UC Berkeley

Metabolic fluxes are the highest level of biochemical phenotype in biology, representing the final outcome of cellular regulation. Measurement of metabolic fluxes requires isotopic tracers, as static measurements of metabolite concentrations do not offer reliable information on metabolic rates and processes. The field at large primarily uses carbon-13 tracers, logically stemming from the fact that metabolism is essentially carbon flow. However, direct tracking of carbon trajectory is limited in capability and not full-proof in the pursuit of uncovering intracellular metabolic state. The use of non-linear models means that any slight deviation will amplify error into inaccurate results. The scope of what can be measured is also limited given that tracers enter locally into metabolism. And, lastly, the cost and logistics behind carbon-13 tracers limits broad applicability, especially with human cohorts.This dissertation proposes an alternative method, using heavy water (2H2O) labeling. Deuterium-enriched solvent body water reacts with enzymes ubiquitous throughout metabolism, conferring isotopic label during traversal through metabolic pathways. Combinatorial analysis (i.e. Mass Isotopomer Distribution Analysis – MIDA) provides a mathematical framework to dissect fractional pathway contributions. MIDA essentially characterizes polymerization as a combinatorial process wherein binomial or multinomial distributions model theoretical isotopic labeling patterns and hence has traditionally been used to calculate synthesis rates. The novelty of this technique is that is also represents the deuterium labeling in specific C-H bonds occurring sequentially through traversal of a metabolic pathway as a combinatorial process and therefore amenable to the same mathematical approach but towards calculating metabolic flux ratios through different pathways that incorporate a different number of H-atoms derived from body water into C-H bonds during the labeling experiment.This approach first developed with targeted pathways such as glycolysis, gluconeogenesis, and glyceroneogenesis. However, it is possible to measure all three of the aforementioned pathways simultaneously along with many more, as LC-MS/MS technology can measure of hundreds of metabolites at once, allowing for the calculation of many metabolic fluxes from one single injection, thus affording the label “-omics” to metabolic fluxes. A simple administration of heavy water can therefore reveal a “snapshot” inside the cell that may supplement or supersede more traditional “-omics” measurements. Just as genomics informs on species or population, and transcriptomics informs on cell type, fluxomics analogously informs on a holistic window into cell state.