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Genomics in multiple sclerosis—Current state and future directions
Journal of Neuroimmunology 187 (2007) 1 – 8 www.elsevier.com/locate/jneuroim
Genomics in multiple sclerosis—Current state and future directions Manuel Comabella a,⁎, Roland Martin a,b a
Unitat de Neuroimmunologia Clinica, Institut de Recerca, Hospital Universitari Vall d'Hebron, Barcelona, Spain b Institució Catalana de Recerca i Estudis Avançats (icrea), Barcelona, Spain Received 6 December 2006; received in revised form 21 February 2007; accepted 22 February 2007
Abstract Microarray-based gene expression profiling of large numbers of genes or even the whole genome has only recently become possible. Several studies have employed this technology in multiple sclerosis (MS) and its animal model, experimental allergic encephalomyelitis (EAE), and although results are promising, microarray-based genomics research is still viewed with skepticism. It is often negatively perceived as a fishing expedition rather than a discovery-oriented effort that takes into account the immense complexity of diseases such as MS. Besides these conceptual concerns, technical reproducibility and the strategies to analyze and interpret the massive amounts of data present problems that can cause considerable variability between studies. In this review, we summarize existing data from different gene expression profiling studies that have been conducted in MS and EAE, discuss potential problems and propose future directions for the use of microarrays in MS. © 2007 Elsevier B.V. All rights reserved. Keywords: Multiple sclerosis; DNA microarrays; Gene expression profiling
1. Introduction MS is considered a T-cell-mediated autoimmune disease of the central nervous system (CNS) that mainly affects young adults between 20 and 40 years of age and leads to significant disability. There are three clinical forms of MS, relapsing– remitting (RR), secondary progressive (SP) and primary progressive (PP) MS. The etiology of MS remains unclear, but both a complex genetic trait with multiple susceptibilityconferring genes as well as environmental influences such as viral infections have been identified as important causes (Sospedra and Martin, 2005). Pathogenetically, autoimmune inflammation, demyelination, glial activation and proliferation and finally damage of neurons and axons are all involved in the process, and their different relative contribution leads to substantial interindividual heterogeneity of MS. This heterogeneity becomes manifest in the clinical course and severity, magnetic ⁎ Corresponding author. Laboratori de Neuroimmunologia Clínica, número 114, Institut de Recerca, Hospital Universitari Vall D'Hebron, 2a planta, Pg. Vall d'Hebron 119-129, 08035 Barcelona, Spain. Tel.: +34 93 2746834; fax: +34 93 2746480. E-mail address: [email protected] (M. Comabella). 0165-5728/$ - see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.jneuroim.2007.02.009
resonance imaging (MRI) findings, histopathological characteristics of lesions, immunological observations and in the treatment response (Lucchinetti et al., 2000; Bielekova et al., 2005). As a result of the complex genetic trait and probably also due to different environmental influences, many processes contribute to the pathogenesis and eventually to clinical signs or treatment responses. Furthermore, particularly in the immune system, many factors such as cytokines, chemokines or surface receptors have pleiotropic and often redundant functions. It is therefore not too surprising that studies in the past that often focused on a single or small sets of molecules have been overall disappointing and to date, there is no single biomarker that correlates strongly with clinical activity, specific pathogenetic aspects or treatment responses in MS (Bielekova and Martin, 2004). Recent developments in the genomics area offer for the first time possibilities to overcome many of these problems. Transcriptional profiling is the identification of a profile or fingerprint of some or all of the genes present in a given tissue or cell population at a particular time. As the human genome has been completed, true profiling of every human gene in any given context has become possible and offers the prospect to understand complex molecular interactions and to identify
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biomarkers that correlate with clinical signs as well as new treatment targets. At present, transcriptional profiling is possible through DNA microarrays. These consist of oligonucleotides or complementary DNA (cDNA) molecules of known composition attached to a surface in an ordered, predetermined fashion at extremely high density (Stoughton, 2005). DNA microarrays are usually categorized into one of two classes, based on the DNA arrayed onto the support. An oligo array is comprised of synthesized oligonucleotides, whereas a cDNA array contains cloned or PCR-amplified complementary DNA molecules (Duggan et al., 1999; Barret and Kawasaki, 2003). Nevertheless, the purpose of either type of array is the same, to profile changes in the expression levels of thousands of genes simultaneously in one hybridization experiment. Large-scale gene expression profiling studies have often been criticized as they are discovery-oriented rather than hypothesis driven. The notion discovery-oriented implies to abandon preconceived hypotheses as to which factors might play a role in a context of interest such as a disease. Instead, the investigator recognizes the limitations of trying to understand complex interactions by rather narrow analyses and therefore studies the expression of as many genes as possible in the disease of interest or the experimental question that one attempts to study. A typical example would be to expose an entire organism such as yeast to a change in environment, e.g. pH or temperature, and examine the transcriptional alterations of all genes of yeast under those conditions. A hypothesis-oriented approach might focus on ion channels and heat shock proteins but at the same time neglect many other alterations that will be discovered by gene expression profiling. The latter has the potential to identify altered expression of novel genes for which little or no prior information is available. Due to the genetic complexity and heterogeneity of MS, we believe that gene expression profiling offers clear advantages and will lead to new hypotheses that would either not have been considered based on current knowledge or are too complex to be examined by conventional approaches. The major potential applications of genomics in MS are the following: (1) to identify transcriptional differences between MS patients and normal controls and between the different clinical forms of the disease, for example between RRMS and PP-MS; (2) to identify changes in cells that predict clinical responses to treatments for MS, such as interferon-β (IFN-β); (3) to identify novel biomarkers for potential use in the diagnosis, prognosis and the treatment of MS; (4) to identify candidate genes for genetic studies; and (5) to identify gene expression patterns or cellular pathways associated with disease phenotypes defined by MRI or histopathology. In the last few years, the size, i.e. the number of genes on the array, as well as the standardization of the methodology have constantly changed. Only recently, arrays have been introduced that contain all known genes and offer very high reproducibility. We therefore intend to summarize the current status of gene expression profiling in MS and EAE, highlight important methodological considerations and propose future directions in this field. Differentially expressed genes (DEG) that have been described repeatedly in previous microarray studies are also listed in order to help researchers to identify genes that may be
relevant in the MS pathogenesis and set the rationale for future studies. 2. Existing data on microarray studies in MS and EAE Comparing the data from the existing gene expression studies is not easy from a number of reasons. These include the different microarray platforms and variable numbers of oligonucleotides or cDNAs, differences in the material that was analyzed, i.e. peripheral blood mononuclear cells (PBMC), brain tissue and material that was examined with respect to a treatment effect, differences in patient populations, variations in the experimental design and others. The studies in the EAE model will be considered as well, although they have been performed with RNA from rodent cells and tissues. In Supplementary Table 1, we summarize the existing gene expression profiling studies in MS, which are based on PBMC (Supplementary Table 1a), brain tissue (1b), those that have been performed in the EAE model (1c) and finally studies related to treatment effects (1d). The main findings of the respective study, the type of array, the sample size and other important characteristics are listed and the specific genes that had been identified as differentially expressed are compiled separately in Supplementary Tables 2–5. The information contained in Supplementary Tables 2–5 relies on and summarizes the analyses and results obtained previously by the authors research groups. While we consider the latter information an important resource, the main purpose of our review is the interpretation of the general approach of gene expression profiling in the context of MS and what conclusions we can draw from the existing data. PBMC have been a preferred source of material for gene expression studies since they are easily accessible. Some of the studies examined only a few patients and used small arrays, while others included up to 72 patients and larger platforms. Between 34 and 1109 genes were found to be differentially expressed (Supplementary Table 1a); however, overall the data from the gene expression profiling of PBMC appear rather heterogeneous because of their varying size, the inclusion of treated and untreated patients, and mixing of patients with single demyelinating episodes, RR-MS, SP-MS and PP-MS patients. Eighty-three genes appeared at least in two different studies (Supplementary Table 2), although the direction of expression, i.e. over- or underexpressed, frequently varied for the individual gene. This may be caused by the overall inhomogeneity of the studies, but also by the fact that disease activity was not measured in parallel to gene expression. Despite these drawbacks, we considered genes that appeared at least twice interesting and worthy of further interpretation. One hundred seventy-four DEG were noted in at least two different studies using MS brain tissue (Supplementary Table 3), and although the number of patients and brain tissue samples was considerably smaller than in the ones using PBMC, we were surprised that the data from brain tissue appeared overall more consistent with respect to direction of gene expression. Of note, acute plaques, chronic active lesions, chronic silent plaques and normal appearing white matter (NAWM) were analyzed, and we will not discuss in detail the
M. Comabella, R. Martin / Journal of Neuroimmunology 187 (2007) 1–8 Table 1 Identical differentially expressed genes shared by studies in MS peripheral blood cells versus MS brain tissue, MS peripheral blood cells versus EAE and MS brain tissue versus EAE MS peripheral blood cells and MS brain tissue CCL2 Chemokine (C-C motif) ligand 2 HSPA1A Heat shock 70-kDa protein 1A JUN v-jun sarcoma virus 17 oncogene homolog MAPK1 Mitogen-activated protein kinase 1 MAPK14 Mitogen-activated protein kinase 14 MYC v-myc myelocytomatosis viral oncogene homolog TIMP1 TIMP metallopeptidase inhibitor 1 MS peripheral blood cells and EAE BTG1 B-cell translocation gene 1, anti-proliferative CCL2 Chemokine (C-C motif) ligand 2 CCL3 Chemokine (C-C motif) ligand 3 CCR5 Chemokine (C-C motif) receptor 5 IL1B Interleukin 1, beta IL1R2 Interleukin 1 receptor type II IL1RN Interleukin 1 receptor antagonist ITGB2 Integrin beta 2 (complement component 3 receptor 3 and 4 subunit) JUN v-jun sarcoma virus 17 oncogene homolog MYC v-myc myelocytomatosis viral oncogene homolog NFKBIA Nuclear factor of kappa light polypeptide gene enhancer in B cells inhibitor, alpha PTPRC Protein tyrosine phosphatase, receptor type C STAT1 Signal transducer and activator of transcription 1 TGFB1 Transforming growth factor, beta 1 TNF Tumor necrosis factor (TNF superfamily, member 2) VCAM1 Vascular cell adhesion molecules 1 MS brain tissue and EAE ARPC1B Actin-related protein 2/3 complex, subunit 1B B2M Beta 2 microglobulin C1QB Complement component 1, q subcomponent, B chain CAPG Capping protein (actin filament), gelsolin-like CCL2 Chemokine (C-C motif) ligand 2 CD14 CD14 molecule CEBPD CCAAT/enhancer binding protein (C/EBP), delta CHI3LI Chitinase 3-like 1 (cartilage glycoprotein-39) COL3A1 Collagen type III, alpha 1 (Ehlers–Danlos syndrome type IV) CSF1R Colony stimulating factor 1 receptor FCGR1A Fc fragment of IgG, high affinity Ia, receptor (CD64) GIP2 Interferon, alpha-inducible protein GFAP Glial fibrillary acidic protein GNAI2 Guanine nucleotide binding protein (G protein), alpha inhibiting GRN Granulin HLA-A Major histocompatibility complex, class I, A HLA-DMA Major histocompatibility complex, class II, DM alpha IFITM3 Interferon induced transmembrane protein (9–27) ISGF3G Interferon stimulated transcription factor 3 ITGB2 Integrin beta 2 (complement component 3 receptor 3 and 4 subunit) JUN v-jun sarcoma virus 17 oncogene homolog LGALS1 Lectin, galactoside-binding, soluble, 1 (galectin 1) MYC v-myc myelocytomatosis viral oncogene homolog SERPINA3 Serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 3 STAT6 Signal transducer and activator of transcription 6, IL-4 induced TNFRSF1B Tumor necrosis factor superfamily, member 1B
differences between lesions in different stages but rather interpret the overall changes and compare it to the other types of materials that have been used.
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EAE studies used various different models and mainly analyzed CNS tissue. Two hundred twenty-five DEG were found in at least 3 studies (Supplementary Table 4) and similar to brain tissue were highly consistent across studies in terms of direction of gene expression. Finally, a relatively small number of studies were performed with the goal of understanding the transcriptional changes in vitro or ex vivo upon IFN-β therapy. Thirty-nine DEG were reported in at least 2 studies (Supplementary Table 5). During the comparison of studies that were performed from the different cells and tissues in MS, as well as in EAE and under IFN-β treatment, we first asked the question, which genes are differentially regulated in PBMC versus MS brain tissue and also between these two sets of studies from MS patients in comparison with the data from EAE experiments. A number of interesting observations can be summarized. Seven genes are repeatedly observed both in studies of PBMC and of brain, and among them are one chemokine (CCL2), one heat shock protein, one transcription factor, one oncogene, two mitogenactivated kinases and one tissue inhibitor of metallopeptidases (Table 1, Fig. 1). Furthermore, 30% of genes that are either identical or similar in function are shared in PBMC and CNS tissue studies (Fig. 1). The comparison of PBMC and EAE results in 16 identical DEG (Table 1), including genes coding for chemokines, cytokines, transcription factors, signalling and adhesion molecules, and 37% of the genes between both sets of studies are either identical or similar in function (Fig. 1). Twenty-six identical genes were shared by the MS brain and EAE studies (Table 1), and these included genes coding for MHC/HLA molecules, complement components, Fc receptors, IFN-regulated proteins, transcription factors, cytokines, chemokines, structural proteins and others. Approximately 20% of genes between these sets of studies were either identical or similar in function (Fig. 1). When comparing DEG from MS PBMC and from IFN treatment-related studies, again 10% were identical and approximately 20% identical or similar in function. We conclude from the considerable percentage of DEG that was identical and similar in function between PBMC, brain tissue and EAE, that PBMC are at least in part
Fig. 1. Percentage of differentially expressed genes (DEG) that are either identical or those that are identical plus those similar in function between studies in MS peripheral blood cells (PBMC) and MS brain tissue, MS peripheral blood cells and EAE, MS peripheral blood cells and those in IFN treatment (IFN tx)related studies, and MS brain tissue and EAE.
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representative of the CNS condition and therefore can be used to profile gene expression in MS patients. The above analysis is still rather crude and provides only limited information with respect to the functional categories of genes that are differentially expressed in MS PBMC, brain or in EAE tissues. In order to extract more detailed information regarding the functional processes that may be dysregulated in MS and hence to gain a better understanding of disease pathogenesis, we functionally annotated all DEG from studies of MS PBMC, MS brain tissue, EAE studies and IFN-β treatment-related studies. We did not rely on the Gene Ontology (GO) annotations but performed a more detailed search for functional characteristics of each gene by considering the available publications. Later, we also determined in which functional context a gene exerts its most important role considering that these studies are meant to learn more about MS. It is obvious that such an analysis is influenced by the expertise and subjective interpretation by the two investigators, who are both neurologists and immunologists, but according to our knowledge there is no better means for such a “weighted analysis”. The functional categories that we considered were immune system, CNS genes, heat shock proteins and stress response, metabolism, cytoskeleton, apoptosis, cell cycle and growth response, IFN and antiviral response, proteases and antigen processing, signalling and function not known (Fig. 2). Fig. 2 summarizes the functional annotation of the genes that were found differentially expressed. This information led us to the following conclusions: (1) There is a clear overrepresentation of immunologically relevant genes (40–50%) in MS PBMC, EAE and IFN treatment-related studies, while the percentage is relatively lower in MS brain tissue. The reverse is true for CNS-related genes. In the PBMC and EAE studies, only a minority of DEG are CNS related, while 20% of DEG in brain tissue studies fall into that functional category. We consider this observation very interesting since the EAE studies also ex-
amined brain and spinal cord tissue. It is therefore reasonable to conclude that EAE is an immunological disease that targets a healthy spinal cord/brain with very little intrinsic alteration of brain function. In contrast, the pathogenetic processes in MS brain tissue appear to involve a target tissue that is functionally altered itself. This finding suggests that many of the difficulties in extrapolating results from EAE treatment studies to MS may in part be caused by this fundamental difference. In other words, MS is more than a pure autoimmune disease but rather involves genetically determined or acquired changes in the brain's response to damage, and this difference is not depicted by the current EAE models. Nevertheless, when interpreting gene expression data in MS patients and EAE, the following factors should be considered. To our knowledge it has not been examined systematically, whether the dynamic range of gene expression is similar or different in CNS versus immune tissues. If they differed greatly, our interpretation as to which genes are pathogenetically relevant in MS might be distorted. Furthermore, we assume that EAE, an autoimmune disease that is forcefully induced by injection of autoantigen in a powerful adjuvant, would more likely show alterations in gene expression profiles of the immune system than of the CNS. Moreover, the autoimmune process in EAE most likely targets a healthy rodent CNS, while CNS vulnerability may be an important factor in MS. As a consequence, one would anticipate differences in CNS gene expression profiles when comparing MS patients and EAE. (2) When considering the other functional categories, apoptosis-related genes, genes involved in cell cycle and growth response and also signalling molecules and transcription factors appear relatively more important in MS than in EAE. Of note, many of the molecules that are listed in the category signalling are also relevant to the function of the immune system. And (3) despite the frequent skepticism towards gene expression profiling studies, both the overrepresentation of immune system-related genes in EAE studies and particularly the DEG
Fig. 2. Functional annotation of differentially expressed genes (DEG) for MS peripheral blood cells (MS PBMC), brain (MS Brain), EAE studies and IFN-β treatmentrelated studies. The percentage of DEG in each of the functional categories is shown.
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within the functional category of IFN and antiviral response in the IFN-β treatment-related studies indicate that what is detected by these studies is relevant and meaningful in the context of the experimental question. 3. Critical evaluation of the data and approaches Microarray experiments involve numerous multiple-step procedures, and hence there are myriad sources of potential error and variability that often lead to high rates of false-positive and false-negative results. As summarized above, a significant body of data on gene expression profiling in MS and EAE has been generated already. Although results are promising, the degree of overlap in DEG across studies should be higher, which points at several important issues that should be considered when approaching complex neurological diseases such as MS with microarrays. Some of the major sources of variability that may contribute to the discrepancies found in MS studies with microarrays include the following: (1) intraindividual and interindividual variations (Whitney et al., 2003); (2) the use of different microarray platforms, which contain different genes and different probes for a given gene; (3) the microarray processing methods, such as cDNA amplification methods, probe labelling, hybridization conditions and washing; and (4) the use of different criteria and statistical approaches to analyze data, i.e. the imposition of a twofold change to select DEG may exclude genes that are expressed at low levels but have important biological effects. Other critical factors that also may contribute to the variability of microarray studies are more specifically related with the experimental design: - Studies in peripheral blood comparing gene expression between MS patients and healthy controls: (1) The use of different sample collection techniques, i.e. whole blood versus PBMC isolated by density gradient centrifugation, and the fraction of PBMC whose gene expression is analyzed, i.e. T cells versus non-T cells, or PBMC depleted of monocytes. (2) The handling of samples ex vivo prior to cell separation or RNA extraction. It has been shown that many genes belonging to a variety of biological pathways are sensitive to ex vivo incubation and changes in their expression may influence data interpretation (Baechler et al., 2004). (3) Alterations in the cellular composition of blood samples, that is, changes in the relative percentage of the main PBMC populations and differences in the activation status of cells that make up the PBMC. (4) Disease stage-related variations, i.e. grouping of patients with different clinical forms, and disease activity variations, i.e. inclusion of patients with more active inflammation and patients in clinical remission. And (5) the occurrence of relapses, infections or treatments in proximity to blood sample collection. - Microarray studies in MS brain tissue: (1) the small number of patients on which most of the brain studies are based; (2) the time-lag between autopsy and brain tissue processing; (3) the inclusion of patients with different clinical forms of MS;
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(4) the samples obtained at different time points in the evolution of the plaque, i.e. active lesions versus chronic lesions; (5) the brain sites from which the tissue has been taken, i.e. MS plaques versus NAWM, or the region of lesion that is analyzed, i.e. margin versus centre; and (6) related to 5, the cellular composition of tissue that is compared, for example with respect to inflammatory cell infiltrates. - Microarray studies in EAE: (1) the use of different species and strains to induce disease; (2) the use of different myelin antigens and adjuvants to immunize animals; (3) the use of different time points to analyze DEG; and (4) the use of different tissues from animals with EAE, i.e. CNS, spleen, lymph nodes or mixed tissues from the CNS. - IFN-β-based or, in general, treatment-related studies: (1) differences in the type of IFN-β used to evaluate the effect of treatment, i.e. IFN-β-1a versus IFN-β-1b; (2) differences in the experimental conditions, i.e. ex vivo versus in vitro testing; (3) differences in the time points selected to assess the in vitro or in vivo effects of therapy; (4) for in vitro studies, the doses of IFN-β used to induce changes in gene expression; and (5) for ex vivo testing, the period of time between last IFN-β injection and sample collection. 4. Criteria that would be desirable for new studies Here, we provide a list of factors that may help to improve data reliability when dealing with microarray experiments: (1) DEG obtained by microarrays should be validated by alternative methods, ideally by real-time quantitative RT-PCR, but also by techniques such as Northern blot, in-situ hybridization or immunohistochemistry. However, the lack of data generated with confirmatory methods does not invalidate the microarray experiments as long they are performed well. Microarray-based detection of relative gene expression has a much narrower dynamic range and lower accuracy than quantitative PCR; however, clear signals in the array experiment are almost always confirmed by PCR. (2) Because of the heterogeneity of the disease, and in order to avoid overgeneralization on the basis of findings in a single group of patients, microarray results should be replicated in an independent cohort of patients. Alternatively, biostatistical methods can be used to separate a larger cohort into a training and test set. (3) To standardize the time of day at which samples are extracted and the time window between sample collection and sample processing. (4) To analyze the leukocyte distribution of the major PBMC populations, i.e. by means of flow cytometry. (5) To avoid using mixed tissues from brain samples or EAE animals. A solution to this problem would be the introduction of new methods to target particular cell types in a standardized way, such as laser-dissection microscopy. And (6) for studies using peripheral blood cells, to exclude concurrent relapses, infections or treatments that may alter gene expression. Other issues that may help to compare microarray data across studies are the following: (1) The use of standardized sample collection methods, i.e. whole blood tubes containing lysis buffer and stabilization solutions, or frozen lysates from PBMC, which all help to minimize sample manipulation. (2) Avoid
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listing only those genes that are considered by the researchers to be the most relevant in a particular experimental condition, i.e. genes with fold amplification over a predefined cut-off. Other genes in proximity to the cut-off may also be meaningful in other contexts. (3) The generation of large amounts of microarray data imposes the necessity of creating repositories of raw gene-array data, which will also allow scientists to compare data from different microarray studies in a format more open than the peer reviewed publications. Such repositories should be in compliance with MIAME (Minimum Information About a Microarray Experiment) guidelines, a standardized data format developed by the Microarray Gene Expression Database (MGED) Society to specify the minimum information needed to describe a microarray experiment (Brazma et al., 2001). (4) Finally, establishing common standards for publications will be also important, i.e. researchers should provide similar annotations for gene identification, names and symbols. 5. Future directions of microarray studies and conclusions A critical issue, which clearly contributes to the discrepancies found in the literature not only in the field of microarrays, but also using other methodological approaches, is disease heterogeneity itself. We need a better understanding of every aspect of the complexities of the disease process, that is, genetic background, environmental triggers, immune reactivity, vulnerability of the target tissue and pathological, clinical and treatment response heterogeneity. We need tools to dissect these processes in the individual patient. These will require integration of knowledge from different areas of research including pathology, neuroimaging and application of large-scale immunologic, proteomics and genomics tools. Genes identified in microarray experiments can be mapped to particular chromosomal regions to see if these correspond to previously identified regions of linkage or association. Candidate genes may then be examined for polymorphisms, which correlate with susceptibility. Nowadays, the availability of highdensity SNP array platforms such as the Affymetrix 500K or the Illumina 550K offers the possibility of determining alleles at hundreds of thousands of loci from DNA samples, allowing whole genome association studies to determine the genetic contribution to complex polygenic disorders. The integration of genetic data from high-density SNP arrays studies with genomics data will be a promising approach to identify candidate biomarkers indicative of the different pathophysiologic processes in MS. In a subsequent step, the inclusion of proteomics and metabolomics will provide further information in the search of MS biomarkers. An important issue derived from these integration studies is the necessity of developing annotation tools that will allow researchers to quickly compare results across different array platforms or even cross-annotation with SNP mapping and proteomics studies. Gene expression profiling studies should be complemented and combined with MRI and neuropathological data. Neuroimaging can be used as an important tool to identify radiological phenotypes that may correlate with the different pathological processes taking place in the CNS of MS patients, i.e. inflam-
mation and/or tissue atrophy (Bielekova et al., 2005). These MRI-defined phenotypes may eventually be used to guide the analysis of gene expression data in order to find expression patterns associated with the radiological phenotypes. Furthermore, genomics studies should be related to clinical data on treatment response. This information should ideally be obtained from therapeutic trials that incorporate pharmacogenomics as part of their design. It has been shown that the effect of IFN-β is partial, and a substantial amount of patients are nonresponders to treatment. At present, there is an absence of clinical, neuroradiological and/or immunological markers that allow to predict the response to therapy. We anticipate that studies of the genetic background and gene expression profile will be instrumental to identify potential biomarkers that predict response or non-response to therapy, and ultimately to guarantee that the patient will receive the most suitable treatment from the onset. In conclusion, this review supports the rationale for employing DNA microarrays to approach the different aspects of MS and gain insight into the heterogeneity of the disease. Nevertheless, there are several factors that need to be considered in order to improve the reliability and reproducibility of data that is derived from genomics studies. We expect that the integration of different large-scale methodological approaches will be a powerful tool in the search for valuable MS biomarkers. Acknowledgement We thank Mireia Sospedra, Ph.D., Institucio Catalana de Recerca i Estudis Avancats (icrea), Unitat de Neuroimmunologia Clinica, Institut de Recerca, Hospital Universitari Vall D'Hebron, Barcelona, Spain, for critically reading the manuscript. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.jneuroim.2007.02.009. References Baechler, E.C., Batliwalla, F.M., Karypis, G., Gaffney, P.M., Moser, K., Ortmann, W.A., Espe, K.J., Balasubramanian, S., Hughes, K.M., Chan, J.P., Begovich, A., Chang, S.Y., Gregersen, P.K., Behrens, T.W., 2004. Expression levels for many genes in human peripheral blood cells are highly sensitive to ex vivo incubation. Genes Immun. 5, 347–353. Barret, J.C., Kawasaki, E.S., 2003. Microarrays: the use of oligonucleotides and cDNA for the analysis of gene expression. Drug Discov. Today 8, 134–141. Bielekova, B., Martin, R., 2004. Development of biomarkers in multiple sclerosis. Brain 127, 1463–1478. Bielekova, B., Kadom, N., Fisher, E., Jeffries, N., Ohayon, J., Richert, N., Howard, T., Bash, C.N., Frank, J.A., Stone, L., Martin, R., Cutter, G., McFarland, H.F., 2005. MRI as a marker for disease heterogeneity in multiple sclerosis. Neurology 65, 1071–1076. Brazma, A., Hingamp, P., Quackenbush, J., Sherlock, G., Spellman, P., Stoeckert, C., Aach, J., Ansorge, W., Ball, C.A., Causton, H.C., Gaasterland, T., Glenisson, P., Holstege, F.C., Kim, I.F., Markowitz, V., Matese, J.C., Parkinson, H., Robinson, A., Sarkans, U., Schulze-Kremer, S., Stewart, J., Taylor, R., Vilo, J., Vingron, M., 2001. Minimum information about a microarray experiment (MIAME)—toward standards for microarray data. Nat. Genet. 29, 365–371.
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Further Reading Achiron, A., Gurevich, M., Friedman, N., Kaminski, N., Mandel, M., 2004a. Blood transcriptional signatures of multiple sclerosis: unique gene expression of disease activity. Ann. Neurol. 55, 410–417. Achiron, A., Gurevich, M., Magalashvili, D., Kishner, I., Dolev, M., Mandel, M., 2004b. Understanding autoimmune mechanisms in multiple sclerosis using gene expression microarrays: treatment effect and cytokine-related pathways. Clin. Dev. Immunol. 11, 299–305. Airla, N., Luomala, M., Elovaara, I., Kettunen, E., Knuutila, S., Lehtimaki, T., 2004. Suppression of immune system genes by methylprednisolone in exacerbations of multiple sclerosis. Preliminary results. J. Neurol. 251, 1215–1219. Baranzini, S.E., Bernard, C.C., Oksenberg, J.R., 2005. Modular transcriptional activity characterizes the initiation and progression of autoimmune encephalomyelitis. J. Immunol. 174, 7412–7422. Bomprezzi, R., Ringner, M., Kim, S., Bittner, M.L., Khan, J., Chen, Y., Elkahloun, A., Yu, A., Bielekova, B., Meltzer, P.S., Martin, R., McFarland, H.F., Trent, J.M., 2003. Gene expression profile in multiple sclerosis patients and healthy controls to disease. Hum. Mol. Genet. 12, 2191–2199. Booth, D.R., Arthur, A.T., Teutsch, S.M., Bye, C., Rubio, J., Armati, P.J., Pollard, J.D., Heard, R.N., Stewart, G.J., 2005. The Southern MS Genetics Consortium. et al. Gene expression and genotyping studies implicate the interleukin 7 receptor in the pathogenesis of primary progressive multiple sclerosis. J. Mol. Med. 83 822–830. Brand-Schieber, E., Werner, P., Iacobas, D.A., Iacobas, S., Beelitz, M., Lowery, S.L., Spray, D.C., Scemes, E., 2005. Connexin43, the major gap junction protein of astrocytes, is down-regulated in inflamed white matter in an animal model of multiple sclerosis. J. Neurosci. Res. 80, 798–808. Carmody, R.J., Hilliard, B., Maguschak, K., Chodosh, L.A., Chen, Y.H., 2002. Genomic scale profiling of autoimmune inflammation in the central nervous system: the nervous response to inflammation. J. Neuroimmunol. 133, 95–107. Chabas, D., Baranzini, S.E., Mitchell, D., Bernard, C.C., Rittling, S.R., Denhardt, D.T., Sobel, R.A., Lock, C., Karpuj, M., Pedotti, R., Heller, R., Oksenberg, J.R., Steinman, L., 2001. The influence of the proinflammatory cytokine, osteopontin, on autoimmune demyelinating disease. Science 294, 1731–1735. Gilgun-Sherki, Y., Barhum, Y., Atlas, D., Melamed, E., Offen, D., 2005. Analysis of gene expression in MOG-induced experimental autoimmune encephalomyelitis after treatment with a novel brain-penetrating antioxidant. J. Mol. Neurosci. 27, 125–135. Graumann, U., Reynolds, R., Steck, A.J., Schaeren-Wiemers, N., 2003. Molecular changes in normal appearing white matter in multiple sclerosis are characteristic of neuroprotective mechanisms against hypoxic insult. Brain Pathol. 13, 554–573. Hong, J., Zang, Y.C., Hutton, G., Rivera, V.M., Zhang, J.Z., 2004. Gene expression profiling of relevant biomarkers for treatment evaluation in multiple sclerosis. J. Neuroimmunol. 152, 126–139. Ibrahim, S.M., Mix, E., Bottcher, T., Koczan, D., Gold, R., Rolfs, A., Thiesen, H.J., 2001. Gene expression profiling of the nervous system in murine experimental autoimmune encephalomyelitis. Brain 124, 1927–1938. Iglesias, A.H., Camelo, S., Hwang, D., Villanueva, R., Stephanopoulos, G., Dangond, F., 2004. Microarray detection of E2F pathway activation and other targets in multiple sclerosis peripheral blood mononuclear cells. J. Neuroimmunol. 150, 163–177.
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Jelinsky, S.A., Miyashiro, J.S., Saraf, K.A., Tunkey, C., Reddy, P., Newcombe, J., Oestreicher, J.L., Brown, E., Trepicchio, W.L., Leonard, J.P., Marusic, S., 2005. Exploiting genotypic differences to identify genes important for EAE development. J. Neurol. Sci. 239, 81–93. Kihara, Y., Ishii, S., Kita, Y., Toda, A., Shimada, A., Shimizu, T., 2005. Dual phase regulation of experimental allergic encephalomyelitis by plateletactivating factor. J. Exp. Med. 202, 853–863. Koike, F., Satoh, J., Miyake, S., Yamamoto, T., Kawai, M., Kikuchi, S., Nomuram, K., Yokoyama, K., Ota, K., Kanda, T., Fukazawa, T., Yamamura, T., 2003. Microarray analysis identifies interferon betaregulated genes in multiple sclerosis. J. Neuroimmunol. 139, 109–118. Lindberg, R.L., De Groot, C.J., Certa, U., Ravid, R., Hoffmann, F., Kappos, L., Leppert, D., 2004. Multiple sclerosis as a generalized CNS diseasecomparative microarray analysis of normal appearing white matter and lesions in secondary progressive MS. J. Neuroimmunol. 152, 154–167. Lock, C., Hermans, G., Pedotti, R., Brendolan, A., Schadt, E., Garren, H., Langer-Gould, A., Strober, S., Cannella, B., Allard, J., Klonowski, P., Austin, A., Lad, N., Kaminski, N., Galli, S.J., Oksenberg, J.R., Raine, C.S., Heller, R., Steinman, L., 2002. Gene-microarray analysis of multiple sclerosis lesions yields new targets validated in autoimmune encephalomyelitis. Nat. Med. 8, 500–508. Maas, K., Chan, S., Parker, J., Slater, A., Moore, J., Olsen, N., Aune, T.M., 2002. Cutting edge: molecular portrait of human autoimmune disease. J. Immunol. 169, 5–9. Mandel, M., Gurevich, M., Pauzner, R., Kaminski, N., Achiron, A., 2004. Autoimmunity gene expression portrait: specific signature that intersects or differentiates between multiple sclerosis and systemic lupus erythematosus. Clin. Exp. Immunol. 138, 164–170. Matejuk, A., Dwyer, J., Zamora, A., Vandenbark, A.A., Offner, H., 2002. Evaluation of the effects of 17beta-estradiol (17beta-e2) on gene expression in experimental autoimmune encephalomyelitis using DNA microarray. Endocrinology 143, 313–319. Matejuk, A., Hopke, C., Dwyer, J., Subramanian, S., Jones, R.E., Bourdette, D.N., Vandenbark, A.A., Offner, H., 2003. CNS gene expression pattern associated with spontaneous experimental autoimmune encephalomyelitis. J. Neurosci. Res. 3, 667–678. Mix, E., Ibrahim, S., Pahnke, J., Koczan, D., Sina, C., Bottcher, T., Thiesen, H.J., Rolfs, A., 2004. Gene-expression profiling of the early stages of MOGinduced EAE proves EAE-resistance as an active process. J. Neuroimmunol. 151, 158–170. Mycko, M.P., Papoian, R., Boschert, U., Raine, C.S., Selmaj, K.W., 2003. cDNA microarray analysis in multiple sclerosis lesions: detection of genes associated with disease activity. Brain 126, 1048–1057. Mycko, M.P., Papoian, R., Boschert, U., Raine, C.S., Selmaj, K.W., 2004. Microarray gene expression profiling of chronic active and inactive lesions in multiple sclerosis. Clin. Neurol. Neurosurg. 106, 223–229. Nicot, A., Ratnakar, P.V., Ron, Y., Chen, C.C., Elkabes, S., 2003. Regulation of gene expression in experimental autoimmune encephalomyelitis indicates early neuronal dysfunction. Brain 126, 398–412. Paintlia, A.S., Paintlia, M.K., Singh, A.K., Stanislaus, R., Gilg, A.G., Barbosa, E., Singh, I., 2004. Regulation of gene expression associated with acute experimental autoimmune encephalomyelitis by lovastatin. J. Neurosci. Res. 77, 63–81. Ramanathan, M., Weinstock-Guttman, B., Nguyen, L.T., Badgett, D., Miller, C., Patrick, K., Brownscheidle, C., Jacobs, L., 2001. In vivo gene expression revealed by cDNA arrays: the pattern in relapsing–remitting multiple sclerosis patients compared with normal subjects. J. Neuroimmunol. 116, 213–219. Satoh, J., Nakanishi, M., Koike, F., Miyake, S., Yamamoto, T., Kawai, M., Kikuchi, S., Nomura, K., Yokoyama, K., Ota, K., Kanda, T., Fukazawa, T., Yamamura, T., 2005. Microarray analysis identifies an aberrant expression of apoptosis and DNA damage-regulatory genes in multiple sclerosis. Neurobiol. Dis. 18, 537–550. Spach, K.M., Pedersen, L.B., Nashold, F.E., Kayo, T., Yandell, B.S., Prolla, T.A., Hayes, C.E., 2004. Gene expression analysis suggests that 1,25-dihydroxyvitamin D3 reverses experimental autoimmune encephalomyelitis by stimulating inflammatory cell apoptosis. Physiol. Genomics 18, 141–151. Sturzebecher, S., Wandinger, K.P., Rosenwald, A., Sathyamoorthy, M., Tzou, A., Mattar, P., Frank, J.A., Staudt, L., Martin, R., McFarland, H.F., 2003.
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Expression profiling identifies responder and nonresponder phenotypes to interferon-beta in multiple sclerosis. Brain 126, 1419–1429. Tajouri, L., Mellick, A.S., Ashton, K.J., Tannenberg, A.E., Nagra, R.M., Tourtellotte, W.W., Griffiths, L.R., 2003. Quantitative and qualitative changes in gene expression patterns characterize the activity of plaques in multiple sclerosis. Brain Res. Mol. Brain Res. 119, 170–183. Wandinger, K.P., Sturzebecher, C.S., Bielekova, B., Detore, G., Rosenwald, A., Staudt, L.M., McFarland, H.F., Martin, R., 2001. Complex immunomodulatory effects of interferon-beta in multiple sclerosis include the upregulation of T helper 1-associated marker genes. Ann. Neurol. 50, 349–357. Weinstock-Guttman, B., Badgett, D., Patrick, K., Hartrich, L., Santos, R., Hall, D., Baier, M., Feichter, J., Ramanathan, M., 2003. Genomic effects of IFNbeta in multiple sclerosis patients. J. Immunol. 171, 2694–2702.
Whitney, L.W., Becker, K.G., Tresser, N.J., Caballero-Ramos, C.I., Munson, P.J., Prabhu, V.V., Trent, J.M., McFarland, H.F., Biddison, W.E., 1999. Analysis of gene expression in multiple sclerosis lesions using cDNA microarrays. Ann. Neurol. 46, 425–428. Whitney, L.W., Ludwin, S.K., McFarland, H.F., Biddison, W.E., 2001. Microarray analysis of gene expression in multiple sclerosis and EAE identifies 5-lipoxygenase as a component of inflammatory lesions. J. Neuroimmunol. 121, 40–48. Zhao, Y., Gran, B., Pinilla, C., Markovic-Plese, S., Hemmer, B., Tzou, A., Whitney, L.W., Biddison, W.E., Martin, R., Simon, R., 2001. Combinatorial peptide libraries and biometric score matrices permit the quantitative analysis of specific and degenerate interactions between clonotypic TCR and MHC peptide ligands. J. Immunol. 167, 2130–2141.
Genomics in multiple sclerosis—Current state and future directions Manuel Comabella a,⁎, Roland Martin a,b a
Unitat de Neuroimmunologia Clinica, Institut de Recerca, Hospital Universitari Vall d'Hebron, Barcelona, Spain b Institució Catalana de Recerca i Estudis Avançats (icrea), Barcelona, Spain Received 6 December 2006; received in revised form 21 February 2007; accepted 22 February 2007
Abstract Microarray-based gene expression profiling of large numbers of genes or even the whole genome has only recently become possible. Several studies have employed this technology in multiple sclerosis (MS) and its animal model, experimental allergic encephalomyelitis (EAE), and although results are promising, microarray-based genomics research is still viewed with skepticism. It is often negatively perceived as a fishing expedition rather than a discovery-oriented effort that takes into account the immense complexity of diseases such as MS. Besides these conceptual concerns, technical reproducibility and the strategies to analyze and interpret the massive amounts of data present problems that can cause considerable variability between studies. In this review, we summarize existing data from different gene expression profiling studies that have been conducted in MS and EAE, discuss potential problems and propose future directions for the use of microarrays in MS. © 2007 Elsevier B.V. All rights reserved. Keywords: Multiple sclerosis; DNA microarrays; Gene expression profiling
1. Introduction MS is considered a T-cell-mediated autoimmune disease of the central nervous system (CNS) that mainly affects young adults between 20 and 40 years of age and leads to significant disability. There are three clinical forms of MS, relapsing– remitting (RR), secondary progressive (SP) and primary progressive (PP) MS. The etiology of MS remains unclear, but both a complex genetic trait with multiple susceptibilityconferring genes as well as environmental influences such as viral infections have been identified as important causes (Sospedra and Martin, 2005). Pathogenetically, autoimmune inflammation, demyelination, glial activation and proliferation and finally damage of neurons and axons are all involved in the process, and their different relative contribution leads to substantial interindividual heterogeneity of MS. This heterogeneity becomes manifest in the clinical course and severity, magnetic ⁎ Corresponding author. Laboratori de Neuroimmunologia Clínica, número 114, Institut de Recerca, Hospital Universitari Vall D'Hebron, 2a planta, Pg. Vall d'Hebron 119-129, 08035 Barcelona, Spain. Tel.: +34 93 2746834; fax: +34 93 2746480. E-mail address: [email protected] (M. Comabella). 0165-5728/$ - see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.jneuroim.2007.02.009
resonance imaging (MRI) findings, histopathological characteristics of lesions, immunological observations and in the treatment response (Lucchinetti et al., 2000; Bielekova et al., 2005). As a result of the complex genetic trait and probably also due to different environmental influences, many processes contribute to the pathogenesis and eventually to clinical signs or treatment responses. Furthermore, particularly in the immune system, many factors such as cytokines, chemokines or surface receptors have pleiotropic and often redundant functions. It is therefore not too surprising that studies in the past that often focused on a single or small sets of molecules have been overall disappointing and to date, there is no single biomarker that correlates strongly with clinical activity, specific pathogenetic aspects or treatment responses in MS (Bielekova and Martin, 2004). Recent developments in the genomics area offer for the first time possibilities to overcome many of these problems. Transcriptional profiling is the identification of a profile or fingerprint of some or all of the genes present in a given tissue or cell population at a particular time. As the human genome has been completed, true profiling of every human gene in any given context has become possible and offers the prospect to understand complex molecular interactions and to identify
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biomarkers that correlate with clinical signs as well as new treatment targets. At present, transcriptional profiling is possible through DNA microarrays. These consist of oligonucleotides or complementary DNA (cDNA) molecules of known composition attached to a surface in an ordered, predetermined fashion at extremely high density (Stoughton, 2005). DNA microarrays are usually categorized into one of two classes, based on the DNA arrayed onto the support. An oligo array is comprised of synthesized oligonucleotides, whereas a cDNA array contains cloned or PCR-amplified complementary DNA molecules (Duggan et al., 1999; Barret and Kawasaki, 2003). Nevertheless, the purpose of either type of array is the same, to profile changes in the expression levels of thousands of genes simultaneously in one hybridization experiment. Large-scale gene expression profiling studies have often been criticized as they are discovery-oriented rather than hypothesis driven. The notion discovery-oriented implies to abandon preconceived hypotheses as to which factors might play a role in a context of interest such as a disease. Instead, the investigator recognizes the limitations of trying to understand complex interactions by rather narrow analyses and therefore studies the expression of as many genes as possible in the disease of interest or the experimental question that one attempts to study. A typical example would be to expose an entire organism such as yeast to a change in environment, e.g. pH or temperature, and examine the transcriptional alterations of all genes of yeast under those conditions. A hypothesis-oriented approach might focus on ion channels and heat shock proteins but at the same time neglect many other alterations that will be discovered by gene expression profiling. The latter has the potential to identify altered expression of novel genes for which little or no prior information is available. Due to the genetic complexity and heterogeneity of MS, we believe that gene expression profiling offers clear advantages and will lead to new hypotheses that would either not have been considered based on current knowledge or are too complex to be examined by conventional approaches. The major potential applications of genomics in MS are the following: (1) to identify transcriptional differences between MS patients and normal controls and between the different clinical forms of the disease, for example between RRMS and PP-MS; (2) to identify changes in cells that predict clinical responses to treatments for MS, such as interferon-β (IFN-β); (3) to identify novel biomarkers for potential use in the diagnosis, prognosis and the treatment of MS; (4) to identify candidate genes for genetic studies; and (5) to identify gene expression patterns or cellular pathways associated with disease phenotypes defined by MRI or histopathology. In the last few years, the size, i.e. the number of genes on the array, as well as the standardization of the methodology have constantly changed. Only recently, arrays have been introduced that contain all known genes and offer very high reproducibility. We therefore intend to summarize the current status of gene expression profiling in MS and EAE, highlight important methodological considerations and propose future directions in this field. Differentially expressed genes (DEG) that have been described repeatedly in previous microarray studies are also listed in order to help researchers to identify genes that may be
relevant in the MS pathogenesis and set the rationale for future studies. 2. Existing data on microarray studies in MS and EAE Comparing the data from the existing gene expression studies is not easy from a number of reasons. These include the different microarray platforms and variable numbers of oligonucleotides or cDNAs, differences in the material that was analyzed, i.e. peripheral blood mononuclear cells (PBMC), brain tissue and material that was examined with respect to a treatment effect, differences in patient populations, variations in the experimental design and others. The studies in the EAE model will be considered as well, although they have been performed with RNA from rodent cells and tissues. In Supplementary Table 1, we summarize the existing gene expression profiling studies in MS, which are based on PBMC (Supplementary Table 1a), brain tissue (1b), those that have been performed in the EAE model (1c) and finally studies related to treatment effects (1d). The main findings of the respective study, the type of array, the sample size and other important characteristics are listed and the specific genes that had been identified as differentially expressed are compiled separately in Supplementary Tables 2–5. The information contained in Supplementary Tables 2–5 relies on and summarizes the analyses and results obtained previously by the authors research groups. While we consider the latter information an important resource, the main purpose of our review is the interpretation of the general approach of gene expression profiling in the context of MS and what conclusions we can draw from the existing data. PBMC have been a preferred source of material for gene expression studies since they are easily accessible. Some of the studies examined only a few patients and used small arrays, while others included up to 72 patients and larger platforms. Between 34 and 1109 genes were found to be differentially expressed (Supplementary Table 1a); however, overall the data from the gene expression profiling of PBMC appear rather heterogeneous because of their varying size, the inclusion of treated and untreated patients, and mixing of patients with single demyelinating episodes, RR-MS, SP-MS and PP-MS patients. Eighty-three genes appeared at least in two different studies (Supplementary Table 2), although the direction of expression, i.e. over- or underexpressed, frequently varied for the individual gene. This may be caused by the overall inhomogeneity of the studies, but also by the fact that disease activity was not measured in parallel to gene expression. Despite these drawbacks, we considered genes that appeared at least twice interesting and worthy of further interpretation. One hundred seventy-four DEG were noted in at least two different studies using MS brain tissue (Supplementary Table 3), and although the number of patients and brain tissue samples was considerably smaller than in the ones using PBMC, we were surprised that the data from brain tissue appeared overall more consistent with respect to direction of gene expression. Of note, acute plaques, chronic active lesions, chronic silent plaques and normal appearing white matter (NAWM) were analyzed, and we will not discuss in detail the
M. Comabella, R. Martin / Journal of Neuroimmunology 187 (2007) 1–8 Table 1 Identical differentially expressed genes shared by studies in MS peripheral blood cells versus MS brain tissue, MS peripheral blood cells versus EAE and MS brain tissue versus EAE MS peripheral blood cells and MS brain tissue CCL2 Chemokine (C-C motif) ligand 2 HSPA1A Heat shock 70-kDa protein 1A JUN v-jun sarcoma virus 17 oncogene homolog MAPK1 Mitogen-activated protein kinase 1 MAPK14 Mitogen-activated protein kinase 14 MYC v-myc myelocytomatosis viral oncogene homolog TIMP1 TIMP metallopeptidase inhibitor 1 MS peripheral blood cells and EAE BTG1 B-cell translocation gene 1, anti-proliferative CCL2 Chemokine (C-C motif) ligand 2 CCL3 Chemokine (C-C motif) ligand 3 CCR5 Chemokine (C-C motif) receptor 5 IL1B Interleukin 1, beta IL1R2 Interleukin 1 receptor type II IL1RN Interleukin 1 receptor antagonist ITGB2 Integrin beta 2 (complement component 3 receptor 3 and 4 subunit) JUN v-jun sarcoma virus 17 oncogene homolog MYC v-myc myelocytomatosis viral oncogene homolog NFKBIA Nuclear factor of kappa light polypeptide gene enhancer in B cells inhibitor, alpha PTPRC Protein tyrosine phosphatase, receptor type C STAT1 Signal transducer and activator of transcription 1 TGFB1 Transforming growth factor, beta 1 TNF Tumor necrosis factor (TNF superfamily, member 2) VCAM1 Vascular cell adhesion molecules 1 MS brain tissue and EAE ARPC1B Actin-related protein 2/3 complex, subunit 1B B2M Beta 2 microglobulin C1QB Complement component 1, q subcomponent, B chain CAPG Capping protein (actin filament), gelsolin-like CCL2 Chemokine (C-C motif) ligand 2 CD14 CD14 molecule CEBPD CCAAT/enhancer binding protein (C/EBP), delta CHI3LI Chitinase 3-like 1 (cartilage glycoprotein-39) COL3A1 Collagen type III, alpha 1 (Ehlers–Danlos syndrome type IV) CSF1R Colony stimulating factor 1 receptor FCGR1A Fc fragment of IgG, high affinity Ia, receptor (CD64) GIP2 Interferon, alpha-inducible protein GFAP Glial fibrillary acidic protein GNAI2 Guanine nucleotide binding protein (G protein), alpha inhibiting GRN Granulin HLA-A Major histocompatibility complex, class I, A HLA-DMA Major histocompatibility complex, class II, DM alpha IFITM3 Interferon induced transmembrane protein (9–27) ISGF3G Interferon stimulated transcription factor 3 ITGB2 Integrin beta 2 (complement component 3 receptor 3 and 4 subunit) JUN v-jun sarcoma virus 17 oncogene homolog LGALS1 Lectin, galactoside-binding, soluble, 1 (galectin 1) MYC v-myc myelocytomatosis viral oncogene homolog SERPINA3 Serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 3 STAT6 Signal transducer and activator of transcription 6, IL-4 induced TNFRSF1B Tumor necrosis factor superfamily, member 1B
differences between lesions in different stages but rather interpret the overall changes and compare it to the other types of materials that have been used.
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EAE studies used various different models and mainly analyzed CNS tissue. Two hundred twenty-five DEG were found in at least 3 studies (Supplementary Table 4) and similar to brain tissue were highly consistent across studies in terms of direction of gene expression. Finally, a relatively small number of studies were performed with the goal of understanding the transcriptional changes in vitro or ex vivo upon IFN-β therapy. Thirty-nine DEG were reported in at least 2 studies (Supplementary Table 5). During the comparison of studies that were performed from the different cells and tissues in MS, as well as in EAE and under IFN-β treatment, we first asked the question, which genes are differentially regulated in PBMC versus MS brain tissue and also between these two sets of studies from MS patients in comparison with the data from EAE experiments. A number of interesting observations can be summarized. Seven genes are repeatedly observed both in studies of PBMC and of brain, and among them are one chemokine (CCL2), one heat shock protein, one transcription factor, one oncogene, two mitogenactivated kinases and one tissue inhibitor of metallopeptidases (Table 1, Fig. 1). Furthermore, 30% of genes that are either identical or similar in function are shared in PBMC and CNS tissue studies (Fig. 1). The comparison of PBMC and EAE results in 16 identical DEG (Table 1), including genes coding for chemokines, cytokines, transcription factors, signalling and adhesion molecules, and 37% of the genes between both sets of studies are either identical or similar in function (Fig. 1). Twenty-six identical genes were shared by the MS brain and EAE studies (Table 1), and these included genes coding for MHC/HLA molecules, complement components, Fc receptors, IFN-regulated proteins, transcription factors, cytokines, chemokines, structural proteins and others. Approximately 20% of genes between these sets of studies were either identical or similar in function (Fig. 1). When comparing DEG from MS PBMC and from IFN treatment-related studies, again 10% were identical and approximately 20% identical or similar in function. We conclude from the considerable percentage of DEG that was identical and similar in function between PBMC, brain tissue and EAE, that PBMC are at least in part
Fig. 1. Percentage of differentially expressed genes (DEG) that are either identical or those that are identical plus those similar in function between studies in MS peripheral blood cells (PBMC) and MS brain tissue, MS peripheral blood cells and EAE, MS peripheral blood cells and those in IFN treatment (IFN tx)related studies, and MS brain tissue and EAE.
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representative of the CNS condition and therefore can be used to profile gene expression in MS patients. The above analysis is still rather crude and provides only limited information with respect to the functional categories of genes that are differentially expressed in MS PBMC, brain or in EAE tissues. In order to extract more detailed information regarding the functional processes that may be dysregulated in MS and hence to gain a better understanding of disease pathogenesis, we functionally annotated all DEG from studies of MS PBMC, MS brain tissue, EAE studies and IFN-β treatment-related studies. We did not rely on the Gene Ontology (GO) annotations but performed a more detailed search for functional characteristics of each gene by considering the available publications. Later, we also determined in which functional context a gene exerts its most important role considering that these studies are meant to learn more about MS. It is obvious that such an analysis is influenced by the expertise and subjective interpretation by the two investigators, who are both neurologists and immunologists, but according to our knowledge there is no better means for such a “weighted analysis”. The functional categories that we considered were immune system, CNS genes, heat shock proteins and stress response, metabolism, cytoskeleton, apoptosis, cell cycle and growth response, IFN and antiviral response, proteases and antigen processing, signalling and function not known (Fig. 2). Fig. 2 summarizes the functional annotation of the genes that were found differentially expressed. This information led us to the following conclusions: (1) There is a clear overrepresentation of immunologically relevant genes (40–50%) in MS PBMC, EAE and IFN treatment-related studies, while the percentage is relatively lower in MS brain tissue. The reverse is true for CNS-related genes. In the PBMC and EAE studies, only a minority of DEG are CNS related, while 20% of DEG in brain tissue studies fall into that functional category. We consider this observation very interesting since the EAE studies also ex-
amined brain and spinal cord tissue. It is therefore reasonable to conclude that EAE is an immunological disease that targets a healthy spinal cord/brain with very little intrinsic alteration of brain function. In contrast, the pathogenetic processes in MS brain tissue appear to involve a target tissue that is functionally altered itself. This finding suggests that many of the difficulties in extrapolating results from EAE treatment studies to MS may in part be caused by this fundamental difference. In other words, MS is more than a pure autoimmune disease but rather involves genetically determined or acquired changes in the brain's response to damage, and this difference is not depicted by the current EAE models. Nevertheless, when interpreting gene expression data in MS patients and EAE, the following factors should be considered. To our knowledge it has not been examined systematically, whether the dynamic range of gene expression is similar or different in CNS versus immune tissues. If they differed greatly, our interpretation as to which genes are pathogenetically relevant in MS might be distorted. Furthermore, we assume that EAE, an autoimmune disease that is forcefully induced by injection of autoantigen in a powerful adjuvant, would more likely show alterations in gene expression profiles of the immune system than of the CNS. Moreover, the autoimmune process in EAE most likely targets a healthy rodent CNS, while CNS vulnerability may be an important factor in MS. As a consequence, one would anticipate differences in CNS gene expression profiles when comparing MS patients and EAE. (2) When considering the other functional categories, apoptosis-related genes, genes involved in cell cycle and growth response and also signalling molecules and transcription factors appear relatively more important in MS than in EAE. Of note, many of the molecules that are listed in the category signalling are also relevant to the function of the immune system. And (3) despite the frequent skepticism towards gene expression profiling studies, both the overrepresentation of immune system-related genes in EAE studies and particularly the DEG
Fig. 2. Functional annotation of differentially expressed genes (DEG) for MS peripheral blood cells (MS PBMC), brain (MS Brain), EAE studies and IFN-β treatmentrelated studies. The percentage of DEG in each of the functional categories is shown.
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within the functional category of IFN and antiviral response in the IFN-β treatment-related studies indicate that what is detected by these studies is relevant and meaningful in the context of the experimental question. 3. Critical evaluation of the data and approaches Microarray experiments involve numerous multiple-step procedures, and hence there are myriad sources of potential error and variability that often lead to high rates of false-positive and false-negative results. As summarized above, a significant body of data on gene expression profiling in MS and EAE has been generated already. Although results are promising, the degree of overlap in DEG across studies should be higher, which points at several important issues that should be considered when approaching complex neurological diseases such as MS with microarrays. Some of the major sources of variability that may contribute to the discrepancies found in MS studies with microarrays include the following: (1) intraindividual and interindividual variations (Whitney et al., 2003); (2) the use of different microarray platforms, which contain different genes and different probes for a given gene; (3) the microarray processing methods, such as cDNA amplification methods, probe labelling, hybridization conditions and washing; and (4) the use of different criteria and statistical approaches to analyze data, i.e. the imposition of a twofold change to select DEG may exclude genes that are expressed at low levels but have important biological effects. Other critical factors that also may contribute to the variability of microarray studies are more specifically related with the experimental design: - Studies in peripheral blood comparing gene expression between MS patients and healthy controls: (1) The use of different sample collection techniques, i.e. whole blood versus PBMC isolated by density gradient centrifugation, and the fraction of PBMC whose gene expression is analyzed, i.e. T cells versus non-T cells, or PBMC depleted of monocytes. (2) The handling of samples ex vivo prior to cell separation or RNA extraction. It has been shown that many genes belonging to a variety of biological pathways are sensitive to ex vivo incubation and changes in their expression may influence data interpretation (Baechler et al., 2004). (3) Alterations in the cellular composition of blood samples, that is, changes in the relative percentage of the main PBMC populations and differences in the activation status of cells that make up the PBMC. (4) Disease stage-related variations, i.e. grouping of patients with different clinical forms, and disease activity variations, i.e. inclusion of patients with more active inflammation and patients in clinical remission. And (5) the occurrence of relapses, infections or treatments in proximity to blood sample collection. - Microarray studies in MS brain tissue: (1) the small number of patients on which most of the brain studies are based; (2) the time-lag between autopsy and brain tissue processing; (3) the inclusion of patients with different clinical forms of MS;
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(4) the samples obtained at different time points in the evolution of the plaque, i.e. active lesions versus chronic lesions; (5) the brain sites from which the tissue has been taken, i.e. MS plaques versus NAWM, or the region of lesion that is analyzed, i.e. margin versus centre; and (6) related to 5, the cellular composition of tissue that is compared, for example with respect to inflammatory cell infiltrates. - Microarray studies in EAE: (1) the use of different species and strains to induce disease; (2) the use of different myelin antigens and adjuvants to immunize animals; (3) the use of different time points to analyze DEG; and (4) the use of different tissues from animals with EAE, i.e. CNS, spleen, lymph nodes or mixed tissues from the CNS. - IFN-β-based or, in general, treatment-related studies: (1) differences in the type of IFN-β used to evaluate the effect of treatment, i.e. IFN-β-1a versus IFN-β-1b; (2) differences in the experimental conditions, i.e. ex vivo versus in vitro testing; (3) differences in the time points selected to assess the in vitro or in vivo effects of therapy; (4) for in vitro studies, the doses of IFN-β used to induce changes in gene expression; and (5) for ex vivo testing, the period of time between last IFN-β injection and sample collection. 4. Criteria that would be desirable for new studies Here, we provide a list of factors that may help to improve data reliability when dealing with microarray experiments: (1) DEG obtained by microarrays should be validated by alternative methods, ideally by real-time quantitative RT-PCR, but also by techniques such as Northern blot, in-situ hybridization or immunohistochemistry. However, the lack of data generated with confirmatory methods does not invalidate the microarray experiments as long they are performed well. Microarray-based detection of relative gene expression has a much narrower dynamic range and lower accuracy than quantitative PCR; however, clear signals in the array experiment are almost always confirmed by PCR. (2) Because of the heterogeneity of the disease, and in order to avoid overgeneralization on the basis of findings in a single group of patients, microarray results should be replicated in an independent cohort of patients. Alternatively, biostatistical methods can be used to separate a larger cohort into a training and test set. (3) To standardize the time of day at which samples are extracted and the time window between sample collection and sample processing. (4) To analyze the leukocyte distribution of the major PBMC populations, i.e. by means of flow cytometry. (5) To avoid using mixed tissues from brain samples or EAE animals. A solution to this problem would be the introduction of new methods to target particular cell types in a standardized way, such as laser-dissection microscopy. And (6) for studies using peripheral blood cells, to exclude concurrent relapses, infections or treatments that may alter gene expression. Other issues that may help to compare microarray data across studies are the following: (1) The use of standardized sample collection methods, i.e. whole blood tubes containing lysis buffer and stabilization solutions, or frozen lysates from PBMC, which all help to minimize sample manipulation. (2) Avoid
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listing only those genes that are considered by the researchers to be the most relevant in a particular experimental condition, i.e. genes with fold amplification over a predefined cut-off. Other genes in proximity to the cut-off may also be meaningful in other contexts. (3) The generation of large amounts of microarray data imposes the necessity of creating repositories of raw gene-array data, which will also allow scientists to compare data from different microarray studies in a format more open than the peer reviewed publications. Such repositories should be in compliance with MIAME (Minimum Information About a Microarray Experiment) guidelines, a standardized data format developed by the Microarray Gene Expression Database (MGED) Society to specify the minimum information needed to describe a microarray experiment (Brazma et al., 2001). (4) Finally, establishing common standards for publications will be also important, i.e. researchers should provide similar annotations for gene identification, names and symbols. 5. Future directions of microarray studies and conclusions A critical issue, which clearly contributes to the discrepancies found in the literature not only in the field of microarrays, but also using other methodological approaches, is disease heterogeneity itself. We need a better understanding of every aspect of the complexities of the disease process, that is, genetic background, environmental triggers, immune reactivity, vulnerability of the target tissue and pathological, clinical and treatment response heterogeneity. We need tools to dissect these processes in the individual patient. These will require integration of knowledge from different areas of research including pathology, neuroimaging and application of large-scale immunologic, proteomics and genomics tools. Genes identified in microarray experiments can be mapped to particular chromosomal regions to see if these correspond to previously identified regions of linkage or association. Candidate genes may then be examined for polymorphisms, which correlate with susceptibility. Nowadays, the availability of highdensity SNP array platforms such as the Affymetrix 500K or the Illumina 550K offers the possibility of determining alleles at hundreds of thousands of loci from DNA samples, allowing whole genome association studies to determine the genetic contribution to complex polygenic disorders. The integration of genetic data from high-density SNP arrays studies with genomics data will be a promising approach to identify candidate biomarkers indicative of the different pathophysiologic processes in MS. In a subsequent step, the inclusion of proteomics and metabolomics will provide further information in the search of MS biomarkers. An important issue derived from these integration studies is the necessity of developing annotation tools that will allow researchers to quickly compare results across different array platforms or even cross-annotation with SNP mapping and proteomics studies. Gene expression profiling studies should be complemented and combined with MRI and neuropathological data. Neuroimaging can be used as an important tool to identify radiological phenotypes that may correlate with the different pathological processes taking place in the CNS of MS patients, i.e. inflam-
mation and/or tissue atrophy (Bielekova et al., 2005). These MRI-defined phenotypes may eventually be used to guide the analysis of gene expression data in order to find expression patterns associated with the radiological phenotypes. Furthermore, genomics studies should be related to clinical data on treatment response. This information should ideally be obtained from therapeutic trials that incorporate pharmacogenomics as part of their design. It has been shown that the effect of IFN-β is partial, and a substantial amount of patients are nonresponders to treatment. At present, there is an absence of clinical, neuroradiological and/or immunological markers that allow to predict the response to therapy. We anticipate that studies of the genetic background and gene expression profile will be instrumental to identify potential biomarkers that predict response or non-response to therapy, and ultimately to guarantee that the patient will receive the most suitable treatment from the onset. In conclusion, this review supports the rationale for employing DNA microarrays to approach the different aspects of MS and gain insight into the heterogeneity of the disease. Nevertheless, there are several factors that need to be considered in order to improve the reliability and reproducibility of data that is derived from genomics studies. We expect that the integration of different large-scale methodological approaches will be a powerful tool in the search for valuable MS biomarkers. Acknowledgement We thank Mireia Sospedra, Ph.D., Institucio Catalana de Recerca i Estudis Avancats (icrea), Unitat de Neuroimmunologia Clinica, Institut de Recerca, Hospital Universitari Vall D'Hebron, Barcelona, Spain, for critically reading the manuscript. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.jneuroim.2007.02.009. References Baechler, E.C., Batliwalla, F.M., Karypis, G., Gaffney, P.M., Moser, K., Ortmann, W.A., Espe, K.J., Balasubramanian, S., Hughes, K.M., Chan, J.P., Begovich, A., Chang, S.Y., Gregersen, P.K., Behrens, T.W., 2004. Expression levels for many genes in human peripheral blood cells are highly sensitive to ex vivo incubation. Genes Immun. 5, 347–353. Barret, J.C., Kawasaki, E.S., 2003. Microarrays: the use of oligonucleotides and cDNA for the analysis of gene expression. Drug Discov. Today 8, 134–141. Bielekova, B., Martin, R., 2004. Development of biomarkers in multiple sclerosis. Brain 127, 1463–1478. Bielekova, B., Kadom, N., Fisher, E., Jeffries, N., Ohayon, J., Richert, N., Howard, T., Bash, C.N., Frank, J.A., Stone, L., Martin, R., Cutter, G., McFarland, H.F., 2005. MRI as a marker for disease heterogeneity in multiple sclerosis. Neurology 65, 1071–1076. Brazma, A., Hingamp, P., Quackenbush, J., Sherlock, G., Spellman, P., Stoeckert, C., Aach, J., Ansorge, W., Ball, C.A., Causton, H.C., Gaasterland, T., Glenisson, P., Holstege, F.C., Kim, I.F., Markowitz, V., Matese, J.C., Parkinson, H., Robinson, A., Sarkans, U., Schulze-Kremer, S., Stewart, J., Taylor, R., Vilo, J., Vingron, M., 2001. Minimum information about a microarray experiment (MIAME)—toward standards for microarray data. Nat. Genet. 29, 365–371.
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Jelinsky, S.A., Miyashiro, J.S., Saraf, K.A., Tunkey, C., Reddy, P., Newcombe, J., Oestreicher, J.L., Brown, E., Trepicchio, W.L., Leonard, J.P., Marusic, S., 2005. Exploiting genotypic differences to identify genes important for EAE development. J. Neurol. Sci. 239, 81–93. Kihara, Y., Ishii, S., Kita, Y., Toda, A., Shimada, A., Shimizu, T., 2005. Dual phase regulation of experimental allergic encephalomyelitis by plateletactivating factor. J. Exp. Med. 202, 853–863. Koike, F., Satoh, J., Miyake, S., Yamamoto, T., Kawai, M., Kikuchi, S., Nomuram, K., Yokoyama, K., Ota, K., Kanda, T., Fukazawa, T., Yamamura, T., 2003. Microarray analysis identifies interferon betaregulated genes in multiple sclerosis. J. Neuroimmunol. 139, 109–118. Lindberg, R.L., De Groot, C.J., Certa, U., Ravid, R., Hoffmann, F., Kappos, L., Leppert, D., 2004. Multiple sclerosis as a generalized CNS diseasecomparative microarray analysis of normal appearing white matter and lesions in secondary progressive MS. J. Neuroimmunol. 152, 154–167. Lock, C., Hermans, G., Pedotti, R., Brendolan, A., Schadt, E., Garren, H., Langer-Gould, A., Strober, S., Cannella, B., Allard, J., Klonowski, P., Austin, A., Lad, N., Kaminski, N., Galli, S.J., Oksenberg, J.R., Raine, C.S., Heller, R., Steinman, L., 2002. Gene-microarray analysis of multiple sclerosis lesions yields new targets validated in autoimmune encephalomyelitis. Nat. Med. 8, 500–508. Maas, K., Chan, S., Parker, J., Slater, A., Moore, J., Olsen, N., Aune, T.M., 2002. Cutting edge: molecular portrait of human autoimmune disease. J. Immunol. 169, 5–9. Mandel, M., Gurevich, M., Pauzner, R., Kaminski, N., Achiron, A., 2004. Autoimmunity gene expression portrait: specific signature that intersects or differentiates between multiple sclerosis and systemic lupus erythematosus. Clin. Exp. Immunol. 138, 164–170. Matejuk, A., Dwyer, J., Zamora, A., Vandenbark, A.A., Offner, H., 2002. Evaluation of the effects of 17beta-estradiol (17beta-e2) on gene expression in experimental autoimmune encephalomyelitis using DNA microarray. Endocrinology 143, 313–319. Matejuk, A., Hopke, C., Dwyer, J., Subramanian, S., Jones, R.E., Bourdette, D.N., Vandenbark, A.A., Offner, H., 2003. CNS gene expression pattern associated with spontaneous experimental autoimmune encephalomyelitis. J. Neurosci. Res. 3, 667–678. Mix, E., Ibrahim, S., Pahnke, J., Koczan, D., Sina, C., Bottcher, T., Thiesen, H.J., Rolfs, A., 2004. Gene-expression profiling of the early stages of MOGinduced EAE proves EAE-resistance as an active process. J. Neuroimmunol. 151, 158–170. Mycko, M.P., Papoian, R., Boschert, U., Raine, C.S., Selmaj, K.W., 2003. cDNA microarray analysis in multiple sclerosis lesions: detection of genes associated with disease activity. Brain 126, 1048–1057. Mycko, M.P., Papoian, R., Boschert, U., Raine, C.S., Selmaj, K.W., 2004. Microarray gene expression profiling of chronic active and inactive lesions in multiple sclerosis. Clin. Neurol. Neurosurg. 106, 223–229. Nicot, A., Ratnakar, P.V., Ron, Y., Chen, C.C., Elkabes, S., 2003. Regulation of gene expression in experimental autoimmune encephalomyelitis indicates early neuronal dysfunction. Brain 126, 398–412. Paintlia, A.S., Paintlia, M.K., Singh, A.K., Stanislaus, R., Gilg, A.G., Barbosa, E., Singh, I., 2004. Regulation of gene expression associated with acute experimental autoimmune encephalomyelitis by lovastatin. J. Neurosci. Res. 77, 63–81. Ramanathan, M., Weinstock-Guttman, B., Nguyen, L.T., Badgett, D., Miller, C., Patrick, K., Brownscheidle, C., Jacobs, L., 2001. In vivo gene expression revealed by cDNA arrays: the pattern in relapsing–remitting multiple sclerosis patients compared with normal subjects. J. Neuroimmunol. 116, 213–219. Satoh, J., Nakanishi, M., Koike, F., Miyake, S., Yamamoto, T., Kawai, M., Kikuchi, S., Nomura, K., Yokoyama, K., Ota, K., Kanda, T., Fukazawa, T., Yamamura, T., 2005. Microarray analysis identifies an aberrant expression of apoptosis and DNA damage-regulatory genes in multiple sclerosis. Neurobiol. Dis. 18, 537–550. Spach, K.M., Pedersen, L.B., Nashold, F.E., Kayo, T., Yandell, B.S., Prolla, T.A., Hayes, C.E., 2004. Gene expression analysis suggests that 1,25-dihydroxyvitamin D3 reverses experimental autoimmune encephalomyelitis by stimulating inflammatory cell apoptosis. Physiol. Genomics 18, 141–151. Sturzebecher, S., Wandinger, K.P., Rosenwald, A., Sathyamoorthy, M., Tzou, A., Mattar, P., Frank, J.A., Staudt, L., Martin, R., McFarland, H.F., 2003.
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Whitney, L.W., Becker, K.G., Tresser, N.J., Caballero-Ramos, C.I., Munson, P.J., Prabhu, V.V., Trent, J.M., McFarland, H.F., Biddison, W.E., 1999. Analysis of gene expression in multiple sclerosis lesions using cDNA microarrays. Ann. Neurol. 46, 425–428. Whitney, L.W., Ludwin, S.K., McFarland, H.F., Biddison, W.E., 2001. Microarray analysis of gene expression in multiple sclerosis and EAE identifies 5-lipoxygenase as a component of inflammatory lesions. J. Neuroimmunol. 121, 40–48. Zhao, Y., Gran, B., Pinilla, C., Markovic-Plese, S., Hemmer, B., Tzou, A., Whitney, L.W., Biddison, W.E., Martin, R., Simon, R., 2001. Combinatorial peptide libraries and biometric score matrices permit the quantitative analysis of specific and degenerate interactions between clonotypic TCR and MHC peptide ligands. J. Immunol. 167, 2130–2141.