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Cross-platform expression microarray performance in a mouse model of mitochondrial disease therapy.

Zhe Zhang1, David L Gasser, Eric F Rappaport

  • 1Division of Biomedical Informatics, Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.

Molecular Genetics and Metabolism
|December 1, 2009
PubMed
Summary

Choosing the right gene expression microarray platform is crucial for metabolic disease research. Newer platforms offer advantages, but probe design and gene expression levels impact cross-platform data integration for pathway analysis.

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Area of Science:

  • Genomics and Bioinformatics
  • Metabolic Disease Research
  • Gene Expression Profiling

Background:

  • Microarray expression profiling is vital for understanding metabolic disease genetics.
  • Emerging gene expression microarray platforms offer updated content and cost efficiencies over older 3'-biased platforms.
  • Selecting the optimal platform for metabolic pathway analysis can be challenging due to platform variations.

Purpose of the Study:

  • To evaluate the practical strengths and weaknesses of four leading expression array platforms for metabolic disease research.
  • To assess the feasibility of integrating expression data from different platforms for biochemical pathway analyses.
  • To provide guidance for researchers in selecting appropriate microarray platforms.

Main Methods:

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  • Liver RNA from mice with coenzyme Q deficiency was analyzed using Affymetrix (430 2.0, Gene 1.0 ST, Exon 1.0 ST) and Illumina (Mouse WG-6) expression arrays.
  • Probes were re-mapped to the current mouse genome, and data were processed using R/Bioconductor.
  • Pathway analyses were conducted using KEGG Atlas and Gene Set Enrichment Analysis (GSEA).

    • Main Results:

    • Expression measurements showed general consistency across platforms, with probe location differences causing significant inter-platform variance.
    • Highly expressed genes exhibited better inter-platform reproducibility than low or intermediate expression level genes.
    • All platforms identified 'steroid biosynthesis' as the most down-regulated pathway following probucol treatment, indicating consistent differential expression patterns.

    Conclusions:

    • This study provides a guide for selecting gene expression microarray platforms for metabolic disease research.
    • Cross-platform integration of biochemical pathway expression data is feasible for well-annotated, highly expressed genes.
    • Gene-level data integration is limited by probe design and expression levels, necessitating advanced bioinformatics tools for robust cross-platform pathway analysis.