HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

Papers In Press, published online ahead of print February 1, 2010
J. Lipid Res., doi:10.1194/jlr.D000760

This Article Full Text Full Text (PDF) Supplemental Data All Versions of this Article: jlr.D000760v1 51/2/431    most recent Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Request Permissions Google Scholar Articles by Kumpula, L. S. Articles by Ala-Korpela, M. PubMed PubMed Citation Articles by Kumpula, L. S. Articles by Ala-Korpela, M. Social Bookmarking                      What's this?

Journal of Lipid Research, Vol. 51, 431-439, February 2010
Copyright © 2010 by American Society for Biochemistry and Molecular Biology

Methods

Characterization of metabolic interrelationships and in silico phenotyping of lipoprotein particles using self-organizing maps^[S]

Linda S. Kumpula^1,*,, Sanna M. Mäkelä^1,,, Ville-Petteri Mäkinen^*,**,, Anna Karjalainen, Johanna M. Liinamaa, Kimmo Kaski^*, Markku J. Savolainen^,, Minna L. Hannuksela^,, and Mika Ala-Korpela^2,,

^* Department of Biomedical Engineering and Computational Science, Helsinki University of Technology, Espoo Finland
Computational Medicine Research Group, Institute of Clinical Medicine Faculty of Medicine, University of Oulu and Biocenter Oulu, University of Oulu, Oulu, Finland
Department of Internal Medicine and Biocenter Oulu, Clinical Research Center, University of Oulu, Oulu, Finland
^** Folkhälsan Institute of Genetics, Folkhälsan Research Center, Biomedicum Helsinki, University of Helsinki, Helsinki, Finland
Division of Nephrology, Department of Medicine, Helsinki University Central Hospital, Helsinki, Finland
Department of Clinical Chemistry, University of Oulu, Oulu, Finland

² To whom correspondence should be addressed. e-mail: mika.ala-korpela{at}computationalmedicine.fi

Plasma lipid concentrations cannot properly account for the complex interactions prevailing in lipoprotein (patho)physiology. Sequential ultracentrifugation (UCF) is the gold standard for physical lipoprotein isolations allowing for subsequent analyses of the molecular composition of the particles. Due to labor and cost issues, however, the UCF-based isolations are usually done only for VLDL, LDL, and HDL fractions; sometimes with the addition of intermediate density lipoprotein (IDL) particles and the fractionation of HDL into HDL₂ and HDL₃ (as done here; n = 302). We demonstrate via these data, with the lipoprotein lipid concentration and composition information combined, that the self-organizing map (SOM) analysis reveals a novel data-driven in silico phenotyping of lipoprotein metabolism beyond the experimentally available classifications. The SOM-based findings are biologically consistent with several well-known metabolic characteristics and also explain some apparent contradictions. The novelty is the inherent emergence of complex lipoprotein associations; e.g., the metabolic subgrouping of the associations between plasma LDL cholesterol concentrations and the structural subtypes of LDL particles. Importantly, lipoprotein concentrations cannot pinpoint lipoprotein phenotypes. It would generally be beneficial to computationally enhance the UCF-based lipoprotein data as illustrated here. Particularly, the compositional variations within the lipoprotein particles appear to be a fundamental issue with metabolic and clinical corollaries.

Supplementary key words metabolism • lipids • ultracentrifugation • subfractions • unsupervised data analysis

Abbreviations: apoB, apolipoprotein B; CE, cholesterol ester; FC, free cholesterol; IDL, intermediate density lipoprotein; PL, phospholipid; SOM, self-organizing map; TG, triglyceride; UCF, ultracentrifugation

CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    What's this?