Moiety modeling framework for deriving moiety abundances from mass spectrometry measured isotopologues

• Published in: BMC bioinformatics 2019-10, Vol.20 (1), p.524-524, Article 524
• Main Authors: Jin, Huan, Moseley, Hunter N B
• Format: Article
• Language: English
• Subjects: Algorithms; Annealing; Atoms; Bayes Theorem; Carbon Isotopes - analysis; Cell Line, Tumor; Cheminformatics; Computational biology; Engineering research; Humans; Isotope analysis; Isotope Labeling; Isotopologue deconvolution; Male; Mass spectrometry; Mass Spectrometry - methods; Metabolites; Metabolomics; Metabolomics - methods; Methods; Moiety model; Prostatic Neoplasms; Software; Spectroscopy; Stable isotope resolved metabolomics (SIRM)
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-019-3096-7

Stable isotope tracing can follow individual atoms through metabolic transformations through the detection of the incorporation of stable isotope within metabolites. This resulting data can be interpreted in terms related to metabolic flux. However, detection of a stable isotope in metabolites by mass spectrometry produces a profile of isotopologue peaks that requires deconvolution to ascertain the localization of isotope incorporation. To aid the interpretation of the mass spectroscopy isotopologue profile, we have developed a moiety modeling framework for deconvoluting metabolite isotopologue profiles involving single and multiple isotope tracers. This moiety modeling framework provides facilities for moiety model representation, moiety model optimization, and moiety model selection. The moiety_modeling package was developed from the idea of metabolite decomposition into moiety units based on metabolic transformations, i.e. a moiety model. The SAGA-optimize package, solving a boundary-value inverse problem through a combined simulated annealing and genetic algorithm, was developed for model optimization. Additional optimization methods from the Python scipy library are utilized as well. Several forms of the Akaike information criterion and Bayesian information criterion are provided for selecting between moiety models. Moiety models and associated isotopologue data are defined in a JSONized format. By testing the moiety modeling framework on the timecourses of C isotopologue data for uridine diphosphate N-acetyl-D-glucosamine (UDP-GlcNAc) in human prostate cancer LnCaP-LN3 cells, we were able to confirm its robust performance in isotopologue deconvolution and moiety model selection. SAGA-optimize is a useful Python package for solving boundary-value inverse problems, and the moiety_modeling package is an easy-to-use tool for mass spectroscopy isotopologue profile deconvolution involving single and multiple isotope tracers. Both packages are freely available on GitHub and via the Python Package Index.