Investigating associations between milk metabolite profiles and milk traits of Holstein cows
Investigating associations between milk metabolite profiles and milk traits of Holstein cows
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DOI：
10.3168/jds.2012-5743
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发表时间：
2013-03-01
影响因子：
3.5
通讯作者：
Repsilber, D.
中科院分区：
文献类型：
--
作者：
Melzer, N.;Wittenburg, D.;Repsilber, D.
在奶牛研究领域，改进对疾病(如乳房炎和酮病)的检测和预防，并监测与健康状况和管理有关的特定性状，是非常有意义的。在标准牛奶性能测试期间，监测传统牛奶特性，并对质量和数量进行筛选。除了标准检测外，现在还可以以高通量的方式分析牛奶代谢物，并将它们与牛奶特性联系起来考虑，以确定也可以作为生物标记候选的重要功能代谢物。本研究对18个商业牧场的1,305头荷斯坦奶牛的190个乳代谢物和14个乳质性状进行了调查，以确定它们之间的相互关系，以及它们与乳品标准生产性能试验中的乳质性状之间的关系，以及牧场和父系效应(半同胞结构)等影响因素。影响因素(如农场)的效果因代谢物和传统牛奶特性而异。对代谢物与牛奶特性之间关系的研究揭示了一组代谢物，例如，与蛋白质和酪蛋白呈正相关，与乳糖和pH呈负相关。另一方面，与所调查的牛奶性状共同相关的代谢物组可以被识别并从功能上进行讨论。为了能够进行多变量研究，应用了两种机器学习方法来检测与所调查的传统牛奶特性高度相关的重要代谢物。体细胞评分检测到尿嘧啶、乳酸等9种重要代谢物。在最近的文献中，乳酸已经被认为是乳房炎的候选生物标记物。总而言之，我们发现了一组有资格预测牛奶特性的代谢物，从而能够从代谢角度分析牛奶特性，并讨论一些已检测到的关联的可能功能背景。
In the field of dairy cattle research, it is of great interest to improve the detection and prevention of diseases (e.g., mastitis and ketosis) and monitor specific traits related to the state of health and management. During the standard milk performance test, traditional milk traits are monitored, and quality and quantity are screened. In addition to the standard test, it is also now possible to analyze milk metabolites in a high-throughput manner and to consider them in connection with milk traits to identify functionally important metabolites that can also serve as biomarker candidates. We present a study in which 190 milk metabolites and 14 milk traits of 1,305 Holstein cows on 18 commercial farms were investigated to characterize interrelations of milk metabolites between each other, to milk traits from the milk standard performance test, and to influencing factors such as farm and sire effect (half-sib structure). The effect of influencing factors (e.g., farm) varied among metabolites and traditional milk traits. The investigations of associations between metabolites and milk traits revealed groups of metabolites that show, for example, positive correlations to protein and casein, and negative correlations to lactose and pH. On the other hand, groups of metabolites jointly associated with the investigated milk traits can be identified and functionally discussed. To enable a multivariate investigation, 2 machine learning methods were applied to detect important metabolites that are highly correlated with the investigated traditional milk traits. For somatic cell score, uracil, lactic acid, and 9 other important metabolites were detected. Lactic acid has already been proposed as a biomarker candidate for mastitis in the recent literature. In conclusion, we found sets of metabolites eligible to predict milk traits, enabling the analysis of milk traits from a metabolic perspective and discussion of the possible functional background for some of the detected associations.