Bioinformatics strategies for lipidomics analysis: characterization of obesity related hepatic steatosis

Laxman Yetukuri, Mikko Katajamaa, Gema Medina-Gomez, Tuulikki Seppänen-Laakso, Antonio Vidal-Puig, Matej Oresic (Corresponding Author)

Research output: Contribution to journalArticleScientificpeer-review

156 Citations (Scopus)

Abstract

Background Lipids are an important and highly diverse class of molecules having structural, energy storage and signaling roles. Modern analytical technologies afford screening of many lipid molecular species in parallel. One of the biggest challenges of lipidomics is elucidation of important pathobiological phenomena from the integration of the large amounts of new data becoming available. Results We present computational and informatics approaches to study lipid molecular profiles in the context of known metabolic pathways and established pathophysiological responses, utilizing information obtained from modern analytical technologies. In order to facilitate identification of lipids, we compute the scaffold of theoretically possible lipids based on known lipid building blocks such as polar head groups and fatty acids. Each compound entry is linked to the available information on lipid pathways and contains the information that can be utilized for its automated identification from high-throughput UPLC/MS-based lipidomics experiments. The utility of our approach is demonstrated by its application to the lipidomic characterization of the fatty liver of the genetically obese insulin resistant ob/ob mouse model. We investigate the changes of correlation structure of the lipidome using multivariate analysis, as well as reconstruct the pathways for specific molecular species of interest using available lipidomic and gene expression data. Conclusions The methodology presented herein facilitates identification and interpretation of high-throughput lipidomics data. In the context of the ob/ob mouse liver profiling, we have identified the parallel associations between the elevated triacylglycerol levels and the ceramides, as well as the putative activated ceramide-synthesis pathways.
Original languageEnglish
Number of pages15
JournalBMC Systems Biology
Volume1
DOIs
Publication statusPublished - 2007
MoE publication typeA1 Journal article-refereed

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Obesity
Bioinformatics
Lipids
Computational Biology
Liver
Pathway
Ceramides
High Throughput
Mouse
Throughput
Technology
Informatics
Insulin
Energy Storage
Scaffold
Multivariate Analysis
Correlation Structure
Fatty Acids
Fatty Liver
Gene Expression Data

Cite this

Yetukuri, Laxman ; Katajamaa, Mikko ; Medina-Gomez, Gema ; Seppänen-Laakso, Tuulikki ; Vidal-Puig, Antonio ; Oresic, Matej. / Bioinformatics strategies for lipidomics analysis: characterization of obesity related hepatic steatosis. In: BMC Systems Biology. 2007 ; Vol. 1.
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Bioinformatics strategies for lipidomics analysis: characterization of obesity related hepatic steatosis. / Yetukuri, Laxman; Katajamaa, Mikko; Medina-Gomez, Gema; Seppänen-Laakso, Tuulikki; Vidal-Puig, Antonio; Oresic, Matej (Corresponding Author).

In: BMC Systems Biology, Vol. 1, 2007.

Research output: Contribution to journalArticleScientificpeer-review

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T1 - Bioinformatics strategies for lipidomics analysis: characterization of obesity related hepatic steatosis

AU - Yetukuri, Laxman

AU - Katajamaa, Mikko

AU - Medina-Gomez, Gema

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AU - Vidal-Puig, Antonio

AU - Oresic, Matej

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N2 - Background Lipids are an important and highly diverse class of molecules having structural, energy storage and signaling roles. Modern analytical technologies afford screening of many lipid molecular species in parallel. One of the biggest challenges of lipidomics is elucidation of important pathobiological phenomena from the integration of the large amounts of new data becoming available. Results We present computational and informatics approaches to study lipid molecular profiles in the context of known metabolic pathways and established pathophysiological responses, utilizing information obtained from modern analytical technologies. In order to facilitate identification of lipids, we compute the scaffold of theoretically possible lipids based on known lipid building blocks such as polar head groups and fatty acids. Each compound entry is linked to the available information on lipid pathways and contains the information that can be utilized for its automated identification from high-throughput UPLC/MS-based lipidomics experiments. The utility of our approach is demonstrated by its application to the lipidomic characterization of the fatty liver of the genetically obese insulin resistant ob/ob mouse model. We investigate the changes of correlation structure of the lipidome using multivariate analysis, as well as reconstruct the pathways for specific molecular species of interest using available lipidomic and gene expression data. Conclusions The methodology presented herein facilitates identification and interpretation of high-throughput lipidomics data. In the context of the ob/ob mouse liver profiling, we have identified the parallel associations between the elevated triacylglycerol levels and the ceramides, as well as the putative activated ceramide-synthesis pathways.

AB - Background Lipids are an important and highly diverse class of molecules having structural, energy storage and signaling roles. Modern analytical technologies afford screening of many lipid molecular species in parallel. One of the biggest challenges of lipidomics is elucidation of important pathobiological phenomena from the integration of the large amounts of new data becoming available. Results We present computational and informatics approaches to study lipid molecular profiles in the context of known metabolic pathways and established pathophysiological responses, utilizing information obtained from modern analytical technologies. In order to facilitate identification of lipids, we compute the scaffold of theoretically possible lipids based on known lipid building blocks such as polar head groups and fatty acids. Each compound entry is linked to the available information on lipid pathways and contains the information that can be utilized for its automated identification from high-throughput UPLC/MS-based lipidomics experiments. The utility of our approach is demonstrated by its application to the lipidomic characterization of the fatty liver of the genetically obese insulin resistant ob/ob mouse model. We investigate the changes of correlation structure of the lipidome using multivariate analysis, as well as reconstruct the pathways for specific molecular species of interest using available lipidomic and gene expression data. Conclusions The methodology presented herein facilitates identification and interpretation of high-throughput lipidomics data. In the context of the ob/ob mouse liver profiling, we have identified the parallel associations between the elevated triacylglycerol levels and the ceramides, as well as the putative activated ceramide-synthesis pathways.

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