oru.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Bioinformatics strategies for lipidomics analysis: characterization of obesity related hepatic steatosis
VTT Technical Research Centre of Finland, Espoo, Finland.
Turku Centre for Biotechnology, Turku, Finland.
University of Cambridge Department of Clinical Biochemistry, Addenbrooke's Hospital, Cambridge, UK.
VTT Technical Research Centre of Finland, Espoo, Finland.
Show others and affiliations
2007 (English)In: BMC Systems Biology, ISSN 1752-0509, E-ISSN 1752-0509, Vol. 1, article id 12Article in journal (Refereed) Published
Abstract [en]

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.

CONCLUSION: 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.

Place, publisher, year, edition, pages
BioMed Central, 2007. Vol. 1, article id 12
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:oru:diva-70904DOI: 10.1186/1752-0509-1-12ISI: 000250001800001PubMedID: 17408502Scopus ID: 2-s2.0-34547825554OAI: oai:DiVA.org:oru-70904DiVA, id: diva2:1345822
Available from: 2019-08-26 Created: 2019-08-26 Last updated: 2019-08-27Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textPubMedScopus

Authority records BETA

Oresic, Matej

Search in DiVA

By author/editor
Oresic, Matej
In the same journal
BMC Systems Biology
Bioinformatics and Systems Biology

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 12 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf