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Bioinformatics strategies for phenotype characterization using lipidomics

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Abstract

Lipids are known to serve as key regulators in the complex biological functional states such as maintenance of energy homeostasis. Phenotype of such complex state is characterised by differential fold change in the levels of some group of lipids, or metabolites in general, in response to genetic or environmental perturbation. Current lipidomic studies, however, are hindered due to lack of matured bioinformatics techniques to distil the information coming from large volumes of data generated from the improved modern analytical methods and therefore, we present bioinformatics strategies involving comprehensive spectral libraries, data processing software and multivariate exploratory analyses to characterize phenotype using lipids. We utilize recently developed nomenclature of lipids (1) in computationally constructed library of lipid compounds represented by the Simplified Molecular Input Line Entry System (SMILES) representation. Each compound entry is linked to the available information on lipid pathways, therefore enabling tracing of known pathways for each identified lipid species from UPLC-MS profiling experiment. Our global profiling approach based on UPLC-MS lipidomic platform involves screening of major lipids, including acylglycerols, phospholipids, sphingolipids, and cholesterol esters. Several data processing steps such as peak detection, alignment, and normalization are performed using MZmine software (2). The resulting peaks are identified utilizing a comprehensive spectral library of lipids, which afford automatic identification and profile comparison of several hundreds of lipid molecular species. Such data then leads to several analyses, such as multivariate exploratory analyses and correlation network analyses, as well as combined analyses of lipid and gene expression profile data. We will demonstrate the utility of the approach in multi-tissue characterization of the recently introduced PPARƒ×2 knock-out mouse model (3), which affords study of associations between the regulation of adipose tissue expandability and accumulation of lipid species in peripheral tissues. References 1. Fahy E, Subramaniam S, Brown HA, et al. A comprehensive classification system for lipids. J Lipid Res 2005; 46(5):839-62. 2. Katajamaa M, Oresic M. Processing methods for differential analysis of LC/MS profile data. BMC Bioinformatics 2005; 6:179. 3. Medina-Gomez G, Virtue S, Lelliott C, et al. The link between nutritional status and insulin sensitivity is dependent on the adipocyte-specific Peroxisome Proliferator-Activated Receptor-{gamma}2 isoform. Diabetes 2005; 54(6):1706-16.

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