Synergy of genetics and lipid metabolism driving feed utilization efficiency in chickens

遗传学和脂质代谢的协同作用驱动鸡的饲料利用效率

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作者:Xiaoli Guo ,Jianbo Li ,Xiaochang Li ,Jia Sun ,Xian Zou ,Jian Ji ,Hao Qu ,Dingming Shu ,Chenglong Luo

Abstract

Residual feed intake (RFI) is a key indicator of feed efficiency, critical for enhancing the economic sustainability of poultry production. However, the genetic and metabolic regulatory mechanisms of RFI remain unclear. This study analyzed the genome, liver transcriptome, metabolome, and lipidome of hens with low and high feed efficiency (N = 60) from the previously established RFI divergent broiler lines (F15). Our results revealed pronounced genetic differentiation between low RFI (LRFI) and high RFI (HRFI) lines and identified genomic signatures of selection associated with feed efficiency. Transcriptomic analysis showed differential expression of genes involved in neural regulation and lipid metabolism. Notably, LRFI chickens exhibited reduced hepatic lipid accumulation, which was associated with decreased fatty acid metabolism and increased cholesterol metabolism (P < 0.05). The lipidomic analysis uncovered distinct profiles of glycerophospholipids (e.g., PE-P and PC-O) and sphingolipids (e.g., ceramides), which were more abundant in LRFI chickens (P < 0.05) and strongly correlated with key lipid metabolism processes (P < 0.05). Despite improved feed efficiency, LRFI chickens demonstrated signs of increased oxidative stress. Moreover, integrative analyses revealed that genes such as MGAT5, GABRA4, and LRRC4C, exhibiting strong selection signatures and higher expression in the LRFI line (P < 0.05), were identified as key regulators of lipid metabolism, potentially contributing to the observed differences in feed efficiency. This comprehensive study highlights the synergistic effect of genetics and lipid metabolism in driving feed utilization efficiency in chickens, establishing a scientific foundation for breeding strategies aimed at improving feed efficiency in poultry production. Keywords: Chicken; Genetics; Lipid metabolism; Multiomics; RFI.

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