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
Phenotype characterisation using integrated gene transcript, protein and metabolite profiling
Beyond Genomics Inc., Waltham, USA; VTT Biotechnology, Espoo, Finland.ORCID iD: 0000-0002-2856-9165
Beyond Genomics Inc., Waltham, USA.
Beyond Genomics Inc., Waltham, USA; Molecular Biophysics and Biochemistry, Yale University, New Haven, USA.
TNO Pharma, Zeist, The Netherlands.
Show others and affiliations
2004 (English)In: Applied bioinformatics, ISSN 1175-5636, Vol. 3, no 4, p. 205-217Article in journal (Refereed) Published
Abstract [en]

Multifactorial diseases present a significant challenge for functional genomics. Owing to their multiple compartmental effects and complex biomolecular activities, such diseases cannot be adequately characterised by changes in single components, nor can pathophysiological changes be understood by observing gene transcripts alone. Instead, a pattern of subtle changes is observed in multifactorial diseases across multiple tissues and organs with complex associations between corresponding gene, protein and metabolite levels. This article presents methods for exploratory and integrative analysis of pathophysiological changes at the biomolecular level. In particular, novel approaches are introduced for the following challenges: (i) data processing and analysis methods for proteomic and metabolomic data obtained by electrospray ionisation (ESI) liquid chromatography-tandem mass spectrometry (LC/MS); (ii) association analysis of integrated gene, protein and metabolite patterns that are most descriptive of pathophysiological changes; and (iii) interpretation of results obtained from association analyses in the context of known biological processes. These novel approaches are illustrated with the apolipoprotein E3-Leiden transgenic mouse model, a commonly used model of atherosclerosis. We seek to gain insight into the early responses of disease onset and progression by determining and identifying--well in advance of pathogenic manifestations of disease--the sets of gene transcripts, proteins and metabolites, along with their putative relationships in the transgenic model and associated wild-type cohort. Our results corroborate previous findings and extend predictions for three processes in atherosclerosis: aberrant lipid metabolism, inflammation, and tissue development and maintenance.

Place, publisher, year, edition, pages
Adis International Ltd. , 2004. Vol. 3, no 4, p. 205-217
Keywords [en]
Enzyme Commission, Kernel Principal Component Analysis, Normal Chow Diet, Bioanalytical Platform, Biomolecular Component
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:oru:diva-70889DOI: 10.2165/00822942-200403040-00002PubMedID: 15702951Scopus ID: 2-s2.0-22944488902OAI: oai:DiVA.org:oru-70889DiVA, id: diva2:1345866
Available from: 2019-08-26 Created: 2019-08-26 Last updated: 2019-08-28Bibliographically 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
Bioinformatics and Systems Biology

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 8 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