BioChip J. (2014) 8(4): 260-268 DOI 10.1007/s13206-014-8403-9
Original Article
Functional Screening of Altered MicroRNA Expression in 3-methylcholanthrene-treated Human Umbilical Vein Endothelial Cells Hye Rim Park1, Seung Eun Lee1, Hana Yang1, Gun Woo Son1 & Yong Seek Park1,* Received: 11 July 2014 / Accepted: 24 July 2014 / Published online: 20 December 2014 � The Korean BioChip Society and Springer 2014
Abstract The common environmental contaminant 3-methylcholanthrene (3MC) is found in cigarette smoke and is produced by incomplete combustion of fat, wood and coal. 3MC is a poly aromatic hydrocarbon that interacts with an aryl hydrocarbon receptor and causes inflammation and induces vascular dysfunction; 3MC forms the DNA adducts structure and regulates cell cycle, which increase oxidative stress and inflammation. MicroRNAs (miRNAs) are non-coding RNA molecules that negatively regulate gene expression; these RNAs also play a role in cellular and molecular responses to toxicants, and can post-transcriptionally regulate gene expression. In this study, we examined whether miRNAs affect the regulation of gene expression in 3MC-treated human umbilical vein endothelial cells (HUVECs). We carried out pair-wise correlation analysis and identified 131 and 116 miRNAs with altered expression upon treatment of HUVECs with 100 nM and 1 μM 3MC, respectively. Furthermore, we identified 188 and 85 mRNAs with altered expression upon treatment with 100 nM and 1 μM 3MC; we subsequently analyzed their anti-correlations. The Gene Ontology (GO) enrichment analysis on the altered expression of miRNA-related genes displayed significant enrichment for genes involved in certain biological processes. Specifically, our results suggest that changes in miRNA expression caused by 3MC treatment are associated with inflammation of endothelial cells and may play a role in cardiovascular disease. 1
Department of Microbiology, School of Medicine, Kyung Hee University, #1 Hoegi-dong, Dongdaemun-gu, Seoul 130-701, Korea *Correspondence and requests for materials should be addressed to Y.S. Park (
[email protected])
Keywords: 3-methylcholanthrene, MicroRNA, Endothelial cells, Inflammation
Introduction 3-methylcholanthrene (3MC) is an environmental toxicant that is produced by cigarette smoke and incomplete combustion of carbon-based fuels and fat1. 3MC belongs to a family of polycyclic aromatic hydrocarbons (PAHs) and has many biological effects, including moderation of cytokine expression, changes in gene expression, production of mucin, and resulting in inflammation through its binding to an aryl hydrocarbon receptor (AhR)2,3. 3MC is known to increase cytochrome P450 (CYP) family that induced reactive oxygen species (ROS)4,5, that is mediated by AhR binding to its heterodimeric partner, the aryl hydrocarbon receptor nuclear translocator (ARNT). This complex regulates gene transcription6,7 and induces immunological response, hepatotoxicity, carcinogenicity, cardiac dysfunction, and oxidative stress8. Specifically, 3MC plays a role in endothelial damage that results in cardiovascular disease such as atherosclerosis3,9. 3MC causes cell cycle arrest and organizes the structure of DNA adducts by up-regulating p21 and p27 levels, which lead to the development of cardiovascular disease2,4,10. MicroRNAs (miRNAs) are highly conserved small non-coding RNAs that are composed of 20-23 nucleotides11,12 More than 1,500 miRNAs have been identified in the human genome, and these miRNAs control post transcriptional gene expression by suppressing protein translation or inhibiting target transcripts13. miRNAs recognize 3’untranslated regions (UTRs) of
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target mRNAs14, which leads to mRNA transcriptional inhibition or degradation11. Thus, miRNAs can mediate cell functions such as proliferation, apoptosis, development, and differentiation15. Toxicant exposure, resulting in oxidative or other cellular stress, affects the expression of miRNAs and can lead to the development of many human disease15,16. Recent studies have shown that miRNAs play a role in endothelial inflammation by adjusting inflammatory signals and inducing phenotypic changes17,18, these changes can result in endothelial dysfunction and cardiovascular disease19. Certain signature patterns of miRNAs expression have been related to specific pathologies, and miRNAs are potential targets for the treatment of cardiovascular disease19,20. Despite their biological significance, miRNAs known to be associated to 3MC exposure have not been extensively studied in endothelial cells. Thus, we examined the expression profiles of miRNAs and miRNA-related genes implicated in vascular disease in 3MC-exposed human umbilical vein endothelial cells (HUVECs).
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posed to 3MC at a concentration of 100 nM and 1 μM for 24 h. Compared to miRNA expression in untreated cells, 131 and 116 miRNAs had altered with 1.5 fold differences when HUVECs were treated with 100 nM and 1 μM 3MC, respectively. Of these, 83 miRNAs were up-regulated and 48 miRNAs were down-regulated in HUVECs treated with 100 nM 3MC; however, 55 miRNAs were up-regulated and 61 miRNAs were down-regulated in HUVECs exposed to 1 μM 3MC treatment (Tables 1 and 2). The significant changes in expression levels of miRNAs in response to different 3MC concentrations demonstrate that 3MC has a specific influence on miRNA expression in HUVECs. Connection of mRNA targets of miRNA dysregulated in 3MC-treted HUVECs
To understand the biological significance of miRNAs, we identified the correlation between miRNAs and mRNAs. All 1.5-fold up-regulated or down-regulated differential gene expression patterns were identified by comparing the gene expression in the treatment group of HUVECs to that in the control group for both 100
Results Cytotoxicity effect of 3MC in HUVECs
The cell viability of 3MC-treated HUVECs was determined by a standard colorimetric assay involving the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay after HUVECs were treated with 50, 100 nM and 1 μM 3MC for 24 h (Figure 1). At all concentrations tested, 3MC treatment did not affect cell viability. In the subsequent experiments described below, cells were only stimulated with 100 nM and 1 μM 3MC. Differential expression of miRNAs in 3MC-treated HUVECs
During the microarray experiment, HUVECs were ex-
Figure 1. Cytotoxicity of 3MC in HUVECs. HUVECs were treated with 50, 100 nM and 1 μM of 3MC for 24 h. Cell viability was analyzed using the MTT assay. Data are represented as the mean±SD values of triplicate experiments.
Table 1. List of expressed miRNAs in HUVEC stimulated with 100 nM 3MC 100 nM 3MC
1.5 fold up-regulated miRNA
has-miR-3613-3p, 1914-3p, 4646-5p, 720, 374b-5p, 1246, 21-5p, 3176, 137, 22-5p, 590-5p, 2355-3p, 374a-5p, 1244,4683, 39-5p, 4708-5p, 4286, 4525, 4284, 424-5p, 3910, 4324, 1262, 3200-5p, 4730, 1269a, 196a-5p, 4750, 4448, 2277-3p, 297, 3156-5p, 4467, 4668-5p, 328, 198, 4428, 3180, 4306, 3180-3p, 4492, 617, 4685-5p, 10b-3p, 744-3p, 3153, 4799-3p, 30b-3p, 4268, 335-5p, 654-3p, 3177-5p, 1976, 601, 1260a, 26b-5p, 4732-5p, 376a-3p, 4280, 4443, 191-3p, 376b, 299-5p, 10a-3p, 4783-5p, 30b-5p, 3691-3p, 625, 379-3p, 5096, 4670-5p, 1260b, 1909-3p, 126a-5p, 4731-3p, 4786-5p, 3144-5p, 148b-3p, 4654, has-let-7f-5p, 7g-3p, 7g-5p
1.5 fold down-regulated miRNA
has-miR-3201, 4725-3p, 4788, 371b-5p, 1208, 1281, 4445-3p, 1184, 3128, 4701-3p, 4423-3p, 3188, 940, 188-5p, 3647-5p, 122-5p, 760, 629-3p, 129-5p, 760, 629-3p, 129-5p, 1273d, 551b-5p, 4539, 4681, 3912, 1299, 4659b-3p, 512-5p, 3163, 4763-3p, 34c-3p, 4713-5p, 4776-5p, 4476, 218-1-3p, 1202, 4687-3p, 3972, 4767, 4446-3p, 4644, 4270, 4440, 1295a, 4689, 590-3p, 150-3p, 4669, 196b-3p
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Table 2. List of expressed miRNAs in HUVEC stimulated with 1 μM 3MC 1 μM 3MC 1.5 fold up-regulated miRNA
has-miR-3128, 30b-3p, 335-5p, 323-5p, 138-5p, 744-3p, 379-3p, 3200-5p, 720, 2355-3p, 2116-5p, 1244, 3611, 4428, 3934, 4423-3p, 26b-3p, 374b-5p, 4730, 3201, 654-3p, 4324, 23b-5p, 2682-5p, 525-5p, 628-5p, 4708-5p, 3613-5p, 27a-5p, 635, 137, 323b-3p, 4654, 340-5p, 4306, 3156-5p, 22-5p, 424-5p, 328, 4670-5p, 196a-5p, 3620, 4668-3p, 484, 4286, 671-3p, 608, 4732-5p, 135a-3p, 4671-3p, 130a-5p, 488-5p, 766-3p, has-let-7e-3p, 7f-5p
1.5 fold down-regulated miRNA
has-miR-4725-3p, 129-5p, 1208, 1184, 4529-3p, 572, 371b-5p, 4539, 3647-5p, 4783-3p, 4788, 602, 3188, 1281, 181c-5p, 4479, 4741, 4651, 4442, 4758-5p, 3912, 4734, 4776-5p, 4721, 3074-5p, 4270, 760, 940, 4763-3p, 3926, 1202, 4793-5p, 2861, 2277-3p, 4476, 4498, 4281, 1915-3p, 885-5p, 4486, 3607-5p, 4713-5p, 1911-3p, 4720-5p, 122-5p, 640, 3162-5p, 4681, 4804-5p, 541-3p, 3185, 103b, 4785, 4707-5p, 4695-5p, 4292, 4687-3p, 150-3p, 4689, 4685-3p, hsa-let-7f-2-3p
Table 3. Number of expressed genes in the HUVECs after treated with 100 nM and 1 μM 3MC
100 nM 3MC 1 μM 3MC
1.5 fold up-regulated genes (¤1.5)
1.5 fold down-regulated genes (⁄0.67)
Total
17 14
171 71
188 85
nM and 1 μM 3MC treatments. Treatment with 100 nM 3MC, resulted in changes in the expression of 188 genes; these changes included 17 and 171 mRNAs whose expression was up-regulated and down-regulated, respectively. Furthermore, treatment with 1 μM 3 MC resulted in changes in the expression of 85 mRNAs; this included 14 and 71 mRNAs whose expression was up-regulated and down-regulated, respectively (Table 3). These differentially expressed genes are involved in many biological processes and were categorized according to their functions. Categories included genes involved in translocation, transport, transcription, signal transduction, response to stress, metabolism, immune response, homeostasis, development, cell proliferation, cell migration, cell growth, cell division, cell differentiation, cell cycle, cell adhesion, behavior, and apoptosis (Figures 2 and 3). Changes in expression of genes related to inflammation and vascular disease mediated by 3MC-induced miRNAs
Vascular disease, as well as atherosclerosis and diabetes, are caused by endothelial cell dysfunction, inflammation, and cell injury. 3MC promotes endothelial damage and inflammation, which can lead to many vascular diseases. Genes encoding molecules associated with inflammation and vascular disease are listed in Table 4. Genes were anti-correlated with miRNA expression patterns that changed upon 3MC treatment. Some of the genes that were up-regulated included CDC42EP3, PHLDA1, PCDHA13, ADAMTS1 and
Figure 2. Classification of functional groups of genes differentially expressed by miRNA expression change with 100 nM 3MC treatment. Functional groups of (A) up-regulated genes after down-regulation of miRNA expression levels in HUVECs treated with 100 nM 3MC; (B) down-regulated genes after upregulation of miRNA expression levels in HUVECs treated with 100 nM 3MC.
TNFSF10, while genes that were down-regulated included IL6ST, WNK1, PTGES3, SGMS1 and TGM2.
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Gene ontology enrichment analysis of differentially expressed miRNAs
We applied a gene ontology approach using DAVID tools to functionally categorize miRNA-correlated genes. DAVID tools are an impartial method for identifying biological functions in list of genes. We observed a large degree of GO term enrichment in each gene set (Tables 5, 6). Among the identified groups, several genes were found to be involved in cell metabolism and cell death. Significant GO terms included nucleobase, nucleoside, nucleotide, and nucleic acid metabolic process (4.6×10-8); cellular nitrogen compound metabolic process (6.5×10-7); regulation of macromolecule metabolic process (2.5×10-5); and protein metabolic process (7.4×10-3). Cellular signal pathway analysis of miRNAs in 3MC-treated HUVECs
To identify the mechanism of the cell signaling pathways induced by 3MC, we analyzed KEGG pathway mapping using DAVID tools. We identified two functional pathways; the lysine degradation pathway and the adherens junction pathway. Some of the differentially expressed miRNAs we identified are associated with cell growth, differentiation, actin polymerization, and tight junctions. These results provide evidence that 3MC may have a role in regulating the expression of adherens junction-related miRNAs (Figure 4). Figure 3. Classification of functional groups of genes differentially expressed by miRNA expression change with 1 μM 3MC treatment. Functional groups of (A) up-regulated genes after down-regulation of miRNA expression levels in HUVECs treated with 1 μM 3MC; (B) down-regulated genes after upregulation of miRNA expression levels in HUVECs treated with 1 μM 3MC.
Discussion 3MC is a highly toxicant molecule in cigarette smoke and can result from the incomplete combustion of
Table 4. Genes related with inflammation and vascular disease in 3MC-treated HUVECs Fold change
Anti-correlated miRNA
NM_006449 NM_007350 NM_018904 NM_199355
2.274 1.760 1.599 1.592
hsa-miR-129-5p hsa-miR-3163 hsa-miR-4644 hsa-miR-3163
NM_001190942
1.538
hsa-miR-4659b-3p
Down-regulated IL6ST interleukin 6 signal transducer (gp130, oncostatin M receptor) WNK1 WNK lysine deficient protein kinase 1 PTGES3 prostaglandin E synthase 3 (cytosolic)
NM_001190981 NM_001184985 NM_006601
0.163 0.465 0.005
SGMS1 TGM2
NM_147156 NM_004613
0.330 0.199
hsa-miR-2355-3p hsa-miR-1244 hsa-miR-4646-5p, hsa-miR-4286 hsa-miR-4306 hsa-miR-4448
Gene symbol
Description
Gene Bank No.
Up-regulated CDC42EP3 PHLDA1 PCDHA13 ADAMTS18 TNFSF10
CDC42 effector protein (Rho GTPase binding) 3 pleckstrin homology-like domain, family A, member 1 protocadherin alpha 13 ADAM metallopeptidase with thrombospondin type 1 motif, 18 tumor necrosis factor (ligand) superfamily, member 10
sphingomyelin synthase 1 transglutaminase 2 (protein-glutamine-gammaglutamyltransferase)
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Table 5. Selective Gene Ontology biological process annotations related with changed expression of 100 nM 3MC-treated genes using the DAVID tool GO:0006139 GO:0010467 GO:0051276 GO:0034641 GO:0044260 GO:0009059 GO:0044249 GO:0060255 GO:0031323 GO:0080090 GO:0048523 GO:0048522 GO:0050794 GO:0019222 GO:0051172 GO:0051171 GO:0048519 GO:0009889 GO:0009890 GO:0048518 GO0031324 GO:0010605 GO:0010604 GO:0009892 GO:0031325 GO:0009893 GO:0051173 GO:0008361 GO:0010941 GO:0045597 GO:0009891 GO:0045595 GO:0022604 GO:0050793 GO:0032535 GO:0051094 GO:0010942 GO:0051128
Term
P-Value
Fold
nucleobase, nucleoside, nucleotide and nucleic acid metabolic process gene expression chromosome organization cellular nitrogen compound metabolic process cellular macromolecule metabolic process macromolecule biosynthetic process cellular biosynthetic process regulation of macromolecule metabolic process regulation of cellular metabolic process regulation of primary metabolic process negative regulation of cellular process positive regulation of cellular process regulation of cellular process regulation of metabolic process negative regulation of nitrogen compound metabolic process regulation of nitrogen compound metabolic process negative regulation of biological process regulation of biosynthetic process negative regulation of biosynthetic process positive regulation of biological process negative regulation of cellular metabolic process negative regulation of macromolecule metabolic process positive regulation of macromolecule metabolic process negative regulation of metabolic process positive regulation of cellular metabolic process positive regulation of metabolic process positive regulation of nitrogen compound metabolic process regulation of cell size regulation of cell death positive regulation of cell differentiation positive regulation of biosynthetic process regulation of cell differentiation regulation of cell morphogenesis regulation of developmental process regulation of cellular component size positive regulation of developmental process positive regulation of cell death regulation of cellular component organization
0.000000046 0.00000032 0.0000005 0.00000065 0.00000074 0.000019 0.000021 0.000025 0.000025 0.000032 0.000066 0.000074 0.000074 0.000085 0.0001 0.00011 0.00013 0.00014 0.00028 0.00043 0.0009 0.0011 0.0019 0.002 0.0025 0.0039 0.0072 0.0075 0.0076 0.012 0.013 0.021 0.024 0.024 0.026 0.029 0.029 0.037
2 2 4.4 1.8 1.6 1.9 1.7 1.8 1.7 1.7 2.2 2.1 1.4 1.6 3.4 1.8 2 1.7 3.1 1.9 2.6 2.6 2.4 2.4 2.3 2.2 2.4 4 2.2 3.6 2.2 2.4 4.5 2.1 3.1 3 2.5 2.3
DAVID tools were used for GO analysis. Results show the GO terms that are overrepresented (fold change¤1.5 and p⁄0.05, Fisher’s exact test) in the anti-correlated targets of miRNAs in 100 nM 3MC-stimulated HUVECs.
almost any fuel in the environment10. This molecule induces a wide variety of toxicities such as teratogenesis, atherogenesis, and carcinogenesis10,21,22. Considerable research has shown that 3MC, as well as oxidative mediators, increase cell dysfunction and oxidative stress. This can cause inflammation and cardiovascular diseases in certain mouse strains expressing the AhR6,23. Furthermore, in mice 3MC has been shown to enhance the size and number of spontaneous atherosclerotic lesions24. Microarray technology is used for a variety of experimental purposes, and it can predict the potential toxicity of unknown elements16. miRNAs are endogenous
non-coding RNAs that play a significant role in various biological processes20,25. miRNAs regulate development, cell growth, apoptosis and proliferation. These RNAs can also contribute to the development of various diseases20. In the present study, we aimed to evaluate how 3MC manipulation affects miRNA expression and miRNArelated gene expression in HUVECs. We determined that the expression of 131 miRNAs was altered in HUVECs exposed to 100 nM 3MC, and the expression of 116 miRNAs was altered in HUVECs treated with 1 μM 3MC. Furethermore, we confirmed anti-correlated miRNA-mRNA pairs. Among the anti-correlated
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Table 6. Selective Gene Ontology biological process annotations related with changed expression of 1 μM 3MC-treated genes using the DAVID tool Term GO:0051276 GO:0006730 GO:0044260 GO:0048523 GO:0019538 GO:0007411 GO:0051128 GO:0040008 GO:0048519 GO:0022604 GO:0006974 GO:0008360 GO:0032535 GO:00050794 GO:0033554 GO:0009890 GO:0031323 GO:0060255 GO:0080090 GO:0009892
chromosome organization one-carbon metabolic process cellular macromolecule metabolic process negative regulation of cellular process protein metabolic process axon guidance regulation of cellular component organization regulation of growth negative regulation of biological process regulation of cell morphogenesis response to DNA damage stimulus regulation of cell shape regulation of cellular component size regulation of cellular process cellular response to stress negative regulation of biosynthetic process regulation of cellular metabolic process regulation of macromolecule metabolic process regulation of primary metabolic process negative regulation of metabolic process
P-Value
Fold
0.00016 0.0012 0.0017 0.0068 0.0074 0.01 0.012 0.013 0.014 0.017 0.019 0.021 0.026 0.027 0.029 0.031 0.035 0.035 0.039 0.042
4.9 10.5 1.5 2.1 1.8 8.8 3.6 4.2 2 7.2 3.8 13.1 4.4 1.3 2.9 2.9 1.5 1.5 1.5 2.4
DAVID tools were used for GO analysis. Results show the GO terms that are overrepresented (fold change¤1.5 and p⁄0.05, Fisher’s exact test) in the anti-correlated targets of miRNAs in 1 μM 3MC-stimulated HUVECs.
Figure 4. 3MC-induced miRNAs involved in the adherens junction signaling pathway.
pairs, we identified genes associated with inflammation or vascular disease. CDC42EP3, PHLDA1, PCD HA13, ADAMTS1 and TNFSF10 were up-regulated whereas IL6ST, WNK1, PTGES3, SGMS1 and TGM2 were down-regulated. CDC42EP3 is well-known Rho GTPase effector protein that interferes with phagocy-
tosis, cytoskeleton rearrangement, and cell spreading by the internalization of IgG-opsonized; this can produce inflammation26,27. TMFSF10 is highly conserved in mammals and is potential therapeutic target for inflammation28. This protein has a proinflammatory function and accelerates inflammation during vascular disease28,29. We found that the down-regulated genes were associated with anti-inflammatory modulation, indicating that 3MC affects vascular disease by miRNA-mediated modulation of gene expression. Cellular signaling pathways stimulated by 3MC include the lysine degradation pathway and adherens junction pathway. The latter is associated with cell growth, differentiation and weak-tight junctions of endothelial cell; it affects vascular conditions such as vasogenic edema and inflammation24,30. In summary, we observed changes in the expression of 131 and 116 miRNAs in HUVECs upon exposer to 100 nM and 1 μM 3MC, respectively. Using a microarray and GO functional analysis, we additionally identified 188 and 85 miRNA target genes for 100 nM and 1 μM 3MC, respectively, and examined the biological processes in which these target genes were involved. Our findings reveal changes in the expression levels of miRNAs underlying 3MC-related conditions, and indicate that 3MC regulates the progression of vascular disease. Further study is necessary to confirm the detailed mechanisms by which 3MC regualtes miRNA-based gene expression.
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Materials and Methods Materials
3-methylcholanthrene (3MC), 3-(4,5-dimethylthiazol2-yl)-2,5-diphenyltetrazolium bromide (MTT), and dimethylsulfoxide (DMSO) were purchased from Sigma-Aldrich (St.Louis, MO, USA). Cell culture
HUVECs were purchased from StemCell Technologies (Vancouver, Canada) and cultured in endothelial cell growth medium-2 (EGM-2; Lonza, Walkersville, MD, USA), which consisted of heat-inactivated 2% fetal bovine serum (FBS), epidermal growth factor (EGF), human vascular endothelial growth factor (VEGF), insulin-like growth factor-1 (IFG-1), ascorbic acid, heparin, basic fibroblast growth factor (FGFB), hydrocortisone, amphotericin B and gentamicin sulfate. The cells were grown at 37� C in the presence of 5% CO2 in a humidified atmosphere and maintained by subculturing every 3-4 days. HUVECs were cultured until approximately about 80% confluent at which point they were further incubated with fresh medium. Throughout these experiments, the cells were used within passages 4-931.
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washing steps, the slides were dried by centrifugation at 900 g at room temperature. For identification the of gene expression profile, we applied the RNA samples (25 μg) to a Roche Nimble Gen Human Whole genome 12 plex array. Each extracted total RNA sample was labeled with Cyanine (Cy3)-conjugated dCTP (Amersharm, Piscataway, NJ, USA) and Cyanine (Cy3)-labeled cDNA mixture, and was then concentrated via the ethanol precipitation method. Concentrated Cy3-labeled cDNAs were resuspended in 30 μL of hybridization solution (GenoCheck, Ansan, Korea). Labeled cDNAs were placed on Roche NimbleGen Human Whole genome 12 plex arrays and covered in a chamber. Slides were hybridized for 12 h at 62� C in a MAUI system. GeneSpring GX v11 (Agilent technologies) was used to analyze of miRNA and gene expression arrays. Data applied standard normalization methods for one-channel microarrays: percentile median normalization. Fold-change values were calculated for unpaired comparisons between normal treatment and MG treatment. Fold-change filters included the requirement that the genes be present in at least 150% of controls for up-regulated genes, and lower than 66.67% of controls for down-regulated genes. Among the significant genes, we investigated predicted targets of significantly changed miRNAs using TargetScan 5.1 and microCosm databases.
Cell viability assay
HUVECs (4×103 cells/well) were seeded in a 96-well plate and incubated in EGM-2 media for 24 h at 37� C. Next, 50, 100 nM and 1 μM of 3MC were added, and the cells were kept at 37� C for 24 h. Then, 20 μL of 5 mg/mL MTT solution was added to each well, and the plate was incubated at 37� C for 4 h The MTT solution was then removed and 20 μL of DMSO was added to each well. The plate was kept on a plate shaker for 30 min, and absorbance was measured with a micro plate reader at 540-570 nm32. Gene and miRNA expression microarray experiments
Total RNA was extracted from 3MC-exposed HUVECs using TRI Reagent (MRC, Cincinnati, OH, USA). For analysis of the miRNA expression profile, total RNA sample (100 ng) containing miRNA was labeled with Cyanine 3-pGp (Cy3) using the Agilent miRNA Complete Labeling and Hyb Kit (Agilent Technologies, Foster City, CA, USA). The sample was placed on an Agilent Human miRNA v14 (AMDID 026867) and covered by a Gasket slide (Agilent Technologies). Slides were hybridized for 16 h at 42� C in the Agilent hybridization system. Hybridized slides were washed in the first wash buffer (0.0005% Triton X-102) for 5 min and the second wash buffer for 5 min. After the
Statistical analysis
Data were analyzed by the student’s t-test and the results were expressed as mean±S.D. Pathway mapping for analysis data
The expressed miRNAs were analyzed using DAVID tools (http://david.abcc.ncifcrf.gov/). Followed by categorization with DAVID tools, and the pathway of the grouped profile data was identified using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway mapping. Acknowledgements This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2011-0030 072).
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