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  • 1.
    Clish, Clary B.
    et al.
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Davidov, Eugene
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Oresic, Matej
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Plasterer, Thomas N.
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Lavine, Gary
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Londo, Tom
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Meys, Michael
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Snell, Philip
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Stochaj, Wayne
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Adourian, Aram
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Zhang, Xiang
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Morel, Nicole
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Neumann, Eric
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Verheij, Elwin
    TNO Pharma, Zeist, Netherlands.
    Vogels, Jack T. W. E.
    TNO Pharma, Zeist, Netherlands.
    Havekes, Louis M.
    TNO Prevention and Health, Gaubius Laboratorium, Leiden, Netherlands; Departments of Cardiology and Internal Medicine and Leiden Center for Cardiovascular Research, Leiden University Medical Center, Leiden, Netherlands.
    Afeyan, Noubar
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Regnier, Fred
    Department of Chemistry, Purdue University, Lafayette, Indiana, USA.
    van der Greef, Jan
    Beyond Genomics, Inc., Waltham, Massachusetts, USA; TNO Pharma, Zeist, Netherlands; Division of Analytical Biosciences, Leiden/Amsterdam Centre for Drug Research, Leiden University, Leiden, Netherlands.
    Naylor, Stephen
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Integrative biological analysis of the APOE*3-leiden transgenic mouse2004In: Omics, ISSN 1536-2310, E-ISSN 1557-8100, Vol. 8, no 1, p. 3-13Article in journal (Refereed)
    Abstract [en]

    Integrative (or systems biology) is a new approach to analyzing biological entities as integrated systems of genetic, genomic, protein, metabolite, cellular, and pathway events that are in flux and interdependent. Here, we demonstrate the application of intregrative biological analysis to a mammalian disease model, the apolipoprotein E3-Leiden (APO*E3) transgenic mouse. Mice selected for the study were fed a normal chow diet and sacrificed at 9 weeks of age-conditions under which they develop only mild type I and II atherosclerotic lesions. Hepatic mRNA expression analysis showed a 25% decrease in APO A1 and a 43% increase in liver fatty acid binding protein expression between transgenic and wild type control mice, while there was no change in PPAR-alpha expression. On-line high performance liquid chromatography-mass spectrometry quantitative profiling of tryptic digests of soluble liver proteins and liver lipids, coupled with principle component analysis, enabled rapid identification of early protein and metabolite markers of disease pathology. These included a 44% increase in L-FABP in transgenic animals compared to controls, as well as an increase in triglycerides and select bioactive lysophosphatidylcholine species. A correlation analysis of identified genes, proteins, and lipids was used to construct an interaction network. Taken together, these results indicate that integrative biology is a powerful tool for rapid identification of early markers and key components of pathophysiologic processes, and constitute the first application of this approach to a mammalian system.

  • 2.
    Davidov, Eugene
    et al.
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Clish, Clary B.
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Oresic, Matej
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Meys, Michael
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Stochaj, Wayne
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Snell, Philip
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Lavine, Gary
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Londo, Thomas R.
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Adourian, Aram
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Zhang, Xiang
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Johnston, Mark
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Morel, Nicole
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Marple, Edward W.
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Plasterer, Thomas N.
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Neumann, Eric
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Verheij, Elwin
    TNO Pharma, Zeist, The Netherlands.
    Vogels, Jack T. W. E.
    TNO Pharma, Zeist, The Netherlands.
    Havekes, Louis M.
    TNO Prevention and Health, Gaubius Laboratorium, Leiden, The Netherlands; Departments of Cardiology and Internal Medicine and Leiden/Amsterdam Center for Drug Research, Leiden University Medical Center, Leiden University, Leiden, The Netherlands.
    van der Greef, Jan
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA; TNO Pharma, Zeist, The Netherlands; Departments of Cardiology and Internal Medicine and Leiden/Amsterdam Center for Drug Research, Leiden University Medical Center, Leiden University, Leiden, The Netherlands.
    Naylor, Stephen
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Methods for the differential integrative omic analysis of plasma from a transgenic disease animal model2004In: Omics, ISSN 1536-2310, E-ISSN 1557-8100, Vol. 8, no 4, p. 267-288Article in journal (Refereed)
    Abstract [en]

    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.

  • 3.
    Elo, Laura L.
    et al.
    Department of Mathematics, University of Turku, Turku, Finland; Turku Centre for Biotechnology, Turku, Finland.
    Katajamaa, Mikko
    Turku Centre for Biotechnology, Turku, Finland.
    Lund, Riikka
    Turku Centre for Biotechnology, Turku, Finland.
    Oresic, Matej
    Turku Centre for Biotechnology, Turku, Finland; VTT Biotechnology, Espoo, Finland.
    Lahesmaa, Riitta
    Turku Centre for Biotechnology, Turku, Finland.
    Aittokallio, Tero
    Department of Mathematics, University of Turku, Turku, Finland; Turku Centre for Biotechnology, Turku, Finland; Systems Biology Unit, Institut Pasteur, Paris, France.
    Improving identification of differentially expressed genes by integrative analysis of Affymetrix and Illumina arrays2006In: Omics, ISSN 1536-2310, E-ISSN 1557-8100, Vol. 10, no 3, p. 369-380Article in journal (Refereed)
    Abstract [en]

    Together with the widely used Affymetrix microarrays, the recently introduced Illumina platform has become a cost-effective alternative for genome-wide studies. To efficiently use data from both array platforms, there is a pressing need for methods that allow systematic integration of multiple datasets, especially when the number of samples is small. To address these needs, we introduce a meta-analytic procedure for combining Affymetrix and Illumina data in the context of detecting differentially expressed genes between the platforms. We first investigate the effect of different expression change estimation procedures within the platforms on the agreement of the most differentially expressed genes. Using the best estimation methods, we then show the benefits of the integrative analysis in producing reproducible results across bootstrap samples. In particular, we demonstrate its biological relevance in identifying small but consistent changes during T helper 2 cell differentiation.

1 - 3 of 3
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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  • text
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