Abstract
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.
Highlights
Metabolite kinetics—biochemical aspectsBasic principles in reaction kinetics of biomolecules were described by the work of Guldberg & Waage [1,2,3] more than 150 years ago and recently resumed by Voit et al, 2015 [4] in their perspective article "150 years of mass action"
Putative dynamic biomarker candidates are selected from the pool of analyzed metabolites by combined analysis of maximum fold changes (MFCs) in concentrations and corresponding P-values from statistical hypothesis testing
We have presented a computational methodology for dynamic biomarker classification and modeling of kinetic metabolic patterns in physical activity
Summary
Basic principles in reaction kinetics of biomolecules were described by the work of Guldberg & Waage [1,2,3] more than 150 years ago and recently resumed by Voit et al, 2015 [4] in their perspective article "150 years of mass action". Product C 1⁄4 kÃAÃB ð1Þ where A, B, and C are concentrations changing over time, and k is a rate constant describing the speed of the reaction. The Michaelis-Menten model describes the reaction kinetics of an enzyme-catalyzed singlesubstrate reaction, in which the conversion of a substrate S into a product P takes place via the formation of an intermediate complex ES, where k1, k2 and k3 denote reaction rates [4] k2 EþS ES!
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