Metabolic fluxes for nutritional flexibility of Mycobacterium tuberculosis.
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ABSTRACT:
SUBMITTER: Borah K
PROVIDER: S-EPMC8094261 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Genome-scale metabolic networks can be modeled in a constraint-based fashion. Reaction stoichiometry combined with flux capacity constraints determine the space of allowable reaction rates. This space is often large and a central challenge in metabolic modeling is finding the biologically most relevant flux distributions. A widely used method is flux balance analysis (FBA), which optimizes a biologically relevant objective such as growth or ATP production. Although FBA has proven to be highly useful for predicting growth and byproduct secretion, it cannot predict the intracellular fluxes under all environmental conditions. Therefore, alternative strategies have been developed to select flux distributions that are in agreement with experimental "omics" data, or by incorporating experimental flux measurements. The latter, unfortunately can only be applied to a limited set of reactions and is currently not feasible at the genome-scale. On the other hand, it has been observed that micro-organisms favor a suboptimal growth rate, possibly in exchange for a more "flexible" metabolic network. Instead of dedicating the internal network state to an optimal growth rate in one condition, a suboptimal growth rate is used, that allows for an easier switch to other nutrient sources. A small decrease in growth rate is exchanged for a relatively large gain in metabolic capability to adapt to changing environmental conditions.Here, we propose Maximum Metabolic Flexibility (MMF) a computational method that utilizes this observation to find the most probable intracellular flux distributions. By mapping measured flux data from central metabolism to the genome-scale models of Escherichia coli and Saccharomyces cerevisiae we show that i) indeed, most of the measured fluxes agree with a high adaptability of the network, ii) this result can be used to further reduce the space of feasible solutions iii) this reduced space improves the quantitative predictions made by FBA and contains a significantly larger fraction of the measured fluxes compared to the flux space that was reduced by a uniform sampling approach and iv) MMF can be used to select reactions in the network that contribute most to the steady-state flux space. Constraining the selected reactions improves the quantitative predictions of FBA considerably more than adding an equal amount of flux constraints, selected using a more naïve approach. Our method can be applied to any cell type without requiring prior information.MMF is freely available as a MATLAB plugin at: http://cs.ru.nl/~wmegchel/mmf.
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Project description:The PLP-dependent transaminase (BioA) of Mycobacterium tuberculosis and other pathogens that catalyzes the second step of biotin biosynthesis is a now well-validated target for antibacterial development. Fragment screening by differential scanning fluorimetry has been performed to discover new chemical scaffolds and promote optimization of existing inhibitors. Calorimetry confirms binding of six molecules with high ligand efficiency. Thermodynamic data identifies which molecules bind with the enthalpy driven stabilization preferred in compounds that represent attractive starting points for future optimization. Crystallographic characterization of complexes with these molecules reveals the dynamic nature of the BioA active site. Different side chain conformational states are stabilized in response to binding by different molecules. A detailed analysis of conformational diversity in available BioA structures is presented, resulting in the identification of two states that might be targeted with molecular scaffolds incorporating well-defined conformational attributes. This new structural data can be used as part of a scaffold hopping strategy to further optimize existing inhibitors or create new small molecules with improved therapeutic potential.
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Project description:Tuberculosis, caused by Mycobacterium tuberculosis complex bacteria, remains one of the most pressing health problems. Despite the general trend towards reduction of the disease incidence rate, the situation remains extremely tense due to the distribution of the resistant forms. Most often, these strains emerge through the intra-host microevolution of the pathogen during treatment failure. In the present study, the focus was on three serial clinical isolates of Mycobacterium tuberculosis Beijing B0/W148 cluster from one patient with pulmonary tuberculosis, to evaluate their changes in metabolism during anti-tuberculosis therapy. Using whole genome sequencing (WGS), 9 polymorphisms were determined, which occurred in a stepwise or transient manner during treatment and were linked to the resistance (GyrA D94A; inhA t-8a) or virulence. The effect of the inhA t-8a mutation was confirmed on both proteomic and transcriptomic levels. Additionally, the amount of RpsL protein, which is a target of anti-tuberculosis drugs, was reduced. At the systemic level, profound changes in metabolism, linked to the evolution of the pathogen in the host and the effects of therapy, were documented. An overabundance of the FAS-II system proteins (HtdX, HtdY) and expression changes in the virulence factors have been observed at the RNA and protein levels.
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Project description:BACKGROUND:Mycobacterium tuberculosis (MTB) is the causal agent of the disease tuberculosis (TB). Metabolic adaptations are thought to be critical to the survival of MTB during pathogenesis. Computational tools that can be used to study MTB metabolism in silico and prioritize resource-intensive experimental work could significantly accelerate research. RESULTS:We have developed E-Flux-MFC, an enhancement of our original E-Flux method that enables the prediction of changes in the production of external and internal metabolites corresponding to gene expression measurements. We have used this method to simulate the changes in the metabolic state of Mycobacterium tuberculosis (MTB). We have validated the accuracy of E-Flux-MFC for predicting changes in lipids and metabolites during a hypoxia time course using previously published metabolomics and transcriptomics data. We have further validated the accuracy of the method for predicting changes in MTB lipids following the deletion and induction of two well-studied transcription factors (TFs). We have applied the method to predict the metabolic impact of the induction of each of the approximately 180 MTB TFs using a previously generated and publically available expression data set. CONCLUSIONS:E-flux-MFC can be used to study global changes in MTB metabolites from gene expression data associated with environmental and genetic perturbations. The application of this method to a data set of MTB TF perturbations provides a resource for studying the large number of TFs whose functions remain unknown. Most TFs impact metabolites indirectly through the propagation of gene expression changes through the regulatory network rather than through their direct regulons. E-Flux-MFC is also applicable to any organism for which accurate metabolic models are available.
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Project description:We report here a series of five chemically diverse scaffolds that have in vitro activities on replicating and hypoxic nonreplicating bacilli by targeting the respiratory bc1 complex in Mycobacterium tuberculosis in a strain-dependent manner. Deletion of the cytochrome bd oxidase generated a hypersusceptible mutant in which resistance was acquired by a mutation in qcrB. These results highlight the promiscuity of the bc1 complex and the risk of targeting energy metabolism with new drugs.
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Project description:Sigma factor C (SigC) contributes to Mycobacterium tuberculosis virulence in various animal models, but the stress response coordinated by this transcription factor was undefined. The results presented here indicate that SigC prevents copper starvation. Whole genome expression studies demonstrate short-term (4-h) induction of sigC, controlled from a tetracycline-inducible promoter, upregulates ctpB and genes in the nonribosomal peptide synthase (nrp) operon. These genes are expressed at higher levels after 48-h sigC induction, but also elevated are genes encoding copper-responsive regulator RicR and RicR-regulated copper toxicity response operon genes rv0846-rv0850, suggesting prolonged sigC induction results in excessive copper uptake. No growth and global transcriptional differences are observed between a sigC null mutant relative to its parent strain in 7H9 medium. In a copper-deficient medium, however, growth of the sigC deletion strain lags the parent, and 40 genes (including those in the nrp operon) are differentially expressed. Copper supplementation reverses the growth defect and silences most transcriptional differences. Together, these data support SigC as a transcriptional regulator of copper acquisition when the metal is scarce. Attenuation of sigC mutants in severe combined immunodeficient mice is consistent with an inability to overcome innate host defenses that sequester copper ions to deprive invading microbes of this essential micronutrient.
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Project description:A key component to the success of Mycobacterium tuberculosis as a pathogen is the ability to sense and adapt metabolically to the diverse range of conditions encountered in vivo, such as oxygen tension, environmental pH and nutrient availability. Although nitrogen is an essential nutrient for every organism, little is known about the genes and pathways responsible for nitrogen assimilation in M. tuberculosis. In this study we have used transcriptomics and chromatin immunoprecipitation and high-throughput sequencing to address this. In response to nitrogen starvation, a total of 185 genes were significantly differentially expressed (96 up-regulated and 89 down regulated; 5% genome) highlighting several significant areas of metabolic change during nitrogen limitation such as nitrate/nitrite metabolism, aspartate metabolism and changes in cell wall biosynthesis. We identify GlnR as a regulator involved in the nitrogen response, controlling the expression of at least 33 genes in response to nitrogen limitation. We identify a consensus GlnR binding site and relate its location to known transcriptional start sites. We also show that the GlnR response regulator plays a very different role in M. tuberculosis to that in non-pathogenic mycobacteria, controlling genes involved in nitric oxide detoxification and intracellular survival instead of genes involved in nitrogen scavenging.
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Project description:Genome-scale metabolic models of Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis, have been envisioned as a platform for drug discovery. By systematically probing the networks that underpin such models, the reactions that are essential for Mtb are identified. A majority of these reactions are catalyzed by enzymes and thus represent candidate drug targets to fight an Mtb infection. Nevertheless, this is complicated by the limited knowledge on the environment that Mtb encounters during infection. Modeling the behavior of the bacteria during infection requires knowledge of the so-called biomass reaction that represents bacterial biomass composition. This composition varies in different environments or bacterial growth phases. Accurate modeling of the metabolic state requires a precise biomass reaction for the described condition. In recent years, additional insights in the in-host environment occupied by Mtb have been gained as transcript abundance data of interacting host and pathogen have become available. Therefore, we used transcript abundance data and developed a straightforward and systematic method to obtain a condition-specific biomass reaction for Mtb during in vitro growth and during infection of its host. The method described herein is virtually free of any pre-set assumptions on uptake rates of nutrients, making it suitable for exploring environments with limited accessibility. The condition-specific biomass reaction represents the "metabolic objective" of Mtb in a given environment (in-host growth and growth on defined medium) at a specific time point, and as such allows modeling the bacterial metabolic state in these environments. Five different biomass reactions were used to predict nutrient uptake rates and gene essentiality. Predictions were subsequently compared to available experimental data. Our results show that nutrient uptake can accurately be predicted. Gene essentiality can also be predicted but accurate predictions remain difficult to obtain. In conclusion, a viable strategy to model Mtb metabolism in hard-to-access environments that is virtually free of pre-set assumptions is provided.
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