Literature DB >> 16888765

Metabolomics: current technologies and future trends.

Katherine Hollywood1, Daniel R Brison, Royston Goodacre.   

Abstract

The ability to sequence whole genomes has taught us that our knowledge with respect to gene function is rather limited with typically 30-40% of open reading frames having no known function. Thus, within the life sciences there is a need for determination of the biological function of these so-called orphan genes, some of which may be molecular targets for therapeutic intervention. The search for specific mRNA, proteins, or metabolites that can serve as diagnostic markers has also increased, as has the fact that these biomarkers may be useful in following and predicting disease progression or response to therapy. Functional analyses have become increasingly popular. They include investigations at the level of gene expression (transcriptomics), protein translation (proteomics) and more recently the metabolite network (metabolomics). This article provides an overview of metabolomics and discusses its complementary role with transcriptomics and proteomics, and within system biology. It highlights how metabolome analyses are conducted and how the highly complex data that are generated are analysed. Non-invasive footprinting analysis is also discussed as this has many applications to in vitro cell systems. Finally, for studying biotic or abiotic stresses on animals, plants or microbes, we believe that metabolomics could very easily be applied to large populations, because this approach tends to be of higher throughput and generally lower cost than transcriptomics and proteomics, whilst also providing indications of which area of metabolism may be affected by external perturbation.

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Year:  2006        PMID: 16888765     DOI: 10.1002/pmic.200600106

Source DB:  PubMed          Journal:  Proteomics        ISSN: 1615-9853            Impact factor:   3.984


  120 in total

1.  Plasma metabolomic profile in nonalcoholic fatty liver disease.

Authors:  Satish C Kalhan; Lining Guo; John Edmison; Srinivasan Dasarathy; Arthur J McCullough; Richard W Hanson; Mike Milburn
Journal:  Metabolism       Date:  2010-04-27       Impact factor: 8.694

2.  The Changes of Serum Metabolites in Diabetic GK Rats after Ileal Transposition Surgery.

Authors:  Kemin Yan; Weijie Chen; Huijuan Zhu; Guole Lin; Wei Sun; Xiaoyan Liu; Hui Pan; Linjie Wang; Hongbo Yang; Meijuan Liu; Fengying Gong
Journal:  Obes Surg       Date:  2019-03       Impact factor: 4.129

Review 3.  A cheminformatic toolkit for mining biomedical knowledge.

Authors:  Gus R Rosania; Gordon Crippen; Peter Woolf; David States; Kerby Shedden
Journal:  Pharm Res       Date:  2007-03-24       Impact factor: 4.200

4.  Diverse metabolic model parameters generate similar methionine cycle dynamics.

Authors:  Matthew Piazza; Xiao-Jiang Feng; Joshua D Rabinowitz; Herschel Rabitz
Journal:  J Theor Biol       Date:  2007-12-23       Impact factor: 2.691

5.  Identification and evaluation of cycling yeast metabolites in two-dimensional comprehensive gas chromatography-time-of-flight-mass spectrometry data.

Authors:  Rachel E Mohler; Benjamin P Tu; Kenneth M Dombek; Jamin C Hoggard; Elton T Young; Robert E Synovec
Journal:  J Chromatogr A       Date:  2007-10-25       Impact factor: 4.759

Review 6.  Database resources in metabolomics: an overview.

Authors:  Eden P Go
Journal:  J Neuroimmune Pharmacol       Date:  2009-05-07       Impact factor: 4.147

7.  A metabolic network described in absolute terms.

Authors:  Dietmar Schomburg
Journal:  Nat Chem Biol       Date:  2009-08       Impact factor: 15.040

8.  Capillary electrophoresis with electrospray ionization mass spectrometric detection for single-cell metabolomics.

Authors:  Theodore Lapainis; Stanislav S Rubakhin; Jonathan V Sweedler
Journal:  Anal Chem       Date:  2009-07-15       Impact factor: 6.986

9.  Monitoring dynamic changes in lymph metabolome of fasting and fed rats by electrospray ionization-ion mobility mass spectrometry (ESI-IMMS).

Authors:  Kimberly Kaplan; Prabha Dwivedi; Sean Davidson; Qing Yang; Patrick Tso; William Siems; Herbert H Hill
Journal:  Anal Chem       Date:  2009-10-01       Impact factor: 6.986

10.  Time-dependent profiling of metabolites from Snf1 mutant and wild type yeast cells.

Authors:  Elizabeth M Humston; Kenneth M Dombek; Jamin C Hoggard; Elton T Young; Robert E Synovec
Journal:  Anal Chem       Date:  2008-10-01       Impact factor: 6.986

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