Abstract
Recent advances in 13C-Metabolic flux analysis (13C-MFA) have increased its capability to accurately resolve fluxes using a genome-scale model with narrow confidence intervals without pre-judging the activity or inactivity of alternate metabolic pathways. However, the necessary precautions, computational challenges, and minimum data requirements for successful analysis remain poorly established. This review aims to establish the necessary guidelines for performing 13C-MFA at the genome-scale for a compartmentalized eukaryotic system such as yeast in terms of model and data requirements, while addressing key issues such as statistical analysis and network complexity. We describe the various approaches used to simplify the genome-scale model in the absence of sufficient experimental flux measurements, the availability and generation of reaction atom mapping information, and the experimental flux and metabolite labeling distribution measurements to ensure statistical validity of the obtained flux distribution. Organism-specific challenges such as the impact of compartmentalization of metabolism, variability of biomass composition, and the cell-cycle dependence of metabolism are discussed. Identification of errors arising from incorrect gene annotation and suggested alternate routes using MFA are also highlighted.
Highlights
Metabolism in yeasts is primed for the maintenance of homeostasis while ensuring the ability to adapt to changing environmental conditions [1,2]
The primary requirements for performing MFA at the genome-scale are the availability of a well curated genome-scale metabolic (GSM) model for S. cerevisiae, atom mapping data for every reaction within the GSM model, and intracellular metabolite labeling distribution data to resolve all fluxes within the GSM model
Inability to completely resolve all fluxes included within the GSM model for S. cerevisiae occurs due to lack of probing techniques capable of obtaining compartment-specific labeling distribution of metabolites
Summary
Metabolism in yeasts is primed for the maintenance of homeostasis while ensuring the ability to adapt to changing environmental conditions [1,2]. Isotope tracer techniques such as 13C-metabolic flux analysis (13C-MFA) [27] employ stable isotopes of carbon to estimate intracellular fluxes by minimizing the variance-weighted sum of square of deviation from experimentally observed labeling distribution of intracellular metabolites The strength of this technique lies in its ability to resolve key metabolic branch points such as glycolysis/PPP [28] and fermentation/respiration [29] and reversible reactions by exploiting distinct pathway atom transitions. Most of the MFA studies in yeast are aimed at estimating two key split ratios: glycolysis/PPP and respiration/fermentation In all of these studies, the role of redox homeostasis is inferred based on flux re-distribution while no direct evidence from flux maps is available as the inclusion of cofactor balances is typically not included in yeast MFA models [43].
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