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An Experimental Model of Diet-Induced Metabolic Syndrome in Rabbit: Methodological Considerations, Development, and Assessment
Published on: April 20, 2018
Transcriptomics and 4D-DIA proteomic analysis reveal differences in meat quality between Sichuan white rabbits and
Liangde Kuang1, Yunduan Wang2, Min Lei1
1Animal Genetic Breeding and Reproduction Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu 610066, China.
Abstract:
Understanding the molecular basis of rabbit meat quality is essential for genetic improvement. We compared Sichuan White (S) and New Zealand White (N) rabbits using carcass phenotyping (n = 20), longissimus dorsi transcriptomics (n = 3), and 4D-DIA proteomics (n = 3). Differentially expressed genes (DEGs) were identified using |log₂FoldChange| ≥ 0.3693 and p < 0.05, and differentially expressed proteins (DEPs) were determined using fold change ≥ 1.3 or ≤ 0.7692 and p < 0.05. S rabbits exhibited lower drip loss and shear force but higher intramuscular fat (IMF) content, despite N rabbits displaying greater carcass yield. Integrated omics analysis revealed 848 differentially expressed genes and 140 differentially expressed proteins. The IMF accumulation in S rabbits was driven by upregulated lipid biosynthesis and suppressed fatty acid oxidation. Enrichment of oxidative phosphorylation and thermogenesis indicated enhanced mitochondrial coupling and redox balance, supporting water-holding capacity. Upregulation of ECM-related integrin β-subunits was correlated with improved tenderness. Sixty-eight KEGG pathways were co-enriched across omics layers, highlighting the interplay of lipid metabolism, mitochondrial function and ECM remodeling. Integrated transcriptomic and proteomic analyses revealed that upregulated lipid biosynthesis, suppressed fatty acid oxidation, and enhanced ECM-receptor signaling jointly contribute to superior meat quality in S rabbits. These findings provide candidate molecular targets for improving native rabbit meat quality and offer a foundation for future marker-assisted selection after validation in independent populations.
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