Exploiting genotypic differences to identify genes important for EAE development

Journal of the Neurological Sciences 239 (2005) 81 – 93 www.elsevier.com/locate/jns

Exploiting genotypic differences to identify genes important for EAE development Scott A. Jelinsky a,*, Joy S. Miyashiro b, Kathryn A. Saraf a, Christopher Tunkey a, Padma Reddy a, Jia Newcombe c, Judith L. Oestreicher a, Eugene L. Brown a, William L. Trepicchio a,2, John P. Leonard b,1,3, Suzana Marusic b,1 a

b

Molecular Profiling and Biomarker Discover, Biological Technologies Department, Wyeth Research, 87 Cambridge Park Drive, Cambridge MA 02140, United States Inflammation Department, Wyeth Research, 200 CambridgePark Drive, Cambridge MA 02140, United States c NeuroResource, Department of Neuroinflammation, Institute of Neurology, University College London, 1 Wakefield Street, London WC1N 1PJ, England, UK Received 25 March 2005; received in revised form 8 July 2005; accepted 9 August 2005 Available online 7 October 2005

Abstract Experimental autoimmune encephalomyelitis (EAE) is an animal model of the human autoimmune disease multiple sclerosis (MS) and is primarily driven by T helper type 1 (Th1) cells. Interleukin (IL)-12 and interferon (IFN)-g are important cytokines involved in the differentiation and amplification of Th1 cells, however mice deficient in either IFN-g or IL-12 still develop EAE. We have used microarray analysis of EAE-affected CNS tissues in wild-type, IFN-g
/
and IL-12
/
animals to identify genes critical for development of EAE. Over 500 genes were regulated in at least one genotype and over 94 genes were regulated in all three. Of those, 17 were also upregulated in spleen during the disease. We show that a majority of the genes regulated in EAE are also regulated in diseased regions of human MS tissues. The genes in the pool of 94 are more likely to be found regulated in MS patients than the genes regulated in only one or two of the mouse strains suggesting that analyzing gene expression under these multiple genetic conditions may lead to better identification of the genes critical for disease development. D 2005 Elsevier B.V. All rights reserved. Keywords: Experimental autoimmune encephalomyelitis; Microarray; Interferon gamma; Interleukin 12; Multiple sclerosis

1. Introduction Experimental autoimmune encephalomyelitis (EAE) is a T cell-mediated inflammatory disease of the central nervous system (CNS), which clinically manifests as ascending paralysis. It can be induced in susceptible animals by

* Corresponding author. Tel.: +1 617 665 8887; fax: +1 617 665 7519. E-mail address: [email protected] (S.A. Jelinsky). 1 These authors contributed equally. 2 Current address: Clinical Pharmacogenomics, Millennium Pharmaceuticals, Inc. 40 Landsdowne Street, Cambridge, MA 02139, United States. 3 Current address: Genzyme Drug Discovery and Development, 153 Second Ave, Waltham, MA 02451, United States. 0022-510X/$ - see front matter D 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.jns.2005.08.008

immunizing them with myelin proteins or by injecting them with myelin protein-specific CD4þ cells. EAE shares many clinical and pathological features with multiple sclerosis (MS), and is the commonly used animal model of this human disease [1– 3]. EAE is generally believed to be a Th1-induced disease because of the increased expression of Th1 cytokines in the CNS. Furthermore injection of Th1 but not Th2 T cells into immunocompetent mice is sufficient to induce EAE [4– 7]. Th1 cells produce interferon (IFN)-g, together with other Th1-type cytokines. IFN-g is a potent activator of macrophages, stimulator of expression of MHC class I and II molecules, and activator of adhesion molecules and inflammatory mediators, such as nitric oxide (NO) and

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TNF [8]. Despite this, mice deficient in IFN-g or IFN-gR often develop EAE with higher incidence and severity than wild-type (WT) mice [9 –13]. Members of the Th1-inducing family of cytokines, IL-12, IL-23 and IL-27 all appear to be important in Th1 differentiation and EAE development [14 –16]. IL-12 is a cytokine, composed of two disulfide-linked subunits, designated p40 and p35. It is produced by activated antigen presenting cells and it can induce differentiation of recently activated CD4þ cells into Th1 type [17,18]. IL-12 can induce production of IFN-g, granulocyte-macrophage colony-stimulatory factor (GM-CSF) and TNF, which all play an important role in EAE development [9 – 13,19 – 22]. However, mice deficient in the p35 subunit of IL-12 still develop EAE [23,24]. IL-12p35
/
mice that develop EAE have reduced levels of IFN-g in draining lymph nodes early in disease but these levels increase later during the immune response. Furthermore the expression of IFN-g mRNA in the CNS of IL-12p35
/
mice with EAE is not significantly different from WT controls [23,24] but levels of IL-10 mRNA are increased [23]. This finding may be significant because IL-10 has been suggested to play an important regulatory role in EAE [7,25]. Differences have also been observed between WT and IFN-g
/
mice during EAE. While WT mice have increased mRNA expression of RANTES and MCP-1, the mRNA for these chemokines are not detectable in CNS of IFN-g
/
mice. In addition, mRNA for MIP-2 and TCA-3 are upregulated in CNS of IFN-g
/
, but not WT mice [11]. Collectively, these results clearly indicate that, while IFN-g and IL-12 are not essential for EAE induction, mice deficient in these genes have altered expression of various mediators of inflammation during the course of EAE. These differences, while not critical for EAE development, may provide important insights into the mechanism of EAE and MS development. While a significant progress has been made in understanding mechanisms of EAE and MS development over the past several decades, our understanding of these diseases is still insufficient. New technologies such as gene microarray can facilitate large-scale analysis of gene expression in diseased tissues and help identify genes that play a role in EAE/MS development [26]. Many of these genes may be targets for novel therapies. However, the large numbers of genes identified using array analysis presents a challenge in identifying the most promising potential new therapeutic targets. Different approaches have been used to narrow down the number of genes selected as potential therapeutic targets in various microarray studies. Genes have been selected based on (I) having high fold changes in expression in comparison to unaffected tissue [27], (II) being regulated both in mouse and human diseased tissues [28], (III) having a location on chromosomes adjacent to quantitative trait loci (QTL) for EAE [29], (IV) and by using a combination of large-scale sequencing of mRNA transcripts from cDNA libraries and gene microarray [30]. Most microarray studies identify correlative genes, which are regulated with disease

progression. Since EAE is an inflammatory disorder, it is hard to distinguish genes that are critical for the development of disease from those that are associated with the general activation of the immune system. In the present study, we describe a novel approach to identify the most promising therapeutic targets based on gene expression changes in diseased tissue isolated from genetically modified animals (Table 1). We have used three different mouse genotypes: WT, IFN-g
/
and IL-12p35
/
mice as they are all fully susceptible to EAE despite the fact that major immunological pathways are defective in the latter two genotypes. We have tried to exploit this finding and have performed transcriptional analysis of CNS tissue isolated from diseased mice during the progression of EAE. By selecting only the genes regulated in all three genotypes, we were able to focus on a smaller set of regulated genes in an effort to enrich for genes involved in disease progression.

2. Material and methods 2.1. Mice Female IFN-g
/
mice on C57Bl/6 background and appropriate C57Bl/6 WT control animals were obtained from Jackson Laboratories (Bar Harbor, ME) and used at 6– 10 weeks of age. IL-12p35
/
mice, backcrossed on C57Bl/6 background for 5 generations, and appropriate C57Bl/6 control WT mice were bred at Taconic Farms (Germantown, NY) and 6 –10 week old females were used for experiments. 2.2. EAE induction and tissue collection For EAE induction, all mice were injected subcutaneously with 200 Ag of myelin oligodendrocytes glycoprotein (MOG) peptide 35– 55 in complete Freund’s adjuvant containing 5 mg/ml killed M. tuberculosis. On the same day, the mice received 200 ng pertussis toxin intraperitoneally. Paralysis (EAE) was assessed, starting on day 5 after immunization, when all the mice were still symptom-free. EAE was scored as follows: 1—limp tail, 2—partial hind leg paralysis, 3— complete hind leg paralysis or partial hind and front leg paralysis, 4—complete hind and partial front leg paralysis, 5—moribund. CNS and spleen tissues were collected from the mice at onset, peak and recovery stage of EAE as defined by both severity of EAE and temporal relationship to the EAE development. The onset group animals were collected within 24 h of the first clinical signs of EAE. Most mice that develop EAE (59/63 C57Bl/6 WT mice, 23/24 IFN-g
/
mice, and 29/32 IL-12p35
/
mice) had a clinical EAE score of 1 –2 at this time point and only those mice were used in analysis. Several animals had a higher EAE score at onset and were excluded from analysis to reduce variability within this group. The peak group animals had EAE scores of 3– 4 and were collected within 4 –6 days after the first signs of disease. Animals whose disease severity was below an EAE score of 3

S.A. Jelinsky et al. / Journal of the Neurological Sciences 239 (2005) 81 – 93

83

were not included in the analysis. CNS tissue was a pool of spinal cord and brain stem tissue of each individual mouse. The entire experimental design was repeated over three timeseparated intervals. No systematic differences were observed and therefore data for individually analyzed mice from the three experiments were combined. To isolate RNA, spleens and CNS tissues (spinal cord and brain stem) were collected from at least 10 mice/group/time point and processed individually.

at genes expressed at least two fold above the chip sensitivity as described below. For the human U95Av2 arrays, normalization was achieved by scaling raw intensity data to a target intensity of 100 using Affymetrix MAS 4.0 software. Quality of RNA was verified by the 5V/3Vratio for GAPDH and for h-actin as measured by the arrays. Ratios ranged from 0.8 to 1.1.

2.3. Human CNS tissue

We performed pairwise comparison on log10 transformed frequency values for each disease state to naive animals for each genotype. We calculated the fold change ratio, the P value based on Student’s t-test, the number of present calls, and the expression level for each comparison. A confidence score (CS) was defined as CS(x) = FC(x) + PV(x) + PC(x) + EL(x), where FC, PV, PC, and EL are scores assigned to the fold change, P value, number of present calls, and the expression level, respectively. For each fold change ratio fc(x), FC(x) was assigned a value based on the following rules. If fc(x) is greater than 2.95 then FC(x) = 6; if fc(x) is greater than 1.95 then FC(x) = 6
(3
fc(x)); if fc(x) is greater than 1.5 then FC(x) = 5
((2
fc(x))  6). For each P value (pv(x)), PV(x) was assigned a value based on the following rules. PV(x) was assigned 4 if the P value was less than 0.01; if pv(x) is less than 0.05 then PV(x) = 3
((pv(x)
0.01)  25). PC(x) was assigned 3 if at least 100% of the samples are called P by the Affymetrix algorithm, assigned 2.5 if 50 – 99% of the samples are called P, assigned 1 if only 25 –49% of the samples were called P. EL(x) was assigned 3 if the average frequency of any group had a value of 10 or greater. Penalty points were assigned if the fold change was less than 1.5, the P value was greater than 0.05, or the frequency values were less than 10 ppm. CS(x) ranged from
16 to 16, with qualifiers having a score of 16 considered the most significant changes. Genes with 11 or more points in any one pairwise comparison were considered to be significant and were included for further analysis. Using this paradigm, we have selected genes that have at least 1.5 fold change with a P value  0.05 based on student test. We have included additional filtering criteria above a statistically derived P value to give greater confidence that the changes observed were real and not related to assay noise. These metrics also allowed us to rank order genes and select those with most significant changes for further interpretation. For human U95Av2 data, intensity values were floored to 1 to remove negative values and paired t-test between normal and active plaques and normal and chronic plaques were performed. Genes were considered detectable if 25% of the samples were called P (present) by the Affymetrix MAS 4.0 software and the mean expression level was greater than 50 signal units. Genes with a fold change greater than 1.5 with a P value of less than 0.05 were considered significantly regulated.

CNS tissue was collected and characterized at the NeuroResource tissue bank at the Institute of Neurology, University College London, UK. All samples were analyzed by oil red O and hematoxylin staining of 10 Am snap-frozen sections cut from each tissue block before and after tissue collected for gene expression profiling, and scored for the degree of ongoing or recent demyelination and perivascular cuffing. Acute MS lesions with ongoing or recent demyelination were identified on the basis of the presence of substantial numbers (graded as 3 on a 0 –5 scale) of oil red O-positive macrophages containing neutral lipids resulting from myelin breakdown [31]. 2.4. Array hybridization RNA was quantified spectrophotometrically at 260 nm and the quality and integrity of the samples were verified by running them on 1% agarose gel and confirming the ribosomal RNA bands. Double-stranded cDNA was synthesized from 10 Ag total RNA using the SuperScript System (Invitrogen, Carlsbad, CA). The cDNA was purified and transcribed in vitro using T7 RNA polymerase. Biotinylated cRNA was generated using the labeled biotin labeled UTP and CTP (Perkin Elmer, Boston, MA). Fragmented cRNA were hybridized to a Murine U74Av2 GeneChip\ (Affymetrix, Santa Clara, CA) or to a Human U95Av2 GeneChip\ (Affymetrix, Santa Clara, CA) as recommended by the manufacturer. The chips were scanned using a Hewlett Packard GeneArray Scanner and raw data generated using Affymetrix MAS 4.0 software. For the murine arrays, hybridization intensities on each array were further normalized to a standard curve created from a set of 11 bacterial transcripts spiked in at defined concentrations. This standard curve was used to convert signal values for each qualifier on each array to frequency units expressed as parts per million. Use of the bacterial transcripts allowed for sensitivity for each array to be determined [32]. In 85% of the arrays we were able to detect transcripts expressed at 2.3 parts per million (ppm) and in 15% of the arrays we were able to detect transcripts expressed at 5.7 ppm. For reference, glyceraldehyde-3phosphate dehydrogenase (GAPDH) and h-actin are expressed at 400 ppm and 70 ppm, respectively. To be sure that we were looking at real signal, we chose to look

2.5. Data analysis

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S.A. Jelinsky et al. / Journal of the Neurological Sciences 239 (2005) 81 – 93

Table 1 Gene expression fold change of EAE regulated genes in CNS Gene

Description

Antigen processing and presentation B2M Beta-2 microglobulin CTSS Cathepsin S H2-D1 Histocompatibility 2, D region locus 1 H2-D1 Histocompatibility 2, D region locus 1 H2-K Histocompatibility 2, K region H2-L Histocompatibility 2, L region IFI30 Interferon gamma inducible protein 30 II Ia-associated invariant chain PSME1 Proteasome 28 subunit, alpha M18837 MHC class I Q4 beta-2-microglobulin M27034 MHC class I D

Wild-type

IFG
/


p35
/


Onset

Peak

Onset

Peak

Onset

Mm.98003 Mm.16771

4.8 4.0 3.9 5.3 4.3 12.0 6.1 27.7 2.4 8.2 9.3

6.5 5.9 5.8 7.2 5.9 18.1 7.5 42.2 2.5 13.5 14.6

2.7 2.5 1.4 1.6 1.5 4.5* 4.8 4.6 2.0 2.0* 3.0

2.8 2.9 1.5 1.9 1.7 4.6 3.4 4.3 1.8 2.4 3.4

Acc. num.

Mm.163 Mm.3619 Mm.33263 Mm.33263 Mm.16771 Mm.254175 Mm.30241 Mm.248267

Peak

Active plaque

Chronic plaque

4.5 5.3 4.0 5.2 4.0 7.2 7.6 19.9 2.6 8.3 7.9

5.2 6.4 5.3 6.3 4.8 9.5 8.0 23.5 2.5 11.4 10.0

1.78 NR 2.11 2.11 NM 3.07 3.44 4.81 NM NM NM

1.49 NR 2.06 2.06 NM 2.25 3.69 3.82 NM NM NM

CNS-related GFAP GFAP SCRG1

Glial fibrillary acidic protein Glial fibrillary acidic protein Scrapie responsive gene 1

Mm.1239 Mm.1239 Mm.12886

4.4 3.2 2.1

5.4 3.8 2.3

2.0 2.6 1.9

2.0 2.5 1.9

2.5 2.3 1.8

2.6 2.6 2.1

NM NM NR

NM NM NR

Complement BC026782 C1QA C1QB C1QC C3 C4 CFH SERPING1

Complement factor H related Complement component 1, q Complement component 1, q Complement component 1, q Complement component 3 Complement component 4 Complement component factor h Serine (or cysteine) proteinase inhibitor

Mm.8655 Mm.370 Mm.2570 Mm.3453 Mm.19131 Mm.234200 Mm.8655 Mm.38888

2.8 3.5 4.1 4.0 6.1 4.3 2.8 3.7

3.7 5.3 6.3 5.8 10.0 6.0 3.8 5.5

3.6 2.6 4.2 3.2 4.1 2.8 3.1 2.2

2.4 3.1 4.4 3.4 3.9 3.2 2.6 2.4

3.9 3.9 4.9 4.3 8.9 5.1 4.5 5.3

3.9 4.7 5.9 5.4 11.8 6.1 4.6 6.4

5.93 NM NM 7.46 NM NM 5.93 4.73

4.34 NM NM 5.31 NM NM 4.34 4.4

Mm.193099 Mm.263124 Mm.788 Mm.8245

2.1 3.3 2.1 1.4

2.9 3.2 2.3 1.8

3.7 1.5 1.8 1.9

3.2 1.3 1.7 1.7

3.8 2.7 1.9 2.1

4.7 2.7 2.0 2.3

4.83 NM ND 23.96

2.33 NM ND 8.66

Mm.22339 Mm.24130 Mm.2692 Mm.15819 Mm.2956 Mm.22574 Mm.196617 Mm.4554

4.1 6.0 5.3 2.3 1.4 2.3 6.7 2.7

4.4 7.6 6.7 3.8 1.7 3.0 7.0 3.3

2.9 3.3 4.8 2.5 1.8 2.2 3.8 2.4

2.7 3.0 3.0 2.7 1.8 2.3 2.5* 2.2

5.7 7.1 8.6 3.9 1.6 2.8 10.9 3.4

6.1 7.5 9.8 5.6 1.6 3.6 10.7 3.9

2.7 NM 2.46 ND NR 2.6 14.33 5.06

2 NM 2.54 ND NR 2.54 19.37 5.26

Mm.2639 Mm.3999 Mm.17932 Mm.137 Mm.46301

3.8 6.2 2.1 2.4 5.4

3.9 8.6 1.9 3.6 7.6

2.2 3.0 1.6 4.5* 6.1

1.4* 3.0 1.4 4.2* 5.2

3.9 7.2 2.2 3.4 7.1

3.4 9.1 2.0 5.5 8.8

NM NM NR NM 5.53

NM NM NR NM 5.36

Iron-binding FTL1 Ferritin light chain 1 FTL1 Ferritin light chain 1

Mm.7500 Mm.7500

3.9 1.3

5.7 1.7

7.4 1.6

4.9 1.6

6.6 1.5

7.5 1.9

NM NM

NM NM

Lipid metabolism-related SAA3 Serum amyloid A 3 LCN2 Lipocalin 2 APOD Apolipoprotein D ADFP Adipose differentiation related protein

Mm.14277 Mm.9537 Mm.2082 Mm.381

16.4 22.7 1.3 1.5

29.0 33.8 2.0 1.8

19.1 18.3 2.1 1.8*

14.5 20.5 2.4 1.7

39.0 23.4 1.4 2.0

49.4 32.4 2.0 2.5

NR ND NR 3.41

NR ND NR 3.7

Extracellular FN1 LY6A LY6E TIMP

matrix-related Fibronectin 1 Lymphocyte antigen 6 complex A Lymphocyte antigen 6 complex E Tissue inhibitor of metalloproteinase

Hematopoietic-related AI551257 CD52 CD52 antigen CD53 CD53 antigen CD68 CD68 antigen CD9 CD9 antigen CSF1R Colony stimulating factor 1 receptor GP49A Glycoprotein 49 A LAPTM5 Lysosomal-associated protein transmembrane 5 LY86 Lymphocyte antigen 86 MPEG1 Macrophage expressed gene 1 PNP Purine-nucleoside phosphorylase SCYA6 Small inducible cytokine A6 TYROBP TYRO protein tyrosine kinase binding protein

S.A. Jelinsky et al. / Journal of the Neurological Sciences 239 (2005) 81 – 93

85

Table 1 (continued) Gene

Description

Acc. num.

Wild-type

IFG
/


p35
/


Onset

Peak

Onset

Peak

Onset

Peak

Active plaque

Chronic plaque

NR 127.78 4.01 8.58 NR NR

NR 111.25 2.52 10.22 NR NR

Lipid metabolism-related CTSC Cathepsin C PPGB Protective protein for beta-galactosidase LZP-S P lysozyme structural CTSZ Cathepsin Z CTSH Cathepsin H MAN2B1 Mannosidase 2, alpha B1

Mm.684 Mm.7046 Mm.177539 Mm.156919 Mm.2277 Mm.4219

11.0 1.3* 35.5 2.7 5.6 1.5*

11.5 1.6 70.9 3.8 8.4 2.2

5.2 2.6 34.1 2.7 4.4 2.2

3.2 2.4 38.2 2.6 4.2 2.2

15.5 1.3 50.4 3.8 8.1 1.7

14.3 1.5 79.9 4.6 10.0 2.2

S100 ANXA2 S100A11 S100A6 S100A8

Mm.584 Mm.175848 Mm.100144 Mm.21567

2.0 5.1 3.0 8.4

2.3 6.2 5.0 10.7

2.4 5.8 4.6 7.1

2.2 3.7 5.0 4.5

3.2 8.6 3.6 8.8

3.8 8.6 5.1 7.4

2.32 NM NM NR

2.11 NM NM NR

Mm.24488 Mm.4863 Mm.4639 Mm.170515

3.0* 4.3 3.7 3.3

3.2 3.9 3.5 2.4*

3.1 2.6 2.5 1.8*

2.2 5.5 4.5 2.1

2.6 5.1 4.2 2.0

NM 8.21 2.51 NR

NM 8.03 2.81 NR

2.9 3.0 11.6 3.5 4.4 1.5 17.0 3.4 3.1 2.8 3.7 1.6 2.2 1.5 1.6 1.7* 1.9 3.7 16.7 3.0 1.6 2.9 1.6 3.6 1.5 1.5

17.9 3.8 18.9 10.7 7.2 1.5 18.8 5.3 5.3 3.2 4.2 2.1 1.6 1.4 1.8 8.5 1.3 3.2* 15.8 8.7 1.8 4.8 1.6 3.9 1.6 2.6

17.6 4.9 24.9 11.5 7.7 1.6 40.1 5.7 7.4 3.4 5.1 2.2 1.8 1.6 2.0 8.6 1.7 4.5 21.2 7.9 1.7 4.5 1.7 5.1 1.5 2.3

2.86 2.65 NR NR NR 2.3 NM 2.95 4.97 NM 148.95 58.71 2.73 NR 2.95 8.04 3.2 NM 7.59 11.74 NM 4.83 1.63 NM NM 205.49

3.25 2.06 NR NR NR 1.73 NM 2.51 3.73 NM 29.62 55.81 2.73 NR 2.42 6.98 2.96 NM 5.26 7.07 NM 4.29 4.42 NM NM 169.44

Annexin A2 S100 calcium binding protein A11 (calizzarin) S100 calcium binding protein A6 (calcyclin) S100 calcium binding protein A8 (calgranulin A)

Transcription factor-related 4933407C03RIK CEBPB CCAAT/enhancer binding protein B CEBPD CCAAT/enhancer binding protein D NFKBIA Nuclear factor of kappa light chain in B-cells inhibitor

2.6* 4.6 4.0 4.0

Other

ARG1 BCL2A1B BCL2A1D BGN CHI3L3 CP CYBA DCN GRN IER3 MT1 MT2 PABPC1 PLS2 SAT SEPR SPI2-2 TGFBI TMSB4X AF109905 AI835858 AI850558 C85523 WFS1

1110004C05RIK AA657044 Arginase 1, liver B-cell leukemia/lymphoma 2 related protein A1b B-cell leukemia/lymphoma 2 related protein A1d Biglycan Chitinase 3-like 3 Ceruloplasmin Cytochrome b-245, alpha polypeptide Decorin Granulin Immediate early response 3 Metallothionein 1 Metallothionein 2 Poly A binding protein, cytoplasmic 1 Plastin 2, L Spermidine/spermine N1-acetyl transferase Selenoprotein R Serine protease inhibitor 2-2 Transforming growth factor, beta induced, 68 kDa Thymosin, beta 4, X chromosome MHC class III regions ESTs, moderately similar to TROPOMYOSIN 5 Moderately similar to MURINOGLOBULIN 2 C85523:C85523 Wolfram syndrome 1 homolog

Mm.141021 Mm.203866 Mm.154144

Mm.2608 Mm.4571 Mm.13787 Mm.448 Mm.56769 Mm.1568 Mm.25613 Mm.192991 Mm.142740 Mm.2642 Mm.153911 Mm.2734 Mm.28212 Mm.22650 Mm.14455 Mm.142729 Mm.29524 Mm.27685 Mm.30151 Mm.262607 Mm.20916

15.0 3.4 7.8 5.9 6.2 1.4 6.7 4.7 4.7 2.4 4.1 1.7 1.6 1.4 1.5 2.9 1.3 3.5 9.7 6.8 1.8 4.1 1.6 3.2 1.6 1.9

13.1 4.2 16.2 8.4 8.2 1.6 21.7 4.8 7.2 2.8 5.9 1.8 1.9 1.6 1.9 3.6 1.8 4.8 16.9 7.0 1.9 4.5 1.6 4.2 1.7 1.5

3.8 3.4 15.4 3.6 5.6 1.5 9.7 4.4 3.5 3.3 3.2 2.0 2.2 1.6 1.5 2.4 1.8 3.5 16.1 4.5 1.8 3.6 2.1 3.7 1.9 1.7

NM: not mapped to U95Av2. ND: not detectable on U95Av2. NR: not regulated on U95Av2 ( P > 0.05). * Not significantly regulated (P > 0.05).

2.6. TaqMani quantitative RT-PCR assays Total RNA was DNase treated and purified on a RNAeasy prep column (Qiagen, Valencia, CA). Selected regulated genes identified via GeneChip\ were verified by real-time quantitative RT-PCR. Briefly, TaqMan primers and probes were obtained from Applied Biosystems as part of the pre-

designed, gene-specific TaqMan\ probe and primer sets. All primers and probes were used at 0.3 mM concentrations in the PCR reactions. Quantified RNA (2 Ag) was converted to cDNA using ABI High-Capacity cDNA archive Kit (PE Applied Biosystems). Gene-specific primers and probes were used with the TaqMan Universal PCR Master Mix (PE Applied Biosystems) to amplify the equivalent of 50 ng

S.A. Jelinsky et al. / Journal of the Neurological Sciences 239 (2005) 81 – 93

of RNA generated from the cDNA. Reactions were incubated at 50 -C for 2 min followed by 10 min at 95 -C then 40 cycles of PCR as follows: 95 -C for 15 s then 60 -C for 1 min in an ABI 7900. The data were analyzed using Sequence Detector version 2.0 software (PE Applied Biosystems) and were normalized to GAPDH primer set (PE Applied Biosystems). 2.7. Mapping of murine qualifiers to human U95av2 qualifiers Unigene accession numbers for murine qualifiers were obtained following sequence searches in the murine unigene database. Murine unigene sequences were mapped to human unigene sequences through the HomoloGene database [33]. Affymetrix U95Av2 tiling sequences were searched with Human unigene sequences to identify corresponding human qualifiers. Murine data and human data were merged to identify genes with significant regulation in both EAE and MS.

3. Results 3.1. Clinically similar EAE develops in WT, IL-12p35
/
and IFN-c
/
mice We have examined the susceptibility of IL-12p35
/
mice and IFN-g
/
mice to EAE. Mice were immunized with MOG35 – 55 peptide in complete Freund’s adjuvant, and the development of clinical signs of EAE was monitored. In at least three independent experiments, WT, IL-12p35
/
and IFN-g
/
animals showed comparable average day of disease onset. The average onset of disease in WT animals (N = 63) was 16.4 T 4.5, in IL-12p35
/
animals (N = 32) was 16.13 T 3.0 and in IFN-g
/
animals (N = 24) was 17.4 T 2.9. These differences were not statistically different ( P > 0.1). Disease was statistically significantly less severe in IFN-g
/
compared to WT mice on days 12, 13, 15, 16, 19 and 20 ( P < 0.05) but there was no significant difference in disease severity at other time points ( P > 0.05) (Fig. 1). Although subtle differences in disease progression were evident, all three strains had comparable disease onset, progression and severity. Since the two gene-deficient mice were bred at two separate facilities, two sets of WT B6 mice were examined. We found no significant differences in EAE development or gene expression profiles in the two sets of WT mice (data not shown) and therefore they were treated as a single control group. 3.2. Gene expression analysis Oligonucleotide arrays were used to identify genes regulated during EAE development. We determined the gene expression profiles in CNS and spleen at onset and peak clinical disease in WT, IFN-g
/
and IL-12p35
/
mice and compared them to CNS and spleen tissue from

4

WT IFN-γ -/IL-12p35 -/-

3.5

Mean Clinical Score

86

3 2.5 2 1.5 1 0.5 0 -0.5 -1 9

14

19

24

29

Days Post Immunization Fig. 1. EAE in IL-12p35
/
and IFN-g
/
mice. EAE was induced with MOG35 – 55 in 6 – 10 week old female IFN-g
/
(h) (N = 34), IL-12p35
/
(r) (N = 40) and wild-type (0) (N = 75). Data represent mean EAE score T S.D.

naive animals. Onset was defined as within 24 h of the first clinical signs of EAE with a disease severity score between 1 and 2. Peak was defined as disease lasting for 4 –6 days with a clinical score between grades 3 and 4. RNA from CNS and spleen from at least 10 animals of each genotype at each time point was used for the analysis. Expression profiling, monitoring over 12,000 qualifiers using Affymetrix MG-U74Av2 arrays, was performed on the RNA from individual animals. Because of the large number of replicas, we had statistical confidence that fold changes as low as 1.5 could readily be detected. We first explored whether large-scale difference in expression existed among the different strains. Only 11 (0.09% of the genes monitored) genes showed a significant difference (> 1.5 fold change, P values  0.05) between baseline samples from WT and IL-12p35
/
while 122 (1.02%) qualifiers differed between WT and IFN-g
/
naive mice. Of the 133 qualifiers that differ among the strains, only two genes, GFAP and 4933407C03Rik are regulated in EAE in all three strains. Both genes show a 2– 3 fold increase in the IFN-g
/
naive animals compared to the WT or IL-12p35
/
naive animals. The relatively low number of differently expressed genes suggests the underlying baseline expression profiling is similar in all strains. Therefore any diseaseinduced differences between the strains are likely due to the effect of the specific gene deletion on EAE development. Expression analysis of CNS tissue collected during the course of EAE identified a large number of genes that were upregulated both at the onset and peak of clinical disease (Fig. 2). When all 3 genotypes were combined a total of 449 unique qualifiers were upregulated in CNS tissue (Fig. 2A), while 288 unique qualifiers were downregulated (Fig. 2B). Analysis of individual genotypes identified 233, 180 and 346 qualifiers that were upregulated in WT, IFN-g
/
and IL-12p35
/
animals, respectively (Fig. 2A). Of this subset, 86 (19%) genes were upregulated in all three genotypes, while only 8 (2.5%) genes were downregulated in all three genotypes. Not surprisingly, unique expression

S.A. Jelinsky et al. / Journal of the Neurological Sciences 239 (2005) 81 – 93

WT 233

IFG KO 180 22

8

IFG KO 117

WT 39

73

4

5 86 117

87

100

8

13

22

5

130

144

p35KO 346 p35KO 179 Downregulated

Upregulated

Fig. 2. Classification of genes regulated during EAE in CNS tissue from IL-12p35
/
, IFN-g
/
and WT animals based on microarray analysis. In total, EAE disease resulted in the regulation of 284 genes (233 up/39 down) in WT animals, 296 genes (180 up/117 down) in IFN-g
/
animals and 540 genes (346 up/179 down) in IL-12p35
/
animals. The number of genes whose expression is significantly regulated (see Material and methods) is indicated. A list of the genes is available as supplementary material (Supplemental Table S1).

profiles were obtained for both IFN-g
/
and IL-12p35
/
mice during the course of disease with 48% and 41% of the genes regulated in IFN-g
/
and IL-12p35
/
mice, respectively, not regulated in WT animals. These gene sets may provide valuable information about immunoregulatory pathways under the direct control of IFN-g and IL-12. However, as the frequency of null mutations in IFN-g or IL12p35 in MS patients is likely to be low, we would not expect a significant role for these genes in the pathogenesis of MS. We have therefore concentrated further analysis on genes that were similarly regulated in all three genotypes. Of the 233 genes that were upregulated in CNS tissue from WT mice with EAE, 203 (87%) were also upregulated in IL12p35
/
mice and 94 (40%) were also upregulated in IFN-g
/
mice. Thus, 60% of genes regulated in WT animals during the course of disease are dependent on IFNg, confirming the importance of this pleotropic cytokine in

Scaled Frequency

Scaled Frequency

A

50 #

30 20

*

*

B

10

* *

*

* * *

Peak

0

D *

12

*

80

*

E

18 *

40 20 0

F

# * *

* *

* *

0

0

60

* 6

*

Naive Onset

40

10

0 18

* 80

*

20

C *

* *

30

*

120

*

40

*

*

the local immune response within the CNS. There were 116 genes that were dependent on IFN-g for induction. Of these, at least 22 of the 55 most highly regulated genes (> 5 fold increase in expression) encompassed members of the MHC complex as well as genes reported in the literature to be regulated by IFN-g. These genes showed similar expression patterns in both IL-12p35
/
and wild-type mice consistent with previous observation that IFN-g levels in the CNS of mice with clinical EAE are similar in both genotypes [23,24]. The expression patterns for 6 representative genes (LCN2, S100A8, CD53, TYROBP, S100A11 and C1QB— Fig. 3A – F) that were significantly induced in all 3 genotypes were independently confirmed by quantitative RT-PCR (Fig. 4A – F). The high correlation between microarray and Taqman analysis confirms that these genes are differentially expressed, corroborating the quality of the data.

12

* *

* *

#

6 0

# p<0.01 *p<0.001 Fig. 3. Modulation of gene expression during the clinical course of EAE assessed by microarray analysis. RNA isolated from brain stem and spinal cord (CNS) obtained from mice (WT; left three bars, IFN-g
/
; middle three bars or IL-12p35
/
; right three bars) during onset or peak EAE disease or naive controls were labeled and hybridized onto microarrays. Shown are the average scaled frequency (N = 8 – 14) and standard deviations for six genes, TYROBP (A), S100A8 (B), LCN2 (C), CD53 (D), C1QB (E), and S100A11 (F) that are significantly regulated (see Material and methods) during EAE.

88

S.A. Jelinsky et al. / Journal of the Neurological Sciences 239 (2005) 81 – 93

Fold change

12 10

A

*

150

500

*

* *

8

*

100

400

*

*

*

* *

50

4

*

* *

*

200

* *

0

D

15 *

0

E

15

F

# *

20 10

Naive Onset Peak

100

2

30

C

300

*

*

6

0

Fold change

B

*

10 *

*

*

*

* * 5 0

0

*

* *

*

*

10

* 5

* *

0

*p<0.00001 #p<0.0001 Fig. 4. Verification of microarray results with Taqman assay. RNA from brain stem and spinal cord of mice (WT; left three bars, IFN-g
/
; middle three bars or IL-12p35
/
; right three bars) during onset or peak EAE disease or naive controls were isolated. TaqMan quantitative RT-PCR assay for six genes, TYROBP (A), S100A8 (B), LCN2 (C), CD53 (D), C1QB (E), and S100A11 (F), was performed. Each expression level was normalized by GAPDH. The data represent means T S.D. for four different samples.

3.3. Identification of disease regulated genes in CNS and spleen Data from experimental models of MS have highlighted the critical role of lymphocytes in the pathogenesis of disease. In EAE, CD4+ T cells found in secondary lymphoid organs migrate to the CNS [34,35] where they undergo re-activation following recognition of antigen [34,35]. This re-activation results in the production of multiple inflammatory mediators, which facilitate further influx of inflammatory cells including mononuclear cells, predominately CD4+, CD8+ T cells and macrophages [26,36 –38]. In an attempt to determine the fraction of genes regulated in the CNS that could be attributed to infiltration of activated immune cells, additional expression analysis was performed on spleen samples taken from the same animals during the course of disease. A significant fraction (17/86) of the genes, upregulated in CNS during EAE, were also upregulated in spleens of the same animals. These genes may be related to the influx of the inflammatory cells to the CNS or related to inflammatory processes, which are going on both in spleen and in CNS (Table 2). Further experiments will determine if potential therapeutic targets are specifically enriched among these genes. 3.4. Expression analysis of MS lesions EAE is a widely used model for studying the pathogenesis of MS and as such has been extensively used for target identification and validation. We wished to further validate the potential importance of the 86 upregulated and the 8 downregulated genes selected in our analysis and determine if transcriptional regulation in EAE is comparable to transcrip-

tional regulation in MS. The MS lesions in this study fell into two categories; 12 samples were from chronic MS lesions characterized by demyelination and absence of ORO+ macrophages and 2 samples were from active MS lesions characterized by demyelination accompanied by ORO+ macrophages. We have performed transcriptional profiling using Affymetrix U95Av2 arrays and compared it to the profile obtained from 4 normal control CNS tissue samples. In order to determine if the 86 EAE upregulated genes were also regulated in MS we needed to identify the homologous human U95Av2 qualifiers. We identified human homologs for 71/86 EAE upregulated genes of which 60 were represented on the Affymetrix U95Av2 arrays. A significant fraction, 42/60 (70%), of these human genes are upregulated in the CNS tissues from MS patients (Table 1). Of the 8 downregulated genes 7 were mapped and represented on the Affymetrix U95Av2 arrays, however none of the seven genes showed a significant regulation in MS tissue. We next examined the overlap between genes regulated in each of the three individual mouse strains and MS plaques. Human homologs for the 449 unique upregulated genes and the 288 unique downregulated genes were identified and mapped to the Affymetrix U95Av2 arrays. We were able to identify human qualifiers for 66% for the murine qualifiers. 58% of the genes upregulated during EAE in the wild-type strain were similarly regulated in MS tissue, while 57% and 49% of the genes upregulated during EAE in IFN-g
/
and IL-12p35
/
animals, respectively, were upregulated in MS tissues (Table 3). The downregulated genes had a significantly lower overlap with MS with only 14% of the WT, 10% IFN-g
/
and 6% of the IL-12p35
/
downregulated genes showing

S.A. Jelinsky et al. / Journal of the Neurological Sciences 239 (2005) 81 – 93

89

Table 2 Fold changes of genes commonly regulated in CNS and spleen tissue in EAE mice Qualifier

Acc. num.

Gene

Description

CNS

Spleen

Wild-type

IFG
/


p35
/


Wild-type

IFG
/


p35
/


Onset Peak Onset Peak Onset Peak Onset Peak Onset Peak Onset Peak 100325_at Mm.196617 GP49A Glycoprotein 49 A 6.7 100397_at Mm.46301 TYROBP TYRO protein tyrosine kinase 5.4 binding protein 100569_at Mm.584 ANXA2 Annexin A2 2.0 102712_at Mm.14277 SAA3 Serum amyloid A 3 16.4 103448_at Mm.21567 S100A8 S100 calcium binding protein A8 8.4 (calgranulin A) 103499_at Mm.22339 Vwf Von Willebrand factor homolog 4.1 104374_at Mm.22650 Serpina3n Serine (or cysteine) proteinase 9.7 inhibitor, clade A, member 3N 160275_at Mm.28212 SEPR Selenoprotein R 3.5 160564_at Mm.9537 LCN2 Lipocalin 2 22.7 160894_at Mm.4639 CEBPD CCAAT/enhancer binding protein 3.7 (C/EBP), delta 92694_at Mm.4571 CHI3L3 Chitinase 3-like 3 6.7 92770_at Mm.100144 S100A6 S100 calcium binding protein A6 3.0 (calcyclin) 92852_at Mm.193099 FN1 Fibronectin 1 2.1 92925_at Mm.4863 CEBPB CCAAT/enhancer binding protein 4.3 (C/EBP), beta 93573_at Mm.192991 MT1 Metallothionein 1 1.6 95661_at Mm.2956 CD9 CD9 antigen 1.4 98600_at Mm.175848 S100A11 S100 calcium binding protein A11 5.1 (calizzarin)

similar expression in MS tissue. The individual data for the genes regulated in EAE and MS can be found in Supplemental Tables S2 and S3. Given the differences between EAE and MS, it is extremely encouraging that such a high fraction of overlapping upregulated genes exist and it is suggestive that our murine model accurately reflect MS disease and that the gene targets identified will likely translate into MS. Future studies using larger number of samples with acute lesions will help generate further information. Finally, identifying genes that do overlap between EAE and MS may help prioritize genes for potential drug targets.

7.0 7.6

3.8 6.1

2.5 10.9 5.2 7.1

2.3 2.4 29.0 19.1 10.7 7.1

10.7 8.8

3.3 1.5

2.9 1.3

2.4 1.2

4.3 1.7

3.8 1.8

3.0 1.6

2.2 3.2 14.5 39.0 4.5 8.8

3.8 2.2 49.4 8.2 7.4 10.1

1.9 5.3 10.2

1.5 1.1 5.1

2.2 2.3 6.5 23.5 7.5 6.1

2.3 6.1 7.6

4.4 2.9 16.9 16.1

2.7 5.7 16.7 15.8

6.1 21.2

4.1 2.4

2.6 2.1

4.1 4.6

5.7 2.7

5.5 3.7

4.8 3.5 33.8 18.3 4.0 3.5

3.7 3.2 20.5 23.4 2.5 4.5

4.5 3.4 32.4 59.7 4.2 3.0

3.4 2.3 57.2 16.1 3.1 1.7

4.4 3.8 35.1 27.4 4.1 2.8

3.8 35.1 2.4

21.7 5.0

9.7 4.6

17.0 18.8 5.0 3.6

40.1 31.7 5.1 2.2

34.3 12.4 2.2 1.6

26.2 23.2 2.5 2.8

30.7 2.7

2.9 4.6

3.7 3.9

3.2 2.6

3.8 5.5

4.7 5.1

1.6 2.8

1.9 2.8

1.2 1.5

1.9 3.6

2.0 2.6

1.8 2.7

1.9 1.7 6.2

2.2 1.8 5.8

2.2 1.8 3.7

1.6 1.6 8.6

1.8 1.6 8.6

2.3 1.6 2.1

2.5 1.6 2.0

1.0 1.2 1.3

1.7 2.2 2.1

1.9 2.0 2.1

3.1 2.3 2.2

4.4 1.2

4. Discussion A better understanding of the pathogenesis of EAE and MS development is critical for identifying new therapeutic targets for the treatment of MS. Using gene expression profiling of CNS of mice with three different genotypes, WT, IFN-g
/
and IL-12p35
/
, we have identified a set of genes whose transcripts are increased in all three mouse strains during EAE development. Significant differences in gene expression exist between the three mouse strains during EAE development, but not between naive animals. This suggests that the differences in gene expression are

Table 3 Overlapping genes regulated in EAE and MS disease in CNS tissue

Up in wild-type Down in wild-type Up in IFN-g
/
Down in IFN-g
/
Up in IL-12p35
/
Down in IL-12p35
/
Up in all three Down in all three a b c d e f

Total murine qualifiers

Upregulated human qualifiersa

Downregulated human qualifiersb

% overlap between EAE and MSc

Not mappedd

Not detectede

Not regulatedf

233 39 180 117 346 179 86 8

88 1 70 10 111 15 42 0

6 4 10 8 10 7 0 0

58% 14% 57% 10% 49% 6% 70% 0%

81 11 57 37 121 67 26 1

8 1 6 14 20 13 3 0

50 22 37 48 84 77 15 7

Genes upregulated in MS ( P < 0.05). Genes downregulated in MS ( P < 0.05). Percentage of genes similarly regulated in MS/the number of EAE genes mapped to human arrays. Number of murine qualifier with no homologous qualifier on the human arrays. Number of human qualifiers with expression below 50 signal units. Number of human qualifiers that are not regulated in MS tissue ( P > 0.05).

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S.A. Jelinsky et al. / Journal of the Neurological Sciences 239 (2005) 81 – 93

likely due to different modes of induction of EAE. The genes regulated in all three mouse strains are likely to play the most critical role in EAE development. A significant fraction of these genes are also upregulated in MS lesions, further confirming their importance. It is noteworthy that the genes in the pool of 86 are more likely to be found regulated in MS patients than the genes that are only regulated in one or two of the mouse strains. This further supports the notion that genes regulated in all three mouse strains during EAE may represent a pool of genes enriched for genes most critical to EAE/MS development. Our observation that IL-12p35
/
mice develop EAE with similar severity and incidence confirms previously published findings indicating that IL-12p35 is dispensable for EAE induction [23,24]. However, our observation that IFN-g
/
mice develop EAE similar to WT differs from some previous reports [9– 13]. The differences may be attributed to different genetic backgrounds and antigens used to induce EAE in different studies. Most studies that report increased severity of EAE in IFN-g
/
and IFNgR
/
mice use mouse strains (BALB/c and 129/Sv) that are normally not susceptible to EAE [9 – 13]. In these mice EAE is characterized by a fulminant clinical course and the lesions are dominated by neutrophils. Limited data are reported on clinical course of EAE in IFN-g
/
mice in EAE susceptible strains like B6 or B10.PL [9,39]. EAE induced with MBP in IFN-g
/
B10.PL mice, a strain similar to B6, develop similar histological and clinical severity as WT animals. However, an increase in mortality was observed in IFN-g
/
mice during a prolonged course of EAE [9]. We have not observed an increase in mortality in the IFN-g
/
animals used in these studies. In our experiments, EAE was relatively mild. As a result, most of the mice with an EAE score of 3 or 4 were euthanized to collect CNS and spleen samples during peak EAE. Some were allowed time to partially recover but were euthanized 9– 12 days after onset (Fig. 1). Therefore, most animals that developed relatively severe EAE with a score of 3 or more were not permitted time to develop lethal EAE. In an independent experiment, in which a more severe EAE was induced and the animals were followed through full disease development, a higher mortality in the IFN-g
/
animals was observed. In that experiment, 40% of the IFN-g
/
animals died or needed to be euthanized while 0% of the WT mice developed lethal EAE. In none of the experiments was the fulminant course of disease observed, which is consistent with other reports on EAE development in IFN-g
/
mice on B6 or B10 background [9,38]. Our finding indicates that IFN-g may not always be protective in EAE. It is more likely that the role of IFN-g in EAE depends on the mouse strain, severity of EAE, as well as antigen used to induce the disease. After determining that the genetically modified mouse strains, IFN-g
/
and IL-12p35
/
develop clinically similar EAE to WT mice, we used gene expression profiling

to identify genes upregulated in all three strains. We expected that by focusing on genes upregulated in all three strains, we will be able to identify genes of the greatest importance for EAE development. There were 86 genes upregulated in all three genotypes. It is possible that these genes may only be non-EAE related effects of immunization. However, only 1 of the 86 genes, WSF1, is regulated in immunized animals that never developed any clinical signs of EAE (data not shown) suggesting that the observed regulation was related to EAE development and not related to immunization. We first tried to determine if genes already identified as important in EAE are enriched in this subset. While there were a significant number of genes detected in our study which were previously shown to be upregulated during EAE and MS, our 86-gene list did not include many genes identified in other microarray studies of EAE. Previous studies identified many genes regulated by IFN-g [27,40,41], most of which would have been eliminated from our analysis. Furthermore, our large sample size allowed statistical significant detection of subtle changes (1.5 fold) and the use of a more complete microarray allowed many additional genes to be detected. Since EAE is an inflammatory disease, it is not surprising that the majority of regulated genes have a role in the inflammatory response. A large fraction of these regulated genes have been reported to be involved in antigen processing and presentation (11/86), or are members of complement pathway (8/86). MHC molecules, which are involved in antigen presentation, and members of complement pathway have been shown in multiple studies to play an important role in EAE development [42 – 48]. We have therefore further analyzed the set of 86 upregulated genes in an attempt to identify additional genes, which may be important in EAE development. Several of the 86 regulated genes, including PSME1, IFI30, and cathepsin family members, have been suggested to play a role in antigen processing and presentation. PSME1 is a part of the 11S regulator of the immunoproteasome, which is required for efficient antigen processing [49]. IFI30, also known as GILT (gamma-interferon inducible lysosomal thiol reductase), reduces disulfide bonds in antigens in preparation for further proteolysis and presentation by antigen presenting cells (APC). It is expressed constitutively in APC and inducible by IFN-g in other cell types [50]. Mice lacking IFI30 are defective in processing antigens containing multiple disulfide bonds such as hen egg lysozyme [51]. Five members of the cathepsin cysteine proteinases (A, S, C, H and Z) are all regulated in EAE-affected CNS, of which cathepsin S has been shown to be important for processing of the MHC class II invariant chain and trafficking and maturation of MHC class II molecules [52]. It is possible that at least some of these genes play an important role in processing myelin proteins, thus allowing their presentation to the encephalitogenic T cells. Besides many genes associated with MHC and antigen processing, at least 14 additional genes (Table 1) seem to have their expression restricted mainly to the cells of

S.A. Jelinsky et al. / Journal of the Neurological Sciences 239 (2005) 81 – 93

hematopoietic origin. Several of these have already been shown to be important in EAE and/or immune response development. DAP-12, CD52 and PNP can be considered potential therapeutic targets for EAE [53 –55]. DAP12
/
mice were shown to be resistant to EAE induced by immunization with myelin oligodendrocyte glycoprotein (MOG) peptide [54]. Resistance was associated with a strongly diminished production of IFN-g by myelin-reactive CD4+ T cells due to inadequate T cell priming in vivo [54]. CD52 is highly expressed on lymphocytes and monocytes and antibody treatments have resulted in depletion of lymphocytes and in suppression of clinical and MRI inflammatory activity in MS patients [55]. Humanized monoclonal antibodies against CD52 are currently being developed for treatment of MS [55]. PNP (purine-nucleoside phosphorylase) deficient mice have defective T cell development and functions [56]. Patients with PNP deficiency suffer from recurring infections and lymphopenia associated with reduced T-cell counts. Inhibitors of PNP may thus act as Tcell selective immunosuppressive agents [53,57]. CD53 and LCP1 are also genes primarily expressed in cells of hematopoietic origin and have been associated with defects in immune functions, strongly suggesting that their expression may play a role in regulating immune responses in EAE. CD53 deficiency has been associated with a familiar syndrome of recurrent heterogeneous infectious diseases, caused by bacteria, fungi, and viruses [58]. Defect in LCP1 (lymphocyte cytosolic protein 1) in mice results in impaired neutrophil functions. Neutrophils in these mice are unable to generate an adhesion-dependent respiratory response burst because of a markedly diminished integrindependent syk activation [59]. Since neutrophils have been shown to be important in effector phase of EAE, and depletion of neutrophils results in a reduction of EAE symptoms [60], it is plausible that therapeutics, which would affect neutrophil functions could be beneficial in treatment of EAE and MS. Because of their association with impaired immune responses in vivo, we believe that both CD53 and LCP1 represent potential targets for manipulation of immune responses. In addition, CSF1R or its ligand, CSF-1 (M-CSF, macrophage colony-stimulating factor) may be therapeutic targets in EAE based on their role in survival and proliferation of macrophages, an important effector cell in EAE and MS. Also, CSF-1 expression has been shown to be upregulated in CNS of rats with EAE [61]. Finally, it has recently been shown that CSF-1 deficient mice of MRLFaslpr background have dramatically reduced autoimmune pathology, suggesting a role for CSF-1 in autoimmune lupus [62]. Among the 86 genes identified in this study were also several genes related to lipid metabolism and/or binding of the serum lipoproteins and cholesterol (SAA3, ApoD, LCN2 and ADRP) [63 – 66]. At this point, it is not clear whether these genes play an important role in EAE development. However, the potential immunoregulatory role of genes encoding molecules involved in lipid

91

metabolism has been suggested by recent findings that statins, which are inhibitors of 3-hydroxy-3-methylglutaryl coenzyme A reductase, and cholesterol-lowering drugs, may have immunomodulatory effects [67]. It is therefore possible that other molecules involved in lipid metabolism may also play important immunoregulatory roles in EAE. Several genes (S100A6, S100A8 and S100A11, ANXA2) encoding S100 proteins or S100 interacting proteins are upregulated in EAE. S100 are small, acidic proteins (10 – 12 kDa), which mostly act intracellularly as Ca2+ signaling or Ca2+ buffering proteins [58]. Annexin A2 (ANXA2, lipocortin II) forms tetramers with S100A10. Most S100 proteins form homo- and heterodimers in solution upon Ca2+-binding. Heterodimers are formed with a variety of other proteins including other S100 family members, annexin, annexin II, or peptides derived from p53, CapZ or Ndr-kinase [68]. Interestingly, some S100 proteins are secreted and can act as cytokines. The S100A8/A9 heterodimer acts as chemokine in inflammation, probably binding the receptor for advanced glycation end products (RAGE) [68]. In vivo blockade of RAGE has been shown to suppress disease in several EAE models [69]. At least two additional genes, identified in this study, metallothionein-I and -II, have already been shown to have a protective role in EAE. Metallothioneins are low molecular weight, cysteine-rich, stress response proteins that can act as immunosuppressive agents in antigendependent adaptive immunity [70]. Metallothionein-I and -II can interact with cells of the immune system and modify their functional activities [70]. Elevated levels of metallothionein-I and -II have been found to be associated with rheumatoid arthritis (RA) [71], EAE [73], and MS lesions [73]. Metallothionein treatment reduces the incidence and severity of EAE as well as collagen-induced arthritis [71,72]. On the other hand, mice with targeted disruptions of metallothionein-I and -II genes have increased EAE incidence and severity after immunization with MOG [74]. It is possible that the genes regulated during EAE are part of a protective response to the disease state and not indicators of the disease itself. However, the finding that a large fraction of the 86 identified genes have already been shown to play a role in EAE or to be potential therapeutic targets for EAE strongly suggests that further validation of these genes will yield additional therapeutic targets for demyelinating autoimmune diseases such as EAE and MS. In addition, the observation that the genes regulated in all three mouse genotypes are more likely to be found regulated in MS patients than the genes regulated in only one or two of the mouse strains further suggests that these genes may be enriched for therapeutic targets for MS. The understanding of the biology of these upregulated genes is clearly the next step. Finally, our investigation of MS brain tissue revealed many genes regulated in EAE are similarly regulated in MS confirming EAE as a suitable model of this human disease.

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S.A. Jelinsky et al. / Journal of the Neurological Sciences 239 (2005) 81 – 93

Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.jns.2005.08. 008.

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