Unbiased identification of susceptibility genes might provide new insights into pathogenic mechanisms that govern complex inflammatory diseases such as multiple sclerosis. In this study we fine mapped Eae18a, a region on rat chromosome 10 that regulates experimental autoimmune encephalomyelitis (EAE), an animal model for multiple sclerosis. We utilized two independent approaches: (1) in silico mapping based on sequence similarity between human multiple sclerosis susceptibility regions and rodent EAE quantitative trait loci and (2) linkage mapping in an F₁₀ (DA x PVG.AV1) rat advanced intercrossed line. The linkage mapping defines Eae18a to a 5-Mb region, which overlaps one intergenomic consensus region identified in silico. The combined approach confirms experimentally, for the first time, the accuracy of the in silico method. Moreover, the shared intersection between the results of both mapping techniques defines a 1.06-Mb region containing 13 candidate genes for the regulation of neuroinflammation in humans, rats, and mice.

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MULTIPLE sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system. As current treatments are only partly beneficial, MS constitutes a significant health burden for individuals and society. The development of better treatment strategies requires the specification of crucial disease mechanisms and thus a better understanding of the causes of MS. It is well established that susceptibility to MS is determined by multiple genes acting in concert with the environment (EBERS et al. 1986, 1995; SADOVNICK et al. 1996; WILLER et al. 2003). An unbiased identification of susceptibility genes would therefore provide new insights into disease pathophysiology and facilitate the identification of suitable drug targets.

Substantial amounts of data have been generated in linkage and association studies of MS (KENEALY et al. 2003) as well as in genetic studies of the animal model, experimental autoimmune encephalomyelitis (EAE) in rats and mice (OLSSON et al. 2000; ANDERSSON and KARLSSON 2004; BECANOVIC et al. 2004). However, apart from the HLA complex (HILLERT and OLERUP 1993; WEINSHENKER et al. 1998), many decades of MS research have as yet failed to determine an unequivocal gene that governs disease susceptibility. Different approaches, based mainly on increasing the power and resolution and/or decreasing the heterogeneity and environmental variation, aimed to facilitate identification of genes that regulate neuroinflammation in humans and rodents. Initial attempts to combine available data in humans have been reported as meta-analyses of whole-genome linkage analyses (WISE et al. 1999; TRANSATLANTIC MULTIPLE SCLEROSIS GENETICS COOPERATIVE 2001; GAMES and TRANSATLANTIC MULTIPLE SCLEROSIS GENETICS COOPERATIVE 2003). A second integrative approach combines data from independent studies of multiple species to identify common regions linked to or associated with different features of neuroinflammation. An example of such a region is Eae18 on rat chromosome 10q24 (DAHLMAN et al. 1999; ROTH et al. 1999; JAGODIC et al. 2004), which shares several homologous genes with other regions on mouse chromosome 11B4 (BUTTERFIELD et al. 2000; KARLSSON et al. 2003) and human chromosome 17p13–q11 (CHATAWAY et al. 1998; BAN et al. 2002; WEBER et al. 2003), which have also been linked to and/or associated with EAE and MS, respectively.

A further development of this integrative approach is the recently reported in silico study that combines data from animal models and human studies (SERRANO-FERNÁNDEZ et al. 2004). One may hypothesize that orthologous chromosomal regions (syntenic blocks) that appear in disease-associated loci are of particular interest since orthologous genes may be responsible for a similar phenotype. The in silico approach yields considerably narrowed intergenomic consensus regions that are linked to or associated with MS/EAE in all three species. These intergenomic consensus regions define the subregions within known quantitative trait loci (QTL) in which genes that contribute to common pathological mechanisms of neuroinflammation are likely to be identified. Nonetheless, every consensus region remains hypothetical unless confirmed by other methods as addressed in this work.

Eae3, a previously described QTL for myelin-induced EAE residing on rat chromosome 10 (ROTH et al. 1999) that comprises Eae18a, harbors several narrow intergenomic consensus regions (SERRANO-FERNÁNDEZ et al. 2004). This article describes a fine mapping of Eae18a by independent in silico mapping and by linkage analysis using the 10th generation of a rat advanced intercross line (AIL) (DARVASI and SOLLER 1995).

AIL:

The AIL used in this study originated from the EAE-susceptible DA and EAE-resistant PVG.AV1 rat strains that share the RT1.AV1 MHC haplotype, allowing identification of non-MHC genes regulating disease. The AIL was produced as previously described (JAGODIC et al. 2004), with the exception that breeding was continued for three additional generations. In the F₁₀ generation, three litters, similar in size and with approximately equal numbers of males and females, were produced for myelin oligodendrocyte glycoprotein (MOG)–EAE experiments.

Inbred DA rats were originally obtained from the Zentralinstitut für Versuchstierzucht (Hannover, Germany) and PVG.AV1 rats from Harlan UK (Blackthorn, UK). Animals were bred and housed at the Karolinska University Hospital (Stockholm), in polystyrene cages containing aspen-wood shavings and with free access to food and standard rodent chow with a 12 hr light/dark cycle. The animals were routinely monitored for pathogens according to a health-monitoring program for rats at the National Veterinary Institute, in Uppsala, Sweden. The local ethical committee in northern Stockholm approved the experiments.

EAE induction and phenotypic evaluation:

Recombinant MOG (aa 1–125 from the N terminus) was expressed in Escherichia coli and purified to homogeneity by chelate chromatography as previously described (AMOR et al. 1994). The purified protein, dissolved in 6 M urea, was dialyzed against PBS to obtain a semiprecipitated preparation that was stored at –20°. Rats between 8 and 16 weeks of age were anesthetized with isofluorane (Forene, Abbott Laboratories, Abbot Park, IL) and immunized with a single subcutaneous injection in the dorsal base of the tail with 200 µl of inoculum containing recombinant MOG (aa 1–125) (20 µg/rat) in saline emulsified with incomplete Freund's adjuvant (Sigma Aldrich, St. Louis). The rats were weighed and monitored daily for clinical signs of EAE from days 7 to 10 until the sacrifice 31–38 days post-immunization (p.i.). The clinical score was graded as follows: 0, no clinical signs of EAE; 1, tail weakness or tail paralysis; 2, hind-leg paraparesis or hemi-paresis; 3, hind-leg paralysis or hemi-paralysis; 4, tetraplegia or moribund; 5, death. The following clinical parameters were assessed: EAE incidence (clinical signs for >1 day), onset of EAE (the first day that clinical signs were observed), maximum EAE score (the highest clinical score observed during EAE), cumulative EAE score (the sum of daily clinical scores), duration of EAE (the number of days with EAE), and weight loss [(weight at day 8 p.i. – minimum weight during the experiment)/weight at day 8 p.i.]. A relapsing/remitting disease was defined as a disease course in which the rats had a nonzero EAE score for at least 2 consecutive days, followed by remission where scores 3 and 4 decreased to 1 or any nonzero score decreased to zero for a minimum of 2 consecutive days, followed by a relapse with an increase in score of a minimum of 2 points with the bout of disease lasting a minimum of 2 days. A monophasic disease course appeared as one bout of disease (nonzero score at least 2 consecutive days) with recovery (score 0 or stable a minimum of 2 consecutive days) and no further signs of disease until day 35. A chronic disease course was defined as a stable nonzero score with no remissions.

Genotyping:

Genotyping of animals was performed on DNA extracted from ear/tail-tip tissues according to a standard protocol (LAIRD et al. 1991). In total, 794 rats were genotyped (428 females and 366 males), corresponding to 772 clinically monitored rats and 22 additional rats for which phenotypic data could not be obtained due to death following anesthesia or too early weaning. Genotype data could not be obtained for 1 of the 772 clinically monitored rats. The region analyzed in the F₁₀ AIL includes the Eae18a region previously linked to MOG–EAE (JAGODIC et al. 2004). The 9-Mb region delimited by markers D10Mgh24 and D10Rat195, respectively, was genotyped with 16 microsatellite markers (Proligo, Paris). PCR amplification was performed as previously described (JACOB et al. 1995) with [-³³P]ATP end-labeled forward primers. The PCR products were size fractioned on 6% polyacrylamide gels and visualized by autoradiography. All genotypes were evaluated manually by two independent observers.

Linkage analysis:

Linkage analysis was performed using GNU R 2.0.1 (http://www.r-project.org) with the R/qtl package version 0.98-57 (BROMAN et al. 2003) using the Haley–Knott multiple regression method, with sex as an interactive covariate, for calculation of LOD scores for all phenotypes. The binary model, used to analyze incidence, and the nonparametric model, used to analyze all other not normally distributed EAE phenotypes, generated comparable results (data not shown). We, however, choose to present data from the Haley–Knott multiple regression method since it allows for analysis of covariates. A LOD score of 4.3 was used as the threshold for significant linkage (LANDER and KRUGLYAK 1995). The marker map was originally determined from the rat genome sequence in Ensembl version 29 and rearranged according to recombination fractions calculated within R/qtl. A confidence interval of 95% for linkage was defined by the closest outmost microsatellite marker after a drop of 1 in LOD. For each phenotype, a drop of 2 in LOD falls within the borders of the combined confidence interval (OT85.22–D10Rat159). A drop of 1 in LOD that previously defined confidence intervals in the F₇ analysis was used to compare F₇ and F₁₀ data.

In silico fine mapping:

The in silico intergenomic fine mapping is based on a systematic analysis of conserved syntenic blocks linked to or associated with MS/EAE in rats, mice, and humans. Both methodology and references for the human, mouse, and rat disease-associated regions have been previously reported (SERRANO-FERNÁNDEZ et al. 2004, 2005).

Starting from QTL regions for EAE in the rat genome, any chromosomal stretch in the mouse genome with a high similarity was regarded as a syntenic region. Overlapping sequences and sequences separated by gaps of selectable maximum size were merged to avoid redundancy and to reduce complexity, respectively. Thereafter the syntenic regions were used in a corresponding way to identify consensus regions in the human genome.

For a global analysis of Eae3, gaps of a maximum size of 1 Mb were merged to segregate well-delimited consensus regions and to estimate the number of genes relevant to the disease that are located in the region. Subsequently, the maximum gap size was reduced to 1 kb for a more detailed analysis. Data from the linkage studies in the 7th and 10th generations of the AIL were not included in the in silico analysis.

Confidence intervals for human susceptibility regions were kept within the limits of ±1 Mb from the linked or associated marker. The database for synteny analyses and gene retrieval was accordingly updated to Ensembl version 29 (http://mar2005.archive.ensembl.org). All genetic positions, marker positions, gene names, and ortholog predictions were retrieved from the Ensembl version 29, if not otherwise stated.

MOG–EAE in an F₁₀ (DA x PVG.AV1) AIL:

Rats immunized with MOG were monitored for clinical signs of EAE up to 35 days post-immunization. The incidence of disease was 29% (224/772), and disease affected more females than males (152:72) (Figure 1). The affected animals displayed different degrees of severity and different disease courses, including relapsing–remitting (25.5%), monophasic (39.7%), and chronic (29.5%) disease courses as well as an ambiguous disease course (5.3%). Registered clinical parameters such as cumulative and maximum EAE score, weight loss, and onset and duration of EAE reflect the spectrum of severity and duration of the disease in the affected animals (Figure 1).

View larger version (15K): In this window In a new window Download PPT slide   FIGURE 1.— Summary of sex distribution and clinical parameters for animals that displayed signs of EAE after immunization. In total, 224 (DA x PVG.AV1) F10 animals displayed signs of EAE after MOG immunization. Number of animals in each phenotypic group is given on the y-axis. The clinical parameters are described in MATERIALS AND METHODS.

 

AIL mapping of Eae18a defines a 5-Mb region that regulates MOG–EAE:

Linkage analysis in the F₁₀ (DA x PVG.AV1) AIL rats with 16 microsatellite markers covering the previously identified QTL confirmed Eae18a (Figure 2). The strongest linkage was detected for duration of EAE (LOD 16.93) close to marker OT90.48. Maximum LOD scores also were detected close to marker OT90.48 for weight loss, incidence, maximum score, and duration and onset of disease, whereas the cumulative disease score had the highest LOD score close to the adjacent marker D10Rat238 (Table 1). The DA allele at peak marker OT90.42/D10Rat238 promotes the disease in an additive manner for all phenotypes. A combined 95% confidence interval of 5 Mb (OT85.22–D10Rat159) was established corresponding to the outmost microsatellite markers for confidence intervals of all phenotypes. Eae18a was originally described as an 5.5-Mb region in a F₇ (DA x PVG.AV1) for the phenotype "incidence" (JAGODIC et al. 2004). The comparable outmost markers for confidence intervals of all phenotypes in F₇, including weight loss that was not analyzed previously, expand over 8.93 Mb (Table 2). The F₁₀ fine mapping describes a 44% smaller region at the same genomic location.

View larger version (14K): In this window In a new window Download PPT slide   FIGURE 2.— Log-likelihood plot of Eae18a that regulates MOG–EAE in the (DA x PVG.AV1) F10 advanced intercross line. Eae18a displayed significant linkage to all clinical EAE phenotypes (incidence of EAE, weight loss, and cumulative EAE score are presented). LOD score values are given on the y-axis whereas microsatellite markers and their positions are on the x-axis. The 95% confidence interval is indicated by the shaded horizontal bar.

 

View this table: In this window In a new window   TABLE 1 Maximum LOD scores and peak markers for six EAE phenotypes analyzed in 794 (DA x PVG.AV1) F10 animals

 

View this table: In this window In a new window   TABLE 2 Size of 1-LOD drop confidence intervals in F7 and F10 and reduction in percentage and fold change

 

In silico fine mapping divides Eae3 into smaller intergenomic consensus regions, one of which overlaps Eae18a:

Eae3 was originally identified as a QTL on rat chromosome 10 that regulates myelin-induced EAE in a rat (BN x LEW) F₂ intercross (ROTH et al. 1999). It spanned a 30-Mb region on chromosome 10 with the peak marker D10Mgh10. In silico intergenomic fine mapping was performed for Eae3, combining previously published information for synteny and linkage to or association with EAE and MS in mice and humans, respectively (BUTTERFIELD et al. 2000; WEBER et al. 2003). The resulting consensuses divide the QTL into five regions (at rat chromosome 10q21, q23–q24, q24, q24-q25, and q26, respectively), suggesting that Eae3 may harbor at least five genes regulating EAE susceptibility.

One of the regions, 10q23–q24, overlaps Eae18a and was analyzed in detail; i.e., sequences of low similarity >1 kb were considered to separate distinct consensus regions. It could thereby be deducted that the 10q23–q24 region is divided into four smaller contiguous regions (45.95–50.1, 52.02–54.7, 56.73–57.1, and 57.86–59.26 Mb on rat chromosome 10). One of these smaller intergenomic consensus regions (57.86–59.26 Mb) is located within Eae18a. The corresponding syntenic regions associated with or linked to MS or EAE are located on human chromosome 17 (5.19–7.19 Mb) and on mouse chromosome 11 (70.46–71.97 Mb), respectively. The 1.4-Mb consensus region on rat chromosome 10 encloses a total of 22 genes, described in rats and mice or with a high sequence homology to genes described in humans.

Combination of AIL and in silico mapping defines a 1.06-Mb consensus region comprising 13 genes:

The Eae18a regulating MOG–EAE was narrowed down by two independent experimental mapping studies, providing robust evidence for linkage and position of the QTL. One of the consensus regions identified using the in silico approach is located within the larger region identified in the F₇ generation and overlaps the region identified in F₁₀ (Figure 3). The overlap between regions identified in F₇, F₁₀, and the intergenomic consensus region comprises 1.06 Mb, spanning from 58.20 to 59.26 Mb on rat chromosome 10. This 1.06-Mb region is 88% smaller than the Eae18a QTL identified in F₇ (8.93 Mb, combined phenotypes) and 79% smaller than the one narrowed down in F₁₀ (5 Mb, combined phenotypes).

View larger version (22K): In this window In a new window Download PPT slide   FIGURE 3.— Schematic of the 1.06-Mb intergenomic consensus region defined by in silico and AIL mapping. The vertical solid bars represent 95% confidence intervals of Eae18a identified by AIL mapping in F7 and F10 generations. The vertical shaded bar represents the intergenomic consensus region defined by in silico analysis in rats, mice, and humans, comprising 22 genes. The number of genes is reduced to 13, listed at the right with their IDs for human genes for the combined 1.06-Mb region that is common for the F10 confidence interval and the intergenomic consensus region. Boldface type indicates genes identified and described in humans, rats, and mice. Italics indicate genes identified in humans and predicted in rats and mice by sequence homology. Boldface italic type indicates genes identified in rats, mice, and humans but described only in humans and mice. Roman type indicates genes identified in rats, mice, and humans but described in humans alone.

 
The reduction in size by 24% of the intergenomic consensus region (from 1.4 to 1.06 Mb) analogously affects the corresponding regions on mouse chromosome 11 and human chromosome 17. The consensus region on human chromosome 17 contains a total of 13 described genes (Table 3, Figure 2).

View this table: In this window In a new window   TABLE 3 Percentage of identity for the genes in consensus between humans, rats, and mice

 

The combined effort of the two mapping approaches presented in this article has several implications: (1) as the first experimental support of the applicability and accuracy of the in silico approach; (2) its focus on a region that regulates neuroinflammation in multiple species, i.e., humans, rats, and mice, thus conferring a particular relevance to that region; and (3) its refinement to 13 genes, a sufficiently small number to allow for individual analysis. A definitive confirmation of the methodology will have to wait until one of those candidates is demonstrated to be involved in the disease.

The repeated mapping of Eae18a in succeeding generations of AIL confirmed linkage to MOG–EAE in this region and refined the position of the QTL. Despite the differences in sample size and incidence of EAE in the two studies (F₇ and F₁₀), an overlapping region was identified. The improved resolution (here, reduction in QTL size) was 44% between F₇ and F₁₀. The higher resolution enables a more accurate positioning of the QTL and identifies a smaller region for focus of further studies. This is particularly important for gene-dense regions such as Eae18a, in which 150 genes are located within a 5-Mb confidence interval. Although the use of more advanced generations could, in principle, provide an even higher resolution, the degree of improvement is not considerable. The size of the QTL confidence interval decreases exponentially with the number of intercross generations; however, this decrease becomes linear after the 10th generation (DARVASI and SOLLER 1995; XIONG and GUO 1997).

The overlap between one of the consensus regions defined from a broad Eae3 using the in silico approach, and Eae18a identified independently in the AIL, further supports the hypothesis that homologous regions are involved in regulation of rodent EAE and human MS. The experimental setup that led to the identification of the QTL regulating EAE in rodents differs with respect to the selection of inbred strains and induction protocols, such as the antigen and adjuvant applied. These factors are known to influence the incidence, severity, and course of the disease, as well as the histopathological features of EAE (LORENTZEN et al. 1995; STORCH et al. 1998). Nevertheless, a number of consensus regions have been identified to date, indicating that pathological mechanisms are shared between the most commonly used EAE models (SERRANO-FERNÁNDEZ et al. 2004).

In the case of the 1.06-Mb region identified here, four different induction protocols are represented, including spinal cord homogenate, myelin, MOG, and myelin basic protein peptide used independently as antigens. The potency of the in silico approach to identify common regulatory regions regardless of the EAE model or strain combination provides a useful resource for combining different studies of EAE in rodents. Furthermore, this finding supports the concept of common pathways and homologous susceptibility genes in human and rodent neuroinflammation. This does not exclude the possibility of additional genes regulating MOG–EAE in rats, located within F₇ and F₁₀ confidence intervals, but rather directs our attention to pathways shared by three species with effects that are strong enough to be detected.

A number of interesting candidates for regulation of neuroinflammation are present among the 13 genes within the combined 1.06-Mb consensus region. These include genes with regulatory functions in inflammation, cell survival, transcription regulation, and protein degradation. The gene HSXIAPAF1 encodes the protein x-linked inhibitor of apoptosis protein (XIAP)-associated factor-1 isoform 1, an inhibitor of XIAP. The ratio of XIAP/XAF1 is suggested as a regulator of susceptibility to apoptosis of motoneurons after axotomy in adult mice (PERRELET et al. 2004). Thioredoxin-like 5, encoded by TXNL5, is involved in tumor necrosis factor-induced NF-B signaling and apoptosis in vitro (JEONG et al. 2004). A gene similar to arachidonate 15-lipoxygenase, an enzyme involved in regulation of inflammation in asthma (PROFITA et al. 2000) and rheumatoid arthritis (LIAGRE et al. 1999), is also found in this region. Furthermore, both AIPL1, the causative gene for Leber congenital amaurosis type 4 (SOHOCKI et al. 2000), and PITPNM3, which is also involved in retinal degeneration, are located within the combined consensus region. The function of these and other genes as well as regulatory elements within the region suggests possible effects on the pathogenesis of MS and EAE.

The 1.06-Mb consensus region is supported by data from five rat strains in three different crosses in rats, including the AIL reported in this study (DAHLMAN et al. 1999; ROTH et al. 1999). This enables us to determine the allelic state of a susceptibility gene in five inbred strains, where DA/LEW alleles are disease predisposing whereas ACI/BN/PVG alleles are protective. Additionally, four different inbred mouse strains (SJL/J, B10S/DvTe, B10.RIII, RIIIS/J) in two separate crosses support an effect of the homologous region on mouse chromosome 11 (BUTTERFIELD et al. 2000; KARLSSON et al. 2003). Information from the strains supporting the linkage to this region in both species can be used to identify haplotype blocks within this region. Two independent studies have previously reported that the majority of haplotype blocks in the laboratory mouse exceed 1 Mb of length (WADE et al. 2002; WILTSHIRE et al. 2003) but a more recent estimation sets the average size of haplotypes at 45 kb (ZHANG et al. 2005). The resolution of additional mapping depends on the actual haplotypes in the region, but interpolation of these data to the laboratory rat suggests that the 1.06-Mb region can be further constrained using haplotype block mapping. The small number of genes, however, also permits a systematic and unbiased approach to individual evaluation of all genes within this region, including investigation of genetic polymorphisms, expression analyses, and functional studies, as well as studies of association with human disease.

In summary, fine mapping in an advanced intercross line provides the first experimental evidence for the utility of the in silico approach in identifying and narrowing down intergenomic consensus regions. The combined approaches define a 1.06-Mb region with 13 candidate genes that regulates neuroinflammation in humans, rats, and mice.

We thank R. A. Harris for linguistic advice. The study was supported by the Swedish Research Council, Bibbi and Nils Jensen's Foundation, Torsten and Ragnar Söderberg's Foundation, the Montel William's Foundation, The Swedish Association of Persons with Neurological Disabilities, and Federal Ministry of Education and Research project NBL3 (FKZ 01ZZ0108).

² These authors contributed equally to this work.

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