Model-independent fluxome profiling from 2H and 13C experiments for metabolic variant discrimination.
We introduce a conceptually novel method for intracellular fluxome profiling from unsupervised statistical analysis of stable isotope labeling. Without a priori knowledge on the metabolic system, we identified characteristic flux fingerprints in 10 Bacillus subtilis mutants from 132 2H and 13C tracer experiments. Beyond variant discrimination, independent component analysis automatically mapped several fingerprints to their metabolic determinants. The approach is flexible and paves the way to large-scale fluxome profiling of any biological system and condition.
Zamboni
Nicola
N
Institute of Biotechnology, ETH Zürich, CH-8093 Zürich, Switzerland. nzamboni@stanford.edu <nzamboni@stanford.edu>
Sauer
Uwe
U
D000596:Amino Acids / Q000378:metabolism; D001412:Bacillus subtilis / Q000235:genetics / Q000378:metabolism*; D002247:Carbon Isotopes; D003627:Data Interpretation, Statistical; D008401:Gas Chromatography-Mass Spectrometry / Q000379:methods; D006859:Hydrogen; D007553:Isotope Labeling; D008954:Models, Biological; D015999:Multivariate Analysis; D009154:Mutation; D019906:Nuclear Magnetic Resonance, Biomolecular / Q000379:methods
D000596:Amino Acids; D002247:Carbon Isotopes; D006859:Hydrogen