Integrative pathway dissection of molecular mechanisms of moxLDL-induced vascular smooth muscle phenotype transformation
© Karagiannis et al.; licensee BioMed Central Ltd. 2013
Received: 23 February 2012
Accepted: 29 December 2012
Published: 16 January 2013
Atherosclerosis (AT) is a chronic inflammatory disease characterized by the accumulation of inflammatory cells, lipoproteins and fibrous tissue in the walls of arteries. AT is the primary cause of heart attacks and stroke and is the leading cause of death in Western countries. To date, the pathogenesis of AT is not well-defined. Studies have shown that the dedifferentiation of contractile and quiescent vascular smooth muscle cells (SMC) to the proliferative, migratory and synthetic phenotype in the intima is pivotal for the onset and progression of AT. To further delineate the mechanisms underlying the pathogenesis of AT, we analyzed the early molecular pathways and networks involved in the SMC phenotype transformation.
Quiescent human coronary artery SMCs were treated with minimally-oxidized LDL (moxLDL), for 3 hours and 21 hours, respectively. Transcriptomic data was generated for both time-points using microarrays and was subjected to pathway analysis using Gene Set Enrichment Analysis, GeneMANIA and Ingenuity software tools. Gene expression heat maps and pathways enriched in differentially expressed genes were compared to identify functional biological themes to elucidate early and late molecular mechanisms of moxLDL-induced SMC dedifferentiation.
Differentially expressed genes were found to be enriched in cholesterol biosynthesis, inflammatory cytokines, chemokines, growth factors, cell cycle control and myogenic contraction themes. These pathways are consistent with inflammatory responses, cell proliferation, migration and ECM production, which are characteristic of SMC dedifferentiation. Furthermore, up-regulation of cholesterol synthesis and dysregulation of cholesterol metabolism was observed in moxLDL-induced SMC. These observations are consistent with the accumulation of cholesterol and oxidized cholesterol esters, which induce proinflammatory reactions during atherogenesis. Our data implicate for the first time IL12, IFN-α, HGF, CSF3, and VEGF signaling in SMC phenotype transformation. GPCR signaling, HBP1 (repressor of cyclin D1 and CDKN1B), and ID2 and ZEB1 transcriptional regulators were also found to have important roles in SMC dedifferentiation. Several microRNAs were observed to regulate the SMC phenotype transformation via an interaction with IFN-γ pathway. Also, several “nexus” genes in complex networks, including components of the multi-subunit enzyme complex involved in the terminal stages of cholesterol synthesis, microRNAs (miR-203, miR-511, miR-590-3p, miR-346*/miR- 1207-5p/miR-4763-3p), GPCR proteins (GPR1, GPR64, GPRC5A, GPR171, GPR176, GPR32, GPR25, GPR124) and signal transduction pathways, were found to be regulated.
The systems biology analysis of the in vitro model of moxLDL-induced VSMC phenotype transformation was associated with the regulation of several genes not previously implicated in SMC phenotype transformation. The identification of these potential candidate genes enable hypothesis generation and in vivo functional experimentation (such as gain and loss-of-function studies) to establish causality with the process of SMC phenotype transformation and atherogenesis.
Atherosclerosis (AT) is a chronic inflammatory disease of medium and large arteries characterized by the accumulation of inflammatory cells, lipoproteins and fibrous tissue that lead to the formation of atherosclerotic plaques. It is the primary cause of heart attacks and stroke, and the leading cause of death and disability in developed countries [1–3]. AT is a multifactorial disease with genetic, environmental and lifestyle risk factors. A variety of atherogenic stimuli including hemodynamic shear stress, infections, lipids and proinflammatory cytokines induce endothelial cell dysfunction and permit the migration of mononuclear cells into the subendothelial space. This process is associated with the transformation of quiescent contractile smooth muscle cells (SMCs) to a proliferative and migratory phenotype. As a result of this transformation, SMCs migrate to the neointima where they produce an extracellular matrix that stabilizes the atherosclerotic plaque [4–9]. Lipids deposited in atherosclerotic plaques are derived largely from the lower-density lipoproteins [LDL, LDL-1, VLDL, beta-VLDL, and Lp(a)] of the blood [10, 11]. 12/15- lipoxygenase and myeloperoxidase have been identified as lipid-oxidizing enzymes that are involved in the formation of biologically active oxidized lipids (cholesterol ester hydroperoxides). The accumulation of these oxidized lipids may initiate the proinflammatory activation of macrophages and SMCs in atherosclerotic lesions [12, 13]. Mildly or minimally oxidized forms of LDL (moxLDL) activate both cell-mediated and humoral immune responses that perpetuate the chronic inflammatory reactions characteristic of atherosclerosis. The accumulation of cholesterol esters in macrophages and macrophage-like cells induce the release of pro-inflammatory cytokines, chemokines, reactive oxygen radicals, and matrix metalloproteinases [14–16].
Although the majority of foam cells, containing oxidized lipoproteins, in atherosclerotic lesions are derived from macrophages, SMCs also give rise to a significant number of lipid laden cells. SMCs exposed to atherogenic stimuli such as inflammatory cytokines, shear stress, moxLDL or reactive oxygen radicals or lipids [6, 17–19] express high levels of a variety of lipid-binding membrane receptors including LDLR, VLDLR, LOX-1, CD36, type I and type II scavenger receptors, and CXCL16/SR-PSOX for cholesterol uptake . Atherogenic cytokines such as IL-1α, TNF-α, and MCSF further upregulate the expression of LDLR and VLDLR . The binding of moxLDL to these receptors then results in the accumulation of high levels of cholesterol and cholesteryl esters by the macrophages and SMCs, which then transform into foam cells in early fatty streak lesions. These changes characterize the initiation and progression of atherosclerosis and restenosis .
moxLDL has been shown to induce SMC transformation from the “contractile” phenotype to the “migratory, proliferative and synthetic” phenotype, central to intimal hyperplasia and atherogenesis [4, 19]. Activated SMCs also produce cytokines such as PDGF, TGF-β and IFN, which contribute to the initiation and propagation of the inflammatory response of the vessel wall [18, 19, 22, 23].
Recently, a number of investigators have used systematic approaches to investigate atherosclerosis. In these works, biopsies of stable and unstable plaques from symptomatic and asymptomatic patients as well as lesions in mouse models for AT have been examined. Gene expression profiles, pathways and molecular networks were analyzed, that underlie the formation of atherosclerotic plaques [24–31]. As a result, these studies have implicated many potential human atherogenic genes related to lipid homeostasis and have reported changes in the cytokine-induced immune and inflammatory responses as part of the pathogenesis of AT. Such studies have also underscored SMC dedifferentiation as a key process in the initiation and progression of AT.
Despite these advances, the molecular mechanisms of SMC transformation during initiation and progression of atherogenesis are not well-defined. However, the identification of early critical pathways involved in SMC transformation can provide insights into the mechanisms that underlie the pathogenesis of AT and cardiovascular diseases and could deliver potential targets for drug discovery. To facilitate such analyses, we have previously used oligonucleotide microarrays to analyze the genome-wide differential gene expression in quiescent primary human coronary artery SMCs induced with moxLDL for 3h and 21h . This work uncovered several genes not previously implicated in the moxLDL-induced SMC phenotype transformation and described numerous functional categories of genes with altered gene expression.
Here, we significantly extended the original analysis of the resulting gene expression data using a number of pathway analysis tools - Gene Set Enrichment Analysis (GSEA) , Enrichment Map visualization , Ingenuity Pathway Analysis (IPA) and GeneMANIA. We found new, non-previously described functional themes and pathways, which may help elucidate the early and late mechanisms of moxLDL-induced SMC phenotype transformation and the onset and progression of atherogenesis. While the in vitro atherogenesis model involving moxLDL treatment of VSMC, particularly in the absence of endothelial cells and immune and inflammatory cells, is an oversimplified model of the complex process of atherogenesis, our systems analysis on the interactions of moxLDL and VSMC has uncovered several novel gene and pathway changes. These observations now permit hypotheses generation and in vivo functional testing (such as gain and loss-of-function studies) to establish causality with the process of SMC phenotypic transformation and atherogenesis.
The microarray analysis of moxLDL treated cells has been previously described . Briefly, human coronary artery SMCs were purchased from Clonetics (Walkersville, MD) and cultured according to the manufacturer’s instructions and used between passages 4–7. Confluent SMC cultures were synchronized to quiescence by incubation for 48h in basal medium (SmBM) containing 0.5% FBS. The cells were then washed and incubated in SmBM+0.5% FBS in the absence or presence of unoxidized LDL or moxLDL (2 μg/ml) for 3h and 21h. The reactions were performed in quadruplicates. DNA-free mRNA was extracted from the cells and mRNA samples from corresponding cell cultures were pooled to reduce inter-sample variation. Biotinylated cRNA samples were hybridized to HG-U133A oligonucleotide Gene Chip arrays (Affymetrix, Santa Clara, CA). The data files from the arrays were analyzed using Affymetrix GeneChip® Operating Software (GCOS) version 1.0 (Affymetrix, Santa Clara, CA) to identify differentially expressed genes.
Re-processing of gene expression data for Gene Set Enrichment Analysis
The originally published set of differentially expressed genes only contained those surpassing a threshold (at least 2-fold differentially expressed), however GSEA requires input of all genes ranked from most over-expressed to most under-expressed. To gather this information, we reprocessed the original Affymetrix HG-U133A CEL image data files using the Affy library of the Bioconductor package for the R programming language . Three arrays exist in this experiment: control, treatment after 3h and treatment after 21h. Background correction and normalization was performed on the datasets using the RMA method . This data was then reformatted for input into the GSEA software (GCT file format).
Gene Set Enrichment Analysis (GSEA) based pathway analysis
Pathway enrichment analysis was carried out by searching for enriched gene-sets (e.g. pathways, molecular functional categories, complexes) in the early time point (3h) vs. control and the late time point (21h) vs. control using GSEA. It was not possible to use a statistical test to establish a gene ranking, as only gene expression data from one pooled set of samples was available for each experimental condition. Instead, a fold-change metric was used, computed by GSEA, comparing moxLDL-3h vs. Control and moxLDL-21h vs. Control. We used “gene set permutation” with 1000 permutations to compute p-values for enriched gene-sets, followed by GSEA’s standard multiple testing correction. We used GSEA’s built- in gene identifier (ID) conversion system to convert Affymetrix probeset IDs from the expression data matrices to gene symbols for analysis. We used an updated version (September 2, 2011) of a custom gene set collection previously used for pathway analysis  (http://baderlab.org/GeneSets). The collection comprises Gene Ontology annotations , as well as pathways from the HumanCyc , Kyoto Encyclopedia of Genes and Genomes (KEGG) , MSigDB , NCI Nature Pathway Interaction Database (PID) , NetPath  and Reactome  databases.
Enrichment Map pathway analysis visualization
The resulting enrichment results were visualized with the Enrichment Map plugin for the Cytoscape network visualization and analysis software. We loaded GSEA results using a p-value cut-off of 0.005 and a q-value threshold of 0.1. In these maps, each gene set is symbolized by a node in the network. Node size corresponds to the number of genes comprising the gene-set. The enrichment scores for the gene-set are represented by the node’s color (red indicates up-regulation, blue represents down-regulation). The color of the node center indicates the enrichment score for the early time point (3h), and the node border color indicates the score for the late time point (21h). To intuitively identify redundancies between gene sets, the nodes are connected with edges if their contents overlap by more than 50%. The thickness of the edge corresponds to the size of the overlap. We used version 1.2 of the Enrichment Map software in Cytoscape 2.8.2.
GeneMANIA (http://www.genemania.org/) finds other genes that are related to a set of input genes, using a very large set of functional interaction data . Interaction data include protein and genetic interactions, pathways, co-expression, co-localization and protein domain similarity. We searched the GeneMANIA web site using differentially expressed genes underlying specific functional themes to find out how the genes interact with each other. The resulting sub-network containing our query genes and additional related genes helps interpret the mechanistic details of the functional themes we define.
Ingenuity pathway analysis
We also used the commercial software Ingenuity Pathway analysis (Ingenuity® Systems; IPA) (http://www.ingenuity.com/) to identify enriched pathways and functional themes, as reported previously . In particular, genes of interest, defined as those genes that were at least 2-fold differentially expressed, as reported in the original publication  were uploaded into the application as standard human gene symbols. Each gene identifier was mapped to its corresponding gene object in the Ingenuity Pathways Knowledge Base (IPKB). The IPKB, containing a large network of curated molecular interactions and pathways, was searched to find sub-networks enriched in genes of interest. A total of 77 and 205 genes were found to be network eligible for the 3h and 21h moxLDL experiments, respectively. Graphical representations of these sub-networks, containing direct and indirect molecular relationships, were generated.
Results and discussion
Overview of the integrative pathway analysis
In-depth pathway analysis of specific molecular themes of interest
Cholesterol metabolism genes in 21h moxLDL-SMC were more robustly regulated with 26 genes up-regulated and 7 genes down-regulated. The most highly upregulated genes were G6PD, INSIG1, HMGCS1, FDPS and LSS and the most strongly down-regulated genes were APOE, LEPR, INSIG2, CYP51A1 and TNSF4 (Figure 4B). GeneMANIA network analysis indicated that genes encoding enzymes essential for the sequential enzymatic conversion of Acetyl-CoA and Acetoacetyl-CoA to cholesterol were all up-regulated in moxLDL-SMC (Figure 4C). The analysis also showed multiple interactions among the enzymes (including FDFT1, NSDHL, HSD17B7, LSS, SOLE, SC5DL, SC4MOL and CYP51A1) involved in the sequential conversion of farnesyl pyrophosphate to squalene, oxidosqualene, lanosterol and finally cholesterol and suggested that these enzymes are hub proteins or function as a multi-subunit complex.
The ER-bound INSIG-SCAP-SREBP complex is the most important sensor of sterol levels. At high cholesterol levels, the complex is retained in the ER, but at lower levels the SCAP-SREBP enters transport vesicles . In the Golgi, SREBP undergoes two steps of proteolysis, releasing a soluble transcription factor that regulates many genes associated with cholesterol and lipid metabolism. This leads to increased synthesis of cholesterol and LDL receptors. A switch-like response that helps to maintain cellular cholesterol in a narrow range has been demonstrated in the ER. It is currently unclear whether the sharp transition is due to cooperative protein-protein interactions between SCAP molecules or an abrupt change in the chemical activity of cholesterol in the ER membrane when it crosses a threshold value . It has been proposed that the level of expression of INSIG1 protein can influence the cholesterol-dependent transition point, and reduction of cholesterol levels leads to proteasomal degradation of INSIG1, which sensitizes cells to cholesterol depletion . In our study, INSIG-1 is highly expressed at 21h and thus we predict sustained cholesterol synthesis would occur.
PDGF has been shown to regulate ABCA1 expression in SMC . However in our study, both ABCA1 and ABCG1 were not expressed in moxLDL-treated SMC at 3h and 21h, in spite of an increased PDGF expression and cholesterol biosynthesis. We propose that the lack of ABCA1 and ABCG1 in moxLDL-treated SMC, would result in impaired cholesterol efflux leading to its accumulation in SMCs during atherogenesis. This finding is thus analogous to the observed down-regulation of ABCA1 and ABCG1 transporters in lipid laden macrophages which results in a dysregulated reverse cholesterol transport pathway that enhances lipid accumulation and “foam cell” formation in moxLDL-treated macrophages [51–55].
The ER contains acetoacetyl CoA thiolase (ACAT1), the enzyme responsible for esterifying excess cholesterol for storage in lipid droplets . Cholesterol ester storage and accumulation as oil droplets in microsomes occurs during cholesterologenesis and may contribute to formation of fatty streaks. NAD(P)H dehydrogenase-like protein (NSDHL), a C4 demethylase that is involved in the removal of C-4 methyl groups from the cholesterol precursor lanosterol, is localized to the surface of the ER. It also accumulates on the surface of lipid droplets that function as intracellular storage compartments for neutral lipids and cholesterol esters and participates in the regulation of cellular cholesterol content . The up-regulation of NSDHL in moxLDL-SMC may therefore play a role in the accumulation of cholesterol in moxLDL-SMC.
Cholesterol metabolism was tightly regulated in 21h moxLDL-SMC, judging by the differential regulation of the network of LDLR, LDLRAP1, LIPA, RXRA, APOC3 and APOL2 genes (Figure 4C). LDLRAP1 is required for internalization of the LDL-LDLR complex in endocytic vesicles . Lysosomal acid lipase (LIPA) has been reported to play an important role in cellular metabolism by releasing cholesterol, which in turn suppresses further cholesterol synthesis and stimulates esterification of cholesterol within the cell. ApoE knock-out mice spontaneously develop atherosclerosis. However, this effect is counteracted by the retinoid X receptor (RXRA) in the same model . APOC3 inhibits the catabolism and hepatic uptake of apoB-containing lipoproteins and enhances the catabolism of HDL particles, as well as the adhesion of monocytes to vascular endothelial cells and activates inflammatory signaling pathways . The up-regulation of APOC3 in moxLDL-SMC would inhibit cholesterol clearance via HDL. Interestingly, the observations of up-regulation of LDLR, LDLRAP1, INSIG1, SCAP, LIPA, RXRA, NSDHL, APOC3 and APOL2 and the down-regulation of INSIG2 and APOE in moxLDL-SMC further suggest a dysregulation of cholesterol metabolism and clearance in moxLDL-SMC, a situation that favors foam cell formation. APOL2 has not been reported to be expressed in neointima or the media but is up-regulated in HUVECs following prolonged stimulation with TNF-α [61–63].
To date, statins are used therapeutically to inhibit de novo hepatic cholesterol synthesis to lower the levels of plasma LDL-cholesterol, the major risk factor for atherosclerosis and coronary heart disease. Inhibition of HMGCR conversion of HMG CoA to mevalonic acid results in an inhibition of the synthesis of several non-sterols such as dolichols and ubiquinone and contributes to the side effects observed in patients on statin therapy. Consequently, attention has been directed towards enzymes such as squalene synthetase, squalene epoxidase and oxidosqualene cyclase, which are involved in cholesterol synthesis beyond farnesyl pyrophosphate as potential targets. Preclinical studies with oral bioavailable inhibitors have demonstrated the potential of squalene epoxidase inhibitors as hypocholesterolemic agents, however high circulating levels of squalene epoxidase inhibitors are believed to be responsible for dermatitis and neuropathy observed in the participants . To the best of our knowledge, the hypocholesterolemic effects of inhibitors of the enzymes involved in post-squalene cholesterol biosynthesis have not yet been reported. These enzymes [including LSS, SC5DL, CYP51A1, SC4MOL, NSDHL, HSD17B7 and C14orf1 (Figure 4C)] exhibit extensive physical interactions with each other, suggesting they may form a scaffold and act via a multistep multi-enzyme process. Inhibition of any one of the enzymes may destabilize the complex and inhibit cholesterol formation. They, thus, could be considered potential targets for the design of hypocholesterolemic drugs for therapeutic intervention of SMC phenotype transformation and atherogenesis.
The significance of moxLDL-mediated induction of cholesterol synthesis in SMC phenotype transformation is believed to be related to the proinflammatory properties of atherogenic LDL particles. According to the “oxidation hypothesis of atherogenesis”, specific proinflammatory oxidized phospholipids that result from the oxidation of LDL phospholipids containing arachidonic acid are largely generated by potent oxidants produced by the lipoxygenase and myeloperoxidase pathways. These so-called mildly or minimally oxidized LDL and their active components, such as polyoxygenated cholesteryl ester hydroperoxides, are recognized by the innate immune system via Toll-like receptor (TLR) activation (such as TLR4). This leads to the recruitment of spleen tyrosine kinase (Syk), cytoskeletal rearrangements and macropinocytosis which in hyperlipidemic environments leads to excessive lipid accumulation in monocytes, macrophages and SMC in vascular lesions, foam cell formation, vascular inflammation and ultimately the development of atherosclerotic plaques [65, 66]. TLR engagement stimulates multiple signaling pathways, including PI3-K/Akt and p38/p42/p44 MAPKs, which activate transcription factors (e.g. NFkB, IRFs, STAT1, STAT3, AP-1). The activation of these pathways also activates the expression of proinflammatory cytokines (e.g. IL1α TGFβ, MCP-1, IFNγ) and growth factors (e.g. PDGF, IGF, EGF, FGF) involved in mitogenesis of VSMCs [67–76]. The various functional themes and pathways that we have analyzed substantiate these observations and suggest further details about the molecular mechanisms of SMC phenotype transformation.
Inflammatory cytokines and growth factors
MicroRNAs (miRNAs) have recently been implicated in the regulation of atherosclerosis and lipoprotein metabolism, by affecting endothelial integrity, macrophage inflammatory response to atherogenic lipids, vascular smooth muscle cell proliferation, and cholesterol synthesis . We found that certain miRNAs (miR-203, miR-511, miR-590-3p, miR-346*/miR-1207-5p/miR-4763-3p) serve as organizational hubs of several signal transduction pathways in one of our IPA networks (Figure 5D). Since miRNAs are implicated in inflammatory processes that accompany heart failure, AT, coronary artery disease, obesity and diabetes , we further investigated these pathways. Many of the identified miRNAs, along with clusters of deregulated proteins were, indeed, highly connected to the IFN-γ pathway in the same molecular network (Figure 5D). Interestingly the JAK/STAT, MAPK and IGF signaling pathways, which have been shown to play clearly defined roles in AT pathogenesis [83–85], served as major intracellular mediators of the cytokine pathways in the generated molecular networks. Recent integrative approaches demonstrating a plethora of IFN-γ-regulated mRNAs and targeted mRNAs , coupled with our observation of miRNAs in the IFN-γ-dominated molecular network suggest that inflammatory signaling may be regulated through non-classical miRNA-related cytokine pathways, beyond the classical JAK/STAT and MAPK pathways.
G-protein coupled receptors (GPCRs)
VSMC migration involves a dominant plasma membrane leading lamellae, or leading edge, protruding from the cell to make contact with an extracellular substrate. Binding is accomplished via integrin transmembrane receptors that enable the formation of focal complexes and secure focal adhesions. An intracellular signal transduction cascade, involving G-protein and tyrosine-kinases, results in the alignment of actin filaments and a myosin contraction within the leading edge. Focal adhesions are subsequently disengaged over the remainder of the cell surface, and contractile forces propel the cell forward in the direction of the anchoring leading edge [87–91]. Thus, VSMC migration is predominantly regulated by two receptor-coupled systems, GTP-binding protein (G-protein)-coupled and tyrosine kinase-coupled proteins. Signal transduction pathways from these two systems appear to intersect as signals are transmitted.
Novel Findings of the Integrative Pathway Analysis of moxLDL-induced SMC Phenotype Transformation
Cholesterol synthesis & transport
The observation of upregulated cholesterol biosynthesis coupled with a dysregulation of the reverse cholesterol transport pathway in moxLDL-induced SMC may promote the accumulation of cholesterol and cholesterol esters in the cells, and facilitate foam cell formation during atherogenesis
Observed GPCR signaling may have a role in SMC transformation. GPCR signaling following activation via inflammatory or other microenvironmental stimuli have been implicated in responses, such as change of cell-to-cell and cell-to-matrix adhesion, matrix remodeling, cell proliferation, migration, and immune cell trafficking and regulation. The activation of GPCR pathways may thus play a role in the initiation of SMC dedifferentiation and phenotype transformation.
Cytokine & growth factor
Potential role for IL12, IFN-α, HGF, CSF3 and VEGF signaling in SMC phenotype transformation.
Cell cycle control
Up-regulation of HBP1, a repressor of cyclin D1 and CDKN1B, suggests a negative feedback loop auto-triggered by the up-regulation of the core cell cycle machinery during SMC phenotype transformation.
The deregulation of the ID2 and ZEB1 regulators of differentiation during SMC phenotype transformation is consistent with the onset of SMC de-differentiation.
miRNA & de-differentiation
Potential role of miRNAs in the regulation of SMC phenotype transformation via interactions with the IFN-γ pathway.
Nexus genes in complex networks
Several “nexus” genes, including the components of a multi-subunit complex involved in the terminal stages of cholesterol synthesis, miRNAs (e.g. miR-203, miR-511, miR-590-3p, miR- 346*/miR-1207-5p/miR-4763-3p), members of the GPCR family of proteins (e.g. GPR1, GPR64, GPRC5A,GPR171, GPR176, GPR32, GPR25, and GPR124) and signal transduction pathways were observed. These genes may play important roles in VSMC phenotype transformation and in the pathogenesis of AT and coronary artery disease and may provide novel targets for drug discovery.
Our enrichment map provides information regarding the activity status (up- or down-regulation) of GPCR signaling pathways during SMC transformation, while Ingenuity identifies cross-talk of this pathway with other pathways. Based on these observations, we speculate that GPCR signaling plays a role in SMC transformation. GPCR signaling may mediate the initiation of SMC dedifferentiation following activation via inflammatory or other microenvironmental stimuli. The activation of GPCR pathways might be implicated in a large number of responses, such as change of cell-to-cell/cell-to-matrix adhesion, proliferation, matrix remodeling, migration, and immune cell trafficking and regulation. These traits are consistent with the SMC transformation process. Once these processes have been completed, GPCR signaling is down-regulated, by a mechanism that is yet to be elucidated. The maintenance of the activated SMC phenotype could be regulated by other “maintenance pathways”, such as cytokine signaling pathways, which are up-regulated throughout the course of the disease. We believe that such maintenance pathways exist, given previous literature  and new evidence from our study that the migratory and proliferative phenotype in SMCs is maintained throughout moxLDL treatment by the strongly up-regulated cell cycle control machinery.
Members of the GPCR superfamily are known to mediate G-protein-coupled, cAMP-mediated signal transduction mechanisms for the detection of chemostimuli in the main olfactory epithelium and heterogeneous cells in mammals [95, 96]. Since the olfactory sensing pathway was highly regulated in SMC exposed to moxLDL (Additional file 2: Figure S1), we speculate that in addition to moxLDL receptors, the GPCRs up-regulated in this process may participate in sensing this atherogenic agent.
Cell cycle control
An important aspect of SMC transition into a migratory and proliferative phenotype is the loss of the differentiated and quiescent phenotype. Regulatory factors of cell differentiation most likely regulate this transition. In one of our IPA networks (Figure 8C), we captured two potential regulators of differentiation: (a) DNA-binding protein inhibitor-2 (ID2), a transcriptional regulator which inhibits the function of basic helix-loop-helix transcription factors , and (b) Zinc-finger E-Box-binding homeobox 1 (ZEB1), a transcription factor involved in the generation of more mesenchymal phenotypes . Interestingly, both ID2 and ZEB1 were deregulated in our dataset. Although IL-1β-induced ID2 gene expression has been described in SMCs , ZEB1 has not been reported to be involved in SMC phenotype transformation.
Myogenic contraction mechanism
** The novel findings in this paper are summarized in Table I.
Pathway analysis of the transcriptomic data of the in vitro model of moxLDL induced-VSMC phenotype transformation using GSEA, Enrichment Map Cytoscape plugin, GeneMANIA and IPA software identified several pathways known or expected to be disturbed during SMC transformation in addition to several novel regulators that may play key roles in the onset and progression of AT. The identification of these novel potential regulatory genes now permit hypothesis generation and in vivo functional experimentation (such as gain and loss-of-function studies) to establish causality with the process of SMC phenotype transformation, AT and coronary artery disease and to possibly reveal novel diagnostic markers and targets for drug discovery.
ATP-binding cassette (ABC) transporters A
ATP-binding cassette (ABC) transporters G
Acetoacetyl CoA thiolase
Cluster of differentiation
Cyclin-dependent kinase inhibitor B
Colony stimulating factor
Chemokine CXC motif, ligand
Epidermal growth factor
Fibroblast growth factor
Focal adhesion kinase
GTP-binding protein (G-protein)-coupled receptor
G-protein coupled receptor
Gene Set Enrichment Analysis
High density lipoprotein-binding protein 1
Hepatic growth factor
3-hydroxy-3-methylglutaryl-CoA synthetase 1
Inhibitor of DNA binding-2
Isopentenyl-diphosphate delta-isomerase 1
Insulin-like growth factor
Insulin-induced gene 1
Insulin-induced gene 2
Ingenuity pathway analysis
Ingenuity pathways knowledge base
Interferon regulatory factor
Low density lipoprotein receptor
Mitogen-activated protein kinase
Monocyte chemotactic protein-1
Lymphocyte antigen 96
Minimally oxidized LDL
Nuclear factor kappa B
NAD(P)H steroid dehydrogenase-like protein
Partitioning-defective protein 3
Platelet-derived growth factor
SREBP cleavage-activating protein
Smooth muscle cells
Sterol regulatory element-binding protein
Signal transducer and activator of transcription
Transforming growth factor beta
Toll-like receptor 4
Vascular endothelial cell growth factor
(very) low density lipoprotein
Vascular smooth muscle cell
Zinc-finger E-Box-binding homeobox 1.
We wish to gratefully acknowledge Dr. Eleftherios P. Diamandis for providing us the opportunity to work with Ingenuity Pathway Analysis. This work was supported by US NIH via National Human Genome Research Institute (NHGRI) [grant number P41HG04118] and by the National Resource for Network Biology [P41 RR031228] to GDB, by the University Health Network and Mount Sinai Hospital, Toronto, Ontario, Canada to EPD and by the Dean’s Research Fund, Faculty of Medicine, University of Toronto to JM.
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