Candida albicans, Borrelia burgdorferi, lipopolysaccharide, and Mycobacterium tuberculosis in vitro. By integrating gene expression data of stimulated PBMCs of healthy individuals with the KEGG pathways, we identified both common and pathogen-specific regulated pathways depending on the time of incubation. At 4 h of incubation, pathogenic agents inhibited expression of genes involved in both the glycolysis and oxidative phosphorylation pathways. In contrast, at 24 h of incubation, particularly glycolysis was enhanced while genes involved in oxidative phosphorylation remained unaltered in the PBMCs. In general, differential gene expression was less pronounced at 4 h compared to 24 h of incubation. KEGG pathway analysis allowed differentiation between effects induced by Candida and bacterial stimuli. Application of genome-scale metabolic model further generated a Candida-specific set of 103 reporter metabolites  that might serve as biomarkers discriminating Candida-stimulated PBMCs from bacteria-stimuated PBMCs. Our analysis also identified a set of 49 metabolites that allowed discrimination between the effects of Borrelia burgdorferi, lipopolysaccharide and Mycobacterium tuberculosis. We conclude that analysis of pathogen-induced effects on PBMCs by a combination of KEGG pathways and genome-scale metabolic model provides deep insight in the metabolic changes coupled to host defense.Immunity and cellular metabolism are tightly interconnected but it is not clear whether different pathogens elicit specific metabolic responses. To address this issue, we studied differential metabolic regulation in peripheral blood mononuclear cells (PBMCs) of healthy volunteers challenged by  As the first line of host defense, the innate immune system can immediately sense and combat foreign pathogens , Borrelia burgdorferi (Borrelia), Escherichia coli-derived LPS, and Mycobacterium tuberculosis (MTB). These four are typical stimuli of innate immune pathways. LPS is the prototypical stimulus recognized by TLR 4  has not yet been reported. In the current study, we interrogate which metabolic pathways and metabolites are altered upon activation by various pathogens. To this end, we systematically measured gene expression profiles in human peripheral blood mononuclear cells (PBMCs) stimulated by heat inactivated Candida and bacterial stimuli, and application of genome-scale metabolic model further generated a Candida-specific set of 103 reporter metabolites that might serve as biomarkers discriminating Candida-stimulated PBMCs from bacteria-stimulated PBMCs.To identify gene expression changes involved in metabolism, we ran Kyoto Encyclopedia of Genes and Genomes (KEGG) based metabolic pathway analysis and genome-scale metabolic model (GEM) based reporter metabolite analysis, respectively. KEGG pathway analyses are widely and successfully used in biomedical research over the last decade as a routine step of interpreting gene expression data  (1 × 106 per ml), heat-killed B. burgdorferi, E. coli-derived LPS (10 ng per ml), or heat-killed MTB (1 μg per ml), respectively for 4 or 24 h. PBMCs that were cultured in only RPMI medium were used as controls. Illumina Human HT-12 Expression BeadChips were used to measure gene expression levels at 4 and 24 h. Details about the experiment and processed data are available in GSE42606 archived by Gene Expression Omnibus.As reported in in the previous study , 25 (Borrelia), 19 (LPS), and 18 (MTB). At 24 h, the size of paired samples were 29 (Candida), 29 (Borrelia), 20 (LPS), and N = 30 (MTB). Illumina probe IDs were mapped to Ensembl gene IDs (Ensembl version 73) or Entrez gene IDs by using the lumiHumanIDMapping and biomaRt R packages  was calculated for each metabolite from the gene-level Z scores of its associated genes. Here we calculated summary statistics for metabolites directly with the gene-level modified t statistics:This simple approach was first introduced by Irizarry et al. . ZpathwaZmetabolite is the summary statistics of a metabolite, and ti is the t statistics of gene i associated with the metabolite. Nmetabolite is the number of genes associated with the metabolite.P-value for each gene set based on its background distribution of summary statistics. However, unlike the original reporter metabolite algorithm . Znull refer to the summary statistics of that gene set based on the sample permutations.P-values were then calculated by using the function permp in the statmod R package. The algorithm underlying the permp function was developed by Phipson and Smyth , including “Isolated,” “Artificial reactions,” “Exchange reactions,” “Pool reactions,” “Miscellaneous,” “Other amino acid,” and “Blood group biosynthesis” were not included in the analysis. To simplify data visualization, all the transport subsystems were not included as well. If a metabolite could be mapped to multiple subsystems, all the subsystems were included in the final results.We were interested in metabolic pathways and metabolites that can discriminate To evaluate whether pathogen-specific metabolism corresponded to a specific immune response, we focused on innate immunity genes provided by the database innateDB , Borrelia , LPS , and MTB-stimulated (Enrichment score = −4.17. FDR = 0.0013) PBMCs. Oxidative phosphorylation pathway was also down-regulated in Candida , Borrelia , LPS , and MTB-stimulated (Enrichment score = −3.82. FDR = 0.0013) PBMCs. At 24 h after stimulation, glycolysis pathway was up-regulated in Candida , Borrelia , LPS , and MTB-stimulated  PBMCs. However, oxidative phosphorylation was not significantly changed in PBMCs stimulated by Candida, Borrelia, LPS, and MTB.At 4 h after stimulation, glycolysis pathway was down-regulated in Borrelia, LPS, and MTB, but differed from Candida-stimulated PBMCs at both 4 and 24 h. The detail statistics for pathways were provided in the Supplementary Table Borrelia, LPS, and MTB-stimulated PBMCs, but not in Candida-stimulated PBMCs at 4 h , and fatty acid biosynthesis  and three bacterial inflammatory stimuli  allowed us to identify metabolic signatures of Candida-induced host response, but also host response differences between bacterial challenges.The main finding of our study is that characterization of pathogen-dependent metabolic reprogramming in immune cells treated by various stimuli of innate immune pathway. For this purpose, we performed gene set enrichment analysis on gene expression data of human PBMCs treated with heat-killed A very strong temporal effect on the expression of metabolic genes was observed. This observation is in line with the concept that stimulation period is a critical factor in immune response  discriminated LPS-stimulated PBMCs from Borrelia-stimulated PBMCs from LPS- and MTB-stimulated PBMCs. To explore potential differences in more depth, we ran the reporter metabolite analysis, which is a gene set enrichment analysis with a genome-scale metabolic model. A genome-scale metabolic model is comprised of metabolites and reactions between them. Compared to KEGG metabolic pathway information, the genome-scale metabolic model makes use of detailed information on biochemical reactions of pathways. For instance, for any enzyme catalyzing reaction, we can retrieve the genes encoding that enzyme in the genome-scale metabolic model. Moreover, metabolites can be products of some reactions and meanwhile act as substrates in other reactions. Consequently, reporter metabolite analysis based on genome-scale metabolic model does not repeat but complement results from KEGG pathway analysis. We used HMR2 in our analysis since we did not perform flux balance analysis.The KEGG pathway based analysis failed to identify metabolic pathways that discriminate Candida-stimulated PBMCs, but not in PBMCs stimulated with bacterial stimuli at both 4 and 24 h. A considerable number of these Candida-specific reporter metabolites were found to be related to lipid metabolism. The previous study . With mass spectrometry and isotope labeling techniques, (Mitsche et al., MTB-specific reporter metabolites, we observed two kind of epoxyeicosatrienoic acids, synthesized from arachidonic acid. Epoxyeicosatrienoic acids were reported to inhibit inflammatory gene expression in immune cells and animal models (Thomson et al., In PBMCs stimulated by Candida, Borrelia, LPS, and MTB. For instance, in the case of Candida we identified five differentially regulated pathways spanning metabolic regions from the pentose phosphate pathway to aminoacyl tRNA biosynthesis. Our analysis here, for the first time, provides insight into pathogen-specific metabolism which affects stimulus-dependent signal transduction and cytokine production in stimulated human PBMCs.In summary, by integrating gene expression data with KEGG metabolic pathways in combination with the human genome-scale metabolic model, a very sensitive method to characterize metabolic reprogramming in immune cells is obtained. Applying this methodology, we were able to discriminate metabolic pathways and metabolites in human PBMCs stimulated by XZ: Did the analysis and wrote the manuscript; LJ: Recruited the participants and performed stimulation experiment; AM, JK, YL, MN, and AG: Helped with manuscript writing.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


To characterize the microbial ecosystems, topological properties, taxonomic and metabolic profiles, as well as a Flux Balance Analysis (FBA) were considered. Furthermore, topological and optimization algorithms were implemented to carry out the curation of the models, to ensure the continuity of the fluxes between the metabolic pathways, and to confirm the metabolite exchange between subcellular compartments. The proposed models provide specific information about ecosystems that are generally overlooked in non-compartmentalized or non-curated networks, like the influence of transport reactions in the metabolic processes, especially the important effect on mitochondrial processes, as well as provide more accurate results of the fluxes used to optimize the metabolic processes within the microbial community.Soil microbial communities are responsible for a wide range of ecological processes and have an important economic impact in agriculture. Determining the metabolic processes performed by microbial communities is crucial for understanding and managing ecosystem properties. Metagenomic approaches allow the elucidation of the main metabolic processes that determine the performance of microbial communities under different environmental conditions and perturbations. Here we present the first compartmentalized metabolic reconstruction at a metagenomics scale of a microbial ecosystem. This systematic approach conceives a meta-organism without boundaries between individual organisms and allows the  Soil represents one of the most diverse and complex natural environments on Earth in terms of species diversity and community niches . The soiin silico characterization of ecosystems under different environmental conditions  Addition of information related to mass, charge, and formula, assuming an intracellular pH of 7.2 (for which data are reported) for each of the metabolites that made up a reaction (S4 Table). This information was obtained from the MetaNetX database :Z=c:Z=c8]:imizes Z .S is the stoichiometric matrix with m (metabolites) by n (reactions), v is the vector of metabolic fluxes, vUB is a vector of the upper bounds for all fluxes, vLB is a vector of the lower bounds for all fluxes and c is the vector defining the contribution of different fluxes to the objective function. To solve the linear problem, we used the General Algebraic Modeling System (GAMS), a high-level modeling system for mathematical programming problems. The values for upper and lower bounds were 100 and 0 mmol gDW-1 h-1, respectively, except for the reversible reactions in which the lower bounds were -1000 mmol gDW-1 h-1. These values were selected after varying the upper bound from 1000 to 100 mmol gDW-1 h-1, where we finally obtained an optimal solution that maximized the number of active fluxes.A canonical FBA calculation can be formally expressed as the following linear programing problem:i) the clustering coefficient to evaluate the number of interactions among one metabolite and its neighbors and ii)the degree distributions to determine the number of metabolites directly connected to a given metabolite m. In the evaluation of the networks, we differentiated between the in-degree distribution (when the edges target the metabolite m) and the out-degree distribution (when the edges target the adjacent neighbors of m) . For this reason, we discarded the presence of these two compartments within both metabolic reconstructions.Using our metagenomic annotation we implemented the CELLO algorithm  to obtaiSaccharomyces cerevisiae, Arabidopsis thaliana, Chlamydomonas reinhardtii, Cyanothece sp. ATCC 51142, Synechocystis sp., Leishmania major, and Pseudomonas putida. The metabolic network reconstruction of these species are reported in the MetanetX database . In both microbial communities a considerable number of biochemical reactions and metabolic functions may be missing. Results revealed that none of the cellular compartments contain completely connected metabolites (S14 Table).A total of 1,389 and 1,305 metabolites that could not be produced under any uptake condition were identified for samples S1_C (non-intervened) and S2_C (intervened), respectively. As shown in Table 3, there was a high number of common gaps between the cytoplasm and the other cellular compartments. Notably, around 50% of the gaps of the mitochondria and between 9 to 40% of the gaps in the inner compartments are shared with the cytoplasm. Therefore, the gaps in the inner compartments can be solved identifying the production pathways of cytoplasm and adding adequate exchange reactions.The GapFill algorithm  was usedTable 4shows the total number of metabolites and reactions added to each compartment. As expected, it was necessary to add more reactions and metabolites to restore the non-intervened compartmentalized network (S1_C), since this model had a higher number of reactions and metabolites; it was more difficult to restore the connectivity of the network. Added reactions were manually evaluated in order to ensure that they were present in previous identified taxa.A total of 1,459 and 1,203 unique reactions of MetanetX database . The unique topological significant difference between both soil samples was the network diameter, higher for the non-intervened soil sample (S1), because this ecosystem has more metabolites and reactions, therefore more nodes and edges to connect. The average path length for all analyzed metabolic networks was ≈3.3.A common feature of many metabolic networks is their small-world property, where all of the nodes in the networks are connected by a relatively short path to each other . This pret al. [Escherichia coli [Another important topological feature of the networks is the average clustering coefficient, which is a measure of the potential modularity of the network. Contrary to what was expected, the average clustering coefficient for metagenomic networks was lower than the reported for the organisms studied by Jeong et al.  and for hia coli . This mi). As expected according to measures of centralities such as in-out degree and edge count, the central metabolites for all evaluated networks were H, H2O, ATP, NAD, NADH, NADP, CO2, Pi, NH4, and O2, which play a central role in the energetics and central pathways of cells. These results are in agreement with the PCA, which showed that these were the most central metabolites because they were connected with almost every other metabolite. Notably, in the compartmentalized models the most connected metabolites were distributed between the cytoplasm and the mitochondria, which also showed the higher values of intermediation, indicating that these compounds control the fluxes between the cytoplasm and the mitochondria.On the other hand, we ranked each metabolite in the networks according to their topological properties, to detect the central and intermediate nodes that determine the topology of the network , where less restoration of flows in the inner cellular compartments were achieved, and therefore, a lower number of associated fluxes.et al. [et al. [E. coli [The topology of the reconstructed metabolic networks followed the power-law of node degree distribution. This feature of true complex biological networks was previously reported by our research group in studies of the topology of metabolic networks at a metagenomics scale . The topet al. . The facet al. . The ave [et al. , as well[E. coli . Metabol[E. coli . Meanwhi[E. coli . The topE. coli and the multi-compartment model of S. cerevisiae. Significant differences between the percentages of disconnected metabolites in the genomic-scale and metagenomic-scale models were found. Kumar et al. [E. coli model were disjointed from the rest of the metabolism, while in the non-compartmentalized models of the microbial communities about 84.4% of all metabolites were disconnected. In the case of the multi-compartment model of S. cerevisiae, Kumar et al. [E. coli and S. cerevisiae are extensively studied and well characterized models.To address the impact of the metagenomic scale on the metabolic reconstruction process, we compared the obtained results with those reported for the genome-scale networks of the non-compartmentalized model of r et al.  showed tr et al.  reportedThe effects of the process of compartmentalization on the gaps features of the network connection and in the resulting network after the curation process were assessed. The non-intervened soil sample contained more gaps, possibly due to the fact that this non-intervened ecosystem has more reactions and metabolites to reconnect. During the curation process, a higher percentage of reconnections for the non-compartmentalized models was obtained. This was because non-compartmentalized models did not show problems in the restoration of flows between inner compartments and the cytoplasm. These results show that the compartmentalization processes have a significant impact on the curation process. The reconstruction of compartmentalized metabolic networks for the microbial genomes is significantly more challenging than that for individual microorganisms because of the larger size of the networks and the considerable variation of metabolic activity between microorganisms The modeling of microbial communities through this systems biology approach highlighted the main features of the studied ecosystems and the main and more determinant steps in the metabolic network reconstruction at a metagenomics-scale. One of the most critical steps in this study was the curation of the metabolic networks. This step had a significant effect on the results obtained for each model, because the flow restoration affected the interconnection between the central and the secondary metabolism, as well as the ability to interchange metabolites between the inner cellular compartments and the cytoplasm. Another important step in this metabolic reconstruction was the compartmentalization process given that a detailed distribution and cellular localization of metabolic functions at a metagenomic scale is very difficult to determine. This is because of the size of the networks and the considerable variation in the subcellular compartmentalization of the metabolic processes between different microorganisms. Therefore, the predicted cellular compartments for the metabolic networks may be improved by using other methods that complement the predictions of CELLO and the literature review.The integration of the results of the taxonomic and metabolic profiles of the ecosystems and of the FBA, indicates that the non-intervened soil had a greater capacity to regulate biogeochemical cycles. On the other hand, the topological analysis showed that there were no significant structural differences between the compartmentalized and non-compartmentalized networks in either the intervened and non-intervened soil samples. Results also show that the metabolic networks at a metagenomics scale conserved the main characteristics of the metabolic networks at a genomics scale. At a metagenomics scale, metabolic networks fit to a type of scale-free network and conserve the small-world property, minimizing transition times between metabolic states, thus increasing the network efficiency.The compartmentalized metabolic networks highlight information of ecosystems that is generally underestimated in non-compartmentalized models, like the role of metabolic fluxes involved in the transport between the inner compartments and the cytoplasm and between the cytoplasm and the extracellular space. Notably, the curation and reconstruction processes of compartmentalized networks are more challenging and more expensive in terms of time and computational resources. Therefore, it must be determined if the purpose of the research justifies the resources invested in the process of compartmentalization.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 TableA pH of 7.2 was assumed.(XLSX)Click here for additional data file.S4 TableA pH of 7.2 was assumed.(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 TableThe set of reactions added were those entirely composed by compounds belonging to the metabolic networks analyzed. Transport and exchange reactions were searched for in the BiGG and Metacyc databases.(XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S10 Table(XLSX)Click here for additional data file.S11 Table(XLSX)Click here for additional data file.S12 Table(XLSX)Click here for additional data file.S13 Table(XLSX)Click here for additional data file.S14 Table(XLSX)Click here for additional data file.S15 Table(XLSX)Click here for additional data file.S16 Table(XLSX)Click here for additional data file.S17 Table(XLSX)Click here for additional data file.S18 Table(XLSX)Click here for additional data file.S19 Table(XLSX)Click here for additional data file.S20 Table(XLSX)Click here for additional data file.


Nonetheless, constructing the 13C-MFA model and performing flux calculation are demanding for new learners, because they require knowledge of metabolic networks, carbon transitions, and computer programming. To facilitate and standardize the 13C-MFA modeling work, we set out to publish a user-friendly and programming-free platform (WUFlux) for flux calculations in MATLAB®.Flux analyses, including flux balance analysis (FBA) and 13C-MFA. Using GUIDE  in MATLAB, we built a user interface that allows users to modify models based on their own experimental conditions. WUFlux is capable of directly correcting mass spectrum data of TBDMS (N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide)-derivatized proteinogenic amino acids by removing background noise. To simplify 13C-MFA of different prokaryotic species, the software provides several metabolic network templates, including those for chemoheterotrophic bacteria and mixotrophic cyanobacteria. Users can modify the network and constraints, and then analyze the microbial carbon and energy metabolisms of various carbon substrates . WUFlux also offers several ways of visualizing the flux results with respect to the constructed network. To validate our model’s applicability, we have compared and discussed the flux results obtained from WUFlux and other MFA software. We have also illustrated how model constraints of cofactor and ATP balances influence fluxome results.We constructed an open-source platform for steady-state 13C-MFA, WUFlux, with a user-friendly interface and easy-to-modify templates, is now available at http://www.13cmfa.org/or (http://tang.eece.wustl.edu/ToolDevelopment.htm). We will continue documenting curated models of non-model microbial species and improving WUFlux performance.Open-source software for The online version of this article (doi:10.1186/s12859-016-1314-0) contains supplementary material, which is available to authorized users. FBA can unravel microbial metabolism based on the stoichiometry of the metabolic reactions as well as measurements of the inflow (substrate uptake) and outflow fluxes (biomass and product synthesis). To facilitate the development of genome scale models, much software has been developed  glucose and 50 % [U-13C6] glucose) in labeling experiments. The final experimental information is the measured fluxes of any chemicals produced in the cell culture. The measured fluxes will be used in the objective function.In the ‘Experiment Data’ section, experimental information must be provided before flux calculations can be made Fig. . The firlgorithm , which pThe ‘Settings’ section allows users to customize the optimization parameters . Thereafter, the flux calculation is ready to start. To determine the fluxome, we used the element metabolite unit algorithm  to simulχ2 test is applied to determine the goodness of fit, which users can use as the reference to determine whether the fitting is statistically acceptable.The Monte Carlo method is used in the model to determine the confidence intervals of central metabolic fluxes. Briefly speaking, MID data are randomly perturbed with normally distributed noises (within the average range of measurement errors), and the flux profile is then recalculated multiple times, which is customizable in WUFlux. The 95 % confidence intervals, for example, are consequently determined by the upper and lower 2.5 % data via the bootstrap method. Additionally, the Finally, all the flux values and confidence intervals are presented in the ‘Results’ panel, which can be exported to an Excel file. To better present the results, we have included functions that provide direct ways of visualizing the computed fluxes with respect to the constructed metabolic network and visualizing the comparisons between simulated and experimental MID data  Choose a suitable template, 2) Modify the metabolic network and constraints, 3) Import the experimental data, 4) Customize the optimization parameters, 5) Estimate the flux distribution and determine the confidence intervals, and 6) Visualize the fluxes. More detailed information is provided in Additional file Figure E. coli strains, which were published in our previous paper [E. coli strains with corrected MID data. The results were then compared with those generated from METRAN and INCA  in this study. Here, we demonstrate how to apply WUFlux to study energy metabolism by using the isotopomer data from reference [2 loss, and acetate production. The results prove that the engineered strain can successfully direct more carbon flow towards fatty acid production, while the control strain uses the majority of the carbons for biomass synthesis. Additionally, we can use flux data to analyze cellular energy expenditure. For example, ATP loss for maintenance energy in the engineered strain was estimated to be two-fold larger than that in the control strain . Under such an assumption, the P/O ratios may impact flux calculation results. Figure E. coli strain. The results show that flux estimation is insensitive to P/O ratios under ‘energy unbalanced’ conditions (when the flux towards ATP maintenance loss is unconstrained, Fig. eference . In Fig.ain Fig. , suggest13C-MFA is a powerful tool for metabolism analysis, but the overall process of performing 13C-MFA is usually not fast enough for biologists to characterize novel microbial species or to provide timely insights into engineered strains in the design-build-test-learn cycle. To overcome this problem, we have designed an open-source MATLAB-based platform, WUFlux, which provides programming-free and straightforward ways of performing 13C-MFA. By testing WUFlux against the other software, we showed that WUFlux can correct raw MS data and reproduce the flux estimation of previously published flux analysis studies. Because the MATLAB codes of all function files in WUFlux are open to researchers, users can extend or enhance its capabilities. By using this platform, we can standardize and document the details of 13C-MFA studies. We will continue to update the software package by including more flux models of non-model microbial species.Project name: WUFluxwww.13cmfa.orgProject homepage: Operating systems: Preferably Windows OS 7 or higherProgramming language: MATLABOther requirements: MATLAB 2012b or higher with optimization toolbox, symbolic math toolbox, and statistic toolbox.License: WUFlux is freely available.Any restrictions to use by non-academics: none


Mycobacterium tuberculosis (Mtb) better manages its intracellular lifestyle by executing adaptive metabolism. Here, we used metabolomics and identified glutamate synthase (GltB/D) that converts glutamine to glutamate (Q → E) as a metabolic effort used to neutralize cytoplasmic pH that is acidified while consuming host propionate carbon through the methylcitrate cycle (MCC). Methylisocitrate lyase, the last step of the MCC, is intrinsically downregulated in BCG, leading to obstruction of carbon flux toward central carbon metabolism, accumulation of MCC intermediates, and interference with GltB/D mediated neutralizing activity against propionate toxicity. Indeed, vitamin B12 mediated bypass MCC and additional supplement of glutamate led to selectively correct the phenotypic attenuation in BCG and restore the adaptive capacity of BCG to the similar level of Mtb phenotype. Collectively, a defective crosstalk between MCC and Q → E contributes to attenuation of intracellular BCG. Furthermore, GltB/D inhibition enhances the level of propionate toxicity in Mtb. Thus, these findings revealed a new adaptive metabolism and propose GltB/D as a synergistic target to improve the antimicrobial outcomes of MCC inhibition in Mtb.Metabolic networks in biological systems are interconnected, such that malfunctioning parts can be corrected by other parts within the network, a process termed adaptive metabolism. Unlike Bacillus Calmette-Guérin (BCG),  Mycobacterium tuberculosis (Mtb), the etiological agent of tuberculosis (TB), claimed 1.8 million human lives in 2016, the highest cause of human mortality among infectious diseases including AIDS1. Chemotherapies developed around 50 years ago rendered TB initially curable, but the incidence has resurged due to the emergence of multidrug-resistant (MDR)-TB strains that are virtually untreatable with standard TB treatment. The urgent need to develop new anti-infectives against drug-resistant TB has been compounded by the threat of extensively drug-resistant (XDR)2 and totally drug-resistant (TDR) TB strains3. Drug-resistance in Mtb is a serious concern that will undoubtedly worsen the global TB pandemic. Unfortunately, the spread of drug-resistance in Mtb has outpaced the introduction of new drugs.4. Instead, Mtb has evolved to undergo adaptive metabolism by altering its lifestyle to ensure persistence in the host, and to maximize tolerance against host imposed stresses including immune response5. As such, adaptive metabolism to nutritionally variable, but incompletely defined, niches inside hosts is a hallmark that Mtb uses for species conservation. When entering the host environments, Mtb continuously switches its cellular compartments of residence and acquires anabolic sources from the host while evading the immune system mediated inflammation. Mtb stimulates the import and subsequent degradation of intracellular storage molecules into compounds that can be catabolized. For this, Mtb remodels its metabolic networks in order to assimilate new nutrients and, more importantly, to neutralize any adverse consequences that may occur during catalysis, processes collectively termed adaptive metabolism10. This metabolic feature allows for maximum efficiency in the utilization of host nutrients and optimal virulence11. Such metabolic plasticity coupled with the ineffective standard TB treatment strategy renders Mtb one of the most successful pathogens.Despite the inextricable link between uncontrolled TB incidence and intracellular adaptation, Mtb appears to lack classical virulent factors such as specialized modulators that interact with the host immune systemMycobacterium bovis by consecutive subculture for 12 years on nutrient enriched media, during which the mother M. bovis strain lost its virulence due to an inadequately-defined manufacturing process12. Consequently, BCG uses the host imposed carbon substrates inefficiently and fails to manage its intracellular lifestyle within host environments. Although many studies have attempted to reveal the factors that caused its attenuation and sought to apply these findings pharmacologically14, there is still no clear explanation. Comparative genomics of Mtb and BCG identified multiple truncations in genetic regions in BCG including region of difference (RD) 1 locus, 506 synonymous and 769 nonsynomynous SNPs compared with Mtb H37Rv strain15, tandem duplications of DU1 and DU216. Among the intensive genetic modifications, truncation of RD-1 is observed across all BCG strains and RD-1 locus is shown to encode mediators promoting Mtb pathogenesis20. The RD1 locus of Mtb includes early secreted antigenic target (ESAT-6), secretion system-1 (ESX-1), and culture filtrate protein (CFP-10), all of which were known to play significant roles in Mtb virulence and interaction with host macrophages24. However, these genetic defects in BCG have yet to fully explain its phenotypic attenuation in the host25.Bacillus Calmette-Guérin (BCG), the only approved TB vaccine, was developed from virulent 27. In the present study, we hypothesized that BCG lacks the adaptive metabolic plasticity needed to efficiently consume host nutrients; this metabolic defect in BCG would allow it to be exploited to reveal conceptually novel anti-TB drug targets. We used metabolomics profiling of Mtb and BCG and identified Mtb-unique adaptive strategies. It includes the methylcitrate cycle (MCC) as an assimilation route of host carbons such as propionate, and the glutamine (Q)-glutamate (E) converting (Q → E) activity by glutamate synthase (GltB/D) as a neutralization effort to mitigate propionate toxicity while using MCC. Efficient coordination of the interaction between MCC and Q → E activity is an essential component for Mtb’s intracellular lifestyle, this functional crosstalk is relatively inefficient in BCG metabolism that may contribute to intracellular phenotypic attenuation. The discovery of functional interaction between MCC and Q → E identifies GltB/D as a new potential drug target whose inhibition mimics the bactericidal properties of Mtb’s MCC activity by maximizing the propionate toxicity.Recent studies have shown that Mtb has evolved to optimize specific metabolic activities that enable the use of multiple carbon sources simultaneously, thereby increasing its ability to survive in a nutritionally adverse environment37Rv wild type M. tuberculosis (WT Mtb), whose in vitro growth is supported by a wide range of carbon species28, BCG (Pasteur 1173P2) has a much narrower carbon source preference to support its in vitro growth. Among the carbon species tested in the present study, in vitro BCG growth was greatest under 0.2% glycerol while there was no recognizable growth under either 0.2% propionate or 0.2% dextrose as a single source of carbon. However, there was intermediate growth under 0.2% acetate  in the presence of dual carbons (glycerol and propionate) over a one-week incubation  metabolism such as the methylcitrate cycle (MCC) (purple box) and the TCA cycle (red box) and II nitrogen metabolism such as glutamine (Q)-glutamate (E) pathway and GABA shunt (blue box), we observed the following specific metabolic defects in BCG  metabolomics of BCG and WT Mtb following exposure to “glycerol and dextrose” (G/D) or “glycerol and propionate” (G/P) medium Fig. . We thenBCG Fig. .Figure 130. Although there were seemingly similar consequences for both strains, exposure to the same amount of G/P carbons reproducibly induced around 3~4 times greater accumulation of 2MC and 2MI in BCG than in WT Mtb  and 2-methylisocitrate (2MI) in both BCG and WT Mtb under G/P carbons, but not under G/D carbons  and glutamine (Q) were upregulated with a reciprocal depletion of glutamate (E) as compared to those under G/D carbons; this occurred in the opposite direction in WT Mtb Fig. , implyinM. bovis strain (ΔRD1) by a recombineering technology as was previously applied to generate ΔRD1 in WT Mtb31. The RD1 truncation in M. bovis was confirmed by whole genome sequencing  and multiplex PCR analyses using validated PCR primer sets  and glutamine (Q)-glutamate (E) metabolism of M. bovis following exposure to G/P carbons resemble those of WT Mtb under G/P carbons   Fig.  comparedvis Fig. . Furtherges Fig. , presumages Fig. . Based oIn summary, WT Mtb metabolism required for the efficient utilization of G/P carbons comprises MCC mediated propionate assimilation and functionally linked Q-E pathway Fig. , that arBCG showed slowed intracellular replication compared to that of WT Mtb Fig. . To elucin vitro metabolomics profiles and growth of BCG, we directed specific attention to propionate metabolism. As shown in Fig. 33. The Metabolomics profile of THP-1 free of infected bacilli lost the chromatograms of 2MC/2MI and 2MA, but retained that of itaconate , we conducted activity based metabolomics profiling (ABMP)34. The ABMP reactions included purified recombinant ICL1, divalent cation (Mg2+), and small metabolite extracts (SME) of either WT Mtb or BCG grown under G/P carbons. ICL1 (as an MCL enzyme)-mediated changes of pool sizes in SME metabolites were monitored by LC-MS. A time course MCL-ABMP allowed us to trace the changes in abundance of 2MI and succinate as substrate and product of MCL, respectively 29. To prove that BCG displays an analogous metabolic perturbation to ICL KO in response to G/P carbons, we monitored the cytoplasmic pH of BCG using a pH sensitive GFP construct35. Exposure of BCG to G/P carbons rapidly acidified the cytoplasmic pH from ~7.0 to ~6.3 in a time- and propionate concentration- dependent manner  can activate an alternate propionate metabolism, the methylmalonyl-CoA pathway37, subsequently protecting ICL KO from bactericidal effects due to propionate toxicity29. Thus, we tested the ability of VB12 to rescue BCG growth under G/P carbons. Indeed, BCG growth was partially restored by VB12 treatment  as a product of glutamate synthase  or glutamine as a substrate of GltB/D on BCG growth. We observed that MSG, not glutamine, further improved the VB12-mediated rescue of BCG growth Fig.  to a levin vitro GltB/D enzyme reaction. To monitor the GltB/D activity, we added αKG and glutamine as GltB/D’s substrates and BCG lysates prepared from the G/D culture or G/P culture as a source of GltB/D enzyme   equivalent to the pH when BCG was exposed to 0.2% glycerol and 0.8% or greater propionate concentrations  Fig. 44. Treattor Fig.  and thisect Fig. . A singlons Figs  and S1D.ons Figs . The MSG45 and identified that the in vitro growth of ICL KD under 0.2% glycerol and 0.1% propionate relied on tetracycline (ATc) induction of ICL expression. ICL KD without ATc was unable to grow because of the occlusion of the MCC caused by lacking ICL’s MCL activity. However, addition of at least 10 ng/mL ATc gradually induced ICL expression, with a subsequent release of sequestered carbons into the TCA cycle, restoring ICL KD growth and viability ity Fig. . Metabolity Fig. . We exteity Fig. . We coulity Fig. . With inity Fig.  without 46. Although it is advantageous that a wide range of nutrients are available to infected Mtb bacilli, this diversity is metabolically challenging47. Following phagocytosis of Mtb by macrophages, differential gene expression48 and metabolomics profiles28 have shown substantial upregulation of genes and their products involved in fatty acid degradation and metabolism fad, gltA (citrate synthase), icl1, pckA (PEP carboxykinase), mez ; in MCC -prpC, prpD, acn1, and icl1; in cholesterol uptake and consumption mce4 and mce6; and moderate upregulation of genes involved in gluconeogenesis including ppdK (pyruvate phosphate dikinase) and fba 48. Collectively, diverse species carbon sources ranging from cholesterol and fatty acids (acetate or propionate) to dextrose and glycerol are possible carbon sources for Mtb while residing inside hosts. In vivo experiments using Mtb harboring defined loss-of-function mutations in specific enzymes involved in carbon uptake and/or metabolism support this notion53. The success of Mtb as an intracellular human pathogen has thus been achieved by its ability to adapt to nutritionally changing environments.During the course of infection, Mtb is exposed to various nutrient conditions as phagocytosed Mtb bacillus inside alveolar macrophages are delivered deeper within the infected lung, ultimately forming granulomas28. The catabolic activities of cholesterol, fatty acids, and fermentable carbohydrates result in the unexpected accumulation of glyoxylate, propionyl-CoA, MCC intermediates and methylglyoxal with potentially toxic metabolic consequences55. Previous studies have shown the importance of the metabolic activities of pyruvate kinase (PYK) to neutralize unfavorable non-enzymatic metabolic side products, such as methylglyoxal that are produced during catabolizing ferment carbohydrate carbons27. Recently, Puckett et al. also revealed a new role of malate synthase (GlcB) in eliminating the accumulated glyoxylate and its mediated harmful consequences during assimilation of even-chain fatty acids through the glyoxylate shunt55. As such, Mtb is evolutionarily able to reprogram its metabolic networks toward efficient catabolism of different carbons in a thermodynamically favorable manner and for this, Mtb requires additional metabolic neutralization efforts to mitigate toxic consequences that arise during the assimilation processes within the context of interconnected metabolic networks.Mtb’s metabolic plasticity for carbon uptake/consumption is now accepted as a hallmark of its virulence as a human pathogen. Recently appreciated multi-carbon co-catabolism is one example of the metabolic strategies used by Mtb to overcome the bioenergetic barrier during adaptation to host- implemented nutrient conditions56. Delineation of these segments has been performed by genome-scale network reconstructions, but is somewhat biased by the scope and experimental conditions with limited integration of multiple hierarchical layers57. Interpretation of metabolic interactions helps reorganize the borders of these units that are shifted upon changing nutritional environments58. Sharing of the functional activities across regulatory structures can be triggered by metabolic networks that are reprogrammed depending upon available carbon sources, this metabolic plasticity is a potential driving force behind adaptive metabolism including emergence of drug-resistance. Evolutionary adaptive metabolism of Mtb permitting its survival in nutritionally adverse host environments may be achieved by these regulatory circuits. Conversely, BCG appears to have limited ability to adapt due to the lack of adaptive metabolism, potentially lost during manufacturing process. This may include the multiple genomic polymorphisms of BCG. Brosch et al. sequenced multiple BCG vaccine strains and identified that the phenotypic attenuation of BCG may partly be associated with multiple genetic insertion and/or deletion events including tandem duplications, DU1 and DU2, repetitive insertion elements, and SNPs in addition to deletion of RD116. For example, nonsynonymous SNPs affect genes that are involved in mycolic acid biosynthesis (mmaA3)59, signaling pathway (sigK)60, central carbon metabolism (pykA)61 or transcriptional regulators (cAMP receptor protein)63. As such, BCG is metabolically distinct from Mtb in that it lacks the ability to catabolize glycerol and fatty acids  and II Gad mediated scavenging protons (glutamate to GABA)65. The impaired functional involvement of Q → E in BCG metabolism to dampen propionate toxicity is associated with its adaptive failure and rapid clearance from hosts. We proved this by the findings that the addition of VB12 and/or MSG was sufficient to protect BCG from the bactericidal effects due to MCC mediated propionate toxicity  through the drainage of toxic intermediates of the propionate metabolism36. Mtb may induce the PDIM biosynthesis as another source of metabolic effort to mitigate the propionate toxicity. However, our study showed that MSG supplement rescued BCG growth in media containing G/P to a level similar to WT Mtb growth, suggesting that crosstalk of MCC and GltB/D plays more crucial role in Mtb propionate metabolism36. Not only does maintenance of a glutamate pool play a role in metabolic neutralization including to buffer cytoplasmic pH, but also is known to be involved in the biosynthesis of γ-glutamylcysteine  and provided an example of evolutionary metabolic networks that are unique to Mtb in catabolic utilization of gluconeogenic carbons such as propionate. We observed that Mtb maintains a lower pool size of toxic MCC intermediates during assimilation of propionate carbons by the ICL’s MCL catalytic activity, the last enzyme in the MCC. We also revealed that Mtb is equipped with GltB/D’s Q → E activity as a metabolic neutralization strategy to mitigate harmful consequences that occur while using MCC. As previously shown, with active Q → E, the conservation of glutamate pool should be expanded for following metabolic neutralizing activities that may include: ity Fig. . This isine Fig. , a sulfuMCC Fig. . The pre53. ICL is a metabolic enzyme used by Mtb to sustain growth on fatty acids through two pathways: the glyoxylate shunt and MCC30. Recent studies have shown that ICL is also essential for Mtb to both establish and maintain infection, when it is thought to persist in a metabolically quiescent state69 and resist to multiple bactericidal anti-TB drugs71. Thus, it is generally accepted that Mtb ICL is a potential drug target to kill Mtb in diverse metabolic states. Efforts to identify pharmacologically relevant ICL inhibitors, however, have been unsuccessful because of fundamental gaps in our knowledge about the impacts of ICL deficiency on Mtb metabolism. We recently provided a systems level insight into the essentiality of Mtb ICL and revealed a functional link between MCC-mediated propionate toxicity and associated impaired membrane bioenergetics29. The results of this study additionally provide evidence of the GltB/D’s Q → E mediated metabolic neutralization efforts to dampen ICL’s MCL deficiency associated propionate toxicity on Mtb metabolism. Collectively, we suggest that previously screened, but pharmacologically irrelevant ICL inhibitors, need to be retested under a new experimental setting in which Q → E activity is interfered.To date, the most attenuated Mtb mutants correspond to Mtb strains lacking annotated ICLIn summary, metabolic adaptation in response to changing environments is a physiological prerogative of all biological systems and requires functional plasticity and crosstalk of metabolic enzymes. This study reveals the evidence that Mtb uses metabolic adaptation strategies to survive intracellular nutrient environments. The interactive crosstalk between MCC and the Q-E pathway can’t be deduced by genetic information, thus exemplifying the importance of metabolomics to resolve the essential metabolic capabilities of intracellular WT Mtb. From a pharmacological perspective, inhibition of this metabolic crosstalk by combinatorial inhibition in WT Mtb will be effective in eradicating WT Mtb by preventing efficient adaptation to the host environments Fig. .Mycobacterium bovis bacillus Calmette-Guérin (BCG) Pasteur (1173P2), Swedish (TMC 1009), Danish (TMC 1010), Mtb H37Rv (WT Mtb), and icl knock-down (ICL KD) Mtb strain were cultured in either Middlebrook (m) 7H9 broth or m7H10 agar media  supplemented with 0.5% fraction V bovine serum albumin (BSA), 0.085% NaCl, 0.04% tyloxapol (broth only) with 0.2% glycerol, dextrose or varying concentrations (0–0.8% vol/vol) of propionate. For culturing M. bovis and ΔRD1, both m7H9 broth or m7H10 agar media was generated with aforementioned supplements and additional sodium pyruvate. For generation of acidic agar plates (pH 5.5), m7H9 broth was solidified by adding 15% w/v Bactoagar after pH adjustment using HCl. For metabolomic profiling, WT Mtb, BCG, M. bovis, or ΔRD1-laden filters were generated as previously described68. Mtb strains of H37Rv, ICL KD, M. bovis and ΔRD1 were cultured in a containment of biosafety level 3 facility.M. bovis, or ΔRD1-laden filters were incubated at 37 °C for 5 days to reach the midlogarithmic phase of growth. The filters were then transferred onto chemically identical m7H10 agar containing fresh carbon sources and metabolites were harvested after 24 hrs. WT Mtb, BCG, M. bovis, or ΔRD1 was metabolically quenched by plunging the bacteria-laden filters into LC-MS-grade acetonitrile:methanol:water (40:40:20) solution that was precooled to −40 °C. Metabolites were extracted by mechanical lysis with 0.1 mm Zirconia beads in a Precellys tissue homogenizer  for 8 min  twice under continuous cooling at or below 2 °C. Lysates were clarified by centrifugation and then filtered across 0.22 µm Spin-X columns. Residual protein content of metabolite extracts was measured  to normalize samples to cell biomass. Itaconic acid, azaserine, vitamin B12, monosodium glutamate, and sodium pyruvate were purchased from Sigma-Aldrich  and used at 2 mM, 1.0 µg/mL, 10 µg/mL, 10 mM, and 40 mM respectively.WT Mtb, ICL KD, BCG, M. bovis, and ΔRD1 metabolites were performed with an Agilent Accurate Mass 6230 TOF coupled with an Agilent 1290 Liquid Chromatography system using a Cogent Diamond Hydride Type C column  using solvents and configuration as previously described68. An isocratic pump was used for continuous infusion of a reference mass solution to allow mass axis calibration. Detected ions were classified as metabolites based on unique accurate mass-retention time identifiers for masses showing the expected distribution of accompanying isotopologues. Metabolites were analyzed using Agilent Qualitative Analysis B.07.00 and Profinder B.06.00 software  with a mass tolerance of <0.005 Da. Standards of authentic chemicals of known amounts were mixed with bacterial lysates and analyzed to generate the standard curves used to quantify metabolite levels. All data obtained by metabolomics were the average of at least two independent triplicates for each condition tested.Liquid chromatography mass spectrometry (LC-MS) differentiation and detection of WT Mtb, ICL knock-down, BCG, 2. Undifferentiated cells were removed by washing with prewarmed PBS three times. For infection, early logarithmic phase bacterial cultures were prepared by dilution to a predefined viable number of bacilli in antibiotic-free RPMI. THP-1 then were infected at a multiplicity of infection (MOI) of 1.0 of CFU-precalibrated BCG and WT Mtb. Uninfected bacilli were removed by PBS after 4 hrs. Infected cells were washed three times with prewarmed PBS and extracellular bacilli were killed by overnight treatment with 30 µg/mL gentamycin. Cells were lysed with 0.5% Triton X-100 and released bacilli were enumerated by plating serial dilutions of the lysate on m7H10 agar plates 0 and 5 days post-infection.THP-1 (human monocyte cell line) were cultured in RPMI1640 supplemented with 10% FBS and differentiated to macrophages by treatment of 100 ng/mL of TPA (12-O-tetra-decanoylphorbol-13-acetate) for 48 hrs at 37 °C and 5% CO6 THP-1 were seeded for infection with either BCG or WT Mtb at MOI 5.0, and for treatment with 10 µg/mL LPS (Sigma-Aldrich) in triplicate in 6 well plates. After a 24-hour infection, supernatants were removed and adherent cells were thoroughly washed with ice-cold PBS. Released bacilli were removed by centrifugation and filtration through 0.2 µm Spin-X columns. Cells were then lysed with 1 mL of cold 80% methanol with or without pretreatment with 0.025% SDS (to remove infected bacilli). Either total metabolome or bacilli free metabolome was extracted at −80 °C overnight and subsequently analyzed by LC-MS33.For extraction of metabolites, 1 × 10TM Thermal Cycler Instrument. The primers used for amplification were 5′-GCACTGTGGCACAAGATTTC-3′ (forward) and 5′-TTCGTCCACGATCACTTCAC-3′ (reverse) for prpD, 5′-GCGTACTACCTGATGGGATTC-3′ (forward) and 5′-CGTGGCCTGTTCCATGAT-3′ (reverse) for prpC, and 5′-ATCGCCAAGTTCCAGAAGG-3′ (forward) and 5′-GCCAGATCGAACATCGAGTAG-3′ (reverse) for icl1. Fold changes were calculated by ΔCT values that were normalized to sigA transcript levels and depicted as log2 values relative to growth permissive carbon condition as controls.WT Mtb or BCG were grown on filter membranes as done in metabolite extraction, and exposed to various carbon conditions (G/D or G/P carbons) for 24 hours. Total RNA was extracted by scraping the bacilli into TRIZOL solution and mechanically lysing with 0.1 mm Zirconia beads in a Precellys tissue homogenizer. Lysates were clarified by centrifugation and TRIZOL supernatant was removed and used for RNA extraction. RNA was isolated using a Qiagen RNA extraction kit. Isolated RNA was treated with DNase I to remove DNA contamination (Sigma). RNA concentrations were determined using a Nanodrop and qrtPCR reactions were conducted using a iQ SYBR-Green Supermix  and analyzed by C1000In vitro GltB/D reactions were performed with a 30 µL reaction volumes containing 100 mM glycine-KOH (pH 8.5), 2 mM glutamine, 2 mM αKG, 5 mM MgSO4, and 0.25 mM NADPH. The reactions were initiated by adding 0.1 µg of cell lysates and quenched by adding 70 µL of 100% acetonitrile with 0.2% formic acid. The production of glutamate was monitored by LC-MS. The 2MI mediated GltB/D inhibition was measured by adding various concentrations of 2MI (0.2~2.0 mM) in each reaction prior to addition of cell lysate.Cytoplasmic fractions were prepared from Mtb or BCG laden filters (cultured similarly to samples used for metabolomics profiling). Mtb or BCG bacilli were resuspended in 1 mL Tris-Cl (pH 7.4) and transferred to 2 mL screw-cap tubes containing 0.4 mL of 0.1 mm diameter Zirconia beads. Cells were disrupted by 30s pulses in Precellys tissue homogenizer for 30s pulses three times  twice under continuous cooling at or below 2 °C. Lysates were then centrifuged at 13,000 g for 10 min at 4 °C. and supernatants were subsequently transferred to fresh tubes. Concentration of cytoplasmic fractions were determined by BCA protein assay kit . 595 0.05 Mtb or BCG was incubated in m7H9 broth containing various carbon sources in 24-well microplates. Bacterial cultures were then serially diluted and plated on m7H10 agar with supplements: 0.5% glycerol, 0.2% dextrose, 0.5 g/L BSA, and 0.085% w/v NaCl for 3 weeks at 37 °C. Growth was monitored by measuring the OD595 of the liquid cultures by using GENESYSTM 20 (Thermo Scientific). All assays were performed as two independents with triplicates.Midlogarithmic phase of Mtb or BCG was used. An OD72. BCG-pHGFP cultures were grown to midlogarithmic phase (OD595 ~ 0.6) and resuspended in m7H9 with 0.2% glycerol and various concentrations of propionate (0 to 0.8%) either with or without 2 mM itaconic acid and 1 µg/mL azaserine at various time points. Fluorescence was measured using a FLUOstar Omega plate reader  at excitation 355 nm, emission 520 nm (reading 1) and at excitation 485 nm, emission 520 nm (reading 2). Intrabacterial pH was inferred from the ratio of reading 1 to reading 2 by reference to a calibration curve performed on lysates of BCG-pHGFP as described72.We used Mtb expressing a reported, pH-sensitive ratiometric green fluorescent protein (BCG Pasteur-pHGFP) as previously describedicl1 gene (rv0467) encoding ICL1 was amplified by PCR using the primer set; forward, 5′-CAT ATG TCT GTC GTC GGC ACC CCG A-3′ and reverse, 5′-AAG CTT CTA GTG GAA CTG GCC CTC TTC GGT GGA-3′ (NdeI and HindIII restriction site underlined). The amplified gene and pET28a  were double digested with NdeI and HindIII and ligated to construct pET28a::icl1. Once the sequence was confirmed, E. coli BL21(DE3) was used as an expression host. A hexa-His ICL1 was purified by standard Ni-NTA chromatography as performed elsewhere. Protein was dialyzed, concentrated, aliquoted, and stored at −80 °C. The final buffer contained 20 mM Tris-Cl (pH 7.4), 1 mM DTT, 175 mM NaCl and the purity was confirmed by a Coomassie Blue staining assay.The 73. Mtb or BCG was grown in 100 mL m7H9 with ADN. The pellet was suspended in 1 mL of acidic ACN solution  and cells were disrupted by bead beating. The soluble fraction was obtained by centrifugation at 20,000 g for 10 min at 4 °C, flash-freezing, and lyophilizing. Half of the dry SME was resuspended in 1 mL of 20 mM Tris-Cl (pH 7.4) and the insoluble fraction removed by centrifugation. Aliquots were stored at – 80 °C. Samples  of the SME were incubated for various times with or without 600 nM recombinant purified ICL1, and 1 mM Mg2+ in a final volume of 100 uL of 20 mM Tris-Cl. Cold ACN containing 0.2% formic acid was added for quenching, yielding 70% ACN final mixtures. After centrifugation at 20,000 g for 10 min at 4 °C, samples were kept at −80 °C until analyzed by LC-MS as described earlier.Small metabolite extracts of Mtb and BCG were prepared as previously describedM. bovis, as was previously used to delete RD1 in Mtb31. Briefly, genomic regions (~800 bp each) flanking the RD1 locus were amplified and cloned into the pKO (pKO-RD1) to replace RD1 with the KanR cassette and pKO-RD1 was introduced into M. bovis by electroporation. Colonies were selected on m7H10 plates containing 10% sucrose and hygromycin (100 µg/mL). Once confirmed as single cross-over candidates, double cross-over candidates were selected by checking the selective sensitivity against hygromycin but resistance to both 10% sucrose and kanamycin (50 µg/mL). Final colonies were tested by both whole genome sequencing  and multiplex PCR32 as used to screen for RD1 depletion in many Mycobacterium spp. including M. bovis and BCG.Recombineering technology was employed to delete the RD1 locus (ΔRD1) in the chromosome of 595 0.5, the culture was further diluted 10-fold and 2 µL was spotted onto m7H9 medium solidified by agar containing either G/D or G/P carbons in the presence varying doses of ATc . 0 or 1 µg/mL azaserine was additionally added and the plates were incubated at 37 °C until colony formation.After ICL knock-down at midlogarithmic phase was diluted to OD1H 500 MHz, 13C 125 MHz) or 400  spectrometer. Chemical shifts (δ) in parts per million are given relative to Me4Si, coupling constants (J) are given in hertz. Analytical TLC was carried out in either Merck Silica 60 F254 or RP-18 F254s plates. The components were observed under ultraviolet light (254 nm) and/or dipped in 5% H2SO4 in MeOH followed by heating using a heat gun. Other chemicals used were purchased from Sigma-Aldrich or Alfa Aesar  unless otherwise stated. Synthesis of D,L-theo-2-methyl isocitrate was as described by Munoz-Elias et al.74 with a few modifications.NMR spectra were recorded on a Bruker Advance 500 (Trans-epoxymethylsuccinic acid (2): Mesaconate (1)  was suspended in water (5 mL) and NaOH  was added dropwise with stirring. The mixture was stirred and cooled to 0 °C and Na2WO4-2H2O  was added followed by dropwise addition of a 30% aqueous solution of H2O2 . After stirring at 0 °C for 1 hr, the mixture was heated at 65 °C for 8 hrs, then cooled to RT and evaporated to dryness. The residue was partitioned between water (10 mL), ethyl acetate (20 mL) and ether (20 mL). Concentrated H2SO4 (1 mL) dissolved in ether (10 mL) was added and the mixture stirred at RT for 3hrs. The organic layer was separated, dried (Na2SO4) and evaporated to afford the epoxide 2 as a white powder in quantitative yield. 1H NMR  δ 13.44 , 3.75 , 1.45 .Dimethyl trans-epoxymethylsuccinate (3). To a solution of epoxide (2)  in anhydrous MeOH (10 mL) at 0 °C, was added SO2CI2  and the resulting mixture allowed to warm up to room temperature and stirred for 8 hrs. The reaction mixture was concentrated under vacuum and the residue partitioned between ether and saturated aqueous NaHCO3. The ether layer was separated, dried and evaporated to afford the dimethyl ester 3 as a clear oil in quantitative yield and used without any further purification. 1H NMR  δ 6.46 , 6.09 , 4.94 , 4.62 , 3.74 , 3.69 , 3.67 , 1.71 , 1.44 .2-Methyl-5-oxo-tetrahydro-2,3,4-tricarbomethoxytetrahydrofuran (4). Sodium metal  was added to cooled anhydrous MeOH (15 mL) under argon atmosphere at 0 °C and stirred until the metal dissolved. Dimethyl malonate  was added to the sodium methoxide thus formed, and the resulting mixture was stirred at RT until a white precipitate formed (20–30 min). Epoxide 3  was added and the mixture stirred for 3 days at RT. Concentrated HCI was added and the stirring continued for another 2 hrs. The solid formed (NaCI) was filtered and the filtrate concentrated to afford crude lactone 4 as a white solid in good yield and used without any further purification.DL-threo-2-methylisocitrate (6). To the crude lactone 4 obtained above (1.5 g) 6 N HCI (50 mL) was added and the mixture refluxed at RT for 3 hrs. The reaction mixture was concentrated; to remove traces of HCI, the residue was redissolved in water and evaporated. The brown residue thus obtained was dissolved in water and treated with activated charcoal at 60 °C. After 30 min, the mixture was filtered and the filtrate evaporated to afford colorless crude lactone 2-Methyl-5-oxo-tetrahydrofuran-2,3-dicarboxylic acid (5). To a solution of lactone 5  in H2O (10 mL), NaOH  was added and the mixture heated at 80 °C for 12 hrs, then neutralized with Amberlite H+ resin to pH 7.0. After solvent removal, the residue was redissolved in H2O and treated with activated charcoal. The mixture was filtered and the filtrated evaporated to afford D,L-threo-2-methylisocitrate 6 in a good yield (60%).Fig. S1-S10, Table 1, 2