Uncertainty-aware integration of deuterium metabolic imaging with genome-scale metabolic modeling reveals diet-associated feasible-space remodeling in mouse skeletal muscle

Abstract

Deuterium metabolic imaging (DMI) measures glucose-derived labeling in vivo, but noisy time courses complicate network-level interpretation. We developed an uncertainty-aware workflow that integrates dynamic DMI data with genome-scale metabolic modeling (GEM) through soft flux constraints. After injection of [6,6'-2H2]glucose, dynamic spectra were acquired from gastrocnemius, soleus, and tibialis anterior muscles of control and high-fat diet (HFD) mice. A hierarchical four-state kinetic model estimated labeling parameters while accounting for shared plasma input, tissue-specific kinetics, voxel-level fluxes, isotope effects, and measurement uncertainty. Because glucose labeling was more reliable than lactate and glutamate/glutamine signals, only two DMI-derived quantities were used as GEM anchors: glucose processing mapped to hexokinase flux, and glucose-derived oxidative entry mapped to mitochondrial pyruvate dehydrogenase flux. Two-sided soft target intervals with weighted penalties defined DMI-compatible feasible regions for each mouse and muscle. Flux variability analysis and sampling assessed changes across reactions and metabolic subsystems. The constraints were compatible with the muscle network and affected feasible activity in glycolysis, lactate metabolism, pyruvate oxidation, the TCA cycle, oxidative phosphorylation, and redox pathways. HFD-associated changes differed by muscle: tibialis anterior and soleus showed reduced glucose-linked feasible activity, whereas gastrocnemius showed mixed changes with preserved or increased TCA and redox activity. This framework connects noninvasive tracer imaging with network-level metabolic inference while avoiding strong claims from poorly identified signals.