Skip to main content
NCBI home page
Genetics logoLink to Genetics
. 2006 Feb;172(2):1147–1153. doi: 10.1534/genetics.105.049049

eae36, a Locus on Mouse Chromosome 4, Controls Susceptibility to Experimental Allergic Encephalomyelitis in Older Mice and Mice Immunized in the Winter

Cory Teuscher *,1, R W Doerge †, Parley D Fillmore ‡, Elizabeth P Blankenhorn §
PMCID: PMC1456213  PMID: 16299394

Abstract

Genetic factors are believed to contribute to multiple sclerosis (MS) susceptibility; however, strong evidence implicating intrinsic and environmental factors in the etiopathogenesis of MS also exists. Susceptibility to experimental allergic encephalomyelitis (EAE), the principal animal model of MS, is also influenced by nongenetic factors, including age and season at immunization. This suggests that age- and season-by-gene interactions exist and that different susceptibility loci may influence disease as a function of the two parameters. In this study, linkage analysis based on genome exclusion mapping was carried out using age and season at immunization restricted cohorts of (B10.S × SJL/J) F2 intercross mice in an effort to identify such linkages. Significant linkage of EAE to eae4 and eae5 was detected with 6- to 12-week-old and summer cohorts. In contrast, significant linkage of EAE to eae4 and eae5 was not detected with the >12-week-old and winter/spring populations. Rather, significant linkage to D4Mit203 at 128.50 Mb on chromosome 4 was detected with animals that were >12 weeks old at the time of immunization or were immunized in the winter. This previously unidentified locus has been designated eae36. These results support the existence of age- and season-by-gene-specific interactions in the genetic control of susceptibility to autoimmune inflammatory disease of the central nervous system and suggest that late-onset MS may be immunogenetically distinct.

MULTIPLE sclerosis (MS) is the major inflammatory demyelinating disease of the central nervous system (CNS) in humans. The etiology of MS is unknown but is believed to have an autoimmune basis arising in genetically susceptible individuals as a consequence of an environmental insult (Compston and Coles 2002). Support for a genetic component in MS exists (Dyment et al. 2004) and considerable effort has gone into identifying the loci contributing to susceptibility; however, with the exception of linkage and association to HLA, there is little consensus and reproducibility across studies (Compston and Sawcer 2003; Herrera and Ebers 2003). These results and the 70–90% discordance rates in identical twins indicate that nongenetic factors also play a strong role in disease risk (Willer et al. 2003).

A recent systematic review of potential nongenetic factors in MS indicates that solar ultraviolet radiation and sex hormones are the best candidates (Coo and Aronson 2004). Additionally, parent-of-origin (POO) effects have been reported to influence MS susceptibility and outcome (Hupperts et al. 2001). A recent study utilizing a half-sibling approach supports a significant maternal POO effect (2.35% for shared mother and 1.31% for shared father) (Ebers et al. 2004). Importantly, the risk for siblings who share only a mother compared with the risk for full siblings was not significantly different (2.34% vs. 3.11%, P = 0.1), suggesting that the maternal POO effect may be the major component underlying familial aggregation (Giordano and Momigliano-Richiardi 2004). A recent study using 17,874 Canadian patients and 11,502 British patients with MS also revealed a significant association between month of birth and MS risk (Willer et al. 2005). This effect was more pronounced in familial cases, implying that gene-environment interactions may underlie the association.

Susceptibility to experimental allergic encephalomyelitis (EAE), the foremost autoimmune model of MS, is also influenced by nongenetic factors. These include both adult (Teuscher et al. 1998; Blankenhorn et al. 2000; Fillmore et al. 2003) and neonatal environmental influences (Dimitrijevic et al. 1994; Laban et al. 1995a,b). We also recently demonstrated that gender, age, and season at immunization uniquely influence susceptibility to EAE in the mouse (Teuscher et al. 2004). Gender was shown to be more important than age and season at immunization in influencing histopathological disease, and in contrast to CNS lesions, age and season at immunization significantly and independently influence susceptibility to clinical signs and do so equally in both males and females. Moreover, linkage to eae5, the H2-linked locus controlling susceptibility to clinical disease, was age and season dependent. These data suggest that age- and season-by-gene interactions exist and that different subsets of susceptibility loci may control clinical disease as a function of the two parameters. In this study we carried out linkage analysis based on genome exclusion mapping using phenotyped age and season restricted cohorts of (B10.S × SJL/J) F2 intercross mice to map such loci. We report that linkage to eae4, as with eae5, is restricted to 6- to 12-week-old and summer cohorts (Teuscher et al. 2004); and the mapping of a previously unidentified locus on chromosome 4, designated eae36, controls susceptibility to EAE in F2 intercross mice that were >12 weeks old at the time of immunization or were immunized in the winter.

MATERIALS AND METHODS

Animals:

The mice used in this study comprise 760 genotyped (B10.S/DvTe × SJL/J) F2 mice used in our earlier studies (Teuscher et al. 2004) and an additional cohort of 366 genotyped (B10.S/SgMcdJ × SJL/J) F2 mice generated at the University of Illinois at Urbana–Champaign (Fillmore et al. 2004). Animals were maintained under standard environmental conditions, including controlled temperature, humidity, and a 12-hr light:12-hr dark cycle and in accordance with the Animal Welfare Act and the Public Health Service Policy on the Humane Care and Use of Laboratory Animals. Additionally, the infectious disease status of the colony was monitored serologically using a standard sentinel program. No change in the serologic profile of the animals was observed over the course of the experiments.

Induction and evaluation of EAE:

Mice were immunized for the induction of EAE as described previously (Butterfield et al. 1998; Teuscher et al. 2004). Briefly, mouse spinal cord homogenate (MSCH) was generated using retired breeder SJL/J mice purchased from either the Jackson Laboratory or the Charles River Laboratory (Wilmington, MA). MSCH–complete Freund's adjuvant (CFA) emulsions were prepared by syringe extrusion using disposable syringes and a 21-gauge double-hub microemulsifying needle. Animals received 0.3 ml of SJL/J MSCH–CFA emulsion via two subcutaneous injections in the posterior right and left flank (2 × 0.15 ml). One week later all mice were similarly injected at two sites on the right and left flanks anterior of the initial injection sites. In this way each animal received a total of 2.0 mg dry weight SJL/J MSCH and 30.0 μg of Mycobacterium tuberculosis H37Ra. Animals were monitored for clinical signs of EAE starting at day 10 after injection through day 60. All animals that exhibited any clinical signs greater or equal to a flaccid tail and/or hind leg weakness for 2 or more consecutive days were considered affected.

Linkage analysis:

Informative microsatellite primers were either purchased from Research Genetics (Huntsville, AL) or synthesized according to sequences obtained through Mouse Genome Informatics. Polymerase chain reaction parameters for microsatellite typing were as previously described (Butterfield et al. 1998). Microsatellite size variants were resolved by electrophoresis on large-format denaturing polyacrylamide gels and visualized by autoradiography on Kodak film (Eastman-Kodak, Rochester, NY).

A chi-square test statistic was employed to test single-marker linkage in 2 × 3 contingency tables at an average marker density of ∼10 cM. To accommodate the well-known statistical issues associated with single-marker linkage analysis (Churchill and Doerge 1994), we relied on permutation testing to assess the statistical significance. The phenotypic data were randomized, while holding the genotypic information fixed, and the chi-square test statistic was calculated for each stratified marker (Good 1993). The experimentwise threshold values were calculated as previously described (Churchill and Doerge 1994). Given 1000 permutations of the original data, the 5% experimentwise threshold was used to indicate significant linkage to marker loci.

Candidate gene analysis:

Gene-specific restriction fragment length polymorphisms were generated for nonsynonymous single nucleotide polymorphisms in Csf3r, Pnrc2, and Htr1d (http://www.aretha.jax.org/pub-cgi/phenome/mpdcgi?rtn=docs/home). A 916-bp Csf3r product was PCR amplified with the primers 5′-CCTCTCCAACCCTCCTAACAG-3′ and 5′-CAGATTCAGGCGGATTTCTG-3′. Restriction of the B10.S product with ApoI results in 289- and a 627-bp fragments whereas cleaving the SJL/J allele with ApoI gives rise to 201-, 289-, and 426-bp fragments. For Pnrc2, a 918-bp product was PCR amplified with the primers 5′-GAGCCTAATGCCATCTTGTC-3′ and 5′-CTCTGGGTACTTATCCACTGC-3′. Restriction with AflII gives rise to 386- and 532-bp fragments for SJL/J and 247-, 285-, and 386-bp fragments with the B10.S allele. A 1026-bp Htr1d product was PCR amplified (5′-CAGTAGAGTGTCAAAGGCGAG-3′ and 5′-GCGGCCATACAGGATAATG-3′) and the products restricted with Tsp45I. The SJL/J allele exhibits two fragments (326 and 700 bp) while the B10.S allele results in three fragments (259, 326, and 441 bp). In all cases, genomic DNA was amplified under standard conditions and the products/fragments were electrophoresed in 2% agarose gels and visualized by ethidium bromide.

RESULTS AND DISCUSSION

Genome exclusion mapping was carried out utilizing 170 informative microsatellite markers and DNA isolated from a cohort of 760 phenotyped (B10.S × SJL/J) F2 intercross mice (Butterfield et al. 1998; Teuscher et al. 2004) having a mean age at immunization of 15.3 ± 10.5 weeks. When all animals were included in the analysis, significant linkage to marker loci on chromosomes 7, 16, and 17 was detected (Table 1). Linkage to other marker loci at or above the suggestive level (90–95% level for permutation-derived thresholds) was not detected. These linkages are consistent with prior results based on the incidence of clinical disease and reflect linkage to eae4, eae11, and eae5, respectively (http://www.informatics.jax.org/).

TABLE 1.

Linkage of EAE susceptibility to marker loci is a function of age and season at immunization

Marker Mba Allb 6–12 wkc >12 wkd Summere Winterf eae locus
D7Mit85 45.74 21.1; >99% 17.2; 95–99% 4.8; <90% 16.8; 95–99% 4.9; <90% eae12
D7Mit233 62.26 23.0; >99% 19.6; >99% 5.6; <90% 20.7; >99% 3.6; <90% eae4
D16Mit140 69.73 16.0; 95–99% 7.6; <90% 8.5; <90% 10.1; <90% 7.8; <90% eae11
H2-K 31.70 eae1
H2-D 32.97
D17Mit105 38.72 14.3; 90–95% 13.5; <90% 3.0; <90% 15.0; 90–95% 1.5; <90% eae5
D17Mit176 40.15 22.2; >99% 19.8; >99% 5.1; <90% 22.2; >99% 2.6; <90%
D17Mit51 40.91 15.8; 95–99% 15.8; 95–99% 3.5; <90% 18.4; >99% 1.2; <90%
D17Mit115 44.99 16.0; 95–99% 11.6; <90% 5.2; <90% 15.8; 90–95% 1.9; <90%
D17Mit70 50.12 17.1; 95–99% 15.0; 90–95% 3.0; <90% 17.7; 95–99% 1.8; <90%
Open in a new tab

Mice were immunized for the induction of EAE and scored as affected and unaffected starting on day 10 (Butterfield et al. 1998).

a

Locations are based on the March 2005 mouse (Mus musculus) draft genome data obtained from the Build 34 assembly by NCBI.

b

Experimentwise critical values for significance levels with 1000 permutations of the linkage data: 90% = 14.22, 95% = 15.73, 99% = 20.57; n = 760, marker number = 153.

c

Experimentwise critical values for significance levels with 1000 permutations of the linkage data: 90% = 14.08, 95% = 15.62, 99% = 18.43; n = 374, marker number = 170.

d

Experimentwise critical values for significance levels with 1000 permutations of the linkage data: 90% = 13.55, 95% = 14.90, 99% = 16.99; n = 288, marker number = 153.

e

Experimentwise critical values for significance levels with 1000 permutations of the linkage data: 90% = 14.40, 95% = 15.83, 99% = 18.35; n = 540, marker number = 170.

f

Experimentwise critical values for significance levels with 1000 permutations of the linkage data: 90% = 13.87, 95% = 15.52, 99% = 18.91; n = 220, marker number = 153.

Stratification by age and season at immunization (Teuscher et al. 2004) resulted in significant linkage to eae4 and eae5 with both the 6- to 12-week-old and summer cohorts (Table 1). Importantly, linkage to these loci was not detected even at the suggestive level with the >12-week-old and winter cohorts, indicating that genetic linkage of disease susceptibility to both eae4 and eae5 is age and season dependent; i.e., age and season at immunization are capable of overriding the major genetic checkpoints controlling susceptibility to EAE in younger animals and in animals immunized in the summer.

The absence of significant linkage to marker loci with the >12-week-old and winter/spring cohorts suggests that disease susceptibility in older animals and in animals immunized in the winter may be less dependent on genetic factors and thus would not support the concept that polymorphism in different genes controls susceptibility to autoimmune inflammatory disease of the CNS as a function of age and season at immunization. Alternatively, the inability to detect a statistically significant linkage in these two cohorts in part may be due to limited sample size and insufficient power (228 in the >12-week-cohort and 220 in the winter cohort). To address this issue, an independent cohort of 366 mice consisting primarily of animals immunized in the winter and having a mean age of 31.0 ± 12 weeks at immunization was studied. As with the stratified cohorts, significant linkage to eae4, eae11, and eae5 was not detected. However, significant linkage of EAE to D4Mit221, D4Mit203, and D4Mit204 was detected (Table 2). Thus, age- and season-by-gene-specific interactions in older mice and mice immunized in the winter involve one or more loci on chromosome 4 that do not contribute to age- and season-by-gene-specific interaction in younger mice and in mice immunized in the summer. Linkage of EAE to chromosome 4 markers has not been reported previously; consequently, we have designated this locus eae36. Interestingly, eae36 colocalizes with Lbw2, Lmb1, Arvm2, Sles2, Idd9.1, and Idd11, raising the possibility that this region of chromosome 4 harbors one or more shared autoimmune disease loci (Teuscher 1985; Sudweeks et al. 1993; Meeker et al. 1995; Ma et al. 2002).

TABLE 2.

Linkage of EAE susceptibility to marker loci in older F2 intercross mice immunized in the winter

Marker Mba Allb >12 wkc Winterd eae locus
D4Mit221 122.23 17.8; 95–99% 14.8; 90–95% 13.0; <90% eae36
D4Mit203 128.50 21.6; >99% 18.9; >95–99% 17.6; 95–99%
D4Mit204 132.29 23.3; >99% 20.9; >99% 21.1; >99%
D7Mit85 45.74 7.0; <90% 7.3; <90% 7.1; <90% eae12
D7Mit233 62.26 7.0; <90% 7.3; <90% 7.0; <90% eae4
D16Mit140 69.73 1.3; <90% 2.5; <90% 2.2; <90% eae11
H2-K 31.70 eae1
H2-D 32.97
D17Mit105 38.72 0.7; <90% 0.4; <90% 2.3; <90% eae5
D17Mit176 40.15 0.2; <90% 0.3; <90% 1.0; <90%
D17Mit51 40.91 0.2; <90% 0.4; <90% 0.3; <90%
D17Mit115 44.99
D17Mit70 50.12 1.9; <90% 2.6; <90% 0.5; <90%
Open in a new tab

Mice were immunized for the induction of EAE and scored as affected and unaffected starting on day 10 (Butterfield et al. 1998).

a

Locations are based on the March 2005 mouse (Mus musculus) draft genome data obtained from the Build 34 assembly by NCBI.

b

Experimentwise critical values for significance levels with 1000 permutations of the linkage data: 90% = 14.05, 95% = 15.88, 99% = 19.43; n = 366, marker number = 125.

c

Experimentwise critical values for significance levels with 1000 permutations of the linkage data: 90% = 13.47, 95% = 14.50, 99% = 20.70; n = 337, marker number = 125.

d

Experimentwise critical values for significance levels with 1000 permutations of the linkage data: 90% = 13.56, 95% = 15.21, 99% = 18.48; n = 250, marker number = 125.

Taken together, these data suggest that age- and season-by-gene interactions exist and that different subsets of susceptibility loci appear to control clinical disease as a function of the two parameters. Age-by-gene interactions are of potential significance in the genetics of MS. MS is considered to be primarily a disease of young adults since the age range of disease onset is usually between 20 and 40 years (Poser et al. 1982) with a mean age of onset of 27 years (Kurtzke 1993). However, late-onset MS, defined as the first presentation of clinical symptoms after 50, has been reported to be in the range of 1.1–10.0% (Noseworthy et al. 1983; Marra 1984; Safran 1989; White et al. 1990; Azzimondi et al. 1994; Polliack et al. 2001; Delalande et al. 2002). Importantly, a number of studies (Noseworthy et al. 1983; Hooge and Redekop 1992; Azzimondi et al. 1994; Trefouret et al. 1996; Polliack et al. 2001; Delalande et al. 2002), albeit not all (White et al. 1990), report a poorer prognosis and a more rapid progression to disability in late-onset MS patients compared to younger patients. This suggests that late-onset MS may represent a phenotypically and genotypically distinct subset of patients. In this regard our data support the concept that late-onset autoimmune inflammatory disease of the CNS is in fact immunogenetically distinct from young-adult-onset disease.

The mechanisms underlying the age and season effects on the genetic control of susceptibility to EAE are unknown. However, they may be acting at the level of the CNS, the immune system, or both since seasonal variation in the plasticity of the structure and function of the adult brain (Tramontin and Brenowitz 2000) and expression of immunological factors (Haus and Smolensky 1999) is well documented. Similar age-associated changes in immune function also exist. Peripheral immunological functions decline with age (Miller 1996), whereas within the brain immunologically important cells such as astrocytes (Goss et al. 1991; O'Callaghan and Miller 1991; Nichols et al. 1993; Morgan et al. 1997) and microglia (Mattiace et al. 1990; Peters et al. 1991; Ogura et al. 1994; Rozovsky et al. 1998; Sheffield and Berman 1998; Sheng et al. 1998) exhibit increased activation with age. Age-related changes associated with microglial activation include increased phagocytic activity (Dickson et al. 1990), expression of MHC class II genes (Mattiace et al. 1990; Sheffield and Berman 1998), and secretion of proinflammatory cytokines (Takao et al. 1996; Sheng et al. 1998; Nanamiya et al. 2000). Additionally, increased expression of cytokine and chemokine genes and their receptors occurs within the aging cortex, midbrain, hippocampus, and cerebellum (Felzien et al. 2001; Terao et al. 2002).

Although MHC class I and II alleles do not segregate in this cross, and therefore cannot underlie eae1 or eae5 (Butterfield et al. 1998), they nevertheless serve to illustrate a scenario whereby environmental factors influencing the expression of structurally polymorphic disease susceptibility genes can exhibit gene-by-environment-dependent genetic linkage. Genetic resistance due to suboptimal affinity of a peptide ligand for MHC alleles may effectively be overcome by age-related increases in cell-surface density. Consequently, genetic linkage to MHC alleles will be detected only when antigen recognition is primarily a function of peptide–MHC affinity and not of the density of peptide–MHC complexes on the surface of antigen-presenting cells. Importantly, genetic linkage to any structural polymorphism whose functional difference can be overcome by increased expression of either the receptor or the ligand has the potential to exhibit similar behavior in genetic linkage studies.

The Mouse Phenome Database (http://www.aretha.jax.org/pub-cgi/phenome/mpdcgi?rtn=docs/home) was used to search for nonsynonymous SNPs in immunologically relevant candidate receptor–ligand systems linked to eae36 (±5 cM of D4Mit204 or from 118 to 137 Mb). Using this approach, we identified three receptors (Table 3). The first was colony-stimulating factor 3 receptor (Csf3r). The SJL/J and C57BL/10 Csf3r alleles are distinguished by an N379S polymorphism (rs13477964). Functionally, colony stimulating factor 3 (Csf3) has been shown to elicit long-lived protective effects on both clinical and histopathologic EAE (Lock et al. 2002; Zavala et al. 2002] and treatment of lupus-prone MRL-lpr/lpr (Zavala et al. 1999) and NOD mice (Kared et al. 2005) with Csf3 induces substantial protection from disease. The second candidate in this region is proline-rich nuclear receptor coactivator 2 (Pnrc2). The SJL/J and C57BL/10 alleles of Pnrc2 are distinguished by an L41H substitution (rs13478000). Pnrc2 interacts with nuclear receptors using a proline-rich sequence to modulate their transcriptional activity. Importantly, Pnrc2 interacts with a number of orphan receptors in a ligand-independent manner and with the estrogen, glucocorticoid, progesterone, thyroid, retinoic acid, and retinoid X receptors in a ligand-dependent manner (Zhou and Chen 2001). Interestingly, Pnrc1/B4-2, the first proline-rich nuclear receptor coregulatory protein identified, was isolated from a natural killer (NK) minus T-cell subtractive library, suggesting that this newly identified class of nuclear receptor coregulatory proteins may play a significant role in nuclear receptor transcriptional activity in cells of the innate immune system (Chen et al. 1995). NK cells have also been shown to regulate CD4 T-cell responses and the outcome of B-cell-mediated autoimmune responses (Shi et al. 2000; Dowdell et al. 2003). Finally, 5-hydroxytryptamine (5-HT/serotonin) receptor 1D (Htr1d) is found in this interval. The SJL/J and C57BL/10 alleles of Htr1d are distinguished by an A22V substitution (rs13478001). 5-HT is an indolamine that interacts with multiple receptors mediating a wide range of physiological functions. These include neurotransmitter (Aghajanian and Sanders-Bush 2002), immunomodulatory (Mossner and Lesch 1998), and vascular functions, including regulation of brain microcirculation and blood brain barrier permeability (Cohen et al. 1996). In this regard, SJL/J mice, the prototypical EAE susceptible strain, exhibit hypersensitivity to the vasoactive effects of 5-HT (Linthicum and Frelinger 1982), a phenotype that can be blocked by the 5-HT1 receptor antagonist methiothepin mesylate (Khare et al. 2000). In addition, C67BL/6 mice lacking the 5-HT transporter (Slc6a4) purportedly develop attenuated clinical and histopathologic EAE as compared to wild-type mice. This difference is associated with reduced production of INFγ by neuroantigen-specific splenocytes and is sexually dimorphic in that it is more pronounced in female mice (Hofstetter et al. 2005).

TABLE 3.

Linkage of EAE susceptibility to Csf3r, Pnrc2, and Htr1d SNP markers in older F2 intercross mice and mice immunized in the winter

Marker Mba Allb >12 wkc Winterd
D4Mit221 122.23 17.8; 95–99% 14.8; 90–95% 13.0; <90%
Csf3r 125.05 18.8; 95–99% 15.7; 95–99% 15.5; 95–99%
D4Mit203 128.50 21.7; >99% 18.9; >99% 17.6; >99%
D4Mit204 132.29 23.4; >99% 20.9; >99% 21.1; >99%
Pnrc2 134.75 19.6; >99% 18.5; >99% 20.4; >99%
Htr1d 135.30 19.8; >99% 18.9; >99% 21.0; >99%
Open in a new tab

Mice were immunized for the induction of EAE and scored as affected and unaffected starting on day 10 (Butterfield et al. 1998).

a

Locations are based on the March 2005 mouse (Mus musculus) draft genome data obtained from the Build 34 assembly by NCBI.

b

Experimentwise critical values for significance levels with 1000 permutations of the linkage data: 90% = 13.48, 95% = 15.16, 99% = 19.50; n = 366, marker number = 127.

c

Experimentwise critical values for significance levels with 1000 permutations of the linkage data: 90% = 13.77, 95% = 15.01, 99% = 18.27; n = 337, marker number = 127.

d

Experimentwise critical values for significance levels with 1000 permutations of the linkage data: 90% = 13.81, 95% = 15.51, 99% = 17.59; n = 243, marker number = 127.

In terms of candidate genes, phenotypes controlled by Csf3/Csf3r and the serotonin/5-HT1 receptor class exhibit seasonal- (Brewerton 1989; Smaaland et al. 2002) and age- (Meltzer et al. 1998; Berkahn and Keating 2004) related variations. Similarly, age- and season-dependent fluctuations in nuclear hormone levels are well documented. Under limiting conditions, a Pnrc2 polymorphism could significantly impact gene expression by influencing the transcriptional activity of multiple nuclear hormone receptors, and thus all three candidate receptors could qualify for the eae36 culprit gene. Clearly, the identification of the polymorphism underlying eae36 will aid in delineating the mechanisms underlying age- and season-by-gene-specific interaction in autoimmune inflammatory demyelinating diseases of the CNS.

Acknowledgments

This work was supported by grants from the National Institutes of Health (NS36526, AI45105, AI41747, and AI45666) and the National Multiple Sclerosis Society (grant RG-3129).

References

  1. Aghajanian, G. K., and E. Sanders-Bush, 2002. Serotonin, pp. 15–34 in Neuropsychopharmacology: The Fifth Generation of Progress, edited by K. L. Davis, D. Charney, J. T. Coyle and C. Nemeroff. Lippencott Williams & Wilkins, Philadelphia.
  2. Azzimondi, G., A. Stracciari, R. Rinaldi, R. D'Alessandro and P. Pazzaglia, 1994. Multiple sclerosis with very late onset: report of six cases and review of the literature. Eur. Neurol. 34: 332–336. [DOI] [PubMed] [Google Scholar]
  3. Berkahn, L., and A. Keating, 2004. Hematopoiesis in the elderly. Hematology 9: 159–163. [DOI] [PubMed] [Google Scholar]
  4. Blankenhorn, E. P., R. J. Butterfield, R. Rigby, L. Cort, D. Giambrone et al., 2000. Genetic analysis of the influence of pertussis toxin on experimental allergic encephalomyelitis susceptibility: an environmental agent can override genetic checkpoints. J. Immunol. 164: 3420–3425. [DOI] [PubMed] [Google Scholar]
  5. Brewerton, T. D., 1989. Seasonal variation of serotonin function in humans: research and clinical implications. Ann. Clin. Psychiatry 1: 153–164. [Google Scholar]
  6. Butterfield, R. J., J. D. Sudweeks, E. P. Blankenhorn, R. Korngold, J. C. Marini et al., 1998. New genetic loci that control susceptibility and symptoms of experimental allergic encephalomyelitis in inbred mice. J. Immunol. 161: 1860–1867. [PubMed] [Google Scholar]
  7. Chen, J., L. Liu and B. Pohajdak, 1995. Cloning a cDNA from human NK/T cells which codes for a protein with high proline content. Biochim. Biophys. Acta 1264: 19–22. [DOI] [PubMed] [Google Scholar]
  8. Churchill, G. A., and R. W. Doerge, 1994. Empirical threshold values for quantitative trait mapping. Genetics 138: 963–971. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Cohen, Z., G. Bonvento, P. Lacombe and E. Hamel, 1996. Serotonin in the regulation of brain microcirculation. Prog. Neurobiol. 50: 335–362. [DOI] [PubMed] [Google Scholar]
  10. Compston, A., and A. Coles, 2002. Multiple sclerosis. Lancet 359: 1221–1231. [DOI] [PubMed] [Google Scholar]
  11. Compston, A., and S. Sawcer, 2003. Genetic analysis of multiple sclerosis in Europeans (GAMES). J. Neuroimmunol. 143: 1–139. [DOI] [PubMed] [Google Scholar]
  12. Coo, H., and K. J. Aronson, 2004. A systematic review of several potential non-genetic risk factors for multiple sclerosis. Neuroepidemiology 23: 1–12. [DOI] [PubMed] [Google Scholar]
  13. Delalande, S., J. De Seze, D. Ferriby, T. Stojkovic and P. Vermersch, 2002. Late onset multiple sclerosis. Rev. Neurol. 158: 1082–1087. [PubMed] [Google Scholar]
  14. Dickson, D. W., A. Wertkin, Y. Kress, H. Ksiezak-Reding and S. H. Yen, 1990. Ubiquitin immunoreactive structures in normal human brains. Distribution and developmental aspects. Lab. Invest. 63: 87–99. [PubMed] [Google Scholar]
  15. Dimitrijevic, M., O. Laban, S. von Hoersten, B. M. Markovic and B. D. Jankovic, 1994. Neonatal sound stress and development of experimental allergic encephalomyelitis in Lewis and DA rats. Int. J. Neurosci. 78: 135–143. [DOI] [PubMed] [Google Scholar]
  16. Dowdell, K. C., D. J. Cua, E. Kirkman and S. A. Stohlman, 2003. NK cells regulate CD4 responses prior to antigen encounter. J. Immunol. 171: 234–239. [DOI] [PubMed] [Google Scholar]
  17. Dyment, D. A., G. D. Ebers and A. D. Sadovnick, 2004. Genetics of multiple sclerosis. Lancet Neurol. 3: 104–110. [DOI] [PubMed] [Google Scholar]
  18. Ebers, G. C., A. D. Sadovnick, D. A. Dyment, I. M. Yee, C. J. Willer et al., 2004. Parent-of-origin effect in multiple sclerosis: observations in half-siblings. Lancet 363: 1773–1774. [DOI] [PubMed] [Google Scholar]
  19. Felzien, L. K., J. T. McDonald, S. M. Gleason, N. E. J. Berman and R. M. Klein, 2001. Increased chemokine gene expression during aging in the murine brain. Brain Res. 890: 137–146. [DOI] [PubMed] [Google Scholar]
  20. Fillmore, P. D., M. Brace, S. A. Troutman, E. P. Blankenhorn, S. Diehl et al., 2003. Genetic analysis of the influence of neuroantigen-complete Freund's adjuvant emulsion structures on the sexual dimorphism and susceptibility to experimental allergic encephalomyelitis. Am. J. Pathol. 163: 1623–1632. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Fillmore, P. D., E. P. Blankenhorn, J. F. Zachary and C. Teuscher, 2004. Adult gonadal hormones selectively regulate sexually dimorphic quantitative traits observed in experimental allergic encephalomyelitis. Am. J. Pathol. 164: 167–175. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Giordano, M., and P. Momigliano-Richiardi, 2004. Maternal effect in multiple sclerosis. Lancet 363: 1748–1749. [DOI] [PubMed] [Google Scholar]
  23. Good, P., 1993. Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses. Springer-Verlag, New York.
  24. Goss, J. R., C. D. Finch and A. Bjorklund, 1991. Age-related changes in glial fibrillary acidic protein mRNA in the mouse brain. Neurobiol. Aging 12: 165–170. [DOI] [PubMed] [Google Scholar]
  25. Haus, E., and M. H. Smolensky, 1999. Biologic rhythms in the immune system. Chronobiol. Int. 16: 581–622. [DOI] [PubMed] [Google Scholar]
  26. Herrera, B. M., and G. C. Ebers, 2003. Progress in deciphering the genetics of multiple sclerosis. Curr. Opin. Neurol. 16: 253–258. [DOI] [PubMed] [Google Scholar]
  27. Hofstetter, H. H., R. Mossner, K. P. Lesch, R. A. Linker, K. V. Toyka et al., 2005. Absence of reuptake of serotonin influences susceptibility to clinical autoimmune disease and neuroantigen-specific interferon-gamma production in mouse EAE. Clin. Exp. Immunol. 142: 39–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Hooge, J. P., and W. K. Redekop, 1992. Multiple sclerosis with very late onset. Neurology 42: 1907–1910. [DOI] [PubMed] [Google Scholar]
  29. Hupperts, R., S. Broadley, A. Mander, D. Clayton, D. A. S. Compston et al., 2001. Patterns of disease in concordant parent-child pairs with multiple sclerosis. Neurology 57: 290–295. [DOI] [PubMed] [Google Scholar]
  30. Kared, H., A. Masson, H. Adle-Biassette, J. F. Bach, L. Chatenoud et al., 2005. Treatment with granulocyte colony-stimulating factor prevents diabetes in NOD mice by recruiting plasmacytoid dendritic cells and functional CD4+CD25+ regulatory T-cells. Diabetes 54: 78–84. [DOI] [PubMed] [Google Scholar]
  31. Khare, S., K. Gokulan and D. S. Linthicum, 2000. Vasoactive amine responses in murine cerebrovascular endothelial cells as measured by extracellular acidification rates. J. Neurosci. Res. 60: 356–361. [DOI] [PubMed] [Google Scholar]
  32. Kurtzke, J. F., 1993. Epidemiological evidence for MS as an infection. Clin. Microbiol. Rev. 6: 382–427. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Laban, O., M. Dimitrijevic, S. von Hoersten, B. M. Markovic and B. D. Jankovic, 1995. a Experimental allergic encephalomyelitis in adult DA rats subjected to neonatal handling or gentling. Brain Res. 676: 133–140. [DOI] [PubMed] [Google Scholar]
  34. Laban, O., B. M. Markovic, M. Dimitrijevic and B. D. Jankovic, 1995. b Maternal deprivation and early weaning modulate experimental allergic encephalomyelitis in the rat. Brain Behav. Immun. 9: 9–19. [DOI] [PubMed] [Google Scholar]
  35. Linthicum, D. S., and J. A. Frelinger, 1982. Acute autoimmune encephalomyelitis in mice. II. Susceptibility is controlled by the combination of H-2 and histamine sensitization genes. J. Exp. Med. 156: 31–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Lock, C., G. Hermans, R. Pedotti, A. Brendolan, E. Schadt et al., 2002. Gene-microarray analysis of multiple sclerosis lesions yields new targets validated in autoimmune encephalomyelitis. Nat. Med. 8: 500–508. [DOI] [PubMed] [Google Scholar]
  37. Ma, R. Z., J. Gao, N. D. Meeker, P. D. Fillmore, K. S. Tung et al., 2002. Identification of Bphs, an autoimmune disease locus, as histamine receptor H1. Science 297: 620–623. [DOI] [PubMed] [Google Scholar]
  38. Marra, T. R., 1984. Multiple sclerosis with onset after age 60. J. Am. Geriatr. Soc. 32: 16–18. [DOI] [PubMed] [Google Scholar]
  39. Mattiace, L. A., P. Davies and D. W. Dickson, 1990. Detection of HLA-DR on microglia in the human brain is a function of both clinical and technical factors. Am. J. Pathol. 136: 1101–1114. [PMC free article] [PubMed] [Google Scholar]
  40. Meeker, N. D., W. F. Hickey, R. Korngold, W. K. Hansen, J. D. Sudweeks et al., 1995. Multiple loci govern the bone marrow-derived immunoregulatory mechanism controlling dominant resistance to autoimmune orchitis. Proc. Natl. Acad. Sci. USA 92: 5684–5688. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Meltzer, C. C., G. Smith, S. T. DeKosky, B. G. Pollock, C. A. Mathis et al., 1998. Serotonin in aging, late-life depression, and Alzheimer's disease: the emerging role of functional imaging. Neuropsychopharmacology 18: 407–430. [DOI] [PubMed] [Google Scholar]
  42. Miller, R. A., 1996. The aging immune system: primer and prospectus. Science 273: 70–74. [DOI] [PubMed] [Google Scholar]
  43. Morgan, T. E., I. Rozovsky, S. K. Goldsmith, D. J. Stone, T. Yoshida et al., 1997. Increased transcription of the astrocyte gene GFAP during middle-age is attenuated by food restriction: implications for the role of oxidative stress. Free Radic. Biol. Med. 23: 534–628. [DOI] [PubMed] [Google Scholar]
  44. Mossner, R., and K. P. Lesch, 1998. Role of serotonin in the immune system and in neuroimmune interactions. Brain Behav. Immun. 12: 249–271. [DOI] [PubMed] [Google Scholar]
  45. Nanamiya, W., T. Takao, K. Asaba, E. B. De Souza and K. Hashimoto, 2000. Effect of orchidectomy on the age-related modulation of IL-1 beta and IL-1 receptors following lipopolysaccharide treatment in the mouse. Neuroimmunomodulation 8: 13–19. [DOI] [PubMed] [Google Scholar]
  46. Nichols, N. R., J. R. Day, N. J. Laping, S. A. Johnson and C. E. Finch, 1993. GFAP mRNA increases with age in rat and human brain. Neurobiol. Aging 14: 421–429. [DOI] [PubMed] [Google Scholar]
  47. Noseworthy, J., D. Paty, T. Wonnacott, T. Feasby and G. Ebers, 1983. Multiple sclerosis after age 50. Neurology 33: 1537–1544. [DOI] [PubMed] [Google Scholar]
  48. O'Callaghan, J. P., and D. B. Miller, 1991. The concentration of glial fibrillary acid protein increases with age in the mouse and rat brain. Neurobiol. Aging 12: 171–174. [DOI] [PubMed] [Google Scholar]
  49. Ogura, K., M. Ogawa and M. Yoshida, 1994. Effects of ageing on microglia in the normal rat brain: immunohistochemical observations. Neuroreport 5: 1224–1226. [DOI] [PubMed] [Google Scholar]
  50. Peters, A., K. Josephson and S. L. Vincent, 1991. Effects of aging on the neuroglial cells and pericytes within area 17 of the rheusus monkey cerebral cortex. Anat. Rec. 229: 384–398. [DOI] [PubMed] [Google Scholar]
  51. Polliack, M. L., Y. Barak and A. Achiron, 2001. Late-onset multiple sclerosis. J. Am. Geriatr. Soc. 49: 168–171. [DOI] [PubMed] [Google Scholar]
  52. Poser, S., N. D. Raun and W. Poser, 1982. Age at onset, initial symptomatology and the course of multiple sclerosis. Acta Neurol. Scand. 66: 355–362. [DOI] [PubMed] [Google Scholar]
  53. Rozovsky, I., C. E. Finch and T. E. Morgan, 1998. Age-related activation of microglia and astrocytes: in vitro studies show persistent phenotypes of aging, increased proliferation and resistance to down-regulation. Neurobiol. Aging 19: 97–103. [DOI] [PubMed] [Google Scholar]
  54. Safran, A. B., 1989. Late onset multiple sclerosis: clinical pattern of optic nerve involvement. Metab. Pediatr. Syst. Ophthalmol. 12: 58–60. [PubMed] [Google Scholar]
  55. Sheffield, L. G., and N. D. Berman, 1998. Microglial expression of MJC class II increases in normal aging of nonhuman primates. Neurobiol. Aging 19: 47–55. [DOI] [PubMed] [Google Scholar]
  56. Sheng, J. G., R. E. Mrak and W. E. Friggin, 1998. Enlarged and phagocytic, but not primed, interleukin-1 alpha-immunoreactive microglia increase with age in normal human brain. Acta Neuropathol. 95: 229–234. [DOI] [PubMed] [Google Scholar]
  57. Shi, F. D., H. B. Wang, H. Li, S. Hong, M. Taniguchi et al., 2000. Natural killer cells determine the outcome of B cell-mediated autoimmunity. Nat. Immunol. 1: 245–251. [DOI] [PubMed] [Google Scholar]
  58. Smaaland, R., R. B. Sothern, O. D. Laerum and J. F. Abrhamsen, 2002. Rhythms in human bone marrow and blood cells. Chronobiol. Int. 19: 101–127. [DOI] [PubMed] [Google Scholar]
  59. Sudweeks, J. D., J. A. Todd, E. P. Blankenhorn, B. B. Wardell, S. R. Woodward et al., 1993. Locus controlling Bordetella pertussis-induced histamine sensitization (Bphs), an autoimmune disease-susceptibility gene, maps distal to T-cell receptor beta-chain gene on mouse chromosome 6. Proc. Natl. Acad. Sci. USA 90: 3700–3704. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Takao, T., I. Nagano, C. Toho, T. Takemura, S. Makino et al., 1996. Age-related reciprocal modulation of interleukin-1beta and interleukin-1 receptors in the mouse brain-endocrine-immune axis. Neuroimmunomodulation 3: 205–212. [DOI] [PubMed] [Google Scholar]
  61. Terao, A., A. Apte-Deshpande, L. Dousman, S. Morairty, B. P. Eynon et al., 2002. Immune response gene expression increases in the aging murine hippocampus. J. Neuroimmunol. 132: 99–112. [DOI] [PubMed] [Google Scholar]
  62. Teuscher, C., 1985. Experimental allergic orchitis in mice. II. Association of disease susceptibility with the locus controlling Bordetella pertussis-induced sensitivity to histamine. Immunogenetics 22: 417–425. [DOI] [PubMed] [Google Scholar]
  63. Teuscher, C., W. F. Hickey, C. M. Grafer and K. S. Tung, 1998. A common immunoregulatory locus controls susceptibility to actively induced experimental allergic encephalomyelitis and experimental allergic orchitis in BALB/c mice. J. Immunol. 160: 2751–2756. [PubMed] [Google Scholar]
  64. Teuscher, C., J. Y. Bunn, P. D. Fillmore, R. J. Butterfield, J. F. Zachary et al., 2004. Gender, age and season at immunization uniquely influence the genetic control of susceptibility to histopathological lesions and clinical signs of experimental allergic encephalomyelitis: implications for the genetics of multiple sclerosis. Am. J. Pathol. 165: 1593–1602. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Tramontin, A. D., and E. A. Brenowitz, 2000. Seasonal plasticity in the adult brain. Trends Neurosci. 23: 251–258. [DOI] [PubMed] [Google Scholar]
  66. Trefouret, S., J. P. Zaulay, J. Pouget, J. Boucraut and G. Serratrice, 1996. Late-onset multiple sclerosis and serum monoclonal gammopathy: An incidental association? Rev. Neurol. 152: 554–556. [PubMed] [Google Scholar]
  67. White, A. D., R. J. Swingler and D. A. Compston, 1990. Features of multiple sclerosis in older patients in South Wales. Gerontology 36: 159–164. [DOI] [PubMed] [Google Scholar]
  68. Willer, C. J., D. A. Dyment, N. J. Risch, A. D. Sadovnick, G. D. Ebers et al., 2003. Twin concordance and sibling recurrence rates in multiple sclerosis. Proc. Natl. Acad. Sci. USA 100: 12877–12882. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Willer, C. J., D. A. Dyment, A. D. Sadovnick, P. M. Rothwell, T. J. Murray et al., 2005. Timing of birth and risk of multiple sclerosis: population based study. BMJ 330: 120. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Zavala, F., A. Masson, K. Hadaya, S. Ezine, F. Schneider et al., 1999. Granulocyte-colony stimulating factor treatment of lupus autoimmune disease in MRL-lpr/lpr mice. J. Immunol. 163: 5125–5132. [PubMed] [Google Scholar]
  71. Zavala, F., S. Abad, S. Ezine, V. Taupin, A. Masson et al., 2002. G-CSF therapy on ongoing experimental allergic encephalomyelitis via chemokine- and cytokine-based immune deviation. J. Immunol. 168: 2011–2019. [DOI] [PubMed] [Google Scholar]
  72. Zhou, D., and S. Chen, 2001. PNRC2 is a 16 kDa coactivator that interacts with nuclear receptors through an SH3-binding motif. Nucleic Acids Res. 29: 3939–3948. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Genetics are provided here courtesy of Oxford University Press

RESOURCES