Literature DB >> 15703476

Methods for the differential integrative omic analysis of plasma from a transgenic disease animal model.

Eugene Davidov1, Clary B Clish, Matej Oresic, Michael Meys, Wayne Stochaj, Philip Snell, Gary Lavine, Thomas R Londo, Aram Adourian, Xiang Zhang, Mark Johnston, Nicole Morel, Edward W Marple, Thomas N Plasterer, Eric Neumann, Elwin Verheij, Jack T W E Vogels, Louis M Havekes, Jan van der Greef, Stephen Naylor.   

Abstract

Multitiered quantitative analysis of biological systems is rapidly becoming the desired approach to study hierarchical functional interactions between proteins and metabolites. We describe here a novel systematic approach to analyze organisms with complex metabolic regulatory networks. By using precise analytical methods to measure biochemical constituents and their relative abundance in whole plasma of transgenic ApoE*3-Leiden mice and an isogenic wild-type control group, simultaneous snapshots of metabolic and protein states were obtained. Novel data processing and multivariate analysis tools such as Impurity Resolution Software (IMPRESS) and Windows-based linear fit program (WINLIN) were used to compare protein and metabolic profiles in parallel. Canonical correlations of the resulting data show quantitative relationships between heterogeneous components in the TG animals. These results, obtained solely from whole plasma analysis allowed us, in a rapid manner, to corroborate previous findings as well as find new events pertaining to dominant and peripheral events in lipoprotein metabolism of a genetically modified mammalian organism in relation to ApoE3, a key mediator of lipoprotein metabolism.

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Year:  2004        PMID: 15703476     DOI: 10.1089/omi.2004.8.267

Source DB:  PubMed          Journal:  OMICS        ISSN: 1536-2310


  6 in total

1.  Unraveling human complexity and disease with systems biology and personalized medicine.

Authors:  Stephen Naylor; Jake Y Chen
Journal:  Per Med       Date:  2010-05       Impact factor: 2.512

Review 2.  Biomarkers for mitochondrial respiratory chain disorders.

Authors:  Anu Suomalainen
Journal:  J Inherit Metab Dis       Date:  2010-10-13       Impact factor: 4.982

3.  Candidate proteins, metabolites and transcripts in the Biomarkers for Spinal Muscular Atrophy (BforSMA) clinical study.

Authors:  Richard S Finkel; Thomas O Crawford; Kathryn J Swoboda; Petra Kaufmann; Peter Juhasz; Xiaohong Li; Yu Guo; Rebecca H Li; Felicia Trachtenberg; Suzanne J Forrest; Dione T Kobayashi; Karen S Chen; Cynthia L Joyce; Thomas Plasterer
Journal:  PLoS One       Date:  2012-04-27       Impact factor: 3.240

4.  Processing methods for differential analysis of LC/MS profile data.

Authors:  Mikko Katajamaa; Matej Oresic
Journal:  BMC Bioinformatics       Date:  2005-07-18       Impact factor: 3.169

5.  Genetic networks of liver metabolism revealed by integration of metabolic and transcriptional profiling.

Authors:  Christine T Ferrara; Ping Wang; Elias Chaibub Neto; Robert D Stevens; James R Bain; Brett R Wenner; Olga R Ilkayeva; Mark P Keller; Daniel A Blasiole; Christina Kendziorski; Brian S Yandell; Christopher B Newgard; Alan D Attie
Journal:  PLoS Genet       Date:  2008-03-14       Impact factor: 5.917

6.  Cross-species comparison of genes related to nutrient sensing mechanisms expressed along the intestine.

Authors:  Nikkie van der Wielen; Mark van Avesaat; Nicole J W de Wit; Jack T W E Vogels; Freddy Troost; Ad Masclee; Sietse-Jan Koopmans; Jan van der Meulen; Mark V Boekschoten; Michael Müller; Henk F J Hendriks; Renger F Witkamp; Jocelijn Meijerink
Journal:  PLoS One       Date:  2014-09-12       Impact factor: 3.240

  6 in total

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