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Prepublished online as a Blood First Edition Paper on October 31, 2002; DOI 10.1182/blood-2002-06-1837.
IMMUNOBIOLOGY
From the Department of Immunotechnology, Lund
University, Sweden; and Department of Complex Systems, Lund University,
Sweden.
A number of studies have implicated a role for the cell surface
glycoprotein CD44 in several biologic events, such as lymphopoiesis, homing, lymphocyte activation, and apoptosis. We have earlier reported
that signaling via CD44 on naive B cells in addition to B-cell receptor
(BCR) and CD40 engagement generated a germinal center-like phenotype.
To further characterize the global role of CD44 in B differentiation,
we examined the expression profile of human B cells cultured in vitro
in the presence or absence of CD44 ligation, together with
anti-immunoglobulin (anti-Ig) and anti-CD40 antibodies. The data sets
derived from DNA microarrays were analyzed using a novel statistical
analysis scheme created to retrieve the most likely expression pattern
of CD44 ligation. Our results show that genes such as interleukin-6
(IL-6), IL-1 B-cell differentiation is highly regulated by
components in the surrounding microenvironment1 and is a
central process in the humoral immune response. This often involves
germinal center reactions eventually leading to population of the
memory compartment as well as to generation of plasma
cells.2 Important players in this process are, for
example, adhesion molecules from the families of integrins, selectins,
and immunoglobulins, as well as chemoattractants, such as the
chemokines.3
The cell surface glycoprotein CD44 is a member of the
hyaladherin or link protein superfamily (LPSF) that interacts
with the polysaccharide hyaluronan (HA) in the extracellular matrix
(ECM). It is widely distributed in the body and mediates cell-cell and cell-matrix interactions. In the hematopoietic system CD44 is expressed
on all cell types and has been shown to play a role in lymphopoiesis
and lymphocyte homing4 as well as in lymphocyte activation5,6 and apoptosis.6-7 CD44 has
furthermore been associated with several different pathologic states
where the linkage to cancer and autoimmune diseases is the most
notable.4,8
All mechanisms supporting B-cell differentiation from a mature naive B
cell to an immunoglobulin (Ig)-producing plasma cell are still not
entirely understood, although many components have been
identified.2 We and others have previously investigated the role of CD44 in the regulation of T-cell-dependent B-cell activation9 and subsequent germinal center
formation,10 where ligation of CD44 was shown to
contribute to the induction of a phenotype closely resembling a
germinal center (GC) B cell.
B cells up-regulate CD44 upon activation,9,11,12 and
HA-CD44 interactions induce activation of mature B cells in
vivo.5 However, CD44 is down-regulated during the germinal
center reaction.13-15 Interestingly, ectopic GC-like
structures are found in several autoimmune and inflammatory
diseases,16,17 indicating that a microenvironment
promoting formation of GC-like follicular structures is generated in
these pathologic states. Apart from T cells and follicular dendritic
cells at these GC-like structures,18,19 an increase in
expression levels of CD44 was evident.20,21
To further elucidate the functional effects of CD44 ligation on B
cells, we assessed the transcriptional profiles from
anti-CD44-stimulated naive B cells also costimulated via CD40 and the
B-cell receptor (BCR). The transcriptional profile of approximately
6800 genes was evaluated by using a high-density DNA microarray
technique. To facilitate the analysis of the complex patterns in the
data set, we developed a novel statistical analysis scheme that
accounts for inherent errors in the experimental handling and process
often seen in these types of studies.22,23 In summary, the
CD44-dependent regulated genes in our analysis were found to mainly
pertain to proteins involved in inflammation and immunomodulation.
Interestingly, the expression patterns regulated by CD44 fall into 2 groups: (1) genes augmented or repressed by CD44 in a temporal fashion and (2) genes directly regulated Antibodies
Cells
Cell culture condition IgD+/CD38 B cells
(2.0 × 106) from the 4 different human donors were
separately cultured at 37°C, 5% CO2, for a total of 6 hours, 24 hours, and 72 hours on 1.5 × 105
CD32-transfected fibroblasts (162CG7) with 0.2 µg/mL anti-IgM (AF6)
and 1.0 µg/mL anti-CD40 (S2C6) antibodies, with or without 1.0 µg/mL anti-CD44 (BU52) antibodies. The culture was performed in
flat-bottomed 24-well plates (Costar, Corning, NY) using complete medium: RPMI 1640 supplemented with 2 mM L-glutamine, 1%
nonessential amino acids, 50 µg/mL gentamicin (Gibco Life
Technologies, Gaithersburg, MD), and 10% FBS (Hyclone Laboratories,
Logan, UT).
Isolation of mRNA Freshly isolated cells from each separate culture were lysed in Trizol (Life Technologies). The RNA was extracted from the cell lysate by adding 0.2 vol chloroform. The aqueous phase containing the RNA was separated and subsequently precipitated with isopropanol and washed in 75% ethanol. The RNA pellet was dissolved in diethylene pyrocarbonate (DEPC)-treated water and further purified with the RNeasy Mini Kit (QIAGEN, Hilden, Germany). The total RNA content was assessed spectrophotometrically (GeneQuant II, Amersham, Pharmacia Biotech) within a 260/280 nm OD (optic density) ratio of 1.9:2.1. After a second precipitation step in 2.5 vol ethanol and subsequent wash, the RNA was resuspended in DEPC-treated water. Five micrograms of total RNA was used for the cDNA and cRNA synthesis, as previously described24; cRNA is quality controlled by gel electrophoresis.Hybridization and scanning of the DNA chips A hybridization cocktail was prepared with the biotinylated and fragmented cRNA at 50 µg/mL, as described previously,24 and hybridized onto HuGeneFL microarrays (Affymetrix, Santa Clara, CA). The probe array was then stained with a solution of 2 mg/mL acetylated bovine serum albumin (BSA) and 10 µg/mL streptavidin R-phycoerythrin (Molecular Probes, Eugene, OR). A secondary stain was performed with acetylated BSA, normal goat IgG (Sigma Chemical, St Louis, MO), and biotinylated goat antistreptavidin antibody (Vector Laboratories, Burlingame, CA) for amplification. A final staining step with streptavidin R-phycoerythrin was performed before the probe arrays were scanned in the gene array scanner and checked using the Micro Array Suite 4.0 (Affymetrix), as described previously.24 Several controls assessing the overall processing, the hybridization, and the quality of the material are included on the microarray. A total of 6 arrays were run for every donor, one for every time point for both the anti-CD44-stimulated and nonstimulated cells. In total, 24 arrays were evaluated.Statistical analysis A brief review of the mathematical details of the statistical data analysis is given here, and a more detailed description is available elsewhere (S.B., T.B., and M. Sigvardsson, unpublished data, 2002). The data consist of 6 different biologic varieties: anti-CD44-treated cells at 6, 24, and 72 hours after the onset of treatment and untreated cells sampled at the same time points. Each variety consists of 4 samples that is, from the 4 different human
donors. For each variety, data are discretized using the Affymetrix
"present/absent" calls so that each gene is represented by a vector
S {0,1}4. The distribution of observed states S
in the variety is assumed to be generated by 3 underlying biologic states: 0 for expression below the detection
level of the chips, 1 for expression above
detection level, and T for genes with varying
expression levels in the 4 samples. The latter state is assumed to give
rise to random vectors S with equal probabilities of 0 or 1 at each
position. For each measurement, a certain noise characteristic is
assumed. This noise characteristic is modeled by
misclassification probabilities
{P![]() ![]() ![]() To simplify the notation, we introduce the S dependent variables:
2 minimization of the unweighted errors. With
the parameter estimates generated from the fit, we may now calculate
the belief in terms of probability for an underlying state given the
observed one. P( |S) = [P(S| )P( )]/P(S)
where
{ 0, T, 1}.
For the 36 = 729 possible expression profiles over the
whole set of varieties {6h+, 24h+, 72h+, 6h ,
24h , 72h }, we calculate the probability of each one of them by
P(![]() ![]() ![]() For simplicity, the underlying states Flow cytometry and ELISA Freshly isolated cells from each separate culture were assayed for IL-1 surface expression by flow cytometry using a FACScan (BD)
and for IL-6 expression in the supernatant using enzyme-linked immunosorbent assay (ELISA). Anti-IL-1 -PE was purchased
from PharMingen, while the human IL-6 ELISA kit was purchased from R&D
Systems (Minneapolis, MN).
To study the transcriptional changes associated with a CD44 stimuli on naive B cells, the entire cell population from ectomized pediatric tonsils was fractionated and a naive B-cell subset, defined by immunoglobulin (Ig) D cell surface expression and a lack of CD38, was collected. These cells were propagated in the presence of anti-CD40 and anti-IgM antibodies and immobilized on CD32-transfected fibroblasts.25 The phenotypic and functional changes then attributed to a CD44 stimulation were evaluated at 6, 24, and 72 hours over 4 separate samples using high-density DNA microarrays displaying probes for approximately 6800 genes and several hundred expressed sequence tags (ESTs). To investigate the sample coherence, we constructed a Hamming distance
matrix of all samples (Figure 1). This
validation clearly shows how samples labeled 1 in both treated
and untreated cells at 72 hours deviate from the other samples. The
deviation is also noted as raised misclassification
probabilities for these samples (data not shown). Because the algorithm
is designed to handle deviations even of this magnitude, the samples
were retained.
To assess the difference in gene expression induced by CD44
ligation, the genes were clustered according to their most probable expression profile over the 6 varieties. These expression profiles can
be viewed as 6-letter strings composed by 0, T, or 1 in the following
order: 6h+, 24h+, 72h+, 6h To select genes directly regulated by CD44 (ie, induced or repressed by
CD44) at all times, we extracted those having an expression pattern
matching (1/T, 1/T, 1/T, 0, 0, 0) and (0, 0, 0, 1/T, 1/T, 1/T). To
select temporally regulated genes (ie, genes having an earlier or
delayed induction by CD44), we extracted those having an expression
pattern matching (1/T, 1/T/0, 1/T/0, 0, 1/T/0, 1/T/0) and (0, 1/T/0,
1/T/0, 1/T, 1/T/0, 1/T/0). The selected clusters were further refined
by applying a ranking of the genes having the highest appearance
frequency within each specific pattern. In this way the most
significant members were assessed. Tables 1 and
2 show genes directly regulated
by CD44, and Tables 3 and
4 show genes temporally regulated by
CD44.
The ligation of CD44 on a naive B-cell population was recently shown by us to partially induce a germinal center phenotype,10 as defined by an up-regulation of CD10, CD77, and CD95. The present analysis confirmed the induced presence of CD10 (MME) and CD95 (TNFRSF6) on the transcriptional level (Table 2). The neutral glycosphingolipid CD77 is not represented on the microarray. In the present setting the difference in CD10 and CD95 expression was attributed to a temporal induction, because the CD95 transcript was found in the 11101T cluster and the CD10 is found in the 11T0TT cluster. This indicated that CD44-dependent induction of these molecules occurred at the earlier time points. The list of directly regulated genes represents several novel members.
In the cluster designated 00T000, representing genes differentially
expressed at 72 hours by CD44 ligation, is the SERPINC1 transcript for
antithrombin III found. This cluster also contains the RGS3 transcript
for the G-protein signaling regulator 3 protein, which is involved in
the regulation of chemoattractant-mediated migration in
lymphocytes.26,27 The cluster 0TT000, representing genes
that are differentially up-regulated after 6 hours by the CD44
ligation, contains the transcript for the In the cluster 0T1000 we found the platelet-derived endothelial cell
growth factor (ECGF1). This cytokine is known for its involvement in
cancer and rheumatoid arthritis and has, for instance, been shown to
induce inflammation and hyperplasia in synovial cells.29
In the cluster described by TTT000, the immunomodulating interleukins
IL-6 and IL-1 The up-regulation of IL-1
The hyaluronan (HA) receptor CD44 has been implicated in the regulation of the immune system, and as a member of the hyaladherin family of proteins its role in adhesion and extracellular interactions has been extensively investigated.4 Furthermore, a crucial role of CD44 in inflammation has also lately been demonstrated.35 In the present study we assessed the transcriptional outcome of ligation of CD44 on mature naive tonsillar B cells. The dataset was analyzed employing a novel statistical analysis scheme created to retrieve the most likely expression pattern of an observed distribution. We were thus able to extract the patterns for genes classified as directly or temporally regulated by the anti-CD44 stimuli (Tables 1-2). Our previous observation that CD44 ligation in naive B cells in fact
induced a GC B-cell-like phenotype was corroborated in the present
transcriptional analysis, in that CD10 (MME) and CD95 (TNFRSF6) were
shown to be temporally regulated by CD44. The neutral endopeptidase
CD10 has a strong association to activated germinal center B-cell
populations,36 and the only other stage in B-cell ontogeny
where CD10 is expressed is at the pro-B-cell stage in bone marrow. The
functional role of CD10 is associated to inflammation, and CD10
provides protection to several neuropeptides and other mediators of
inflammation in tissue. Some of the substrates include endothelin,
bradykinin, substance P, calcitonin gene-related peptide, neuropeptide
Y, and IL-1 Among the components that were found to be directly regulated by CD44
were the cytokines IL-6 and IL-1 Direct regulation by the CD44 stimuli was evident also for the IL-1 CD44 has also been implicated in lymphocyte migration. In this study
SERPINC1 and RSG3, both potent regulators of migration, were shown to
be regulated by CD44. The serpin, antithrombin III (SERPINC1), is an
important regulator of the coagulation cascade as well as being
involved in inflammation.48 Antithrombin III has also been
found to inhibit chemokine-mediated migration through the heparan
sulfate proteoglycans syndecan-4 49 and by a similar interaction also to interfere with nuclear factor- Another up-regulated immunoregulatory gene dependent on CD44
stimulation is the Interestingly, a recent study demonstrated rapid down-regulation of
CD44 on NK cells by NE treatment,57 and it is possible to
speculate on a similar mechanism in the heavily innervated marginal
zone. Such a mechanism presents a possible scenario where the CD44
participates in the decision making of the B-cell faith. For example,
antigen-induced CD44 9,11,12 will as a costimultory molecule9 participate in a T cell-B cell cognate
interaction with a sequential up-regulation of the In summary, our results suggest a novel role for CD44 in
immunoregulation and inflammation. More specifically, possible
mechanisms for CD44 involvement in the humoral response and the
formation of germinal center reactions based on the up-regulation of
transcript for CD10, CD95, IL-6, IL-1
Submitted June 21, 2002; accepted October 29, 2002.
Prepublished online as Blood First Edition Paper, October 31, 2002; DOI 10.1182/blood-2002-06-1837.
Supported by Cancerfonden.
The publication costs of this article were defrayed in part by page charge payment. Therefore, and solely to indicate this fact, this article is hereby marked "advertisement" in accordance with 18 U.S.C. section 1734.
Reprints: Carl A. K. Borrebaeck, Department of Immunotechnology, Lund University, PO Box 7031, SE-220 07 Lund, Sweden; e-mail: carl.borrebaeck{at}immun.lth.se.
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