31P NMR spectroscopy is a powerful research and clinical tool to study in vivo dynamics of skeletal muscle metabolism. However, any straightforward interpretation of the data is complicated by skeletal muscle tissue heterogeneity. Skeletal muscle contains three distinct muscle fiber types, which are organized in motor units. The 31P NMR data represents the behavior of the whole muscle and thus, the mean behavior of all muscle fibers. We developed a multi-scale model of human skeletal muscle, which was used to extract the dynamic behavior of different types of muscle fibers / cells from 31P NMR measurements. At the fiber level the model is composed of detailed kinetic models of mitochondria1, glycolysis2, ATP buffering2 and ATP consumption (model input). A set of fiber type specific parameters was identified and parameterized for the three different types of muscle fibers, yielding three fiber type specific models. The whole muscle scale was modeled as the average behavior of a representative pool of motor units of different types. Individual motor unit models consisted of 27 ODEs and 131 parameters and were numerically solved in Matlab using the ODE15s. Dynamic data of [PCr], [Pi] and [ATP] were obtained from 31P NMR spectra measured during in magnet bicycle exercise. During exercise [PCr] dropped, accompanied by an increase in [Pi], while [ATP] remained stable. At the highest workload [PCr] decreased >90% indicating all muscle fibers have been recruited. The mitochondrial enzyme content in each of the three types of muscle fibers was fine tuned based upon the 31P NMR data. The predicted rate of post-exercise [PCr] recovery was different for each type of muscle fibers, being the highest in slow twitch fibers, intermediate in oxidative fast twitch fibers and the lowest in glycolytic fast twitch fibers. These [PCr] recovery rates agreed well with values available in literature3. The computational model is able to reproduce human skeletal muscle dynamics as obtained with 31P NMR spectroscopy and predicts the behavior of different fiber types. Validation of the method is possible by analysis of muscle biopsy samples, obtained 90s after exercise. Although Karatzatafi et al. used different subjects; the similarity between their results and the model predictions is at least remarkable, but moreover, promising. Potentially, this method can become a valuable tool in the evaluation of therapeutic training protocols for e.g. type 2 diabetes or muscular diseases; it allows measuring non-invasively whether the beneficial effects apply to all or only a subset of motor units, providing information to adapt / personalize these protocols.
|Title of host publication
|Proceedings of the 2nd Dutch BME Conference 2009 (BME 2009), 22-23 January 2009, Egmond aan Zee, The Netherlands
|Published - 2009