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Integrated Multi-Omics Analysis Reveals Lipid Metabolism as a Key Contributor to the Growth-Meat Quality Trade-Off
Ying Li1, Rongqin Huang1,2, Li Zhang1,2
1Guangdong Provincial Key Laboratory of Animal Breeding and Nutrition, State Key Laboratory of Swine and Poultry Breeding Industry, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China.
Background:
Improving meat quality while maintaining growth efficiency remains a major challenge in poultry production. However, the molecular mechanisms underlying breed-specific meat quality variation remain unclear. This study aimed to investigate how breed-specific growth patterns influence meat quality and elucidate metabolic and transcriptional mechanisms involved.
Methods:
Pectoralis major meat quality traits and multi-omics profiles were characterized in three genetically distinct chicken breeds-the fast-growing Small White-Feathered chicken (XBJ), the slow-growing Huiyang Bearded chicken (HXJ), and the layer-type Hy-Line Brown chicken (HLH)-at 50, 180, and 300 days of age. Twelve birds per breed per age were used for phenotypic measurement (n = 108 in total), and eight birds per breed per age were subjected to metabolomic and transcriptomic profiling. Phenotypes were analyzed using linear mixed-effects models with breed, age, and their interaction as fixed effects and pen nested within breed as a random effect, followed by Tukey-adjusted pairwise comparisons (p < 0.05). Differential metabolites were screened by OPLS-DA (VIP > 1, p < 0.05), and differentially expressed genes were identified using DESeq2 (|log2FC| ≥ 1, FDR < 0.05). Integrative analyses were performed to identify key genes, metabolites, and pathways associated with meat quality.
Results:
Phenotypic evaluation revealed a breed-dependent growth-meat quality trade-off, with XBJ exhibiting superior growth but poorer water-holding capacity and meat color, whereas HXJ and HLH showed better tenderness and color at the expense of growth. Metabolomic analysis revealed lipid metabolism as a major contributor to breed-specific divergence, with triglyceride-driven divergence predominating at early and middle stages, whereas later-stage differences involved glycerophospholipid and amino acid metabolism. Transcriptomic analysis revealed significant breed-specific differences in expressed genes at 50 and 180 days, enriched in pathways related to muscle structure, ECM remodeling, and energy metabolism, consistent with metabolic and phenotypic divergence. Integrated analyses identified 28 candidate genes and 71 core metabolites associated with meat quality traits, with PLIN1 and SLC1A6 emerging as key regulators associated with TG species, drip loss, shear force, and BMW.
Conclusions:
These findings reveal molecular mechanisms underlying the growth-meat quality trade-off and highlight lipid metabolic regulation as a key contributor to meat quality variation. The identified gene-metabolite networks provide insights for molecular breeding to improve chicken meat quality.

