Luke Timothy D W, Pryce Jennie E, Elkins Aaron C, Wales William J, Rochfort Simone J
Agriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia.
School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia.
Metabolites. 2020 Apr 30;10(5):180. doi: 10.3390/metabo10050180.
Most livestock metabolomic studies involve relatively small, homogenous populations of animals. However, livestock farming systems are non-homogenous, and large and more diverse datasets are required to ensure that biomarkers are robust. The aims of this study were therefore to (1) investigate the feasibility of using a large and diverse dataset for untargeted proton nuclear magnetic resonance (H NMR) serum metabolomic profiling, and (2) investigate the impact of fixed effects (farm of origin, parity and stage of lactation) on the serum metabolome of early-lactation dairy cows. First, we used multiple linear regression to correct a large spectral dataset (707 cows from 13 farms) for fixed effects prior to multivariate statistical analysis with principal component analysis (PCA). Results showed that farm of origin accounted for up to 57% of overall spectral variation, and nearly 80% of variation for some individual metabolite concentrations. Parity and week of lactation had much smaller effects on both the spectra as a whole and individual metabolites (< 3% and < 20%, respectively). In order to assess the effect of fixed effects on prediction accuracy and biomarker discovery, we used orthogonal partial least squares (OPLS) regression to quantify the relationship between NMR spectra and concentrations of the current gold standard serum biomarker of energy balance, β-hydroxybutyrate (BHBA). Models constructed using data from multiple farms provided reasonably robust predictions of serum BHBA concentration (0.05 ≤ RMSE ≤ 0.18). Fixed effects influenced the results biomarker discovery; however, these impacts could be controlled using the proposed method of linear regression spectral correction.
大多数家畜代谢组学研究涉及相对较小的、同质化的动物群体。然而，家畜养殖系统并非同质化，需要大量且更多样化的数据集来确保生物标志物的稳健性。因此，本研究的目的是：（1）研究使用大量且多样化的数据集进行非靶向质子核磁共振（H NMR）血清代谢组分析的可行性，以及（2）研究固定效应（原产农场、胎次和泌乳阶段）对早期泌乳奶牛血清代谢组的影响。首先，在使用主成分分析（PCA）进行多变量统计分析之前，我们使用多元线性回归对一个大型光谱数据集（来自13个农场的707头奶牛）的固定效应进行校正。结果表明，原产农场占总体光谱变异的比例高达57%，对某些个体代谢物浓度的变异占近80%。胎次和泌乳周数对整个光谱和个体代谢物的影响要小得多（分别<3%和<20%）。为了评估固定效应对预测准确性和生物标志物发现的影响，我们使用正交偏最小二乘法（OPLS）回归来量化NMR光谱与当前能量平衡的金标准血清生物标志物β-羟基丁酸（BHBA）浓度之间的关系。使用来自多个农场的数据构建的模型对血清BHBA浓度提供了相当稳健的预测（0.05≤RMSE≤0.18）。固定效应影响生物标志物发现的结果；然而，使用所提出的线性回归光谱校正方法可以控制这些影响。
