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Related Experiment Videos

Phenotype characterisation using integrated gene transcript, protein and metabolite profiling.

Matej Oresic1, Clary B Clish, Eugene J Davidov

  • 1Beyond Genomics Inc., 40 Bear Hill Road, Waltham, MA 02451, USA.

Applied Bioinformatics
|February 11, 2005
PubMed
Summary
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This study introduces novel methods for analyzing multifactorial diseases by integrating gene, protein, and metabolite data. These approaches help identify early disease markers and understand complex biological changes.

Area of Science:

  • Genomics
  • Proteomics
  • Metabolomics
  • Systems Biology

Background:

  • Multifactorial diseases pose challenges for traditional functional genomics due to complex, multi-component effects.
  • Understanding disease pathophysiology requires analyzing changes across multiple tissues and biomolecular levels (genes, proteins, metabolites).

Purpose of the Study:

  • To present novel methods for exploratory and integrative analysis of pathophysiological changes at the biomolecular level.
  • To develop approaches for processing and analyzing proteomic and metabolomic data (ESI LC/MS).
  • To conduct association analyses of integrated omics patterns and interpret results within biological contexts.

Main Methods:

  • Exploratory and integrative analysis of omics data (gene, protein, metabolite).

Related Experiment Videos

  • Data processing and analysis for electrospray ionization (ESI) liquid chromatography-tandem mass spectrometry (LC/MS).
  • Association analysis to identify patterns descriptive of pathophysiological changes, using the apolipoprotein E3-Leiden transgenic mouse model of atherosclerosis.

    • Main Results:

    • Identification of integrated gene, protein, and metabolite patterns associated with early disease onset in atherosclerosis.
    • Corroboration of previous findings and extension of predictions for lipid metabolism, inflammation, and tissue development in atherosclerosis.
    • Discovery of molecular signatures preceding overt pathogenic manifestations.

    Conclusions:

    • The developed methods enable comprehensive analysis of multifactorial diseases by integrating multi-omics data.
    • This integrative approach provides insights into early disease mechanisms and identifies potential biomarkers.
    • The study advances the understanding of atherosclerosis pathogenesis through systems biology approaches.