Dataset Information

Beste2007 - Genome-scale metabolic network of Mycobacterium tuberculosis (GSMN_TB)

ABSTRACT: Beste2007 - Genome-scale metabolic network of Mycobacterium tuberculosis (GSMN_TB) This model is described in the article: GSMN-TB: a web-based genome-scale network model of Mycobacterium tuberculosis metabolism. Beste DJ, Hooper T, Stewart G, Bonde B, Avignone-Rossa C, Bushell ME, Wheeler P, Klamt S, Kierzek AM, McFadden J. Genome Biol. 2007; 8(5): R89 Abstract: BACKGROUND: An impediment to the rational development of novel drugs against tuberculosis (TB) is a general paucity of knowledge concerning the metabolism of Mycobacterium tuberculosis, particularly during infection. Constraint-based modeling provides a novel approach to investigating microbial metabolism but has not yet been applied to genome-scale modeling of M. tuberculosis. RESULTS: GSMN-TB, a genome-scale metabolic model of M. tuberculosis, was constructed, consisting of 849 unique reactions and 739 metabolites, and involving 726 genes. The model was calibrated by growing Mycobacterium bovis bacille Calmette Guérin in continuous culture and steady-state growth parameters were measured. Flux balance analysis was used to calculate substrate consumption rates, which were shown to correspond closely to experimentally determined values. Predictions of gene essentiality were also made by flux balance analysis simulation and were compared with global mutagenesis data for M. tuberculosis grown in vitro. A prediction accuracy of 78% was achieved. Known drug targets were predicted to be essential by the model. The model demonstrated a potential role for the enzyme isocitrate lyase during the slow growth of mycobacteria, and this hypothesis was experimentally verified. An interactive web-based version of the model is available. CONCLUSION: The GSMN-TB model successfully simulated many of the growth properties of M. tuberculosis. The model provides a means to examine the metabolic flexibility of bacteria and predict the phenotype of mutants, and it highlights previously unexplored features of M. tuberculosis metabolism. This model is hosted on BioModels Database and identified by: MODEL1507180021. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

SUBMITTER: Nicolas Le Novère

PROVIDER: MODEL1507180021 | BioModels | 2015-07-30

REPOSITORIES: BioModels

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	MODEL1507180021?filename=MODEL1507180021-biopax2.owl	Owl
	MODEL1507180021?filename=MODEL1507180021-biopax3.owl	Owl
	MODEL1507180021?filename=MODEL1507180021.m	Other
	MODEL1507180021?filename=MODEL1507180021.pdf	Pdf
	MODEL1507180021?filename=MODEL1507180021.png	Other

Items per page:

1 - 5 of 11

Publications

GSMN-TB: a web-based genome-scale network model of Mycobacterium tuberculosis metabolism.

Beste Dany J V DJ Hooper Tracy T Stewart Graham G Bonde Bhushan B Avignone-Rossa Claudio C Bushell Michael E ME Wheeler Paul P Klamt Steffen S Kierzek Andrzej M AM McFadden Johnjoe J

Genome biology 20070101 5

<h4>Background</h4>An impediment to the rational development of novel drugs against tuberculosis (TB) is a general paucity of knowledge concerning the metabolism of Mycobacterium tuberculosis, particularly during infection. Constraint-based modeling provides a novel approach to investigating microbial metabolism but has not yet been applied to genome-scale modeling of M. tuberculosis.<h4>Results</h4>GSMN-TB, a genome-scale metabolic model of M. tuberculosis, was constructed, consisting of 849 un ...[more]

PMID: 17521419

Similar Datasets

Project description:Human infection with Mycobacterium tuberculosis results in a continuum of ill-defined, clinical manifestations with stable latent M. tuberculosis infection (LTBI) and severe active disease at the ends. Identifying different states of infection is of importance to tuberculosis (TB) control since risk of developing active disease varies among different asymptomatic states while infectiousness varies among patients with different bacterial burden. We investigated changes in proteome-scale antibody responses during disease progression in a non-human primate model of tuberculosis. We probed M. tuberculosis proteome microarrays with serial sera collected from three infection-outcome groups (active, reactivation, and latent). We found that each infection outcome is associated with characteristic changes in the antibody levels and number of antigenic targets, which suggested an association between antibody responses and bacillary burden. Additional proteome-scale serological profiling of > 400 human TB suspects established that antibody responses are positively associated with bacterial load. Thus tuberculosis-specific antibody levels and number of antigenic targets increases with disease progression. Serum samples collected from adult patients with suspected tuberculosis during a multi-site study was used to probe whole proteome microarrays. Subject recruitment was conducted under uniform protocols approved by the institutional ethics committee at each site. Final diagnosis of active TB was based on positive M. tuberculosis culture results. The active TB patients were further subdivided into smear-positive and negative disease based on results of Ziehl-Neelsen staining of sputum smears for acid fast bacilli. Active TB was excluded as a diagnosis (Non-TB Disease [NTBD] patients) based on having negative M. tuberculosis culture and smear results and on having an alternate diagnosis. All subjects were presumably negative for HIV infection given the very low incidence of HIV infection in the study sites. Sera from 169 TB and 242 NTBD patients were selected for microarray probing. The control sera (n = 14) which was used to generate negative control distribution for each protein were negative to latent M. tuberculosis infection, as indicated by negative results to tuberculin skin test.

Project description:The innate immune system provides the first response to pathogen infection and orchestrates the activation of the adaptive immune system. Though a large component of the innate immune response is common to all infections, pathogen-specific innate immune responses have been documented as well. The innate immune response is thought to be especially critical for fighting infection with Mycobacterium tuberculosis (MTB), the causative agent of tuberculosis (TB). While TB can be a deadly disease, only 5-10% of individuals infected with MTB develop active disease, and this inter-individual variation is, at least partly, heritable. Studies of inter-individual variation in the innate immune response to MTB infection may therefore shed light on the genetic basis for variation in susceptibility to TB. Yet, to date, we still do not know which properties of the innate immune response are specific to MTB infection and which represent a general response to pathogen infection. To begin addressing this gap, we infected macrophages with eight different bacterial pathogens, including different MTB strains and related mycobacteria, and studied the transcriptional response to infection. We found that although the gene expression changes were largely consistent across the bacterial infection treatments, we were able to identify a novel subset of genes whose regulation was affected specifically by infection with mycobacteria. Genetic variants that are associated with regulatory differences in these genes should be considered candidate loci for explaining inter-individual susceptibility TB. RNA-seq of monocyte-derived macrophages isolated from 6 healthy European males at 4, 18, and 48 hours post-infection with the following 8 bacteria: Mycobacterium tuberculosis (MTB) H37Rv, Mycobacterium tuberculosis GC1237, MTB GC1237, bacillus Calmette-Guérin (BCG), Mycobacterium smegmatis, Yersinia pseudotuberculosis, Salmonella typhimurium, and Staphylococcus epidermidis. table-s1.txt is a tab-delimited text file that contains the batch-corrected log2 counts per million for each of the 156 samples, as well as the Ensembl gene ID and gene name. BCG = bacillus Calmette-Guérin GC = Mycobacterium tuberculosis GC1237 Rv = Mycobacterium tuberculosis (MTB) H37Rv Rv+ = heat-inactivated MTB H37Rv Salm = Salmonella typhimurium Smeg = Mycobacterium smegmatis Staph = Staphylococcus epidermidis Yers = Yersinia pseudotuberculosis