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Transcriptomic meta-analysis of multiple sclerosis and its experimental models.

Barbara B R Raddatz1, Florian Hansmann1, Ingo Spitzbarth1

  • 1Department of Pathology, University of Veterinary Medicine Hannover, Hannover, Germany ; Center for Systems Neuroscience, University of Veterinary Medicine Hannover, Hannover, Germany.

Plos One
|January 30, 2014
PubMed
Summary

This meta-analysis reveals that comparing differentially expressed genes (DEGs) alone is too restrictive for multiple sclerosis (MS) research. Gene Set Enrichment Analysis (GSEA) is a more effective approach for identifying key pathways in MS and its models.

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Area of Science:

  • Neuroscience
  • Immunology
  • Genomics

Background:

  • Numerous microarray studies on multiple sclerosis (MS) and its animal models exist.
  • Meta-analyses are valuable for integrating multi-study data to find common pathways.

Purpose of the Study:

  • To perform a meta-analysis of gene expression profiling studies in MS and experimental models.
  • To identify commonly affected genes and pathways using different analytical methods.

Main Methods:

  • Screened ArrayExpress, Gene Expression Omnibus, and PubMed for relevant microarray studies.
  • Included studies on MS, EAE (rat and mouse), TMEV-IDD, and TNFtg mice with available raw data.
  • Performed cross-study analysis using differentially expressed gene (DEG) comparisons and Gene Set Enrichment Analysis (GSEA).

Main Results:

  • Simple DEG intersection identified 12 macrophage-related genes common to MS, EAE, TMEV-IDD, and TNFtg.
  • GSEA revealed significantly more differentially regulated pathways, including coagulation and JAK/STAT signaling.

Conclusions:

  • Direct comparison of DEGs in meta-analyses can be overly conservative.
  • GSEA is a more powerful approach for discovering both known and novel candidate pathways in MS research.