Dataset Information

Gene expression diversity among Mycobacterium tuberculosis clinical isolates

ABSTRACT: While several groups, including our own, have examined variability of M. tuberculosis at the DNA level, this is the first systematic survey of variability in mRNA expression among clinical isolates of M. tuberculosis. Genes whose expression varies among isolates when assayed under a single growth condition may make poor drug targets and vaccine antigens and may affect molecular diagnostics, so they can be used to narrow down lists of candidate molecules. Because the measurement of gene expression is extremely sensitive to environmental conditions, comparison of gene expression is labour intensive. In this study, we surveyed 12 strains. These strains are a subset of those for which we have already published genomic deletion information (Kato-Maeda et al., 2001). In order to ensure maximum reproducibility of the experiments and avoid complications caused by differences in growth conditions, we measured gene expression under well-controlled in vitro conditions. Our aims were to provide an overview of gene expression variability among clinical isolates under a single growth condition and to test whether gene functional classes are related to variability in expression. Set of arrays that are part of repeated experiments Keywords: Biological Replicate

ORGANISM(S): Mycobacterium tuberculosis

PROVIDER: GSE3201 | GEO | 2005/08/26

SECONDARY ACCESSION(S): PRJNA92867

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Jamshidi2007 - Genome-scale metabolic network of Mycobacterium tuberculosis (iNJ661) This model is described in the article: Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets. Jamshidi N, Palsson BØ. BMC Syst Biol 2007; 1: 26 Abstract: BACKGROUND: Mycobacterium tuberculosis continues to be a major pathogen in the third world, killing almost 2 million people a year by the most recent estimates. Even in industrialized countries, the emergence of multi-drug resistant (MDR) strains of tuberculosis hails the need to develop additional medications for treatment. Many of the drugs used for treatment of tuberculosis target metabolic enzymes. Genome-scale models can be used for analysis, discovery, and as hypothesis generating tools, which will hopefully assist the rational drug development process. These models need to be able to assimilate data from large datasets and analyze them. RESULTS: We completed a bottom up reconstruction of the metabolic network of Mycobacterium tuberculosis H37Rv. This functional in silico bacterium, iNJ661, contains 661 genes and 939 reactions and can produce many of the complex compounds characteristic to tuberculosis, such as mycolic acids and mycocerosates. We grew this bacterium in silico on various media, analyzed the model in the context of multiple high-throughput data sets, and finally we analyzed the network in an 'unbiased' manner by calculating the Hard Coupled Reaction (HCR) sets, groups of reactions that are forced to operate in unison due to mass conservation and connectivity constraints. CONCLUSION: Although we observed growth rates comparable to experimental observations (doubling times ranging from about 12 to 24 hours) in different media, comparisons of gene essentiality with experimental data were less encouraging (generally about 55%). The reasons for the often conflicting results were multi-fold, including gene expression variability under different conditions and lack of complete biological knowledge. Some of the inconsistencies between in vitro and in silico or in vivo and in silico results highlight specific loci that are worth further experimental investigations. Finally, by considering the HCR sets in the context of known drug targets for tuberculosis treatment we proposed new alternative, but equivalent drug targets. This model is hosted on BioModels Database and identified by: MODEL1507180001. 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.

Dataset Information

Gene expression diversity among Mycobacterium tuberculosis clinical isolates

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets