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Meta-Analysis
. 2014 Jan 27;9(1):e86643.
doi: 10.1371/journal.pone.0086643. eCollection 2014.

Transcriptomic meta-analysis of multiple sclerosis and its experimental models

Affiliations
Meta-Analysis

Transcriptomic meta-analysis of multiple sclerosis and its experimental models

Barbara B R Raddatz et al. PLoS One. .

Abstract

Background: Multiple microarray analyses of multiple sclerosis (MS) and its experimental models have been published in the last years.

Objective: Meta-analyses integrate the information from multiple studies and are suggested to be a powerful approach in detecting highly relevant and commonly affected pathways.

Data sources: ArrayExpress, Gene Expression Omnibus and PubMed databases were screened for microarray gene expression profiling studies of MS and its experimental animal models.

Study eligibility criteria: Studies comparing central nervous system (CNS) samples of diseased versus healthy individuals with n >1 per group and publically available raw data were selected.

Material and methods: Included conditions for re-analysis of differentially expressed genes (DEGs) were MS, myelin oligodendrocyte glycoprotein-induced experimental autoimmune encephalomyelitis (EAE) in rats, proteolipid protein-induced EAE in mice, Theiler's murine encephalomyelitis virus-induced demyelinating disease (TMEV-IDD), and a transgenic tumor necrosis factor-overexpressing mouse model (TNFtg). Since solely a single MS raw data set fulfilled the inclusion criteria, a merged list containing the DEGs from two MS-studies was additionally included. Cross-study analysis was performed employing list comparisons of DEGs and alternatively Gene Set Enrichment Analysis (GSEA).

Results: The intersection of DEGs in MS, EAE, TMEV-IDD, and TNFtg contained 12 genes related to macrophage functions. The intersection of EAE, TMEV-IDD and TNFtg comprised 40 DEGs, functionally related to positive regulation of immune response. Over and above, GSEA identified substantially more differentially regulated pathways including coagulation and JAK/STAT-signaling.

Conclusion: A meta-analysis based on a simple comparison of DEGs is over-conservative. In contrast, the more experimental GSEA approach identified both, a priori anticipated as well as promising new candidate pathways.

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Conflict of interest statement

Competing Interests: Arno Kalkuhl and Ulrich Deschl are employed by Boehringer Ingelheim Pharma GmbH&Co KG. There are no patents, products in development or marketed products to declare. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.

Figures

Figure 1
Figure 1. Flow diagram of systematic database search.
ArrayExpress and Gene Expression Omnibus (GEO) were searched for microarray gene expression profiling studies with publically available raw data of multiple sclerosis and its experimental animal models with the search terms “Multiple sclerosis”, “EAE” and “Theiler virus”, “Cuprizone” and “Ethidium bromide”. For multiple sclerosis, additional information was gathered from PubMed database using the keywords “multiple sclerosis AND human AND microarray AND (brain OR spinal cord)”. All microarray studies published prior to October 24th 2013, the last time-point of database search, were screened. n = number of records.
Figure 2
Figure 2. Venn diagram of the list comparison of differentially expressed genes (DEGs).
Intersections comparing lists of DEGs in multiple sclerosis (MS), experimental autoimmune encephalomyelitis (EAE), Theiler’s murine encephalomyelitis virus-induced demyelinating disease (TMEV-IDD) and transgenic tumor necrosis factor-overexpressing mice (TNFtg). The numbers in the subsets represent the numbers of the comprised differentially expressed genes, and the grey boxes list the associated enriched biological modules. * = subsets without associated significantly enriched biological modules.
Figure 3
Figure 3. Gene set similarity map retrieved by Gene Set Enrichment Analysis (GSEA).
Pathways significantly enriched and positively correlated with disease versus control in multiple sclerosis, experimental autoimmune encephalomyelitis, Theiler’s murine encephalomyelitis virus-induced demyelinating disease and transgenic tumor necrosis factor-overexpressing mice as revealed by cross-study Gene Set Enrichment Analysis. The graph displays a similarity map of GO terms, retrieved by the leading edge analysis GSEA. The intensity of the colour gradient represents a measure for the relative overlap of genes in the respective GO terms, ranging from 100% overlap (dark green) to 0% overlap (white) of the leading edge genes within the GO terms on the x- and y- axis.
Figure 4
Figure 4. Venn diagram of differentially expressed genes within each gene signature.
Intersections of differentially expressed genes in experimental autoimmune encephalomyelitis (EAE), Theiler’s murine encephalomyelitis virus-induced demyelinating disease (TMEV-IDD) and transgenic tumor necrosis factor-overexpressing mice (TNFtg) within the gene signatures for the GO terms “T cell mediated immunity”, “immunoglobulin mediated immune response”, “positive regulation of apoptotic process” and “myelination”. The numbers in the intersections represent the absolute numbers of the comprised differentially expressed genes, and the grey boxes list the associated gene symbols.

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