Integration of Metabolomics and Transcriptomics Reveals a Complex Diet of Mycobacterium tuberculosis during Early Macrophage Infection

ABSTRACT The nutrients consumed by intracellular pathogens are mostly unknown. This is mainly due to the challenge of disentangling host and pathogen metabolism sharing the majority of metabolic pathways and hence metabolites. Here, we investigated the metabolic changes of Mycobacterium tuberculosis, the causative agent of tuberculosis, and its human host cell during early infection. To this aim, we combined gene expression data of both organisms and metabolite changes during the course of infection through integration into a genome-wide metabolic network. This led to the identification of infection-specific metabolic alterations, which we further exploited to model host-pathogen interactions quantitatively by flux balance analysis. These in silico data suggested that tubercle bacilli consume up to 33 different nutrients during early macrophage infection, which the bacteria utilize to generate energy and biomass to establish intracellular growth. Such multisubstrate fueling strategy renders the pathogen’s metabolism robust toward perturbations, such as innate immune responses or antibiotic treatments. Nutrient acquisition from the host environment is crucial for the survival of intracellular pathogens, but conceptual and technical challenges limit our knowledge of pathogen diets. To overcome some of these technical roadblocks, we exploited an experimentally accessible model for early infection of human macrophages by Mycobacterium tuberculosis, the etiological agent of tuberculosis, to study host-pathogen interactions with a multi-omics approach. We collected metabolomics and complete transcriptome RNA sequencing (dual RNA-seq) data of the infected macrophages, integrated them in a genome-wide reaction pair network, and identified metabolic subnetworks in host cells and M. tuberculosis that are modularly regulated during infection. Up- and downregulation of these metabolic subnetworks suggested that the pathogen utilizes a wide range of host-derived compounds, concomitant with the measured metabolic and transcriptional changes in both bacteria and host. To quantify metabolic interactions between the host and intracellular pathogen, we used a combined genome-scale model of macrophage and M. tuberculosis metabolism constrained by the dual RNA-seq data. Metabolic flux balance analysis predicted coutilization of a total of 33 different carbon sources and enabled us to distinguish between the pathogen’s substrates directly used as biomass precursors and the ones further metabolized to gain energy or to synthesize building blocks. This multiple-substrate fueling confers high robustness to interventions with the pathogen’s metabolism. The presented approach combining multi-omics data as a starting point to simulate system-wide host-pathogen metabolic interactions is a useful tool to better understand the intracellular lifestyle of pathogens and their metabolic robustness and resistance to metabolic interventions. IMPORTANCE The nutrients consumed by intracellular pathogens are mostly unknown. This is mainly due to the challenge of disentangling host and pathogen metabolism sharing the majority of metabolic pathways and hence metabolites. Here, we investigated the metabolic changes of Mycobacterium tuberculosis, the causative agent of tuberculosis, and its human host cell during early infection. To this aim, we combined gene expression data of both organisms and metabolite changes during the course of infection through integration into a genome-wide metabolic network. This led to the identification of infection-specific metabolic alterations, which we further exploited to model host-pathogen interactions quantitatively by flux balance analysis. These in silico data suggested that tubercle bacilli consume up to 33 different nutrients during early macrophage infection, which the bacteria utilize to generate energy and biomass to establish intracellular growth. Such multisubstrate fueling strategy renders the pathogen’s metabolism robust toward perturbations, such as innate immune responses or antibiotic treatments.

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FastMM: an efficient toolbox for personalized constraint-based metabolic modeling

BMC Bioinformatics ◽

10.1186/s12859-020-3410-4 ◽

2020 ◽

Vol 21 (1) ◽

Cited By ~ 3

Author(s):

Gong-Hua Li ◽

Shaoxing Dai ◽

Feifei Han ◽

Wenxing Li ◽

Jingfei Huang ◽

...

Keyword(s):

Flux Balance Analysis ◽

Large Scale ◽

Computing Time ◽

Metabolic Modeling ◽

The Cancer Genome Atlas ◽

Flux Balance ◽

Mcmc Sampling ◽

Genome Wide ◽

Balance Analysis ◽

User Friendly

Abstract Background Constraint-based metabolic modeling has been applied to understand metabolism related disease mechanisms, to predict potential new drug targets and anti-metabolites, and to identify biomarkers of complex diseases. Although the state-of-art modeling toolbox, COBRA 3.0, is powerful, it requires substantial computing time conducting flux balance analysis, knockout analysis, and Markov Chain Monte Carlo (MCMC) sampling, which may limit its application in large scale genome-wide analysis. Results Here, we rewrote the underlying code of COBRA 3.0 using C/C++, and developed a toolbox, termed FastMM, to effectively conduct constraint-based metabolic modeling. The results showed that FastMM is 2~400 times faster than COBRA 3.0 in performing flux balance analysis and knockout analysis and returns consistent outputs. When applied to MCMC sampling, FastMM is 8 times faster than COBRA 3.0. FastMM is also faster than some efficient metabolic modeling applications, such as Cobrapy and Fast-SL. In addition, we developed a Matlab/Octave interface for fast metabolic modeling. This interface was fully compatible with COBRA 3.0, enabling users to easily perform complex applications for metabolic modeling. For example, users who do not have deep constraint-based metabolic model knowledge can just type one command in Matlab/Octave to perform personalized metabolic modeling. Users can also use the advance and multiple threading parameters for complex metabolic modeling. Thus, we provided an efficient and user-friendly solution to perform large scale genome-wide metabolic modeling. For example, FastMM can be applied to the modeling of individual cancer metabolic profiles of hundreds to thousands of samples in the Cancer Genome Atlas (TCGA). Conclusion FastMM is an efficient and user-friendly toolbox for large-scale personalized constraint-based metabolic modeling. It can serve as a complementary and invaluable improvement to the existing functionalities in COBRA 3.0. FastMM is under GPL license and can be freely available at GitHub site: https://github.com/GonghuaLi/FastMM.

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Core Metabolism Shifts during Growth on Methanol versus Methane in the Methanotroph Methylomicrobium buryatense 5GB1

mBio ◽

10.1128/mbio.00406-19 ◽

2019 ◽

Vol 10 (2) ◽

Cited By ~ 7

Author(s):

Yanfen Fu ◽

Lian He ◽

Jennifer Reeve ◽

David A. C. Beck ◽

Mary E. Lidstrom

Keyword(s):

Flux Balance Analysis ◽

Metabolic Pathways ◽

Scale Model ◽

Obligate Methylotroph ◽

Content Type ◽

Advantages And Disadvantages ◽

The Core ◽

Flux Balance ◽

Balance Analysis ◽

Core Metabolism

ABSTRACT Methylomicrobium buryatense 5GB1 is an obligate methylotroph which grows on methane or methanol with similar growth rates. It has long been assumed that the core metabolic pathways must be similar on the two substrates, but recent studies of methane metabolism in this bacterium suggest that growth on methanol might have significant differences from growth on methane. In this study, both a targeted metabolomics approach and a 13C tracer approach were taken to understand core carbon metabolism in M. buryatense 5GB1 during growth on methanol and to determine whether such differences occur. Our results suggest a systematic shift of active core metabolism in which increased flux occurred through both the Entner-Doudoroff (ED) pathway and the partial serine cycle, while the tricarboxylic acid (TCA) cycle was incomplete, in contrast to growth on methane. Using the experimental results as constraints, we applied flux balance analysis to determine the metabolic flux phenotype of M. buryatense 5GB1 growing on methanol, and the results are consistent with predictions based on ATP and NADH changes. Transcriptomics analysis suggested that the changes in fluxes and metabolite levels represented results of posttranscriptional regulation. The combination of flux balance analysis of the genome-scale model and the flux ratio from 13C data changed the solution space for a better prediction of cell behavior and demonstrated the significant differences in physiology between growth on methane and growth on methanol. IMPORTANCE One-carbon compounds such as methane and methanol are of increasing interest as sustainable substrates for biological production of fuels and industrial chemicals. The bacteria that carry out these conversions have been studied for many decades, but gaps exist in our knowledge of their metabolic pathways. One such gap is the difference between growth on methane and growth on methanol. Understanding such metabolism is important, since each has advantages and disadvantages as a feedstock for production of chemicals and fuels. The significance of our research is in the demonstration that the metabolic network is substantially altered in each case and in the delineation of these changes. The resulting new insights into the core metabolism of this bacterium now provide an improved basis for future strain design.

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Genome-Wide Transposon Mutagenesis Indicates that Mycobacterium marinum Customizes Its Virulence Mechanisms for Survival and Replication in Different Hosts

Infection and Immunity ◽

10.1128/iai.03050-14 ◽

2015 ◽

Vol 83 (5) ◽

pp. 1778-1788 ◽

Cited By ~ 41

Author(s):

Eveline M. Weerdenburg ◽

Abdallah M. Abdallah ◽

Farania Rangkuti ◽

Moataz Abd El Ghany ◽

Thomas D. Otto ◽

...

Keyword(s):

Insertion Site ◽

Mycobacterium Marinum ◽

Host Cells ◽

Intracellular Pathogens ◽

Virulence Determinants ◽

Phagocytic Cells ◽

General Hypothesis ◽

Content Type ◽

Genome Wide ◽

A Genome

The interaction of environmental bacteria with unicellular eukaryotes is generally considered a major driving force for the evolution of intracellular pathogens, allowing them to survive and replicate in phagocytic cells of vertebrate hosts. To test this hypothesis on a genome-wide level, we determined for the intracellular pathogenMycobacterium marinumwhether it uses conserved strategies to exploit host cells from both protozoan and vertebrate origin. Using transposon-directed insertion site sequencing (TraDIS), we determined differences in genetic requirements for survival and replication in phagocytic cells of organisms from different kingdoms. In line with the general hypothesis, we identified a number of general virulence mechanisms, including the type VII protein secretion system ESX-1, biosynthesis of polyketide lipids, and utilization of sterols. However, we were also able to show thatM. marinumcontains an even larger set of host-specific virulence determinants, including proteins involved in the modification of surface glycolipids and, surprisingly, the auxiliary proteins of the ESX-1 system. Several of these factors were in fact counterproductive in other hosts. Therefore,M. marinumcontains different sets of virulence factors that are tailored for specific hosts. Our data imply that although amoebae could function as a training ground for intracellular pathogens, they do not fully prepare pathogens for crossing species barriers.

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Metabolic flux balance analysis of an industrially useful microorganism Corynebacerium glutamicum by a genome-scale reconstructed model

Proceedings of the Third International Conference on Bio-Inspired Models of Network Information and Computing Systems (Bionetics 2008) ◽

10.4108/icst.bionetics2008.4704 ◽

2008 ◽

Author(s):

Hiroshi Shimizu ◽

Yohei Shinfuku ◽

Masahiro Sono ◽

Chikara Furusawa ◽

Takashi Hirasawa

Keyword(s):

Flux Balance Analysis ◽

Metabolic Flux ◽

Flux Balance ◽

A Genome ◽

Balance Analysis ◽

Genome Scale ◽

Reconstructed Model

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Flux Balance Analysis with Objective Function Defined by Proteomics Data—Metabolism of Mycobacterium tuberculosis Exposed to Mefloquine

PLoS ONE ◽

10.1371/journal.pone.0134014 ◽

2015 ◽

Vol 10 (7) ◽

pp. e0134014 ◽

Cited By ~ 8

Author(s):

Daniel Montezano ◽

Laura Meek ◽

Rashmi Gupta ◽

Luiz E. Bermudez ◽

José C. M. Bermudez

Keyword(s):

Mycobacterium Tuberculosis ◽

Objective Function ◽

Flux Balance Analysis ◽

Proteomics Data ◽

Flux Balance ◽

Balance Analysis

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Flux balance analysis of Corynebacterium glutamicum using a genome-scale metabolic model

Journal of Bioscience and Bioengineering ◽

10.1016/j.jbiosc.2009.08.450 ◽

2009 ◽

Vol 108 ◽

pp. S166

Author(s):

Chikara Furusawa ◽

Yohei Shinfuku ◽

Natee Sorpitiporn ◽

Masahiro Sono ◽

Takashi Hirasawa ◽

...

Keyword(s):

Corynebacterium Glutamicum ◽

Flux Balance Analysis ◽

Metabolic Model ◽

Flux Balance ◽

A Genome ◽

Balance Analysis ◽

Genome Scale

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Bacterial Genome-Wide Association Identifies Novel Factors That Contribute to Ethionamide and Prothionamide Susceptibility in Mycobacterium tuberculosis

mBio ◽

10.1128/mbio.00616-19 ◽

2019 ◽

Vol 10 (2) ◽

Cited By ~ 9

Author(s):

Nathan D. Hicks ◽

Allison F. Carey ◽

Jian Yang ◽

Yanlin Zhao ◽

Sarah M. Fortune

Keyword(s):

Mycobacterium Tuberculosis ◽

Antibiotic Susceptibility ◽

Experimental Validation ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Second Line ◽

Content Type ◽

Clinical Strains ◽

Genome Wide ◽

A Genome

ABSTRACT In Mycobacterium tuberculosis, recent genome-wide association studies have identified a novel constellation of mutations that are correlated with high-level drug resistances. Interpreting the functional importance of the new resistance-associated mutations has been complicated, however, by a lack of experimental validation and a poor understanding of the epistatic factors influencing these correlations, including strain background and programmatic variation in treatment regimens. Here we perform a genome-wide association analysis in a panel of Mycobacterium tuberculosis strains from China to identify variants correlated with resistance to the second-line prodrug ethionamide (ETH). Mutations in a bacterial monooxygenase, Rv0565c, are significantly associated with ETH resistance. We demonstrate that Rv0565c is a novel activator of ETH, independent of the two known activators, EthA and MymA. Clinically prevalent mutations abrogate Rv0565c function, and deletion of Rv0565c confers a consistent fitness benefit on M. tuberculosis in the presence of partially inhibitory doses of ETH. Interestingly, Rv0565c activity affects susceptibility to prothionamide (PTH), the ETH analog used in China, to a greater degree. Further, clinical isolates vary in their susceptibility to both ETH and PTH, to an extent that correlates with the total expression of ETH/PTH activators (EthA, MymA, and Rv0565c). These results suggest that clinical strains considered susceptible to ETH/PTH are not equally fit during treatment due to both Rv0565c mutations and more global variation in the expression of the prodrug activators. IMPORTANCE Phenotypic antibiotic susceptibility testing in Mycobacterium tuberculosis is slow and cumbersome. Rapid molecular diagnostics promise to help guide therapy, but such assays rely on complete knowledge of the molecular determinants of altered antibiotic susceptibility. Recent genomic studies of antibiotic-resistant M. tuberculosis have identified several candidate loci beyond those already known to contribute to antibiotic resistance; however, efforts to provide experimental validation have lagged. Our study identifies a gene (Rv0565c) that is associated with resistance to the second-line antibiotic ethionamide at a population level. We then use bacterial genetics to show that the variants found in clinical strains of M. tuberculosis improve bacterial survival after ethionamide exposure.

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