditions for growth and feed efficiency. Marine feed ingredients must be replaced to meet global demand, with challenges for fish health and sustainability. Metabolic models can address this by connecting genomes to metabolism, which converts nutrients in the feed to energy and biomass, but such models are currently not available for major aquaculture species such as salmon. We present SALARECON, a model focusing on energy, amino acid, and nucleotide metabolism that links the Atlantic salmon genome to metabolic fluxes and growth. It performs well in standardized tests and captures expected metabolic (in)capabilities. We show that it can explain observed hypoxic growth in terms of metabolic fluxes and apply it to aquaculture by simulating growth with commercial feed ingredients. Predicted limiting amino acids and feed efficiencies agree with data, and the model suggests that marine feed efficiency can be achieved by supplementing a few amino acids to plant- and insect-based feeds. SALARECON is a high-quality model that makes it possible to simulate Atlantic salmon metabolism and growth. It can be used to explain Atlantic salmon physiology and address key challenges in aquaculture such as development of sustainable feeds.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Amino Acids / genetics
  • Animal Feed* / analysis
  • Animals
  • Aquaculture
  • Salmo salar* / genetics

Substances

  • Amino Acids

Grants and funding

This work was funded by the Research Council of Norway (https://www.forskningsradet.no/en/) grant 248792 (DigiSal) with support from grant 248810 (Centre for Digital Life Norway). The grant was awarded to JOV. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.