Breit Marc, Netzer Michael, Weinberger Klaus M, Baumgartner Christian
Research Group for Clinical Bioinformatics, Institute of Electrical and Biomedical Engineering (IEBE), University for Health Sciences, Medical Informatics and Technology (UMIT), Hall in Tirol, Austria.
Research Group for Clinical Bioinformatics, Institute of Electrical and Biomedical Engineering (IEBE), University for Health Sciences, Medical Informatics and Technology (UMIT), Hall in Tirol, Austria; sAnalytiCo Ltd, Belfast, United Kingdom.
PLoS Comput Biol. 2015 Aug 28;11(8):e1004454. doi: 10.1371/journal.pcbi.1004454. eCollection 2015 Aug.
The objectives of this work were the classification of dynamic metabolic biomarker candidates and the modeling and characterization of kinetic regulatory mechanisms in human metabolism with response to external perturbations by physical activity. Longitudinal metabolic concentration data of 47 individuals from 4 different groups were examined, obtained from a cycle ergometry cohort study. In total, 110 metabolites (within the classes of acylcarnitines, amino acids, and sugars) were measured through a targeted metabolomics approach, combining tandem mass spectrometry (MS/MS) with the concept of stable isotope dilution (SID) for metabolite quantitation. Biomarker candidates were selected by combined analysis of maximum fold changes (MFCs) in concentrations and P-values resulting from statistical hypothesis testing. Characteristic kinetic signatures were identified through a mathematical modeling approach utilizing polynomial fitting. Modeled kinetic signatures were analyzed for groups with similar behavior by applying hierarchical cluster analysis. Kinetic shape templates were characterized, defining different forms of basic kinetic response patterns, such as sustained, early, late, and other forms, that can be used for metabolite classification. Acetylcarnitine (C2), showing a late response pattern and having the highest values in MFC and statistical significance, was classified as late marker and ranked as strong predictor (MFC = 1.97, P < 0.001). In the class of amino acids, highest values were shown for alanine (MFC = 1.42, P < 0.001), classified as late marker and strong predictor. Glucose yields a delayed response pattern, similar to a hockey stick function, being classified as delayed marker and ranked as moderate predictor (MFC = 1.32, P < 0.001). These findings coincide with existing knowledge on central metabolic pathways affected in exercise physiology, such as β-oxidation of fatty acids, glycolysis, and glycogenolysis. The presented modeling approach demonstrates high potential for dynamic biomarker identification and the investigation of kinetic mechanisms in disease or pharmacodynamics studies using MS data from longitudinal cohort studies.
这项工作的目标是对动态代谢生物标志物候选物进行分类，并对人体代谢中响应体育活动引起的外部扰动的动力学调节机制进行建模和表征。研究了来自4个不同组的47名个体的纵向代谢浓度数据，这些数据来自一项自行车测力计队列研究。总共通过靶向代谢组学方法测量了110种代谢物（在酰基肉碱、氨基酸和糖类类别中），该方法将串联质谱（MS/MS）与稳定同位素稀释（SID）概念相结合用于代谢物定量。通过对浓度的最大倍数变化（MFC）和统计假设检验得出的P值进行联合分析来选择生物标志物候选物。通过利用多项式拟合的数学建模方法识别特征动力学特征。通过应用层次聚类分析对具有相似行为的组的建模动力学特征进行分析。对动力学形状模板进行了表征，定义了不同形式的基本动力学响应模式，如持续、早期、晚期和其他形式，可用于代谢物分类。乙酰肉碱（C2）表现出晚期响应模式，在MFC和统计显著性方面具有最高值，被分类为晚期标志物并被列为强预测因子（MFC = 1.97，P < 0.001）。在氨基酸类别中，丙氨酸显示出最高值（MFC = 1.42，P < 0.001），被分类为晚期标志物和强预测因子。葡萄糖产生延迟响应模式，类似于曲棍球棒函数，被分类为延迟标志物并被列为中度预测因子（MFC = 1.32，P < 0.001）。这些发现与运动生理学中受影响的中心代谢途径的现有知识一致，如脂肪酸的β氧化、糖酵解和糖原分解。所提出的建模方法在使用纵向队列研究的MS数据进行疾病或药效学研究中的动态生物标志物鉴定和动力学机制研究方面显示出很高的潜力。
