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Use of reconstituted metabolic networks to assist in metabolomic data visualization and mining.

Jourdan F, Cottret L, Huc L, Wildridge D, Scheltema R, Hillenweck A, Barrett MP, Zalko D, Watson DG, Debrauwer L - Metabolomics (2010)

Bottom Line: However, important information can be gleaned even from sparse datasets, which can be facilitated by placing the results within the context of known metabolic networks.Biologically relevant metabolic sub-networks were extracted from both datasets.ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0196-9) contains supplementary material, which is available to authorized users.

View Article: PubMed Central - PubMed

ABSTRACT
Metabolomics experiments seldom achieve their aim of comprehensively covering the entire metabolome. However, important information can be gleaned even from sparse datasets, which can be facilitated by placing the results within the context of known metabolic networks. Here we present a method that allows the automatic assignment of identified metabolites to positions within known metabolic networks, and, furthermore, allows automated extraction of sub-networks of biological significance. This latter feature is possible by use of a gap-filling algorithm. The utility of the algorithm in reconstructing and mining of metabolomics data is shown on two independent datasets generated with LC-MS LTQ-Orbitrap mass spectrometry. Biologically relevant metabolic sub-networks were extracted from both datasets. Moreover, a number of metabolites, whose presence eluded automatic selection within mass spectra, could be identified retrospectively by virtue of their inferred presence through gap filling. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0196-9) contains supplementary material, which is available to authorized users.

No MeSH data available.


Green nodes are metabolites identified in the dataset. Purple nodes are the ones inferred by our gap filling method. Square nodes are reactions nodes, when the border is red it means that the reaction can occur in both directions. This pathway is part of amino acids degradation. The sub-network reported here was produced from the current version of Trypanocyc. It is notable that this version has not benefited from expert pruning so the reactions catalysed by spermine synthase and spermidine dehydrogenase have not been reported in T. brucei. Furthermore the reaction catalysed by trypanothione reductase, usually converts the oxidised form of trypanothione to its reduced form, which is then involved in multiple cellular reductions prior to its being enzymatically reconverted to the reduced form
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Fig6: Green nodes are metabolites identified in the dataset. Purple nodes are the ones inferred by our gap filling method. Square nodes are reactions nodes, when the border is red it means that the reaction can occur in both directions. This pathway is part of amino acids degradation. The sub-network reported here was produced from the current version of Trypanocyc. It is notable that this version has not benefited from expert pruning so the reactions catalysed by spermine synthase and spermidine dehydrogenase have not been reported in T. brucei. Furthermore the reaction catalysed by trypanothione reductase, usually converts the oxidised form of trypanothione to its reduced form, which is then involved in multiple cellular reductions prior to its being enzymatically reconverted to the reduced form

Mentions: Trypanosomes contain an unusual “signature” metabolite, trypanothione, that comprises two molecules of glutathione linked through a spermidine. Its presence, along with metabolites involved in its biosynthesis, offers an interesting proof of concept for the pathway extraction method. Fig. 6 shows that the trypanothione biosynthetic pathway sub-network was indeed correctly extracted.Fig. 6


Use of reconstituted metabolic networks to assist in metabolomic data visualization and mining.

Jourdan F, Cottret L, Huc L, Wildridge D, Scheltema R, Hillenweck A, Barrett MP, Zalko D, Watson DG, Debrauwer L - Metabolomics (2010)

Green nodes are metabolites identified in the dataset. Purple nodes are the ones inferred by our gap filling method. Square nodes are reactions nodes, when the border is red it means that the reaction can occur in both directions. This pathway is part of amino acids degradation. The sub-network reported here was produced from the current version of Trypanocyc. It is notable that this version has not benefited from expert pruning so the reactions catalysed by spermine synthase and spermidine dehydrogenase have not been reported in T. brucei. Furthermore the reaction catalysed by trypanothione reductase, usually converts the oxidised form of trypanothione to its reduced form, which is then involved in multiple cellular reductions prior to its being enzymatically reconverted to the reduced form
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2874485&req=5

Fig6: Green nodes are metabolites identified in the dataset. Purple nodes are the ones inferred by our gap filling method. Square nodes are reactions nodes, when the border is red it means that the reaction can occur in both directions. This pathway is part of amino acids degradation. The sub-network reported here was produced from the current version of Trypanocyc. It is notable that this version has not benefited from expert pruning so the reactions catalysed by spermine synthase and spermidine dehydrogenase have not been reported in T. brucei. Furthermore the reaction catalysed by trypanothione reductase, usually converts the oxidised form of trypanothione to its reduced form, which is then involved in multiple cellular reductions prior to its being enzymatically reconverted to the reduced form
Mentions: Trypanosomes contain an unusual “signature” metabolite, trypanothione, that comprises two molecules of glutathione linked through a spermidine. Its presence, along with metabolites involved in its biosynthesis, offers an interesting proof of concept for the pathway extraction method. Fig. 6 shows that the trypanothione biosynthetic pathway sub-network was indeed correctly extracted.Fig. 6

Bottom Line: However, important information can be gleaned even from sparse datasets, which can be facilitated by placing the results within the context of known metabolic networks.Biologically relevant metabolic sub-networks were extracted from both datasets.ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0196-9) contains supplementary material, which is available to authorized users.

View Article: PubMed Central - PubMed

ABSTRACT
Metabolomics experiments seldom achieve their aim of comprehensively covering the entire metabolome. However, important information can be gleaned even from sparse datasets, which can be facilitated by placing the results within the context of known metabolic networks. Here we present a method that allows the automatic assignment of identified metabolites to positions within known metabolic networks, and, furthermore, allows automated extraction of sub-networks of biological significance. This latter feature is possible by use of a gap-filling algorithm. The utility of the algorithm in reconstructing and mining of metabolomics data is shown on two independent datasets generated with LC-MS LTQ-Orbitrap mass spectrometry. Biologically relevant metabolic sub-networks were extracted from both datasets. Moreover, a number of metabolites, whose presence eluded automatic selection within mass spectra, could be identified retrospectively by virtue of their inferred presence through gap filling. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0196-9) contains supplementary material, which is available to authorized users.

No MeSH data available.