Transcriptome Profile Analysis of Saturated Aliphatic Aldehydes

Jul 11, 2014 - In the current study, we aimed to investigate the transcriptomic responses and identify specific molecular signatures of low-molecular-...
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Transcriptome Profile Analysis of Saturated Aliphatic Aldehydes Reveals Carbon Number-Specific Molecules Involved in Pulmonary Toxicity Mi-Kyung Song,† Han-Seam Choi,† Hyo-Sun Lee,† and Jae-Chun Ryu*,†,‡ †

Cellular and Molecular Toxicology Laboratory, Korea Institute of Science & Technology P.O. Box 131, Cheongryang, Seoul 130-650, Korea ‡ Department of Pharmacology and Toxicology, Human and Environmental Toxicology, Korea University of Science and Technology, Gajeong-Ro 217, Yuseong-gu, Daejeon 305-350, Korea S Supporting Information *

ABSTRACT: In the current study, we aimed to investigate the transcriptomic responses and identify specific molecular signatures of low-molecular-weight saturated aliphatic aldehydes (LSAAs). To evaluate the change in gene expression levels, A549 human alveolar epithelial cells were exposed to six LSAAs (propanal, butanal, pentanal, hexanal, heptanal, and octanal) for 48 h. Clustering analysis of gene expression data show that the low carbon number group (LCG; propanal, butanal, and pentanal) was distinguished from the high carbon number group (HCG; hexanal, heptanal, and octanal). Also, transcriptomic profiling indicates that the LCG exposure group was more sensitive in gene alterations than the HCG group. Supervised analysis revealed 703 LCG specific genes and 55 HCG specific genes. After gene ontology (GO) analysis on LCG specific genes, we determined several key pathways which are known as being related to increase pulmonary toxicity such as cytokine−cytokine receptor interaction and chemokine signaling pathway. However, we did not find pulmonary toxicity-related pathways through GO analysis on HCG specific genes. Genes that are expressed in only the low carbon LSAA exposure group were regarded as biomarkers of aldehyde-induced pulmonary toxicity. In conclusion, this study describes changes in gene expression profiles in the in vitro respiratory system in response to exposure to 6 LSAAs with different carbon numbers and relates these gene alterations to pulmonary toxicity-related pathways. Moreover, novel carbon number-specific genes and pathways can be more widely implemented in combination with the traditional technique for assessment and prediction of exposure to environmental toxicants.

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several compounds used to enhance the aroma and flavor of various products.10 Despite their usefulness, little research has been conducted on the toxicity of LSAAs at the genetic level. LSAAs have been found in the blood of patients with cancer, and hexanal and heptanal are lung cancer biomarkers.11,12 Thus, LSAA-induced adverse effects have been proposed, and several research groups are assessing the risk and toxicity of LSAAs using human and animal models; however, studies focused on physicochemical properties may not be sufficient to determine how LSAAs affect cells at the molecular level.13 Therefore, a toxicological study of the LSAA-induced gene alterations should be performed; this will likely result in the identification of predictive gene signatures and help to elucidate the mechanisms of exposure-induced toxic effects. Highthroughput screening tools, such as global gene expression analysis using microarrays, have allowed for more comprehensive functional analyses of environmental pollutants impacts.14

INTRODUCTION The human exposure to chemicals and environmental toxicants is steadily increasing. Aldehydes are a group of volatile organic compounds (VOCs) and important indoor air pollutants that are associated with health risks. They are emitted from building materials, furniture, paints, carpets, rubber, home electrics, detergents, cleaning products, cooking oil, and fried foods.1−6 Aldehydes are produced through various physiological processes and biotransformation events. Some aldehydes are necessary for particular functional processes, while others are believed to be cytotoxic intermediates with various functions, including in transcriptomic regulation, signal transduction, and cell proliferation.7,8 Low-molecular-weight saturated aliphatic aldehydes (LSAAs) are ubiquitous in the environment.9 Propanal is used to synthesize rubber and is an ingredient in preservatives. Hexanal is an essential chemical in the manufacture of food additives, synthetic resins, and insecticides. Heptanal and octanal, are widely used in perfumes because of their distinctive odors. Also, heptanal is an important starting material for the production of © 2014 American Chemical Society

Received: September 26, 2013 Published: July 11, 2014 1362

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Chemical Treatment. A549 cells were treated with each compound at 0.1% and 1.0% of final solvent (DMSO) concentration in well plates. Bceause of the volatility of the compound, well plates were sealed with sealing films after chemical treatment. The cells were seeded at a density of 7 × 104 cells/mL per well in 500-μL media for the cytotoxicity assay and seeded in a 6-well plate at a density of 25 × 104 cells/mL for RNA extraction. After incubation for 24 h, the cells were treated with six LSAAs for 48 h. Determination of Cell Viability. To determine the LSAAinduced cytotoxic effects, the MTT [3-(4,5-dimethylthaizol-2-yl)-2,5diphenyltetrazolium bromide] cell proliferation assay was performed using the modified methods of Mosmann.18 The experiments were done in triplicate for each sample. Microarray Analysis and Data Processing. Gene expression analysis was conducted using 44 K whole human genome microarray (Agilent Technologies,Santa Clare, CA, USA). Analyses were performed in triplicate for each chemical, simultaneously. Labeling and hybridization were performed using Agilent’s Low RNA Input Linear Amplification Kit (Agilent Technology) according to the manufacturer’s instructions. The labeled cRNA target was quantified using an ND-1000 spectrophotometer. After checking labeling efficiency, fragmentation of cRNA was performed by adding 10× blocking agent and 25× fragmentation buffer and incubating at 60 °C for 30 min. The fragmented cRNA was resuspended with 2× hybridization buffer and directly pipetted onto assembled Agilent’s Human Oligo Microarray (4x44K). Hybridization was performed in a hybridization oven at 65 °C for 17 h. The hybridized slides were washed according to the manufacturer’s washing protocol (Agilent Technology). The hybridized arrays were scanned using a DNA microarray scanner (Agilent Technology), and the images of each spot were quantified with Feature Extraction Software (Agilent Technology) to obtain gene expression ratios. Data normalization and selection of significantly altered genes were performed using GeneSpring GX software (Agilent Technology). The averages of normalized ratios were calculated by dividing the average normalized signal channel intensity by the average normalized control channel intensity. After Log2 transform, interarray normalization of expression levels was performed by quantile normalization to correct possible experimental distortions. Normalization was applied to the expression data for all experiments, and the values of spot replicates within arrays were then averaged. Furthermore, Feature Extraction Software provides spot quality measures to evaluate the goodness and the reliability of hybridization. The flag “glsFound” (set to 1 if the spot has an intensity value significantly different from the local background, 0 otherwise) was used to exclude substandard spots (values of 0 will be noted as “not available (NA)”) from further analysis. Also, in an effort to make the statistical analysis more robust and unbiased, probes with a high proportion of NA values were not included in the data set. For each gene, a p-value was calculated to assess the statistical significance of differential expression. A permutation procedure was used to determine the p-value for each permutation, and t-test statistics were computed for each gene. Differentially expressed genes, which displayed either greater than or equal to a 1.5 fold up- and down-regulation, were considered for this study. To avoid the influence of technical variance on gene ranking and classification exercises due to data use over a long time period, all expression data from the treated samples were normalized to their matched vehicle control. Hierarchical clustering tool was used to detect cluster patterns of the significantly expressed genes (p < 0.05) and principle component analysis (PCA), whose dimensionality reduction techniques were used to analyze the high-dimensional gene expression data set. Functional classification and pathway construction were performed using the DAVID (http://david.abcc.ncifcrf.gov/home.jsp) functional annotation clustering tool by choosing the default option.19 Quantitative Real-Time Reverse Transcription Polymerase Chain Reaction (qRT-PCR). The mRNA levels for the genes of interest were analyzed by quantitative real-time RT-PCR using a BioRad iCycler system (Bio-Rad). The mRNAs were reverse-transcribed into cDNAs using an Omniscript RT kit (Qiagen). The primer specificity was tested by running a regular PCR for 45 cycles at 95 °C for 20 s and 60 °C for 1 min, followed by agarose gel electrophoresis.

Microarrays have been widely used for comprehensive analysis of transitory changes in gene expression, mutations, and singlenucleotide polymorphism detection. Notably, large-scale gene expression microarray analyses monitor the significant changes in thousands of genes at onces.15,16 Moreover, array-based highthroughput technologies provide an opportunity to identify toxicity-related genes in response to toxicant exposure.17 These techniques are very effective because many genes can be screened in one experiment, and they can characterize pathways and mechanisms. Additionally, significantly altered genes can be utilized as a “fingerprint” biomarker. Likewise, microarray analysis can identify the biological effects of chemical exposures and provide huge amounts of data. To analyze these amounts of data effectively, it is necessary to use diverse analytical tools. The gene ontology (GO) analysis (http://david.abcc.ncifcrf. gov/home.jsp) tool, which provides common language to describe aspects of a gene product’s biology, and the Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www. genome.jp/kegg/), which is a widely used integrated database for pathways, chemical reactions, genomes and expression, are useful for functional classification of genes.16 Thus far, most transcriptomic studies have investigated the chemical-induced toxicity or effects of their toxic metabolite on gene regulation. Few studies have addressed the effects of structural features like the number of carbon atoms or the position of a functional group on transcriptome changes. Currently, we thought that aliphatic compounds like LSAAs may induce distinct gene expression profiles depending on carbon number. Studies on the determination of the carbon numberspecific gene expression profiling provide useful information for the assessment of aliphatic compound toxicity. In current studies, we aimed to utilize genome-wide information on the carbon number-specific gene expression profiles induced by LSAAs in A549 human alveolar cells to determine the mechanisms involved in the pulmonary toxicity of LSAAs. To this end, A549 cells were exposed to six LSAAs selected based on their carbon number. The compounds evaluated were propanal (C3), butanal (C4), pentanal (C5), hexanal (C6), heptanal (C7), and octanal (C8). We performed global transcriptome analyses of LSAA-exposed A549 cells and identified the exposure-induced alterations in gene expression profiles, predicting different responses to the carbon number of LSAAs by genomic expression analysis. Microarray data for octanal was published by our research group17 and used for the comprehensively analysis of this study. Thus, identifying the global genomic response through transcriptome changes in an in vitro system may provide valuable information for identifying indicators of chemical property-related toxicity.

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EXPERIMENTAL PROCEDURES

Chemicals and Reagents. Propanal, butanal, pentanal, hexanal, heptanal, octanal, dimethyl sulfoxide (DMSO), and 3-(4,5-dimethylthaizol2-yl)-2,5-diphenyltetrazolium bromide (MTT) were purchased from Sigma-Aldrich Co. (St. Louis, MO, USA). The following culture media and buffer solutions were purchased from Gibco (Grand Island, NY, USA): Roswell Park Memorial Institute (RPMI) 1640, Dulbecco’s phosphate-buffered saline (PBS), fetal bovine serum (FBS), and antibiotics (penicillin and streptomycin). All chemicals used were of analytical grade or of the highest grade available. Cell Culture. The human adenocarcinoma lung epithelial cell line (A549) was obtained from Korea Cell Line Bank (Seoul, Korea) and was grown in RPMI 1640 medium supplemented with 10% FBS, sodium bicarbonate, HEPES, and penicillin/streptomycin at 37 °C in a 5% CO2 atmosphere. For cell growth, the medium was renewed every 2 or 3 days, and cells were subcultured twice per week. 1363

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Table 1. Primer Sequences for the qRT-PCR Used in This Study accession no.

gene

NM_003782

B3GALT4

NM_005771

DHRS9

NM_013447

EMR2

NM_018689

KIAA1199

NM_004321

KIF1A

NM_000499

CYP1A1

NM_001166034

SBSN

NM_001216

CA9

NM_001135195

SLC39A5

NM_002046

GAPDH

primer sequence (5′→3′) F R F R F R F R F R F R F R F R F R F R

The real time RT-PCR was performed by using a SYBR Supermix kit (Bio-Rad) and running for 45 cycles at 95 °C for 20 s and 60 °C for 1 min. The PCR efficiency was examined serially diluting the template cDNA, and melting curve data were collected to check the PCR specificity. Each cDNA sample was analyzed in triplicate, and the corresponding no-RT mRNA sample was included as a negative control. A glyceraldehyde-3-phosphate dehydrogenase (GAPDH) primer was used as an endogenous control. The mRNA level of each sample for each gene was normalized to that of the GAPDH mRNA. The relative mRNA level was presented as 2[(Ct/GAPDH‑Ct/gene of interest)], and the data were shown as means ± standard deviation (SD) of three separate experiments. The primer sequences used for the qRT-PCR are listed in Table 1.

microarrays containing approximately 44,000 human oligonucleotides. Generally, transcriptomic changes occur through dynamic regulatory networks that respond to multiple environmental stressors; thus, characteristics and differences in molecular profiles reflect these stimuli. So, we investigated the stress-induced gene expression profiles by using an unsupervised hierarchical clustering analysis. Through the initial filtering process, 34,127 genes were passed and included in further analysis. From the clustering pattern, as shown in Figure 1A, hierarchical clustering analysis of these gene sets yielded three subclusters in a dendrogram, and the changes in gene expression are represented as differential gene expression patterns following LSAA exposure. Gene expression profiles of the high carbon number group (HCG; hexanal, heptanal, and octanal) showed strong similarity with each other, and they were distinguished from the low carbon number group (LCG; propanal, butanal, and pentanal). The transcriptional levels of randomly selected six genes (DHRS9, EMR2, KIF1A, KIAA1199, CYP1A1, and SBSN) that were upregulated and three genes (B3GALT4, CA9, and SLC39A5) that were downregulated following LSAA exposure were assessed by qRT-PCR to validate the microarray. The expression of these genes showed similar patterns as in the microarray analysis except for B3GALT4 expression in octanaltreated A549 cells (Supporting Information, Figure S1). Next, differentially regulated genes were selected using statistical analysis. A p-value 1.5 and p < 0.05) induced in A549 cells after six LSAA exposure at IC20 dose.

we subjected the genes to Expression Analysis Systematic Explorer (EASE) analysis to identify biological processes that were significantly overrepresented in the gene lists. Key biological processes and pathways that were significantly affected by the LSAAs are shown in Tables 3 and 4. For the 703 LCG exposure-specific genes, the biological processes included “response to wounding,” “cell−cell signaling,” “defense response,” “immune response,” “regulation of apoptosis,” and “regulation of cell death” (Table 3). Additionally, the KEGG pathway analysis revealed that “cytokine−cytokine receptor interaction,” “chemokine signaling pathway,” “cell adhesion molecules,” and “intestinal immune network for IgA production,” which are known as related to increase pulmonary toxicity, were prominently annotated with LCG specific genes (Table 4). However, there were no annotated biological processes, and only “antigen processing and presentation” was detected for the 55 HCG-specific genes through the KEGG pathway analysis (Table 5). Genes that are expressed in only the LCG exposure group may be regarded as a biomarker of LSAA-induced pulmonary toxicity. These functional analyses provide a valuable mechanistic explanation of low carbon LSAA-induced toxic responses in human cellular systems.

(619 up- and 767 downregulated) was altered by hexanal, 1,091 genes (525 up- and 566 downregulated) was altered by heptanal, and 625 genes (345 up- and 280 downregulated) was altered by octanal. Next, we thought that there are different toxic responses depending on the carbon number of LSAAs. So, we first identified classifier genes that could distinguish between aldehydes with large carbon number and small carbon number. Through the analysis of gene expression profiles, we identified 577 up- and 126 down-regulated LCG specific genes and 19 up- and 36 down-regulated HCG specific genes (Supporting Information, Table S1 and S2). Clustering showed that the 703-gene set was unique to the LCG and that the 55-gene set was unique to the HCG (Figure 2A). PCA for these 758 genes confirmed that they could clearly separate LCG and HCG (Figure 2B). Also, we found 16 genes (3 up- and 13 downregulated) were commonly expressed by these two groups (Supporting Information, Figure S2 and Table S3). Functional Classification of Genes That Are Differentially Expressed in LSAA-Exposed A549 Cells. The genes that were specifically modulated in the LCG and HCG were classified according to their GO biological processes and KEGG pathways to analyze the specific molecular mechanism related to LSAA exposure. Using DAVID (http://david.abcc.ncifcrf.gov/), 1365

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Figure 2. Identification of the molecular markers for the LCG and HCG exposure groups. (A) Expression profiling showing three clusters (control, LCG, and HCG) according to the expression profiles of the 758 molecular markers. Columns represent probe measurements. Color saturation reflects differences in expression between LSAA-treated RNA and control RNA. (B) Results of a PCA for the set of 758 genes presented as a twodimensional scatterplot showing three different classes, control, LCG, and HCG.

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DISCUSSION

responses. As the lung is the main respiratory organ and also represents a major target organ of volatile chemical toxicants, in vivo and in vitro models based on pulmonary systems are frequently used to assess toxicity. A549 cells may not completely reflect the normal lung cell properties, but A549 cells have several advantages for air toxicants studies. Epithelial cells are the lining of the airway, and their proportions vary all along the

We observed the effects of LSAAs on human lung transcriptome. Exposure to environmental pollution remains a major health threat throughout the world, and the list of potentially hazardous chemicals to which humans are exposed continues to grow. In this study, we used in vitro models of human lung epithelial A549 cells to identify the LSAA-induced transcriptome 1366

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concern is mounting over the potential risks of human exposure to LSAAs. Although the environmental distribution and metabolism of LSAAs have been well reported, no studies have determined the gene expression patterns of LSAA compounds. In recent years, expression profiling analysis using microarray technologies have provided biologists with an opportunity to identify the transcriptome profiling in cancer biology and other biomedical responses.30,31 A specific challenge in the array-based approach is to detect genes with expression levels that change differentially and can be considered potential biomarkers. Here, using the smallest known toxic LSAA compounds, propanal, butanal, pentanal, hexanal, heptanal, and octanal, we investigated the effect of carbon number on gene expression and pulmonary toxicity in A549 cells. The aim of this study was to identify genes expressed in pulmonary toxicity-relevant pathways using toxicogenomic technology to elucidate the cellular response of mammalian cells to LSAAs. Microarray and statistical analyses showed that expression of substantially more genes was affected by LCG exposure than by HCG exposure. The expression of 719 genes was altered >1.5-fold after exposure to an IC20 dose of propanal, butanal, or pentanal, compared with only 71 genes after exposure to an IC20 dose of hexanal, heptanal, or octanal. Studies suggest that this pattern of gene expression reflects a carbon-number-based biological response, with a lower number of carbons inducing more function responses compared to a higher number of carbons. Among these genes, 16 genes (3 up- and 13 down-regulated) commonly changed their expression by both LCG and HCG exposure. We can suggest that these 16 genes are regarded as strong potential biomarkers of six LSAAs, regardless of carbon number. Next, we identified genes whose expression was only significantly altered in the LCG or HCG exposure. The expression of 703 genes was significantly altered in the LCG group compared with 55 genes in the HCG group. To study the effects of carbon number at the biological process level, we performed GO enrichment and pathway analyses of genes whose expression was regulated in a cluster-specific way. As shown in Table 2, genes that contributed to the enrichment of “response to wounding,” “cell−cell signaling,” “defense response,” “immune response,” and “regulation of apoptosis” included LCGregulated genes. However, no related GO biological processes were detected among the high-carbon-number-regulated genes. Genes whose expression was only altered in low-carbonnumber LSAA-treated A549 cells were related to inflammatory response pathways. A significant proportion of the genes affected by the LCG (propanal, butanal, and pentanal) was related to cytokine−cytokine receptor interactions, chemokine signaling pathways, and the p53 signaling pathway (Table 4). The cytokine−cytokine receptor interaction pathway contains genes known to be induced by lesions in lung epithelial cells.32 Genes in this pathway included one interleukin family gene (IL32) and four cytokine/chemokine genes (CSF1R, CCL5, CXCL2, and CSF2RA). Previous results have demonstrated that multiple members of the cytokine−cytokine receptor interaction pathway are strongly activated in A549 cells.33,34 We found that expression of the cytokine−cytokine receptor interaction pathway-related genes AQP3, CDCP1, GCOM1, MCTP1, STXBP6, DPYSL5, KIAA1199, BMP7, CHST1, STXBP6, FGA, AIM1L, CFI, LTPD1, LOC389634, PDLIM7, AHNAK2, and TMEM52 was also altered >1.5-fold in the propanal, butanal, and pentanal IC20 dose−exposure groups. These genes may play an important role in the cytokine−cytokine receptor

Table 3. GO Functional Category Analysis of Low Carbon Group (LCG)-Specific 703 Genesa term

genesc

p-valueb

(A) Biological Process (Top 10 out of 205 Total) cell surface receptor linked signal transduction 84 0.008 intracellular signaling cascade 58 0.021 response to wounding 52 3.13 × cell−cell signaling 46 8.80 × defense response 45 4.02 × immune response 45 6.84 × regulation of apoptosis 42 0.008 regulation of programmed cell death 42 0.009 regulation of cell death 42 0.0104 biological adhesion 41 0.001 (B) Cellular Component (Top 10 out of 24 Total) plasma membrane 176 1.15 × plasma membrane part 121 2.56 × extracellular region 118 9.56 × intrinsic to plasma membrane 74 3.65 × integral to plasma membrane 72 6.06 × extracellular region part 71 3.52 × extracellular space 55 2.07 × cell fraction 52 0.020 cell projection 40 0.002 cell junction 31 0.005 (C) Molecular Function (Top 10 out of 14 Total) calcium ion binding 55 1.09 × carbohydrate binding 24 0.003 cytokine activity 21 1.63 × growth factor activity 15 0.001 polysaccharide binding 14 0.002 pattern binding 14 0.002 glycosaminoglycan binding 13 0.003 chemokine activity 10 2.51 × chemokine receptor binding 10 4.27 × cytokine binding 9 0.037

10−11 10−07 10−06 10−05

10−05 10−07 10−09 10−06 10−06 10−09 10−08

10−04 10−05

10−05 10−05

a

DAVID functional annotation bioinformatics microarray analysis software was used to obtain the GO functional categories. bStatistically significant GO terms were only considered (p-value ≤0.05). cSome genes are counted in more than one annotation category.

respiratory tract from the nasal, trachea, bronchi, to alveoli. In vitro culture systems have been established for all lung cell types,20 but alveolar cell lines, especially A549 cells have been frequently used to assess the potential toxicity of environmental toxicants on the respiratory system.21−23 The conditions used in this study were IC20 dose exposure to subcytotoxic doses for 48 h. Actually, it is correct to conduct the study of transcriptome analysis using actual concentrations exposed in the environment. However, in a study using an in vitro model, exposure to minimal-volume and low-concentration toxicants involves difficulties observing clear genetic and toxicological changes. Therefore, we selected a concentration that exhibits changes specific to gene and toxicity expression without excessive changes in the survival of cells. Also, IC20 doses are considered appropriate for microarray analysis as such an exposure level provides meaningful outcomes in in vitro toxicogenomic studies of carcinogenesis24,25 and immunology.26−29 Notably, despite international agreements intended to restrict and ultimately eliminate the production, use, release, and storage of LSAAs, these compounds persist indoors and in the atmosphere. Because of their ubiquity in the environment, 1367

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Table 4. Functional Classification and Gene List of Molecular Signature of Low Carbon Group (LCG)-Specific 703 Genes [KEGG pathway] gene symbol

GenBank accession no.

[KEGG pathway] gene symbol

description

[Cytokine−Cytokine Receptor Interaction] CSF1R NM_005211 colony stimulating factor 1 receptor AQP3 NM_004925 aquaporin 3 (Gill blood group) CDCP1 NM_022842 CUB domain containing protein 1 GCOM1 NM_001018100 GRINL1A complex locus MCTP1 NM_024717 multiple C2 domains, transmembrane 1 IL32 NM_001012631 interleukin 32 CCL5 NM_002985 chemokine (C−C motif) ligand 5 STXBP6 NM_014178 syntaxin binding protein 6 (amisyn) DPYSL5 NM_020134 dihydropyrimidinase-like 5 CXCL2 NM_002089 chemokine (C-X-C motif) ligand 2 KIAA1199 NM_018689 KIAA1199 CSF2RA NM_172249 colony stimulating factor 2 receptor, alpha, lowaffinity (granulocyte-macrophage) BMP7 NM_001719 bone morphogenetic protein 7 CHST1 NM_003654 carbohydrate (keratan sulfate Gal-6) sulfotransferase 1 STXBP6 NM_014178 syntaxin binding protein 6 (amisyn) CXCL2 NM_002089 chemokine (C-X-C motif) ligand 2 FGA NM_021871 fibrinogen alpha chain AIM1L NM_001039775 absent in melanoma 1-like CFI NM_000204 complement factor I LYPD1 NM_144586 LY6/PLAUR domain containing 1 LOC389634 NR_024420 hypothetical LOC389634 PDLIM7 NM_005451 PDZ and LIM domain 7 (enigma) AHNAK2 NM_138420 AHNAK nucleoprotein 2 TMEM52 NM_178545 transmembrane protein 52 (Entrez:3569) TMEM52 NM_178545 transmembrane protein 52 (Entrez:55504) [Chemokine Signaling Pathway] CXCR4 NM_001008540 chemokine (C-X-C motif) receptor 4 CCL5 NM_002985 chemokine (C−C motif) ligand 5 CCL20 NM_004591 chemokine (C−C motif) ligand 20 CCL26 NM_006072 chemokine (C−C motif) ligand 26 CXCL2 NM_002089 chemokine (C-X-C motif) ligand 2 VAV1 NM_005428 vav 1 guanine nucleotide exchange factor RASGRP2 NM_153819 RAS guanyl releasing protein 2 (calcium and DAGregulated) CXCL1 NM_001511 chemokine (C-X-C motif) ligand 1 (melanoma growth stimulating activity, alpha) CCL7 NM_006273 chemokine (C−C motif) ligand 7 CCL2 NM_002982 chemokine (C−C motif) ligand 2 CXCL3 NM_002090 chemokine (C-X-C motif) ligand 3 ARRB1 NM_004041 arrestin, beta 1 IL8 NM_000584 interleukin 8 CCL3 NM_002983 chemokine (C−C motif) ligand 3 TIAM1 NM_003253 T-cell lymphoma invasion and metastasis 1 [Cell Adhesion Molecules (CAMs)] ICAM1 NM_000201 intercellular adhesion molecule 1 HLA-DPB1 NM_002121 major histocompatibility complex, class II, DP beta 1 ESAM NM_138961 endothelial cell adhesion molecule ITGA4 NM_000885 integrin, alpha 4 (antigen CD49D, alpha 4 subunit of VLA-4 receptor) CD40 NM_001250 CD40 molecule, TNF receptor superfamily member 5 L1CAM NM_000425 L1 cell adhesion molecule NRXN2 NM_138732 neurexin 2 CD8A NM_001768 CD8a molecule CDH15 NM_004933 cadherin 15, type 1, M-cadherin (myotubule) HLA-DOA NM_002119 major histocompatibility complex, class II, DO alpha CLDN4 NM_001305 claudin 4 CLDN3 NM_001306 claudin 3 HLA-F NM_001098478 major histocompatibility complex, class I, F NFASC NM_001005388 neurofascin homologue (chicken)

F7 FGB PLAUR FGA CFI PLAT F2 F5 TFPI C1S F13B CXCR4 NGEF EPHA2 DPYSL5 ABLIM3 FYN UNC5B EFNB2 NTN1 RAC3 L1CAM CCL5 CXCL2 IL6 CXCL1 CCL7 CCL2 BIRC3 TNFAIP3 IL8 CD82 IGFBP3 GADD45A TP53 CCND2 GADD45G BAI1 IGF2 ICA1 HLA-DPB1 GAD1 HLA-DOA HLA-F CXCR4 HLA-DPB1 ITGA4 CD40 IL6 HLA-DOA

1368

GenBank accession no.

description

[Complement and Coagulation Cascades] NM_000131 coagulation factor VII (serum prothrombin conversion accelerator) NM_005141 fibrinogen beta chain NM_001005377 plasminogen activator, urokinase receptor NM_021871 fibrinogen alpha chain NM_000204 complement factor I NM_000930 plasminogen activator, tissue NM_000506 coagulation factor II (thrombin) NM_000130 coagulation factor V (proaccelerin, labile factor) NM_006287 tissue factor pathway inhibitor (lipoprotein-associated coagulation inhibitor) NM_201442 complement component 1, s subcomponent NM_001994 coagulation factor XIII, B polypeptide [Axon Guidance] NM_001008540 chemokine (C-X-C motif) receptor 4 NM_019850 neuronal guanine nucleotide exchange factor NM_004431 EPH receptor A2 NM_020134 dihydropyrimidinase-like 5 NM_014945 actin binding LIM protein family, member 3 NM_002037 FYN oncogene related to SRC, FGR, YES NM_170744 unc-5 homologue B (C. elegans) NM_004093 ephrin-B2 NM_004822 netrin 1 NM_005052 ras-related C3 botulinum toxin substrate 3 (rho family, small GTP binding protein Rac3) NM_000425 L1 cell adhesion molecule [NOD-Like Receptor Signaling Pathway] NM_002985 chemokine (C−C motif) ligand 5 NM_002089 chemokine (C-X-C motif) ligand 2 NM_000600 interleukin 6 (interferon, beta 2) NM_001511 chemokine (C-X-C motif) ligand 1 (melanoma growth stimulating activity, alpha) NM_006273 chemokine (C−C motif) ligand 7 NM_002982 chemokine (C−C motif) ligand 2 NM_001165 baculoviral IAP repeat-containing 3 NM_006290 tumor necrosis factor, alpha-induced protein 3 NM_000584 interleukin 8 [p53 Signaling Pathway] NM_002231 CD82 molecule NM_001013398 insulin-like growth factor binding protein 3 NM_001924 growth arrest and DNA-damage-inducible, alpha NM_000546 tumor protein p53 NM_001759 cyclin D2 NM_006705 growth arrest and DNA-damage-inducible, gamma NM_001702 brain-specific angiogenesis inhibitor 1 [Type I Diabetes Mellitus] NM_000612 insulin-like growth factor 2 (somatomedin A) NM_004968 islet cell autoantigen 1, 69 kDa NM_002121 major histocompatibility complex, class II, DP beta 1 NM_000817 glutamate decarboxylase 1 (brain, 67 kDa) NM_002119 major histocompatibility complex, class II, DO alpha NM_001098478 major histocompatibility complex, class I, F [Intestinal Immune Network for IgA Production] NM_001008540 chemokine (C-X-C motif) receptor 4 NM_002121 major histocompatibility complex, class II, DP beta 1 NM_000885 integrin, alpha 4 (antigen CD49D, alpha 4 subunit of VLA-4 receptor) NM_001250 CD40 molecule, TNF receptor superfamily member 5 NM_000600 interleukin 6 (interferon, beta 2) NM_002119 chemokine (C-X-C motif) receptor 4

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response to LSAA exposure. Our results show that the carbonnumber-specific gene expression patterns were associated with pulmonary toxicity induced by a low carbon number. Classifying the gene expression changes, analyzing the gene expression patterns, and understanding the mechanisms associated with low-carbon-number-induced toxicity should allow earlier identification of environmentally relevant toxicological results during compound screening and aid the development of new chemicals to reduce pulmonary toxicity. In conclusion, transcriptome analysis through using microarray technology was an efficient approach to evaluating how environmental toxicants affect gene expression related to adverse health effects and offers the possibility of identifying molecular markers. Moreover, GO analysis is an excellent tool for predicting the mechanisms associated with DEGs in cells or organisms influenced by various environmental toxicants. Our results show that changes in gene expression were associated with toxicity induced by LSAAs and that the genes affected could be promising biomarkers of LSAA-induced toxicity in human pulmonary cells.

Table 5. Functional Classification and Gene List of Molecular Signature of High Carbon Group (HCG)-Specific 55 Genes [KEGG pathway] gene symbol

GenBank accession no.

description

[Antigen Processing and Presentation] HLA-DOB NM_002120 major histocompatibility complex, class II, DO beta KLRC1 NM_002259 killer cell lectin-like receptor subfamily C, member 1 KLRC3 NM_007333 killer cell lectin-like receptor subfamily C, member 3

interaction pathway and may alter environmental toxicant-induced pulmonary toxicity. Chemokines are a specialized family of chemotactic cytokines which are organized into four subclasses termed C, CC, CXC, and CXXXC, according to the arrangement of the amino terminal cysteine residues (C) and the intervening amino acids (X). Recent studies have provided irrefutable evidence that chemokines play an important role in the pathophysiology of several inflammatory diseases.35,36 Multiple chemokines are expressed during an allergic reaction, and they direct inflammatory cell recruitment during pulmonary inflammation.37,38 Exposure to LSAAs may affect cytokines/chemokines and inflammation. Although studying the gene expression responses of complete biological processes or pathways is useful, the expression profiles of individual genes within each pathway may provide additional information about the induced gene expression response. Our results show that exposure to LSAAs significantly altered the expression of genes that are regulated by the chemokine signaling pathway (CXCR4, CCL5, CCL20, CCL26, CXCL2, VAV1, RASGRP2, CXCL1, CCL7, CCL2, CXCL3, ARRB1, IL8, CCL3, and TIAM1). Among these, CXCR4 is an appealing candidate gene for inflammatory disease states associated with the epithelium.39 Previous studies have shown that different types of chemokine genes changed their expression in A549 cells. Tumor necrosis factor (TNF)-α induced expression of CCL2 and CCL5 was observed in A549 cells, whereas H2O2 only induces CCL2 expression.40 Several distinct signaling pathways appear to regulate the transcription of chemokines in A549 cells. These genes may play an important role in the chemokine signaling pathway and may alter environmental toxicant-induced pulmonary toxicity. The p53 signaling pathway plays an important role in stressinduced cell growth regulation and apoptosis. Seven genes were in the p53 pathway (Table 3), including TP53, a critical effector molecule of the p53 pathway, which may inhibit the p53 signaling and apoptosis-related factors in lung cancer cells.41 These reports appear to be consistent with our data showing the induction of tp53 mRNA expression by LSAA exposure and altered expression of genes related to the p53 signaling pathway. We observed enrichment of the GO processes “response to wounding,” “cell−cell signaling,” “defense response,” “immune response,” and “regulation of apoptosis” with the LCG at IC20 doses. Our findings of DEGs for cytokine−cytokine receptor interactions and chemokine signaling pathways are consistent with the suggestion that these pathways are fundamental to the response to LCG LSAAs. However, no related GO biological processes were detected among the high-carbon-number-regulated genes. This is the first study to determine the carbon-numberspecific expression of toxicity-related genes in A549 cells in

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ASSOCIATED CONTENT

* Supporting Information S

qRT-PCR data and Venn diagram for differentially expressed genes; and spreadsheets of all LCG and HCG significant genes. This material is available free of charge via the Internet at http://pubs.acs.org. Accession Codes

Raw data from Agilent microarrays is available at the Gene Expression Omnibus (GEO) site (accession number GSE56005).

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AUTHOR INFORMATION

Corresponding Author

*Fax: 082-02-958-5899. E-mail: [email protected]. Funding

This study was supported by the Korea Research Foundation Grants from Korea Ministry of Environment (grant numbers 412-111-010) as “The Eco-Innovation Project” and KIST Program 2E24651 of the Republic of Korea. Notes

The authors declare no competing financial interest.

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ABBREVIATIONS VOC, volatile organic compound; LSAA, low-molecular-weight saturated aliphatic aldehyde; LCG, low carbon number group; HCG, high carbon number group; GO, gene ontology; C3, propanal; C4, butanal; C5, pentanal; C6, hexanal; C7, heptanal; C8, octanal

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REFERENCES

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Chemical Research in Toxicology

Chemical Profile

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