© 2000 American Heart Association, Inc.
Vascular Biology |
Determination of Temporal Expression Patterns for Multiple Genes in the Rat Carotid Artery Injury Model
From CV Therapeutics (J.T.N.T., E.E.B., J.D.R., R.M.L., D.S.) and Incyte Pharmaceuticals (S.L., R.S.) Palo Alto, Calif.
Correspondence to Dr Dov Shiffman, CV Therapeutics, 3172 Porter Dr, Palo Alto, CA 94304. E-mail ds{at}cvt.com
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Abstract |
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Abstract—Vascular injury induces extensive alteration to the extracellular matrix (ECM). These changes contribute to lesion formation and promote cell migration and proliferation. To elucidate ECM response to arterial injury, we used real-time polymerase chain reaction monitoring to quantitate the expression levels of 81 genes involved in the synthesis and breakdown of ECM as well as receptors and signaling proteins that communicate and respond to ECM molecules. The temporal regulation of gene expression in the carotid was measured at 1, 3, 5, 7, 9, 14, and 28 days postinjury. Among the 68 genes that showed detectable expression by our method, 47 (69%) were significantly induced or repressed over time, confirming the extensive ECM gene response in this model. More ECM-related genes (31) were regulated at day 1 than at any other time point, and the number of regulated genes decreased over time. However, 14 of the genes were still induced or repressed at day 28, indicating that return to preinjury expression patterns did not occur and no new steady state was achieved over 28 days. In spite of the large number of changes in gene expression, only a small number of expression patterns was observed, suggesting that ECM-related genes could potentially be coregulated.
Key Words: restenosis • carotid artery injury • gene expression profiling • extracellular matrix • matrix metalloproteinases
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Introduction |
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Restenosis, defined as recurrent lumen narrowing, is a significant clinical problem that occurs in 20% to 50% of patients undergoing percutaneous transluminal angioplasty.1 2 In the rat carotid balloon injury model of restenosis, the replication of smooth muscle cells in the tunica media is considered the initial wave of response that occurs during the first 2 days postinjury.3 Replication is followed by migration of smooth muscle cells from their normal location in the tunica media to the newly formed intimal layer. The subsequent proliferation of intimal smooth muscle cells eventually leads to the formation of a thickened neointima and consequent reduction in luminal diameter. Cell proliferation and migration events are promoted by the continuous breakdown and synthesis of a complex network of extracellular matrix (ECM) macromolecules that shape the vascular structure. It has been argued that although proliferation results in an increase of the total cell number, it is the ECM that accounts for the bulk of the neointimal volume in the restenotic lesion.4
Previous research has shown that ECM genes are significantly regulated after injury in the rat model and are responsive to numerous growth factors, cytokines, and stretch stimulation.5 However, expression studies using conventional methods usually generate information on a limited number of genes and fail to give a more comprehensive view of the transcriptional events governing these changes. Recently, it has been proposed6 7 8 9 that temporal transcription profiles of multiple genes during a model physiological process could be measured and clustered on the basis of similarities in their expression patterns.10 This information could potentially be used to propose regulatory circuits that control gene expression in the model system.11
Using real-time polymerase chain reaction (PCR) monitoring, we determined the expression profiles of 68 genes over 28 days in the rat carotid balloon injury model. The clustering of the temporal expression patterns of these genes yielded 4 waves of differential regulation, 1 of which could be additionally divided into 4 smaller clusters. More genes were regulated at 1 day after injury than at any other time point, and no new equilibrium was reached as late as 28 days postinjury. This systematic approach also led to the identification of genes previously not known to take part in neointima formation and could provide additional insight into the pathophysiology of the disease.
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Methods |
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Rat Tissue and RNA Isolation
Male Sprague-Dawley rats (400 to 500 g) (Charles River Breeding Laboratories, Wilmington, Mass) were anesthetized with an intraperitoneal injection of 0.45 to 0.55 mL of 58% ketamine and 42% xylazine mixture. A 2 French Fogarty catheter was used to induce vascular injury, as previously described.12 Rat neonatal smooth muscle cells were cultured, as described previously.12 RNA was extracted from pools of 10 carotid arteries or cultured smooth muscle cells using RNA STAT-60 (Tel-Test, Inc.) according to the instructions by the manufacturer and stored at -80°C.
mRNA Quantitation
Quantitation of gene expression was performed on the ABI Prism
Sequence Detection System 5700 (PE Applied Biosystems). A set of
primers was designed for each gene using Primer Express (PE Applied
Biosystems). DNA sequence was obtained from GenBank (see online
supplement at www.ahajournals.org), and amplicons of 100 to 200 base
pairs with melting temperature between 68° and 85°C were
selected. Reverse transcriptase (RT) reactions consisted of 0.1 to 1
µg of DNase-treated total RNA, 5.5 mmol/L of magnesium chloride,
500 µmol/L each of dNTP, 2.5 µmol/L of random hexamers,
0.4 U/µL of RNase inhibitor, and 2.5 µL of 50 U/µL
MultiScribe RT in a final volume of 100 µL. The reaction conditions
were 25°C for 10 minutes followed by 48°C for 30 minutes and
inactivation at 95°C for 5 minutes. An aliquot of the RT reaction was
used for a 40-cycle PCR amplification in the presence of SYBR green
fluorescent dye according to a protocol provided by the
manufacturer (PE Applied Biosystems). PCR product generation was
monitored by measuring the increase in fluorescence caused by
the binding of SYBR green to double-stranded DNA at each annealing
phase. A dissociation curve was generated at the end of the 40th cycle
to verify that a single product was amplified. A standard curve for
each amplicon was obtained using serial dilutions of total RNA prepared
from rat vascular smooth muscle cells grown in culture.
Data Analysis
The results from duplicate PCR reactions for a given gene in
each time point were used to determine its RNA quantity relative to the
corresponding standard curve. Two sets of PCR reactions, each with
cyclophilin A13 or ribosomal protein S9 primers, were
performed for each batch of RT reaction. The relative RNA quantity for
a given gene sampled from the same RT reaction was divided by the value
obtained for either of these 2 genes to correct for fluctuations in
input RNA levels and varying efficiencies of RT reactions. The
differential expression curves obtained using either of these genes as
the correction factor consistently showed the same pattern.
Clustering
Each measurement was converted to the
log2 of the ratio of injured over control sample
for each time point (1, 3, 5, 7, 9, 14, and 28 days after injury),
generating 7 expression ratio values for each gene. Considering that
our data represent a time series, we were interested in the
variation from one time point to the next. To emphasize these changes
for clustering, the expression differences between consecutive time
points were calculated. Consequently, the expression pattern for each
gene was converted into a vector with 7 expression ratios and 6
expression differences. The Euclidean distance between these vectors
was used as a similarity measure for the expression responses.
An iterative agglomerative algorithm of N-1 steps (N=number of genes) was used to construct a dendrogram similar to the phylogenetic tree familiar to most biologists.8 14 Starting with N clusters containing a single gene, at each step in the iteration the 2 closest clusters were merged into a larger cluster. The distance between clusters was defined as the distance between their average expression patterns. After N-1 steps, all the data points were merged together. This clustering process defines a hierarchical tree.
We can automatically assign genes to a cluster by cutting the tree
between the root and each gene branch with a set of lines separated by
equal distance (see Branch Levels in Figure 2
). We first
normalize the tree so that each branch is at the same distance from the
root. To preserve the distance between the closest genes, we distort
the tree at the branch farthest from the leaf. The number of
intersections at each branch level determines the number of the
grouping in that hierarchy.
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Results |
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TopAbstractIntroductionMethodsResultsDiscussionReferences |
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mRNA Quantitation Using Real Time PCR Monitoring
The relative abundance of individual mRNA species was measured by RT-PCR, as described in Methods. Two potential sources of measurement errors are associated with real-time PCR monitoring. First, fluctuations in the quantity of input RNA and the varying efficiency of different RT reactions can affect the initial amount of cDNA in the PCR reaction. Second, the stochastic nature of the early cycles of PCR reactions can spuriously amplify small differences between samples. To reduce the noise level in this study, we normalized the message abundance with an internal control gene. It is important to choose a gene that shows a constant level of expression across all experimental conditions. Traditionally used housekeeping genes (glyceraldehyde-3-phosphate dehydrogenase and
-actin) failed to meet this criterion. We found that the expression
level ratios of cyclophilin A and ribosomal protein S9 were
consistent in all of our experimental samples, indicating that
these genes are either tightly coregulated or, more probably, expressed
at constant levels (data not shown). To evaluate the measurement errors
in our study, we measured the quantity of 16 mRNA species twice, using
a pair of independent RT-PCR reactions of the same sample. For most of
the genes (75%), a 1- to 2.4-fold change was measured (Figure 1), indicating that a measured 2.4-fold
change or less between samples was not reliable enough to indicate a
real difference. The results of duplicated PCR reactions taken from the
same RT reactions were more reproducible (<26% SD), suggesting that
the variation was mainly attributable to differences in the efficiency
of independent RT reactions. On the basis of these results, a 2.5-fold
differential expression was considered a significant change. This
cutoff level was a compromise between inclusion of data that are within
the measurement error (false positives) and exclusion of genes that are
regulated at low levels (false negatives).
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Genes Surveyed in the Study
A list of 81 known rat genes was compiled for the study
(Table 1) (for a complete list
online of accession numbers and primers used, please see
www.ahajournals.org). It included components of the vascular matrix,
their corresponding receptors, enzymes involved in matrix remodeling,
and the family of transforming growth factor (TGF)-ß
cytokines and their receptors. Several cell-cycle progression
markers were also included. Some of the genes are known to be regulated
in this model. However, to systematically analyze the
regulation of all the ECM components, we also included genes not
reported or expected to be expressed in the carotid artery, such as
aggrecan and neurocan. The temporal expression profiles of all 81 genes
were initially analyzed at 4 time points: 1, 3, 7, and 14 days
postinjury, because it has been observed that key events of cell
migration proliferation occur during this period of time. Thirteen of
the 81 genes were not detectable either in normal or injured carotid
tissue by our method. Although it is possible that these genes are
expressed at other time points after injury, they were excluded from
subsequent analysis, because the information obtained would not
be sufficient to generate a complete temporal expression profile. The
remaining 68 genes were then measured at a higher sampling density (1,
3, 5, 7, 9, 14, and 28 days postinjury).
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Global Expression Changes
A total of 47 (69%) of the genes showed
2.5-fold differential expression at one or multiple time points (Figure 2
) (log2 values
>1.3 or <-1.3). The largest number of differentially regulated genes
was observed at the first day postinjury. Thirty-one genes were found
to be at least 2.5-fold upregulated or downregulated at this time
(Figure 3). Downregulated genes (21)
outnumbered the upregulated genes (10). The number of differentially
regulated genes decreased at later time points, a trend indicating a
partial return to steady state. However, even at day 28, 14 of the
genes (20%) were differentially regulated, indicating an ongoing
process of ECM modulation.
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Cluster Analysis
The expression profiles were clustered on the basis of
similarities in the trend and magnitude of the differential expression
pattern. The data were normalized using the log2
values of the expression levels ratio between the injured and the
normal arteries at each time point. The magnitude of the normalized
change and the slope of the curve were weighted when a dendrogram of
the expression profiles was generated (see Methods). Each gene was
assigned a numerical tag, which corresponded to the branching pattern
for that gene. Thus, genes can be clustered at different levels of
similarity using this tag (Figure 2). Four clusters were
generated by cutting the expression profile tree at branch level 5
(Figure 2A). Waves 1 and 4 consisted of 40 genes, accounting for
85% of the differentially regulated genes. Thus, the expression
profiles of a vast majority of the surveyed genes can be captured in
these 2 clusters. Genes in wave 1 can be additionally segmented into 4
distinct groups by cutting the clustering tree at branch level 8
(Figure 2B), resulting in a total of 7 waves of expression
profiles (Figure 4). Waves 1.A through
1.D shared an early repression feature but had distinct characteristics
in the timing and extent of differential regulation. Genes in wave 1.A
were moderately repressed from days 1 to 5, whereas the genes in wave
1.C were downregulated at days 1 and 3. The 3 genes in wave 1.D,
collagen 1 type III, collagen
2 type V, and glypican, were sharply
downregulated at day 1 only. Wave 1.B showed a unique pattern of
downregulation at day 1 and upregulation at day 9. Altogether, wave 1
comprised the largest number (25) of differentially regulated genes
and, therefore, can be considered the predominant regulation trend for
ECM genes in this model. Several genes in wave 1 belong to the group of
late matrix components: 4 collagens, 7 proteoglycans, and 1 elastin.
The expression pattern of some of these genes has been previously
described15 16 and largely corresponds to our data, but
these results were interpreted only as a late induction of these genes,
and very little was reported regarding their early regulation. Waves 2
and 3 represented two opposite trends of induction. They
deviated from other genes in the cluster tree at branch level 1 and 3,
suggesting that their expression profiles are distinctively different
from other clusters. The 3 genes in wave 2 showed peak induction at day
1 (Figure 4). Matrix metalloproteinase-8 (MMP-8) is a
neutrophil-derived matrix metalloproteinase, and its early
upregulation might be indicative of early neutrophil adhesion in this
model. Early neutrophil adhesion has been reported in similar injury
models of rabbit17 and mouse.18 Wave 3 also
consisted of only 3 genes: tenascin-C, aggrecan, and MMP-12. They were
all significantly and continuously induced until day 28. This group of
genes also showed the greatest increase in its expression level,
because an average of 42-fold induction over normal was observed at day
7 (Figure 4). Wave 4 can be characterized by intermediate
induction from days 3 to 7. It has been reported that genes involved in
cell proliferation and hyaluronic acid (HA)-mediated motility are
induced between 3 to 7 days postinjury in this
model.19 20 21 Not surprisingly, 2 genes involved in the
regulation of cell-cycle progression, cyclin-dependent kinase 1 (CDK1)
and cyclin B, and 3 members of the HA family, HA synthase, CD44, and
receptor for hyaluronan-mediated motility (RHAMM), are among the genes
clustered in wave 4 (Figure 4). Some of the genes in this
cluster were not significantly induced between days 3 and 7. For
example, versican V3 isoform is only significantly upregulated at day
1. However, the clustering algorithm is designed to capture both the
shape of the curve and magnitude of change. The change in expression
for versican V3 isoform between days 3 and 9 generated a shape
characteristic of wave 4, even though the data points were within the
noise level. Therefore, clustering data should be examined critically,
taking into account the limitations of the method and noise level in
the data. One gene, sciatic nerve integrin ß, displayed unique
expression profiles and did not cluster into any of the 7 waves
described above (Figure 2A).
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Discussion |
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We have successfully used real-time PCR to measure the expression fingerprints of the major ECM and representative cell-cycle control genes in the rat carotid artery restenosis model. Traditionally, RNA isolation posed a major limitation for gene expression studies in rat carotid arteries, because the tissue sections are small in size (
10x2 mm) and have a high ECM content.
Typically, pools of 5 to 10 carotids were needed to obtain enough RNA
for a Northern blot analysis of a single gene. Because RT-PCR
requires a small amount of RNA, 10- to 50-mRNA species can be measured
from a single carotid (J.T., unpublished data, May 1999). This
highly sensitive method allowed us to generate expression information
for all but 13 of the genes analyzed. The rat carotid artery is undergoing continuous alteration in tissue composition during the process of restenosis. Smooth muscle cell proliferation and immune cell invasion result in different proportions of adventitia, media, neointima, and invading immune cells when compared with normal carotid arteries at various time points. However , the changing composition of the tissue could additionally complicate data interpretation, because gene induction could be attributable to either an increase in the number of cells expressing a given gene in the injured tissue or to an increase in the expression level in a fixed population of cells. Thus, changes in expression of MMP-8 could either reflect early neutrophil adhesion or the induction of MMP-8 expression in the existing tissue. Although the presence of neutrophils in mice18 and rabbit17 arteries after injury was observed previously, the localization of these differential expression events awaits in situ hybridization analysis.
The systematic expression profiling of genes involved in ECM synthesis and response revealed an extensive differential regulation of these genes in the rat carotid balloon injury model. A total of 47 genes showed differential regulation of 2.5-fold from normal at one or multiple time points. The largest number of differentially regulated genes was observed at the earliest measurement, 1 day after injury. As the tissue is responding to the recent trauma generated by endothelial denudation and stretching, early changes in gene expression can be expected. Because we monitored only a small number of genes relative to the whole genome, it remains to be seen whether this pronounced early downregulation of gene expression will hold for a much larger sample of genes. It is apparent from our study that significant differential gene expression still persists 4 weeks after injury, even though smooth muscle cell proliferation and migration are believed to subside by 2 weeks in this model.22 A total of 14 genes were differentially regulated at 28 days postinjury. Previously, 4 other genes were reported to be regulated in similar time frame,23 which would suggest that the system does not return to the preinjury state.
The number of repressed versus induced genes decreases over time
(Figure 3). At day 1, 21 genes were repressed and 10 were
induced. By day 28, only 2 were repressed and 12 were induced. This
trend could be attributed to the large number of ECM genes monitored in
this study, because it has been shown that accumulation of ECM
increases in the late phases of arterial repair in similar
models.24 25 Our observation that more genes are induced
at late time points is consistent with the roles these matrix
components play in the injury response.
The measurement of the expression levels of numerous genes led to the
identification of regulation events not recognized previously. For
example, the inductions of cartilage oligomeric matrix protein (COMP),
collagen 1 type XII, and aggrecan have not
been documented in this model previously. Recently it has been shown
that the expression of collagen 1 type XII and
tenascin-C is induced by mechanical stress,26 27 and
stretch-responsive enhancer regions were found in the promoters of
these 2 genes.28 In our analysis, these genes
showed significant induction by day 3 after injury, and both peaked at
days 5 and 7. Because it is known that the accumulation of matrix
proteins increases the tensile strength of the arterial
wall after balloon injury,29 it is possible that the
induction of these 2 genes in this model was in response to ECM
synthesis.
Both COMP (a glycoprotein) and aggrecan (a chondrotin sulfate proteoglycan) are proteins found almost exclusively in the cartilage.30 31 The expression levels of COMP and aggrecan are low in normal carotid tissues and were induced 15- and 64-fold, respectively, at day 28 postinjury. Osteopontin, an acidic noncollagenous protein of the ECM of the cartilage, was also induced in our model (6-fold on day 3). Osteopontin is shown to be elevated after rat carotid injury,23 32 in human atherosclerotic plaques,33 and at sites of dystrophic calcification, and it may play a regulatory role in calcification.34 35 36 Because vascular calcification is a common finding in atherosclerosis and aortic stenosis, the induction of these 3 genes suggests a similar calcification process occurring in the rat restenosis model.
Clustering the expression profiles we measured can be used to generate
new research questions. It is not surprising that most of the collagen
genes we measured cluster together in wave 1; however, collagen
1 type XII has a different expression pattern
(wave 4) and might indicate its different role in the
restenosis process. Waves 2 and 3 were sharply different from
the other genes we studied and branched off the hierarchical tree
earlier than other clusters (Figure 2A). We do not know whether
these 2 groups reflect distinct novel biological processes or whether
the tight coregulation we observed for these 2 clusters is
coincidental. Elucidating the transcription control mechanisms of genes
in these clusters and identification of common regulatory factors could
clarify the biological significance of these findings.
This study demonstrates the feasibility of expression analysis of nearly 100 genes in a complex tissue source with limiting amounts of RNA. The quantitative nature of our data allows easy comparison to expression information generated in other studies and makes it a useful database for expression profiles of ECM gene families in the rat injury model. Future experiments can assess the effects of pharmacological intervention on gene expression and provide a better understanding of drug effects in restenosis. The study is limited by the sensitivity of the method and by its noise level. However, our ability to detect a gene expressed primarily in neutrophils demonstrates the potential utility of our methodology. The number and profile of the clusters we observed also increased our confidence in the validity of our data, because the expression profiles we obtained were not random and did not include the entire spectrum of possible expression patterns.
Received April 3, 2000; accepted July 21, 2000.
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 V. Lindner, Q. Wang, B. A. Conley, R. E. Friesel, and C. P.H. Vary Vascular Injury Induces Expression of Periostin: Implications for Vascular Cell Differentiation and Migration Arterioscler Thromb Vasc Biol, January 1, 2005; 25(1): 77 - 83. [Abstract] [Full Text] [PDF]
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 A. Krettek, G. K. Sukhova, U. Schonbeck, and P. Libby Enhanced Expression of CD44 Variants in Human Atheroma and Abdominal Aortic Aneurysm: Possible Role for a Feedback Loop in Endothelial Cells Am. J. Pathol., November 1, 2004; 165(5): 1571 - 1581. [Abstract] [Full Text] [PDF]
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 A. P. Miller, W. Feng, D. Xing, N. M. Weathington, J. E. Blalock, Y.-F. Chen, and S. Oparil Estrogen Modulates Inflammatory Mediator Expression and Neutrophil Chemotaxis in Injured Arteries Circulation, September 21, 2004; 110(12): 1664 - 1669. [Abstract] [Full Text] [PDF]
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D. L. Myers, K. J. Harmon, V. Lindner, and L. Liaw Alterations of Arterial Physiology in Osteopontin-Null Mice Arterioscler Thromb Vasc Biol, June 1, 2003; 23(6): 1021 - 1028. [Abstract] [Full Text] [PDF]
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M. Qin, Z. Zeng, J. Zheng, P. K. Shah, S. M. Schwartz, L. D. Adams, and B. G. Sharifi Suppression Subtractive Hybridization Identifies Distinctive Expression Markers for Coronary and Internal Mammary Arteries Arterioscler Thromb Vasc Biol, March 1, 2003; 23(3): 425 - 433. [Abstract] [Full Text] [PDF]
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Y. Chu, D. D. Heistad, K. L. Knudtson, K. G. Lamping, and F. M. Faraci Quantification of mRNA for Endothelial NO Synthase in Mouse Blood Vessels by Real-Time Polymerase Chain Reaction Arterioscler Thromb Vasc Biol, April 1, 2002; 22(4): 611 - 616. [Abstract] [Full Text] [PDF]
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 Y. Aihara, H. Kasuya, H. Onda, T. Hori, J. Takeda, and F. M. Faraci Quantitative Analysis of Gene Expressions Related to Inflammation in Canine Spastic Artery After Subarachnoid Hemorrhage Editorial Comment Stroke, January 1, 2001; 32(1): 212 - 217. [Abstract] [Full Text] [PDF]
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J. N. Topper Genes, Matrix, and Restenosis Arterioscler Thromb Vasc Biol, October 1, 2000; 20(10): 2173 - 2174. [Full Text] [PDF]
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Y. Chu, D. D. Heistad, K. L. Knudtson, K. G. Lamping, and F. M. Faraci Quantification of mRNA for Endothelial NO Synthase in Mouse Blood Vessels by Real-Time Polymerase Chain Reaction Arterioscler Thromb Vasc Biol, April 1, 2002; 22(4): 611 - 616. [Abstract] [Full Text] [PDF]
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