Estimating Relative Changes of Metabolic Fluxes

Abstract

Fluxes are the central trait of metabolism and Kinetic Flux Profiling (KFP) is an effective method of measuring them. To generalize its applicability, we present an extension of the method that estimates the relative changes of fluxes using only relative quantitation of 13C-labeled metabolites. Such features are directly tailored to the more common experiment that performs only relative quantitation and compares fluxes between two conditions. We call our extension rKFP. Moreover, we examine the effects of common missing data and common modeling assumptions on (r)KFP, and provide practical suggestions. We also investigate the selection of measuring times for (r)KFP and provide a simple recipe. We then apply rKFP to 13C-labeled glucose time series data collected from cells under normal and glucose-deprived conditions, estimating the relative flux changes of glycolysis and its branching pathways. We identify an adaptive response in which de novo serine biosynthesis is compromised to maintain the glycolytic flux backbone. Together, these results greatly expand the capabilities of KFP and are suitable for broad biological applications.

A roadmap for interpreting 13 C metabolite labeling patterns from cells

“…Hence, X U / X T is the fraction of M+0 in the metabolite of interest. In the second case isotopically non-stationary 13 C flux analysis [30,40,85] or kinetic flux profiling [51,90,91] can be performed. These methods require the same additional data and information as steady state 13 C flux analysis and/or measurements of metabolite levels along the pathway of interest.…”

A roadmap for the XCMS family of software solutions in metabolomics

“…Additionally, by comparing labeling patterns between different phenotypes using global metabolomic technologies, it is possible to identify relative changes in flux distributions. [39]…”

Understanding metabolism with flux analysis: From theory to application

“…Furthermore, flux ratio analysis could also be extended to flux analysis in isotopic non-steady state (Hörl et al, 2013), but the relative fluxes are evaluated by iterative fitting instead of direct calculation, which is the case of isotopically stationary flux ratio analysis. Also these methods can be extended to evaluate fold changes in metabolic fluxes that occur in the more common setting of comparing two conditions such as wild type and knockout or vehicle vs. drug treated (Huang et al, 2014). Other strategies for improving efficiency of isotopically non-stationary flux analysis include parallel measurements of intracellular and extracellular metabolite pools (Shlomi et al, 2014), using hybrid model containing both isotopically stationary and non-stationary dynamics of metabolites (Tedeschi et al, 2015).…”