Wang Xiao, Kadarmideen Haja N
Quantitative Genomics, Bioinformatics and Computational Biology Group, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 324, 2800 Kongens Lyngby, Denmark.
Metabolites. 2019 Jul 23;9(7):151. doi: 10.3390/metabo9070151.
Residual feed intake (RFI) is designed to estimate net efficiency of feed use, so low RFI animals are considered for selection to reduce feeding costs. However, metabolic profiling of cows and availability of predictive metabolic biomarkers for RFI are scarce. Therefore, this study aims to generate a better understanding of metabolic mechanisms behind low and high RFI in Jerseys and Holsteins and identify potential predictive metabolic biomarkers. Each metabolite was analyzed to reveal their associations with two RFIs in two breeds by a linear regression model. An integrative analysis of metabolomics and transcriptomics was performed to explore interactions between functionally related metabolites and genes in the created metabolite networks. We found that three main clusters were detected in the heat map and all identified fatty acids (palmitoleic, hexadecanoic, octadecanoic, heptadecanoic, and tetradecanoic acid) were grouped in a cluster. The lower cluster were all from fatty acids, including palmitoleic acid, hexadecanoic acid, octadecanoic acid, heptadecanoic acid, and tetradecanoic acid. The first component of the partial least squares-discriminant analysis (PLS-DA) explained a majority (61.5%) of variations of all metabolites. A good division between two breeds was also observed. Significant differences between low and high RFIs existed in the fatty acid group ( < 0.001). Statistical results revealed clearly significant differences between breeds; however, the association of individual metabolites (leucine, ornithine, pentadecanoic acid, and valine) with the RFI status was only marginally significant or not significant due to a lower sample size. The integrated gene-metabolite pathway analysis showed that pathway impact values were higher than those of a single metabolic pathway. Both types of pathway analyses revealed three important pathways, which were aminoacyl-tRNA biosynthesis, alanine, aspartate, and glutamate metabolism, and the citrate cycle (TCA cycle). Finally, one gene (2-hydroxyacyl-CoA lyase 1 () associated with two metabolites (-α-ketoglutarate and succinic acid) were identified in the gene-metabolite interaction network. This study provided novel metabolic pathways and integrated metabolic-gene expression networks in high and low RFI Holstein and Jersey cattle, thereby providing a better understanding of novel biochemical mechanisms underlying variation in feed efficiency.
剩余采食量（RFI）旨在评估饲料利用的净效率，因此低RFI的动物被视为选育对象以降低饲养成本。然而，关于奶牛的代谢谱以及RFI的预测性代谢生物标志物的研究却很少。因此，本研究旨在更好地理解泽西牛和荷斯坦牛中低RFI和高RFI背后的代谢机制，并识别潜在的预测性代谢生物标志物。通过线性回归模型分析每种代谢物，以揭示它们与两个品种的两种RFI之间的关联。对代谢组学和转录组学进行综合分析，以探索在创建的代谢物网络中功能相关的代谢物和基因之间的相互作用。我们发现，在热图中检测到三个主要聚类，所有鉴定出的脂肪酸（棕榈油酸、十六烷酸、十八烷酸、十七烷酸和十四烷酸）都聚集在一个聚类中。较低的聚类均来自脂肪酸，包括棕榈油酸、十六烷酸、十八烷酸、十七烷酸和十四烷酸。偏最小二乘判别分析（PLS-DA）的第一个成分解释了所有代谢物大部分（61.5%）的变异。两个品种之间也观察到了良好的区分。脂肪酸组中低RFI和高RFI之间存在显著差异（<0.001）。统计结果清楚地显示了品种之间的显著差异；然而，由于样本量较小，个别代谢物（亮氨酸、鸟氨酸、十五烷酸和缬氨酸）与RFI状态的关联仅具有边缘显著性或不显著。综合基因-代谢物通路分析表明，通路影响值高于单一代谢通路。两种类型的通路分析都揭示了三个重要通路，即氨酰-tRNA生物合成、丙氨酸、天冬氨酸和谷氨酸代谢以及柠檬酸循环（TCA循环）。最后，在基因-代谢物相互作用网络中鉴定出一个与两种代谢物（α-酮戊二酸和琥珀酸）相关的基因（2-羟酰基辅酶A裂解酶1）。本研究提供了高RFI和低RFI荷斯坦牛和泽西牛的新代谢途径以及综合代谢-基因表达网络，从而更好地理解了饲料效率变异背后的新生化机制。
