Metabolic fluxes for nutritional flexibility of Mycobacterium tuberculosis
2021; Springer Nature; Volume: 17; Issue: 5 Linguagem: Inglês
10.15252/msb.202110280
ISSN1744-4292
AutoresKhushboo Borah, Tom A. Mendum, N. Hawkins, Jane L. Ward, Michael H. Beale, Gérald Larrouy-Maumus, Apoorva Bhatt, Martine Moulin, Michael Haertlein, Gernot A. Strohmeier, Harald Pichler, V. Trevor Forsyth, Stephan Noack, Celia W. Goulding, Johnjoe McFadden, Dany J. V. Beste,
Tópico(s)Diagnosis and treatment of tuberculosis
ResumoArticle4 May 2021Open Access Transparent process Metabolic fluxes for nutritional flexibility of Mycobacterium tuberculosis Khushboo Borah Khushboo Borah orcid.org/0000-0001-9030-2085 Department of Microbial and Cellular Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK Search for more papers by this author Tom A Mendum Tom A Mendum orcid.org/0000-0002-6331-2605 Department of Microbial and Cellular Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK Search for more papers by this author Nathaniel D Hawkins Nathaniel D Hawkins orcid.org/0000-0001-9806-312X Department of Computational and Analytical Sciences, Rothamsted Research, Harpenden, UK Search for more papers by this author Jane L Ward Jane L Ward Department of Computational and Analytical Sciences, Rothamsted Research, Harpenden, UK Search for more papers by this author Michael H Beale Michael H Beale Department of Computational and Analytical Sciences, Rothamsted Research, Harpenden, UK Search for more papers by this author Gerald Larrouy-Maumus Gerald Larrouy-Maumus orcid.org/0000-0001-6614-8698 MRC Centre for Molecular Bacteriology and Infection, Department of Life Sciences, Faculty of Natural Sciences, Imperial College London, London, UK Search for more papers by this author Apoorva Bhatt Apoorva Bhatt orcid.org/0000-0002-6655-1636 School of Biosciences, University of Birmingham, Edgbaston, UK Search for more papers by this author Martine Moulin Martine Moulin Life Sciences Group, Institut Laue-Langevin, Grenoble Cedex 9, France Partnership for Structural Biology, Grenoble Cedex 9, France Search for more papers by this author Michael Haertlein Michael Haertlein Life Sciences Group, Institut Laue-Langevin, Grenoble Cedex 9, France Partnership for Structural Biology, Grenoble Cedex 9, France Search for more papers by this author Gernot Strohmeier Gernot Strohmeier Austrian Centre of Industrial Biotechnology, Graz, Austria Institute of Organic Chemistry, NAWI Graz, Graz University of Technology, Graz, Austria Search for more papers by this author Harald Pichler Harald Pichler Austrian Centre of Industrial Biotechnology, Graz, Austria Institute of Organic Chemistry, NAWI Graz, Graz University of Technology, Graz, Austria Institute of Molecular Biotechnology, NAWI Graz, BioTechMed Graz, Graz University of Technology, Graz, Austria Search for more papers by this author V Trevor Forsyth V Trevor Forsyth Life Sciences Group, Institut Laue-Langevin, Grenoble Cedex 9, France Partnership for Structural Biology, Grenoble Cedex 9, France Faculty of Natural Sciences, Keele University, Staffordshire, UK Search for more papers by this author Stephan Noack Stephan Noack orcid.org/0000-0001-9784-3626 Institute of Bio- and Geosciences 1: Biotechnology 2, Forschungszentrum Jülich GmbH, Jülich, Germany Search for more papers by this author Celia W Goulding Celia W Goulding orcid.org/0000-0001-5582-0565 Department of Pharmaceutical Sciences & Molecular Biology & Biochemistry, University of California Irvine, Irvine, CA, USA Search for more papers by this author Johnjoe McFadden Johnjoe McFadden orcid.org/0000-0003-2145-0046 Department of Microbial and Cellular Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK Search for more papers by this author Dany J V Beste Corresponding Author Dany J V Beste [email protected] orcid.org/0000-0001-6579-1366 Department of Microbial and Cellular Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK Search for more papers by this author Khushboo Borah Khushboo Borah orcid.org/0000-0001-9030-2085 Department of Microbial and Cellular Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK Search for more papers by this author Tom A Mendum Tom A Mendum orcid.org/0000-0002-6331-2605 Department of Microbial and Cellular Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK Search for more papers by this author Nathaniel D Hawkins Nathaniel D Hawkins orcid.org/0000-0001-9806-312X Department of Computational and Analytical Sciences, Rothamsted Research, Harpenden, UK Search for more papers by this author Jane L Ward Jane L Ward Department of Computational and Analytical Sciences, Rothamsted Research, Harpenden, UK Search for more papers by this author Michael H Beale Michael H Beale Department of Computational and Analytical Sciences, Rothamsted Research, Harpenden, UK Search for more papers by this author Gerald Larrouy-Maumus Gerald Larrouy-Maumus orcid.org/0000-0001-6614-8698 MRC Centre for Molecular Bacteriology and Infection, Department of Life Sciences, Faculty of Natural Sciences, Imperial College London, London, UK Search for more papers by this author Apoorva Bhatt Apoorva Bhatt orcid.org/0000-0002-6655-1636 School of Biosciences, University of Birmingham, Edgbaston, UK Search for more papers by this author Martine Moulin Martine Moulin Life Sciences Group, Institut Laue-Langevin, Grenoble Cedex 9, France Partnership for Structural Biology, Grenoble Cedex 9, France Search for more papers by this author Michael Haertlein Michael Haertlein Life Sciences Group, Institut Laue-Langevin, Grenoble Cedex 9, France Partnership for Structural Biology, Grenoble Cedex 9, France Search for more papers by this author Gernot Strohmeier Gernot Strohmeier Austrian Centre of Industrial Biotechnology, Graz, Austria Institute of Organic Chemistry, NAWI Graz, Graz University of Technology, Graz, Austria Search for more papers by this author Harald Pichler Harald Pichler Austrian Centre of Industrial Biotechnology, Graz, Austria Institute of Organic Chemistry, NAWI Graz, Graz University of Technology, Graz, Austria Institute of Molecular Biotechnology, NAWI Graz, BioTechMed Graz, Graz University of Technology, Graz, Austria Search for more papers by this author V Trevor Forsyth V Trevor Forsyth Life Sciences Group, Institut Laue-Langevin, Grenoble Cedex 9, France Partnership for Structural Biology, Grenoble Cedex 9, France Faculty of Natural Sciences, Keele University, Staffordshire, UK Search for more papers by this author Stephan Noack Stephan Noack orcid.org/0000-0001-9784-3626 Institute of Bio- and Geosciences 1: Biotechnology 2, Forschungszentrum Jülich GmbH, Jülich, Germany Search for more papers by this author Celia W Goulding Celia W Goulding orcid.org/0000-0001-5582-0565 Department of Pharmaceutical Sciences & Molecular Biology & Biochemistry, University of California Irvine, Irvine, CA, USA Search for more papers by this author Johnjoe McFadden Johnjoe McFadden orcid.org/0000-0003-2145-0046 Department of Microbial and Cellular Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK Search for more papers by this author Dany J V Beste Corresponding Author Dany J V Beste [email protected] orcid.org/0000-0001-6579-1366 Department of Microbial and Cellular Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK Search for more papers by this author Author Information Khushboo Borah1, Tom A Mendum1, Nathaniel D Hawkins2, Jane L Ward2, Michael H Beale2, Gerald Larrouy-Maumus3, Apoorva Bhatt4, Martine Moulin5,6, Michael Haertlein5,6, Gernot Strohmeier7,8, Harald Pichler7,8,9, V Trevor Forsyth5,6,10, Stephan Noack11, Celia W Goulding12, Johnjoe McFadden1 and Dany J V Beste *,1 1Department of Microbial and Cellular Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK 2Department of Computational and Analytical Sciences, Rothamsted Research, Harpenden, UK 3MRC Centre for Molecular Bacteriology and Infection, Department of Life Sciences, Faculty of Natural Sciences, Imperial College London, London, UK 4School of Biosciences, University of Birmingham, Edgbaston, UK 5Life Sciences Group, Institut Laue-Langevin, Grenoble Cedex 9, France 6Partnership for Structural Biology, Grenoble Cedex 9, France 7Austrian Centre of Industrial Biotechnology, Graz, Austria 8Institute of Organic Chemistry, NAWI Graz, Graz University of Technology, Graz, Austria 9Institute of Molecular Biotechnology, NAWI Graz, BioTechMed Graz, Graz University of Technology, Graz, Austria 10Faculty of Natural Sciences, Keele University, Staffordshire, UK 11Institute of Bio- and Geosciences 1: Biotechnology 2, Forschungszentrum Jülich GmbH, Jülich, Germany 12Department of Pharmaceutical Sciences & Molecular Biology & Biochemistry, University of California Irvine, Irvine, CA, USA *Corresponding author. Tel: +44 1483 686785; E-mail: [email protected] Molecular Systems Biology (2021)17:e10280https://doi.org/10.15252/msb.202110280 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract The co-catabolism of multiple host-derived carbon substrates is required by Mycobacterium tuberculosis (Mtb) to successfully sustain a tuberculosis infection. However, the metabolic plasticity of this pathogen and the complexity of the metabolic networks present a major obstacle in identifying those nodes most amenable to therapeutic interventions. It is therefore critical that we define the metabolic phenotypes of Mtb in different conditions. We applied metabolic flux analysis using stable isotopes and lipid fingerprinting to investigate the metabolic network of Mtb growing slowly in our steady-state chemostat system. We demonstrate that Mtb efficiently co-metabolises either cholesterol or glycerol, in combination with two-carbon generating substrates without any compartmentalisation of metabolism. We discovered that partitioning of flux between the TCA cycle and the glyoxylate shunt combined with a reversible methyl citrate cycle is the critical metabolic nodes which underlie the nutritional flexibility of Mtb. These findings provide novel insights into the metabolic architecture that affords adaptability of bacteria to divergent carbon substrates and expand our fundamental knowledge about the methyl citrate cycle and the glyoxylate shunt. Synopsis Quantitative metabolic analysis using stable isotopes, lipid fingerprinting, and mathematical modelling are applied to investigate the metabolic network of Mycobacterium tuberculosis growing slowly in a steady state chemostat system. The tubercle bacillus efficiently co-metabolises cholesterol or glycerol, in combination with two-carbon generating substrates without compartmentalisation of metabolism. Metabolic flux profiles of M. tuberculosis growing slowly on the dual carbon sources are described using an expanded 13C isotopomer model. Partitioning of metabolite flux between the TCA cycle and the glyoxylate shunt combined with a reversible methyl citrate cycle are critical nodes underlying the metabolic flexibility of M. tuberculosis. Introduction Mycobacterium tuberculosis (Mtb) is the causative agent of a global tuberculosis (TB) pandemic which has now reached staggering levels making Mtb once again a leading cause of death globally (World Health Organisation, 2020). The terrifying trend of increasing antibiotic-resistant TB is destabilising TB control measures making new therapeutics which target drug-resistant strains of Mtb an urgent priority (Singh et al, 2020). Mtb is an unusual bacterial pathogen, which has the remarkable ability to cause both acute life-threatening disease and clinically latent infections that can persist for the lifetime of the human host. Mycobacterium tuberculosis spends much of its life cycle growing intracellularly within the phagosomal compartment of macrophages where nutrient availability will fluctuate (Warner, 2014; Huang et al, 2018). In addition to replicating within the phagosomal compartment, it has been shown that Mtb can escape the intracellular environment to survive extracellularly (Grosset, 2003), in other cell types and within the diverse and dynamic microenvironments of granulomas (Bussi & Gutierrez, 2019). Mtb must therefore be able to survive in a plethora of different microenvironments and nutrients. Experimental evidence has identified central carbon metabolism as instrumental to this pathogenic strategy, and therefore, our research is focused on investigating the metabolic capabilities of Mtb both in vitro and ex vivo (Beste et al, 2007a; Beste et al, 2011; Beste et al, 2013; Lofthouse et al, 2013; Basu et al, 2018; Borah et al, 2019; López-Agudelo et al, 2020). Mycobacterium tuberculosis maintains a functional tricarboxylic acid (TCA) cycle, pentose phosphate pathway (PPP) and Embden–Meyerhof–Parnas (EMP) pathway, as well as enzymes providing a metabolic link between glycolysis and the TCA cycle (Beste & McFadden, 2010). Mtb also has two alternative pathways (methyl citrate cycle and the B12-dependent methylmalonyl pathway) for metabolising propionyl-CoA derived from the metabolism of sterols, uneven branched chain fatty acids and amino acids (Eoh & Rhee, 2014). These pathways allow Mtb to utilise a wide range of carbon sources that includes carbohydrates, sugars, fatty acids, amino acids and sterols (Warner, 2014). Whilst the basic architecture of central carbon metabolism of Mtb is now well established, there are still many questions regarding how the flux of metabolites through this network is modulated under various different nutritional conditions. Using stable isotope-labelled nutrients for studying the metabolism of Mtb has proved extremely informative (de Carvalho et al, 2010; Marrero et al, 2010; Beste et al, 2011; Beste et al, 2013; Eoh & Rhee, 2014; Nandakumar et al, 2014; Basu et al, 2018; Borah et al, 2019). Metabolic labelling experiments using 13C-labelled combinations of acetate, glycerol and glucose have demonstrated that Mtb is able to co-catabolise two carbon sources simultaneously demonstrating that Mtb does not use carbon catabolite repression to regulate metabolism (de Carvalho et al, 2010). This work also suggested that Mtb not only co-catabolised these carbon substrates but did so in a compartmentalised and segregated manner. However, importantly this work was not able to determine the metabolic flux profiles on these combinations of carbon substrates. Understanding metabolic fluxes of Mtb during co-catabolism of multiple carbon sources will allow us to identify nodes of metabolism most amenable to therapeutic intervention. By combining isotopomer labelling with our steady-state chemostat model system of mycobacterial growth allowed us to perform 13C-metabolic flux analysis (MFA) at different growth rates in carbon limited conditions (Beste et al, 2011). Previously, we identified the activity of a novel GAS pathway for pyruvate dissimulation when Mtb was growing slowly on glycerol and oleic acid and demonstrated that the pathway requires isocitrate lyase and the enzymes of the anaplerotic node (Beste et al, 2011) both of which are important for the survival of Mtb in the host (McKinney et al, 2000; Basu et al, 2018). Glycerol was not considered an important carbon source for Mtb as glycerol kinase (glpK), which is an essential gene for the conversion of glycerol to glycerol 3-phosphate (Beste et al, 2009), is dispensable for the growth of Mtb in a murine TB model (Pethe et al, 2010). However, the detection of glycerol in human-like TB lesions has compelled a re-evaluation of the role of glycerol and glycerol containing metabolites in the life cycle of Mtb (Safi et al, 2019). Moreover, Mtb has been shown to co-metabolise a mixture of carbon substrates when growing in host macrophage cells that included an unknown glycolytic C3 substrate (Beste et al, 2013) that could potentially be glycerol. Metabolomic and gene-deletion studies have highlighted fatty acids and cholesterol as critical to the nutrition, survival and virulence of Mtb (McKinney et al, 2000; Pandey & Sassetti, 2008) and that Mtb encounters and co-metabolises both substrates simultaneously in vivo (Wilburn et al, 2018). Cholesterol, uneven chain fatty acids and branched chain amino acids are all catabolised to provide Mtb with the metabolite propionyl-CoA which although required for the synthesis of important virulence cell wall lipids (phthiocerol dimycocerosate (PDIM), polyacylated trehalose and sulpholipids) is also toxic if allowed to accumulate intracellularly (Griffin et al, 2012; Lee et al, 2013). In addition to metabolising propionyl-CoA through the methyl citrate cycle (MCC) or in the presence of vitamin B12, the methylmalonyl cycle, Mtb can also sequester propionyl-CoA into methyl branched cell wall lipids (Lee et al, 2013). During infection, it is thought that Mtb uses fatty acids to prime this process further suggesting that cholesterol and fatty acid metabolism occur simultaneously in vivo (Lee et al, 2013; Wilburn et al, 2018). Despite numerous biochemical studies to elucidate the biochemical degradation pathways and studies exploring the role of specific enzymes in cholesterol and fatty acid metabolism, the metabolic flux profile of Mtb growing on this combination of substrates has never been directly measured. Therefore, in this study we performed 13C-MFA on steady-state, slowly growing cultures of Mtb using an extended version of our 13C isotopomer model (Beste et al, 2011), which includes the MCC. We compared the metabolic flux profile of chemostat cultures of Mtb growing in defined carbon limited conditions growing with cholesterol/acetate with those growing on glycerol/Tween 80 to reflect carbon sources available to Mtb during the life cycle of this pathogen within the host. We demonstrate that when Mtb is growing slowly on cholesterol and acetate, Mtb utilises a complete TCA cycle in combination with the glyoxylate shunt. There is very little demand for the MCC in these conditions as lipid fingerprinting identified that the propionyl-CoA-derived cholesterol is being preferentially incorporated into lipids. Conversely when growing on glycerol and Tween 80, Mtb utilises an incomplete TCA cycle and the methyl citrate cycle is reversed to provide propionyl-CoA for the synthesis of virulence lipids. This work highlights that re-routing fluxes through the TCA cycle, MCC and the glyoxylate shunt and in particular the ability to alternate the direction of the MCC, whilst co-metabolising carbon substrates during slow growth is critical to the metabolic flexibility of Mtb. Results Isotopic profiling of chemostat grown Mtb demonstrated efficient co-catabolism of carbon sources without metabolic compartmentalisation To define the metabolic profile of Mtb growing on cholesterol and acetate, Mtb H37Rv cultures were grown in carbon limited chemostats operating at a dilution rate of µ = 0.01 h (doubling time (td) = 69 h). We adopted this dilution rate as we have previously demonstrated that the transcriptional response at this growth rate has many similarities to the transcriptional response characteristic of the adaptation of Mtb to the macrophage environment, and others have shown a similar profile from Mtb isolated from sputum (Beste et al, 2007b). The cultures were grown in Roisin's minimal medium (Beste et al, 2005) with a combination of either cholesterol/acetate (CHL-ACE) or glycerol/Tween 80 (GLY-OLA) as previously described (Beste et al, 2011). After approximately three volume changes, little variation was observed in the CO2 and biomass production rates, indicating that a metabolic steady-state conditions had been attained (Fig EV1). Data from the chemostat cultures demonstrated that Mtb was able to co-metabolise acetate and cholesterol simultaneously with similar yields of bacteria to cultures growing on glycerol and Tween 80. Substrate uptake rates were also comparable under the two growth conditions, but the CO2 production rate was significantly higher in CHL-ACE (Table 1). Click here to expand this figure. Figure EV1. Metabolic and isotopic steady-state growth of Mycobacterium tuberculosis A–D. Continuous cultures of Mtb were grown in bioreactors with either CHL-ACE or GLY-OLA acid as carbon sources at a dilution rate of 0.01 h−1 (td = 69 h). Metabolic steady state was achieved after three volume changes as evidenced from by stable cell density (OD600) and CO2 production rates (A and B). After metabolic steady state was achieved, the feed was replaced with identical media containing 13C-labelled carbon substrates until the culture attained an isotopic steady state as evidenced by steady levels of 13C incorporation into proteinogenic amino acids. Download figure Download PowerPoint Table 1. Steady-state characteristics of Mycobacterium tuberculosis grown in carbon limited chemostats. Carbon sources CHL-ACE GLY-OLA Dilution rate (h−1) 0.01 0.01 Biomass (g dry weight l−1) 0.49 2.18 CFU (× 107 ml−1) 3.9 33 Cell weight (pg dry weight cfu−1) 12.5 6.7 Substrate consumption rate (mmol g biomass−1 h−1) Acetate = 0.26 Glycerol = 0.23 Cholesterol = 0.0085 Oleic acid = 0.002 CO2 production rate (mmol g biomass−1 h−1) 0.245 0.178 Yield (g biomass−1 mmol carbon−1) 0.013 0.012 For the labelling experiments, steady-state chemostat cultures were switched to identical media containing 13C-labelled substrates (30% [13C3]glycerol OR 100% [13C2]acetate). Samples were taken every volume change for a total of four volume changes to ensure that an isotopic stationary state was attained. Labelling of proteinogenic amino acids and intracellular metabolites was measured using gas chromatography–mass spectrometry (GC-MS) or liquid chromatography–mass spectrometry (LC-MS) as previously described (Beste et al, 2011). Proteinogenic amino acids are commonly measured for 13C-MFA as they are much more abundant and stable than their precursors and provide extensive labelling information. Moreover, amino acids can be used to directly deduce the labelling patterns of their precursor metabolites. The labelling pattern of the amino acids changed very little between the third and fourth volume change confirming that the culture had reached an isotopic steady state which is essential for determining intracellular fluxes using 13C-MFA (Table 1; Fig EV1C and D). Previous studies indicated that Mtb was operating a form of compartmentalised metabolism when growing on solid 7H10 agar containing combinations of the carbon sources glucose, glycerol and acetate whereby individual substrates had distinct metabolic fates (de Carvalho et al, 2010). However, our 13C labelling data from chemostat cultures at metabolic and isotopic steady state showed no evidence of such compartmentalisation when Mtb was grown with either GLY-OLA or CHL-ACE as evidenced by uniform distribution of both unlabelled and 13C-labelled substrates (Fig 1). Figure 1. 13C incorporation into the proteinogenic amino acids and intracellular metabolites from metabolic and isotopic steady-state chemostat Mtb cultures Label distribution is shown in metabolites from Mtb grown in 30% [13C3] glycerol and unlabelled Tween 80 which provides Mtb with oleic acid (GLY-OLA) and 100% [13C2] acetate and unlabelled cholesterol (CHL-ACE). Average 13C incorporation was calculated for metabolites harvested at a steady-state growth and are shown as the amount labelled (13C) and unlabelled (12C) using 3–4 independent measurements. Data are average of 3 replicate measurements. DHAP (dihydroxyacetone phosphate), PYR (pyruvate) ALA (alanine), GLY (glycine), SER (serine), LYS (lysine), MET (methionine), ASP/N (aspartate/asparagine), THR (threonine), ILE (isoleucine), ORN (ornithine), GLU/N (glutamate/glutamine), SUC (succinate), LEU (leucine), ILE (isoleucine), VAL (valine), PHE (phenylalanine), TYR (tyrosine), HIS (histidine), S7P (sedoheptulose-7-phosphate), measured for CHL-ACE and R5P (ribose 5-phosphate) and measured for GLY-OLA cultures, are plotted on a metabolic map showing reactions for glycolysis, PPP, GLX (glyoxylate shunt) and the TCA cycle. Aspartate/asparagine and glutamate/glutamine pools are lumped as both asparagine and glutamine were reduced to aspartate and glutamate, respectively, during acid hydrolysis. Download figure Download PowerPoint For steady-state CHL-ACE Mtb cultures, the 13C labelling profiles reflect the entry points of cholesterol into central carbon metabolism. Cholesterol catabolism yields four propionyl-CoA, four acetyl-CoA, one pyruvate and one succinyl-CoA of which succinyl-CoA, pyruvate and acetyl-CoA enter central carbon metabolism directly (Crowe et al, 2017). Propionyl-CoA is toxic to Mtb and can be metabolised by either: (i) the methyl citrate cycle to pyruvate, (ii) the B12-dependent methylmalonyl pathway leading to succinyl-CoA, or (iii) used in cell wall lipogenesis. In our experiments, the methylmalonyl pathway is not active (as Roisin's media lacks vitamin B12). For the CHL-ACE experiments, the labelling profile of succinate (SUC), pyruvate (PYR) and the pyruvate-derived amino acids (alanine (ALA), valine (VAL) and leucine (LEU)) had ≥ 50% unlabelled carbon indicating that the carbon backbone of these metabolites was predominantly derived from unlabelled cholesterol. This is expected as cholesterol enters central carbon metabolism as succinate and pyruvate. Canonical 13C labelling patterns were measured in the other metabolites reflecting that 60% of the total carbon was derived from 13C-labelled acetate and the remainder derived from unlabelled cholesterol indicating that metabolism was also not compartmentalised in these conditions (Fig 1). For GLY-OLA grown cultures, the labelling profiles were consistent across the different metabolites analysed; the backbone of these metabolites was synthesised primarily from glycerol, demonstrating that metabolism of GLY-OLA was also not compartmentalised. 13C isotopologue analysis of amino acids defines their biosynthetic routes We performed 13C isotopologue analysis of proteinogenic amino acids. As expected, the profiles of all metabolites derived from CHL-ACE were different to that derived from GLY-OLA grown Mtb (Fig 2). The labelling profiles of amino acids reflect their biosynthetic origin for both GLY-OLA and CHL-ACE grown Mtb. For the GLY-OLA cultures, the labelling patterns of aspartate (ASP/N), threonine (THR), isoleucine (ILE), lysine (LYS) and methionine (MET) were similar, consistent with a common biosynthetic origin of these amino acids from oxaloacetate. The isotopologue composition of tyrosine (TYR) and phenylalanine (PHE) was also alike reflecting their common precursors – phosphoenolpyruvate and erythrose-4-phosphate. Similarly, ornithine (ORN) and glutamate (GLU/N), which are both derived from α-ketoglutarate, had similar labelling patterns. The isotopologue profiles of proteinogenic amino acids derived from steady-state cultures grown in CHL-ACE also reflect their biosynthetic origin. THR and MET had near identical profiles with ASP/N with M + 2 and M + 4 isotopomers having the highest 13C labelling, indicating their synthesis from ASP. GLU/N and ORN, TYR and PHE and ALA and SER all had very similar profiles indicating the common biosynthetic precursor for each of these pairs of amino acids. Whilst manual analysis of 13C isotopologue data allows qualitative conclusions, systems level analysis is required in order to quantitate the metabolic fluxes. Figure 2. 13C isotopologue analysis of proteinogenic amino acids from Mtb grown on CHL-ACE and GLY-OLA Measurements were obtained from Mtb grown under steady-state chemostat conditions. 13C incorporation is shown for the mass isotopomers, which are labelled as 13C1, 13C2, etc. Measurements are shown for alanine (ALA), leucine (LEU), valine (VAL), glycine (GLY), serine (SER), methionine (MET), histidine (HIS), aspartate–asparagine (ASP/N), threonine (THR), lysine (LYS), isoleucine (ILE), glutamate–glutamine (GLU/N), ornithine (ORN), tyrosine (TYR) and phenylalanine (PHE). Glycolysis, pentose phosphate pathway (PPP), glyoxylate shunt (GLX) and the TCA cycle are depicted. Values are shown as mean ± error bars (s.d) of 3–4 replicates. Download figure Download PowerPoint Improved 13C-Metabolic Flux Analysis using an expanded isotopomer model 13C-MFA is the preferred tool for quantitative characterisation of metabolic phenotypes in steady-state cultures (Wiechert, 2001). Previously, we developed an isotopomer model of Mtb's central carbon metabolism comprising of the TCA cycle, glycolysis, pentose phosphate pathway (PPP) and anaplerotic reactions, which allowed us to analyse 13C-isotopomer data and compute metabolic fluxes (Beste et al, 2011). For this study, we have expanded our capacity for predicting metabolic fluxes by adding the methyl citrate cycle (MCC) and amino acid degradation pathways to our previous 13C-isotopomer model (Table EV1). Absolute flux distributions were derived from the two steady-state chemostat conditions (Fig 3) using the INCA platform (Young, 2014). In our previous study, we were unable to determine the flux values unambiguously for Mtb growing in GLY-OLA. Here by using our extended isotopomer model, we obtained a unique metabolic flux profile for these conditions building on our previous work (Fig 3). A comparison of the current and the previous metabolic flux profiles of Mtb growing in GLY-OLA in steady-state cultures (Fig EV2) illustrates the similarities between the solutions and shows that extending the isotopomer model has improved our ability to resolve the metabolic fluxes of Mtb. Figure 3. Flux distributions of Mtb grown on CHL-ACE (A) and GLY-OLA (B) carbon substrate combinations A, B. Flux distributions were calculated for the extended Mtb metabol
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