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: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: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:Cellular metabolism can influence host immune responses to Mycobacterium tuberculosis. Using a systems biology approach, differential expression of 292 metabolic genes involved in glycolysis, glutathione, pyrimidine, and inositol phosphate pathways was evident at the site of a human tuberculin skin test challenge in patients with active tuberculosis infection. For 28 metabolic genes, we identified single nucleotide polymorphisms that were trans-acting for in vitro cytokine responses to M. tuberculosis stimulation, including glutathione and pyrimidine metabolism genes that alter production of Th1 and Th17 cytokines. Our findings identify novel therapeutic targets in host metabolism that may shape protective immunity to tuberculosis.
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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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