13C Metabolic Flux Analysis

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13C metabolic flux analysis (13CMFA) is a collective term to indicate a set of methods to experimentally measure in vivo rates through metabolic pathways and reactions. Since reaction rates are not di per se detectable, stable isotopic tracers are used to infer them form the propagation of 13C over time (in dynamic experiments) or from the labeling patterns that emerge when 12C and 13C fragments are merged in metabolism (in end-point measurements).

Mass spectrometry is absolutely instrumental to record the labeling patterns in metabolic intermediates and end-products. In real-life problems the increased problem complexity calls for denser and more accurate 13C data. Consequently, 13CMFA strongly depends on improvements in sensitivity, throughput, and coverage of metabolomics.

Traditional flux analysis methods were developed in the context of metabolic engineering, i.e. for microbial systems and media with single carbon sources. Unfortunately, these systems transfer poorly to more complex conditions. Our current efforts aim at developing new approaches and implement the necessary software to measure fluxes in complex situations (ie in higehr cells) and measure fluxes at high-throughputs. 

Targeted 13C Metabolic Flux Analysis

Early methods of flux analysis aimed at quantifying possibly many fluxes in central carbon metabolism. This was important in a biotechnological setting, to get a global map of subtrate utilization or derive cellular-wide ATP or redox balances. In contemporary cellular biology, the challenge is different. Quantification of all fluxes is prohibitively difficult. Instead, other approaches such as RNAseq or metabolomics are proficiently used to formulate hypothesis on reactions and pathways that are of particular relevance to drive the metabolic phenotype of interest. For this purpose 13CMFA is used to quantify fluxes of very specific reacions or single pathways.

In analogy to othe omics areas, there is a trend towards developing targeted methods that are specialized in a specific task, i.e. offer the best sensitivty while preserving computational and data demand low. We published two such methods for targeted 13CMFA: one for non-stationary data (doi) and one for stationary data (SUMOFLUX link doi).


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  • Kogadeeva M, Zamboni N, SUMOFLUX: A Generalized Method for Targeted 13C Metabolic Flux Ratio Analysis, PLOS Computational Biology, 2016, doi
  • Hörl M, Schnidder J, Sauer U, Zamboni N, Non-stationary 13C-metabolic flux ratio analysis, Biotechnol Bioeng. 2013 Pubmed doi
  • Rühl M, Rupp B, Nöh K, Wiechert W, Sauer U, Zamboni N, Collisional fragmentation of central carbon metabolites in LC-MS/MS increases precision of 13C metabolic flux analysis, Biotechnol Bioeng. 2012 Mar; 109(3):763-71 Pubmed doi
  • Zamboni N, Fendt SM, Rühl M, Sauer U, 13C-based metabolic flux analysis, Nat protocols 2009; 4(6):878-892 Pubmed doi
  • Zamboni, Fischer, and Sauer, FiatFlux - a software for metabolic flux analysis from 13C-glucose experiments. BMC Bioinformatics. 2005; 6:209 Pubmed 
  • Fischer E, Zamboni N, Sauer U, High-throughput metabolic flux analysis based on gas chromatography-mass spectrometry derived 13C constraints, Anal Biochem, 2004; 325(2):308-16 Pubmed
  • Zamboni N, Sauer U, Model-independent fluxome profiling from 2H and 13C experiments for metabolic variant discrimination, Genome Biol. 2004;5(12):R99 Pubmed
  • Zamboni N, 13C metabolic flux analysis in complex systems, Curr Opin Biotechnol. 2011;22(1):103-108 doi
  • Zamboni N, Sauer U, Novel biological insights through metabolomics and 13C-flux analysis, Curr Opin Microbiol. 2009;12(5):553-8 doi
  • Zamboni N, Saghatelian A, Patti GJ, Defining the Metabolome: Size, Flux, and RegulationMol Cell. 2015, 58: 699–706, doi
  • Büscher et al, A roadmap for interpreting 13C metabolite labeling patterns from cellsCurr Opin Biotechnol. 2015, 34: 189-201 doi
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Tue Jun 27 10:48:43 CEST 2017
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