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Systems-based approaches to probing metabolic variation within the Mycobacterium tuberculosis complex.

ABSTRACT: The Mycobacterium tuberculosis complex includes bovine and human strains of the tuberculosis bacillus, including Mycobacterium tuberculosis, Mycobacterium bovis and the Mycobacterium bovis BCG vaccine strain. M. bovis has evolved from a M. tuberculosis-like ancestor and is the ancestor of the BCG vaccine. The pathogens demonstrate distinct differences in virulence, host range and metabolism, but the role of metabolic differences in pathogenicity is poorly understood. Systems biology approaches have been used to investigate the metabolism of M. tuberculosis, but not to probe differences between tuberculosis strains. In this study genome scale metabolic networks of M. bovis and M. bovis BCG were constructed and interrogated, along with a M. tuberculosis network, to predict substrate utilisation, gene essentiality and growth rates. The models correctly predicted 87-88% of high-throughput phenotype data, 75-76% of gene essentiality data and in silico-predicted growth rates matched measured rates. However, analysis of the metabolic networks identified discrepancies between in silico predictions and in vitro data, highlighting areas of incomplete metabolic knowledge. Additional experimental studies carried out to probe these inconsistencies revealed novel insights into the metabolism of these strains. For instance, that the reduction in metabolic capability observed in bovine tuberculosis strains, as compared to M. tuberculosis, is not reflected by current genetic or enzymatic knowledge. Hence, the in silico networks not only successfully simulate many aspects of the growth and physiology of these mycobacteria, but also provide an invaluable tool for future metabolic studies.

SUBMITTER: Lofthouse EK

PROVIDER: S-EPMC3783153 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Systems-based approaches to probing metabolic variation within the Mycobacterium tuberculosis complex.

Lofthouse Emma K EK Wheeler Paul R PR Beste Dany J V DJ Khatri Bhagwati L BL Wu Huihai H Mendum Tom A TA Kierzek Andrzej M AM McFadden Johnjoe J

PloS one 20130917 9

The Mycobacterium tuberculosis complex includes bovine and human strains of the tuberculosis bacillus, including Mycobacterium tuberculosis, Mycobacterium bovis and the Mycobacterium bovis BCG vaccine strain. M. bovis has evolved from a M. tuberculosis-like ancestor and is the ancestor of the BCG vaccine. The pathogens demonstrate distinct differences in virulence, host range and metabolism, but the role of metabolic differences in pathogenicity is poorly understood. Systems biology approaches h ...[more]

PMID: 24098743

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Project description:Identifying Mycobacterium tuberculosis persistence genes is important for developing novel drugs to shorten the duration of tuberculosis (TB) treatment. We developed computational algorithms that predict M. tuberculosis genes required for long-term survival in mouse lungs. As the input, we used high-throughput M. tuberculosis mutant library screen data, mycobacterial global transcriptional profiles in mice and macrophages, and functional interaction networks. We selected 57 unique, genetically defined mutants (18 previously tested and 39 untested) to assess the predictive power of this approach in the murine model of TB infection. We observed a 6-fold enrichment in the predicted set of M. tuberculosis genes required for persistence in mouse lungs relative to randomly selected mutant pools. Our results also allowed us to reclassify several genes as required for M. tuberculosis persistence in vivo. Finally, the new results implicated additional high-priority candidate genes for testing. Experimental validation of computational predictions demonstrates the power of this systems biology approach for elucidating M. tuberculosis persistence genes.Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), has a genetic repertoire that permits it to persist in the face of host immune responses. Identification of such persistence genes could reveal novel drug targets and elucidate mechanisms by which the organism eludes the immune system and resists drugs. Genetic screens have identified a total of 31 persistence genes, but to date only 15% of the ~4,000 M. tuberculosis genes have been tested experimentally. In this paper, as an alternative to brute force experimental screens, we describe computational methods that predict new persistence genes by combining known examples with growing databases of biological networks. Experimental testing demonstrated that these predictions are highly accurate, validating the computational approach and providing new information about M. tuberculosis persistence in host tissues. Using the new experimental results as additional input highlights additional genes for testing. Our approach can be extended to other data types and target organisms to characterize host-pathogen interactions relevant to this and other infectious diseases.

Project description:Network medicine relies on different types of networks: from the molecular level of protein?protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein?protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs-including long non-coding RNAs (lncRNAs) -competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes-called switch genes-critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes.

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Systems-based approaches to probing metabolic variation within the Mycobacterium tuberculosis complex.

Publications

Systems-based approaches to probing metabolic variation within the Mycobacterium tuberculosis complex.

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