GSMN-TB: a web-based genome-scale network model of Mycobacterium tuberculosismetabolism

Beste, Dany J. V.; Hooper, T.; Stewart, Graham R.; Bonde, Bhushan; Avignone–Rossa, Claudio; Bushell, Michael E.; Wheeler, Paul R.; Klamt, Steffen; Kierzek, Andrzej M.; McFadden, Johnjoe

doi:10.1186/gb-2007-8-5-r89

Cited by 204 publications

(299 citation statements)

References 97 publications

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“…Cultures, during log phase, were labeled with [1][2][3][4][5][6][7][8][9][10][11][12][13][14] C]acetate. The density of bacteria, 12 to 18 mg (dry weight)/100 ml, was chosen to maximize the yield of log-phase bacteria.…”

Section: Methodsmentioning

confidence: 99%

Global Effects of Inactivation of the Pyruvate Kinase Gene in the Mycobacterium tuberculosis Complex

Chavadi

Wooff²,

Coldham³

et al. 2009

J Bacteriol

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To better understand the global effects of "natural" lesions in genes involved in the pyruvate metabolism in Mycobacterium bovis, null mutations were made in the Mycobacterium tuberculosis H37Rv ald and pykA genes to mimic the M. bovis situation. Like M. bovis, the M. tuberculosis ⌬pykA mutant yielded dysgonic colonies on solid medium lacking pyruvate, whereas colony morphology was eugonic on pyruvate-containing medium. Global effects of the loss of the pykA gene, possibly underlying colony morphology, were investigated by using proteomics on cultures grown in the same conditions. The levels of Icd2 increased and those of Icl and PckA decreased in the ⌬pykA knockout. Proteomics suggested that the synthesis of enzymes involved in fatty acid and lipid biosynthesis were decreased, whereas those involved in ␤-oxidation were increased in the M. tuberculosis ⌬pykA mutant, as confirmed by direct assays for these activities. Thus, the loss of pykA from M. tuberculosis results in fatty acids being used principally for energy production, in contrast to the situation in the host when carbon from fatty acids is conserved through the glyoxylate cycle and gluconeogenesis; when an active pykA gene was introduced into M. bovis, the opposite effects occurred. Proteins involved in oxidative stress-AhpC, KatG, and SodA-showed increased synthesis in the ⌬pykA mutant, and iron-regulated proteins were also affected. Ald levels were decreased in the ⌬pykA knockout, explaining why an M. tuberculosis ⌬pykA ⌬ald double mutant showed little additional phenotypic effect. Overall, these data show that the loss of the pykA gene has powerful, global effects on proteins associated with central metabolism.Comparison of the genome sequences of Mycobacterium bovis and Mycobacterium tuberculosis revealed Ͼ99.95% identity at the nucleotide level; however, these pathogens differ in terms of host tropism, phenotype, and virulence (16). Eleven regions of difference (RD) were observed in the M. bovis genome (2 to 12.7 kb) compared to M. tuberculosis, while one region deleted from M. tuberculosis was present in M. bovis (5,16). In addition to the RDs, there are over 2,400 single nucleotide polymorphisms (SNPs) between M. tuberculosis and M. bovis (16). Some SNPs cosegregate with regions of deletions or other genetic markers (5); one such SNP is in the pykA gene, which cosegregates with the RD9 deletion. This SNP results in an inactive pyruvate kinase (PykA) being produced due to a Glu220Asp mutation (20). Glu220 is in the active site of the enzyme (21, 24), and its substitution results in complete loss of the enzyme activity in M. bovis (20). Thus, the pykA SNP explains one of the classic distinctions between M. bovis and M. tuberculosis, namely, the requirement for pyruvate. Neither glycerol, the preferred carbon source for isolation of tubercle bacilli, nor glucose support the growth of M. bovis when they are not supplemented with pyruvate (38), due to the inactive pyruvate kinase.On the routinely used Middlebrook 7H11 agar, containing glycerol and ol...

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Section: Methodsmentioning

confidence: 99%

Global Effects of Inactivation of the Pyruvate Kinase Gene in the Mycobacterium tuberculosis Complex

Chavadi

Wooff²,

Coldham³

et al. 2009

J Bacteriol

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“…A wealth of data from wellcontrolled experiments, coupled with advancements in methods for computational network analysis, have allowed these models to aid interrogation of metabolic behavior. In addition, an iterative process to model development-cycles of in silico model predictions, experimental (i.e., wet lab) validation, and subsequent model refinement-has enabled the development of methods that have contributed to biological discovery, such as in determination of likely drug targets in Mycobacterium tuberculosis (3,26), metabolic engineering of cells for production of valuable compounds (5, 32, 34), and development of novel frameworks for contextualizing high-throughput "-omics" data sets (15,24,64).Pseudomonas aeruginosa is a ubiquitous gram-negative bacterium that is capable of surviving in a broad range of natural …”

mentioning

confidence: 99%

Genome-Scale Metabolic Network Analysis of the Opportunistic Pathogen Pseudomonas aeruginosa PAO1

et al. 2008

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Pseudomonas aeruginosa is a major life-threatening opportunistic pathogen that commonly infects immunocompromised patients. This bacterium owes its success as a pathogen largely to its metabolic versatility and flexibility. A thorough understanding of P. aeruginosa's metabolism is thus pivotal for the design of effective intervention strategies. Here we aim to provide, through systems analysis, a basis for the characterization of the genome-scale properties of this pathogen's versatile metabolic network. To this end, we reconstructed a genome-scale metabolic network of Pseudomonas aeruginosa PAO1. This reconstruction accounts for 1,056 genes (19% of the genome), 1,030 proteins, and 883 reactions. Flux balance analysis was used to identify key features of P. aeruginosa metabolism, such as growth yield, under defined conditions and with defined knowledge gaps within the network. BIOLOG substrate oxidation data were used in model expansion, and a genome-scale transposon knockout set was compared against in silico knockout predictions to validate the model. Ultimately, this genome-scale model provides a basic modeling framework with which to explore the metabolism of P. aeruginosa in the context of its environmental and genetic constraints, thereby contributing to a more thorough understanding of the genotype-phenotype relationships in this resourceful and dangerous pathogen.With sequenced genomes now routinely being made available to the public, detailed annotations and various publicly available genomic resources have enabled the formation of genome-scale models of metabolism for a wide variety of organisms (12,21,26,42,63,65). A wealth of data from wellcontrolled experiments, coupled with advancements in methods for computational network analysis, have allowed these models to aid interrogation of metabolic behavior. In addition, an iterative process to model development-cycles of in silico model predictions, experimental (i.e., wet lab) validation, and subsequent model refinement-has enabled the development of methods that have contributed to biological discovery, such as in determination of likely drug targets in Mycobacterium tuberculosis (3,26), metabolic engineering of cells for production of valuable compounds (5, 32, 34), and development of novel frameworks for contextualizing high-throughput "-omics" data sets (15,24,64).Pseudomonas aeruginosa is a ubiquitous gram-negative bacterium that is capable of surviving in a broad range of natural

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“…A major benefit of these global profiling datasets is the ability to attribute predicted action to genes of unknown function using gene regulatory or protein network modelling [24,50]. Such interaction networks, together with metabolic models of M.tb [51], will be required to map and digest the huge quantity of mRNA abundance data generated from microarray and sequencing projects. Finally, in vitro transcriptional profiling of mycobacterial responses to selected stresses, metabolites and growth constraints in controlled settings will allow the multi-factorial signatures captured in vivo to be dissected [3,9,12,22,26,33,39,44,49,52,53].…”

Section: Regulation Of Gene Expressionmentioning

confidence: 99%

Reprogramming the Mycobacterium tuberculosis transcriptome during pathogenesis

Waddell

2010

Drug Discovery Today: Disease Mechanisms

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Transcriptional profiling has revealed that Mycobacterium tuberculosis adapts both its metabolic and respiratory states during infection, utilising lipids as a carbon source and switching to alternative electron acceptors. These global gene expression datasets may be exploited to identify virulence determinants and to screen for new targets for rational drug design. Characterising the changing physiological predicament of distinct M.tb populations during infection will help expose the fundamental biology of M.tb; highlighting mechanisms that influence tuberculosis pathogenicity.

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GSMN-TB: a web-based genome-scale network model of Mycobacterium tuberculosismetabolism

Cited by 204 publications

References 97 publications

Global Effects of Inactivation of the Pyruvate Kinase Gene in the Mycobacterium tuberculosis Complex

Global Effects of Inactivation of the Pyruvate Kinase Gene in the Mycobacterium tuberculosis Complex

Genome-Scale Metabolic Network Analysis of the Opportunistic Pathogen Pseudomonas aeruginosa PAO1

Reprogramming the Mycobacterium tuberculosis transcriptome during pathogenesis

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