| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
Cutting Edge |
* Division of Rheumatology, Departments of Medicine,
Microbiology and Immunology, and
Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232
 |
Abstract |
|---|
 Top Abstract IntroductionMaterials and MethodsResults and DiscussionReferences |
|---|
|
Introduction |
|---|
|
TopAbstractIntroductionMaterials and MethodsResults and DiscussionReferences |
|---|
cells in
type 1 diabetes (3), myelin basic protein in multiple
sclerosis (MS)3
(4), thyroglobulin or thyroid peroxidase in thyroiditis
(5), or more systemic: the synovial lining, lung, or heart
in rheumatoid arthritis (RA) (6), or skin, kidney, or
heart in systemic lupus erythematosus (SLE) (7). Despite
these differences, clinically distinct autoimmune diseases exhibit
common elements. First, most autoimmune diseases are thought to arise
from alterations in the immune system. Second, coassociation of
different autoimmune diseases is often found in families or
individuals. Third, analysis of genome-wide linkage results
demonstrates that multiple autoimmune diseases share common
susceptibility loci. Taken together, these findings suggest that common
groups of genes may contribute to development of clinically distinct
forms of autoimmune disease (8, 9, 10, 11). cDNA microarray technology represents a powerful tool to investigate differences in gene expression profiles. We wanted to compare these profiles in a common cell population from individuals with clinically distinct forms of autoimmune disease. Given the ease of obtaining blood, we first tested whether PBMC represented such a source. To do so, we performed gene expression profiling of PBMC in control individuals following immunization with the influenza vaccine. The results show that the normal immune response is a highly dynamic process with coordinate changes in expression of genes that encode proteins with common functions. In contrast, genes with a distinct expression pattern in autoimmunity are conserved among the different autoimmune diseases but are not "immune response" genes.
|
Materials and Methods |
|---|
|
TopAbstractIntroductionMaterials and MethodsResults and DiscussionReferences |
|---|
Nine control subjects (27–58 years of age) were studied before
and after influenza vaccination. Patients with RA (n =
20; 46–68 years of age), SLE (n = 24; 22–73 years),
type I diabetes (n = 5; 20–46 years), and MS
(n = 4; 37–54 years) were also enrolled in the study.
A clinical diagnosis of each autoimmune disorder was the sole criterion
for inclusion. Unaffected family members were also included in the
study (n = 4, 33–54 years); three were parents of
individuals with SLE and one was the child of an individual with RA.
The ratio of females to males in the test groups was 
3:1.
Sample preparation
PBMC were isolated from heparinized blood by centrifugation on a Ficoll-Hypaque (Sigma-Aldrich, St. Louis, MO) gradient. Leukocyte distribution in PBMC was determined by flow cytometry. Total RNA was isolated with Tri-Reagent (Molecular Research Center, Cincinnati, OH), processed, and 5 µg were hybridized to Research Genetics GF-211 membranes (Huntsville, AL). Filters were exposed to imaging screens for 24 h and screens were scanned by a phosphor imager (Molecular Dynamics, Piscateway, NJ). Data were normalized to yield an average intensity of 1.0 for each clone (4329) represented on the microarray. Reproducibility of the method was established by performing replicate hybridizations to separate microarrays. Linear regression analysis demonstrated that separate hybridizations yielded R2 values ranging from 0.87 to 0.96. Different exposure lengths of identical filters also produced high R2 values (0.99).
Data analysis
Eisen’s Cluster and Treeview software (Stanford University, Palo Alto, CA) (12) were used to compare similarities among individual samples. Data sets were analyzed using hierarchical, K-means, and self-organizing map algorithms (13). Research Genetics Pathways 3.0 program was used to identify differentially expressed genes in the immune and autoimmune disease classes. Expression levels of genes that did not change significantly (99% confidence, Chen test) over any of the conditions were removed from the database (14). The remaining genes in the data set were clustered using an unsupervised K-means clustering algorithm with ten centroids (12, 13).4
|
Results and Discussion |
|---|
|
TopAbstractIntroductionMaterials and MethodsResults and DiscussionReferences |
|---|
A, upper panel).
Thus, total gene expression patterns remained relatively unchanged
after immunization. To focus on distinctions that arose from the most
differentially expressed genes, we filtered out genes for which
expression levels did not vary by >3 SD from their respective means.
After filtering, expression profiles were segregated primarily by pre-
and postimmune status (Fig. 1A, lower panel).
This suggested that uniform changes in expression levels of a smaller
subset of genes distinguished pre- and postimmunization groups. To
identify these genes, we used K-means clustering to group genes on the
basis of similarity in expression patterns.
|
B). The first cluster consisted of 304 genes
that were overexpressed 3 days after immunization. This cluster mainly
contained genes that encode proteins involved in key signal
transduction pathways (e.g., protein kinase C, phospholipase C,
1,2-diacylglycerol kinase, mitogen-activated protein kinase,
STATs, and STAT inhibitors, AP-1 transcription factors, interferon
regulatory factors, and proteins required for proliferation).
Genes in this cluster exhibited an increase in expression from 3- to
21-fold compared with the control group. The second cluster of 88 late (19–21 days) response genes represented a shift away from signaling and proliferation pathways toward increased functional activity. Among the late immune response gene cluster, chemokines (SCYA3, SCYA13, SCYA14), complement components (C1S), IFN-inducible proteins (IFI35), and leukocyte homing/adhesion (ICAM2) genes were overexpressed. Receptors for serotonin, glutamate, estrogen, and retinoic acid were also overexpressed. Increases in expression levels of this group of genes varied from 2- to 11-fold.
The final immune response cluster contained 78 genes that exhibited reduced expression levels over the entire time course. Over 15% of these genes encode ribosomal proteins. This represents a decrease in the expression of one-third of all ribosomal protein-encoding genes present on the microarrays. Coordinate changes in ribosomal protein gene expression have been linked to differentiation in eukaryotic cells (15) and the observed changes may reflect differentiation of lymphocytes to an effector state in response to immunization. Taken together, these data illustrate dynamic, coordinate changes in mRNA expression that accompany the immune response, in vivo. First, genes were induced that are required for signal transduction and cell proliferation, two key elements of the early immune response. Later, we observed a shift away from these genes to other classes that are necessary to undertake the immune functions of lymphocytes.
Given the above results, we wanted to compare gene expression profiles
of the normal immune response to those of autoimmune disease. We
obtained samples from patients diagnosed with one of four common
autoimmune disorders: RA, MS, type I diabetes, and SLE. The relatedness
of global gene expression profiles associated with autoimmune
disease was examined relative to the normal immune response using a
hierarchical clustering algorithm (Fig. 2A). Other clustering
algorithms yielded similar results (data not shown). Comparison between
the RA/SLE class and the normal immune response class yielded four
major branches from the clustering analysis. One major branch contained
all normal immune samples and none of the autoimmune samples. The
autoimmune samples segregated into the other three major branches. This
analysis revealed that some of the RA samples (e.g., RA2 and RA5, or
RA1, RA6, and RA4) and some of the SLE samples (e.g., SLE2, SLE3, and
SLE4, or SLE6, SLE8, and SLE9) were highly related. However, unlike
distinctions between the RA/SLE and the normal immune response samples,
it was not possible to segregate the majority of RA samples from the
majority of SLE samples. This argues that RA and SLE may represent a
common autoimmune class that is distinct from the immune class. Similar
results were obtained from clustering of normal immune response samples
with MS/type I diabetes samples. Again, there was good segregation of
the normal immune response group from the MS/type I diabetes group, but
MS and type I diabetes profiles did not segregate from each other. This
inability to segregate within autoimmune class was retained even when
invariant genes were removed from the data set (data not shown). One
explanation for the inability of the clustering algorithm to
distinguish among the phenotypically distinct autoimmune diseases is
that the gene expression profiles are relatively similar.
|
Two clusters of differentially expressed genes distinguished between 1)
patients with autoimmune disease and 2) control and immune individuals
(Fig. 2B). The first major cluster consisted of 95 genes
that were overexpressed in all four autoimmune diseases (type I
diabetes, MS, RA, and SLE). The genes in this overexpressed autoimmune
cluster were relatively heterogeneous, representing several distinct
functional categories: receptors (CSF3R, HLA-DMB,
HLALS, TGFBR2, and BMPR2),
inflammatory mediators (MSTP9, BDNF,
CES1, ELA3, and CYR61),
signaling/second messenger molecules (FASTK,
DGKA, and DGKD), and autoantigens
(GARS and GAD2). The second major cluster
contained 117 genes that were strongly underexpressed in all autoimmune
groups. Levels of expression of these genes did not change in the
immune response group. Many of the down-regulated genes play key roles
in apoptosis (TRADD, TRAP1,
TRIP, TRAF2, CASP6, CASP8,
TP53, and SIVA) and ubiquitin/proteasome function
(UBE2M, UBE2G2, and POH1). Inhibitors
of various cellular functions were also widely represented in this
cluster. These include direct inhibitors of cell cycle progression
(CDKN1B, CDKN2A, and BRCA1), as well
as inducers of cell differentiation (LIF and
CD24). Certain enzyme inhibitors (APOC3 and
KAL1) were also found in this class.
A striking result of the K-means clustering was that we were unable to identify clusters of genes that overlapped between the immune and autoimmune classes. This argues that the expression patterns that characterize the normal immune response bear little resemblance to those found in autoimmune disease. In addition, we were unable to identify clusters of genes that distinguished among the distinct autoimmune diseases. Rather, the results point to pervasive changes in gene expression that are relatively uniform among four distinct autoimmune diseases.
To examine this hypothesis in greater detail, we compared expression
levels of single genes between preimmune controls and individuals with
each of four autoimmune diseases. We selected 10 genes that exhibited
the greatest level of over- and underexpression (Fig. 3) at the population level and were
highly consistent in each individual with autoimmune disease.
Overexpressed genes in the autoimmune population showed greater
individual variation in expression (Fig. 3A). In this group,
no individual gene was overexpressed in all autoimmune individuals
compared with all control individuals. However, each of these genes was
significantly overexpressed in the autoimmune population
(p < 0.05). In contrast, expression levels of
underexpressed genes (Fig. 3B) were lower in all autoimmune
individuals than in all control individuals.
|
10% monocytes, 5% B cells, and <1% neutrophils. Second, we
determined whether expression levels of genes that are either
restricted to a given subpopulation or reflect activation status were
differentially expressed in the control compared with the autoimmune
population (Table I). Expression levels
of these genes varied by less than 2-fold between the control and
autoimmune groups and this difference did not achieve statistical
significance. Taken together, these data argue that alterations in the
composition or activation status of PBMC did not account for the
observed differences in gene expression between the control and
autoimmune populations.
|
), we
wanted to determine whether we could use this pattern to classify
individuals with autoimmune disease and predict whether new samples
were derived from autoimmune or control individuals. First, we selected
35 genes that were most underexpressed in the SLE population compared
with the control population with the greatest statistical significance.
Second, to give each gene equal weight in the determination, we summed
the average of the control population and the SLE population for each
gene and divided by 2 ((control + SLE)/2). Third, we inspected
expression levels of each of the 35 genes in each sample and assigned
them a value of 0 if they were less than the average and 1 if they were
greater than the average. In this analysis, the maximum possible score
was 35 and the minimum possible score was 0 (Fig. 4). The range of scores for control
individuals was 18–35. Most achieved a score of 35 (8 of 11). The
range of scores for the normal immune samples was 26–35. In contrast,
the range of scores in individuals with autoimmune disease was 0–5
(SLE), 0–6 (RA), 0–1 (type I diabetes), and 0 (MS)
(p < 0.000001). Next, we tested a new set of
SLE and RA patients that were not included in the initial data analysis
to determine whether these individuals would obtain a similar score.
The range of scores for this second autoimmune group was 0–5 (SLE) and
0–6 (RA).
|
In designing this study, we set out to test the hypothesis that
individuals with autoimmune disease exhibited uniform changes in gene
expression in PBMC. For this reason, we studied patients with variable
disease activity, duration of disease, modes of therapy, and type of
autoimmune disease. In our population, the most common therapy for RA
is methotrexate, for SLE it is prednisone (5–10 mg/day), for type I
diabetes it is insulin, and for MS it is IFN-. None of these
patients were receiving cyclophosphamide. The fact that the observed
changes in gene expression are independent of these parameters and are
also observed in first-degree unaffected family members argues that
these changes are genetic in origin rather than a reflection of the
disease process or therapy. Analysis of patients on prednisone
indicates that this treatment partially corrects the expression levels
of certain genes (data not shown), but these genes are not included in
the set that comprises the common gene expression profile found in
autoimmune disease. This suggests that other clusters of genes, as yet
unidentified, are involved in the disease process.
Individual genes represented within the autoimmune expression profile illustrate the potential impact of these variations upon the immune response. First, a large number of genes that encode proteins involved in multiple apoptosis pathways are underexpressed. Defects in apoptosis have been clearly demonstrated in animal models of autoimmune disease and in human autoimmune disease (16, 17, 18, 19). However, the pervasive nature of defects in the expression of a large number of genes involved in apoptosis pathways was unexpected. Second, inhibitors of a variety of functions, ranging from cell cycle control, proteasome function, enzyme activities, and cell differentiation are also underexpressed. Third, genes required for induction of tolerance are also underexpressed.
Defects in expression of these genes may increase the likelihood that lymphocytes avoid the normal processes used by the immune system to eliminate unwanted lymphocytes or to down-regulate an immune response (20, 21, 22). Normally, autoreactive lymphocytes that survive the process of thymic selection and circulate in the periphery can respond to self-Ag if presented by the correct MHC. This process is held in check by the combined actions of apoptosis and anergy and results in a state of peripheral tolerance. However, if patients carry this autoimmune gene expression signature, signaling pathways essential for the maintenance of tolerance may not function properly. This may permit lymphocytes to escape tolerance and adopt a prosurvival agenda that increases the likelihood of autoimmune diseases
|
Acknowledgments |
|---|
|
Footnotes |
|---|
2 Address correspondence and reprint requests to Dr. Thomas M. Aune, Medical Center North T3219, Vanderbilt University Medical Center, 21st and Garland Avenues, Nashville, TN 37232. E-mail address: Thomas.Aune{at}mcmail.vanderbilt.edu
3 Abbreviations used in this paper: MS, multiple sclerosis; RA, rheumatoid arthritis; SLE, systemic lupus erythematosus.
4 Complete data tables are available upon request.
Received for publication March 7, 2002. Accepted for publication May 6, 2002.
|
References |
|---|
|
TopAbstractIntroductionMaterials and MethodsResults and DiscussionReferences |
|---|
This article has been cited by other articles:
|
|
 |
|
  M. J. Mattapallil, A. Sahin, P. B. Silver, S.-H. Sun, C.-C. Chan, E. F. Remmers, J. F. Hejtmancik, and R. R. Caspi Common Genetic Determinants of Uveitis Shared with Other Autoimmune Disorders J. Immunol., May 15, 2008; 180(10): 6751 - 6759. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. Bouwens, L. A Afman, and M. Muller Fasting induces changes in peripheral blood mononuclear cell gene expression profiles related to increases in fatty acid {beta}-oxidation: functional role of peroxisome proliferator activated receptor {alpha} in human peripheral blood mononuclear cells Am. J. Clinical Nutrition, November 1, 2007; 86(5): 1515 - 1523. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S. C. Fossey, C. L. Vnencak-Jones, N. J. Olsen, S. Sriram, G. Garrison, X. Deng, P. S. Crooke III, and T. M. Aune Identification of Molecular Biomarkers for Multiple Sclerosis J. Mol. Diagn., April 1, 2007; 9(2): 197 - 204. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 X Li, V Rider, B F Kimler, and N I Abdou Estrogen does not regulate CD154 mRNA stability in systemic lupus erythematosus T cells Lupus, December 1, 2006; 15(12): 852 - 857. [Abstract] [PDF]
|
|
|
|
|
|
M Mandel and A Achiron Gene Expression Studies in Systemic Lupus Erythematosus Lupus, July 1, 2006; 15(7): 451 - 456. [Abstract] [PDF]
|
|
|
|
|
|
M. E. Burczynski, R. L. Peterson, N. C. Twine, K. A. Zuberek, B. J. Brodeur, L. Casciotti, V. Maganti, P. S. Reddy, A. Strahs, F. Immermann, et al. Molecular Classification of Crohn's Disease and Ulcerative Colitis Patients Using Transcriptional Profiles in Peripheral Blood Mononuclear Cells J. Mol. Diagn., February 1, 2006; 8(1): 51 - 61. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 Z. Liu, K. Maas, and T. M. Aune Identification of gene expression signatures in autoimmune disease without the influence of familial resemblance Hum. Mol. Genet., February 1, 2006; 15(3): 501 - 509. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K. Maas, H. Chen, Y. Shyr, N. J. Olsen, and T. Aune Shared gene expression profiles in individuals with autoimmune disease and unaffected first-degree relatives of individuals with autoimmune disease Hum. Mol. Genet., May 15, 2005; 14(10): 1305 - 1314. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
C. A. Acevedo-Suarez, C. Hulbert, E. J. Woodward, and J. W. Thomas Uncoupling of Anergy from Developmental Arrest in Anti-Insulin B Cells Supports the Development of Autoimmune Diabetes J. Immunol., January 15, 2005; 174(2): 827 - 833. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 W. Li, M. Simarro, N. Kedersha, and P. Anderson FAST Is a Survival Protein That Senses Mitochondrial Stress and Modulates TIA-1-Regulated Changes in Protein Expression Mol. Cell. Biol., December 15, 2004; 24(24): 10718 - 10732. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 N Olsen, T Sokka, C L Seehorn, B Kraft, K Maas, J Moore, and T M Aune A gene expression signature for recent onset rheumatoid arthritis in peripheral blood mononuclear cells Ann Rheum Dis, November 1, 2004; 63(11): 1387 - 1392. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 G. Henriksson, A. Sallmyr, L. Du, A. Larsson, R. Manthorpe, and A. Bredberg Enhanced DNA-dependent protein kinase activity in Sjogren's syndrome B cells Rheumatology, September 1, 2004; 43(9): 1109 - 1115. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. G. Barnes, B. J. Aronow, L. K. Luyrink, M. B. Moroldo, P. Pavlidis, M. H. Passo, A. A. Grom, R. Hirsch, E. H. Giannini, R. A. Colbert, et al. Gene expression in juvenile arthritis and spondyloarthropathy: pro-angiogenic ELR+ chemokine genes relate to course of arthritis Rheumatology, August 1, 2004; 43(8): 973 - 979. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 B. Bielekova and R. Martin Development of biomarkers in multiple sclerosis Brain, July 1, 2004; 127(7): 1463 - 1478. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
P J Utz Multiplexed assays for identification of biomarkers and surrogate markers in systemic lupus erythematosus Lupus, May 1, 2004; 13(5): 304 - 311. [Abstract] [PDF]
|
|
|
|
|
|
R. Bomprezzi, M. Ringner, S. Kim, M. L. Bittner, J. Khan, Y. Chen, A. Elkahloun, A. Yu, B. Bielekova, P. S. Meltzer, et al. Gene expression profile in multiple sclerosis patients and healthy controls: identifying pathways relevant to disease Hum. Mol. Genet., September 1, 2003; 12(17): 2191 - 2199. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 R Bomprezzi, P E Kovanen, and R Martin New approaches to investigating heterogeneity in complex traits J. Med. Genet., August 1, 2003; 40(8): 553 - 559. [Abstract] [Full Text] [PDF]
|
|
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
) and 25
individuals (
) with autoimmune disease. The ten most overexpressed
(A) and underexpressed (B) genes were
analyzed.