Literature DB >> 25540606

Characterizing phenotype with tracer based metabolomics.

Wai Nang P Lee1.   

Abstract

In the post-genomic era, a pressing challenge to biological scientists is to understand the organization of gene functions, the interaction between gene and nutrient environment, and the genesis of phenotypes. Metabolomics, the quantitation of low molecular weight compounds, has been used to provide a phenotypic description of a cell or tissue by a set of metabolites. Gene function is hypothesized from its correlation with the corresponding set of macromolecules by transcriptomics or proteomics. Another approach to genotype-phenotype correlation is by the reconstruction of genome-scale metabolic maps. The utilization of specific pathways as predicted by reaction network analysis provides the phenotypic characterization of a cell, which can be plotted on a phenotypic phase plane. Tracer based metabolomics is the experimental approach to reaction network analysis using stable isotope tracers. The redistribution of the isotope tracer among metabolic intermediates is used to identify a finite number of pathways, the utilization of which is characteristic of the phenotypic behavior of cells. In this paper, we review tracer based metabolomic methods for the construction of phenotypic phase plane plots, and discuss the functional implications of phenotypic phase plane analysis. Examples of phenotypic changes in response to differentiation, inhibition of signaling pathways and perturbation in nutrient environment are provided.

Entities:  

Keywords:  constraint based modeling; phenotypic phase plane analysis; reaction network; tracer based metabolomics

Year:  2006        PMID: 25540606      PMCID: PMC4271195          DOI: 10.1007/s11306-006-0017-3

Source DB:  PubMed          Journal:  Metabolomics        ISSN: 1573-3882            Impact factor:   4.290


  37 in total

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Authors:  C H Schilling; S Schuster; B O Palsson; R Heinrich
Journal:  Biotechnol Prog       Date:  1999 May-Jun

Review 2.  Genome-scale microbial in silico models: the constraints-based approach.

Authors:  Nathan D Price; Jason A Papin; Christophe H Schilling; Bernhard O Palsson
Journal:  Trends Biotechnol       Date:  2003-04       Impact factor: 19.536

Review 3.  Thirteen years of building constraint-based in silico models of Escherichia coli.

Authors:  Jennifer L Reed; Bernhard Ø Palsson
Journal:  J Bacteriol       Date:  2003-05       Impact factor: 3.490

Review 4.  Metabonomics: a platform for studying drug toxicity and gene function.

Authors:  Jeremy K Nicholson; John Connelly; John C Lindon; Elaine Holmes
Journal:  Nat Rev Drug Discov       Date:  2002-02       Impact factor: 84.694

5.  Hepatic de novo lipogenesis in stable low-birth-weight infants during exclusive breast milk feedings and during parenteral nutrition.

Authors:  Meena Garg; Sara Bassilian; Cynthia Bell; Samuel Lee; W N Paul Lee
Journal:  JPEN J Parenter Enteral Nutr       Date:  2005 Mar-Apr       Impact factor: 4.016

6.  Appendix. Analysis of tricarboxylic acid cycle using mass isotopomer ratios.

Authors:  W N Lee
Journal:  J Biol Chem       Date:  1993-12-05       Impact factor: 5.157

7.  Transforming growth factor beta2 promotes glucose carbon incorporation into nucleic acid ribose through the nonoxidative pentose cycle in lung epithelial carcinoma cells.

Authors:  L G Boros; J S Torday; S Lim; S Bassilian; M Cascante; W N Lee
Journal:  Cancer Res       Date:  2000-03-01       Impact factor: 12.701

8.  Fatty acid cycling in human hepatoma cells and the effects of troglitazone.

Authors:  W N Lee; S Lim; S Bassilian; E A Bergner; J Edmond
Journal:  J Biol Chem       Date:  1998-08-14       Impact factor: 5.157

9.  Isotopomer study of lipogenesis in human hepatoma cells in culture: contribution of carbon and hydrogen atoms from glucose.

Authors:  W N Lee; L O Byerley; S Bassilian; H O Ajie; I Clark; J Edmond; E A Bergner
Journal:  Anal Biochem       Date:  1995-03-20       Impact factor: 3.365

10.  High-throughput classification of yeast mutants for functional genomics using metabolic footprinting.

Authors:  Jess Allen; Hazel M Davey; David Broadhurst; Jim K Heald; Jem J Rowland; Stephen G Oliver; Douglas B Kell
Journal:  Nat Biotechnol       Date:  2003-05-12       Impact factor: 54.908

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  5 in total

1.  Cyclin-dependent kinases 4 and 6 control tumor progression and direct glucose oxidation in the pentose cycle.

Authors:  Miriam Zanuy; Antonio Ramos-Montoya; Oscar Villacañas; Nuria Canela; Anibal Miranda; Esther Aguilar; Neus Agell; Oriol Bachs; Jaime Rubio-Martinez; Maria Dolors Pujol; Wai-Nang P Lee; Silvia Marin; Marta Cascante
Journal:  Metabolomics       Date:  2011-07-08       Impact factor: 4.290

2.  The Warburg effect: a balance of flux analysis.

Authors:  B Vaitheesvaran; J Xu; J Yee; Lu Q-Y; V L Go; G G Xiao; W N Lee
Journal:  Metabolomics       Date:  2015-08       Impact factor: 4.290

Review 3.  Tracer-based metabolomics: concepts and practices.

Authors:  W-N Paul Lee; Paulin N Wahjudi; Jun Xu; Vay Liang Go
Journal:  Clin Biochem       Date:  2010-08-14       Impact factor: 3.281

4.  Inhibition of protein phosphorylation in MIA pancreatic cancer cells: confluence of metabolic and signaling pathways.

Authors:  Hengwei Zhang; Rui Cao; Wai-Nang Paul Lee; Caishu Deng; Yingchun Zhao; Joan Lappe; Robert Recker; Yun Yen; Qi Wang; Ming-Ying Tsai; Vay Liang Go; Gary Guishan Xiao
Journal:  J Proteome Res       Date:  2010-02-05       Impact factor: 4.466

Review 5.  From correlation to causation: analysis of metabolomics data using systems biology approaches.

Authors:  Antonio Rosato; Leonardo Tenori; Marta Cascante; Pedro Ramon De Atauri Carulla; Vitor A P Martins Dos Santos; Edoardo Saccenti
Journal:  Metabolomics       Date:  2018-02-27       Impact factor: 4.290

  5 in total

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