代谢组学在反刍动物营养代谢应用中的研究进展

为了解近年来代谢组学应用于反刍动物营养代谢研究的进展，同时为代谢组学在该研究领域的广泛应用提供参考，本文采用文献综述和归纳总结的方法，从代谢组学的基本概念、分类和应用范围入手，结合近5年来代谢组学在反刍动物的瘤胃、血液和动物产品等方面的应用实例，以期借助代谢组学技术的研究优势，阐释更多机体营养代谢的机制。结果表明：1）应用代谢组学能从更加基础的层面全面探究营养利用与转化过程，极大推动了反刍动物营养学的发展。2）瘤胃代谢组学发展迅速，目前主要的研究集中于探究瘤胃酸中毒、营养物质高效利用以及影响生产性能的调节机制。3）血液代谢组学主要用以发现反映营养代谢状况以及影响生产性能的生物标志物。4）代谢组学为提高乳和肉产量、改善乳和肉品质提供了更加全面的参考。针对以上研究现状，提出目前代谢组学应用存在的局限性和未来发展方向：基于单一的检测手段可能无法得到十分全面的代谢物信息；定性和功能数据库标准不统一；非靶向与靶向代谢组学结合得到的数据可能更加可靠；代谢组学所得到的复杂数据需要生物信息学等学科的充分解析；多组学技术整合与相互补充验证是营养代谢研究的新趋势。

Advance in the application of metabolomics to nutrition research of ruminant livestock

In order to understand the process of metabonomics in ruminant nutrition and metabolism research in recent years, and provide reference for the wide application of metabonomics in the research field, this paper summarized the literature and based on the introduction, classification and application scope of metabolomics. The application of metabolomics in rumen, blood and animal products of ruminants in recent five years was introduced to elucidate more mechanisms of nutrient metabolism in the body by taking advantage of the research advantages of metabolomics. The results showed that: 1)Metabonomics can comprehensively explore the process of nutrition utilization and transformation from a more basic level, which greatly promotes the development of ruminant nutrition. 2)Rumen metabolomics has developed rapidly. At present, the main research focuses on rumen acidosis, efficient utilization of nutrients and the regulatory mechanism affecting production performance. 3)Blood metabolomics is mainly used to discover biomarkers that reflect nutritional metabolism status and affect production performance. 4)Metabolomics provides a more comprehensive reference for improving milk and meat yield and quality. In view of the above research status, the limitations and future development direction of the current application of metabonomics are proposed as follows: A single detection method may not be able to obtain a very comprehensive metabolite information; Qualitative and functional database standards are unified; The combination of un-targeted and targeted metabolomics may be more reliable; Complex data need to be fully analyzed by bioinformatics etc. The integration and mutual complementation of multiple omics techniques is a new trend in nutrient metabolism research.