Open Access

Carbon tetrachloride affects inflammation-related biochemical networks in the mouse liver as identified by a customized cDNA microarray system

  • Hidekuni Inadera1Email author,
  • Shinjiro Tachibana1,
  • Aya Suzuki1 and
  • Akiko Shimomura1
Environmental Health and Preventive Medicine200915:117

https://doi.org/10.1007/s12199-009-0117-6

Received: 26 July 2009

Accepted: 15 October 2009

Published: 13 November 2009

Abstract

Objectives

We have attempted to upgrade and validate an in-house cDNA microarray system developed by our group for the evaluation of chemical toxicity.

Methods

To establish an in-house microarray, we selected genes that play pivotal roles in detoxifying exogenous substances and maintaining homeostasis in the liver. To validate the system, we examined gene expression profiles in mouse liver following treatment with different doses of carbon tetrachloride (CCl4). The data were also analyzed by pathway analysis tools.

Results

We upgraded our array system by collecting genes that are responsive to xenobiotic receptors, apoptosis-related genes, and stress-responsive genes. The acute toxicity of CCl4 was confirmed by elevated levels of serum transaminase and histopathological findings. The microarray data showed the CCl4 treatment induced significant changes in gene expression in the mouse liver, and the ingenuity pathways analysis revealed alterations in gene expression in inflammation-related networks.

Conclusions

We have established a focused microarray system that may be useful for use in toxicogenomics studies. Using this array system, we gained insight into the mechanisms by which CCl4 exerts its toxic effects. The results of our study also indicate that the combination of focused arrays and bioinformatics tools is helpful in the mechanistic analysis of chemical toxicity.

Keywords

BioinformaticsCarbon tetrachlorideMicroarrayPathway analysisToxicogenomics

Introduction

A considerable body of evidence supports the ability of DNA microarrays to monitor simultaneously the expression of thousands of genes [1]. One of the advantages of microarrays is that they enable a comprehensive analysis of gene responses under specific conditions, thereby revealing gene networks and providing useful clues on their mechanisms of interest [2]. Although large-scale and whole transcriptome analyses are initially necessary to identify the diagnostic gene set, once these diagnostic sets of indicator transcripts have been identified, focused array systems may enable detailed analyses [3]. Thus, gene expression analysis using focused microarrays is attracting increasing attention in the toxicology field.

Carbon tetrachloride (CCl4) is a highly toxic chemical agent, and the hepatotoxicity of CCl4 has been a long-term focus of toxicological studies [4, 5]. Acute toxicity by CCl4 results in liver steatosis and centrilobular necrosis, while the chronic effects of CCl4 exposure include cirrhosis, fibrosis, and carcinogenicity [6, 7]. To date, CCl4-induced hepatotoxicity has been extensively studied in animal models, and changes in biochemical pathways and gene expression analysis during toxic insults have been explored [8, 9].

The liver is the first significant site of chemical exposure and the major site of xenobiotic metabolism. In an earlier publication, we reported our development of a customized DNA microarray of 355 transcripts, which we designated the mouse Liver Stress Array ver. 1.0 [10]. To establish this array system, we selected liver-related genes based on the liver transcriptome as revealed by serial analysis of gene expression (SAGE) in order to eliminate liver-unrelated genes and facilitate the efficient detection of the signals [10, 11]. In the study reported here, we upgraded this array system by including genes important in detoxifying exogenous substances and sensing homeostasis in the liver. Using our upgraded focused array system, we have investigated the mechanism by which CCl4 may exert its toxic effects. In particular, using bioinformatics tools, our aim was to clarify the biochemical networks that are affected by CCl4 intoxication.

Materials and methods

Reagents

CCl4 was purchased from Wako Pure Chemicals (Osaka, Japan). The RNAlater solution, Oligotex-dT30 mRNA Purification kit, and SYBR Green PCR Master Mix were obtained from Takara (Osaka, Japan). The Trizol reagent was purchased from Invitrogen (Carlsbad, CA), the Superscript III Reverse Transcriptase (RT) and mouse Cot-1 DNA were from Life Technologies (Rockville, MD), the fluorescent nucleotide Cy5- and Cy3-deoxyuridine triphosphates (dUTPs) were from PerkinElmer (Boston, MA), the QIAquick Purification kit was from Qiagen (Valencia, CA), and the yeast tRNA and poly(dA) polymer were from Sigma–Aldrich (St. Louis, MO).

cDNA microarray system

Our upgraded cDNA microarray, the mouse Liver Stress Array ver. 2.0, contains 399 cDNAs and was constructed in collaboration with Kaken Geneqs (Chiba, Japan). The mouse Liver Stress Array ver. 1.0 comprised a total of 355 individual transcripts and contained major liver-related proteins based on the liver transcriptome, cDNAs encoding drug metabolism, and inflammation-related proteins [10, 11]. To upgrade our microarray system, we selected genes whose expressions are reported to be affected by known toxicants, namely, responsive-genes of the xenobiotic receptors, stress-responsive genes, and apoptosis-related genes. The list of genes selected to upgrade ver. 1.0 is presented in Table 1. The complete list of the transcripts in ver. 2.0 is available at http://www.med.u-toyama.ac.jp/pubhlth/index.html. The PCR products confirmed by sequencing were purified by ethanol precipitation, re-suspended in spotting buffer, and spotted onto amino silane-coated glass slides using a modified, robotic DNA arrayer (Kaken Geneqs). Each cDNA clone was spotted four or five times onto each slide.
Table 1

List of the selected genes used to upgrade the mouse Liver Stress Array ver. 1.0

Gene

Description

Full name

Accession no.

Forward primer 5→3

Reverse primer 5→3

Size (bp)

Gsta2

CAR-responsive

Glutathione S-transferase, alpha 2

NM_008182

GCAAAAGACAGGACCAAAA

GCACAATAGCCAGAATCAAC

384

SLC21A6

CAR-responsive

Liver-specific transporter-1/organic anion transporter

AF250912

TTTTTCACTGCACTGGGAAG

TACAGGTTCTGGGTTTCCTT

387

CAR

PXR-responsive

Constitutive androstane receptor

NM_009803

CATCTCCAGGAGCCTGAGTA

CAGCATTTCATTGCCACT

410

MRP2

CAR, PXR-responsive

Multidrug resistance protein

NM_013806

GATTGTTGAATACGGCAGTC

GGGGGTTTTAGGGATCA

413

Sod1

PPARalpha-responsive

Superoxide dismutase 1, soluble

NM_011434

ACAGGATTAACTGAAGGCCAGC

ATGGTTTGAGGGTAGCAGATGAG

430

Sucla1

PPARalpha-responsive

Succinyl-CoA synthetase

NM_019879

AGTCTTCCTGAATGATCCAGCC

TCCCACATACTGAGAGACCAACC

450

Casp3

Apoptosis

Caspase 3, apoptosis related cysteine protease

NM_009810

GTCTTTGTACGCTACCACTGCCT

ATGGTGGTCCAACTCTTCACTTC

418

Bax

Apoptosis

Bcl2-associated X protein

NM_007527

TGACATGTTTGCTGATGGCA

TCCCACCCCTCCCAATAATT

425

Cryz

AhR-responsive

Crystallin, zeta/quinone reductase

NM_009968

AGTGATGCACACTACCACACACAG

GACCATACTGAAGACCACAGACCC

470

PPARalpha

Nuclear receptor

Peroxisome proliferators-activated receptor alpha

NM_011144

ACGAGGGTGTGATTTCATCCA

CAGGAACCAAGCCCCTCC

417

Cpt2

PPARalpha-responsive

Carnitine palmitoyltransferase 2

NM_009949

CAGTGAGCCTCGGTGGCTT

TCCAACCCGATCTCCTTAAGAA

405

CEBPdelta

Transcription factor

CCAAT/enhancer binding protein, delta

NM_007679

GCCTCTACTTTCCTCAATCT

GCACTGTCACCCATACAAT

437

CyE1

Cell cycle

Cyclin E1

NM_007633

GCAGATGCTGTGCTCTATGGAG

CATGAAGGCAGGACATGCTG

401

Por

PXR-responsive

NADPH-cytochrome P450 oxidoreductase

NM_008898

GCCCACAAGGTCTATGTTCA

CTAGGCTGCCTGGTACAATG

422

Bak

Apoptosis

BCL2-antagonist/killer 1

NM_007523

TCTGAACACATCCATCAGGCTC

CCCTATGTTGTGCAAGACAGAGA

388

Fah

Metabolizing enzyme

Fumarylacetoacetate hydrolase

NM010176

AGAAGGAATGAGCCAGGCG

ATAAGGCAAGGGTGTGTCTTGTG

403

AhR repressor

AhR-responsive

Aryl-hydrocarbon receptor repressor

NM_009644

AGTGGCATGAATCCCAGCA

GAATGAGCAAAAGTGCAAGTTGG

456

Ddit3

Stress-responsive

DNA-damage inducible transcript 3/CHOP10/gadd153

NM_007837

GCAAGGAAGAACTAGGAAACGGA

CAATGTACCGTCTATGTGCAAGC

394

Jun

Transcription factor

Jun oncogene

BC021888

TGCCCCAACAGATCCCG

GCCCTGACAGTCTGTTCTCAAA

403

m36B

Housekeeping gene

Acidic ribosomal phosphoprotein P0 (Arbp)

NM_007475

CAACGGCAGCATTTATAA

CCCATTGATGATGGAGTGTGG

171

Nrf2

Transcription factor

Nuclear factor, erythroid-derived 2, like 2

NM_010902

GAAAAGGGAGAAAACGACAG

CCCAAATGGTGCCTAAGA

429

Ccng2

Cell cycle

CyclinG2

NM_007635

GGTGGTGTATTGTCCGATA

CATCACCACACAGAATTGCT

438

MT1

Stress-responsive

Metallothionein 1

BC027262

ACGACTTCAACGTCCTGAGTACCT

CACGTACTCGGTAGAAAACGGG

382

MT2

Stress-responsive

Metallothionein 2

NM_008630

AAACCGATCTCTCGTCGATCTTC

CAAGTCAACGGCTTTTATTGTCAG

384

Gadd45a

DNA-damage-inducible

Growth arrest and DNA-damage-inducible 45 alpha

NM_007836

CGGGAAAGTCGCTAC

AAAAATACCCAAACTATTGC

421

Bad

Apoptosis

Bcl-associated death promoter

NM_007522

CTGGACGCGCATTATCCAGT

GACCTCGGGTCCCCAGTTAT

420

p21

Cell cycle

Cyclin-dependent kinase inhibitor 1A

NM_007669

CTTGGTGGTGAGACAGGCCT

TTATTGAGCACCAGCTTTGGG

451

MRP1

CAR, PXR-responsive

Multidrug resistance protein

AF022908

GGAGAAAAGGTGGGTATTGT

GCCTCGTCCAACACTAGAAT

410

CD36

PPARalpha-responsive

CD36 antigen

NM_007643

ACTTCAATTTCTGTAGACCC

CAGCATGGAACATGACG

461

Lfas

PPARalpha-responsive

Long chain fatty acyl CoA synthetase

U15977

GATGGCTGGTTACACACGGG

TCTTCGCCTTCAGTGTTGGA

455

PXR

PXR-responsive

Pregnane X receptor

AF031814

GTCGGCCATATCCTGCTC

GCGGGATTTGGAAGTTTATT

397

Rad23b

DNA-damage-inducible

RAD23b homolog

NM_009011

CCCATTCTTCGTGTTCA

TAAAGGCAGCAGTTTGACTC

426

STA2

CAR, PXR-responsive

Hydroxysteroid sulfotransferase (mSta2)

L27121

ATGAAGACATGAAAAAGGAT

TTTCCACAACAGGTTTTATT

413

Tnfrsf6

Apoptosis

Tumor necrosis factor receptor superfamily, member6

NM_007987

ACCTTGGAAAATCAACCC

GTCTTCAATTAACTGCGAGT

433

HSP60

Stress-responsive

Heat shock 60-kDa protein

X53584

GCGGCATGTTCTAACTCCTA

TCCACAGAAAGGCTGTCTTC

413

MnSOD

Anti-oxidant enzymes

Manganese superoxide dismutase 2

X04972

GAGTTGCTGGAGGCTATCAA

CCAGTCATAGTGCTGCAATG

423

OATP2

PXR-responsive

Organic anion transporter 2

AY007379

CTCCTGCAGGCTGTATGACT

GCATACCTTTGGAAGCAGAA

391

OSP

Stress-responsive

Oxidative stress-induced protein

U40930

GGAATGTGTGATGCCTTTTC

CCATTAGAAAGGGGGACAGT

435

p53

Stress-responsive

Cellular tumor antigen p53

X01237, K01700

CTCCAGCTACCTGAAGACCA

GGTGGATAAATGCAGACAGG

463

PCNA

Stress-responsive

Proliferating cell nuclear antigen

X53068

TTTTTCACAAAAGCCACTCC

TGCATTTAGCGTCAAGATCA

446

Rad50

Stress-responsive

DNA damage repair and recombination protein 50 homolog

U66887

AAGCCATCGGTGATCATTTA

GAGGTGACACCCAACAAATC

469

Rad51

Stress-responsive

DNA damage repair and recombination protein 51 homolog

D13473

TCACAGGCTCATGTTGTGAC

TGCAGGGATCGTAGTCTAGC

415

Tst

Metabolizing enzyme

Thiosulfate sulfurtransferase

S80191

ACCGGAGCCGGATATAGTAG

AACCTACAGCAGGTCACACC

415

Vim

Stress-responsive

Vimentin

X51438

ATTGAGATCGCCACCTACAG

TCTTTTGGGGTGTCAGTTGT

402

The complete list of the transcripts is available at http://www.med.u-toyama.ac.jp/pubhlth/index.html

Animals and treatment procedures

Six-week-old male C57BL/6J mice (Sakyo Laboratory, Toyama, Japan) were used throughout this study. The mice were housed in the animal center at University of Toyama under specific pathogen-free conditions, at a constant temperature and under a 12/12-h light/dark cycle. All animal procedures were carried out in accordance with our institutional guidelines. The mice were acclimated for 1 week prior to use. After acclimation, animals were randomly assigned to five groups (n = 5 each): a control group and four treatment groups. The indicated doses of CCl4 dissolved in corn oil (2% v/v) were administered into the peritoneal cavity of the mice. Control animals received an equal volume of corn oil only. At 8 h post-injection, the mice were killed and their livers removed for pathological examination and gene expression analysis.

Measurement of serum transaminases

Blood samples were immediately collected in tubes, kept at room temperature for 1 h, and centrifuged at 1000 g for 10 min to obtain serum. Serum aspartate aminotransferase (AST) and alanine aminotransferase (ALT) activities were measured using a Transaminase CII-Test kit (Wako Pure Chemicals) according to the manufacturer’s protocol.

Histopathological examination

The liver was immediately removed and cross-sectioned into small pieces. Each piece was fixed in 10% buffered formalin, dehydrated through a graded ethyl alcohol series, and embedded in paraffin. Four micrometer-thick sections were stained with hematoxylin/eosin (H/E). Histopathological assessment of the H/E-stained sections was performed under a light microscope.

RNA isolation and microarray hybridization

RNA isolation and microarray hybridization were performed as described previously [10]. Briefly, the dissected liver was immediately soaked in RNAlater solution to prevent RNA degradation. Total RNA was isolated from the liver using Trizol reagent, from which mRNA was enriched using the Oligotex-dT30 mRNA Purification kit. Fluorescent-labeled cDNA probes were constructed from 1-µg aliquots of mRNA samples by oligo(dT)-primed reverse transcription using Superscript III RT. The fluorescent nucleotide Cy5- and Cy3-deoxyuridine triphosphates (dUTPs) were used to label the cDNA of the experimental and reference tissue, respectively. The probes were purified using the QIAquick Purification kit. The blocking reagents, 20 µg of yeast tRNA, 20 µg of poly(dA) polymer, and 20 µg of mouse Cot-1 DNA, were added to the probes. Hybridization was performed at 65°C for 16 h in a hybridization cassette (Kaken Geneqs). After washing, the slides were dried and subjected to image processing. A twofold change in gene expression was used as the cut-off threshold. The analysis was repeated in two independent experiments. The data were further analyzed using GeneSpring software (Silicon Genetics, Redwood City, CA) to extract the significant genes.

Ingenuity pathways analysis

To examine the gene ontology, including biological processes, cellular components, molecular functions, and genetic networks, we analyzed the data using Ingenuity Pathways Analysis (IPA) tools (Ingenuity Systems, Mountain View, CA; http://www.ingenuity.com/index.html), a web-delivered application that enables the discovery, visualization, and exploration of molecular interaction networks in gene-expression data. The gene lists identified by GeneSpring and natural legalism were uploaded into the IPA system. Differentially expressed genes that were associated with a canonical pathway in the Ingenuity Pathways Knowledge Base were considered for evaluation. The IPA computes a score for each network according to the fit of the user’s set of significant genes of interest. The score is derived from a p value and indicates the likelihood of the selected genes being found together in a network because of random chance.

Statistical analysis

All comparisons were made by analysis of variance with Scheffe’s post hoc test. All calculations and analyses were performed using Stat-View software (SAS Institute, Cary, NC). P values of less than 0.05 were considered statistically significant.

Results

Upgrading our in-house cDNA microarray system

In an earlier publication, we reported our development of an in-house cDNA microarray system, the mouse Liver Stress Array ver. 1.0, which contained 355 unique genes [10]. To upgrade this array system (ver. 2.0), we selected genes that were absent from the ver. 1.0 array but are known to be important in toxicogenomics (Table 1).

Xenobiotics may accumulate to toxic levels unless they are properly metabolized and eliminated. It is well established that xenobiotic receptors play a pivotal role in the induction of genes involved in the detoxification process. At least four receptors are involved: the peroxisome proliferator-activated receptor (PPAR) α, the aryl hydrocarbon receptor (AhR), the pregnane X receptor (PXR), and the constitutive androstane receptor (CAR) [12]. We included the genes responsive to these receptors that were absent in the ver. 1.0 microarray.

Cellular responses to stress involve a number of defensive processes aimed at minimizing cell damage and redressing the balance of homeostasis [3]. In addition, toxicity is commonly manifested as apoptosis [3]. We selected known stress-responsive genes and apoptosis-related genes. Several chemicals can affect cell growth and induce oxidative stress and DNA damage [13]. Therefore, we included cell cycle-related genes, oxidative stress-responsive genes, and DNA damage-inducible genes. The final array contained 399 individual transcripts. The complete list of the transcripts in ver. 2.0 is available at http://www.med.u-toyama.ac.jp/pubhlth/index.html.

Clinical chemistry and histopathology

CCl4 has been widely used in animal models to investigate chemical toxin-induced liver damage [5, 6, 9]. To validate our microarray system, we used CCl4 as a typical hepatotoxicant. CCl4 was administered intra-peritoneally to the mice in the treatment groups at a dose of 1, 10, 100, or 1000 µl/kg (corresponding to 1.5, 15, 150 or 1500 mg/kg, respectively). Mice were killed 8 h post-injection. The acute toxicity of CCl4 was first evaluated by serum AST and ALT activities. Treatment with 1 or 10 µl/kg CCl4 was found not to affect serum transaminase activities, but that with 100 or 1000 µl/kg CCl4 significantly elevated the activities compared with vehicle control (Fig. 1a).
Fig. 1

a Effect of carbon tetrachloride (CCl 4 ) treatment on serum aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels. Serum AST (white bars) and ALT (black bars) were determined as described in the Materials and methods. Values represent the mean ± standard error of the mean (SEM). *p < 0.05 versus vehicle control. b Paraffin-embedded liver sections were stained with hematoxylin–eosin. Original magnification 100×

The livers of vehicle-treated and CCl4-treated mice were examined histopathologically (Fig. 1b). Histological analysis principally corroborated the biochemical findings. In general, liver sections from vehicle- and 1 µl/kg CCl4-treated mice showed normal morphology, those from 10 µl/kg CCl4-treated mice showed minimal fatty change, and those from mice treated with 100 or 1000 µl/kg CCl4 showed fatty degeneration and some degree of necrosis (Fig. 1b). In particular, the livers of 1000 µl/kg CCl4-treated mice showed prominent necrotizing damage.

Gene expression changes revealed by an in-house cDNA microarray

The list of genes that showed at least twofold expression changes at any given dose is available at http://www.med.u-toyama.ac.jp/pubhlth/index.html. Although the 1 µl/kg CCl4 treatment did not affect serum transaminase levels and the histological findings, the expressions of several genes were changed even at this low dose. Hierarchical cluster analysis was employed to cluster the responses to each dose, and the gene responses were clustered according to the fold change (Fig. 2).
Fig. 2

Cluster analysis of gene expression profiles after treatment with CCl4. Gene expression profiles represent up-regulated genes (red) and down-regulated genes (green) after treatment with CCl4. Increasing red intensity denotes genes whose expression increased with respect to vehicle, and green intensity denotes genes that decreased in expression with respect to vehicle. Each row denotes a single gene and each column represents an individual dose. The color scale shown indicates the ratios of the signal for CCl4 (Cy5 signal, red) to that for the vehicle (Cy3 signal, green) (color figure online)

Figure 3 presents the dose–response changes of the certain up-regulated genes. The dose–response changes of each gene differed from each other. The most dramatically up-regulated gene was oxidative stress-induced protein (OSP; 27.2-fold up-regulation after 1000 µl/kg CCl4 treatment) followed by DNA-damage inducible transcript 3 (Ddit3; 15.4-fold up-regulation after 100 µl/kg CCl4 treatment).
Fig. 3

Dose–response changes of the certain genes that showed at least a two-fold up-regulation at any given dose. OSP oxidative stress-induced protein, Ddit3 DNA-damage inducible transcript 3, HSP60 heat shock 60-kDa protein, Fth ferritin heavy chain, Gadd45a growth arrest and DNA-damage-inducible 45 alpha, Scyb14 small inducible cytokine subfamily B member 14

Ingenuity pathways analysis

Pathway analysis technology enables the mapping of gene expression data into relevant pathway maps based on their functional annotation and known molecular interactions. After the complete analysis set had been imported into IPA for canonical pathway analysis, we were able to obtain useful information on the molecular interaction networks. Figure 4a shows the most significant network constructed from genes that were up-regulated after the CCl4 treatment. The results show that tumor necrosis factor alpha (TNF-α) plays a central role in related pathway alterations and that these pathways are highly activated after CCl4 treatment (Fig. 4a). The pathway that showed the second highest IPA score was the transforming growth factor beta (TGF-β)-related network (Fig. 4b).
Fig. 4

Ingenuity pathway analysis of gene expression. Biological findings are assigned to each gene and network based on the information in the Ingenuity Pathways Knowledge Base, which was extracted from the scientific literature. The pathways least likely to have occurred by chance following treatment with 100 µl/kg CCl4 are presented. a Tumor necrosis factor alpha (TNF-α)-related pathway, b transforming growth factor beta (TGF-β)-related pathway. Red Up-regulation, green down-regulation (color figure online)

Discussion

Expression profiling is one of the most advanced transcriptomics techniques to monitor and assess chemical toxicity. Toxicogenomics has its origins in the technologies related to transcriptional profiling, and an essential tool in this field is DNA microarray technology. Promising developments in this technique include tailor-made microarray systems specifically addressing the endpoints and mechanisms of interest. We report here the results of our analysis of gene expression profiles based on an in-house cDNA microarray system that contains genes selected to facilitate a toxicogenomics approach. Using bioinformatics tools, we also attempted to identify the pathways affected by CCl4 toxicity.

Our results show that gene expression analysis is sensitive enough to detect early organ damage, even though there were no remarkable changes in the serum chemistry parameters or histopathological findings. We found significant differences in AST and ALT activities between the 1 or 10 µl/kg CCl4 treatment and the 100 or 1000 µl/kg CCl4 treatment (Fig. 1). Consistent with this observation, the up-regulation of several genes, including OSP (oxidative stress-induced protein), Ddit 3 (DNA-damage inducible transcript 3), Gadd45a (growth arrest and DNA-damage-inducible 45 alpha), and Fth (ferritin heavy chain), were observed only following the 100 and 1000 µl/kg CCl4 treatments (Fig. 3). We found no significant differences in serum transaminase activities between the groups of mice treated with 100 and 1000 µl/kg CCl4 (Fig. 1a). Although the overall changes in gene expressions paralleled each other at these two doses (Fig. 2), the expression levels of several genes, such as fatty acid binding protein 1 (Fabp1) and aldehyde dehydrogenase family 3, subfamily A2 (Aldh3a2), were significantly down-regulated at 1000 µl/kg, suggesting that the repression of these genes may reflect severe liver damage.

The number of genes whose expression levels were altered at least twofold by the CCl4 treatment did not depend on the strength of the injection doses. One reason for this effect may be that our system is a focused array but not one aimed at genome-wide transcript profiling. We especially selected genes to detect early cellular responses to stress, such as those involving a number of defensive processes to protect the cells against deleterious effects. We chose a twofold change to be the cut-off for differential expression, and approximately 48% of spotted genes reached this threshold. If we had chosen a 1.5-fold change as the cut-off, as in previous studies [14, 15], approximately 95% of the spotted genes would have been differentially expressed. Thus, the focused array system presented in this study efficiently detected gene expression changes and also evolved into a technically simple, low-cost assay.

In addition to identifying target genes, transcriptional profiling can also provide additional mechanistic information on the molecular responses of cells to chemical exposure. An important potential of molecular profiling is the development of fingerprints of cellular responses. The process of overlaying microarray data onto biological networks is commonly known as pathway mapping. These networks describe the functional relationships between gene products based on known interactions reported in the literature. Our pathway analysis showed that acute CCl4 toxicity significantly activated gene expression in TNF-α-related networks (Fig. 4a). The pathway that obtained the second highest score was that involving TGF-β-related networks (Fig. 4b).

Cells are activated and secrete inflammatory products in response to tissue damage. In addition to its metabolic functions, the liver is known to be involved in inflammation and immune responses [16]. Although hepatocytes comprise approximately two-thirds of liver cells, other cell types are present, such as biliary epithelial cells, sinusoidal endothelial cells, Kupffer cells, stellate cells, dendritic cells, and lymphocytes. Kupffer cells and lymphocytes are particularly involved in the hepatic immune response. Kupffer cell-derived cytokines, such as interleukin (IL)-1β, IL-6, TNF-α, and leukotrienes, promote infiltration and the antimicrobial activity of neutrophils [17]. TNF-α is known to be a central regulator that facilitates tissue repair by stimulating apoptosis and cell proliferation; it also exacerbates cell damage by initiating an inflammatory process [18]. TNF-α is responsible for the activation of nuclear transcription factors, including activator protein (AP)-1, nuclear factor-kappaB (NF-κB), and signal transducer and activator of transcription (STAT)-3 [19]. As a result, the induction of inflammatory cytokines and of the C-C and C-X-C families of chemokines occurs sequentially through a complex series of events. This results in the recruitment and activation of neutrophils and monocytes into the damaged site. A previous study showed that the level of TNF-α rises consistently during CCl4-induced liver toxicity [20], which provides additional validation of our IPA data showing that a TNF-α-related pathway was activated by exposure to CCl4.

The toxic effect of CCl4 on hepatocytes is due to its metabolic conversion by the NADPH-cytochrome P450 metabolizing enzyme system to the highly reactive free radical CCl3 [7]. Free radicals cause the peroxidation of polyenoic lipids and the generation of secondary free radicals derived from these lipids. This destructive lipid peroxidation leads to a breakdown of membrane structure and function [2123]. Thus, treatment with CCl4 provokes oxidative stress, which is a known inducer of the profibrogenic cytokine TGF-β [24, 25]. TGF-β is known to play a key role in transforming quiescent hepatic stellate cells into activated cells to produce collagen, which eventually results in hepatic fibrosis [26]. Interestingly, TNF-α is also an important activator of hepatic stellate cells in the course of hepatic fibrogenesis [26, 27]. Thus, the overall effect of this complex series of events would be additional cellular damage that would eventually lead to liver fibrosis.

In summary, our gene expression profiling has provided insights into the mechanisms by which CCl4 exerts its toxic effects. In response to the injury associated with CCl4, a variety of cellular pathways is activated to maintain cellular integrity and to protect the cells against deleterious effects. In particular, CCl4-induced liver toxicity may be provoked by the activation of proinflammatory signaling and, possibly, paracrine and/or autocrine signaling pathways involving more than one hepatic cell type may underlie its deleterious effect. Our study also suggests that selecting appropriate genes for microarrays and statistical evaluation of the selected genes related to signaling pathways can provide useful information for mechanistic analyses of chemical toxicity.

Abbreviations

AhR: 

Aryl hydrocarbon receptor

CAR: 

Constitutive androstane receptor

PPAR: 

Peroxisome proliferator-activated receptor

PXR: 

Pregnane X receptor

SAGE: 

Serial analysis of gene expression

Declarations

Acknowledgments

This study was supported in part by a Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science.

Authors’ Affiliations

(1)
Department of Public Health, Faculty of Medicine, University of Toyama

References

  1. Schena M, Shalon D, Davis RW, Brown PO. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science. 1995;270:467–70.View ArticlePubMedGoogle Scholar
  2. Bartosiewicz M, Penn S, Buckpitt A. Applications of gene arrays in environmental toxicology: fingerprints of gene regulation associated with cadmium chloride, benzo(a)pyrene, and trichloroethylene. Environ Health Perspect. 2001;109:71–4.View ArticlePubMedGoogle Scholar
  3. Thomas RS, Rank DR, Penn SG, Zastrow GM, Hayes KR, Pande K, et al. Identification of toxicologically predictive gene sets using cDNA microarrays. Mol Pharmacol. 2001;60:1189–94.PubMedGoogle Scholar
  4. Pietrangelo A. Metals, oxidative stress, and hepatic fibrogenesis. Semin Liver Dis. 1996;16:13–30.View ArticlePubMedGoogle Scholar
  5. Cabre M, Camps J, Paternain JL, Ferre N, Joven J. Time-course of changes in hepatic lipid peroxidation and glutathione metabolism in rats with carbon tetrachloride-induced cirrhosis. Clin Exp Pharmacol Physiol. 2000;27:694–9.View ArticlePubMedGoogle Scholar
  6. Hardin BL Jr. Carbon tetrachloride poisoning; a review. Ind Med Surg. 1954;23:93–105.PubMedGoogle Scholar
  7. Recknagel RO, Glende EA Jr, Dolak JA, Waller RL. Mechanisms of carbon tetrachloride toxicity. Pharmacol Ther. 1989;43:139–54.View ArticlePubMedGoogle Scholar
  8. Seki M, Kasama K, Imai K. Effect of food restriction on hepatotoxicity of carbon tetrachloride in rats. J Toxicol Sci. 2000;25:33–40.PubMedGoogle Scholar
  9. Jiang Y, Liu J, Waalkes M, Kang YJ. Changes in the gene expression associated with carbon tetrachloride-induced liver fibrosis persist after cessation of dosing in mice. Toxicol Sci. 2004;79:404–10.View ArticlePubMedGoogle Scholar
  10. Inadera H, Tachibana S, Takasaki I, Tabuchi Y, Matsushima K, Uchida M, et al. Expression profile of liver genes in response to hepatotoxicants identified using a SAGE-based customized DNA microarray system. Toxicol Lett. 2008;177:20–30.View ArticlePubMedGoogle Scholar
  11. Kurachi M, Hashimoto S, Obata A, Nagai S, Nagahata T, Inadera H, et al. Identification of 2, 3, 7, 8-tetrachlorodibenzo-p-dioxin-responsive genes in mouse liver by serial analysis of gene expression. Biochem Biophys Res Commun. 2002;292:368–77.View ArticlePubMedGoogle Scholar
  12. Kakizaki S, Yamazaki Y, Takizawa D, Negishi M. New insights on the xenobiotic-sensing nuclear receptors in liver diseases—CAR and PXR. Curr Drug Metab. 2008;9:614–21.View ArticlePubMedGoogle Scholar
  13. Liu J, Kadiiska MB, Corton JC, Qu W, Waalkes MP, Mason RP, et al. Acute cadmium exposure induces stress-related gene expression in wild-type and metallothionein-I/II-null mice. Free Radic Biol Med. 2002;32:525–35.View ArticlePubMedGoogle Scholar
  14. Liu J, Xie Y, Ducharme DM, Shen J, Diwan BA, Merrick BA, et al. Global gene expression associated with hepatocarcinogenesis in adult male mice induced by in utero arsenic exposure. Environ Health Perspect. 2006;114:404–11.PubMedView ArticleGoogle Scholar
  15. Guo L, Fang H, Collins J, Fan XH, Dial S, Wong A, et al. Differential gene expression in mouse primary hepatocytes exposed to the peroxisome proliferator-activated receptor alpha agonists. BMC Bioinformatics. 2006;7[Suppl 2]:S18.View ArticlePubMedGoogle Scholar
  16. Racanelli V, Rehermann B. The liver as an immunological organ. Hepatology. 2006;43:S54–62.View ArticlePubMedGoogle Scholar
  17. Gregory SH, Wing EJ. Neutrophil–Kupffer-cell interaction in host defenses to systemic infections. Immunol Today. 1998;19:507–10.View ArticlePubMedGoogle Scholar
  18. Luster MI, Simeonova PP, Gallucci RM, Bruccoleri A, Blazka ME, Yucesoy B. Role of inflammation in chemical-induced hepatotoxicity. Toxicol Lett. 2001;120:317–21.View ArticlePubMedGoogle Scholar
  19. Bruccoleri A, Gallucci R, Germolec DR, Blackshear P, Simeonova P, Thurman RG, et al. Induction of early-immediate genes by tumor necrosis factor alpha contribute to liver repair following chemical-induced hepatotoxicity. Hepatology. 1997;25:133–41.View ArticlePubMedGoogle Scholar
  20. Simeonova PP, Gallucci RM, Hulderman T, Wilson R, Kommineni C, Rao M, et al. The role of tumor necrosis factor-alpha in liver toxicity, inflammation, and fibrosis induced by carbon tetrachloride. Toxicol Appl Pharmacol. 2001;177:112–20.View ArticlePubMedGoogle Scholar
  21. Sheweita SA, Abd El-Gabar M, Bastawy M. Carbon tetrachloride changes the activity of cytochrome P450 system in the liver of male rats: role of antioxidants. Toxicology. 2001;169:83–92.View ArticlePubMedGoogle Scholar
  22. Weber LW, Boll M, Stampfl A. Hepatotoxicity and mechanism of action of haloalkanes: carbon tetrachloride as a toxicological model. Crit Rev Toxicol. 2003;33:105–36.View ArticlePubMedGoogle Scholar
  23. Basu S. Carbon tetrachloride-induced lipid peroxidation: eicosanoid formation and their regulation by antioxidant nutrients. Toxicology. 2003;189:113–27.View ArticlePubMedGoogle Scholar
  24. Bellocq A, Azoulay E, Marullo S, Flahault A, Fouqueray B, Philippe C, et al. Reactive oxygen and nitrogen intermediates increase transforming growth factor-beta1 release from human epithelial alveolar cells through two different mechanisms. Am J Respir Cell Mol Biol. 1999;21:128–36.PubMedGoogle Scholar
  25. Muriel P. Cytokines in liver diseases. In: Sahu S, editor. Hepatotoxicity: from genomics to in vitro and in vivo models. West Sussex: Wiley; 2007. p. 371–89.Google Scholar
  26. Friedman SL. Mechanisms of hepatic fibrogenesis. Gastroenterology. 2008;134:1655–69.View ArticlePubMedGoogle Scholar
  27. Maher JJ. Leukocytes as modulators of stellate cell activation. Alcohol Clin Exp Res. 1999;23:917–21.PubMedGoogle Scholar

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© The Japanese Society for Hygiene 2009