Gao Lutao, Zhang Lilian, Chen Jian, Peng Lin, Guo Lujiale, Yang Linnan
College of Food Science and Technology, Yunnan Agricultural University, Kunming, Yunnan, China; College of Big Data, Yunnan Agricultural University, Kunming, Yunnan, China; Yunnan Engineering Technology Research Center of Agricultural Big Data, Kunming, Yunnan, China; Yunnan Engineering Research Center for Big Data Intelligent Information Processing of Green Agricultural Products, Kunming, Yunnan, China.
College of Big Data, Yunnan Agricultural University, Kunming, Yunnan, China; Yunnan Engineering Technology Research Center of Agricultural Big Data, Kunming, Yunnan, China; Yunnan Engineering Research Center for Big Data Intelligent Information Processing of Green Agricultural Products, Kunming, Yunnan, China.
Gene. 2025 Apr 29;962:149416. doi: 10.1016/j.gene.2025.149416.
Beef quality is a crucial factor affecting both consumer preferences and the economic efficiency of the industry. With the rapid advancements in high-throughput technologies, including genomics, transcriptomics, proteomics, and metabolomics, integrated multi-omics analysis has emerged as a new research paradigm for deeply investigating the mechanisms underlying beef quality. This review systematically summarizes recent progress in multi-omics research related to beef quality, encompassing various levels such as genomics, transcriptomics, proteomics, metabolomics, and phenomics. At the genomic level, the use of genome-wide association studies (GWAS) and genomic selection techniques has markedly improved the precision of selecting meat quality traits. Studies in transcriptomics and proteomics have identified key genes involved in muscle growth and fat deposition, along with their expression regulation networks. Metabolomics analyses have highlighted critical metabolites that influence beef flavor and tenderness, as well as their biosynthetic pathways. The integration of multi-omics data has led to the construction of a comprehensive regulatory network linking genotype to phenotype, providing a theoretical foundation for precision breeding and quality control. However, current research faces challenges such as limited sample sizes and the need for more advanced data integration methods. Future research should prioritize: (1) increasing sample sizes and conducting large-scale omics data collection across diverse breeds and environmental conditions; (2) developing sophisticated computational methods for deeper integration of multi-omics data to create more accurate quality prediction models, and (3) enhancing functional validation experiments to elucidate the roles of key genes and metabolites. This review offers a systematic perspective on the molecular mechanisms driving beef quality and is of significant importance for guiding precision breeding and quality control in the beef industry.
牛肉品质是影响消费者偏好和牛肉产业经济效率的关键因素。随着高通量技术的迅速发展,包括基因组学、转录组学、蛋白质组学和代谢组学,整合多组学分析已成为深入研究牛肉品质潜在机制的一种新的研究范式。本文综述系统总结了与牛肉品质相关的多组学研究的最新进展,涵盖了基因组学、转录组学、蛋白质组学、代谢组学和表型组学等各个层面。在基因组水平上,全基因组关联研究(GWAS)和基因组选择技术的应用显著提高了肉质性状选择的精度。转录组学和蛋白质组学研究已经确定了参与肌肉生长和脂肪沉积的关键基因及其表达调控网络。代谢组学分析突出了影响牛肉风味和嫩度的关键代谢物及其生物合成途径。多组学数据的整合导致构建了一个将基因型与表型联系起来的综合调控网络,为精准育种和质量控制提供了理论基础。然而,目前的研究面临样本量有限以及需要更先进的数据整合方法等挑战。未来的研究应优先考虑:(1)增加样本量,并在不同品种和环境条件下进行大规模组学数据收集;(2)开发复杂的计算方法,以更深入地整合多组学数据,创建更准确的品质预测模型;(3)加强功能验证实验,以阐明关键基因和代谢物的作用。本文综述为驱动牛肉品质的分子机制提供了系统的观点,对指导牛肉产业的精准育种和质量控制具有重要意义。