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Temporal Toxicogenomic Analysis of TCDF & TCDD

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Comparative Temporal Toxicogenomic Analysis of TCDD-and TCDF-Mediated Hepatic Effects in Immature Female C57BL/6 Mice

A N’Jai*,†, DR Boverhof*,†, E Dere*,†, LD Burgoon*,†, YS Tan*,†, JC Rowlands‡, RA Budinsky‡, KE Stebbins‡, and TR Zacharewski*,†,§

*Department of Biochemistry & Molecular Biology, †Center for Integrative Toxicology & National Food Safety & Toxicology Center, Michigan State University, East Lansing, MI 48824, ‡The Dow Chemical Company, Midland, MI 48674

§Corresponding Author:

Tim ZacharewskiMichigan State UniversityBiochemistry & Molecular Biology501 Biochemistry BuildingWilson RoadEast Lansing, MI 48824-1319Fax: 517-353-9334E-mail: tzachare@msu.edu

Running Title: Comparative Temporal Toxicogenomic Analysis of TCDF & TCDD

Key words: TCDD; TCDF; Microarray; Liver; Mouse; Temporal

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Abstract

Temporal analyses were performed on hepatic tissue from immature female C57BL/6

mice in order to compare the gene expression profiles for 2,3,7,8-tetrachlorodibenzo-p-dioxin

(TCDD) and 2,3,7,8-tetrachlorodibzofuran (TCDF). Time course studies conducted with a

single oral dose of 300 µg/kg TCDF or 30 µg/kg TCDD were used to compare differential gene

expression on cDNA microarrays containing 13,361 features, representing 8,194 genes at 2, 4, 8,

12, 24, 72, 120 and 168 hrs. 195 genes were identified as differentially regulated by TCDF, of

which 116 genes were in common with TCDD, with 109 exhibiting comparable expression

profiles (correlation coefficients > 0.3). In general, TCDF was less effective in eliciting hepatic

vacuolization, and differential gene expression compared to TCDD when given at an equipotent

dose based on a toxic equivalence factor (TEF) of 0.1 for TCDF, especially after 72 hrs post

administration. For example, the induction of Cyp1a1 mRNA by TCDF was weaker when

compared TCDD. Moreover, TCDF induced less severe hepatocyte cytoplasmic vacuolization

consistent with lower lipid accumulations which significantly subsided by 120 and 168 hrs when

compared to TCDD. TCDF-elicited responses correlated with their hepatic tissue levels which

gradually decreased between 18 and 168 hrs. Although both compounds elicited comparable

gene expression profiles, especially at early time points, the TCDF responses were generally

weaker. Collectively, these results suggest that the weaker TCDF responses could be attributed

to differences in pharmacokinetics. However, more comprehensive dose-response studies are

required at optimal times for each end point of interest in order to investigate the effect of

pharmacokinetic differences on relative potencies that are important in establishing TEFs.

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Introduction

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) and related compounds, including 2,3,7,8-

tetrachlorodibenzofuran (TCDF), are ubiquitous environmental contaminants that are inadvertent

by-products of various processes including municipal waste combustion and phenoxy acid

herbicides production (Mason and Safe, 1986; Poland and Glover, 1973; Safe et al., 1982).

TCDD and related compounds, including TCDF, elicit a broad range of species-specific

biochemical and toxic effects in animals such as xenobiotic enzyme induction, wasting

syndrome, tumor promotion, teratogenicity, immunotoxicity, hepatotoxicity, and endocrine

system modulation (Abbott et al., 1987; Birnbaum, 1995; Birnbaum et al., 1987; Davis and Safe,

1989; Viluksela et al., 2000). Many, if not all of these effects are due to the inappropriate

regulation of gene expression mediated by the aryl hydrocarbon receptor (AhR) (Denison and

Heath-Pagliuso, 1998; Denison et al., 2002; Poland and Knutson, 1982; Safe, 2001). The

proposed mechanism involves ligand binding, activation, and translocation of the cytoplasmic

AhR to the nucleus where it heterodimerizes with the AhR nuclear translocator (ARNT). This

complex induces changes in the expression of genes possessing dioxin response elements

(DREs) within their regulatory region.

The ability of a large number of structurally diverse chemicals to bind to the AhR and

their existence as complex mixtures presents significant challenges in assessing their potential

risk to human and ecological health (Ahlborg, 1994; Birnbaum and DeVito, 1995; DeVito et al.,

1994; Safe, 1990; Santosfefano et al., 1994). Assessment approaches have focused on their

dioxin-like properties and the use of toxic equivalency factors (TEFs) based on endpoint-specific

relative potencies (REFs) (Ahlborg, 1994; Barnes et al., 1991; Birnbaum and DeVito, 1995;

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Haws et al., 2006; Safe, 1990; Van den Berg et al., 1998; Van den Berg et al., 2006). The TEF

approach assumes that at submaximal doses, the contributions of individual congeners are

essentially additive (Harris et al., 1993; Safe, 1997), and that TEFs are independent of dose,

time-point and tissue (Poland and Knutson, 1982; Safe, 1990). However, pharmacokinetic and

dispositional differences between congeners may affect their relative potencies (Budinsky et al.,

2006; DeVito et al., 1997; DeVito et al., 1998; Diliberto et al., 2001; Safe, 1995).

TEFs for 29 polychlorinated dibenzo-p-dioxins (PCDDs), dibenzofurans (PCDFs), and

biphenyls (PCBs), have been established relative to TCDD, the reference chemical which is

assigned a TEF of 1 (Van den Berg et al., 2006). For example, 36 separate in vivo REPs, from

13 different peer-reviewed publications and one thesis were available to the expert panel

convened by the World Health Organization (WHO) to establish a TEF for TCDF. Assuming in

vitro data was only used in the absence of sufficient in vivo data, and the panel adopted the

criteria developed for REP 2004, 17 of 36 in vivo studies were considered when the TEF of 0.1

for TCDF was set, with the understanding that it could vary by a half-log unit (Van den Berg et

al., 2006). Of these studies, 3 were in the Wistar rat, 1 was in the Hartley guinea pig, and 13 used

C57BL/6J and C57BL/6N mice. The endpoints examined included enzyme assays

(ethoxyresorufin-O-deethylase (EROD), acetanilide 4-hydroxylation (ACOH), benzo(a)pyrene

hydroxylase), body weight gain, kidney damage, cleft palate, relative liver weight, and

immunosuppression measured by the plaque forming cell assay. Consequently, the potential

toxicity of a mixture containing these 29 TCDDs, TCDFs and PCBs could be estimated relative

to TCDD by calculating the sum of the concentrations of each individual congener multiplied by

their corresponding TEF.

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Comparative toxicogenomics is a powerful approach to further elucidate the mechanisms

of toxicity of TCDD and related compounds across species. Temporal analyses using these

technologies facilitates the identification of linkages between differential gene expression and

injury, and can also distinguish adaptive differential gene expression from responses associated

with adverse effects by correlation to other concurrently assessed apical endpoints (i.e.,

phenotypic anchoring) (Afshari et al., 1999; Hamadeh et al., 2002; Nuwaysir et al., 1999).

Published studies examining the hepatotoxicity of TCDD using the same model, dosing regimen,

cDNA microarrays and data analysis methods have linked some differentially expressed genes to

TCDD elicited histopathology (Boverhof et al., 2005). Furthermore, using the same models and

approaches facilitates the identification of ligand-specific responses that may be important when

evaluating potency. In this study, comparable comprehensive differential gene expression time

course cDNA microarrays with complementary histopathology and tissue level analyses were

conducted to compare the effects elicited by TCDD and TCDF at equipotent-based TEF doses.

Collectively, 300 µg/kg TCDF elicited a weaker subset of the responses induced by 30 µg/kg

TCDD, especially at later time points, consistent with the decreasing hepatic tissue levels of

TCDF. Consequently, pharmacokinetics is an important factor when establishing TEF values.

However, more comprehensive time optimized dose-response studies are required to provide

additional REP data for reconsideration of the TCDF TEF, in context with all other TCDF REP

data.

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Materials and Methods

Animal husbandry

Female C57BL/6 mice, all having body weights (BW) within 10% of the average BW,

were obtained from Charles River Laboratories (Raleigh, NC) on postnatal day (PND) 25. The

mice were housed in polycarbonate cages containing cellulose fiber chips (Aspen Chip

Laboratory Bedding, Northeastern Products, Warrensberg, NY) in a 23°C Hepa-filtered

environment with 30-40% humidity and 12 h light/dark cycle (0700 h-1900 h). Animals were

allowed free access to deionized water and fed ad libitum Harlan Tekad 22/5 Rodent Diet 8640

(Madison, WI), and acclimatized for 4 days prior to dosing. On the fourth day, animals were

weighed and stock solutions of TCDD and TCDF (provided by The DOW Chemical Company,

Midland, MI) were diluted in sesame oil (Sigma, St. Louis, MO) to achieve the desired dose

based on the average weight. All procedures were performed with the approval of the Michigan

State University All-University Committee on Animal Use and Care.

In-Life Study Design

TCDD and TCDF doses chosen were based on, 1) a published comprehensive TCDD

time course and dose-response study (Boverhof et al., 2005), and 2) the Toxic Equivalency

Factor (TEF) for TCDF (0.1) as determined by an expert panel of the World Health Organization

(WHO) (Van den Berg et al., 2006). 30 µg/kg TCDD was initially selected for use in the

Boverhof et al. study because it elicited maximum induction of Cyp1a1 and 1a2 mRNA levels

while not inducing significant changes in body weight gain (Boverhof et al., 2005). It was used

again in the present study, to facilitate comparisons between studies that employed the same

species, experimental design, cDNA microarray platform, and analysis methods. However, note

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that this study used intact immature female C57BL/6 mice while previous studies in this lab have

used ovariectomized immature female C57BL/6 mice (Boverhof et al., 2005). 300 µg/kg TCDF

was used to examine the hypothesis that it would elicit hepatic effects comparable to 30 µg/kg

TCDD, based on the TCDF TEF of 0.1. The vehicle groups were not the same between the

current TCDF study and the Boverhof et al. study, but the same vehicle controls were used for

the internal TCDD treated mice in the current TCDF study.

Mice were orally gavaged with 0.1 mL of sesame oil for a nominal dose of 0 (vehicle

control), 300 µg/kg BW of TCDF, or 30 µg/kg BW of TCDD. Five animals were treated per

dose group and time point, and housed in separate cages (Figure 1). TCDF-treated and time-

matched vehicle control animals were sacrificed at 2, 4, 8, 12, 18, 24, 72, 120, or 168 h post

dosing. Mice were treated with 30 µg/kg BW TCDD were sacrificed at 4, 12, 72, 120, or 168 h

post dose. This limited number of time points was meant to serve as a few internal controls to

facilitate comparisons with a more comprehensive TCDD time course study (Boverhof et al.,

2005). TCDD and TCDF doses were chosen to elicit moderate hepatic effects while avoiding

overt toxicity in longer term studies. Animals were sacrificed by cervical dislocation and tissue

samples were excised, weighed, flash frozen in liquid nitrogen, and stored at -80°C until further

use. The right lobe of the liver was fixed in 10% neutral buffered formalin (NBF, Sigma) for

histological analysis.

Histological Analysis

Sections taken through the center of the right middle lobes were processed sequentially in

formalin, alcohol, butanol, xylene, and paraffin in a Tissue Tek VIP 5 vacuum infiltration

processor (Torrance, CA). Tissues were then embedded in paraffin with a Miles Tissue Tek

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embedding center, after which paraffin blocks were sectioned at 6 µm with a rotary microtome.

Sections were placed on glass microscope slides, dried, and stained with hematoxylin and eosin.

Histological evaluations were performed by a board certified veterinary pathologist.

Quantification of TCDF and TCDD in Liver Tissues

Liver samples were processed in parallel with lab blanks and a reference or background

sample at The Dow Chemical Company. The samples were weighed, spiked with 13C12-labeled

TCDD or TCDF surrogate, digested and extracted by shaking overnight in a solution

containing concentrated hydrochloric acid and a 5% benzene:hexane solution. The organic

phase was processed through a series of three clean-up columns. The first column consisted

from bottom to top: silica gel, caustic silica gel (33% NaOH/Silica Gel), silica gel, acid silica gel

(44% H2SO4/Silica Gel), and silica gel. The second and third clean-up

columns contained silver nitrate (10% silver nitrate/silica gel) and basic alumina, respectively.

The final cleaned-up extract was concentrated and spiked with an injection standard. The

analysis was performed on a high resolution gas chromatography/high resolution mass

spectrometer (HRGC/HRMS) using a Hewlett Packard 5890 series II GC (Palo Alto, CA)

interfaced to a VG 70SE HRMS (VG Analytical, Manchester, UK). The HRMS was operated in

the electron impact (EI)/selected ion recording (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. The injection method was splitless with an injection volume of 2

µL. All calculations were performed via the Isotope-Dilution Mass Spectrometric procedure.

When appropriate, the system and laboratory performance was monitored using the guidelines

specified in EPA method 1613b.

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RNA Isolation

Frozen liver samples (approximately 70-100 mg) were transferred to 1.3 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. RNA was resuspended in RNA storage solution (Ambion Inc.,

Austin, TX), quantified (A260) for concentration, and the purity determined by A260/A280 ratio

and by visual inspection of 1.0µg on a denaturing gel electrophoresis.

Microarray Assay

TCDF treated samples were co-hybridized with time-matched vehicles controls using an

independent reference design (Yang and Speed, 2002). cDNA microarrays were also performed

for the ‘internal’ TCDD-treated group of mice, which used the same vehicle controls as in the

TCDF microarray design. In the Boverhof et al. study, independent groups of the TCDD-treated

and vehicle control mice were used. All experiments were performed with three biological

replicates with two independent labelings of each sample (dye swap) for each time point or dose

group, using custom mouse cDNA microarrays containing 13,361 features representing 8,516

unique genes (UniGene build 144).

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

chipmaker 2 pins (Telechem, Atlanta, GA) at the Research Technology Support Facility

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(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 PCR purification kit (Qiagen, Valencia, CA). After cDNA labeling, 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. The hybridization 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 18-24 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 a GenePix Personal 4100A

microarray scanner (Molecular Devices, Union City, CA). Scanned Images were analyzed for

feature and background intensities using GenePix Pro 6.0 (Molecular Devices).

cDNA Microarray Data Analysis

All microarray data used within this study passed the laboratory quality assurance

protocol (Burgoon et al., 2005), although there was more variability relative to previous studies

(Boverhof et al., 2005; Boverhof et al., 2006), possibly due to the use of intact immature 28-35

day old C57BL/6 mice. Microarray data were normalized using a semiparametric approach

(Eckel et al., 2004; Eckel et al., 2005), and the posterior probabilities were calculated using an

empirical Bayes analysis on a per gene and time point or dose basis (Eckel et al., 2004).

Normalization and empirical Bayes analysis were performed using SAS version 9.1 (SAS

Institute Inc, Cary, NC) and R version 2.3.1. Gene expression data were ranked and prioritized

using a P1(t) cutoff of 0.999 and ± 1.4 fold change to identify an initial subset of differentially

expressed genes for further investigation and data interpretation. Relaxed filtering criteria (from

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P1(t)=0.999; > 1.4 fold absolute fold change to P1(t)=0.9; > 1.4 absolute fold change) were also

used to examine overlapping differentially regulated genes to minimize classifying genes as

TCDF- or TCDD-specific as a result of using stringent cut-offs. Hierarchical clustering of

differentially expressed genes was performed using GeneSpring GX 7.3.1 software (Agilent

Technologies, Santa Clara, CA) and standard correlation tool as the similarity metric. Trajectory

analysis was performed in R using singular value decomposition. Regression analysis was also

performed in R using the nonlinear, robust loess with 1,000 iterations. The slope was estimated

empirically using the loess model.

Multiple features spotted on our cDNA microarray represent the same gene (e.g.

Cyp1a1). To obtain the number of unique genes, the features were first screened by their

corresponding Entrez Gene IDs. If several features had the same Entrez Gene ID, they were all

considered to be representative of the same genes and counted as one gene. Due to this

redundancy, and because of missing annotation and changes to annotation in the mouse genome,

the 13,361 features spotted on our cDNA microarray correspond to 8,516 unique genes based on

the annotation provided by UniGene build 144.

Quantitative Real-Time PCR

Quantitative real-time PCR verification of microarray responses was performed as

described previously (Boverhof et al., 2005). Briefly, 1.0 µg of total RNA was reverse

transcribed by Superscript II using an anchored oligo-dT primer as described by the

manufacturer (Invitrogen). The cDNA (1.0 µL) was used as a template in a 30 µL PCR reaction

containing 0.1 µM of forward and reverse gene-specific primers, 3 mM MgCl2, 1.0 mM dNTPs,

0.025 IU AmpliTaq Gold, and 1x SYBR Green PCR buffer (Applied Biosystems). Gene names,

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accession numbers, forward and reverse primer sequences and amplicon sizes are listed in

Supplementary Table 1. PCR amplification was conducted on an Applied Biosystems PRISM

7000 Sequence Detection System. cDNAs were quantified using a standard curve approach and

the copy number of each sample was standardized to 3 housekeeping genes (Actb, Gapdh, Hprt)

to control for differences in RNA loading, quality and cDNA synthesis.

Identification of DREs

The regulatory regions (-10,000 relative to the transcription start site [TSS] through the

5’- untranslated region [UTR]) for all genes with a mature Refseq accession were obtained from

the University of California, Santa Cruz, Genome Browser for the mouse (build 34). All

sequences were deposited into dbZach, an in-house data management solution (Burgoon et al.,

2006). Core DRE sequences (5’-GCGTG-3’) were identified using a response element

application developed in Java (Sun et al., 2004).

Statistical Analysis

Statistical analysis, unless otherwise defined, was performed using SAS version 9.1.

Data were analyzed using analysis of variance followed by Dunnett’s or Tukey’s post hoc tests.

Differences between treatment groups were considered significant when p<0.05. Half-life

estimates were derived using WinNonlin (Pharsight, Mountain View CA).

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Results

Organ and Body Weights

Changes in body weight, liver weight and cell morphology are characteristic rodent

responses following exposure to TCDD and related compounds (Poland and Knutson, 1982).

Mice treated with 30 µg/kg of TCDD showed significant (p<0.05) increases in relative liver

weight (RLW) at 72, 120, and 168 h (Table 1), as previously reported (Boverhof et al., 2005).

Similar treatment with the TEQ dose of TCDF (300 µg/kg) also elicited significant (p<0.05)

increases in RLWs at 24, 72, 120, and 168 h (Table1). Despite reports of wasting in TCDD

treated rodents, mice in this study did not exhibit significant treatment related alterations in body

weight at the doses used, consistent with published studies (Boverhof et al., 2005; Fletcher et al.,

2001).

Hepatic TCDD/ TCDF Concentrations

Hepatic TCDD and TCDF levels per wet weight (n=3) were quantified to assess the

temporal relationship and relative potency between TCDD and TCDF in relation to RLW,

histopathology and gene expression responses. Hepatic levels of TCDD reached maximal levels

at 12 h which were sustained through 72 h, followed by gradual decreases at 120 and 168 h

(Figure 2). Similarly, TCDF hepatic levels increased between 2 and 12 h, followed by a more

rapid decrease between 18 and 168 h compared to TCDD (Figure 2). These findings are

consistent with published reports on the disposition of TCDF and TCDD in rats and mice

(DeVito and Birnbaum, 1995; DeVito et al., 1997; Hamm et al., 2003) and the capacity of

Cyp1a1 induction to metabolically clear TCDF (McKinley et al., 1993; Olson et al., 1994; Tai et

al., 1993). Fitting of the liver concentration versus time data yielded estimated hepatic clearance

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half-lives of 40 and 177 hours (1.7 and 7.4 days) for TCDF and TCDD, respectively.

Comparison of hepatic levels of TCDD and TCDF, when expressed as TEQ, revealed no

significant differences in hepatic tissue levels at 12 h, however, TCDD levels were significantly

greater than TCDF levels at 72 h and 168 h (Figure 2), consistent with the longer half-life (10

days) of TCDD compared to 2 days for TCDF (DeVito and Birnbaum, 1995). These data suggest

that ligand specific pharmacokinetic and disposition factors may contribute to differences in

relative potencies across end points. Consequently, hepatic TCDD and TCDF levels are

important factors in phenotypically anchoring gene expression to RLW and histopathology.

Histopathology

The principal treatment related alteration in response to TCDF or TCDD was very slight

to moderate cytoplasmic vacuolization of hepatocytes, consistent with lipid accumulation,

primarily observed in the periportal and midzonal regions of the liver. In the time course study,

TCDD induced cytoplasmic vacuolization at 72 h, which peaked at 120 h with all animals

exhibiting moderate vacuolization, and then decreased in severity by 168 h (Table 2). In

comparison, TCDF induced cytoplasmic vacuolization at 24 h, which became more severe by 72

h, but lessened at 120 h, and significantly subsided to very slight effects by 168 h (Table 2). In

addition, at 120 and 168 h TCDD and TCDF increased numbers of inflammatory cell aggregates

consisting of lymphocytes, neutrophils and macrophages that were frequently associated with

multiple degenerative and necrotic hepatocytes. Treatment-related centrilobular hypertrophy of

hepatocytes was noted in at least two out of five animals treated with TCDD or TCDF at 168 h.

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Hepatic Gene Expression Response to TCDF and TCDD

Analysis of temporal hepatic gene expression responses to TCDD and TCDF was

performed using custom mouse cDNA microarrays containing 13,361 features representing

8,194 unique genes. For TCDD, empirical Bayes analysis identified 242 features, representing

208 unique genes, which were differentially expressed (P1(t)>0.999 and │fold change│ >1.4)

relative to time-match vehicle controls at one or more time points. For TCDF, 233 features

representing 195 unique genes were differentially expressed at one or more time points in the

time course study (Figures 3A and 3B) (complete listing of all gene expression data is available

in Supplementary Table 2).

As was seen with effects on RLW, hepatic concentrations and liver histopathology,

TCDF elicited fewer differential gene expression responses over time. TCDD displayed a time-

dependent increase in number of genes induced between 2 and 4 h, which remained stable

through 18 h, and increased further between 24 and 168 h (Boverhof et al., 2005). In contrast,

TCDF elicited differential gene expression was highest at early time points, with maximal

induction at 12 h, followed by dramatic decreases in number of differently expressed genes after

24 h (Figure 3A), consistent with decreasing hepatic TCDF levels.

Comparison of the differentially expressed gene lists identified 116 genes regulated by

both TCDD and TCDF (Figure 3B) (complete listing of all genes used in the comparison are

available in Supplementary Table 3). There was significant overlap in gene expression responses

at all time points. TCDF-specific differential gene expression typically included marginal

responses that were selected due to the use of a stringent statistical cut-off. When the selection

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criteria were relaxed (P1(t) > 0.9; fold change > 1.4), almost all of the apparent TCDF-specific

responses also exhibited differential gene expression in the TCDD active list (data not shown).

Consequently, TCDF elicited differential gene expression was comparable to gene expression

changes elicited by TCDD. This is consistent with TCDF eliciting comparable hepatic effects,

although the induction of RLW (Table 1) and vacuolization (Table 2) was weaker at later time

points.

Comparison of TCDD and TCDF Elicited Differential Gene Expression

Responses

Hierarchical clustering of the microarray data by experimental time point illustrates the

induction and repression of early and late differential gene expression responses. Moreover,

early (4-72 h) TCDD and TCDF elicited responses clustered together based on time point, while

later time points (120-168 h) clustered according to treatment (Figure 4A). Overall, differential

gene expression responses elicited by TCDF were similar in magnitude to TCDD responses at

the early time points, but were lower from 120-168 h relative to TCDD consistent with the

decreasing hepatic TCDF tissue levels.

Pearson’s correlation analysis of the temporal gene expression (fold change) and

significance (P1(t)) profiles of the 116 commonly regulated genes exhibited similar differential

expression patterns (Figure 4B). For this analysis, TCDD and TCDF paired data were plotted on

a coordinate axis with the x-axis as the gene expression correlation and the y-axis as the

significance correlation. A majority of the gene relationships fell into the upper right hand

quadrant representing TCDD and TCDF responsive genes that exhibit highly correlated

differential gene expression and significance patterns. Overall, 109 of the 116 genes regulated by

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TCDD and TCDF exhibited a gene expression correlation greater than 0.3, indicating similarity

in gene expression patterns. Correlations less than 0.3 tended to occur with genes exhibiting

differential gene expression after 24 h.

Functional Analysis of the Common Gene Responses

Functional annotation of TCDD and TCDF elicited differential gene expression was

associated with phase 1 and 2 enzymes, development and differentiation, fatty acid uptake and

metabolism, gluconeogenesis, immune signaling, transcription regulation, apoptosis, transport,

and endocrine disruption (Table 3). Many AhR battery genes were induced by both compounds,

including cytochrome P450s and glutathione transferases (Boverhof et al., 2006; Fletcher et al.,

2005; Nebert et al., 2000; Puga et al., 1992; Stahl, 1995; Stahl et al., 1993; Tian et al., 1999;

Viluksela et al., 1999; Weber et al., 1991). A more thorough discussion of the association

between differential gene expression, functional annotation, and elicited hepatic effects has been

previously published (Boverhof et al., 2005; Boverhof et al., 2006).

Despite TCDD and TCDF eliciting comparable hepatic effects, there were differences in

temporal differential gene regulation and efficacy as illustrated by their divergent paths in the

trajectory plots (Figure 5A). Regression analysis was conducted on 2-24 and 72-120 h grouped

temporal data based on the hierarchical clustering and Pearson’s correlation analyses in order to

further investigate differences in ligand efficacy and temporal regulation, relative to tissue levels.

Tissue levels of both compounds were comparable from 2-24 h, and the slope of the nonlinear

regression function for gene expression efficacy approaches 1.00, indicating that TCDD and

TCDF exhibit equal efficacy at these earlier time points (Figure 5B). However, the slope of the

nonlinear regression function is approximately 0.52 suggesting significantly lower TCDF

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efficacy between 72-168 h (Figure 5C). This is consistent with decreasing TCDF tissue levels,

and the lower RLWs and vacuolization effects (Tables 1 and 2). In general, it is also consistent

with the lower induction of secondary and tertiary responses associated with inflammatory cell

accumulation and fatty acid transport and metabolism (Table 3).

Verification of Microarray Response

QRTPCR was used to verify the differential expression for a selected subset of

differentially expressed genes from Table 3 representing different response profiles and

functions (Figure 6). In general, there was good agreement in the level of differential expression

when comparing microarray and QRTPCR data. However, microarray data compression was

evident for Cyp1a1 due to the limited dynamic fluorescence intensity range (0-65,535), which

results in signal saturation for highly induced 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 in comparison to other, more gene-specific measurement

techniques (Yuen et al., 2002).

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Discussion

The WHO assigned TEF of 0.1 for TCDF is based on expert judgment considering all

toxicity data, under the assumption that in vitro data is only used in the absence of sufficient in

vivo data (Barnes, 1991; Birnbaum and DeVito, 1995; Haws et al., 2006; Safe, 1993; Toyoshiba

et al., 2004; van Birgelen et al., 1996; Van den Berg et al., 2006). The present in vivo study used

a comprehensive toxicogenomic study design with complementary histopathology and hepatic

tissue concentration analysis to compare the hepatic effects elicited by equipotent doses of TCDF

and TCDD based on the WHO TEF in order to assess time-dependent differences in differential

gene expression. These temporal changes provide essential baseline data for subsequent dose

response studies.

Hepatic clearance estimates for TCDF and TCDD are consistent with previous reports

(Birnbaum, 1986; Decad et al., 1981; Gasiewicz et al., 1983). The estimated hepatic half-life for

TCDF was approximately 40 h (1.7 days), almost identical to the hepatic half-life reported in

C57BL/6 and DBA/2J mice (Decad et al., 1981). The hepatic half-life of 7.4 days for TCDD is

less than the 10 to 12 day half-life previously reported (Birnbaum, 1986; Gasiewicz et al., 1983).

Reduced adipose tissue in the immature mice used in this study may partially explain the

apparent enhanced clearance. The difference may also be a function of examining hepatic

clearance versus whole body clearance, as reported in these other studies. Since adipose tissue

concentrations were not obtained it is not possible to calculate whole-body clearance and half-

life estimates from this study.

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Ligand specific pharmacokinetic and dispositional characteristics may lead to differences

in the relative potencies of dioxin-like congeners across endpoints (Chen et al., 2001; DeVito

and Birnbaum, 1995; DeVito et al., 1997; DeVito et al., 2000; DeVito et al., 1998). There were

significantly lower hepatic TCDF levels at later time points when expressed as TEQs that can be

attributed to ligand specific pharmacokinetic properties consistent with previously published

studies (DeVito and Birnbaum, 1995; Diliberto et al., 1995; Diliberto et al., 2001; Hamm et al.,

2003). For example, the relative potencies of TCDD and TCDF are dependent on their

pharmacokinetics in female B6C3F1 mice (DeVito and Birnbaum, 1995). The TEFs accurately

estimated the relative potency of steady state levels of TCDF after 4 weeks based on EROD

activity. However, after 13 weeks the TEF overestimates potency as hepatic EROD induced by

TCDD and TCDF were 41- and 6-fold, respectively. This is in agreement with the levels of

hepatotoxicity observed in this study and the reported half-life of 2 and 15 days for TCDF and

TCDD, respectively (Birnbaum, 1986; Diliberto et al., 1995). TCDF is reported to induce its

own metabolic clearance via induction of Cyp1a1 (Budinsky et al., 2007).

TCDD’s disposition (% dose/g tissue) and retained dose levels (21-34%) were also

greater when compared to TCDF (2.5-6.2%) (DeVito et al., 1998). Binding of dioxin-like

chemicals to inducible proteins such as Cyp1a2 may also contribute to differential hepatic

sequestration in rats and mice (Chen et al., 2001; DeVito et al., 2000; Diliberto et al., 1995;

Diliberto et al., 1997; Diliberto et al., 1999). Cyp1a2-null mice exhibited little TCDD and other

dioxin-like chemical accumulation, suggesting that Cyp1a2 expression is important in the

pharmacokinetics and disposition of these compounds (Diliberto et al., 1997; Diliberto et al.,

1999). Overall, the relative potency, and thus the estimates of a TEF value for a specific

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dibenzo-p-dioxin or dibenzofuran congener, relative to TCDD, may be dependent on its

metabolic clearance (i.e., Cyp1a1 hydroxylation of TCDF) or hepatic sequestration (Cyp1a2

binding of TCDD). The issue of external TEFs based on administered dose and those of internal

TEFs based on tissue concentration or body burden metrics was a concern expressed by the 2005

WHO panel charged with updating the TEF values (Van den Berg et al., 2006).

TCDD- and TCDF-induced temporal-dependent increases in RLW and differential gene

expression associated with fatty vacuolization are consistent with previously published studies

(Boverhof et al., 2005; DeVito et al., 1998; Diliberto et al., 1999; Fletcher et al., 2005; Viluksela

et al., 1998). Differences in TCDD and TCDF RLWs can be directly attributed to lower TCDF

hepatic levels, which were observed in this study. Histopathology revealed time-dependent

differences in cytoplasmic vacuolization consistent with lipid accumulation indicative of

alterations in triglyceride metabolisms/and or lipoprotein trafficking, and inflammation with

associated hepatocellular degeneration and necrosis. As with the effects on RLW, TCDF induced

less hepatocellular cytoplasmic vacuolization, especially at later time points, consistent with its

shorter half-life. The linkages between differential gene expression (e.g., lipid metabolism and

transport; immune response) and elicited liver histopathology have been previously described

(Boverhof et al., 2005, 2006).

In general, responses elicited by TCDF were consistent with the hepatic tissue levels and

histopathology effects. There was also substantial overlap in the early differential gene

expression responses elicited by TCDD and TCDF. Both compounds induced the well

characterized AhR gene battery (i.e., Cyp1a1, Nqo1, Ugdh), as well as other genes involved in

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gluconeogenesis, fatty acid metabolism, development, and oxidative stress (Nebert et al., 1993;

Safe, 1995) (complete listing of all gene expression data is available in Supplementary Table 2).

Hierarchical clustering revealed strong concordance between the administered dose and the

transcriptional responses at the early time points while clustering was ligand dependent at the

later time points. Trajectory analysis further illustrated the similarity in gene expression at early

time points with divergence at 24 h and continuing separation at 168 h. The slope (0.52) of the

PCA scatter plot nonlinear regression line after 24 h clearly indicates the weaker potency of

TCDF, consistent with its shorter half-life and hepatic clearance (Birnbaum, 1986; DeVito and

Birnbaum, 1995). Although early differential gene expression responses are similar, they are not

sufficient to elicit comparable levels of toxicity which represents a continuum of effects

governed by temporal and spatial factors as well as exposure conditions. For example, 300 µg/kg

TCDF was generally less potent than 30 µg/kg TCDD in inducing immune response and fatty

acid metabolism genes, which are associated with the secondary response leading to

hepatotoxicity. Moreover, TCDD toxicity appears to be more sustained over time compared to

TCDF, which continuously diminished after 24 h, consistent with its shorter half-life, increased

rate of hepatic clearance, and recovering histopathology effects.

Comprehensive gene expression data that can be phenotypically anchored to

complementary histopathology can be used to further elucidate the mechanisms involved in the

adaptive and toxic responses elicited by TCDD and TCDF. For example, there were fewer lipid

transport and metabolism gene expression changes elicited by 300 µg/kg TCDF from 24 to 168 h

when compared to 30 µg/kg TCDD, consistent with the recovery in hepatic fatty accumulation.

Similar to TCDF, a recent toxicogenomic study of PCB126 hepatic effects using the same model,

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study design and analysis methods also reported that hepatocellular vacuolization, and lipid

transport and metabolism differential gene expression diminished at later time points (Kopec et

al., 2007). However, unlike PCB126 which continued to accumulate in the liver throughout the

study, decreases in TCDF elicited hepatocellular vacuolization, and lipid transport and

metabolism differential gene expression is consistent with the enhanced hepatic clearance of

TCDF. Nevertheless, the possibility of specific differential gene expression elicited by TCDF

cannot be ruled out since the cDNA microarrays used in this study did not include a feature

representative of each gene in the mouse genome. Collectively, these results suggest that the TEF

value of 0.1 for TCDF decreases with time after exposure/dose. However, in order to more

accurately determine the effects of pharmacokinetic differences on the potency of TCDF relative

to TCDD, more comprehensive dose-response studies are required at times that are optimal for

each end point of interest.

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Poland, A. and Knutson, J. C. (1982). 2,3,7,8-tetrachlorodibenzo-p-dioxin and related halogenated aromatic hydrocarbons: examination of the mechanism of toxicity. Annu Rev Pharmacol Toxicol 22, 517-54.

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Puga, A., Nebert, D. W. and Carrier, F. (1992). Dioxin induces expression of c-fos and c-jun proto-oncogenes and a large increase in transcription factor AP-1. DNA Cell Biol 11, 269-81.

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Safe, S. (1997). Limitations of the toxic equivalency factor approach for risk assessment of TCDD and related compounds. Teratog Carcinog Mutagen 17, 285-304.

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Table 1: Terminal Body, Whole Liver and Relative Liver Weight (RLW) for Mice Treated with Vehicle, 300 µg/kg of TCDF or 30 µg/kg TCDD

Time (h) Treatment Body Weight (g) Liver Weight (g) RLWa

Vehicle 12.66 ± 0.59 0.66 ± 0.07 0.052 ± 0.0062

TCDF1 13.14 ± 0.77 0.66 ± 0.04 0.050 ± 0.002

Vehicle 12.74 ± 0.92 0.63 ± 0.06 0.049 ± 0.002TCDF1 12.46 ± 1.20 0.65 ± 0.08 0.052 ± 0.0044TCDD2 12.40 ± 1.01 0.58 ± 0.11 0.046 ± 0.007

Vehicle 13.00 ± 0.68 0.57 ± 0.04 0.044 ± 0.0028

TCDF1 12.30 ± 0.85 0.55 ± 0.05 0.044 ± 0.003

Vehicle 12.98 ± 0.98 0.58 ± 0.05 0.045 ± 0.002TCDF1 13.22 ± 0.60 0.62 ± 0.03 0.047 ± 0.00212TCDD2 12.88 ± 0.78 0.60 ± 0.06 0.046 ± 0.003

Vehicle 13.72 ± 0.96 0.71 ± 0.08 0.052 ± 0.00318

TCDF1 12.98 ± 1.03 0.70 ± 0.05 0.054 ± 0.001

Vehicle 12.46 ± 1.32 0.60 ± 0.10 0.048 ± 0.00324

TCDF1 12.90 ± 1.01 0.76 ± 0.10 0.059 ± 0.004*

Vehicle 14.10 ± 1.61 0.74 ± 0.08 0.053 ± 0.002TCDF1 14.46 ± 0.42 0.95 ± 0.04 0.066 ± 0.002*72TCDD2 14.06 ± 0.74 0.92 ± 0.06 0.065 ± 0.002*

Vehicle 14.60 ± 1.01 0.77 ± 0.08 0.053 ± 0.002TCDF1 14.38 ± 1.00 0.87 ± 0.07 0.061 ± 0.003*120TCDD2 14.78 ± 0.79 0.99 ± 0.07 0.067 ± 0.004*

Vehicle 15.68 ± 1.04 0.80 ± 0.07 0.051 ± 0.002TCDF1 15.28 ± 0.61 0.92 ± 0.02 0.060 ± 0.001*168TCDD2 14.44 ± 0.61 0.95 ± 0.07 0.066 ± 0.003*

a relative liver weight (liver weight/body weight)* p < 0.05 relative to time matched vehicle controls1300 µg/kg TCDF230 µg/kg TCDD

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Table 2: Liver Histopathology Observations for Mice Treated with 300 µg/kg of TCDF or 30 µg/kg TCDD

Inflammation & Necrosis VacuolizationTreatment

72 h 120 h 168 h 24 h 72 h 120 h 168 h

300 µg/kg TCDF NEa ++ ++ ++ +++ ++ +30 µg/kg TCDD NEa + ++ NAb +++ +++ ++

a no effect on liver observedb not availableDegree of serverity from very slight (+), slight (++), to moderate (+++)

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Table 3: Functional Categorization and Temporal Regulation of Select cDNA Microarray Differentially Expressed Hepatic Genes in Response to TCDF and TCDD

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Fold Changea Direction of RegulationFunctional

CategoryUnigene Cluster

Entrez GeneID Gene Name Gene

TCDDb TCDFc TCDDb TCDFc

DREd

Mm.14089 13076 cytochrome P450,family 1, subfamily a1 Cyp1a1 38.35 65.27 ▲ ▲ Yes

Mm.344831 22235 UDP-glucose dehydrogenase Ugdh 3.06 3.46 ▲ ▲ Yes

Mm.252 18104 NADPH dehydrogenase, quinone 1 Nqo1 4.58 3.32 ▲ ▲ Yes

Mm.29415 68198 NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 2

Ndufb2 -4.24 -2.55 ▼ ▼ Yes

Mm.181473 66082 abhydrolase domain containing 6 Abhd6 2.84 3.00 ▲ ▲ Yes

Mm.299292 14870 glutathione-s-transferase, pi 2 Gstp1 2.51 3.45 ▲ ▲ Yes

Mm.172411 27366 thioredoxin-like 4 Txnl4 2.10 1.81 ▲ ▲ Yes

Mm.276389 15368 heme oxygenase (decycling) 1 Hmox1 2.02 1.72 ▲ ▲ Yes

Mm.14063 20148 dehydrogenase/reductase (SDR family) member 3

Dhrs3 2.00 1.92 ▲ ▲ Yes

Mm.4512 109857 carbonyl reductase 3 Cbr3 2.41 2.87 ▲ ▲ Yes

Mm.3879 15251 hypoxia inducible factor 1, alpha subunit Hif1a 1.57 1.82 ▲ ▲ Yes

Mm.197422 14857 glutathione S-transferase, alpha 1 (Ya) Gsta1 7.23 3.21 ▲ ▲ Yes

Mm.250901 13119 cytochrome P450, family 4, subfamily a, polypeptide 14

Cyp4a14 -4.00 -2.69 ▼ ▼ Yes

Mm.39750 381813 protein arginine N-methyltransferase 8 Prmt8 1.59 1.67 ▲ ▲ Yes

Metabolizing Enzymes

Mm.196045 68273 protein O-linked mannose beta1,2-N-acetylglucosaminyltransferase

Pomgnt1 2.80 2.45 ▲ ▲ Yes

Mm.272206 17165 MAP kinase-activated protein kinase 5 Mapkapk5 2.99 2.45 ▲ ▲ Yes

Mm.264645 319188 histone 1, H2bp Hist1h2bp -3.68 -3.04 ▼ ▼ Yes

Mm.4132 216233 suppressor of cytokine signaling 2 Socs2 -3.04 -3.68 ▼ ▼ Yes

Mm.298893 12995 casein kinase 2, alpha 1 polypeptide Csnk2a1 -2.26 -2.26 ▼ ▼ Yes

Mm.37516 67838 DnaJ (Hsp40) homolog, subfamily B, member 11

Dnajb11 1.80 1.62 ▲ ▲ Yes

Mm.261570 14886 general transcription factor II I Gtf2i 1.50 1.48 ▲ ▲ Yes

Mm.289818 67920 RNA binding motif protein 13 Rbm13 1.66 1.51 ▲ ▲ Yes

Mm.29389 209039 tensin like C1 domain-containing phosphatase

Tenc1 -2.03 -2.12 ▼ ▼ Yes

Mm.7328 64660 mitochondrial ribosomal protein S24 Mrps24 -5.00 -3.04 ▼ ▼ Yes

Transcription & Translation

Mm.215860 320632 activating signal cointegrator 1 complex subunit 3-like 1

Ascc3l1 1.80 1.56 ▲ ▲ Yes

Mm.392411 18128 Notch gene homolog 1 (Drosophila) Notch1 2.42 4.52 ▲ ▲ Yes

Mm.255332 21928 tumor necrosis factor, alpha-induced protein 2

Tnfaip2 5.52 16.17 ▲ ▲ Yes

Mm.1360 17873 growth arrest and DNA-damage-inducible 45 beta

Gadd45b 4.64 2.82 ▲ ▲ Yes

Mm.293771 108645 methionine adenosyltransferase II, beta Mat2b 2.56 2.62 ▲ ▲ Yes

Mm.297906 14369 frizzled homolog 7 (Drosophila) Fzd7 2.66 3.08 ▲ ▲ Yes

Mm.930 13039 cathepsin L Ctsl -2.19 -2.5 ▼ ▼ Yes

Mm.4512 109857 carbonyl reductase 3 Cbr3 2.41 2.86 ▲ ▲ Yes

Mm.237941 106369 yippee-like 1 (Drosophila) Ypel1 1.89 1.79 ▲ ▲ Yes

Mm.159684 21917 thymopoietin Tmpo 1.92 2.27 ▲ ▲ Yes

Mm.2661 21877 thymidine kinase 1 Tk1 -2.37 -2.41 ▼ ▼ Yes

Development & Differentiation

Mm.35691 116701 fibroblast growth factor receptor-like 1 Fgfrl1 -2.03 -2.50 ▼ ▼ Yes

Mm.296191 70025 acyl-CoA thioesterase 7 Acot7 2.55 2.73 ▲ ▲ Yes

Mm.4527 12500 CD3 antigen, delta polypeptide Cd3d -3.50 -3.04 ▼ ▼ Yes

Mm.290044 26458 solute carrier family 27 (fatty acid transporter) 2

Slc27a2 1.95 1.69 ▲ ▲ Yes

Mm.741 16592 fatty acid binding protein 5, epidermal Fabp5 3.91 1.98 ▲ ▲ Yes

Mm.18628 12491 CD36 antigen Cd36 3.41 2.46 ▲ ▲ Yes

Mm.268710 56360 acyl-CoA thioesterase 9 Acot9 2.12 3.00 ▲ ▲ Yes

Mm.41989 23801 arachidonate lipoxygenase 3 Aloxe3 -2.19 -2.50 ▼ ▼ Yes

Mm.259916 218865 choline dehydrogenase Chdh 1.64 1.51 ▲ ▲ Yes

Fatty Acid Uptake & Metabolism

Mm.261984 109801 glyoxalase 1 Glo1 2.36 2.05 ▲ ▲ Yes

Mm.220358 103988 glucokinase Gck -3.33 -4.00 ▼ ▼ Yes

Mm.22374 26384 glucosamine-6-phosphate deaminase 1 Gnpda1 -2.19 -2.03 ▼ ▼ Yes

Mm.19039 14718 glutamate oxaloacetae transaminase 1, soluble

Got1 -3.11 -3.68 ▼ ▼ Yes

Mm.266867 18534 phpsphoenolpyruvate carboxykinase 1, cytosolic

Pck1 -2.98 -3.50 ▼ ▼ Yes

Mm.374868 225913 dihydroxyacetone kinase 2 homolog (yeast)

Dak -3.33 -4.00 ▼ ▼ Yes

Mm.220982 26903 dysferlin Dysf 2.75 2.54 ▲ ▲ Yes

Mm.274852 104174 glycine decarboxylase Gldc -2.19 -2.55 ▼ ▼ Yes

Gluconeogenesis

Mm.13000 15109 histidine ammonia lyase Hal -3.04 -2.86 ▼ ▼ Yes

Apoptosis Mm.256858 56455 dynein light chain LC8-type 1 Dynll1 2.07 2.88 ▲ ▲ Yes

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Mm.12930 12029 B-cell CLL/lymphoma 6, member B Bcl6b 2.14 3.78 ▲ ▲ Yes

Mm.259702 11746 annexin A4 Anxa4 -3.04 -2.98 ▼ ▼ Yes

Mm.195663 12575 cyclin-dependent kinase inhibitor 1A (P21)

Cdkn1a 2.75 6.30 ▲ ▲ Yes

Mm.246520 12445 cyclin D3 Ccnd3 1.48 1.50 ▲ ▲ Yes

Mm.202358 16650 karyopherin (importin) alpha 6 Kpna6 3.96 6.01 ▲ ▲ Yes

Mm.389461 15168 hyperpolarization-activated, cyclic nucleotide-gated K+ 3

Hcn3 -3.78 -2.46 ▼ ▼ Yes

Mm.344708 207175 centrin 4 Cetn4 2.57 2.68 ▲ ▲ Yes

Mm.55812 212919 potassium channel tetramerisation domain containing 7

Kctd7 -2.26 -2.59 ▼ ▼ Yes

Mm.29194 74287 potassium channel modulatory factor 1 Kcmf1 -3.11 -2.80 ▼ ▼ Yes

Transport

Mm.4930 13340 solute carrier family 29 (nucleoside transporters), member 2

Slc29a2 2.51 2.15 ▲ ▲ Yes

Mm.254067 14961 histocompatibility 2, class II antigen A, beta 1

H2-Ab1 3.00 2.04 ▲ ▲ Yes

Mm.22564 14969 histocompatibility 2, class II antigen E, beta 1

H2-Eb1 3.34 3.51 ▲ ▲ Yes

Mm.235338 14960 histocompatibility 2, class II antigen A, alpha

H2-Aa 2.67 2.27 ▲ ▲ Yes

Mm.653 14962 complement factor B Cfb 1.92 1.84 ▲ ▲ Yes

Immune Response

Mm.218981 380928 LIM domain only 7 Lmo7 3.11 2.33 ▲ ▲ No

Mm.293314 20845 steroidogenic acute regulatory protein Star -2.69 -3.78 ▼ ▼ YesSteroid Hormone Metabolism Mm.28768 64292 prostaglandin E synthase Ptges 1.52 1.69 ▲ ▲ Yes

aMaximum significant fold change (|fold change| >1.4 and P1(t) > 0.999)b 30 µg/kg TCDDc 300 µg/kg TCDFd DRE identified within -1500 to +1500 of the transcriptional start site

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Figure 1. Time Course In Vivo Study Design. Female C57BL/6 mice (PND 25, n = 5) were administered a single oral dose of sesame oil (vehicle), 30 µg/kg TCDD or 300 µg/kg TCDF. Animals were sacrificed at the indicated time point and samples were collected for histopathology, tissue level, and gene expression analyses.

Figure 2. HRGC/HRMS Analysis of Hepatic TCDD and TCDF Tissue Levels.Hepatic tissue concentrations are expressed as parts per billion (ppb). Error bars represent the SEM for the average. N=3 animals per group. The asterisk (*) indicates a significant (p < 0.05) difference between the vehicle control and treatment groups based on a two- and one-way ANOVA test followed by Dunnett’s test. The symbol (a) indicates a significant (p < 0.05) difference between TEF-adjusted values for TCDD and TCDF treatment groups based on a two-and one-way ANOVA test followed by Dunnett’s test. Absolute values are provided in Supplementary Table 4.

Figure 3. Identification and Comparison of TCDD and TCDF Elicited Gene Expression Responses. A. Identification of differentially expressed features at each time point relative to time-matched vehicle controls (P1(t)>0.999 and Іfold changeІ >1.4). B. 208 and 195 unique differentially expressed genes at one or more time points were identified for TCDD and TCDF, respectively. 116 genes of these genes were differentially regulated by both TCDD and TCDF.

Figure 4. Comparison of TCDD and TCDF Temporal Gene Expression Data. A. Hierarchical clustering of the 116 differentially expressed genes differentially regulated by both TCDD and TCDF. This depiction provides a more qualitative assessment of the similarities of the gene expression of the 116 differentially expressed genes. B. Correlation analysis of temporal gene expression and fold change significance value. It provides a more quantitative examination of the similarities in the expression patterns of the 116 differentially expressed genes by determining the Pearson’s correlation between the temporal gene expression (fold change) and significance (P1(t)) profiles of the 116 genes differentially regulated by both ligands. TCDD and TCDF paired data are plotted on a coordinate axis with the x-axis as the gene expression correlation and the y-axis as the significance correlation. A majority of the elicited responses fall into upper right quadrant identifying responses that are highly correlated between both gene expression and significance. There are also some genes that fall into lower right quadrant which represents genes with high correlation of expression but a lower correlation for significance.

Figure 5. Temporal Analysis of TCDD and TCDF Gene Expression Profiles. A. Trajectory analysis of the TCDF (blue line)- and TCDD (red line)-mediated temporal gene expression illustrates convergence only at early time-points (2 – 24 hrs; the first two principal components are visualized, and represent 62% of the variance). B. A scatter plot of TCDD (red line) and TCDF (blue line) elicited fold changes in gene expression at early time points (2 – 24 hrs). The slope approaches 1.0 suggesting a strong similarity in TCDD- and TCDF-elicited gene expression patterns at early time points. C. A scatter plot of TCDD (red line) and TCDF (blue line) elicited fold changes in gene expression at later time points (72 – 168 hrs). The slope of 0.52 indicates a divergence in gene expression patterns consistent with decreasing hepatic TCDF levels.

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Figure 6. QRTPCR Verification of Microarray Gene Expression Responses. The same RNA used for cDNA microarray analysis was examined by QRTPCR. All fold changes were calculated relative to time-matched vehicle controls. Bars (left axis) and lines (right axis) represent data obtained by QRTPCR and cDNA microarray data, respectively. The x-axis represents the time points. Genes are represented by official gene symbols and the QRTPCR results are the average of four TCDD and five TCDF biological replicates. Error bars represent the SEM for the average fold change. The asterisk (*) represent a significant (p<0.05) difference relative to time-matched controls for QRTPCR.

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Figure 1. Time Course In Vivo Study Design. Female C57BL/6 mice (PND 25, n = 5) were administered a single oral dose of sesame oil (vehicle), 30 µg/kg TCDD or 300 µg/kg

TCDF. Animals were sacrificed at the indicated time point and samples were collected for histopathology, tissue level, and gene expression analyses.

276x78mm (600 x 600 DPI)

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Figure 2. HRGC/HRMS Analysis of Hepatic TCDD and TCDF Tissue Levels. Hepatic tissue concentrations are expressed as parts per billion (ppb). Error bars represent the SEM for the average. N=3 animals per group. The asterisk (*) indicates a significant (p < 0.05) difference between the vehicle control and treatment groups based on a two- and one-way ANOVA test followed by Dunnett's test. The symbol (a) indicates a significant (p <

0.05) difference between TEF-adjusted values for TCDD and TCDF treatment groups based on a two- and one-way ANOVA test followed by Dunnett's test. Absolute values are

provided in Supplementary Table 4. 162x78mm (600 x 600 DPI)

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Figure 3. Identification and Comparison of TCDD and TCDF Elicited Gene Expression Responses. A. Identification of differentially expressed features at each time point

relative to time-matched vehicle controls (P1(t)>0.999 and Іfold changeІ >1.4). B. 208 and 195 unique differentially expressed genes at one or more time points were identified for TCDD and TCDF, respectively. 116 genes of these genes were differentially regulated

by both TCDD and TCDF. 148x156mm (600 x 600 DPI)

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Figure 4. Comparison of TCDD and TCDF Temporal Gene Expression Data. A. Hierarchical clustering of the 116 differentially expressed genes differentially regulated by both TCDD and TCDF. This depiction provides a more qualitative assessment of the similarities of the

gene expression of the 116 differentially expressed genes. B. Correlation analysis of temporal gene expression and fold change significance value. It provides a more quantitative examination of the similarities in the expression patterns of the 116

differentially expressed genes by determining the Pearson's correlation between the temporal gene expression (fold change) and significance (P1(t)) profiles of the 116 genes

differentially regulated by both ligands. TCDD and TCDF paired data are plotted on a coordinate axis with the x-axis as the gene expression correlation and the y-axis as the

significance correlation. A majority of the elicited responses fall into upper right quadrant identifying responses that are highly correlated between both gene expression and

significance. There are also some genes that fall into lower right quadrant which represents genes with high correlation of expression but a lower correlation for

significance. 230x125mm (600 x 600 DPI)

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Figure 5. Temporal Analysis of TCDD and TCDF Gene Expression Profiles. A. Trajectory analysis of the TCDF (blue line)- and TCDD (red line)-mediated temporal gene expression

illustrates convergence only at early time-points (2 � 24 hrs; the first two principal components are visualized, and represent 62% of the variance). B. A scatterplot of TCDD

(red line) and TCDF (blue line) elicited fold changes in gene expression at early time points (2 � 24 hrs). The slope approaches 1.0 suggesting a strong similarity in TCDD- and TCDF-elicited gene expression patterns at early time points. C. A scatterplot of TCDD (red line) and TCDF (blue line) elicited fold changes in gene expression at later time points (72

� 168 hrs). The slope of 0.52 indicates a divergence in gene expression patterns consistent with decreasing hepatic TCDF levels.

181x170mm (600 x 600 DPI)

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Figure 6. QRTPCR Verification of Microarray Gene Expression Responses. The same RNA used for cDNA microarray analysis was examined by QRTPCR. All fold changes were

calculated relative to time-matched vehicle controls. Bars (left axis) and lines (right axis)represent data obtained by QRTPCR and cDNA microarray data, respectively. The x-axis

represents the time points. Genes are represented by official gene symbols and the QRTPCR results are the average of four TCDD and five TCDF biological replicates. Error

bars represent the SEM for the average fold change. The asterisk (*) represent a significant (p<0.05) difference relative to time-matched controls for QRTPCR.

218x113mm (600 x 600 DPI)

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