title: Phenotype characterisation using integrated gene transcript, protein and metabolite profiling.
abstract: 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.
authors.affiliation: Beyond Genomics Inc., 40 Bear Hill Road, Waltham, MA 02451, USA.
authors.forename: Matej
authors.initials: M
authors.lastname: Oresic
authors.forename: Clary B
authors.initials: CB
authors.lastname: Clish
authors.forename: Eugene J
authors.initials: EJ
authors.lastname: Davidov
authors.forename: Elwin
authors.initials: E
authors.lastname: Verheij
authors.forename: Jack
authors.initials: J
authors.lastname: Vogels
authors.forename: Louis M
authors.initials: LM
authors.lastname: Havekes
authors.forename: Eric
authors.initials: E
authors.lastname: Neumann
authors.forename: Aram
authors.initials: A
authors.lastname: Adourian
authors.forename: Stephen
authors.initials: S
authors.lastname: Naylor
authors.forename: Jan
authors.initials: J
authors.lastname: van der Greef
authors.forename: Thomas
authors.initials: T
authors.lastname: Plasterer
mesh_terms: D000465:Algorithms; D000818:Animals; D002478:Cells, Cultured; D003198:Computer Simulation; D005786:Gene Expression Regulation / Q000502:physiology; D008099:Liver / Q000378:metabolism*; D013058:Mass Spectrometry / Q000379:methods*; D051379:Mice; D008954:Models, Biological; D010449:Peptide Mapping / Q000379:methods*; D010641:Phenotype; D020543:Proteome / Q000737:chemistry / Q000235:genetics / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*; D018511:Systems Integration; D014157:Transcription Factors / Q000737:chemistry / Q000235:genetics / Q000378:metabolism*
chemical_list: D020543:Proteome; D014157:Transcription Factors