p13CMFA: Parsimonious 13C metabolic flux analysis

PLoS Comput Biol. 2019 Sep 6;15(9):e1007310. doi: 10.1371/journal.pcbi.1007310. eCollection 2019 Sep.

Abstract

Deciphering the mechanisms of regulation of metabolic networks subjected to perturbations, including disease states and drug-induced stress, relies on tracing metabolic fluxes. One of the most informative data to predict metabolic fluxes are 13C based metabolomics, which provide information about how carbons are redistributed along central carbon metabolism. Such data can be integrated using 13C Metabolic Flux Analysis (13C MFA) to provide quantitative metabolic maps of flux distributions. However, 13C MFA might be unable to reduce the solution space towards a unique solution either in large metabolic networks or when small sets of measurements are integrated. Here we present parsimonious 13C MFA (p13CMFA), an approach that runs a secondary optimization in the 13C MFA solution space to identify the solution that minimizes the total reaction flux. Furthermore, flux minimization can be weighted by gene expression measurements allowing seamless integration of gene expression data with 13C data. As proof of concept, we demonstrate how p13CMFA can be used to estimate intracellular flux distributions from 13C measurements and transcriptomics data. We have implemented p13CMFA in Iso2Flux, our in-house developed isotopic steady-state 13C MFA software. The source code is freely available on GitHub (https://github.com/cfoguet/iso2flux/releases/tag/0.7.2).

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Carbon Isotopes / metabolism*
  • Computational Biology / methods*
  • Gene Expression Profiling / methods*
  • Glycolysis
  • HCT116 Cells
  • Human Umbilical Vein Endothelial Cells
  • Humans
  • Metabolic Flux Analysis / methods*
  • Metabolic Networks and Pathways
  • Models, Biological
  • Transcriptome

Substances

  • Carbon Isotopes
  • Carbon-13

Grants and funding

This work was supported by European Commission (EC-654241, FP7-PEOPLE-264780), MINECO-European Commission FEDER funds – “Una manera de hacer Europa” (SAF2017-89673-R; SAF2015-70270-REDT), Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) – Generalitat de Catalunya (2017SGR1033) and Instituto de Salud Carlos III (CIBEREHD, CB17/04/00023). CF acknowledges the support received through “Becas de la Caixa para estudios de doctorado en universidades españolas” funded by the “La Caixa” foundation. CF also acknowledges the support from the Spanish National Bioinformatics Institute (INB-ISCIII-ES-ELIXIR). MC acknowledges the support received through the prize “ICREA Academia” for excellence in research, funded by ICREA foundation – Generalitat de Catalunya. The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript.