Contents lists available at ScienceDirect Metabolic Engineering journal homepage: www.elsevier.com/locate/meteng Mapping Salmonella typhimurium pathways using 13 C metabolic flux analysis Daniela M. Correia a,1 , Cintia R. Sargo a,1 , Adilson J. Silva a,1 , Sophia T. Santos b , Roberto C. Giordano a , Eugénio C. Ferreira b , Teresa C. Zangirolami a , Marcelo P.A. Ribeiro a , Isabel Rocha b,c, ⁎ a Graduate Program of Chemical Engineering, Federal University of São Carlos, Rodovia Washington Luís, Km 235, São Carlos, SP 13565-905, Brazil b CEB—Centre of Biological Engineering, University of Minho, Campus De Gualtar, Braga 4710-057, Portugal c Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB-NOVA), Oeiras, Portugal ARTICLE INFO Keywords: Salmonella typhimurium Chemostat culture 13 C-MFA Genome-scale metabolic model In silico simulation ABSTRACT 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 meta- bolic 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 13 C-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% 13 C-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 reac- tions were poorly described. 1. Introduction Salmonella enterica serovar Typhimurium (S. typhimurium) is an in- tracellular mammalian pathogen that belongs to the Enterobacteriaceae family. Several studies addressing virulence, pathogenicity, host-mi- crobe interactions, and genetics of S. typhimurium have been published (Dandekar et al., 2012, 2015; Kaufmann et al., 2001). Besides the re- cognized importance as a pathogen itself, in the last years, Salmonella has gained increasing attention in the biotechnological area as a po- tential host to produce several pharmaceutical compounds (Silva et al., 2014). Among the products that can be produced with Salmonella ty- phimurium are flagellin, capsular polysaccharide Vi, lipopolysacchar- ides, and vaccines, with applications in human and veterinary medicine (Braga et al., 2010; Kong et al., 2013; Kothari et al., 2014; Oliveira et al., 2011). Attenuated strains of Salmonella are being used as Live Bacterial Vectors (LBV), for immunization against itself or to deliver heterologous antigens (Silva et al., 2014). In addition, promising results have been obtained with the utilization of Salmonella in treatment and vaccination against non-infectious diseases, such as several types of cancer, including melanoma, and breast, prostate, pancreatic and cervix cancers (Bolhassani and Zahedifard, 2012; Forbes, 2010; Heimann and Rosenberg, 2003). Metabolic engineering has been applied, with success, in genetic improvement of a variety of organisms. Through the rational selection of gene targets to be manipulated, it is possible, for instance, to improve the production of a given molecule or to reduce the secretion of an undesirable metabolite (Stephanopoulos et al., 1998). To this end, genome-scale metabolic models are important tools that allow pre- dicting phenotypes under different conditions, supporting the devel- opment of metabolic engineering strategies. Another important appli- cation of genome-scale metabolic models is the identification of metabolic drug targets, with a possible relevant contribution in fighting https://doi.org/10.1016/j.ymben.2018.11.011 Received 19 March 2018; Received in revised form 26 November 2018; Accepted 28 November 2018 ⁎ Corresponding author at: Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB-NOVA), Oeiras, Portugal. E-mail address: irocha@itqb.unl.pt (I. Rocha). 1 These authors contributed equally to this work. Metabolic Engineering 52 (2019) 303–314 Available online 04 December 2018 1096-7176/ © 2018 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved. T