Combinatorial metabolomic and transcriptomic analysis of muscle growth in hybrid striped bass (female white bass Morone chrysops x male striped bass M. saxatilis)

Abstract

Abstract Background Understanding growth regulatory pathways is important in aquaculture, fisheries, and vertebrate physiology generally. Machine learning pattern recognition and sensitivity analysis were employed to examine metabolomic small molecule profiles and transcriptomic gene expression data generated from liver and white skeletal muscle of hybrid striped bass (white bass Morone chrysops x striped bass M. saxatilis) representative of the top and bottom 10 % by body size of a production cohort. Results Larger fish (good-growth) had significantly greater weight, total length, hepatosomatic index, and specific growth rate compared to smaller fish (poor-growth) and also had significantly more muscle fibers of smaller diameter (≤ 20 µm diameter), indicating active hyperplasia. Differences in metabolomic pathways included enhanced energetics (glycolysis, citric acid cycle) and amino acid metabolism in good-growth fish, and enhanced stress, muscle inflammation (cortisol, eicosanoids) and dysfunctional liver cholesterol metabolism in poor-growth fish. The majority of gene transcripts identified as differentially expressed between groups were down-regulated in good-growth fish. Several molecules associated with important growth-regulatory pathways were up-regulated in muscle of fish that grew poorly: growth factors including agt and agtr2 (angiotensins), nicotinic acid (which stimulates growth hormone production), gadd45b, rgl1, zfp36, cebpb, and hmgb1; insulin-like growth factor signaling (igfbp1 and igf1); cytokine signaling (socs3, cxcr4); cell signaling (rgs13, rundc3a), and differentiation (rhou, mmp17, cd22, msi1); mitochondrial uncoupling proteins (ucp3, ucp2); and regulators of lipid metabolism (apoa1, ldlr). Growth factors pttg1, egfr, myc, notch1, and sirt1 were notably up-regulated in muscle of good-growing fish. Conclusion A combinatorial pathway analysis using metabolomic and transcriptomic data collectively suggested promotion of cell signaling, proliferation, and differentiation in muscle of good-growth fish, whereas muscle inflammation and apoptosis was observed in poor-growth fish, along with elevated cortisol (an anti-inflammatory hormone), perhaps related to muscle wasting, hypertrophy, and inferior growth. These findings provide important biomarkers and mechanisms by which growth is regulated in fishes and other vertebrates as well.

Keywords

• Machine learning
• Genome
• Growth
• Fish
• Striped bass
• Metabolomics