Development and analysis of an in vivo-compatible metabolic network of Mycobacterium tuberculosis

<p>Abstract</p> <p>Background</p> <p>During infection, <it>Mycobacterium tuberculosis </it>confronts a generally hostile and nutrient-poor <it>in vivo </it>host environment. Existing models and analyses of <it>M. tuberculosis </it>metabolic networks are able to reproduce experimentally measured cellular growth rates and identify genes required for growth in a range of different <it>in vitro </it>media. However, these models, under <it>in vitro </it>conditions, do not provide an adequate description of the metabolic processes required by the pathogen to infect and persist in a host.</p> <p>Results</p> <p>To better account for the metabolic activity of <it>M. tuberculosis </it>in the host environment, we developed a set of procedures to systematically modify an existing <it>in vitro </it>metabolic network by enhancing the agreement between calculated and <it>in vivo-</it>measured gene essentiality data. After our modifications, the new <it>in vivo </it>network contained 663 genes, 838 metabolites, and 1,049 reactions and had a significantly increased sensitivity (0.81) in predicted gene essentiality than the <it>in vitro </it>network (0.31). We verified the modifications generated from the purely computational analysis through a review of the literature and found, for example, that, as the analysis suggested, lipids are used as the main source for carbon metabolism and oxygen must be available for the pathogen under <it>in vivo </it>conditions. Moreover, we used the developed <it>in vivo </it>network to predict the effects of double-gene deletions on <it>M. tuberculosis </it>growth in the host environment, explore metabolic adaptations to life in an acidic environment, highlight the importance of different enzymes in the tricarboxylic acid-cycle under different limiting nutrient conditions, investigate the effects of inhibiting multiple reactions, and look at the importance of both aerobic and anaerobic cellular respiration during infection.</p> <p>Conclusions</p> <p>The network modifications we implemented suggest a distinctive set of metabolic conditions and requirements faced by <it>M. tuberculosis </it>during host infection compared with <it>in vitro </it>growth. Likewise, the double-gene deletion calculations highlight the importance of specific metabolic pathways used by the pathogen in the host environment. The newly constructed network provides a quantitative model to study the metabolism and associated drug targets of <it>M. tuberculosis </it>under <it>in vivo </it>conditions.</p

Metabolic modeling of mycobacterium tuberculosis through the integration of large-scale genomics datasets

Thesis (Ph. D.)--Boston UniversityMycobacterium tuberculosis (MTB) is the bacterium that is the causal agent of tuberculosis. MTB is estimated to infect one-third of the world's population. The emergence of multi drug-resistant and extensively drug-resistant strains of the bacterium are becoming a larger threat to global health as they decrease the efficacy of current treatments and make the disease more fatal. These factors combine to make MTB an interesting target for study with novel systems biology approaches. Genome-scale metabolic models have emerged as important platforms for the analysis of datasets that describe highly-interconnected biological processes. We have the first comprehensive profiling of mRNA, proteins, metabolites, and lipids in MTB during an in vitro model of infection that includes a time course of induced hypoxia andre-aeration. Hypoxia and reaeration are important cues during infection of the human host and act to model the environment seen in the host. We use genome-scale metabolic modeling methods to integrate these data with our metabolic model will allow us to generate experimentally testable predictions about metabolic adaptations that occur in response to experimental perturbations that represent an in vitro model of important environmental cues present during infection, dormancy, and re-activation in the human host

Modeling metabolism of Mycobacterium tuberculosis

Approximately one-fourth of the Mycobacterium tuberculosis (Mtb) genome contains genes that encode enzymes directly involved in its metabolism. These enzymes represent potential drug targets that can be systematically probed with constraint based (CB) models through the prediction of genes essential (or the combination thereof) for the pathogen to grow. However, gene essentiality depends on the growth conditions and, so far, no in vitro model precisely mimics the host at the different stages of mycobacterial infection, limiting model predictions. A first step in creating such a model is a thoroughly curated and extended genome-scale CB metabolic model of Mtb metabolism. The history of genome-scale CB models of Mtb metabolism up to model sMtb are discussed and sMtb is quantitatively validated using 13C measurements. The human pathogen Mtb has the capacity to escape eradication by professional phagocytes. During infection, Mtb resists the harsh environment of phagosomes and actively manipulates macrophages and dendritic cells to ensure prolonged intracellular survival. In contrast to many other intracellular pathogens, it has remained difficult to capture the transcriptome of mycobacteria during infection due to an unfavorable host-to-pathogen ratio. The human macrophage-like cell line THP-1 was infected with the attenuated Mtb surrogate Mycobacterium bovis Bacillus Calmette–Guérin (M. bovis BCG). Mycobacterial RNA was up to 1000-fold underrepresented in total RNA preparations of infected host cells. By combining microbial enrichment with specific ribosomal RNA depletion the transcriptional responses of host and pathogen during infection were simultaneously analyzed using dual RNA sequencing. Mycobacterial pathways for cholesterol degradation and iron acquisition are upregulated during infection. In addition, genes involved in the methylcitrate cycle, aspartate metabolism and recycling of mycolic acids are induced. In response to M. bovis BCG infection, host cells upregulate de novo cholesterol biosynthesis presumably to compensate for the loss of this metabolite by bacterial catabolism. By systematically probing the metabolic network underpinning sMtb, 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. 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 all fluxes through metabolism under a given condition at a moment in time, the so called metabolic state, requires a precise description of the biomass reaction for the described condition. The transcript abundance data obtained by dual RNA sequencing was used to develop 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 predict nutrient uptake rates and gene essentiality. Predictions were subsequently compared to available experimental data. Nutrient uptake can accurately be predicted, but accurate gene essentiality predictions remain difficult to obtain. By combining sMtb and a model of human metabolism, model sMtb-RECON was developed and used to predict the metabolic state of Mtb during infection of the host. Amino acids are predicted to be used for energy production as well as biomass formation. Subsequently the effect of increasing dosages of drugs, targeting metabolism, on the metabolic state of the pathogen was assessed and resulting metabolic adaptations and flux rerouting through various pathways is predicted. In particular, the TCA cycle becomes more important upon drug application, as well as alanine, aspartate, glutamate, proline, arginine and porphyrin metabolism, while glycine, serine and threonine metabolism become less important for survival. Notably, an effect of eight out of eleven metabolically active drugs could be recreated and two major profiles of the metabolic state were predicted. The profiles of the metabolic states of Mtb affected by the drugs BTZ043, cycloserine and its derivative terizidone, ethambutol, ethionamide, propionamide, and isoniazid were very similar, while TMC207 is predicted to have quite a different effect on metabolism as it inhibits ATP synthase and therefore indirectly interferes with a multitude of metabolic pathways.</p

Investigation of RelBE1 toxin-antitoxin function in the carbon-dependent metabolic adaptation of Mycobacterium tuberculosis

2022 Summer.Includes bibliographical references.Tuberculosis (TB) is a devastating disease with suboptimal treatment regimens and a single vaccine with variable efficacy. Reducing the global burden of TB requires a refined arsenal of methods to prevent and treat the disease, which necessitates a better understanding of M. tuberculosis (Mtb) pathogenesis during infection. Mtb undergoes continuous metabolic reprogramming throughout acute and chronic stages of infection in order to survive and persist harsh host conditions, and the regulatory network responsible for mediating metabolic adaptation has not been fully defined. Mtb harbors at least 88 Toxin-antitoxin (TA) loci that have been proposed to function as regulatory modules in response to stress. TA systems are uniquely abundant in Mtb, making them viable targets for the treatment of both active and latent infection. Several RelBE TA systems are present in Mtb, and the RelE toxins function as ribonucleases to inhibit translation when not bound to RelB antitoxins. The genes encoding relBE1 are adjacent to a gene that encodes an enzyme involved in central carbon metabolism, which could suggest a regulatory role for RelBE1 in carbon metabolism. We aimed to explore the relationship between the RelBE1 TA system and carbon-mediated metabolic adaptation. This work incorporated in vitro transcriptional and genetic studies under defined carbon sources to investigate the activity of RelBE1 and the requirement of RelE1 in Mtb metabolism, growth, and viability in the presence of different carbon sources. We observed transcriptional and physiological trends consistent with the hypothesis that RelBE1 contributes to iii adaptation of Mtb metabolism in the presence of cholesterol and oleate. Additionally, we found evidence that supports the necessity of RelE1 in Mtb metabolism under conditions depleted of nutrients. To investigate if multiple RelBE systems work redundantly or cooperatively in Mtb metabolic adaptation, we applied CRISPRi to simultaneously silence three RelBE TA loci. CRISPRi construction of knockdown mutants resulted in variable success but did not fully resolve the question regarding the cooperative or redundant functions of RelBE systems in Mtb metabolism. Nonetheless, the study provided the building blocks for efficient genetic manipulation of multiple TA systems in Mtb that are essential for exploring the coordination of TA systems in their contribution to Mtb pathogenesis. This thesis work contributes to the debate regarding TA system function in Mtb stress response and adaptation during infection. Given the limitations of the presented studies, further work is warranted to elucidate the relationship between TA systems and Mtb pathogenesis. Expanding our understanding of TA systems in TB disease would provide novel avenues in research to improve treatments against TB

The Genetic Requirements for Fast and Slow Growth in Mycobacteria

Mycobacterium tuberculosis infects a third of the world's population. Primary tuberculosis involving active fast bacterial replication is often followed by asymptomatic latent tuberculosis, which is characterised by slow or non-replicating bacteria. Reactivation of the latent infection involving a switch back to active bacterial replication can lead to post-primary transmissible tuberculosis. Mycobacterial mechanisms involved in slow growth or switching growth rate provide rational targets for the development of new drugs against persistent mycobacterial infection. Using chemostat culture to control growth rate, we screened a transposon mutant library by Transposon site hybridization (TraSH) selection to define the genetic requirements for slow and fast growth of Mycobacterium bovis (BCG) and for the requirements of switching growth rate. We identified 84 genes that are exclusively required for slow growth (69 hours doubling time) and 256 genes required for switching from slow to fast growth. To validate these findings we performed experiments using individual M. tuberculosis and M. bovis BCG knock out mutants. We have demonstrated that growth rate control is a carefully orchestrated process which requires a distinct set of genes encoding several virulence determinants, gene regulators, and metabolic enzymes. The mce1 locus appears to be a component of the switch to slow growth rate, which is consistent with the proposed role in virulence of M. tuberculosis. These results suggest novel perspectives for unravelling the mechanisms involved in the switch between acute and persistent TB infections and provide a means to study aspects of this important phenomenon in vitro

Analysis of complex metabolic behavior through pathway decomposition

<p>Abstract</p> <p>Background</p> <p>Understanding complex systems through decomposition into simple interacting components is a pervasive paradigm throughout modern science and engineering. For cellular metabolism, complexity can be reduced by decomposition into pathways with particular biochemical functions, and the concept of elementary flux modes provides a systematic way for organizing metabolic networks into such pathways. While decomposition using elementary flux modes has proven to be a powerful tool for understanding and manipulating cellular metabolism, its utility, however, is severely limited since the number of modes in a network increases exponentially with its size.</p> <p>Results</p> <p>Here, we present a new method for decomposition of metabolic flux distributions into elementary flux modes. Our method can easily operate on large, genome-scale networks since it does not require all relevant modes of the metabolic network to be generated. We illustrate the utility of our method for metabolic engineering of <it>Escherichia coli </it>and for understanding the survival of <it>Mycobacterium tuberculosis </it>(MTB) during infection.</p> <p>Conclusions</p> <p>Our method can achieve computational time improvements exceeding 2000-fold and requires only several seconds to generate elementary mode decompositions on genome-scale networks. These improvements arise from not having to generate all relevant elementary modes prior to initiating the decomposition. The decompositions from our method are useful for understanding complex flux distributions and debugging genome-scale models.</p