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Identification of inflammatory gene modules based on variations of human endothelial cell responses to oxidized lipids Peter 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 GOAL: Understand the complex biological system/disease Evolution of approaches: 1. gene cloning and single gene regulation 2. identification of gene-gene relationships (pathways) 3. regulation of a pathway in the given system 4. integration of a given pathway/genome into complex and dynamic biological system (current challenge) NEW TECHNOLOGIES (Expression arrays): Initial use in gene expression mapping: Identify all genes regulated by Inflammatory Stimuli (Oxidized Lipids) Classical approach to exploratory expression array experiments Dose response oxPAPC (4hrs) 10 μg/ml 30 μg/ml HAEC 50 μg/ml Data analysis LPS (2ng/ml) Time course Multiple time points 0 - 4hrs (50 μg/ml) HAEC Data analysis Major Differences in Gene Regulation Between LPS and OxPAPC oxPAPC (50 ug/ml) Bacterial LPS (2 ng/ml) 459 70 vs. 283 742 genes 17 87 genes Many Genes and Pathways are Regulated by Oxidized Lipids (complex system!!!) LDL Inflammatory response Endothelial Cells Oxidation Oxidized Nitric Oxide Phospholipids Unfolded Protein Response SREBP ~ 800 genes ERK/EGR-1 CREB/HO-1 Src/Jak/STAT GPCR, cAMP Can we take advantage of the large amount of data collected from differentially perturbed states to learn more about the biological system? Approach: Weighted Gene Co-expression NETWORK Analysis (WGCNA) • Identifies network modules that can be used to explain gene regulation and function (pathway analysis) •Hierarchical clustering with the topological overlap matrix • Uses intramodular connectivity to identify important genes •References •Bin Zhang and Steve Horvath (2005) "A General Framework for Weighted Gene Co-Expression Network Analysis", Statistical Applications in Genetics and Molecular Biology: Vol. 4: No. 1, Article 17. • Horvath S, Zhang B, Carlson M, Lu KV, Zhu S, Felciano RM, Laurance MF, Zhao W, Shu, Q, Lee Y, Scheck AC, Liau LM, Wu H, Geschwind DH, Febbo PG, Kornblum HI, Cloughesy TF, Nelson SF, Mischel PS (2006) "Analysis of Oncogenic Signaling Networks in Glioblastoma Identifies ASPM as a Novel Molecular Target", PNAS Hypothesis: Genetic variation modulates inflammatory responses to oxidized phospholipids in human population Interleukin 8: Pro-inflammatory cytokine implicated in atherogenesis Mediates adhesion of monocytes to EC Highly induced by oxPAPC IL8 levels are higher in patients with unstable CAD then in healthy individuals Elevated plasma IL8 levels are associated with increased risk for future CAD Genetic background influences inflammatory responses to oxidized lipids in human EC 1400 oxPAPC PAPC 1200 IL8 (pg/ml) 1000 800 600 400 200 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 DONOR HAEC # Inflammatory Responses are Preserved Between Cell Passages IL8 ELISA 1600 correlation=0.825 p<0.001 1400 2nd (pg/ml) 1200 1000 800 600 400 200 0 0 200 400 600 800 1st (pg/ml) 1000 1200 1400 Co-Expression Network of Endothelial Responses to Oxidized Phospholipids ENDOTHELIAL CELL DONORS 1 2 3 4 5 6 7 8 Oxidized Phospholipids IL8 Gene X Gene Y EXPRESSION PATTERNS 9 10 11 12 Experimental Design: ENDOTHELIAL CELL DONORS 1 2 3 4 5 6 7 8 9 10 11 TREATMENT (4hrs) 1. PAPC (40 ug/ml) 2. oxPAPC (40ug/ml) 1043 Genes Regulated by OxPAPC Data Analysis Using Gene Co-expression Network Approach 12 Genetic Perturbation Approach to Study Gene Regulation oxPAPC Endothelial cell line (1) SREBP activity (+) LOW Expression of SREBP- regulated genes (+) LOW oxPAPC Endothelial cell line (2) SREBP activity (+++) HIGH Expression of SREBP- regulated genes (+++) HIGH 1043 genes in the oxPAPC network are separated into 15 modules Topological Overlap Matrix Plot 12 cell lines Brown Module is enriched in SREBP Pathway Genes gene INSIG1 6.257772 Highest INSIG1 6.194221 SLC2A3 6.061201 INSIG1 5.695922 SLC2A14 5.606994 SLC2A14 5.227064 SLC2A14 4.260267 NQO1 3.984579 SQLE 3.5742 SLC2A3 3.483622 LPIN2 3.087652 Ranking ADRB2 based 2.922237 SC4MOL on connectivity 2.915552 CYP51A1 2.373458 CPNE8 2.241534 SQSTM1 1.861886 CYP51A1 1.784242 --- 1.722028 LOC285148 1.674725 --- 1.602659 --- 1.528179 SQLE 1.36481 LTB4DH 0.84509 LOC283219 0.790956 ID3 0.691711 --- 0.255479 Brown module has 26 genes 8 of 14 SREBP targets are in Brown module (p-value 1.26x10-10 ) Blue and Red module are enriched in UPR genes RED MODULE (52 genes) BLUE MODULE (256 genes) 5 out of top 10 genes are UPR genes 22 out of top 100 genes are UPR genes CEBPB 10.82586 GIT2 9.623114 ATF4 9.178292 SLC7A5 8.612143 CEBPG 7.563844 MGC4504 7.446907 Ranking 7.270555on based network 7.019388 connectivity XBP1 KIAA0582 MTHFD2 Ranking based on network connectivity 6.86908 SPTLC2 6.824852 DDIT4 6.682974 EEF2K 6.475676 KIAA0582 6.40407 KIAA0121 6.288301 VEGF 6.155599 RALA 6.062034 LOC148418 6.031962 C14orf27 5.904039 IMAP1 5.65993 MLYCD 5.586476 RED module UPR enrichment (p-value 0.049 ) BLUE module UPR enrichment (p-value 1.3x10-13 ) Gene network separates genes into modules based on mechanism of regulation SREBP genes (Brown module) (p-value 1.26x10-10 ) UPR genes (Blue and Red module) (p-value 1.3x10-13 and 0.049) IL8 (Blue module) IL8 expression in cell lines is highly correlated with UPR genes Screen for UPR regulatory sites in 1043 network genes Endoplasmic Reticulum IRE1 PERK ATF6 XBP1 ATF4 UPR genes UPRE 5’-TGACGTGG-3’) ERSE-I 5’- CCAAT(N9)CCACG -3’ ERSE-II 5 –ATTGGNCCACG- 3’ C/EBP-ATF 5’-TTGCATCA -3’ CRE-like site found in IL8 promoter XBP1 and ATF6 ATF4 ATF4 siRNA inhibits IL8 expression in primary human aortic ECs mRNA (% of control) 400 p=0.001 IL8 Scrambled siRNA ATF4 siRNA 300 UPR Blue module p=0.0002 200 p=0.0006 68% 100 72% 74% 0 400 ATF4 ATF4 OX TUN p=0.003 300 p<0.0001 1000 200 Scrambled siRNA ATF4 siRNA INSIG1 p<0.0001 100 81% 71% 85% 0 CONT OX TUN mRNA (% of control) mRNA (% of control) CONT Scrambled siRNA ATF4 siRNA 800 600 400 200 0 CONT OX TUN SREBP Brown module Co-expression network can be applied to new gene-function discovery (MGC4504 in red module is regulated by ATF4) Gene of unknown function present in UPR module 400 8000 ATF4 ATF4 7000 p=0.003 300 p<0.0001 200 p<0.0001 100 81% 71% 85% 0 mRNA (% of control) mRNA (% of control) MGC4504 Scrambled siRNA ATF4 siRNA 6000 OX TUN p=0.0007 5000 p=0.003 4000 3000 2000 p=0.0008 1000 0 CONT Scrambled siRNA ATF4 siRNA MGC4504 89% CONT 96% OX 97% TUN SUMMARY Common genetic variations in human population have significant impact on inflammatory responses to oxidized lipids Genetic variation-based gene co-expression network approach was used to: subdivide genes into pathways based on mechanism of regulation (UPR versus SREBP pathway) predict UPR involvement in regulation of IL8 and MGC4504 ER homeostasis and associated stress pathways may play a central role in mediating endothelial inflammatory responsiveness to oxidized phospholipids and possibly other stimuli