HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Abstract Full Text (PDF) Supplementary Material All Versions of this Article: 25/2/194    most recent 00240.2005v1 Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (4) Citing Articles via Google Scholar Google Scholar Articles by Tabibiazar, R. Articles by Quertermous, T. Search for Related Content PubMed PubMed Citation Articles by Tabibiazar, R. Articles by Quertermous, T.

Proteomic profiles of serum inflammatory markers accurately predict atherosclerosis in mice

Donald W. Reynolds Cardiovascular Clinical Research Center, Division of Cardiovascular Medicine, Stanford University, Stanford, California

	ABSTRACT

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION GRANTS REFERENCES

 
At a population level, inflammatory markers have been shown to predict outcome and response to therapy in patients with atherosclerotic cardiovascular disease. However, current markers are not sufficiently sensitive or specific to provide clinical utility for managing individual patients. We hypothesize that measurement of multiple circulating disease-related inflammatory factors will be more informative, allowing the early identification of vascular wall disease activity. We have investigated whether protein microarray-based abundance measurements of circulating proteins can predict the severity of atherosclerotic disease. Using a longitudinal experimental design with apolipoprotein E-deficient mice and control C57Bl/6J and C3H/HeJ wild-type mice, we measured the time-related serum protein expression of 30 inflammatory markers using a protein microarray. We were able to identify a subset of proteins that classify and predict the severity of atherosclerotic disease with a high level of accuracy. The time-specific vascular expression of these markers was verified by showing that their gene expression in the mouse aorta correlated closely to the temporal pattern of serum protein levels. In conclusion, these data suggest that quantification of multiple disease-related inflammatory proteins can provide a more sensitive and specific methodology for assessing atherosclerotic disease activity in humans, and identify candidate biomarkers for such studies.

protein microarray; biomarker; genomics; vascular

	INTRODUCTION

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION GRANTS REFERENCES

 
ATHEROSCLEROTIC CARDIOVASCULAR disease is the primary cause of morbidity and mortality in the developed world (1, 2). Unfortunately, due to the lack of sensitive and specific early biomarkers, the first clinical presentation of more than one-half of these patients is either myocardial infarction or death (19, 20). Moreover, despite appropriate evidence-based treatments, recurrence and mortality rates remain high even among patients with long-established atherosclerotic cardiovascular disease.

Inflammation has been implicated in all stages of atherosclerosis (12, 26, 41), and elevated serum inflammatory markers have been shown to stratify cardiovascular risk and assess response to therapy (39, 40). However, current known inflammatory markers are not expressed primarily in the vascular wall and hence may lack specificity for vascular disease, increasing false-positive findings in the case of nonvascular inflammatory processes. Although available inflammatory markers have been shown to predict outcome and response to therapy in large groups of patients with cardiovascular disease, they are not useful in screening for atherosclerotic disease or, more importantly, for prediction of the likelihood of a first cardiovascular event at the level of the individual patient (38). Hence, there is a critical need for identification of inflammatory markers that are more specific to vascular disease and can be used for highly sensitive and specific assays capable of detecting and quantifying atherosclerotic cardiovascular disease. We hypothesize that patterns of multiple circulating disease-related serum biomarkers can accurately predict the severity of atherosclerotic disease.

Mouse genetic models of atherosclerosis allow systematic temporal study of the disease process from its earliest stages and can be used as optimal models for biomarker discovery. Apolipoprotein (apo)E-deficient mice predictably develop spontaneous atherosclerotic plaques with many features similar to human lesions (31, 32, 36). On a high-fat diet, the rate and extent of progression of lesions is accelerated. In addition to environmental influences such as diet, the genetic background of mice has also been found to have an important role in disease development and progression (14, 47). Using parallel longitudinal experiments, it is possible to account for these variables (diet, age, and genetic background) and identify differentially expressed proteins with serum levels that correlate with the extent of the vascular disease. In a recent study (47), we have shown that C57Bl/6 mice are more prone to develop inflammation in response to atherogenic stimuli. Using high-throughput gene expression studies of the vascular wall in apoE-deficient mice, we have been able to identify a small subset of genes, primarily inflammatory, whose level of transcription can classify vascular disease in both mice and humans (45). These findings are supported by other studies showing the importance of vascular wall expression of chemokines and chemokine receptors in vascular disease (5, 27, 28).

To identify patterns of serum protein expression that can be correlated to both disease progression and gene expression in the vascular wall, we have taken advantage of a longitudinal experimental design and mouse genetic model and diet combinations that produce varying degrees of atherosclerosis. Here, we have utilized a protein microarray to identify a set of inflammatory biomarkers that are differentially expressed in the sera of mice at levels that correlate with various severity levels of disease. The vascular wall gene expression for a subset of these markers was also evaluated by quantitative real-time reverse transcriptase polymerase chain reaction (RT-PCR). Using classification algorithms to identify a set of the most sensitive discriminators, we were able to show that unique signature patterns of vascular-derived inflammatory biomarkers can accurately predict different severities of atherosclerotic disease in mice.

	METHODS

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION GRANTS REFERENCES

 
Experimental design, serum collection, and RNA preparation.
All experiments were approved by the Stanford Committee on Animal Research. The general experimental design has been described previously (45). Three-week-old female apoE knockout (C57BL/6J-Apoe^tm1Unc), C57Bl/6J, and C3H/HeJ mice were purchased from Jackson Laboratory (Bar Harbor, ME). At 4 wk of age, the mice were either continued on normal chow or were fed a high-fat diet that included 21% anhydrous milkfat and 0.15% cholesterol (Dyets no. 101511; Dyets, Bethlehem, PA) for a maximum period of 40 wk. Serum was collected by retroorbital approach for five to nine individual mice at every time point for apoE-deficient mice on the high-fat diet from the same cohort of mice as described previously. To control for diet and genetic differences, serum was also collected at baseline and at 40 wk from apoE knockout mice (C57BL/6J-Apoe^tm1Unc) on normal chow and from wild-type C57Bl/6J and C3H/HeJ mice on normal chow and high-fat diets. Aortas from 15 mice (3 pools of 5) were harvested for RNA isolation, as described previously (45), at each of the time points for each of the conditions (strain-diet combination) to parallel serum collection schedule. Total RNA was isolated as described previously using a modified two-step purification protocol (45, 47). Quantification of aortic atherosclerotic plaque (determined as percent lesion area in entire aorta) previously has been performed on this cohort of mice and described in a prior publication (45). Serum and aortas from a separate independent cohort of 16-wk-old apoE-deficient mice on high-fat diet for 2 wk (4 pools of 3–4 animals) were also used for classification purposes. The rationale for pooling RNA and serum samples for microarray hybridizations has been discussed previously (45–47, 49). All sample processing and protein hybridization were performed at the same time to negate any potential technical variability.

Protein biochip hybridization and data processing.
Serum samples were hybridized to Zyomyx Murine Cytokine BioChips (Zyomyx, Hayward, CA) following the manufacturer's instructions, using the Zyomyx 1200 Assay station (Zyomyx). Nine-point calibration curves were generated for each analyte for accurate determination of protein levels in test sera (please see Supplement S4 for individual calibration curves; available at the Physiological Genomics web site).¹ Protein biochips were scanned using a Zyomyx 100 fluorescence scanner, and microarray gridding was performed using GenPix Pro and Zyomyx ZDR version 4001 software. Intrachip (ratio of standard deviation of all negative control features over the average intensity for those features) and interchip variability (ratio of average standard deviation over average of median intensities) were determined as measures of quality control. Protein arrays present control variability ranging from 3 to 15% and sensitivity from 1 to 1,000 pg/ml depending on the analyte (please see Supplemental Calibration Curves for each analyte) (11). Values that were not in the linear portion of the calibration curves were marked as missing values. Numerical raw data were then migrated into an Oracle relational database (CoBi) that has been designed specifically for microarray data analysis (GeneData). Heat maps were generated using HeatMap Builder software (7). Detailed Supplemental Methods are available.

Protein selection algorithms and disease classification.
Protein selection and classification algorithms have been described previously (45). Briefly, for supervised analyses, we used Expressionist software version 5.0 (GeneData), which employs a number of classification algorithms to rank genes based on their utility for class discrimination between time points of 0, 10, 24, and 40 wk in apoE mice on high-fat diet. These algorithms included analysis of variance (ANOVA), support vector machine (SVM) (4), and recursive feature elimination (RFE) (16), which is a recursive version of the SVM weight where genes are ranked repeatedly and a fixed fraction of worst scorers are removed each time (35). We also used the previously described prediction analysis of microarray (PAM) as an additional classification algorithm (48). Each method was then used to determine the optimal number of ranked genes to classify the experiments into their correct groups at minimal error rate. The optimal error rate or misclassification was calculated by cross-validation with 25% of the experiments as the test group and the rest as the training group. This was reiterated 1,000 times for ANOVA, SVM, and RFE algorithms. In our analyses, we used a linear kernel for SVM and RFE; a nonlinear Gaussian kernel yielded similar results. This minimal subset of classifier genes was then used for cross-validation as well as classification of another independent data set. Detailed methods are provided in the Supplemental Materials.

Cross-validation and analysis of independent data sets.
To determine the accuracy of classification based on the small subset of proteins identified earlier, we utilized the SVM algorithm (linear kernel) to generate a confusion matrix using cross-validation with repeated splits into 75% training and 25% test sets. Results are represented in tabular fashion. We also utilized the SVM algorithm for classification of independent groups of experiments as described previously (45, 50). In this analysis, we used the four time points in apoE-deficient mice as the training set and the independent set of experiments as the test set. SVM output for each experiment based on one-vs.-all comparisons was represented graphically in a heat map format (see Fig. 3), which is the normalized margin value for each of the four SVM classifiers mentioned above. The SVM output allows us to view how a new experiment is classified according to the four SVM hyperplanes. Detailed methods are available in the Supplemental Materials.

Quantitative real-time RT-PCR.
Primers and probes for 10 genes of interest were obtained from Applied Biosystems Assays-on-Demand for Taqman analysis (Supplemental Table S1). Reactions were performed in triplicate assays using representative RNA samples derived from three pools of five aortas as described previously (45–47).

	RESULTS

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION GRANTS REFERENCES

 
Temporal patterns of protein expression during atherogenesis in apoE-deficient mice.
We have demonstrated previously (45) the extent of atherosclerotic lesions in this cohort of apoE-deficient mice. Given the extensive atherosclerotic lesions in the aorta as well as the aortic valve of the apoE-deficient mice, other vascular beds were not examined in these studies. To identify serum markers that correlate with the extent of atherosclerotic lesions, we have utilized a protein microarray to simultaneously measure the serum level of 30 inflammatory markers in apoE-deficient mice on a high-fat diet throughout the time course of disease development. For control groups, we utilized the apoE-deficient mice on normal diet as well as wild-type C57Bl/6J and C3H/HeJ mice at two time points. Eight out of the thirty markers measured did not reveal significant serum expression levels (see Supplemental Table S2 for the entire data set). Twenty-two markers revealed unique time-related patterns of expression, some of which closely correlated with the extent of atherosclerotic lesions in the aorta previously described in this cohort of mice (Fig. 1) (45). These markers included various chemokines (Ccl2, Ccl9, Ccl11, Ccl19, Ccl21, Cxcl1, and Cxcl2) and several cytokines (Il2, Il4, Il5, Il6, Il10, and Il12) as well as other inflammatory proteins (Csf1, Csf2, Csf3, Ifng, Tnfsf11) and Vegfa. The vast majority of these markers had higher expression in apoE-deficient mice compared with control wild-type C57Bl/6J and C3H/HeJ mice (Fig. 2). As described previously, under similar conditions, the control mice did not develop histologically evident atherosclerotic lesions (47); therefore, disease-related changes can be readily distinguished from other factors such as high-fat diet and aging.

View larger version (53K): [in this window] [in a new window]   Fig. 1. Time-dependent serum inflammatory protein expression during progression of atherosclerosis in apolipoprotein (apo)E-deficient mice on high-fat diet. The heat map is a graphic representation of the serum concentration levels with individual serum samples arranged along the x-axis and protein markers along the y-axis. Values represent serum protein expression levels from apoE-deficient mice at baseline (T00; n = 5) and at 10 (T10; n = 5), 16 (T16; n = 4), 24 (T24; n = 5), and 40 wk (T40; n = 5) on high-fat diet. Please note that for the 16-wk time point, values were derived from a 2nd independent data set.

View larger version (30K): [in this window] [in a new window]   Fig. 2. Serum inflammatory protein expression levels in apoE-deficient mice and in control mice. The heat map is a graphic representation of row-normalized expression values depicted by color intensity, from highest (bright red) to lowest (bright green) expression. Values represent average serum protein expression levels (±SD) (log2), in pg/ml, of replicate apoE-deficient mice at baseline (n = 9) and at 40 wk (n = 9) on high-fat diet as well as C57Bl/6J (n = 5) and C3H/HeJ mice (n = 3) at baseline and at 40 wk on high-fat diet (n = 5 and 5, respectively). While apoE-deficient mice on high-fat diet have the highest levels of inflammatory markers, C3H/HeJ mice have the lowest levels, despite being on high-fat diet. N-way analysis of variance (ANOVA) was used to identify statistically significant variations among the various conditions. Because the P values reported do not take into account possible interactions between diet, strain, and time, the effects of these factors and their interactions with each other are reported in Supplemental Table S3.

Simple ANOVA revealed at least 12 markers that were differentially expressed among the various diet-strain-time combinations (Fig. 2). To account for possible interactions among the three independent variables, we utilized three-way ANOVA. Three independent variables have three first-order interactions (time-strain, time-diet, strain-diet) and one second-order interaction (time-strain-diet). Accounting for interactions among all three factors, we identified five proteins as differentially expressed (3-way ANOVA, P < 0.05), including Ccl9, Ccl21, Ccl11, Csf1, and Il12b. For a complete list of proteins and other possible interactions, see Supplemental Table S3.

At the later time points, the high-fat diet also stimulated an inflammatory response in C57Bl/6 wild-type mice, as represented by elevated serum levels for a number of inflammatory markers (Fig. 2). C3H/HeJ mice, on the other hand, had the lowest levels of inflammatory markers, even when on the high-fat diet. This finding is consistent with observations from our prior study comparing the aortic vascular wall gene expression in C3H/HeJ mice with that of C57Bl/6J mice. That study concluded C57Bl/6J mice have a higher genetic propensity for the expression of inflammatory markers in atherosclerosis.

Identification of time-specific protein expression signature pattern in mouse serum.
Classification approaches to human cancer have provided significant insights regarding the clinical features of the tumor, including propensity to metastasis, drug responsiveness, and long-term prognosis (13, 23, 33, 43). For atherosclerosis, the clinical utility of classification algorithms will be in prediction of future events. In a previous study, we have applied classification algorithms to establish a panel of genes whose expression in the vessel wall could accurately classify disease severity in atherosclerotic vascular tissue derived from both mice and humans (45). In the current study, we have employed a similar approach to identify a minimal subset of serum proteins to accurately classify each proteomic experiment with one of the four defined stages of atherosclerosis in mice (Fig. 3). Here we utilized several well-known classification algorithms to identify the variables that can best distinguish between the mice with different disease states. These algorithms included RFE, SVM, and ANOVA. We also used PAM as an additional classification algorithm. These algorithms rank the proteins based on their utility for class discrimination between time points of 0, 10, 24, and 40 wk in apoE mice on high-fat diet. Our results demonstrated that a small subset of proteins (Ccl21, Ccl9, Csf3, Tnfsf11, Vegfa, Ccl11, Ccl2) were identified by a majority of the algorithms (Fig. 3A). The predictive power of the signature pattern of this panel was superior to any single marker, since no individual marker was able to accurately classify the various disease states (analysis not shown). To determine the utility of serum levels of these proteins for classification of mice with different disease states, we utilized the SVM algorithm (linear kernel) to generate a confusion matrix using cross-validation with repeated splits into 75% training and 25% test sets. This algorithm demonstrated that the signature pattern of expression of these serum proteins can distinguish groups of mice with and without disease with up to 100% accuracy (Fig. 3B). Mice with intermediate stages of the disease are also distinguished from the other stages with a high degree of accuracy (79.6–100%) (Fig. 3B).

View larger version (20K): [in this window] [in a new window]   Fig. 3. Proteomic signature patterns of serum inflammatory markers in classification of atherosclerosis in mice. A: identification of the atherosclerosis classification protein subset. Various classification algorithms, including prediction analysis for microarrays (PAM), recursive feature elimination (RFE), support vector machine (SVM), and ANOVA, were used to rank a subset of markers based on their ability to accurately discriminate between mice with 4 different stages of atherosclerotic disease (apoE-deficient mice at baseline and 10, 24, and 40 wk on high-fat diet). A number of these markers were ranked in all classification algorithms. B: classification accuracy of mouse atherosclerotic disease (confusion matrix). To determine the accuracy of mouse classifier proteins in predicting disease severity, we used the top-ranking protein markers identified earlier (Ccl21, Ccl9, Csf3, Tnfsf11, Vegfa, Ccl11, Ccl2). The SVM algorithm was utilized for cross-validation of mouse experiments grouped on the basis of stages of disease. Accuracy of classification was determined with a 1,000-step N-fold cross-validation method, with 25% of experiments employed as the test group and the rest as the training group. Results are represented in tabular fashion with the confusion matrix as described in METHODS. The notation "TRUE" refers to "Actual Disease State," whereas "Predicted" refers to "Predicted Disease State." C: classification of an independent data set. Using the SVM algorithm, we can classify an independent data set ("test") to closest time point from the original set of experiments ("known"). The known experiments include the 4 time points in our original analysis from which the set of protein classifiers was derived. The independent set of experiments was derived from the 16-wk time point, which was not included in the original set. SVM scores (affinity) for each experiment, based on one-vs.-all comparisons, are represented graphically in the heat map. The protein profile of the 16-wk time point correlated more closely with the 10-wk time point of the original data set.

Biomarker serum protein levels correlate with vascular wall gene expression levels.
Those biomarkers whose circulating protein levels correlate with molecular events and expression levels in the vessel wall are expected to be most informative about vascular disease. To investigate such correlations, and to gain insights from the biomarker data regarding the pathophysiology of atherosclerosis, we have investigated vascular wall gene expression patterns for genes encoding informative biomarkers. Using quantitative real-time RT-PCR, we were able to correlate serum protein levels of several markers with their vascular RNA expression. Among the markers studied, Ccl21 (r = 0.91), Ccl2 (r = 0.97), Ccl19 (r = 0.80), and Ccl11 (r = 0.67) revealed a remarkably high correlation between time-related increase in gene expression and in serum levels (Fig. 4). Although these data do not exclude expression of these markers in other tissues, they suggest that expression is particularly associated with the atherosclerotic vascular wall.

View larger version (21K): [in this window] [in a new window]   Fig. 4. Correlation between serum level and vascular gene expression of top classifier markers. A: to investigate the disease-related gene expression for a subset of these serum markers, we studied their temporal gene expression in aortas of mice from which the sera were obtained. Using quantitative real-time RT-PCR (qRT-PCR), we were able to correlate the time-dependent serum protein levels of these markers with their vascular wall gene expression. Pearson correlation was determined for log10-normalized average expression ratios of serum protein levels and aortic gene expression values. The average ratio of protein levels was determined by protein microarray at each time point divided by levels for apoE-deficient mice at baseline (n = 4–9). Average ratio of gene expression levels was determined by replicate qRT-PCR reaction at each time point divided by values obtained for apoE-deficient mice at baseline. Please note that, for the 16-wk time point, the values were derived from a separate independent data set. B: correlation matrix summary table for Pearson correlation values comparing normalized average ratios of serum protein level, vascular gene expression, and time on high-fat diet (log10 of no. of wk on diet). Correlations were considered significant at 0.05 (2 tailed).

	DISCUSSION

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION GRANTS REFERENCES

Currently, the general markers of inflammation, although proposed for use in risk stratification of patients with atherosclerotic disease, are not used in the screening of asymptomatic patients for accurate disease classification and, more importantly, for prediction of first cardiovascular events. The lack of specificity of markers such as C-reactive protein (CRP) and fibrinogen may stem from the fact that they are not derived from the vasculature and may signal inflammation in any organ. It is also possible that, because of heterogeneity among the population at risk, a single marker cannot provide sufficient information for accurate prediction of disease. For similar reasons, these general markers of inflammation such as CRP and sedimentation rate (ESR) have been long abandoned as specific diagnostic markers in other inflammatory diseases such as lupus (SLE) and rheumatoid arthritis (RA).

We have shown previously with RNA profiling studies of mouse aortic tissues, with the same experimental design as that used here, that it is possible to identify a small number of genes capable of classifying disease severity (45). Obviously, given that the vascular tissue is not readily accessible, identification of protein markers in the serum can have practical implications in developing diagnostic tools for diagnosis of coronary artery disease in humans. In the work reported here, we have investigated inflammatory serum biomarker abundance patterns and whether a subset of these biomarkers can be used to classify animals with respect to disease progression. Scientifically, these two types of information are complementary and provide significantly greater insights into the detailed molecular mechanisms of the disease, from gene transcription to translation to intracellular pathways to secretion of mediators into the serum. As noted above, identification of the serum marker profile for a given disease state allows the development of noninvasive diagnostic approaches that can be used in humans. Because we also have a detailed microarray-based picture of the transcriptional landscape in the diseased tissue, we can use this view to assess upstream components in the pathways that lead to inflammatory mediator expression, the first step in developing highly targeted therapeutics. Indeed, serum assays such the one described here can then be used to assay the ultimate effects of such therapeutics.

We utilized protein microarrays for simultaneous protein expression profiling of sera from various mouse models of atherosclerosis with different susceptibilities and severities of atherosclerosis. Using classification algorithms similar to those utilized in classifying cancer progression and type, we were able to show that the unique signature patterns of these vascular-derived biomarkers could accurately predict different severities of atherosclerotic disease in mice.

In the prior study (45), our analysis revealed that the microarray gene expression profile of the independent data set derived from the 16-wk time point associated more closely with the 24-wk time point, whereas, in the present study, the protein profiles of the similar time point correlated more closely with the 10-wk time point. This finding may offer a number of interesting hypotheses. Given the limited number of probes in the current protein microarray, the protein classifiers in the current study are different from the gene classifiers identified in the prior study. It is also possible that time-related increase in serum protein expression lags behind changes at the level of vascular wall gene expression.

Because there may not be a direct correlation between vascular gene expression and serum protein levels for the same markers because of various factors such as posttranscriptional modification and protein stability, an important validation of these data was the demonstration of disease-related vascular gene expression for a subset of these markers. We show a correlation between the time-related serum levels of these markers and their gene expression in the vessel wall. The time-dependent correlation of disease progression and vascular gene expression suggests that the primary site of marker production is the vessel wall. However, the vasculature may not be the sole source of the inflammatory markers, and it is possible that other tissues such as muscle, spleen, adipose tissue, or liver may contribute to the serum levels of these markers, as suggested by previous reports (22). One marker evaluated in our studies, Il6, is known to be produced in muscle and liver as well as the vascular wall. Interestingly, the serum abundance of Il6 did not correlate with the temporal development of disease, correlating only weakly with gene expression in the vascular wall. These findings suggest that other tissues may contribute to serum levels of some markers, such as Il6, but that the levels of these were not correlated with the disease state studied and do not contribute to the classification panel.

The serum level of some of the systemic inflammatory markers may also be confounded by differences in metabolic parameters among the various mice studied. It has been demonstrated that a high-fat diet stimulates an inflammatory response in the liver (22). The level of expression of these genes remains high throughout the high-fat feeding period. We controlled for these systemic effects by comparing mice fed high-fat diets during both the early and late atherosclerosis stages, so that serum lipid levels are constant (14) but the degree of atherosclerosis changes. These metabolic parameters therefore have a poor correlation with the serum level of markers which demonstrate a linear increase with time. Thus temporal changes in vascular-derived marker serum levels correlate more closely with the degree of atherosclerosis and not lipid levels.

The markers identified in this study provide strong support for the inflammatory nature of atherosclerosis, and the individual markers identified offer some insights into the underlying mechanisms of the disease in mice. These markers include important chemokines specific for both macrophages and T cells. Ccl21 (originally Exodus-2/SLC/6Ckine/TCA4) is the most powerful chemoattractant yet identified for T cells and plays an important role in T cell adhesion and trafficking from the vasculature to tissue sites of inflammation (30). Related chemokines Cxcl12 and Ccl19, also expressed at high levels in our experiments, mediate the firm adherence of T cells to the endothelium by stimulating lymphocyte function-associated antigen-1 (LFA-1) (6, 15). Importantly, Ccl21 is not thought to play a role in T cell effector function during a normal immune response but has been found to be highly induced in endothelial cells in T cell-mediated autoimmune diseases (8). Therefore, the novel finding of disease-related high-level circulating Ccl21, and highly correlated expression of CCL21 in the diseased vessel wall, raises the question of whether autoimmune pathways may play a role in the development of atherosclerosis in mice (44). CCL21 levels in human disease remain to be measured. Ccl19 [macrophage inflammatory protein (MIP)-3b] has a somewhat similar function to Ccl21. It binds the same receptor, Ccr7, and is a potent chemoattractant for both T cells and B cells. But unlike Ccl21, it appears to also play a role in normal T cell function. Its expression in the atherosclerotic vasculature and the high correlation between serum levels and aortic gene expression are both novel findings.

The roles of Ccl2 (Mcp1 or JE) (3) and Ccl11 (Eotaxin) (10, 17) in atherosclerosis are well established and confirm our findings. We have also documented that the serum levels of both Cxcl2 (MIP-2) and Cxcl1 (KC) are elevated in sera of atherosclerotic mice, consistent with serum levels described by other investigators (29). As was described in that study (29), we found levels of Cxcl2 (MIP-2) to be less reliable. Moreover, given the lower correlation of serum levels with aortic gene expression, it appears that significant amounts of Cxcl2 may be produced by nonvascular tissues, confirming previous observations (29). Nonetheless, we found that the correlation with vascular gene expression of Cxcl2 was still better than other markers such as Il6 and Csf3. Despite the increased levels of Cxcl1 (KC), we did not find this marker to be a consistent predictor of disease, which is consistent with a recent study (34). Vegfa has recently been described as an independent predictor of acute coronary syndrome (18, 24). Our study supports Vegfa as a reasonable classifier in at least three of the algorithms used, confirming its potential utility in monitoring human disease. Another very interesting finding in our study is the role of Tnfsf11 (TRANCE) in atherosclerosis. Tnfsf11 is a member of tumor necrosis factor (TNF) cytokine family and a ligand for osteoprotegerin which functions as a key factor for osteoclast differentiation and activation. This protein is also known to be a dentritic cell survivor factor and is involved in the regulation of T cell-dependent immune response. Osteoprotegerin has recently been identified as a potential risk factor for progressive atherosclerosis and cardiovascular disease in humans (21, 37). Other cytokines that have been speculated to play a role in atherosclerosis include Il12b (25) and Il5 (9). Although we demonstrated their serum level to be predictive of disease state, we failed to confirm vascular-specific expression of Il12b in atherosclerotic lesions.

In summary, the top serum protein classifiers identified in our study encompass a wide range of atherosclerotic biological processes including macrophage chemoattraction (Ccl9, Ccl2), T cell chemokine activity (Ccl21 and Ccl19), innate immunity (Il5), vascular calcification (Tnfsf11), angiogenesis (Vegfa), and high fat-induced inflammation (Cxcl1 and possibly leptin). The signature pattern derived from simultaneous measurement of these markers, which represent diverse atherosclerosis-related biological processes, will likely add to the specificity needed for diagnosis of atherosclerotic disease. Further validation of this approach with appropriate prospective trials in human subjects may lead to improved screening diagnostic tools in atherosclerosis and coronary artery disease.

	GRANTS

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION GRANTS REFERENCES

 
This work was supported by the Donald W. Reynolds Cardiovascular Clinical Research Center at Stanford University.

	ACKNOWLEDGMENTS

 
We thank Lienchi Nguyen for expert assistance.

	FOOTNOTES

 
Article published online before print. See web site for date of publication (http://physiolgenomics.physiology.org).

Address for reprint requests and other correspondence: R. Tabibiazar or T. Quertermous, Stanford Medical School, Division of Cardiovascular Medicine, 300 Pasteur Dr., Falk CVRC, Stanford, CA 94305 (e-mail: rtabibiazar{at}cvmed.stanford.edu or tomq1{at}stanford.edu, respectively).

¹ The Supplemental Material for this article is available online at http://physiolgenomics.physiology.org/cgi/content/full/00240.2005/DC1.

	REFERENCES

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION GRANTS REFERENCES

 

1. [No author listed.] Fact Book Fiscal Year 2003. Bethesda, MD: National Heart, Lung, and Blood Institute, 2003.
2. [No author listed.] Morbidity and Mortality Chartbook, 2002. Bethesda, MD: National Heart, Lung, and Blood Institute, 2002.
3. Aiello RJ, Bourassa PA, Lindsey S, Weng W, Natoli E, Rollins BJ, and Milos PM. Monocyte chemoattractant protein-1 accelerates atherosclerosis in apolipoprotein E-deficient mice. Arterioscler Thromb Vasc Biol 19: 1518–1525, 1999.[Abstract/Free Full Text]
4. Burges CJC. A tutorial on support vector machines for pattern recognition. Data Mining Knowledge Discov 2: 121–167, 1998.[CrossRef]
5. Bursill CA, Channon KM, and Greaves DR. The role of chemokines in atherosclerosis: recent evidence from experimental models and population genetics. Curr Opin Lipidol 15: 145–149, 2004.[CrossRef][Medline]
6. Campbell JJ, Hedrick J, Zlotnik A, Siani MA, Thompson DA, and Butcher EC. Chemokines and the arrest of lymphocytes rolling under flow conditions. Science 279: 381–384, 1998.[Abstract/Free Full Text]
7. Chen MM, Ashley EA, Deng DX, Tsalenko A, Deng A, Tabibiazar R, Ben-Dor A, Fenster B, Yang E, King JY, Fowler M, Robbins R, Johnson FL, Bruhn L, McDonagh T, Dargie H, Yakhini Z, Tsao PS, and Quertermous T. Novel role for the potent endogenous inotrope apelin in human cardiac dysfunction. Circulation 108: 1432–1439, 2003.[Abstract/Free Full Text]
8. Christopherson KW 2nd, Hood AF, Travers JB, Ramsey H, and Hromas RA. Endothelial induction of the T-cell chemokine CCL21 in T-cell autoimmune diseases. Blood 101: 801–806, 2003.[Abstract/Free Full Text]
9. Daugherty A, Rateri DL, and King VL. IL-5 links adaptive and natural immunity in reducing atherosclerotic disease. J Clin Invest 114: 317–319, 2004.[Medline]
10. Economou E, Tousoulis D, Katinioti A, Stefanadis C, Trikas A, Pitsavos C, Tentolouris C, Toutouza MG, and Toutouzas P. Chemokines in patients with ischaemic heart disease and the effect of coronary angioplasty. Int J Cardiol 80: 55–60, 2001.[CrossRef][ISI][Medline]
11. Feezor RJ, Baker HV, Xiao W, Lee WA, Huber TS, Mindrinos M, Kim RA, Ruiz-Taylor L, Moldawer LL, Davis RW, and Seeger JM. Genomic and proteomic determinants of outcome in patients undergoing thoracoabdominal aortic aneurysm repair. J Immunol 172: 7103–7109, 2004.[Abstract/Free Full Text]
12. Glass CK and Witztum JL. Atherosclerosis. The road ahead. Cell 104: 503–516, 2001.[CrossRef][ISI][Medline]
13. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, and Lander ES. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286: 531–537, 1999.[Abstract/Free Full Text]
14. Grimsditch DC, Penfold S, Latcham J, Vidgeon-Hart M, Groot PH, and Benson GM. C3H apoE(–/–) mice have less atherosclerosis than C57BL apoE(–/–) mice despite having a more atherogenic serum lipid profile. Atherosclerosis 151: 389–397, 2000.[CrossRef][ISI][Medline]
15. Gunn MD, Tangemann K, Tam C, Cyster JG, Rosen SD, and Williams LT. A chemokine expressed in lymphoid high endothelial venules promotes the adhesion and chemotaxis of naive T lymphocytes. Proc Natl Acad Sci USA 95: 258–263, 1998.[Abstract/Free Full Text]
16. Guyon I, Weston J, Barnhill S, and Vapnik V. Gene selection for cancer classification using support vector machines. Machine Learning 46: 389, 2002.[CrossRef][ISI]
17. Haley KJ, Lilly CM, Yang JH, Feng Y, Kennedy SP, Turi TG, Thompson JF, Sukhova GH, Libby P, and Lee RT. Overexpression of eotaxin and the CCR3 receptor in human atherosclerosis: using genomic technology to identify a potential novel pathway of vascular inflammation. Circulation 102: 2185–2189, 2000.[Abstract/Free Full Text]
18. Heeschen C, Dimmeler S, Hamm CW, Fichtlscherer S, Simoons ML, and Zeiher AM. Pregnancy-associated plasma protein-A levels in patients with acute coronary syndromes: comparison with markers of systemic inflammation, platelet activation, and myocardial necrosis. J Am Coll Cardiol 45: 229–237, 2005.[Abstract/Free Full Text]
19. Kannel WB and McGee DL. Epidemiology of sudden death: insights from the Framingham Study. Cardiovasc Clin 15: 93–105, 1985.[Medline]
20. Kannel WB and Schatzkin A. Sudden death: lessons from subsets in population studies. J Am Coll Cardiol 5: 141B–149B, 1985.[Medline]
21. Kiechl S, Schett G, Wenning G, Redlich K, Oberhollenzer M, Mayr A, Santer P, Smolen J, Poewe W, and Willeit J. Osteoprotegerin is a risk factor for progressive atherosclerosis and cardiovascular disease. Circulation 109: 2175–2180, 2004.[Abstract/Free Full Text]
22. Kim S, Sohn I, Ahn JI, Lee KH, and Lee YS. Hepatic gene expression profiles in a long-term high-fat diet-induced obesity mouse model. Gene 340: 99–109, 2004.[CrossRef][ISI][Medline]
23. Lapointe J, Li C, Higgins JP, van de Rijn M, Bair E, Montgomery K, Ferrari M, Egevad L, Rayford W, Bergerheim U, Ekman P, DeMarzo AM, Tibshirani R, Botstein D, Brown PO, Brooks JD, and Pollack JR. Gene expression profiling identifies clinically relevant subtypes of prostate cancer. Proc Natl Acad Sci USA 101: 811–816, 2004.[Abstract/Free Full Text]
24. Lee SH, Wolf PL, Escudero R, Deutsch R, Jamieson SW, and Thistlethwaite PA. Early expression of angiogenesis factors in acute myocardial ischemia and infarction. N Engl J Med 342: 626–633, 2000.[Abstract/Free Full Text]
25. Lee TS, Yen HC, Pan CC, and Chau LY. The role of interleukin 12 in the development of atherosclerosis in ApoE-deficient mice. Arterioscler Thromb Vasc Biol 19: 734–742, 1999.[Abstract/Free Full Text]
26. Libby P. Inflammation in atherosclerosis. Nature 420: 868–874, 2002.[CrossRef][Medline]
27. Lucas AD and Greaves DR. Atherosclerosis: role of chemokines and macrophages. Expert Rev Mol Med 2001: 1–18, 2001.[Medline]
28. Luster AD. Chemokines—chemotactic cytokines that mediate inflammation. N Engl J Med 338: 436–445, 1998.[Free Full Text]
29. Murphy N, Bruckdorfer KR, Grimsditch DC, Overend P, Vidgeon-Hart M, Groot PH, Benson GM, and Graham A. Temporal relationships between circulating levels of CC and CXC chemokines and developing atherosclerosis in apolipoprotein E*3 Leiden mice. Arterioscler Thromb Vasc Biol 23: 1615–1620, 2003.[Abstract/Free Full Text]
30. Nagira M, Imai T, Hieshima K, Kusuda J, Ridanpaa M, Takagi S, Nishimura M, Kakizaki M, Nomiyama H, and Yoshie O. Molecular cloning of a novel human CC chemokine secondary lymphoid-tissue chemokine that is a potent chemoattractant for lymphocytes and mapped to chromosome 9p13. J Biol Chem 272: 19518–19524, 1997.[Abstract/Free Full Text]
31. Nakashima Y, Plump AS, Raines EW, Breslow JL, and Ross R. ApoE-deficient mice develop lesions of all phases of atherosclerosis throughout the arterial tree. Arterioscler Thromb 14: 133–140, 1994.[Abstract/Free Full Text]
32. Napoli C, Palinski W, Di Minno G, and D'Armiento FP. Determination of atherogenesis in apolipoprotein E-knockout mice. Nutr Metab Cardiovasc Dis 10: 209–215, 2000.[Medline]
33. Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, Baehner FL, Walker MG, Watson D, Park T, Hiller W, Fisher ER, Wickerham DL, Bryant J, and Wolmark N. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 351: 2817–2826, 2004.[Abstract/Free Full Text]
34. Parkin SL, Pritchett JP, Grimsditch DC, Bruckdorfer KR, Sahota PK, Lloyd A, Overend P, and Benson GM. Circulating levels of the chemokines JE and KC in female C3H apolipoprotein-E-deficient and C57BL apolipoprotein-E-deficient mice as potential markers of atherosclerosis development. Biochem Soc Trans 32: 128–130, 2004.[Medline]
35. Ramaswamy S, Tamayo P, Rifkin R, Mukherjee S, Yeang CH, Angelo M, Ladd C, Reich M, Latulippe E, Mesirov JP, Poggio T, Gerald W, Loda M, Lander ES, and Golub TR. Multiclass cancer diagnosis using tumor gene expression signatures. Proc Natl Acad Sci USA 98: 15149–15154, 2001.[Abstract/Free Full Text]
36. Reddick RL, Zhang SH, and Maeda N. Atherosclerosis in mice lacking apo E. Evaluation of lesional development and progression. Arterioscler Thromb 14: 141–147, 1994.[Abstract/Free Full Text]
37. Rhee EJ, Lee WY, Kim SY, Kim BJ, Sung KC, Kim BS, Kang JH, Oh KW, Oh ES, Baek KH, Kang MI, Woo HY, Park HS, Kim SW, Lee MH, and Park JR. The relationship of serum osteoprotegerin levels with coronary artery disease severity, left ventricular hypertrophy and C-reactive protein. Clin Sci (Lond) 108: 237–243, 2004.
38. Ridker PM, Brown NJ, Vaughan DE, Harrison DG, and Mehta JL. Established and emerging plasma biomarkers in the prediction of first atherothrombotic events. Circulation 109: IV6–IV19, 2004.
39. Ridker PM, Cannon CP, Morrow D, Rifai N, Rose LM, McCabe CH, Pfeffer MA, and Braunwald E. C-reactive protein levels and outcomes after statin therapy. N Engl J Med 352: 20–28, 2005.[Abstract/Free Full Text]
40. Rifai N and Ridker PM. Inflammatory markers and coronary heart disease. Curr Opin Lipidol 13: 383–389, 2002.[CrossRef][ISI][Medline]
41. Ross R. Atherosclerosis—an inflammatory disease. N Engl J Med 340: 115–126, 1999.[Free Full Text]
42. Saadeddin SM, Habbab MA, and Ferns GA. Markers of inflammation and coronary artery disease. Med Sci Monit 8: RA5–RA12, 2002.[Medline]
43. Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS, Thorsen T, Quist H, Matese JC, Brown PO, Botstein D, Eystein Lonning P, and Borresen-Dale AL. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA 98: 10869–10874, 2001.[Abstract/Free Full Text]
44. Stemme S, Faber B, Holm J, Wiklund O, Witztum JL, and Hansson GK. T lymphocytes from human atherosclerotic plaques recognize oxidized low density lipoprotein. Proc Natl Acad Sci USA 92: 3893–3897, 1995.[Abstract/Free Full Text]
45. Tabibiazar R, Wagner RA, Ashley EA, King JY, Ferrara R, Spin JM, Sanan DA, Narasimhan B, Tibshirani R, Tsao PS, Efron B, and Quertermous T. Signature patterns of gene expression in mouse atherosclerosis and their correlation to human coronary disease. Physiol Genomics 22: 213–226, 2005.[Abstract/Free Full Text]
46. Tabibiazar R, Wagner RA, Liao A, and Quertermous T. Transcriptional profiling of the heart reveals chamber-specific gene expression patterns. Circ Res 93: 1193–1201, 2003.[Abstract/Free Full Text]
47. Tabibiazar R, Wagner RA, Spin JM, Ashley EA, Narasimhan B, Rubin EM, Efron B, Tsao PS, Tibshirani R, and Quertermous T. Mouse strain-specific differences in vascular wall gene expression and their relationship to vascular disease. Arterioscler Thromb Vasc Biol 25: 302–308, 2005.[Abstract/Free Full Text]
48. Tibshirani R, Hastie T, Narasimhan B, and Chu G. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci USA 99: 6567–6572, 2002.[Abstract/Free Full Text]
49. Wagner RA, Tabibiazar R, Powers J, Bernstein D, and Quertermous T. Genome-wide expression profiling of a cardiac pressure overload model identifies major metabolic and signaling pathway responses. J Mol Cell Cardiol 37: 1159–1170, 2004.[CrossRef][ISI][Medline]
50. Yeang CH, Ramaswamy S, Tamayo P, Mukherjee S, Rifkin RM, Angelo M, Reich M, Lander E, Mesirov J, and Golub T. Molecular classification of multiple tumor types. Bioinformatics 17, Suppl 1: S316–S322, 2001.[Abstract]

This article has been cited by other articles:

				J. A. Wingrove, S. E. Daniels, A. J. Sehnert, W. Tingley, M. R. Elashoff, S. Rosenberg, L. Buellesfeld, E. Grube, L. K. Newby, G. S. Ginsburg, et al. Correlation of Peripheral-Blood Gene Expression With the Extent of Coronary Artery Stenosis Circ Cardiovasc Genet, October 1, 2008; 1(1): 31 - 38. [Abstract] [Full Text] [PDF]

				A. V. G. Edwards, M. Y. White, and S. J. Cordwell The Role of Proteomics in Clinical Cardiovascular Biomarker Discovery Mol. Cell. Proteomics, October 1, 2008; 7(10): 1824 - 1837. [Abstract] [Full Text] [PDF]

				M. S. Goligorsky Frontiers in Nephrology: Viewing the Kidney through the Heart Endothelial Dysfunction in Chronic Kidney Disease J. Am. Soc. Nephrol., November 1, 2007; 18(11): 2833 - 2835. [Full Text] [PDF]

This Article Abstract Full Text (PDF) Supplementary Material All Versions of this Article: 25/2/194    most recent 00240.2005v1 Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (4) Citing Articles via Google Scholar Google Scholar Articles by Tabibiazar, R. Articles by Quertermous, T. Search for Related Content PubMed PubMed Citation Articles by Tabibiazar, R. Articles by Quertermous, T.