Mapping Salmonella typhimurium pathways using 13C metabolic flux analysis

Daniela M. Correia, Cintia R. Sargo, Adilson J. Silva, Sophia T. Santos, Roberto C. Giordano, Eugénio C. Ferreira, Teresa C. Zangirolami, Marcelo P.A. Ribeiro, Isabel Rocha

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1 Citation (Scopus)


In the last years, Salmonella has been extensively studied not only due to its importance as a pathogen, but also as a host to produce pharmaceutical compounds. However, the full exploitation of Salmonella as a platform for bioproduct delivery has been hampered by the lack of information about its metabolism. Genome-scale metabolic models can be valuable tools to delineate metabolic engineering strategies as long as they closely represent the actual metabolism of the target organism. In the present study, a 13C-MFA approach was applied to map the fluxes at the central carbon pathways of S. typhimurium LT2 growing at glucose-limited chemostat cultures. The experiments were carried out in a 2L bioreactor, using defined medium enriched with 20% 13C-labeled glucose. Metabolic flux distributions in central carbon pathways of S. typhimurium LT2 were estimated using OpenFLUX2 based on the labeling pattern of biomass protein hydrolysates together with biomass composition. The results suggested that pentose phosphate is used to catabolize glucose, with minor fluxes through glycolysis. In silico simulations, using Optflux and pFBA as simulation method, allowed to study the performance of the genome-scale metabolic model. In general, the accuracy of in silico simulations was improved by the superimposition of estimated intracellular fluxes to the existing genome-scale metabolic model, showing a better fitting to the experimental extracellular fluxes, whereas the intracellular fluxes of pentose phosphate and anaplerotic reactions were poorly described.

Original languageEnglish
Pages (from-to)303-314
Number of pages12
JournalMetabolic Engineering
Publication statusPublished - 1 Mar 2019


  • C-MFA
  • Chemostat culture
  • Genome-scale metabolic model
  • In silico simulation
  • Salmonella typhimurium


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