Analysis of PPARα-dependent and PPARα-independent transcript regulation following fenofibrate treatment of human endothelial cells
Fenofibrate is a synthetic ligand for the nuclear receptor peroxisome proliferator-activated receptor (PPAR) alpha and has been widely used in the treatment of metabolic disorders, especially hyperlipemia, due to its lipid-lowering effect. The molecular mechanism of lipid-lowering is relatively well defined: an activated PPARα forms a PPAR–RXR heterodimer and this regulates the transcription of genes involved in energy metabolism by binding to PPAR response elements in their promoter regions, so-called “trans-activation”. In addition, fenofibrate also has anti-inflammatory and anti-athrogenic effects in vascular endothelial and smooth muscle cells. We have limited information about the anti-inflammatory mechanism of fenofibrate; however, “trans-repression” which suppresses production of inflammatory cytokines and adhesion molecules probably contributes to this mechanism. Furthermore, there are reports that fenofibrate affects endothelial cells in a PPARα-independent manner. In order to identify PPARα-dependently and PPARα-independently regulated transcripts, we generated microarray data from human endothelial cells treated with fenofibrate, and with and without siRNA-mediated knock-down of PPARα. We also constructed dynamic Bayesian transcriptome networks to reveal PPARα-dependent and -independent pathways. Our transcriptome network analysis identified growth differentiation factor 15 (GDF15) as a hub gene having PPARα-independently regulated transcripts as its direct downstream children. This result suggests that GDF15 may be PPARα-independent master-regulator of fenofibrate action in human endothelial cells.
KeywordsEndothelial cells Fenofibrate PPARα Transcriptome network
Peroxisome proliferator-activated receptor alpha (PPARα) is a ligand-activated transcription factor belonging to the family of nuclear receptors. PPARα is highly expressed in liver, skeletal muscle, kidney, and heart and it regulates the transcription of genes involved in energy metabolism [1, 2]. Over the past decade, PPARα has been investigated as a therapeutic target and drugs targeting PPARα have been developed. Fenofibrate is one of the synthetic ligands of PPARα and has been widely used for the treatment of hyperlipidemia, type 2 diabetes, and cardiovascular diseases due to its lipid-lowering effects . A reported molecular mechanism of the lipid-lowering effect is “trans-activation”, i.e., PPARα activated by fenofibrate forms a PPAR–RXR heterodimer complex, which binds to PPAR response elements (PPREs) in the promoter regions of genes involved in beta-oxidation and lipoprotein/cholesterol transport . In addition, fenofibrate also has anti-inflammatory and anti-atherogenic functions, which are thought to be based on “trans-repression” mechanisms in endothelial cells, smooth muscle cells, and other vascular cells .
While the lipid-lowering molecular mechanism in the liver is well known, the anti-inflammatory mechanisms in vascular cells have not been fully investigated. In addition, there are some PPARα-independent drug effects in human endothelial cells. For example, fenofibrate has been shown to regulate the survival of cultured human retinal endothelial cells in PPARα-independent manner, since pretreatment with the PPARα antagonist, MK 886, did not alter this effect and since another selective agonist for PPARα, WY-14643, had no significant effect on cell survival . Moreover, in human umbilical vein endothelial cells (HUVECs), fenofibrate has been shown to increase AMPK phosphorylation, but neither bezafibrate nor WY-14643 had the same effect . Therefore, we speculate that fenofibrate has PPARα-independent actions in human endothelial cells.
The objectives of the present study are (1) to identify transcripts in HUVECs regulated by fenofibrate in a PPARα-dependent and -independent manner, (2) to construct dynamic Bayesian transcriptome networks to reveal PPARα-independent mechanisms of action of fenofibrate, and the master regulators of PPARα-independent transcripts, based on computational data analysis techniques. In order to do this, we treated HUVECs with fenofibrate after pretreatment (or not) with small interfering RNAs (siRNA) targeting PPARα, and then profiled mRNA abundance using microarrays. PPARα-dependent and -independent transcripts regulated by fenofibrate were identified from the microarray data. We also estimated a Bayesian transcriptome regulatory network showing gene–gene regulatory relationships that were activated by fenofibrate. Our transcriptome network analysis indicated that growth differentiation factor (GDF) 15 is a master regulator of fenofibrate action, which acts in a PPARα-independent manner. Based on our results, we conclude that effects of fenofibrate in HUVECs are largely PPARα-independent and GDF15 is a new candidate, which acts as a key mediator of fenofibrate action in endothelial cells.
Materials and methods
Human umbilical vein endothelial cells were isolated from umbilical cords by collagenase digestion and cultured at 37°C/5%CO2 in basal culture medium supplemented with a proprietary mixture of heparin, hydrocortisone, epidermal growth factor, FGF, and 2% fetal calf serum (FCS; EGM-2, Cambrex, Workingham, UK) as previously described .
Small interference RNA
Small interfering RNAs treatment was performed through lipid-based transfection method  with either PPARα siRNA (Cat. No. M-00343400) or control siRNA (Cat. No. D-001206-13), using SmartPool reagents (Dharmacon, Lafayette CO, USA) according to the manufacturers’ instructions. HUVECs from 10 individuals were pooled, plated at 2.2 × 105 cells in six-well plates, and allowed to recover for 24 h at 37°C/5%CO2, at which time they were ~80% confluent. For transfection, 5 μl/well of 20 μM RNAi (PPARα or control), 42 μl/well of siFECTAMINE (ICVEC, UK no longer available), and 500 μl/well of serum-free medium were incubated at room temperature for 5 min. The relevant transfection mix was then added to each well containing 500 μl of fully supplemented media and incubated at 37°C/5%CO2 for 3 h, after which the transfection medium was replaced with 3 ml of fresh fully supplemented medium. About 24 h post transfection, relevant wells were treated with 25 μmol fenofibrate (Sigma–Aldrich, UK) dissolved in DMSO (Sigma–Aldrich, UK). Cells were incubated at 37°C/5%CO2 for the time points in transcriptional profiling.
Transcriptional profiling using microarrays
We first compared fenofibrate-treated cells in data set A in Fig. 1 with untreated control cells in data set N (comparison 1). We also compared PPARα siRNA-treated cells in data set C with control siRNA-treated cells in data set B (comparison 2). In the comparison 1, we identified fenofibrate-regulated transcripts based on the Significant Analysis of Microarray (SAM) statistical test, which takes multiple testing into account by estimating a false discovery rate, “q value” . If a transcript was up- or down-regulated 1.7-fold or more in data set A rather than in data set N, and had a SAM q value of ≤0.05, we regarded that transcript as a fenofibrate-regulated transcript. In the comparison 2, if a transcript was up- or down-regulated 1.7-fold or more in data set C rather than in data set B, we regarded that transcript as a PPARα-regulated transcript. From two comparisons, if a transcript was oppositely regulated by fenofibrate and PPARα siRNA, we regarded that transcript as a fenofibrate-regulated transcript through PPARα-dependent mechanisms. The remaining fenofibrate-regulated transcripts that did not meet the criterion in the comparison 2 were considered PPARα-independently regulated transcripts.
In Gene Ontology analysis of the comparison 1 by MetaGP , SAM P value of each gene at each time point is used to evaluate significance of Gene Ontology annotations (biological process). The Benjamini and Hochberg’s method  is used for the adjustment of multiplicity.
Transcriptome network estimation
For time-course microarray data, various computational techniques have been developed in bioinformatics for estimating transcriptome networks that show causal dependencies between expression data of genes in terms of Granger causality. Among them, we use dynamic Bayesian network (DBN) and non-parametric regression method , because this method does not require any data discretization and can capture non-linear dependencies between transcripts. Also, we use the node-set separation method , in order to find dynamical changes of activities of transcriptome networks by dosing with fenofibrate. From this combination, we can find which networks are activated over our observed time course. In this study, we estimated five transcriptome networks based on node-set separation method . Each node-set consists of regulated transcripts at (1) 2 h, (2) 2 or 4 h, (3) 4 or 6 h, (4) 6 or 8 h, and (5) 8 and 18 h, respectively.
We briefly introduce our model, the DBN and non-parametric regression, as follows: let x(t) be the transcript expression value vector observed at time t (t = 1,…,T). The DBN model can be represented by statistical density functions as f(x(1),…,x(T)) = ΠtΠjfj(xj(t)|pj(t − 1), θj), where pj(t) is the expression value vector of the parent genes of jth gene and θj is the parameter vector. The essential point of the node-set separation method is to define a node-set for an observed time. In order to create the node-set, the first-order Markov process of the DBN is used, i.e., a node-set Nt at time t is defined by the union of the sets of active transcript sets At and At−1, where At is the active transcript set defined as the set of transcript whose expression values are significantly different from the control expression profiles. By using the DBNs, we estimated the transcriptome network Gt on Nt, and the final transcriptome network can be obtained as G = G1∪…∪GT. The final transcriptome network allows us to identify which transcripts are important depending on time and which pathways are activated by the drug. In the trascriptome network estimation, a non-redundant gene set was used, so if a transcript has multiple probes on microarray, the probe with the highest intensity was used.
In order to identify transcripts regulated by fenofibrate in HUVECs in a PPARα-dependent or -independent manner, we generated two types microarray data (Fig. 1). First, we selected HUVEC transcripts regulated by fenofibrate drug treatment by comparing time-course microarray data from fenofibrate-treated cells (data set A in Fig. 1) with untreated control cells (data set N in Fig. 1). Next, PPARα-dependently or -independently regulated genes were identified by comparing time-course microarray data from cells treated with fenofibrate in the presence or absence or anti-PPARα siRNAs, i.e., by the combination of experiments N and A and experiments B and C, as shown in Fig. 1. Then, we constructed dynamic Bayesian transcriptome networks to further analyze fenofibrate’s mode of action, especially focusing on PPARα-independent actions.
Fenofibrate treatment time-course microarray data on HUVECs
The number of transcripts regulated by fenofibrate (1.7-fold or more with SAM q value ≤ 0.05)
Time point (h)
PPARα siRNA knocked down
Identification of PPARα-dependent and -independent transcriptional regulation by fenofibrate on HUVECs
The number of transcripts oppositely regulated by fenofibrate and PPARα siRNA-mediated knock-down
Fenofibrate up regulation-PPARα siRNA down regulation
Fenofibrate down regulation-PPARα siRNA up regulation
Dynamic transcriptome network
Gene list having PPARα-dependently (a) and -independently (b) regulated transcripts as its child in transcriptome network with statistical significance (P value < 0.01)
(a) Gene list having PPARα-dependently regulated transcripts
RecQ protein-like 5
Dickkopf homolog 1
Regulatory solute carrier protein family 1 member 1
Chromosome 1 open reading frame 24
URB2 ribosome biogenesis 2 homolog
Peroxisome proliferator-activated receptor alpha
(b) Gene list having PPARα-independently regulated transcripts
Solute carrier family 1 member 4
Chromosome 4 open reading frame 34
Hypothetical protein LOC148189
Growth differentiation factor 15
Hub genes in transcriptome networks or protein–protein interaction networks are recognized as master regulators in E. coli , in yeast , and in mammalian cells [7, 21, 22]. Therefore, we focused on the hub transcripts in our network. We defined hub transcripts as those transcripts with the top 5% of numbers of direct children in the network (Supplementary Table S7). Reassuringly, PPARα, a direct target of fenofibrate, is listed as a hub transcript. This seems reasonable because a drug targeted molecule is likely to be important for downstream gene–gene regulation relationships.
The child genes of GDF15 in transcriptome network
ADAM metallopeptidase with thrombospondin type 1 motif, 1
Adrenergic, beta-2-, receptor, surface
Ariadne homolog 2 (drosophila)
Chromosome 1 open reading frame 24
Chromosome 14 open reading frame 139
Cdc6 cell division cycle 6 homolog (S. cerevisiae)
Cyclin-dependent kinase-like 3
Centrosomal protein 72 kDa
Ectodermal-neural cortex (with btb-like domain)
Family with sequence similarity 46, member a
3-Hydroxymethyl-3-methylglutaryl-coenzyme a lyase
Heparan sulfate (glucosamine) 3-o-sulfotransferase 1
Heat shock 70 kDa protein 1B
Hypothetical protein loc157562
Hypothetical protein loc256273
Minichromosome maintenance complex component 3
Minichromosome maintenance complex component 10
NEDD4 binding protein 2-like 2
n-myc downstream regulated gene 1
Protocadherin alpha subfamily c, 2
Solute carrier family 5, member 6
Tbc1 domain family, member 2
Transforming growth factor, beta 2
Tubulin tyrosine ligase-like family, member 1
Uridine monophosphate synthetase
Vasoactive intestinal peptide
X-ray repair complementing defective repair in chinese hamster cells 2
Zinc finger protein 251
Fenofibrate is a synthetic ligand of a nuclear receptor PPARα that has been widely used for the treatment of metabolic disorders over the past decade [2, 3]. This drug has lipid-lowering action on liver, and in addition anti-inflammatory and anti-atherogenic action on endothelial cells and smooth muscle cells. The lipid-lowering actions of fenofibrate are thought to be based on PPARα’s binding to the PPREs within promoter regions of genes involved in lipid, lipoprotein, and glucose metabolism—a “trans-activation” mechanism [1, 2]. The anti-inflammatory and anti-atherogenic actions in vascular cells are thought to be mediated in part by PPARα-ligand complexes suppressing inflammatory signaling pathways through interaction with nuclear factor-κB (NF-κB) and activated protein 1 (AP-1) and through up regulation of IκBa [1, 4]. Nevertheless, the molecular mechanisms of fenofibrate actions in vascular cells remain poorly understood. For example, there are some reports suggesting that fenofibrate acts in PPRAα-independent manner on human endothelial cells [5, 6]. Revealing the mechanisms of fenofibrate’s actions on endothelial cells may lead to new drug development for diseases related to endothelial dysfunction, such as atherosclerosis, thrombosis, and hypertension.
In this study, we used microarray analysis of HUVECs treated with fenofibrate in the presence or absence of pretreatment with siRNA directed against PPARα (Fig. 1). We first identified transcripts that were significantly regulated by fenofibrate. The number of regulated transcripts gradually increased with time of fenofibrate exposure (Fig. 1) and fenofibrate’s effects were first clearly observed after 6 h of treatment which is consistent with previous report . Interestingly, the transcript encoding PPARα was not detected to be upregulated until after 8 h of treatment. Then, we used the MetaGP tool to biologically interpret microarray data at each time point. MetaGP evaluated the significance of Gene Ontology terms based on a P value at each time point. This analysis suggested that inflammation related genes annotated by inflammatory response (GO:0006954), angiogenesis (GO:0001525), and cell adhesion (GO:0007155) were highly significant at middle or late time points (6, 8, and 18 h).
Next, we identified PPARα-dependently and -independently regulated transcripts by using microarray analysis in presence and absence of anti-PPARα siRNA prior to the treatment of fenofibrate (Fig. 1). Contrary to our expectations, data for most fenofibrate-regulated transcripts were consistent with PPARα-independent fenofibrate mechanisms of action, in spite of the fact that PPARα is a target of fenofibrate (Table 2). It is interesting to speculate about the possible explanations for this observation. Fenofibrate and other PPARα ligands appear to predominantly modulate the expression of genes that are traditionally thought to be activated by inflammatory pathways. These genes may not been significantly regulated by fenofibrate responses in HUVECs without a prior inflammatory stimulus, as was the case in our study. This lead to our observation of mainly PPARα-independent regulation of transcript abundance by fenofibrate. Another possible explanation is that fenofibrate might mainly act through PPARα-independent mechanisms in endothelial cells. Unlike the mechanism of lipid-lowering actions based on the binding PPREs of promoter regions of PPARα targeting genes, the anti-inflammatory mechanisms may be very complicated.
In order to address PPARα-dependent and -independent mechanisms of fenofibrate action in human endothelial cells, we estimated a transcriptome network by the DBN and node-set separation model . In this network, PPARα has a significant number of transcripts as children, which we also showed were regulated by siRNA targeted towards PPARα (P value < 0.01) (Table 3). This suggests that our network estimation method may be capable of capturing gene–gene regulation relationships mediated by fenofibrate through PPARα. Despite this, a limitation of our network estimation method is the small number of time points of microarray data—this method might prove even more informative with more time points of microarray data.
Our microarray data show that many transcripts are regulated by fenofibrate in HUVECs in a PPARα-independent manner. Therefore, we focused on the PPARα-independently regulated transcripts in transcriptome networks in an attempt to reveal fenofibrate’s PPARα-independent molecular mechanisms. Some network hubs had significant numbers of PPARα-independently regulated transcripts as children (P value < 0.01) (Table 3). Based on the relevance of their biological functions and drug efficacy of fenofibrate, we focused on GDF15/MIC-1 belonging to the transforming growth factor beta (TGF-beta) superfamily. Previous work has shown that in HUVECs, GDF15 regulated the MMP-2 activity and cell migration that contributes to atherosclerotic lesion formation and increases the risk of plaque hemorrhage . It is important to note that fenofibrate appears to affect similar pathways to GDF15 in HUVECs [18, 27]. GDF15 also induces apoptosis in tumor cells  as does fenofibrate [16, 29]. In fact, there are 11 apoptosis or cell death related genes (ADRB2, ASNS, CCNB1, CDC6, CDKL3, HSPA1B, MCM10, NDRG1, TGFB2, VIP, and XRCC2) downstream of GDF15 in our transcriptome network (Table 4). Moreover, although there is no study that reports the regulation of GDF15 expression by fenofibrate in human endothelial cells, several groups reported that PPARγ agonists induce GDF15 expression through PPARγ independent pathway in cancer cell lines [30, 31, 32]. Two molecular mechanisms are proposed in this regulation; transcriptional and post-transcriptional regulation. In the former, PPARγ agonists induce the expression of early growth response 1 (EGR-1) transcription factor and binding to the promoter regions of GDF15 [30, 31]. In the latter, PPARγ agonist inhibits GDF15 RNA degradation, thereby increasing GDF15 expression . In these two proposed mechanisms, the phosphorylation of ERK1/2 by PPARγ agonists plays very important role [30, 32, 33]. Fenofibrate also induces the phosphorylation of ERK1/2 in adult rat ventricular myocytes . Taken together, we hypothesize that GDF15 might be a novel target point of fenofibrate via the ERK pathway independent of PPARα activation in human endothelial cells.
In conclusion, there are several reports of PPARα-independent actions of fenofibrate, including experiments in human endothelial cells. Here, we used microarray analysis of HUVECs treated with fenofibrate with or without siRNA targeted towards PPARα and identified transcripts regulated by fenofibrate through putative PPARα-dependent and -independent mechanisms. Unexpectedly, most fenofibrate-regulated genes appeared to be regulated in a PPARα-independent manner. In order to further reveal PPARα-independent mechanisms, we estimated a transcriptome network by using DBN model. This network suggested that GDF15 may be an important regulator of PPARα-independent fenofibrate action in HUVECs. Previous studies have reported that in HUVECs treated by GDF15, cell migration is inhibited and MMP-2 activity is suppressed. Therefore, GDF15 may provide a novel mechanism for fenofibrate’s PPARα-independent actions in human endothelial cells.
The authors would like to thank two reviewers for their constructive comments and suggestions. Computation time was provided by the Super Computer System, Human Genome Center, Institute of Medical Science, University of Tokyo.
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