Brain, Vol. 124, No. 10, 1927-1938,
October 2001
© 2001 Oxford University Press
Gene expression profiling of the nervous system in murine experimental autoimmune encephalomyelitis
1 Departments of Immunology and 2 Neurology, University of Rostock, Rostock and 3 The Clinical Research Group for Multiple Sclerosis and Neuroimmunology, Department of Neurology, University of Würzburg, Germany
Correspondence to:
Saleh M. Ibrahim, MD, Department of Immunology, University of Rostock, Schillingallee 70, 18055 Rostock, Germany E-mail: saleh.ibrahim{at}med.uni-rostock.de
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Abstract |
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Multiple sclerosis is thought to be a polygenic disease driven by dysregulation of the immune system leading to an autoimmune response against one or several antigens of cerebral white matter tissue. Experimental autoimmune encephalomyelitis (EAE) is a mouse model that is used to study the aetiology and pathogenesis of multiple sclerosis and new therapeutic approaches. We used oligonucleotide microarrays to determine gene expression profiles of the inflamed spinal cords of EAE mice at the onset and at the peak of the disease. Of the ~11 000 genes studied, 213 were regulated differentially and 100 showed consistent differential regulation throughout the disease. Inflammation resulted in a profile of increased gene expression of immune-related molecules, extracellular matrix and cell adhesion molecules and molecules involved in cell division and transcription, and differential regulation of molecules involved in signal transduction, protein synthesis and metabolism. Of the 104 genes with defined chromosomal locations, 51 mapped to known EAE-linked quantitative trait loci and as such are putative candidate genes for susceptibility to EAE.
EAE; multiple sclerosis; oligonucleotide microarrays
EAE = experimental autoimmune encephalomyelitis; IFN = interferon; IL = interleukin; MHC = major histocompatibility complex; PCR = polymerase chain reaction; QTL = quantitative trait locus/loci; R = receptor; TNF = tumour necrosis factor
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Introduction |
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TopAbstractIntroductionMaterial and methodsResultsDiscussionAcknowledgementsReferences |
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Experimental autoimmune encephalomyelitis (EAE) induced by myelin oligodendrocyte glycoprotein is a one of the best animal models of multiple sclerosis (Slavin et al., 1998
; Conlon et al., 1999; Gold et al., 2000). In particular, it is very similar to multiple sclerosis with regard to the chronicity of disease and the degree of myelination (Storch et al., 1998). As in multiple sclerosis, susceptibility to disease has been linked to the major histocompatibility complex (MHC) (H2 locus) (Encinas et al., 1996a). However, expression of the linked MHC haplotype is not necessarily sufficient to determine susceptibility to EAE, indicating important non-MHC genetic contributions. The disease is characterized by progressive ascending paralysis resulting from infiltration of the central nervous system by mononuclear cells, predominantly antigen-specific CD4+ and CD8+ T cells, and macrophages. Current models of the pathogenesis of the disease involve the generation of activated antigen-specific T and B cells, their migration to the nervous system, local reactivation by resident antigen-presenting cells, the activation of local glial and microglial cells, the secretion of chemokines, cytokines and other inflammatory mediators, e.g. complement, and NO and, eventually, the demyelination of neuronal axons (Lucchinetti et al., 2000).
The genetic dissection of the EAE susceptibility trait has been performed in a variety of mouse strains with the use of different antigens and immunization protocols and has led to the identification of several EAE-linked quantitative trait loci (QTL). Several of them overlap between mouse strains and also represent QTL for other murine autoimmune diseases (Holmdahl, 1998; Griffiths et al., 1999). However, identifying putative susceptibility genes in these QTL has not been feasible so far. We attempted to use gene expression profiling not only to establish a global profile of genes involved in disease pathogenesis, but also to explore the possibility of combining profiling with linkage analysis to identify new susceptibility genes in EAE.
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Material and methods |
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Animals, antigen, immunization and assessment of disease
C57Bl/6 mice were obtained from Harlan–Winklemann (Borchen, Germany) and kept under standard conditions at the animal facility of Rostock University. Mice aged 7 weeks were immunized subcutaneously with 150 µg of rat myelin oligodendrocyte glycoprotein peptide 35–55 in complete Freund's adjuvant. Mice received an intraperitoneal injection of 500 ng pertussis toxin on Day 1 after immunization. Clinical scores were assessed directly before immunization (Day 0) and thereafter daily until Day 28 after immunization. The severity of paresis was graded as follows: 0 = normal; 1 = flaccid tail; 2 = moderate paraparesis; 3 = severe paraparesis; 4 = tetraparesis. Mice were killed at the first signs of disease (onset of disease, Day 16) or after consistently exhibiting grade 3 paresis for more than 2 days (peak of disease, Day 22). Control mice were immunized with complete Freund's adjuvant only. Spinal cords were dissected and immediately transferred into Fast-prepRNA tubes (Bio101, Carlsbad, Calif., USA) with 500 µl lysis buffer (Qiagen, Hilden, Germany) for RNA preparation. All experiments were approved by the competent authorities, Ministry of Agriculture, of the state of Mecklenburg-Vorpommern, Germany.
Sample preparation, high-density oligonucleotide array hybridization and data analysis
All experiments involved three mice per group and their mRNA was pooled. Total RNA was extracted from homogenized spinal cords using an RNA extraction kit according to the manufacturer's instructions (Qiagen). RNA concentrations were determined spectrophotometrically at 260 nm. RNA probes were labelled according to the supplier's instructions (Affymetrix, Santa Clara, Calif., USA). Analysis of gene expression was carried out with the Mu11K array, which has a capacity of ~11 000 genes (Affymetrix). Hybridization and washing of the gene chips was done as described previously (Teague et al., 1999). Microarrays were analysed by laser scanning (Hewlett-Packard Gene Scanner) and the expression levels were calculated with commercially available software provided by Affymetrix. Data are given as fold increases in gene expression in the inflamed spinal cords (onset and peak) compared with expression in the normal spinal cords. A change in the level of expression for any gene was considered significant if it was more than fourfold.
Quantification of mRNA levels using kinetic PCR analysis
Differential expression analysis was confirmed by quantitative reverse transcription–polymerase chain reaction (RT-PCR) using the LightCycler (Roche, Mannheim, Germany) (Wittwer et al., 1997). We used the same mRNA pools for both microarray and LightCycler-PCR analysis. Briefly, 500 ng was reverse-transcribed with 100 ng random hexanucleotides (Gibco, Karlsruhe, Germany). For LightCycler PCR using gene-specific primers, ß-actin served as a positive amplification control and for additional external standardization. The LightCycler quantification programme was used to determine the cross point of each amplicon in order to quantify the cDNA content.
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Results |
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Analysis of gene expression in inflamed spinal cords of EAE mice
Oligonucleotide microarrays representing 11 000 genes [~6000 full-length genes and ~ 5000 ESTs (expressed sequence tags)] were used to determine the gene expression profiles of the inflamed spinal cords of EAE mice at the time of onset and the peak of clinical disease (Days 16 and 22 after immunization, respectively) in comparison with normal tissue. The onset and peak of disease were determined by the clinical evaluation of disease. The oligonucleotide microarray data were validated independently by the use of a quantitative RT-PCR method to analyse a subset of genes that were either down- or upregulated. In all cases, the PCR results confirmed that the genes were expressed differentially, as detected by the microarrays (Fig. 1
). Thus, despite being semiquantitative in nature, the microarray technique enabled us to identify differentially expressed genes with a high degree of validity. To identify the most relevant genes, the threshold of differential expression was set to fourfold (Zhu et al., 1998).
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Two hundred and thirteen genes showed more than fourfold differential expression from baseline. This was observed consistently at the onset and peak of disease in 100 genes, only at the onset in 45 genes and only at the peak of disease in 68 genes (Table 1
). No major changes between the onset and peak of disease were observed; only three genes (IgK constant region, annexin V and TMS-1) showed a change of more than fourfold between these two times. Most of the 158 genes observed had well-characterized full-length sequences reported in databases and only 55 were unknown (ESTs). One hundred and sixty-six of the 213 genes were upregulated and only 47 were downregulated. The profile of gene expression supported current disease models in which genes involved in antigen processing and presentation (i.e. MHC genes and proteasome) are highly upregulated and, together with genes encoding other immune-related molecules (e.g. complement components, cytokines and chemokines), constitute the largest group of differentially expressed genes (n = 72). Inflammation also resulted in a profile of increased expression of genes for extracellular matrix and cell adhesion molecules and molecules involved in cell division and death and transcription, and the differential regulation of molecules involved in signal transduction (18 upregulated, nine downregulated), protein synthesis and cell metabolism (12 upregulated, nine downregulated). Interestingly, almost all of the nervous system-related genes observed (13 out of 14) were downregulated (Table 1).
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Analysis of expression of genes mapping to EAE-linked loci
In this analysis we used gene expression profiling to explore the possibility of combining this technique with linkage analysis to identify new susceptibility genes for EAE. We analysed 104 differentially expressed genes with known chromosomal locations in relation to known murine EAE susceptibility loci. For this purpose, we assumed that linkage is observed within a 20 cM (centimorgan) distance from the peak of a disease locus, as suggested by Todd and colleagues (Todd et al., 1991
), i.e. an EAE QTL was defined as the interval covering 10 cM either side of the genetic marker showing the highest lod score, or as the interval reported in the reference given in the table. Fifty-one differentially expressed genes (49%) mapped to previously identified QTL that contribute to susceptibility to EAE or the severity of EAE in various strains and immunization protocols (Table 2). Since EAE-linked loci cover ~200 cM, i.e. 13.3% of the total mouse genome [which is estimated to span 1503 cM (Dietrich et al., 1996)], the distribution in our data set was significantly different from random distribution (P < 0.001, 
2-test), suggesting that putative susceptibility genes are among the genes that are expressed differentially in our data set.
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Discussion |
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How autoimmune dysregulation leads to tissue destruction in EAE remains unresolved despite significant work. An impediment to progress is the complexity of autoimmune processes, which involve multiple immune cell types, target cells and pathways. Despite considerable knowledge of the model, a global survey of gene expression, in particular of differentially expressed genes, is still lacking. In this study we used oligonucleotide microarrays to detect new EAE-associated genes contributing to earlier stages of disease. Roughly 3000 genes were expressed and 213 of these were expressed differentially. Most of them reflected the immune-mediated nature of EAE. However, although many of the classical elements of the immune system, e.g. complement components, MHC, Ig and adhesion molecules, were seen, proinflammatory cytokines such as tumour necrosis factor (TNF)-
, interleukin (IL)-6, IL-12 and interferons (IFNs) were not detected. Interestingly, the only upregulated integrin subunit was ß7, which has been shown recently to play a pivotal role in disease progression in myelin oligodendrocyte glycoprotein-induced EAE (Kanwar et al., 2000). We explored the possibility of using microarrays and linkage analysis to identify new susceptibility genes that may contribute to these stages of EAE. Indeed, when we analysed the differentially expressed genes in relation to known EAE susceptibility loci, 51 of the 104 (49.0%) genes with known chromosomal positions mapped to these loci, suggesting that our data set includes some putative susceptibility genes.
As expected, genes in the MHC (EAE1 locus) are among this group, confirming the central role of MHC molecules in disease susceptibility. Other upregulated genes that have been suggested as candidate susceptibility genes in linkage studies include those for IgK, the T-cell receptor ß subunit and C3. The influence of these genes on susceptibility to disease has been studied exhaustively, and mice lacking T cells or complement are resistant to EAE (Koh et al., 1992; Davoust et al., 1996; Elliot et al., 1996; Lyons et al., 1999). Only the genes for one cytokine, IFN-ß, and two chemokines, Scya-5 [RANTES (regulated upon activation, normal T-cell expressed and secreted chemokine)] and Scya-9, map to known EAE QTL. Interestingly, IFN-ß is highly upregulated in the inflamed spinal cord of EAE, probably reflecting an attempt to downregulate the inflammatory process at an early stage of disease. IFN-ß is currently a promising therapy for multiple sclerosis. Unexpectedly, apart from IFN-ß, the cytokines IFN-ß, TNF-, IL-1, IL-2, IL-4, IL-10 or IL-12 did not appear in our data set despite their well-documented role in disease pathogenesis (Olsson, 1995). Genes for cytokine receptors (R) were nevertheless featured (e.g. IL3R, the common cytokine 
receptor subunit, and TNFR2), probably reflecting the fact that the majority of cells in our sample were target cells. TNFR2 has been suggested as a susceptibility gene in linkage analysis in human multiple sclerosis patients (Croxford et al., 1997). The failure to detect several classical proinflammatory cytokines/chemokines and adhesion molecules may have had different causes, e.g. (i) the mRNA is produced by infiltrating cells that are in a minority within whole spinal cord; (ii) the expression kinetics and half-life of cytokine mRNAs may affect their detection, although the mRNAs of some cytokine regulatory, cytokine-induced proteins and chemokines were detected, e.g. IFN-related proteins [IFN-induced 15 kDa protein, IFN-inducible protein 1–8D, macrophage IFN-inducible protein IP-10, IFN- induced Mg11, MIG (monokine-induced by IFN-), IFN regulatory factor 1, mirf-5 (IFN regulatory factor 5) and mirf-7 (IFN regulatory factor 7)]; and (iii) the respective oligonucleotides are not yet present in available mouse DNA microarrays.
Our analysis identified a new set of genes as candidates for susceptibility to EAE (Table 2
). They include the genes for CD52, a molecule involved in the downregulation of the immune response, CD53, an adhesion and costimulatory molecule, and Fc- RI, another immune-related molecule. The gene for a macrophage-secreted protein, YM-1, also mapped to EAE quantitative trait loci (QTL), as did IFR1 (interferon regulatory factor 1), a transcription factor involved in IFN regulation and monocyte/macrophage differentiation. The involvement of IRF-1 in autoimmunity has been suggested through experiments using mutant mice lacking this molecule that were resistant to antigen-induced autoimmune disease (Tada et al., 1997). Miscellaneous genes that have not yet been linked to EAE and appear in our data set include the genes for acrogranin, a growth factor involved in germ cell differentiation, ADFP (adipose differentiation-related protein), Hox2.2 (Hoxb6), lissencephaly-1 protein and the gene for ß Ig-h3 (a transforming growth factor ß-inducible gene). Many genes (n = 109) have not yet been mapped, suggesting that the number of candidate genes may increase in the future. The new candidate genes should be of special interest as QTL overlap between different mouse strains and other murine autoimmune diseases. It will be the task of a future study to identify their anatomical localization (e.g. nerve cells versus infiltrating inflammatory cells) and to elucidate their biological role in the induction and progression of EAE by in situ hybridization, immunohistochemistry, knock-out technology and other functional assays.
In conclusion, our data establish a gene expression profile for the early stages of EAE and demonstrate that gene expression profiling could become a powerful way to identify new putative susceptibility genes in predefined QTL for autoimmune polygenic diseases.
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Acknowledgements |
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The work was supported by grants from the University of Rostock and the German Federal Ministry of Education and Research (BMBF) to S.M.I., A.R., D.K. and H.-J.T. and by SFB 581, project A1, to R.G.
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Received January 25, 2001. Revised April 30, 2001. Accepted May 3, 2001.
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