Identification of inflammatory gene modules basedon variations of human endothelial cell responsesto oxidized lipidsPeter S. Gargalovic*†‡§, Minori Imura*†‡, Bin Zhang‡¶, Nima M. Gharavi�, Michael J. Clark*†‡, Joanne Pagnon*†‡,Wen-Pin Yang**, Aiqing He**, Amy Truong**, Shilpa Patel‡, Stanley F. Nelson‡, Steve Horvath‡¶,Judith A. Berliner�, Todd G. Kirchgessner**, and Aldons J. Lusis*†‡§

Departments of *Medicine, †Microbiology, Immunology, and Molecular Genetics, ‡Human Genetics, ¶Biostatistics, and �Pathology, Universityof California, Los Angeles, CA 90095; and **Bristol–Myers Squibb Pharmaceutical Research Institute, P.O. Box 5400, Princeton, NJ 08543

Communicated by Jan L. Breslow, The Rockefeller University, New York, NY, June 29, 2006 (received for review June 1, 2006)

Oxidized phospholipids are thought to promote atherogenesis bystimulating endothelial cells (ECs) to produce inflammatory cyto-kines, such as IL-8. In studies with mouse models, we previouslydemonstrated that genetic variation in inflammatory responses ofendothelial cells to oxidized lipids contributes importantly toatherosclerosis susceptibility. We now show that similar variationsoccur in cultured aortic ECs derived from multiple heart transplantdonors. These variations were stably maintained between pas-sages and, thus, reflect either genetic or epigenetic regulatorydifferences. Expression array analysis of aortic EC cultures derivedfrom 12 individuals revealed that >1,000 genes were regulated byoxidized phospholipids. We have used the observed variations inthe sampled population to construct a gene coexpression networkcomprised of 15 modules of highly connected genes. We show thatseveral identified modules are significantly enriched in genes forknown pathways and confirm a module enriched for unfoldedprotein response (UPR) genes using siRNA and the UPR inducertunicamycin. On the basis of the constructed network, we pre-dicted that a gene of unknown function (MGC4504) present in theUPR module is a target for UPR transcriptional activator ATF4. Ourdata also indicate that IL-8 is present in the UPR module and isregulated, in part, by the UPR. We validate these by using siRNA.In conclusion, we show that interindividual variability can be usedto group genes into pathways and predict gene–gene regulatoryrelationships, thus identifying targets potentially involved in sus-ceptibility to common diseases such as atherosclerosis.

genetic � interleukin 8 � atherosclerosis � unfolded proteinresponse � network

A therosclerosis, the major cause of heart disease, is character-ized by the accumulation of cholesterol, inflammatory cells,

smooth muscle cells, and fibrous elements beneath the endothelialcell (EC) monolayer that lines the artery wall (1). Althoughnumerous risk factors for atherosclerosis, such as elevated bloodpressure, hypercholesterolemia, and smoking, have been recog-nized, these factors do not alone account for the genetic contribu-tion to risk (2). An important mechanism contributing to therecruitment of inflammatory cells in atherosclerosis is the inductionof adhesion molecules, growth factors, and cytokines in vascularECs by oxidized phospholipids, such as oxidized 1-palmitoyl-2-arachidonyl-sn-3-glycero-phosphorylcholine (oxPAPC) derivedfrom lipoproteins trapped in the vessel wall (3).

We have previously demonstrated that ECs from different strainsof mice show differences in the induction of inflammatory geneswhen treated with oxidized lipoproteins, and that these differencessegregate with susceptibility to atherosclerosis (4, 5). Studies inhuman populations show significant variability in the plasma levelsof inflammatory mediators associated with atherosclerosis, includ-ing IL-8 and C-reactive protein (6–8). The plasma levels of cyto-kines are influenced by genetic and environmental factors. In thisstudy, we examined human aortic EC cultures derived from mul-

tiple heart transplant donors and showed the existence of strikingdifferences in the level of inflammatory gene induction by oxPAPC.These differences were maintained between individual passages ofthe ECs and thus are likely to represent either genetic or epigeneticregulatory variations.

The mechanisms underlying the inflammatory effects of ox-PAPC are not well understood, and here we describe a uniqueapproach for understanding the global pathways involved. Theapproach is based on the fact that a natural population will exhibitmultiple genetic or epigenetic variations that perturb the expressionof many individual genes, as well as entire pathways. These varia-tions can be quantitated by expression array profiling and analyzedby using systems biology approaches, which provide a way to bridgethe gap between individual genes and complex biological systems(9–11). Here, we take advantage of the naturally occurring vari-ability in human EC responses to oxidized phospholipids and show,using a gene coexpression network approach, that the unfoldedprotein response (UPR) pathway is one of the key regulators of IL-8production.

ResultsVariation in Inflammatory Responses of Primary Human EndothelialCells to Oxidized Phospholipids. Based on our previous studies inmouse ECs (4, 5), we hypothesized that the inflammatory responsesof human ECs to oxidized lipids may also differ among individualsin the population. We tested this possibility by performing an initialscreen of IL-8 secretion in response to oxPAPC treatment in earlypassages of human aortic ECs (HAECs) derived from 23 separateheart transplant donors. As shown in Fig. 1A, we observed signif-icant differences in the amount of IL-8 protein secreted into mediaafter oxPAPC treatment. These differences between EC cultureswere quite stable and preserved during multiple cell passages (Fig.1B). We conclude that EC cultures derived from different individ-uals in the population exhibit either common genetic or epigeneticvariations that perturb their responses to oxidized lipids.

Identification of Genes Regulated by oxPAPC. The observed differ-ences in IL-8 secretion among individual cell lines indicated that wecould expect similar variability at the level of gene transcription.Thus, we reasoned that we would be able to identify groups offunctionally related, coexpressed genes (modules) that are respon-sive to oxPAPC by profiling endothelial cells derived from unre-lated individuals in the population and testing for coexpression.

Conflict of interest statement: W.-P.Y., A.H., A.T., and T.G.K. are employees of Bristol–MyersSquibb Pharmaceutical Research Institute.

Abbreviations: EC, endothelial cell; oxPAPC, oxidized 1-palmitoyl-2-arachidonyl-sn-3-glycero-phosphorylcholine; UPR, unfolded protein response; HAEC, human aortic EC;SREBP, sterol regulatory element-binding protein; Q-PCR, quantitative PCR.

§To whom correspondence may be addressed. E-mail: pgargalo@ucla.edu or jlusis@mednet.ucla.edu.

© 2006 by The National Academy of Sciences of the USA

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Therefore, we profiled global mRNA expression levels in HAECcultures derived from 12 individual donors, where each culture wastreated with either unoxidized PAPC (40 �g�ml) as control or withoxPAPC (40 �g�ml) for 4 h. Each condition was profiled induplicate, so that altogether 48 arrays were examined. Varyinglevels of secreted IL-8 protein among the 12 donors correlatedsignificantly with the respective IL-8 mRNA levels (r � 0.824, P �0.001), supporting the use of expression array data as a predictivemeasure of the inflammatory response to oxPAPC (Fig. 5, which ispublished as supporting information on the PNAS web site).

We restricted the analysis to genes differentially expressed be-tween the control group and the group treated with oxPAPC. Theselection criteria were as follows: (i) fold change of group means�1.5 or �0.667; (ii) t test P value �0.01; (iii) absolute difference ofgroup means �500; and (iv) ‘‘QC’’ called present in at least one ofthe compared groups. For network analysis purposes, each indi-vidual array probe set was treated as a unique gene. This methodyielded 1,043 differentially expressed genes (probe sets) with a

corresponding mean false discovery rate (FDR) of 0.25% (seeMaterials and Methods). Some of these genes, including IL-8,HMOX1 and members of the sterol regulatory element-bindingprotein (SREBP) pathway, have previously been shown to beregulated by oxPAPC, but the vast majority represented noveloxPAPC targets (Table 1, which is published as supporting infor-mation on the PNAS web site; refs. 12 and 13).

Gene Coexpression Network of oxPAPC-Responsive Genes. A genecoexpression network was constructed according to a recentlydescribed methodology (14). The connectivity (k) was determinedfor all network genes by taking the sum of their connectionstrengths (coexpression similarity) with all other genes in thenetwork. To identify modules of highly coregulated genes, we usedaverage linkage hierarchical clustering to group genes based on thetopological overlap of their connectivity (see Materials and Methodsfor details).

The resulting gene coexpression network was composed of 1043oxPAPC-regulated genes separated into 15 modules of highlycorrelated genes. Each module was assigned a unique color iden-tifier (Fig. 2A), with the remaining, poorly connected genes coloredgray. In a topological overlap matrix (TOM) plot, the increasingcolor intensity indicates higher connectivity (coexpression similar-ity) among genes in the network (Fig. 2A). Genes with the greatestconnectivity index (k) represent network ‘‘hubs’’ and are localizedin the center of individual modules. Because the network iscomprised of differentially expressed genes, the control group andthe group treated with oxPAPC were distinguished by their geneexpression profiles. Overall, the most highly induced�suppressedgenes have the largest number of connections to other genes (Fig.6, which is published as supporting information on the PNASweb site).

Genetic or Epigenetic Variations Perturb Multiple Genes and PathwaysInvolved in Responses to Oxidized Phospholipids. Because of theindicated importance of hubs in the network (15), we ranked geneswithin each of the 15 modules based on their intramodular con-nectivity (k.in) to identify module hubs (Table 1). Interestingly,INSIG1, a key regulator of the SREBP pathway activation (16), hadthe highest connectivity in the brown module and represented themain hub. The brown module was also the most highly enriched inSREBP-regulated genes (Fisher’s exact test, P � 10�9; Table 2,which is published as supporting information on the PNAS website). Altogether, eight of the 26 total genes in the brown modulewere SREBP targets (17).

Next, we focused on examining modules containing the IL-8gene. There are two probes on the array chip measuring IL-8, bothof which were present within the blue module. Examination of theblue module revealed the presence of a large number of molecularchaperones (DNAJA1, DNAJA4, DNAJB1, DNAJB6, andDNAJB9) and heat shock proteins (HSPA1A, HSPA1B, andHSPH1), many of which are known to be induced by ER stress aspart of the UPR pathway (Table 1) (18). The blue module alsocontained several other UPR targets including DDIT3, ATF3, andHERPUD1 and ranked first in terms of enrichment for UPR targetgenes (P � 10�12).

Additional searches for UPR genes revealed that the red modulecontained several additional genes known to be regulated by theUPR (ATF4, XBP1, CEBPB, SLC7A5, and STC2) (19). Althoughonly moderately enriched in UPR genes, four of the top 10 mostconnected genes (hubs) in the red module were UPR members,including two key UPR transcription factors ATF4 and XBP1(Tables 1 and 3, which are published as supporting information onthe PNAS web site) (20).

Induction of UPR-target genes was a strong indication thatoxPAPC induces the UPR pathway in HAECs. The UPR has threebranches, each acting via separate transcription factor representedby ATF4, ATF6, and XBP1 (20). The blue module contained large

Fig. 1. Primary human aortic endothelial cells isolated from different donorsexhibit differences in IL-8 induction in response to oxPAPC treatment. (A) HAEClines derived from human individuals were examined for IL-8 induction in re-sponse to oxPAPC or control PAPC by ELISA. Individual HAEC lines from 23 donorswere treated with oxPAPC (40 �g�ml) or PAPC (40 �g�ml) for 4 h, media werecollected, and IL-8 levels were analyzed by ELISA. Absolute levels of secreted IL-8protein (picograms per milliliter) after the treatment were plotted for eachindividual HAEC line. HAEC donors were numbered according to the level of IL-8induction in response to oxPAPC. (B) Eleven individual HAEC cell lines wereexamined for differences in IL-8 protein secretion between successive cell pas-sages. HAECs were treated with oxPAPC (40 �g�ml) or PAPC (40 �g�ml) for 4 h,and IL-8 protein was measured by ELISA (first passage). The same analysis wasperformed after freezing and replating cells to obtain the subsequent cell pas-sage (second passage).

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number of ER chaperones, which are known to be regulatedprimarily by XBP1, whereas many of the red module UPR genes(SLC7A5, ATF4, and STC2) are known targets of ATF4 (18, 19,21). This finding suggests that differences in transcriptional regu-latory mechanisms may underlie separation of genes into modules.This was visualized by further clustering analysis (Fig. 2B and Table4, which is published as supporting information on the PNAS website), which showed that the blue and red UPR subgroups of geneswere very similar to each other but strikingly different from theSREBP genes present in the brown module.

The other modules also contained genes that could be function-ally related, although many of the identified transcripts have notbeen functionally characterized. For example, the yellow modulecontained HMOX1 and other genes involved in the response tooxidative stress such as GCLM and GCLC (Table 1) (13).

In an effort to further understand the functional significance ofthe network modules, we carried out a gene-enrichment analysiswith respect to GO ontology and KEGG pathway databases. Thisproved to be problematic, given that �25% of the network geneswere annotated in the ontology databases and only 10% werepresent in the KEGG pathway database. Nevertheless, the bluemodule was enriched for genes with heat shock protein andchaperone activity, and the brown module for genes involved insterol biosynthesis (Table 5, which is published as supportinginformation on the PNAS web site).

Network Application: The UPR Pathway Contributes to Expression ofthe Inflammatory Cytokine IL-8 and the Gene MGC4504. As illustratedabove, the constructed network contains modules with genesfunctionally linked on the basis of their mRNA regulation. This

approach can therefore be applied to identification of biologicallyimportant regulatory mechanisms, in this case the oxPAPC-mediated IL-8 induction. Alternatively, it can help in the functionalannotation of uncharacterized genes. To address this, we focused onIL-8 (blue module) and the MGC4504, a gene of unknown functionin the red module. MGC4504 was chosen based on its highestconnectivity value among unannotated genes in the red (UPR)module (Table 1). To examine whether IL-8 and MGC4504 ex-pression is regulated by the UPR, we incubated HAECs with twowell established inducers of ER stress and UPR, tunicamycin andDTT (22). Treatment with either tunicamycin or DTT resulted ininduction of IL-8 (5- and 11-fold, respectively) and MGC4504 (200-and 100-fold, respectively). UPR genes ATF3, ATF4, and XBP1were also induced, with only marginal changes observed in non-UPR genes, INSIG1 (brown module), and HMOX1 (yellow mod-ule) (Fig. 3A).

Next, we searched the promoters (the 2-kb 5� flanking region) ofall of the network genes for regulatory elements known to conferresponsiveness to the UPR (18, 23). Sixty-nine of the 746 networkgenes with unique promoters contained at least one of the foursearched UPR-binding sites. These included many known UPRgenes (Table 6, which is published as supporting information on thePNAS web site). This analysis revealed that the MGC4504 pro-moter contains a highly conserved (human, mouse, rat) C�EBP-ATF composite element 5�-TTGCATCA-3� at �323 to �315 bp,which is known to bind ATF4 and lead to induction of UPR genesDDIT3 and HERPUD1 (23). None of the searched sites werepresent in the proximal promoter of IL-8.

To examine whether ATF4 expression can lead to IL-8 andMGC4504 induction, we transfected HeLa cells with a plasmid

Fig. 2. Gene coexpression network of the 1,043 genes regulated by oxPAPC. (A) Pearson correlations between expression profiles of all pairs of genes weretransformed into network connection strengths (denoted by intensity of red color) by using a power function (see Materials and Methods). Average linkagehierarchical clustering with the topological overlap dissimilarity measure was used to identify gene coexpression modules, each of which was assigned a uniquecolor. Rows and columns are symmetric and represent genes. (B) A color-coded version of the correlation matrix (Table 4) involving the SREBP genes from thebrown module and the UPR genes from blue and red modules. The intensity of red color represents the absolute value of Pearson correlation coefficients. Therows and columns have been sorted by the gene clustering tree. Note that there are two broad clusters corresponding to SREBP and UPR genes.

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constitutively expressing ATF4. HeLa cells were used due to theextremely low plasmid transfectability of primary human EC. Thesecells were previously shown to respond to oxPAPC treatment muchlike primary endothelial cells, up-regulating IL-8, activatingSREBP (12), and inducing the UPR (P.S.G., unpublished data).Expression of ATF4 resulted in a significant induction of IL-8 andMGC4504 mRNA (9- and 47-fold, respectively) (Fig. 3B). Nosignificant changes were observed in expression of INSIG1 andHMOX1.

To determine whether ATF4 is required for MGC4504 and IL-8induction by oxPAPC, we used an siRNA approach (see Materialsand Methods). Oligo-based siRNA targeting effectively reduced theendogenous ATF4 mRNA (control, 78%; oxPAPC, 87%; tunica-mycin, 91%) and protein levels in primary HAECs (Fig. 4A). ATF4down-regulation resulted in a significant decrease in IL-8 mRNAlevels (control, 74%; oxPAPC, 68%; tunicamycin, 72%) and se-creted protein levels (Fig. 4B and Fig. 7, which is published assupporting information on the PNAS web site). Inhibition of ATF4had a striking impact on MGC4504 expression, which decreased onaverage by �90% relative to control siRNA (Fig. 4C). The siRNA

targeting was selective, without significant decrease in INSIG1 andHMOX1 in any of the treatment conditions (Fig. 7). Altogether,these data confirm the network-based predictions and demonstratethat ATF4 is a necessary mediator of MGC4504 expression inHAECs. These data also indicate that ATF4 is a significantregulator of IL-8 expression.

DiscussionAortic endothelial cells are a crucial source of inflammatorycytokines contributing to atherosclerosis. We previously showed

Fig. 3. UPR regulates expression of IL-8 and MGC4504. (A) Effect of UPRinducers on gene expression. HAECs were treated for 4 h in medium alone(control), medium containing oxPAPC (50 �g�ml), or UPR-inducing agentstunicamycin (10 �g�ml) or DTT (1 mM). Expression levels of IL-8 (blue module),MGC4504 (red module), INSIG1 (brown module), HMOX1 (yellow module),and UPR genes from blue and red module (ATF3, ATF4, and XBP1) weremeasured by quantitative PCR (Q-PCR) as described in Materials and Methods.Q-PCR results are expressed as the mean differences for each treatment groupand control group (set at 100%) � 1 SD. (B) Effect of ATF4 overexpression onIL-8 and MGC4504. HeLa cells were transfected with an expression plasmid forhuman ATF4 (pATF4) or a control plasmid (empty) as described in Materialsand Methods. Forty-eight hours after transfection, expression levels of IL-8,MGC4504, INSIG1, and HMOX1 mRNA were analyzed by Q-PCR. ATF4 proteinlevels in isolated nuclear extracts were measured by immunoblotting. HistoneH3 (H3) antibody was used as a protein-loading control. Asterisks indicatesignificantly different mean expression value of pATF4 group from controlgroup (P � 0.005).

Fig. 4. Effect of the ATF4 siRNA on IL-8 and MGC4504 expression. HAECs weretransfected with siRNA directed against human ATF4 or with control scrambledoligonucleotide (see Materials and Methods). Transfected cells were treated for4 h with medium only (control), oxPAPC (50 �g�ml), or tunicamycin (10 �g�ml).mRNA expression levels of ATF4 (A), IL-8 (B), and MGC4504 (C) were measured byQ-PCR. The levels of ATF4 protein were measured in isolated nuclear extracts byimmunoblotting (A). Q-PCR data were set at 100% for untreated cells (control)incubatedwithscrambledsiRNA.Percentagevaluesabovebars indicatethemeanexpression decrease in the ATF4 siRNA group versus control siRNA group for eachtreatment � 1 SD.

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that certain inbred strains of mice differing in susceptibility toatherosclerosis exhibit striking differences in the response of aorticECs to oxidized lipoproteins and that, in genetic crosses, thesusceptibility to atherosclerosis segregated with the responsivenessof ECs to oxidized lipoproteins (4, 5). In the present study weexamined the responses of human aortic EC cultures isolated fromdifferent heart transplant donors to oxPAPC. Striking differenceswere observed in the expression of many genes important inatherosclerosis, including IL-8 cytokine (Fig. 1A). These differ-ences are unlikely to be caused by experimental variability orvariation in culturing conditions, as indicated by high preservationof inflammatory responsiveness among successive cell passages(Fig. 1B). Although we hypothesize that the genetic makeup of eachindividual contributes to observed variation in IL-8 expression, wecannot exclude possible role of epigenetic changes taking placebefore and after the collection of cell explants. Epigenetic changesare caused by heritable changes in gene expression that are inde-pendent of nucleotide sequence. They include methylation of DNA,biochemical modifications of core histones and modulation of thehigher-order chromatin structure (24). For most of the cytokinesstudied, plasma level heritability is �50% with environmentalfactors likely playing a significant role (25).

Expression array analyses of a subset of the EC cultures revealedregulation of surprisingly large number of genes (�1,000) byoxPAPC. These changes were unlikely due to toxic effects ofoxPAPC, because oxPAPC at concentrations up to 50 �g�ml wasnot toxic to ECs and had no effect on caspase-3 activity (Fig. 8,which is published as supporting information on the PNASweb site).

We hypothesized that we could identify groups of genes that arebiologically related with respect to regulatory mechanisms. This wasbased on the assumption that variation in the expression level of akey transcription factor(s) or the degree of receptor activationwould perturb a whole series of downstream target genes. Ourapproach explores either genetic or epigenetic variability to focuson mechanisms underlying clinically highly relevant interactions ofoxidized phospholipids and HAECs. Our results demonstrate thatthis approach can be used to subdivide genes into pathways on thebasis of the regulation of their expression. For example, genesinvolved in the SREBP pathway were highly induced in some ECcultures but not others, whereas the UPR pathway genes alsoexhibited very similar expression profiles among the EC cultures butthe overall responses were quite distinct from genes of the SREBPpathway (Fig. 2B). Thus, there appeared to be multiple genetic orepigenetic variations that are responsible for independent regula-tion of the individual pathways in response to oxPAPC.

In addition to segregating genes into pathways, we showed thatthe intramodular network connectivity is a very useful measure foridentifying biologically important genes within pathways. A case inpoint is the red and brown modules, where hub genes with highconnectivity value included some of the key regulators of the UPR(ATF4 and XBP1) and SREBP (INSIG1) pathways.

One of the exciting features of gene networks is their ability toidentify novel target genes. Using MGC4504 as an example, weshowed that this previously uncharacterized gene represents aUPR-target, induction of which requires ATF4. MGC4504 andATF4 were both among the top six hub genes in the red (UPR)module. To illustrate the advantage of the network approach, weused simple correlation and analyzed all network genes on the basisof their correlation with ATF4 in oxPAPC-treated cells. MGC4504ranked as 99th most correlated gene with ATF4. Therefore, usingsimple correlation, this gene would likely have been missed. Basedon its protein sequence, MGC4504 appears to be a highly conservedhuman homologue of a putative protein mediating cation transportin bacteria, which could play an important role in ER homeostasis.

The power of this network approach is further highlighted by thefact that we used a relatively small sample of individuals. Eventhough successful in predicting gene–gene regulatory relationships,

our study using heart transplant donor ECs does not allow for followup comparison of the cell culture results with plasma cytokine levelsof individuals. However, similar network approaches could beapplied to large human populations, using easily obtainable cellssuch as blood monocytes, and further combined with SNP analysis.This approach would permit comparison of the cell culture and invivo data as well as examination of any genetic variations controllingthe expression of genes contributing to atherosclerosis and othercomplex traits.

A particularly important finding of the present study is thatHAEC exposure to oxidized phospholipids results in the inductionof the UPR, which directly participates in modulating the inflam-matory response. Interestingly, homocysteine, a common risk factorfor cardiovascular disease, is also known to induce UPR in ECs (26)as well as the synthesis of MCP1 and IL-8 (27). Our study indicatesthat UPR, and more specifically, ATF4, is an important mediatorof IL-8 expression in HAECs. Previous studies have shown thatinduction of IL-8 by oxPAPC is complex and likely mediated bymultiple pathways, including c-src and SREBP (12, 28). Ourfindings are consistent with this, and we show that UPR appears tobe required, but its induction by tunicamycin is not sufficient, tomaximally induce IL-8 expression levels in HAEC. This finding isin contrast to the MGC4504 gene, which behaved as a soleUPR-target. Tunicamycin interferes with protein processing andsecretion in the ER, which is likely the reason for the observeddecrease in IL-8 protein secreted into medium after tunicamycintreatment, despite increased IL-8 mRNA levels.

Aside from the SREBP and UPR, many as yet unknown path-ways are likely to be mediating endothelial responses to oxPAPC.KEGG and GO enrichment analysis of the network was relativelyuninformative, because only a limited number of genes on the arraywere present in these databases. Future studies using targeteddisruption of hub genes, particularly those coding for transcriptionfactors, may provide greater insight into underlying gene–generegulatory relationships.

Materials and MethodsReagents and Antibodies. OxPAPC was prepared and analyzed bymass spectrometry to confirm the lipid profile described (29). Allreagents were determined to have �2 pg�ml LPS as determined bya kit from MA Biowhittaker (Walkersville, MD; catalog no.5064U). Antibodies used were from the following sources: ATF4(catalog no. sc-200; Santa Cruz Biotechnology, Santa Cruz, CA),beta-actin antibody (catalog no. A2066; Sigma, St. Louis, MO),Histone H3 antibody (catalog no. 06-599; Upstate Cell Signaling,Charlottesville, VA). Western blots were developed by using ECL-Plus reagent (Amersham Pharmacia, Pittsburgh, PA). siRNA du-plexes were from Qiagen (Valencia, CA), and PCR primers werefrom Invitrogen (Carlsbad, CA) (see Table 7, which is published assupporting information on the PNAS web site). A plasmid express-ing human ATF4 was kindly provided by Pierre Fafournoux (fromINRA, Theix, France).

Cell Culture and siRNA Knock-Down Experiments. Human aorticendothelial cells were isolated from aortic samples retrieved at thetime of organ harvest for cardiac transplantation and used atpassages 4–7 (see Supporting Text, which is published as supportinginformation on the PNAS web site). For ELISA screening assays,cells were cultured in 96-well dishes, and for gene expression arrayanalysis, cells were cultured in 35-mm dishes. For siRNA experi-ments, cells were transfected for 16 h with 5 nM siRNA in mediumcontaining 10% FBS using HiPerFect reagent (Qiagen) accordingto the manufacturer’s protocol and treated 40 h after transfection.Cells were treated with PAPC or oxPAPC (10–50 �g�ml), tuni-camycin (10 �g�ml), or DTT (1 mM) in medium containing 1%FBS. HeLa cells were transfected by using FuGene 6 (Roche, Basel,Switzerland) at a ratio of 1:3 of �g DNA to FuGene and harvested24 or 48 h after transfection.

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Nuclear Protein Extraction and Immunoblotting. Nuclear proteinextraction was performed by using standard procedures (see Sup-porting Text). Isolated protein (20 �g) was separated on 4–12%PAGE and transferred to PVDF membrane. After blocking using5% dry milk, membranes were incubated in primary antibodies(1:1,000 dilution) followed by appropriate secondary antibodies for1 h at room temperature. ECL-Plus (Amersham Pharmacia, Upp-sala, Sweden) reagent was used to detect bound antibodies.

ELISA. IL-8 levels in HAEC supernatants were measured by usingan ELISA kit (Quantikine Immunoassay R & D Systems, Minne-apolis, MN) as described (30).

Expression Array Analysis. Twelve individual HAEC lines weretreated in duplicate (in 35-mm dishes) for 4 h in media containingPAPC (40 �g�ml) or oxidized PAPC (40 �g�ml). CytoplasmicRNA was isolated by using an RNAeasy kit (Qiagen) and analyzedon an Agilent 2100 Bioanalyzer (Agilent, Palo Alto, CA) to assessRNA integrity. Double-stranded cDNAs were synthesized fromtotal RNA by using the cDNA Synthesis System (Invitrogen,Carlsbad, CA). Biotin-labeled cRNA was generated and used toprobe Affymetrix (Santa Clara, CA) Human Genome U133A andB arrays according to the manufacturer’s recommendations (Af-fymetrix). The Microarray Suite 5.0 software (Affymetrix) was usedto analyze image data and make the absolute call for each mea-surement. The array data were normalized with the median invari-ant method (31). To filter out differentially expressed genes, weadopted the standard t test in a pairwise comparison involvingPAPC and oxPAPC. All genes in the network followed a normaldistribution except one (238755�at) based on the Kolmogorov–Smirnov test (cutoff P value � 0.05). The normality test for eachgene was based on 24 measurements (absolute gene expressionvalues) from 12 PAPC cell lines and 12 oxPAPC cell lines. The 1,043array probe sets that passed the selection criteria were used for thegene coexpression network analysis. The computed mean falsediscovery rate (FDR) was 0.25% (see Supporting Text). In thesubsequent analyses, we also used the absolute value of the t test Pvalue as a measure of gene significance. Thus, the gene significancemeasures the responsiveness to oxPAPC treatment.

Gene Coexpression Network Construction and Analysis. A weightedgene coexpression network was constructed to identify groups ofgenes (modules) involved in various activated pathways following apreviously described algorithm (14). Briefly, we first computed thePearson correlation between each pair of 1,043 selected genesyielding a similarity (correlation) matrix (sij). Then, a power func-tion, aij � Power(sij, �)' �sij��, was used to transform the similaritymatrix into an adjacency matrix A � [aij], where aij is the strength

of a connection between two nodes (genes) i and j in the network.The connectivity (k) was determined for all differentially expressedgenes by taking the sum of their connection strengths with all othergenes in the network. A characteristic of many biological networksis that they are approximately scale-free (32). The parameter � waschosen by using the scale-free topology criterion (14), such that theresulting network connectivity distribution approximated scale-freetopology (Fig. 9, which is published as supporting information onthe PNAS web site). A major advantage of weighted networks isthat they are highly robust with respect to the choice of theparameter �. R software code, a tutorial, and a technical report forgenerating weighted gene coexpression networks are available onrequest. The adjacency matrix was then used to define a measureof node dissimilarity, based on the topological overlap matrix. Toidentify gene modules, we performed hierarchical clustering on thetopological overlap matrix. The details of the algorithm are avail-able on request. Fisher’s exact test was used to determine whetherenrichment in pathway genes within the module is random (seeSupporting Text).

Real-Time QPCR Analysis. In a typical experiment each treatment wasdone in triplicate. RNA was isolated from cells by using RNeasyisolation kit (Qiagen). One microgram of total RNA was reversetranscribed by using random hexamers and Superscript-III reversetranscriptase (Invitrogen). Quantitative RT-PCR was performedby using an ABI Prism 7700 (Applied Biosystems, Foster City, CA)and SYBR Green detection (SYBR Green Taq ready mix; Sigma).Primers were designed by using PrimerQuest software (IDT Tech-nologies) and verified by using BLAST search. Sequences ofprimers can be found in Table 7. The PCR consisted of a 2-min stepat 94°C and 40 cycles of 94°C for 15 s, 60°C for 1 min, and 72°C for1 min, and ending with a slow heating step from 55°C to 95°C togenerate the melting curve data. The correct sizes of the PCRproducts were verified by agarose gel electrophoresis. Serial dilu-tions of the pooled samples were used to construct the standardcurve and determine the real-time PCR efficiency for each primerpair by using the ABI Prism 7700 software. Each individual samplecDNA was analyzed separately and corrected for the �2M expres-sion. The final data are expressed as an average relative expressionof the treatment group versus the control group (set as 100%).

This work has been supported by National Institutes of Health GrantsHL30568 (to A.J.L. and J.A.B.), HL64731 (to J.A.B.), and HL72367 (toS.F.N.); an unrestricted research award from Bristol–Myers Squibb (toA.J.L.); an American Heart Association (AHA) Postdoctoral Fellowshipaward (to P.S.G.); an AHA Predoctoral Fellowship award (to N.M.G.);and the Laubisch Fund [University of California, Los Angeles (UCLA)].Microarray work was performed in the UCLA DNA Microarray Facilityand supported by the UCLA�National Heart, Lung, and Blood InstituteShared Microarray Resource.

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