Transcriptomic Analysis Reveals Early Signs of Liver Toxicity in

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Chemical Profile Transcriptomic Analysis Reveals Early Signs of Liver Toxicity in Female MRL +/+ Mice Exposed to the Acylating Chemicals Dichloroacetyl Chloride and Dichloroacetic Anhydride Rolf König,*,† Ping Cai,‡ Xin Guo,† and G. A. S. Ansari‡ Department of Microbiology & Immunology, and Department of Pathology, The UniVersity of Texas Medical Branch, GalVeston, Texas 77555 ReceiVed July 27, 2007

Dichloroacetyl chloride (DCAC) is a reactive metabolite of trichloroethene (TCE). TCE and its metabolites have been implicated in the induction of organ-specific and systemic autoimmunity, in the acceleration of autoimmune responses, and in the development of liver toxicity and hepatocellular carcinoma. In humans, effects of environmental toxicants are often multifactorial and detected only after long-term exposure. Therefore, we developed a mouse model to determine mechanisms by which DCAC and related acylating agents affect the liver. Autoimmune-prone female MRL +/+ mice were injected intraperitoneally with 0.2 mmol/kg of DCAC or dichloroacetic anhydride (DCAA) in corn oil twice weekly for six weeks. No overt liver pathology was detectable. Using microarray gene expression analysis, we detected changes in the liver transcriptome consistent with inflammatory processes. Both acylating toxicants up-regulated the expression of acute phase response and inflammatory genes. Furthermore, metallothionein genes were strongly up-regulated, indicating effects of the toxicants on zinc ion homeostasis and stress responses. In addition, DCAC and DCAA induced the up-regulation of several genes indicative of tumorigenesis. Our data provide novel insight into early mechanisms for the induction of liver disease by acylating agents. The data also demonstrate the power of microarray analysis in detecting early changes in liver function following exposure to environmental toxicants. Introduction 1

Dichloroacetyl chloride (DCAC) is a reactive metabolite of trichloroethene (TCE) (1). TCE has been implicated in the induction of organ-specific and systemic autoimmunity, in the acceleration of autoimmune responses (2, 3), and in the development of liver diseases (4, 5). Occupational exposure to TCE has also been reported to cause liver diseases, such as hepatic necrosis, fatty liver, and cirrhosis (6, 7). Epidemiological (8) and population-based case control studies (9) determined an increase in liver cancer following exposure to TCE. The precise mechanisms by which TCE and its metabolites cause liver dysfunction, fatty liver, and necrosis of hepatocytes remain unclear (10–12). Two irreversible pathways metabolize TCE. In the liver, the main pathway involves oxidation by the microsomal cytochrome P450 oxidase system. Cytochrome P450 2E1 (CYP2E1) is * To whom correspondence should be addressed. The University of Texas Medical Branch, Department of Microbiology & Immunology, 301 University Boulevard, Galveston, TX 77555. Tel: (409) 747-0395. Fax: (409) 772-5065. E-mail: [email protected]. † Department of Microbiology & Immunology. ‡ Department of Pathology. 1 Abbreviations: DCAC, dichloroacetyl chloride; TCE, trichloroethene; DCAA, dichloroacetic anhydride; GCOS, GeneChip Operating Software; SAM, significance analysis of microarrays; ANOVA, analysis of variance; IPKD, Ingenuity Pathways Knowledge Database; RT-PCR, reverse transcriptase-polymerase chain reaction; FDR, false discovery rate; PCA, principal component analysis, ROS, reactive oxygen species.

considered essential for the generation of toxic TCE metabolites (13, 14). CYP2E1 oxidizes TCE to trichloroethene oxide, an unstable intermediate that leads to the formation of oxalic acid and chloral/chloral hydrate (15–17). Reduction of chloral/chloral hydrate by alcohol dehydrogenase to trichloroethanol or oxidation of chloral/chloral hydrate by aldehyde dehydrogenase leads to trichloroacetic acid, which can form dichloroacetic acid by reductive dechlorination (18, 19). Alternatively, trichloroethene oxide via rearrangement leads to DCAC and its subsequent hydrolysis to dichloroacetic acid (20, 21). The second pathway, conjugation with glutathione by glutathione S-transferases to S-1,2-dichlorovinyl-L-glutathione, is important in the renal system, but of only minor consequence in the liver (19). Khan, Ansari, and co-workers previously reported the immunogenicity of both TCE and DCAC (22, 23). TCE and its metabolites induce and exacerbate autoimmune responses in autoimmune-prone MRL +/+ mice (22–27). Furthermore, TCE and particularly its toxic metabolites promote inflammatory responses in MRL +/+ (27–29) and B6C3F1 mice (29). However, liver pathology and lymphocyte infiltration into the liver can only be detected after long-term exposure to TCE (28). A persistent challenge for researchers in toxicology is to predict the consequences of long-term exposure to low doses of toxic chemicals. Exposure for six weeks to DCAC or the related, acylating chemical dichloroacetic anhydride (DCAA) induces inflammatory responses by splenic T cells, systemic autoimmunity, thickening of alveolar septa, and other histologi-

10.1021/tx7002728 CCC: $40.75  2008 American Chemical Society Published on Web 02/23/2008

Toxicogenomics of Acylating Agents in Mouse LiVer

cal changes in the lung (27). However, no overt pathological changes in the liver can be detected after a six-week exposure period2. One approach to alleviate the problems hindering the early detection of pathological effects of toxic chemicals can be the use of microarray gene chips for the global analysis of gene expression. Microarray analysis can simultaneously measure the expression level of over 30,000 mouse genes. Altered expression of many genes with related biological functions can indicate mechanisms of toxicity and future manifestations of chemical exposure (30, 31). To determine whether autoimmune and inflammatory responses induced by short-term exposure to DCAC or DCAA were associated with alterations in gene expression that could predict liver dysfunction or disease, we performed a transcriptomic analysis of liver samples from female MRL +/+ mice. Samples of mice exposed to DCAC or DCAA for six weeks were compared to age-matched controls. Changes in gene expression were predictive of altered liver function. Our data show that DCAC and DCAA differentially induced multiple acute phase response and inflammatory genes in the liver, suggesting inflammation as the mechanism of hepatic injury following exposure to acylating agents.

Experimental Procedures Chemicals. Dichloroacetyl chloride (DCAC; purity 99%) and dichloroacetic anhydride (DCAA; purity 85%) were purchased from Sigma Aldrich, Inc. (St. Louis, MO). Dichloroacetic anhydride was further purified by distillation to a final purity of ∼99%. DCAC and DCAA were dissolved in corn oil at a concentration of 50 mM. Animals. All experiments were performed in accordance with the guidelines of the National Institutes of Health and the Guiding Principles in the Use of Animals in Toxicology, and were approved by the Institutional Animal Care and Use Committee of the University of Texas Medical Branch. Animals were housed in facilities accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International. Four-weekold female MRL +/+ mice were purchased from Jackson Laboratory (Bar Harbor, ME) and housed in an animal room maintained at ∼22 °C and 50–60% relative humidity with a 12-h light/dark cycle. Laboratory chow and drinking water were provided ad libitum, and mice were acclimatized for one week before starting the treatment. The mice were randomly divided into three groups and injected intraperitoneally with 0.2 mmol/kg of DCAC or DCAA in 100 µL of corn oil twice weekly for six weeks. Control animals received only corn oil. Mice were sacrificed 24 h after the last treatment. Isolation of RNA and Preparation of Biotinylated Target cRNA. Immediately upon dissection, small pieces of the liver (∼125 mm3) were frozen on dry ice, and stored at -70 °C until further processing. Samples from three mice per group were independently processed, thus yielding three biological replicates. Total RNA was isolated using the ToTALLY RNA kit from Ambion (Austin, TX) and further processed by passing through CHROMA SPIN-100 columns (BD Biosciences Clonetech, Mountain View, CA). The RNA concentration was determined using a NanoDrop ND-1000 spectrophotometer (Nanodrop Inc., Wilmington, DE). The integrity of the RNA samples was tested using an Agilent 2100 Bioanalyzer and RNA 6000 LabChip kit (Agilent Technologies, Palo Alto, CA). Only RNA samples without signs of degradation were used. Reverse transcription was performed using 10–25 µg of total RNA and an oligo-dT-primer that encodes a T7 RNA polymerase promoter sequence for first-strand cDNA synthesis. Second-strand synthesis converted the cDNA into double-stranded DNA templates for use in an in Vitro transcription reaction. Bacteriophage T7 RNA polymerase and biotinylated nucleotides were used in the in Vitro transcription reaction to produce biotinylated cRNA. The biotin2 Cai, P., Boor, P. J., König, R. and Ansari, G.A.S., unpublished observations.

Chem. Res. Toxicol., Vol. 21, No. 3, 2008 573 labeled cRNA was fragmented to a mean size of 200 bases, and the biotinylated cRNA-fragments served as targets for microarray hybridization. Microarrays and Hybridization. The biotin-labeled antisense cRNA fragments were hybridized to Affymetrix GeneChip Mouse Genome 430 2.0 arrays (Affymetrix, Santa Clara, CA) using standard protocols and GeneChip Hybridization Oven 640 (Affymetrix). The microarrays were washed and labeled with Streptavidin-Phycoerythrin in GeneChip Fluidics Station 400 (Affymetrix) using software-selected protocols specific for the Mouse Genome 430 2.0 array. This microarray contains 45,037 probe sets and provides expression analysis of over 39,000 transcripts on a single array with multiple independent measurements for each transcript. Expression levels of over 34,000 characterized mouse genes can be analyzed using this chip. The probe sets for this array were derived from GenBank, dbEST, and RefSeq. In addition, the GeneChips contained probe sets for several bacterial genes. Prelabeled target sequences for bioB (biotin synthetase), bioC, and bioD (dethiobiotin synthetase) from the E. coli biotin synthesis pathway and the recombinase gene cre from the bacteriophage P1 were spiked into the hybridization cocktail. The Bacillus subtilis genes dapB, lys (diaminopimelate decarboxylase), pheA (monofunctional prephenate dehyratase), pheB (phenylalanine biosynthesis associated protein), thrB (homoserine kinase), and thrC (threonine synthase) were expressed from a plasmid vector that introduced a poly A sequence at the 3′-end, added as synthetic RNAs, and labeled in parallel with the sample RNA. The constant-expression control genes encoding β-actin, glyceraldehyde-3-phosphate dehydrogenase, transferrin receptor, pyruvate carboxylase, and 18S rRNA served as normalization controls. Arrays were scanned on a GeneChip Scanner 3000 (Affymetrix) with GeneChip Operating Software (GCOS). Microarray Data Analysis. Data were analyzed using GeneChip Analysis Suite 4.0 software (Affymetrix), Spotfire DecisionSite 8.1 for Functional Genomics (Spotfire Inc., Somerville, MA), Significance Analysis of Microarrays (SAM; Stanford University, Stanford, CA), the National Institute of Aging’s Array Analysis tool (http:// lgsun.grc.nia.nih.gov/ANOVA/index.html), and analysis of variance (ANOVA). Computational hierarchical cluster analysis and principal component analysis were performed using Spotfire DecisionSite 8.1. Pathways and network analysis was performed using Ingenuity Pathways Analysis and the Ingenuity Pathways Knowledge Database (IPKD, Ingenuity, Mountain View, CA). All microarray experiments adhere to the Minimum Information About a Microarray Experiment (MIAME) guidelines (32). The data set is available at NCBI GEO (http://www.ncbi.nlm.nih.gov/geo/) with accession number GSE5241. Affymetrix GCOS-processed data containing calculated signal intensities, absent/present calls, and detection p-values have been deposited. Significance in Global Functions and Pathways. The significance is expressed by p-value, which is calculated using the righttailed Fisher’s Exact Test. In this method, the p-value is calculated by comparing the number of user-specified genes of interest (i.e., Global Analysis Genes) that participate in a given function or pathway, relative to the total number of occurrences of these genes in all functional/pathway annotations stored in the IPKD. The significance value associated with a function in Global Analysis is a measure for how likely it is that genes from the data set file under investigation participate in that function. In the right-tailed Fisher’s Exact Test, only over-represented functional/pathway annotations, which have more Functions/Canonical Pathways Analysis Genes than expected by chance (right-tailed annotations), are used. Underrepresented annotations (left-tailed annotations), which have significantly fewer Functions/Canonical Pathways Analysis Genes than expected by chance, are not shown. Real-Time Reverse Transcriptase-Polymerase Chain Reaction. Induction of mRNA was also quantified by a two-step realtime reverse transcriptase-polymerase chain reaction (real-time RTPCR). Aliquots of the same RNA samples isolated for microarray analysis were used. Equal quantities of total RNA (1 µg) were added to each reverse transcriptase reaction. Random hexamer-primed

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reverse transcription with TaqMan Reverse Transcription Reagents (Applied Biosystems, Foster City, CA) was performed at 25 °C for 10 min followed by 48 °C for 30 min and 95 °C for 5 min. Quantitative PCR was performed with 1/10 of the RT product using 900 nM of each primer and 250 nM of the probe. The PCR primer sets were purchased from Integrated DNA Technologies (Skokie, IL). The primers were designed to span exon-exon junctions in order to avoid detecting genomic DNA. The TaqMan MGB probes, labeled with 6FAM, were purchased from Applied Biosystems. All primer and probe sequences were searched against the Celera database to confirm specificity. The sequences of the primers and probes used are shown in Supporting Information Table 1. The PCR reactions were performed with TaqMan Universal PCR mix (Applied Biosystems) under the following cycling conditions on a Prism 7000 Sequence Detection System (Applied Biosystems): one cycle at 50 °C for 2 min and 95 °C for 10 min; 40 cycles at 95 °C for 15 s and 60 °C for 1 min. For quantification, the comparative cycle threshold (Ct) method was used (33). The amount of target was obtained by normalizing to an endogenous reference (mouse glyceraldehyde-3-phosphate dehydrogenase supplied as a control reagent by Applied Biosystems) relative to a calibrator. A sample from an untreated mouse of the same age that was housed under the same conditions as those of the mice in the treatment groups served as a calibrator.

Results DCAC and DCAA Affect Gene Expression in the Liver of Female MRL +/+ Mice. Affymetrix GeneChip Mouse Genome 430 2.0 arrays were used to measure the expression of 39,415 transcripts in liver RNA from female MRL +/+ mice. Samples from three mice of each group (vehicle control, DCACtreated, and DCAA-treated) were independently processed and analyzed. Hierarchical clustering of all genes significant at p < 0.01 by ANOVA was performed with Spotfire DecisionSite 8.1 for Functional Genomics. This process revealed groups of genes that were differentially regulated in the treatment groups (Figure 1). We also observed that DCAC and DCAA treatment had differential effects, which can be seen in Figure 1. For example, genes involved in regulating immune system processes were in general highly expressed (red in Figure 1) following DCAA treatment, but not after DCAC treatment (Figure 1a). However, IK cytokine genes were highly expressed in untreated, agematched control mice, but not in either DCAC- or DCAA-treated mice (black or green, respectively, in Figure 1). Several stress response genes were highly expressed after either treatment with DCAC or DCAA (Figure 1b). A set of nine cell cycle regulators was highly expressed (red) after DCAA treatment, whereas a different set of four cell cycle regulators was poorly expressed (green) after DCAA treatment, yet highly expressed in untreated, age-matched controls (Figure 1c). For further analysis of microarray data, we used the most stringent criteria to determine whether a change in gene expression should be considered significant. These criteria included a p-value of 0.01 and a greater than 2-fold change in expression in either direction. In addition, differential gene expression at these levels had to occur in at least two out of three biological replicates. On the basis of these criteria, DCACtreatment induced up-regulation of 31 genes in the livers of female MRL +/+ mice and repressed the expression of 34 genes. Treatment with DCAA up-regulated 70 genes and suppressed 63 genes (Supporting Information Table 3). We compiled the genes identified by at least two of the three methods used and have tabulated the fold-changes reported by Spotfire, SAM, and the NIA Array Analysis tool in Tables 1 (DCAC-treatment) and 2 (DCAA-treatment). In these tables, we also identify the FDR for each gene. A Venn diagram shows

König et al.

Figure 1. Hierarchical cluster of liver genes of female MRL +/+ mice. Raw intensity data for each experiment were used for the calculation of Z-scores. Z-scores were calculated by subtracting the overall average gene intensity across all samples from the raw intensity data for each gene and dividing that result by the SD of all of the measured intensities using Spotfire DecisionSite 8.1 for Functional Genomics. The gene set affected by exposure to DCAC or DCAA with a confidence level g99% (p e 0.01), which comprised 289 transcripts, was then subjected to an unsupervised clustering algorithm, sorting both genes and arrays simultaneously. The Gene Ontology Browser function in Spotfire was then used to identify genes belonging to specific biological processes. The clustering results for immune system process (a), stress response (b), and cell cycle (c) are shown as intensity plots with the column dendrogram showing the similarity between the individual arrays and the row dendrogram grouping the genes according to the similarity of their expression patterns. The color gradient shows the Z-scores ranging from –2.0 (bright green) to +2.0 (bright red). A Z-score of 0 is represented by black.

that half of the genes up-regulated by DCAC were also upregulated by DCAA (Figure 2). Many of the genes up-regulated by treatment with DCAC or DCAA were associated with acute phase or inflammatory responses. Six of the 11 genes upregulated by both DCAC and DCAA were in these two groups. In addition, genes involved in metal ion homeostasis, an indication of stress responses, were up-regulated by both DCAC and DCAA. The affected genes predominantly regulate zinc ion homeostasis. Furthermore, genes encoding proteins with catalytic activity expressed in hepatocytes were affected by treatment with DCAC and DCAA, indicating disruption of normal liver cell metabolic activities. Both treatments also affected several signaling pathways. The Venn diagram also shows that several genes were differentially regulated by DCAC and DCAA and that the gene expression profiles for DCAC and DCAA differed for down-regulated genes. DCAC and DCAA Increase the Expression of Genes that Regulate the Acute Phase Response, Inflammation, and Stress Responses in the Liver of Female MRL +/+ Mice. All methods employed to analyze the regulation of gene expression in female MRL +/+ mice treated with DCAC or DCAA identified the up-regulation of acute phase response and inflammatory genes (Tables 1 and 2 and Supporting Information Table 5). In addition, several liver genes known to be regulated by inflammatory cytokines were up-regulated in treated mice [e.g., Slc3a1 (34), Cyb561 (35)]. Furthermore, genes that regulate metal ion homeostasis in response to stress (e.g., metallothioneins and Slc39 family members) or express enzymes that eliminate toxic metabolites [e.g., Afmid (36) and Nnmt (37)] were strongly up-regulated. Expression of Atp2a1, which


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Table 1. Liver Genes Affected by Exposure of Female MRL +/+ Mice to DCACa fold-change GeneBank

SAM

spotfire

NIA

NM_011314 NM_009117 NM_011016 NM_011315 NM_013623

33.79 18.45 16.01 3.91 3.65

24.42 14.80 11.67 4.03 2.28

22.94 11.53 10.52 3.03 2.55

NM_008491 NM_007805 NM_008176

19.32 N/A 3.58

14.35 3.69 2.89

12.49 5.11 2.47

NM_031188

3.88

2.32

2.30

NM_001014836

2.90

5.92

8.34

NM_008630 NM_013602 NM_013808

5.94 3.47 3.19

6.01 2.60 1.07

4.21 2.41 2.33

3.16

6.86

NM_026232

N/A

FDR (%)

gene symbol

acute phase response 0.00 Saa2 (1)b 0.00 Saa1 0.00 Orm2 (1) 0.39 Saa3 0.34 Orm3 (1) inflammation 0.00 Lcn2 (1) 0.00 Cyb561 (1) 0.56 Cxcl1 (1) 2.32

Mup1 (1)

immune response 3.74 4930404N11Rik metal ion binding/homeostasis 0.00 Mt2 (1) 0.00 Mt1 (1) 1.09 Csrp3 stress response 0.00 Slc25a30

NM_013560

0.09

0.10

0.10

NM_009205

0.75

2.33

2.27

NM_007824

5.81

6.03

3.98

NM_023627

2.42

5.11

6.09

0.00

Isyna1 (1)

NM_010924 NM_027907

3.99 0.38

3.18 0.35

2.66 0.29

0.01 0.00

Nnmt (1) Agxt2l1

0.12

0.13

0.75

Hsd3b5

NM_008295 NM_177341

N/A 0.10

1.07

metabolic processes 1.06 Cyp7a1 (2)

regulation of signaling 0.42 Socs2

1.41

4.55

NM_008745

0.49

0.34

0.33

NM_028082

4.82

5.57

7.92

8.20 6.41 0.09

metabolic transport processes 1.03 Slc3a1 (1)

0.09

3.75

3.10 6.64 0.11

Hspb1 (1)

calcium ion transport 0.94 Trpm3

NM_007706

NM_027128 NM_172595 52230

0.00

8.32 7.20 0.11

0.75

Ntrk2

regulation of transcription 3.52 Cnot2 unknown process 1.06 2310011G06Rik 1.64 Arl15 2.46 D5Ertd121e

gene name (function) serum amyloid A 2 serum amyloid A 1 orosomucoid 2 serum amyloid A 3 orosomucoid 3 lipocalin 2 Cytochrome b-561 (ferric reductase) chemokine (C-X-C motif) ligand 1/KC chemokine major urinary protein 1 whn-dependent transcript 1 (similar to fzr1) metallothionein 2 (zinc ion binding) metallothionein 1 (zinc ion binding) cysteine and glycine-rich protein 3 (zinc ion binding) solute carrier family 25, member 30 (antioxidant defense) heat shock protein 1/Hsp25 solute carrier family 3 (carbohydrate metabolism) cytochrome P450, family 7, subfamily a, polypeptide 1 (cholesterol metabolism) myo-inositol 1-phosphate synthase A1 (phospholipid biosynthesis) nicotinamide N-methyltransferase alanine-glyoxylate aminotransferase 2-like 1 3β-hydroxysteroid dehydrogenase-5 melastatin 2 (calcium channel) suppressor of cytokine signaling 2 (JAK-STAT signaling) neurotrophic tyrosine kinase (MAPK signaling) CCR4-NOT transcription complex APin protein ADP-ribosylation factor-like 15 ERATO Doi 121

a All transcripts that were identified by the NIA Array Analysis tool with FDR < 5%, and a fold-change of at least 2 are shown in the table. b The numbers in parentheses following the gene symbol indicate whether the gene belongs to one of two Principal Components. Bolded GeneBank IDs identify genes regulated by both DCAC and DCAA.

encodes an ATPase that regulates calcium ions in the ER and maintains cellular calcium signaling, was also up-regulated by DCAA. DCAC and DCAA Affect Signaling Regulatory Genes in the Liver of Female MRL +/+ Mice. Genes that regulate signal transduction or transcription were also affected by DCAC and DCAA, but the direction of the effect depended on the specific signaling pathways. For example, expression of the suppressor of cytokine signaling 2 (Socs2) was strongly upregulated by treatment with either DCAC or DCAA. This gene encodes a member of the STAT-induced STAT inhibitor (SSI) family, also known as the SOCS family. SSI family members are cytokine-inducible negative regulators of cytokine signaling.

The expression of Socs2 can be induced by a subset of cytokines, including, GM-CSF, IL-10, IL-12, and IFN-γ (38–40). The protein encoded by Socs2 interacts with the cytoplasmic domain of insulin-like growth factor-1 receptor (IGF1-R) and is involved in the regulation of IGF1-R mediated cell signaling. It also acts as an inhibitor of the JAK/STAT signaling pathway. However, expression of Pim3, a gene that encodes a serine/ threonine kinase previously reported to be suppressed by Socs2 expression (41), was also up-regulated by DCAA-treatment. Similar to the suppressive effects of DCAC and DCAA treatment on cytokine signaling via Socs2 up-regulation, DCAAtreatment suppressed the expression of Gab2, the gene that encodes Grb2-associated binding protein 2, an adapter for

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Table 2. Liver Genes Affected by Exposure of Female MRL +/+ Mice to DCAAa fold-change GeneBank

SAM

spotfire

NIA

FDR (%)

gene symbol

NM_011314 NM_009117 NM_011016

28.45 17.75 13.25

20.56 14.24 9.66

29.71 11.01 7.92

acute phase response 0.00 Saa2 (1)b 0.00 Saa1 1.85 Orm2 (1)

NM_008491 NM_008176 NM_031188 NM_020581 NM_175217 NM_013653

18.98 4.58 5.04 3.76 3.41 0.28

14.10 3.69 3.01 2.87 2.34 0.21

11.28 3.25 3.08 2.95 2.48 0.18

0.98 0.00 1.32 0.00 0.87 3.32

NM_013793 NM_008391

24.43 2.41

12.90 2.07

18.24 2.29

immune response 0.08 Klra15 (1) 3.09 Irf2 (1)

NM_001031664

2.64

12.57

13.33

NM_028051

0.32

6.07

5.87

0.87

Slc39a5

NM_008630 NM_013602 NM_008202

5.89 3.83 0.46

5.95 2.86 0.38

4.29 2.75 0.46

1.95 3.49 2.84

Mt2 (1) Mt1 (1) Slc39a7

NM_022310

0.57

0.39

0.43

0.30

inflammation Lcn2 (1) Cxcl1 (1) Mup1 (1) Angptl4 (1) Mmd2 (1) Ccl5

metal ion binding/homeostasis 0.77 Nudt11 (1)

stress response Hspa5

metabolic transport processes 2.14 Folr1 0.01 Slc3a1 (1)

gene name (function) serum amyloid A 2 serum amyloid A1 orosomucoid 2 lipocalin 2 chemokine (C-X-C motif) ligand 1/KC chemokine major urinary protein 1 angiopoietin-like 4 monocyte to macrophage differentiation-associated chemokine (C-C motif) ligand 5 killer cell lectin-like receptor, subfamily A, member 15 interferon regulatory factor 2 nudix (nucleoside diphosphate-linked moiety X)-type motif 11 (magnesium ion binding) solute carrier family 39, member 5 (zinc ion transporter) metallothionein 2 (zinc ion binding) metallothionein 1 (zinc ion binding) solute carrier family 39, member 7 (zinc ion transporter) heat shock 70 kD protein 5/glucose-regulated protein

NM_008034 NM_009205

0.09 0.74

3.75 2.30

7.17 2.34

NM_011731

0.02

0.13

0.14

NM_020025

3.34

24.71

17.98

metabolic processes 1.06 B3galt2

NM_027827 XP_129214

5.47 9.08

20.47 12.04

11.75 11.71

0.94 0.98

Afmid (1) Pcsk5

NM_010924 NM_023792 NM_173030

4.20 2.70 8.32

3.34 2.11 1.09

2.90 2.17 14.76

0.08 4.97 0.45

Nnmt (1) Pank1 Galnt13

NM_010359 NM_053200 NM_009272

0.66 0.52 0.55

0.49 0.49 0.39

0.48 0.45 0.46

0.94 0.08 1.56

Gstm3 (1) Ces3 (1) Srm

NM_007822

0.13

0.31

0.27

0.00

Cyp4a14

NM_053200 NM_010011

0.33 0.26

0.25 0.25

0.28 0.28

0.78 0.08

Ces3 (1) Cyp4a10

NM_016773 NM_007504

1.56 4.78

10.66 8.20

8.20 5.08

calcium ion transport 2.04 Nucb2 (1) 0.92 Atp2a1

nucleobindin 2 (Ca2+ homeostasis) ATPase, cardiac muscle, fast twitch 1

NM_001039050 NM_007706 NM_145478 NM_028303 NM_010248

3.50 4.17 0.82 0.57 0.05

23.36 9.66 1.96 0.36 0.20

10.42 8.88 2.25 0.40 0.12

regulation 1.85 4.01 1.72 0.07 4.40

cAMP-dependent protein kinase inhibitor beta suppressor of cytokine signaling 2 Pim3 oncogene (ser/thr protein kinase) PDZ domain containing 11 Grb2-associated binding protein 2

NM_011480

0.67

0.43

0.48

regulation of transcription 0.48 Srebf1 (1)

NM_009716 NM_008321 NM_009107 NM_008719 NM_009324

0.51 0.42 0.38 0.14 0.03

0.39 0.35 0.28 0.11 0.12

0.43 0.40 0.25 0.13 0.09

0.92 0.92 0.87 0.92 1.56

NM_026410

3.85

2.68

3.34

1.67

4.54

Slc6a20

of signaling Pkib (1) Socs2 Pim3 Pdzd11 Gab2

Atf4 Idb3 (1) Rxrg (1) Npas2 Tbx2 (1) cell cycle Cdca5 (1)

folate receptor 1 (folate reabsorption) solute carrier family 3, member 1 (carbohydrate metabolism) solute carrier family 6 (neurotransmitter transporter), member 20 (proline reabsorption) UDP-Gal:betaGlcNAc beta 1,3-galactosyltransferase, polypeptide 2 (oligosaccharide biosynthesis) arylformamidase (tryptophan catabolism) proprotein convertase subtilisin/kexin type 5 (serine-type endopeptidase) nicotinamide N-methyltransferase pantothenate kinase 1 (coenzyme A biosynthesis) UDP-N-acetyl-R-D-galactosamine:polypeptide N-acetylgalactosaminyltransferase 13 glutathione S-transferase, mu 3 carboxylesterase 3 spermidine synthase (arginine and proline metabolism) cytochrome P450, family 4, subfamily a, polypeptide 14 (monooxygenase) carboxylesterase 3 cytochrome P450, family 4, subfamily a, polypeptide 10

sterol regulatory element binding factor 1 (lipid metabolism) activating transcription factor 4 (gluconeogenesis) inhibitor of DNA binding 3 retinoid X receptor gamma neuronal PAS domain protein 2 T-box 2 (repressor) cell division cycle-associated 5


Chem. Res. Toxicol., Vol. 21, No. 3, 2008 577 Table 2. Continued

fold-change GeneBank

SAM

spotfire

NIA

FDR (%)

NM_007631

0.29

0.36

0.43

1.05

NM_020293 NM_028044 NM_146116 NM_009609 NM_009450 NM_029720

6.92 0.46 0.46 0.30 0.61 0.57

6.10 1.09 0.44 0.42 0.40 0.35

12.06 0.43 0.46 0.45 0.43 0.45

AA414992 NM_026983 NM_008016

5.71 4.47 0.40

26.10 10.17 0.41

27.59 7.41 0.43

NM_144810 NM_133797

0.10 0.03

0.16 0.10

0.19 0.11

gene symbol

gene name (function)

Ccnd1 (1)

cyclin D1

structural proteins 1.06 1.85 0.83 3.38 0.94 0.98

Cldn9 Cnn3 Tubb2c Actg1 (1) Tubb2a Creld2

claudin 9 calponin 3, acidic (actin binding) tubulin beta 2c (GTP binding) actin, gamma, cytoplasmic 1 tubulin beta 2a (GTP binding) cysteine-rich with EGF-like domains 2

unknown process 0.00 3.84 0.87

(1) 1810033M07Rik Fin15

expressed sequence RIKEN cDNA 1810033M07 gene fibroblast growth factor inducible 15 kelch domain containing 8A RIKEN cDNA 4833439L19 gene

2.95 0.09

Klhdc8a 4833439L19Rik

a All transcripts that were identified by the NIA Array Analysis tool with FDR < 5%, and a fold-change of at least 2 are shown in the table. b The numbers in parentheses following the gene symbol indicate whether the gene belongs to one of two Principal Components. Bolded GeneBank IDs identify genes regulated by both DCAC and DCAA.

Figure 2. Venn diagram of liver genes differentially regulated by treatment with DCAC or DCAA. Only genes that fulfilled the criteria for differential expression (i.e., p < 0.01 by ANOVA, expression foldchange > 2, and differential gene expression in at least 2 out of 3 biological replicates) and were identified by at least two out of three independent analysis methods (i.e., Spotfire, SAM, and NIA Array Analysis) were used in the Venn diagram. Genes with increased or decreased expression following treatment are represented separately. The size of the circles is proportional to the number of genes represented. The numbers in the circles or overlapping areas indicate the number of individual genes.

transmitting signals in response to cytokine and growth factor receptor stimulation. The gene product of Gab2 is an adapter protein that acts as the principal activator of phosphatidylinositol-3 kinase (42). Interestingly, inhibition of phosphatidylinositol-3 kinase promotes the expression of interferon regulatory factor 2 (Irf2) in mice (43). We found Irf2 to be up-regulated after DCAA-treatment. Also, DCAA strongly up-regulated the expression of Pkib, which encodes a competitive inhibitor of the cAMP-dependent protein kinase A inhibitor (44). However, the gene Hspb1 encoding stress-induced heat-shock protein involved in multiple signaling pathways was strongly down-regulated following DCAC treatment. This gene has been suggested to be primarily an anti-inflammatory stimulus (45). Confirmation of Effects of DCAC and DCAA on Liver Gene Expression by Quantitative Real-Time RT-PCR. To confirm the microarray data, we performed quantitative twostep real-time RT-PCR on selected transcripts using the same

RNA samples analyzed in the microarray experiments. The primer and probe sequences used in RT-PCR experiments are listed in Supporting Information Table 1. The genes were selected on the basis of observed changes in expression levels, the signal intensity in the microarray assay, and characteristics of the probe region. Supporting Information Table 6 shows foldchanges measured by real-time RT-PCR in comparison to foldchanges determined by Spotfire. In general, the quantitative realtime RT-PCR experiments confirmed the data obtained by microarray analysis with regard to directionality and magnitude of the induced change in expression. Furthermore, both methods detected common patterns of expression in genes expected to be coregulated (e.g., acute phase response and inflammationrelated genes). However, the acute phase response genes Orm2 and Saa2 were found to be drastically up-regulated in samples from both DCAC- and DCAA-treated mice (Supporting Information Table 6). The up-regulation of these genes as measured by RT-PCR was much more pronounced than was found by microarray analysis. Other investigators have reported such quantitative discrepancies between microarray and real-time RTPCR data (46, 47). Identification of Biological Pathways Affected by Treatment of Female MRL +/+ Mice with DCAC or DCAA. The statistical analysis of microarray expression data provided information regarding changes in gene expression. Genes with observed changes in expression of more than 2-fold were grouped in categories that characterize their function in general biological processes (Tables 1 and 2). To further understand how treatment of mice with DCAC or DCAA affected regulatory and signaling functions of the liver, we used Ingenuity Pathways Analysis. Ingenuity Pathways Analysis uses the Gene IDs in the data set file and maps them to genes in the IPKD. Biologically relevant networks were generated from the list of 15,918 genes that were called present by the Affymetrix GeneChip Suite 4.0 software. First, focus genes were used to identify molecular networks that indicate how these genes may influence each other. Focus genes, that is, the genes eligible for generating networks, were defined as those genes that met the following requirements: (a) they showed expression value changes of greater than 2-fold with an FDR of less than 5% following treatment with DCAC or DCAA and (b) they interact with other genes in the IPKD. Focus genes are also present in Tables 1 (DCAC) or 2 (DCAA). The focus genes directly or

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Figure 3. Ingenuity Pathways Analysis of liver genes affected by exposure of female MRL +/+ mice to DCAC. This network contains 15 focus genes and has a score of 37. In addition to the focus genes, the network also includes genes present in the microarray expression analysis that did not meet the selection criteria. Furthermore, genes called absent by the Affymetrix GeneChip Suite 4.0 software but that have known relationships with other genes in the network are also shown (white nodes). The fold-change in expression level is indicated by the number below the gene symbol. Red nodes indicate genes upregulated more than 2-fold, and green nodes indicate genes downregulated more than 2-fold. * indicates that the input gene had duplicate identifiers in the data set file mapping to a single gene in the IPKD. Solid lines indicate direct interactions between connected proteins, and broken lines indicate indirect interactions. An arrow pointing from node A to node B signifies that A has been reported in the IPKD to act on B, whereas a connecting edge ending in a perpendicular line signifies inhibition. Simple edges connecting two nodes signify that only binding relationships have been reported. The following canonical pathways were affected: p38 MAPK, ERK, Jak/Stat, IL-6, Wnt, and TGF-β signaling pathways; nicotinate and nicotinamide metabolism; bile acid biosynthesis; starch and sucrose metabolism; and aminosugar metabolism.

indirectly interact with other genes in the IPKD, which consists of direct or indirect physical, enzymatic, and transcriptional interactions between orthologous mammalian genes from published, peer-reviewed articles. The application added other genes from the data set file, that are not focus genes, to networks during later steps of network generation, on the basis of the relationships contained in the IPKD. Furthermore, the application added genes that were not present in the data set file to networks on the basis of the relationships contained in the IPKD. It is important to realize that these networks do not represent biochemical pathways but are based on protein interactions. Thus, a single network can combine proteins that participate in several biochemical pathways. We show significant networks for each treatment. The only significant network for DCAC treatment that fulfilled all our requirements stated above had a score of 37 and contained 15 focus genes (Figure 3). The score indicates that the likelihood of the focus genes to be found in this network together due to random chance was less than 10-37. The Global Analysis function in the Ingenuity software suite revealed that this network was strongly indicative of hemorrhage of the liver (p ) 7.47 × 10-6), oxidative stress of hepatocytes (p ) 7.43

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× 10-5), and liver damage (p ) 2.87 × 10-4). The network depicted in Figure 3 contains several genes involved in the acute phase response (Orm2, Saa1, Saa2, and Saa3) and in inflammation (Cxcl1 and Lcn2). The network also contains several genes encoding inflammatory cytokines that were not present in the data set file (IL-6, IFN-γ, and TNF-R). These genes are expressed by inflammatory effector cells and the cytokines are secreted. Thus, they can act in a paracrine manner and affect liver cells even if not detectably expressed by cells in the liver. Their effects were also evident by the strong up-regulation of several focus genes that are associated with protective hepatocyte responses to oxidative stress (Mt1 and Mt2) and liver cell damage (Mt1, Mt2, and Saa2). Additional evidence for effects of inflammatory cytokines on liver cell gene expression was provided by the observation that the expression of Socs2 was up-regulated. Furthermore, the increase in Cyb561 expression following DCAC-treatment may have been caused by inflammatory cytokines such as INF-γ and INF-R (35) and the increase in Slc3a1 expression by INF-γ and other inflammatory cytokines that activate NF-κB (34). Additional evidence of stress responses by the liver following DCAC treatment was the up-regulation of Nnmt, the gene encoding nicotinamide N-methyltransferase, by almost 4-fold. N-methylation is one method by which drug and other xenobiotic compounds are metabolized by the liver. Another gene involved in drug metabolism, Cyp7a1, was up-regulated 4-fold. The product of Cyp7a1 is a liver-specific endoplasmic reticulum membrane monooxygenase that catalyzes the first reaction in the cholesterol catabolic pathway in the liver, which converts cholesterol to bile acids (48). Mitochondria are the primary cellular target of oxidative stress because aerobic respiration at the inner mitochondrial membrane produces reactive oxygen species (ROS) as harmful byproducts of oxidative phosphorylation (49). One directly damaging effect of ROS is to impair mitochondrial function, followed by increased intracellular mitochondrial mass, presumably as a compensatory mechanism (50). The Mt1 gene, which encodes a mitochondrial protein (51), was up-regulated by DCACtreatment, indicating that oxidative stress may have acted on liver cells following exposure to DCAC. However, DCACtreatment strongly suppressed the expression of Hspb1, a gene that encodes the mitochondrial outer membrane protein, Hsp27 (52). A comparison of Tables 1 and 2 shows that although many genes were affected similarly by DCAC- and DCAA-treatment, the latter chemical impacted the expression of a wider range of genes than did DCAC. Further evidence that DCAA affected more genes than did DCAC came from Ingenuity Pathways Analysis. We identified three highly significant networks of genes with scores of 25, 23, and 18, respectively (Figure 4 and Supporting Information Figures 5 and 6). The networks contained 14, 13, and 11 focus genes, respectively. These networks were strongly indicative of the hemorrhage of the liver (p ) 4.41 × 10-5), oxidative stress of hepatocytes (p ) 4.35 × 10-4), and liver damage (p ) 3.75 × 10-3). Several of the genes affected by DCAC-treatment and represented in Figure 3 were also present in the networks of genes affected by DCAAtreatment. Acute phase response genes were present in network 3 (Saa1, Saa2, and Orm2). The lipocalin 2 gene was also up-regulated by DCAA and present in network 1 (Figure 4). Metallothionein genes were up-regulated and present in networks 1 and 3 (Mt1 and Mt2, respectively). Furthermore, Socs2 was present in network 1 and Cxcl1 in network 2. Both genes are involved in


Figure 4. Ingenuity Pathways Analysis of liver genes affected by exposure of female MRL +/+ mice to DCAA; network 1. This network contains 14 focus genes and has a score of 25. In addition to the focus genes, the network also includes genes present in the microarray expression analysis that did not meet the selection criteria. Symbols and expression levels are consistent with the description given for Figure 3. The following canonical pathways were affected: xenobiotic metabolism; glutathione metabolism; urea cycle and metabolism of amino groups; fatty acid metabolism; starch and sucrose metabolism; NF-κB, β-adrenergic, IL-6, IL-10, interferon, JAK/Stat, p38 MAPK, nitric oxide, PPAR, calcium, and cAMP-mediated signaling pathways.

the regulation of inflammation. Expression of Slc3a1 (in network 1) and Nnmt (in network 3) was also up-regulated. Although not a focus gene, IL1F9 expression (in network 1) was increased 2.5-fold by DCAA treatment. This cytokine gene is a member of the interleukin-1 family of genes. Its expression is increased by inflammatory cytokines including INF-γ, TNF-R, and IL1β (53). In addition to focus genes also present in the DCAC-affected gene network (Figure 3), network 1 of genes affected by DCAA contained interferon regulatory factor 2, Irf2. This factor inhibits transcriptional activation of type I interferons (54). Expression of Irf2 is also required for the protection of Kupffer cells from apoptosis following inflammatory stimuli (55). In addition, DCAA treatment up-regulated two negative regulators of apoptosis, Angptl4 and Atp2a1. The former is secreted by hepatocytes and inhibits apoptosis (56), and the latter regulates calcium ion flux into the ER, thus preventing apoptosis in hepatocytes (57). Two genes in network 1 that are involved in the peroxidation of lipids, Cyp4a10 and Cyp4a14 (58), were suppressed following treatment with DCAA. In addition, Gstm3, which encodes a glutathione S-transferase that belongs to the mu class, was suppressed by DCAA. These enzymes conjugate electrophilic substrates with glutathione, thereby detoxifying carcinogens, therapeutic drugs, environmental toxins, and products of oxidative stress. DCAA treatment also up-regulated Pim-3, a gene in network 1 that is transcriptionally regulated by Socs-2 (41).

Discussion Exposure to environmental or occupational toxicants such as TCE and related chlorohydrocarbons (tetrachloroethene, 1,2dichloroethene, and vinyl chloride) has been associated with health problems in humans. Often, long-term exposure to

Chem. Res. Toxicol., Vol. 21, No. 3, 2008 579

multiple toxicants masks the mechanisms that induce diseases such as organ-specific and systemic autoimmunity, and cancer. Therefore, we have developed a mouse model to determine the immunotoxic effects of TCE and its metabolites using the autoimmune-prone mouse strain MRL +/+ (22, 23, 27). In this investigation, we measured and compared the toxicogenomic effects of the TCE-metabolite DCAC and the related acylating agent DCAA on the livers of female MRL +/+ mice following an exposure period of six weeks. No histopathological changes of the liver were apparent during this period (data not shown). Using stringent criteria to restrict the number of genes identified as affected by the toxicants, we found several genes involved in acute phase, stress, and inflammatory responses to be upregulated following exposure to DCAC or DCAA. Metabolic liver enzymes were also affected by both acylating agents; however, the gene expression profiles of the two acylating chemicals differed in this group of genes. The gene expression profile of DCAA-treated mice was also more diverse in groups of genes that regulate cellular signaling, transcription, and the cell cycle. In an aprotic solvent, DCAC will be a more reactive acylating agent than DCAA. However, in aqueous environments, as is the case in ViVo, the hydrolysis of the chemicals competes with acylation. The hydrolysis of DCAA yields twice as much dichloroacetic acid as does the hydrolysis of DCAC (see Table of Contents graphic for chemical formulas). Exposure to dichloroacetic acid causes liver toxicity in MRL +/+ and B6C3F1 mice (29). In addition, dichloroacetic acid induces liver tumors in B6C3F1 mice (10). Thus, the different concentrations of dichloroacetic acid resulting from the hydrolysis of DCAC and DCAA may be responsible for the differential gene expression profiles induced by the two chemicals. Stress and Inflammation. We have previously reported that splenic T cells from MRL +/+ mice treated with DCAC or DCAA secreted significantly more IL-1R, IL-1β, IL-3, IL-6, IFN-γ, G-CSF, and KC following T cell receptor-mediated stimulation than did splenic T cells from untreated mice (22). T cells from mice exposed to DCAC also secreted more IL-17 upon stimulation. All of the eight cytokines mentioned are involved in either acute (e.g., IL-1, IL-6, IL-17, G-CSF, and KC) or chronic (e.g., IL-3 and IFN-γ) inflammation. If we assume that liver infiltrating lymphocytes were also biased to increased secretion of inflammatory cytokines, we can explain the mechanism for increased expression of acute phase and inflammatory genes in the livers of MRL +/+ mice exposed to acylating chemicals. Compensatory Changes in Liver Gene Expression to Control Liver Damage. DCAA up-regulated expression of the apoptosis regulators Atp2a and Angptl4. Atp2a1 prevents apoptosis in hepatocytes by maintaining calcium ion homeostasis in the ER (57) and Angptl4 negatively regulates apoptosis (56). These regulatory effects on gene expression suggest a compensatory mechanism by which liver damage may be controlled. Additional evidence for early changes in liver function came form the observation that two genes involved in the peroxidation of lipids, Cyp4a10 and Cyp4a14 (58), were suppressed following treatment with DCAA. Expression of these genes is also suppressed by the bacterial endotoxin lipopolysaccharide, which induces liver inflammation (59). Genes encoding zinc-binding proteins, such as metallothioneins (Mt1 and Mt2), members of the Slc39a family of proteins, and Csrp3 (gene alias Mlp) function as antioxidant and stress response genes. Both DCAC and DCAA up-regulated the expression of several zinc-binding proteins, most prominently the metallothionein genes Mt1 and Mt2. Oxidative stress and

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heavy metals induce transcriptional activation of metallothionein genes (60, 61). Metallothionein gene expression is also induced by inflammatory cytokines, such as TNF-R and IL-6 (62, 63). The antioxidant functions of metallothioneins in response to heavy metal-induced damage (64) and inflammation (65, 66) have been recognized. In addition, metallothioneins protect DNA from oxidative damage (67). Expression of the hepatitis C virus core protein induces the expression of metallothioneins and Nnmt (68), suggesting a cellular response to intracellular oxidative stress due to core protein-induced mitochondrial injury and subsequent accumulation of ROS (69). The Csrp3 gene encodes a zinc-binding protein that is required for the activation of the stress-responsive calcineurin signaling pathway (70). Another antioxidant encoding gene up-regulated by DCAC was Slc25a30 (gene alias Kmcp1). Expression of this gene is increased in situations of enhanced mitochondrial metabolism that lead to cellular oxidant states (71). Thus, our data indicate increased gene expression of inhibitors of oxidative damage and suggest the activation of multiple antioxidant pathways following exposure to DCAC or DCAA. Metallothionein gene expression has also been proposed as a marker for aggressive progression in many tumors (72–77). The correlation of metallothionein gene expression with aggressive tumor growth may be due to the antioxidant and antiapoptotic functions of metallothioneins, which could protect rapidly growing tumors from ROS caused by increased mitochondrial metabolism and chemotherapeutic drugs. In this context, it is interesting that chemical-induced inflammation has been linked to hepatocarcinogenesis (78). In this model, compensatory proliferation of hepatocytes driven by inflammatory cytokines following a chemical insult with diethyl nitrosamine that increased ROS was required for tumor progression. In this chemical profile, we have not analyzed the potential of DCAC or DCAA to promote hepatocarcinogenesis because tumorigenesis requires at least six months or longer before sizable tumors develop. However, our findings that DCAC and DCAA up-regulated the expression of metallothionein and other genes involved in antioxidant defenses and of inhibitors of apoptosis (e.g., Angptl4, and Atp2a1), together with our previous report demonstrating inflammatory responses following DCACand DCAA-exposure (27), suggest that chronic low-level inflammation may form a link between exposure to acylating agents and hepatocarcinogenesis. Toxicogenomic Analyses in Rodents. Previous reports have demonstrated the effects of short-term exposure to TCE or its metabolite dichloroacetic acid on gene expression (79–81). An early study using a limited set of only 148 unique genes provided proof-of-concept that microarray analysis following short-term exposure can discriminate between different classes of toxicants (82). Using microarrays containing a total of 1316 unique genes, Thai identified 24 genes with altered expression in the livers of B6C3F1 mice after exposure to hepatocarcinogenic doses of dichloroacetic acid for four weeks (80). The identified genes belong to three functional groups: tissue remodeling and angiogenesis-related groups; xenobiotic metabolism; and damage response genes. The stress response gene ER p72 and the DNA repair gene MHR 23A were both suppressed in the livers of treated mice. In our study, we identified several genes upregulated by DCAC that are involved in the metabolism of xenobiotics (Nnmt and Cyp7a1), antioxidant defense (Slc25a30), or zinc ion homeostasis (Mt1, Mt2, and Csrp3). Exposure to DCAA also induced xenobiotic metabolizing (Nnmt) and metal ion homeostasis genes (Mt1, Mt2, and Nucb2). Similar to the observations reported in dichloroacetic acid-treated B6C3F1

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mice (80), we found the suppression of stress response genes by DCAC (Hspb1) and DCAA (Hspa5). However, the genes identified in our experiments using DCAC or DCAA differed from those identified by Thai. In a different study, microarray analysis of livers from SV129 mice treated with high doses of TCE by gavage (1,500 mg/kg thrice daily for 3 days) using a set of ∼1,200 genes identified 43 differentially expressed genes (81). Similar to our findings, Hspa5 was suppressed. Almost all changes in gene expression were dependent on the expression of the peroxisome proliferator-activated receptor R (PPARR) because in PPARR-null mice, only three of the 43 affected genes were expressed in the same direction as that in wild-type mice (81).

Conclusions Exposure to environmental toxicants is often long-term, and symptoms of disease remain hidden for prolonged periods of time. In most exposures, a multitude of toxicants at low doses are involved. Thus, effects of the exposure on disease induction and progression are multifactorial and difficult to ascertain. Here, we report the first comprehensive analysis of early in ViVo changes in gene expression induced by short-term exposure to low doses of DCAC and DCAA. In the absence of histopathological changes, we observed differential expression of liver genes that regulate the acute phase response and inflammatory processes, consistent with chronic low-level inflammation. Acknowledgment. This work was made possible by grant ES11584 (to G. A. S. Ansari) from the National Institute of Environmental Health Sciences (NIEHS) and its contents are solely the responsibility of the authors and do not necessarily represent the views of the NIH or NIEHS. Affymetrix GeneChip hybridization and analysis, and real-time PCR were performed by the Molecular Genomics Core Facility (Director T. G. Wood, Ph.D.) at UTMB. Purification of DCAA was done by the Synthetic Organic Chemistry Core Laboratory at UTMB. Both core facilities are supported by NIEHS Center grant P30ES06676. We gratefully acknowledge Ms. Mala Sinha from the Bioinformatics Program at UTMB for help with the software programs Spotfire DecisionSite and Ingenuity Pathways Analysis for the analysis of Affymetrix GeneChip data. We have no conflicting interests in reporting the data of this study. Supporting Information Available: Microarray data have been deposited and are available at www.ncbi.nlm.nih.gov/ projects/geo under accession number GSE5241. Additional analysis of Affymetrix microarray data by significance analysis of microarrays (SAM) and principal component analysis (PCA). Tables of primer and probe sequences used for quantitative realtime RT-PCR; δ-values generated by SAM; number of transcripts differentially regulated by DCAC or DCAA; Eigenvalues and Eigenvectors determined by PCA; liver genes that correlate with principal components 1 and 2; and quantitative real-time RT-PCR data on selected liver genes. Figures of SAM plots for the comparison of the control versus treatment groups; logratio plots for control versus treatment groups; PCA of gene expression in liver from DCAC- or DCAA-exposed MRL +/+ mice; principal component-based clustering of genes; and networks generated by Ingenuity Pathways Analysis of liver genes affected by exposure to DCAA. This material is available free of charge via the Internet at http://pubs.acs.org.

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