SALARECON connects the Atlantic salmon genome to growth and feed efficiency

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.

SEEK ID: https://fairdomhub.org/publications/644

DOI: 10.1371/journal.pcbi.1010194

Projects: DigiSal

Publication type: Journal

Journal: PLOS Computational Biology

Citation:

Date Published: 1st Jun 2022

URL: https://doi.org/10.1371/journal.pcbi.1010194

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Citation
Zakhartsev, M., Rotnes, F., Gulla, M., Øyås, O., van Dam, J. C. J., Suarez-Diez, M., Grammes, F., Hafþórsson, R. A., van Helvoirt, W., Koehorst, J. J., Schaap, P. J., Jin, Y., Mydland, L. T., Gjuvsland, A. B., Sandve, S. R., Martins dos Santos, V. A. P., & Vik, J. O. (2022). SALARECON connects the Atlantic salmon genome to growth and feed efficiency. In A. Zelezniak (Ed.), PLOS Computational Biology (Vol. 18, Issue 6, p. e1010194). Public Library of Science (PLoS). https://doi.org/10.1371/journal.pcbi.1010194
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Created: 24th May 2022 at 12:20

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