Genome-scale analysis of <i>Streptomyces coelicolor</i> A3(2) metabolism

Borodina, Irina; Krabben, Preben; Nielsen, Jens

doi:10.1101/gr.3364705

Cited by 226 publications

(203 citation statements)

References 61 publications

(51 reference statements)

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“…The availability of the complete genome sequence now provides the basis for systematic approaches to identify and manipulate such feeder pathways with the aim of increasing polyketide production. It also provides the starting point for an integrated genome-scale analysis of S. erythraea metabolism, as provided recently for S. coelicolor 41 .…”

Section: A R T I C L E Smentioning

confidence: 99%

Complete genome sequence of the erythromycin-producing bacterium Saccharopolyspora erythraea NRRL23338

et al. 2007

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Section: A R T I C L E Smentioning

confidence: 99%

Complete genome sequence of the erythromycin-producing bacterium Saccharopolyspora erythraea NRRL23338

et al. 2007

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“…A combination of automated and manual procedures can help turn a genome sequence into a metabolic model, and a variety are available (e.g. Refs [26][27][28][29][30]). The qualitative metabolic network or logical graph, popularised in the biochemical wall charts [25] and resources such as Kyoto Encyclopedia of Genes and Genomes (KEGG) [31], is then the starting point for metabolic modelling.…”

Section: Metabolic Reconstruction Is Now Mature and Timelymentioning

confidence: 99%

“…Ref. [26]) give some 1200 reactions and 650 metabolites, with slightly smaller but broadly similar numbers for bacteria such as Escherichia coli [54][55][56] and Streptomyces coelicolor [28], most with a MW <500 [27,55]. The curated human metabolome [as reconstructed semi-manually from the consensus human genome sequence, build 31 [29] or 35, (Bernhard Palsson, pers.…”

Section: Sizes Of the Human And Other Metabolomesmentioning

confidence: 99%

Systems biology, metabolic modelling and metabolomics in drug discovery and development

Kell

2006

Drug Discovery Today

265

163

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Unlike signalling pathways, metabolic networks are subject to strict stoichiometric constraints. Metabolomics amplifies changes in the proteome, and represents more closely the phenotype of an organism. Recent advances enable the production (and computer-readable encoding as SBML) of metabolic network models reconstructed from genome sequences, as well as experimental measurements of much of the metabolome. There is increasing convergence between the number of human metabolites estimated via genomics ( 3000) and the number measured experimentally. It is thus both timely, and now possible, to bring these two approaches together as an integrated (if distributed) whole to help understand the genesis of metabolic biomarkers, the progress of disease, and the modes of action, efficacy, off-target effects and toxicity of pharmaceutical drugs. Systems biology and metabolic modelling in the 21st CenturyAlthough there are many individual definitions, most commentators (including this one [1,2]) take it that systems biology involves an iterative interplay between more or less high-throughput and high-content 'wet' experiments, technology development, theory and computational modelling, and that it is the involvement of computational modelling, in particular, in the process that sets systems biology apart from the more traditional and more reductionist molecular biology. Metabolomics illustrates this amply. There is also the view that the perceived decrease in the effectiveness of the target-based drug discovery process [3][4][5], including the still-high levels of attrition [6], means that we must move towards understanding organisms at something more akin to a whole-system level [7][8][9][10][11].The question then arises as to what part of a system one might first beneficially model? Although there has, unsurprisingly, been considerable interest in modelling the major signalling pathways (e.g. Refs [12,13]), there are several reasons why it is timely to turn our attention to the level of small molecule metabolism, which is the focus of this review. Metabolism is more discriminatingIt has long been known, and proven through the formalism of metabolic control analysis [7,10,[14][15][16], that whereas small changes in the concentrations of enzymes (and the transcripts that encode them) have only small effects on the fluxes through metabolic pathways, they have substantial effects on the concentrations of metabolic intermediates. Because the metabolome (nominally the concentrations of 'all' the metabolites measured in a system of interest [17]) is downstream of the proteome, it is thereby 'amplified' both in theory [18] and in practice [19,20] and represents a more sensitive level of organisation than do the macromolecular 'omes for understanding a complex biological system, and the changes in it that might be occasioned by disease or pharmaceutical intervention [21,22]. Metabolic reconstruction is now mature and timelyAn attractive feature for the purposes of modelling is that metabolism, in contrast to signalling pathways, ...

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“…In recent years, there has been an effort to reconstruct the genomescale metabolic networks for hundreds species [2][3][4][5][6][7]. In principle, the reconstruction of metabolic networks is an iterative multi-stage process [8,9], which starts from gene annotation, and goes all the way to network development.…”

Section: Introductionmentioning

confidence: 99%

“…But the annotation through the network reconstruction methods have failed to find the corresponding gene in G that is responsible for that reaction R. We distinguish two types of metabolic gaps: local metabolic gap where the corresponding gene responsible for R can be found in other related organisms and global metabolic gap where the corresponding gene responsible for R has not been found in any known organism or have not been so annotated in any genome. [5], and bacterium Streptomyces coelicolor [6], between 6% to 19% of the biochemical reactions are metabolic gaps. …”

mentioning

confidence: 99%

Megafiller: A Retrofitted Protein Function Predictor for Filling Gaps in Metabolic Networks

Nguyen¹,

Vongsangnak²,

Shen³

et al. 2014

J Proteomics Bioinform

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BackgroundThe metabolic network of a cell is the complete set of interconnected metabolic processes that determine the physiological and biochemical properties of the cell. In recent years, metabolic networks have enormously contributed to our understanding of metabolic genotype and phenotype relationship. This leads to important applications through systems biology and metabolic engineering. Recently, metaproteome-scale metabolic network reconstruction has also emerged as a promising and challenging approach for investigating the metabolism of microbial communities [1].In recent years, there has been an effort to reconstruct the genomescale metabolic networks for hundreds species [2][3][4][5][6][7]. In principle, the reconstruction of metabolic networks is an iterative multi-stage process [8,9], which starts from gene annotation, and goes all the way to network development. Several sophisticated techniques have been developed for metabolic network reconstruction [10][11][12][13][14][15]. Metabolic Gaps and their ImplicationsHowever, most of the reconstructed networks remain incomplete, namely there are significant numbers of metabolic gaps [16,17]. A metabolic gap in a network for genome (G) is a metabolic reaction (R) (described by its EC number) that is present in the network. But the annotation through the network reconstruction methods have failed to find the corresponding gene in G that is responsible for that reaction R. We distinguish two types of metabolic gaps: local metabolic gap where the corresponding gene responsible for R can be found in other related organisms and global metabolic gap where the corresponding gene responsible for R has not been found in any known organism or have not been so annotated in any genome. [5], and bacterium Streptomyces coelicolor [6], between 6% to 19% of the biochemical reactions are metabolic gaps. Metabolic gaps impede downstream biological analysis of these metabolic networks. For examples, in the reconstructed metabolic networks of yeast Saccharomyces cerevisiae [2], filamentous fungi Aspergillus oryzae [3], Aspergillus nidulans [4], and Aspergillus nigerFilling these metabolic gaps (and thus, enhancing these networks) is the most time-consuming task that may take years to complete since there is a lot of manual curation involved [16][17][18]. This can be a bottleneck for gaining high quality metabolic networks. Our work therefore proposes to fill those metabolic gaps by considering new algorithmic methods. Current Metabolic Gap Filling MethodsCurrent direct methods for filling local metabolic gaps (i.e. genes that are un-annotated in the target organism, but have been found in Abstract Background: A bottleneck in investigating the cellular metabolism and physiology of organisms is the presence of metabolic gaps in the genome-scale metabolic networks. Metabolic gaps are reactions in the network that the corresponding genes have not yet been identified. Previous gap filling methods are generally based on identifying protein family in related organisms and then use ...

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Genome-scale analysis of Streptomyces coelicolor A3(2) metabolism

Cited by 226 publications

References 61 publications

Complete genome sequence of the erythromycin-producing bacterium Saccharopolyspora erythraea NRRL23338

Complete genome sequence of the erythromycin-producing bacterium Saccharopolyspora erythraea NRRL23338

Systems biology, metabolic modelling and metabolomics in drug discovery and development

Megafiller: A Retrofitted Protein Function Predictor for Filling Gaps in Metabolic Networks

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