Brain, Vol. 126, No. 5, 1048-1057,
May 2003
© 2003 Guarantors of Brain
doi: 10.1093/brain/awg107
cDNA microarray analysis in multiple sclerosis lesions: detection of genes associated with disease activity
1 Department of Neurology, Medical University of Lodz, Lodz, Poland, 2 Serono Research Institute, Geneva, Switzerland and 3 Departments of Pathology (Neuropathology) and Neurology, Albert Einstein College of Medicine, New York, USA
Correspondence to: Krzysztof W. Selmaj, MD, PhD, Department of Neurology, Medical University of Lodz, Kopcinskiego Street 22, 90–153 Lodz, Poland E-mail: kselmaj{at}afazja.am.lodz.pl
Received September 1, 2002. Revised December 14, 2002. Accepted December 16, 2002.
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cDNA microarray analysis of the regions of pathologically proven different activity of multiple sclerosis lesions was performed. Major differences in gene expression (DGE) occurred between the lesion margin and lesion centre in active lesions studied (57 and 69 genes differentially expressed, respectively), whereas the margins and centres of silent lesions showed markedly reduced heterogeneity (only 11 and two genes differentially expressed, respectively). To compare differences between chronic active and silent lesions, we performed DGE comparison of the pooled data from both types of lesions. The major DGE occurred at the lesion margin, 156 (26; 5%), the greater number representing upregulated genes at the margin of active lesions (15%). Fourteen genes were found to be significantly upregulated in marginal versus central zones in active lesions examined. These genes comprised predominantly inflammation/immune-related factors. We also performed DGE analysis of pooled genes upregulated at the margin of active lesions and found that among the 50 genes showing differences, nine out of 14 were identified in the previous analysis of overlapping differentially expressed genes. Thus this microarray analysis has identified a novel set of genes associated with lesion activity in multiple sclerosis, many of them not previously linked with the disease.
Keywords: multiple sclerosis; brain tissue; differential gene expression; chronic active lesions; chronic silent lesions
Abbreviations: CCNA1= cyclin A1; DGE = differential gene expression; EAE = experimental autoimmune encephalomyelitis; EDDR1 = epithelial discoidin receptor 1; H&E = haematoxylin and eosin; HSP90 = heat shock protein 90; IFN
= interferon gamma; IL = interleukin; MAPKK1 = MAP kinase kinase 1; MyTI = myelin transcription factor I; NGF = nerve growth factor; RT–PCR = reverse transcriptase–polymerase chain reaction; TNF = tumour necrosis factor
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Introduction |
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Multiple sclerosis is a primary demyelinating disease of humans. The disease is characterized pathologically by disseminated regions of focal destruction of myelin and axonal loss. Inflammatory infiltrates occur throughout early, active lesions and at later stages of disease development are restricted to the lesion edge (Raine, 1994
). The lesion expands from the periphery suggesting that inflammatory activity at the lesion edge is instrumental in disease progression. Chronicity and progression of multiple sclerosis lesions is one of the most characteristic features of the disease. The composition of infiltrating cells comprises predominantly T lymphocytes and macrophages, and strongly suggests an immune basis for lesion pathogenesis. Several investigations in situ using immunocytochemistry (Gay et al., 1997; De Groot et al., 2001) and hybridization techniques (Liu et al., 2001) have demonstrated expression of a number of immune molecules system at the edge of multiple sclerosis lesions. The results of these studies have indicated that the predominant type of immune reaction in multiple sclerosis involves Th1-type cytokines (Steinman, 1996). This conclusion is supported by recent experiments on T cell clones derived from multiple sclerosis patients which demonstrated that interleukin-12 (IL-12) plays a central role in the initiation of inflammation (Karp et al., 2001). The growing understanding of mechanisms of immune reactions in multiple sclerosis has added hundreds of molecules to the simplified Th1/Th2 paradigm. It is known that the immune reaction is influenced not only by cytokine expression, but also by co-stimulatory molecules, chemokines, surface receptors, intracellular signalling molecules, transcription factors and cell cycle proteins. The complexity of the molecular interplay in the fine tuning of the final immune reaction in multiple sclerosis imposes severe limitations on standard immunoassays currently applied to pathological tissue.
cDNA microarray technology affords the simultaneous measurement of expression of thousands of genes thus enabling the identification of the gene activation patterns in a specific tissue at specific time points (Steinman, 2001). Microarray technology, together with similar high throughput sequencing of cDNA, has been only recently been applied to investigations of multiple sclerosis brain tissue. Initial applications have already led to the discovery of new transcripts in multiple sclerosis like osteopontin (Chabas et al., 2001).
In the present study, we have performed for the first time cDNA microarray analysis on multiple sclerosis brain tissue from the regions of the lesion with different activity, margin versus centre, and obtained from the lesions of different type, active and inactive. The analysis revealed that the activity of multiple sclerosis process associated with the lesion margin is linked with upregulation of several known immune-related genes as well as unique transcripts, which could contribute to the understanding of multiple sclerosis.
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Tissue
Brain tissue was obtained at early autopsy (<8 h post-mortem) from four multiple sclerosis patients. All multiple sclerosis patients fulfilled the Poser’s criteria for clinically definitive multiple sclerosis (Poser et al., 1983
). Patients were two males and two females. The mean age of the patients at the time of autopsy was 49 ± 19 years and the mean duration of disease was 18 ± 11 years. All patients were initially diagnosed as remitting–relapsing multiple sclerosis but, at the time of autopsy, they were in the secondary progressive phase although still with relapses occurring. None of the patients was receiving any disease-modifying medication and they were not on steroids for several months prior to autopsy. For gene expression analysis, tissue blocks with apparent multiple sclerosis plaques were dissected from each brain; one part of each plaque was fixed in 4% buffered formalin and embedded in paraffin and the other part was quick-frozen on dry ice and stored at –80°C. Paraffin sections from the fixed tissue were cut at 7 µm and stained with haematoxylin–eosin (H&E) for microscopic analysis. Histology was performed to characterize lesion type. Based on the H&E staining pattern, the frozen tissue blocks were dissected for further analysis of the less cellular lesion centre and the more active lesion edge with adjacent white matter.
Expression analysis
Total RNA was extracted from the frozen samples of CNS tissue using TRIZOL (Gibco, Life Technologies, Paisley, UK) according to the manufacturer’s protocol. The differential gene expression (DGE) analysis was performed using Atlas Glass Human 1.0 Human Broad-Coverage Microarrays (Clontech, Palo Alto, CA, USA). Each microarray covers 588 different gene cDNAs. Major groups of genes housed in the microarray include genes encoding transcription factors and transcription repressors, intracellular transducers, cell-cycle regulatory proteins, membrane receptors, heat shock and stress response proteins, growth factors, cytokines, neurotransmitters, chemokines, hormones, apoptosis- and cell death-associated proteins, DNA damage, synthesis and repair proteins, RNA synthesis, transport, processing and turnover proteins, and housekeeping genes. For the full list of the genes, refer to http://www.clontech.com/atlas/genelists/Hbroad.txt.
Isolation of total RNA, cDNA synthesis, labelling of cDNA and hybridization of cDNA probes to the Atlas Microarray were performed according to the manufacturer’s protocol (Clontech). Briefly, 2 µg of RNA was used to generate first-stranded cDNA in the presence of [32P]dATP. Atlas Microarrays were prehybridized at 68°C for 2 h and hybridized at 68°C for 16–20 h followed by four washings. Atlas Microarrays were exposed onto phosphoimager screens (BioRad Laboratories, Hercules, CA, USA). Imaging screens were scanned at a resolution of 50 µm using a BioRad Personal FX phosphoimager. The resultant 16-bit digital file was analysed using Arrayvision software (Imaging Research Inc., St. Catharines, Ontario, Canada). For each sample, we measured the pixel intensity of the 588 genes spotted in duplicate on the filter. The background signal was subtracted and an average pixel intensity for each pair of spots in the array was generated. All the microarrays used were derived from the same lot and reproducibility between arrays was about 90%.
Spot intensities between arrays were normalized to the average set of nine housekeeping genes. Data were analysed by DGE analysis software developed at the Serono Research Institute, Geneva, Switzerland. The gene was recognized as significantly upregulated/downregulated when the expression level ratio in two analysed samples was >2.0. To strengthen the significance of DGE, a ratio of 4.0 was considered the cut-off level for some experiments.
Polymerase chain reaction
Selected genes, interferon gamma (IFN), CD4, MAP kinase kinase 1 (MAPKK1) and cyclin A1 (CCNA1)—representing the highest fold change genes found in DGE analysis from either of the cellular compartment groups—were amplified by PCR with specifically designed pairs of primers. The primer sequences were: for the IFN primer pair (5'–3') GCATCGTTTTGGGTTCTCTTGGC and CAGCATCTGA CTCCTTTTTCGC; for the CD4 primer pair (5'–3') GTG GAGTTCAAAATAGACATCGTG and CAGCACCCAC ACCGCCTTCTCCCGCTT; for the MAPKK1 primer pair (5'–3') TCATGAGTGCAACTCTCCGTACATC and GGA GTTGGCCATGGAGTCGAT; for the CCNA1 primer pair (5'–3') CGGACACATAGAAAGATAACGACGG and AA AAGCATAGCTGCTGTTCCTACGA; and for the ß-actin primer pair (5'–3') GTGGGGCGCCCCAGGCACCA and CTCCTTAATGTCACGCACGATTTC. PCR amplification was performed under the following conditions: 35 cycles at 94°C for 30 s, 65°C for 30 s and 72°C for 1 min, with Taq polymerase (Gibco, Life Technologies, Paisley, UK). PCR analysis of cDNA of upregulated genes was run in parallel with housekeeping gene amplification and analysed in a semi-quantitative manner.
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Histological typing of lesions
The multiple sclerosis lesions sampled possessed both inflammatory and demyelinating features. In all lesions studied, the inflammatory infiltrates were restricted to the edge of lesions. Accordingly, the lesions were classed as chronic although significant variation in the inflammatory component was observed (see below). The samples were analysed in two groups, one consisting of marginal zone/adjacent white matter containing inflammatory cell infiltrates and the other of the less cellular gliotic central zone of the lesion. Two of the plaques (3 and 4) had numerous infiltrating cells in the marginal zone, typical of chronic active lesions, while the other two (1 and 2) displayed less evidence of inflammation, fulfilling the criteria for chronic inactive/silent multiple sclerosis plaques (Lassmann et al., 1998
) (see Fig. 1). In order to detect differences in gene expression pattern related to ongoing activity of the multiple sclerosis lesion, comparative DGE analysis was performed between marginal and central zones of plaques and between chronic active and chronic silent lesions.
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Quantitative DGE analysis between margin and centre of the lesions
The major DGE between marginal and central regions occurred in both chronic active lesions, 3 and 4 (57 and 69 genes differentially expressed, respectively), whereas lesions 1 and 2 showed markedly reduced heterogeneity (only 11 and two genes differentially expressed, respectively) (see Fig. 2). Interestingly, the majority of the differentially expressed genes in lesions 3 and 4 comprised genes overexpressed at the margin of the plaque (82% and 65%, respectively), whereas for lesions 1 and 2 (chronic silent), the DGE was comparable between the periphery and centre (45% versus 55% and 50% versus 50%). Furthermore, analysis of the mean fold difference in gene expression between the marginal versus central zones showed higher overall values in lesions 3 and 4 compared with lesions 1 and 2.
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Quantitative DGE analysis between active and silent lesions
Since the previous analysis showed significant levels of similarity between both samples of chronic active lesions (3 and 4) and between both chronic silent lesions (1 and 2), we performed DGE comparison of pooled data from lesions 3 and 4 versus that from lesions 1 and 2 in order to analyse differences between active and inactive plaques. Data pooling of samples was performed by computer software and was based on normalization to the expression levels of nine housekeeping genes. This comparative analysis confirmed the presence of significant heterogeneity of the gene profile between chronic active versus inactive plaques, both at the lesion edge and in the central zone (Fig. 3). The majority of differentially expressed genes occurred at the lesion margin, 156 (26.5%), of which the largest part represented upregulated genes at the margin of active lesions (15%). Less striking differences in DGE were observed between the lesion centres of chronic active and chronic silent lesions. However, the greatest difference was still represented by genes upregulated in active lesions. Comparative analysis of the mean fold difference in gene expression also differed between chronic active and inactive plaques, which revealed significantly higher gene expression levels in chronic active lesions (Fig. 3). Interestingly, in this comparison, both marginal and central zones showed similar differences between two different multiple sclerosis lesion types. This may suggest that heterogeneity of the gene expression profile in different types of multiple sclerosis plaques might not be restricted to marginal zones, but may involve the entire lesion area.
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Qualitative DGE between active and silent chronic lesions
In order to identify qualitative differences of the DGE between the chronic active lesions (3 and 4) and the chronic inactive lesions (1 and 2), we performed a search for overlapping differentially expressed genes in both of the active and silent lesions. The search yielded 14 genes, which were significantly differentially expressed in marginal versus central plaque zones that were present in DGE analysis in both lesions 3 and 4 (Table 1). Interestingly, these genes were only in the group showing significantly higher expression in the marginal zone; no gene overlap was noted in genes showing higher representation in the lesion centre. Although only found to be upregulated in the marginal zone, the overlapping genes represented more then 20% of all differentially expressed genes found in lesions 3 and 4, suggesting a significant level of similarity between the two lesions. These overlapping genes of higher expression could be grouped to several pools according to known gene product function, representing both intra- and extracellular-acting proteins (Table 1). Of note is that this list of genes consists mainly of inflammation-related factors. Similar analysis of chronic silent lesions, 1 and 2, yielded no gene overlap—neither at the margin nor in the lesion centre. Reverse transcriptase–polymerase chain reaction (RT–PCR) analysis was performed for the selected genes (CD4, IFN
, MAPKK1 and CCNA1) to confirm their differential expression in lesions 3 and 4 margins versus centres. Tested genes showed significant overexpression in lesion margins versus lesion centres for both 3 and 4, which correlated with microarray analysis (data not shown).
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To analyse further DGE between active versus non-active lesions, we pooled genes which showed higher expression in the marginal zones of both active lesions 3 and 4, and compared them with the pooled genes of higher expression in the marginal zones of inactive lesions, 1 and 2. The list of genes differentially higher expressed in the two active lesions is shown in Table 2. To strengthen the significance of DGE analysis, we applied the cut-off fold increase at 4.0. Interestingly, among the 50 genes expressed higher in marginal zones of active lesions, nine out of 14 were identified in the previous analysis of the overlapping differentially expressed genes in both of the active and silent lesions, perhaps indicating their significance in immune mechanisms in multiple sclerosis. The majority of the remaining 41 genes also encoded inflammatory/immune mediators. Similar DGE analysis of pooled genes expressed higher in the centres of active and inactive lesions showed only 15 genes (Table 3).
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Discussion
Lesion activity in the chronic multiple sclerosis plaque is well known to be associated with the lesion edge (Raine, 1994
). In most cases, the centres of chronic lesions lack activity, there are no inflammatory infiltrates and the astrocytic reaction leads to glial scarring. Therefore, pathological events differ in the centre and at the edge of the chronic lesion (Raine, 1994; Lassmann et al., 1998). Lesion activity is not always restricted to the marginal zone and may extend into adjacent white matter since inflammatory infiltrates and reactive microglial cells may be present in normal appearing white matter (Gobin et al., 2001). Using MRI with magnetization transfer, it has been shown that tissue destruction in multiple sclerosis can be detected outside focal lesions in normal appearing white matter (Guo et al., 2001; Siger-Zajdel and Selmaj, 2001). Based on these findings, we decided to compare the pattern of gene expression in the silent lesion centre with the marginal zone of lesion together with adjacent white matter. In chronic active lesions, we found significant DGE between the lesion centre and the lesion margin with 13.2% of investigated genes being upregulated in the margin and only 5.7% of genes upregulated in the centre. The similarity in DGE analysis between the centre and the margin in silent lesions revealed remarkably less evidence of a difference in gene number and expression ratio. These results clearly indicate that, in chronic silent lesions, the less different pattern of gene expression in the centre and the lesion margin than that showing in chronic active lesions corresponds well to the patterns of pathological activity.
We next compared DGE between chronic active and chronic silent lesions. As expected, we found significant differences in gene expression pattern in the marginal zones of active and silent lesions, which also corresponded well to the differences in disease activity between these two types of lesions. Fifteen percent of the investigated genes were upregulated at the margin of active lesions. Unexpectedly, we found that 11% of genes were also upregulated in the centre of active lesions, indicating that at this stage of lesion development, the centre is transcriptionally active. The qualitative DGE was based on the analysis of frequency distribution in tissue samples and close attention was paid to the detection of overlapping genes in matching samples. This analysis revealed 14 overlapping genes that were upregulated at the margin of chronic active lesions. This finding was supported by results from DGE analyses between pooled genes at marginal zones of active versus silent lesions. Of 50 genes displaying higher expression in active lesions, nine were identified in the frequency distribution analyses. All of these genes were related to inflammation and many of their protein products have been demonstrated by immunohistochemistry in active multiple sclerosis lesions (Raine, 1994; Cannella and Raine, 1995; Lassmann et al., 1998). The finding of upregulation of genes for CD4 antigens and IFN at the margin of active lesions validated the current microarray DGE analysis, since CD4 and IFN represent the most abundantly expressed proteins at the lesion edge and in adjacent white matter (Raine, 1994; Cannella and Raine, 1995; Woodroofe et al., 1986; Simpson et al., 2000). However, our analysis also revealed a number of novel genes to be overexpressed in samples from the active lesion margin. These include a group of genes encoding signalling molecules:
(i) MAPKK1, which demonstrated the second highest fold increase in expression and has been shown to be involved in T cell activation signalling (Lee et al., 1999), as well as in the cellular death pathway (Chen et al., 2001);
(ii) Caspase 9, an apoptotic protease involved in downstream signalling of mitochondria-derived factors, has been implicated in oligodendrocyte death in multiple sclerosis lesions (Soane et al., 2001);
(iii) Cbl-b, a negative regulator of receptor clustering and raft aggregation in T cells (Krawczyk et al., 2000), influences the CD28 dependence of T cell activation (Chiang et al., 2000). Mice deficient in Cbl-b are highly susceptible to experimental autoimmune encephalomyelitis (EAE) and it has been suggested that dysregulation of Cbl-b signalling pathways might contribute to autoimmunity in multiple sclerosis (Bachmaier et al., 2000; Chiang et al., 2000);
(iv) Epithelial discoidin receptor 1 (EDDR1), recently shown to be upregulated in dendritic cells derived from CD14+ monocytes (Lapteva et al., 2001), has not yet been documented in CNS tissue;
(v) Heat shock protein 90 (HSP90), a molecule involved in protein chaperoning, might have several functions in multiple sclerosis lesions ranging from myelin repair (Brosnan et al., 1996) to antigen presentation (Basu et al., 2001).
We would also like to draw attention to the presence of upregulated gene for SL cytokine (FLT3 ligand), a potent haematopoietic cytokine that affects growth and differentiation of progenitor and stem cells (Pulendran et al., 1999; McKenna, 2001). NGF has been shown by some to induce oligodendrocyte death in vitro (Casaccia-Bonnefil et al., 1996), although NGF receptor has not been demonstrated thus far to be upregulated on oligodendrocytes in multiple sclerosis lesions (Valdo et al., 2002). Of particular interest was the upregulation of adenosine A1 receptor in marginal zone samples of chronic active lesions. Adenosine, an endogenous purine nucleoside, inhibits IL-12 and independently increases IL-10 production (Hasko et al., 2000). Particularly novel was the group of cell cycle proteins with the demonstration of upregulation of zinc-finger DNA binding protein. Myelin transcription factor I (MyTI) is a zinc-dependent DNA binding protein (Kim and Hudson, 1992) which is integral during early stages of oligodendrocyte development and myelin production. MyTI mRNA transcripts are expressed at high levels in oligodendrocyte progenitors in comparison to differentiated oligodendrocytes (Armstrong et al., 1995). The enhanced presence of oligodendrocyte progenitors is well established at the edge of active multiple sclerosis lesions (Blakemore and Keirstead, 1999; Chang et al., 2000). Interestingly, we also found several immune-related genes in the centre of active lesions, perhaps indicating the presence of protracted inflammatory activity in this region. Alternatively, these genes might act as immunoregulatory agents rather than proinflammatory mediators, as shown in the other systems (Wensky et al., 2001).
Prior to this study, DGE analysis in multiple sclerosis had been reported on tissue obtained from a single case of primary progressive multiple sclerosis (Whitney et al., 1999). This analysis revealed 62 differentially expressed genes in active lesions compared with the gene expression profile of normal white matter. Of these 62 genes, 29 were overexpressed in more than one experiment. A parallel analysis of a normalized cDNA library prepared from mRNA obtained from the same multiple sclerosis tissue revealed the presence of 54 cDNAs that were associated with immune activation and autoantigens of other autoimmune disorders. More recent data were derived from a large-scale sequencing analysis of non-normalized cDNA brain libraries obtained from three multiple sclerosis tissue specimens (two libraries) and one non-pathological brain (Chabas et al., 2001). This analysis yielded 54 genes that showed increased expression in two multiple sclerosis libraries. The highest average clones and fold-difference yielded osteopontin, 
B-crystallin, an inducible heat shock protein, prostaglandin D synthase, prostatic binding protein, ribosomal and brain-derived genes, and KIAA genes. The most recent cDNA microarray analysis of multiple sclerosis tissue (Lock et al., 2002) compared acute versus silent lesions and revealed increased transcripts of several genes encoding inflammatory molecules associated with lesion activity. In contrast, the present DGE analysis is the first to directly compare different regions of multiple sclerosis lesions from lesions displaying different activity from the same individuals.
Several immune/inflammatory related genes were not seen to be upregulated in this study. Multiple sclerosis lesions demonstrate enormous heterogeneity in terms of development, inflammation intensity, localization, growth and degree of repair. The sampling of lesions of different ages might result in the detection of gene expression related to different stages of development. In recently published gene expression profiles in EAE, a number of classical proinflammatory molecules were also not detected (Ibrahim et al., 2001). Large-scale throughput microarray analysis using hundreds of multiple sclerosis samples might overcome this problem, but will require great effort and extensive access to multiple sclerosis tissue suitable for DGE.
To date, whole genome screen analysis in multiple sclerosis patients has not identified multiple sclerosis-specific genes, but has highlighted several loci potentially associated with multiple sclerosis (Ebers et al., 1996; Multiple Sclerosis Genetics Group, 1996; Sawcer et al., 1996; Chataway et al., 1998). It is of interest that one of the genes identified in this study was associated with the margin of active chronic lesions: EDDR1, the human genome locus of which is located in 6p21.3, the region most highlighted by the genome screen analysis in multiple sclerosis (Ebers et al., 1996; Multiple Sclerosis Genetics Group, 1996; Sawcer et al., 1996; Chataway et al., 1998). Further studies are warranted to unravel the potential role of the EDDR1 gene in genetic susceptibility to multiple sclerosis.
In conclusion, our DGE analysis has identified a new set of genes associated with lesion activity in chronic multiple sclerosis, which are expressed during lesion progression. Most of these genes were immune-related, thus indicating perhaps that expansion of the chronic multiple sclerosis lesion depends upon protracted immune/inflammatory reactions.
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Acknowledgements |
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This work was supported in part by grant from KBN nr 4 P05A 005 19 to M.P.M. and HHS grants NS 11920 and NS 08952 to C.S.R.
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References |
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|---|
Armstrong RC, Kim JG, Hudson LD. Expression of myelin transcription factor I (MyTI), a ‘zinc-finger’ DNA-binding protein, in developing oligodendrocytes.Glia 1995; 14: 303–21.[CrossRef][ISI][Medline]
Bachmaier K, Krawczyk C, Kozieradzki I, Kong YY, Sasaki T, Oliveira-dos-Santos A, et al. Negative regulation of lymphocyte activation and autoimmunity by the molecular adaptor Cbl-b. Nature 2000; 403: 211–6.[CrossRef][Medline]
Basu S, Binder RJ, Ramalingam T, Srivastava PK. CD91 is a common receptor for heat shock proteins gp96, hsp90, hsp70, and calreticulin. Immunity 2001; 14: 303–13.[CrossRef][ISI][Medline]
Blakemore WF, Keirstead HS. The origin of remyelinating cells in the central nervous system. J Neuroimmunol 1999; 98: 69–76.[CrossRef][ISI][Medline]
Brosnan CF, Battistini L, Gao Y-L, Raine CS, Aquino DA. Heat shock proteins and multiple sclerosis. J Neuropathol Exp Neurol 1996; 55: 389–402.[ISI][Medline]
Cannella B, Raine CS. The adhesion molecule and cytokine profile of multiple sclerosis lesions. Ann Neurol 1995; 37: 424–35.[CrossRef][ISI][Medline]
Casaccia-Bonnefil P, Carter BD, Dobrowsky RT, Chao MV. Death of oligodendrocytes mediated by the interaction of nerve growth factor with its receptor p75. Nature 1996; 383: 716–9.[CrossRef][Medline]
Chabas D, Baranzini SE, Mitchell D, Bernard CC, Rittling SR, Denhardt DT, et al. The influence of the proinflammatory cytokine, osteopontin, on autoimmune demyelinating disease. Science 2001; 294: 1731–5.
Chang A, Nishiyama A, Peterson J, Prineas J, Trapp BD. NG2-positive oligodendrocyte progenitor cells in adult human brain and multiple sclerosis lesions. J Neurosci 2000; 20: 6404–12.
Chataway J, Feakes R, Coraddu F, Gray J, Deans J, Fraser M, et al. The genetics of multiple sclerosis: principles, background and updated results of the United Kingdom systematic genome screen. Brain 1998; 121: 1869–87.
Chen J, Fujii K, Zhang L, Roberts T, Fu H. Raf-1 promotes cell survival by antagonizing apoptosis signal-regulating kinase 1 through a MEK-ERK independent mechanism. Proc Natl Acad Sci USA 2001; 98: 7783–8.
Chiang YJ, Kole HK, Brown K, Naramura M, Fukuhara S, Hu RJ, et al. Cbl-b regulates the CD28 dependence of T-cell activation. Nature 2000; 403: 216–20.[CrossRef][Medline]
DeGroot CJ, Bergers E, Kamphorst W, Ravid R, Polman CH, Barkhof F, et al. Post-mortem MRI-guided sampling of multiple sclerosis brain lesions: increased yield of active demyelinating and (p)reactive lesions. Brain 2001; 124: 1635–45.
Ebers GC, Kukay K, Bulman DE, Sadovnick AD, Rice G, Anderson C, et al. A full genome search in multiple sclerosis. Nature Genet 1996; 13: 472–6.[CrossRef][ISI][Medline]
Gay FW, Drye TJ, Dick GW, Esiri MM. The application of multifactorial cluster analysis in the staging of plaques in early multiple sclerosis. Identification and characterization of the primary demyelinating lesion. Brain 1997; 120: 1461–83.
Gobin SJ, Montagne L, Van Zutphen M, Van Der Valk P, Van Den Elsen PJ, De Groot CJ. Upregulation of transcription factors controlling MHC expression in multiple sclerosis lesions. Glia 2001; 36: 68–77.[CrossRef][ISI][Medline]
Guo AC, Jewells VL, Provenzale JM. Analysis of normal-appearing white matter in multiple sclerosis: comparison of diffusion tensor MR imaging and magnetization transfer imaging. AJNR Am J Neuroradiol 2001; 22: 1893–900.
Hasko G, Kuhel DG, Chen JF, Schwarzschild MA, Deitch EA, Mabley JG, et al. Adenosine inhibits IL-12 and TNF-[] production via adenosine A2a receptor-dependent and independent mechanisms. FASEB J 2000; 14: 2065–74.
Ibrahim SM, Mix E, Bottcher T, Koczan D, Gold R, Rolfs A, et al. Gene expression profiling of the nervous system in murine experimental autoimmune encephalomyelitis. Brain 2001; 124: 1927–38.
Karp CL, van Boxel-Dezaire AH, Byrnes AA, Nagelkerken L. Interferon-ß in multiple sclerosis: altering the balance of interleukin-12 and interleukin-10? Curr Opin Neurol 2001; 14: 361–8.[CrossRef][ISI][Medline]
Kim JG, Hudson LD. Novel member of the zinc finger superfamily: C2-HC finger that recognizes a glia-specific gene. Mol Cell Biol 1992; 12: 5632–9.
Krawczyk C, Bachmaier K, Sasaki T, Jones GR, Snapper BS, Bouchard D, et al. Cbl-b is a negative regulator of receptor clustering and raft aggregation in T cells. Immunity 2000; 13: 463–73.[CrossRef][ISI][Medline]
Lapteva N, Ando Y, Nieda M, Hohjoh H, Okai M, Kikuchi A, et al. Profiling of genes expressed in human monocytes and monocyte-derived dendritic cells using cDNA expression array. Br J Haematol 2001; 114: 191–7.[CrossRef][ISI][Medline]
Lassmann H, Raine CS, Antel J, Prineas JW. Immunopathology of multiple sclerosis: report on an international meeting held at the Institute of Neurology of the University of Vienna. J Neuroimmunol 1998; 86: 213–7.[CrossRef][ISI][Medline]
Lee IH, Li WP, Hisert KB, Ivashkiv LB. Inhibition of interleukin 2 signaling and signal transducer and activator of transcription (STAT)5 activation during T cell receptor-mediated feedback inhibition of T cell expansion. J Exp Med 1999; 190: 1263–74.
Liu JS, Zhao ML, Brosnan CF, Lee SC. Expression of inducible nitric oxide synthase and nitrotyrosine in multiple sclerosis lesions. Am J Pathol 2001; 158: 2057–66.
Lock C, Hermans G, Pedotti R, Brendolan A, Schadt E, Garren H, et al. Gene-microarray analysis of multiple sclerosis lesions yields new targets validated in autoimmune encephalomyelitis. Nat Med 2002; 8: 500–8.[CrossRef][ISI][Medline]
McKenna HJ. Role of hematopoietic growth factors/flt3 ligand in expansion and regulation of dendritic cells. Curr Opin Hematol 2001; 8: 149–54.[CrossRef][ISI][Medline]
Multiple Sclerosis Genetics Group. A complete genomic screen for multiple sclerosis underscores a role for the major histocompatibility complex. Nature Genet 1996; 13: 469–71.[CrossRef][ISI][Medline]
Poser CM, Paty DW, Scheinberg L, McDonald WI, Davis FA, Ebers GC, et al. New diagnostic criteria for multiple sclerosis: guidelines for research protocols. Ann Neurol 1983; 13: 227–31.[CrossRef][ISI][Medline]
Pulendran B, Smith JL, Caspary G, Brasel K, Pettit D, Maraskovsky E, et al. Distinct dendritic cell subsets differentially regulate the class of immune response in vivo. Proc Natl Acad Sci USA 1999; 96: 1036–41.
Raine CS. The Dale E. McFarlin Memorial Lecture: the immunology of the multiple sclerosis lesion. Ann Neurol 1994; 36 Suppl: S61–72.
Sawcer S, Jones HB, Feakes R, Gray J, Smaldon N, Chataway J, et al. A genome screen in multiple sclerosis reveals susceptibility loci on chromosome 6p21 and 17q22. Nature Genet 1996; 13: 464–8.[CrossRef][ISI][Medline]
Siger-Zajdel M, Selmaj K. Magnetization transfer ratio analysis of normal appearing white matter in patients with familial and sporadic multiple sclerosis. J Neurol Neurosurg Psychiatry 2001; 71: 752–6.
Simpson JE, Newcombe J, Cuzner ML, Woodroofe MN. Expression of the interferon-gamma-inducible chemokines IP-10 and Mig and their receptor, CXCR3, in multiple sclerosis lesions. Neuropathol Appl Neurobiol 2000; 26: 133–42.[CrossRef][ISI][Medline]
Soane L, Cho HJ, Niculescu F, Rus H, Shin ML. C5b-9 terminal complement complex protects oligodendrocytes from death by regulating Bad through phosphatidylinositol 3-kinase/Akt pathway. J Immunol 2001; 167: 2305–11.
Steinman L. Multiple sclerosis: a coordinated immunological attack against myelin in the central nervous system. Cell 1996; 85: 299–302.[CrossRef][ISI][Medline]
Steinman L. Gene microarrays and experimental demyelinating disease: a tool to enhance serendipity. Brain 2001; 124: 1897–9.
Valdo P, Stegagno C, Mazzucco S, Zuliani E, Zanusso G, Moretto G, et al. Enhanced expression of NGF receptors in multiple sclerosis lesions. J Neuropathol Exp Neurol 2002; 61: 91–8.[ISI][Medline]
Wensky A, Marcondes MC, Lafaille JJ. The role of IFN- in the production of Th2 subpopulations: implications for variable Th2-mediated pathologies in autoimmunity. J Immunol 2001; 167: 3074–81.
Whitney LW, Becker KG, Tresser NJ, Caballero-Ramos CI, Munson PJ, Prabhu VV, et al. Analysis of gene expression in multiple sclerosis lesions using cDNA microarrays. Ann Neurol 1999; 46: 425–8.[CrossRef][ISI][Medline]
Woodroofe MN, Bellamy AS, Feldmann M, Davison AN, Cuzner ML. Immunocytochemical characterization of the immune reaction in the central nervous system in multiple sclerosis. Possible role for microglia in lesion growth. J Neurol Sci 1986; 74: 135–52.[CrossRef][ISI][Medline]
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  J. Olesen, M. G Baker, T. Freund, M. di Luca, J. Mendlewicz, I. Ragan, and M. Westphal Consensus document on European brain research J. Neurol. Neurosurg. Psychiatry, August 1, 2006; 77(suppl_1): i1 - i49. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 W. Lin, A. Kemper, J. L. Dupree, H. P. Harding, D. Ron, and B. Popko Interferon-{gamma} inhibits central nervous system remyelination through a process modulated by endoplasmic reticulum stress Brain, May 1, 2006; 129(5): 1306 - 1318. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 R P Lisak, J A Benjamins, B Bealmear, B Yao, S Land, L Nedelkoska, and D Skundric Differential effects of Th1, monocyte/macrophage and Th2 cytokine mixtures on early gene expression for immune-related molecules by central nervous system mixed glial cell cultures Multiple Sclerosis, April 1, 2006; 12(2): 149 - 168. [Abstract] [PDF]
|
|
|
|
|
|
C. Stadelmann, S. Ludwin, T. Tabira, A. Guseo, C. F. Lucchinetti, L. Leel-Ossy, A. T. Ordinario, W. Bruck, and H. Lassmann Tissue preconditioning may explain concentric lesions in Balo's type of multiple sclerosis Brain, May 1, 2005; 128(5): 979 - 987. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
B. Bielekova and R. Martin Development of biomarkers in multiple sclerosis Brain, July 1, 2004; 127(7): 1463 - 1478. [Abstract] [Full Text] [PDF]
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|
W. Sun, U. Popat, G. Hutton, Y. C. Q. Zang, R. Krance, G. Carrum, G. A. Land, H. Heslop, M. Brenner, and J. Z. Zhang Characteristics of T-cell receptor repertoire and myelin-reactive T cells reconstituted from autologous haematopoietic stem-cell grafts in multiple sclerosis Brain, May 1, 2004; 127(5): 996 - 1008. [Abstract] [Full Text] [PDF]
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M Bradl and R Hohlfeld Molecular pathogenesis of neuroinflammation J. Neurol. Neurosurg. Psychiatry, October 1, 2003; 74(10): 1364 - 1370. [Abstract] [Full Text] [PDF]
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|
 |
|
 Searching for Activity: Microarrays in MS Journal Watch Neurology, July 24, 2003; 2003(724): 6 - 6. [Full Text]
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