Physiol Genomics 38: 233–240, 2009. First published March 31, 2009; doi:10.1152/physiolgenomics.90364.2008.
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Gene expression profiles in peripheral blood mononuclear cells of chronic heart failure patients Claudia Cappuzzello,2* Monica Napolitano,1* Diego Arcelli,3 Guido Melillo,1 Roberta Melchionna,1 Luca Di Vito,4 Daniele Carlini,1 Lorena Silvestri,5 Salvatore Brugaletta,4 Giovanna Liuzzo,4 Filippo Crea,4 and Maurizio C. Capogrossi1 1
Laboratorio di Patologia Vascolare, Istituto Dermopatico dell’Immacolata IRCCS, Rome; 2Laboratorio di Biologia Vascolare e Terapia Genica, Centro Cardiologico Monzino-IRCCS, Milan; 3Laboratorio di Oncologia Molecolare, Bioinformatic Unit, Istituto Dermopatico dell’Immacolata-IRCCS, Rome; and 4Institute of Cardiology, Catholic University, Ospedale Gemelli and 5Laboratorio di Analisi, Istituto Dermopatico dell’Immacolata-IRCCS, Rome, Italy Submitted 30 October 2008; accepted in final form 30 March 2009
Cappuzzello C, Napolitano M, Arcelli D, Melillo G, Melchionna R, Di Vito L, Carlini D, Silvestri L, Brugaletta S, Liuzzo G, Crea F, Capogrossi MC. Gene expression profiles in peripheral blood mononuclear cells of chronic heart failure patients. Physiol Genomics 38: 233–240, 2009. First published March 31, 2009; doi:10.1152/physiolgenomics.90364.2008.—The present study was aimed at identifying chronic heart failure (CHF) biomarkers from peripheral blood mononuclear cells (PBMCs) in patients with ischemic (ICM) and nonischemic dilated (NIDCM) cardiomyopathy. PBMC gene expression profiling was performed by Affymetrix in two patient groups, 1) ICM (n ⫽ 12) and 2) NIDCM (n ⫽ 12) New York Heart Association (NYHA) III/IV CHF patients, vs. 3) age- and sex-matched control subjects (n ⫽ 12). Extracted RNAs were then pooled and hybridized to a total of 11 microarrays. Gene ontology (GO) analysis separated gene profiling into functional classes. Prediction analysis of microarrays (PAM) and significance analysis of microarrays (SAM) were utilized in order to identify a molecular signature. Candidate markers were validated by quantitative real-time polymerase chain reaction. We identified a gene expression profiling that distinguished between CHF patients and control subjects. Interestingly, among the set of genes constituting the signature, chemokine receptor (CCR2, CX3CR1) and early growth response (EGR1, 2, 3) family members were found to be upregulated in CHF patients vs. control subjects and to be part of a gene network. Such findings were strengthened by the analysis of an additional 26 CHF patients (n ⫽ 14 ICM and n ⫽ 12 NIDCM), which yielded similar results. The present study represents the first large-scale gene expression analysis of CHF patient PBMCs that identified a molecular signature of CHF and putative biomarkers of CHF, i.e., chemokine receptor and EGR family members. Furthermore, EGR1 expression levels can discriminate between ICM and NIDCM CHF patients.
a complementary diagnostic/prognostic value, in addition to well-established diagnostic tools, for CHF (12). It is well established that inflammation, and the underlying cellular and molecular mechanisms, plays an important pathophysiological role in vascular remodeling in ischemic cardiovascular disease, in atherosclerosis, and in the progression toward CHF (27, 34). Several studies have reported that elevated levels of inflammatory mediators, including tumor necrosis factor (TNF)-␣, IL-1, and IL-6, may contribute to cardiac remodeling during CHF; this is characterized by chamber dilation, cardiomyocyte hypertrophy, contractile dysfunction, and fibrosis (2, 31). The powerful tool of gene expression analysis by microarray has been successfully applied to cardiovascular research (6, 18). Several studies have been performed, using myocardial biopsies or explanted hearts from transplant recipients, with the goal of identifying diagnostic and/or prognostic marker genes of CHF (4, 23, 24, 33). In contrast, to our knowledge, only one small-scale cDNA array analysis has been performed on CHFderived versus control peripheral blood mononuclear cells (PBMCs) (40). Nevertheless, PBMCs have been successfully utilized for gene expression studies from transplant (20) and stroke (28) patients. In our study, we performed PBMC gene profiling by Affymetrix of New York Heart Association (NYHA) III/IV ischemic and nonischemic CHF patients, with the goal of identifying differentially expressed genes that may represent a molecular signature and diagnostic markers of disease.
microarray; cardiomyopathy; leukocyte
(CHF) is a functional consequence of acute myocardial infarction or of several types of cardiomyopathies and is characterized by progressive cardiac insufficiency that leads to high morbidity and premature death (17). Biomarkers such as brain natriuretic peptide (BNP) may have
CHRONIC HEART FAILURE
* C. Cappuzzello and M. Napolitano made equal contributions to this work. Address for reprint requests and other correspondence: M. C. Capogrossi and M. Napolitano, Laboratorio di Patologia Vascolare, Istituto Dermopatico dell’Immacolata-IRCCS, Via Monti di Creta 104, 00167 Rome, Italy (e-mail: [email protected]
; [email protected]
Patient characteristics. The study population consisted of caucasian patients with stable CHF classified as NYHA functional class III/IV and left ventricular ejection fraction (LVEF) ⬍40%. Table 1 shows the characteristics of NYHA III/IV CHF patients used in the Affymetrix study. None of the patients had infections, malignancies, autoimmune disorders, diabetes, pulmonary disease, myocardial infarction, unstable angina, or myocarditis in the 6 mo before enrollment in the study and blood sample collection. The cause of heart failure was classified as 1) ischemic cardiomyopathy (ICM) in patients (n ⫽ 12) with history of myocardial infarction and coronary atherosclerosis with stenosis ⬎70% in at least one major coronary artery branch or 2) nonischemic dilated cardiomyopathy (NIDCM) in patients (n ⫽ 12) with no history of myocardial
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SIGNATURE OF HEART FAILURE
Table 1. “Development set” patient characteristics
Age mean (range), yr Sex, M/F NYHA class LVEF, % LVIDd, mm Medications, % ␤-Antagonists ACE inhibitors Loop diuretics Aldosterone antagonists Digitoxin Statins
ICM (n ⫽ 12)
NIDCM (n ⫽ 12)
66.5⫾2.7 (53–77) 10/2 III/IV 24⫾1.7 70.2⫾2.6
59.1⫾2.6 (49–75) 11/1 III/IV 23.2⫾1.9 72.1⫾1.8
83 66 100 66 8 83
100 75 91 50 16 25
Values are means ⫾ SE for n subjects. ICM, ischemic cardiomyopathy; NIDCM, nonischemic dilated cardiomyopathy; NYHA, New York Heart Association; LVEF, left ventricular ejection fraction; LVIDd, left ventricular internal diastolic diameter; ACE, angiotensin-converting enzyme.
infarction and angiographically normal coronary arteries. LVEF and left ventricular internal diastolic diameter (LVIDd, mm) were calculated from two-dimensional and M-mode echocardiographic images. All patients were selected by the Cardiology Unit, Catholic University, Rome. Blood samples were also collected by the Laboratory of Analysis, Istituto Dermopatico dell’ImmacolataIRCCS, Rome, from age- and sex-matched control subjects [n ⫽ 12; age: 60.9 ⫾ 1.84 yr (mean ⫾ SE), sex: 9/3 (M/F)] with no overt cardiac disease and dyslipidemia. Furthermore, inflammatory indexes, such as C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR), were negative and no leukocytosis was observed. After Affymetrix analysis and validation of candidate marker (see Fig. 4A) were performed, a new set of NYHA III/IV patients (ICM n ⫽ 14; NIDCM n ⫽ 12) and normal donors (n ⫽ 17) were recruited applying the same selection criteria described above (Table 2). The total number of NYHA III/IV patients analyzed in the study was 50 (ICM ⫽ 26; NIDCM ⫽ 24). The study complied with the Declaration of Helsinki and was approved by the Ethics Committee of the Catholic University, Rome, and all subjects gave written informed consent. Microarray sample preparation and hybridization. PBMCs were obtained from 20 ml of heparinized blood by Histopaque Ficoll (Sigma Diagnostics, St. Louis, MO) gradient centrifugation (blood samples were collected from CHF patients and control subjects between 9:00 and 11:00 AM and immediately processed). Total RNA was obtained from PBMCs by TRIzol reagent (Invitrogen) and checked for integrity as previously described (9). Preparation of labeled cRNA and hybridization to GeneChip Human Genome U133A Array (Affymetrix) was performed according to the manufacturer’s instructions. Briefly, a total of 10 g of RNA was converted into cRNA and hybridized to each microarray. Samples (“development sets”) were constituted of two to five patients’ pooled mRNAs; control subjects (n ⫽ 12) were divided into C1, C2, and C3; ICM patients (n ⫽ 12) were divided into ICM1, 2, 3, and 4; and NIDCM patients (n ⫽ 12) were divided into NIDCM1, 2, 3, and 4 for a total of 11 samples. The strategy of pooling samples, successfully utilized by other groups (32, 40), was adopted to limit expenses. Prediction analysis of microarrays and significance analysis of microarrays. Data analysis was performed with “R”, an open-sourceinterpreted computer language for statistical computation and graphics, and tools from the Bioconductor project (16). The first approach was to test internal consistency and to explore the relationship among samples and underlying features of gene expression, applying an unsupervised two-way hierarchical clustering to check whether the individual samples clustered together according to their features as previously shown (14). For clusterPhysiol Genomics • VOL
ing, we used 3,916 probe sets of our data set, previously filtered by the interquartile range method (IQR), to remove genes with low intensity values or low variance across the samples. Prediction analysis of microarrays (PAM) and significance analysis of microarrays (SAM) analyses were applied to subsets from our sample data set to examine differential expression patterns among ICM, NIDCM, and control samples. SAM analysis was performed with the samr R statistical package. SAM software was applied to identify differentially expressed genes. SAM calculates a score for each gene on the basis of the change in expression relative to the standard deviation of all measurements by computed t-statistic and then performs a set of permutations to determine the false discovery rate (FDR), an adjustment method for multiple testing. Once the program reported the list of ranked genes, the “delta value,” which defines the threshold of false positive in the validated data set, was adjusted to a stringent false discovery rate (FDR ⬍ 1) (35). To identify functional classes of expressed genes, gene ontology (GO) analysis was performed on biological processes and molecular functions classes by FatiGO⫹ (http://babelomics.bioinfo.cipf.es/), a webbased program that provides a functional interpretation of the data. GO analysis was performed by increasing confidence level to achieve a wider functional class annotation. Raw microarray data described in this manuscript have been deposited in NCBI Gene Expression Omnibus (GEO) and are accessible through GEO series accession number GSE9128. Validation by real-time PCR. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to validate the prediction profile obtained by Affymetrix. Twenty-six additional CHF patients (n ⫽ 14 ICM, n ⫽ 12 NIDCM; Table 2) and 17 additional control subjects were recruited, with the same inclusion criteria described in Patient characteristics, and tested for gene expression. cDNAs were used as templates for Taqman qRT-PCR with ABI Assays-on Demand on an ABI Prism 7000 (Applied Biosystems, Foster City, CA) sequence detection system. Primers for EGR1, EGR2, EGR3, CX3CR1, and CCR2 were designed with Primer Express software version 2.0. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was amplified on the same plate for each sample for normalization purposes. For statistical analysis, see below. Western blot analysis. PBMCs were isolated from CHF patients and control subjects, washed in PBS, and counted. Similar cell numbers for each patient were lysed in Laemmli buffer, and ⬃3 ⫻ 105 cells/lane were resolved on 10% SDS-PAGE and transferred onto nitrocellulose membranes. Blots were then probed with rabbit anti-EGR1 (15F7) monoclonal antibody (1:1,000, New England Biolabs) and normalized with anti-tubulin antibody (1:3,000, Santa Cruz Biotechnology). Enhanced chemiluminescence (ECL; Amersham, Arlington Heights, IL) was used as the detection system.
Table 2. “Validation set” patient characteristics
Age mean (range), yr Sex, M/F NYHA class LVEF, % LVIDd, mm Medications, % ␤-Antagonist ACE inhibitors Loop diuretics Aldosterone antagonists Digitoxin Statins
ICM (n ⫽ 14)
NIDCM (n ⫽ 12)
65.9⫾2.0 (53–80) 12/2 III/IV 36.6⫾2.3 60⫾2.7
59.1⫾2.9 (52–80) 11/1 III/IV 23.2⫾2.1 72.1⫾2.3
92 57 78 42 0 92
100 83 66 41 25 66
Values are means ⫾ SE for n subjects. www.physiolgenomics.org
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Statistical analysis. Statistical analysis of age of control subjects and ICM and NIDCM patients was performed by ANOVA and showed a P value ⬎0.05. Statistical analysis applied to real-time PCR data was performed by comparing ⌬Ct values (cycle numbers at the threshold level of log-based fluorescence normalized to the GAPDH control gene) by Wilcoxon rank-sum test, with two-sided P ⬍ 0.05 indicating statistical significance (41). Mean differences in ⌬Ct (⌬⌬Ct) were used to calculate fold differences in gene expression by the following formula: fold ⫽ 2⫺⌬⌬Ct (41). RESULTS
Gene expression profiles in PBMCs of CHF patients vs. control subjects. Affymetrix screening was performed on CHF patient (Table 1) versus normal donor PBMCs. As shown in Fig. 1, a hierarchical cluster, derived from data analysis, well separated control subjects from CHF patients and, among the latter, ICM from NIDCM. PAM and SAM analyses identified 65 genes that defined a molecular signature of CHF patients versus control subjects (Table 3). From the integration of PAM and SAM analyses several cytokine/cytokine receptor genes were found to be upregulated; i.e., the chemokine receptors CCR1, CCR2, CX3CR1, IL-8, CXCL2, TNF family member 10, and TNF-␣-induced protein 3. Furthermore, among upregulated genes were identified early growth response (EGR) gene family members, i.e., EGR1, 2, and 3, important molecules in cardiovascular biology. In contrast, few genes, including vascular endothelial growth factor (VEGF) A and SMAD family member 7 (SMAD7), showed reduced expression in CHF patients versus control subjects.
Fig. 1. Hierarchical cluster dendrogram. Left: clusters of functionally related categories. Red, upregulated; blue, downregulated. Samples tested for gene profiling, nonischemic dilated cardiomyopathy (NIDCM)1– 4, ischemic cardiomyopathy (ICM)1– 4, and control (C)1–3, are shown at bottom. Physiol Genomics • VOL
PAM- and SAM-derived data were then extended, broadening the confidence levels, adjusting SAM delta value and PAM threshold still in a range of low FDR. Three hundred seventeen significant genes were selected to be subjected to GO analysis. GO allowed identification of up- or downregulated genes, in CHF patients versus control subjects, among several distinct functional classes including cellular metabolic processes, signal transduction, transcription, response to stress, immune response, and chemotaxis (Fig. 2). By means of bioinformatic analysis, i.e., by using the www. pubgene.org program, EGR1 expression was found to be linked, in terms of expression or function, to several other genes identified by the PAM analysis, i.e., ID1, JAK2, STAT6, EGR2, EGR3, PTPN1, JUN, and MYC, as well as to molecules associated with heart failure, such as endothelin (ET)-1, angiotensin (ANG) II, and adrenergic receptor ␤1 (ADRB1) in a gene network (Fig. 3). Interestingly, key molecules, such as JAK2 and MYC, were found to link EGR1 to CCR2 (Fig. 3). Validation of gene expression. Validation of the Affymetrix-derived CHF signature was then assessed on each patient or control subject (Fig. 4). At first, analysis was performed on the “development set” utilized for the Affymetrix screening, i.e., the 12 NIDCM, 12 ICM patients, and 12 control subjects (Fig. 4A). Validation, by qRT-PCR, was carried out on some upregulated genes that may play a role in cardiovascular diseases, such as CX3CR1, CCR2, EGR1, EGR2, and EGR3. The CX3CR1 gene was chosen because it was found in our study to be the most significantly upregulated gene in CHF patients versus control subjects and was also described to be upregulated in two previous studies, along with CCR2 (13, 40). Furthermore, EGR1 plays an important role in cardiovascular biology (22, 39), and EGR2 and EGR3 are controlled by VEGF expression. Results are shown as fold increase in mRNA expression of the tested genes versus control subjects, placed at 1. To strengthen our findings, validation was also carried out on an independent set of patients, i.e., the “validation set”, constituted of 12 NIDCM and 14 ICM patients in addition to 17 control subjects, and proved accurate (Fig. 4B). Figure 4C shows the results on the total number of patients, i.e., the “development set” plus the “validation set.” Fold change in mRNA expression (⫾SE) of ICM vs. NIDCM patients (total number) yielded no statistically significant differences for all genes tested but EGR1 (Fig. 4D). Furthermore, we assessed whether EGR1 was also upregulated at the protein level in CHF patients, and this was the case, as shown in Fig. 5. Similar to what was observed by qRT-PCR, EGR1 levels were increased in both NIDCM and ICM patients versus control subjects. Although, as expected, EGR1 was expressed more in ICM than NIDCM patients, the difference did not reach statistical significance, possibly because of the higher sensitivity of qRT-PCR compared with Western blot analysis or because of the small number of patients analyzed. Intersection of Affymetrix data from PBMCs and heart samples. We next compared data obtained from gene profiling, in CHF patients versus control subjects, derived from studies utilizing PBMCs (GEO accession number of the present study: GSE9128) and heart sources, in order to assess whether shared features could be found among different tissues. www.physiolgenomics.org
SIGNATURE OF HEART FAILURE
Table 3. PAM and SAM analysis of NIDCM and ICM classes versus control subjects PAM Score Symbol
SAM Score (multiclass)
CX3CR1 RORA JUN EGR3 CREM MOAP1 TNFSF10 TNFAIP3 EGR2 PPP1R16B PI4KII STIP1 CTSD RGS1 CCR2 ARRB1 ATP2B1 GIMAP4 NFKBIE LILRA2 CITED2 FCGR3B CD69 EGR1 SMAD7 ECGF1 PRKACB PICALM RGS2 LEF1 IFI16 PRKAR1A FAS PIK3R1 PTPN6 PPP1R2 PBEF1 LY9 PSME4 ACADVL ABI1 ARTS-1 VEGFA C3AR1 CD58 ITGA4 DDX3Y ARL4C TXNIP GAB2 EIF5 IL8 ITK TRAT1 ATP8A1 FPRL1 HSPA8 HDAC5 ITGA6 BCL2A1 ADD3 CXCL2 SYNE2 FCGR2C CCNG2
Chemokine (C-X3-C motif) receptor-1 RAR-related orphan receptor A jun oncogene Early growth response-3 cAMP responsive element modulator Modulator of apoptosis-1 Tumor necrosis factor (ligand) superfamily, member-10 Tumor necrosis factor, ␣-induced protein3 Early growth response 2 (Krox-20 homolog, Drosophila) Protein phosphatase 1, regulatory subunit 16B Phosphatidylinositol 4-kinase type II Stress-induced-phosphoprotein-1 cathepsin D Regulator of G-protein signaling-1 Chemokine (C-C motif) receptor-2 Arrestin, ␤-1 ATPase, Ca2⫹ transporting GTPase member-4 NFKB inhibitor, ε Leukocyte immunoglobulin-like receptor, subfamily A Cbp/p300-interacting transactivator Fc fragment receptor (CD16b) CD69 molecule Early growth response-1 SMAD family member-7 Endothelial cell growth factor-1 Protein kinase, cAMP-dependent Phosphatidylinositol binding clathrin assembly protein Regulator of G-protein signaling-2 Lymphoid enhancer-binding factor-1 Interferon, ␥-inducible protein-16 Protein kinase, cAMP-dependent Fas (TNF receptor superfamily, m6) Phosphoinositide-3-K, regulatory subunit-1 Protein tyrosine phosphatase Protein phosphatase-1 preB-cell colony enhancing factor-1 Lymphocyte antigen-9 Proteasome activator Acyl-coenzyme A dehydrogenase abl-interactor-1 Type-1 tumor necrosis factor receptor shedding regulator Vascular endothelial growth factor A Complement component 3a receptor-1 CD58 molecule Integrin, ␣4 DEAD box polypeptide 3, Y-linked ADP-ribosylation factor-like 4C Thioredoxin interacting protein GRB2-associated binding protein-2 Eukaryotic translation initiation factor-5 Interleukin-8 IL-2-inducible T-cell kinase T cell receptor associated adaptor-1 ATPase transporter Formyl peptide receptor-like 1 Heat shock 70-kDa protein-8 Histone deacetylase-5 Integrin, ␣-6 BCL2-related protein A1 Adducin-3 Chemokine (C-X-C motif) ligand 2 Spectrin repeat containing, nuclear envelope 2 Fc fragment of IgG, receptor for (CD32) Cyclin G2
⫺1.528 1.382 1.042 ⫺0.635 0.831 0.663 ⫺0.585 0.578 ⫺0.577 0.556 0.548 ⫺0.537 0 0.436 ⫺0.415 ⫺0.401 0 ⫺0.347 0.346 0 ⫺0.327 ⫺0.320 0.102 ⫺0.304 0.296 0 0 ⫺0.232 ⫺0.223 0 0 ⫺0.203 0 0 0 0.181 ⫺0.179 ⫺0.175 0.163 0 0 0 0.146 ⫺0.138 ⫺0.105 0 0 0 0 0.068 ⫺0.056 ⫺0.054 0 0 0 ⫺0.038 0 0 0 ⫺0.025 0 ⫺0.019 0 ⫺0.008 ⫺1e-04
0.211 0 0 0.852 0 0 0 0 0.302 0 ⫺0.220 0 ⫺0.119 0 0 0 0 0.059 ⫺0.308 0 0 0 0 0 0 ⫺0.292 0.283 0.207 0 0.215 0.216 0 0.214 0 0 0 0.014 0 0 ⫺0.160 0.151 0.148 0 0 0.131 0.122 0 0 0.089 0 0 0.048 0 0.043 0 0 0.038 ⫺0.029 0.026 0 0.023 0 0.008 0 0
0 ⫺0.426 0 0 0 ⫺0.244 0 0 0 0 0 0 0.503 0 0 0 ⫺0.352 0 0 0.328 0 0 ⫺0.319 0 0 0.279 0 0 0 ⫺0.220 0 0 0 ⫺0.196 0.191 ⫺0.095 0 0 0 0 0 0 0 0 0 0 ⫺0.115 ⫺0.113 0 0 0 0 ⫺0.050 0 ⫺0.041 0 0 0 0 0 0 0 0 0 0
4.001 4.438 4.484 3.033 2.829 3.183 2.371 2.458 2.367 2.783 3.320 2.973 3.627 2.154 2.117 3.117 2.372 2.201 3.144 3.152 2.134 1.764 2.299 1.252 1.962 2.446 1.810 2.133 2.308 2.696 1.700 1.345 2.132 0.937 2.470 2.544 2.088 2.172 2.298 3.229 2.296 1.983 1.989 1.795 2.706 1.586 1.945 2.189 1.631 2.297 2.051 1.507 2.032 1.577 1.853 1.593 1.502 1.916 1.791 1.743 1.518 1.474 1.728 1.635 1.419
Genes were selected by the intersection of prediction analysis of microarrays (PAM) and significance analysis of microarrays (SAM) analyses and ranked by statistic scores for each class. PAM scores indicate predictive values for each single gene among classes. SAM analysis assigns a score on the basis of change in gene expression relative to the SD of repeated measurements for that gene. SAM multiclass score is derived by the analysis of the 3 groups of subjects. C, control subjects. Physiol Genomics • VOL
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Fig. 2. Gene ontology. Functional categories of up- and downregulated genes in chronic heart failure (CHF) patients and control (C) subjects. APO, apoptosis; CYC, cell cycle; PRO, proliferation; REC, cell surface receptor; MET, cell metabolism; CTX, chemotaxis; IR, immune response; STR, stress response; SIG, signal transduction; TRA, transcription.
Two different microarray studies, deposited in the GEO database (www.ncbi.nlm.nih.gov/geo) and accessible through GEO series accession numbers GSE1869 and GSE3585, were selected as they assessed gene profiling using the Affymetrix array platform HGU133A. The GSE1869 series was part of a study performed on heart biopsies of end-stage cardiomyopathy (ICM and NIDCM) versus nonfailing hearts (24), while the GSE3585 series was performed on septal heart biopsies of dilated cardiomyopathy (DCM) versus nonfailing donor hearts (4). Each data set was analyzed with the same R script program (16). The significant genes from SAM analysis of all data sets were intersected with the present study. CCR2 and RAR-related orphan receptor A (RORA) were present in the GSE1869 series and in our study, while SMAD7 and GRB2associated binding protein 2 (GAB2) were identified by comparing the GSE3585 series and the present study. SMAD7, GAB2, and RORA were similarly regulated, i.e., downregulated in both CHF hearts and PBMCs versus control, while CCR2 was upregulated in our study, using
PBMCs, in CHF patients versus control subjects, and downregulated in the GSE1869 series. Furthermore, we performed GO analyses on GSE1869 and GSE3585 series, with the same parameters utilized in our studies, and intersected each of them with the present study. The analysis revealed shared functional terms, present in all studies, including apoptosis, cell cycle, proliferation, cell surface receptor, cell metabolism, immune response, stress response, signal transduction, and transcription. The chemotaxis functional term was common only between GSE1869 and our study. DISCUSSION
The present work represents the first large-scale study, of 18,400 genes, that analyzed gene expression profiles of CHF patient PBMCs versus those of healthy control subjects, and that separately analyzed ICM versus NIDCM patients. Microarrays represent important tools to identify biomarkers of disease (8, 37). Biomarkers can be constituted by single
Fig. 3. Network of coexpressed genes in CHF patients vs. control subjects. A selected gene network, based on Affymetrix data and generated with www.pubgene.org, is shown. Lines link identified network of genes (black) whose expression or function is affected by neighboring genes. Gray genes represent key molecules modulated in heart failure: brain natriuretic peptide (BNP), endothelin-1 (ET-1), adrenergic ␤1 receptor (ADRB1), angiotensin II (ANG II); their interconnection to the genes identified in the present study is shown.
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Fig. 4. Validation of gene expression by quantitative real-time polymerase chain reaction (qRT-PCR). A–C: fold change in mRNA expression (⫾SE) of ICM and NIDCM patients vs. control subjects. A: New York Health Association (NYHA) III/IV CHF patients, i.e., “development set” (12 NIDCM and 12 ICM patients vs. 12 control subjects) on which the Affymetrix screening was performed. B: independent set of newly analyzed NYHA III/IV CHF patients, i.e., “validation set” (12 NIDCM and 14 ICM vs. 17 control subjects). C: total number, i.e., “development set” ⫹ “validation set,” of NYHA III/IV patients. D: fold change in mRNA expression (⫾SE) of NYHA III/IV ICM vs. NIDCM patients (total number). *P ⬍ 0.05 vs. control subjects; †P ⬍ 0.05, NYHA III/IV ICM vs. NIDCM patients.
genes and/or by gene expression patterns, or clusters of genes, the latter often consistent among studies even if conducted on a small scale (26). Although the Affymetrix data obtained in the present study used a limited patient population, data validation was successful not only on the “development set” but also on the “validation set” of patients. The vast majority of modulated genes in CHF patients versus control subjects belong to the category of genes involved in cellular metabolism, in keeping with the energy metabolism changes present in heart failure (29). In fact, a hallmark of heart failure is the alteration in cardiac energy metabolism as a consequence of impaired substrate utilization, oxidative phosphorylation, and/or ATP transfer/ utilization. Our data suggest that CHF is associated with changes in energy metabolism and mitochondrial function not only in the failing myocardium but also in circulating mononuclear cells. Furthermore, 20.4% of “immune response” genes were upregulated, many of which are involved in chemotaxis, such as CCR1, CCR2, CCR5, and CX3CR1, while 2.7% were downregulated. Furthermore, Physiol Genomics • VOL
several genes fell into the categories of signal transduction and transcription. A number of genes, among those found to be modulated in our study, are known to be associated with and/or play a role in heart failure. Example are represented by RXRA, the IL-6 signal transducer gp130, calpain7, calmodulin, VEGFA, and many members of the ␤-adrenergic receptor signaling pathways (15, 30, 38). Interestingly, PAM analysis identified members belonging to the EGR gene family, which showed, for the first time, a strong association to CHF patients versus control subjects. EGR1 was previously shown to be an important player in cardiovascular biology because it promotes atherogenesis, induces intimal thickening after acute vascular injury, is upregulated by hypoxia, and is involved in ischemia-reperfusion damage (22, 39). Interestingly, EGR1, EGR2, and EGR3 were upregulated in both ICM and NIDCM patient PBMCs versus those of control subjects, and because they are master regulators they are likely to coordinate target gene expression to generate responses/amplify myocardial damage. www.physiolgenomics.org
SIGNATURE OF HEART FAILURE
Fig. 5. Validation of early growth response (EGR)1 expression by Western blot (WB) analysis. EGR1 protein levels of CHF patients and control subjects were analyzed by WB analysis. A: densitometric analysis shows average optical density (OD) of EGR1 protein expression by peripheral blood mononuclear cells on an independent set of ICM (n ⫽ 5) and NIDCM (n ⫽ 3) patients and control subjects (n ⫽ 3), as assessed by WB. Tubulin was utilized as an internal normalization control. *P ⬍ 0.05. B: Representative WB of control subjects and ICM and NIDCM patients. Since in the original blot the lanes shown were not adjacent, a separation vertical line was inserted to indicate removal of unnecessary lanes.
EGR1 regulates ID1 expression, which is in turn regulated by SMAD7, the latter targeted by SMURF1 and SIAH1. All these genes are modulated in CHF patients versus control subjects, thus suggesting that this pathway is associated to CHF. Furthermore, ET-1, ANG II, and ADRB1, whose levels/ activities are modulated in CHF, regulate or are regulated by EGR1 (3, 7, 25) (Fig. 3). We therefore hypothesize that EGR1 is an important master gene that is part of a gene network altered in CHF. To the best of our knowledge there are only two other published studies on PBMC gene expression profiles in CHF, i.e., 1) the study by Yndestad et al. (40), a 375-cytokine/ cytokine receptor gene cDNA array screening of CHF NYHA II/III patient PBMCs from pooled coronary artery disease (CAD)/idiopathic cardiomyopathy patients, and 2) the study by Damas et al. (13) on NYHA II/III CHF patients that analyzed, by RNase protection assay (RPA) 21 chemokine/chemokine receptor expression levels. Comparison of these studies with the present Affymetrix array screening on 18,400 genes, which represents the first large-scale gene expression analysis, further supports a role of CX3CR1 and CCR2 chemokine receptors as CHF biomarkers. Another study has been recently published on cell surface chemokine receptor expression, by FACS analysis, in human heart failure, but it was performed on discrete leukocyte subpopulations (1). In the present work CX3CR1 was the most significantly upregulated gene in the two groups of CHF patient versus control PBMCs. Increased fractalkine (FKN), the CX3CR1 Physiol Genomics • VOL
receptor ligand, levels are found in several conditions including atherosclerotic plaques and vascular injury as FKN expressed on ECs may be recognized by CX3CR1-expressing cytotoxic NK and CD8⫹ T cells, leading to damage. Furthermore, CX3CR1 is an important player in cardiovascular diseases (36), as shown by the lower prevalence of atherosclerosis and acute coronary syndrome in patients bearing the CX3CR1M280 mutation and in lower susceptibility to atherosclerosis in CX3CR1-deficient mice (11). Finally, a threefold increase of FKN in human failing hearts and in plasma of CHF patients has been recently reported (21), possibly constituting a recruiting signal of CX3CR1⫹ PBMCs to the damaged heart. Our study suggests that the FKN/CX3CR1 axis may play a role in cardiovascular diseases also independently from atherosclerosis in CHF. In fact, the marked CX3CR1 upregulation on CHF PBMCs was similar in both ICM and NIDCM, the latter with no angiographic evidence of coronary atherosclerotic plaques. The monocyte chemoattractant protein-1 (MCP-1)/CCR2 axis has been implicated in the initiation of atherosclerosis (10) and is thought to be crucial for monocyte activation/ recruitment underlying the pathogenesis of cardiovascular diseases, including heart failure. In fact, MCP-1 overexpression in the heart generates vasculopathy followed by ischemia and heart failure, and impairment of the MCP-1/CCR2 axis diminishes postischemic myocardial remodeling and heart failure (19). It is known that MCP-1 is highly expressed in chronic failing myocardium and in serum in human end-stage heart failure (2) and that it is upregulated in a rat model of volume-overload congestive heart failure in the myocardium (5). Therefore it is possible that CHF PBMCs are induced to migrate toward the failing myocardium, in which MCP-1 levels are known to be increased, thus contributing to inflammation-induced cardiac remodeling and heart failure. CCR2, CX3CR1, EGR1, EGR2, and EGR3 could be regarded as biomarkers of CHF, their expression being merely associated with the disease or having a pathogenetic role. Further studies are needed to experimentally address such issues. Interestingly, all validated genes were upregulated in both ICM and NIDCM versus control subjects (Fig. 4, A–C), thus indicating that although the two forms of cardiomyopathies leading to CHF have quite distinct pathogenetic origins, several shared molecular pathways are triggered in both ICM and NIDCM. Although CCR2, CX3CR1, and EGR1 have a role in atherogenesis, in CHF patients they may be regarded as markers of heart failure, being upregulated in both ICM and NIDCM (Fig. 4D). Among the tested genes, only the expression levels of EGR1 were significantly different between the two classes of cardiomyopathies (Fig. 4D), possibly because of a contribution of the etiology to EGR1 dysregulation. In conclusion, the identified gene expression profiles that differentiated healthy control subjects from CHF patients may represent a novel tool contributing to diagnosis of CHF and of different types of cardiomyopathies. GRANTS This work was supported by grants from the Ministry of Health to M. C. Capogrossi and to M. Napolitano and by a fellowship from Istituto “Giuseppe Toniolo” to C. Cappuzzello. www.physiolgenomics.org
SIGNATURE OF HEART FAILURE
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