title: Impact of kinetic isotope effects in isotopic studies of metabolic systems.
abstract: BACKGROUND
  Isotope labeling experiments (ILEs) are increasingly used to investigate the functioning of metabolic systems. Some enzymes are subject to kinetic isotope effects (KIEs) which modulate reaction rates depending on the isotopic composition of their substrate(s). KIEs may therefore affect both the propagation of isotopes through metabolic networks and their operation, and ultimately jeopardize the biological value of ILEs. However, the actual impact of KIEs on metabolism has never been investigated at the system level.
  
  
  RESULTS
  First, we developed a framework which integrates KIEs into kinetic and isotopic models of metabolism, thereby accounting for their system-wide effects on metabolite concentrations, metabolic fluxes, and isotopic patterns. Then, we applied this framework to assess the impact of KIEs on the central carbon metabolism of Escherichia coli in the context of (13)C-ILEs, under different situations commonly encountered in laboratories. Results showed that the impact of KIEs strongly depends on the label input and on the variable considered but is significantly lower than expected intuitively from measurements on isolated enzymes. The global robustness of both the metabolic operation and isotopic patterns largely emerge from intrinsic properties of metabolic networks, such as the distribution of control across the network and bidirectional isotope exchange.
  
  
  CONCLUSIONS
  These results demonstrate the necessity of investigating the impact of KIEs at the level of the entire system, contradict previous hypotheses that KIEs would have a strong effect on isotopic distributions and on flux determination, and strengthen the biological value of (13)C-ILEs. The proposed modeling framework is generic and can be used to investigate the impact of all the isotopic tracers ((2)H, (13)C, (15)N, (18)O, etc.) on different isotopic datasets and metabolic systems. By allowing the integration of isotopic and metabolomics data collected under stationary and/or non-stationary conditions, it may also assist interpretations of ILEs and facilitate the development of more accurate kinetic models with improved explicative and predictive capabilities.
authors.affiliation: MCISB, Manchester Institute of Biotechnology, University of Manchester, Manchester, UK. pierre.millard@insa-toulouse.fr.|School of Computer Science, University of Manchester, Manchester, UK. pierre.millard@insa-toulouse.fr.|Université de Toulouse; INSA, UPS, INP; LISBP, Toulouse, France. pierre.millard@insa-toulouse.fr.|INRA, UMR792, Ingénierie des Systèmes Biologiques et des Procédés, Toulouse, France. pierre.millard@insa-toulouse.fr.|CNRS, UMR5504, Toulouse, France. pierre.millard@insa-toulouse.fr.
authors.forename: Pierre
authors.initials: P
authors.lastname: Millard
authors.affiliation: Université de Toulouse; INSA, UPS, INP; LISBP, Toulouse, France. jean-charles.portais@insa-toulouse.fr.|INRA, UMR792, Ingénierie des Systèmes Biologiques et des Procédés, Toulouse, France. jean-charles.portais@insa-toulouse.fr.|CNRS, UMR5504, Toulouse, France. jean-charles.portais@insa-toulouse.fr.
authors.forename: Jean-Charles
authors.initials: JC
authors.lastname: Portais
authors.affiliation: MCISB, Manchester Institute of Biotechnology, University of Manchester, Manchester, UK. pedro.mendes@manchester.ac.uk.|School of Computer Science, University of Manchester, Manchester, UK. pedro.mendes@manchester.ac.uk.|Center for Quantitative Medicine and Department of Cell Biology, UConn Health, Farmington, Connecticut, USA. pedro.mendes@manchester.ac.uk.
authors.forename: Pedro
authors.initials: P
authors.lastname: Mendes
mesh_terms: D002247:Carbon Isotopes / Q000378:metabolism / Q000494:pharmacology*; D004798:Enzymes / Q000378:metabolism; D004926:Escherichia coli / Q000187:drug effects* / Q000378:metabolism; D049490:Systems Biology / Q000379:methods* / Q000592:standards
chemical_list: D002247:Carbon Isotopes; D004798:Enzymes