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Gene profiling of scleroderma skin reveals robust signatures of disease that are imperfectly reflected in the transcript profiles of explanted fibroblasts.

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ARTHRITIS & RHEUMATISM
Vol. 54, No. 6, June 2006, pp 1961–1973
DOI 10.1002/art.21894
© 2006, American College of Rheumatology
Gene Profiling of Scleroderma Skin Reveals Robust Signatures
of Disease That Are Imperfectly Reflected in the
Transcript Profiles of Explanted Fibroblasts
Humphrey Gardner,1 Jeffrey R. Shearstone,1 Raj Bandaru,1 Tom Crowell,1 Matthew Lynes,1
Maria Trojanowska,2 Jaspreet Pannu,2 Edwin Smith,2 Stefania Jablonska,3 Maria Blaszczyk,3
Filemon K. Tan,4 and Maureen D. Mayes4
Objective. To determine whether biopsy specimens obtained from systemic sclerosis (SSc) lesions
show a distinctive gene profile, whether that gene profile
is maintained in fibroblasts cultured from SSc skin
biopsy specimens, and whether results from tissue obtained from multiple clinical centers can be combined to
yield useful observations in this rare disease.
Methods. Biopsy samples and passaged fibroblasts were stored in RNAlater solution prior to processing for RNA. RNA from SSc and control skin biopsy
specimens, as well as SSc and control explanted passage
4 fibroblasts, from 9 patients and 9 controls was hybridized to Affymetrix HG-U133A arrays. Data were analyzed
using the BRB ArrayTools system. When appropriate,
findings were followed up with immunohistochemical
analysis or TaqMan studies.
Results. Biopsy samples obtained from patients
with SSc had a robust and distinctive gene profile, with
⬃1,800 qualifiers distinguishing normal skin from SSc
skin at a significant level. The SSc phenotype was the
major driver of sample clusters, independent of origin.
Alterations in transforming growth factor ␤ and Wnt
pathways, extracellular matrix proteins, and the CCN
family were prominent. Explanted fibroblasts from SSc
biopsy samples showed a far smaller subset of changes
that were relatively variable between samples, suggesting that either nonfibroblast cell types or other aspects
of the dermal milieu are required for full expression of
the SSc phenotype.
Conclusion. SSc has a distinct gene profile that is
not confounded by geographic location, indicating that
extended multicenter studies may be worthwhile to
identify distinct subsets of disease by transcript profiling. Explanted SSc fibroblasts show an incomplete
reflection of the SSc phenotype.
Systemic sclerosis (SSc; scleroderma) is a devastating disease of connective tissue, the etiology and
pathogenesis of which are poorly understood. Major
pathogenetic components include endothelial injury and
microvascular damage, excess production of extracellular matrix, and production of multiple autoantibodies,
many of which are directed against nuclear and nucleolar components, and some of which are also associated
with other connective tissue disorders such as rheumatoid arthritis and systemic lupus erythematosus.
Disease outcome in SSc is not easily predicted,
although general clinical patterns are discernable that,
to an incomplete extent, overlap with autoantibody
profiles (1,2). Most patients present with symptoms and
signs of skin thickening and hardening in the distal
extremities, which are frequently presaged (for months
to years) by Raynaud’s phenomenon. Rapid progression
of skin thickening is suggestive of a worse outcome, with
a higher likelihood of internal organ involvement. However, different patients tend to have different patterns of
end-organ damage in an unpredictable manner, and
patients with limited disease have a distinctly increased
1
Humphrey Gardner, MD, Jeffrey R. Shearstone, MSc, Raj
Bandaru, MSc, Tom Crowell, BSc, Matthew Lynes, BA: Biogen Idec,
Cambridge, Massachusetts; 2Maria Trojanowska, PhD, Jaspreet
Pannu, PhD, Edwin Smith, MD: Medical University of South Carolina,
Charleston; 3Stefania Jablonska, MD, Maria Blaszczyk, MD: Warsaw
Medical Academy, Warsaw, Poland; 4Filemon K. Tan, MD, PhD,
Maureen D. Mayes, MD, MPH: University of Texas Medical School at
Houston.
Dr. Gardner has stock options in Novartis.
Address correspondence and reprint requests to Humphrey
Gardner, MD, Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, MA 02139. E-mail: Humphrey.
gardner@novartis.com.
Submitted for publication October 4, 2005; accepted in revised form February 21, 2006.
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GARDNER ET AL
risk of pulmonary hypertension. It is possible that the
myriad outcomes in SSc represent different diseases that
might ultimately be diagnosable by expression profiling.
Fibroblast activation and the potential involvement of the transforming growth factor ␤ (TGF␤) axis
have long been a focus of study in SSc, not least because
explanted dermal fibroblasts are a highly accessible
source of tissue, and cells from the same individual can
be studied by multiple investigators. At the same time,
there is concern that examination of SSc fibroblasts
results in a focus on one potential aspect of the pathogenesis of the disease while missing the contribution of
other tissues, e.g., endothelial cells and cells of the
immune system. Furthermore, many studies and anecdotal reports have suggested that the phenotypic peculiarities of SSc fibroblasts are not stable and wear off
with successive passages (3). Studies of fibroblasts in
other contexts have suggested that fibroblasts retain
some characteristics, such as transcript patterns enabling
them to be clustered by site of origin (4), while losing
others, such as surface integrin profiles (5).
For these reasons, establishing whether the transcript profile of explanted SSc fibroblasts reflects the
gene profiles associated with SSc skin biopsy specimens
is of some importance. It is also advantageous to analyze
in parallel both biopsy specimens and fibroblasts derived
from them. In this way, at least to a first degree of
approximation, fibroblast and nonfibroblast components
of the SSc gene profile can be separated from one
another.
A current challenge with transcript profile studies
of human tissue is variation in collection methodology or
RNA preservation, leading to evanescent signatures that
are more associated with artifacts of collection than with
the disease of interest. In common diseases, rigorous
collection protocols and studies limited to single sites
have enabled identification of important prognostic signatures, using unsupervised clustering (6). However, to
use similar techniques in rare diseases such as SSc, in
which samples necessarily are collected from multiple
sites, we need to understand and control collection
artifacts. We attempted to control these sources of
variation with our methodology and explicitly tested for
their presence during our analysis.
PATIENTS AND METHODS
Patients and controls. All patients fulfilled the American College of Rheumatology (formerly, the American Rheumatism Association) preliminary classification criteria for
scleroderma (7), and all but 1 had diffuse cutaneous disease
according to the classification criteria described by LeRoy et al
(8). Consecutive outpatients with early disease (⬍3 years from
the onset of the first non–Raynaud’s phenomenon scleroderma
disease manifestation) and either diffuse disease or skin thickening involving the forearms were chosen, in an attempt to
diminish heterogeneity of disease expression. To be eligible for
the study, patients could not have been receiving doses of
prednisone ⱖ20 mg during the 4 weeks prior to the biopsy, nor
could they have been receiving other immunosuppressive
treatment within this time period.
Healthy controls were chosen on the basis of a lack of
Raynaud’s phenomenon by history, lack of a diagnosis of a
systemic autoimmune disease, and willingness to participate.
Control subjects listed their medications, and none were
receiving glucocorticoids or immunosuppressive agents. This
study was approved by the institutional review boards of the
University of Texas Health Science Center at Houston and the
Medical University of South Carolina. Written informed consent was obtained from all participants.
Biopsy method. Two 3-mm punch biopsy specimens
were obtained from the same distal forearm (forearm with
involved skin in patients with scleroderma) of each subject, in
a side-by-side manner. One biopsy specimen was immediately
placed in RNAlater (Ambion, Austin, TX), shipped on wet ice
to Affymetrix (Santa Clara, CA), and stored at 4°C prior to
RNA preparation. RNA was prepared within 15 days of tissue
harvest. The other biopsy piece was placed in culture.
Fibroblast culture conditions. Biopsy tissue was rinsed
several times with antibiotic–antimycotic solution (catalog no.
15240-062; Life Technologies, Gaithersburg, MD). Tissue was
then placed in 1 ml of collagenase solution and incubated for
24 hours at 37oC. Collagenase solution was prepared at the
Medical University of South Carolina and was distributed to all
investigators. The collagenase solution contained 0.25% collagenase 1 (Sigma, St. Louis, MO) and 0.05% DNase I (Sigma)
in Dulbecco’s modified Eagle’s medium (DMEM) with 20%
fetal bovine serum (HyClone, Logan, UT). The entire 1-ml
solution was mixed together with 5 ml of media (DMEM plus
20% fetal calf serum), plated into a 25-cm2 flask, and left
undisturbed for 48 hours at 37°C in a 5% CO2 atmosphere.
The resulting confluent culture was then designated passage 1
(P1). Cells were then split (1:4 ratio) to generate 4 ⫻ 25–cm2
flasks (P2). Two flasks at passage 4 were trypsinized, the
trypsin was then neutralized using soybean trypsin inhibitor,
and cell pellets were washed twice with phosphate buffered
saline prior to the addition of RNAlater (Ambion). Cell pellets
were shipped frozen on dry ice by overnight delivery and
stored frozen at ⫺80°C at the Affymetrix facility prior to RNA
preparation.
Total RNA purification. Skin biopsy specimens were
removed from the RNAlater solution (Ambion), placed in a
weighing dish containing 1 ml of TRIzol reagent (Invitrogen,
Carlsbad, CA), minced using a razor blade, and poured into a
2-ml tube. RNAlater was removed from the fibroblast pellets,
which were then resuspended in 1 ml of TRIzol reagent.
Fibroblasts and biopsy specimens were homogenized using a
PowerGen 125 homogenizer (Fisher Scientific, Hampton, NH)
for 2–5 minutes at top speed. Total RNA was extracted from
TRIzol according to the manufacturer’s protocol. The extraction process included an optional centrifugation step that was
suggested for biopsy samples with a high content of fat and
DISTINCT GENE PROFILE OF SCLERODERMA
extracellular material. Total RNA was resuspended in 100 ␮l
of water and further purified using an RNeasy Mini column
(Qiagen, Valencia, CA), according to the manufacturer’s protocol.
Probe labeling, hybridization, and scanning. Sample
labeling, hybridization, and staining were carried out according
to the Eukaryotic Target Preparation protocol in the Affymetrix Technical Manual (701021 rev. 4) for Genechip
Expression Analysis (Affymetrix). According to this protocol,
1–5 ␮g of purified total RNA was used in a 20-␮l first-strand
reaction with 200 units of SuperScript II (Invitrogen) and 0.5
␮g of dT-T7 primer in 1⫻ first-strand buffer (Invitrogen), with
incubation at 42°C for 1 hour.
Second-strand synthesis was carried out by adding 40
units of Escherichia coli DNA polymerase, 2 units of E coli
RNase H, and 10 units of E coli DNA ligase in 1⫻ secondstrand buffer (Invitrogen), followed by incubation at 16°C for
2 hours. The second-strand synthesis reaction was purified
using the Genechip Sample Cleanup Module according to the
manufacturer’s protocol (Affymetrix). Purified complementary
DNA (cDNA) was amplified and biotinylated using the BioArray HighYield RNA Transcript Labeling Kit (Enzo Life
Sciences, Farmingdale, NY) according to the manufacturer’s
protocol. Fifteen micrograms of labeled complementary RNA
was fragmented and resuspended in 300 ␮l of 1⫻ hybridization
buffer containing 100 mM MES, 1M Na⫹, 20 mM EDTA,
0.01% Tween 20, 0.5 mg/ml aceylated bovine serum albumin,
0.1 mg/ml herring sperm DNA, Control Oligo B2 (Affymetrix),
and eukaryotic control transcripts. Sample was applied to a
Human Genome 133A GeneChip (Affymetrix). Hybridized
arrays were washed and stained on a GeneChip Fluidics
Station 450 and visualized using a GeneChip Scanner 3000.
Quantitative reverse transcription–polymerase chain
reaction (RT-PCR). First-strand cDNA was synthesized from
RNA using the SuperScript III Platinum Two-Step qRT-PCR
Kit (Invitrogen). For each reaction, 1 ␮l of RNA containing
100 ng–1 ␮g was used. RT-PCRs were set up in Optical 96-well
reaction plates (Applied Biosystems, Foster City, CA) using
TaqMan Gene Expression assays protocol for 50-␮l reactions
(Applied Biosystems). The concentration of cDNA was determined by spectroscopy, using a BioPhotometer (Eppendorf,
Madison, WI). For all reactions, 40 ng of cDNA was used. The
RT-PCR thermal profile was as follows: 3 minutes at 95°, then
55 cycles of 15 seconds at 95°, and 1 minute at 56°. RT-PCR
data were collected using an Mx3000P PCR system (Stratagene, La Jolla, CA) and analyzed using Mx3000P software
(Stratagene). Standard TaqMan probes were used, as follows:
for COL11A1, Hs00266273_m; for COL1A2, Hs00164099_m1;
for COL1A1, Hs00164004_m1; for GAPDH, Hs99999905_m1.
Array analysis. Array data in the form of CEL files
were imported into BRB ArrayTools, developed by Dr. Richard Simon and Amy Peng Lam (http://linus.nci.nih.gov/BRBArrayTools.html). Biopsy data and fibroblast data were imported and normalized independently, using the robust
multichip average (RMA) algorithm. Data sets normalized
using the Affymetrix MicroArray Suite 5.0 probe reduction
algorithm were used when comparisons between fibroblasts
and biopsy specimens were necessary. Class comparisons and
class predictions were carried out using the BRB software
package. Specific methods for given analyses are described in
the figure legends.
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Immunohistochemical analysis. Specimens from the
individuals involved in the Affymetrix analysis were not available for immunohistochemical analysis. Recuts were obtained
from paraffin-embedded biopsy specimens (5 SSc and 2 control) from subjects whose age distribution and length of history
of SSc were similar to those of the Affymetrix group. Pretreatment of tissue sections for antigen retrieval was required and
was performed as follows: for type I collagen, pretreatment
with pepsin (P7000; Sigma) at 1 mg/ml in 0.5N acetic acid for
2 hours at 37°C; for CD3, pretreatment with 1 mM EDTA (pH
8.2) in a pressure cooker (Biocare Medical, Walnut Creek,
CA). Endogenous peroxidase was quenched using 0.3% hydrogen peroxide in methanol for 30 minutes. Tissue sections were
rinsed in several changes of 50 mM Tris HCl, 150 mM NaCl,
0.05% Tween 20 (pH 7.6) prior to immunostaining. Unconjugated primary antibodies included monoclonal mouse antihuman CD20, clone L26 (Dako, Carpinteria CA); monoclonal
mouse anti-human fibrillin 1, clone 12A5.18 (LabVision, Fremont, CA); monoclonal rat anti-human CD3, clone CD3-12
(Serotec, Raleigh, NC); and polyclonal rabbit anti-human type
I collagen (Biogenesis, Poole, UK). Binding of primary antibodies to tissue sections was detected using biotinylated secondary antibodies (Vector, Burlingame, CA) followed by an
avidin–biotin–horseradish peroxidase reagent (Elite ABC;
Vector), using diaminobenzidine as the substrate.
RESULTS
Biopsy and cell pellet RNA recovery and hybridization. We noted in pilot studies that yields from
fresh-frozen human skin samples were ⬃2-fold higher
than those from skin samples stored in RNAlater (data
not shown). However, the attraction of the RNAlater
method in terms of ease of use was thought to outweigh
the reduced yield. In general, a 3-mm biopsy sample
stored in RNAlater according to the manufacturer’s
instructions provided ample material for a labeling and
hybridization reaction without additional amplification.
Twenty-eight biopsy specimens were processed.
Of the specimens submitted for processing, 16 of 28 gave
yields in excess of 2 ␮g RNA and were hybridized. One
group of 10 biopsy samples processed as a single batch
during the study yielded ⬍0.1 ␮g RNA, but there were
no clear parameters distinguishing these samples from
those preceding or following them, and these failures
were assumed to be attributable to an undiagnosed error
in processing. Of the fibroblast culture pellets, all
yielded at least 5 ␮g of RNA, but 3 of 26 failed
hybridization. As an indicator of RNA quality, the total
mean ⫾ SD percentage of present calls was noted to be
54.9 ⫾ 4.6% (range 61.3–46.4%) for the biopsy samples
and 50.7 ⫾ 2.9 (range 57.0–45.8%) for the fibroblast
samples.
Overall, samples from 9 patients and 9 controls
provided data. Twelve subjects (5 patients and 7 con-
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Figure 1. Dendrogram showing results of unsupervised clustering of
the biopsy specimens used in this study, with relevant clinical information. Intersection of the lists created from the 50th intensity percentile
for each sample resulted in 8,736 probe set qualifiers with a consistently strong signal in all samples. These probe sets were used as the
basis for hierarchical clustering using the BRB ArrayTools system with
a centered correlation metric and average linkage. Note that disease
status was more important than any other parameter in classifying the
biopsy specimens. SSc ⫽ systemic sclerosis; NL ⫽ normal; SC ⫽ site 1;
TX ⫽ site 2; C ⫽ caucasian; AA ⫽ African American.
trols) had both biopsy samples and fibroblast cultures
analyzed. The mean ⫾ SD age of patients was 45.7 ⫾ 8.5
years (range 29–56 years), and that of controls was
46.9 ⫾ 8.8 years (range 34–56 years). The mean ⫾ SD
disease duration among patients was 10.6 ⫾ 6.1 months
(range 3–24 months).
Comparison of normal and SSc biopsy specimens. Unsupervised (agglomerative) clustering of all
samples (Figure 1) demonstrated that the scleroderma
phenotype was the dominant influence on the expression profile and was not confounded by patient sex,
age, or race, or by the origin of the biopsy sample.
Class comparisons on all 22,283 qualifiers between
normal and SSc biopsy specimens showed 1,839 qualifiers distinguishing normal skin from SSc skin at a
univariate P value of ⬍0.01, and 506 qualifiers distinguishing normal skin from SSc skin at a univariate P
value of ⬍0.001 (by unpaired t-test, with random
variance model). Of these, the 50 most significant
(according to P value) are shown in Figure 2.
GARDNER ET AL
Comparison of normal and SSc fibroblasts and
relationship to the biopsy specimens. Class comparisons
between normal and SSc fibroblasts showed 223 qualifiers distinguishing normal from SSc at a P value of ⬍0.01,
of which 21 were significant at a P value of ⬍0.001.
These results reflect the far higher variance seen in the
fibroblast culture profiles than in the biopsy specimens.
Of the 223 qualifiers, 105 were up-regulated in SSc
samples, and 118 were down-regulated. The intersection
between qualifiers dysregulated in biopsy specimens and
those dysregulated in fibroblasts (n ⫽ 26 [9 discordant,
17 concordant]) (Figure 3A) was far greater than that
which would be seen by chance (P ⱕ 0.02 by Fisher’s
exact test of observing an intersection of that many or
more by random sampling of gene lists from a pool of
23,000 qualifiers), and included a large proportion of
extracellular matrix genes, including type VIII collagen,
fibulin 1, fibrillin 2, and decorin. Interestingly, a subset
of these showed inverse regulation in the context of the
biopsy samples and the fibroblasts, notably ephrin B2.
Determining fibroblast and nonfibroblast components of the transcript profile of SSc and normal
biopsy specimens. To make an approximation of the
cellular source of differentially expressed genes in SSc
versus normal biopsy specimens, we reasoned that genes
5-fold up-regulated in cultured fibroblasts compared
with biopsy samples might be considered as likely to be
of fibroblast origin in the biopsy specimen, and, similarly, that genes down-regulated 5-fold in fibroblasts
relative to the biopsy sample were unlikely to be of
fibroblast origin in the biopsy specimen, with genes
between these 2 extremes of differential expression
being considered indeterminate in source. To ensure
that genes influenced by the disease process would not
be averaged out, class comparisons of SSc biopsy specimens versus SSc fibroblasts were made independently of
control biopsy specimens versus control fibroblasts, and
a union was made of the 5-fold up-regulated or downregulated qualifiers. These lists were then intersected
with the class comparison of SSc versus control biopsy
samples. Thus, for example, qualifiers q dysregulated
in the fibroblast component were found, such that
([(qfibro(ssc) ⬎ 5 ⫻ qbiopsy(ssc); P ⬍ 0.01) or (qfibro(control)
⬎ 5 ⫻ qbiopsy(control); P ⬍ 0.01)] and [qbiopsy(control) ⫽
qbiopsy(ssc); P ⬍ 0.01]). Using these criteria, 121 probable
fibroblast and 358 probable nonfibroblast genes were
identified; expression of the 30 most significant of each
is shown in Figures 4A and B, respectively.
In order to provide an independent form of
confirmation of the probable cellular source of transcripts, we created an independent RMA data set com-
DISTINCT GENE PROFILE OF SCLERODERMA
Figure 2. Top 50 (by P value) class-distinguishing genes for systemic sclerosis (SSc) versus normal biopsy specimens.
Class comparison between SSc and control biopsy specimens was performed on the robust multichip average–
normalized data set in BRB ArrayTools using a nominal significance level of 0.01 for each univariate test. Log2 intensity
values for each biopsy sample are presented as a color map for each qualifier (left group of columns of color maps). Nine
colors, from bright red to bright green, represent equal-size bins, from the highest to the lowest values in the row. In
order to give a rough guide as to the likely source of the transcript, the pair of columns in the middle of the figure
represent the ratio of the mean expression in biopsy specimens versus that in fibroblasts, as determined in a MicroArray
Suite–normalized data set containing all fibroblast and biopsy samples, with bright red indicating ⬎16-fold higher
expression in biopsy specimens, and bright green representing ⬎16-fold lower expression. The left column of the pair
represents the comparison of control biopsy specimens versus control fibroblasts, and the right column represents
scleroderma biopsy specimens versus scleroderma fibroblasts. The group of columns on the right represents data for SSc
and control fibroblasts for the same qualifiers, in the same format and representation as for the biopsy specimens. These
are heat-mapped relative to one another, independent of the biopsy specimens, and illustrate that very few of the genes
that distinguish SSc from control biopsy specimens also distinguish SSc from control fibroblasts.
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GARDNER ET AL
Figure 3. A, Intersection of qualifiers in class comparisons of biopsy specimens and fibroblasts. Class comparisons
identified 1,839 qualifiers distinguishing normal skin from systemic sclerosis (SSc) skin and 223 qualifiers distinguishing
normal fibroblasts from SSc-derived fibroblasts (P ⱕ 0.01). Intersection of these lists yielded 26 qualifiers, 17 of which
demonstrated concordant disease state expression between fibroblasts and biopsy specimens (probe sets highlighted
in green), while 9 showed discordant values (probe sets highlighted in red). The heat map of the expression intensity
data is false colored, as described in Figure 2. B, A subset of fibroblast classifier genes was successful in predicting the
disease class of the biopsy specimens (see Patients and Methods). The upper part of the figure shows heat maps of
expression of these genes in SSc and normal biopsy samples, and the group of columns on the right shows expression
of the cognate HU95A qualifiers in SSc and normal biopsy specimens based on the data set described by Whitfield et
al (9). The lower part of the figure illustrates whether each biopsy specimen was correctly classified as SSc or normal,
using this 26-gene set or the subset of cognate qualifiers present in the data set of Whitfield et al. Note that 1 of the SSc
biopsy specimens from this study was misclassified by all but 1 algorithm, and 1 of the normal biopsy specimens in the
study by Whitfield et al was misclassified by 1 algorithm. All others were correctly classified by all algorithms. Ctl ⫽
control.
DISTINCT GENE PROFILE OF SCLERODERMA
Figure 4. A and B, Top 30 genes (by P value) derived from the class comparison shown in Figure
2, separated into those showing 5-fold increased expression in fibroblasts relative to biopsy
specimens (A) and those showing 5-fold increased expression in biopsy specimens relative to
fibroblasts (B). The 3 columns on the right (F ⫽ fibroblast, K ⫽ keratinocyte, E ⫽ endothelial)
represent the mean of 3 samples each of cultured fibroblasts, basal keratinocytes, and endothelial
cells for the same qualifiers in an independent robust multichip average–normalized data set. These
illustrate that, on the whole, the distinction between fibroblast and nonfibroblast attained by
comparing fibroblasts with biopsy specimens is consistent with an estimate of the cellular source by
comparing cultured cell lines. Notable exceptions are IGF1, COMP, and SFRP4, which are likely
to originate from fibroblasts but appear to be dramatically down-regulated in culture.
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GARDNER ET AL
Figure 5. A, Summary of dysregulation of extracellular matrix genes in systemic sclerosis (SSc). B, Relative expression of qualifiers for different
collagens in biopsy specimens and fibroblasts. Data are expressed as the ratio of the geometric means of SSc versus normal fluorescence intensity.
All significantly dysregulated collagens are ⬃2-fold up-regulated in SSc biopsy specimens relative to control biopsy specimens, with the notable
exception of type XI collagen, which is increased to at least 4-fold. Explanted SSc fibroblasts show relatively minor and attenuated differences in
collagen expression relative to control. C, Quantitative reverse transcription–polymerase chain reaction for COL1A1, COL1A2, and COL11A1 in
4 SSc biopsy specimens and 7 normal skin samples, normalized to GAPDH expression. Up-regulation of type XI collagen expression is far more
dramatic than that of type I collagen expression in the SSc samples. Values are the mean and SD.
bining published expression profiles on HU133A chips
for cultured basal keratinocytes (3 lines), endothelial
cells (3 lines), and fibroblasts (3 lines). The mean values
for the same set of qualifiers for each of these cell types
were heat-mapped relative to one another, independently of the biopsy specimens, and placed to the right of
the heat-mapped data for the biopsy specimens. This
comparison showed that although, in general, tissue
assignments based on the fibroblast-to-biopsy comparison were consistent with evidence from the cell lines,
there were exceptions. Examples include secreted
Frizzled-related protein 4 (SFRP4), insulin-like growth
factor 1 (IGF-1), and cartilage oligomeric matrix protein
(COMP), which may well be derived predominantly
from fibroblasts but which are so down-regulated in
tissue culture relative to the biopsy samples that they are
interpreted, using this approach, as being of nonfibroblast origin.
Comparison of our data set with previously published data. The generous provision of Affymetrix data
by Whitfield et al (9) enabled us to compare the genes
that in their study were determined to be significantly
modulated in SSc with the genes in our study. We
generated a list of qualifiers in common between
HU95A and HU133A chips and generated classifier lists
at a significance level of P ⬍ 0.05 for our data set and
that of Whitfield et al. Overall, 10,508 qualifiers from
the HU95A chip were matched to 9,530 unique qualifiers on HU133A chips. Among these 9,530 qualifiers,
2,250 were significantly different between control and
SSc biopsy specimens in our data set. Of those, 572 were
also present in the data set described by Whitfield and
DISTINCT GENE PROFILE OF SCLERODERMA
colleagues (P ⬍ 10⫺44). To give a better idea of concordance of genes of high significance in each data set, 52 of
the 100 most significant qualifiers in the data set described by Whitfield et al were also present in our data
set, at a significance level of P ⬍ 0.05, and 60 of the most
significant qualifiers in our data set were present at the
same level of significance in the Whitfield data set. In
our experience, these levels of concordance compare
favorably with those observed in other repeated studies
using different platforms.
Generation of a predictive qualifier set from
fibroblasts. We reasoned that, should there be an expression profile predictive of SSc in fibroblasts, we
should be able to identify a group of qualifiers that
would distinguish normal from SSc fibroblasts and also
effectively distinguish SSc biopsy specimens from controls. To this end, we performed a class comparison
between SSc and normal fibroblasts, at a P value of
⬍0.01. The 223-member qualifier list that resulted from
this comparison (described above) was used to generate
a classifier for the biopsy specimens. Leave-one-out
cross-validation analysis selected a subset of 26 genes
that were successful, by multiple models, in distinguishing SSc from normal biopsy specimens. Fifteen of these
could be matched to qualifiers in the HU95A chip.
Those 15 were successful at distinguishing SSc from
normal biopsy specimens in the data set described by
Whitfield et al (9), although close examination of the
expression levels suggests that some qualifiers, especially
those down-regulated in SSc in our data set, were not
similarly dysregulated in the data presented by Whitfield
and colleagues (Figure 3B). Among those found in
common were 3 interferon targets: OAS1, MX2, and
IFI16.
To reach a more general understanding of the
relationship between cultured fibroblasts and disease,
we examined clustering of normal and SSc fibroblast and
biopsy gene sets by principal component analysis, using
various qualifier lists (data not shown). The list of 223
genes from the class comparison of fibroblasts enabled
distinction of SSc and control fibroblasts as loose but
resolvable clusters, and could also divide SSc and control
biopsy specimens into tighter independent clusters.
However, no qualifier list derived from a class comparison of SSc versus control biopsy samples, even the
probable fibroblast subset, could resolve the fibroblast
cultures into SSc and control groups, whereas with each
gene list used the biopsy samples were resolved into 2
tight clusters. As well as suggesting that nonfibroblast
drivers of disease may be important in the biopsy
specimens, this finding is consistent with the possibility
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that cultured fibroblasts may derive from quite small
subpopulations within the biopsy samples. From biopsy
specimen to biopsy specimen, these may be more or less
representative of the scleroderma phenotype.
Collagens in SSc. Collagens are well known to be
up-regulated in SSc. We therefore surveyed qualifiers
for collagens. Qualifiers showing significant upregulation in SSc are shown in Figure 5B. Two qualifiers
for type XI collagen showed a remarkably high level of
up-regulation in SSc compared with other qualifiers. We
therefore reconfirmed this observation using quantitative RT-PCR for 3 collagens: COL1A1, COL1A2, and
COL11A1 (Figure 5C).
Immunohistochemical analysis of SSc biopsy
specimens. Five SSc biopsy specimens and 3 control
specimens were examined by immunohistochemistry for
fibrillin 1, type I collagen, CD3, and CD20, of which a
representative subset is shown in Figure 6. Infiltration of
T cells and B cells in SSc biopsy specimens was sparse
and not significantly different from that in controls. Type
I collagen and fibrillin 1 showed significantly increased
staining in SSc specimens. Immmunostaining for
smooth-muscle actin showed staining in dermal adnexae
and arterioles in control and SSc samples, but no dermal
fibroblasts were positive for ␣-smooth muscle actin in
any sample (data not shown).
DISCUSSION
The abundance of autoantibodies in SSc indicates
involvement of the immune system, but the precise role
of the immune system in SSc remains unclear. Immune
cell signals in the biopsy specimens include upregulation of the T cell markers CD4 and CD28, the
monocyte markers CD14, CD163, and CD11b, the
antigen-presenting cell costimulatory B7-2 protein
(CD86), and B cell antigens CD83 and CD84 as well as
Ig␬. There were no obvious differences in CD3 (T cell)
counts between control and SSc samples, and staining of
CD20 cells (B cells) showed very few cells in all specimens (Figure 6). This suggests that very small numbers
of lymphocytes can be detected by gene profiling. The
extent of immune cell infiltration is known to decline
with time from the onset of disease, which was 3 months
to 2 years for both the biopsy specimens submitted for
RNA analysis and those used for immunohistochemical
analysis. However, our histology findings are typical and
are rather dissimilar from the findings described by
Whitfield et al (9), in which some SSc biopsy specimens
appeared to include secondary lymphoid tissue, interpreted as B cell aggregates. In summary, although there
1970
GARDNER ET AL
Figure 6. CD20, CD3, type I collagen, and fibrillin immunostaining in biopsy specimens from 3 patients with systemic sclerosis (SSc) and 2 normal
controls. A few CD20⫹ cells can be seen adjacent to an adnexal structure in 1 biopsy specimen (top left panel), but otherwise CD20⫹ cells were
not seen. CD3⫹ cells were present in a predominantly perivascular distribution in all biopsy specimens and were not increased in SSc. Both type
I collagen and fibrillin 1 show increased staining in the SSc biopsy specimens.
is undeniably an immune cell signature from B cells, T
cells, and macrophages, consistent with the abundant
autoantibodies seen in the disease, these results do not
clarify our understanding of the relationship between
autoimmunity and disease pathogenesis.
Changes in mRNA expression for extracellular
matrix proteins found in SSc biopsy specimens are
summarized in Figure 5A. It has long been known that
collagens are up-regulated in SSc, but the major focus
has been on the fibrillar collagens, types I, III, and V.
However, chondrogenesis-associated collagens, such as
type XI collagen and type X collagen, as well as the
chondrogenesis-associated protein COMP, are also upregulated, as well as the basement membrane type IV
collagen, the network-forming collagens types X and
VIII, and the endothelial basement membrane type XV
collagen, and the collagen- and biglycan-processing en-
zyme bone morphogenetic protein 1 (10). Most of these
up-regulations have also been seen in TGF␤-treated
fibroblasts (11). Significantly, in this study, type XI
collagen was up-regulated disproportionately relative to
other collagens (Figures 5B and C) (⬃5-fold versus
⬃2-fold for most others). Type V collagen forms fibrils
with type I collagen to control fibril diameter, and type
XI collagen forms fibrils with type II collagen in an
analogous manner. Settings in which type XI collagen is
found in association with type I collagen in normal tissue
include fibrocartilage of the intervertebral disc, as well
as embryonic tendon. Thus, it is possible that the dermal
fibrotic response is a specific developmental program
directed toward fibrocartilage (12) rather than a nonspecific response. In contrast, in a study of lung fibroblast responses to TGF␤, type IV collagen was the only
collagen that was significantly dysregulated (13).
DISTINCT GENE PROFILE OF SCLERODERMA
The small leucine-rich family of proteoglycans
(SLRPs) also show some characteristic alterations in
SSc. Decorin is well known to be down-regulated in SSc
and is believed to have some TGF␤ antagonistic properties of its own. In contrast, biglycan, versican, and
lumican are up-regulated, as is asporin, a relative of
decorin. The potential regulatory roles of the SLRPs in
matrix assembly are beginning to be scrutinized. Although all of them are associated with collagen fibrils,
each may be synthesized by different subsets of cell types
(14). All of these proteins seem to have generally
inhibitory effects on collagen fibril diameter and on
fibroblast proliferation; thus, the reciprocal downregulation of decorin and up-regulation of biglycan and
lumican in SSc have a pathophysiologic significance that
is not fully understood. However, these abnormalities in
SLRPs may be connected to ultrastructural and biochemical changes in skin, namely increased collagen
solubility (15) and fibril abnormalities, including both
abnormally thin fibrils (16) and abnormally tightly
packed and thick fibrils (17).
The strength of activation of the TGF␤ pathway
in cultured SSc fibroblasts has been discussed extensively (18). Our study and that by Whitfield et al (9)
show extensive evidence of dysregulation of the TGF␤
and Wnt pathways in vivo. Many TGF␤ targets are
up-regulated in SSc biopsy specimens and are extremely
similar to those seen in normal fibroblasts treated with
TGF␤ (11). Many of these targets are no longer differentially expressed in explanted fibroblasts, notably the
bulk of the collagens, which suggests that the profibrotic
drivers are from cell types that do not persist in fibroblast culture. Transcript for TGF␤ itself is not increased
in SSc biopsy specimens, suggesting that increased
TGF␤ signal is attributable to increased activation of
latent TGF␤ protein. One of the best known activators
of latent TGF␤ is thrombospondin 1, the transcript of
which was reported to be significantly up-regulated in
SSc biopsy specimens and in cultured SSc fibroblasts in
some studies (19). Studies have suggested that the subset
of pulmonary fibroblasts that are Thy-1 positive are in
fact TGF␤ insensitive due to failure of TGF␤ activation
(20). Thy-1, along with thrombospondin 1, is strongly
up-regulated in SSc skin, suggesting that this relationship does not hold in dermal fibrosis.
Changes in the Wnt pathway that are seen in SSc
biopsy samples include down-regulation of Wnt inhibitory factor 1, Frizzled-related protein, and Frizzled
homolog 7, as well as up-regulation of SFRP4. Taken
together, these changes are consistent with a general
1971
up-regulation of Wnt signaling. It is notable that a Wnt
regulatory system has been shown to be active in mesenchymal stem cells in culture: down-regulations in Dkk
and reciprocal up-regulations in Wnt-5A cause a deceleration in cell growth (21). Synergies between the Wnt
and TGF␤ pathways have been proposed, which may be
mediated by interaction of Smad4 and lymphoid enhancer factor 1 (22).
The best known member of the CCN family is
connective tissue growth factor (CTGF; CCN2), a putative downstream target of TGF␤ and a profibrotic
cytokine in its own right, which is well known to be
up-regulated in SSc skin and SSc fibroblasts (23–25).
CTGF requires insulin or IGF-1 (the level of IGF-1 is
also elevated in SSc biopsy samples) for induction of
collagen in SSc fibroblasts (26). Cyr61 (CCN1), which
provides integrin-dependent promigratory stimuli to fibroblasts and vascular smooth muscle cells (27,28), is
also up-regulated in SSc, while Wnt-1–inducible signaling pathway protein 2 (WISP2; CCN5) is downregulated. Loss of Cyr61 is associated with differentiation of mesenchymal stem cells into any daughter
lineage (29), and thus its up-regulation may reflect
expansion of an uncommitted fibroblast phenotype.
Down-regulation of WISP2 and reciprocal up-regulation
of CTGF have also been noted in the response of normal
fibroblasts to TGF␤ (11), but the significance of this
remains unclear. CCN3 (NOV) was not dysregulated at
the transcript level in SSc.
Using the observations described above, profiling
the data from both biopsy samples and fibroblasts
enabled us to make some informed guesses about the
tissue-type contribution of different genes in SSc. For
example, up-regulation of the monocyte/macrophage
genes CD14, Toll-like receptor 1 (TLR-1) and TLR-2,
and integrins ␤2, ␣X, and ␣M appears to be associated
with up-regulation of interleukin-6 (IL-6), IL-16, and
CXCL3, all of which might be synthesized by fibroblasts.
Similarly, examination of IGFs, CCN family members,
and proteases showed down-regulation of IGF binding
protein 5 (IGFBP-5), IGFBP-6, and WISP2 and reciprocal up-regulation of IGFBP-3 and IGFBP-4 as well as
CTGF, plasminogen activator inhibitor 1, and C1q
(which was likely from a nonfibroblast source). It was
also noteworthy that the vast majority of fibroblastassociated transcripts showing significant change in SSc
biopsy specimens were up-regulated (105 of 121 [87%]),
whereas approximately half of the fibroblast-associated
transcripts in the nonfibroblast compartment (161 of 358
[45%]) were down-regulated. A theme may be emerging
involving loss of nonfibroblast-derived inhibitory signals
1972
and concomitant up-regulation of fibroblast profibrotic
behavior in SSc lesions. It is possible that some of the
down-regulation of epithelium-derived genes is attributable to loss of epithelial adnexae in sclerodermatous
skin. However, although there was a 2-fold downregulation of keratin 15, associated with early hair
follicle “bulge” cells (30), no other markers of follicles
such as keratin 19 and the hair keratins were significantly or consistently altered in the SSc biopsy specimens, nor were adnexae lost in the biopsy samples
examined by immunohistochemistry. Furthermore, a
systematic review of endothelial, keratinocyte, and
fibroblast-specific transcripts of the highest abundance
did not show a change in the relative size of these 3
compartments in the SSc biopsy specimens (data not
shown).
This study includes the first extensive comparison
of fibroblast cultures and primary biopsy specimens in
SSc and provides an opportunity to examine fibroblastdriven versus non–fibroblast-driven behavior. We reasoned that if fibroblasts truly reflect the disease state,
then a subset of genes that distinguishes between SSc
fibroblasts and normal fibroblasts should be able to
distinguish SSc skin from normal skin. Leave-one-out
cross-validation analysis selected a subset of genes that
were successful, by multiple models, in distinguishing
SSc specimens from normal biopsy specimens, in both
our data set and that of Whitfield et al (Figure 3B).
From this we conclude that there is a set of gene
expression profiles that distinguishes SSc from normal
fibroblasts; however, the number of genes that distinguish SSc fibroblasts from control fibroblasts is far
reduced compared with the number of genes that distinguish SSc biopsy specimens from control specimens at
the same level of significance (223 versus 1,839; P ⫽
0.01). Indeed, Figure 2 demonstrates that the majority of
the most significant differentially expressed genes in SSc
biopsy specimens are not consistently differentially expressed in explanted fibroblasts.
Differences between the differential gene profiles of fibroblasts and biopsy specimens suggest further
conclusions. For example, CTGF, which is well known as
a fibrosis driver downstream of TGF␤, as well as Thy-1
are up-regulated in scleroderma biopsy specimens relative to normal controls, but expression in cultured
fibroblasts does not clearly correlate with disease. Aspects of the SSc phenotype that are retained in fibroblasts include primarily a small residual subset of
changes in matrix gene expression. One interpretation of
this is that there is a key disease-driver population of a
GARDNER ET AL
different cell type in the dermis, such as the first known
target of SSc-mediated injury, the endothelial cell, or its
partner the pericyte. Another possibility is that a small
driver population of aberrant fibroblasts or pericytes,
which creates an altered cytokine milieu and matrix, is
progressively lost in culture. A third, related possibility is
that fibroblast-synthesized matricellular proteins assembled in the dermis contribute to an autocrine loop
causing altered fibroblast behavior, and that the process
of explantation and growth in monolayer causes progressive loss of those signals.
Our results demonstrate that multicenter studies
of the gene profiles in SSc skin can generate meaningful
results that are not confounded by the source of material, and show that the SSc transcript profile is very
robust. We also observed previously unreported changes
in matrix expression, such as a disproportionate upregulation of type XI collagen, which suggest a move
toward fibrocartilage in the fibrotic process. Without
comparison with other fibrotic conditions, it is impossible to comment on the specificity of the gene profile
changes for scleroderma, but the consistency does suggest that this method of analysis is sufficiently stable
that, with larger sample numbers, different subtypes of
SSc may become discernable. Indeed, a sample obtained
from 1 patient with SSc, who was notably younger than
the other patients, was something of an outlier (Figure
1). We have validated the use of a room-temperature/
4°C RNA-stabilizing agent (RNAlater) to ease sample
collection, and have since determined that this form of
preservation is effective for immunohistochemical analyses (data not shown).
Thus, prospects are good for obtaining meaningful results from remote biopsy specimens and centralized Affymetrix analysis for a large number of patients
with this and other relatively rare diseases. Conversely,
the utility of explanted scleroderma fibroblasts in the
study of SSc is clearly circumscribed. Phenotypic characteristics of SSc fibroblasts appear to be degraded
independent of the dermal milieu, since their differential expression versus that of control fibroblasts is reduced relative to the biopsy samples and shows higher
variance, for reasons that are yet to be elucidated.
Nonetheless, fibroblast culture remains a necessary
technique for assessing therapeutic strategies and the
function of humoral factors ex vivo. Finally, in this study,
we did not witness a strong signature from B cells or
other cells of the immune system.
Dedicated to the memory of Joseph Korn, professor,
advocate, and student of scleroderma.
DISTINCT GENE PROFILE OF SCLERODERMA
1973
ACKNOWLEDGMENTS
Dr. Gardner thanks Anthony Guidi and Jim Kiernan
for consultation in sample handling, Margaret McCrann for
pilot hybridization experiments, John McCoy for resource
support, and Uwe Hansen for valuable discussions. Dr. Mayes
thanks Julio Charles for assistance in biopsy processing.
15.
16.
17.
REFERENCES
1. Gussin HA, Ignat GP, Varga J, Teodorescu M. Anti–
topoisomerase I (anti–Scl-70) antibodies in patients with systemic
lupus erythematosus. Arthritis Rheum 2001;44:376–83.
2. Kane GC, Varga J, Conant EF, Spirn PW, Jimenez S, Fish JE.
Lung involvement in systemic sclerosis (scleroderma): relation to
classification based on extent of skin involvement or autoantibody
status. Respir Med 1996;90:223–30.
3. Krieg T, Perlish JS, Fleischmajer R, Braun-Falco O. Collagen
synthesis in scleroderma: selection of fibroblast populations during
subcultures. Arch Dermatol Res 1985;277:373–6.
4. Chang HY, Chi JT, Dudoit S, Bondre C, van de Rijn M, Botstein
D, et al. Diversity, topographic differentiation, and positional
memory in human fibroblasts. Proc Natl Acad Sci U S A 2002;99:
12877–82.
5. Szulgit G, Rudolph R, Wandel A, Tenenhaus M, Panos R,
Gardner H. Alterations in fibroblast ␣1␤1 integrin collagen receptor expression in keloids and hypertrophic scars. J Invest Dermatol
2002;118:409–15.
6. Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald
A, et al. Distinct types of diffuse large B-cell lymphoma identified
by gene expression profiling. Nature 2000;403:503–11.
7. Subcommittee for Scleroderma Criteria of the American Rheumatism Association Diagnostic and Therapeutic Criteria Committee. Preliminary criteria for the classification of systemic sclerosis
(scleroderma). Arthritis Rheum 1980;23:581–90.
8. LeRoy EC, Black C, Fleischmajer R, Jablonska S, Krieg T,
Medsger TA, Jr, et al. Scleroderma (systemic sclerosis): classification, subsets and pathogenesis. J Rheumatol 1988;15:202–5.
9. Whitfield ML, Finlay DR, Murray JI, Troyanskaya OG, Chi JT,
Pergamenschikov A, et al. Systemic and cell type-specific gene
expression patterns in scleroderma skin. Proc Natl Acad Sci U S A
2003;100:12319–24.
10. Scott IC, Imamura Y, Pappano WN, Troedel JM, Recklies AD,
Roughley PJ, et al. Bone morphogenetic protein-1 processes
probiglycan. J Biol Chem 2000;275:30504–11.
11. Gardner H, Strehlow D, Bradley L, Widom R, Farina A, de
Fougerolles A, et al. Global expression analysis of the fibroblast
transcriptional response to TGF␤. Clin Exp Rheumatol 2004;22(3
Suppl 33):S47–57.
12. Benjamin M, Ralphs JR. Biology of fibrocartilage cells. Int Rev
Cytol 2004;233:1–45.
13. Renzoni EA, Abraham DJ, Howat S, Shi-Wen X, Sestini P,
Bou-Gharios G, et al. Gene expression profiling reveals novel
TGF␤ targets in adult lung fibroblasts. Respir Res 2004;5:24.
14. Matheson S, Larjava H, Hakkinen L. Distinctive localization and
function for lumican, fibromodulin and decorin to regulate colla-
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
gen fibril organization in periodontal tissues. J Periodontal Res
2005;40:312–24.
Uitto J, Ohlenschlager K, Lorenzen I. Solubility of skin collagen in
normal human subjects and in patients with generalised scleroderma. Clin Chim Acta 1971;31:13–8.
Davis EC, Blattel SA, Mecham RP. Remodeling of elastic fiber
components in scleroderma skin. Connect Tissue Res 1999;40:
113–21.
Sapadin AN, Esser AC, Fleischmajer R. Immunopathogenesis of
scleroderma: evolving concepts. Mt Sinai J Med 2001;68:233–42.
Pannu J, Trojanowska M. Recent advances in fibroblast signaling
and biology in scleroderma. Curr Opin Rheumatol 2004;16:
739–45.
Mimura Y, Ihn H, Jinnin M, Asano Y, Yamane K, Tamaki K.
Constitutive thrombospondin-1 overexpression contributes to autocrine transforming growth factor-␤ signaling in cultured scleroderma fibroblasts. Am J Pathol 2005;166:1451–63.
Zhou Y, Hagood JS, Murphy-Ullrich JE. Thy-1 expression regulates the ability of rat lung fibroblasts to activate transforming
growth factor-␤ in response to fibrogenic stimuli. Am J Pathol
2004;165:659–69.
Gregory CA, Singh H, Perry AS, Prockop DJ. The Wnt signaling
inhibitor dickkopf-1 is required for reentry into the cell cycle of
human adult stem cells from bone marrow. J Biol Chem 2003;278:
28067–78.
Attisano L, Labbe E. TGF␤ and Wnt pathway cross-talk. Cancer
and Metastasis Rev 2004;23:53–61.
Igarashi A, Nashiro K, Kikuchi K, Sato S, Ihn H, Grotendorst GR,
et al. Significant correlation between connective tissue growth
factor gene expression and skin sclerosis in tissue sections from
patients with systemic sclerosis. J Invest Dermatol 1995;105:280–4.
Shi-wen X, Pennington D, Holmes A, Leask A, Bradham D,
Beauchamp JR, et al. Autocrine overexpression of CTGF maintains fibrosis: RDA analysis of fibrosis genes in systemic sclerosis.
Exp Cell Res 2000;259:213–24.
Trojanowska M. Molecular aspects of scleroderma. Front Biosci
2002;7:608–18.
Gore-Hyer E, Pannu J, Smith EA, Grotendorst G, Trojanowska
M. Selective stimulation of collagen synthesis in the presence of
costimulatory insulin signaling by connective tissue growth factor
in scleroderma fibroblasts. Arthritis Rheum 2003;48:798–806.
Grzeszkiewicz TM, Kirschling DJ, Chen N, Lau LF. CYR61
stimulates human skin fibroblast migration through integrin ␣ v␤
5 and enhances mitogenesis through integrin ␣v␤3, independent of
its carboxyl-terminal domain. J Biol Chem 2001;276:21943–50.
Grzeszkiewicz TM, Lindner V, Chen N, Lam SC, Lau LF. The
angiogenic factor cysteine-rich 61 (CYR61, CCN1) supports vascular smooth muscle cell adhesion and stimulates chemotaxis
through integrin ␣(6)␤(1) and cell surface heparan sulfate proteoglycans. Endocrinology 2002;143:1441–50.
Schutze N, Noth U, Schneidereit J, Hendrich C, Jakob F. Differential expression of CCN-family members in primary human bone
marrow-derived mesenchymal stem cells during osteogenic, chondrogenic and adipogenic differentiation. Cell Commun Signal
2005;3:5.
Liu Y, Lyle S, Yang Z, Cotsarelis G. Keratin 15 promoter targets
putative epithelial stem cells in the hair follicle bulge. J Invest
Dermatol 2003;121:963–8.
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