Altered expression of metabolic pathways and immune-related genes revealed by RNA sequencing and metabolomic analysis in meat-type chickens with different growth rates

Abstract

Differences in growth rates among broiler chickens within a single commercial genetic line have important economic implications; however, their molecular basis remains incompletely understood. This study analyzed male Ross 308 broilers classified into early- and late-growth lines based on weight gain from 1 to 5 days of age. We integrated RNA sequencing, metabolomics based on gas chromatography-mass spectrometry, and exploratory SNP analysis of pectoralis major muscle tissue collected at 35 days of age. Our integrative analysis revealed contrasting energy utilization programs. Slow early-growth phenotype chickens showed a Warburg-like metabolic profile characterized by glycolytic reliance, lactate fermentation, ketone metabolism, and enhanced proteolysis, accompanied by a bottleneck in mitochondrial oxidative phosphorylation. In contrast, fast early-growth phenotype chickens displayed enhanced oxidative phosphorylation, elevated glycerol-3-phosphate levels, and coordinated activation of pathways related to mitochondrial function and immune responses. Notably, reduced CARNS1 expression in the fast early-growth group suggested a potential trade-off with muscle quality, consistent with the role of carnosine in pH buffering and maintaining redox balance. Multi-omics integrated analysis revealed coordinated changes in metabolites and gene expression within glycolysis, lipid metabolism, and mitochondrial pathways. These findings indicate that the weight gain phenotype during early growth is associated with specific transcriptional and metabolic states during later development.

Keywords: Energy metabolism; Growth rate; Immune response; Metabolomic analysis; RNA sequencing.

Fig. 1

Transcriptomic, metabolomic, and integrative analyses of breast muscle from fast- and slow-growing chickens. (A) Volcano plot of differentially expressed genes (DEGs) identified by RNA-seq (FDR < 0.1). The x-axis shows log₂(fold-change) (slow/fast), and the y-axis shows the −log₁₀(FDR). Blue and red dots indicate significantly downregulated and upregulated genes, respectively, in slow early-growth phenotype chickens compared to those in the fast early-growth phenotype group. Gray dots represent genes not reaching significance. Dashed lines denote the thresholds for statistical significance. (B) Volcano plot of metabolites detected by GC–MS/MS analysis. The x-axis shows the log₂(fold-change) (slow/fast), and the y-axis shows the −log₁₀ (nominal P-value). Red and blue dots indicate metabolites with higher levels in slow and fast early-growth phenotype chickens, respectively, based on a nominal P < 0.05 (unadjusted). Gray dots represent metabolites that do not meet this threshold. Dashed lines denote a |log₂(fold-change)| = 1 (vertical) and P = 0.05 (horizontal). (C) Block sparse partial least squares discriminant analysis (block sPLS-DA) of metabolomic data (left) and transcriptomic data (right). Each point represents an individual sample, with orange triangles indicating fast early-growth phenotype and blue circles indicating slow early-growth phenotype chickens. Ellipses represent the 95% confidence intervals for each group. The clear separation of groups is visible along the first two principal components. (D) Integrative network analysis linking differentially expressed genes (blue) and metabolites (green). Edges represent significant correlations, with red lines indicating positive and blue lines indicating negative associations.

Fig. 2

Enriched pathways identified by GO, KEGG, and Reactome analyses of differentially expressed genes.