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

E. Davidov, C.B. Clish, Matej Oresic, M. Meys, W. Stochaj, P. Snell, G. Lavine, T.R. Londo, A. Adourian, X. Zhang, M. Johnston, N. Morel, E.W. Marple, T.N. Plasterer, E. Neumann, E. Verheij, J.T.W.E. Vogels, L.M. Havekes, J. Van Der Greef, S. Naylor

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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.
Original languageEnglish
Pages (from-to)267 - 288
Number of pages22
JournalOMICS: A Journal of Integrative Biology
Volume8
Issue number4
DOIs
Publication statusPublished - 2004
MoE publication typeA1 Journal article-refereed

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Genetically Modified Animals
Animals
Animal Models
Plasmas
Metabolism
Lipoproteins
Apolipoprotein E3
Genetically Modified Organisms
Proteins
Metabolome
Apolipoproteins E
Biological systems
Metabolites
Systems Analysis
Metabolic Networks and Pathways
Software
Multivariate Analysis
Impurities
Control Groups
Chemical analysis

Cite this

Davidov, E. ; Clish, C.B. ; Oresic, Matej ; Meys, M. ; Stochaj, W. ; Snell, P. ; Lavine, G. ; Londo, T.R. ; Adourian, A. ; Zhang, X. ; Johnston, M. ; Morel, N. ; Marple, E.W. ; Plasterer, T.N. ; Neumann, E. ; Verheij, E. ; Vogels, J.T.W.E. ; Havekes, L.M. ; Van Der Greef, J. ; Naylor, S. / Methods for the differential integrative Omic analysis of plasma from a transgenic disease animal model. In: OMICS: A Journal of Integrative Biology. 2004 ; Vol. 8, No. 4. pp. 267 - 288.
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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.",
author = "E. Davidov and C.B. Clish and Matej Oresic and M. Meys and W. Stochaj and P. Snell and G. Lavine and T.R. Londo and A. Adourian and X. Zhang and M. Johnston and N. Morel and E.W. Marple and T.N. Plasterer and E. Neumann and E. Verheij and J.T.W.E. Vogels and L.M. Havekes and {Van Der Greef}, J. and S. Naylor",
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Davidov, E, Clish, CB, Oresic, M, Meys, M, Stochaj, W, Snell, P, Lavine, G, Londo, TR, Adourian, A, Zhang, X, Johnston, M, Morel, N, Marple, EW, Plasterer, TN, Neumann, E, Verheij, E, Vogels, JTWE, Havekes, LM, Van Der Greef, J & Naylor, S 2004, 'Methods for the differential integrative Omic analysis of plasma from a transgenic disease animal model', OMICS: A Journal of Integrative Biology, vol. 8, no. 4, pp. 267 - 288. https://doi.org/10.1089/omi.2004.8.267

Methods for the differential integrative Omic analysis of plasma from a transgenic disease animal model. / Davidov, E.; Clish, C.B.; Oresic, Matej; Meys, M.; Stochaj, W.; Snell, P.; Lavine, G.; Londo, T.R.; Adourian, A.; Zhang, X.; Johnston, M.; Morel, N.; Marple, E.W.; Plasterer, T.N.; Neumann, E.; Verheij, E.; Vogels, J.T.W.E.; Havekes, L.M.; Van Der Greef, J.; Naylor, S.

In: OMICS: A Journal of Integrative Biology, Vol. 8, No. 4, 2004, p. 267 - 288.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Methods for the differential integrative Omic analysis of plasma from a transgenic disease animal model

AU - Davidov, E.

AU - Clish, C.B.

AU - Oresic, Matej

AU - Meys, M.

AU - Stochaj, W.

AU - Snell, P.

AU - Lavine, G.

AU - Londo, T.R.

AU - Adourian, A.

AU - Zhang, X.

AU - Johnston, M.

AU - Morel, N.

AU - Marple, E.W.

AU - Plasterer, T.N.

AU - Neumann, E.

AU - Verheij, E.

AU - Vogels, J.T.W.E.

AU - Havekes, L.M.

AU - Van Der Greef, J.

AU - Naylor, S.

PY - 2004

Y1 - 2004

N2 - 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.

AB - 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.

U2 - 10.1089/omi.2004.8.267

DO - 10.1089/omi.2004.8.267

M3 - Article

VL - 8

SP - 267

EP - 288

JO - OMICS: A Journal of Integrative Biology

JF - OMICS: A Journal of Integrative Biology

SN - 1536-2310

IS - 4

ER -