Human Molecular Genetics

Human Molecular Genetics Advance Access originally published online on July 8, 2003

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Human Molecular Genetics, 2003, Vol. 12, No. 17 2191-2199
DOI: 10.1093/hmg/ddg221
© 2003 Oxford University Press

Gene expression profile in multiple sclerosis patients and healthy controls: identifying pathways relevant to disease

Roberto Bomprezzi^1,*, Markus Ringnér^1,2, Seungchan Kim^1,3, Michael L. Bittner^1,3, Javed Khan⁴, Yidong Chen¹, Abdel Elkahloun¹, Aimee Yu⁵, Bibiana Bielekova⁵, Paul S. Meltzer¹, Roland Martin⁵, Henry F. McFarland⁵ and Jeffrey M. Trent³

¹Cancer Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, ²Department of Theoretical Physics, Lund University, SE-223 62 Lund, Sweden, ³Translational Genomics Research Institute, Phoenix, AZ 85004, USA, ⁴Advanced Technology Center, National Cancer Institute, National Institutes of Health, Gaithersburg, MD 20877, USA and ⁵Neuroimmunology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA

Received May 3, 2003; Revised June 12, 2003; Accepted June 26, 2003

	ABSTRACT

TOP ABSTRACT INTRODUCTION RESULTS DISCUSSION MATERIALS AND METHODS SUPPLEMENTARY MATERIAL REFERENCES

 
Multiple sclerosis (MS) and other T cell-mediated autoimmune diseases develop in individuals carrying a complex susceptibility trait, probably following exposure to various environmental triggers. Owing to the presumed weak influence of single genes on disease predisposition and the recognized genetic heterogeneity of autoimmune disorders in humans, candidate gene searches in MS have been difficult. In an attempt to identify molecular markers indicative of disease status rather than susceptibility genes for MS, we show that gene expression profiling of peripheral blood mononuclear cells by cDNA microarrays can distinguish MS patients from healthy controls. Our findings support the concept that the activation of autoreactive T cells is of primary importance for this complex organ-specific disorder and prompt further investigations on gene expression in peripheral blood cells aimed at characterizing disease phenotypes.

	INTRODUCTION

TOP ABSTRACT INTRODUCTION RESULTS DISCUSSION MATERIALS AND METHODS SUPPLEMENTARY MATERIAL REFERENCES

 
Multiple sclerosis (MS) is a chronic inflammatory demyelinating disorder of the central nervous system (CNS) with various degrees of axonal damage. MS affects mainly young adults with predominance for females and a prevalence in the USA and northern Europe of 100 per 100 000 people (1). Since it often leads to substantial disability, MS has major socioeconomic impact.

Intensive investigations over the last two to three decades have indicated that MS is pathogenetically a T cell-mediated autoimmune disease (2). Its etiology remains elusive, but the interplay between environment and genetic factors is ultimately thought to be critical to the development of the disease. Epidemiological observations at the population level as well as familial aggregation studies (3–6) have prompted, in different MS populations, linkage studies and candidate gene searches in the attempt to dissect genetic components of the susceptibility to MS. The results were similar to other autoimmune diseases, suggesting that MS is genetically complex and there may be no single genes of major attributable risk. Instead MS may result from the contribution of several genes exhibiting low or moderate effect (7,8). While none of the candidate genes suggested to date has been shown to be essential or sufficient for disease development, the most robust association has clearly been established with alleles of the major histocompatibility complex (MHC; HLA in humans, on chromosome 6p21) and in particular with the haplotypes HLA-DQB1*0602, -DQA*0102, -DRB1*1501, -DRB5*0101 (9,10). In summary, etiologic and genetic heterogeneity, epistatic gene interactions and epigenetic modifications are only some of the possible confounding factors to consider in interpreting the results of studies on genetic susceptibility to MS (11).

Evidence of phenotypic heterogeneity in MS was recently reported by Lucchinetti et al. (12) who described, in a large series of actively demyelinating lesions from MS patients, different pathology patterns suggestive of distinct pathogenic mechanisms (12).

The method of global gene expression analysis using cDNA microarrays has proven to be a sensitive method to develop and refine the molecular taxonomy of cancer and other disorders, as well as a tool to investigate molecular heterogeneity (13). Here, using this technology, complemented with a novel, powerful, analytical method, we compared the gene expression profiles of peripheral blood mononuclear cells (PBMC) from a series of MS patients with those from healthy donors. The specific aim of the study was to identify a panel of molecular markers indicative of disease status and not necessarily the identification of a gene(s) causative for MS. Nonetheless, identifying genes capable of molecular classification of MS from healthy controls using PBMC would be a significant advance, also providing insight into the pathogenetic mechanisms of MS. Results from previous microarray-based studies sustain the rationale for employing microarrays to test PBMC in autoimmune disorders (14–16). In accordance with the reports by Ramanathan et al. (14) and Maas et al. (15), we found that the differences existing between MS patients and control individuals comprise immune related as well as cell cycle related genes, supporting the idea that, besides the lymphocyte activation and cell–cell interaction, other cellular events play relevant roles during the course of autoimmune disease.

	RESULTS

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The method of analysis we employed (see Methods) presents two major advantages: (a) extract data from strong feature sets (17) to separate samples having the smallest variance within the group and being least susceptible to both biological and technical noise; (b) test the combined information in sets of genes (pairs or triplets), so that if multiple causes are acting in the sample set, such as gene interaction, the discriminative power can contribute to the overall separation. The disadvantage is the very intense computation requirements. We identified more than a thousand pairs of genes that could distinguish MS samples from controls in our initial ‘training set’ (termed set 1). The pairs of genes separating MS from healthy controls were strongly dominated by cDNA clones for the heat shock protein-70 followed by CKS2. For example, HSPA1L together with H1F2 and CKS2 together with PAFAH1B1 by themselves discriminated well between MS and controls (Fig. 1A and B). The results demonstrated a -error of less than 5% (_n=0.6) for the best candidate pairs. Using these pairs, we predicted whether a sample in an independent test cohort (termed set 2), could be molecularly ‘diagnosed’ with MS, and achieved reasonably good prediction results (Fig. 1C and D). For both pairs, only four individuals were misclassified corresponding to 80% correct predictions. This was also the case for most of the highest ranked pairs. They typically resulted in an 80% classification performance, generally with the same four healthy individuals misclassified as for HSPA1L and H1F2 (Fig. 1C). Even for normalized profiles, the range of values is slightly different between sets 1 and 2 (Fig. 1), either reflecting differences in microarray batches (shift of ranges) or sample preparation (fresh versus frozen, enlarged or reduced variation). Nevertheless, the classifiers identified in set 1 showed very good predictive capability for set 2.

View larger version (22K): [in this window] [in a new window]   Figure 1. Pairs of genes separating MS patients from healthy controls. The expression levels of HSPA1L and H1F2 (A) and CKS2 and PAFAH1B1 (B) are shown for the MS (blue) and healthy (yellow) samples in set 1. Based on these samples and a -error as indicated by the dotted circles around each sample, linear classifiers were designed (solid lines). These classifiers were then used to predict the samples in the test set 2 (C and D). Each gene pair misclassified four samples, giving a correct prediction rate of 80%.

 
These results indicate that a set of genes differentially expressed in MS patients can be used to predict the disease state in an independent test set. Hence, we proceeded to find candidate discriminatory genes using all individuals from both sample sets. Using minimal criteria requiring each pair to have a 1.4-fold difference in average expression between the two groups, we identified 303 pairs having -error less than 14% (_n=0.6). From this group, 112 represented unique genes that could discriminate MS patients from controls in both sets (for a complete list see Supplementary Material). Of the 112 genes, 53 appeared in at least two pairs (Fig. 2). Of interest, when we used strong feature sets based on gene triplets (rather than pairs) we did not gain significant improvement in misclassification error. For this study, we therefore limited our analysis to pairs. It is worth mentioning that, by generating the final discriminatory gene list from the combination of both sets, we intended to minimize potential experimental artifacts, such as low number of samples. Obviously, differences exist between the two sets, and we are aware of the gender mismatch of the control group of set 1. However, we feel that the validation by a properly matched group (set 2) strengthens the findings. Furthermore, even though the classification error obtained from combining both sets of samples increased when compared with the <5% error achieved in set 1 only, we believe the grouping of both sets may approximate more closely the real biological variation. Being outside the aim of the study, we did not attempt any sub-grouping of the patient samples. We rather see the heterogeneity existing in all the samples (affected and control) and the sample processing itself (fresh and frozen) as ‘noise’, from which we expect the real signal to emerge.

View larger version (129K): [in this window] [in a new window]   Figure 2. Expression of the 53 discriminatory genes. Each row represents one of the 53 discriminatory genes found in our analysis. Each column represents a sample ordered such that the normal samples are on the left- and the MS samples on the right-hand side. For each gene, red indicates a high level of expression relative to the mean; green indicates a low level of expression relative to the mean. The scale below indicates the number of standard deviations from the mean.

 
In a complex disease like MS, we recognize that both multiple interactions of different components of the immune system in vivo, and the complexity of the intracellular pathways must be considered in the interpretation of microarray experiments (18). While it is currently impossible to understand the biological outcome of these interaction patterns, we will below highlight some of the genes involved in specific pathways that are of potential interest for MS. These informative genes are divided into two groups, based on their up- or down-regulation in MS versus healthy donors (Table 1).

View this table: [in this window] [in a new window]   Table 1. Genes with differential expression in MS and controls

 
Genes with comparatively high expression in MS
The PAFAH1B1 gene encodes for the non-catalytic alpha subunit of the intracellular Ib isoform of platelet-activating factor acetylhydrolase, which is highly specific for platelet-activating factor (PAF). A mutation of this gene is responsible for the human Miller–Dieker lissencephaly, suggesting its importance in controlling PAF levels during brain development (19). Moreover, PAF has a key role as a chemo-attractant, mediating inflammation and allergy.

We found increased expression of the TNF receptor (CD27) transcript in MS patients. CD27 is a disulfide-linked homodimer that serves as a costimulatory molecule during T cell activation. Its production is exclusive to cells of lymphoid lineage (NK, T and B cells) and is crucial for the development and maintenance of immune responses (20).

Unlike those from healthy donors, MS samples showed increased expression of genes related to T cell function and activation, like the transcript for the T cell receptor alpha locus and its zeta-chain associated protein kinase (ZAP70). T cell receptors are essential for any specific T cell-mediated immune response and have been implicated in MS susceptibility by genetic studies (21). ZAP70 is an important link between T cell receptor engagement and intracellular signaling steps during T cell activation (22). Also, the zinc finger protein 148 (ZNF148), which is known to bind the CACCC box promoter of both the human and mouse TCR genes to activate transcription (23), and the transcription factor-7, a T cell-specific down-stream enhancer element that controls CD3E gene expression (24), were up-regulated in the MS samples.

Another gene of interest is the interleukin 7 receptor gene (IL7R). IL7R mediates the effects of IL7 during lymphopoiesis, and its function is required for B and T cells, and in particular for gamma/delta T cell development. Of note, increased expression of IL7R was observed in a microarray study in PBMC from MS patients (14). Furthermore, an observation on the regulation of this gene and its relationship to T cell activation events has recently been reported (25).

Genes with comparatively low expression in MS
Statistically most significant in our study was the relative underexpression in the MS samples of the transcript for the heat shock protein 70 kDa (HSP70). This ubiquitous, highly conserved protein, previously suggested as a potential autoantigen in MS (26), maps on chromosome 6p21.3, the MHC/HLA-region. However, HSP70 may not only be of relevance as a potential autoantigen, but may also be particularly important for its involvement in the mRNA degradation of the cytokine in the ubiquitin–proteasome pathway (27) and its chaperone function (28).

Next to HSP70, messengers for histone proteins were detected as differentially regulated. The histone family genes map within a cluster of 35 histone genes to 6p21.3, again in the MHC/HLA region. Lever et al. (29) demonstrated that histone H1 is subject to rapid exchange in vivo and is modulated by phosphorylation (29). Not surprisingly, we also found that the expression of CKS2 (CDC28 protein kinase-2 gene) was lower in MS than in healthy donors, and it proved to be a good discriminator of MS versus controls in our study. This latter gene encodes for a protein kinase that is activated during cell cycle, pushes cells into mitosis, and may have histone H1 as a substrate for phosphorylation (30). Moreover, histones are functionally regulated upon acetylation, known to be a mechanism in gene activation, chromatin structure and gene silencing.

Other genes of interest underexpressed in the MS samples included TNF and the oncogene JUN. The tissue inhibitor of metalloproteinase 1 (TIMP1) is also down-regulated in MS, as is SERPINE1, which is known to contain several regulatory sequences including heat-shock- and retinoic acid-responsive elements (31).

It is worth noting that nine of the over- (2) or under-expressed (7) discriminatory genes map to chromosome 6p21, a region that has reproducibly shown the strongest association with MS and other autoimmune diseases (see Supplementary Material).

	DISCUSSION

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During recent years, research into the etiology, pathogenesis and clinical presentation of MS has indicated that it probably represents a heterogeneous group of disorders rather than a single disease entity (12), and dissecting the multitude of factors that contribute to the disease development has been difficult and frustrating not only in MS, but also in other autoimmune diseases (32). Within the immune system, many of the cytokines or chemokines and their respective receptors have pleiotropic and redundant functions, and act in complex networks rather than as single factors (33). This complexity results in subtle changes that are difficult if at all possible to detect (34).

The simultaneous analysis of the expression of thousands of genes allows the exploration of interactions within many components of entire networks, becoming elective instrument to recognize pathophysiological pathways (35,36). The results of our investigation clearly support these points of view. We have found that, even without obtaining perfect separation, subtle but distinguishable gene expression differences between healthy and MS subjects can be identified in peripheral blood cells. In our opinion the misclassification of a few samples of healthy individuals is not surprising and it is conceivable that the set of genes we have identified that best classify MS samples will probably be complemented and change. In fact, consistent with the model of a multifactorial disease, neither single genes nor a fixed group of genes are expected to function as perfect classifiers, and limits are posed by the study design (case–control) with a relatively restricted number of samples (37).

Nevertheless, our findings are in line with previous reports in MS, underscore basic principles and document new concepts. We propose that the process which culminates in an inflammatory cascade in the white matter of the CNS, can be evaluated in the periphery where pathogenetic events take place. However, as also pointed out in a recent microarray study (16), we acknowledge that there is a distinction between the genes that predispose to the disease (susceptibility alleles) and those whose expression is altered in an affected individual as a downstream effect of other genes. Subsets of genes confer ‘identity’ and account for direct genotype–phenotype correlation, whereas others may function as indicators of the disease status, and by identifying the former we may dissect the interaction of multiple genes each weakly contributing to disease susceptibility. Thus gene expression profiling may serve as an important adjunct in the search for candidate genes in multifactorial disorders (38).

With respect to the identified genes, some of potential interest for MS are presented in the Results section. Pathways may be defined and it is remarkable how the potentially coordinated regulation of genes can be outlined (see Results). For many of the transcripts for which we report a differential expression for the disease samples (e.g. IL-7R, ZAP70, TNFRSF7) (39), the role in autoimmunity is intuitive. However, for other genes it is less so and we gain confidence of their involvement on the solid basis of similar findings in analogous studies (14,15). Among the most informative candidates, we find worth particular attention the reduced transcription of the heat shock protein-70, together with the group of the histones and the kinase-2 of the CDC28 complex (CKS2). Although the real link of these candidates to autoimmunity remains speculative, we fully endorse the interpretation provided by Maas and colleagues (15) that these genes as a whole signal for an altered regulation, relative to controls, of the cell cycle in favor of an anti-apoptotic program. The direct correlation between autoimmunity and regulation of apoptosis is a relatively new concept of increasing interest (40), strongly supported by characteristics of the autoimmune lympho-proliferative syndrome (ALPS) in human (41,42), and mouse models of systemic lupus erythemathosus (SLE) (43), and our data suggest that parallels also exist in this T cell-mediated disease, i.e. MS.

When performing a case–control study the information obtained depends on the samples employed. As mentioned above, this may represent an important limit because the samples may not be representative of the general population. It is conceivable, therefore, that two distinct studies may identify different genes. However, we believe that the revealed pathways should be focused on. For instance, UBE2G, UBE2M and POH1 encode for proteins functioning in the ubiquitin–proteasome complex and were detected as underexpressed in the autoimmune group of samples in the study by Maas et al. (15). We consider the presence in our gene list of HISPA1A, also a relatively reduced transcript, as further evidence of the involvement of this pathway in autoimmunity, probably due to its role both in the intracellular processing of cytokine mRNA (27), and the cell cycle (44), or perhaps due to function in protein degradation during antigen presentation by the antigen presenting cells (45), present in lower percentage in PBMC. Similarly, Ramanathan et al. (14), reporting the overexpression of transcripts for LCK, a protein–tyrosine kinase, a key component of the TCR signal transduction, agrees with our finding on ZAP70 and transcription factors involved in TCR expression regulation (e.g. ZNF148, TCF7), in sustaining the relevance of this pathway for autoimmunity. Further examples may be extracted, again by simple means of comparison, such as the recognition of increased levels of matrix metalloproteinase-19 (MMP-19) in the previous study (14), paralleled by low levels of the inhibitor of metalloproteinase-1 (TIMP1) in our investigation.

In regard to the search for susceptibility loci, it is noteworthy how in this study the chromosome region 6p21 stands out among the others for harboring both the HLA complex, the histone cluster and the heat shock protein-70.

In conclusion, we are providing new evidence that the ‘expression signature’ of MS as detected in PBMC shows an overall weak signal, suggesting that a fine regulation of a large number of factors has to be kept in place to maintain immunological tolerance and a slight deregulation may account for its unbalance and subsequent pathology. In light of our results, further investigations addressing specific questions on the mechanisms underlying the etiology and pathogenesis of MS will be needed. Such research should encompass longitudinal follow-up of larger patient cohorts, and the information should be complemented by patient stratification based on MS pathology, immunological markers, clinical and magnetic resonance imaging phenotype as well as response to therapeutic intervention. We anticipate that gene expression profiling, besides being utilization for focused research into the role of individual functional pathways, will evolve into an important technique to bridge studies on diseases of complex immunogenetic background, such as MS, to protein expression profiling.

	MATERIALS AND METHODS

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Patient samples
PBMC samples were either collected fresh or isolated from frozen cells and divided into two sets as they were hybridized onto different batches of slides (see below). Set 1 included freshly collected (14 MS and seven normals) as well as frozen samples (three MS and two normals). Set 2 was composed of 10 MS and 10 healthy donors whose PBMC were stored frozen at the cell bank of the NIB/NIH. All patients enrolled in the study were free of therapy for at least 2 months. Patient and donor characteristics are shown in Table 2. Fresh PBMC were isolated from lymphocytopheresis by density gradient centrifugation (Biowitthaker, Walkersville, MD, USA), the cells were lysed by TRIzol Reagent (Invitrogen, Carlsbad, CA, USA) and then frozen at -80°C. RNA isolation was performed according to a standard protocol in use at NHGRI (13). RNA from frozen PBMC was prepared as above.

View this table: [in this window] [in a new window]   Table 2. Sample characteristics

 
Microarray slides
The experiments were conducted with fluorescent labeling of mRNA samples according to the standard protocol of NHGRI, followed by hybridization onto glass slides (13). Two sets of samples were hybridized onto two sets of slides, with partial overlap of the cDNA clones. Set 1 was hybridized onto slides containing about 6500 clones [4000 obtained under a Cooperative Research and Development Agreement with ResGen (Huntsville, AL) and 2500 from the germinal center library of the ‘lymphochip’] (46). For set 2 we used arrays containing 7500 cDNA clones (6500 from ResGen, 1000 from the lymphochip).

Data analysis
We filtered genes by requiring that a gene should have a minimal intensity across all experiments. Each slide was then normalized such that the relative (or normalized) intensity (RI) for each gene was defined as RI=intensity of that spot/mean intensity of filtered genes (47). The logarithm of RI was used as a measure of the expression levels.

To predict whether samples were from MS patients or healthy donors, we used a recently developed algorithm that finds strong feature sets and design classifiers based on those feature sets (17). The analysis of gene expression data poses a fundamental conceptual problem: there are very large numbers of genes (features) and typically a small number of experiments (samples), rendering it difficult to find the best features from which to construct a classifier (48). Furthermore, once a set of features is selected, a classifier is designed and its error estimated. For small number of samples, an error estimator may still be unbiased, but due to large variance it often yields very optimistic estimates (over-fitting). As a consequence, a large number of feature sets and classifiers may result in low error estimates, while the true error remains high. Conversely, our novel method of analysis mitigates the small-sample problem. (a) It designs classifiers using only a small number of features, and (b) once the features are identified, assesses the probability distribution that results from spreading the mass of the sample points. This makes the classification more difficult, while maintaining sample geometry. By considering increasing amounts of spread, the algorithm finds feature sets whose classification accuracy remains good relative to greater spreading of the sample. The error then gives a measure of the strength of the feature set as a function of the spread.

We then used linear classifiers because of their simplicity and an available analytical representation for both the classifier design and its error, which help exploit many possible feature sets, more than 10 billion in this study. We tested single, two and three genes in a classifier, for four different amounts of spread (_n). The misclassification error, defined in terms of error as described (17), was estimated for _n=0.6. A error of 5% has been shown for _n=0.6 to result in robust classifiers with good predictive ability (17). After designing the classifiers using set 1, we applied them to set 2 as an independent set to assess their predictive ability. Lastly, the two sets of samples were combined to generate the final list of marker genes.

	SUPPLEMENTARY MATERIAL

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Supplementary Material is available at HMG Online.

	ACKNOWLEDGEMENTS

 
We acknowledge S. Panavally for her help in the data analysis, D. Leja for help with the illustrations, and A. Glatfelter and J. Leuders for excellent technical support. We also would like to thank S. Leitman and her staff, Department of Transfusion Medicine, Clinical Center, NIH, for providing leukaphereses, T. Simonis for HLA typing, J. Ohayon, J. McCartin and H. Griffith, for patient scheduling and nursing care, and E. Suh and R. Martino for providing computing resources at the Center for Information Technology, NIH. M.R. was supported by a post-doctoral fellowship from the Swedish Research Council.

	FOOTNOTES

 
^* To whom correspondence should be addressed at: Cancer Genetics Branch, National Human Genome Research Institute, National Institutes of Health, 50 South Drive, Bldg 50 Room 5150, Bethesda, MD 20892-8000, USA. Tel: +1 3014964655; Fax: +1 3014023241; Email: rbomprez{at}nhgri.nih.gov

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