* Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing Michigan; Department of Pharmacology and Toxicology, Michigan State University, East Lansing Michigan;
Department of Pathobiology and Diagnostic Investigation, Michigan State University, East Lansing Michigan;
Department of Physiology, Michigan State University, East Lansing Michigan; ¶ Center for Integrative Toxicology, Michigan State University, East Lansing Michigan; and || Wellington Laboratories Inc., Guelph Ontario, Canada
1 To whom correspondence should be addressed at Department of Biochemistry and Molecular Biology, Michigan State University, 223 Biochemistry Building, Wilson Road, East Lansing, MI, 488241319. Fax: (517) 353-9334. E-mail: tzachare{at}msu.edu.
Received February 10, 2005; accepted March 22, 2005
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ABSTRACT |
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Key Words: TCDD; microarray; liver; mouse; temporal; dose response.
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INTRODUCTION |
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The obligatory involvement of the AhR/ARNT signaling pathway in mediating the toxic and biochemical responses to TCDD is supported by studies demonstrating that mice with low-affinity AhR alleles are less susceptible than other mice to the effects of TCDD (Okey et al., 1989), and that AhR-null mice are resistant to the prototypical toxicities of TCDD and related compounds (Gonzalez and Fernandez-Salguero, 1998
; Peters et al., 1999
; Vorderstrasse et al., 2001
). More recent studies have shown that mice possessing mutations in the AhR nuclear localization/DRE binding domain, as well as mice harboring a hypomorphic ARNT allele, fail to exhibit the classical TCDD toxicities (Bunger et al., 2003
; Walisser et al., 2004b
). Furthermore, the AhR/ARNT signaling pathway plays an important role in development, differentiation, and growth, as AhR null mice experience various liver, heart, thymus, and immune system abnormalities. Developmental effects are most notable in the liver as AhR null mice exhibit reduced liver weight, transient microvesicular fatty metamorphosis, prolonged extramedullary hematopoiesis, and portal hypercellularity with thickening and fibrosis (Schmidt et al., 1996
). Moreover, mice expressing a constitutively active AhR exhibit increased hepatocarcinogenesis, which has further implicated AhR activation in tumor promotion (Moennikes et al., 2004
).
Although the mechanisms of AhR/ARNT-mediated changes in gene expression are fairly well established, TCDD-elicited modulation of gene expression and the pathways associated with toxicity remains poorly understood. Well-characterized AhR-inducible genes are limited to various xenobiotic-metabolizing enzymes, including cytochrome P450s 1a1, 1a2, and 1b1. However, a significant role for cytochrome P450 induction alone in the observed adverse responses is questionable (Schmidt and Bradfield, 1996). Global gene expression technologies provide a comprehensive strategy whereby critical AhR-regulated target genes can be identified and used to elucidate target pathways involved in the etiology of TCDD and related compound toxicity.
Sustained activation of the AhR and its target genes has been hypothesized as a prerequisite for toxicity that typically requires days or weeks to develop. Alternatively, activation of the AhR may initiate a cascade of secondary and tertiary gene expression changes leading to the compromised physiological state. Hepatotoxicity is a classical end point of TCDD exposure and is characterized by hepatomegaly accompanied by hepatocyte hypertrophy, fat accumulation, immune infiltration, necrosis, and alterations in liver enzymes (Pohjanvirta and Tuomisto, 1994), which likely contribute to tumor promotion and hepatocarcinogenesis. To identify gene expression changes that cause hepatotoxicity and carcinogenesis, and to further characterize the spectrum of AhR/ARNT-responsive transcripts, temporal and dose-dependent effects of TCDD on hepatic gene expression were examined in the context of complementary histological and clinical chemistry end points. This integrative approach has provided a powerful strategy in the comprehensive assessment of the in vivo effects of TCDD.
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MATERIALS AND METHODS |
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Time-course and dose-response studies.
For the time-course study, mice were treated by gavage with 0.1 ml of sesame oil for a nominal dose of 0 (vehicle control) or 30 µg/kg bw of TCDD. Eight animals were treated per dose group and time point, and groups for each dose and time point were housed in separate cages. Mice were sacrificed 2, 4, 8, 12, 18, 24, 72, or 168 h after dosing. An untreated group of mice was also included, and it was sacrificed at time zero, the time at which the other animals were dosed. For the dose-response study, five mice per group were gavaged with 0.1 ml of vehicle or 0.001, 0.01, 0.1, 1, 10, 100, or 300 µg/kg TCDD and sacrificed 24 h after dosing. In both studies, treatment was staggered to ensure that the exposure duration was within 5% of the desired length. Animals were sacrificed by cervical dislocation and tissue samples were removed, weighed, flash frozen in liquid nitrogen, and stored at 80°C until further use. For the dose-response study, the right lobe of the liver was fixed in 10% neutral buffered formalin (Sigma), for histological analysis.
Clinical chemistry and histological analyses.
Mice were gavaged with 0 (vehicle control) or 30 µg/kg bw of TCDD and were sacrificed 2, 4, 8, 12, 18, 24, 72, or 168 h after dosing. At sacrifice, mice were anesthetized with 0.1 ml of a 5% solution of sodium-pentobarbital, and blood was collected by cardiac puncture and placed in Vacutainer SST gel and clot activator tubes (Becton Dickinson, Franklin Lakes, NJ). Samples were allowed to clot, and serum was separated by spinning at 1500 x g for 10 min, after which the samples were stored at 80°C until analysis. Because sampling was limited, only select clinical chemistry end points were monitored: blood urea nitrogen (BUN), creatinine (CREA), free fatty acids (FFA), glucose (GLU), total bilirubin (TBIL), alanine aminotransferase (ALT), cholesterol (CHOL), and triglycerides (TG). End points were monitored by standard clinical chemistry assays with an Olympus AU640 Automated Chemistry Analyzer (Olympus America Inc., Melville, NY) at the Clinical Pathology Laboratory at MSU (http://cvm.msu.edu/clinpath/new.htm).
Tissues were harvested and fixed in 10% neutral buffered formalin (NBF, Sigma). Sectioned tissues were processed sequentially in ethanol, xylene, and paraffin in a Thermo Electron Excelsior tissue processor (Waltham, MA). Tissues were then embedded in paraffin with a Miles Tissue Tek II embedding center, after which paraffin blocks were sectioned at 5 µm with a rotary microtome. Sections were placed on glass microscope slides, dried, and stained with the standard hematoxylin and eosin stain. All histological processing was performed at the Histology Laboratory at MSU (http://www.lahms.msu.edu). Histological evaluations were preformed by a veterinary pathologist. For Oil Red O staining, liver cryosections were fixed in NBF, stained with Oil Red O solution, washed, and counterstained with hematoxylin.
Quantification of TCDD in liver samples.
Liver samples were processed in parallel with lab blanks and a reference or background sample at Wellington Laboratories Inc. (Guelph, ON, Canada). Samples were weighed, spiked with 13C12 TCDD surrogate, digested with sulfuric acid, and then extracted. Extracts were cleaned, concentrated, and spiked with an injection standard. Analysis was performed on a high resolution gas chromatograph/high resolution mass spectrometer (HRGC/HRMS) using a Hewlett Packard 5890 Series II GC interfaced to a VG 70SE HRMS. The HRMS was operated in the EI/SIR mode at 10,000 resolution. A 60-m DB5 column (J&W Scientific, Folsom, CA) with an internal diameter of 0.25 mm and film thickness of 0.25 µm was employed. Injection volumes were 2 µl and a splitless injection was used.
RNA isolation.
Frozen liver samples (approximately 70 mg) were transferred to 1.0 ml of Trizol (Invitrogen, Carlsbad, CA) and homogenized in a Mixer Mill 300 tissue homogenizer (Retsch, Germany). Total RNA was isolated according to the manufacturer's protocol with an additional phenol:chloroform extraction. Isolated RNA was resuspended in RNA storage solution (Ambion Inc., Austin, TX), quantified (A260), and assessed for purity by determining the A260/A280 ratio and by visual inspection of 1.0 µg on a denaturing gel.
Microarray experimental design.
Changes in gene expression were assessed using customized cDNA microarrays containing 13,362 features representing 7952 unique genes (Unigene build #144; see Table 1 of the Supplementary Material online). For temporal analysis, TCDD-treated samples were compared to time-matched vehicle controls with an independent reference design. In this design, a treated animal is compared to a time-matched vehicle control with two independent labelings per sample (dye swap) for a total of 16 arrays per replicate (8 time points x 2 arrays/time point comparison). Four replicates of this design were performed, each using different animals, for a total of four biological replicates and 64 arrays.
Doseresponse changes in gene expression were analyzed using a common reference design in which samples from TCDD-treated mice are co-hybridized with a common vehicle control. Each design replicate uses one of the five animals from each dose group, with two independent labelings per sample (dye swap) for a total of 14 arrays (7 doses x 2 arrays/dose comparison). Four replicates of this design were performed, each using different animals, for a total of four biological replicates and 56 microarrays.
Microarray analysis of differential gene expression.
Detailed protocols for microarray preparation, labeling of the cDNA probe, sample hybridization, and washing can be found at http://dbzach.fst.msu.edu/interfaces/microarray.html. Briefly, polymerase chain reaction (PCR) amplified DNA was robotically arrayed onto epoxy-coated glass slides (Schott-Nexterion, Duryea, PA) using an Omnigrid arrayer (GeneMachines, San Carlos, CA) equipped with 48 (4 x 12) Chipmaker 2 pins (Telechem) at the Genomics Technology Support Facility (http://www.genomics.msu.edu). Total RNA (30 µg) was reverse transcribed in the presence of Cy3- or Cy5-deoxyuridine triphosphate (dUTP) to create fluor-labeled cDNA, which was purified using a Qiagen polymerase chain reaction (PCR) purification kit (Qiagen, Valencia, CA). Cy3 and Cy5 samples were mixed, vacuum dried, and resuspended in 48 µl of hybridization buffer (40% formamide, 4x SSC, 1% sodium dodecyl sulfate [SDS]) with 20 µg polydA and 20 µg of mouse COT-1 DNA (Invitrogen, Carlsbad, CA) as competitor. This probe mixture was heated at 95°C for 3 min and hybridized on the array under a 22 x 40 mm lifterslip (Erie Scientific Company, Portsmouth, NH) in a light-protected and humidified hybridization chamber (Corning Inc., Corning, NY) for 1824 h in a 42°C water bath. Slides were then washed, dried by centrifugation, and scanned at 635 nm (Cy5) and 532 nm (Cy3) on an Affymetrix 428 Array Scanner (Santa Clara, CA). Images were analyzed for feature and background intensities using GenePix Pro 5.0 (Molecular Devices, Union City, CA).
Microarray data normalization and analysis.
Data were normalized using a semiparametric approach (Eckel et al., 2004b). Model-based t-values were calculated from normalized data, comparing treated and vehicle responses per time point or dose group. Empirical Bayes analysis was used to calculate posterior probabilities (P1(t)-value) of activity on a per gene and time-point or dose-group basis using the model-based t-value (Eckel et al., 2004a
). A stringent P1(t) cutoff of 1.0 was used to obtain a subset of differentially regulated genes to initially focus analysis and data interpretation on the most reproducible differentially regulated genes,. Gene expression changes that passed the threshold were subsequently analyzed by hierarchical and K-means clustering (GeneSpring 6.0, Silicon Genetics, Redwood City, CA). Doseresponse analysis was performed with Graph Pad Prism 4.0 (GraphPad Software, San Diego, CA). The P1(t) value is a Bayesian posterior probability that is different from the p value in that it can be used to provide an initial ranking of genes, based on their expression, in order to prioritize those transcripts for further investigation relative to biologic/toxic relevance. It is only a guide to rank the probability of identifying the most active genes, and is not equivalent to a p value. Therefore, it is not intended to be used for hypothesis testing. Posterior probabilities generated by Bayesian analyses are better suited for microarray data when compared to parametric analyses since no assumptions are required regarding the distribution of the gene expression data, which typically are not normally distributed. Consequently, gene expression changes that approach the initial P1(t) cutoff will also be considered, provided supporting published evidence indicates its relevance in the emerging pathway. These genes would also be candidates for verification by quantitative RT-PCR (QRTPC).
Quantitative real-time PCR.
For each sample, 1.0 µg of total RNA was reverse transcribed by SuperScript II using an anchored oligo-dT primer as described by the manufacturer (Invitrogen, Carlsbad, CA). The cDNA (1.0µl) was used as a template in a 30 µl PCR reaction containing 0.1µM of forward and reverse gene-specific primers designed using Primer3 (Rozen and Skaletsky, 2000), 3 mM MgCl2, 1.0 mM dNTPs, 0.025 IU AmpliTaq Gold, and 1x SYBR Green PCR buffer (Applied Biosystems, Foster City, CA). Gene names, accession numbers, forward and reverse primer sequences, and amplicon sizes are listed in Table 2 of the Supplementary Material online. Polymerase chain reaction amplification was conducted in MicroAmp Optical 96-well reaction plates (Applied Biosystems) on an Applied Biosystems PRISM 7000 Sequence Detection System under the following conditions: initial denaturation and enzyme activation for 10 min at 95°C, followed by 40 cycles of 95°C for 15 s and 60°C for 1 min. A dissociation protocol was performed to assess the specificity of the primers and the uniformity of the PCR-generated products. Each plate contained duplicate standards of purified PCR products of known template concentration covering 7 orders of magnitude to interpolate relative template concentrations of the samples from the standard curves of log copy number versus threshold cycle (Ct). No template controls (NTC) were also included on each plate. Samples with a Ct value within 2 SD of the mean Ct values for the NTCs were considered below the limits of detection. The copy number of each unknown sample for each gene was standardized to the geometric mean of three house-keeping genes (ß-actin, Gapd, and Hprt) to control for differences in RNA loading, quality, and cDNA synthesis. For graphing purposes, the relative expression levels were scaled such that the expression level of the time-matched control group was equal to 1.
Statistical analysis.
Statistical analysis was performed with SAS 8.02 (SAS Institute, Cary, NC). Data were analyzed by analysis of variance (ANOVA) followed by Dunnett's or Tukey's post hoc tests. Differences between treatment groups were considered significant when p < 0.05.
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RESULTS |
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In the time-course study, cytoplasmic vacuolization was observed in the periportal and midzonal regions with extension into the centriacinar regions at later time points. Minimal vacuolization was first observed at 18 h, with severity progressing from mild to moderate at 24 and 72 h, respectively. Marked cytoplasmic vacuolization was noted at 168 h and was accompanied by individual cell apoptosis and immune cell accumulation (Fig. 2A and 2B). Oil Red O staining confirmed that the dose- and time-dependent vacuolization was due to lipid accumulation (Fig. 2C and 2D). Analysis of liver lipid extracts by thin layer chromatography revealed increases in triglycerides (TG), free fatty acids (FFA), and cholesterol esters (CHOL) in TCDD-treated mice (data not shown).
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Data Clustering
Hierarchical clustering of the dose-response data by treatment revealed a strong concord between gene expression responses and the administered dose of TCDD (Fig. 5A). The two low-dose groups clustered together, as did the five highest doses. The high-dose cluster also branched out sequentially by dose, with the top dose of 300 µg/kg exhibiting the greatest difference from low-dose groups, as expected. Three K-means clusters, one downregulated cluster and two up-regulated clusters, most accurately represented the dose-response data (data not shown). The existence of two upregulated clusters for the dose-response data indicates that TCDD elicits gene-specific dose-dependent responses. For example, genes such as Cyp1a2, phosphoenolpyruvate carboxykinase 1 (Pck1), and Got1 displayed similar ED50 values to that of Cyp1a1 (0.370.95 µg/kg). The ED50 values for Notch gene homolog 1 (Notch1) and NAD(P)H dehydrogenase, quinone 1 (Nqo1) induction were an order of magnitude greater (2.178.8 µg/kg), whereas tumor necrosis factor, alpha-induced protein 2 (Tnfaip2), and Cyp1b1 were two orders of magnitude greater (2839.5 µg/kg) (Fig. 6). These results may be due to gene-specific thresholds, differential temporal regulation, or differing basal expression levels, which would affect the dose at which transcriptional regulation may be initiated or detected.
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Functional Categorization of Microarray Data
Functional annotation extracted from public databases revealed that many of the transcriptional responses were associated with metabolizing enzymes, development and differentiation, fatty acid uptake and metabolism, gluconeogenesis, immune signaling, and apoptosis (Table 3). Metabolizing enzymes included oxidoreductases, monooxygenases, and xenobiotic metabolizing enzymes such as the well-characterized TCDD-inducible genes Cyp1a1 and Nqo1. Novel responsive oxidoreductase and xenobiotic metabolizing genes included abhydrolase domain containing 6 (Abhd6), carbonyl reductase 3 (Cbr3), dehydrogenase/reductase (SDR family) member 3 (Dhrs3), epoxide hydrolase 1 (Ephx1), and UDP-glucose dehydrogenase (Ugdh). Glutathione S-transferases alpha2, alpha4, and pi2 (Gsta2, a4, and p2) as well as glutamate-cysteine ligase (Gclc) and glutathione synthetase (Gss) were also regulated by TCDD, which is consistent with the induction of both phase I and II metabolizing enzymes by TCDD, commonly referred to as the "AhR gene battery."
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Effects on fatty acid uptake and metabolism, immune signaling, and apoptosis are consistent with the observed hepatic histological findings. Hematoxylin and eosin and Oil Red O staining revealed marked fatty vacuolization of hepatocytes at 24 h, with maximal effects at 168 h. Numerous genes involved in fatty acid transport, including fatty acid binding protein 4 and 5 (Fabp4 and 5), CD36 antigen (Cd36), solute carrier family 27, member 2 (Slc27a2), and lipoprotein lipase (Lpl) were significantly induced and may mediate the fatty accumulation. Induced apoptotic genes included receptor tumor necrosis factor rabbit primary synovial fibroblast (TNFRSF)-interacting serine-threonine kinase 1 (Ripk1), caspase 6 (Casp6), BCL2-like 11 (Bcl2l11), and huntingtin interacting protein 1 (Hip1), which is also consistent with the histopathologic identification of hepatocyte apoptosis at 168 h. In general, these gene expression responses preceded or paralleled the observed histopathology for each functional category. In contrast, the induction of immune signaling genes was largely confined to 168 h coincident with the histology. Consequently, these gene expression changes are likely due to the infiltration of immune cells and not to changes in hepatocyte gene expression. Moreover, all functional categories included genes in each of the five identified clusters of upregulated immediate early, early, late, and sustained, as well as downregulated responses. The exceptions were the TCDD-elicited changes in gluconeogenesis and immune signaling, which were primarily represented by downregulated and upregulated late clusters, respectively.
Verification of Microarray Responses
Qualitative RT-PCR was used to verify changes in transcript levels for a selected subset of active genes representing different responses and functional categories in Table 3 (Fig. 7). In total, 24 genes were verified by QRTPCR, all of which displayed temporal expression patterns comparable with the microarray data (See Table 2 of the Supplementary Material online for a complete list of genes). For genes such as Myc, Tnfaip2, Fabp5, and Cd36, there was also good agreement in the magnitude of the fold-change when comparing microarray and QRTPCR data. However, microarray data compression was evident for Cyp1a1 and Gstp2 due to the smaller dynamic fluorescence intensity range (065,535) of the microarrays, which resulted in signal saturation for these genes and compression of the true induction. Cross hybridization of homologous probes to a given target sequence on the microarray may also be a contributing factor, especially when in comparison to other, more gene-specific measurement techniques (Yuen et al., 2002).
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DISCUSSION |
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TCDD Induction of Metabolizing Enzymes and Oxidative Stress
Genes encoding products associated with oxidoreductase, monooxygenase, and xenobiotic metabolism activities were induced by TCDD, some of which have been previously characterized as members of the "AhR gene battery," including Cyp1a1, 1a2, and 1b1, as well as Nqo1 and Ugt1a6 (Nebert et al., 2000). Although their induction serves an important role in detoxification, their activity also contributes to the formation of reactive oxygen species (ROS), which can lead to cellular oxidative stress, lipid peroxidation, and DNA fragmentation (Bagchi et al., 2002
; Barouki and Morel, 2001
). TCDD is a particularly potent mediator of ROS formation as a result of its pronounced induction of P450 enzymes such as Cyp1a1 (Bagchi et al., 2002
), whereas Cyp1a2 is considered only a minor contributor (Slezak et al., 1999
). However, Cyp1a1 null mice still exhibit the hallmarks of TCDD toxicity, suggesting the involvement of additional members of the AhR gene battery in mediating these adverse effects (Uno et al., 2004
).
Further examination identified previously uncharacterized TCDD-induced transcripts encoding enzymes with oxidoreductase activity. As with classic members of the AhR gene battery, xanthine dehydrogenase (Xdh), Ugdh, Dhrs3, and Cbr3 were upregulated early (within 4 h) and dose dependently, and they were likely significant contributors to TCDD-mediated oxidative stress. For example, Xdh is a known major producer of ROS in ischemiareperfusion injury because of its ability to catalyze the reduction of molecular oxygen leading to the formation of superoxide anions and hydrogen peroxide (Zimmerman and Granger, 1994). Xanthine dehydrogenase transcript induction also complements reports of sustained induction of hepatic Xdh enzyme activity after TCDD treatment (Sugihara et al., 2001
). Moreover, comparative computational scanning using a PWM has identified high-scoring putative DREs in the proximal promoter sequences of each of these enzymes.
Induction of ROS-generating enzymes was accompanied by increases in glutathione transferases (GSTsGsta2, a4, and p2), epoxide hydrolase (Ephx1), and Ugdh, which prevent cellular damage by oxidative stress. Glutathione transferases catalyze the conjugation of reduced glutathione (GSH) to electrophiles and products of oxidative stress, thereby facilitating their elimination. Although GSH protects against oxidative stress, production of ROS by TCDD depletes cellular GSH levels, leaving cells susceptible to oxidative damage. Consistent with this finding, the two GSH-synthesis enzymes, glutamate-cysteine ligase, which catalyzes the first and rate-limiting step, and glutathione synthetase, which catalyzes the second step, were induced by TCDD. UDP glucose dehydrogenase catalyzes the formation of UDP-glucuronic acid (UDPGA) from UDP-glucose. In subsequent phase II glucuronidation reactions, UDPGA is conjugated to reactive xenobiotics to facilitate their elimination. Interestingly, these conjugation reactions are catalyzed by Ugt1a6 and 1a7, both members of the AhR gene battery, indicating that TCDD induces multiple levels of this phase II metabolism pathway. The induction of these phase II enzymes may play an important protective role in response to TCDD-elicited oxidative stress.
TCDD-Induced Fatty Acid Uptake and Metabolism
The integration of histopathology and clinical chemistry with microarray data provides compelling evidence that TCDD-mediated increases in liver weight can be attributed to fatty accumulation involving the disruption of hepatic lipid uptake and metabolism. Cellular uptake of lipids from chylomicrons and very low density lipoprotein (VLDL) occurs via hydrolysis by lipoprotein lipase (Lpl) and hepatic lipase, which enables FFA to accumulate via membrane-associated transporters such as fatty acid binding proteins (Fabp), fatty acid translocase, and fatty acid transport proteins (Jump and Clarke, 1999). Lipoprotein lipase mRNA was upregulated within 18 h and achieved maximum induction by 168 h, which would increase FFA availability for hepatic uptake. In addition, TCDD induction of Fabp4 and 5, Slc27a2, and Cd36 would facilitate increased hepatic fatty acid uptake and a resultant fatty liver. Cell models with increased or reduced expression of Fabp exhibit increased and decreased fatty acid uptake, respectively (Haunerland and Spener, 2004
). Fatty acid transporter 2 (Slc27a2), which was upregulated at 4 h and remained elevated through to 168 h, facilitates long chain fatty acid transport across the plasma membrane and accounts for high affinity and specific FA transport in hepatocytes (Hirsch et al., 1998
). Fatty acid translocase (Cd36), upregulated in a pattern similar to that of Lpl, is a key enzyme involved in the uptake of FA and oxidized LDL across the plasma membrane. Null mutations of Cd36 result in reduced FA uptake, whereas overexpression increases FA uptake and metabolism (Bonen et al., 2004
; Febbraio et al., 1999
). Lipoprotein receptorrelated protein-2 (Lrp2) was also upregulated for the duration of the time course and may account for the increased hepatic cholesterol ester content and reduced serum levels. Lipin2 was upregulated throughout the time course and belongs to a family of genes whose deficiency prevents normal lipid accumulation and the induction of key lipogenic enzymes in adipocytes (Peterfy et al., 2001
; Phan et al., 2004
). Furthermore, mice deficient in Lipin exhibit dramatically reduced Lpl activity (Langner et al., 1989
). Consequently, TCDD induction of Lipin2 may be linked to the subsequent upregulation of Lpl. Collectively, induction of these key genes supports an environment for increased lipid uptake into the liver. Furthermore, their dose-dependent and temporal expression profiles precede or parallel the observed histological increases in hepatic fat accumulation, strongly suggesting that their induction is involved in mediating this response.
Fatty acids also act as signaling molecules that regulate gene expression. Fatty acid synthase (Fasn), involved in de novo lipogenesis, and Apoa1 were both downregulated late following TCDD treatment, consistent with their previously reported decreases in transcript and activity levels after increases in hepatic FA accumulation (Duplus et al., 2000). Similarly, the oxidative phosphorylation uncoupling gene, uncoupling protein-2 (Ucp2), was induced late, in agreement with its regulation by FA (Reilly and Thompson, 2000
). These results suggest that a subset of the late changes in gene expression may be secondary to the increased FA content of the liver, and not direct AhR-mediated responses.
Inhibition of Gluconeogenic Enzymes
TCDD-induced lethality involves feed refusal, body weight loss, and exhaustion of energy stores, collectively referred to as a "wasting syndrome" (Viluksela et al., 1995). However, feed refusal alone does not sufficiently account for the wasting effect, as pair-fed animals still exhibit this response (Chapman and Schiller, 1985
). TCDD exposure also inhibits gluconeogenesis by repressing key gluconeogenic enzymes, which, in combination with feed refusal, is thought to result in TCDD-induced wasting syndrome lethality (Viluksela et al., 1995
). Phosphoenolpyruvate carboxykinase 1, pyruvate carboxylase (Pcx), and to a lesser extent G6pc (glucose-6-phosphatase) are known to be repressed by TCDD. In this study, repression of Pck1, Gpd2, and Got1 was also detected. Of these, Gpd2 catalyzes the irreversible conversion of glycerol phosphate to dihydroxyacetone phosphate (DHAP) required for the formation of fructose-16-bisphosphate; Got1 is involved in the malate-aspartate shuttle and the conversion of aspartate to oxaloacetate, which provides substrate for use by Pck1 in gluconeogenesis. The multiple gluconeogenic enzymes downregulated further implicate this pathway as a target in the etiology of TCDD-induced wasting syndrome.
In adipose tissue, TCDD inhibits lipid synthesis, decreases uptake of FFA due to reduced Lpl activity, and increases the mobilization of fat (Lakshman et al., 1989). Gluconeogenic enzymes also serve roles in glyceroneogenesis, a process that plays an integral but opposite role in fatty acid cycling and triglyceride turnover in hepatic and adipose tissues. In adipose tissue, inhibition of glyceroneogeneis induces FFA release caused by decreased triglyceride storage. Increased FFA output combined with the reduced uptake from downregulation of adipose Lpl, is a likely contributor to the increased serum FFA levels observed in the present study. Furthermore, inhibition of hepatic glyceroneogenesis reduces triglyceride output, thereby contributing to the TCDD-elicited fatty liver. Collectively, FFA mobilization from adipose tissue and the decreased triglyceride export from the liver, combined with the increased expression of genes for fatty acid uptake, would facilitate the loss of body fat (i.e., wasting) and its accumulation in the liver.
Immune Cell Accumulation
Histological analysis revealed the presence of immune cell accumulation, primarily in the centrilobular regions, coincident with the upregulation of numerous immune signaling genes at 168 h. These genes included a number of cluster of differentiation and lymphocyte antigens (Cd and Ly antigens), as well as major histocompatabilty complex (MHC) molecules. Cluster of differentiation and Ly antigens are surface molecules on hemopoietic cells that are important in a number of immune signaling functions, including rolling and migration, as well as T-cell activation (Lai et al., 1998; Sumoza-Toledo and Santos-Argumedo, 2004
). H2-Ab1 and H2-Eb1 belong to the MHC class II and are involved in antigen presentation and processing (Alfonso et al., 2001
). Changes in immune gene expression are likely a secondary AhR-independent response to hepatic damage mediated by ROS or fatty accumulation, as induction was confined to the 168-h time point when immune cell infiltration was detected by histology, a finding consistent the absence of DREs in the promoters of these genes. These results further illustrate the importance of complementary histology to facilitate the interpretation of changes in gene expression in complex tissue analysis.
Apoptosis
Induction of a number of genes involved in the initiation of apoptosis was also detected by microarray analysis, including Rip1k, Casp6, and Hip1. Both Rip1k and Hip1 are able to activate apoptotic pathways (Gervais et al., 2002; Grimm et al., 1996
), whereas Casp6 induction lowers the threshold for apoptotic signals (MacLachlan and El-Deiry, 2002
). The collective induction of these and other genes is supportive of a cellular environment conducive to apoptosis, in agreement with histological evidence of late apoptotic events. Although induction of these apoptotic genes may be a response secondary to oxidative stress and toxicity, it is also possible that these are primary response genes involved in TCDD-mediated apoptosis or alterations in differentiation.
TCDD Regulates Genes Involved in Development and Differentiation
TCDD treatment also resulted in the induction of a number of genes involved in development and differentiation, including Tnfaip2 and Notch1. Although these genes may not be involved in mediating the hepatotoxicity observed in this study, they may play an important role in normal AhR signaling during hepatic development, as AhR null mice are known to exhibit reduced liver size and altered hepatic vasculature (Fernandez-Salguero et al., 1995; Lahvis et al., 2000
; Schmidt et al., 1996
). Both genes have previously been implicated in tissue development and exhibit specific patterns of expression in the developing liver (Wolf et al., 1994
) (Harper et al., 2003
; Loomes et al., 2002
). Activation of Notch receptors induce the hairy and enhancer of split (Hes) family of genes, whose expression mediates many aspects of Notch signaling (Pissarra et al., 2000
). Consistent with this regulation, induction of Hes6 at the 72- and 168-h time points was also observed. The hepatic expression patterns of Tnfaip2 and Notch1 occur during embryonic days 1218 (Loomes et al., 2002
; Wolf et al., 1994
), which coincides with the period of AhR activation required for normal liver development (Walisser et al., 2004a
). Furthermore, treatment of AhR hypomorphs, which exhibit a 90% reduction in AhR levels and display altered hepatic development, with TCDD on embryonic days 1218, is able to restore normal liver development, presumably as a result of the potent activation of low levels of AhR (Walisser et al., 2004a
). Therefore, these genes provide putative candidates for mediating the hepatic developmental role of the AhR.
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SUMMARY |
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ACKNOWLEDGMENTS |
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REFERENCES |
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