Multiple sclerosis: Genomic rewards

Journal of Neuroimmunology 113 (2001) 171–184 www.elsevier.com / locate / jneuroin

Review article

Multiple sclerosis: Genomic rewards Jorge R. Oksenberg*, Sergio E. Baranzini, Lisa F. Barcellos, Stephen L. Hauser Department of Neurology, School of Medicine, University of California, San Francisco, CA 94143 -0435, USA Received 7 September 2000; received in revised form 3 November 2000; accepted 3 November 2000

Abstract A large body of immunologic, epidemiologic, and genetic data indicate that tissue inury in multiple sclerosis (MS) results from an abnormal immune response to one or more myelin antigens that develops in genetically susceptible individuals after exposure to an as-yet undefined causal agent. The genetic component of MS etiology is believed to result from the action of several genes of moderate effect. The incomplete penetrance of MS susceptibility alleles probably reflects interactions with other genes, post transcriptional regulatory mechanisms, and significant nutritional and environmental influences. Equally significant, it is also likely that genetic heterogeneity exists, meaning that specific genes influence susceptibility and pathogenesis in some affects but not in others. Results in multiplex MS families confirm the genetic importance of the MHC region in conferring susceptibility of MS. Susceptibility may be mediated by the class II genes themselves (DR, DQ or both), related to the known function of these molecules in the normal immune response, e.g. antigen binding and presentation and T cell repertoire determination. The possibility that other genes in the MHC or the telomeric region of the MHC are responsible for the observed genetic effect cannot be excluded. The data also indicate that although the MHC region plays a significant role in MS susceptibility, much of the genetic effect in MS remains to be explained. Some loci may be involved in the initial pathogenic events, while others could influence the development and progression of the disease. The past few years have seen real progress in the development of laboratory and analytical approaches to study non-Mendelian complex genetic disorders and in defining the pathological basis of demyelination, setting the stage for the final characterization of the genes involved in MS susceptibility and pathogenesis. Their identification and characterization is likely to define the basic etiology of the disease, improve risk assessment and influence therapeutics.  2001 Elsevier Science B.V. All rights reserved. Keywords: Multiple sclerosis; Genes; Genomics; Immunity; MHC

1. Introduction: multiple sclerosis as a genetic disease Multiple sclerosis (MS) is a common inflammatory disease of the central nervous system (CNS) characterized by myelin loss, gliosis, varying degrees of axonal pathology, and progressive neurological dysfunction (Hauser and Goodkin, 2001). A large body of data indicates that tissue injury in MS results from an abnormal immune response to one or several myelin antigens occurring in genetically susceptible individuals after exposure to an as yet undefined environmental factor or factors. A genetic etiology is indicated foremost by the recurrence risk in family members of the affected individual (Ebers and Sadovnick, 1994; Oksenberg et al., 1996) (Table 1). One measure of familial aggregation is the ls statistic, defined as the ratio of the lifetime risk to siblings of affected individuals (Ks ) *Corresponding author. Tel.: 11-415-476-1335; fax: 11-415-4765229. E-mail address: [email protected] (J.R. Oksenberg).

versus the population prevalence (K) of the disease ( ls 5 Ks /K). A ls 51 would indicate non-familial clustering of cases; for MS, the total ls is between 20 (0.02 / 0.001) and 40 (0.04 / 0.001) (Risch, 1992). Studies of half-siblings (Sadovnick et al., 1996) and adoptees (Ebers et al., 1995) support the concept that genetic, and not environmental factors, are primarily responsible for familial aggregation. Furthermore, twin studies from different populations consistently indicate that a monozygotic twin of an MS patient is at higher risk (25–30% concordance) for MS than is a dizygotic twin (2–5%) (Mumford et al., 1994; Sadovnick et al., 1993), providing additional evidence for a significant, but complex, genetic etiology. The data derived from family cohorts raise a number of key questions regarding the role of genetic factors in MS. The first relates to the strength of these effects. Only 20% of MS cases have a known affected relative. A second question concerns the mode of inheritance of MS (Table 2). The derivation of a genetic model for MS based upon patterns of familial recurrence is a risky undertaking but

0165-5728 / 01 / $ – see front matter  2001 Elsevier Science B.V. All rights reserved. PII: S0165-5728( 00 )00444-6

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Table 1 Multiple sclerosis as a genetic disease (1) Familial aggregation of MS cases (a) Increased relative risk to sibs ( ls 520–40) (b) MS sibling pairs tend to cluster by age of onset, rather than year of onset (c) No detectable effect of shared environment on MS susceptibility in first-degree non-biological relatives (spouses, adoptees) (d) High disease concordance in monozygotic twins (25–30%) compared with dizygotic twins and non-twin siblings (3–5%) (2) Racial clustering of MS cases. Resistant ethnic groups residing in high risk regions (3) Suggestive correlations between certain polymorphic loci and disease susceptibility

nonetheless supports a polygenic model in which each gene contributes a small or at most a moderate effect to the overall risk. It is also likely that primary genetic heterogeneity exists in MS, meaning that different genes can cause identical forms of the disease. In addition, beyond the impact of genes that are inherited and act in their germline configuration, MS risk may be influenced by a number of post-genomic DNA changes. These include genes that rearrange from their position in the germline to encode a vast variety of T cell receptors and immunoglobulins, somatic mutations, post transcriptional regulatory mechanisms, and incorporation of retroviral sequences. The final layer of complexity in this analysis is encountered when interactions with nutritional, geographical, infectious, and other environmental influences are considered.

2. An interplay of genes and environmental factors Migration studies have been widely used to illustrate potential environmental influences on MS (Kurtzke, 1983; Martyn and Gale, 1997). Children born to parents who have migrated from a high-risk area to a low-risk area for MS appear to have a lower lifetime risk than their parents. Conversely, migration of parents from a low-risk area to a high-risk area may confer a higher risk for MS in the children. Although the interpretation of migration studies has been difficult, in part because the small number of migrants who develop MS in the individual reports, they

Table 2 Multiple sclerosis as a complex genetic disease (1) Etiological heterogeneity Identical genes, different phenotypes (2) Genetic heterogeneity Different genes, identical phenotypes (3) Unknown genetic parameters Single versus multiple genes Dominant versus recessive mode of inheritance Incomplete penetrance (4) Gene–gene interactions (5) Post-genomic mechanisms (6) Unidentified non-heritable (environmental) factors

have been influential and do suggest the existence of a critical age before puberty, between 13 and 20 years of age, for exposure to a putative environmental disease agent (Alter et al., 1996; Dean and Kurtzke, 1971; Gale and Martyn, 1995). A large number of environmental exposures have been investigated in MS. Those include viral and bacterial infections, nutritional and dietary factors, well water, exposure to animals, minerals, trauma due to accident or surgery, chemical agents, metals, organic solvents, geographical influences, and various occupational hazards (Bachmann and Kesselring, 1998; Dalgleish, 1997; Lauer, 1997). Reports of clusters or outbreaks of MS, such as those described in the Faroe Islands, while also problematic, underscore the potential role of an infectious agent (Kurtzke and Hyllested, 1979). Various microbes have been implicated in MS pathogenesis (Brocke et al., 1994). Eighty years ago, spirochetes were claimed to be the cause of MS. This was a reasonable assumption given that syphilis can cause a relapsing / remitting CNS inflammatory disease with synthesis of oligoclonal immunoglobulins in the cerebrospinal fluid. Since then, more than 20 infectious agents, ranging from retroviruses to mycobacteria have been associated with MS onset or relapses (Granieri and Casetta, 1997; Karpuj et al., 1997; Soldan et al., 1997; Sriram et al., 1998). However, attempts to isolate the causative pathogen have been largely unproductive and failed to provide major insights into mechanisms of disease susceptibility and pathogenesis. No single infectious agent has proven to have enough specificity and universality to be considered an ‘MS pathogen’. Nevertheless, if pathogens were involved in disease etiology, then genetic resistance or susceptibility to the microbial agent would surely have significant consequences for the host. For example, polymorphisms within chemokine receptors that also serve as co-receptor for HIV, have been shown to significantly influence infection and progression to AIDS (Carrington et al., 1999). Similarly, polymorphisms in cell receptor(s) or other target molecules utilized for microbial entry may also function as MS susceptibility genes. Climatic factors such as pollution, solar radiation, temperature, rainfall, humidity, have all been investigated; however, the only consistent effect appears to be due to latitude. MS is in general a disease of temperate climates

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with a population prevalence that decreases with decreasing latitude. High incidence rates are found in Scandinavia, Iceland, and the British Isles (about one in 1000). Even within the United States, distinctions exist in the prevalence of MS between populations living north and south of the 37th parallel (Hogancamp et al., 1997). MS is uncommon in Japan (two per 100 000) and other Asian nations, in the sub-Saharan Africa, the Indian sub-continent, and in the native populations of Oceania and the Americas. According to some observers, this characteristic geographical distribution implicates in the etiology of MS a pathogen that is not ubiquitously distributed. However, this prevalence pattern can also be explained by the geographical clustering of Northern Europeans and their descendants with susceptible genetic profiles. The high occurrence of MS in populations of Northern European origin irrespective of geographic location (Compston, 1997; Ebers and Sadovnick, 1994; Oksenberg et al., 1996; Poser, 1995), and the observation of resistant ethnic groups residing in high risk regions, as for example Gypsies in Bulgaria (Milanov et al., 1999) or Japanese in the United States (Lauer, 1994), suggest that the differential risk observed in different population groups results primarily from genetic susceptibility or resistance. The geographic distribution of MS may alternatively be related to the degree of sunlight exposure catalyzing the production of the hormonally active form of vitamin D, 1,25-(OH) 2 D 3 or vitamin D 3 . Exposure to UV light may have a protective effect in MS. In Switzerland for example, MS risk increases at low altitude and decreases at high altitude. In addition to its role in calcium homeostasis and bone metabolism, vitamin D 3 possesses both anti-inflammatory and immunomodulatory properties (Cippitelli and Santoni, 1998; Issa et al., 1998). Studies in experimental autoimmune demyelination (EAE) demonstrated that feeding of vitamin D prevented disease induction and reversed established disease progression (Cantorna et al., 1996). Furthermore, vitamin D 3 deficiency accelerated EAE onset. Additional evidence is provided by clinical studies which have shown that vitamin D 3 deficiency is more prevalent in MS patients, and that MS patients have more frequent fractures and bone loss when compared to ageand sex-matched controls (Cosman et al., 1998; Nieves et al., 1994). The *GC-1f allele of the vitamin D binding protein (DBP) gene (Arnason et al., 1980), and an allelic polymorphism in the 1,25( OH)2 D3 receptor (VDR) gene (Fukazawa et al., 1999a) were found to be associated with MS in Icelandic (n563) and Japanese (n577) patients, respectively. On the other hand, a recent study in multiplex MS Canadian families revealed no significant linkage or association with genes involved in the metabolism and function of vitamin D — VDR, DBP and 25( OH)D3 1a hydroxylase (Steckley et al., 2000). Perhaps the genetic factors operating in MS will be more evident when genomic and non-genomic data are combined together for analysis. Hence, for the examination of vitamin D-related

173

genes, it will be of interest to stratify patients by the geographical region in which study participants lived before and after the age of 15.

3. Gene identification in MS

3.1. Population versus family-based studies The genetic analysis of MS has traditionally focused on population-based association studies of candidate polymorphic genes, in which the frequencies of marker alleles in groups of patients and healthy controls are compared, and the difference is subjected to statistical analysis. The association is often expressed as the relative risk that an individual will develop the disorder if he / she carries the particular allele or marker, compared to an individual who does not carry the allele or marker. Candidate genes are defined as genes that are logical possibilities to play a role in a disease; for MS, candidate genes might encode cytokines, immune-receptors, myelin components, and proteins involved in viral clearance (http: / / www.ucsf.edu / msdb / r]ms]candidate]genes.html; http: / / www.pam.bris. ac.uk / services / GAl / cytokine4). With the notable exception of the major histocompatibility complex (MHC) locus, case–control association studies have met with only modest success in identifying disease-causing genes in MS, in part because of the difficulty in selecting from among the many candidate gene possibilities without a unifying model of disease pathogenesis. When the candidate gene is truthfully involved in the pathogenic process (e.g., the MHC), associations are consistently confirmed (Seboun et al., 1996). The lack of replication may also arise from low power, random variation, or population-specific effects. Some investigators have argued that a case–control association study design may not be able to distinguish between a true genetic effect and a disease irrelevant marker of population admixture. Thus, positive results with specific alleles from candidate genes could arise simply from the migration of Northern European individuals into the other population. Even when cases and controls are adequately matched, or relatives are used as controls for cases, previous study designs have in general involved relatively small sample sizes, which lack the statistical power to detect modest or moderate gene effects. Welldesigned and comprehensive population-based association studies are, nevertheless, a powerful and effective tool to discover disease susceptibility alleles (Risch, 2000). However, because linkage disequilibrium can vary across small physical distances, multiple polymorphisms within each of the candidate genes must be tested to ensure that any true effect is identified. An alternative strategy for the identification of disease genes merges the basic principles of Mendelian genetics with the power of molecular biology. This approach, which is known as positional cloning, involves first determining

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the chromosomal region of the genomic defect by genetic linkage analysis and then isolating the disease gene without any prior knowledge of the protein product and function. The notable track record of this strategy in many diseases with Mendelian (monogenic) inheritance, and the early success with complex traits such as the discovery of the role of APOE in late-onset Alzheimer’s disease (Pericak-Vance et al., 1991), powered the rationale for the wide application of this approach in non-Mendelian disorders. As shown for monogenic diseases, positional cloning is greatly facilitated by the presence of chromosomal abnormalities, such as deletions or translocations, which indicate the location of the gene responsible for the phenotype under investigation. In MS, no gross chromosomal abnormalities have been identified; disease genes are most likely characterized by subtle allelic variations (i.e., polymorphisms common in the population) with weak but cumulative effects and penetrance. Hence the establishment of genetic linkage and gene localization requires the collection of multiple pedigrees with more than one affected member to track the inheritance of discrete chromosomal segments that deviate from independent segregation and co-segregate with the disease. The availability of detailed maps of highly polymorphic markers (i.e., microsatellites) for all chromosomes facilitates efficient screening for linkage. The theoretical foundation and methodologies are currently available to examine family data in a variety of ways based upon the structure of pedigrees in a given study (Haines and Pericak-Vance, 1998; Martin et al., 2000). For example, family collections may consist of extended multigenerational pedigrees with more than one affected individual (multiplex families), or affected sib pairs, alone or with parents and other affected sibs. The frequency and penetrance of the susceptibility allele may determine the optimal ascertainment strategy. To date, however, positional cloning of complex disease genes has been an elusive goal. In MS, with the exception of the MHC in some populations (MS Genetics Group, 1998), family-based studies have also failed to identify major MS-disease genes (http: / / www.ucsf.edu / msdb / r]ms]candidate]genes.html). Similarly to case–control studies, the reported negative data may be attributable to heterogeneity and the limited size of the familial datasets analyzed. The smaller the contribution of specific genes, the greater the required sample size, and more stringent the inclusion criteria to minimize effects of heterogeneity. It has been argued that because of the low relative risk of any MS gene-variant, incomplete penetrance, and relative high frequency in the population at large, the linkage approach will not provide evidence for the location of the disease gene except in the case that unrealistically large samples are available for analysis (Risch, 2000). Others will concede to the inherent limitations of current mapping and associations approaches, and propose the analysis of individuals or families at very high disease risk (Weiss and Terwilliger, 2000). These individuals are in the tails of the

risk distribution, which facilitates the ascertainment of alleles with substantial phenotypic effect, high penetrance and typical Mendelian inheritance. On the other hand, the potential of positional cloning for gene identification in complex diseases was recently highlighted in a landmark study of type 2 diabetes (Horikawa et al., 2000). The investigators followed original linkage data that implicated the distal long arm of chromosome 2 and identified a disease-associated intronic polymorphism in calpain-10, an ubiquitously expressed member of the calpain-like cysteine protease family.

3.2. Genome scans in MS Genetic studies in MS in the previous decade were dominated by three large multi-stage whole genome screens performed in multiple-affected families ascertained in the US, UK and Canada. Designed in the early 1990s to detect genes with a ls $3, the studies were completed and published in 1996 (Ebers et al., 1996; MS Genetics Group, 1996; Sawcer et al., 1996). Follow-up screenings in confirmatory and additional datasets have been completed as well (Kuokkanen et al., 1997; Chataway et al., 1998; D’Alfonso et al., 1999; Oturai et al., 1999; Seboun et al., 1999; Xu et al., 1999). The US group is currently engaged in a second-generation genome screen with a substantially larger DNA dataset and a more informative and dense Table 3 Regions of overlap between whole genome scans in multiple sclerosis US a

UK b

Canada c

Finland d

2p23

1p36–p33 2p23–p21 3p14–p13

1p36–p33 2p23–21 3p14–p13 3q22–q24

3q22–q24

3q22–q24 4q31–qter 5q13–q23 6p21 6q27 7q11–q22

4q31–qter 5q12–q13 6p21 6q22–27

5p14–p12 5q12–q13 6p21 (LD)

a

6p21

7q21–q22 17q22

18p11 19q13 e

5p14–22

19q12–13

17q22–24 18p11 19q13

19q13

52 families, 81 affected sib-pairs, 58 affected relative pairs. 129 families, 143 affected sib-pairs, 0 affected relative pairs. c 61 families, 100 affected sib-pairs, 0 affected relative pairs. d 16 families, 30 affected sib-pairs, 25 affected relative pairs. e Chromosome 19q13 has been of consistent interest since the first description of positive linkage results by Haines et al. (1993). In addition, one study (Barcellos et al., 1997b) showed an association with the 184-base pair allele of D19S574 in a Caucasian dataset of sporadic cases. This region contains many attractive candidate genes. The well-documented involvement of APOE in neurological disease for example, makes this gene an interesting candidate for MS studies (Hogh et al., 2000; Weatherby et al., 2000). Additional suggestive candidate genes include TGFb1, immunoglobulin-like transcripts (ILT), killer cell inhibitory receptors (KIR), the leukocyte-associated inhibitory receptors (LAIR), the Fc receptor, IL-11, the heavy chain of the MHC class I-like Fc receptor, and APOC4. b

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battery of microsatellite markers. Analysis is expected to be completed in 2001. It was not surprising that the original all-genome studies generated considerable interest in the neurology and immunology communities, nor was it surprising that the data was received with certain discouragement. All together, about 60 genomic regions were identified as having a potential involvement in MS. Some critics emphasized the absence of total or even predominant replication between the different genomic screens (Bell and Lathrop, 1996). It is possible that study designs in each case

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underestimated the confounding influence of disease heterogeneity and the limitations of parametric statistical methods of analysis. It should be noted, however, that because each study used a somewhat overlapping but different set of genetic markers and different clinical inclusion criteria, the direct comparison of results is not straightforward. Furthermore, the studies were probably under-powered to reproduce modest genomic effects. Nevertheless, the careful analysis of the composite data indicates common regions of interest between the different studies (Table 3). A recent meta-analysis of the published

Fig. 1. Genes of interest in the 6p21–23 region. The full sequence of the MHC region has been completed and reported by the MHC sequencing consortium in 1999. From 224 identified loci, 128 are predicted to be expressed and about 40% to have immune-response functions. The diagram shows the relative positions of class I and II loci involved in antigen presentation. Other genes mapped in the MHC region include complement proteins, genes for the 21-hydroxylase, tumor necrosis factor and heat-shock proteins, collectively known as class III. Given the extended linkage disequilibrium in this region, the detected association with the HLA-DR2 allele could also represent linkage disequilibrium with a susceptibility allele at another locus in the extended 6p21–p23 segment. To examine this possibility, we have genotyped many additional 6p21–23 markers. The data suggest that a locus (or loci) of interest exists telomeric the class I MHC region in a subgroup of MS families (manuscript in preparation). Suggestive candidate genes include myelin oligodendrocyte glycoprotein (MOG), butyrophilin (BTN), hemochromatosis (HH ), and prolactin (PRL).

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data singled out discrete overlapping MS-susceptibility chromosomal regions at chromosomes 5, 6, 17 and 19 (Wise et al., 1999). A second type of meta-analysis attempted to cluster autoimmune-susceptibility loci from a comparison of the linkage results from 23 human and experimental immune-mediated diseases, including MS and EAE (Becker et al., 1998). Overlapping of susceptibility loci was detected, suggesting that in some cases, part of the pathophysiology of clinically distinct autoimmune disorders may be controlled by a common set of genes. Undoubtedly, these common regions will be the focus of intensive investigation with dense batteries of genetic markers for the direct analysis of candidate genes mapped to these regions.

4. The MHC and multiple sclerosis The MHC chromosomal region is the strongest and most consistent effect identified in MS, and must be discussed in detail. MHC class I and class II molecules are polymorphic cell surface glycoproteins whose primary role in an immune response is to display and present short antigenic peptide fragments to antigen-specific CD41 and CD81 T cells, which can then become activated by a second stimulatory signal and initiate an immune response. In addition, MHC molecules present on stromal cells on the thymus help determine the specificity of the mature T cell repertoire. The human MHC (the human leukocyte antigen — HLA — system; Fig. 1) consists of linked gene clusters located on the short arm of chromosome 6 at 6p21.3, spanning almost four million base pairs. Many of the HLA genes are highly polymorphic, resulting in the generation of enormously diverse numbers of different genotypic combinations or haplotypes. The polymorphic residues that define an HLA allele are clustered in the antigen–peptidebinding grove of the molecule. Hence, the ability to respond to an antigen, whether foreign or self, and the nature of that response, is to a large extent, determined by the unique amino acid sequences of HLA alleles, an observation that provided the rationale for focusing on associations between HLA genotypes and susceptibility to autoimmune disease (Begovich and Oksenberg, 2000). The genetic association between MS susceptibility and the HLA locus has been known for almost 30 years. The majority of HLA population studies in MS have focused on Caucasians of Northern European descent where predisposition to disease has been consistently associated with the class II HLA-DRB1*1501 -DQA1*0102 -DQB1*0602 haplotype (the molecular designation for the serologically defined ‘DR2’ haplotype) (Olerup and Hillert, 1991). Attempts to further localize the MS susceptibility gene with in the DR or DQ regions of the MHC have not provided consensus. When the effect of DRB1*1501 is removed from the DR2 haplotype, evidence against (Allen et al., 1994) and in favor (Spurkland et al., 1991) of a primary role for the DQa1*0102 / b1*0602 heterodimer in

MS susceptibility have been reported. The strong linkage disequilibrium (LD) across the DR-DQ region (Begovich et al., 1992) prevented a clear resolution of the relative contribution of each gene. LD refers to the presence of alleles at neighboring loci occurring together more frequently than would be expected by independent segregation. The recombination rate between HLA-A and HLA-B for example, is 0.31%, much smaller than expected for a physical distance of 1.4 Mbp. The maintenance of these extended haplotypes may reflect the competitive advantage of certain combination of alleles at two or more loci in the immune response against pathogens. Because the patterns of genomic disequilibrium are shaped by the population history (founding size, bottlenecks, expansions), the association of particular DRB1, DQA1 and DQB1 alleles within a given population, can be unique. For example, in Caucasians DRB1*0405 is linked to DQA1*0301 and DQB1*0302, but in Japanese it is coupled to DQA1*0301 and DQB1*0401, in Africans to DQA1*0301 and DQB1*0201, and in Filipinos to DQA1*0101 and DQB1*0503. Hence, by conducting association studies in various ethnic groups with different haplotypic combinations, the different contribution of individual genes can be discerned. This principle was demonstrated in studies of HLA and narcolepsy, and more recently suggested in a cohort of Afro-Brazilian MS patients. In Caucasians, the associations with DRB1*1501 and DQB*0602 are virtually indistinguishable because of the strong LD, whereas in Black narcoleptic and MS patients DQB*0602 segregates independently from DRB1*1501 and shows a primary effect (Rogers et al., 1997; Caballero et al., 1999). The mechanism(s) underlying the genetic association of HLA-DRB1*1501 -DQA1*0102 -DQB1*0602 with MS are not yet fully understood. These MHC molecules may fail to negatively select (delete) autoreactive T cells within the embryonic thymic microenvironment. Alternatively, HLA-DRA1*0101 -DRB1*1501 and / or DQA1*0102 DQB1*0602 genes may encode class II recognition molecules with a propensity to bind peptide antigens of myelin and stimulate encephalitogenic T cells. The HLADRa0101-DRb1501 heterodimer binds with high affinity to the myelin basic protein (MBP) 89–98 peptide. X-ray crystallography of the DR-MBP peptide complex reveals a DRb1501 structure different from other DRb molecules in that aromatic residues are preferred in the P4 pocket of the peptide binding domain (Fig. 2) (Gauthier et al., 1998; Smith et al., 1998). In addition, it was found that two peptide side chains of the p85–99 MBP immuno-dominant peptide, Val89 and Phe92, are the primary anchors and account for the high-affinity binding of the MBP peptide to HLA-Dra0101 / DRb1501. The structural analysis also revealed that only two primary T cell receptor contact residues of MBP p85–99 had to be conserved to properly stimulate antigen-specific clones (Hausmann et al., 1999). The data suggest that microbial peptides with only limited sequence identity with a self-peptide may activate autoreactive T cells.

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5. Susceptibility genes versus modifiers As summarized above, the MHC locus has consistently demonstrated both association and linkage with MS in case–control and family studies, however the role of a gene within this region in determining clinical features or subtypes of MS is unclear. HLA-DR2 has been reported to be associated with lower age at onset, gender, severe,

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relapsing-remitting, and mild MS courses, or to have no influence (Celius et al., 2000; Kira et al., 1996; Masterman et al., 2000; McDonnell et al., 1999, Olerup et al., 1989; Runmarker et al., 1994; Weinshenker et al., 1998). In the EAE disease model it appears that MHC genes primarily influence susceptibility and penetrance, whereas other loci modulate specific phenotypes such as location in brain or spinal cord, demyelination, and severity of inflammation (Butterfield et al., 2000; Encinas et al., 1996). By analogy, it will be of interest to identify which loci are involved in the initial pathogenic events or influence the development and progression of the disease. Several studies examining the influence of non-HLA genes (IL-1R, TNF, APOE, CTLA4, and CCR5 among others) on disease course and severity in MS have been reported and await confirmation (Barcellos et al., 2000; Fukazawa et al., 1999b; Schrijver et al., 1999, Sciacca et al., 1999; Sellebjerg et al., 2000; Weinshenker et al., 1997).

6. Gene expression studies During the process of lesion formation, lymphocyte activation and recruitment, extravasation, and effector functions involve several cellular phenotypic changes triggered by specific gene expression pathways. Cytokines, adhesion molecules, growth factors, and other molecules, such as free radicals, proteases and vasoactive amines, induce and regulate numerous critical cell functions. The comprehensive analysis of these cellular transcriptional programs, the transcriptome, both in the CNS and the periphery, should provide the molecular fingerprint of the demyelinating process and help identify the complete array of MS disease-genes. The first generation of methods for the analysis of gene transcription included the dot blot assay, which constituted a simple but low-resolution way to measure RNA abundance. Northern blot analysis enhanced specificity by incorporating the electrophoretic

Fig. 2. Crystal structure graphic representation of HLA-DRA*0101, DRB*1501 in complex with the MBP peptide 85–99. (A) Top view of the HLA-DR 2 groove with the MBP peptide. Color coding in the DR2specific MHC residues chains: Valb86 in pink (P1 pocket), Argb13 in red (P4 and P6 pockets) Alab71 in red (P4 pocket), Prob9 in blue (P6 pocket), Pheb47 in magenta (P7 pocket), Serb37 in green (P9 pocket). In the MBP peptide, in yellow backbone, highlighted in red and green are P1Val and P4 Phe, respectively, occupying the P1 and P4 hydrophobic pockets, respectively, and serve as anchor residues of the peptide. P2 His, P3 Phe, and P5 Lys, also in red, are putative contact residues important for TCR recognition. Crystallographic data was downloaded from the Protein Databank (www.rcsb.org.pdb). Figures were drawn with Swiss PDBViewer V.3.662, and rendered with POV-Ray V.3.1a. Further details are described in Smith et al. (1998). (B) Side view of the HLA-DR 2 grove with the MBP peptide. TCR contact residues P2 His, P3 Phe and P5 Lys are prominent. (C) Side view of the HLA-DR 2 grove with the MBP peptide. P5 Lys has been substituted by Ala (in green). The Lys.Ala single substitution dramatically changes the cellular response to MBP, and has been suggested has an altered peptide ligand reagent for the treatment of MS (Kappos et al., 2000).

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Table 4 General methods for analysis of gene expression Nucleic acid based

Protein based

Conventional

New generation

Dot blot Northern blot RNAse protection assay Differential display Subtracted cDNA libraries RT-PCR In situ hybridization

Single-pass sequencing of cDNA libraries cDNA fragment fingerprinting SAGE Gene microarrays Kinetic (real time) PCR

size-separation of the RNA population before the blotting step. More sensitive approaches for transcriptional analysis were subsequently developed, including RNAse protection assay, reverse transcriptase (RT)-PCR, subtracted cDNA libraries and differential display (Table 4).

Western blot ELISA Two-dimensional gels Mass spectrometry Protein / peptide microarrays Immunohistochemistry Immunofluorescence Immunoperoxidase Immunogold

Immunohistochemical and molecular (i.e., RT-PCR) analyses of MS-CNS samples, particularly dissected plaques from autopsy tissue and cerebrospinal fluid cells, provide support for a model of lesion development driven by a Th1-type inflammatory response (Balashov et al.,

Table 5 Expression of cytokines and chemokines in MS a Cytokine / chemokine

Chromosomal location

Function

Compartment

Method of analysis

Representative reference

IL-1 IL-2 IL-3 IL-4 IL-6

2q14 4q26–q27 5q31.1 5q31.1 7p21

Th1 Th1 Th2 Th2 Th2

IL-10

1q31–q32

Th2

CSF PBMC PBMC TCC CSF / serum Dendritic cells PBMC TCC

↑ RIA ↓ PCR ↓ ELISA ↑ PCR, ELISA – ELISA ↑ ELISPOT ↓ PCR ↑ PCR, ELISA

IL-12p40

5q31–q33

Th1

PBMC

IL-15 IL-16 IL-17 IL-18

4q31 15q26.3 2q31 11q22.2–22.3

Th1 Th1 Th1 Th1

PBMC / CSF TCC PBMC / CSF CNS

IFN-a IFN-b IFN-g

9p22 12q14

Th1 Th1 / Th2 Th1

TNF-a

6p21.3

Th1

PBMC PBMC PBMC TCC PBMC

TGFb1

19q13.1–13.3

Th1

LT TNF-b

6p21.3

Th1

↑ PCR ↓ PCR ↑ PCR ↑ ELISA ↑ PCR – ELISA ↑ IHC ↓ ELISA ↓ ELISA ↑ ELISA ↑ ELISA, PCR ↑ PCR ↓ PCR ↑ IHC ↓ ELISA, PCR ↑ ELISA ↑ PCR, ELISA

Hauser et al., 1990 Musette et al., 1996 Huberman et al., 1993 Rohowsky-Kochan et al., 2000 Padberg et al., 1999; Huang, Y.M., et al., 1999 van Boxel-Dezaire et al., 1999; Huang, Y.M., et al., 1999; Rohowsky-Kochan et al., 2000 van Boxel-Dezaire et al., 1999; Rohowsky-Kochan et al., 2000 Kivisakk et al., 1998 Biddison et al., 1997 Matusevicius et al., 1999 Fassbender et al., 1999; Balashov et al., 1999 Wandinger et al., 1997 Wandinger et al., 1997 Balashov et al., 1999; Rohowsky-Kochan et al., 2000 Huang, W.X., et al., 1999; Musette et al., 1996 de Groot et al., 1999; Rohowsky-Kochan et al., 2000; Nicoletti et al., 1998 Raine et al., 1998

MCP-1 MIP-1a RANTES

17q11.2–q12

Th1 / Th2 Th1 Th1

CCR5

3p21

Th1

Brain TCC PBMC Astrocytes / oligodendrocytes Astrocytes Microglia CSF PBMC Microglia

↑ ↑ ↑ ↑ ↑ ↑

IHC IHC ELISA ELISA FACS IHC

Balashov et Balashov et Sorensen et Hvas et al., Balashov et

al., 1999 al., 1999 al., 1999; 1997 al., 1999

a Recent technological advances facilitated the accumulation of expression data in prokaryotic and eukaryotic organisms at a speed that far exceeds our understanding of the biological processes. The construction of gene expression databases is being pursued by public and private efforts under the rationale that this information will permit to understand how regulatory information within the cell is organized and interpreted (www.ncbi.nlm.nih.gov / cgap; www.nhgri.nih.gov / DIR / LCC / 15K / HTML / ; www.icr.ac.uk / molcarc / teams / molpath.html; http: / / genome-www4.stanford.edu / MicroArray / MDEV/ ).

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1999; Cannella and Raine, 1995; Jurewicz et al., 1999; Sorensen et al., 1999). In addition, the high expression of MHC class II molecules in MS brains suggests that the local microenvironment may be enriched in MHC-activating factors such as interferon (IFN)-g, and that antigen is presented to T cells. However, the dogmatic application of the Th1 / Th2 paradigm to human demyelination is undoubtedly simplistic (Baranzini et al., 2000; Genain et al., 1996; de Groot et al., 1999; Laman et al., 1998; Steinman, 1997). Indeed, a large number of pro-inflammatory (IL-1, IL-6, RANTES, MIP-1a, TNF-a, TNF-b, INF-g) and regulatory (IL-10, IL-4, TGF-b1) cytokines have been detected in brain, CSF, peripheral blood, and myelin reactive clones isolated from MS patients (Table 5). It is possible that acting in both paracrine and autocrine ways, they constitute a functional network that regulates the cellular interactions that operate in MS. However, most studies of gene expression have been limited to the analysis of single or a few molecular targets, preventing the formulation of a unifying hypothesis of pathogenesis. The Human Genome Project has facilitated the development of a second generation of nucleic acid-based methodologies for the high-throughput analysis of differential gene expression, allowing progress in functional interpretation of genomic information (Table 4) (Baranzini et al., 2000; Brenner et al., 2000; Heller et al., 1996; Higuchi et al., 1993; Khan et al., 1992; Schena et al., 1995; Schena et al., 1996; Velculescu et al., 1995). In MS research, the application of comprehensive methods such as single-pass sequencing of cDNA libraries or microarrays has been restricted so far to the analysis of very few specimens (Becker et al., 1997; Lock et al., 1999; Whitney et al., 1999). A particularly informative way of analyzing transcriptomes requires the assembly of large gene expression datasets into clusters that share the temporal and / or topographic transcriptional patterns (Schena et al., 1995). Cluster analysis can reveal unknown interactions, not previously identified by isolated experiments. This complex set of interactions, the genetic network, is what ultimately defines the function and, therefore, the phenotype. Under appropriate conditions, these integrative models could place molecular features into a rational scenario of network operation, and help to predict the responses of a particular biochemical pathway under a variety of different stimuli, simulated feedback loops, or other interactions. The mathematical modeling of gene networks is largely an unexplored, but emerging field (Mc Adams and Shapiro, 1995; Liang, 1998; Smolen et al., 2000). By using high-capacity technologies, the combined analysis of genomic and transcriptional information, together with the modeling of genetic networks will define a useful conceptual model of pathogenesis and a framework for understanding the mechanisms of action of existing therapies for this disorder, as well as the rationale for novel curative strategies.

179

7. Genetic heterogeneity in MS Studies with multiplex families confirmed the genetic linkage to the MHC region and the specific association with the HLA-DRB1*1501 allele (MS Genetics Group, 1998). Interestingly, 25% of the families, most of them HLA-DRB1*1501 negative, showed no linkage to the MHC locus. We also detected an increased frequency of the DRB1*1501 susceptibility marker in families with many affecteds compared to families with smaller number of affecteds. Both observations suggest the presence of substantial genetic heterogeneity among familial MS in Caucasians. Indeed, it is conceivable that in addition to the recognized phenotypic variations (i.e., primary progressive versus relapsing remitting), MS encompasses more than one fundamental etiological entity. In Asians for example, one form of MS (‘Western-type’) is characterized by disseminated CNS involvement and is associated with the DR2 haplotype, whereas more restricted forms of disease in which optic nerve and / or spinal cord involvement predominate are not associated with DR2 (Kira et al., 1996). Lesions in the non-DR2 associated condition are frequently more severe and necrotizing than in the disseminated form. In addition, there is the intriguing observation that MS in African-American and West Indian young patients may follow a more debilitating course than in Caucasians (Elian and Dean, 1987; Sheremata et al., 1981; Zelnik et al., 1991). Whether differences in genotype underlie different phenotypes of MS or whether a common genotype can produce different phenotypes in response to different causative agents or triggers, is not known. It is likely that clinical and paraclinical (e.g., MRI) variables will assume increasing importance as stratifying elements for future genetic studies of MS.

8. A model of inheritance A simple model of inheritance for all MS is unlikely and cannot account for the nonlinear decrease in disease risk in families with increasing genetic distance from the proband. As summarized above, the available data is most compatible with a complex multifactorial etiology, including both genetic and environmental factors (Table 6). Recurrence risk estimates in multiplex families combined with twin data, predict that the MS-prone genotype results from multiple independent or interacting polymorphic genes, each exerting a small or moderate effect. Thus, the abundance of recent data supports the long-held view that MS is a polygenic disorder, although a Mendelian-like genetic etiology cannot be ruled out for a small subset of pedigrees. Equally significant, it is also likely that genetic heterogeneity exists, meaning that specific genes influence susceptibility and pathogenesis in some affecteds but not in others. Results in multiplex families confirm the genetic importance of the MHC region in conferring susceptibility

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Table 6 Model of genetic contributions to MS Multiple genes with common allelic variants of small or moderate and cumulative effect No major MS gene / locus with the exception of the MHC Susceptibility versus disease modifier genes Complex gene–gene and gene–environment interactions Genetic heterogeneity may result in clinical isoforms

to MS. Susceptibility may be mediated by the class II genes themselves (DR, DQ or both), related to the known function of these molecules in the normal immune response, e.g., antigen binding and presentation and T cell repertoire determination. The possibility that other genes in the MHC or the telomeric region of the MHC are responsible for the observed genetic effect cannot be excluded. The data also indicate that although the MHC region plays a significant role in MS susceptibility, much of the genetic effect in MS remains to be explained. Some loci may be involved in the initial pathogenic events, while others could influence the development and progression of the disease. Their characterization will help to define the basic etiology of the disease, improve risk assessment and influence therapeutics.

9. Conclusions and future directions The confluence of recent technological and analytical advances proposes a new experimental view to elucidate the pathogenic mechanisms of this disease. Specifically: 1. Groups and consortia with the appropriate experimental, clinical and financial resources will continue the analysis of the MS genome using larger familial DNA datasets and dense and informative genetic markers to further narrow the chromosomal segments harboring disease genes. The potentially critical importance of identifying and studying rare families that might have a single gene variant of MS cannot be overstated; this approach has been extraordinary successful in genetic dissection of complex neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases. Similarly, semi-isolated or homogeneous populations could prove to be informative as well (Haines et al., 2000). 2. Population-based case–control studies of candidate genes with limited statistical power will be substituted by the analysis of large collections of nuclear families (the patient and the biological parents, or the patient and healthy siblings) (Spielman et al., 1993; Spielman and Ewens, 1998), or extremely well matched cases and controls. With the advances in deciphering the human genome code and sequences readily available in the public domain, studies will focus on the analysis of single nucleotide polymorphisms (SNP) in candidate genes, in particular genes located in chromosomal segments linked to disease susceptibility or shown to

3.

4.

5.

6.

affect EAE. SNPs are less informative but have several advantages over conventional microsatellite markers in which allele differences are based upon size (Kruglyak, 1997). SNPs are more frequent in the genome than microsatellite markers, and are more stable. They are easy to score across different study sites and permit direct screening of coding and regulatory sequences. Haplotypic association studies using SNPs in non-repetitive sequences, particularly exons, but also introns and regulatory regions, may provide critical information for the final positional cloning of MS-associated genes. Key to the success of the proposed studies will be the availability of rapid, reliable, non-labor intensive methods for high-throughput polymorphism screening (Cheng et al., 1999; Lindblad-Toh et al., 2000). Using a pooled DNA approach can significantly reduce the time and expense of the gene-identification process in MS and other complex diseases (Risch and Teng, 1998). Equal amounts of DNA from a large number of individuals sharing disease status can be mixed together for amplification and genotyping (Barcellos et al., 1997a; Germer et al., 2000). Pools of affecteds and matched controls are grouped separately and then allele frequency estimates are compared. Simplex or trio families (patient, mother and father) can also be used in DNA pooling studies. In this case, parents are pooled separately from the affected offspring, and non-transmitted allele frequencies can be compared with those obtained from the patient pool. In all likelihood, the use of phenotypic (clinical and paraclinical), epidemiological, and demographic variables will assume increasing importance as stratifying elements for genetic studies of MS, and to address the fundamental question of genotype–phenotype correlation in autoimmune demyelination. These studies will be necessarily linked to the development of novel mathematical formulations designed to identify modest genetic effects, as well as interactions between multiple genes, and interactions between genetic, clinical and environmental factors. The analysis of non-Caucasian patient populations with low and intermediate disease risk, both in their native environment and after migration, will provide important new insights and clues about MS genetic and clinical heterogeneity. Gender differences in MS have been well documented. Rigorous studies assessing the potential role of genetic factors in MS sexual dimorphism have not yet been performed. Further, reproductive history in females such as pregnancy and breastfeeding may influence disease pathogenesis. Genomic, clinical and reproductive information will be combined to investigate potential MS risk factors in large groups of female patients. Expression studies to look at the coordinated expression of critical genes encoding cytokines, adhesion molecules, metalloproteinases, molecules involved in apop-

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tosis, and molecules participating in myelin destruction and repair, both in the CNS and the periphery, will contribute to our understanding of how these genes influence susceptibility and pathogenesis in MS. Emerging advances in protein analysis (mass spectroscopy, NMS spectroscopy, X-ray crystallography, yeast twoor three-hybrid systems) (Pandey and Mann, 2000) will facilitate the transition from gene identification to gene function. 7. A significant proportion of MS patients is refractory to currently available treatments. Disease heterogeneity provides an opportunity to determine the relative efficacy of different therapeutic strategies. Genetic polymorphisms in drug receptors, metabolizing enzymes, transporters and targets have been linked to interindividual differences in efficacy and toxicity of many medications (Evans and Relling, 1999). Pharmacogenomic studies will directly address the question of genetic heterogeneity in MS and the response to immunotherapy by analysis of the correlation between different genotypes and clinical response to therapeutic modalities. Having successfully teased generations of immunologists and virologists, the development of new molecular tools to define the immune response in MS and to characterize its genetic basis are opening a window into the intricacies of genetic and environmental interactions that take place in this disorder.

Acknowledgements The authors are supported by the National Multiple Sclerosis Society, the National Institute of Health, and the Nancy Davis and Sandler Foundations. LFB is a National Multiple Sclerosis Society post-doctoral fellow. The concepts and discussion presented in this paper represent the invaluable continuous interactions with Drs. Jonathan L. Haines (Vanderbilt University), Margaret Pericak-Vance (Duke University) and their colleagues.

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