An integrated proteomics and metabolomics investigation of feed efficiency in seawater reared Chinook salmon (Oncorhynchus tshawytscha)

Aquaculture(2023)

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摘要
Feed efficiency (FE) is a critical phenotype for aquaculture sustainability, yet it is poorly understood at the molecular level. In this study, the molecular basis of FE was investigated using a multi-omics approach to integrate proteome-level data and metabolites associated with differences in feed efficiency. To achieve this, Chinook salmon (Oncorhynchus tshawytscha) were reared in seawater and the liver, blood plasma, and white muscle of feed efficient (EFF) and inefficient (INEFF) fish were analysed using proteomics and metabolomics. By comparing 21 EFF and 19 INEFF fish, 2748 liver and 703 white muscle proteins, as well as 140, 127, and 70 metabolites in the liver, plasma, and muscle, respectively, were measured. According to Gene Set Enrichment Analysis (GSEA) approach, protein synthesis was enriched in both liver and white muscle tissues of the EFF group. Furthermore, in the liver and white muscle of INEFF fish, pathways associated with protein degradation (amino acid catabolism and proteolysis) and glucose metabolism were the most affected processes. The metabolite data showed that pyruvate metabolism in the liver of the EFF group and phenylalanine, tyrosine and tryptophan metabolism in the plasma of the EFF group were enriched. Glutathione metabolism and valine, leucine and isoleucine metabolism were the top enriched pathways in both the plasma and muscle samples of the INEFF group. Integrated analysis of the datasets revealed a connection of metabolites related to lipids and amino acids with catabolism of proteins in the INEFF group. Utilising an integrated multi-omics approach, this study gained insights into the FE phenotype of Chinook salmon and it is hypothesised that protein turnover is a key component of FE in Chinook salmon reared in seawater.
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关键词
Multi-omics data integration,Systems biology,Chinook salmon,Feed conversion ratio,Protein turnover
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