Multiple sclerosis (MS) is a common and severe neurological disorder associated with an autoimmune response directed against myelin components within the CNS. Lymphocyte activation, extravasation, and recruitment, as well as effector function, involves the turning on and off of a number of genes, thus triggering specific transcriptional pathways. The characterization of the transcriptome in MS lesions should provide a better understanding of the mechanisms that generate and sustain the pathogenic immune response in this disease. Here we performed transcriptional profiling of 56 relevant genes in brain specimens from eight MS patients and eight normal controls by kinetic RT-PCR. Results showed a high transcriptional activity for the gene coding for myelin basic protein (MBP); however, it was not differentially expressed in MS samples, suggesting that remyelination is an active process also in the noninflammatory brain. CD4 and HLA-DRα transcripts were dramatically increased in MS as compared with controls. This reveals a robust MHC class II up-regulation and suggests that Ag is being presented locally to activated T cells. Although analysis of cytokine and cytokine receptor genes expression showed predominantly increased levels of several Th1 molecules (TGF-β, RANTES, and macrophage-inflammatory protein (MIP)-1α) in MS samples, some Th2 genes (IL-3, IL-5, and IL-6/IL-6R) were found to be up-regulated as well. Similarly, both proinflammatory type (CCR1, CCR5) and immunomodulatory type (CCR4, CCR8) chemokine receptors were differentially expressed in the MS brain. Overall, our data suggest a complex regulation of the inflammatory response in human autoimmune demyelination.

Multiple sclerosis (MS)3 is a common and severe neurological disorder associated with an autoimmune response directed against myelin proteins within the CNS. The cause of MS remains unknown, although evidence indicates a complex and multifactorial etiology with an underlying genetic susceptibility likely acting in concert with undefined environmental exposures. It has been proposed that lymphocytes activated in the periphery by a microbial mimic home to the CNS, become attached to receptors on endothelial cells, and then proceed to cross the blood-brain barrier directly into the interstitial matrix (1). T cells are then reactivated in situ by fragments of myelin Ags exposed in the context of MHC molecules. Reactivation induces the release of cytokines and other small molecules that open further the blood-brain barrier and stimulate chemotaxis, resulting in a second larger wave of pathogenic inflammatory cell recruitment and leakage of Ab and other plasma proteins into the nervous system (2).

The pathological hallmark of MS is the plaque, a well-demarcated white matter lesion characterized histologically by inflammation, particularly macrophages and T cells, demyelination, and gliosis, and different degrees of axonal loss (3, 4). Immunohistochemical and molecular (i.e., RT-PCR) analyses of CNS samples, particularly dissected MS plaques from autopsy tissue and cerebrospinal fluid cells, provide support for a model of lesion development driven by a Th1 type inflammatory response (5, 6, 7, 8, 9, 10, 11). However, although patterns of local proinflammatory cytokine production correlate fairly well with disease in models of MS, particularly in rodents, the dogmatic application of the Th1/Th2 paradigm to human demyelination is considered somehow simplistic (12). Indeed, most studies of gene expression in MS were limited to the analysis of single or a few molecular targets, preventing the formulation of a unifying hypothesis of MS pathogenesis. In contrast, the recent application of comprehensive methods such as single-pass sequencing of cDNA libraries or high capacity microarrays was limited to very few specimens (13, 14, 15).

A PCR-based method for sensitive nucleic acid quantification has been described by Higuchi and colleagues (16). Reverse transcriptase-initiated real-time PCR (kinetic RT-PCR, kRT-PCR) provides a way to monitor product formation as the reaction proceeds, allowing for precise quantification of the initial amount of mRNA present in the sample tube (17, 18). Furthermore, kRT-PCR allows for automation and the simultaneous analysis of multiple samples with a moderately large number of transcripts. Compared with microarrays, the greater sensitivity of PCR assures that most of the transcripts assayed will be detected. Here we use kRT-PCR to analyze the expression profile of 56 genes in brain samples from eight MS patients with active demyelinating lesions and eight controls. Selected targets include several cytokines, chemokines, their receptors, and other immunologically relevant genes as well as genes encoding for myelin components. To our knowledge, this study constitutes to date the most comprehensive gene expression analysis using kRT-PCR. Results show complex, but consistent patterns of predominantly inflammatory immune pathways.

Postmortem MS and non-MS control brain specimens were obtained from The Rocky Mountain Tissue Bank (Englewood, CO). Frozen sections from eight individuals with clinical history of MS were divided in two for both molecular and immunohistochemical analyses. Each analyzed MS brain sample was stained with luxol fast blue (LFB) and counterstained with Harris hematoxylin for myelin integrity, trichrome (Tri) for astrocytes, oil red O (ORO) counterstained with hematoxylin for neutral lipids, and the anti-macrophage Ab EBM 11 for macrophages. These techniques allow for standard grading of MS plaques (19). With the exception of samples MS-7 (type III) and MS-8 (type IV), all of the MS specimens chosen for this study represent the most active stages of plaque activity (Table I). Sample MS-5 is mostly grade II but it has a marginal region displaying a beginning of a grade III plaque. Control samples consisted of eight non-MS, noninflammatory white matter brain specimens. Total RNA was isolated from 100–200 mg of frozen brain tissue homogenized with Trizol reagent (Life Technologies, Gaithersburg, MD) according to the manufacturer’s recommendations.

Table I.

Histopathology of MS samplesa

SampleMacrophages/LymphocytesAstrocytosisNeutral LipidsDisease CourseLesion Type
MS-1 SP (N/A) 
MS-2 ++ ++ − SP (10) 
MS-3 ++ − PP (8) 
MS-4 −/+ ED N/A II 
MS-5 −/+ N/A II 
MS-6 ++ +++ +++ N/A II 
MS-7 ++ SP (10) III 
MS-8 ++ − SP (19) IV 
SampleMacrophages/LymphocytesAstrocytosisNeutral LipidsDisease CourseLesion Type
MS-1 SP (N/A) 
MS-2 ++ ++ − SP (10) 
MS-3 ++ − PP (8) 
MS-4 −/+ ED N/A II 
MS-5 −/+ N/A II 
MS-6 ++ +++ +++ N/A II 
MS-7 ++ SP (10) III 
MS-8 ++ − SP (19) IV 
a

+, Present; −, absent; ED, evenly distributed; −/+, scarce. Disease course: PP, primary progressive; SP, secondary progressive. Disease duration (if known) is included in parentheses. Lesion grading: type I (most active), area of hypercellularity with macrophages evenly distributed, no ORO staining and good LFB integrity; type II (active), well-defined plaque with macrophages evenly distributed, mild ORO staining at plaque edge, intense ORO staining at interior of plaque, lack of LFB staining integrity; type III (modestly active), well-defined plaque, hypocellular at center, macrophages at plaque edges, intense ORO staining in macrophages at lesion edge, No LFB staining within lesion; type IV (inactive), well-defined plaque, hypocellular throughout (scattered macrophages present), little, if any, ORO staining, and no LFB staining within lesion.

One-step RT-PCRs (20) were performed in low-transmissiveness 96-well reaction plates (MicroAmp Optical; PE Applied Biosystems, Foster City, CA) containing 50 ng total RNA and the following: 50 mM Bicine, pH 8.3; 125 mM KOAc, pH 7.5; 2.5% glycerol (including contribution from the enzyme); 200 μM each of dATP, dCTP, dGTP, and 400 μM dUTP; 0.2 μM each oligonucleotide primer, 0.2× SYBR (Molecular Probes, Eugene, OR) in DMSO (1% final concentration); 1 U uracyl N-glycosilase (AmpErase; PE Applied Biosystems); 4 mM Mn(OAc)2; and 5 U rTth polymerase (GeneAmp; PE Applied Biosystems). Total reaction volume was 50 μl. An initial incubation of 10 min at 45°C was performed to activate uracyl N-glycosilase followed by a reverse transcription step of 30 min at 60°C. All reactions were conducted for 50 cycles of 95°C for 15 s and 55°C for 30 s on an ABI-5700 Sequence Detector (Applied Biosystems). To cover the entire battery of primers, each RNA sample required nine 96-well plates. Cellular GAPDH was amplified from all samples on each plate as a housekeeping gene to normalize expression between different samples and to monitor assay reproducibility. A control without added template was included for each target analyzed. To calibrate quantitation among different runs, a 10-fold dilution series of a GAPDH run-off transcript (106 to 102 initial GAPDH mRNA copies) was included in each reaction plate.

Output data was generated by the instrument on-board software Geneamp 5700 SDS (PE Applied Biosystems) and subsequently transferred to a custom designed MS Excel 2000 spreadsheet for analysis. A log-linear calibration graph was generated by plotting GAPDH copy number (from run-off transcript) for each of the six 10-fold dilutions of GAPDH mRNA against the number of cycles it took for each reaction product to exceed a preset fluorescence threshold (Ct). Ct values for each sample were then compared with those obtained in the standard curve fitted to the points of the calibration graph to obtain quantitative measurements. Finally, all readings were standardized to the amplification values obtained for the housekeeping gene so that the copy number of each transcript is expressed as relative to GAPDH. A cut-off value of Ct = 30 was set as this is the number of cycles it usually took for the fewest GAPDH molecules (100) to reach the threshold in the calibration curve. After normalization for cellular GAPDH content, this Ct corresponds approximately to a value of 10 GAPDH units. Therefore, expression values of <10 were considered background. Although potentially entailing some loss of information, this procedure eliminates variability arising from the assay itself or from tissue handling, and allows the comparison of transcription patterns among different samples. Replicate reactions were conducted to confirm statistically significant differences in gene expression.

Parametric (Student’s t) and nonparametric (Mann-Whitney rank-sum) tests were conducted to challenge the hypothesis that there were no differences in the expression levels of the analyzed genes between samples and controls. There was a close agreement between both tests. No adjustment for multiple comparisons was performed in our data to avoid overlooking potentially true differences in gene expression. This type of correction was considered too conservative an approach because it usually mechanizes the interpretive problem negating the value of much of the information in large bodies of data (21). Statistical analysis was performed in consultation with the University of California at San Francisco statistical unit.

Expression data was analyzed using Gene Cluster and visualized with Tree View software tools (22) (http://rana.stanford.edu/software). A different weight was assigned to targets according to their level of significance for the Rank-Sum Test. A weight of 0.99 was given to genes showing p < 0.01, 0.95 to those with p < 0.05, and 0.5 to those that did not exhibit statistically significant expression changes. A weight of 0.8 was assigned to genes that reached significance only on the Student t test.

We analyzed the expression profile of 56 selected genes in a set of eight well-characterized MS and eight noninflammatory control brain samples by kRT-PCR. Targets included several cytokines, ILs, chemokines, their receptors, and transcripts encoding for myelin components. All MS samples were analyzed histologically to confirm inflammation and to allow for standard grading of the plaques. Except samples 7 and 8, all MS samples represent the most active stages of plaque evolution. Perivascular cuffing and intense lymphocytic and macrophage infiltration were seen in all MS samples (Table I). Aberrant MHC expression and well-defined areas of demyelination were also identified.

Kinetic RT-PCR provides an efficient experimental approach for the comparison of expression levels of a relatively large number of transcripts from multiple samples. The primary data output in kRT-PCR is the Ct, defined as the number of PCR cycles needed to exceed a predefined fluorescence threshold for each sample. When this threshold is set within the exponential phase of the reaction, the Ct is inversely proportional to the log of the starting number of copies of the target RNA. A 10-fold dilution series of a GAPDH run-off transcript of known copy number is included in each experiment to build a standard curve for quantification (Fig. 1). Such a quantification would allow the accurate assessment of the actual starting copy number of a given target RNA assuming that RT and PCR efficiency are the same than those of GAPDH transcripts. Because the fact that these efficiencies are the same cannot be proven for all transcripts, expression values are provided as GAPDH equivalents rather than true copy number (17).

FIGURE 1.

Description of kinetic RT-PCR. A, Representative fluorescence growth curves after 50 cycles of RT-PCR amplification in a series of 10-fold dilutions of a GAPDH run-off transcript. NTC, nontemplate control. B, Calibration standard plot. Ct values are interpolated in this chart to obtain quantitative measurements.

FIGURE 1.

Description of kinetic RT-PCR. A, Representative fluorescence growth curves after 50 cycles of RT-PCR amplification in a series of 10-fold dilutions of a GAPDH run-off transcript. NTC, nontemplate control. B, Calibration standard plot. Ct values are interpolated in this chart to obtain quantitative measurements.

Close modal

From the 56 targets investigated, statistical analysis revealed that 31 transcripts (54%) were significantly increased in the MS lesions. Of these, nine showed a p value of 0.05, and 22 showed a p value of 0.01 (Table II). Thirteen targets showed no difference in expression between MS and non-MS specimens. Twelve and 17 transcripts in MS and control samples, respectively, showed a value <10 GAPDH units, which was the cut-off value for detection,. Average differences in expression are displayed in Fig. 2. The most dramatic difference in expression was detected in CD4 transcripts, which showed an average fold increase (AFI) of 8.51 for MS samples (maximum fold increase, 70.26; Table II). This finding is consistent with the presence of activated T helper cells in the brain parenchyma of MS patients and correlates well with our histological data (Table I). Conversely, CD8 transcripts were not detected in the MS or in the control brain. Other markedly up-regulated genes (AFI >4) included the IL-2 receptor β and γ genes (IL-2Rβ and IL-2Rγ), the C-C chemokine RANTES, IL-6 (B cell stimulation factor 2), and the complement component C1r. Most of the remaining transcripts considered significant had an AFI between 2 and 4. Targets in this group included the Th1 markers TNF-α receptor 1 (TNF-α-R1), IL-6R, CCR1, CCR5, IL-12Rβ1, caspase-1, IL-1β, prolactin, and IL-18, all with p values of 0.01 (Table II). Also in this set of targets, TGFβ1, TGFβ3, and CCR4 were marginally significant (p = 0.05).

Table II.

Differential gene expression in MS samples

TargetExpression LevelaMaximum Fold ChangebAverage Fold Changecp Valued
SamplesCTRL
MBP 36921 22994 −40.41 1.61 NS 
TGF-β1 7382 2666 30.34 2.77 0.05 
DRα 5217 1639 20.25 3.18 0.01 
MOG-α 4693 8605 −28.37 −1.83 NS 
β-2 micro 4408 1264 12.85 3.49 0.01 
TNFR1 2674 944 17.92 2.83 0.01 
MOG-β 2662 4077 −45.52 −1.53 NS 
FcRI 2246 1492 11.05 1.51 0.05 
TGF-β3 2015 842 16.33 2.39 0.05 
10 HLA-C 1905 737 18.42 2.58 NS 
11 TGF-β2 1329 686 15.22 1.94 0.05 
12 TNFRp55 872 261 6.80 2.41 0.01 
13 MAG 467 1084 −42.97 −2.32 NS 
14 LMP2 360 199 11.40 1.80 NS 
15 IFN-αR 338 178 9.11 1.89 0.01 
16 IL-6R 311 88 11.02 3.51 0.01 
17 IL-2Rγ 225 56 46.89 4.01 0.01 
18 IL-18 167 61 21.57 2.70 0.01 
19 HLA-E 160 146 −11.27 1.10 NS 
20 IL-8 136 39 33.38 3.49 NS 
21 IL-6 135 32 30.14 4.13 0.01 
22 IL-5 131 59 18.44 2.22 0.01 
23 IL-1β 130 42 14.14 3.07 0.01 
24 IL-15 115 38 18.87 2.98 0.01 
25 Caspase-1 109 38 13.98 2.84 0.01 
26 LMP7 E-2 108 40 17.49 2.65 0.01 
27 CD4 95 11 70.26 8.51 0.01 
28 CCR5 61 16 20.20 3.76 0.01 
29 TNFRp75 48 34 26.31 1.39 NS 
30 RANTES 45 (5) 23.78 5.19 0.01 
31 MIP-1α 45 22 42.04 1.99 0.05 
32 CCR1 41 16 22.72 2.62 0.01 
33 IL-1R 41 26 10.68 1.56 NS 
34 IFN-α 35 23 −19.14 1.53 NS 
35 IL-12Rβ1 33 11 11.87 2.83 0.01 
36 IL-9 32 17 12.91 1.90 0.05 
37 LMP7 E-1 25 10 7.79 2.31 0.01 
38 CCR4 24 11 26.67 2.17 0.05 
39 C1r 25 (5) 38.44 4.69 0.01 
40 PRL 17 (5) 34.72 3.85 0.01 
41 IL-12Rβ2 14 12 47.23 1.14 NS 
42 IL-3 14 (5) 34.66 2.60 0.05 
43 HSCalBR 13 55 −95.18 −4.30 NS 
44 PFR-1 11 (5) 50.84 2.17 0.05 
45 IL-2Rβ BTe BT – – – 
46 IL-7 BT BT – – – 
47 CCR8 BT BT – – – 
48 IL-1a BT BT – – – 
49 IL-10 BT BT – – – 
50 IL-11 BT BT – – – 
51 IL-12p35 BT BT – – – 
52 IL-12p40 BT BT – – – 
53 IL-13 BT BT – – – 
54 IFN-β BT BT – – – 
55 IL-4 BT BT – – – 
56 CD8α BT BT – – – 
TargetExpression LevelaMaximum Fold ChangebAverage Fold Changecp Valued
SamplesCTRL
MBP 36921 22994 −40.41 1.61 NS 
TGF-β1 7382 2666 30.34 2.77 0.05 
DRα 5217 1639 20.25 3.18 0.01 
MOG-α 4693 8605 −28.37 −1.83 NS 
β-2 micro 4408 1264 12.85 3.49 0.01 
TNFR1 2674 944 17.92 2.83 0.01 
MOG-β 2662 4077 −45.52 −1.53 NS 
FcRI 2246 1492 11.05 1.51 0.05 
TGF-β3 2015 842 16.33 2.39 0.05 
10 HLA-C 1905 737 18.42 2.58 NS 
11 TGF-β2 1329 686 15.22 1.94 0.05 
12 TNFRp55 872 261 6.80 2.41 0.01 
13 MAG 467 1084 −42.97 −2.32 NS 
14 LMP2 360 199 11.40 1.80 NS 
15 IFN-αR 338 178 9.11 1.89 0.01 
16 IL-6R 311 88 11.02 3.51 0.01 
17 IL-2Rγ 225 56 46.89 4.01 0.01 
18 IL-18 167 61 21.57 2.70 0.01 
19 HLA-E 160 146 −11.27 1.10 NS 
20 IL-8 136 39 33.38 3.49 NS 
21 IL-6 135 32 30.14 4.13 0.01 
22 IL-5 131 59 18.44 2.22 0.01 
23 IL-1β 130 42 14.14 3.07 0.01 
24 IL-15 115 38 18.87 2.98 0.01 
25 Caspase-1 109 38 13.98 2.84 0.01 
26 LMP7 E-2 108 40 17.49 2.65 0.01 
27 CD4 95 11 70.26 8.51 0.01 
28 CCR5 61 16 20.20 3.76 0.01 
29 TNFRp75 48 34 26.31 1.39 NS 
30 RANTES 45 (5) 23.78 5.19 0.01 
31 MIP-1α 45 22 42.04 1.99 0.05 
32 CCR1 41 16 22.72 2.62 0.01 
33 IL-1R 41 26 10.68 1.56 NS 
34 IFN-α 35 23 −19.14 1.53 NS 
35 IL-12Rβ1 33 11 11.87 2.83 0.01 
36 IL-9 32 17 12.91 1.90 0.05 
37 LMP7 E-1 25 10 7.79 2.31 0.01 
38 CCR4 24 11 26.67 2.17 0.05 
39 C1r 25 (5) 38.44 4.69 0.01 
40 PRL 17 (5) 34.72 3.85 0.01 
41 IL-12Rβ2 14 12 47.23 1.14 NS 
42 IL-3 14 (5) 34.66 2.60 0.05 
43 HSCalBR 13 55 −95.18 −4.30 NS 
44 PFR-1 11 (5) 50.84 2.17 0.05 
45 IL-2Rβ BTe BT – – – 
46 IL-7 BT BT – – – 
47 CCR8 BT BT – – – 
48 IL-1a BT BT – – – 
49 IL-10 BT BT – – – 
50 IL-11 BT BT – – – 
51 IL-12p35 BT BT – – – 
52 IL-12p40 BT BT – – – 
53 IL-13 BT BT – – – 
54 IFN-β BT BT – – – 
55 IL-4 BT BT – – – 
56 CD8α BT BT – – – 
a

Expression levels of all targets are shown for samples and controls. If expression levels in controls (but not in MS) fall below the threshold they are shown in parentheses to allow comparison.

b

Maximum fold change was calculated as the difference between the highest expression level for a sample and the lowest for a control (positive values) or vice versa (negative values), whichever is greater.

c

Average fold change.

d

Statistical significance.

e

BT, Below threshold.

FIGURE 2.

Average fold increase in expression for MS samples. Targets are organized from left to right in decreasing order. Fold changes >4 are indicated in black, those between 2 and 4 are in gray. Fold changes of <2 are shown in white. Significance is indicated by asterisks next to each target: ∗∗, p = 0.01; ∗, p = 0.05.

FIGURE 2.

Average fold increase in expression for MS samples. Targets are organized from left to right in decreasing order. Fold changes >4 are indicated in black, those between 2 and 4 are in gray. Fold changes of <2 are shown in white. Significance is indicated by asterisks next to each target: ∗∗, p = 0.01; ∗, p = 0.05.

Close modal

The high expression of the MHC class II transcript DRα (AFI = 3.18, p < 0.01) as well as CD4 suggest that the local microenvironment is enriched with MHC-activating factors, and that Ag is possibly being presented to T cells. Unexpectedly, IFN-γ, a known up-regulator of MHC molecules, was not detected consistently in all samples in repeated amplification attempts.

Transcripts that fell below the established cut-off included the CD8 receptor and the Th2-related transcripts IL-4, IL-7, IL-10, IL-11, IL-13, and CCR8 (Table II). Surprisingly, transcripts for IL-12 (p35 and p40 subunits) failed to be detected even though differential expression was previously reported in PBMC and cerebrospinal fluid from MS patients (23). Because IL-12-Rβ2 seems to play a pivotal role in the generation of pathogenic autoreactive Th1 cells, this transcript was expected to be up-regulated in MS samples. However, IL-12-Rβ1 but not IL-12-Rβ2 transcripts were found increased in the MS sample panel. IFN-α, HLA-class I genes C and E, IL-1R, and IL-8 were detected at similar levels in MS and controls.

Although no significant differences between MS samples and controls were observed, myelin basic protein (MBP) transcripts displayed the highest level of expression relative to GAPDH, being almost 10 times more abundant than the next most abundant target (Table II). Of interest, high transcriptional levels of the principal myelin component, MBP, and glial acidic fibrillary protein (GFAP, a marker for astrocytes) have also been found in the analysis of single-pass sequenced cDNA libraries from MS patients (S.E.B. and J.R.O., unpublished observation). Myelin oligodendrocyte glycoprotein (MOG)α, MOGβ, and myelin-associated glycoprotein showed, on average, decreased levels of expression in MS samples (Fig. 2, Table II), although these differences were statistically not significant. Another transcript found to be less expressed in MS samples was Calbindin (27K), a vitamin D-dependent calcium binding protein (-4.3 on average, not statistically significant, Table II). However, this target was detected at levels very close to the lower limit of the dynamic range of the assay (10 GAPDH units).

Data was further analyzed by a hierarchical clustering algorithm that distributed genes and samples along a two-dimensional colored chart according to their transcriptional profile (Fig. 3). Most of the differentially expressed targets were linked in the computer-generated dendrogram by shorter branches, suggesting closer distances and therefore a similar expression profile. Genes showing no differential expression were automatically placed at the top of the picture and connected to each other through longer branches in the dendrogram. In this group, two families of transcripts (α and β) from the alternative spliced myelin gene MOG, were placed next to each other linked with very short branches, reflecting their coordinated expression. Other myelin genes, such as myelin-associated glycoprotein and MBP, were also located nearby due to a similar expression profile.

FIGURE 3.

Cluster representation of gene expression in MS and control samples. Relative expression levels are colored in shades of red for high values and of green for low values. A computer algorithm calculates distances across samples for each target and organizes them into nodes. The most similar expression profiles are placed together, and the distance to the next target is then calculated. A dendrogram-like figure links all genes according to their expression profile. Samples are also clustered according to their expression pattern across genes. Most of the differentially expressed targets are placed together and interconnected by nodes shorter than those not showing statistically significant changes. A group of genes with a highly similar pattern of expression is highlighted in yellow.

FIGURE 3.

Cluster representation of gene expression in MS and control samples. Relative expression levels are colored in shades of red for high values and of green for low values. A computer algorithm calculates distances across samples for each target and organizes them into nodes. The most similar expression profiles are placed together, and the distance to the next target is then calculated. A dendrogram-like figure links all genes according to their expression profile. Samples are also clustered according to their expression pattern across genes. Most of the differentially expressed targets are placed together and interconnected by nodes shorter than those not showing statistically significant changes. A group of genes with a highly similar pattern of expression is highlighted in yellow.

Close modal

When samples were clustered according to their overall pattern of gene expression, the MS specimens grouped together (Fig. 3) and were joined by shorter branches than those linking the controls, reflecting a higher and more homogeneous expression profile in the MS group. The relative wide range of maximum fold increase (Table II) reflects a certain degree of variability among the MS samples due to, most likely, pathogenic heterogeneity. Interestingly, the less active plaques DA (type III) and RR (type IV) were linked together in the same cluster; within this cluster was also included sample ME, classified as type II but with an emerging type III histology at the edge of the plaque.

The MS plaque is considered the result of an inflammatory process leading to pathogenic myelin destruction. The characterization of gene expression in these lesions should provide a better understanding of the mechanisms that trigger and sustain the pathogenic immune response in this disease. At present, gene microarrays and kRT-PCR emerge as the tools of choice to carry out such experiments. The one-step kRT-PCR approach confers the ability of real-time inspection of reaction products as they accumulate, allowing the facile analysis of gene expression with remarkable sensitivity. Here we evaluated the expression of 56 relevant genes by kRT-PCR in the injured MS tissue.

From all targets analyzed, CD4 was the most clearly overexpressed in MS samples (AFI = 8.51). This observation provides evidence of helper T lymphocytes having homed to the brain of MS patients. Equally relevant is the elevated expression of the DRα transcripts. Up-regulation of MHC class II genes has been proposed as a marker of plaque activity (19). Because the level of cell surface expression of MHC class II molecules directly affects the nature and magnitude of the immune response, the study of the mechanisms involved in the regulation of class II expression in the MS plaque is essential for understanding the inflammatory response in the affected brain. It is also possible that enhanced or deregulated MHC class II expression by APCs within the CNS mediates the autoimmune process, as is the case in experimental allergic encephalomyelitis (24, 25). However, it is important to note that in many silent plaques devoid of T cell infiltrates, class II MHC may be expressed at high levels on reactive microglia. In addition, up-regulation of MHC class II Ags is not unique to MS tissue, as it has also been detected in other neurodegenerative diseases or following trauma (2). Although the costimulatory molecules ICAM-1 and B7 have been identified on microglia from humans and mice (26), the case for an effective Ag presentation in the brain requires further experimentation.

The very high level of expression detected for MBP in both MS and control samples is of special interest. This transcript showed a concentration high above the rest of the targets analyzed (∼10-fold higher than the next most abundant target). Although transcriptional activity does not necessarily correlate with protein abundance, this could be indicative of either a high mRNA stability or a continuous synthesis that keeps an internal pool to readily suffice the need of such proteins and regenerate the myelin sheath during normal turnover or under a demyelination event (27, 28).

Most of the knowledge on the role of cytokines in autoimmune demyelination has been obtained through studies in experimental models or isolated lymphocytes activated in vitro with myelin Ags. Although the majority of the studies suggest a bias toward a Th1 type of response, others insinuate a less than strict polarization of the cytokine profile (29). Our analysis shows a predominant expression pattern of Th1 cytokines mainly represented by the MIP-1α/RANTES/CCR5 and Caspase-1/IL-1β/IL-18 axes. Surprisingly, key inflammatory-type molecules such IL-2, IFN-γ, and TNF-α did not display consistent and reproducible expression patterns. In contrast, concurrently with elevated expression of IL-5 and IL-6/IL-6R, prototypic Th2-type molecules such as IL-4, IL-10, IL-13, and CCR8 were undetected. Altogether, this particular pattern of cytokine expression suggests a complex, not fully polarized regulation of the local immune response in human autoimmune demyelination.

Finally, it has been shown that B cell activation and Ab responses play an important role in the development of demyelination both in human and experimental disease (30, 31, 32). Abs may participate in myelin destruction through different mechanisms such as myelin opsonization facilitating phagocytosis by macrophages, and/or complement fixation (33). Also, CNS Igs may induce myelinolysis via activation of a Ca2+-dependent myelin-associated protease acting on MBP (34). The up-regulation in MS plaques for transcripts encoding IL-6 (AFI = 4.13) and C1r (AFI = 2.22) is consistent with the potential pathogenic role of Ab-producing plasmocytes in the injured MS tissue. Interestingly, in the Theiler’s virus experimental model of autoimmune demyelination, mAbs directed against the oligodendrocyte surface promote remyelination (35, 36). Hence, a pathogenic as well as reparative role for the humoral immune response could be postulated.

In conclusion, our data indicate a multifaceted cellular and humoral immune response underlying autoimmunity in MS. The comprehensive analysis of tissue-specific transcriptional programs in MS, together with the development of advanced computational algorithms to integrate the descriptive data into metabolic and regulatory circuits, will reveal the molecular fingerprint of the demyelinating process and help identify the complete array of MS disease genes.

1

This work was supported by National Multiple Sclerosis Society Grant RG 2901.

3

Abbreviations used in this paper: MS, multiple sclerosis; MBP, myelin basic protein; kRT-PCR, kinetic RT-PCR; Ct, fluorescence threshold; AFI, average fold increase; MOG, anti-myelin/oligodendrocyte glycoprotein; ORO, oil red O; LFB, luxol fast blue.

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