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

被引：0
作者：
Guo, Xiaoli [1 ,2 ,3 ]
Li, Jianbo [4 ]
Li, Xiaochang [5 ,6 ]
Sun, Jia [1 ,2 ,3 ]
Zou, Xian [1 ,2 ,3 ]
Ji, Jian [1 ,2 ,3 ]
Qu, Hao [1 ,2 ,3 ]
Shu, Dingming [1 ,2 ,3 ]
Luo, Chenglong [1 ,2 ,3 ]
机构：
[1] Guangdong Acad Agr Sci, State Key Lab Swine & Poultry Breeding Ind, Guangzhou 510640, Peoples R China
[2] Guangdong Acad Agr Sci, Guangdong Key Lab Anim Breeding & Nutr, Guangzhou 510640, Peoples R China
[3] Guangdong Acad Agr Sci, Inst Anim Sci, Guangzhou 510640, Peoples R China
[4] Guangdong Acad Agr Sci, Agrobiol Gene Res Ctr, State Key Lab Swine & Poultry Breeding Ind, Guangzhou 510640, Peoples R China
[5] China Agr Univ, State Key Lab Anim Biotech Breeding, Beijing 100193, Peoples R China
[6] China Agr Univ, Frontier Sci Ctr Mol Design Breeding, Beijing 100193, Peoples R China
来源：
POULTRY SCIENCE | 2025年 / 104卷 / 03期
基金：
中国博士后科学基金;
关键词：
Chicken; RFI; Genetics; Lipid metabolism; Multiomics; OXIDATIVE STRESS; GENOME; ASSOCIATION; EXPRESSION; SELECTION; BEHAVIOR; RECEPTOR; DISEASE; FORMAT; DNA;
D O I：
10.1016/j.psj.2025.104885
中图分类号：
S8 [畜牧、 动物医学、狩猎、蚕、蜂];
学科分类号：
0905 ;
摘要：
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.
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页数：12
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共 66 条
  • [1] Alexander D.H., Novembre J., Lange K., Fast model-based estimation of ancestry in unrelated individuals, Genom. Res., 19, pp. 1655-1664, (2009)
  • [2] Alexandre P.A., Kogelman L.J., Santana M.H., Passarelli D., Pulz L.H., Fantinato-Neto P., Silva P.L., Leme P.R., Strefezzi R.F., Coutinho L.L., Ferraz J.B., Eler J.P., Kadarmideen H.N., Fukumasu H., Liver transcriptomic networks reveal main biological processes associated with feed efficiency in beef cattle, BMC Genom., 16, (2015)
  • [3] Anders S., Pyl P.T., Huber W., HTSeq–a Python framework to work with high-throughput sequencing data, Bioinformatics, 31, pp. 166-169, (2015)
  • [4] Arroyave-Ospina J.C., Wu Z., Geng Y., Moshage H., Role of oxidative stress in the pathogenesis of non-alcoholic fatty liver disease: implications for prevention and therapy, Antioxid. (Basel), 10, (2021)
  • [5] Badmus O.O., Hillhouse S.A., Anderson C.D., Hinds T.D., Stec D.E., Molecular mechanisms of metabolic associated fatty liver disease (MAFLD): functional analysis of lipid metabolism pathways, Clin. Sci. (Lond.), 136, pp. 1347-1366, (2022)
  • [6] Bai H., Guo Q., Yang B., Dong Z., Li X., Song Q., Jiang Y., Wang Z., Chang G., Chen G., Effects of residual feed intake divergence on growth performance, carcass traits, meat quality, and blood biochemical parameters in small-sized meat ducks, Poult. Sci., 101, (2022)
  • [7] Bastian K., Scott E., Elliott D.J., Munkley J., FUT8 alpha-(1,6)-fucosyltransferase in cancer, Int. J. Mol. Sci., 22, (2021)
  • [8] Bottje W.G., Carstens G.E., Association of mitochondrial function and feed efficiency in poultry and livestock species, J. Anim. Sci., 87, pp. E48-E63, (2009)
  • [9] Browning B.L., Tian X., Zhou Y., Browning S.R., Fast two-stage phasing of large-scale sequence data, Am. J. Hum. Genet., 108, pp. 1880-1890, (2021)
  • [10] Chen S., Zhou Y., Chen Y., Gu J., fastp: an ultra-fast all-in-one FASTQ preprocessor, Bioinformatics, 34, pp. i884-i890, (2018)
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