convISA: A simple, convoluted method for isotopomer spectral analysis of fatty acids and cholesterol

Isotopomer spectral analysis (ISA) is a simple approach for modelling the cellular synthesis of fatty acids and cholesterol in a stable isotope labelling experiment. In the simplest model, fatty acid biosynthesis is described by two key parameters: the fractional enrichment of acetyl-CoA from the labelled substrate, D, and the fractional de novo synthesis of the fatty acid during the exposure to the labelled substrate, g(t). The model can also be readily extended to include synthesis via elongation of unlabelled shorter fatty acids. This modelling strategy is less complex than metabolic flux analysis and only requires the measurement of the mass isotopologues of a single metabolite. However, software tools to perform these calculations are not freely available. We have developed an algorithm (convISA), implemented in MATLAB(™), which employs the convolution (Cauchy product) of mass isotopologue distributions (MIDs) for ISA of fatty acids and cholesterol. In our method, the MIDs of each molecule are constructed as a single entity rather than deriving equations for individual isotopologues. The flexibility of this method allows the model to be applied to raw data as well as to data that has been corrected for natural isotope abundance. To test the algorithm, convISA was applied to 238 MIDs of methyl palmitate available from the literature, for which ISA parameters had been calculated via other methods. A very high correlation was observed between estimates of the D and g(t) parameters from convISA with both published values, and estimates generated by our own metabolic flux analysis using a simplified stoichiometric model (r=0.981 and 0.944, and 0.996 and 0.942). We also demonstrate the application of the convolution ISA approach to cholesterol biosynthesis; the model was applied to measurements made on MCF7 cells cultured in U-(13)C-glucose. In conclusion, we believe that convISA offers a convenient, flexible and transparent framework for metabolic modelling that will help facilitate the application of ISA to future experiments.