Genomic Identification of Potential Risk Factors during

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Genomic Identification of Potential Risk Factors during Acetaminophen-Induced Liver Disease in Susceptible and Resistant Strains of Mice Kevin D. Welch,*,† Timothy P. Reilly,†,‡ Mohammed Bourdi,† Thomas Hays,† Cynthia A. Pise-Masison,§ Michael F. Radonovich,§ John N. Brady,§ David J. Dix,| and Lance R. Pohl† Molecular and Cellular Toxicology Section, Laboratory of Molecular Immunology, National Heart, Lung, and Blood Institute, and Laboratory of Cellular Oncology, Virus Tumor Biology Section, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Department of Health and Human SerVices, Bethesda, Maryland 20892, and National Center for Computational Toxicology, Office of Research and DeVelopment, U.S. EnVironmental Protection Agency, Research Triangle Park, North Carolina 27711 ReceiVed October 13, 2005

Drug-induced liver disease (DILD) continues to cause significant morbidity and mortality and impair new drug development. Mounting evidence suggests that DILD is a complex, multifactorial disease in which no one factor is likely to be an absolute indicator of susceptibility. As an approach to better understand the multifactorial basis of DILD, we recently compared the hepatic proteomes of mice that were resistant (SJL) and susceptible (C57Bl/6) to APAP-induced liver disease (AILD) wherein we identified potential risk factors and mechanistic pathways responsible for DILD. In this study, we have uncovered additional potential risk factors by comparing hepatic mRNA expression profiles of the same two strains of mice with that of SJLxB6-F1 hybrid (F1) mice, which were found to be of intermediate susceptibility to AILD. Global hepatic gene expression profiling over a 24 h period following APAP treatment revealed elevated patterns in the mRNA expression of cytoprotective genes in resistant SJL mice as compared to susceptible B6 mice, while F1 mice had intermediate mRNA expression levels of these genes. One of these genes encoded for heat shock protein (HSP) 70 whose relative protein expression among the three strains of mice was found to parallel that of their mRNA levels, suggesting that this protein had a protective role against AILD. However, there was no difference in the susceptibility of HSP70 knockout (KO) mice to AILD as compared to wild-type (WT) mice. There were also protoxicant genes, such as osteopontin (OPN), with elevated mRNA expression levels in the B6 mice as compared to the SJL mice and with intermediate levels in the F1 mice, suggesting that they may play a role in exacerbating liver injury after APAP treatment. In support of this hypothesis, OPN KO mice were found to be more resistant to AILD than WT mice. Additionally, the results from both the proteomic and the genomic studies were compared. The two approaches were found to be complementary to each other and not simply overlapping. Our findings suggest that comparative gene expression analysis of susceptible and resistant mouse strains may lead to the identification of factors that could have a role in determining the susceptibility of individuals to DILD. Introduction (DILD)1

Drug-induced liver disease is often life-threatening and is one of the major reasons new drugs never reach the market or are withdrawn postmarketing (1). Most cases of DILD are classified as idiosyncratic reactions due in large part to the unpredictability of these reactions, the lack of animal models, the scarcity of information concerning the nature of susceptibility factors, and the insufficient knowledge of the mechanisms of * To whom correspondence should be addressed. Tel: 301-496-4841. Fax: 301-480-4852. E-mail: [email protected]. † National Heart, Lung, and Blood Institute. ‡ Current address: Department of Immunotoxicology, Drug Safety Evaluation, Pharmaceutical Research Institute, Bristol-Myers Squibb Company, Syracuse, NY, 13057. § National Cancer Institute. | U.S. Environmental Protection Agency. 1 Abbreviations: ALT, alanine aminotransferase; AILD, APAP-induced liver disease; APAP, acetaminophen; B6, C57Bl/6; DILD, drug-induced liver disease; F1, SJLxB6-F1 hybrid cross; HSP, heat shock protein; IL, interleukin; NAPQI, N-acetyl-p-benzoquinone imine; KO, knockout; GO, gene ontology; OPN, osteopontin; WT, wild-type.

10.1021/tx050285z

liver disease caused by drugs. Nevertheless, the majority of studies with drugs and other xenobiotics that cause liver injury in animal models, including acetaminophen (APAP), halothane, ethanol, cocaine, carbon tetrachloride, bromobenzene, and thioacetamide, indicate that liver injury caused by both nonallergic and allergic pathways of toxicity is probably initiated in many cases by reactive metabolites formed in the liver (2-6). The relatively low incidence and idiosyncratic nature of most cases of DILD suggest that susceptibility to this class of disease might be determined by multiple genetic and/or acquired risk factors. In line with this hypothesis, it has been postulated that both genetic and acquired environmental factors that can affect drug metabolism and reactive metabolite formation, such as cytochrome P450 and N-acetyltransferase polymorphisms, drug interactions, gender, and extrahepatic diseases, may have contributed to the susceptibility of individuals to a variety of DILDs (7). More recent studies of APAP-induced liver disease (AILD) in transgenic knockout (KO) mice have indicated that multiple factors unrelated to drug metabolism may also con-

This article not subject to U.S. Copyright. Published 2006 by the American Chemical Society Published on Web 01/11/2006

224 Chem. Res. Toxicol., Vol. 19, No. 2, 2006

tribute to the susceptibility to DILD. These include the hepatoprotectants, interleukin (IL)-10 (8), IL-6 (9), Nrf2 (10), and cyclooxgenase-2 (11) and the protoxicants, lipopolysaccharide (12, 13), interferon-γ (14), and macrophage migration inhibitory factor (15). A number of studies have shown that there are species differences in susceptibility of rats, mice, and guinea pigs to various organ injuries caused by xenobiotics (16-23). However, little is known about strain differences within the same species, especially mice, regarding susceptibility to DILD. An intraspecies comparison may thus serve as an experimental model to help elucidate the complex multifactorial basis of DILD. In this regard, we have recently identified resistant (SJL) and susceptible [C57Bl/6 (B6)] strains of mice to AILD and compared their hepatic proteomes following APAP treatment (24). Several novel factors were identified that were more highly expressed in the livers of SJL mice, including those involved in stress response, cell proliferation and tissue regeneration, and protein modification, implicating these proteins as potential hepatoprotective factors. There was also a selective loss of several mitochondrial proteins from the livers of the B6 mice, suggesting that the loss of functional mitochondria may indeed play a role in AILD. In this study, as a complement to the proteomics study, we compared the hepatic mRNA expression profiles of SJL, B6, and SJL-B6-F1 hybrid (F1) mice following APAP treatment. GeneChip microarray analysis led to the identification of a number of factors that might play a role in the regulation of not only AILD in mice but also idiosyncratic DILD in humans. Two of the factors shown to have differential mRNA expression between the strains were further investigated using KO mice. Finally, we compared the results from this genomics study to our proteomics findings.

Experimental Procedures Animal Handling and Drug Treatment. SJL, F1, and B6.CgSpp1tm1Blh/J [osteopontin (OPN) KO mice on a pure B6 background] were obtained from Jackson Laboratories (Bar Harbor, ME). B6 mice were obtained from both Taconic Farms (Terrytown, NY) and Jackson Laboratories. HSP70.1/3 KO mice (C57/129 hybrid background) (25, 26) and wild-type (WT) littermates were obtained from the National Health and Environmental Effects Research Laboratory of the U.S. Environmental Protection Agency (Research Triangle Park, NC). Mice were acclimated to a 12 h light/dark cycle in a temperature- and humidity-controlled, specific pathogen-free environment in autoclaved, microisolator cages for at least 6-7 days according to National Institutes of Health standards. Mice were allowed free access to autoclaved food and water until experimental use. Before each experiment, food was withheld from the animals overnight (>16 h) to deplete hepatic glutathione stores uniformly as previously described (11). All mice were male, 8-9 weeks old, and weighed 20-25 g at the time that they were treated. APAP (300 mg/kg, dissolved in warm saline) or saline was then administered intraperitoneally whereupon food supplies were restored. At varying times thereafter, blood samples were taken by retro-orbital puncture and preselected mice were killed to obtain liver tissues for histological, mRNA, and protein analyses. The remaining mice were monitored for 48-72 h for the occurrence of APAP-induced deaths. Assessment of Liver Injury. Liver damage was evaluated by measuring the serum activity of alanine aminotransferase (ALT) using a microtiter plate adaptation of a commercially available kit (Sigma, St. Louis, MO). Liver sections that had been fixed in buffered formalin were processed by standard histological techniques, stained with hematoxylin and eosin (H/E), and examined for histopathological evidence of liver injury. Immunoblot Analysis. Sections of liver tissues were homogenized in 100 mM Tris-HCl, pH 7.5, containing 1 mM EDTA, 250

Welch et al. mM sucrose, and a protease inhibitor cocktail (Complete, Boehinger-Mannheim) (buffer A). Equivalent amounts of protein were then diluted in sample buffer under reducing conditions (125 mM Tris-HCl, pH 6.8, 0.5% SDS, 20% glycerol, 40 mM dithiotheitol, and bromophenol blue), boiled, and resolved on 10% acrylamide gels. After transfer to nitrocellulose (BioRad, Hercules, CA), nonspecific binding was blocked with 5% nonfat dried milk, and blots were probed with a rabbit anti-APAP polyclonal antibody (1: 2000, generously provided by Drs. Neil Pumford and Jack Hinson, University of Arkansas), mouse anti-actin monoclonal antibody (1: 5000, catalog no. MAB1501R, Chemicon International, Temecula, CA), or mouse anti-HSP70 monoclonal antibody (1:000, catalog no. SPA-810, Stressgen, Victoria, BC, Canada), followed by incubation with an appropriate peroxidase-conjugated secondary antibody (1:5000, Chemicon International). Protein signals were visualized using chemiluminescent detection (ECL, Amersham Pharmacia Biotech, Piscataway, NJ). Exposed X-ray films or direct chemiluminescent detection from nitrocellulose membranes were scanned and analyzed with a Kodak Image Station 2000RT imager and its software (Eastman Kodak, Rochester, NY). OPN Analysis. Sections of liver tissues were homogenized in buffer A and centrifuged at 100000g for 30 min. Supernatants were snap frozen in liquid nitrogen and stored at -80 °C, until OPN protein levels were measured by Pierce Biotechnology, Inc. (Woburn, MA) using Pierce/Perbio Searchlight Proteome Array technology (Pierce Biotechnology Inc., Rockford, IL) (27). Transcriptional Profiling. Global gene expression within the liver was evaluated with high-density oligonucleotide microarrays exactly as detailed by the microarray manufacturer (Murine U74, v2 A, B, and C arrays; Affymetrix, Santa Clara, CA). Pooled RNA samples comprised of three individual mice were used to obtain a representative gene expression profile for each treatment group as described previously (28). The validity of this pooling approach for investigational purposes has been discussed in detail by other investigators (29). Data analysis was performed using GeneChip Analysis Suite (version 5.0, Affymetrix) and scaled to 1000 to allow direct comparisons among arrays using GeneSpring (Silicon Genetics, Redwood City, CA). Effects on gene expression were performed using 3 h saline-treated SJL expression levels as an arbitrary baseline measurement for analysis and data filtered using the exclusion criteria outlined previously (28). Gene Ontology (GO) analysis of the gene expression data was performed using the software program GoMiner (30). Prior to GO analysis, the Affymetrix probe set IDs were converted to HUGO symbols using the software program MatchMiner (31). Only GO categories with a p value lower than that obtained using a random list of genes of the same size were considered significant. Statistical Analysis. Statistical comparisons between two groups were made using a Student’s T-test and between various treatments using ANOVA with a posthoc test of significance between individual groups, unless indicated otherwise in the figure legends. Statistical analyses of correlations were performed using the Spearman correlation test. Differences were considered significant when p < 0.05.

Results Consistent with an earlier report (24), B6 mice were more susceptible to AILD than SJL mice as determined biochemically by the measurement of serum ALT activities (Figure 1A) and histologically (Figure 1B). In addition, F1 mice had an intermediate susceptibility, which was statistically different as compared to both parental strains (Figure 1). Additionally, there was no histological indication of kidney damage in any of the strains (data not shown). The marked differences in susceptibility of B6, SJL, and F1 mice to AILD did not appear to be due to a disparity in the metabolism of APAP into its reactive N-acetylp-benzoquinoneimine (NAPQI) metabolite, because the levels of APAP-protein adducts formed in the liver 3 h following APAP treatment did not differ appreciably between the three

Strain Susceptibility to APAP-Induced LiVer Disease

Figure 1. Comparison of AILD in SJL, B6, and F1 mice. (A) Time course of ALT activity in sera after treatment of mice with 300 mg/kg APAP. Results represent means ( SEM from a compellation of multiple studies (n ) 5-26 mice per group per time point); *, p < 0.05 as compared to B6 mice at the same time point; †, p < 0.05 as compared to SJL mice at the same time point. (B) Photomicrographs (10×, objective lens) of H/E-stained liver sections of mice 8 h after treatment with APAP. Results shown are representative of each group (n ) 3-5 per group). The liver section depicted for the saline treatment is from an SJL mouse, which is representative of all of the saline-treated animals from all three strains.

strains (data not shown) as found previously when similar comparisons were made exclusively between B6 and SJL mice (24). Other potential factors unrelated to drug metabolism that might have a role in determining the strain differences in susceptibility to AILD were discovered when gene expression profiles of liver mRNAs from B6, SJL, and F1 mice were compared at 3, 6, 12, and 24 h following APAP treatment (for a complete listing of mRNA expression data, see the Supporting Information). Analysis of the results using the software program GoMiner, which clusters genes into GO categories, revealed a number of GO categories that were significantly enriched with genes that were up-regulated, 2-fold or greater, in the SJL mice as compared to the B6 mice. These included the GO categories acute-phase response, heat shock protein (HSP) binding, response to unfolded protein, and complement activation (Table 1). Many factors within these GO categories could play important roles in protection against AILD. Similarly, there were a number of GO categories enriched with genes up-regulated, 2-fold or greater, in the B6 mice as compared to the SJL mice, such as response to wounding and blood coagulation (Table 2). Increased levels of many of the factors within these GO categories could exacerbate liver injury produced by APAP.

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A more detailed analysis of the expression profiles of genes of interest in the enriched GO categories revealed that SJL mice had a much higher mRNA expression profile for a number of potentially cytoprotective genes including those of several HSPs such as inducible HSP70 (HSP70.3 or Hspala). The expression profile for HSP70 was considerably higher in SJL mice as compared to B6 mice, while F1 mice had an intermediate level of HSP70 mRNA expression (Figure 2A). Similar expression differences of HSP70 were seen at the protein level by immunoblot analysis of liver homogenates 8 h after APAP treatment (Figure 2B). Noteworthy, the F1 mouse with the highest serum ALT activity had the lowest level of hepatic HSP70 protein expression among the F1 mice. In fact, a statistically significant negative correlation was found when sera ALT activities from individual animals from all three mouse strains were plotted against hepatic HSP70 levels (R ) -0.87 and p < 0.001). On the basis of these findings and the established cytoprotective roles of HSP70 and other HSPs against a variety of stress and toxicant insults (9, 15, 32-34), it seemed possible that the elevated levels of HSP70 in the liver of SJL mice may play a role in protecting these mice against AILD. To test this hypothesis, we compared the susceptibility of HSP70.1/3 KO (deficient in both HSP70.1 and 70.3 genes) and WT mice to AILD. No statistical differences were found in the extent of liver injury (Figure 3A) or mortality (Figure 3B) between the two strains of mice, although there was a trend toward increased lethality of the KO mice. Similarly, there was no difference in the susceptibility of HSP70.1/3 KO mice as compared to WT mice when the dose of APAP was reduced from 300 to 200 mg/kg (data not shown). Another gene of potential interest uncovered from the analysis of the enriched GO categories was that of OPN. In contrast to the expression profile of HSP70 (Figure 2A), OPN mRNA levels were higher in the B6 mice as compared to the SJL mice while those of the F1 mice remained intermediate between the two parental strains (Figure 4A). These strain differences in OPN hepatic mRNA expression were confirmed at the protein level 12 h after APAP treatment (Figure 4B). A statistically significant correlation existed between hepatic OPN mRNA levels and serum ALT activities (R ) 0.57 and p < 0.03), suggesting a protoxicant role of OPN in AILD. Indeed, we found OPN KO mice to be more resistant to AILD than WT mice, in regard to both the extent of liver injury (Figure 5A) and the mortality (Figure 5B). The levels of APAP-protein adducts 2 h after APAP treatment were found to be similar between the KO and the WT mice (Figure 5C), indicating that the decreased susceptibility of the OPN KO mice was not due to a lack of bioactivation of APAP, a prerequisite event for toxicity. Finally, we compared the GeneChip expression data collected 3 and 6 h after treatment of SJL and B6 mice with APAP with our 6 h proteomics results (24). For the 524 factors for which we had sufficient information to compare the genomic and proteomic results, approximately 24% had significant (2-fold) changes in either mRNA or protein expression between the two strains (see the Supporting Information). Although 56 mRNA transcripts from the 3 h GeneChip data showed 2-fold, or greater, differences in mRNA expression between SJL and B6 mice, only seven of the corresponding proteins (highlighted in bold) had a similar change in expression (Table 3). Similarly, the expression ratios of only eight proteins (highlighted in bold) corresponded to the 62 mRNA transcripts having 2-fold or greater differences in expression 6 h after treatment (Table 4).


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Table 1. GO Categories Enriched with Genes More Highly Expressed in SJL as Compared to B6 Mice following APAP Treatmenta time (h) 3

6

12

24

GO IDb

totalc

changedd

overe

P value (over)f

category name

5783 6953 30333 19886 19882 48002 6118 19752 4497 42598 5792 6082 5737 6986 16712 42598 5792 4497 6457 9266 9408 51082 42789 6629 6082 44255 16705 42598 19752 5792 8610 8202 9058 5783 6986 8152 6953 51082 6457 6066 44249 16491 42445 6519 44237 31072 6091 4497 16125 9308 5319 6694 16709 3824 4522 30659 16892 5624 9063 6958 6956 6953 6950 6118 6091 44237 16705 16491 4497 42598 42445 5792 5783 5739 3824

401 32 36 11 42 7 229 358 71 114 114 360 2727 41 19 114 114 71 153 39 27 111 2 464 360 390 72 114 358 114 186 116 803 401 41 4605 32 111 153 201 770 467 54 231 4410 29 404 71 59 268 56 61 12 3231 12 12 13 479 51 25 34 32 862 229 404 4410 72 467 71 114 54 114 401 637 3231

40 8 7 4 7 3 25 36 11 13 13 36 181 10 8 19 19 13 15 8 6 12 2 65 46 59 17 21 46 21 34 26 80 47 9 335 7 13 15 31 76 52 12 30 315 6 48 17 16 34 11 16 5 241 4 4 4 55 9 8 9 9 101 50 73 385 23 90 24 29 13 29 59 80 307

24 7 7 4 6 3 14 18 7 9 9 18 81 10 6 13 13 10 15 7 6 12 2 34 26 31 10 13 26 13 17 15 43 31 9 164 7 13 15 17 40 28 8 18 155 6 25 9 8 19 8 8 4 118 4 4 4 27 7 8 8 9 61 29 41 224 12 53 16 20 11 20 37 51 179

0.0000 0.0000 0.0000 0.0001 0.0003 0.0004 0.0006 0.0010 0.0010 0.0010 0.0010 0.0011 0.0013 0.0000 0.0000 0.0001 0.0001 0.0001 0.0001 0.0002 0.0002 0.0002 0.0011 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0002 0.0002 0.0002 0.0002 0.0002 0.0003 0.0003 0.0003 0.0003 0.0003 0.0004 0.0005 0.0005 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

endoplasmic reticulum acute-phase response antigen processing antigen processing, exogenous antigen via MHC class II antigen presentation antigen presentation, peptide antigen electron transport carboxylic acid metabolism monooxygenase activity vesicular fraction microsome organic acid metabolism cytoplasm response to unfolded protein oxidoreductase activity, acting on paired donors, with... vesicular fraction microsome monooxygenase activity protein folding response to temperature response to heat unfolded protein binding mRNA transcription from Pol II promoter lipid metabolism organic acid metabolism cellular lipid metabolism oxidoreductase activity, acting on paired donors, with... vesicular fraction carboxylic acid metabolism microsome lipid biosynthesis steroid metabolism biosynthesis endoplasmic reticulum response to unfolded protein metabolism acute-phase response unfolded protein binding protein folding alcohol metabolism cellular biosynthesis oxidoreductase activity hormone metabolism amino acid and derivative metabolism cellular metabolism HSP binding generation of precursor metabolites and energy monooxygenase activity sterol metabolism amine metabolism lipid transporter activity steroid biosynthesis oxidoreductase activity, acting on paired donors, with... catalytic activity pancreatic ribonuclease activity cytoplasmic vesicle membrane endoribonuclease activity, producing 3′-phosphomonoesters membrane fraction amino acid catabolism complement activation, classical pathway complement activation acute-phase response response to stress electron transport generation of precursor metabolites and energy cellular metabolism oxidoreductase activity, acting on paired donors, with... oxidoreductase activity monooxygenase activity vesicular fraction hormone metabolism microsome endoplasmic reticulum mitochondrion catalytic activity



Table 1. (Continued) time (h) 24

GO IDb

totalc

changedd

overe

P value (over)f

category name

8152 6730 6082 16903 9605 16712 4364 5737 5579 50896

4605 28 360 29 961 19 20 2727 3 1499

407 8 45 10 110 8 7 282 3 151

235 7 30 7 62 6 6 144 3 88

0.0000 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001

metabolism one-carbon compound metabolism organic acid metabolism oxidoreductase activity, acting on the aldehyde... response to external stimulus oxidoreductase activity, acting on paired donors, with... glutathione transferase activity cytoplasm membrane attack complex response to stimulus

a May be a result of up-regulation in SJL mice and/or down-regulation in B6 mice. b GO category identification number used by the program GoMiner. Total number of genes in the GO category. d Number of genes in the SJL-B6 comparison changed (2-fold difference in expression of the mRNA in the SJL strain vs the B6 strain). e Number of up-regulated (higher in SJL as compared to B6 mice) genes in the GO category. f The arbitrary p value assigned by the software GoMiner for overexpressed genes in the GO category.

c

Table 2. GO Categories Enriched with Genes More Highly Expressed in B6 as Compared to SJL Mice following APAP Treatmenta time (h) 3

6

12

24

GO IDb

totalc

changedd

overe

P value (over)f

category name

15071 17018 5737 8420 5622 163 158 5775 3860 48519 6590 42403 16019 5856 7596 50817 7599 42060 50878 8395 44255 8610 6928 9611 16491 6694 16126 1944 6091 16705 5737 6629 6066 8610 6096 48514 1568 6695 4859

15 12 2727 13 4926 15 15 2 2 605 7 7 2 584 64 64 68 86 80 10 390 186 276 337 467 61 27 102 404 72 2727 464 201 186 39 88 100 21 8

5 4 181 4 279 4 4 2 2 52 3 3 2 34 14 14 14 16 14 7 59 34 29 36 90 19 10 18 73 23 282 66 34 31 10 16 17 7 4

5 4 100 4 160 4 4 2 2 27 3 3 2 25 11 11 11 12 11 4 28 17 22 25 37 11 8 14 32 11 138 35 20 19 8 12 13 6 4

0.0000 0.0002 0.0003 0.0003 0.0005 0.0006 0.0006 0.0007 0.0007 0.0002 0.0003 0.0003 0.0005 0.0007 0.0000 0.0000 0.0000 0.0001 0.0001 0.0003 0.0005 0.0005 0.0005 0.0006 0.0000 0.0000 0.0000 0.0000 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001

protein phosphatase type 2C activity myosin phosphatase activity cytoplasm CTD phosphatase activity intracellular protein phosphatase type 1 activity protein phosphatase type 2A activity vacuolar lumen 3-hydroxyisobutyryl-CoA hydrolase activity negative regulation of biological process thyroid hormone generation thyroid hormone metabolism peptidoglycan receptor activity cytoskeleton blood coagulation coagulation hemostasis wound healing regulation of body fluids steroid hydroxylase activity cellular lipid metabolism lipid biosynthesis cell motility response to wounding oxidoreductase activity steroid biosynthesis sterol biosynthesis vasculature development generation of precursor metabolites and energy oxidoreductase activity, acting on paired donors, with... cytoplasm lipid metabolism alcohol metabolism lipid biosynthesis glycolysis blood vessel morphogenesis blood vessel development cholesterol biosynthesis phospholipase inhibitor activity

a May indicate up-regulation in B6 mice and/or down-regulation in SJL mice. b GO category identification used by the program GoMiner. c Total number of genes in the GO category. d Number of genes in the SJL-B6 comparison changed (2-fold difference in expression of the mRNA in the SJL strain vs the B6 strain). e Number of up-regulated (higher in B6 as compared to SJL mice) genes in the GO category. f The arbitrary p value assigned by the software GoMiner for overexpressed genes in the GO category.

Discussion The mechanistic features of DILD and the factors that determine susceptibility remain unknown. Metabolic idiosyncrasy has historically been proposed as a primary determinant of DILD, but more recent investigations have focused upon alterations in proinflammatory as well as hepatoprotective, antiinflammatory, and/or proregenerative responsive mechanisms as key determinants of susceptibility (8-11, 15, 3537). In fact, there is no consensus on what range of factors may

be key contributors to DILD or what genetic or phenotypic alterations, or combinations of alterations, actually confer susceptibility. To begin to address the complexity of DILD, we applied a classical genetics approach (38) to a model of acute liver injury caused by the analgesic and antipyretic drug, APAP. In this regard, we discovered that B6 mice are susceptible whereas the SJL mice are resistant to AILD (24). Several potential risk-associated and protective factors were identified by comparing the hepatic proteomes of these two strains before


Figure 2. Hepatic expression profiles of HSP70 in SJL, B6, and F1 mice after APAP treatment. (A) Time-dependent hepatic mRNA expression profile of HSP70 following APAP treatment, as determined by Affymetrix GeneChip analysis. (B) Immunoblot detection of hepatic HSP70 protein levels in mice 8 h after APAP treatment. Results represent means ( SD; *, p < 0.05 as compared to B6 mice at the same time point.

and after treatment with APAP including ubiquitin-like activating enzyme E1B, peroxiredoxins, mitochondrial thioredoxin, and numerous GSTs (24). In the current study, we have investigated the genetic basis of the susceptibility differences of the B6 and SJL mice to AILD. We initially compared APAP susceptibility of B6 and SJL mice to their F1 hybrid offspring. If only a single dominant gene controlled susceptibility or resistance to AILD, based upon Mendelian inheritance, all F1 mice would be expected to behave like one or the other parental strain. However, we found the F1 mice to have nearly intermediate toxicity to APAP as compared to the parental strains (Figure 1), indicating that genes inherited from both resistant (SJL) and susceptible (B6) parents were expressed and in competition with one another in promoting or counteracting AILD. To more fully understand the potential multifactorial genetic nature of susceptibility and resistance to AILD, we monitored the time-dependent changes in hepatic mRNA expression of over 36000 genes and ESTs in the SJL, B6, and F1 mice following APAP treatment. Analysis of the GeneChip data indicated that there were several HSPs including HSP10, HSP40, HSP70.1, HSP70.3, HSP86, and HSP105 that were more highly expressed in livers of SJL than B6 mice. This finding was potentially important because HSPs have cytoprotective functions (39-41) and they

Welch et al.

Figure 3. Comparison of HSP70 KO and WT mice to AILD. (A) Time course of ALT activity in sera following treatment with 300 mg/ kg of APAP. Results represent means ( SEM of 5-9 mice per group. (B) Survival rates of HSP70 following APAP treatment. The figure represents data from two independent experiments (n ) 15-22).

have been suggested to play a vital role in protecting mice from liver injury caused by APAP and other xenobiotics (9, 11, 15, 42, 43). In fact, it has been shown that pretreatment of mice with amphetamines not only causes a significant induction of HSP25 and HSP70 but also protects mice against AILD (32). The findings of SJL mice with higher hepatic mRNA and protein expression levels of HSP70 than that of B6 mice while the F1 mice had intermediate HSP70 mRNA and protein levels (Figure 2) suggested that this HSP might have a protective role against AILD. However, when we compared the susceptibility of HSP70.1/3 KO mice to their WT littermates, the findings did not validate the hypothesis that HSP70 protects against AILD (Figure 3). That being said, the hepatoprotective role of HSP70 in regulating AILD may be strain-dependent. The background of the HSP70 KO mice is a B6/129 hybrid, both of which are very susceptible to AILD (unpublished observations). It is possible that if the background of the KO animal is changed to the SJL strain, or another APAP resistant strain that also has high hepatic HSP70 protein levels following APAP treatment, the lack of HSP70 might then make a significant difference in susceptibility to AILD. Perhaps in the B6 mice there is not enough HSP70 protein retained in the hepatocytes to have a protective effect. Additionally, in the HSP70 KO mice, other factors including additional HSPs may compensate for the lack of HSP70 in response to insult by toxicants such as APAP. More in depth research is needed to clarify the role of HSPs in AILD.



Figure 4. Hepatic expression profile of OPN in SJL, B6, and F1 mice following APAP treatment. (A) Time-dependent mRNA expression profiles of OPN following APAP treatment, as determined by Affymetrix GeneChip analysis. (B) Hepatic OPN protein levels in mice 12 h after APAP treatment, as determined by Searchlight analysis. Results represent means ( SEM of 3-5 mice per group; *, p < 0.05 as compared to the B6 APAP group.

Of the proinflammatory factors whose mRNA expression was higher in the B6 mice following APAP treatment, the immune mediator, OPN, was of particular interest. OPN is an important immune mediator known to be involved in supporting adhesion and migration of inflammatory cells, in addition to its properties as an immunoregulatory cytokine (44-46). For example, OPN has been shown to polarize cytokine production to a Th1 response by directly inducing macrophages to produce IL-12 while concomitantly inhibiting IL-10 production (46). Also, the overexpression of OPN has been associated in various models of inflammatory liver diseases such as hepatic granuloma, fulminant hepatitis, and Con A-induced liver disease (47-50). OPN is produced by NK and NKT cells, among other cell types (51-53). Activated NKT cells secrete various cytokines that activate resident Kupffer cells and recruit macrophages to produce TNF-R, which can lead to liver injury (54, 55). In this regard, OPN has been shown to play an important role in the infiltration of Kupffer cells and other macrophages into necrotic areas of the liver after carbon tetrachloride treatment in rats (53). Moreover, NKT and NK cells have been shown to have a role in AILD (56). All of these findings suggested that OPN might play a significant role in the exacerbation of liver injury after APAP treatment. This hypothesis was supported by the finding that OPN KO mice were less susceptible to AILD than WT mice (Figure 5). Additionally, because SJL mice are deficient in NK

Figure 5. Comparison of OPN KO and WT mice to AILD. (A) Time course of serum ALT activity in mice following APAP treatment. Results represent means ( SEM of 6-9 mice per group; *, p < 0.05. (B) Survival rates following APAP treatment. The figure represents data from two independent experiments (n ) 12-16; *, p < 0.05). (C) Immunoblot analysis (100 µg/lane, from individual mice) of APAP-protein adducts 2 h after APAP treatment.

and NKT cells (57-59) and had lower hepatic levels of OPN than B6 mice after APAP treatment (Figure 4), one possible mechanism for the resistance of SJL mice to AILD may be related to their inability to activate a sufficient number of NK and NKT cells after initial liver injury, which could prevent the exacerbation of toxicity seen in the B6 strain. Interestingly, it has been shown that a number of single-nucleotide polymorphisms (SNPs) are present in the human OPN gene and one of these SNPs has been associated with systemic lupus erythematosus (60). Perhaps other polymorphisms in the OPN gene may lead to alterations in OPN expression and/or function so as to predispose individuals to a higher susceptibility to DILD.


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Table 3. Comparison of 3 h Transcriptional Data to 6 h Proteomics Data in APAP-Treated Mice Hugo symbol

3 h mRNA ratioa

6 h protein ratiob

protein name

Plg Fkbp8 Bag3 Hdlbp B430201G11Rik Por 4921511I16Rik Vars2 Aco1 Alad Camk2 g Son Egfr Serpina3k Cyp7a1 Car5a Tpmt Slc6a12 Slc38a3 Oplah Gfer Nipsnap1 Cyp2a4 Prodh Lifr Cyp2f2 Sgpl1 Cyp1a2 Dirc2 Acads Ctsd 1110032D12Rik Hal G6pc Catna1 Anxa11 D7Rp2e Fabp1 Pgam1 Atox1 Ehhadh Ehd3 Vcl Gclc Lrp12 St13 Acadm Igfbp1 Acsl5 Adh1 Mdh1 Xdh Nnt Thbs1 Cyp3a13 Mgst1

13.4 11.5 6.4 6.1 5.7 4.7 4.5 3.7 3.6 3.2 3.0 3.0 2.7 2.7 2.7 2.6 2.6 2.5 2.5 2.5 2.4 2.4 2.4 2.4 2.3 2.3 2.2 2.2 2.1 2.0 2.0 2.0 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.4 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.2 0.2 0.2

0.7 1.2 2.0 1.5 2.6 1.4 0.8 0.9 1.7 2.3 0.9 1.9 1.0 0.7 0.7 0.8 2.2 1.4 2.4 0.9 0.6 1.5 0.6 3.3 1.1 1.3 1.5 1.3 1.4 1.3 1.2 0.9 1.4 1.7 1.7 1.1 1.0 1.2 1.3 1.9 1.5 0.7 0.7 1.5 0.9 1.5 0.8 0.5 1.4 1.8 1.2 1.2 1.1 1.4 1.6 1.6

plasminogen precursor 38 kDa FK-506 binding protein homolog BAG-family molecular chaperone regulator-3 high-density lipoprotein-binding protein RIKEN cDNA B430201G11 NADPH-cytochome P450 reductase putative serine-rich protein valyl-tRNA synthetase 2 iron-responsive element binding protein 1 δ-aminolevulinic acid dehydratase calcium/calmodulin-dependent protein kinase type II γ-chain SON protein epidermal growth factor receptor precursor serine proteinase inhibitor A3K precursor cytochome P450 7A1 carbonic anhydrase VA, mitochondrial precursor thiopurine S-methyltransferase sodium- and chloride-dependent betaine transporter solute carrier family 38, member 3 5-oxoprolinase augmenter of liver regeneration NipSnap1 protein cytochome P450 2A4 proline oxidase, mitochondrial precursor leukemia inhibitory factor receptor precursor cytochome P450 2F2 sphingosine phosphate lyase 1 cytochome P450 1A2 disrupted in renal carcinoma 2 acyl-CoA dehydrogenase, short-chain specific, mitochondrial precursor cathepsin D precursor cop-coated vesicle membrane protein p24 precursor histidine ammonia-lyase glucose-6-phosphatase R-1 catenin annexin A11 testosterone-regulated RP2 protein fatty acid-binding protein, liver phosphoglycerate mutase 1 copper transport protein ATOX1 peroxisomal bifunctional enzyme EH-domain containing protein 3 vinculin glutamate-cysteine ligase catalytic subunit low-density lipoprotein receptor-related protein 12 Hsc70-interacting protein acyl-CoA dehydrogenase, medium-chain specific, mitochondrial precursor insulin-like growth factor binding protein 1 precursor similar to fatty acid coenzyme A ligase, long chain 5 alcohol dehydrogenase A chain malate dehydrogenase, cytoplasmic xanthine dehydrogenase/oxidase NAD(P) transhydrogenase, mitochondrial precursor thombospondin 1 precursor cytochome P450 3A13 microsomal glutathione S-transferase 1

a Results are expressed as a ratio of mRNA expression of SJL vs B6 mice 3 h after APAP treatment. b Results are expressed as a ratio of protein expression of SJL vs B6 mice 6 h after APAP treatment.

Previously, we compared the hepatic proteome of SJL and B6 mice after treatment with APAP (24). A large number of factors were identified that could potentially play an important role in protecting the SJL mice from AILD. One key attribute of proteomics studies is the ability to directly determine differences in levels of proteins, the functional molecules of cells, in response to treatment. However, even with all of the advancements in proteomic technologies today, there is still a deficiency in the ability to analyze large numbers of samples in a timely fashion and to be able to efficiently identify and quantify the entire proteome in each analysis. These two technical issues are resolved, in part, with the high-speed efficiency and genome-wide coverage of gene expression

microarray analyses. Therefore, we compared the data obtained from our proteomics study to the genomics findings in the current study (Tables 3 and 4), to determine the extent of overlap between the two methods and perhaps to help determine to what extent one can rely on transcriptional profiling data. The number of factors for which we were able to compare the results of the two technologies was limited due to the lack of sufficient annotation so as to be confident that a gene identified in the genomics study encodes for the same protein uncovered in the proteomics analysis. We were only able to compare 524 factors for which we could obtain matching Hugo symbols from both platforms, out of the 36000 plus mRNA transcripts and the 1600 plus proteins. It was no surprise that



Table 4. Comparison of 6 h Transcriptional Data vs 6 h Proteomics Data in APAP-Treated Mice Hugo symbol

6 h mRNA ratioa

6 h protein ratiob

protein name

Pts Cyp7a1 Adh4 Cyp2a4 Mod1 Alad Ddc Stard5 Tpmt D10Ertd214e HSP105 Cyp1a2 HSPca Car5a Aldh1a1 C4bp Cyp2c40 Hsd17b4 Rgn Atp6v1 g1 HSPb8 Cops5 Ctsz Fcgrt Cpox Cyp3a25 Arsa Cyp3a11 Lifr Shmt1 Bag3 Slc6a12 Nat2 Gsto1 Prodh Pfc Gfer Psma7 Atp1b3 Adfp Gclc Sirt5 Cyp4b1 Spnb2 Igfbp1 Acad9 Mlp Srr Ipo7 Cyp3a13 Chkb Csrp1 Tgm2 Bub3 Nubp1 Mgst1 Nnt Lrp12 Xdh 4921511I16Rik Thbs1 Mtap4

8.6 7.6 6.2 4.1 3.1 3.1 3.1 2.9 2.9 2.9 2.8 2.7 2.7 2.7 2.6 2.6 2.5 2.3 2.3 2.3 2.3 2.2 2.2 2.2 2.2 2.1 2.1 2.1 2.1 2.1 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2

0.8 0.7 1.6 0.6 1.7 2.3 1.6 1.2 2.2 1.4 1.8 1.3 1.5 0.8 2.0 0.7 1.0 1.4 3.1 1.1 1.1 0.7 0.8 1.2 1.1 0.9 0.7 2.1 1.1 1.0 2.0 1.4 0.8 1.8 3.3 0.3 0.6 1.5 6.7 1.1 1.5 3.1 1.3 1.3 0.5 1.1 1.0 1.4 1.6 1.6 0.6 1.0 1.9 1.4 1.3 1.6 1.1 0.9 1.2 0.8 1.4 1.5

6-pyruvoyl tetrahydrobiopterin synthase cytochome P450 7A1 alcohol dehydrogenase II cytochome P450 2A4 NADP-dependent malic enzyme δ-aminolevulinic acid dehydratase aromatic-L-amino acid decarboxylase StAR-related lipid transfer protein 5 thiopurine S-methyltransferase hematopoietic stem/progenitor cells protein MDS029 homolog HSP 105 kDa cytochome P450 1A2 HSP 90-R carbonic anhydrase VA, mitochondrial precursor aldehyde dehydrogenase 1A1 C4b-binding protein precursor cytochome P450 2C40 estradiol 17 β-dehydrogenase 4 senescence marker protein-30 vacuolar ATP synthase subunit G 1 R-crystallin C chain COP9 signalosome complex subunit 5 cathepsin Z precursor IGG receptor FCRN large subunit P51 precursor coproporphyrinogen III oxidase, mitochondrial precursor cytochome P450 3A25 arylsulfatase A precursor cytochome P450 3A11 leukemia inhibitory factor receptor precursor serine hydroxymethyltransferase, cytosolic BAG-family molecular chaperone regulator-3 sodium- and chloride-dependent betaine transporter arylamine N-acetyltransferase 2 glutathione transferase ω-1 proline oxidase, mitochondrial precursor properdin (fragment) augmenter of liver regeneration proteasome subunit R type 7 sodium/potassium-transporting ATPase β-3 chain adipophilin glutamate-cysteine ligase catalytic subunit NAD-dependent deacetylase sirtuin 5 cytochome P450 4B1 β-spectrin 2, nonerythocytic insulin-like growth factor binding protein 1 precursor hypothetical protein MARCKS-related protein serine racemase RanBP7/importin 7 cytochome P450 3A13 choline/ethanolamine kinase CYSTEIN rich protein-1 protein-glutamine γ-glutamyltransferase mitotic checkpoint protein BUB3 nucleotide-binding protein 1 microsomal glutathione S-transferase 1 NAD(P) transhydrogenase, mitochondrial precursor low-density lipoprotein receptor-related protein 12 xanthine dehydrogenase/oxidase putative serine-rich protein thombospondin 1 precursor microtubule-associated protein 4

a Results are expressed as a ratio of mRNA expression of SJL vs B6 mice 6 h after APAP treatment. b Results are expressed as a ratio of protein expression of SJL vs B6 mice 6 h after APAP treatment.

for the majority of the factors (approximately 76%), there was no significant (2-fold) change in either the mRNA or the protein expression when comparing their levels between the SJL and the B6 mice. However, it was surprising that only approximately 13% of the factors that did have a significant (2-fold) change

in mRNA expression correlated with a similar significant change in protein expression. Although this may be due to differential kinetics between RNA and protein expression, the lack of correlation between the genomic and the proteomic data indicates that there are still significant advances that need to


be made regarding the fate of newly expressed mRNA transcripts. Because of the uncertainty in the relationship between transcriptional and translational events for a given factor (61), future studies will ideally need to focus on a more systematic kinetic analysis using both platforms. By comparing the hepatic gene expression microarray data from an APAP-resistant and an APAP-susceptible strain of mouse, we identified a number of potential susceptibility and protective factors. The conceptual conclusion from this study is that a model such as this, which aims to better evaluate the multifactorial nature of complex toxicological processes, may be used to identify factors involved in the susceptibility and/or resistance to DILD, with the ultimate goal of predicting the likelihood of idiosyncratic DILD in animal models and in humans. Acknowledgment. We thank Drs. Neil Pumford and Jack Hinson (University of Arkansas, Little Rock, AR) for their contribution of anti-APAP serum. We also acknowledge the expert technical assistance of Jose Flores and John W. George (NHLBI, Bethesda, MD). This research was supported in part by the Intramural Research Program of the NIH, NHLBI, and NCI. The information in this document has been funded in part by the U.S. Environmental Protection Agency. It has been subjected to review by the National Center for Computational Toxicology and approved for publication. Approval does not signify that the contents reflect the views of the Agency, nor does mention of trade names or commercial products constitute endorsement or recommendation for use. Supporting Information Available: Tables with complete listing of mRNA expression data and tables with complete listings of the comparison of 3 and 6 h GeneChip data to 6 h proteomics data. This material is available free of charge via the Internet at http://pubs.acs.org.

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TX050285Z

Genomic Identification of Potential Risk Factors during

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