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Characterization of metabolic interrelationships and in silico phenotyping of lipoprotein particles using self-organizing maps.

ABSTRACT: 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 HDL(2) and HDL(3) (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.

SUBMITTER: Kumpula LS

PROVIDER: S-EPMC2803246 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature

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Characterization of metabolic interrelationships and in silico phenotyping of lipoprotein particles using self-organizing maps.

Kumpula Linda S LS Mäkelä Sanna M SM Mäkinen Ville-Petteri VP Karjalainen Anna A Liinamaa Johanna M JM Kaski Kimmo K Savolainen Markku J MJ Hannuksela Minna L ML Ala-Korpela Mika M

Journal of lipid research 20090905 2

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 ...[more]

PMID: 19734566

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Project description:BackgroundParallel high-throughput microarray and sequencing experiments produce vast quantities of multidimensional data which must be arranged and analyzed in a concerted way. One approach to addressing this challenge is the machine learning technique known as self organizing maps (SOMs). SOMs enable a parallel sample- and gene-centered view of genomic data combined with strong visualization and second-level analysis capabilities. The paper aims at bridging the gap between the potency of SOM-machine learning to reduce dimension of high-dimensional data on one hand and practical applications with special emphasis on gene expression analysis on the other hand.ResultsThe method was applied to generate a SOM characterizing the whole genome expression profiles of 67 healthy human tissues selected from ten tissue categories (adipose, endocrine, homeostasis, digestion, exocrine, epithelium, sexual reproduction, muscle, immune system and nervous tissues). SOM mapping reduces the dimension of expression data from ten of thousands of genes to a few thousand metagenes, each representing a minicluster of co-regulated single genes. Tissue-specific and common properties shared between groups of tissues emerge as a handful of localized spots in the tissue maps collecting groups of co-regulated and co-expressed metagenes. The functional context of the spots was discovered using overrepresentation analysis with respect to pre-defined gene sets of known functional impact. We found that tissue related spots typically contain enriched populations of genes related to specific molecular processes in the respective tissue. Analysis techniques normally used at the gene-level such as two-way hierarchical clustering are better represented and provide better signal-to-noise ratios if applied to the metagenes. Metagene-based clustering analyses aggregate the tissues broadly into three clusters containing nervous, immune system and the remaining tissues.ConclusionsThe SOM technique provides a more intuitive and informative global view of the behavior of a few well-defined modules of correlated and differentially expressed genes than the separate discovery of the expression levels of hundreds or thousands of individual genes. The program is available as R-package 'oposSOM'.

Dataset Information

Characterization of metabolic interrelationships and in silico phenotyping of lipoprotein particles using self-organizing maps.

Publications

Characterization of metabolic interrelationships and in silico phenotyping of lipoprotein particles using self-organizing maps.

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