HEPATIC TOXICITY AND POLYCHLORINATED BIPHENYLS:
TOXICOGENOMIC EXAMINATION OF SINGLE CONGENER
AND MIXTURE EFFECTS IN C57BL/6 MICE
By
Anna Katarzyna Kopec

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
Biochemistry and Molecular Biology - Environmental Toxicology
2010

ABSTRACT

HEPATIC TOXICITY AND POLYCHLORINATED BIPHENYLS:
TOXICOGENOMIC EXAMINATION OF SINGLE CONGENER
AND MIXTURE EFFECTS IN C57BL/6 MICE
By
Anna Katarzyna Kopec

Human exposures to persistent organic pollutants rarely occur in isolation but rather as
complex mixtures. Interactions between environmental contaminants, such as 2,3,7,8tetrachlorodibenzo-p-dioxin (TCDD) and polychlorinated biphenyls (PCBs), can produce
additive, synergistic or antagonistic interactions, which may influence risk assessment.
Therefore, scientifically based risk and health hazard evaluation requires toxicological data from
chemical mixture studies, in addition to traditional studies involving single chemical exposure.
Dioxins and PCBs are ubiquitous toxicants that persist in the environment due to their
lipophilicity and propensity to bioaccumulate in biological tissues. These chemicals elicit tissueand species-specific effects including hepatotoxicity, immune suppression, endocrine disruption,
and carcinogenicity. Structural similarity to the most toxic coplanar dioxin, TCDD, determines
the dioxin-like effects of individual PCBs and the spectrum of aryl hydrocarbon receptor (AhR)mediated biochemical and toxic responses. In contrast, non-coplanar PCB congeners are more
abundant in the environment and elicit responses that are unique and non-AhR-mediated. The
main objective of this research was to evaluate how mixture interactions between these

structurally different, dioxin and non-dioxin-like chemicals affect the gene expression responses
underlying the hepatic toxicity in the immature C57BL/6 mouse model.
Comprehensive time course and dose-response hepatic gene expression analyses were
performed for dioxin-like 3,3’,4,4’,5-pentachlorobiphenyl (PCB126) and non-dioxin-like
2,2’,4,4’,5,5’-hexachlorobiphenyl (PCB153) relative to TCDD, and differentially expressed
genes were phenotypically anchored to changes in physiological endpoints to establish a
quantitative baseline of toxic responses. Assessment of the toxic effects exerted by a mixture of
TCDD and PCB153 was performed using a 1 to 10,000 ratio, respectively, to reflect relative
environmental concentrations of each of the chemicals and to compare obtained results to
previous literature reports examining non-additive AhR interactions.
In summary, the single chemical studies demonstrated that each compound elicited a
complex and unique temporal and dose-dependent gene expression profile that could be linked to
the physiological outcomes. In the mixture studies, microarray profiling and statistical doseresponse modeling identified a small subset of non-additive, synergistically induced gene
expression responses, which were consistent with effects on relative liver weights,
histopathology, hepatocellular lipid accumulation and tissue level pharmacokinetics.

To my family

iv

ACKNOWLEDGMENTS
It is my great pleasure and honor to thank all those who supported me throughout the
doctoral program at Michigan State University. Without you, I could not have completed this
journey.
I first wish to thank my advisor Dr. Tim Zacharewski for accepting me into his laboratory
as an undergraduate student, and later as a graduate assistant. I am very thankful for his patience,
faith in my abilities and all the extraordinary opportunities he provided that allowed me to
develop scientifically and professionally. I could not have asked for a better mentor. I also wish
to extend my gratitude to Jana Zacharewski, the warmest and sweetest person who always cared
for all the Zacharewski lab students with the greatest passion and love. Thank you for always
looking out for me.
I would like to express my gratitude to my Committee Members, Drs. Dave Dewitt, Eric
Hegg, Norb Kaminski and John Wang for your guidance, support and insightful comments
during our meetings that provided me with a broader perspective on my research.
I also wish to acknowledge the Department of Biochemistry & Molecular Biology and
the Environmental and Integrative Toxicological Science program at the Center for Integrative
Toxicology for the invaluable training and funding that facilitated my travel to scientific
meetings. I extend special thanks to Amy Swagart, Carol Chvojka, Teresa Vollmer, Jessica
Lawrence and Lesley Reed for their fantastic support during my graduate years.
Next, I wish to think all the members of the Zacharewski lab for their friendship, advice
and support. I owe my deepest gratitude to Darrell Boverhof who introduced me to the area of
toxicogenomics, provided answers to every single question and was the best project supervisor
v

every new student could ever wish for. I would also like to thank Lyle Burgoon who gave me an
invaluable insight into the bioinformatic world and offered priceless management advice. Next, I
extend my gratitude to Josh Kwekel, the “cave king” for his support, patience and a great ear to
listen. I am extremely grateful to Qi Ding and Suntae Kim for all the conversions on life and
culture inside and outside of science. Without Ed Dere (the Kuler Color Guru), I would have
never gained the appreciation for color and finesse for graphical representation of the data. I
would also like to thank Michelle Angrish and Agnes Forgacs for their amazing friendship,
shopping, gym trips, and endless conversations and for always having my back! Big thanks to
the past lab members, including Bryan Mets, May Tan, Naisha Shah, Sharon Dubois, Andrea
Lee, and Michelle (Little Michelle) D’Souza for their invaluable support in and outside of the
lab.
I would also like to give a shout-out to my loving Polish friends, especially Marta
Szumko, Magda Kedzierska, Dominika Bijos, Jakub Olszowiec, Anna Behrendt, and Michal
Migalski. Thank you for your friendship, constant support, advice and for always being there for
me when I needed you. Also, I cannot forget about my darling friend from Michigan and a
fellow graduate student – Dorothy Tappenden – thank you for making me feel like a part of your
family, you and your girls will always be dear to my heart. Dr. Matthews thank you for all the
crazy love and support!
Finally, I wish to thank my family for their love and support, my mom Elzbieta, my dad
Stanislaw, my sweet little brother Mateusz and my grandparents. Thank you for pushing me in
the right direction and always supporting my choices. If it was not for you, I would not be here. I
hope to repay you one day for everything you have taught me.

vi

TABLE OF CONTENTS
LIST OF TABLES ....................................................................................................................... ix
LIST OF FIGURES ..................................................................................................................... xi
LIST OF ABBREVIATIONS ................................................................................................... xiv
CHAPTER 1
Review of the Literature: Persistent Organic Pollutants, Human Exposure, Single
Congener and Mixture Toxicity .............................................................................................. 1
Introduction ............................................................................................................................. 2
Polychlorinated Biphenyls and Dioxins: Production, Use and Human Exposure .................. 3
Dioxin-Like PCBs: Toxicity and Mode of Action.................................................................. 6
Toxic Equivalency Factors ................................................................................................... 11
Non-Dioxin-Like PCBs: Toxicity and Mode of Action ....................................................... 13
Mixture Effects ..................................................................................................................... 16
Conclusions ........................................................................................................................... 17
References ............................................................................................................................. 19
CHAPTER 2
Rationale, Hypothesis and Specific Aims.............................................................................. 29
Rationale ............................................................................................................................... 30
Hypothesis............................................................................................................................. 30
Specific Aims ........................................................................................................................ 31
CHAPTER 3
Comparative Toxicogenomic Examination of the Hepatic Effects of PCB126 and TCDD
in Immature, Ovariectomized C57BL/6 Mice ...................................................................... 32
Abstract ................................................................................................................................. 33
Introduction ........................................................................................................................... 34
Materials & Methods ............................................................................................................ 36
Results ................................................................................................................................... 43
Discussion ............................................................................................................................. 66
References ............................................................................................................................. 75
CHAPTER 4
Automated Dose-Response Analysis and Comparative Toxicogenomic Evaluation of the
Hepatic Effects Elicited by TCDD, TCDF, and PCB126 in C57BL/6 Mice ...................... 80
Abstract ................................................................................................................................. 81
Introduction ........................................................................................................................... 82
Materials & Methods ............................................................................................................ 84
Results ................................................................................................................................... 91
Discussion ........................................................................................................................... 106
vii

References ........................................................................................................................... 115
CHAPTER 5
PCB153-Elicited Hepatic Responses in the Immature, Ovariectomized C57BL/6 Mice:
Comparative Toxicogenomic Effects of Dioxin and Non-Dioxin-Like Ligands .............. 122
Abstract ............................................................................................................................... 122
Introduction ......................................................................................................................... 123
Materials & Methods .......................................................................................................... 124
Results ................................................................................................................................. 133
Discussion ........................................................................................................................... 155
References ........................................................................................................................... 158
CHAPTER 6
Non-Additive Hepatic Gene Expression Effects Elicited by 2,3,7,8-Tetrachlorodibenzo-pdioxin and 2,2’,4,4’,5,5’-Hexachlorobiphenyl in C57BL/6 Mice ...................................... 165
Abstract ............................................................................................................................... 165
Introduction ......................................................................................................................... 166
Materials & Methods .......................................................................................................... 169
Results ................................................................................................................................. 177
Discussion ........................................................................................................................... 203
References ........................................................................................................................... 209
CHAPTER 7
Conclusions and Future Research ....................................................................................... 219
Considerations for Cross-Species Comparisons ................................................................. 220
Toxicogenomic Evaluation of Non-Additive Gene Expression ......................................... 221
In Vitro Considerations for Mixture Evaluation ................................................................. 222
References ........................................................................................................................... 223

viii

LIST OF TABLES
Table 1.
Summary of WHO 2005 TEF values. ........................................................................................... 12
Table 2.
Temporal effects of 300 µg/kg PCB126 or 30 µg/kg TCDD on terminal body, whole liver, and
RLW. ............................................................................................................................................. 45
Table 3.
Dose-dependent effects of PCB126 on terminal body, whole liver, and RLW at 72 h. ............... 46
Table 4.
Comparison of temporal incidence and severity of liver microscopic changes in PCB126 and
TCDD experiments [17]. .............................................................................................................. 51
Table 5.
Temporal incidence and severity of liver microscopic changes in the vehicle-, PCB126- and
TCDD-treated mice. ...................................................................................................................... 52
Table 6.
Dose-dependent incidence and severity of liver microscopic changes in the vehicle-, PCB126and TCDD-treated mice at 72 h. ................................................................................................... 53
Table 7.
Functional categorization and regulation of select hepatic genes identified as differentially
regulated in response to PCB126 and TCDD [17]........................................................................ 60
Table 8.
The 24 h dose-response study design............................................................................................ 86
Table 9.
Absolute hepatic tissue levels (in pg/g or ppt per wet weight) for TCDD-, PCB126-, and TCDFtreated samples and vehicle controls measured by HRGC-HRMS. ............................................. 94
Table 10.
Dose-dependent incidence and severity of hepatic histopathological responses in the vehicle,
TCDD, PCB126 and TCDF treated mice at 24 h.......................................................................... 96
Table 11.
Temporal effects of 300 mg/kg PCB153 and 30 µg/kg TCDD on terminal body, body weight
gain, absolute liver weight, and relative liver weight. ................................................................ 134

ix

Table 12.
Dose-dependent effects of PCB153 on terminal body, body weight gain, absolute liver weight
and relative liver weight at 24 h. ................................................................................................. 135
Table 13.
Functional categorization and regulation of select hepatic genes identified as differentially
regulated in response to 300 mg/kg PCB153 and 30 µg/kg TCDD [19]. ................................... 142
Table 14.
GC-MS analysis of fatty acid composition. ................................................................................ 184
Table 15.
Functional categorization and potential response element regulation of select hepatic genes
differentially expressed by 30 µg/kg TCDD, 300 mg/kg PCB153, and MIX. ........................... 190

x

LIST OF FIGURES
Figure 1.
Structural comparisons between dioxin and non-dioxin-like chemicals. ....................................... 7
Figure 2.
Aryl hydrocarbon receptor-mediated signaling. ............................................................................. 9
Figure 3.
Constitutive androstane/pregnane X receptor-mediated signaling. .............................................. 15
Figure 4.
Hepatic PCB126 and TCDD levels............................................................................................... 47
Figure 5.
Representative histopathology results from vehicle, PCB126 and TCDD treated mice at 72 h. . 50
Figure 6.
Thin layer chromatograph of hepatic lipid extracts from vehicle-, PCB126-, and TCDD- treated
samples in the time course study. ................................................................................................. 55
Figure 7.
Number of PCB126 differentially expressed genes. ..................................................................... 57
Figure 8.
Agglomerative hierarchical clustering of PCB126 gene expression data. .................................... 58
Figure 9.
Gene expression comparisons and correlation analysis. ............................................................... 64
Figure 10.
QRTPCR verification of microarray responses. ........................................................................... 67
Figure 11.
Dose-dependent changes in the RLWs. ........................................................................................ 92
Figure 12.
Hepatic tissue level quantification. ............................................................................................... 93
Figure 13.
Representative hematoxylin and eosin stained liver sections 24 h post exposure to PCB126,
TCDF, or TCDD. .......................................................................................................................... 97

xi

Figure 14.
EROD activity............................................................................................................................... 99
Figure 15.
Comprehensive TCDD, TCDF, and PCB126 dose-response analysis. ...................................... 101
Figure 16.
Three-way Venn analysis. ........................................................................................................... 105
Figure 17.
QRTPCR verification of microarray responses. ......................................................................... 108
Figure 18.
Hepatic PCB153 and TCDD levels per g liver wet weight measured using HRGC-HRMS. ..... 136
Figure 19.
Representative histopathology results from vehicle, 30 µg/kg TCDD and 300 mg/kg PCB153treated mice at 168 h. .................................................................................................................. 138
Figure 20.
GC-MS lipid profile analysis at 24, 72 and 168 h. ..................................................................... 139
Figure 21.
Fatty acid methyl ester (FAME) GC-MS analysis of PCB153- and TCDD-treated mouse livers at
168 h............................................................................................................................................ 141
Figure 22.
QRTPCR verification of selected PCB153-induced genes......................................................... 147
Figure 23.
QRTPCR verification of selected TCDD-regulated genes. ........................................................ 149
Figure 24.
Dose-response modeling. ............................................................................................................ 151
Figure 25.
Gene expression comparisons. .................................................................................................... 153
Figure 26.
Temporal and dose-dependent changes in the relative liver weights following exposure to
TCDD, PCB153, and MIX.......................................................................................................... 178
Figure 27.
Tissue level quantification. ......................................................................................................... 180

xii

Figure 28.
Representative histopathology results......................................................................................... 181
Figure 29.
Principal component analysis of lipid profiles............................................................................ 183
Figure 30.
GC-MS fatty acid profiles. .......................................................................................................... 185
Figure 31.
Three-way Venn analysis. ........................................................................................................... 188
Figure 32.
QRTPCR verification of AhR, CAR, and PXR-regulated genes................................................ 196
Figure 33.
Temporal and dose-dependent QRTPCR verification of putative non-additive genes............... 198
Figure 34.
Dose-response non-linear logistic modeling of the dose-response QRTPCR results from Figure
33................................................................................................................................................. 201

xiii

LIST OF ABBREVIATIONS
1,2,3,4-TCDD

1,2,3,4-Tetrachlorodibenzo-p-dioxin

1,2,3,4-TCDF

1,2,3,4-Tetrachlorodibenzofuran

AhR

Aryl hydrocarbon receptor

ALT

Alanine aminotransferase

ANOVA

Analysis of variance

ARNT

Aryl hydrocarbon receptor nuclear translocator

bHLH

Basic-helix-loop-helix

BHT

2,6-di-tert-butyl-4-methylphenol

BW

Body weight

CAR

Constitutive androstane receptor

CARE

Constitutive androstane receptor response element

cDNA

Complementary deoxyribonucleic acid

CHOL

Cholesterol

CV

Central vein

dNTP

Deoxynucleotide triphosphate

DRE

Dioxin response element

DTT

Dithiothreitol

ED50

Effective dose 50

EDTA

Ethylene-di-amine-tetra-acetic acid

EROD

Ethoxyresorufin-O-deethylase

xiv

FAME

Fatty acid methyl ester

GC-MS

Gas chromatography-mass spectrometry

GLU

Glucose

HEPES

4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid

HRGC-HRMS

High resolution gas chromatography-high resolution mass spectrometry

IU

International unit

Min

Minutes

MSS

Matrix similarity score

MUFA

Monounsaturated fatty acid

N/A

Not available

NC

No change

NADPH

Nicotinamide adenine dinucleotide phosphate

NEFA

Non-esterified fatty acids

NTP

National Toxicology program

ORO

Oil red O

PAS

Per-Arnt-Sim

PCA

Principal component analysis

PCAH

Polycyclic aromatic hydrocarbon

PCB

Polychlorinated biphenyl

PCB111

2,3,3’,5,5’-Pentachlorobiphenyl

PCB126

3,3',4,4',5-Pentachlorobiphenyl

PCB153

2,2',4,4',5,5'-Hexachlorobiphenyl

PCDD

Polychlorinated dibenzo-p-dioxin

xv

PCDF

Polychlorinated dibenzofurans

PND

Postnatal day

POP

Persistent organic pollutant

PPT

Parts per trillion

PUFA

Polyunsaturated fatty acid

PV

Portal vein

PWM

Position weight matrix

PXR

Pregnane X receptor

PXRE

Pregnane X receptor response element

QRTPCR

Quantitative real time polymerase chain reaction

REP

Relative potency

RLW

Relative liver weight

RNA

Ribonucleic acid

RT

Room temperature

RXR

Retinoid X receptor

SD

Standard deviation

SE

Standard error

Sec

Seconds

SFA

Saturated fatty acid

T

2,3,7,8-Tetrachlorodibenzo-p-dioxin

TCDD

2,3,7,8-Tetrachlorodibenzo-p-dioxin

TCDF

2,3,7,8-Tetrachlorodibenzofuran

TEF

Toxic equivalency factor

xvi

TEQ

Toxic equivalents

TG

Triglycerides

TLC

Thin layer chromatography

TSS

Transcriptional start site

UTR

Untranslated region

V

Vehicle

VEH

Vehicle

WHO

World Health Organization

xvii

CHAPTER 1

1

CHAPTER 1
REVIEW OF THE LITERATURE: PERSISTENT ORGANIC POLLUTANTS,
HUMAN EXPOSURE, SINGLE CONGENER AND MIXTURE TOXICITY
INTRODUCTION
Persistent organic pollutants (POPs) have become widespread environmental
contaminants and currently represent a global toxicological threat. The Stockholm Convention
coordinated by the United Nations Environment Programme has been convened to protect human
health and the environment from POPs [1]. In implementing the Convention, over 150
participating countries (including Poland and the United States) are required to take appropriate
measures to eliminate or significantly reduce the production and use of these persistent
chemicals. More specifically, the Convention identified an initial list of 12 POPs (the so called
“dirty dozen”), including polychlorinated biphenyls (PCBs), polychlorinated dibenzo-p-dioxins
(PCDDs), and polychlorinated dibenzofurans (PCDFs) as a significant hazard to human and
wildlife health on a global scale [2, 3]. The toxicity and environmental persistence of POPs is
enhanced by their lipophilic properties and capability of long-range, transboundary atmospheric
transport and deposition. POPs are resistant to degradation through chemical and biological
processes, allowing them to bioaccumulate in the fatty tissues of animals and humans and
biomagnify through the food chain, having potentially detrimental impact on the environment
and human health [4].

2

Human exposure to POPs, and potential interactions between the individual chemicals,
can significantly influence the spectrum of elicited toxic responses, including effects on the liver,
as one of the major target organs of PCB and PCDD toxicity. Comprehensive transcriptomic and
phenotypic level examination of hepatic effects elicited by individual chemicals and their
mixtures using an in vivo model can significantly expand current understanding of the toxic
mode of action and may have major implications for human risk assessment.

POLYCHLORINATED BIPHENYLS AND DIOXINS: PRODUCTION, USE AND HUMAN
EXPOSURE
PCBs represent a class of oily liquid or solid man-made chemicals that are colorless to
light yellow, have no smell or taste, and do not naturally occur in the environment. PCBs were
commonly found as commercial Aroclor mixtures of individual chlorinated biphenyl components
(congeners) with varying number and position of chlorine substituents [5]. PCBs were
manufactured for use predominantly as coolants, lubricants, insulating fluids for transformers
and capacitors, lighting ballasts, surface coatings (paints, carbonless copy paper, flame
retardants), inks and plasticizers (gasket sealers, caulking), due to their general chemical
inertness and thermal stability [6, 7]. In the United States the manufacture of PCBs continued for
about 50 years until it was banned in 1977, however in Poland, PCBs were still produced until
1981 [8-10]. Unlike the deliberate manufacture and use of PCBs, the continuous release of
PCDDs and PCDFs into the environment occurs as by-products of municipal waste and sewage
sludge incineration, herbicide production, pulp and paper bleaching, and other industrial
processes [11-14].
Industrial regulations have led to a significant decrease in PCB and dioxin levels over the
years [15, 16]. However, these POPs still enter the environment from poorly maintained
3

hazardous waste sites, accidental spills and leaks, and from illegal or improper disposal of
wastes, where incomplete combustion or heating of PCBs can lead to formation of PCDFs [9,
17-19]. Exposure can also occur from ambient inhalation of lighter congeners present in the
atmosphere or from natural weathering of the caulking used around windows and expansion
joints resulting in elevated levels of PCBs in the soil surrounding the deteriorating buildings [2023]. In addition, backyard barrel burning of household waste has risen dramatically over the
years, becoming the primary source of dioxin emissions [16]. For the general population,
however, the main source of PCB, PCDD and PCDF exposures continues to be the consumption
of contaminated food, such as fish, meat and dairy products [8, 9]. Breast-fed infants of mothers
who have diets high in contaminated fish are at particularly high risk due to significant PCB and
dioxin accumulation via lactational transfer [24-26].
Direct exposure to PCBs has been well documented in the two large-scale food poisoning
cases, in Japan (1968) and Taiwan (1979), where a PCB mixture used as a heat-transfer medium
leaked into rice oil during the manufacturing process [27-30]. It was later found that the cooking
oil was also contaminated with PCDFs [31]. The illnesses, commonly referred to as “Yusho” (oil
disease in Japanese) and “Yu-Cheng” (oil disease in Chinese), were characterized by a syndrome
of persistent events presented as general malaise, eye discharge, neuropathy, hyperpigmentation,
severe chloracne, as well as hepatic toxicity, including elevated levels of serum alanine
aminotransferase, indicative of liver damage [27, 30]. Follow-up reports indicated a 2.7-fold
increase in mortality due to chronic liver disease and cirrhosis compared to the general
population [32].
Of more recent and local relevance is contamination of the Great Lakes and the Saginaw
and Tittabawassee rivers that have been significantly impacted by historical industrial activities

4

[33]. Recent studies reported that consumption of fish from Lake Michigan correlated with PCB
levels detected in human serum and maternal milk [34]. In addition, although PCB, PCDD and
PCDFs concentrations in several Lake Michigan fish have declined, reproductive effects have
persisted in aquatic birds, representing an ongoing environmental concern [35].
Human exposure to one of the most toxic PCDDs, 2,3,7,8-tetrachlorodibenzo-p-dioxin
(TCDD), was reported after an accidental contamination of Agent Orange used as a herbicide
and defoliant during the Vietnam War [36, 37]. It is estimated that between 1961 and 1971,
approximately 360 kg of dioxin was sprayed multiple times over 2.6 million acres, resulting in
an estimated 4.8 million exposed or potentially exposed Vietnam residents and war veterans
[36]. Similar mass dioxin poisoning occurred in Seveso, Italy (1976), where a residential
population was exposed to high levels of TCDD as a result of an industrial accident in a
chemical plant manufacturing pesticides and herbicides [38]. The most recent and well known
case of TCDD exposure in humans is that of Victor Yushchenko, a candidate for the presidential
election in Ukraine, poisoned with dioxin in 2004. Analysis of Yushchenko’s blood revealed
108,000 pg/g lipid weight of TCDD, exceeding background dioxin levels in general population
by at least 50,000 fold [39]. The documented acute and long term toxic effects following human
exposure to TCDD include chloracne, general fatigue and weakness, nausea, hepatic toxicity,
neurotoxicity, lymphoid atrophy, birth defects, miscarriages, type 2 diabetes and increased
incidences of cancer [37, 38, 40-44].
The significant overlap of the endocrine-disrupting and toxic effects observed in response
to PCBs and dioxins further emphasizes that human exposure to environmental POPs rarely
occurs in isolation and that some chemicals contribute to the observed toxicity more than others.

5

Structural similarity to the most toxic dioxin, TCDD (Figure 1A-D), has been used as a criterion
for toxicity and mode of action classification for PCBs and other POPs and is discussed below.

DIOXIN-LIKE PCBS: TOXICITY AND MODE OF ACTION
PCBs represent a family of theoretically 209 possible congeners, consisting of two
benzene rings with up to ten possible chlorine substitutions [5]. Commercial PCB mixtures
typically consist of a smaller number of congeners, but only about half are considered
environmentally relevant [9, 45]. Naturally occurring PCB mixtures often do not match any of
the known commercial formulations, because they have been subjected to chemical and
biological transformation, including reductive dechlorination by anaerobic bacteria [46-48]. In
this process, chlorine is removed from the biphenyl ring and replaced by hydrogen, resulting in a
product mixture with diminished number of chlorines. Chlorines substituted in the meta and para
positions are preferentially removed by this process, but ortho chlorines are rarely removed [47].
Reductive dechlorination also helps to convert highly chlorinated (and highly persistent)
congeners into forms that are more amenable to aerobic degradation [49].
PCB congeners’ potencies for several distinct toxic effects vary according to structures
and chlorine substitution on the biphenyl rings. PCBs with chlorine substituents at the meta and
para positions are the most toxic because structurally they resemble TCDD, assuming a coplanar
configuration. Hence, the coplanar PCBs are termed dioxin-like and elicit a spectrum of TCDDlike toxic responses, including dermal toxicity, tumor promotion, immune suppression and
hepatotoxicity [50]. 3,3’,4,4’,5-Pentachlorobiphenyl (PCB126) is the most potent PCB congener
and accounts for 40-60% of the total toxic potency of all dioxin-like PCBs [17].

6

Figure 1. Structural comparisons between dioxin and non-dioxin-like chemicals.
For interpretation of the references to color in this and all other figures, the reader is referred to the electronic version of this
dissertation. (A) TCDD and (B) TCDF assume coplanar conformation. (C) PCB126 has chlorine substitutions in meta (3,5,5’) and
para (4,4’) positions and also assumes a coplanar configuration. (D) PCB153 has two ortho (2,2’) substitutions resulting in steric
hindrance and repulsion between the rings leading to its non-coplanar conformation.

7

To date, numerous studies have suggested that many, if not all, of the toxic effects
elicited by TCDD, PCB126 and other dioxin-like chemicals are mediated by the aryl
hydrocarbon receptor (AhR), a ligand-dependent basic-helix-loop-helix PER-ARNT-SIM
(bHLH-PAS) domain transcription factor (Figure 2) [19, 51, 52]. Multiple studies have
demonstrated that the AhR is present in multiple tissues and different species and that it shares
functional and structural characteristics with members of the nuclear receptor superfamily.
However, the primary structure of the AhR and the lack of the zinc-finger domain, typical of
steroid receptors, prevented its classification into the nuclear receptor family [53, 54].
The AhR resides in the cytoplasm complexed with a dimer of heat shock proteins 90
(Hsp90), aryl hydrocarbon receptor interacting protein (AIP also known as ARA9 or XAP2) and
prostaglandin E synthase 3 (p23) [55]. Upon binding of a dioxin-like ligand, the chaperone
proteins dissociate and the AhR translocates to the nucleus and heterodimerizes with the AhR
nuclear translocator (ARNT), another member of the bHLH-PAS family of transcription factors.
The activated AhR-ARNT heterodimer binds to the 5’-GCGTG-3’ core sequences termed dioxin
response elements (DREs) in the regulatory regions of target genes to drive transcriptional
events, which ultimately lead to observed toxic and biochemical responses [56, 57]. Some of the
well-characterized AhR target genes belonging to the “AhR gene battery” include phase I and II
enzyme families involved in xenobiotic metabolism, principally Cyp1a1, Cyp1a2, Tiparp, Nqo1,
Aldh3a1 and Ugt1a6 [58, 59]. In addition to classic AhR ligands like TCDD or dioxin-like
chemicals, the AhR is also known to be activated by a wide range of natural and synthetic
compounds, including indolo-[3,2-b]-carbazole, UV photoproducts of tryptophan, bilirubin, and
flavonols such as quercetin [60, 61].

8

Figure 2. Aryl hydrocarbon receptor-mediated signaling.
In the absence of the ligand, the aryl hydrocarbon receptor (AhR) is bound by chaperone proteins (Hps90 dimer, ARA9 and p23).
Following binding of the coplanar ligand (e.g. TCDD, PCB126 or TCDF), the chaperone proteins dissociate and the ligand-bound
receptor translocates to the nucleus where it heterodimerizes with the AhR nuclear translocator (ARNT). The AhR/ARNT heterodimer
binds specific DNA response elements called dioxin response elements (DREs) in the promoter of target genes, leading to changes in
gene expression.

9

Multiple reports have demonstrated the obligatory involvement of the AhR in mediating
dioxin-like toxicity. For example, mice with low-affinity AhR alleles are less susceptible to
TCDD’s toxic effects, while complete lack of the AhR abolishes inducible expression of Cyp1a1
and Cyp1a2 and leads to an overall resistance to toxic and teratogenic effects of TCDD [62-65].
The AhR-null mice, however, exhibit significantly reduced growth rates in the first three weeks
of life, and also reveal defects in liver development and the immune system compared to wild
type animals [63, 66]. It has also been reported that AhR-null female mice have difficulty
maintaining conception, lactation and rearing pups to weaning [67].
Data from a significant number of reports clearly illustrate the necessary requirement for
the AhR signaling in mediating toxic effects of TCDD and dioxin-like compounds. Few reports,
however, comment on the involvement of an alternative pathway, which does not involve the
AhR or ARNT. For example, the inflammatory action of TCDD has been proposed to act
through a rapid increase in intracellular calcium concentration, enzymatic activation of
phospholipase A2 (cPLA2) and Cox-2, as artificial suppression of these enzymes leads to a
significant reduction of TCDD-elicited wasting syndrome and hydronephrosis [68-70]. In
addition, AhR-null mice treated with 2000 µg/kg TCDD revealed occasional hepatocyte necrosis
with moderate portal fibrosis, blood vessel proliferation and mild oval cell hyperplasia [62]. The
same treatment elicited a significant increase in lymphocyte and macrophage infiltrates in the
lung of AhR-deficient mice, suggesting that at very high doses, TCDD-exerted effects on lung
tissue are apparently AhR-independent [62]. Although published results support the existence of
the alternative pathway, there is an overall lack of acceptance of this proposed mode of action,
probably to due insufficient amount of experimental evidence and the overwhelming dominance
of the classical model based on the AhR activation of target genes [68].

10

TOXIC EQUIVALENCY FACTORS
Risk assessment of dioxin-like chemicals uses a toxic equivalency factor (TEF) approach
established by the World Health Organization (WHO), relating the potency of the individual
dioxin-like compounds to that of TCDD, the most toxic congener [71-76]. The TEF method is
based on a congener’s structural similarity to TCDD, binding and activation of the AhRmediated biochemical and toxic responses, and persistence and bioaccumulation potential in the
food chain [76]. Among 75, 135 and 209 possible PCDD, PCDF and PCB chemicals,
respectively, only 7 PCDDs, 10 PCDFs and 12 PCBs are considered dioxin-like and have TEF
values ranging from 0.00003 to 1, where 1 is the maximum possible TEF value [76]. Table 1
summarizes 2005 WHO TEF values for the dioxin-like chemicals [76]. For example, PCB126
and 2,3,7,8-tetrachlorodibenzofuran (TCDF) have a TEF of 0.1, indicating that when compared
to TCDD, a 10-fold higher dose of PCB126 or TCDF is required to elicit comparable toxic
effects [75, 76].
TEFs are single point potency estimates that are derived from a wide range of relative
potency values using different biological endpoints and species, and are therefore considered
estimates than can vary by a half-log unit [7, 19, 74, 76-79]. TEFs are used to calculate toxic
equivalents (TEQs) defined by the sum of the products of the concentration of each dioxin-like
chemical multiplied by its TEF to estimate the total TCDD-like activity of a mixture [76].
The development of the TEF/TEQ concept was largely based on the structure-activity
relationships and bioassays that suggested that at the submaximal doses, the contributions of
individual congeners are additive and that TEFs are independent of dose, time and tissue [7, 19,
78]. Environmental mixtures change over time and contain relatively low concentrations of
dioxin-like chemicals, compared to the high levels of di-ortho-substituted, non-coplanar PCBs,

11

Table 1. Summary of WHO 2005 TEF values.
Dioxin-like compounds

WHO 2005 TEF

PCDDs
2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD)
1,2,3,7,8-Pentachlorodibenzo-p-dioxin
1,2,3,4,7,8-Hexachlorodibenzo-p-dioxin
1,2,3,6,7,8-Hexachlorodibenzo-p-dioxin
1,2,3,7,8,9-Hexachlorodibenzo-p-dioxin
1,2,3,4,6,7,8-Heptachlorodibenzo-p-dioxin
Octachlorodibenzo-p-dioxin (OCDD)

1
1
0.1
0.1
0.1
0.01
0.0003

PCDFs
2,3,7,8-Tetrachlorodibenzo-p-furan (TCDF)
1,2,3,7,8-Pentachlorodibenzo-p-furan
2,3,4,7,8-Pentachlorodibenzo-p-furan
1,2,3,4,7,8-Hexachlorodibenzo-p-furan
1,2,3,6,7,8-Hexachlorodibenzo-p-furan
1,2,3,7,8,9-Hexachlorodibenzo-p-furan
2,3,4,7,8,9-Hexachlorodibenzo-p-furan
1,2,3,4,6,7,8-Heptachlorodibenzo-p-furan
1,2,3,4,7,8,9-Heptachlorodibenzo-p-furan
Octachlorodibenzo-p-furan (OCDF)

0.1
0.03
0.3
0.1
0.1
0.1
0.1
0.01
0.01
0.0003

Non-ortho-substituted PCBs
3,3',4,4'-Tetrachlorobiphenyl (PCB77)
3,4,4',5-Tetrachlorobiphenyl (PCB81)
3,3',4,4',5-Pentachlorobiphenyl (PCB126)
3,3',4,4',5,5'-Hexachlorobiphenyl (PCB169)
Mono-ortho-substituted PCBs
2,3,3',4,4'-Pentachlorobiphenyl (PCB105)
2,3,4,4',5-Pentachlorobiphenyl (PCB114)
2,3',4,4',5-Pentachlorobiphenyl (PCB118)
2',3,4,4',5-Pentachlorobiphenyl (PCB123)
2,3,3',4,4',5-Hexachlorobiphenyl (PCB156)
2,3,3',4,4',5'-Hexachlorobiphenyl (PCB157)
2,3',4,4',5,5'-Hexachlorobiphenyl (PCB167)
2,3,3',4,4',5,5'-Heptachlorobiphenyl (PCB189)

12

0.0001
0.0003
0.1
0.03
0.00003
0.00003
0.00003
0.00003
0.00003
0.00003
0.00003
0.00003

which do not have TEF values and do not activate AhR, and this can profoundly complicate
mixture risk assessment [78, 80]. There are also pharmacokinetic and distributional differences
between the congeners resulting from the hepatic sequestration of dioxin-like chemicals that may
affect their relative potencies, additional aspects which are not accounted for in the TEF method
[81-85].

NON-DIOXIN-LIKE PCBS: TOXICITY AND MODE OF ACTION
Research into the PCB toxic mode of action has focused mainly on the AhR and the
coplanar, non-ortho-substituted, dioxin-like congeners. However, it has become more evident
that certain non-coplanar PCBs with low affinity for the AhR also exhibit important biological
activities. Originally thought to be inactive, current evidence suggests that these non-dioxin-like
congeners show evidence of toxic effects, including neurotoxicity, carcinogenicity and endocrine
disruption [45, 86-90]. Non-dioxin-like PCBs are primarily found in blood and other tissues of
humans, wildlife and fish exposed via the food chain. Due to their high abundance, prevalence
and distribution in the environment, non-dioxin-like congeners are also termed “indicator PCBs”
[91]. Compared to dioxin-like PCBs, which use TEFs for toxicity evaluation, no unifying method
currently exists for the risk assessment of non-dioxin-like PCBs, even though humans are
exposed to significantly higher levels of non-coplanar PCBs. Recent reports, however, suggest
the use of alternative biomarkers, like thyroid hormone levels for the generation of a new and
dynamic TEF scheme that would be applicable to non-dioxin-like PCBs and other structurally
related chemicals. Ideally, the new TEF would parallel the WHO TEF for dioxin-like chemicals
and become a useful tool for non-dioxin-like PCBs in risk assessment to address the endocrinedisrupting and the potentially neurotoxic effects of PCBs [45].

13

The 2,2’,4,4’,5,5’-hexachlorobiphenyl (PCB153) is the most abundant PCB congener and
is found at the highest concentrations in humans and in the environment on a molar basis [92,
93]. The di-ortho-substitution on the biphenyl ring leads to steric hindrance and the non-coplanar
conformation of PCB153, resulting in its minimal structural resemblance to TCDD.
Consequently, PCB153 has very low binding affinity for the AhR, is not assigned a TEF value
and exerts its toxic effects via a mechanism independent of the Ah receptor activation [76, 94,
95]. Based on a structural similarity to phenobarbital, the mode of action for PCB153 has been
proposed to be mediated via members of the nuclear receptor family, the constitutive androstane
and pregnane X receptors (CAR and PXR) (Figure 3) [96-100].
CAR and PXR share a lot of structural and functional similarities and both belong to the
type I family of nuclear receptors that also includes the vitamin D receptor [97, 101, 102].
Following ligand binding, these receptors dissociate from their co-chaperone partners, including
cytoplasmic CAR retention protein (Ccrp) and Hsp90, and translocate to the nucleus to
heterodimerize with the retinoid X receptor (RXR) [101, 103]. Similar to the AhR signaling, the
CAR/RXR or PXR/RXR heterodimers bind to CAR and PXR response elements in the promoter
regions of target genes, recruiting co-activators and leading to transcriptional changes, including
expression of cytochrome P450 genes from the 2b and 3a families (e.g. Cyp2b10, Cyp3a11) [97,
101, 103-107]. It has also been shown that the DNA binding domains of CAR and PXR are
similar in amino acid composition and therefore bind similar DNA motifs. However, similarities
between the ligand binding domains of human and rodent CAR and PXR are significantly lower,
suggesting that ligand-specificity may differ drastically between species [97]. Some of the most
well characterized and potent CAR agonists include phenobarbital and TCPOBOP, while

14

Figure 3. Constitutive androstane/pregnane X receptor-mediated signaling.
In the absence of ligand, the constitutive androstane or pregnane X receptors (CAR/PXR) are bound by chaperone proteins (Hsp90
and Ccrp). Following binding of a non-coplanar ligand (e.g. PCB153), the chaperone proteins dissociate and the ligand-bound
receptors translocate to the nucleus, where they heterodimerize with the retinoid X receptor (RXR). The CAR/RXR or PXR/RXR
heterodimers bind to specific DNA response elements, the CAR or PXR response elements (CAREs/PXREs), in the promoter region
of target genes, leading to changes in gene expression.

15

pregnanes (e.g. PCN) and glucocorticoids (e.g. dexamethasone) have been shown to activate the
PXR [97].

MIXTURE EFFECTS
Understanding the interactions between the dioxin and non-dioxin-like chemicals is of
importance because environmental extracts usually contain lower levels of dioxin-like chemicals
and relatively high concentration of non-coplanar chemicals, confounding the risk assessment of
a mixture. Two or more chemicals can exert an additive effect, represented as the arithmetic sum
of effects elicited by individual chemicals, or the resulting effect can be non-additive. The nonadditive interactions include synergism, where the mixture has a total effect that is greater than
the additive effect, or antagonism, where the mixture interactions lead to a lower effect than the
one based on additivity [108]. In addition, different types of interactions can be observed at the
receptor level. Full receptor agonists are capable of eliciting a maximum response, whereas
partial agonists are unable to elicit a full response even when they are present at very high
concentrations or doses [109]. On the contrary, inverse agonists bind the same receptor site as
full or partial agonists, but they exert the opposite effects through reversal of ligand-induced
constitutive activity [110].
Several groups have investigated non-additive interactions between non-coplanar
PCB153 and AhR agonists, like PCB126 or TCDD. PCB153 has been shown to antagonize
TCDD- and PCB126-mediated cleft palate and immunotoxicity, as well as suppress hepatic
microsomal ethoxyresorufin-O-deethylase (EROD) induction in C57BL/6 mice [111, 112]. At
low doses of AhR agonists, PCB153 significantly antagonized TCDD- and PCB126-induced
relative Cyp1a1 mRNA expression in CH12.LX B cells [113]. Co-treatment studies in chick
embryos revealed that PCB153 significantly reduced PCB126-induced embryo malformations,
16

edema and liver lesions [114]. Other studies reported synergistic interactions between dioxin and
non-dioxin-like ligands. For example, treatment with PCB153 markedly enhanced the TCDDinduced hepatic porphyrin levels in Sprague-Dawley rats [115], while PCB126 and PCB153 cotreatment synergistically altered hepatocellular foci development and expression of γglutamyltranspeptidase [116]. Co-treatment of PCB153 and TCDD resulted in a significant
synergistic induction of hepatic EROD and aryl hydrocarbon hydroxylase activity compared to
TCDD treatment alone [117]. In addition, immunohistochemistry of rat livers following cotreatment with PCB126 and PCB153 showed differential induction of Cyp1a1 compared to
PCB126 alone [118]. Consequently, a more thorough examination of PCB153 in mixtures with
dioxin-like ligands is warranted in order to further elucidate the mechanisms involved in these
non-additive activities.

CONCLUSIONS
Exposure to dioxin and non-dioxin-like chemicals occurs due to their environmental
persistence, lipophilicity and bioaccumulation in the food chain and results in a spectrum of
tissue and species-specific responses, including immune suppression, endocrine disruption, and
hepatotoxicity. Understanding the toxicity of individual chemicals is necessary to establish a
quantitative baseline of toxic responses, but it is not completely adequate for risk assessment
since human and animal exposure to these chemicals rarely occurs in isolation. Dose addition is
currently the underlying assumption behind the TEF approach, implying that the exposures or the
responses to the mixture components are additive, even though multiple studies report on the
non-additive interactions in mixtures of dioxin and non-dioxin-like chemicals at varying
concentrations and doses. In practice, however, the risk assessment of these chemical mixtures is
frequently inhibited by the lack of desirable dose-response evaluation of the individual
17

components and their mixtures [119, 120]. Mathematical formulas allow for the determination of
predicted and observed responses of chemical mixtures and enable statistical assessment of the
non-additive effects [120]. Comprehensive dose-response and time course evaluation of single
congener and mixture effects on gene expression and physiological endpoints will increase our
understanding of the hepatotoxic mode of action and address limitations with current approaches
to human and wildlife mixture risk assessment.

18

REFERENCES

19

REFERENCES

1.

Fisher, B.E., Most unwanted. Environ Health Perspect, 1999. 107(1): p. A18-23.

2.

Mansour, S.A., Persistent organic pollutants (POPs) in Africa: Egyptian scenario. Hum
Exp Toxicol, 2009. 28(9): p. 531-66.

3.

Kaiser, J. and M. Enserink, Environmental toxicology. Treaty takes a POP at the dirty
dozen. Science, 2000. 290(5499): p. 2053.

4.

Schecter, A., et al., Chlorinated dioxins and dibenzofurans in human tissue from general
populations: a selective review. Environ Health Perspect, 1994. 102 Suppl 1: p. 159-71.

5.

Mullin, M.D., et al., High-resolution PCB analysis: synthesis and chromatographic
properties of all 209 PCB congeners. Environ. Set Technol., 1984. 18: p. 468-476.

6.

Guidelines for the identification of PCBs and materials containing PCBs. United Nations
Environment Programme, 1999(First Issue): p. 1-40.

7.

Safe, S., Polychlorinated biphenyls (PCBs), dibenzo-p-dioxins (PCDDs), dibenzofurans
(PCDFs), and related compounds: environmental and mechanistic considerations which
support the development of toxic equivalency factors (TEFs). Crit Rev Toxicol, 1990.
21(1): p. 51-88.

8.

Toxicological profile for polychlorinated biphenyls (PCBs). U.S. DEPARTMENT OF
HEALTH AND HUMAN SERVICES, Public Health Service, Agency for Toxic
Substances and Disease Registry, 2000: p. 1-948.

9.

Kimbrough, R.D., Polychlorinated biphenyls (PCBs) and human health: an update. Crit
Rev Toxicol, 1995. 25(2): p. 133-63.

10.

Dolnośląska Fundacja Ekorozwoju: Program
http://www.pcb.pl/pcb_ministerstwo.shtml.

11.

Mason, G. and S. Safe, Synthesis, biologic and toxic effects of the major 2,3,7,8tetrachlorodibenzo-p-dioxin metabolites in the rat. Toxicology, 1986. 41(2): p. 153-9.

12.

Poland, A. and E. Glover, Studies on the mechanism of toxicity of the chlorinated
dibenzo-p-dioxins. Environ Health Perspect, 1973. 5: p. 245-51.

13.

Commoner, B., K. Shapiro, and T. Webster, The Origin and Health Risks of PCDD and
PCDF. Waste Management & Research, 1987. 5: p. 327-346.

14.

Safe, S., et al., Halogenated biphenyls: molecular toxicology. Can J Physiol Pharmacol,
1982. 60(7): p. 1057-64.

20

PCB

Stop.

Available

from:

15.

Rappolder, M., et al., Temporal trends and spatial distribution of PCDD, PCDF, and
PCB in pine and spruce shoots. Chemosphere, 2007. 67(9): p. 1887-1896.

16.

The Chlorine Chemistry Division of the American Chemistry Council: Trends in Dioxin
Emissions
and
Exposure
in
the
United
States.
Available
from:
http://www.dioxinfacts.org/sources_trends/trends_emissions.html.

17.

NTP, NTP toxicology and carcinogenesis studies of 3,3',4,4',5-pentachlorobiphenyl (PCB
126) (CAS No. 57465-28-8) in female Harlan Sprague-Dawley rats (Gavage Studies).
NTP Technical Report, 2006(520): p. 4-246.

18.

Hopf, N.B., A.M. Ruder, and P. Succop, Background levels of polychlorinated biphenyls
in the U.S. population. Sci Total Environ, 2009.

19.

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

20.

Herrick, R.F., D.J. Lefkowitz, and G.A. Weymouth, Soil contamination from PCBcontaining buildings. Environ Health Perspect, 2007. 115(2): p. 173-5.

21.

Harrad, S.J., et al., Polychlorinated biphenyls (PCBs) in the British environment: sinks,
sources and temporal trends. Environ Pollut, 1994. 85(2): p. 131-46.

22.

Hu, D., A. Martinez, and K.C. Hornbuckle, Discovery of non-aroclor PCB (3,3'dichlorobiphenyl) in Chicago air. Environ Sci Technol, 2008. 42(21): p. 7873-7.

23.

Zuccato, E., et al., PCB concentrations in some foods from four European countries.
Food Chem Toxicol, 2008. 46(3): p. 1062-7.

24.

Gallenberg, L.A. and M.J. Vodicnik, Potential mechanisms for redistribution of
polychlorinated biphenyls during pregnancy and lactation. Xenobiotica, 1987. 17(3): p.
299-310.

25.

Schecter, A., et al., Comparison of dibenzodioxin levels in blood and milk in agricultural
workers and others following pentachlorophenol exposure in China. Chemosphere, 1994.
29(9-11): p. 2371-80.

26.

Vodicnik, M.J. and J.J. Lech, The transfer of 2,4,5,2',4',5'-hexachlorobiphenyl to fetuses
and nursing offspring. I. Disposition in pregnant and lactating mice and accumulation in
young. Toxicol Appl Pharmacol, 1980. 54(2): p. 293-300.

27.

Lung, S.C., Y.L. Guo, and H.Y. Chang, Serum concentrations and profiles of
polychlorinated biphenyls in Taiwan Yu-cheng victims twenty years after the incident.
Environ Pollut, 2005. 136(1): p. 71-9.

28.

Ryan, J.J., et al., Blood serum levels of PCDFs and PCBs in Yu-Cheng children perinatally exposed to a toxic rice oil. Chemosphere, 1994. 29(6): p. 1263-78.

21

29.

Masuda, Y., Health status of Japanese and Taiwanese after exposure to contaminated
rice oil. Environ Health Perspect, 1985. 60: p. 321-5.

30.

Yoshimura, T., Yusho in Japan. Ind Health, 2003. 41(3): p. 139-48.

31.

Nagayama, J., M. Kuratsune, and Y. Masuda, Determination of chlorinated
dibenzofurans in kanechlors and "yusho oil". Bull Environ Contam Toxicol, 1976. 15(1):
p. 9-13.

32.

Yu, M.L., et al., Increased mortality from chronic liver disease and cirrhosis 13 years
after the Taiwan "yucheng" ("oil disease") incident. Am J Ind Med, 1997. 31(2): p. 1725.

33.

Wan, Y., et al., Bioaccumulation of polychlorinated dibenzo-p-dioxins, dibenzofurans,
and dioxin-like polychlorinated biphenyls in fishes from the Tittabawassee and Saginaw
Rivers, Michigan, USA. Sci Total Environ, 2010. 408(11): p. 2394-401.

34.

Tryphonas, H., Immunotoxicity of PCBs (Aroclors) in relation to Great Lakes. Environ
Health Perspect, 1995. 103 Suppl 9: p. 35-46.

35.

Giesy, J.P., et al., Contaminants in fishes from Great Lakes-influenced sections and
above dams of three Michigan rivers. I: Concentrations of organo chlorine insecticides,
polychlorinated biphenyls, dioxin equivalents, and mercury. Arch Environ Contam
Toxicol, 1994. 27(2): p. 202-12.

36.

Ngo, A.D., et al., Association between Agent Orange and birth defects: systematic review
and meta-analysis. Int J Epidemiol, 2006. 35(5): p. 1220-30.

37.

Frumkin, H., Agent Orange and cancer: an overview for clinicians. CA Cancer J Clin,
2003. 53(4): p. 245-55.

38.

Bertazzi, P.A., et al., The Seveso studies on early and long-term effects of dioxin
exposure: a review. Environ Health Perspect, 1998. 106 Suppl 2: p. 625-33.

39.

Sorg, O., et al., 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) poisoning in Victor
Yushchenko: identification and measurement of TCDD metabolites. Lancet, 2009.
374(9696): p. 1179-85.

40.

Birnbaum, L.S. and J. Tuomisto, Non-carcinogenic effects of TCDD in animals. Food
Addit Contam, 2000. 17(4): p. 275-88.

41.

Sweeney, M.H. and P. Mocarelli, Human health effects after exposure to 2,3,7,8-TCDD.
Food Addit Contam, 2000. 17(4): p. 303-16.

42.

Hobson, L.B., Human effects of TCDD exposure. Bull Environ Contam Toxicol, 1984.
33(6): p. 696-701.

22

43.

Consonni, D., et al., Mortality in a population exposed to dioxin after the Seveso, Italy,
accident in 1976: 25 years of follow-up. Am J Epidemiol, 2008. 167(7): p. 847-58.

44.

Michalek, J.E. and M. Pavuk, Diabetes and cancer in veterans of Operation Ranch Hand
after adjustment for calendar period, days of spraying, and time spent in Southeast Asia.
J Occup Environ Med, 2008. 50(3): p. 330-40.

45.

Yang, J.M., A.G. Salmon, and M.A. Marty, Development of TEFs for PCB congeners by
using an alternative biomarker - Thyroid hormone levels. Regul Toxicol Pharmacol,
2010. 56(2): p. 225-36.

46.

Quensen, J.F., S.A. Boyd, and J.M. Tiedje, Dechlorination of Four Commercial
Polychlorinated Biphenyl Mixtures (Aroclors) by Anaerobic Microorganisms from
Sediments. Appl Environ Microbiol, 1990. 56(8): p. 2360-2369.

47.

Ganey, P.E. and S.A. Boyd, An approach to evaluation of the effect of bioremediation on
biological activity of environmental contaminants: dechlorination of polychlorinated
biphenyls. Environ Health Perspect, 2005. 113(2): p. 180-5.

48.

Wu, Q., K.R. Sowers, and H.D. May, Microbial Reductive Dechlorination of Aroclor
1260 in Anaerobic Slurries of Estuarine Sediments. Appl Environ Microbiol, 1998. 64(3):
p. 1052-1058.

49.

Bedard, D.L., et al., Rapid assay for screening and characterizing microorganisms for
the ability to degrade polychlorinated biphenyls. Appl Environ Microbiol, 1986. 51(4): p.
761-8.

50.

Denison, M.S. and S. Heath-Pagliuso, The Ah receptor: a regulator of the biochemical
and toxicological actions of structurally diverse chemicals. Bull Environ Contam
Toxicol, 1998. 61(5): p. 557-68.

51.

Poland, A., E. Glover, and A.S. Kende, Stereospecific, high affinity binding of 2,3,7,8tetrachlorodibenzo-p-dioxin by hepatic cytosol. Evidence that the binding species is
receptor for induction of aryl hydrocarbon hydroxylase. J Biol Chem, 1976. 251(16): p.
4936-46.

52.

Swanson, H.I. and C.A. Bradfield, The AH-receptor: genetics, structure and function.
Pharmacogenetics, 1993. 3(5): p. 213-30.

53.

Evans, R.M., The steroid and thyroid hormone receptor superfamily. Science, 1988.
240(4854): p. 889-95.

54.

Okey, A.B., An aryl hydrocarbon receptor odyssey to the shores of toxicology: the
Deichmann Lecture, International Congress of Toxicology-XI. Toxicol Sci, 2007. 98(1):
p. 5-38.

23

55.

Patel, R.D., et al., Aryl-hydrocarbon receptor activation regulates constitutive
androstane receptor levels in murine and human liver. Hepatology, 2007. 46(1): p. 20918.

56.

Hankinson, O., The aryl hydrocarbon receptor complex. Annu Rev Pharmacol Toxicol,
1995. 35: p. 307-40.

57.

Swanson, H.I., W.K. Chan, and C.A. Bradfield, DNA binding specificities and pairing
rules of the Ah receptor, ARNT, and SIM proteins. J Biol Chem, 1995. 270(44): p. 26292302.

58.

Nebert, D.W., et al., Role of the aromatic hydrocarbon receptor and [Ah] gene battery in
the oxidative stress response, cell cycle control, and apoptosis. Biochem Pharmacol,
2000. 59(1): p. 65-85.

59.

Boutros, P.C., et al., Dioxin-dependent and dioxin-independent gene batteries:
comparison of liver and kidney in AHR-null mice. Toxicol Sci, 2009. 112(1): p. 245-56.

60.

Flaveny, C.A., I.A. Murray, and G.H. Perdew, Differential gene regulation by the human
and mouse aryl hydrocarbon receptor. Toxicol Sci, 2010. 114(2): p. 217-25.

61.

Denison, M.S., et al., Ligand binding and activation of the Ah receptor. Chem Biol
Interact, 2002. 141(1-2): p. 3-24.

62.

Fernandez-Salguero, P.M., et al., Aryl-hydrocarbon receptor-deficient mice are resistant
to 2,3,7,8-tetrachlorodibenzo-p-dioxin-induced toxicity. Toxicol Appl Pharmacol, 1996.
140(1): p. 173-9.

63.

Schmidt, J.V., et al., Characterization of a murine Ahr null allele: involvement of the Ah
receptor in hepatic growth and development. Proc Natl Acad Sci U S A, 1996. 93(13): p.
6731-6.

64.

Mimura, J., et al., Loss of teratogenic response to 2,3,7,8-tetrachlorodibenzo-p-dioxin
(TCDD) in mice lacking the Ah (dioxin) receptor. Genes Cells, 1997. 2(10): p. 645-54.

65.

Okey, A.B., L.M. Vella, and P.A. Harper, Detection and characterization of a low
affinity form of cytosolic Ah receptor in livers of mice nonresponsive to induction of
cytochrome P1-450 by 3-methylcholanthrene. Mol Pharmacol, 1989. 35(6): p. 823-30.

66.

Mimura, J. and Y. Fujii-Kuriyama, Functional role of AhR in the expression of toxic
effects by TCDD. Biochim Biophys Acta, 2003. 1619(3): p. 263-8.

67.

Abbott, B.D., et al., Adverse reproductive outcomes in the transgenic Ah receptordeficient mouse. Toxicol Appl Pharmacol, 1999. 155(1): p. 62-70.

68.

Matsumura, F., The significance of the nongenomic pathway in mediating inflammatory
signaling of the dioxin-activated Ah receptor to cause toxic effects. Biochem Pharmacol,
2009. 77(4): p. 608-26.

24

69.

Dong, B. and F. Matsumura, Roles of cytosolic phospholipase A2 and Src kinase in the
early action of 2,3,7,8-tetrachlorodibenzo-p-dioxin through a nongenomic pathway in
MCF10A cells. Mol Pharmacol, 2008. 74(1): p. 255-63.

70.

Puga, A., D.W. Nebert, and F. Carrier, Dioxin induces expression of c-fos and c-jun
proto-oncogenes and a large increase in transcription factor AP-1. DNA Cell Biol, 1992.
11(4): p. 269-81.

71.

Ahlborg, U.G., Human health risk assessment and risk perception related to the Baltic
Sea. Arch Toxicol Suppl, 1994. 16: p. 53-9.

72.

Barnes, D.G., Toxicity equivalents and EPA's risk assessment of 2,3,7,8-TCDD. Sci Total
Environ, 1991. 104(1-2): p. 73-86.

73.

Birnbaum, L.S. and M.J. DeVito, Use of toxic equivalency factors for risk assessment for
dioxins and related compounds. Toxicology, 1995. 105(2-3): p. 391-401.

74.

Haws, L.C., et al., Development of a refined database of mammalian relative potency
estimates for dioxin-like compounds. Toxicol Sci, 2006. 89(1): p. 4-30.

75.

Van den Berg, M., et al., Toxic equivalency factors (TEFs) for PCBs, PCDDs, PCDFs for
humans and wildlife. Environ Health Perspect, 1998. 106(12): p. 775-92.

76.

Van den Berg, M., et al., The 2005 World Health Organization reevaluation of human
and Mammalian toxic equivalency factors for dioxins and dioxin-like compounds.
Toxicol Sci, 2006. 93(2): p. 223-41.

77.

Starr, T.B., et al., The trouble with TEFs. Environ Health Perspect, 1999. 107(10): p.
A492-3.

78.

Safe, S., Limitations of the toxic equivalency factor approach for risk assessment of
TCDD and related compounds. Teratog Carcinog Mutagen, 1997. 17(4-5): p. 285-304.

79.

Harris, M., T. Zacharewski, and S. Safe, Comparative potencies of Aroclors 1232, 1242,
1248, 1254, and 1260 in male Wistar rats--assessment of the toxic equivalency factor
(TEF) approach for polychlorinated biphenyls (PCBs). Fundam Appl Toxicol, 1993.
20(4): p. 456-63.

80.

Wolfle, D., Interactions between 2,3,7,8-TCDD and PCBs as tumor promoters:
limitations of TEFs. Teratog Carcinog Mutagen, 1997. 17(4-5): p. 217-24.

81.

Budinsky, R.A., et al., Recommended relative potency factors for 2,3,4,7,8pentachlorodibenzofuran: the impact of different dose metrics. Toxicol Sci, 2006. 91(1):
p. 275-85.

82.

DeVito, M.J., et al., Dose-response relationships for polyhalogenated dioxins and
dibenzofurans following subchronic treatment in mice. I. CYP1A1 and CYP1A2 enzyme
activity in liver, lung, and skin. Toxicol Appl Pharmacol, 1997. 147(2): p. 267-80.

25

83.

DeVito, M.J., et al., Dose-response relationships for disposition and hepatic
sequestration of polyhalogenated dibenzo-p-dioxins, dibenzofurans, and biphenyls
following subchronic treatment in mice. Toxicol Sci, 1998. 46(2): p. 223-34.

84.

Diliberto, J.J., et al., Subchronic Exposure of [3H]- 2,3,7,8-tetrachlorodibenzo-p-dioxin
(TCDD) in female B6C3F1 mice: relationship of steady-state levels to disposition and
metabolism. Toxicol Sci, 2001. 61(2): p. 241-55.

85.

Safe, S.H., Modulation of gene expression and endocrine response pathways by 2,3,7,8tetrachlorodibenzo-p-dioxin and related compounds. Pharmacol Ther, 1995. 67(2): p.
247-81.

86.

Fischer, L.J., et al., Symposium overview: toxicity of non-coplanar PCBs. Toxicol Sci,
1998. 41(1): p. 49-61.

87.

Seegal, R.F., Epidemiological and laboratory evidence of PCB-induced neurotoxicity.
Crit Rev Toxicol, 1996. 26(6): p. 709-37.

88.

Shain, W., B. Bush, and R. Seegal, Neurotoxicity of polychlorinated biphenyls: structureactivity relationship of individual congeners. Toxicol Appl Pharmacol, 1991. 111(1): p.
33-42.

89.

Crofton, K.M., et al., Thyroid-hormone-disrupting chemicals: evidence for dosedependent additivity or synergism. Environ Health Perspect, 2005. 113(11): p. 1549-54.

90.

Knerr, S. and D. Schrenk, Carcinogenicity of "non-dioxinlike" polychlorinated biphenyls.
Crit Rev Toxicol, 2006. 36(9): p. 663-94.

91.

Kim, M., et al., Comparison of seven indicator PCBs and three coplanar PCBs in beef,
pork, and chicken fat. Chemosphere, 2004. 54(10): p. 1533-1538.

92.

Schecter, A., et al., Polychlorinated biphenyl levels in the tissues of exposed and
nonexposed humans. Environ Health Perspect, 1994. 102 Suppl 1: p. 149-58.

93.

NTP, NTP technical report on the toxicology and carcinogenesis studies of 2,2',4,4',5,5'hexachlorobiphenyl (PCB 153) (CAS No. 35065-27-1) in female Harlan Sprague-Dawley
rats (Gavage studies). Natl Toxicol Program Tech Rep Ser, 2006(529): p. 4-168.

94.

Sipka, S., et al., Oral administration of PCBs induces proinflammatory and prometastatic
responses. Environ Toxicol Pharmacol, 2008. 25(2): p. 251-259.

95.

Kafafi, S.A., et al., Binding of polychlorinated biphenyls to the aryl hydrocarbon
receptor. Environ Health Perspect, 1993. 101(5): p. 422-8.

96.

Craft, E.S., M.J. DeVito, and K.M. Crofton, Comparative responsiveness of
hypothyroxinemia and hepatic enzyme induction in Long-Evans rats versus C57BL/6J
mice exposed to TCDD-like and phenobarbital-like polychlorinated biphenyl congeners.
Toxicol Sci, 2002. 68(2): p. 372-80.

26

97.

Honkakoski, P., T. Sueyoshi, and M. Negishi, Drug-activated nuclear receptors CAR and
PXR. Ann Med, 2003. 35(3): p. 172-82.

98.

Pascussi, J.M., et al., The Tangle of Nuclear Receptors that Controls Xenobiotic
Metabolism and Transport: Crosstalk and Consequences. Annu Rev Pharmacol Toxicol,
2007.

99.

Strathmann, J., et al., PCB 153, a non-dioxin-like tumor promoter, selects for betacatenin (Catnb)-mutated mouse liver tumors. Toxicol Sci, 2006. 93(1): p. 34-40.

100.

Tabb, M.M., et al., Highly chlorinated PCBs inhibit the human xenobiotic response
mediated by the steroid and xenobiotic receptor (SXR). Environ Health Perspect, 2004.
112(2): p. 163-9.

101.

Timsit, Y.E. and M. Negishi, CAR and PXR: the xenobiotic-sensing receptors. Steroids,
2007. 72(3): p. 231-46.

102.

Moore, J.T., et al., Functional and structural comparison of PXR and CAR. Biochim
Biophys Acta, 2003. 1619(3): p. 235-8.

103.

Squires, E.J., T. Sueyoshi, and M. Negishi, Cytoplasmic localization of pregnane X
receptor and ligand-dependent nuclear translocation in mouse liver. J Biol Chem, 2004.
279(47): p. 49307-14.

104.

Lemaire, G., et al., Discovery of a highly active ligand of human pregnane x receptor: a
case study from pharmacophore modeling and virtual screening to "in vivo" biological
activity. Mol Pharmacol, 2007. 72(3): p. 572-81.

105.

Blumberg, B. and R.M. Evans, Orphan nuclear receptors--new ligands and new
possibilities. Genes Dev, 1998. 12(20): p. 3149-55.

106.

Masahiko, N. and P. Honkakoski, Induction of drug metabolism by nuclear receptor
CAR: molecular mechanisms and implications for drug research. Eur J Pharm Sci, 2000.
11(4): p. 259-64.

107.

Honkakoski, P., et al., The nuclear orphan receptor CAR-retinoid X receptor heterodimer
activates the phenobarbital-responsive enhancer module of the CYP2B gene. Mol Cell
Biol, 1998. 18(10): p. 5652-8.

108.

Staal, Y.C., et al., Binary PAH mixtures cause additive or antagonistic effects on gene
expression but synergistic effects on DNA adduct formation. Carcinogenesis, 2007.
28(12): p. 2632-40.

109.

Zhu, B.T., Mechanistic explanation for the unique pharmacologic properties of receptor
partial agonists. Biomed Pharmacother, 2005. 59(3): p. 76-89.

110.

Kenakin, T., Principles: receptor theory in pharmacology. Trends Pharmacol Sci, 2004.
25(4): p. 186-92.

27

111.

Biegel, L., et al., 2,2',4,4',5,5'-hexachlorobiphenyl as a 2,3,7,8-tetrachlorodibenzo-pdioxin antagonist in C57BL/6J mice. Toxicol Appl Pharmacol, 1989. 97(3): p. 561-71.

112.

Zhao, F., et al., Inhibition of 3,3',4,4',5-pentachlorobiphenyl-induced fetal cleft palate
and immunotoxicity in C57BL/6 mice by 2,2',4,4',5,5'-hexachlorobiphenyl. Chemosphere,
1997. 34(5-7): p. 1605-13.

113.

Suh, J., et al., Antagonism of aryl hydrocarbon receptor-dependent induction of CYP1A1
and inhibition of IgM expression by di-ortho-substituted polychlorinated biphenyls.
Toxicol Appl Pharmacol, 2003. 187(1): p. 11-21.

114.

Zhao, F., et al., Inhibition of 3,3',4,4',5-pentachlorobiphenyl-induced chicken
embryotoxicity by 2,2',4,4',5,5'-hexachlorobiphenyl. Fundam Appl Toxicol, 1997. 35(1):
p. 1-8.

115.

van Birgelen, A.P., et al., Synergistic effect of 2,2',4,4',5,5'-hexachlorobiphenyl and
2,3,7,8-tetrachlorodibenzo-p-dioxin on hepatic porphyrin levels in the rat. Environ
Health Perspect, 1996. 104(5): p. 550-7.

116.

Bager, Y., et al., Interaction of 3,4,5,3',4'-pentachlorobiphenyl and 2,4,5,2',4',5'hexachlorobiphenyl in promotion of altered hepatic foci in rats. Pharmacol Toxicol,
1995. 77(2): p. 149-54.

117.

Bannister, R. and S. Safe, Synergistic interactions of 2,3,7,8-TCDD and 2,2',4,4',5,5'hexachlorobiphenyl in C57BL/6J and DBA/2J mice: role of the Ah receptor. Toxicology,
1987. 44(2): p. 159-69.

118.

Chubb, L.S., et al., Regional induction of CYP1A1 in rat liver following treatment with
mixtures of PCB 126 and PCB 153. Toxicol Pathol, 2004. 32(4): p. 467-73.

119.

Borgert, C.J., et al., Evaluating chemical interaction studies for mixture risk assessment.
Human and Ecological Risk Assessment, 2001. 7(2): p. 259-305.

120.

Hertzberg, R.C. and L.K. Teuschler, Evaluating quantitative formulas for dose-response
assessment of chemical mixtures. Environ Health Perspect, 2002. 110 Suppl 6: p. 965-70.

28

CHAPTER 2

29

CHAPTER 2
RATIONALE, HYPOTHESIS AND SPECIFIC AIMS
RATIONALE
Human exposure to toxicants typically occurs as complex mixtures of environmental
chemicals with varying potencies and toxicities. The more toxic, dioxin-like chemicals, including
2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and 3,3’,4,4’,5-pentachlorobiphenyl (PCB126),
elicit their toxicity through the AhR-mediated pathway, while the more abundant, non-dioxinlike congeners like 2,2’,4,4’,5,5’-hexachlorobiphenyl (PCB153) elicit toxic effects through
unique, non-AhR-mediated mechanisms. Interactions between these chemicals can lead to
responses that deviate from the dose-additive assumption underlying mixture risk assessment
approaches. Comprehensive mixture dose-response and time course evaluation, in addition to
single chemical, single time point or single dose studies, is required for scientifically based risk
and health hazard assessment. The systematic transcriptomic and phenotypic examination of
single congener and mixture toxicity will provide valuable insight into elucidation of potential
non-additive effects occurring at both molecular and physiological levels. These comprehensive
studies have the potential of significantly improving quantitative risk assessment of human
exposures to ubiquitously persistent environmental toxicants.

HYPOTHESIS
The binary mixture of TCDD and PCB153 elicits non-additive hepatic gene expression
and phenotypic effects compared to single chemical exposure.

30

SPECIFIC AIMS
To address this hypothesis, the following specific aims with the application of genomewide transcript profiling and phenotypic anchoring of the gene expression responses will be
used:
1.

Establish baseline quantitative temporal and dose-dependent data on the hepatic effects
elicited by dioxin-like PCB126 in comparison to TCDD in a mouse model.

2.

Establish baseline quantitative temporal and dose-dependent data on the hepatic effects
elicited by non-dioxin-like PCB153 in a mouse model.

3.

Characterize the effects of a reconstituted binary mixture of TCDD and PCB153 on
eliciting hepatic toxicity in a mouse model.

31

CHAPTER 3

Kopec AK, Boverhof DR, Burgoon LD, Ibrahim-Aibo D, Harkema JR, Tashiro C, Chittim B,
Zacharewski TR: Comparative Toxicogenomic Examination of the Hepatic Effects of
PCB126 and TCDD in Immature, Ovariectomized C57BL/6 Mice. Toxicol Sci 2008, 102:6175.

32

CHAPTER 3
COMPARATIVE TOXICOGENOMIC EXAMINATION OF THE HEPATIC
EFFECTS OF PCB126 AND TCDD IN IMMATURE, OVARIECTOMIZED
C57BL/6 MICE
ABSTRACT
Polychlorinated biphenyls are persistent environmental pollutants that elicit a wide range
of effects in humans and wildlife, mediated by the aryl hydrocarbon receptor. 3,3’,4,4’,5pentachlorobiphenyl (PCB126) is the most potent congener with relative effect potencies (REPs)
ranging from 0.0026 to 0.857, and a toxic equivalency factor (TEF) of 0.1 set by an expert panel
of the World Health Organization. In this study, the hepatic effects elicited by 300 μg/kg
PCB126 were compared with 30 μg/kg 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) in
immature, ovariectomized female C57BL/6 mice. Comprehensive hepatic gene expression
analyses with complementary histopathology, high-resolution gas chromatograph/high resolution
mass spectrometer tissue analysis, and clinical chemistry were examined. For temporal analysis,
mice were orally gavaged with PCB126 or sesame oil vehicle and sacrificed after 2, 4, 8, 12, 18,
24, 72, 120, or 168 h. In the dose-response study, mice were gavaged with 0.3, 1, 3, 10, 30, 100,
300, 1000 μg/kg PCB126, 30 or 100 μg/kg TCDD and sacrificed after 72 h. 251 and 367 genes
were differentially expressed by PCB126 at one or more time points or doses, respectively,
significantly less than elicited by TCDD. In addition, there was less vacuolization and necrosis,
and no immune cell infiltration, despite comparable or higher TEF-adjusted hepatic PCB126

33

levels. The functional annotation of differentially expressed genes was consistent with the
observed histopathology. Collectively, the data indicate that 300 μg/kg PCB126 elicited a subset
of weaker effects compared with 30 μg/kg TCDD in immature, ovariectomized C57BL/6 mice.

INTRODUCTION
Polychlorinated biphenyls (PCBs) are ubiquitous environmental contaminants that are
found as mixtures of individual congeners. There are 209 possible PCB congeners with different
degrees of chlorination [1]. Many commercial PCB mixtures are known by their industrial name
Aroclor, followed by a number designating the number of carbon atoms and the percent chlorine
by weight. PCBs were produced between 1930 and 1977 for use as coolants, lubricants, and
dielectric insulating fluids for capacitors and transformers, due to their chemical inertness and
stability [1]. Even though production has ceased, they are still released into the environment
through the improper use and disposal of PCB containing products [2]. Once released, PCB
mixtures are continuously altered through volatilization, partitioning and biochemical
transformations [3, 4]. 3,3’,4,4’,5-pentachlorobiphenyl (PCB126) is the most potent PCB
congener and accounts for 40-60% of the total toxic potency of all dioxin-like PCBs [2].
Dioxin and related compounds elicit a broad spectrum of species- and tissue-specific
biochemical and toxic effects including wasting syndrome, dermal toxicity, tumor promotion,
teratogenicity, immunotoxicity, and hepatotoxicity [5]. Many, if not all, of these toxic responses
are mediated through the activation of the aryl hydrocarbon receptor (AhR), a basic-helix-loophelix-PAS (bHLH-PAS) protein [6]. Dioxin and related compounds bind to the cytoplasmic
AhR, which then translocates to the nucleus to form a heterodimer with the AhR nuclear
translocator (ARNT), another member of the bHLH-PAS family [7]. The activated AhR/ARNT
complex interacts with dioxin response elements (DREs) located in the regulatory region of
34

target genes, leading to changes in gene expression [8]. The involvement of AhR/ARNT
signaling pathway in mediating these responses is supported by several complementary lines of
evidence including studies with low affinity AhR allele mice [9], structure activity studies [10],
and AhR-null mice [11].
The toxic equivalency factor (TEF)/toxic equivalents (TEQ) approach is used to assess
the potential risks associated with exposure to mixtures of polychlorinated dibenzo-p-dioxins
(PCDDS), polychlorinated dibenzofurans (PCDFs), and PCBs. It assumes that PCDDS, PCDFs
and PCBs structurally similar to 2,3,7,8-tetrachlorobidenzo-p-dioxin (TCDD) elicit their toxicity
through the AhR. The relative effect potency (REP) for a specific endpoint is determined for
individual congeners relative to TCDD, the most potent of the dioxin-like chemicals [12]. REPs
were used by an expert panel convened by the World Health Organization (WHO) to establish a
TEF point estimate with the understanding that it may vary by a half-log unit. The potential
toxicity of a mixture could then be represented by the sum of the concentrations of individual
congeners multiplied by their corresponding TEFs to obtain an estimated toxicity relative to
TCDD. This approach assumes that at submaximal doses, the contributions of individual
components are essentially additive [10] and that TEFs are independent of dose, time point, and
tissue [13, 14].
To facilitate the creation of TEFs, the expert panel used the REP2004 Database, a
comprehensive listing of REP values for all known dioxins and dioxin-like compounds [15, 16].
In vitro data were only considered when there were insufficient in vivo data. For PCB126 there
were 318 separate in vivo REPs, from 33 different peer-reviewed publications, a thesis, or
government technical report for PCB126. The 2005 WHO expert panel excluded 64 mouse
studies due to discrepancies between mouse and rat enzyme activity assay data. Although the

35

official list of studies considered is not available, the Committee stated that they used the
REP2004 Database criteria for inclusion and exclusion. The rat studies that were considered
included a range of endpoints from short-term enzyme induction to hepatocellular adenomas
following chronic exposures (1-2 years). Although the tight range of REPs for PCB126 in rat
studies supports a TEF of 0.1, information from mouse and some human studies, especially for
enzyme induction, suggests it may have been too high [16]. The 2005 WHO expert panel
concluded that there was insufficient information to change the PCB126 TEF of 0.1, but called
for further studies.
In this study, comprehensive time course and dose-response gene expression analyses
were conducted with complementary histopathology, clinical chemistry, and high-resolution gas
chromatograph/high resolution mass spectrometer (HRGC-HRMS) tissue level analyses to
compare the hepatic effects of 300 μg/kg PCB126 (TEF=0.1) to 30 μg/kg TCCD (TEF=1) in the
immature, ovariectomized female C57BL/6 mice. Comparisons were also made to a previously
published TCDD study that used the same animal species, experimental design, cDNA
microarray platform, and analysis methods [17]. Collectively, and consistently, 300 μg/kg
PCB126 elicited weaker responses and only a subset of effects induced by 30 μg/kg TCDD.
However, more comprehensive time optimized dose-response studies are required for each
endpoint of interest in order to provide REP data that could be used, in the context of all other
available data, when considering the TEF for PCB126.

MATERIALS & METHODS
ANIMAL HANDLING
Female C57BL/6 mice, ovariectomized by the supplier on postnatal day (PND) 20, with
body weights (BW) within 19% of the average, were obtained from Charles Rivers Laboratories
36

(Wilmington, MA) on PND 25. Animals were housed in polycarbonate cages containing
cellulose fiber chips (Aspen Chip Laboratory Bedding, Northeastern Products, Warrensberg,
NY) with 30-40% humidity and a 12-h light/dark cycle (07:00 A.M. – 7:00 P.M.). Mice had free
access to deionized water and Harlan Teklad 22/5 Rodent Diet 8640 (Madison, WI). Animals
were acclimatized prior to being dosed on PND 28. The immature ovariectomized mouse was
used to facilitate comparisons with other data sets obtained in the same model [17]. The
comparisons of the hepatotoxic potency of PCB126 were made to either ‘internal’ TCDD-treated
group of mice or to an independent, previously published comprehensive TCDD time course
study by Boverhof et al. that used the same animal model [17]. All procedures were carried out
with the approval of the Michigan State University All-University Committee on Animal Use
and Care.
TIME COURSE AND DOSE-RESPONSE STUDIES
A stock solution of PCB126 (99.7% purity, AccuStandard, New Haven, CT) was
prepared by first dissolving it in acetone (J.T. Baker), then diluting it with sesame oil (Sigma, St.
Louis, MO), and evaporating the acetone under a mild stream of nitrogen gas. The PCB126 stock
solution was further diluted in sesame oil to achieve the desired dose. For the time course study,
mice (n=5 per group) were orally gavaged with either 300 μg/kg PCB126, 30 μg/kg TCDD (gift
from the Dow Chemical Company, Midland, MI), or sesame oil vehicle. PCB126 and vehicletreated animals were sacrificed at 2, 4, 8, 12, 18, 24, 72, 120, or 168 h post exposure, whereas
TCDD animals were sacrificed at 72 h. 30 μg/kg TCDD was initially selected to study because it
elicited maximum induction of Cyp1a1 and 1a2 mRNA levels while not inducing significant
changes in BW gain [17]. It was used again in the present study to facilitate comparisons
between studies that employed the same model species, experimental design, cDNA platform,

37

and analysis methods. A concentration of 300 μg/kg PCB126 was used to examine the
hypothesis that it would elicit hepatic effects comparable to 30 μg/kg TCDD, based on the
PCB126 TEF of 0.1. The vehicle groups were not the same between the current PCB126 study
and Boverhof et al. study, but the same vehicle controls were used for the internal TCDD-treated
mice in the current PCB126 study.
For the dose-response study, mice were gavaged with a single dose of 0.3, 1, 3, 10, 30,
100, 300, or 1000 μg/kg of PCB126, 30 or 100 μg/kg TCDD, or vehicle and sacrificed 72 h
following treatment. All mice were sacrificed by cervical dislocation and tissue samples were
removed, weighed, flash frozen in liquid nitrogen, and stored at –80°C. For both the time course
and dose-response studies, the right lobe of the liver was fixed in 10% neutral buffered formalin
(Sigma) for histological analysis.
CLINICAL CHEMISTRY AND HISTOLOGICAL ANALYSIS
Blood samples were collected by submandibular vein puncture and blood was allowed to
clot in the Microtainer Serum Separator Tubes (VWR International, Batavia, IL). Serum was
separated by spinning at 10,000  g for 5 min, after which the samples were stored at –80°C.
Serum triglycerides (TG), non-esterified fatty acids (NEFA), cholesterol (CHOL), alanine
aminotransferase (ALT), and glucose (GLU) were measured using an Olympus AU640
Automated Chemistry Analyzer (Olympus America Inc., Melville, NY) by the Michigan State
University Clinical Pathology Laboratory (http://cvm.msu.edu/clinpath/new.htm).
H

H

Fixed liver tissues were sectioned and processed in ethanol, xylene, and paraffin using a
Thermo Electron Excelsior tissue processor (Waltham, MA). Tissues were then embedded in
paraffin with 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

38

stained with the standard hematoxylin and eosin stain. All histological processing was performed
at

the

Michigan

State

University

Histology

Laboratory

(http://humanpathology.msu.edu/histology/index.html).
H

H

THIN LAYER CHROMATOGRAPHY
Liver samples were first homogenized (Polytron PT2100, Kinematica AG, Luzern, CH)
in 1% methanol and acidified with concentrated HCl. Lipids were extracted with chloroform:
methanol (2:1) containing 1 mM 2,6-di-tert-butyl-4-methylphenol (BHT; Sigma). The protein
and aqueous phases were re-extracted with chloroform and the organic phases were pooled, dried
under nitrogen gas, and resuspended in chloroform with 1 mM BHT, and stored at –80°C in
glass vials with PTFE caps (VWR International). Lipid extracts were then fractionated by thin
layer chromatography on silica gel adsorption plates (TLC; LK6D Silica G 60A; Whatman Inc.,
Florham Park, NJ) with hexane:diethyl ether:acetic acid (90:30:1) and developed with iodine
(Sigma). Lipid migrations were compared to triacylglycerol, diacylglycerol and cholesterol ester
standards (Nu-Chek Prep, Elysian, MN).
QUANTIFICATION OF HEPATIC PCB126 AND TCDD LEVELS
Liver samples were processed in parallel with laboratory blanks and a reference or
background sample at Wellington Laboratories Inc. (Guelph, ON, Canada). Samples were
weighed, spiked with

13

C12-2,3,7,8-TCDD or

13

C12 -PCB126 surrogate, digested with sulfuric

acid, and extracted. Extracts were cleaned, concentrated, and spiked with
or

13

13

C12-1,2,3,4-TCDD

C12-PCB111 as injection standards. 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 electron

39

ionization/selective ion recording mode (EI/SIR) 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 (on average ~100 mg) were retrieved from –80°C storage and
immediately transferred to 1 ml of TRIzol (Invitrogen, Carlsbad, CA) and homogenized using a
Mixer Mill 300 tissue homogenizer (Retsch, Germany). Total RNA was isolated according to the
manufacturer’s protocol with an additional acid phenol:chloroform extraction. Isolated RNA was
resuspended in RNA storage solution (Ambion Inc., Austin, TX), quantified (A260), and quality
was assessed by determining the A260/A280 ratio and by visual inspection of 2 μg on a
denaturing gel.
CDNA MICROARRAY EXPERIMENTAL DESIGN AND PROTOCOLS

In the time course study, PCB126-treated samples were co-hybridized with time-matched
vehicles controls using an independent reference design [18]. Dose-dependent changes in gene
expression were analyzed using a common reference design, where PCB126 samples were
compared to a common vehicle control. cDNA microarrays were also performed for the
‘internal’ TCDD-treated group of mice, which used the same vehicle controls as in the PCB126
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 152).

40

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.
H

H

Microarrays were printed at the Michigan State University Research Technology Support
Facility (http://www.genomics.msu.edu/). Briefly, PCR amplified mouse cDNAs were
H

H

robotically arrayed onto epoxy-coated glass slides (Schott-Nexterion, Duryea, PA) using an
Omnigrid arrayer (GeneMachines, San Carlos, CA) equipped with 48 (412) Chipmaker 2 pins
(TeleChem, Sunnyvale, CA). Total RNA (30 μg) was reverse transcribed in the presence of Cy3or Cy5-dUTP (Amersham, Piscataway, NJ) to create fluor-labeled cDNA which was purified
using a Qiagen PCR purification kit (Qiagen, Valencia, CA). Cy3 and Cy5 samples were mixed,
vacuum dried, and resuspended in 48 μl of hybridization buffer (40% formamide, 4 sodium
chloride sodium citrate, 1% sodium dodecyl sulfate) with 20 μg polydA and 20 μg of mouse
COT-1 DNA (Invitrogen) as competitor. This probe mixture was heated at 95°C for 3 min and
hybridized on the array under a 22  60 mm lifterslip (Erie Scientific Company, Portsmouth,
NH) 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 4100A scanner (Molecular Devices,
Union City, CA). Images were analyzed for feature and background intensities using GenePix
Pro 6.0 (Molecular Devices).
CDNA

MICROARRAY DATA NORMALIZATION

AND

ANALYSIS

AND

FEATURE-TO-GENE

FILTERING CRITERIA
All microarray data used within this study passed the laboratory quality assurance
protocol [19]. Microarray data were normalized using a semiparametric approach [20], and the
posterior probabilities were calculated using an empirical Bayes analysis on a per gene and time
point or dose basis [21]. Gene expression data were ranked and prioritized using a P1(t) cut-off

41

≥0.9999 and |fold change|≥1.5 to identify treatment active genes and to obtain an initial subset of
differentially regulated genes for further investigation and data interpretation. Relaxed filtering
criteria (from P1(t)≥0.9999 and |fold change|≥1.5 to P1(t)≥0.99 and |fold change|≥1.2) were also
used to examine overlapping, differentially regulated genes to minimize classifying genes as
PCB126 or TCDD specific as a result of using hard cut-offs. Active genes were analyzed by
agglomerative hierarchical clustering using a standard correlation distance metric implemented
in GeneSpring 6.0 (Agilent Technologies; Santa Clara, CA).
Multiple features spotted on our cDNA microarray may 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 gene and counted as one gene. Due to this
redundancy, and because of the changes to the mouse genome annotation, the 13,361 features
spotted on our cDNA microarray correspond to 8,516 unique genes based on the annotation
provided by UniGene build 152.
QUANTITATIVE REAL-TIME PCR
Quantitative real-time PCR (QRTPCR) verification of microarray responses was
performed as described [17]. Briefly, 1 μg of total RNA was reverse transcribed by SuperScript
II (Invitrogen) using an anchored oligo-dT primer as described by the manufacturer. 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 mM dNTPs, 0.025 IU AmpliTaq Gold, and 1 SYBR
Green PCR buffer (Applied Biosystems, Foster City, CA). PCR amplification was conducted on
an Applied Biosystems PRISM 7500 Sequence Detection System. cDNAs were quantified using
a standard curve approach and the copy number of each sample was standardized to 3
42

housekeeping genes (ActB, Gapdh, Hprt) to control for the differences in RNA loading, quality,
and cDNA synthesis [22]. For graphing purposes, the relative expression levels were scaled such
that the expression level of the time-matched control group was equal to one.
DOSE-RESPONSE MODELING
A Java application was developed to identify the best-fit dose-response model, by
minimizing the Euclidean distance, for differential gene expression responses in the doseresponse study. The algorithm uses particle swarm optimization [23] to identify the best-fit
model (i.e., the model with the parameter set that best fits the experimental data) within each of
five classes (sigmoidal, exponential, linear, Gaussian, parametric), termed the best in-class
model. The algorithm then chooses the best-fit of the five best in-class models. The best-fit
model is used to calculate model-specific end points, such as the ED50, ED99, ED01,
probabilistic point of departure, and the benchmark dose.
FUNCTIONAL GENE ANNOTATION AND STATISTICAL ANALYSIS
Annotation and functional categorization of differentially regulated genes was performed
using a Database for Annotation, Visualization and Integrated Discovery (DAVID) [24]. All
statistical analyses were performed with SAS 8.02 (SAS Institute, Cary, NC). Data were
analyzed by analysis of variance followed by Tukey’s and Dunnett’s post hoc tests. Differences
between treatment groups were considered significant when p<0.05.

RESULTS
ORGAN AND BODY WEIGHTS
Increases in liver weight and decreases in BW gain are hallmark, dose-dependent toxic
responses following treatment with TCDD and related compounds including PCB126 [5].

43

Significant (p<0.05) increases in relative liver weights (RLW) were seen with 30 μg/kg TCDD at
72 h and 300 μg/kg PCB126 at 168 h in the time course study (Table 2). In the dose-response
study, PCB126 elicited modest increases in RLW that were not significant due to the greater
response variance (Table 3). However, TCDD at doses of 30 and 100 μg/kg significantly
(p<0.05) increased RLW at 72 h (Table 3). No other significant treatment-related changes in BW
or BW gain were observed at any time point or dose, consistent with a comparable published
TCDD study [17], indicating that neither a single dose of 30 μg/kg TCDD nor 300 μg/kg
PCB126 elicits a ‘wasting syndrome’ response within 168 h.
HEPATIC PCB126 AND TCDD TISSUE LEVELS
Absolute hepatic PCB126 and TCDD levels per wet weight of three individual liver
samples were determined at each time point and dose level. In order to facilitate comparisons
between the two compounds, PCB126 TEQs were calculated by multiplying the tissue
concentration by the TEF value of 0.1 [16]. In the time course study, PCB126 levels continued to
increase throughout the study, achieving the highest concentrations at 120 and 168 h (Figure
4A). In contrast, TCDD levels significantly decreased after 72 h [17] and approached vehicle
control levels by 24 weeks (Boverhof et al., manuscript in preparation). Moreover, PCB126
TEQs increased (p<0.05) in a dose-dependent manner (Figure 4B) at 72 h with 30 and 100 μg/kg
TCDD achieving similar levels.
Hepatic concentrations of PCB126 in this study are comparable with reports in rats using
similar exposure regimens. For example, a single bolus dose of 275 μg/kg to male rats resulted in
3,300,000 pg/g of PCB126 in the liver after 7 days [25], similar to the 3,000,000 pg/g in this
study at 168 h. The National Toxicology Program study on PCB126 in female rats reported that

44

Table 2. Temporal effects of 300 µg/kg PCB126 or 30 µg/kg TCDD on terminal body, whole
liver, and RLW.
Sacrifice
Treatment
time (h)

BW (g)

Liver weight (g)

RLW

2

Vehicle
PCB126

12.62 ± 0.44
12.00 ± 0.80

0.702 ± 0.037
0.563 ± 0.323

0.056 ± 0.004
0.058 ± 0.004

4

Vehicle
PCB126

12.26 ± 0.65
12.12 ± 0.99

0.689 ± 0.080
0.651 ± 0.077

0.056 ± 0.004
0.054 ± 0.003

8

Vehicle
PCB126

12.08 ± 1.65
12.08 ± 0.89

0.613 ± 0.084
0.617 ± 0.074

0.051 ± 0.001
0.051 ± 0.003

12

Vehicle
PCB126

12.92 ± 0.89
12.82 ± 0.82

0.694 ± 0.058
0.687 ± 0.073

0.054 ± 0.002
0.053 ± 0.003

18

Vehicle
PCB126

13.38 ± 1.03
12.82 ± 1.31

0.750 ± 0.106
0.779 ± 0.063

0.056 ± 0.005
0.061 ± 0.003

24

Vehicle
PCB126

12.68 ± 1.16
12.86 ± 1.21

0.709 ± 0.090
0.814 ± 0.094

0.056 ± 0.004
0.063 ± 0.002

72

Vehicle
PCB126
TCDD

14.32 ± 2.37
13.88 ± 0.92
16.54 ± 1.59

0.822 ± 0.131
0.889 ± 0.127
1.008 ± 0.163

0.057 ± 0.002
0.064 ± 0.007
0.069 ± 0.004*

120

Vehicle
PCB126

15.86 ± 0.72
15.46 ± 1.42

0.885 ± 0.066
0.973 ± 0.134

0.056 ± 0.002
0.063 ± 0.004

168

Vehicle
PCB126

17.46 ± 0.69
16.54 ± 1.59

0.997 ± 0.043
1.087 ± 0.088

0.057 ± 0.001
0.066 ± 0.003*

Note. Values represent averages ± SD (n=5).
Asterisk (*) indicates p<0.05 (PCB126 vs. vehicle and TCDD vs. vehicle).

45

Table 3. Dose-dependent effects of PCB126 on terminal body, whole liver, and RLW at 72
h.
Dose
(µg/kg)

Treatment

BW (g)

Liver weight (g)

RLW

0

Vehicle

15.37 ± 0.87

0.843 ± 0.095

0.055 ± 0.005

0.3
1
3
10
30
100
300
1000

PCB126
PCB126
PCB126
PCB126
PCB126
PCB126
PCB126
PCB126

15.42 ± 1.39
15.62 ± 0.79
16.22 ± 1.41
15.99 ± 0.82
15.17 ± 1.31
15.78 ± 0.26
15.34 ± 0.56
14.66 ± 0.72

0.885 ± 0.113
0.890 ± 0.058
0.975 ± 0.153
0.912 ± 0.077
0.817 ± 0.117
0.967 ± 0.086
0.912 ± 0.125
0.906 ± 0.188

0.057 ± 0.003
0.057 ± 0.003
0.060 ± 0.006
0.057 ± 0.002
0.054 ± 0.005
0.061 ± 0.005
0.059 ± 0.008
0.061 ± 0.011

30
100

TCDD
TCDD

14.78 ± 1.08
15.20 ± 2.31

0.944 ± 0.050
1.058 ± 0.205

0.064 ± 0.004*
0.069 ± 0.005*

Note. Values represent averages ± SD (n=5). Asterisk (*) indicates p<0.05
(PCB126 vs. vehicle and TCDD vs. vehicle).

46

Figure 4. Hepatic PCB126 and TCDD levels.
Hepatic tissue levels from the (A) time course and (B) dose-response studies measured using
HRGC-HRMS. The results are displayed as the mean ± standard error of at least three
independent samples. Tissue levels (per liver wet weight) were multiplied by the corresponding
TEF value for each compound to facilitate comparisons. Animals were dosed with 300 μg/kg
PCB126 and 30 μg/kg TCDD in the time course study. Doses used in the dose-response study
are represented in the graph. Dose-response data are displayed on a log scale to visualize tissue
concentrations at all doses. An asterisk (*) indicates a significant (p<0.05) difference between
the treated samples and vehicle controls. (C) Comparison of PCB126 tissue levels to published
TCDD levels [17]. An asterisk (*) indicates significant (p<0.05) difference between PCB126 and
TCDD TEQ hepatic levels at 168 h. Results are displayed as the mean ± standard error of three
independent replicates.

47

48

the highest concentrations of PCB126 were discovered in the liver, followed by fat, with the
lowest concentrations in blood [2].
Comparison of hepatic PCB126 to published TCDD levels indicates that the TEQ levels
are comparable at every time point except for 168 h, where a significant decrease in TCDD
hepatic concentration was observed [17] (Figure 4C). This single-dose finding is similar to a
chronic exposure study (5 days/week over 13 weeks), where PCB126 was found to be
sequestered within the liver to a greater extent than TCDD, which was in part mediated by
binding to inducible Cyp1a2 [26].
PATHOLOGY
Hepatocellular vacuolization was observed in vehicle, PCB126- and TCDD-treated livers,
mainly in the periportal and midzonal regions and frequently extended into the centrilobular
region in more severely affected animals. Affected hepatocytes were characterized by
perinuclear and/or midcellular cytoplasmic loss and replacement by poorly delineated clear
vacuoles (Figure 5A-F). At 72 h, TCDD-treated animals also exhibited mixed cell infiltration
(neutrophils and mononuclear cells) (Figure 5F). In the PCB126 time course, vacuolization was
present in both treated and control animals. However, TCDD-elicited increases in vacuole
formation were significantly greater than the changes in vehicles and PCB126-treated livers at all
time points and doses. PCB126 elicited a pronounced dose-dependent increase in vacuolization,
(Figure 5B and 5C), however, microscopic changes (vacuolization and mixed cell infiltration)
elicited by TCDD were in any case more severe (Table 4, Figure 5D and 5E) [17]. Furthermore,
PCB126 did not induce necrosis in the time course and dose-response study, whereas TCDD
induced necrosis in the time course and dose-response studies (Table 5 and 6). Together, these

49

Figure 5. Representative histopathology results from vehicle, PCB126 and TCDD treated
mice at 72 h.
Liver sections from (A) vehicle showed minimal vacuolization most likely due to free access to
chow. (B) 300 μg/kg PCB126 elicited slight/moderate hepatocellular vacuolization, which
exhibited (C) dose-dependent increase at 1000 μg/kg PCB126. (D) 30 μg/kg TCDD elicited
marked vacuolization and minimal/slight necrosis, with (E) more pronounced vacuolization and
(F) mixed cell infiltration at 100 μg/kg TCDD. Arrows indicate necrotic hepatocytes. Bars =
50μm.

50

Table 4. Comparison of temporal incidence and severity of liver microscopic changes in PCB126 and TCDD experiments [17].
Time (h) and treatment
18

24

72

120

168

V

P

V

T

V

P

V

T

V

P

V

T

V

P

V

P

V

T

1.6

1.0

1.3

3.0

1.4

2.4

2.3

4.0

2.0

1.8

1.3

3.3

1.4

1.4

1.6

1.4

1.3

3.0

0

0

0

0

0

0

0

0

0

0

0

0.8

0

0

0

0

0

1.5

0

0

0

0

0

0

0

0

0

0

0

1

0

0

0

1

0

1.3

Hepatocellular
vacuolization:
Average
severity
Hepatocellular
necrosis:
Average
severity
Mixed cell
infiltration:
Average
severity

Note. The average severity scores are reported as a weighted average of minimal (grade of 1), slight (grade of 2), moderate (grade
of 3), and/or marked (grade of 4) responses divided by the total number of examined animals. V, vehicle; P, PCB126; T, TCDD.

51

Table 5. Temporal incidence and severity of liver microscopic changes in the vehicle-, PCB126- and TCDD-treated mice.
Time (h) and treatment
2
V
Hepatocellular
vacuolization:
0.4
Average
severity
Hepatocellular
necrosis:
Average
severity
Mixed cell
infiltration:
Average
severity

4

8

P

V

P

0

0.4 0.6

V

0

12
P

V

18
P

24

72

V

P

V

P

0.2 0.4 0.2 1.6

1

1.4 2.4

V

2

120
P

V

168
P

V

72
P

T

1.8 1.4 1.4 1.6 1.4 2.4

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0.8

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

0.2

Note. The average severity scores are reported as a weighted average of minimal (grade of 1), slight (grade of 2),
moderate (grade of 3), and/or marked (grade of 4) responses divided by the total number of examined animals. V,
vehicle; P, PCB126; T, TCDD.

52

Table 6. Dose-dependent incidence and severity of liver microscopic changes in the vehicle-,
PCB126- and TCDD-treated mice at 72 h.
Treatment and dose (µg/kg)
Vehicle

PCB126

TCDD

0
Hepatocellular
vacuolization:
Average
severity
Hepatocellular
necrosis:
Average
severity
Mixed cell
infiltration:
Average
severity

0.3

1

3

10

30

100

300 1000

30

100

0.6

1

0.8

1.2

1.2

1.4

1.8

1.8

2.2

2

3

0

0

0

0

0

0

0

0

0

0.4

1

0

0

0

0

0

0

0

0

0

0

0.6

Note. The average severity scores are reported as a weighted average of minimal (grade of
1), slight (grade of 2), moderate (grade of 3), and/or marked (grade of 4) responses divided
by the total number of examined animals.

53

data indicate that 300 μg/kg PCB126 is not equivalent to 30 μg/kg TCDD at inducing
histopathological changes.
Analysis of liver lipid extracts by TLC revealed increases in TG in TCDD- and PCB126treated animals at 72 and 168 h, respectively (Figure 6), consistent with the increases in RLW,
suggesting that fatty accumulation may contribute to increases in liver weight. These findings are
consistent with general hallmarks of liver toxicity due to dioxin and PCB exposure [27, 28].
Serum samples from vehicle and PCB126-treated animals were examined for changes in
TG, NEFA, CHOL, GLU, and ALT levels at 12, 24, 72, 120, and 168 h. Unlike TCDD, which
significantly increased serum TG, NEFA, and ALT, and decreased CHOL levels [17], PCB126
only increased (p<0.05) ALT levels at 168 h, indicative of slight liver injury.
TEMPORAL GENE EXPRESSION CHANGES
Hepatic gene expression was examined using custom mouse cDNA microarrays with
13,361 features representing 8,516 unique genes. In the PCB126 time course, 294 features
corresponding to 251 unique genes were differentially regulated (P1(t)≥0.9999 and |fold
change|≥1.5) at one or more time points relative to the time-matched vehicle controls (Figure
7A). Application of the same filtering criteria for the internal TCDD data set at 72 h identified
221 differentially regulated features, corresponding to 182 unique genes, representing
approximately twice as many dysregulated genes than PCB126 (Figure 7A).
Fifty-eight genes regulated by 30 μg/kg TCDD and 300 μg/kg PCB126 at 72 h were
identified. TCDD-specific differential expression correlated with the emergence of inflammatory
cell aggregates associated with degenerative and necrotic hepatocytes. Examples include
lymphocyte antigen 6 complex, locus A (Ly6a) and complement component 1, s subcomponent
(C1s) which were down-regulated by TCDD, but unaffected following PCB126 treatment.

54

Figure 6. Thin layer chromatograph of hepatic lipid extracts from vehicle-, PCB126-, and
TCDD- treated samples in the time course study.
Lipids from homogenized liver samples were extracted using chloroform:methanol. The extracts
were spotted on TLC plates, fractionated with hexane:diethyl ether:acetic acid mixture (90:30:1)
and developed with iodine. Lanes 1-4 represent vehicle, PCB126, vehicle and TCDD at 72 h,
respectively. Lane 5 represents the standard, while lanes 6-9 correspond to vehicle and PCB126
at 120 h and vehicle and PCB126 at 168 h, respectively. Increasing amounts of TG could be
observed in the treated samples when compared to the time-matched vehicle controls.

55

Responses specific to PCB126 typically included marginal differential expression that
were selected due to the hard statistical cut-offs. When the selection criteria were relaxed
(P1(t)≥0.99 and |fold change|≥1.2), the number of overlapping genes dramatically increased.
Consequently, the majority of genes differentially expressed following PCB126 treatment simply
missed the cut-offs or were regulated at time points other than 72 h. In contrast, TCDD elicited
robust gene expression responses associated with inflammatory cell infiltration and necrosis,
consistent with the histopathology assessment.
Clustering of microarray data provides a general view of the similarity of the temporal
profiles between the two compounds. Agglomerative hierarchical clustering of temporal gene
expression data by time point resulted in three main branches: early (2 and 4 h), mid (8-24 h),
and late (72-168 h) time points (Figure 8A). The clustering of 72 h TCDD with larger clustered
group of 8 + 12 h PCB126 and 18 + 24 h PCB126 indicates that the two chemicals are most
similar at these time points relative to the other time points, despite the differences in intensity
and gene expression patterns (Figure 8A).
DOSE-RESPONSE GENE EXPRESSION CHANGES
Analysis of the dose-response data at 72 h identified 436 microarray features representing
367 unique annotated genes, which were differentially expressed (P1(t)≥0.9999 and |fold
change|≥1.5) relative to vehicle controls, at one or more doses (Figure 7B). Among the 244
genes regulated by two TCDD doses and 265 genes regulated by 1000 μg/kg PCB126, 137 genes
were commonly regulated by both compounds. 249 of the 436 differentially expressed features,
corresponding to 214 unique genes, exhibited a sigmoidal dose-response profile as determined
by the particle swarm optimization [23] Java application. This tool first examines the dose-

56

Figure 7. Number of PCB126 differentially expressed genes.
Number of PCB126 differentially expressed genes in (A) time course and (B) dose-response
studies. The microarray data were filtered using a P1(t)≥0.9999 and |fold change|≥1.5 to identify
differentially expressed genes. The number of gene expression changes induced by 300 μg/kg
PCB126 increased over the course of the study, but did not exhibit the level of activity reported
for 30 μg/kg TCDD when compared to internal TCDD treatments and in a comparable TCDD
study [17]. Genes identified from the dose-response study were further analyzed to identify dosedependent changes in gene expression using a Java application to identify the best-fit doseresponse model.

57

Figure 8. Agglomerative hierarchical clustering of PCB126 gene expression data.
Agglomerative hierarchical clustering of PCB126 gene expression data in the (A) time course and (B) dose-response studies. Temporal microarray data clustered into early (2-4 h),
middle (8-24 h) and late (72-168 h) time point branches. 72 h TCDD gene expression data clustered with the 18 and 24 h PCB126 profiles. The PCB126 dose-response data
followed a positive correlation between gene expression and the administered dose, forming low, medium and high dose clusters. 30 and 100 μg/kg TCDD gene expression data
clustered with the 1000 μg/kg PCB126 gene expression profile.
58

response data for each gene using sigmoidal, exponential, linear, Gaussian, parametric classes to
identify the best-fit dose-response model (i.e. the model with the parameter set that best fits the
experimental data). The algorithm then chooses the model that best fits the data and calculates
the ED50.
ED50 values for differential gene expression ranged from 2.21 to 513 μg/kg dose of
PCB126. Because the PCB126 and Boverhof et al. TCDD dose-response studies were done at
different time points (72 h vs. 24 h, respectively), comprehensive comparisons between the ED50s
are not possible. In general, PCB126 exhibited higher ED50 values for Cyp1a1 (24.5 vs. 0.3
μg/kg), Nqo1 (301.4 vs. 8.8 μg/kg), and Pck1 (144.1 vs. 0.4 μg/kg) when compared with TCDD.
Hierarchical clustering of the dose-response gene expression data clustered according to
lower (1, 3 and 10 μg/kg), intermediate (30 and 100 μg/kg) and high (300 and 1000 μg/kg)
PCB126 doses with 30 and 100 μg/kg TCDD clustering with the high PCB126 group (Figure
8B).
FUNCTIONAL ANNOTATION OF DIFFERENTIALLY EXPRESSED GENES
Functional annotation of the 251 PCB126 elicited temporal gene expression changes was
associated with metabolizing enzymes, lipid metabolism, gluconeogenesis/glucose metabolism,
development and differentiation, necrosis, and immune signaling (Table 7). Metabolism
functions included the catalytic action of monooxygenases, oxidoreductases and xenobiotic
metabolizing enzymes such as the classical TCDD-inducible “AhR gene battery” members
Cyp1a1, Tiparp and Nqo1. Others included P450 oxidoreductase (Por), epoxide hydrolase 1,
microsomal (Ephx1), dehydrogenase/reductase (sdr family) member 3 (Dhrs3), glutaredoxin
(Glrx) and xanthine dehydrogenase (Xdh). A majority of the glutathione S-transferase family
(Gsta2, Gsta4, Gstm3, Gstt2) were also differentially regulated by PCB126. Genes associated
59

Table 7. Functional categorization and regulation of select hepatic genes identified as differentially regulated in response to
PCB126 and TCDD [17].

Metabolizing enzymes

Entrez
Functional
Gene
category
ID

11671
13076
13119
20148
13849
14629
93692
14860
14864
14870
14872
18104
18984
99929
53376
22235
22436

Gene
symbol

Gene name

Aldehyde dehydrogenase family 3, subfamily A2
Aldh3a2
Cytochrome P450, family 1, subfamily a, polypeptide 1
Cyp1a1
Cytochrome P450, family 4, subfamily a, polypeptide 14 Cyp4a14
Dehydrogenase/reductase (sdr family) member 3
Dhrs3
Epoxide hydrolase 1, microsomal
Ephx1
Glutamate-cysteine ligase, catalytic subunit
Gclc
Glutaredoxin
Glrx
Glutathione S-transferase, alpha 4
Gsta4
Glutathione S-transferase, mu 3
Gstm3
Glutathione S-transferase, pi 1
Gstp1
Glutathione S-transferase, theta 2
Gstt2
NAD(P)H dehydrogenase, quinone 1
Nqo1
P450 (cytochrome) oxidoreductase
Por
TCDD-inducible poly(ADP-ribose) polymerase
Tiparp
Ubiquitin specific protease 2
Usp2
UDP-glucose dehydrogenase
Ugdh
Xanthine dehydrogenase
Xdh

60

TCDD
Fold
internal
a
change controls

1.5
92
-1.9
1.8
1.8
1.7
1.6
1.7
1.8
4.4
1.7
2.1
2.2
14
2.1
2.7
2

No
Yes
Yes
Yes
No
No
No
Yes
Yes
Yes
No
Yes
Yes
Yes
No
Yes
Yes

TCDD
time
course
[17]

DRE

No
Yes
Yes
Yes
Yes
Yes
No
Yes
No
Yes
Yes
Yes
No
Yes
No
Yes
Yes

No
Yes
No
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No

b

Table 7 (cont’d).

Development/
differentiation

Lipid metabolism

Functional
category

Entrez
Gene
ID

11806
23801
64436
16956

Gene
symbol

Gene name

Fold
a
change

TCDD
internal
controls

TCDD
time
course
[17]

DRE

Apoa1
Aloxe3
Inpp5e
Lpl

-1.9
-1.6
1.5
2.5

Yes
Yes
No
Yes

Yes
Yes
No
Yes

No
Yes
No
No

Serpina6

2.1

Yes

No

Yes

26458
20249
20787
22359

Apolipoprotein A-I
Arachidonate lipoxygenase 3
Inositol polyposphate-5-phosphatase e
Lipoprotein lipase
Serine (or cysteine) proteinase inhibitor, clade A,
member 6
Solute carrier family 27 (fatty acid transporter),
member 2
stearoyl-coenzyme a desaturase 1
Sterol regulatory element binding factor 1
Very low density lipoprotein receptor

Slc27a2
Scd1
Srebf1
Vldlr

2.1
2.1
-2.1
1.6

Yes
No
Yes
Yes

Yes
No
Yes
Yes

Yes
No
No
Yes

27494
56484
15251
18128
21928

Angiomotin
Forkhead box o3a
Hypoxia inducible factor 1, alpha subunit
Notch gene homolog 1 (Drosophila)
Tumor necrosis factor, alpha-induced protein 2

Amot
Foxo3a
Hif1a
Notch1
Tnfaip2

1.7
1.8
1.7
2.4
3.6

No
No
Yes
Yes
Yes

Yes
Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes
Yes

12401

61

b

Table 7 (cont’d).
Entrez
Gene
ID

Gene name

26384
14381
14718
14571

Glucosamine-6-phosphate deaminase 1
Glucose-6-phosphate dehydrogenase x-linked
Glutamate oxaloacetate transaminase 1, soluble
Glycerol phosphate dehydrogenase 2, mitochondrial

18534

Phosphoenolpyruvate carboxykinase 1, cytosolic

TCDD
Fold
Gene
a internal
symbol change
controls

TCDD
time
course
[17]

DRE

-1.6
-1.5
-1.7
-1.7

No
No
Yes
Yes

Yes
No
Yes
Yes

Pck1

-2.2

Yes

Yes

Yes
No
Yes
Yes
Yes

Bnip3
Hip1

1.8
1.8

No
Yes

No
Yes

Yes
Yes

Necrosis/
apoptosis

Gnpda1
G6pdx
Got1
Gpd2

a

12176 Bcl2/adenovirus e1b interacting protein 1, nip3
215114 Huntingtin interacting protein 1
17869

Myelocytomatosis oncogene

Myc

1.7

Yes

Yes

Yes

Immune
response

Glucose
metabolism/
gluconeogenesis

Functional
category

12500
20299
15439

CD3 antigen, delta polypeptide
Chemokine (c-c motif) ligand 22
Haptoglobin

Cd3d
Ccl22
Hp

-1.7
1.7
-2

No
Yes
Yes

Yes
No
No

Yes
Yes
Yes

Maximum expression (|fold change| ≥1.5, P1(t) ≥ 0.9999).

b

DRE identified in -1500 to + 1500 of the transcriptional start site (TSS) [29].

62

b

with metabolism exhibited the highest fold change across the time course study. For example,
Cyp1a1 was induced 92-fold at the 18 h time point in the PCB126 time course, whereas Tiparp
was induced 14-fold at 4 h. PCB126 treatment also induced glutathione S-transferases by 1.7- to
4.4-fold at late time points.
Lipid metabolism genes like very low density lipoprotein receptor (Vldlr), lipoprotein
lipase (Lpl), apolipoprotein (Apoa1), stearoyl-Coenzyme A desaturase 1 (Scd1), arachidonate
lipoxygenase 3 (Aloxe3), and sterol regulatory element binding factor 1 (Srebf1) were either
induced or repressed by PCB126 treatment. For example, Srebf1 was repressed between 8-24 h,
whereas Aloxe3 and Apoa1 were repressed at later time points. In contrast, Vldlr, Lpl, and Scd1
were induced at least 1.6-fold at mid and late time points.
PCB126 and TCDD [17] elicited gene expression changes associated with development
and differentiation, including Notch gene homolog 1 (Drosophila) (Notch1), tumor necrosis
factor, alpha-induced protein 2 (Tnfaip2) and hypoxia inducible factor 1, and alpha subunit
H

(Hif1a). Apart from Notch1, which was continuously up-regulated, both Tnfaip2 and Hif1a were
induced at early and mid time points with fold changes ranging from 1.7- to 3.6-fold.
Overall, PCB126 differentially regulated the same gene functions as TCDD except for
changes associated with immune cell infiltration and hepatocellular necrosis, in agreement with
the histopathology observations. A more thorough discussion of the association between
differential gene expression and pathology has been previously published [17, 30]. Moreover,
the number of PCB126-elicited gene expression changes was approximately five times lower
than that of TCDD (Figure 9A) when compared with a TCDD study using the same experimental
design [17]. After relaxing the filtering criteria (Figure 9A), the number of TCDD regulated

63

Figure 9. Gene expression comparisons and correlation analysis.
(A) Differentially regulated genes sets for 300 μg/kg PCB126 and 30 μg/kg TCDD were
compared at 2, 4, 8, 12, 18, 24, 72 and 168 h with stringent filtering criteria (P1(t)≥0.9999 and
|fold change|≥1.5) and using relaxed criteria (P1(t)≥0.99 and |fold change|≥1.2) to further
examine ligand specific gene expression changes. Numbers in the Venn diagram represent
unique genes. (B) Toxicogenomic correlation plot of genes regulated by PCB126 and TCDD at
relaxed filtering criteria. Correlation analysis was used to visualize significance and expression
profiles comparisons to identify similarities and differences between PCB126 and TCDD [17]
temporal data sets. A vast majority of genes was found within the upper right hand quadrant and
exhibited profiles that were positively correlated in both gene expression and significance.
Overall, PCB126-elicited gene expression responses were a subset of TCDD regulated genes,
suggesting PCB126 does not elicit the full spectrum of responses induced by TCDD as indicated
in the histopathology and clinical chemistry results.

64

65

genes was significantly greater, with a majority of the common differentially expressed genes
having the same temporal expression pattern (Figure 9B). Nevertheless, at equipotent doses,
based on the TEF of 0.1, 300 μg/kg PCB126 was less effective in eliciting gene expression
responses when compared with 30 μg/kg TCDD.
VERIFICATION OF MICROARRAY RESPONSES
QRTPCR verified the temporal and dose-dependent changes in transcript levels for a
selected subset of differentially regulated genes identified by microarray analysis (Figure 10A
and 10B, respectively). However, data compression was evident for Cyp1a1 due in part to the
limited dynamic fluorescence intensity range (0-65,535) of microarrays, which resulted in signal
saturation and compression of the true level of induction.

DISCUSSION
The present study compared the hepatic effects of 300 μg/kg PCB126 to 30 μg/kg TCDD
using a comprehensive time course and dose-response toxicogenomic approach with
complementary histopathology, tissue-level analysis, and clinical chemistry. In order to use a
published report examining the hepatic effects of 30 μg/kg TCDD using the same experimental
design [17] as a comparator for this study, an equipotent dose of 300 μg/kg PCB126 was used in
this study based on the WHO TEF of 0.1. Given the conserved AhR mediated mechanism of
action, as well as the use of “equipotent” doses, we examined the hypothesis that 300 μg/kg
PCB126 would quantitatively and qualitatively elicit comparable effects when compared with 30
μg/kg TCDD. In general, the similar elicited effects were consistent with the AhR mediated
mechanism of action. However, there were also notable qualitative and quantitative differential
gene expression, pathology, and clinical chemistry differences suggesting that 30 μg/kg PCB126
does

not

elicit

comparable

responses

compared
66

with

30

μg/kg

TCDD.

Figure 10. QRTPCR verification of microarray responses.
QRTPCR verification of selected PCB126 (A) temporal and (B) dose-dependent microarray gene
expression responses. The same RNA that was used for cDNA microarrays was examined by
QRTPCR. All fold changes were calculated relative to time-matched vehicle controls. Bars (left
y-axis) and lines (right y-axis) represent QRTPCR and microarray data respectively with the xaxis representing the time points or dose. The genes are represented by their official gene
symbols. The error bars represent the standard error of the mean of five independent replicates.
Asterisks (*) indicate a significant change (p<0.05) for QRTPCR.

67

A
* *
* *

* *

4000
2000

*

Cyp1a1

*

60

4

Notch1

*

2

* *
*

*

*
*

20

3
2
1
0

20

*

15
10

Tiparp

*
*

5

*
*

2
4
8
12
18
24
72
12
0
16
8

Time (h)

Time (h)

2.0

*

2

Gsta4
2.0

1.5

*

* *

0.5

1.5

1.0
0.5

1.0

0.0

1.5

1.0

1.0
0.5
0.0

0.0
2
4
8
12
18
24
72
12
0
16
8

0.0

2.0

1.5

2.0

Pck1
2.0

2
4
8
12
18
24
72
12
0
16
8

0

1.0

2.5

2.5
Fold change

*

2.5
Fold change

2.5

*

Nqo1 *
8

2
4
8
12
18
24
72
12
0
16
8

Fold change

10

4

5
0

2
4
8
12
18
24
72
12
0
16
8

0

Time (h)

6

15
10

2
4
8
12
18
24
72
12
0
16
8

1
0

10

0

3

4
Fold change

6000

110
Fold change

Fold change

8000

Time (h)

Time (h)

Time (h)

68

Figure 10 (cont’d).

B

3000
10

*

1000
0

1

1.2

0

*

7.5

*

3

5.0

2

2.5

1

0.0

0

Dose (g/kg)

69

2.5
2.0

0.8

Pck1

1.5

0.6

1.0
0.5
0.0

0.4

1

1.00

Dose (g/kg)

1

0.
3

1.25

Gsta4

4

Fold change

* *

1.50

10.0

0.
3

*

1.75

Fold change

*

*

2

Dose (g/kg)

12.5

2.00

*

2.5

Dose (g/kg)

1
3
10
30
10
0
30
10 0
00

4.5
4.0 Nqo1
3.5
3.0
2.5
2.0
1.5
1.0

0.
3

Fold change

Dose (g/kg)

Tiparp

0.0

0.9

0.
3

1
3
10
30
10
0
30
10 0
00

0.
3

0

1.5

1
3
10
30
10
0
30
10 0
00

2000

*

2

3

1
3
10
30
10
0
30
10 0
00

20

5.0

3
10
30
10
0
30
10 0
00

*

4000

Notch1

1.8

0.
3

*

*

1
3
10
30
10
0
30
10 0
00

5000 Cyp1a1

3

Fold change

30

Fold change

Fold change

6000

Dose (g/kg)

For example, PCB126, like TCDD, significantly increased RLW. Complementary
microarray, clinical chemistry, and histopathology data suggest the increased RLW was due to
fatty accumulation resulting from the disruption of hepatic lipid uptake and metabolism.
PCB126-induced lipoprotein lipase (Lpl) expression may involve the hydrolysis of lipids from
chylomicrons and very low density lipoproteins, enabling free fatty acid accumulation [17].
PCB126 and TCDD also induced very low density lipoprotein receptor (Vldlr), which is required
for Lpl regulation. A decrease of Vldlr may result in hypertriglyceridemia associated with
decreased Lpl activity [31]. In addition, apolipoprotein A-1 was inhibited by PCB126 and
TCDD, consistent with reported decreases in transcript and activity levels after hepatic fatty acid
accumulation [32]. PCB126 and TCDD also induced solute carrier family 27 (fatty acid
transporter), member 2 (Slc27a2), which supports hepatocellular vacuolization through transport
of fatty acids into hepatocytes [33]. Direct comparison of hematoxylin and eosin stained slides
indicates that 300 μg/kg PCB126 is less effective in inducing hepatocellular vacuolization than
30 μg/kg TCDD [17] (Table 4) at later time points (18-168 h). This may also be attributed to the
TCDD-elicited differential expression of fatty acid synthase (Fasn), lipin 2 (Lpin2), low density
lipoprotein receptor-related protein 2 (Lrp2), CD36 antigen (Cd36), and fatty acid binding
protein 5, epidermal (Fabp5) [17], which were not induced by PCB126.
The presence of mixed cell infiltrates at later time points has been associated with the
expression of immune signaling genes [17]. Minimal PCB126-mediated inflammation was
observed at 168 h and was not observed in the 72 h dose-response study. It was coincident with
(C-C motif) ligand 22 (Ccl22) induction, which is produced in response to activated murine B
lymphocytes and dendritic cells [34]. Significant down-regulation of CD3 antigen, delta
polypeptide (Cd3d) and haptoglobin (Hp) at earlier time points preceded histological

70

inflammation. CD antigens are important in select immune signaling functions, including rolling
and migration, as well as T-cell activation [35], whereas inhibition of haptoglobin is involved in
hepatic acute-phase response [36]. Although Ccl22 and Cd3d were comparably regulated by
PCB126 and TCDD, haptoglobin showed more significant repression only in response to
PCB126. In contrast, TCDD-induced mixed cell infiltration was observed at 72 and 168 h in the
time course study and in the dose-response study at 100 μg/kg [17]. Lymphocyte antigen 6
complex, locus A (Ly6a), CD44 antigen (Cd44) involved in T-cell activation [37], as well as the
major histocompatibility complex (MHC) class II genes, H2-Ab1 and H2-Eb1, involved in
mediating antigen presentation and processing [38] were differentially regulated by TCDD.
However, none of these genes was regulated in response to PCB126, suggesting that they are key
players in eliciting TCDD-induced hepatic inflammation. The mixed cell infiltrates are likely a
response to tissue damage and therefore, the late regulation of immune signaling genes is not
directly mediated by the AhR. The attenuated immune signaling gene responses relative to 30
μg/kg TCDD are further evidence of a weaker hepatic response to 300 μg/kg PCB126.
A common hallmark of TCDD exposure is feed refusal, BW loss and depletion of energy
stores commonly referred to as “wasting syndrome” [5]. However, because pair-fed animals still
experience wasting, feed refusal alone is not sufficient to account for the effect. Like TCDD,
PCB126 repressed several gluconeogenesis genes including phosphoenolpyruvate carboxykinase
1 (Pck1), glycerol phosphate dehydrogenase 2, mitochondrial (Gpd2), as well as glutamate
oxaloacetate transaminase 1, soluble (Got1), albeit at lower efficacy. For the genes involved in
gluconeogenesis, 30 μg/kg TCDD caused more significant repression than 300 μg/kg PCB126.
Even though there were no significant changes in BW or BW gain at the doses used, the

71

inhibition of gluconeogenesis may still contribute to hepatotoxicity and an eventual wasting
effect [39].
TCDD also induced minimal to slight necrotic changes at 72 h in this study and in
published study at 72 and 168 h. Although, there was no evidence of necrosis in the PCB126
time course and dose-response sections, a number of genes involved in necrosis and apoptosis,
including BCL2/adenovirus E1B interacting protein 1, NIP3 (Bnip3), huntingtin interacting
protein 1 (Hip1), and myelocytomatosis oncogene (Myc) were regulated by PCB126. Even
though upregulation of Hip1 activates apoptosis [40] and Bnip3 mediates apoptosis and oncosis
in rodent models [41], 300 μg/kg and higher doses of PCB126 were not sufficient to cause
necrosis further indicating that 300 μg/kg PCB126 does not elicit comparable effects when
compared with 30 μg/kg TCDD.
Other genes of interest also exhibited lower induction by PCB126 when compared with
TCDD treatment. For example, members of the “AhR gene battery” were induced by PCB126
and TCDD including Cyp1a1, Nqo1, and Xdh. Their induction serves an important role in
detoxification, but may also contribute to reactive oxygen species formation, leading to cellular
oxidative stress and DNA fragmentation [17, 42, 43]. The induction of reactive oxygen speciesgenerating enzymes was accompanied by increases in glutathione transferases (Gsta1, Gsta4,
Gstp1, Gstt2, Gstm3). This contributes to the biotransformation of xenobiotics by catalyzing the
conjugation of reduced glutathione to electrophiles and products of oxidative stress to facilitate
their excretion [44]. In addition, Notch1 and Tnfaip2 were induced by PCB126 and TCDD. Both
exhibit specific patterns of expression in the developing liver and have been implicated in tissue
development [45]. Their role in hepatotoxicity is unknown, but they may be important in normal
AhR signaling during hepatic development because AhR null mice have reduced liver size and

72

distorted hepatic vasculature [46]. Although these genes were differentially expressed by both
compounds with similar profiles, in general, PCB126-elicited differential gene expression was
lower and for a shorter duration, again suggesting that 300 μg/kg PCB126 does not elicit
responses comparable to 30 μg/kg TCDD.
In summary, there were significant qualitative and quantitative differences in the effects
elicited by PCB126 when compared with TCDD. This included differences in gene expression,
histopathology, and clinical chemistry. Overall, 30 μg/kg TCDD elicited greater fold changes in
gene expression compared with 300 μg/kg PCB126. In addition, vacuolization, necrosis, and
mixed cell infiltration were less pronounced in mice treated with 300 μg/kg PCB126 compared
to 30 μg/kg TCDD. Furthermore, unlike TCDD, PCB126 did not alter circulating NEFA, TG,
CHOL or GLU levels. These differences cannot be attributed to metabolism because PCB126
tissue levels were comparable and even exceeded TCDD levels at later time points according to
HRGC-HRMS tissue analysis.
The data collectively and consistently indicate that 300 μg/kg PCB126 does not elicit
responses comparable to 30 μg/kg TCDD, suggesting that the 0.1 TEF for PCB126 is an over
estimate of its hepatotoxicity in immature, ovariectomized C57BL/6 mice. However, other
studies have found that the mammalian TEF of 0.1 for PCB126 accurately reflects its toxicity
[2]. Therefore, outside the context of other studies and given that the TEF is a point estimate that
may vary by a half-log unit, additional research is needed to warrant adjusting the PCB126 TEF
of 0.1. There are significant species differences in toxicity [47], and sensitivity [48], as well as
pharmacokinetic differences between congeners [10, 26], that must also be considered. Other
factors, including the lack of positionally conserved DREs [29], and non-additive (antagonistic)
interactions [10, 26], also confound the establishment of appropriate TEFs for human and

73

wildlife risk assessment. Consequently, there continue to be significant gaps in knowledge
regarding the validity of the PCB126 TEF and the accuracy of the 0.1 point estimate [16]. In
order to determine a more accurate TEF for the hepatotoxicity of PCB126, more comprehensive
dose-response studies at multiple times optimal for each specific endpoint are required. Ideally,
these would be comparative and include multiple species to account for species-specific
differences that may not be relevant to human or wildlife toxicity [29, 30].

74

REFERENCES

75

REFERENCES
1.

Mullin, M.D., et al., High-resolution PCB analysis: synthesis and chromatographic
properties of all 209 PCB congeners. Environ. Set Technol., 1984. 18: p. 468-476.

2.

NTP, NTP toxicology and carcinogenesis studies of 3,3',4,4',5-pentachlorobiphenyl (PCB
126) (CAS No. 57465-28-8) in female Harlan Sprague-Dawley rats (Gavage Studies).
NTP Technical Report, 2006(520): p. 4-246.

3.

Ganey, P.E. and S.A. Boyd, An approach to evaluation of the effect of bioremediation on
biological activity of environmental contaminants: dechlorination of polychlorinated
biphenyls. Environ Health Perspect, 2005. 113(2): p. 180-5.

4.

Wu, Q., K.R. Sowers, and H.D. May, Microbial Reductive Dechlorination of Aroclor
1260 in Anaerobic Slurries of Estuarine Sediments. Appl Environ Microbiol, 1998. 64(3):
p. 1052-1058.

5.

Denison, M.S. and S. Heath-Pagliuso, The Ah receptor: a regulator of the biochemical
and toxicological actions of structurally diverse chemicals. Bull Environ Contam
Toxicol, 1998. 61(5): p. 557-68.

6.

Safe, S., Molecular biology of the Ah receptor and its role in carcinogenesis. Toxicol
Lett, 2001. 120(1-3): p. 1-7.

7.

Hankinson, O., The aryl hydrocarbon receptor complex. Annu Rev Pharmacol Toxicol,
1995. 35: p. 307-40.

8.

Nebert, D.W., et al., Role of the aromatic hydrocarbon receptor and [Ah] gene battery in
the oxidative stress response, cell cycle control, and apoptosis. Biochem Pharmacol,
2000. 59(1): p. 65-85.

9.

Okey, A.B., L.M. Vella, and P.A. Harper, Detection and characterization of a low
affinity form of cytosolic Ah receptor in livers of mice nonresponsive to induction of
cytochrome P1-450 by 3-methylcholanthrene. Mol Pharmacol, 1989. 35(6): p. 823-30.

10.

Safe, S., Limitations of the toxic equivalency factor approach for risk assessment of
TCDD and related compounds. Teratog Carcinog Mutagen, 1997. 17(4-5): p. 285-304.

11.

Schmidt, J.V., et al., Characterization of a murine Ahr null allele: involvement of the Ah
receptor in hepatic growth and development. Proc Natl Acad Sci U S A, 1996. 93(13): p.
6731-6.

12.

Van den Berg, M., et al., Toxic equivalency factors (TEFs) for PCBs, PCDDs, PCDFs for
humans and wildlife. Environ Health Perspect, 1998. 106(12): p. 775-92.

13.

Poland, A. and J.C. Knutson, 2,3,7,8-tetrachlorodibenzo-p-dioxin and related
halogenated aromatic hydrocarbons: examination of the mechanism of toxicity. Annu
Rev Pharmacol Toxicol, 1982. 22: p. 517-54.
76

14.

Safe, S., Polychlorinated biphenyls (PCBs), dibenzo-p-dioxins (PCDDs), dibenzofurans
(PCDFs), and related compounds: environmental and mechanistic considerations which
support the development of toxic equivalency factors (TEFs). Crit Rev Toxicol, 1990.
21(1): p. 51-88.

15.

Haws, L.C., et al., Development of a refined database of mammalian relative potency
estimates for dioxin-like compounds. Toxicol Sci, 2006. 89(1): p. 4-30.

16.

Van den Berg, M., et al., The 2005 World Health Organization reevaluation of human
and Mammalian toxic equivalency factors for dioxins and dioxin-like compounds.
Toxicol Sci, 2006. 93(2): p. 223-41.

17.

Boverhof, D.R., et al., Temporal and dose-dependent hepatic gene expression patterns in
mice provide new insights into TCDD-Mediated hepatotoxicity. Toxicol Sci, 2005. 85(2):
p. 1048-63.

18.

Yang, Y.H. and T. Speed, Design issues for cDNA microarray experiments. Nat Rev
Genet, 2002. 3(8): p. 579-88.

19.

Burgoon, L.D., et al., Protocols for the assurance of microarray data quality and process
control. Nucleic Acids Res, 2005. 33(19): p. e172.

20.

Eckel, J.E., et al., Normalization of two-channel microarray experiments: a
semiparametric approach. Bioinformatics, 2005. 21(7): p. 1078-83.

21.

Eckel, J.E., et al., Empirical bayes gene screening tool for time-course or dose-response
microarray data. J Biopharm Stat, 2004. 14(3): p. 647-70.

22.

Vandesompele, J., et al., Accurate normalization of real-time quantitative RT-PCR data
by geometric averaging of multiple internal control genes. Genome Biol, 2002. 3(7): p.
RESEARCH0034.

23.

Shokooh-Saremi, M. and R. Magnusson, Particle swarm optimization and its application
to the design of diffraction grating filters. Opt Lett, 2007. 32(8): p. 894-6.

24.

Dennis, G., Jr., et al., DAVID: Database for Annotation, Visualization, and Integrated
Discovery. Genome Biol, 2003. 4(5): p. P3.

25.

Fisher, J.W., et al., Effect of PCB 126 on hepatic metabolism of thyroxine and
perturbations in the hypothalamic-pituitary-thyroid axis in the rat. Toxicol Sci, 2006.
90(1): p. 87-95.

26.

DeVito, M.J., et al., Dose-response relationships for disposition and hepatic
sequestration of polyhalogenated dibenzo-p-dioxins, dibenzofurans, and biphenyls
following subchronic treatment in mice. Toxicol Sci, 1998. 46(2): p. 223-34.

77

27.

NTP toxicology and carcinogenesis studies of 2,3,7,8-tetrachlorodibezno-p-dioxin
(TCDD) (CAS No. 1746-01-6) in female Harlan Sprague-Dawley rats (Gavage Studies).
Natl Toxicol Program Tech Rep Ser, 2006(521).

28.

NTP toxicology and carcinogenesis studies of 3,3',4,4',5-pentachlorobiphenyl (PCB 126)
(CAS No. 57465-28-8) in female Harlan Sprague-Dawley rats (Gavage Studies), in Natl
Toxicol Program Tech Rep Ser. 2006. p. 4-246.

29.

Sun, Y.V., et al., Comparative analysis of dioxin response elements in human, mouse and
rat genomic sequences. Nucleic Acids Res, 2004. 32(15): p. 4512-23.

30.

Boverhof, D.R., et al., Comparative toxicogenomic analysis of the hepatotoxic effects of
TCDD in Sprague Dawley rats and C57BL/6 mice. Toxicol Sci, 2006. 94(2): p. 398-416.

31.

Yagyu, H., et al., Very low density lipoprotein (VLDL) receptor-deficient mice have
reduced lipoprotein lipase activity. Possible causes of hypertriglyceridemia and reduced
body mass with VLDL receptor deficiency. J Biol Chem, 2002. 277(12): p. 10037-43.

32.

Duplus, E., M. Glorian, and C. Forest, Fatty acid regulation of gene transcription. J Biol
Chem, 2000. 275(40): p. 30749-52.

33.

Hirsch, D., A. Stahl, and H.F. Lodish, A family of fatty acid transporters conserved from
mycobacterium to man. Proc Natl Acad Sci U S A, 1998. 95(15): p. 8625-9.

34.

Schaniel, C., et al., Activated murine B lymphocytes and dendritic cells produce a novel
CC chemokine which acts selectively on activated T cells. J Exp Med, 1998. 188(3): p.
451-63.

35.

Lai, L., et al., Mouse cell surface antigens: nomenclature and immunophenotyping. J
Immunol, 1998. 160(8): p. 3861-8.

36.

Venteclef, N., et al., Liver receptor homolog 1 is a negative regulator of the hepatic
acute-phase response. Mol Cell Biol, 2006. 26(18): p. 6799-807.

37.

Sumoza-Toledo, A. and L. Santos-Argumedo, The spreading of B lymphocytes induced
by CD44 cross-linking requires actin, tubulin, and vimentin rearrangements. J Leukoc
Biol, 2004. 75(2): p. 233-9.

38.

Alfonso, C., et al., The impact of H2-DM on humoral immune responses. J Immunol,
2001. 167(11): p. 6348-55.

39.

Viluksela, M., B.U. Stahl, and K.K. Rozman, Tissue-specific effects of 2,3,7,8Tetrachlorodibenzo-p-dioxin (TCDD) on the activity of phosphoenolpyruvate
carboxykinase (PEPCK) in rats. Toxicol Appl Pharmacol, 1995. 135(2): p. 308-15.

40.

Gervais, F.G., et al., Recruitment and activation of caspase-8 by the Huntingtininteracting protein Hip-1 and a novel partner Hippi. Nat Cell Biol, 2002. 4(2): p. 95-105.

78

41.

Copple, B.L., et al., Modes of cell death in rat liver after monocrotaline exposure.
Toxicol Sci, 2004. 77(1): p. 172-82.

42.

Barouki, R. and Y. Morel, Repression of cytochrome P450 1A1 gene expression by
oxidative stress: mechanisms and biological implications. Biochem Pharmacol, 2001.
61(5): p. 511-6.

43.

Zimmerman, B.J. and D.N. Granger, Mechanisms of reperfusion injury. Am J Med Sci,
1994. 307(4): p. 284-92.

44.

Raza, H., et al., Multiple isoforms of mitochondrial glutathione S-transferases and their
differential induction under oxidative stress. Biochem J, 2002. 366(Pt 1): p. 45-55.

45.

Harper, J.A., et al., Notch signaling in development and disease. Clin Genet, 2003. 64(6):
p. 461-72.

46.

Lahvis, G.P., et al., Portosystemic shunting and persistent fetal vascular structures in
aryl hydrocarbon receptor-deficient mice. Proc Natl Acad Sci U S A, 2000. 97(19): p.
10442-7.

47.

Huang, S. and G.G. Gibson, Species and congener specific induction of hepatic
cytochrome P4504A by polychlorinated biphenyls. Biochem Pharmacol, 1992. 43(3): p.
637-9.

48.

Silkworth, J.B., et al., Comparison of TCDD and PCB CYP1A induction sensitivities in
fresh hepatocytes from human donors, sprague-dawley rats, and rhesus monkeys and
HepG2 cells. Toxicol Sci, 2005. 87(2): p. 508-19.

79

CHAPTER 4

Kopec AK, Burgoon LD, Ibrahim-Aibo D, Burg AR, Lee AW, Tashiro C, Potter D, Sharratt B,
Harkema JR, Rowlands JC, Budinsky RA, Zacharewski TR: Automated Dose-Response
Analysis and Comparative Toxicogenomic Evaluation of the Hepatic Effects Elicited by
TCDD, TCDF, and PCB126 in C57BL/6 Mice. Toxicol Sci 2010, 118:286-297.

80

CHAPTER 4
AUTOMATED DOSE-RESPONSE ANALYSIS AND COMPARATIVE
TOXICOGENOMIC EVALUATION OF THE HEPATIC EFFECTS ELICITED
BY TCDD, TCDF, AND PCB126 IN C57BL/6 MICE
ABSTRACT
The toxic equivalency factor (TEF) approach recommended by the World Health
Organization is used to quantify dioxin-like exposure concentrations for mixtures of
polychlorinated dibenzo-dioxins, -furans, and polychlorinated biphenyls (PCBs), including
2,3,7,8-tetrachlorodibenzofuran (TCDF) and 3,3’,4,4’,5-pentachlorobiphenyl (PCB126) relative
to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Whole-genome microarrays were used to
evaluate the hepatic gene expression potency of TCDF and PCB126, relative to TCDD with
complementary histopathology, tissue level analysis, and ethoxyresorufin-O-deethylase (EROD)
assay results. Immature ovariectomized C57BL/6 mice were gavaged with 0.001, 0.01, 0.03, 0.1,
0.3, 1, 3, 10, 30, 100, and 300 μg/kg TCDD and TEF-adjusted doses (TEF for TCDF and
PCB126 is 0.1) of TCDF or PCB126 (1, 3, 10, 30, 100, 300, 1000, and 3000 μg/kg of TCDF or
PCB126), or sesame oil vehicle and sacrificed 24 h post dose. In general, TCDD, TCDF and
PCB126 tissue levels, as well as histopathological effects, were comparable when comparing
TEF-adjusted doses. Automated dose-response modeling (ToxResponse Modeler) of the
microarray data identified 210 TCDF and 40 PCB126 genes that exhibited sigmoidal doseresponse curves with comparable slopes when compared to TCDD. These similar responses were
81

used to calculate a median TCDF gene expression REP of 0.06 and a median PCB126 gene
expression REP of 0.02. REPs of 0.02 were also calculated for EROD induction for both
compounds. Collectively, these data suggest that differences in the ability of the liganded aryl
hydrocarbon receptor:AhR nuclear translocator complex to elicit differential hepatic gene
expression, in addition to pharmacokinetic differences between ligands, influence their potency
in immature ovariectomized C57BL/6 mice.

INTRODUCTION
Polychlorinated aromatic hydrocarbons (PCAHs), including 2,3,7,8-tetrachlorodibenzop-dioxin (TCDD) and related chemicals, such as 2,3,7,8-tetrachlorodibenzofuran (TCDF) and
3,3’,4,4’,5-pentachlorobiphenyl (PCB126) are ubiquitous environmental contaminants that elicit
a broad spectrum of species-specific biochemical and toxic effects. Polychlorinated dibenzo-pdioxins (PCDDs) and dibenzofurans (PCDFs) are by-products of waste combustion, herbicide
production, and other industrial processes [1-4]. In contrast, polychlorinated biphenyls (PCBs)
were manufactured between 1930 and 1977 as mixtures that included PCB126, as well as other
congeners [5]. PCBs were used as coolants, lubricants and dielectric insulating fluids for
transformers and capacitors [4, 6, 7]. Even though their production has ceased, they continue to
be released into the environment through the mishandling of PCB-containing products [8].
Although there are 75, 135, and 209 possible PCDD, PCDF and PCB congeners,
respectively, only 7, 10, and 12 are considered dioxin-like based on their ability to bind and
activate the aryl hydrocarbon receptor (AhR) [9]. The AhR is a cytosolic ligand-activated basic
helix-loop-helix Per-Arnt-Sim domain containing transcription factor [10-13]. Following ligand
binding, chaperone proteins that maintain the AhR in an inactive state dissociate from the AhR
allowing translocation to the nucleus and heterodimerization with AhR nuclear translocator
82

(ARNT). The AhR:ARNT heterodimer complex then interacts with dioxin response elements in
the regulatory regions of target genes followed by recruitment of co-regulatory proteins, leading
to changes in gene expression [12, 14].
Environmental exposure to PCDDs, PCDFs, and PCBs typically occurs as a complex
mixture. In order to estimate the risk associated with a mixture, the concentration and potency of
each toxic PCDD, PCDF, and PCB congeners is taken into account based on its toxic
equivalency factor (TEF) relative to TCDD, the most toxic congener [9, 15-19]. TEFs are single
point potency estimates that were developed from relative potency (REP) values calculated by
comparing the effective dose (ED50) value of a single response elicited by TCDD and dividing it
by the ED50 for the same response elicited by the congener of interest [9, 19]. Consequently, the
uncertainty of a TEF point estimate can extend over orders of magnitude, reflecting the range of
relative potency values available for a particular congener in various model systems [7, 9, 11, 18,
20-22]. TEFs are independent of dose, time point, and tissue and largely reflect biochemical
effects such as enzyme induction. Although results from cancer bioassays or developmentalreproductive studies have been considered and are the critical human health risk assessment
endpoints for PCDDs and PCDFs, no TEF value is based exclusively on these endpoints.
Furthermore, it is assumed that at submaximal doses, the contribution of each congener is
additive. There are also pharmacokinetic and distributional differences between congeners that
may affect their REPs [23-27].
The TEFs for TCDF and PCB126 are currently set at 0.1, indicating that they are 10
times less potent than TCDD. However, our recent studies suggest that the TEF of 0.1 does not
accurately reflect the hepatic potency of TCDF and PCB126 relative to TCDD in C57BL/6 mice
[28-30]. Although pharmacokinetic differences are an important factor, temporal and dose83

dependent microarray data suggest that TCDF- and PCB126-elicited gene expression responses
are also not equivalent, in terms of potency and efficacy, relative to TCDD at TEF-adjusted
equivalent doses. To further investigate this hypothesis, parallel TCDD, TCDF and PCB126
dose-response studies were conducted at 24 h to minimize the pharmacokinetic and distributional
differences between these compounds. In addition to using the same species, comparable study
designs and analysis methods were also used as previously reported [28-30]. Moreover,
complementary histopathology, tissue level analysis, and ethoxyresorufin-O-deethylase (EROD)
activities were assessed. Results from this study not only expand the available REP data in
C57BL/6 mice but also suggest that TCDF- and PCB126-elicited differential gene expression
responses are not comparable to TCDD at TEF-adjusted equivalent doses at 24 h when
pharmacokinetic and distributional differences are minimized.

MATERIALS & METHODS
ANIMAL HUSBANDRY
Female C57BL/6 mice, ovariectomized by the supplier on postnatal day (PND) 20, with
body weights (BW) within 10% of the average, were obtained from Charles Rivers Laboratories
(Kingston, NY) on PND 25. Animals were housed in polycarbonate cages containing cellulose
fiber chips (Aspen Chip Laboratory Bedding, Northeastern Products, Warrensberg, NY) with 3040% humidity and a 12-h light/dark cycle (0700 h – 1900 h). Mice had free access to deionized
water and Harlan Teklad 22/5 Rodent Diet 8640 (Madison, WI). Animals were dosed on PND 28
following acclimatization for 3 days. Immature mice were used because they are more
responsive to AhR ligands and to facilitate comparisons with other data sets obtained using the
same model, study design and analysis methods [29-31]. Animals were ovariectomized to negate
potential interactions with estrogens produced by the developing ovaries because some animals
84

are approaching reproductive maturity. All procedures were carried out with the approval of the
Michigan State University Institutional Animal Care and Use Committee.
DOSE-RESPONSE STUDY
A stock solution of PCB126 (99.7% purity, AccuStandard, New Haven, CT) was
dissolved in acetone (J.T. Baker), followed by dilution in sesame oil (Sigma, St. Louis, MO), and
evaporation of the acetone using a mild stream of nitrogen gas. The PCB126 stock was further
diluted in sesame oil to achieve the desired doses. TCDD and TCDF were gifts from The Dow
Chemical Company (Midland, MI). Animals were orally gavaged using 1.5-inch feeding needle
with a 2.25-mm ball end (Cadence Science, Lake Success, NY). Mice received 0.1 ml of a single
dose of 0.001, 0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30, 100, and 300 μg/kg of TCDD or 1, 3, 10, 30, 100,
300, 1000, and 3000 μg/kg of PCB126 or TCDF or 0.1 ml sesame oil vehicle and sacrificed 24 h
post dose. PCB126 and TCDF doses were TEF adjusted to be equivalent to the TCDD doses [9]
(Table 8). Mice were sacrificed by cervical dislocation and tissue samples were removed,
weighed, flash frozen in liquid nitrogen, and stored at –80°C. The right lobe of the liver was
fixed in 10% neutral buffered formalin (Sigma) for histological analysis.
HISTOLOGICAL ANALYSIS
Fixed liver tissues were sectioned and processed in ethanol, xylene, and paraffin using a
Thermo Electron Excelsior tissue processor (Waltham, MA). Tissues were embedded in paraffin
with Miles Tissue Tek II embedding center, after which paraffin blocks were sectioned at 5 μm
with a rotary microtome. Liver sections were placed on glass microscope slides, washed twice in
xylene for 5 min, followed by four quick washes in ethanol and rinsed in water. Slides were
placed in Gill 2 hematoxylin (Thermo Fisher Scientific, Waltham, MA) for 1.5 min followed by
two to three quick dips in 1% glacial acetic acid water and rinsed with running water for 2-3 min.

85

Table 8. The 24 h dose-response study design.

Dose
(µg/kg)

0
0.001
0.01
0.03
0.1
0.3
1
3
10
30
100
300
1000
3000

Number of animals
TCDD PCB126 TCDF
4
4
4
4
4
4
4
4
4
4
4
-

4
4
4
4
4
4
4
4

86

4
4
4
4
4
4
4
4

Vehicle
(sesame oil)
7
-

Slides were then rinsed in ethanol and counterstained with 1% eosin Y-phloxine B solution
(Sigma) followed by multiple rinses in ethanol and xylene. Coverslips were attached using
aqueous mounting media. All the histological processing was performed at Michigan State
University Investigative HistoPathology Laboratory, Division of Human Pathology, using a
modified version of previously published procedures [32].
QUANTIFICATION OF HEPATIC TCDD, PCB126, AND TCDF LEVELS
Liver samples were processed in parallel with laboratory blanks and a reference or
background sample at Wellington Laboratories Inc. (Guelph, ON, Canada). The samples (100500 mg) were transferred to a tared screw cap culture tube and weights were recorded. Samples
were then spiked with a mixture of

13

C12-2,3,7,8-TCDD,

13

C12-PCB126, and

13

C12-2,3,7,8-

TCDF surrogates and digested with HCl. Each digest was split between two screw cap tubes and
3-4 ml hexane was added to each tube followed by vigorous mixing. The tubes were centrifuged,
and the organic layer was removed. The hexane extraction was performed three times per tube
with the six hexane fractions combined. The hexane fraction was then split and one fraction was
archived. The other fraction was processed further using a small multilayer (acid/base/neutral)
silica gel column eluted with 20-25 ml of hexane. The eluate was concentrated and transferred to
a conical microvial with pentane and dichloromethane rinses and allowed to dry. Immediately
prior to injection on the high-resolution gas chromatograph/high-resolution mass spectrometer
(HRGC-HRMS) system, a mixture of

13

C12-1,2,3,4-TCDD,

13

C12-PCB111, and

13

C12-1,2,3,4-

TCDF injection standards were added to the conical microvial. TCDD, PCB126, and TCDF
analyses were performed using an Agilent (Santa Clara, CA, USA) 6890 series HRGC with
direct capillary interface to a Waters (Milford, MA, USA) Autospec Ultima HRMS.
Chromatographic separations were carried out on a 60-m DB5 (0.25 mm ID, 0.25 µm film
87

thickness) column in constant flow mode (Helium, 1 ml /min). All injections were 1 µl using
splitless injection. The mass spectrometer was operated in positive electron ionization selective
ion recording mode at a mass resolving power of 10,000 or greater.
EROD ASSAY
Microsomes were extracted from ~100 mg samples by differential centrifugation [33].
Tissue was minced and homogenized using Tri-R Stir-R homogenizer (Tri-R Instruments, Inc.,
Rockville Centre, NY) in 0.05 M Tris (Invitrogen), 1.15% KCl (JT Baker, Phillipsburg NJ), pH
7.5, and then centrifuged at 4°C, 10,000  g for 10 min. The supernatant was centrifuged at 4°C,
100,000  g for 30 min. The microsomal pellets were re-suspended in 0.01 M EDTA
(Invitrogen), and 1.15% KCl, pH 7.4, and re-centrifuged at 100,000  g for 60 min. Final pellets
were resuspended in a stabilizing buffer (20% glycerol [J.T. Baker], 0.1 M KH2PO4 [J.T. Baker],
and 1 mM EDTA and 1 mM DTT [Roche Applied Science, Indianapolis IN], pH 7.25) and
stored at –80°C. Extracted hepatic microsomes from vehicle, TCDD-, PCB126-, and TCDFtreated mice were assayed for EROD activity by monitoring the production of resorufin
measured at 590 nm in a 96-well plate (Costar, Corning, NY) using a Fluoroskan Ascent
fluorometer (Thermo Fisher Scientific, Waltham, MA) and corresponding software (version 2.6).
The assay was performed in 0.05 M HEPES (Sigma), pH 7.8, and 3.35 mM 7-ethoxyresorufin
(Molecular Probes, Eugene OR). Catalytic activity was initiated by addition of 0.3 mM NADPH
(Sigma), and fluorescence was measured every 2 min. After 30 min, the assay was terminated by
addition of 36 µg fluorescamine (Sigma) in acetonitrile. Protein concentrations were measured at
460 nm using bovine serum albumin (Roche) as a protein standard. Linear portions of each
kinetic profile analysis were used to determine picomoles of resorufin produced per minute and
standardized to the total protein (mg).
88

RNA ISOLATION
Frozen liver samples (left lobe, ~100 mg stored at –80°C) were immediately transferred
to 1 ml TRIzol (Invitrogen, Carlsbad, CA) and homogenized using a Mixer Mill 300 tissue
homogenizer (Retsch, Germany). Total RNA was isolated according to the manufacturer’s
protocol with an additional acid phenol:chloroform extraction. Isolated RNA was resuspended in
RNA storage solution (Ambion Inc., Austin, TX) and quantified (A260). RNA quality was
assessed by determining the A260/A280 ratio and visual inspection of 2 μg on a denaturing gel.
MICROARRAY EXPERIMENTAL DESIGN
Treated and vehicle RNA samples were individually hybridized to 4  44 K Agilent
oligonucleotide microarrays (Agilent Technologies, Inc., Santa Clara, CA). Three biological
replicates were performed using one-color labeling (Cy3) for each dose according to the
manufacturer’s protocol (Agilent Manual: G4140-90040 v. 5.7). Microarray slides were scanned
at 532 nm (Cy3) on a GenePix 4000B scanner (Molecular Devices, Union City, CA). Images
were analyzed for feature and background intensities using GenePix Pro 6.0 (Molecular
Devices). All data were archived in TIMS dbZach data management system [34].
MICROARRAY ANALYSIS
All microarray data in this study passed our laboratory quality assurance protocol [35].
TCDD, PCB126, and TCDF data sets were independently normalized due to the overall size of
the files using a semiparametric approach [36]. Posterior probabilities were then calculated using
an empirical Bayes method on a per-gene and dose basis using model-based t values [37]. Gene
expression data were then ranked and prioritized (P1(t) values >0.99 or >0.90 and |fold
change|>1.5) to identify differentially expressed genes.

89

QUANTITATIVE REAL-TIME PCR
Quantitative real-time PCR (QRTPCR) was performed as previously described [31].
Briefly, 1 μg of total RNA was reverse transcribed by SuperScript II (Invitrogen) using an
anchored oligo-dT primer. The complementary DNA (cDNA) (1 μl) was used as a template in a
30-μl reaction containing 0.1 μM of forward and reverse gene-specific primers, 3 mM MgCl2, 1
mM dNTPs, 0.025 IU AmpliTaq Gold, and 1 SYBR Green PCR buffer (Applied Biosystems,
Foster City, CA). Amplification was conducted on an Applied Biosystems PRISM 7500
Sequence Detection System. The cDNAs were quantified using a standard curve approach, and
the copy number of each sample was standardized to 3 housekeeping genes (Actb, Gapdh, Hprt)
[38]. For graphing purposes (GraphPad Prism 5.0), the relative expression levels were scaled
such that the expression level of the time-matched control group was equal to one.
DOSE-RESPONSE MODELING
Dose-response modeling was performed using the ToxResponse Modeler [39].
ToxResponse Modeler performs automated dose-response modeling by identifying the best fit
model among five different mathematical models (linear, exponential, Gaussian, sigmoidal, and
quadratic). The algorithm then chooses the best-fit of the five best in-class models. The overall
best-fit model is then used to calculate the ED50 values. Microarray dose-response data were
first filtered using a P1(t)>0.90 cut-off and filtered to identify genes exhibiting a sigmoidal doseresponse profile. REP values were calculated on a per-feature/per-gene basis using model-based
ED50 values:

REP

TCDD ED
TCDF or PCB126 ED

90

STATISTICAL ANALYSIS
All statistical analyses were performed with SAS 9.1 (SAS Institute, Cary, NC). All data
(with the exception of microarray data) were analyzed by ANOVA followed by Dunnett’s or
Tukey’s post hoc tests. Differences between treatment groups were considered significant when
p<0.05.

RESULTS
LIVER AND BWS
Rodents exposed to PCAHs including dioxins, PCBs, and furans exhibit temporal and
dose-dependent hepatic changes characterized by increases in liver weight [8, 12, 40]. PCB126
and TCDF have TEFs of 0.1 [9], indicating that 10 times more chemical is required to elicit
similar effects compared to an equivalent dose of TCDD. In our study, PCB126 and TCDF doses
were TEF-adjusted to theoretically match the potency of TCDD. Significant (p<0.05) increases
in relative liver weight (RLW) were observed with 300 µg/kg TCDD, 300 µg/kg PCB126, and
1000 and 3000 µg/kg TCDF in agreement with previous reports using the same compounds,
animal models and study designs (Figure 11) [29-31, 41]. No changes in BW or BW gain relative
to vehicle controls were observed within 24 h, indicating no systemic toxicity or wasting
syndrome response at the doses used.
HEPATIC TCDD, PCB126, AND TCDF LEVELS
TCDD, PCB126, and TCDF levels per liver wet weight (in pg/g) were measured in three
individual samples per dose using HRGC-HRMS. A dose-dependent increase in the hepatic
concentration of all three compounds was observed (Figure 12A-C and Table 9). The tissue
levels of TCDD, PCB126, and TCDF were significantly (p<0.05) higher compared to vehicle
controls, except for the lowest doses of TCDD (0.001-0.1 µg/kg), because of the presence of low
91

Figure 11. Dose-dependent changes in the RLWs.
Dose-dependent changes in the relative liver weights 24 h post dose for TCDD, PCB126, and
TCDF. Results are displayed as mean ± SE of at least 4 independent replicates. Data were
analyzed by ANOVA followed by Dunnett’s post hoc test. Asterisk (*) indicates p<0.05 for
vehicle vs. treated samples. No additional significant treatment-related alterations in liver or
organ weights were noted in the dose-response study.

92

*

10

0

*

*

10 0
0
30 0
00

0

30

VE
H

*

10 0
0
30 0
00

*

*

30

*

*

3
10
30

*

1

VE
H

8
7
6
5
4
3
2
1
0

*

*

PCB126 dose (g/kg)

TCDD dose (g/kg)
Log hepatic concentration
(pg/g)

C

*

*

*

*

10

*

*

*

3
10
30

*

*
* *

*

8
7
6
5
4
3
2
1
0

1

8
7
6
5
4
3
2
1
0

Log hepatic concentration
(pg/g)

B

V
0. EH
00
0. 1
0
0. 1
03
0.
1
0.
3
1
3
10
3
100
300
0

Log hepatic concentration
(pg/g)

A

TCDF dose (g/kg)

Figure 12. Hepatic tissue level quantification.
Hepatic (A) TCDD, (B) PCB126 and (C) TCDF levels per g liver wet weight measured 24 h post
dose using HRGC-HRMS. The data are displayed on a log scale to visualize tissue
concentrations at all doses. The results are displayed as mean ± SE of at least three independent
samples. Data were analyzed by ANOVA followed by Dunnett’s post hoc test: *p<0.05 for
vehicle vs. treated samples.

93

Table 9. Absolute hepatic tissue levels (in pg/g or ppt per wet weight) for TCDD-, PCB126-, and TCDF-treated samples and
vehicle controls measured by HRGC-HRMS.
Absolute hepatic tissue levels (in pg/g or ppt per wet weight)
Dose (µg/kg)

TCDD

PCB126
  

Vehicle
(sesame oil)
0.001
0.01

TCDF
  

2

1

2

2

-

-

1

1

-

-

2

1

-

-

2

1

-

-

3

2

-

-

3

3

4

3

4

4

5

4

5

4

6

5

1.0010 ± 9.8410

3

1.2210 ± 1.9810

2.0710 ± 2.3910
8.8310 ± 7.1210

0.03

1.0910 ± 1.2910

0.1

3.1010 ± 2.0610

0.3

1.0810 ± 2.2110

1

6.7310 ± 2.2110

3

1.9010 ± 4.8510

10

8.7010 ± 2.7810

30

2.2510 ± 1.0610

100

4.7510 ± 5.0810

300

1.5210 ± 3.0710

2.2910 ± 4.9210

1000

-

6.4110 ± 1.9510

3000

-

1.2510 ± 1.0110

3

3

3

4

3

4

4

5

4

5

5

6

5

6

6

7

6

8.6610 ± 2.5810
2.7210 ± 9.2410
6.0810 ± 1.2310
2.8910 ± 2.7210
8.3910 ± 2.0610

Note. The values represent mean ± SD of at least 3 independent replicates.
94

3

3

1.0510 ± 1.6810

3

3

4

3

4

3

5

5

5

5

6

5

6

5

6

5

4.7910 ± 1.5610
1.8810 ± 1.7010
5.8110 ± 6.3610
1.8010 ± 3.8110
6.8110 ± 2.2410
1.2910 ± 3.5210
2.9610 ± 4.6610
6.6310 ± 4.0510

background levels of TCDD. At TEF-equivalent doses, the levels of all three compounds were
comparable, except for statistical differences at 0.1, 0.3, 100, and 300 µg/kg. The use of TEF
values for determining tissue concentrations increases the uncertainty and reliability of potency
estimates [9]. Despite this limitation, the use of TEF-adjusted tissue level data suggests that the
potencies of TCDD, TCDF, and PCB126 should be comparable because there were minimal
differences in hepatic absorption, distribution and metabolism between compounds at 24 h. This
is significant because pharmacokinetic differences between these ligands have been reported to
contribute to substantial differences in potencies at later time points [29, 30, 42, 43].
HISTOPATHOLOGY
Hematoxylin and eosin staining was examined on the three highest TEF-equivalent doses
(30, 100, and 300 µg/kg TCDD; 300, 1000, and 3000 µg/kg PCB126 and TCDF). TCDD,
PCB126, and TCDF elicited dose-dependent increases in periportal hepatocellular vacuolization
and multifocal mixed inflammatory infiltration (Table 10, Figure 13). TCDD and the equivalent
TEF-matched TCDF doses elicited comparable hepatic vacuolization, while TEF-equivalent
PCB126 doses exhibited slightly less vacuolization (Table 10). Hepatocellular single cell
necrosis was present only in the highest dosed groups for all three compounds with the most
pronounced necrosis occurring with 3000 µg/kg TCDF (Table 10, Figure 13). Comparable levels
of dose-dependent mixed inflammatory cell foci (lymphocytes, monocytes and neutrophils) were
observed. Qualitatively, these results suggest that TEF-matched doses of TCDD, PCB126, and
TCDF elicit comparable histopathological effects at 24 h.
EROD ACTIVITY
Hepatic EROD activity was assessed to anchor our results to data within the World
Health Organization (WHO) REP database [18]. TCDD induced a dose-dependent, sigmoidal

95

Table 10. Dose-dependent incidence and severity of hepatic histopathological responses in the vehicle, TCDD, PCB126 and
TCDF treated mice at 24 h.
Treatment and dose (µg/kg)
Vehicle

TCDD

PCB126

TCDF

0

30

100

300

300

1000

3000

300

1000

3000

0

1

1.75

2.5

1

1.5

2

1.25

1.75

2.5

0

0

0

0.5

0

0

0.5

0

0

1.25

0

0.25

0.25

0.5

0

0.5

0.5

0

0.25

0.75

Hepatocellular
vacuolization
Average severity:
Hepatocellular
single cell necrosis
Average severity:
Mixed cell
infiltration
Average severity:

Note. The average severity scores are reported as a weighted average based on the following scoring scheme: minimal (grade of 1),
slight (grade of 2), moderate (grade of 3), and/or marked (grade of 4) responses divided by the total number of examined animals.

96

Figure 13. Representative hematoxylin and eosin stained liver sections 24 h post exposure to PCB126, TCDF, or TCDD.
Selected doses resulted in vacuolization, single cell necrosis and/or immune cell infiltration. (A) Vehicle treatment resulted in no
visible hepatic alterations. (B) 300 μg/kg PCB126 elicited minimal vacuolization, compared to (C) 1000 μg/kg TCDF and (D) 300
μg/kg TCDD which exhibited more pronounced vacuolization. (E) 3000 μg/kg TCDF resulted in immune cell infiltration, as well as
(F) instances of necrosis (arrow). Bars = 50 μm.

97

increase in EROD activity with an ED50 of 1.1 µg/kg, calculated using the ToxResponse
Modeler [39] (Figure 14). TEF-matched doses of PCB126 and TCDF elicited comparable EROD
induction (Figure 14), but were less potent than TCDD (PCB126 ED50=53.6 µg/kg, TCDF
ED50=54.2 µg/kg). These EROD activity REPs (0.02 for PCB126 and TCDF) are consistent
with values reported in the WHO REP database [18]. Moreover, in our published study
examining TEF-equivalent doses of TCDD and TCDF at 72 h, the ToxResponse Modeler
identified a comparable EROD REP value (TCDF EROD REP72h of 0.04) [28].
HEPATIC TCDF AND PCB126 GENE EXPRESSION REPS
TCDF and PCB126 microarray data were analyzed to determine both per-gene and
aggregate gene expression REP values using set theory and our ToxResponse Modeler (Figure
15A-C). The analysis abides by assumptions underlying the TEF concept (e.g., same mechanism
of action, parallel dose-response curves). TCDD, TCDF, and PCB126 microarray data sets were
individually normalized [36] and analyzed using an Empirical Bayes approach to identify
differentially expressed features. Compared to previously published microarray studies [29-31], a
more relaxed P1(t) cut-off was used (P1(t)>0.90) to be more inclusive, yielding higher statistical
power at the risk of including more false-positives at this early stage in the dose-response
analysis.
TCDD elicited ~1.6 times more differentially expressed features when compared to
TCDF (13,476 vs. 8655, respectively) (Figure 15A) at comparable hepatic levels. This trend is
similar to the 72 h dose-response analysis, where TCDD elicited the differential expression of
almost twice as many features compared to TCDF, but at lower hepatic TCDF levels due to

98

Figure 14. EROD activity.
Dose-dependent induction of EROD activity following exposure to TEF-equivalent doses of
TCDD, PCB126 and TCDF. The ToxResponse Modeler calculated ED50 values of 1.1 µg/kg,
53.6 µg/kg and 54.2 µg/kg for TCDD, PCB126, and TCDF, respectively, yielding PCB126 and
TCDF EROD REPs of 0.02. Symbols represent the mean ± SE of four independent samples.
Sigmoidal dose-response curve fitting was done using GraphPad Prism 5.0.

99

ligand-specific pharmacokinetics [26, 28, 42-45]. Likewise, TCDD differentially expressed ~1.3
times more features compared to PCB126 (13,476 vs. 10,983, respectively) (Figure 15A). It
should be noted that TCDD consistently elicited the greatest number of differentially expressed
features (and genes in subsequent analysis) compared to either TCDF or PCB126.
Unions of the TCDD plus TCDF (16,227 features) and TCDD plus PCB126 (17,053
features) data sets were taken to include all differentially regulated features for further doseresponse and REP analysis. Features missing data at one or more doses, as well as the Agilent
included controls were removed and not considered further. Features were then mapped back to
genes to investigate dose-dependent changes in expression (Figure 15A).
Genes with a P1(t) > 0.90 were also filtered for a >1.5-fold change in expression for at
least one dose, relative to time-matched controls. This resulted in 5191 TCDD and 3919 TCDF
genes that exhibited an expression change greater than 1.5-fold for at least one dose with a
corresponding P1(t)>0.90 and an ED50 value within the experimental dose range (Figure 15B).
This means that of the 5191 genes differentially expressed by TCDD for at least one dose, 3120
genes were not represented in the TCDF data set, and of the 3919 TCDF genes, 1848 were not
represented in the TCDD set (Figure 15B). Compared with the 72 h dose-response analysis [28],
approximately five times more TCDD and TCDF genes were identified at 24 h (5191 TCDD and
3919 TCDF genes at 24 h compared with 1027 TCDD and 837 TCDF genes at 72 h).
The ToxResponse Modeler then identified the best linear, exponential, Gaussian,
sigmoidal, and quadratic models for each dose-responsive differentially expressed gene (e.g.,
best in class). The model that best fit the data among the different classes (e.g., best in show) was
then used to calculate an ED50 for each dose-responsive gene. Only those genes with an ED50
within the experimental dose-response range were retained for subsequent REP analysis.

100

Figure 15. Comprehensive TCDD, TCDF, and PCB126 dose-response analysis.
(A) Differentially regulated features were identified using a relaxed P1(t) cut-off of 0.90 to
maximize the number of differentially expressed genes under consideration. TCDD elicited ~1.6
times more differentially expressed features compared to TCDF (13,476 vs. 8655) and ~1.3
times more differentially expressed features compared to PCB126 (13,476 vs. 10,983). Unions
were taken to identify all differentially regulated features. Features missing data at any dose, as
well as Agilent controls, were removed and not considered further. Features were mapped to
specific genes for further analysis.
(B) Differentially expressed genes were analyzed further if the change in expression was greater
than 1.5-fold for at least one dose. Genes exhibiting a sigmoidal dose-response were identified
and intersected to identify genes responsive to both TCDD and TCDF and to TCDD and
PCB126 at 24 h. Genes were examined further if the model-based ED50 value, calculated by
ToxResponse Modeler, was within the experimental dose range. Genes exhibiting a sigmoidal
dose-response curve for both TCDD and TCDF were intersected to identify 1506 genes that
exhibited an expression change greater than 1.5-fold for at least one dose, a P1(t)>0.90, a
sigmoidal profile and an ED50 value within the experimental range. Identical analysis yielded
only 388 sigmoidal genes for TCDD and PCB126. PCB126 lost a majority of differentially
expressed, dose-responsive genes from further consideration because the ED50s were not within
the experimental dose range.
(C) In the final analysis, assumptions regarding similarities in the slopes and shapes of
corresponding TCDD and TCDF as well as TCDD and PCB126 sigmoidal dose-response curves
were assessed by calculating the correlation coefficients of the elicited dose-response curves.
The correlation analysis identified 210 genes from TCDD vs. TCDF comparisons, and only 40
genes from TCDD and PCB126 comparisons with correlation coefficients greater than 0.60. The
distribution of individual gene expression REPs is provided. The median REP for hepatic gene
expression in the immature, ovariectomized C57BL/6 mouse at 24 h was calculated to be 0.06
and 0.02 for TCDF and PCB126, respectively.

101

102

The intersection of 5191 TCDD and 3919 TCDF genes identified 2071 genes that were used to
calculate gene-specific REPs. Furthermore, 3120 and 1848 genes were TCDD- and TCDFspecific, respectively. Of those, 2137 TCDD and 1630 TCDF genes exhibited sigmoidal doseresponse curves. The intersection of 2071 genes in common between TCDD and TCDF with
reasonable ED50 values identified 1506 genes with sigmoidal dose-response curves (Figure
15B).
In order to comply with WHO TEF assumptions regarding similar slopes and curve
shapes, only those genes that exhibited correlation coefficient greater than 60% were considered
further for gene-specific REP analysis. In this study, the coefficient was lowered compared to the
72 h TCDF dose-response analysis (correlation coefficient> 80%) [28] to include more genes
and to account for the smaller overlap between TCDD and PCB126 data sets, thus facilitating
comparisons between PCB126 and TCDF at 24 h. Correlation analysis identified 210 genes with
similar slopes and curve shapes elicited by TCDD and TCDF (Figure 15C). Sixteen of these
genes had gene specific REP values between 0 and 0.01, 28 between 0.01 and 0.03, 89 between
0.03 and 0.1, 51 between 0.1 and 0.3, 16 between 0.3 and 1, and 10 genes had REPs greater than
10. The median REP for all 210 genes was 0.06, lower than the 72 h median REP of 0.1 (0.096)
calculated using 83 genes with similar slopes and curves for TCDD and TCDF [28]. Relaxing the
correlation coefficient used for the 72 h dose-response study [28] to 60% resulted in an almost
identical median gene expression REP of 0.1 (0.112).
An identical approach was used to determine PCB126 gene-specific REPs and a median
REP (Figure 15C). In summary, only 40 genes exhibited similar slope and sigmoidal curves, of
which 14 genes had REPs between 0 and 0.01, 12 between 0.01 and 0.03, 11 between 0.03 and
0.1, 2 between 0.3 and 1, and only 1 gene had a REP greater than 1. The median PCB126 gene
103

expression REP for the 40 sigmoidal genes was 0.02. The PCB126 data set lost a majority of
genes from further consideration because the ED50s were not within the experimental dose
range. The low number of dose-responsive genes considered for the PCB126 REP analysis can
be attributed to the lower number of differentially expressed genes with sufficient potency when
compared to TCDF and TCDD.
REPs based on tissue levels were also calculated for a subset of responsive genes used to
determine REPs based on administered dose. However, a vast majority of tissue level-based
REPs could not be calculated due to differences in curve shapes and slopes. Overall, the median
tissue level gene expression REPs for TCDF and PCB126 were comparable to administered
dose-based REPs for the limited number of genes considered (TCDF REPadministered dose =
0.06, TCDF REP tissue level = 0.04, and PCB126 REPsadministered dose and tissue level = 0.02).
Note that approximately 60% of the tissue level-based REPs were within an order of magnitude
of the administered dose-based REPs.
Microarray analysis suggests there were a number of TCDF- and PCB126-specific
responses. Three-way Venn analysis of the 3280, 2343, and 1411 gene expression changes
elicited by TCDD, TCDF, and PCB126 (P1(t)>0.99 and |fold change|>1.5), respectively,
identified only 202 genes differentially expressed by all 3 compounds (Figure 16A). However,
after relaxing the statistical cut-off (P1(t)>0.90 and |fold change|>1.5) (Figure 16B), almost all
PCB126- and TCDF-elicited gene expression changes overlapped, consistent with the conserved
AhR-mediated mechanism of action. Overlaps between TCDD versus TCDF and TCDD versus
PCB126 also significantly increased. The high number of TCDD-specific genes could be
attributed to a wider range of doses used in the study (11 doses of TCDD vs. 8 doses of PCB126

104

Figure 16. Three-way Venn analysis.
Three-Way Venn analysis of TCDD, PCB126 and TCDF elicited differentially expressed genes
at (A) stringent (|fold change|>1.5, P1(t)>0.99) and (B) relaxed statistical criteria (|fold
change|>1.5, P1(t)>0.90).

105

and TCDF), and the greater potency and efficacy of TCDD even at TEF-adjusted doses of TCDF
and PCB126.
These results are in agreement with reports suggesting that TCDF- and PCB126-elicited
responses are a subset of TCDD regulated genes and consistent with a common AhR-mediated
mode of action [29, 30]. At later time points, TCDD elicits more pronounced histopathological
responses and changes in serum clinical chemistry compared with TCDF and PCB126. A more
thorough discussion of the association between differential gene expression and pathology was
previously reported [29-31, 41]. QRTPCR confirmed the dose-dependent changes in expression
for a subset of AhR-regulated genes (Figure 17). The classic members of the “AhR gene battery”
involved in phase I and phase II metabolism including Cyp1a1, Cyp1a2, and Nqo1 [46] were
induced. In addition, Tiparp previously reported to exhibit robust AhR-dependent expression [47,
48], and Notch1, implicated in cell differentiation and suggested to have a role in normal AhR
signaling during liver development [31, 49], were also significantly expressed. The results from
QRTPCR analysis were in good agreement with the microarray data.

DISCUSSION
In this study, the dose-dependent hepatic gene expression effects of TEF-adjusted,
equipotent doses of TCDF and PCB126 (WHO TEF = 0.1) relative to TCDD (WHO TEF = 1)
were examined at 24 h. In addition, complementary histopathology and EROD activity were
assessed to associate dose-dependent changes in gene expression to higher order effects, at
comparable TEF-adjusted TCDD, TCDF, and PCB126 tissue levels. Unlike comparable studies
at later time points, the TEF-adjusted tissue levels for TCDD, TCDF, and PCB126 were
comparable at 24 h, except for slight statistical differences at 0.1, 0.3, 100 and 300 µg/kg (dose 
TEF). Therefore, differences in pharmacokinetics cannot be used to explain the weaker responses
106

elicited by equipotent, TEF-adjusted doses of TCDF and PCB126. By minimizing the differences
in TEF-adjusted levels of TCDD, TCDF, and PCB126 in the liver, we further investigated
qualitative and quantitative gene expression differences that may contribute to the weaker
responses elicited by TCDF and PCB126.
Overall, the results of this 24 h study are consistent with our previous 72 h studies using a
similar model, the same oligonucleotide microarrays, and the same analysis methods [28-30]. At
the highest doses, 3000 µg/kg TCDF, 3000 µg/kg PCB126, and 300 µg/kg TCDD elicited
comparable increases in RLW accompanied by similar levels of hepatocellular vacuolization,
and minimal evidence of necrosis and immune cell infiltration at 24 h. Consequently, TEFadjusted, equipotent doses of TCDF and PCB126 elicited comparable phenotypic level effects
when compared to TCDD. However, at later time points (72 and 168 h), with more apparent
differences in tissue levels, TCDD, TCDF, and PCB126 elicited different levels of vacuolization,
necrosis, and immune cell infiltration [28-31], consistent with reported ligand-specific
pharmacokinetics [25].
Despite comparable levels of vacuolization, necrosis, and immune cell infiltration at 24 h,
there were significant differences in EROD activity induced by TCDF, PCB126, and TCDD.
TCDF and PCB126 induced comparable EROD activity levels, resulting in ED50 values of 53.6
and 54.2 µg/kg, respectively. The EROD ED50 for TCDD was 1.1 µg/kg, comparable to the
ED50 of 2.5 µg/kg in B6 mice at 24 h [50] and 1.6 µg/kg in C57BL/6 mice at 7 days [51]. This is
also in agreement with the TCDF EROD ED50 at 72 h in terms of maximum EROD induction, as
well as the ED50 values and REPs [28]. In addition, ED50 values for TCDD, TCDF, and
PCB126 induction of Cyp1a1 mRNA levels (0.09, 2.5, and 3.9 µg/kg, respectively) were
107

Figure 17. QRTPCR verification of microarray responses.
QRTPCR verification of selected AhR-regulated genes: Cyp1a1, Cyp1a2, Nqo1, Tiparp and
Notch1 for (A) TCDD, (B) PCB126, and (C) TCDF at 24 h. The same RNA samples used in the
dose-response microarray studies were also used for QRTPCR analysis. All fold changes were
calculated relative to vehicle controls. Bars (left y-axis) and lines (right y-axis) represent
QRTPCR and microarray data, respectively. The genes are represented by their official gene
symbols. Bars represent the mean ± SE of four independent QRTPCR samples. QRTPCR data
were analyzed by ANOVA followed by Dunnett’s post hoc test: *p<0.05 for vehicle vs. treated
samples.

108

TCDD dose (g/kg)

25

20 Tiparp

15

0

* * *

10

*

*
20

10

0
3

2

109

1
3
10
3
10 0
300
0

Fold change (QRT-PCR)

10

*

30

*
*
*

4 Notch1

* *

*

TCDD dose (g/kg)

*

*

0

5

*

*

* *

6
5
4
3
2
1
0

4

3

2

1
1

0
0

Fold change (Microarray)

TCDD dose (g/kg)
30

Fold change (Microarray)

40

40 Cyp1a2

TCDD dose (g/kg)

TCDD dose (g/kg)

Fold change (QRT-PCR)

4

2

*
* *
1
3
10
3
10 0
300
0

0.
00
1
0.
0
0. 1
03
0.
0.1
3

0
20

1
3
10
3
10 0
300
0

1000

0.
00
1
0.
0
0. 1
03
0.
0.1
3

*

0.
00
1
0.
0
0. 1
03
0.
1
0.
3

*
* * * *

Fold change (QRT-PCR)

Cyp1a1
50
10
10

8 Nqo1

6

* *
* * *
*
8

0

6

4

2

0

Fold change (Microarray)

5
1
3
10
3
10 0
300
0

2000

1
3
10
3
10 0
300
0

0.
00
1
0.
0
0. 1
03
0.
1
0.
3

3000

350
300
250
200
150
100
50
0
Fold change (Microarray)

0.
00
1
0.
0
0. 1
03
0.
1
0.
3

Fold change (QRT-PCR)

4000

Fold change (Microarray)

Fold change (QRT-PCR)

A

Figure 17 (cont’d).

10
0
30
10 0
00
30
00

3
10
30

0
1

0
PCB126 dose (g/kg)

*

3
2

2

*
1

1
0

0
10
0
30
10 0
00
30
00

5

5

3

4 Notch1

3
10
30

10

PCB126 dose (g/kg)

110

*

4

*

2

4

*

* *

3
2
1

0

0
10
0
30
10 0
00
30
00

PCB126 dose (g/kg)

5

1

*
*

Fold change (QRT-PCR)

15

10

0

Fold change (Microarray)

20
15

*

PCB126 dose (g/kg)

Fold change (Microarray)

Fold change (QRT-PCR)

PCB126 dose (mg/kg)

Tiparp

2

0

10
0
30
10 0
00
30
00

3
10
30

0
1

0

*

10

*

5

Nqo1

3
10
30

100

* *

4

6

1

2000

*

20

Fold change (QRT-PCR)

*

6

Cyp1a2

10
0
30
10 0
00
30
00

200

30

3
10
30

300

1

*

Fold change (QRT-PCR)

*

4000

*

Fold change (Microarray)

Cyp1a1

Fold change (Microarray)

6000

Fold change (Microarray)

Fold change (QRT-PCR)

B

Figure 17 (cont’d).

10
0
30
10 0
00
30
00

TCDF dose (g/kg)

4

*

*

*

*

2

4
3
2
1

0

0

10
0
30
0
10
00
30
00

0

Notch1

3
10
30

10

5

1

*

Fold change (QRT-PCR)

* *

3
10
30

1

Fold change (QRT-PCR)

20

6

TCDF dose (g/kg)

111

4

*

*

*

*

4
2

2
0

0
TCDF dose (g/kg)

Fold change (Microarray)

30

*

6

10
0
30
10 0
00
30
00

0

6

8 Nqo1

3
10
30

0
TCDF dose (g/kg)

Fold change (Microarray)

*

4
2

10

TCDF dose (g/kg)

30
25 Tiparp
20
15
10
5
0

* *

*

10

1

20

10
0
30
10 0
00
30
00

3
10
30

0

1

0

*

Fold change (QRT-PCR)

100

350

*

10
0
30
10 0
00
30
00

*

30

3
10
30

200

6

Cyp1a2

1

700

*

*

Fold change (QRT-PCR)

*

*

40

Fold change (Microarray)

1050

300

Cyp1a1

Fold change (Microarray)

1400

Fold change (Microarray)

Fold change (QRT-PCR)

C

consistently lower compared with the corresponding EROD values (1.1, 53.6, and 54.2 µg/kg,
respectively). The 0.09 µg/kg TCDD Cyp1a1 mRNA ED50 from this study is almost identical to
that reported by Abel et al. in C57BL/6 mice at 24 h (0.08 µg/kg) [52]. Others have also reported
lower ED50s values for Cyp1a1 mRNA compared with EROD activity in B6C3F1 at the same
time point (mRNA ED50 of 1.6 µg/kg and EROD ED50 of 5.3 µg/kg) [53]. Therefore, dosedependent changes in gene expression may provide a more sensitive indicator of exposure, and
possibly toxicity, provided an association can be established between gene function and the
etiology of an adverse effect, assuming that a change in gene expression correlates with protein
expression and activity.
TEF-adjusted, equipotent doses of TCDD, TCDF, and PCB126 did not elicit comparable
gene expression changes. TCDD elicited the greatest number of differentially expressed genes in
comparison to TCDF and PCB126 at TEF-adjusted equipotent doses. Furthermore, TCDF- and
PCB126-elicited changes in gene expression were a subset of the genes differentially expressed
by TCDD. Suggestions of TCDF- and PCB126-specific gene expression responses have been
previously shown to be a statistical artifact. When hard cut-off values (P1(t)>0.99 and |fold
change|>1.5) are relaxed (P1(t) >0.90 and |fold change|>1.5), almost all PCB126- and TCDFelicited genes become a subset of genes differentially expressed by TCDD [29, 30] (Figure 16).
However, the quantitatively lower number of TCDF- and PCB126-elicited differentially
expressed genes cannot be fully attributed to differences in ligand pharmacokinetics. HRGCHRMS analysis indicates the hepatic levels of TCDD, TCDF, and PCB126 at 24 h were
essentially equipotent when TEF-adjusted. Consequently, the ability of the liganded AhR:ARNT
complex to efficiently recruit the same complement of coactivators may also be an important

112

factor for overall congener potency. For example, gene transactivation is reported to be congener
structure-, coactivator-, and cell context dependent [54].
In order to calculate gene-specific REP values, TCDD-, TCDF-, and PCB126-elicited
gene expression changes with a P1(t) >0.90 and |fold change|>1.5 were analyzed using our
automated dose-response modeler [39]. REPs for only 210 TCDF versus TCDD genes and 40
PCB126 versus TCDD genes were calculated to comply with WHO guidelines requiring
sigmoidal dose-response curves with similar slopes and shapes [9, 18]. Overall, the TCDF
median REP values at 24 (0.06) and 72 h (0.1) are similar. This difference may be attributed, in
part, to the use of a wider dose range and higher doses of TCDF and TCDD in the 24 h study,
and the use of intact mice in the 72 h study. In contrast, the PCB126 REP of 0.02 reflects its
weaker potency even at TEF-adjusted doses. PCB126 treatment also resulted in a lower number
of differentially regulated genes exhibiting a sigmoidal dose-response when compared to TCDD.
Many PCB126-elicited gene expression changes were excluded due to poor induction (<1.5-fold)
or because they did not exhibit a sigmoidal dose-response curve. Alternatively, REPs based on
points of departure [39], lowest observable adverse effect level, and/or no observable adverse
effect level could be also considered [9].
In summary, this study expands the available hepatic REP data for TCDF and PCB126
using immature ovariectomized C57BL/6 mice. It further suggests that at TEF-adjusted doses
and at equipotent TEQ hepatic tissue levels, TCDF and PCB126 elicit weaker responses
compared to TCDD. Therefore, in addition to pharmacokinetic differences, congener structure
also plays an important role in gene expression potency and efficacy. Further associations of
specific dose-responsive genes to toxic events are compromised by limited gene annotation and

113

an incomplete understanding of the mechanisms involved in the etiology of the toxic responses
elicited by TCDD and related compounds.

114

REFERENCES

115

REFERENCES
1.

Mason, G. and S. Safe, Synthesis, biologic and toxic effects of the major 2,3,7,8tetrachlorodibenzo-p-dioxin metabolites in the rat. Toxicology, 1986. 41(2): p. 153-9.

2.

Poland, A. and E. Glover, Studies on the mechanism of toxicity of the chlorinated
dibenzo-p-dioxins. Environ Health Perspect, 1973. 5: p. 245-51.

3.

Commoner, B., K. Shapiro, and T. Webster, The Origin and Health Risks of PCDD and
PCDF. Waste Management & Research, 1987. 5: p. 327-346.

4.

Safe, S., et al., Halogenated biphenyls: molecular toxicology. Can J Physiol Pharmacol,
1982. 60(7): p. 1057-64.

5.

Mayes, B.A., et al., Comparative carcinogenicity in Sprague-Dawley rats of the
polychlorinated biphenyl mixtures Aroclors 1016, 1242, 1254, and 1260. Toxicol Sci,
1998. 41(1): p. 62-76.

6.

Mullin, M.D., et al., High-resolution PCB analysis: synthesis and chromatographic
properties of all 209 PCB congeners. Environ. Set Technol., 1984. 18: p. 468-476.

7.

Safe, S., Polychlorinated biphenyls (PCBs), dibenzo-p-dioxins (PCDDs), dibenzofurans
(PCDFs), and related compounds: environmental and mechanistic considerations which
support the development of toxic equivalency factors (TEFs). Crit Rev Toxicol, 1990.
21(1): p. 51-88.

8.

NTP, NTP toxicology and carcinogenesis studies of 3,3',4,4',5-pentachlorobiphenyl (PCB
126) (CAS No. 57465-28-8) in female Harlan Sprague-Dawley rats (Gavage Studies).
NTP Technical Report, 2006(520): p. 4-246.

9.

Van den Berg, M., et al., The 2005 World Health Organization reevaluation of human
and Mammalian toxic equivalency factors for dioxins and dioxin-like compounds.
Toxicol Sci, 2006. 93(2): p. 223-41.

10.

Safe, S., Molecular biology of the Ah receptor and its role in carcinogenesis. Toxicol
Lett, 2001. 120(1-3): p. 1-7.

11.

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

12.

Denison, M.S. and S. Heath-Pagliuso, The Ah receptor: a regulator of the biochemical
and toxicological actions of structurally diverse chemicals. Bull Environ Contam
Toxicol, 1998. 61(5): p. 557-68.

13.

Denison, M.S., et al., Ligand binding and activation of the Ah receptor. Chem Biol
Interact, 2002. 141(1-2): p. 3-24.
116

14.

Nebert, D.W., et al., Role of the aromatic hydrocarbon receptor and [Ah] gene battery in
the oxidative stress response, cell cycle control, and apoptosis. Biochem Pharmacol,
2000. 59(1): p. 65-85.

15.

Ahlborg, U.G., Human health risk assessment and risk perception related to the Baltic
Sea. Arch Toxicol Suppl, 1994. 16: p. 53-9.

16.

Barnes, D.G., Toxicity equivalents and EPA's risk assessment of 2,3,7,8-TCDD. Sci Total
Environ, 1991. 104(1-2): p. 73-86.

17.

Birnbaum, L.S. and M.J. DeVito, Use of toxic equivalency factors for risk assessment for
dioxins and related compounds. Toxicology, 1995. 105(2-3): p. 391-401.

18.

Haws, L.C., et al., Development of a refined database of mammalian relative potency
estimates for dioxin-like compounds. Toxicol Sci, 2006. 89(1): p. 4-30.

19.

Van den Berg, M., et al., Toxic equivalency factors (TEFs) for PCBs, PCDDs, PCDFs for
humans and wildlife. Environ Health Perspect, 1998. 106(12): p. 775-92.

20.

Starr, T.B., et al., The trouble with TEFs. Environ Health Perspect, 1999. 107(10): p.
A492-3.

21.

Safe, S., Limitations of the toxic equivalency factor approach for risk assessment of
TCDD and related compounds. Teratog Carcinog Mutagen, 1997. 17(4-5): p. 285-304.

22.

Harris, M., T. Zacharewski, and S. Safe, Comparative potencies of Aroclors 1232, 1242,
1248, 1254, and 1260 in male Wistar rats--assessment of the toxic equivalency factor
(TEF) approach for polychlorinated biphenyls (PCBs). Fundam Appl Toxicol, 1993.
20(4): p. 456-63.

23.

Budinsky, R.A., et al., Recommended relative potency factors for 2,3,4,7,8pentachlorodibenzofuran: the impact of different dose metrics. Toxicol Sci, 2006. 91(1):
p. 275-85.

24.

DeVito, M.J., et al., Dose-response relationships for polyhalogenated dioxins and
dibenzofurans following subchronic treatment in mice. I. CYP1A1 and CYP1A2 enzyme
activity in liver, lung, and skin. Toxicol Appl Pharmacol, 1997. 147(2): p. 267-80.

25.

DeVito, M.J., et al., Dose-response relationships for disposition and hepatic
sequestration of polyhalogenated dibenzo-p-dioxins, dibenzofurans, and biphenyls
following subchronic treatment in mice. Toxicol Sci, 1998. 46(2): p. 223-34.

26.

Diliberto, J.J., et al., Subchronic Exposure of [3H]- 2,3,7,8-tetrachlorodibenzo-p-dioxin
(TCDD) in female B6C3F1 mice: relationship of steady-state levels to disposition and
metabolism. Toxicol Sci, 2001. 61(2): p. 241-55.

117

27.

Safe, S.H., Modulation of gene expression and endocrine response pathways by 2,3,7,8tetrachlorodibenzo-p-dioxin and related compounds. Pharmacol Ther, 1995. 67(2): p.
247-81.

28.

Burgoon, L.D., et al., Automated Dose-Response Analysis of the Relative Hepatic Gene
Expression Potency of TCDF in C57BL/6 Mice. Toxicol Sci, 2009. 112(1): p. 221-228.

29.

Kopec, A.K., et al., Comparative Toxicogenomic Examination of the Hepatic Effects of
PCB126 and TCDD in Immature, Ovariectomized C57BL/6 Mice. Toxicol Sci, 2008.
102(1): p. 61-75.

30.

N'Jai, A., et al., Comparative temporal toxicogenomic analysis of TCDD- and TCDFmediated hepatic effects in immature female C57BL/6 mice. Toxicol Sci, 2008. 103(2): p.
285-97.

31.

Boverhof, D.R., et al., Temporal and dose-dependent hepatic gene expression patterns in
mice provide new insights into TCDD-Mediated hepatotoxicity. Toxicol Sci, 2005. 85(2):
p. 1048-63.

32.

Sheehan, D.C. and B.B. Hrapchak, Theory and Practice of Histotechnology. second ed
ed. 1980, St. Louis: Mosby.

33.

Moore, J.N., et al., Hepatic P450 enzyme activity, tissue morphology and histology of
mink (Mustela vison) exposed to polychlorinated dibenzofurans. Arch Environ Contam
Toxicol, 2009. 57(2): p. 416-25.

34.

Burgoon, L.D. and T.R. Zacharewski, dbZach toxicogenomic information management
system. Pharmacogenomics, 2007. 8(3): p. 287-91.

35.

Burgoon, L.D., et al., Protocols for the assurance of microarray data quality and process
control. Nucleic Acids Res, 2005. 33(19): p. e172.

36.

Eckel, J.E., et al., Normalization of two-channel microarray experiments: a
semiparametric approach. Bioinformatics, 2005. 21(7): p. 1078-83.

37.

Eckel, J.E., et al., Empirical bayes gene screening tool for time-course or dose-response
microarray data. J Biopharm Stat, 2004. 14(3): p. 647-70.

38.

Vandesompele, J., et al., Accurate normalization of real-time quantitative RT-PCR data
by geometric averaging of multiple internal control genes. Genome Biol, 2002. 3(7): p.
RESEARCH0034.

39.

Burgoon, L.D. and T.R. Zacharewski, Automated quantitative dose-response modeling
and point of departure determination for large toxicogenomic and high-throughput
screening data sets. Toxicol Sci, 2008. 104(2): p. 412-8.

118

40.

NTP, NTP technical report on the toxicology and carcinogenesis studies of 2,3,7,8tetrachlorodibenzo-p-dioxin (TCDD) (CAS No. 1746-01-6) in female Harlan SpragueDawley rats (Gavage Studies). Natl Toxicol Program Tech Rep Ser, 2006(521): p. 4-232.

41.

Boverhof, D.R., et al., Comparative toxicogenomic analysis of the hepatotoxic effects of
TCDD in Sprague Dawley rats and C57BL/6 mice. Toxicol Sci, 2006. 94(2): p. 398-416.

42.

DeVito, M.J. and L.S. Birnbaum, The importance of pharmacokinetics in determining the
relative
potency
of
2,3,7,8-tetrachlorodibenzo-p-dioxin
and
2,3,7,8tetrachlorodibenzofuran. Fundam Appl Toxicol, 1995. 24(1): p. 145-8.

43.

Budinsky, R.A., et al., A pilot study of oral bioavailability of dioxins and furans from
contaminated soils: Impact of differential hepatic enzyme activity and species differences.
Chemosphere, 2008. 70(10): p. 1774-86.

44.

Hamm, J.T., C.Y. Chen, and L.S. Birnbaum, A mixture of dioxins, furans, and non-ortho
PCBs based upon consensus toxic equivalency factors produces dioxin-like reproductive
effects. Toxicol Sci, 2003. 74(1): p. 182-91.

45.

Diliberto, J.J., et al., Dose-response relationships of tissue distribution and induction of
CYP1A1 and CYP1A2 enzymatic activities following acute exposure to 2,3,7,8tetrachlorodibenzo-p-dioxin (TCDD) in mice. Toxicol Appl Pharmacol, 1995. 130(2): p.
197-208.

46.

Nebert, D.W., D.D. Petersen, and A.J. Fornace, Jr., Cellular responses to oxidative
stress: the [Ah] gene battery as a paradigm. Environ Health Perspect, 1990. 88: p. 13-25.

47.

Ma, Q., et al., TCDD-inducible poly(ADP-ribose) polymerase: a novel response to
2,3,7,8-tetrachlorodibenzo-p-dioxin. Biochem Biophys Res Commun, 2001. 289(2): p.
499-506.

48.

Tijet, N., et al., Aryl hydrocarbon receptor regulates distinct dioxin-dependent and
dioxin-independent gene batteries. Mol Pharmacol, 2006. 69(1): p. 140-53.

49.

Harper, J.A., et al., Notch signaling in development and disease. Clin Genet, 2003. 64(6):
p. 461-72.

50.

Ma, X., N.A. Mufti, and J.G. Babish, Protein tyrosine phosphorylation as an indicator of
2,3,7,8-tetrachloro-p-dioxin exposure in vivo and in vitro. Biochem Biophys Res
Commun, 1992. 189(1): p. 59-65.

51.

Lin, F.H., et al., The effects of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) on the
hepatic estrogen and glucocorticoid receptors in congenic strains of Ah responsive and
Ah nonresponsive C57BL/6J mice. Toxicol Appl Pharmacol, 1991. 108(1): p. 129-39.

52.

Abel, J., et al., Dose-response relationship of cytochrome P4501b1 mRNA induction by
2,3,7,8-tetrachlorodibenzo-p-dioxin in livers of C57BL/6J and DBA/2J mice. Arch
Toxicol, 1996. 70(8): p. 510-3.
119

53.

Narasimhan, T.R., et al., Relative sensitivities of 2,3,7,8-tetrachlorodibenzo-p-dioxininduced Cyp1a-1 and Cyp1a-2 gene expression and immunotoxicity in female B6C3F1
mice. Fundam Appl Toxicol, 1994. 23(4): p. 598-607.

54.

Zhang, S., C. Rowlands, and S. Safe, Ligand-dependent interactions of the Ah receptor
with coactivators in a mammalian two-hybrid assay. Toxicol Appl Pharmacol, 2008.
227(2): p. 196-206.

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CHAPTER 5

Kopec AK, Burgoon LD, Ibrahim-Aibo D, Mets BM, Tashiro C, Potter D, Sharratt B, Harkema
JR, Zacharewski TR: PCB153-Elicited Hepatic Responses in the Immature, Ovariectomized
C57BL/6 Mice: Comparative Toxicogenomic Effects of Dioxin and Non-dioxin-like
Ligands. Toxicol Appl Pharmacol 2010, 243:359-371.

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CHAPTER 5
PCB153-ELICITED HEPATIC RESPONSES IN THE IMMATURE,
OVARIECTOMIZED C57BL/6 MICE: COMPARATIVE TOXICOGENOMIC
EFFECTS OF DIOXIN AND NON-DIOXIN-LIKE LIGANDS
ABSTRACT
Polychlorinated biphenyls (PCBs) are ubiquitous contaminants found as complex
mixtures of coplanar and non-coplanar congeners. The hepatic temporal and dose-dependent
effects of the most abundant non-dioxin-like congener, 2,2’,4,4’,5,5’-hexachlorobiphenyl
(PCB153), were examined in immature, ovariectomized C57BL/6 mice, and compared to
2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), the prototypical aryl hydrocarbon receptor (AhR)
ligand. Animals were gavaged once with 300 mg/kg PCB153 or sesame oil vehicle and
sacrificed 4, 12, 24, 72 or 168 h post dose. In the dose-response study, mice were gavaged with
1, 3, 10, 30, 100 or 300 mg/kg PCB153 or sesame oil for 24 h. Significant increases in relative
liver weights were induced with 300 mg/kg PCB153 between 24 and 168 h, accompanied by
slight vacuolization and hepatocellular hypertrophy. The hepatic differential expression of 186
and 177 genes was detected using Agilent 4  44 K microarrays in the time course (|fold
change|≥1.5, P1(t)≥0.999) and dose-response (|fold change|≥1.5, P1(t)≥0.985) studies,
respectively. Comparative analysis with TCDD suggests that the differential gene expression
elicited by PCB153 was not mediated by the AhR. Furthermore, constitutive androstane and
pregnane X receptor (CAR/PXR) regulated genes including Cyp2b10, Cyp3a11, Ces2, Insig2,
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and Abcc3 were dose-dependently induced by PCB153. Collectively, these results suggest that
the hepatocellular effects elicited by PCB153 are qualitatively and quantitatively different from
TCDD and suggestive of CAR/PXR regulation.

INTRODUCTION
Polychlorinated biphenyls (PCBs), manufactured in the United States from 1929 to 1977,
had various applications as coolants, insulating fluids for transformers and capacitors,
plasticizers in paints and cements, pesticide extenders, lubricating oils, and sealants [1, 2]. Their
chemical stability and lipophilic properties facilitated their distribution, persistence, and
biomagnification in the food chain, particularly in fatty tissues [3].
PCBs and related compounds elicit tissue- and species-specific effects including
hepatotoxicity, immune suppression, reproductive toxicity, endocrine disruption, developmental
toxicity, and carcinogenicity [4, 5]. They are classified as coplanar, designated dioxin-like based
on their structural similarity to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), and non-coplanar
[1]. The effects of TCDD and dioxin-like PCBs, such as 3,3’,4,4’,5-pentachlorobiphenyl
(PCB126), are mediated by the aryl hydrocarbon receptor (AhR), facilitating the toxicity
assessment of dioxin-like PCB mixtures to be expressed as toxic equivalents relative to TCDD
[6]. TCDD and related dioxin-like PCBs are more toxic than non-coplanar PCBs, which contain
ortho chlorine substituents on the biphenyl ring, which significantly reduces their binding
affinity for the AhR [7]. Despite their non-dioxin-like characteristics, non-coplanar PCBs
account for a majority of the PCBs found in environmental and biological samples.
Non-coplanar 2,2’,4,4’,5,5’-hexachlorobiphenyl (PCB153) is the congener found at the
highest concentrations in human samples on a molar basis [8, 9]. PCB153 bears little structural
resemblance to TCDD, has little to no AhR binding affinity, and elicits a unique toxicity profile
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relative to TCDD and other coplanar PCBs, and therefore is not assigned a TEF value. Its mode
of action has not been fully elucidated, but based on its structural similarity to phenobarbital and
limited gene expression activity, it may be mediated via the constitutive androstane receptor
(CAR)/ pregnane X receptor (PXR) [10-14].
Previous studies have demonstrated non-additive interactions between PCB153 and
dioxin-like compounds [15-18]. PCB153 partially antagonizes TCDD-mediated cleft palate and
immunotoxicity, as well as suppresses hepatic microsomal ethoxyresorufin-O-deethylase
(EROD) induction in C57BL/6 mice [15]. Immunohistochemistry of rat livers following cotreatment with PCB126 and PCB153 also showed differential induction of Cyp1a1 compared to
PCB126 alone [16]. In addition, PCB126 and PCB153 co-treatment synergistically altered
hepatocellular foci development and expression of γ-glutamyltranspeptidase [18]. Consequently,
a more thorough examination of PCB153 is warranted in order to further elucidate the
mechanisms involved in these non-additive activities.
In this report, time course and dose-dependent gene expression studies with
complementary histopathology, gas chromatography/mass spectrometry (GC/MS) lipid profiling
and high-resolution gas chromatography/high-resolution mass spectrometry (HRGC-HRMS)
tissue level analyses were conducted to investigate the hepatic effects elicited by PCB153 in
immature, ovariectomized C57BL/6 mice. Comparisons were made to comparable TCDD and
PCB126 studies, which used the same model, study design and data analysis strategies [19, 20].

MATERIALS & METHODS
ANIMAL HUSBANDRY
Immature female C57BL/6 mice, ovariectomized by the supplier on postnatal day (PND)
20, with body weights (BW) within 10% of the average, were obtained from Charles Rivers
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Laboratories (Portage, MI) on PND 25. Animals were housed in polycarbonate cages (n=5 per
cage) containing cellulose fiber chips (Aspen Chip Laboratory Bedding, Northeastern Products,
Warrensberg, NY) with 30-40% humidity and a 12 h light/dark cycle (07:00 h – 19:00 h). Mice
had free access to deionized water and were fed ab libitum with Harlan Teklad 22/5 Rodent Diet
8640 (Madison, WI). Animals were acclimatized for 3 days prior to dosing on PND 28. The
immature ovariectomized mouse was used to facilitate comparisons with other data sets obtained
using the same model, study design and analysis methods [19, 20]. Immature animals were used
because they are more responsive to AhR ligands. The animals were ovariectomized to negate
potential interactions with estrogens produced by the developing ovaries, since some animals in
our studies are approaching reproductive maturity. All procedures were carried out with the
approval of the Michigan State University All-University Committee on Animal Use and Care.
DOSE-RESPONSE AND TIME COURSE STUDIES
A stock solution of PCB153 (99.9% purity, AccuStandard, New Haven, CT) was first
dissolved in acetone (J.T. Baker), followed by a dilution with sesame oil (Sigma, St. Louis, MO),
and evaporation of the acetone using nitrogen gas. The PCB153 stock solution was further
diluted using sesame oil to achieve the desired dose. Animals were orally gavaged using 1.5 inch
feeding needle with a 2.25 mm ball end (Cadence Science: formerly Popper and Sons, Lake
Success, NY). For the dose-response study, mice (n=5 per group) were administered 0.1 ml of a
single dose of 1, 3, 10, 30, 100 or 300 mg/kg of PCB153 or 0.1 ml pure sesame oil vehicle
(Sigma) and sacrificed 24 h post-treatment. The 300 mg/kg PCB153 dose was used in a
complementary time course study, as it was the highest dose examined that did not alter body
weight gain. Animals were gavaged with either 300 mg/kg PCB153 or sesame oil vehicle and
sacrificed at 4, 12, 24, 72 or 168 h. For direct comparisons between dioxin-like and non-dioxin-

125

like responses, additional animals were treated with 30 μg/kg TCDD and sacrificed at the same
time points. Furthermore, additional data sets generated using the same animal model, study
design and analysis methods for 30 μg/kg TCDD and 300 μg/kg PCB126 [19, 20] served as
comparators for this study. Mice were sacrificed by cervical dislocation and tissue samples were
removed, weighed, flash frozen in liquid nitrogen, and stored at –80°C. For both the time course
and dose-response studies, the section of the right liver lobe was fixed in 10% neutral buffered
formalin (Sigma) for histological analysis.
HISTOLOGICAL ANALYSES
Fixed liver tissues were sectioned and processed in ethanol, xylene, and paraffin using a
Thermo Electron Excelsior tissue processor (Waltham, MA). Tissues were embedded in paraffin
with Miles Tissue Tek II embedding center, after which paraffin blocks were sectioned at 5 μm
with a rotary microtome. Liver sections were placed on glass microscope slides, washed twice in
xylene for 5 min, followed by four quick washes in ethanol and rinsing in water. Slides were
placed in Gill 2 hematoxylin (Thermo Fisher Scientific, Waltham, MA) for 1.5 min followed by
2-3 quick dips in 1% glacial acetic acid water and rinsed with running water for 2-3 min. Slides
were then rinsed in ethanol and counterstained with 1% eosin Y-phloxine B solution (Sigma)
followed by multiple rinses in ethanol and xylene. For lipid staining, liver sections were frozen
in Tissue-Tek O.C.T. compound (Sakura, Torrance, CA) before further processing. Liver
samples were sectioned at 6 μm, fixed in 10% neutral buffered formalin for 5 min, rinsed with
water and immersed in 100% propylene glycol for 5 min. The slides were stained with Oil Red O
solution (Sigma) for 8 min at 60°C. Following staining, slides were placed in 80% propylene
glycol for 5 min and rinsed in water for 15 min. Slides were counterstained with Gill 2
hematoxylin for 30 sec and washed with water for 30 min. Coverslips were attached using

126

aqueous mounting media. All the histological processing was performed at Michigan State
University Investigative HistoPathology Laboratory, Division of Human Pathology using a
modified version of previously published procedures [21].
LIPID ANALYSIS BY GAS CHROMATOGRAPHY-MASS SPECTROMETRY (GC-MS)
Liver samples (~100 mg) from the 24, 72, 168 h PCB153, TCDD and vehicle control
groups were homogenized (Polytron PT2100, Kinematica AG, Luzern, CH) in 40% methanol
and acidified with concentrated HCl. Lipids were extracted with chloroform: methanol (2:1)
containing 1 mM 2,6-di-tert-butyl-4-methylphenol (BHT; Sigma) and extraction efficiency
controls (19:1n9 FFA [free fatty acid] and 19:0 TAG [triacylglycerol]) were added (Nu-Chek
Prep, Elysian, MN). Protein and aqueous phases were re-extracted with chloroform and the
organic phases were pooled. A derivatization standard (19:2n6 FFA; Nu-Chek Prep) was added
and samples were dried under nitrogen, resuspended in 2% non-aqueous methanolic HCl and
incubated at 60ºC overnight. Samples were cooled to room temperature (RT) and 0.9% (w/v)
NaCl and hexane were added. The organic phase was collected and a loading control (17:1n9
fatty acid methyl ester [FAME]; Nu-Chek Prep) was added. Samples were dried under nitrogen,
resuspended in equal volumes of hexane and separated on Agilent 6890N gas chromatograph
interfaced to Agilent 5973 mass spectrometer with DB23 column (30 m length, 0.25 mm internal
diameter, 0.25 m film thickness). Samples were run on the following temperature program:
50°C to 150°C at 40°C/ min, to 185°C at 5°C/ min, to 235°C at 3°C/ min, to 250°C at 10°C/ min.
Principal component analysis (PCA) of lipid profiles was performed in R 2.6.0, data were
extracted and used to generate PCA plots in GraphPad Prism 4.0.

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HEPATIC TRIGLYCERIDE MEASUREMENT
Frozen liver samples (~100 mg) were homogenized (Polytron PT2100, Kinematica) in 1
ml of 1.15% KCl. Triglycerides were extracted from 200 µl of hepatic homogenate with 800 µl
of isopropyl alcohol by vortex-mixing for 10 min. The samples were centrifuged for 5 min at 800
x g at RT and supernatant was collected into separate vials. The concentration of hepatic
triglycerides was determined using a commercial L-Type Triglyceride M kit (Wako Diagnostics,
Richmond, VA) with Multi-Calibrator Lipids as a standard (Wako Diagnostics). The
measurements were performed according to manufacturer's protocol with 20 µl of the
triglyceride extract incubated with 150 µl of Reagent 1 followed by incubation with 50 µl of
Reagent 2. Final results were normalized to the starting amount of liver.
QUANTIFICATION OF PCB153 AND TCDD TISSUE LEVELS
Liver samples were processed in parallel with laboratory blanks and a reference or
background sample at Wellington Laboratories Inc. (Guelph, ON, Canada). The samples (100 to
500 mg) were transferred to a tared screw cap culture tube and weight recorded. The sample was
spiked with

13

C12-PCB153 and

13

C12-2,3,7,8-TCDD surrogates and then digested with

hydrochloric acid. Each digested sample was then split between two screw cap tubes and hexane
(3-4 ml) was added to each tube followed by vigorous mixing. The tubes were centrifuged, and
the organic layer removed. The hexane extraction was performed three times per screw cap tube
and the six hexane fractions were combined. The hexane fraction was then split evenly prior to
clean-up and one fraction was archived. The other fraction was cleaned-up using a small
multilayer (acid/base/neutral) silica gel column eluted with 20-25 ml of hexane. The eluate was
concentrated on a rotary evaporator and then transferred to a conical microvial with pentane and
dichloromethane rinses and allowed to dry. Immediately prior to injection on the high-resolution
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gas chromatograph/high-resolution mass spectrometer (HRGC-HRMS) system,
and

13

13

C12-PCB111

C12-1,2,3,4-TCDD injection standards were added to the conical microvial.
Sesame oil (control) samples (100 to 500 mg) were weighed into tared screw cap tubes,

spiked with

13

C12-PCB153 and

13

C12-2,3,7,8-TCDD surrogates, and diluted in 5 ml of hexane.

The controls were then mixed and digested with 5 ml of sulfuric acid. The tube was centrifuged
and the hexane layer was removed, split evenly with one half subjected to the same clean-up as
the liver samples.
Harlan Teklad 22/5 Rodent Diet 8640 feed samples were crushed and weighed (~8 g) into
a pre-extracted cellulose thimble. The samples were then spiked with

13

C12-PCB153 and

13

C12-

2,3,7,8-TCDD surrogates prior to Soxhlet extraction overnight with dichloromethane. The
samples were concentrated on a rotary evaporator and, transferred to a screw cap tube with
hexane. The samples were then mixed and digested with 5 ml of sulfuric acid. Following
centrifugation, the hexane layer was removed, split evenly, and processed as described above.
The identification and quantification of PCB153 and TCDD was performed using an
Agilent (Santa Clara, CA, USA) 6890 series HRGC with direct capillary interface to a Waters
(Milford, MA, USA) Autospec Ultima HRMS. Chromatographic separations were carried out on
a 60 m DB5 (0.25 mm ID, 0.25 µm film thickness) column in constant flow mode (Helium, 1 ml
/min). All injections were 1 µl and a splitless injection was used. The mass spectrometer was
operated in EI+ selective ion recording mode (SIR) at a mass resolving power of 10,000 or
greater.

129

RNA ISOLATION
Frozen liver samples (~100 mg, stored at –80°C) were immediately transferred to 1 ml
TRIzol (Invitrogen, Carlsbad, CA) and homogenized using a Mixer Mill 300 tissue homogenizer
(Retsch, Germany). Total RNA was isolated according to the manufacturer’s protocol with an
additional acid phenol:chloroform extraction. Isolated RNA was resuspended in RNA storage
solution (Ambion Inc., Austin, TX), quantified (A260), and quality was assessed by determining
the A260/A280 ratio and by visual inspection of 2 μg on a denaturing gel.
MICROARRAY EXPERIMENTAL DESIGN
PCB153-, TCDD-treated and vehicle samples were individually hybridized to 4  44K
whole mouse genome oligo microarrays (Agilent Technologies, Inc., Santa Clara, CA).
Hybridizations were performed with three biological replicates using one-color labeling (Cy3)
for each time point and dose, according to the manufacturer’s protocol (Agilent Manual: G414090040 v. 5.7). Published reports suggest that one- and two-color microarrays provide comparable
data, with no significant differences detected when compared to studies utilizing Cy3 and Cy5
labeling [22]. Microarray slides were scanned at 532 nm (Cy3) on a GenePix 4000B scanner
(Molecular Devices, Union City, CA). Images were analyzed for feature and background
intensities using GenePix Pro 6.0 (Molecular Devices). All data were managed in TIMS dbZach
data management system [23].
MICROARRAY ANALYSIS
All microarray data in this study passed our laboratory quality assurance protocol [24].
Microarray data were normalized using a semiparametric approach [25] and the posterior
probabilities were calculated using an empirical Bayes method based on a per gene and time
point or dose basis using model-based t values [26]. Gene expression data were ranked and
130

prioritized using a |fold change|≥1.5 and P1(t) values ≥0.999 for the temporal PCB153 and
TCDD data sets. However, relaxed P1(t) values (≥0.985) were also used for the PCB153 doseresponse data to identify differentially expressed genes that approached the selection cut-offs to
ensure the identification of ligand-specific regulation. In addition, the different statistical cut-offs
were used to obtain a comparable number of differentially expressed genes between the temporal
and dose-dependent microarray data sets and to account for studies being performed at two
different times.
DOSE-RESPONSE MODELING
Dose-response modeling was performed using a grid-enabled version of the ToxResponse
Modeler [27]. ToxResponse Modeler performs automated dose-response modeling by identifying
the best fit model amongst five different mathematical model families (linear, exponential,
Gaussian, sigmoidal, quadratic). The algorithm then chooses the best-fit of the five best in-class
models. The overall best-fit model is then used to calculate the ED50 values. The microarray
dose-response PCB153 data set was first filtered using a P1(t)>0.90 cut-off and the filtered data
were analyzed to identify genes exhibiting a sigmoidal dose-response profile.
DNA RESPONSE ELEMENT ANALYSIS MODELING
Dioxin response elements (DREs) [28], constitutive androstane receptor response
elements (CAREs) [29] and pregnane X receptor response elements (PXREs) were identified
computationally using position weight matrices (PWMs) specific to each site. The PXRE PWM
was generated using seven published response elements that were aligned to the genome [30-35].
Gene regulatory regions (-10,000 relative to the transcriptional start site [TSS] together with 5’untranslated region [UTR]) were obtained from the University of California, Santa Cruz,
Genome Browser for mouse (build 37), computationally searched, and each DRE, CARE and

131

PXRE was scored. Matrix similarity scores (MSS) >0.80 are considered to be putative functional
response elements.
QUANTITATIVE REAL-TIME PCR (QRTPCR)
QRTPCR verification of selected microarray data was performed as described previously
[19]. Briefly, 1 μg of total RNA was reverse transcribed by SuperScript II (Invitrogen) using an
anchored oligo-dT primer as described by the manufacturer. 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 mM dNTPs, 0.025 IU AmpliTaq Gold, and 1 SYBR Green PCR
buffer (Applied Biosystems, Foster City, CA). PCR amplification was conducted on an Applied
Biosystems PRISM 7500 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 the differences in RNA loading, quality, and cDNA synthesis
[36] . For graphing purposes, the relative expression levels were scaled such that the expression
level of the time-matched control group was equal to one.
FUNCTIONAL GENE ANNOTATION AND STATISTICAL ANALYSIS
Annotation and functional categorization of differentially regulated genes were
performed using Database for Annotation, Visualization and Integrated Discovery (DAVID)
[37]. All statistical analyses were performed with SAS 9.1 (SAS Institute, Cary, NC). All data
were analyzed by analysis of variance (ANOVA) followed by Tukey’s or Dunnett’s post hoc
tests. Differences between treatment groups were considered significant when p<0.05.

132

RESULTS
ORGAN AND BODY WEIGHTS
In the time course study, 300 mg/kg PCB153 increased (p<0.05) relative liver weight
(RLW) at 72 and 168 h (Table 11), comparable to other reports using lower doses [9, 10]. In the
dose-response study, 300 mg/kg significantly increased RLW at 24 h (Table 12), similarly to 30
μg/kg TCDD and 300 μg/kg PCB126, which also increased RLW at later time points (Table 11)
[19, 20]. No significant decreases in body weight gain were observed at any of the PCB153
doses or time points, in agreement with other reports with doses as high as 360 mg/kg [10, 15].
HEPATIC TISSUE LEVEL QUANTIFICATION
HRGC-HRMS analysis of liver samples at 24 and 168 h time points indicate that PCB153
levels (in pg/g) exhibited different hepatic accumulation kinetics relative to TCDD (Figure 18A).
PCB153 levels dramatically decreased (~3.3 fold) after 7 days, compared to TCDD which
exhibited modest reductions (~1.4 fold), and in contrast to PCB126 which continued to increase
throughout the study, reaching the highest concentration at 7 days [20]. These differences may be
partially due to the induction of Cyp1a2 (~30 fold), which sequesters TCDD and dioxin-like
compounds in the liver [38] (Figure 23A). PCB153 only marginally induced Cyp1a2 (~1.8 fold)
(Figure 23B), which may account for the time dependent decrease of hepatic PCB153 levels.
PCB153 exhibited a dose-dependent increase in hepatic levels that was significantly
different from controls (Figure 18B). Interestingly, hepatic PCB153 levels for sesame oil treated
controls (Figure 18B) were ~500 times higher than the corresponding PCB126 or TCDD levels
[20]. High levels in vehicle animals have been attributed to the ingestion of PCB153 found in
rodent chow [39, 40]. However, only 62.1 pg/g of PCB153 was detected in Harlan Teklad 22/5
Rodent Diet 8640 using HRGC-HRMS, while levels in sesame oil were below the limits of
133

Table 11. Temporal effects of 300 mg/kg PCB153 and 30 µg/kg TCDD on terminal body,
body weight gain, absolute liver weight. and relative liver weight.
Sacrifice
time (h)

Treatment

Terminal body
weight (g)

Body
weight gain

Liver weight
(g)

Relative liver
weight

4

Vehicle
PCB153
TCDD

12.5 ± 0.5
12.5 ± 0.8
13.6 ± 0.8

1.0 ± 0.0
1.0 ± 0.0
1.0 ± 0.0

0.666 ± 0.028
0.712 ± 0.048
0.733 ± 0.044

0.053 ± 0.002
0.057 ± 0.001
0.054 ± 0.003

12

Vehicle
PCB153
TCDD

14.0 ± 0.7
13.0 ± 0.6
12.8 ± 0.7

1.0 ± 0.0
1.0 ± 0.0
1.1 ± 0.0

0.766 ± 0.048
0.730 ± 0.075
0.724 ± 0.025

0.055 ± 0.001
0.056 ± 0.003
0.056 ± 0.001

24

Vehicle
PCB153
TCDD

14.4 ± 0.9
13.3 ± 0.9
14.2 ± 0.9

1.1 ± 0.0
1.1 ± 0.0
1.1 ± 0.0

0.814 ± 0.070
0.813 ± 0.137
0.973 ± 0.088

0.056 ± 0.003
0.061 ± 0.009
0.068 ± 0.003*

72

Vehicle
PCB153
TCDD

15.6 ± 1.5
15.7 ± 0.7
15.1 ± 0.7

1.2 ± 0.0
1.1 ± 0.0
1.2 ± 0.1

0.918 ± 0.055
1.086 ± 0.135
1.068 ± 0.060

0.059 ± 0.005
0.069 ± 0.007*
0.071 ± 0.003*

168

Vehicle
PCB153
TCDD

17.6 ± 0.4
17.9 ± 1.2
17.5 ± 0.4

1.4 ± 0.1
1.4 ± 0.1
1.3 ± 0.0

1.017 ± 0.082
1.280 ± 0.074
1.263 ± 0.080

0.058 ± 0.004
0.072 ± 0.002*
0.072 ± 0.003*

Note. Values represent mean ± SD of five independent replicates. Body weight gain is
represented as terminal body weight divided by body weight prior to dosing. An asterisk (*)
indicates p<0.05 vs. vehicle.

134

Table 12. Dose-dependent effects of PCB153 on terminal body, body weight gain, absolute
liver weight and relative liver weight at 24 h.
Dose
(mg/kg)

Treatment

Terminal body
weight (g)

Body
weight gain

Liver weight
(g)

Relative liver
weight

0

Vehicle

11.8 ± 1.9

1.1 ± 0.0

0.694 ± 0.085

0.059 ± 0.006

1
3
10
30
100
300

PCB153
PCB153
PCB153
PCB153
PCB153
PCB153

14.3 ± 1.8
14.4 ± 1.7
12.7 ± 1.1
13.4 ± 1.0
13.8 ± 1.1
12.7 ± 1.9

1.1 ± 0.0
1.1 ± 0.0
1.1 ± 0.1
1.1 ± 0.0
1.1 ± 0.0
1.1 ± 0.0

0.850 ± 0.121
0.865 ± 0.119
0.721 ± 0.073
0.836 ± 0.066
0.829 ± 0.071
0.855 ± 0.152

0.059 ± 0.002
0.060 ± 0.002
0.056 ± 0.004
0.062 ± 0.001
0.060 ± 0.002
0.067 ± 0.003*

Note. Values represent mean ± SD of five independent replicates. Body weight gain is
represented as terminal body weight divided by body weight prior to dosing. An asterisk (*)
indicates p<0.05 vs. vehicle.

135

Figure 18. Hepatic PCB153 and TCDD levels per g liver wet weight measured using
HRGC-HRMS.
(A) Mice were gavaged with 300 mg/kg PCB153 or 30 µg/kg TCDD. Hepatic levels of PCB153
showed a dramatic decrease (~3.3 fold) after 7 days, compared to TCDD, which exhibited more
modest decreases (~1.4 fold). (B) PCB153 levels attained in the 24 h dose-response study. The
results are displayed as the mean ± standard error (SE) of at least three independent samples.
Dose-response data are displayed on a log scale to visualize tissue levels at all doses. An asterisk
(*) indicates a significant (p<0.05) difference between the treated samples and vehicle (VEH)
controls.
136

detection (data not shown). Others suggest that non-dioxin-like PCBs may also accumulate in
animals via lactational transfer [41].
HISTOPATHOLOGY
PCB153 induced minimal hepatocellular vacuolization (Figure 19C), comparable to the
levels observed in vehicle controls (Figure 19A). In contrast, TCDD-elicited vacuolization was
more severe and localized to the periportal regions and extended to the midzonal and
centrilobular regions in more severely affected mice. TCDD treatment also resulted in moderate
multifocal inflammation (Figure 19B, arrow), which was almost absent in the PCB153-treated
mice. However, PCB153 elicited increasing hypertrophic responses between 24 and 168 h.
Furthermore, Oil Red O staining (ORO) identified significant lipid accumulation only in the
TCDD-treated animals, while PCB153 livers showed no fatty accumulation.
In the dose-response study, 300 mg/kg PCB153 induced the highest levels of
hepatocellular vacuolization; however incidences of hypertrophy were not observed. Compared
to our previous studies [19, 42], 30 μg/kg TCDD elicited more dramatic vacuolization and
necrosis, as well as mixed cell infiltration that was absent in PCB153 treatment.
LIPID PROFILING
Total lipids were extracted from control, TCDD and PCB153-treated livers at 24, 72 and
168 h, derivatized to fatty acid methyl esters (FAMEs) and analyzed by GC-MS. The temporal
and treatment-dependent separation of FAME profiles (as fold changes) is summarized in Figure
20. The cumulative proportion of variance for principal component (PC) 1 and PC2 is 99%,
indicating that the PCA plot accurately represents the separation of the data. Comparison of
PCB153- and TCDD-elicited FAME profiles identified separation around PC1 and PC2
indicating differences due to treatment and time, respectively (Figure 20). In agreement with

137

Figure 19. Representative histopathology results from vehicle, 30 µg/kg TCDD and 300 mg/kg PCB153-treated mice at 168 h.
Liver sections from (A) vehicle showed an overall lack of vacuolization, (B) 30 µg/kg TCDD-treated animal exhibited slight to
moderate vacuolization and instances of multifocal inflammation (arrow), and (C) an animal treated with 300 mg/kg PCB153
exhibited minimal vacuolization and hepatocellular hypertrophy. PCB153 did not elicit immune cell infiltration or necrosis. PV –
Portal Vein; CV – Central Vein. Bars = 50 µm.

138

Figure 20. GC-MS lipid profile analysis at 24, 72 and 168 h.
Principal component analysis indicated temporal and treatment-dependent separation of PCB153
and TCDD fatty acid methyl ester profiles (fold change ratios) relative to their respective timematched controls.

139

ORO staining, TCDD exhibited a time-dependent induction in the level of several individual
FAMEs that was the highest at 168 h (Figure 21) and led to hepatic lipid accumulation [20, 42]
that was not detected in PCB153-treated animals (Figure 21).
Hepatic triglyceride measurement identified a time-dependent increase in triglycerides in
the TCDD group. In contrast, there was no difference in triglyceride levels between vehicle and
PCB153-exposed mice, consistent with ORO and GC-MS FAME analysis.
TEMPORAL AND DOSE-DEPENDENT PCB153 GENE EXPRESSION CHANGES
Hepatic gene expression was assessed using 4  44K Agilent oligonucleotide
microarrays, containing ~21,000 unique annotated genes. PCB153 elicited the differential
expression of 186 unique, annotated genes at one or more time points (P1(t)≥0.999 and |fold
change|≥1.5) relative to vehicle controls with 72 h exhibiting the most changes (127 unique
genes). In the 24 h dose-response study, PCB153 differentially regulated 177 unique genes at one
or more doses (P1(t)≥0.985 and |fold change|≥1.5).
Functional annotation of PCB153-elicited differential gene expression was associated
with xenobiotic metabolism and oxidoreductase activity, lipid metabolism, cell cycle and cell
death, and transport (Table 13). Position weight matrices (PWMs) were used to computationally
identify putative dioxin, PXR/CAR response elements [28-35] in the promoter region (-10,000
bp relative to the [TSS] together with the 5’ UTR) of responsive genes identified from the
microarray analysis (Table 13).
Xenobiotic and oxidoreductase activity functions were the most highly induced among all
functional clusters and almost exclusively included cytochrome P450s (e.g. Cyp2b9, Cyp2b10,
Cyp2c54, Cyp3a25) and glutathione S-transferases (e.g. Gsta2, Gstt3, Gstm4), with Cyp2c55

140

Figure 21. Fatty acid methyl ester (FAME) GC-MS analysis of PCB153- and TCDD-treated mouse livers at 168 h.
Treatment with 30 µg/kg TCDD induced a variety of individual FAMEs compared to 300 mg/kg PCB153 which did not affect FAME
composition relative to control animals. An asterisk (*) indicates a significant (p<0.05) difference between the treated samples and
vehicle controls.

141

Table 13. Functional categorization and regulation of select hepatic genes identified as differentially regulated in response to
300 mg/kg PCB153 and 30 µg/kg TCDD [19].

Metabolizing enzymes/
oxidoreductase activity

Functional
category

Gene
symbol

Entrez
Gene
ID

PCB153
fold
a
change

TCDD
*

TCDD
[19]

DREs

Aox1

11761

2.9

2.2

N/A

Akr1b7

11997

22.1

NC

Akr1c20

116852

-2.3

Ces2

234671

Cyp2b10

Cytochrome P450, family 2,
subfamily b, polypeptide 23

Gene name

PXREs CAREs
c

d

4

5

2

NC

4

6

2

-1.7

N/A

_

1

_

5.8

2.5

1.6

1

4

1

13088

27.5

2.3

NC

5

5

1

Cyp2b23

243881

19.7

NC

N/A

2

5

_

Cytochrome P450, family 2,
subfamily b, polypeptide 9

Cyp2b9

13094

7.6

NC

N/A

_

4

1

Cytochrome P450, family 2,
subfamily c, polypeptide 54

Cyp2c54

404195

2.4

NC

N/A

_

5

1

Cytochrome P450, family 2,
subfamily c, polypeptide 55

Cyp2c55

72082

48.4

1.8

N/A

4

3

1

Cytochrome P450, family 3,
subfamily a, polypeptide 25

Cyp3a25

56388

3.8

NC

N/A

_

5

_

Aldehyde oxidase 1
Aldo-keto reductase family
1, member B7
Aldo-keto reductase family
1, member C20
Carboxylesterase
Cytochrome P450, family 2,
subfamily b, polypeptide 10

142

b

Table 13 (cont’d).

Gene name

Gene
symbol

Entrez
Gene
ID

PCB153
fold
a
change

TCDD
*

TCDD
[19]

DREs

Dhfr

13361

-2.1

NC

-1.5

Fmo5

14263

3.0

NC

Gpx2

14776

1.7

Gsta2

14858

Gstm4
Gstt3
Ugdh

b

PXREs CAREs
c

d

5

5

1

NC

2

5

1

NC

NC

8

9

3

4.1

5.9

7.2

4

10

2

14865
103140
22235

4.0
1.7
2.5

2.4
1.5
2.7

N/A
NC
3.1

2
3
6

5
10
2

1
3
2

Acsl3

74205

-3.1

-2.3

N/A

2

4

_

Agpat4

68262

-1.9

NC

NC

7

4

1

Hmgcr

15357

-5.3

-2.6

N/A

4

5

1

Cds2

110911

1.7

NC

NC

5

4

3

Metabolizing enzymes/
oxidoreductase activity

Dihydrofolate reductase
Flavin containing
monooxygenase 5
Glutathione peroxidase 2
Glutathione S-transferase, alpha
2 (Yc2)
Glutathione S-transferase, mu 4
Glutathione S-transferase, theta 3
UDP-glucose dehydrogenase

Lipid metabolism

  

Functional
category

Acyl-CoA synthetase long-chain
family member 3
1-Acylglycerol-3-phosphate Oacyltransferase 4
(lysophosphatidic acid
acyltransferase, delta)
3-Hydroxy-3-methylglutarylCoenzyme A reductase
CDP-diacylglycerol synthase
(phosphatidate
cytidylyltransferase) 2

143

Table 13 (cont’d).

Gene name

Gene
symbol

Entrez
Gene
ID

PCB153
fold
a
change

TCDD
*

TCDD
[19]

DREs

PXREs

CAREs

Elovl6

170439

-3.8

-4.3

-1.8

3

5

1

Fasn

14104

-3.9

-4.3

-1.9

4

7

1

Hsd17b2

15486

-2.0

2.2

N/A

6

6

_

Insig2
Pla2g12a

72999
66350

5.9
1.7

-1.7
3.1

NC
N/A

1
14

4
9

1
1

Srebf2

20788

-2.1

-1.7

1.5

8

6

4

Srebf1

20787

-2.4

-2.3

-2.1

4

16

1

Mad1l1
Cdca2

17120
108912

2.4
2.8

NC
NC

N/A
N/A

8
2

8
3

1
1

Gadd45a

13197

2.0

-3.6

N/A

3

_

2

Gadd45b

17873

10.7

3.2

4.6

4

14

4

b

c

d

Cell cycle/
cell death

Lipid metabolism

  

Functional
category

ELOVL family member 6,
elongation of long chain fatty
acids (yeast)
Fatty acid synthase
Hydroxysteroid (17-beta)
dehydrogenase 2
Insulin induced gene 2
Phospholipase A2, group XIIA
Sterol regulatory element
binding factor 2
Sterol regulatory element
binding transcription factor 1
Mitotic arrest deficient 1-like 1
Cell division cycle associated 2
Growth arrest and DNAdamage-inducible 45 alpha
Growth arrest and DNAdamage-inducible 45 beta

144

Table 13 (cont’d).
Gene
symbol

Entrez
Gene
ID

PCB153
fold
a
change

TCDD
*

TCDD
[19]

Myc

17869

2.9

3.0

Nol3

78688

4.9

Sphingomyelin
phosphodiesterase 3, neutral

Smpd3

58994

ZW10 interactor

Zwint

Gene name

DREs PXREs

CAREs

b

c

d

3.7

9

2

1

NC

NC

12

6

_

10.8

-2.6

N/A

4

6

1

52696

1.8

NC

N/A

2

5

_

Abcc3

76408

1.9

1.7

1.5

2

9

5

Slc1a4

55963

-2.5

NC

NC

6

10

_

Slc23a1

20522

2.3

-1.7

N/A

3

2

1

Transport

Cell cycle/
cell death

  

Functional
category

Myelocytomatosis oncogene
Nucleolar protein 3 (apoptosis
repressor with CARD domain)

ATP-binding cassette, subfamily C (CFTR/MRP),
member 3
Solute carrier family 1
(glutamate/neutral amino acid
transporter), member 4
Solute carrier family 23
(nucleobase transporters),
member 1

a

Maximum expression (|fold change| ≥1.5, P1(t) ≥ 0.985).
*Maximum expression (|fold change| ≥1.5, P1(t) ≥ 0.90).
b,c,d
Putative DREs, PXREs, and CAREs identified by computational searches (Materials & Methods).
N/A – not available on cDNA microarray.
NC – no change (not meeting the |fold change| ≥1.5, P1(t) ≥ 0.90 cut-off).

145

showing the highest (48-fold) induction in both the time course and dose-response study.
PCB153 modestly induced the typical “AhR-battery” genes such as Cyp1a1, Cyp1a2 and Tiparp
compared to TCDD (Figure 23A-B), while TCDD induction of PCB153-responsive genes was
reciprocally modest (Table 13 and Figure 22C).
PCB153 down-regulated the lipid metabolism acyl-CoA synthetase long-chain family
member 3, Acsl3, and sterol regulatory element binding factors (Srebf1 and Srebf2) genes, -2.1
to -3.1-fold, respectively. Moreover, PCB153 repressed Elovl5 -1.9-fold, while it was induced
2.2-fold by TCDD, suggesting divergent regulation (Figure 23A-B). The down-regulation of
many lipid biosynthesis and metabolism genes by PCB153 is consistent with GS-MS FAMEs
and triglyceride analysis, all indicating a lack of lipid accumulation compared to TCDD.
Genes involved in cell cycle and DNA replication, including Mad1l1 and Zwint, were upregulated 2.4- and 2.0-fold, respectively, by PCB153. However, in contrast to TCDD, PCB153
did not induce necrosis or immune cell infiltration, even though genes involved in cell death and
immune response were differentially regulated. A number of transport genes were also
differentially expressed by PCB153, including ATP-binding cassette and solute carrier family
members, such as Abcc3, Slc1a4, and Slc23a1.
COMPUTATIONAL DOSE-RESPONSE MODELING
Dose-response microarray data were filtered using P1(t)>0.90 cut-off and identified 846
unique annotated genes exhibiting a sigmoidal dose-response profile with reasonable ED50 values
(i.e. between 1 and 300 mg/kg) (Figure 24). 315 genes exhibited ED50s between 1 and 10 mg/kg,
124 genes were between 10 and 30 mg/kg, 140 genes between 30 and 100 mg/kg and 267 genes
had ED50s between 100 and 300 mg/kg. The CAR/PXR regulated genes, Cyp2b10, Cyp2c55,
Nol3, Entpd5 and Abcc3 exhibited ED50s of 38.1, 31.7, 33.9, 34.2 and 2.9 mg/kg, respectively.

146

Figure 22. QRTPCR verification of selected PCB153-induced genes.
The same RNA samples used in the time course and dose-response microarray studies were also
used for QRTPCR analysis. PCB153 induced CAR/PXR regulated genes, Cyp2b10, Cyp3a11,
Cyp2c55 and Gadd45b, in (A) time course and (B) dose-response studies. (C) TCDD elicited
minimal differential expression of CAR/PXR responsive genes. All fold changes were calculated
relative to vehicle controls. Bars (left y-axis) and lines (right y-axis) represent QRTPCR and
microarray data, respectively. The genes are represented by their official gene symbols. Bars
represent the mean ± SE of at least four independent samples. Data were analyzed by ANOVA
followed by Tukey’s and Dunnett’s post hoc tests. The asterisk (*) indicates p<0.05.

147

24 72
Time (h)

168

90
75 Cyp2b10
*
60
45
*
30
15
* *
0
1 3 10 30 100 300
Dose (mg/kg)
3

0

0.0

40
30
20
10

2

1

1

0

0

4

12

24 72
Time (h)

168

6

24 72
Time (h)

Cyp3a11

168

*
*

4

*

2
0

3

2

12

8

0

Cyp2b10

4

1

3 10 30 100 300
Dose (mg/kg)

30

*
168

1.0

*

0.5

4

*

12 24 72
Time (h)

4

168

148

30
20
10

2
1

*
24 72
Time (h)

5

*
4

12

24

72

168

0

Gadd45b

168

*

10

*
5
0

*
1

3

10

30

100

300

6
5
4
3
2
1
0

Dose (mg/kg)

2

12

*

15

5

4

10

*

5

0

3

0

10

0

40

3
1

*

15

0

Cyp2c55 4

*

15

Gadd45b

Time (h)

5

Cyp3a11

15

Fold change

45

35
*
30 Cyp2c55
25
*
20
15
10
*
5
0
1 3 10 30 100 300
Dose (mg/kg)

1.5

0.0

60

Fold change

Fold change
Fold change

12

*

20

75

0

8
Fold change

4

1.5

*

175
150 Cyp2c55 *
125
100
75
50
*
*
25 *
0
4
12 24 72
Time (h)

Fold change

*

B

C

10

3.0

Cyp3a11 *
*

Fold change

0

*

4.5
Fold change

20
10

20

*

*

30

30

*

Fold change

40 Cyp2b10

Fold change

Fold change

50

Fold change

A

6

5

*

Gadd45b 4
3

4

2

2
0

1
4

12 24 72
Time (h)

168

0

Figure 23. QRTPCR verification of selected TCDD-regulated genes.
The same RNA samples used in the time course microarray studies were also used for QRTPCR
analysis. (A) TCDD-induced AhR-responsive genes, Cyp1a1, Cyp1a2, Tiparp, as well as Elovl5.
(B) PCB153 elicited modest regulation of AhR-responsive genes, but down-regulation of Elovl5.
All fold changes were calculated relative to vehicle controls. Bars (left y-axis) and lines (right yaxis) represent QRTPCR and microarray data, respectively. The genes are represented by their
official gene symbols. Bars represent the mean ± SE of at least four independent samples. Data
were analyzed by ANOVA followed by Tukey’s post hoc test. The asterisk (*) indicates p<0.05.

149

A

0

300
200
100

*
4

12

24 72
Time (h)

168

0

*
*

20
10
0

4

*

*

3

*

*

2

*
4

12

24

72

168

30

1

20

Tiparp

*
20

*

*
*
4

12

Time (h)

24

15

2.0 Elovl5

10

*

10
0

2.5

72

*
168

5

Fold change

*

*

30

5

Cyp1a2

Fold change

2500

400

*

7500
5000

40

500

*

10000 Cyp1a1

Fold change

Fold change

12500

0

2.5

*

2.0

1.5

1.5

1.0

1.0

0.5

0.5

0.0

4

12

24

72

168

0.0

Time (h)

Time (h)

B
6

2

4

1

2

0

4

12

24

72

Time (h)

168

0

2.25
2.00 Cyp1a2
1.75
1.50
1.25
1.00 *
0.75
4
12

*

2.5

*

2.0
1.5
1.0
0.5

24

72

168

2.0

0.0

Time (h)

1.5

2.0

Tiparp

1.0

*

0.5
0.0

1.5

4

*

12

24

0.5

Time (h)

150

1.0

72

168

0.0

1.5

Fold change

*

Fold change

3

8

Cyp1a1

Fold change

Fold change

4

1.5

Elovl5
1.0

1.0

*

0.5
0.0

4

12

24 72
Time (h)

0.5

168

0.0

Figure 24. Dose-response modeling.
846 genes exhibited a dose-response profile and were categorized according to their ED50 value.

151

VERIFICATION OF MICROARRAY RESPONSES
QRTPCR was used to verify the temporal and dose-dependent changes in expression for
a subset of genes. In total, 20 differentially regulated genes where confirmed, including
Cyp3a11, which did not satisfy the Agilent microarray data selection criteria. The differential
expression of CAR/PXR-regulated genes Cyp2b10, Cyp3a11 and Gadd45b [11, 43, 44], as well
as Cyp2c55 (48-fold) by PCB153 and TCDD was also confirmed (Figure 22A-C). In addition,
AhR-responsive genes (Cyp1a1, Cyp1a2 and Tiparp) were included as a positive control for
TCDD and a negative control for PCB153 (Figure 23A-B). Elovl5 was included as an example
of divergent regulation (Figure 23A-B). Overall, there was a good agreement in temporal and
dose-dependent expression patterns between QRTPCR and microarray analysis.
COMPARISON OF TCDD AND PCB153 ELICITED DIFFERENTIAL GENE EXPRESSION
Overall, when compared to TCDD and PCB126, PCB153 elicited a significantly different
gene expression profile that did not include “AhR battery genes”, but CAR/PXR regulated genes,
such as Ces2, Cyp2b10, Fmo5, Fasn, Insig2, Abcc3 and Gsta2 (Table 13). To further investigate
these differences, a comprehensive comparison of the Agilent 300 mg/kg PCB153 and 30 μg/kg
TCDD temporal microarray data between 4 and 168 h was performed (Figure 25A). Using the
same filtering criteria (P1(t)≥0.999 and |fold change|≥1.5), 170 and 186 differentially expressed
genes elicited by TCDD and PCB153, respectively, were identified, of which only 14 were
regulated by both compounds. Relaxing the statistical cut-off to P1(t)≥0.985 increased the
overlap to 74 genes (Figure 25A). A correlation plot of expression ratios compared to
significance was then used to identify conserved and divergent responses between TCDD and
PCB153 (Figure 25B). Overall, only 54% of the commonly regulated genes were positively
correlated in terms of fold change and significance, suggesting that TCDD and PCB153 elicit

152

Figure 25. Gene expression comparisons.
(A) Microarray data sets for 30 µg/kg TCDD and 300 mPCB153 were compared at 4, 12, 24, 72
and 168 h with stringent (|fold change|≥1.5, P1(t)≥0.999) and relaxed (|fold change|≥1.5,
P1(t)≥0.985) selection criteria. Numbers in the Venn diagram represent unique genes. (B) The
correlation plot illustrates that the 74 genes regulated by TCDD and PCB153 are poorly
correlated, indicating that many of the genes exhibited divergent regulation. The x-axis
represents the correlation of gene expression, while the y-axis represents the correlation between
significance values. Genes (dots) located within the upper right quadrant exhibit good correlation
across their gene expression profiles and significance values indicating that their expression
patterns are similar. Genes in the lower left quadrant are poorly correlated with respect to fold
induction and significant values indicating that the TCDD and PCB153-elicited expression
patterns are different, suggesting different mechanisms of regulation. Approximately 54% of the
genes (40/74) were located within the upper right hand quadrant, indicating that 34 of the 74
common genes were poorly correlated. In contrast, when PCB126 and 2,3,7,8tetrachlorodibenzofuran (TCDF) were compared to TCDD in the same model, 68% and 82% of
the genes were located in the upper right hand quadrant [20, 45], further illustrating the
differences between PCB153 and TCDD, PCB126 and TCDF.

153

154

different expression patterns via different mechanisms of regulation.

DISCUSSION
Hepatic gene expression, histopathology and lipid profiling analysis quantitatively and
qualitatively indicate that the temporal and dose-dependent effects elicited by PCB153 are
mechanistically different when compared to TCDD and PCB126. These data are consistent with
the effects elicited by PCB153 being regulated by CAR/PXR.
PCB153 elicited a gene expression profile that included the induction of xenobiotic
metabolism genes such as Cyp2c55, Cyp2b10, Cyp3a11, Ces2, Fmo5 and Gsta2 [11, 43, 44] in
the absence of “AhR gene battery” induction. PWM analysis computationally identified putative
CAREs and PXREs within the regulatory region of numerous genes that exhibited differential
expression following PCB153 treatment, further suggesting CAR/PXR regulation. This is also
consistent with the in vivo induction of Cyp3a23 by highly chlorinated non co-planar PCBs [46],
and more specifically, the induction of Gal4-PXR-regulated reporter gene activity [14]. In
addition, Sprague-Dawley rat studies concluded PCB153 did not activate AhR and may be
associated with the induction of phenobarbital-responsive genes [9, 47, 48].
Hepatic responses induced by PCB153 are also in agreement with reported CAR/PXRmediated hepatic hypertrophy [49-51]. In contrast, hepatocyte cell size and number are not
affected by TCDD and PCB126 [19, 20]. PCB153 induced hepatocellular hypertrophy between
24 and 168 h compared to controls, in agreement with the NTP Technical Report that identified
pronounced and persistent increase in cell size elicited by PCB153 [9]. In addition, liver
hypertrophic responses to known CAR inducers (TCPOBOP or phenobarbital) are abolished in
mice lacking the CAR gene [11, 52].

155

TCDD, PCB126 and PCB153 all induced RLW in the time course and dose-response
studies [19, 20]. However, PCB153 elicited minimal vacuolization which completely subsided
by 168 h. Histopathology and lipid profiling also suggest that PCB153 did not induce hepatic
steatosis [19, 20], and microarray analysis indicated that PCB153 down-regulated many lipid
metabolism genes. For example, Elovl5 was repressed by PCB153 but induced by TCDD,
suggesting divergent regulation that may partially explain the histopathology differences.
Interestingly, Elovl5 null mice (Elovl5

-/-

) have higher SREBP1 protein levels that increase fatty

acid synthesis and the development of steatosis [53]. However, PCB153 did not affect Srebf1
expression and did not promote lipid synthesis. Other studies have shown that the CAR/PXRmediated insulin induced genes (Insig1 and Insig2) also repress Srebf1, thereby lowering hepatic
fatty acids [44, 54]. The PCB153 induction of Insig2 by 5.9-fold and repression of Srebf1 by
-2.4-fold suggests that Elovl5 repression does fully explain the observed lipid changes. In
contrast, Insig2 was down-regulated -1.7-fold and Srebf1 was repressed -2.3-fold by TCDD
suggesting that this down-regulation was not sufficient to affect lipid biosynthesis.
Histopathology revealed a lack of PCB153-elicited hepatocellular necrosis/apoptosis,
when compared to TCDD [19], consistent with other studies suggesting PCB153 reduces
apoptosis in mouse hepatocytes [55, 56]. PCB153 induced Gadd45b ~11 fold, a known inhibitor
of c-Jun N-terminal kinase (JNK)-mediated apoptosis [57]. In addition, Gadd45b null mice have
attenuated Cyp2b10 expression, linking Gadd45b with direct co-activation of CAR-mediated
transcription [58]. PCB153 also regulated the expression of genes involved in cell death,
although there was no evidence of necrosis. For example, Myc induction following chronic
PCB153 exposure has been associated with increased apoptosis in HepG2 cells [59]. However,

156

the single dose of PCB153 used in the current study may not be sufficient to induce cell death
within 168 h.
Furthermore, there was a lack of significant immune cell accumulation associated with
PCB153 treatment, unlike TCDD, which increased mixed cell infiltration, primarily in the
centrilobular regions [19]. Nevertheless, PCB153 induced expression of chemokine (C-C motif)
ligand 9 (Ccl9), in agreement with the increased incidence of liver inflammation in chronically
treated rats [9]. This suggests that a single dose of PCB153 may not be sufficient to increase
mixed cell infiltration within a week.
In summary, PCB153 and possibly other non-coplanar congeners elicit responses that are
qualitatively and quantitatively different than TCDD and other planar PCBs. PCB153 elicited no
instances of inflammation or necrosis/apoptosis and did not lead to hepatic lipid accumulation.
These effects are consistent with the differential gene expression responses and suggestive of
CAR/PXR-mediated regulation. However, the relevance of these effects in risk assessment
warrants further investigation due to significant species-specific differences in ligand preference,
binding, and receptor activation when comparing human and rodent CAR/PXR orthologs [14,
43, 60, 61].

157

REFERENCES

158

REFERENCES
1.

Mullin, M.D., et al., High-resolution PCB analysis: synthesis and chromatographic
properties of all 209 PCB congeners. Environ. Set Technol., 1984. 18: p. 468-476.

2.

NTP, NTP toxicology and carcinogenesis studies of 3,3',4,4',5-pentachlorobiphenyl (PCB
126) (CAS No. 57465-28-8) in female Harlan Sprague-Dawley rats (Gavage Studies).
NTP Technical Report, 2006(520): p. 4-246.

3.

Kimbrough, R.D., Polychlorinated biphenyls (PCBs) and human health: an update. Crit
Rev Toxicol, 1995. 25(2): p. 133-63.

4.

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

5.

Knerr, S. and D. Schrenk, Carcinogenicity of "non-dioxinlike" polychlorinated biphenyls.
Crit Rev Toxicol, 2006. 36(9): p. 663-94.

6.

Van den Berg, M., et al., The 2005 World Health Organization reevaluation of human
and Mammalian toxic equivalency factors for dioxins and dioxin-like compounds.
Toxicol Sci, 2006. 93(2): p. 223-41.

7.

Giesy, J.P. and K. Kannan, Dioxin-like and non-dioxin-like toxic effects of
polychlorinated biphenyls (PCBs): implications for risk assessment. Crit Rev Toxicol,
1998. 28(6): p. 511-69.

8.

Schecter, A., et al., Polychlorinated biphenyl levels in the tissues of exposed and
nonexposed humans. Environ Health Perspect, 1994. 102 Suppl 1: p. 149-58.

9.

NTP, NTP technical report on the toxicology and carcinogenesis studies of 2,2',4,4',5,5'hexachlorobiphenyl (PCB 153) (CAS No. 35065-27-1) in female Harlan Sprague-Dawley
rats (Gavage studies). Natl Toxicol Program Tech Rep Ser, 2006(529): p. 4-168.

10.

Craft, E.S., M.J. DeVito, and K.M. Crofton, Comparative responsiveness of
hypothyroxinemia and hepatic enzyme induction in Long-Evans rats versus C57BL/6J
mice exposed to TCDD-like and phenobarbital-like polychlorinated biphenyl congeners.
Toxicol Sci, 2002. 68(2): p. 372-80.

11.

Honkakoski, P., T. Sueyoshi, and M. Negishi, Drug-activated nuclear receptors CAR and
PXR. Ann Med, 2003. 35(3): p. 172-82.

12.

Pascussi, J.M., et al., The Tangle of Nuclear Receptors that Controls Xenobiotic
Metabolism and Transport: Crosstalk and Consequences. Annu Rev Pharmacol Toxicol,
2007.

159

13.

Strathmann, J., et al., PCB 153, a non-dioxin-like tumor promoter, selects for betacatenin (Catnb)-mutated mouse liver tumors. Toxicol Sci, 2006. 93(1): p. 34-40.

14.

Tabb, M.M., et al., Highly chlorinated PCBs inhibit the human xenobiotic response
mediated by the steroid and xenobiotic receptor (SXR). Environ Health Perspect, 2004.
112(2): p. 163-9.

15.

Biegel, L., et al., 2,2',4,4',5,5'-hexachlorobiphenyl as a 2,3,7,8-tetrachlorodibenzo-pdioxin antagonist in C57BL/6J mice. Toxicol Appl Pharmacol, 1989. 97(3): p. 561-71.

16.

Chubb, L.S., et al., Regional induction of CYP1A1 in rat liver following treatment with
mixtures of PCB 126 and PCB 153. Toxicol Pathol, 2004. 32(4): p. 467-73.

17.

Suh, J., et al., Antagonism of aryl hydrocarbon receptor-dependent induction of CYP1A1
and inhibition of IgM expression by di-ortho-substituted polychlorinated biphenyls.
Toxicol Appl Pharmacol, 2003. 187(1): p. 11-21.

18.

Bager, Y., et al., Interaction of 3,4,5,3',4'-pentachlorobiphenyl and 2,4,5,2',4',5'hexachlorobiphenyl in promotion of altered hepatic foci in rats. Pharmacol Toxicol,
1995. 77(2): p. 149-54.

19.

Boverhof, D.R., et al., Temporal and dose-dependent hepatic gene expression patterns in
mice provide new insights into TCDD-Mediated hepatotoxicity. Toxicol Sci, 2005. 85(2):
p. 1048-63.

20.

Kopec, A.K., et al., Comparative Toxicogenomic Examination of the Hepatic Effects of
PCB126 and TCDD in Immature, Ovariectomized C57BL/6 Mice. Toxicol Sci, 2008.
102(1): p. 61-75.

21.

Sheehan, D.C. and B.B. Hrapchak, Theory and Practice of Histotechnology. second ed
ed. 1980, St. Louis: Mosby.

22.

Patterson, T.A., et al., Performance comparison of one-color and two-color platforms
within the MicroArray Quality Control (MAQC) project. Nat Biotechnol, 2006. 24(9): p.
1140-50.

23.

Burgoon, L.D. and T.R. Zacharewski, dbZach toxicogenomic information management
system. Pharmacogenomics, 2007. 8(3): p. 287-91.

24.

Burgoon, L.D., et al., Protocols for the assurance of microarray data quality and process
control. Nucleic Acids Res, 2005. 33(19): p. e172.

25.

Eckel, J.E., et al., Normalization of two-channel microarray experiments: a
semiparametric approach. Bioinformatics, 2005. 21(7): p. 1078-83.

26.

Eckel, J.E., et al., Empirical bayes gene screening tool for time-course or dose-response
microarray data. J Biopharm Stat, 2004. 14(3): p. 647-70.

160

27.

Burgoon, L.D. and T.R. Zacharewski, Automated quantitative dose-response modeling
and point of departure determination for large toxicogenomic and high-throughput
screening data sets. Toxicol Sci, 2008. 104(2): p. 412-8.

28.

Sun, Y.V., et al., Comparative analysis of dioxin response elements in human, mouse and
rat genomic sequences. Nucleic Acids Res, 2004. 32(15): p. 4512-23.

29.

Phillips, J.M., L.D. Burgoon, and J.I. Goodman, Phenobarbital (PB) Elicits Unique,
Early Changes in the Expression of Hepatic Genes That Affect Critical Pathways in
Tumor-Prone B6C3F1 Mice. Toxicol Sci, 2009.

30.

Vyhlidal, C.A., P.K. Rogan, and J.S. Leeder, Development and refinement of pregnane X
receptor (PXR) DNA binding site model using information theory: insights into PXRmediated gene regulation. J Biol Chem, 2004. 279(45): p. 46779-86.

31.

Zhou, J., et al., A novel pregnane X receptor-mediated and sterol regulatory elementbinding protein-independent lipogenic pathway. J Biol Chem, 2006. 281(21): p. 1501320.

32.

Pascussi, J.M., et al., Possible involvement of pregnane X receptor-enhanced CYP24
expression in drug-induced osteomalacia. J Clin Invest, 2005. 115(1): p. 177-86.

33.

Echchgadda, I., et al., Gene regulation for the senescence marker protein DHEAsulfotransferase by the xenobiotic-activated nuclear pregnane X receptor (PXR). Mech
Ageing Dev, 2004. 125(10-11): p. 733-45.

34.

Igarashi, M., et al., Vitamin K induces osteoblast differentiation through pregnane X
receptor-mediated transcriptional control of the Msx2 gene. Mol Cell Biol, 2007. 27(22):
p. 7947-54.

35.

Wang, H., et al., A novel distal enhancer module regulated by pregnane X
receptor/constitutive androstane receptor is essential for the maximal induction of
CYP2B6 gene expression. J Biol Chem, 2003. 278(16): p. 14146-52.

36.

Vandesompele, J., et al., Accurate normalization of real-time quantitative RT-PCR data
by geometric averaging of multiple internal control genes. Genome Biol, 2002. 3(7): p.
RESEARCH0034.

37.

Dennis, G., Jr., et al., DAVID: Database for Annotation, Visualization, and Integrated
Discovery. Genome Biol, 2003. 4(5): p. P3.

38.

DeVito, M.J., et al., Dose-response relationships for disposition and hepatic
sequestration of polyhalogenated dibenzo-p-dioxins, dibenzofurans, and biphenyls
following subchronic treatment in mice. Toxicol Sci, 1998. 46(2): p. 223-34.

39.

Jordan, S.A. and M.M. Feeley, PCB congener patterns in rats consuming diets
containing Great Lakes salmon: analysis of fish, diets, and adipose tissue. Environ Res,
1999. 80(2 Pt 2): p. S207-S212.
161

40.

Luotamo, M., J. Jarvisalo, and A. Aitio, Assessment of exposure to polychlorinated
biphenyls: analysis of selected isomers in blood and adipose tissue. Environ Res, 1991.
54(2): p. 121-34.

41.

Vodicnik, M.J. and J.J. Lech, The transfer of 2,4,5,2',4',5'-hexachlorobiphenyl to fetuses
and nursing offspring. I. Disposition in pregnant and lactating mice and accumulation in
young. Toxicol Appl Pharmacol, 1980. 54(2): p. 293-300.

42.

Boverhof, D.R., et al., Comparative toxicogenomic analysis of the hepatotoxic effects of
TCDD in Sprague Dawley rats and C57BL/6 mice. Toxicol Sci, 2006. 94(2): p. 398-416.

43.

Maglich, J.M., et al., Nuclear pregnane x receptor and constitutive androstane receptor
regulate overlapping but distinct sets of genes involved in xenobiotic detoxification. Mol
Pharmacol, 2002. 62(3): p. 638-46.

44.

Rosenfeld, J.M., et al., Genetic profiling defines the xenobiotic gene network controlled
by the nuclear receptor pregnane X receptor. Mol Endocrinol, 2003. 17(7): p. 1268-82.

45.

N'Jai, A., et al., Comparative temporal toxicogenomic analysis of TCDD- and TCDFmediated hepatic effects in immature female C57BL/6 mice. Toxicol Sci, 2008. 103(2): p.
285-97.

46.

Schuetz, E.G., C. Brimer, and J.D. Schuetz, Environmental xenobiotics and the
antihormones cyproterone acetate and spironolactone use the nuclear hormone
pregnenolone X receptor to activate the CYP3A23 hormone response element. Mol
Pharmacol, 1998. 54(6): p. 1113-7.

47.

Connor, K., et al., Structure-dependent induction of CYP2B by polychlorinated biphenyl
congeners in female Sprague-Dawley rats. Biochem Pharmacol, 1995. 50(11): p. 191320.

48.

Vezina, C.M., N.J. Walker, and J.R. Olson, Subchronic exposure to TCDD, PeCDF,
PCB126, and PCB153: effect on hepatic gene expression. Environ Health Perspect, 2004.
112(16): p. 1636-44.

49.

Blanco-Bose, W.E., et al., C-Myc and its target FoxM1 are critical downstream effectors
of constitutive androstane receptor (CAR) mediated direct liver hyperplasia. Hepatology,
2008. 48(4): p. 1302-11.

50.

Staudinger, J.L., et al., Regulation of drug transporter gene expression by nuclear
receptors. Drug Metab Dispos, 2003. 31(5): p. 523-7.

51.

Wei, P., et al., The nuclear receptor CAR mediates specific xenobiotic induction of drug
metabolism. Nature, 2000. 407(6806): p. 920-3.

52.

Wei, P., et al., Specific and overlapping functions of the nuclear hormone receptors CAR
and PXR in xenobiotic response. Pharmacogenomics J, 2002. 2(2): p. 117-26.
162

53.

Moon, Y.A., R.E. Hammer, and J.D. Horton, Deletion of ELOVL5 leads to fatty liver
through activation of SREBP-1c in mice. J Lipid Res, 2008.

54.

Roth, A., et al., Regulatory cross-talk between drug metabolism and lipid homeostasis:
constitutive androstane receptor and pregnane X receptor increase Insig-1 expression.
Mol Pharmacol, 2008. 73(4): p. 1282-9.

55.

Tharappel, J.C., et al., Regulation of cell proliferation, apoptosis, and transcription factor
activities during the promotion of liver carcinogenesis by polychlorinated biphenyls.
Toxicol Appl Pharmacol, 2002. 179(3): p. 172-84.

56.

Lu, Z., et al., Effect of 2,2',4,4',5,5'-hexachlorobiphenyl (PCB-153) on hepatocyte
proliferation and apoptosis in mice deficient in the p50 subunit of the transcription factor
NF-kappaB. Toxicol Sci, 2004. 81(1): p. 35-42.

57.

Papa, S., et al., Gadd45 beta mediates the NF-kappa B suppression of JNK signalling by
targeting MKK7/JNKK2. Nat Cell Biol, 2004. 6(2): p. 146-53.

58.

Yamamoto, Y. and M. Negishi, The antiapoptotic factor growth arrest and DNAdamage-inducible 45 beta regulates the nuclear receptor constitutive active/androstane
receptor-mediated transcription. Drug Metab Dispos, 2008. 36(7): p. 1189-93.

59.

Ghosh, S., S. De, and S.K. Dutta, Altered Protein Expressions in Chronic PCB-153Induced Human Liver (HepG2) Cells. Int J Toxicol, 2007. 26(3): p. 203-12.

60.

Blumberg, B. and R.M. Evans, Orphan nuclear receptors--new ligands and new
possibilities. Genes Dev, 1998. 12(20): p. 3149-55.

61.

Jones, S.A., et al., The pregnane X receptor: a promiscuous xenobiotic receptor that has
diverged during evolution. Mol Endocrinol, 2000. 14(1): p. 27-39.

163

CHAPTER 6

Kopec AK, D’Souza ML, Mets BM, Burgoon LD, Reese SE, Archer KJ, Potter D, Tashiro C,
Sharratt B, Harkema JR, Zacharewski TR: Non-Additive Hepatic Gene Expression Effects
Elicited by 2,3,7,8-Tetrachlorodibenzo-p-dioxin and 2,2’,4,4’,5,5’-Hexachlorobiphenyl in
C57BL/6 Mice

164

CHAPTER 6
NON-ADDITIVE HEPATIC GENE EXPRESSION EFFECTS ELICITED BY
2,3,7,8-TETRACHLORODIBENZO-P-DIOXIN AND 2,2’,4,4’,5,5’HEXACHLOROBIPHENYL IN C57BL/6 MICE
ABSTRACT
Interactions between environmental contaminants can lead to non-additive effects that
may affect toxicity and influence the risk assessment of a mixture. Comprehensive time course
and dose-response studies with 2,3,7,8-tetrachlorodibenzo-p-dioxins (TCDD), non-dioxin-like
2,2’,4,4’,5,5’-hexachlorobiphenyl (PCB153) and their mixture were performed in immature,
ovariectomized C57BL/6 mice. Mice were gavaged once with 30 µg/kg TCDD, 300 mg/kg
PCB153, a mixture of 30 µg/kg TCDD and 300 mg/kg PCB153 (MIX) or sesame oil vehicle for
4, 12, 24, 72 or 168 h. In the dose-response study, animals were gavaged with TCDD (0.3, 1, 3,
6, 10, 15, 30, 45 µg/kg), PCB153 (3, 10, 30, 60, 100, 150, 300, 450 mg/kg), MIX (0.3+3, 1+10,
3+30, 6+60, 10+100, 15+150, 30+300, 45 µg/kg TCDD + 450 mg/kg PCB153, respectively) or
vehicle for 24 h. Relative liver weights (RLW) were significantly increased by all three
treatments, but MIX induction was significantly (p<0.05) greater compared to TCDD and
PCB153 alone. Histologically, MIX induced marked hepatocellular hypertrophy, vacuolization,
immune cell infiltration, hyperplasia and necrosis, reflecting a combination of TCDD- and
PCB153-elicited responses. Complementary lipid analyses identified significant increases in
hepatic triglycerides in MIX- and TCDD-treated samples, while PCB153 had no effect on lipid
165

accumulation. Hepatic PCB153 levels were also significantly increased with TCDD cotreatment. Microarray analysis identified 568 unique differentially expressed genes, with 167,
185 and 388 differentially expressed in response to TCDD, PCB153 and MIX, respectively (|fold
change|>1.5, P1(t)>0.999). Statistical modeling of quantitative real-time PCR analysis of
Pla2g12a, Serpinb6a, Nqo1, Srxn1 and Dysf identified non-additive expression following MIX
treatment compared to TCDD and PCB153 alone at 24 h. Furthermore, computational searches
identified dioxin, constitutive androstane, and pregnane X receptor response elements in the
promoter region of genes exhibiting non-additive interactions. This study has identified specific
non-additive gene expression responses following co-treatment with TCDD plus PCB153 that
are consistent with effects on gross physiology, histopathology, inflammation, and hepatic lipid
accumulation.

INTRODUCTION
Polyhalogenated aromatic hydrocarbons (PHAHs), including polychlorinated biphenyls
(PCBs) and polychlorinated dibenzo-p-dioxins (PCDDs), are persistent environmental
contaminants that elicit species- and tissue-specific toxic effects. 2,3,7,8-Tetrachlorodibenzo-pdioxin (TCDD) and other PCDDs are by-products of various activities including municipal solid
waste and sewage sludge incineration, herbicide production, pulp and paper bleaching, backyard
barrel burning, and other processes [1-4]. Despite the banning of both the production and the use
of PCBs in many industrial applications in the late 1970s [4-6], various PCBs are still present in
the environment and pose a potential adverse health threat. The chemical stability of PCBs and
PCDDs coupled with their lipophilic nature has led to their bioaccumulation and
biomagnification in the food chain, particularly in fatty tissues [7, 8].

166

The broad spectrum of effects elicited by PCBs and PCDDs include hepatotoxicity,
immune suppression, reproductive toxicity, endocrine disruption, developmental toxicity, and
carcinogenicity [9, 10]. Overall toxicity is determined by the structural similarity of the congener
to TCDD and its ability to bind and activate the aryl hydrocarbon receptor (AhR) [11]. PCB
congeners containing meta and para chlorines on the biphenyl ring (coplanar, dioxin-like PCBs)
are more toxic than non-coplanar PCBs with ortho chlorine substituents reducing their AhR
binding affinity [6, 12].
The responses elicited by dioxin and dioxin-like PCBs are mediated through the ligandactivation of AhR, a cytosolic basic helix-loop-helix Per/ARNT/Sim domain containing
transcription factor [9, 13-15]. Upon ligand binding, chaperone proteins dissociate from the AhR,
allowing its translocation to the nucleus and heterodimerization with AhR nuclear translocator
(ARNT). AhR:ARNT heterodimers interact with dioxin response elements (DREs) in the
regulatory regions of target genes and recruit transcriptional co-regulators, leading to changes in
gene expression [14, 16].
Despite their non-dioxin-like characteristics, non-coplanar PCBs, in particular
2,2’,4,4’,5,5’-hexachlorobiphenyl (PCB153), account for a majority of the PCBs found in
environmental and biological samples [17, 18]. PCB153 does not bind or activate the AhR. In
addition, it elicits a unique response profile compared to TCDD and other dioxin-like PCBs that
is constitutive androstane receptor (CAR)/pregnane X receptor (PXR) mediated [19-22].
Following ligand binding, CAR/PXR translocates to the nucleus to heterodimerize with the
retinoid X receptor. This heterodimer complex can then bind to CAR and PXR response
elements in the regulatory regions of target genes, recruiting co-activators and eliciting changes
in gene expression [23-25].

167

Traditionally, risk assessment of environmental chemical exposure has focused on single
congener toxicity. However, PCBs and dioxins exist as complex mixtures, and interactions
between components can influence the toxicity of a mixture. For example, interactions can lead
to additive effects elicited by two chemicals, or result in non-additive responses (i.e., synergistic
or antagonistic) [26].
Previous studies have documented non-additive interactions between PCB153 and other
dioxin-like chemicals. For example, PCB153 inhibits TCDD- and PCB126-induced cleft palate
and immunotoxicity, and suppresses in vivo and in vitro ethoxyresorufin-O-deethylase (EROD)
induction [27-29]. In contrast, others reported synergistic induction in mouse hepatic EROD and
aryl hydrocarbon hydroxylase activities compared to TCDD treatment alone [30]. PCB153 and
TCDD co-treatment has also been shown to elicit synergistic effects on porphyrin accumulation
in Sprague-Dawley rats [31]. However, there have been no studies examining the non-additive
effects of PCB153 and TCDD co-treatment on gene expression and associating these effects to a
phenotypic response.
In this report, time course and dose-dependent hepatic gene expression studies with
complementary computational response element searches, histopathology, lipid profiling and
tissue analyses of PCB153 and TCDD levels were performed to evaluate the hepatic effects
elicited by a mixture of TCDD and PCB153 in immature, ovariectomized C57BL/6 mice. Doseresponse non-linear regression modeling identified non-additive gene expression responses
elicited by the mixture that were consistent with the non-additive phenotypic responses observed
at later time points.

168

MATERIALS & METHODS
ANIMAL HUSBANDRY
Female C57BL/6 mice, ovariectomized by the supplier on postnatal day (PND) 20, with
body weights (BW) within 10% of the average, were obtained from Charles Rivers Laboratories
(Portage, MI) on PND 25. Animals were housed in polycarbonate cages containing cellulose
fiber chips (Aspen Chip Laboratory Bedding, Northeastern Products, Warrensberg, NY) with 3040% humidity and a 12 h light/dark cycle (07:00 h – 19:00 h), had free access to deionized water
and were fed ad libitum with Harlan Teklad 22/5 Rodent Diet 8640 (Madison, WI). Animals
were acclimatized prior to dosing on PND 28. Immature ovariectomized mice were used to
facilitate comparisons with other data sets obtained using the same model, study design and
analysis methods [22, 32-34]. Immature animals are more responsive to AhR ligands, and
ovariectomy negates potential interactions with estrogens produced by the maturing ovaries. All
procedures were carried out with the approval of the Michigan State University All-University
Committee on Animal Use and Care.
TIME COURSE AND DOSE-RESPONSE STUDIES
A stock solution of PCB153 (99.9% purity, AccuStandard, New Haven, CT) was first
dissolved in acetone (J.T. Baker), followed by dilution with sesame oil (Sigma, St. Louis, MO),
and evaporation of acetone under nitrogen gas. A stock solution of TCDD was a gift from the
Dow Chemical Company (Midland, MI). PCB153 and TCDD stock solutions were diluted using
sesame oil to achieve the desired dose. Animals were orally gavaged using 1.5 inch feeding
needles with 2.25 mm ball ends (Cadence Science, Lake Success, NY). For the time course
study, mice (n=5 per group) were administered 0.1 ml of a single dose of 30 μg/kg TCDD, 300
mg/kg PCB153, a mixture (MIX) of 1:10,000 ratio of TCDD:PCB153 (30 μg/kg TCDD with 300
169

mg/kg PCB153) or sesame oil vehicle and sacrificed after 4, 12, 24, 72 or 168 h. In the doseresponse study, mice (n=5 per group) were gavaged with TCDD (0.3, 1, 3, 6, 10, 15, 30, 45
µg/kg), PCB153 (3, 10, 30, 60, 100, 150, 300, 450 mg/kg), MIX (0.3+3, 1+10, 3+30, 6+60,
10+100, 15+150, 30+300, 45 µg/kg TCDD + 450 mg/kg PCB153, respectively) or vehicle, and
sacrificed 24 h post dose by cervical dislocation. Livers were removed, and sections weighed,
flash frozen in liquid nitrogen, and stored at –80°C. A section of the right liver lobe was fixed in
10% neutral buffered formalin (Sigma) for histological analysis. For lipid staining, the remaining
right lobe of the liver was frozen in Tissue-Tek O.C.T. compound (Sakura, Torrance, CA).
HISTOLOGICAL ANALYSIS
Fixed liver tissues were sectioned and processed in ethanol, xylene, and paraffin using a
Thermo Electron Excelsior tissue processor (Waltham, MA). Tissues were embedded in paraffin
with Miles Tissue Tek II embedding center, after which paraffin blocks were sectioned at 5 μm
with a rotary microtome. Liver sections were placed on glass microscope slides, washed twice in
xylene for 5 min, followed by four quick washes in ethanol and rinsing in water. Slides were
placed in Gill 2 hematoxylin (Thermo Fisher Scientific, Waltham, MA) for 1.5 min followed by
2-3 quick dips in 1% glacial acetic acid water and rinsed with running water for 2-3 min. Slides
were then rinsed in ethanol and counterstained with 1% eosin Y-phloxine B solution (Sigma)
followed by multiple rinses in ethanol and xylene.
For lipid staining, liver samples were sectioned at 6 μm, fixed in 10% neutral buffered
formalin for 5 min, rinsed with water and immersed in 100% propylene glycol for 5 min. Slides
were stained with Oil Red O solution (Sigma) for 8 min at 60°C, placed in 80% propylene glycol
for 5 min and rinsed in water for 15 min. Slides were counterstained with Gill 2 hematoxylin for
30 sec and washed with water for 30 min. Coverslips were attached using aqueous mounting

170

media. All the histological processing was performed at Michigan State University Investigative
Histopathology Laboratory, Division of Human Pathology using a modified version of
previously published procedures [35].
LIPID ANALYSIS BY GAS CHROMATOGRAPHY-MASS SPECTROMETRY (GC-MS)
TCDD, PCB153, MIX and vehicle control liver samples (~100 mg) from the time course
(24, 72, 168 h) and dose-response (24 h) studies were homogenized (Polytron PT2100,
Kinematica AG, Luzern, CH) in 40% methanol and acidified with concentrated HCl. Lipids were
extracted with chloroform:methanol (2:1) containing 1 mM 2,6-di-tert-butyl-4-methylphenol
(BHT; Sigma) and extraction efficiency controls (19:1n9 FFA [free fatty acid] and 19:0 TAG
[triacylglycerol]) were added (Nu-Chek Prep, Elysian, MN). Protein and aqueous phases were
re-extracted with chloroform and the organic phases were pooled. A derivatization standard
(19:2n6 FFA; Nu-Chek Prep) was added and samples were dried under nitrogen, resuspended in
2% non-aqueous methanolic HCl and incubated at 60ºC overnight. Samples were cooled to room
temperature and 0.9% (w/v) NaCl and hexane were added. The organic phase was collected and
a loading control (17:1n9 fatty acid methyl ester [FAME]; Nu-Chek Prep) was added. Samples
were dried under nitrogen, resuspended in equal volumes of hexane and separated on Agilent
6890N gas chromatograph interfaced to Agilent 5973 mass spectrometer with DB23 column (30
m length, 0.25 mm internal diameter, 0.25 m film thickness). Samples were run on the
following temperature program: 50°C to 150°C at 40°C/ min, to 185°C at 5°C/ min, to 235°C at
3°C/ min, to 250°C at 10°C/ min. Peak areas were integrated with MassLynx software (Waters,
Milford, MA) and data were normalized to the starting amount of liver and loading controls.
Principal component analysis of temporal lipid profiles was performed in R 2.6.0, data were
extracted and used to generate PCA plots in GraphPad Prism 5.0 (La Jolla, CA).

171

HEPATIC TRIGLYCERIDE MEASUREMENT
Frozen liver samples (~100 mg) were homogenized (Polytron PT2100, Kinematica) in 1
ml of 1.15% KCl. Triglycerides were extracted from 200 µl of hepatic homogenate with 800 µl
of isopropyl alcohol by vortex-mixing for 10 min. The samples were centrifuged for 5 min at 800
x g at room temperature and supernatant was collected into separate vials. The concentration of
hepatic triglycerides was determined using a commercial L-Type Triglyceride M kit (Wako
Diagnostics, Richmond, VA) with Multi-Calibrator Lipids as a standard (Wako Diagnostics).
The measurements were performed according to the manufacturer's protocol with 20 µl of the
triglyceride extract incubated with 150 µl of Reagent 1 followed by incubation with 50 µl of
Reagent 2. Final results were normalized to the starting amount of liver.
QUANTIFICATION OF HEPATIC PCB153 AND TCDD LEVELS
Liver samples were processed in parallel with laboratory blanks and a reference or
background sample at Wellington Laboratories Inc. (Guelph, ON, Canada). Samples (100 to 500
mg) were transferred to a tared screw cap culture tube and weights were recorded. Samples were
spiked with

13

C12-PCB153 and

13

C12-2,3,7,8-TCDD surrogates and digested with hydrochloric

acid. Each digested sample was then split between two screw cap tubes and 3-4 ml hexane was
added to each tube followed by vigorous mixing. Tubes were centrifuged, and the organic layer
was removed. Hexane extraction was repeated three times per screw cap tube and the six hexane
fractions were combined. The hexane fraction was then split evenly prior to clean-up and one
fraction was archived. The other fraction was cleaned-up using a small multi-layer
(acid/base/neutral) silica gel column eluted with 20-25 ml of hexane. The eluate was
concentrated on a rotary evaporator and then transferred to a conical microvial with pentane and
dichloromethane rinses and allowed to dry. Immediately prior to injection on the high-resolution
172

gas chromatograph/high-resolution mass spectrometer (HRGC-HRMS) system,
and

13

13

C12-PCB111

C12-1,2,3,4-TCDD injection standards were added to the conical microvial. The

identification and quantification of PCB153 and TCDD was performed using an Agilent (Santa
Clara, CA, USA) 6890 series HRGC with direct capillary interface to a Waters Autospec Ultima
HRMS. Chromatographic separations were carried out on a 60 m DB5 (0.25 mm ID, 0.25 µm
film thickness) column in constant flow mode (Helium, 1 ml /min). All injections were 1 µl and
a splitless injection was used. The mass spectrometer was operated in EI+ selective ion recording
mode at a mass resolving power of 10,000 or greater.
RNA ISOLATION
Liver samples (~100 mg stored at –80°C) were immediately transferred to 1 ml TRIzol
(Invitrogen, Carlsbad, CA) and homogenized using a Mixer Mill 300 tissue homogenizer
(Retsch, Germany). Total RNA was isolated according to the manufacturer’s protocol with an
additional acid phenol:chloroform extraction. Isolated RNA was resuspended in RNA storage
solution (Ambion Inc., Austin, TX), quantified (A260), and quality was assessed by determining
the A260/A280 ratio and by visual inspection of 2 μg on a denaturing gel.
MICROARRAY EXPERIMENTAL DESIGN
TCDD, PCB153, MIX and vehicle RNA samples from the time course study were
individually hybridized to 4  44 K Agilent microarrays (Agilent Technologies, Inc., Santa
Clara, CA). Three biological replicates were performed using one-color labeling (Cy3) for each
time point and treatment, according to the manufacturer’s protocol (Agilent Manual: G414090040 v. 5.7). Microarray slides were scanned at 532 nm (Cy3) on a GenePix 4000B scanner
(Molecular Devices, Union City, CA). Images were analyzed for feature and background
173

intensities using GenePix Pro 6.0 (Molecular Devices). All data were managed in TIMS dbZach
data management system [36].
MICROARRAY ANALYSIS
All microarray data passed our laboratory quality assurance protocol [37]. Microarray
data were normalized using a semi-parametric approach [38] and the posterior probabilities were
calculated using an empirical Bayes method based on a per gene and dose basis using modelbased t values [39]. Gene expression data were ranked and prioritized using a P1(t) values
>0.999 and |fold change|>1.5 to identify differentially expressed genes. Data obtained from the
time course microarray were used as a filter to identify putative non-additive candidates for
further verification in the 24 h dose-response study.
QUANTITATIVE REAL-TIME PCR (QRTPCR)
QRTPCR verification of microarray responses was performed as described previously
[32]. Briefly, 1 μg of total RNA was reverse transcribed by SuperScript II (Invitrogen) using an
anchored oligo-dT primer as described by the manufacturer. 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 mM dNTPs, 0.025 IU AmpliTaq Gold, and 1 SYBR Green PCR
buffer (Applied Biosystems, Foster City, CA). PCR amplification was conducted on an Applied
Biosystems PRISM 7500 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 the differences in RNA loading, quality, and cDNA synthesis
[40]. For graphing purposes, the relative expression levels were scaled such that the expression
level of the time-matched control group was equal to one.

174

COMPUTATIONAL DNA RESPONSE ELEMENT IDENTIFICATION
Dioxin response elements (DREs) [41, 42], constitutive androstane receptor response
elements (CAREs) [43] and pregnane X receptor response elements (PXREs) [22] were
computationally identified using position weight matrices specific to each element. Gene
regulatory regions (-10,000 bp relative to the transcriptional start site [TSS] together with 5’untranslated region [UTR]) were obtained from the University of California, Santa Cruz,
Genome Browser for mouse (build 37), computationally searched, and each DRE, CARE and
PXRE was scored. Matrix similarity scores (MSS) >0.80 are considered to be putative response
elements.
DOSE-RESPONSE NON-LINEAR LOGISTIC MODELING
Gene-specific non-linear models were fit to investigate the dose-response relationship for
Dysf, Pla2g12a, Serpinb6a, Srxn1, Nqo1, Got1, Elovl5, Dexi, Akr1c20 and Srebf1 identified in
the microarray study as putative candidates exhibiting non-additive gene expression and further
verified using QRTPCR in the 24 h dose-response study. Specifically, for each gene a non-linear
logistic model for the mean was used to estimate the dose-response relationship for TCDD and
PCB153, according to:

μ

,
 

where β  is the unknown intercept parameter, β ,

 

1,2,3,  is the unknown slope parameter

associated with the ith chemical, χ   is the dose associated with the ith chemical, γ is the
maximum-effect parameter, and µ

is the expected value of the response under the additive

model. Simultaneously, the MIX data were fit with a mixture model:
175

μ

,

where t is the dose associated with MIX data. For a fixed-ratio of TCDD and PCB153 given by

(α , α , such that α
β α

α =1), the slope for the mixture under additivity is θ

β α . Based on preliminary analyses, using the quasi-likelihood estimation

criterion, the assumption is made that variance is a function of the mean:

μ, where

 

is the parameter associated with the functional form of the variance and has to be estimated. The
quasi-likelihood criterion has assumptions only concerning the first and second moments, and
some regularity conditions related to the regression equation:

 μ θ and

μ . It is of interest to maximize the quasi-likelihood function given by:

 

μ

μ .

Gene-specific plots of the mean versus the variance for TCDD, PCB153, and MIX confirmed the
relationship between the mean and the variance. In the study, the additive dose-response model
for the fixed-ratio mixture based on TCDD and PCB153 data alone was compared to the mixture
model obtained using MIX data. Therefore, the null hypothesis states that θ
θ

 and θ

θ

, where

are the parameter vectors assuming the mixture and the additive models,

respectively. The modeling (PROC NLIN) was performed with SAS 9.2 (SAS Institute, Cary,
NC).
FUNCTIONAL GENE ANNOTATION AND STATISTICAL ANALYSIS
Annotation and functional categorization of the differentially regulated genes was
performed using Database for Annotation, Visualization and Integrated Discovery (DAVID) [44]

176

and Ingenuity Pathway Analysis (Ingenuity Systems, Redwood City, CA). All statistical analyses
were performed with SAS 9.2. Unless stated otherwise, all data were analyzed by analysis of
variance (ANOVA) followed by Dunnett’s or Tukey’s post hoc test. Differences between
treatment groups were considered significant when p<0.05.

RESULTS
ORGAN AND BODY WEIGHTS
Exposure to environmental contaminants including dioxins and PCBs leads to temporal
and dose-dependent increases in liver weight in rodents [14, 18, 19, 22, 45, 46]. In the time
course study, significant increases in relative liver weight (RLW) were observed with 300 mg/kg
PCB153 (72, 168 h), 30 μg/kg TCDD, and MIX (24, 72, 168 h), relative to time-matched vehicle
controls (Figure 26A). TCDD and PCB153 treatment resulted in comparable increases in RLW,
while MIX-elicited induction in RLW was significantly greater compared to TCDD and PCB153
at 72 and 168 h (Figure 26A). These results are consistent with reported increases in RLW in
C57BL/6 and B6C3F1 mice 3, 7 and 13 days after a single gavage of TCDD and PCB153
mixture [47, 48].
In the 24 h dose-response study, TCDD and MIX elicited comparable significant
increases in RLW in all but the lowest dose groups (Figure 26B). MIX-induced RLW increase at
15+150 (μg/kg TCDD + mg/kg PCB153, respectively) was also significantly greater when
compared to 150 mg/kg PCB153 (Figure 26B). In contrast, PCB153 increased RLW only at 100
and 300 mg/kg.
Minimal (≤ 10%) decreases in body weight gain were observed with PCB153 (300 and
450 mg/kg) and MIX (three highest dose group combinations) at 24 h, but were not considered
indicative of systemic toxicity [49].
177

B

*

*a

***

* *

45
0

45

+

30
0
+

30

15

+

10
0

15
0

0.000
+

168

*

0.025

60

24
72
Time (h)

**

0.050

10

12

* *

*

+

4

*

6

0.000

*

30

0.025

*

+

0.050

0.075

3

**

10

**

+

*

0.100

1

*

*

3

0.075

*ab

Vehicle
TCDD (g/kg)
PCB153 (mg/kg)
MIX (1:10,000)

+

0.100

ab

0.125

3

Vehicle
TCDD (30 g/kg)
PCB153 (300 mg/kg)
MIX (1:10,000)

0.

0.125

Relative liver weight

Relative liver weight

A

Dose groups

Figure 26.Temporal and dose-dependent changes in the relative liver weights following exposure to TCDD, PCB153, and MIX.
(A) In the time course study, 30 µg/kg TCDD + 300 mg/kg PCB153 were co-administered in a MIX group (1:10,000 ratio of TCDD to
PCB153, respectively). (B) In the dose-response at 24 h, MIX dose groups (represented as µg/kg TCDD + mg/kg PCB153) were also
administered at 1:10,000 ratio of TCDD to PCB153, respectively. Results are displayed as mean ± standard error (SE) of at least 5
independent replicates. Data were analyzed by analysis of variance (ANOVA) followed by Tukey's post hoc test: *p<0.05 vs. vehicle,
a
b
p<0.05 vs. PCB153 and p<0.05 vs. TCDD.

178

HEPATIC TISSUE LEVEL QUANTIFICATION
HRGC-HRMS analysis of liver samples at 24 and 168 h indicate that the hepatic PCB153
accumulation was potentiated following TCDD co-treatment compared to PCB153 alone (Figure
27B). No changes were detected in the hepatic accumulation of TCDD following coadministration with PCB153 (Figure 27A). In addition, PCB153 levels dramatically decreased at
168 h compared to more modest decreases in TCDD levels after seven days [22].
HISTOPATHOLOGY
In the time course study, TCDD elicited moderate multifocal inflammation and marked
hepatocellular vacuolization localized in periportal regions and extending to midzonal and
centrilobular regions at later time points. Instances of vacuolization corresponded with
significant Oil Red O (ORO) staining indicating neutral lipid accumulation (Figure 28B,F).
These changes are consistent with previous reports on the hepatic effects of TCDD in mice [32,
45].
PCB153 elicited levels of vacuolization comparable to those observed in vehicles, as
well as mild to moderate centrilobular hypertrophy with cytoplasmic granularity (Figure 28C,G)
[22]. In contrast to TCDD, PCB153 did not elicit inflammation. Treatment with MIX exhibited a
combination of the TCDD and PCB153 responses. Vacuolization and ORO staining in MIX was
the most severe at 24 h, which decreased by 168 h compared to TCDD-exposed mice. MIXtreated livers also had moderate multifocal inflammation and a temporal increase in cellular
hypertrophy that was comparable to time-matched TCDD and PCB153-exposed livers,
respectively. Furthermore, MIX elicited minimal necrosis and instances of mitotic figures
indicative of hyperplasia (Figure 28D,H).

179

Figure 27. Tissue level quantification.
Hepatic (A) TCDD and (B) PCB153 accumulation levels per g liver wet weight measured by HRGC-HRMS. PCB153 levels were
significantly induced following co-administration with TCDD (in MIX) at 24 and 168 h. However no changes were detected in
TCDD hepatic accumulation following co-treatment with PCB153 (in MIX). Results are displayed as mean ± SE of 3 independent
replicates. Data were analyzed by ANOVA followed by Tukey's post hoc test: *p<0.05 vs. time-matched PCB153 levels alone.

180

Figure 28. Representative histopathology results.
Standard hematoxylin and eosin staining of liver sections at 24 (top panel: A-D) and 168 h (bottom panel: E-H). (A&E) Sesame oil
treatment exhibited minimal vacuolization. (B&F) 30 µg/kg TCDD elicited vacuolization and inflammation (solid arrow). (C&G) 300
mg/kg PCB153 resulted in minimal vacuolization and hypertrophy (star). (D&H) MIX exposure resulted in vacuolization,
inflammation (solid arrow), hypertrophy (star) and necrosis (dashed arrow). Bars = 50 μm.

181

Liver microscopic changes observed in the dose-response study were most severe in the
highest dose groups for each treatment. TCDD elicited moderate periportal hepatocellular
vacuolization and minimal multifocal inflammation (15, 30, 45 μg/kg), while PCB153 elicited
mild to moderate centrilobular hypertrophy (300 and 450 mg/kg) and minimal to mild
hepatocellular vacuolization. MIX treatment resulted in moderate to marked periportal
hepatocellular vacuolization and centrilobular hypertrophy and minimal mixed cell infiltration
(15+15, 30+300, 45 μg/kg TCDD + 450 mg/kg PCB153, respectively).
LIPID PROFILING
Total lipids extracted from TCDD, PCB153, MIX and control livers from the time course
(24, 72 and 168 h) and dose-response studies were derivatized to fatty acid methyl esters. The
levels of twenty one individual fatty acid species were measured and analyzed using GC-MS.
Principal component (PC) analysis (Figure 29) identified a time- and treatment-dependent
separation of fatty acid (FA) profiles (as fold changes) around PC1 and PC2, respectively.
Treatment and time-dependent induction in individual fatty acids is represented in Figure 30A-C.
Compared to all treatments, TCDD induced the most pronounced changes in total FAs
that increased in a time-dependent fashion, with significant inductions at 72 and 168 h (p<0.05
vs. vehicle and PCB153) (Table 14), consistent with the most pronounced ORO staining at 168
h. MIX-elicited increases in total FAs were comparable across time, with significant changes at
24 and 168 h (p<0.05 vs. vehicle and PCB153) (Table 14).
Comparison of saturated (SFA), monounsaturated (MUFA), and n3 and n6
polyunsaturated fatty acids (PUFA) revealed significant differences in MIX inductions compared
to individual chemical exposure (Table 14). In agreement with the lack of ORO staining,

182

1.25

72 h TCDD
0.75

72 h PCB153
24 h TCDD
0.25

PC2

24 h MIX
168 h PCB153
-0.25

24 h PCB153

168 h TCDD

72 h MIX

-0.75

168 h MIX
-1.25

-4

-2

0

2

4

6

8

PC1

Figure 29. Principal component analysis of lipid profiles.
GC-MS lipid profile for TCDD (solid line), PCB153 (dashed line), and MIX (dotted line) at 24,
72 and 168 h. PCA was performed in R as described in the Materials and Methods. The
cumulative proportion of variance for PC1 and PC2 is 95%, indicating that the PCA plot
accurately represents the separation of the data. PCA indicated treatment- and time-dependent
separation of fatty acid species (represented as fold changes).

183

Table 14. GC-MS analysis of fatty acid composition.

Time (h) Treatment

n3 PUFA

n6 PUFA

Total FA

472.71 ± 64.53

170.21 ± 41.37

103.94 ± 9.56

349.80 ± 42.12

1096.65 ± 152.52

TCDD

504.19 ± 57.39

197.54 ± 54.99

106.95 ± 10.60

363.20 ± 32.40

1171.89 ± 148.03

PCB153

422.49 ± 45.64

134.35 ± 31.72

103.70 ± 11.67

331.29 ± 26.02

991.84 ± 97.90

MIX

590.06 ± 37.85*

264.58 ± 27.06*

114.20 ± 6.99

447.42 ± 29.96*

1416.26 ± 96.05*

Vehicle
72

MUFA

Vehicle
24

SFA

470.31 ± 39.09

184.24 ± 33.04

a

a

ab

a

93.26 ± 10.15

317.51 ± 22.72

1065.32 ± 72.23

a

a

TCDD

702.20 ± 80.64*

394.28 ± 63.50*

109.14 ± 2.92

432.07 ± 40.07*

1637.68 ± 180.72*

PCB153

513.23 ± 125.56

213.53 ± 100.64

98.27 ± 15.94

326.28 ± 64.06

1151.31 ± 270.82

b

257.33 ± 67.52

104.47 ± 5.89

394.45 ± 23.05

1314.50 ± 174.81

MIX

558.24 ± 80.12

Vehicle
168

a

455.12 ± 15.19

178.44 ± 24.29
a

97.07 ± 7.28
a

333.55 ± 14.33
a

1064.17 ± 51.85
a

a

TCDD

778.11 ± 105.98*

432.65 ± 75.20*

130.39 ± 6.76*

526.26 ± 46.53*

1867.40 ± 226.62*

PCB153

492.70 ± 59.68

184.20 ± 19.16

104.39 ± 8.46

342.23 ± 26.61

1123.52 ± 110.42

MIX

ab

628.25 ± 60.38*

ab

a

272.31 ± 62.84*

121.32 ± 11.21*

a

461.61 ± 41.32*

a

1483.49 ± 159.37*

Note. Hepatic fatty acids were measured by GC-MS and peak areas were integrated using MassLynx software. The results are
displayed in arbitrary units and reported as mean ± SD of 5 independent replicates. Data were analyzed by ANOVA followed by
a
b
Tukey's post hoc test: *p<0.05 vs. vehicle, p<0.05 vs. PCB153 and p<0.05 vs. TCDD.
184

Figure 30. GC-MS fatty acid profiles.
Fatty acid profiles measured by GC-MS for TCDD, PCB153, and MIX-treated mouse livers at
(A) 24 (B) 72 and (C) 168 h relative to vehicle controls. TCDD elicited time-dependent
induction of multiple fatty acid species compared to PCB153 which did not alter fatty acid
composition. At later time points, the induction of 20:1n9, 20:2n6 and 20:3n3 by TCDD was
significantly repressed following co-treatment with PCB153 (in MIX). Results are displayed as
mean ± SE of 5 independent replicates. Data were analyzed by ANOVA followed by Tukey's
a
b
post hoc test: *p<0.05 vs. vehicle, p<0.05 vs. PCB153 and p<0.05 vs. TCDD.

185

16
16 :0
:1
n7
1
18 8:0
:1
18 n9
:2
18 n6
:3
n3
20
20 :0
:1
20 n9
:2
20 n6
:3
20 n6
:4
20 n6
:3
20 n3
:4
20 n3
:5
n3
22
22 :0
:1
22 n9
:2
22 n6
:5
22 n3
:6
n3
24
24 :0
:1
n9

Fold change

B

C

16
16 :0
:1
n7
18
18 :0
:1
18 n9
:2
18 n6
:3
n3
20
20 :0
:1
20 n9
:2
20 n6
:3
20 n6
:4
20 n6
:3
20 n3
:4
20 n3
:5
n3
22
22 :0
:1
22 n9
:2
22 n6
:5
22 n3
:6
n3
24
24 :0
:1
n9

Fold change
16
16 :0
:1
n7
18
18 :0
:1
18 n9
:2
18 n6
:3
n3
20
20 :0
:1
20 n9
:2
20 n6
:3
20 n6
:4
20 n6
:3
20 n3
:4
20 n3
:5
n3
22
22 :0
:1
22 n9
:2
22 n6
:5
22 n3
:6
n3
24
24 :0
:1
n9

Fold change

A
8

6

2

2
TCDD (30 g/kg)
PCB153 (300 mg/kg)
MIX (1:10,000)

2
a

a

*
b

*a

a
**

*

4

*a *a a*
*

8

6

4

a
*a*a*

24 h

4

ab a
a

*
ab*

*
a

*
ab*

*a

*a

*a
*ab ab a
*

a

*
*a *a
*
ab
*b

a

b

186

*a a
**
ab
*b

Fatty acid species

a

8

a *a
a a a

*a

*a *a
* * a
*
* *a *a
*a *a
a

*a*a

*b
*

0

Fatty acid species

72 h

6

*ab

0

Fatty acid species

168 h

a

0

PCB153 did not change total FAs or any of the FA classes at any time point (Table 14), in
agreement with CAR-mediated inhibition of lipogenesis (Zhai et al., 2010).
Overall, TCDD significantly induced SFAs, MUFAs and PUFAs in a time-dependent
fashion, although changes in n3 PUFAs were modest. Interestingly, MIX significantly repressed
SFAs and MUFAs but increased n6 PUFA levels compared to TCDD at 168 and 24 h,
respectively (Table 14). These non-additive effects on fatty acids are consistent with MIX
inductions in triglyceride levels and ORO staining at 24 h that decreased by 168 h compared to
TCDD at the same time points. GC-MS analysis of FA profiles in the dose-response study
revealed slightly higher increases in MIX compared to TCDD groups, but the differences were
not statistically significant.
TEMPORAL MICROARRAY GENE EXPRESSION
Hepatic gene expression was evaluated using whole genome 4x44K Agilent
oligonucleotide microarrays containing ~21,000 unique annotated genes. Statistical analysis of
the time course study data identified 568 unique gene expression changes, with 167, 185 and 388
genes differentially regulated in response to TCDD, PCB153 and MIX, respectively (|fold
change|>1.5, P1(t)>0.999) at one or more time points. MIX elicited the highest number of
differentially expressed genes at each time point.
Comparative analysis identified 68 TCDD-, 111 PCB153-, and 230 MIX-specific
responses (|fold change|>1.5, P1(t)>0.999). Only 13 genes were differentially expressed by all
three treatments (Figure 31A). When the statistical criteria were relaxed for more inclusive
analysis (|fold change|>1.5, P1(t)>0.95) (Figure 31B), the number of overlapping genes
significantly increased, including the overlaps between MIX and TCDD, and MIX and PCB153.

187

Figure 31. Three-way Venn analysis.
Temporal microarray data sets for 30 µg/kg TCDD, 300 mg/kg PCB153, and MIX (1:10,000
TCDD:PCB153, respectively) groups were compared at (A) stringent (|fold change|>1.5,
P1(t)>0.999) and (B) relaxed (|fold change|>1.5, P1(t)>0.95) selection criteria. Numbers in the
Venn diagram represent unique genes.

188

Functional annotation of the 568 differentially regulated genes was associated with
xenobiotic metabolism, oxidoreduction/oxidative stress, lipid metabolism, immune response, cell
division and differentiation, cell death, and transport. Many of these over-represented functions
(Table 15) could be phenotypically anchored to the changes in RLW, histopathology, and lipid
profiles. For example, the differential expression of genes involved in lipid metabolism and
transport was consistent with the hepatic fatty accumulation, increased hepatic triglyceride
levels, and changes in specific FAMEs. TCDD and MIX induced significant fatty vacuolization
between 24-168 h consistent with the induction of Cd36 (~6 fold) or Fabp12 (~54-34 fold),
which were not induced by PCB153. Down-regulation of Elovl5 (-2 fold) and up-regulation
of Insig2 (~6 fold) by PCB153, in contrast to TCDD, was also consistent with GC-MS fatty
acid profiling indicating lack of lipid accumulation in PCB153-exposed livers.
Histopathology also revealed alterations in cell size (hypertrophy), as well as cell division
and cell death (hyperplasia and necrosis) (Figure 28). TCDD and MIX significantly induced
genes that promote cell death including Bcl2l11 (~2-4 fold), Tnfaip2 (~7 fold), Tnfaip8l1 (~3-4
fold), Traf5 (~2 fold), and Htatip2 (~4 fold), which were not regulated by PCB153, in agreement
with observed lack of cell death.
Furthermore, TCDD significantly induced genes involved in the immune response (~2-8
fold; Clec7a, Fcgr1, H2-Eb1, H2-DMa, H2-DMb1, Irf8, Ltb, Mfg28 and Saa2-3), consistent with
the marked multifocal inflammation at later time points. In contrast, PCB153 did not alter the
expression of these genes, while MIX elicited significant inflammation, reflected by comparable
induction of immune related genes relative to TCDD.

189

Table 15. Functional categorization and potential response element regulation of select hepatic genes differentially expressed
by 30 µg/kg TCDD, 300 mg/kg PCB153. and MIX.
Functional Entrez
category Gene ID

Xenobiotic metabolism/ oxidoreductase activity/
oxidative stress

11761
11997
432720
116852
13076
13077
13088
13089
13094
72082
13113

Gene name
Aldehyde oxidase 1
Aldo-keto reductase family 1,
member B7
Aldo-keto reductase family 1,
member C19
Aldo-keto reductase family 1,
member C20
Cytochrome P450, family 1,
subfamily a, polypeptide 1
Cytochrome P450, family 1,
subfamily a, polypeptide 2
Cytochrome P450, family 2,
subfamily b, polypeptide 10
Cytochrome P450, family 2,
subfamily b, polypeptide 13
Cytochrome P450, family 2,
subfamily b, polypeptide 9
Cytochrome P450, family 2,
subfamily c, polypeptide 55
Cytochrome P450, family 3,
subfamily a, polypeptide 13

Gene
symbol

TCDD
*

PCB153
*

MIX
*

DREs

PXREs

CAREs

Aox1

2.2

2.9

4.8

yes

yes

yes

Akr1b7

NC

22.1

16.8

yes

yes

yes

Akr1c19

NC

1.7

2.0

yes

yes

no

Akr1c20

-1.7

-1.6

-3.0

no

yes

no

Cyp1a1

348.1

NC

348.2

yes

yes

yes

Cyp1a2

2.4

1.7

2.4

yes

yes

yes

Cyp2b10

NC

16.1

24.2

yes

yes

yes

Cyp2b13

NC

1.9

1.9

yes

yes

yes

Cyp2b9

NC

7.6

6.2

no

yes

yes

Cyp2c55

NC

48.4

48.4

yes

yes

yes

Cyp3a13

-1.9

2.5

NC

no

yes

no

  

190

a

a

a

Table 15 (cont’d).
Functional Entrez
category Gene ID

Gene name

Gene
symbol

TCDD PCB153 MIX
*
*
*

DREs

PXREs

CAREs

a

a

a

  

Xenobiotic metabolism/ oxidoreductase activity/
oxidative stress

56388
56050
14776
14858
14863
14865
14872
18104
76650
394435
22235

Cytochrome P450, family 3,
Cyp3a25
subfamily a, polypeptide 25
Cytochrome P450, family
39, subfamily a, polypeptide Cyp39a1
1
Glutathione peroxidase 2
Gpx2
Glutathione S-transferase,
Gsta2
alpha 2 (Yc2)
Glutathione S-transferase,
Gstm2
mu 2
Glutathione S-transferase,
Gstm4
mu 4
Glutathione S-transferase,
Gstt2
theta 2
NAD(P)H dehydrogenase,
s
Nqo1
quinone 1
Sulfiredoxin 1 homolog (S.
s
Srxn1
cerevisiae)
UDP glucuronosyltransferase
Ugt1a6b
1 family, polypeptide A6B
UDP-glucose dehydrogenase
Ugdh

191

NC

3.8

3.0

no

yes

yes

NC

2.5

2.0

yes

yes

no

NC

4.3

8.9

yes

yes

yes

5.8

4.1

11.3

yes

yes

yes

2.0

2.0

2.2

yes

yes

yes

2.4

4.0

5.6

yes

yes

yes

2.0

2.0

2.2

yes

yes

yes

4.6

2.8

12.6

yes

yes

yes

2.6

2.3

3.9

yes

yes

yes

5.6

NC

6.4

yes

yes

no

2.7

2.5

2.7

yes

yes

yes

Table 15 (cont’d).
Functional
category

Entrez
Gene ID
12491
26903

Lipid and glucose metabolism

170439
75497
14104
14718
14571
72999
53357
66350
228026
20250
20788
20787

Gene name
CD36 antigen
Dysferlin
ELOVL family member 6,
elongation of long chain fatty
acids (yeast)
Fatty acid binding protein 12
Fatty acid synthase
Glutamate oxaloacetate
transaminase 1, soluble
Glycerol phosphate
dehydrogenase 2, mitochondrial
Insulin induced gene 2
Phospholipase A2, group VI
Phospholipase A2, group XIIA
Pyruvate dehydrogenase kinase,
isoenzyme 1
Stearoyl-Coenzyme A
desaturase 2
Sterol regulatory element
binding factor 2
Sterol regulatory element
binding transcription factor 1

Gene
symbol

TCDD
*

PCB153
*

MIX
*

DREs

PXREs

CAREs

Cd36

s

Dysf

5.7
3.3

NC
1.5

6.1
14.9

yes
yes

yes
yes

yes
yes

Elovl6

-4.3

-3.8

-5.2

yes

yes

yes

Fabp12
Fasn

53.7
-4.3

-1.5
-3.9

34.4
-6.4

yes
yes

yes
yes

yes
yes

Got1

-2.6

NC

-4.3

yes

yes

yes

Gpd2

-3.0

1.5

-3.3

yes

yes

yes

Insig2
Pla2g6

NC
NC
3.7

5.9
2.5
2.7

4.0
1.8
7.7

yes
yes
yes

yes
yes
yes

yes
yes
yes

Pdk1

-1.6

NC

1.6

yes

yes

no

Scd2

2.0

NC

NC

yes

yes

yes

Srebf2

-1.7

-2.1

-1.6

yes

yes

yes

Srebf1

-2.3

-2.4

-4.6

yes

yes

yes

Pla2g12a

192

s

a

a

a

Table 15 (cont’d).
Functional
category

Entrez
Gene ID
12125
13197
17873

Cell cycle/ cell death

53415
15937
17869
211323
78688
22033
21813
29820
21928
66443

Gene name
BCL2-like 11 (apoptosis
facilitator)
Growth arrest and DNAdamage-inducible 45 alpha
Growth arrest and DNAdamage-inducible 45 beta
HIV-1 tat interactive protein
2, homolog (human)
Immediate early response 3
Myelocytomatosis oncogene
Neuregulin 1
Nucleolar protein 3
Tnf receptor-associated
factor 5
Transforming growth factor,
beta receptor II
Tumor necrosis factor
receptor superfamily,
member 19
Tumor necrosis factor, alphainduced protein 2
Tumor necrosis factor, alphainduced protein 8-like 1

DREs

PXREs

CAREs

4.2

yes

yes

no

9.4

3.1

yes

no

yes

5.7

10.8

29.2

yes

yes

yes

Htatip2

3.5

NC

4.4

yes

yes

no

Ier3
Myc
Nrg1
Nol3

3.5
3.0
2.8
NC

NC
4.2
NC
4.8

3.8
7.2
6.7
4.7

yes
yes
yes
yes

yes
yes
yes
yes

yes
yes
yes
no

Traf5

2.1

NC

2.3

yes

yes

yes

Tgfbr2

1.6

1.8

2.4

yes

yes

yes

Tnfrsf19

3.9

2.6

7.7

yes

yes

yes

Tnfaip2

6.7

NC

6.9

yes

yes

yes

Tnfaip8l1

3.0

NC

4.1

yes

yes

yes

Gene
symbol

TCDD
*

Bcl2l11

1.9

NC

Gadd45a

-3.6

Gadd45b

193

PCB153 MIX
*
*

a

a

a

Table 15 (cont’d).
Functional
category

Entrez
Gene
ID
12774
56644
58239

Immune response

14129
14969
14998
14999
15900
16994
17304
20719
20209
20210

Gene name
Chemokine (C-C motif)
receptor 5
C-type lectin domain family 7,
member a
Dexamethasone-induced
transcript
Fc receptor, IgG, high affinity I
Histocompatibility 2, class II
antigen E beta
Histocompatibility 2, class II,
locus DMa
Histocompatibility 2, class II,
locus Mb1
Interferon regulatory factor 8
Lymphotoxin B
Milk fat globule-EGF factor 8
protein
Serine (or cysteine) peptidase
inhibitor, clade B, member 6a
Serum amyloid A 2
Serum amyloid A 3

Gene
symbol

TCDD PCB153 MIX
*
*
*

DREs

PXREs

CAREs

a

a

a

Ccr5

2.4

-1.6

NC

no

yes

yes

Clec7a

3.9

NC

2.9

yes

yes

yes

Dexi

-1.9

-1.9

-4.3

yes

yes

no

Fcgr1

2.4

NC

1.9

yes

yes

no

H2-Eb1

7.8

NC

5.0

yes

yes

yes

H2-DMa

4.0

NC

2.7

yes

yes

yes

H2-DMb1

4.9

NC

3.2

yes

yes

yes

Irf8
Ltb

3.1
2.9

NC
NC

2.0
2.3

yes
yes

yes
yes

yes
yes

Mfge8

4.6

NC

5.0

yes

yes

yes

5.1

NC

11.0

yes

yes

yes

3.2
7.8

NC
NC

3.2
3.5

yes
yes

yes
yes

yes
no

Serpinb6a
Saa2
Saa3

s

*Maximum fold change in the microarray (|fold change| >1.5, P1(t) > 0.90).
a
Putative DNA response elements identified by computational searches (Materials & Methods).
s

Non-additive, synergistic responses were verified by statistical modeling (Materials & Methods).
NC – no change (not meeting the |fold change| >1.5, P1(t) > 0.90 cut-off).
194

Genes involved in xenobiotic metabolism and oxidoreductase activities exhibited the
highest induction in all three treatment groups. This included cytochrome P450s (e.g. Cyp1a1,
Cyp1a2, Cyp2b10, Cyp2b13, Cyp2b9, Cyp2c55, Cyp23a25, Cyp39a1) and glutathione Stransferases (e.g. Gsta2, Gstm2, Gstt2, Gstm4), as well as Nqo1, Ugdh, and Aox1. For example,
Cyp1a1 was induced ~348 fold by TCDD and MIX. Unlike treatment-specific induction of
Cyp1a (TCDD) and Cyp2b/2c/23a/39a (PCB153) families, MIX elicited the differential
expression of the above listed cytochrome P450s, suggesting activation of both AhR and
CAR/PXR pathways. Computational analysis identified putative dioxin, CAR and PXR response
elements in the promoter regions (-10,000 bp relative to the TSS together with the 5’ UTR) of
the differentially regulated genes (Table 15) [22, 42].
QRTPCR VERIFICATION OF MICROARRAY RESPONSES
In addition to functional categorization of the gene expression responses, the temporal
microarray dataset with corresponding statistics was used to identify genes displaying putative
non-additive expression in MIX compared to TCDD and PCB153 treatments.
QRTPCR was used to verify the temporal and dose-dependent changes in expression of
13 genes. The differential expression of Cyp1a1, Cyp2b10, and Cyp3a11 (Figure 32) was
confirmed in the time course and dose-response study as a positive control for AhR, CAR, and
PXR-mediated responses, respectively [20, 51, 52]. TCDD significantly induced Cyp1a1, while
PCB153 significantly up-regulated Cyp2b10 and Cyp3a11. The induction of CYp1a1, Cyp2b10,
and Cyp3a11 by MIX was comparable to TCDD and PCB153 alone and therefore was not
further considered for non-additive modeling.

195

*

*

30
0

+

+
30

+
15

10

15
0

10
0

*
+

0.
3

Time (h)

*a *a

45
0

*

*

0

168

60

72

6

24

*

*a *a

30

12

3

4

*a * *

+

15

+

0

*a *a

a

3

5

a

*a *a *

a
*a a *a *

45

3000

Cyp1a1

a

+

6000

10

*a *ab a a a
* * * a
*
*a *a
*a *a

+

5000

TCDD
PCB153
MIX (1:10,000)

Cyp1a1

1

15000
10000

9000

TCDD (30 g/kg)
PCB153 (300 mg/kg)
MIX (1:10,000)

Fold change

Fold change

20000

Dose groups

+
45

30

45
0

30
0
+

15
0
+

15

0.
3

Time (h)

*b*

10
0

168

*

*b

b

+

72

10

24

*b

* *

0

3

12

b
b*

b b

b

60

*

4

*

5

+

*

b

*b*

b *b

6

b *b

b

50

30

*b

*

+

*

*ab

+

20
10
5
0

*b

*b

b

b*b

3

40

Cyp2b10
100

10

*

*ab

+

b

1

Cyp2b10
60

150

Fold change

Fold change

80

Dose groups

+
45

30

+

30
0

15
0
+

15

45
0

*

*b

*b *b

*b

*b b

10
0
+

10

6

+

60

0

30

168

+

Time (h)

72

* *
*

2

3

24

b b

10

12

*

4

+

*
*

4

* *

1

0

b b

ab

*

1

*

*b

6

3

*

b

+

2

*

b

3

Cyp3a11

b

0.
3

4

8

Cyp3a11

Fold change

Fold change

5

Dose groups

Figure 32. QRTPCR verification of AhR, CAR, and PXR-regulated genes.
TCDD significantly induced Cyp1a1 (AhR), while PCB153 significantly induced Cyp2b10 and
Cyp3a11 (CAR/PXR). MIX significantly up-regulated all three genes. All fold changes were
calculated relative to time-matched vehicle controls. The genes are represented by their official
gene symbols. Doses are µg/kg for TCDD and mg/kg for PCB153. Results are displayed as mean
± SE of 5 independent replicates. Data were analyzed by ANOVA followed by Tukey's post hoc
a
b
test: *p<0.05 vs. time-matched vehicle, p<0.05 vs. PCB153 and p<0.05 vs. TCDD.

196

STATISTICAL VERIFICATION OF NON-ADDITIVE GENE EXPRESSION
Analysis of gene expression time course data suggested that Nqo1, Dysf, Pla2g12a,
Serpinb6a, Srxn1 (Figure 33) and Elovl5, Srebf1, Dexi, Got1, Akr1c20 exhibited potential nonadditive interactions. The time course and dose-response profiles elicited by TCDD, PCB153,
and MIX were verified using QRTPCR and gene-specific non-linear models were fit to
statistically define the interactions as synergistic, antagonistic, or additive. Initial analysis
confirmed that the variance is proportional to the mean:
provided parameter estimates used as initial values for

. Linear regression
’s and maximum fold expression for

each gene was used as the initial value for . The final model fit for the expected value for each
gene was:
,

where

is the unknown intercept,

is the unknown slope parameter associated with TCDD

doses,

is the unknown parameter associated with PCB153 doses,

associated with doses of the mixture TCDD+PCB153, and

is the parameter

are indicator functions for the

chemical groups, where:

 

 
    

where
parameter

1,

2, and

 
 

 

    for 

1,2,3 ,

3 represent TCDD, PCB153, and MIX, respectively. The intercept

is assumed to be zero. Plots of the residuals against the predicted values were

assessed for goodness of fit of the models, and no abnormalities were found.

197

Figure 33. Temporal and dose-dependent QRTPCR verification of putative non-additive
genes.
Genes exhibiting putative non-additive interactions were initially identified in the time course
study. Putative non-additive interactions were verified by QRTPCR in the time course study and
the 24 h dose-response study. The genes are represented by their official gene symbols. The dose
groups are represented as µg/kg TCDD + mg/kg PCB153. Results are displayed as mean ± SE of
5 independent replicates. Data were analyzed by ANOVA followed by Tukey's post hoc test:
a
b
*p<0.05 vs. time-matched vehicle, p<0.05 vs. PCB153 and p<0.05 vs. TCDD.

198

*

45

+

30
0
+

15
0
30

+

10
0

*

*

*a

a

45
0

*

*a

15

+

1

0.
3

Time (h)

60

168

*

*ab

ab

*a

10

72

0

*

a

+

24

* *a *

*

*a

a

*ab

6

12

4

*
*a

*a

*

5

30

*

ab

*ab

+

a

Nqo1

10

3

*

*

ab

+

0

a
*a *

*

a

*

10

10

ab

TCDD
PCB153
MIX (1:10,000)

15

+

*ab

Nqo1

3

15

5

20

TCDD (30 g/kg)
PCB153 (300 mg/kg)
MIX (1:10,000)

Fold change

Fold change

20

Dose groups
25

*ab

45
0

45

30

+

30
0

**

+

15
0

15

+

10
0
+

0.
3

Time (h)

10

168

*ab

**

*a

*a

*a
60

72

0

*

+

24

* * *

a

6

12

a

a

30

4

5

ab

*ab

ab

+

*a

*a

10

10

0

*ab

*

*ab

15

+

5

*

ab

+

*

10

ab

1

*

ab

Dysf

20

3

15

Fold change

Fold change

Dysf

3

20

Dose groups

*ab

a

*ab
*

*

*

45
0

45

+

30
0

30

+

15
0

15

+
10

+

60
+

0.
3

Time (h)

10
0

0

168

6

72

3

*

*a

*

30

24

*a

+

12

*

a

3

4

**

* *
*

6

10

0

*

a

1

2

*
* *

a

3

4

*ab

9

+

*b

6

*ab

Pla2g12a

+

8

12

Pla2g12a

Fold change

Fold change

10

Dose groups

*

45
0

45

+

30
0
+

30

15

+

10
0
+

0.
3

Time (h)

15
0

0

168

10

72

60

24

+

12

6

4

+

0

2

*a

*a

*

30

2

4

+

*a

3

*a

*ab

*ab

*ab

10

*ab

Serpinb6a
6

+

*

4

ab

8

1

6

*

*a

a

3

8

Serpinb6a

Fold change

Fold change

10

Dose groups

*b
*b

*

Dose groups

199

45
0

45

+

30
0

30

+

15
0
+

15

10

+

10
0

60

0
+

168

6

Time (h)

72

30

24

*ab

*a

*a

* *

2

+

a
* * *a *a

4

3

12

**

*ab

10

4

*

+

*

2
0

*

1

a
*a *

ab

Srxn1
6

3

4

*

+

6

Srxn1

0.
3

8

8

Fold change

Fold change

10

To determine statistically significant deviation from additivity, the quasi-likelihood ratio
test was used to compare the empirical mixture model with the reduced additivity model based
on the F-distribution [53]:
,

, where

where

 and

are the ‘mix ratios’ for TCDD and

PCB153 respectively. The ‘mix ratios’ were obtained by dividing the dose by the total dose. For
example, when the TCDD dose is 1 µg/kg, the PCB153 dose is 10000 µg/kg (10 mg/kg), so the
‘mix ratios’ are

∑

0.0001

and

10000

0.9999

such that

1.
Each graph in Figure 34 illustrates the overlay between the additive model generated

using TCDD and PCB153 data alone and the mixture model fitted using MIX data for each gene.
The quasi-likelihood ratio test determined that the mixture model was significantly (p<0.0001)
different from the additive model for Nqo1, Dysf, Pla2g12a, Serpinb6a and Srxn1, indicating
that co-administration of TCDD plus PCB153 resulted in synergistic gene expression changes.
MIX expression of Srebf1, Akr1c20, and Dexi approached significant non-additive
expression (p=0.0619, p=0.0615, p=0.0806, respectively), while Elovl5 (p=0.5164) and Got1
(p=0.9181) expression was not significantly different from additivity.

200

Figure 34. Dose-response non-linear logistic modeling of the dose-response QRTPCR
results from Figure 33.
Graphs depict the additive model (dashed line) generated using TCDD and PCB153 data and the
mixture model (solid line) fitted using MIX data (black dots). For Nqo1, Dysf, Pla2g12a,
Serpinb6a and Srxn1, the mixture model fit the MIX data significantly better than the additive
model, indicating a synergistic interaction (p<0.0001).

201

+4
5
0

6

4

2
2

0

Dose groups

202
50

10

+4

Serpinb6a

45

Dose groups

00

+4

50

00

5
2

0
0

Dose groups

Srxn1

8

6

4

0

Dose groups

0

2

+4
5

10

45

15

0

25

+3
0

14

30

30 Dysf

12

Fold change

20

0.
3
1+ +3
3+10
6+ 30
10 60
15+10
+1 0
50

45

+3

4

30

6
Fold change

8

+3

50

00

10

0.
3
1+ +3
3+10
6+ 30
10 60
15+10
+1 0
50

+4

+3

Fold change

Nqo1

30

8

Fold change

45

30

0.
3
1+ +3
3+10
6+ 30
10 60
15+10
+1 0
50

12

0.
3
1+ +3
3+10
6+ 30
10 60
15+10
+1 0
50

45

+3
00

Fold change

10

30

0.
3
1+ + 3
3+10
6+ 30
1060
15+10
+1 0
50

14
Pla2g12a

10
8

6

4

0

Dose groups

DISCUSSION
In the current study, comprehensive time course and dose-dependent hepatic gene
expression changes elicited by a mixture of TCDD and PCB153 were anchored to changes in
gross physiology, histopathology, tissue concentrations, and hepatic lipid composition, and these
were compared to effects elicited by TCDD and PCB153 alone. Statistical modeling confirmed
dose- additive and synergistic effects in a subset of differentially expressed genes that could be
associated with specific phenotypic changes.
Exposure to mixtures of dioxin and non-dioxin-like chemicals has been reported to
increase hepatic disposition of these chemicals when compared to single chemical administration
[48]. In the current study, hepatic PCB153 levels were significantly increased following coadministration with TCDD at 24 and 168 h. However the levels of TCDD did not change when
co-administered with PCB153, as previously reported [47, 48]. These results are consistent with
rat studies that also reported TCDD co-treatment increased PCB153 levels [54]. Coadministration of PCB153 with dioxin-like PCB156 also led to significant hepatic retention of
PCB153 after 7 and 14 days in C57BL/6J mice [55]. In B6C3F1 mice, TCDD-elicited increases
in PCB153 levels were specific to the liver and not observed in other tissues [47].
Hepatic sequestration of TCDD and other dioxin-like PCBs is mediated by binding to
Cyp1a2 protein [56]. Cyp1a2 null mice exhibit 10-fold lower hepatic TCDD accumulation
compared to wild type animals [57]. PCB153 has a higher affinity for adipose compared to
hepatic tissue [47] and unlike TCDD and dioxin-like PCBs, it is not sequestered by Cyp1a2
protein. However, exposure to TCDD leads to fatty accumulation in the liver [32], increasing
hepatic lipophilicity, which may contribute to hepatic accumulation of PCB153 following coadministration with TCDD [47, 54].
203

MIX induced xenobiotic metabolism gene expression, comparable to levels elicited by
individual treatments. Cytochrome P450 induction was the highest among all functional
categories, and included typical AhR (Cyp1a1, Cyp1a2) and CAR/PXR (Cyp2b9, Cyp2b10,
Cyp2b13, Cyp23a25, Cyp3a11) induced genes [20, 51, 52]. Differential expression of Nqo1, a
known AhR- and Nrf2- (nuclear factor erythroid 2-related factor 2) inducible gene [58],
displayed synergistic induction following co-administration of TCDD with PCB153.
Synergistically elevated expression of Nqo1 may be cytoprotective against chemical toxicity [59,
60]. MIX also synergistically induced Srxn1, sulfiredoxin 1 homolog, an Nrf2-dependent
oxidoreductase, which has been reported to protect from smoke-induced oxidative injury in the
lung [61, 62]. Synergistic induction of Nqo1 and Srxn1 may support hepatic defense mechanisms
following the increased load of xenobiotic stress induced by MIX.
Among oxidoreductase and xenobiotic metabolism genes, aldo-keto reductase (AKR)
superfamily members were also differentially expressed including the induction of Akr1b7,
Akr1c19 and repression of Akr1c20. AKRs are involved in NAD(P)H-dependent
oxidoreductions of a variety of natural and foreign substrates and have been implicated in
alleviating toxicity associated with lipid peroxidation [63]. Expression of Akr1b7 is abolished in
CAR and PXR null mice [63], in agreement with PCB153 and MIX-mediated Akr1b7 induction
and putative CAREs and PXREs identification. Statistical modeling approached significant
(p=0.0615) synergistic repression of Akr1c20 levels following MIX treatment. Akr1c20
expression is liver-specific, but its function is unknown [64]. Based on structural similarity to
17β-hydroxysteroid dehydrogenase type 5 and enzymatic properties, it may be involved in
steroid metabolism and reduction of non-steroidal α-dicarbonyl compounds [64].

204

Integrating histopathology, ORO staining, GC-MS fatty acid and triglyceride analyses
suggests that TCDD- and MIX-elicited increases in RLW can be partially attributed to hepatic
lipid accumulation. The AhR-mediated fatty accumulation (steatosis) by TCDD can be directly
associated with the differential expression of lipid transport and metabolism genes. For example,
TCDD induces the expression of the fatty acid transporter, Cd36 [65], which was also induced
by MIX. Cd36 facilitates hepatic uptake of free fatty acids and was found up-regulated in nonalcoholic fatty liver disease patients [66]. TCDD and MIX also induced fatty acid binding
protein 12 (Fabp12) [67]. In contrast, PCB153 did not alter Cd36 or Fabp12 expression,
consistent with no steatosis. Instead PCB153 down-regulated a number of lipid metabolism
genes, including Elovl5 was induced by TCDD, suggesting divergent regulation. Deletion of
Elovl5 in mice leads to steatosis through activation of SREBP-1c protein [68]. MIX approached
significant (p=0.0619) synergistic repression of Srebf1 consistent with less fat accumulation at
168 h compared to TCDD which did not affect Srebf1 expression. PCB153 minimally repressed
Srebf1 expression (at 12 h and at 450 mg/kg at 24 h), but did not promote lipid accumulation. In
addition, repression of SREBP-mediated lipogenesis in Zucker diabetic fatty rats is associated
with CAR/PXR-mediated over-expression of insulin induced genes (Insig1/2) [69, 70]. PCB153
and MIX induced Insig2, while TCDD down-regulated Insig2 at 4 h, consistent with the
observed hepatic phenotypes.
MIX also synergistically induced Pla2g12a, a novel secretory phospholipase, which is
involved in the digestion of dietary phospholipids and the production of molecules that induce
inflammatory responses [71]. Although MIX induction of Pla2g12a may be linked to elevated
free fatty acids, previous reports suggest Pla2g12a has very weak catalytic activity and
contributes little to increase cellular fatty acid release [71, 72].

205

TCDD and dioxin-like chemical are associated with feed refusal, body weight loss and
exhaustion of energy sources, collectively referred to as “wasting syndrome” [73]. PCB153 is
linked to increased glucose consumption in MCF-10A human non-tumorigenic mammary
epithelial cells [74]. TCDD and MIX repression of the glucose metabolism genes, including
glutamate oxaloacetate soluble 1, (Got1), glycerol-phosphate dehydrogenase 2, mitochondrial
(Gpd2), and pyruvate dehydrogenase kinase, isoenzyme 1 (Pdk1), was consistent with dioxinmediated repression of gluconeogenesis in chick embryo hepatocytes [75]. MIX also repressed
Got1 expression but statistical modeling did not confirm non-additive expression.
There was a lack of hepatocellular necrosis in PCB153-treated mice compared to TCDD
and MIX, consistent with reports of PCB153-mediated decreases in cell death in mouse
hepatocytes [76, 77]. PCB153 and MIX increased hepatic cell size, in agreement with reported
CAR/PXR-mediated liver hypertrophy [18, 78-80]. In contrast to PCB153, which did not
regulate cell death related genes, TCDD and MIX induced Tnfaip2 and Tnfaip8l1, members of
the tumor necrosis factor (TNF)-signaling pathway, which mediate cell death, proliferation,
differentiation, and inflammation [81]. In addition, TCDD and MIX, but not PCB153, induced
Bcl2l11and Htatip2 [82, 83]. MIX also induced hyperplasia at 168 h, in agreement with Myc
induction [84]. Myc, a transcription factor associated with cell proliferation, growth and
metabolism, regulates early response 3 (Ier3), which was induced by TCDD and MIX, and not
affected by PCB153. Transgenic mice studies indicate that Ier3 is anti-apoptotic and may
stimulate cell proliferation [85].
Histopathology also revealed a lack of inflammatory cells associated with PCB153
treatment, unlike TCDD and MIX, which increased mixed cell infiltration. A number of immune
related genes were induced by TCDD and MIX, including major histocompatibility complex

206

molecules  H2-Eb1, H2-DMa, H2-DMb1, also expressed in mice exposed to hepatic parasites
[86]. PCB153 did not alter expression of immune response related genes.
MIX synergistically induced Serpinb6a, a member of a serine protease inhibitors
involved in diverse processes, such as inflammation and cell death [87]. Serpinb6a is highly
expressed by mast cells in all organs and may regulate the activity of endogenous β-tryptase in
the cytoplasm, a biomarker of mast cell activation and mastocytosis immune response [88].
PCB153 did not affect Serpinb6a expression, in contrast to TCDD induction at later time points
and synergistic induction by MIX, consistent with comparable extent of immune cell infiltration
elicited by TCDD and MIX.
Dexamethasone-induced transcript (Dexi) is regulated by the glucocorticoid analogue
dexamethasone, a very potent PXR ligand [89, 90]. Dexi was down-regulated in this study by all
treatments. Expression of glucocorticoids is linked to suppressed inflammatory response,
partially by regulated expression of TNF [91]. MIX synergistically (p=0.0806) suppressed Dexi
transcript at 24 h and at later time points, concomitant with histopathological findings indicating
significant immune cell infiltration.
The most dramatic synergistic induction was observed in dysferlin (Dysf), following
exposure to MIX. Dysf was only minimally induced by TCDD and was not differentially
expressed following PCB153 treatment. Dysf is a transmembrane protein implicated in calciumdependent sarcolemmal membrane repair. Dysf-deficient skeletal muscles induce and activate
key inflammasome adaptor components, including NACHT, LRR and PYD-containing proteins
[92]. The C2 domains of dysferlin exhibit lipid binding specificity, facilitating interactions with
lipid bilayer components including phosphatidylserine (PtdS), phosphatidylinositol 4-phosphate
(PtdIns[4]P) and phosphatidylinositol 4,5-bisphosphate (PtdIns[4,5]P2) in a calcium-dependent

207

fashion [93-95]. MIX elicited the most significant changes in hepatic architecture that may be
linked to cellular membrane stress. Dysf induction by MIX during membrane injury may lead to
calcium-dependent interactions with PtdS and PtdIns to patch and repair membrane damage [93].
Deficiencies in phosphatidylcholine synthesis, another major membrane component, are linked
to muscular dystrophy [96].
In summary, co-administration of TCDD with PCB153 elicits non-additive, synergistic
AhR/CAR/PXR-mediated gene expression responses that can be associated with changes in
RLW, hepatic cell size and number, immune response and lipid accumulation in ovariectomized
C57BL/6 mice. Non-additive interactions may confound the risk assessment of mixtures, and
therefore require special considerations [97]. For example, dose-response curves for mixture
components should be adequately characterized, the “no-interaction” hypothesis should be
explicitly stated and used as the basis for assessing synergy and antagonism, combinations of
mixture components should be assessed across a sufficient range, statistical tests should be used
to determine departure from additivity, and interactions should be assessed at relevant levels of
biological organization to assure meaningful interpretation of the results. Development of
predictive models integrating in vitro, pharmacokinetic and pharmacodynamic components will
enable a better understanding of the influence of mixture composition on elicited toxic effects.

208

REFERENCES

209

REFERENCES

1.

Mason, G. and S. Safe, Synthesis, biologic and toxic effects of the major 2,3,7,8tetrachlorodibenzo-p-dioxin metabolites in the rat. Toxicology, 1986. 41(2): p. 153-9.

2.

Poland, A. and E. Glover, Studies on the mechanism of toxicity of the chlorinated
dibenzo-p-dioxins. Environ Health Perspect, 1973. 5: p. 245-51.

3.

Commoner, B., K. Shapiro, and T. Webster, The Origin and Health Risks of PCDD and
PCDF. Waste Management & Research, 1987. 5: p. 327-346.

4.

Safe, S., et al., Halogenated biphenyls: molecular toxicology. Can J Physiol Pharmacol,
1982. 60(7): p. 1057-64.

5.

Mullin, M.D., et al., High-resolution PCB analysis: synthesis and chromatographic
properties of all 209 PCB congeners. Environ. Set Technol., 1984. 18: p. 468-476.

6.

Safe, S., Polychlorinated biphenyls (PCBs), dibenzo-p-dioxins (PCDDs), dibenzofurans
(PCDFs), and related compounds: environmental and mechanistic considerations which
support the development of toxic equivalency factors (TEFs). Crit Rev Toxicol, 1990.
21(1): p. 51-88.

7.

Kimbrough, R.D., Polychlorinated biphenyls (PCBs) and human health: an update. Crit
Rev Toxicol, 1995. 25(2): p. 133-63.

8.

Safe, S., Limitations of the toxic equivalency factor approach for risk assessment of
TCDD and related compounds. Teratog Carcinog Mutagen, 1997. 17(4-5): p. 285-304.

9.

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

10.

Knerr, S. and D. Schrenk, Carcinogenicity of "non-dioxinlike" polychlorinated biphenyls.
Crit Rev Toxicol, 2006. 36(9): p. 663-94.

11.

Van den Berg, M., et al., The 2005 World Health Organization reevaluation of human
and Mammalian toxic equivalency factors for dioxins and dioxin-like compounds.
Toxicol Sci, 2006. 93(2): p. 223-41.

12.

Safe, S.H., Polychlorinated biphenyls (PCBs): environmental impact, biochemical and
toxic responses, and implications for risk assessment. Crit Rev Toxicol, 1994. 24(2): p.
87-149.

13.

Safe, S., Molecular biology of the Ah receptor and its role in carcinogenesis. Toxicol
Lett, 2001. 120(1-3): p. 1-7.
210

14.

Denison, M.S. and S. Heath-Pagliuso, The Ah receptor: a regulator of the biochemical
and toxicological actions of structurally diverse chemicals. Bull Environ Contam
Toxicol, 1998. 61(5): p. 557-68.

15.

Denison, M.S., et al., Ligand binding and activation of the Ah receptor. Chem Biol
Interact, 2002. 141(1-2): p. 3-24.

16.

Nebert, D.W., et al., Role of the aromatic hydrocarbon receptor and [Ah] gene battery in
the oxidative stress response, cell cycle control, and apoptosis. Biochem Pharmacol,
2000. 59(1): p. 65-85.

17.

Schecter, A., et al., Polychlorinated biphenyl levels in the tissues of exposed and
nonexposed humans. Environ Health Perspect, 1994. 102 Suppl 1: p. 149-58.

18.

NTP, NTP technical report on the toxicology and carcinogenesis studies of 2,2',4,4',5,5'hexachlorobiphenyl (PCB 153) (CAS No. 35065-27-1) in female Harlan Sprague-Dawley
rats (Gavage studies). Natl Toxicol Program Tech Rep Ser, 2006(529): p. 4-168.

19.

Craft, E.S., M.J. DeVito, and K.M. Crofton, Comparative responsiveness of
hypothyroxinemia and hepatic enzyme induction in Long-Evans rats versus C57BL/6J
mice exposed to TCDD-like and phenobarbital-like polychlorinated biphenyl congeners.
Toxicol Sci, 2002. 68(2): p. 372-80.

20.

Honkakoski, P., T. Sueyoshi, and M. Negishi, Drug-activated nuclear receptors CAR and
PXR. Ann Med, 2003. 35(3): p. 172-82.

21.

Tabb, M.M., et al., Highly chlorinated PCBs inhibit the human xenobiotic response
mediated by the steroid and xenobiotic receptor (SXR). Environ Health Perspect, 2004.
112(2): p. 163-9.

22.

Kopec, A.K., et al., PCB153-elicited hepatic responses in the immature, ovariectomized
C57BL/6 mice: Comparative toxicogenomic effects of dioxin and non-dioxin-like ligands.
Toxicol Appl Pharmacol, 2010. 243: p. 359-71.

23.

Lemaire, G., et al., Discovery of a highly active ligand of human pregnane x receptor: a
case study from pharmacophore modeling and virtual screening to "in vivo" biological
activity. Mol Pharmacol, 2007. 72(3): p. 572-81.

24.

Blumberg, B. and R.M. Evans, Orphan nuclear receptors--new ligands and new
possibilities. Genes Dev, 1998. 12(20): p. 3149-55.

25.

Masahiko, N. and P. Honkakoski, Induction of drug metabolism by nuclear receptor
CAR: molecular mechanisms and implications for drug research. Eur J Pharm Sci, 2000.
11(4): p. 259-64.

26.

Staal, Y.C., et al., Binary PAH mixtures cause additive or antagonistic effects on gene
expression but synergistic effects on DNA adduct formation. Carcinogenesis, 2007.
28(12): p. 2632-40.
211

27.

Biegel, L., et al., 2,2',4,4',5,5'-hexachlorobiphenyl as a 2,3,7,8-tetrachlorodibenzo-pdioxin antagonist in C57BL/6J mice. Toxicol Appl Pharmacol, 1989. 97(3): p. 561-71.

28.

Zhao, F., et al., Inhibition of 3,3',4,4',5-pentachlorobiphenyl-induced fetal cleft palate
and immunotoxicity in C57BL/6 mice by 2,2',4,4',5,5'-hexachlorobiphenyl. Chemosphere,
1997. 34(5-7): p. 1605-13.

29.

Suh, J., et al., Antagonism of aryl hydrocarbon receptor-dependent induction of CYP1A1
and inhibition of IgM expression by di-ortho-substituted polychlorinated biphenyls.
Toxicol Appl Pharmacol, 2003. 187(1): p. 11-21.

30.

Bannister, R. and S. Safe, Synergistic interactions of 2,3,7,8-TCDD and 2,2',4,4',5,5'hexachlorobiphenyl in C57BL/6J and DBA/2J mice: role of the Ah receptor. Toxicology,
1987. 44(2): p. 159-69.

31.

van Birgelen, A.P., et al., Synergistic effect of 2,2',4,4',5,5'-hexachlorobiphenyl and
2,3,7,8-tetrachlorodibenzo-p-dioxin on hepatic porphyrin levels in the rat. Environ
Health Perspect, 1996. 104(5): p. 550-7.

32.

Boverhof, D.R., et al., Temporal and dose-dependent hepatic gene expression patterns in
mice provide new insights into TCDD-Mediated hepatotoxicity. Toxicol Sci, 2005. 85(2):
p. 1048-63.

33.

Kopec, A.K., et al., Comparative Toxicogenomic Examination of the Hepatic Effects of
PCB126 and TCDD in Immature, Ovariectomized C57BL/6 Mice. Toxicol Sci, 2008.
102(1): p. 61-75.

34.

Kopec, A.K., et al., Automated Dose-Response Analysis and Comparative Toxicogenomic
Evaluation of the Hepatic Effects Elicited by TCDD, TCDF and PCB126 in C57BL/6
Mice. Toxicol Sci, 2010. 118: p. 286-97.

35.

Sheehan, D.C. and B.B. Hrapchak, Theory and Practice of Histotechnology. second ed
ed. 1980, St. Louis: Mosby.

36.

Burgoon, L.D. and T.R. Zacharewski, dbZach toxicogenomic information management
system. Pharmacogenomics, 2007. 8(3): p. 287-91.

37.

Burgoon, L.D., et al., Protocols for the assurance of microarray data quality and process
control. Nucleic Acids Res, 2005. 33(19): p. e172.

38.

Eckel, J.E., et al., Normalization of two-channel microarray experiments: a
semiparametric approach. Bioinformatics, 2005. 21(7): p. 1078-83.

39.

Eckel, J.E., et al., Empirical bayes gene screening tool for time-course or dose-response
microarray data. J Biopharm Stat, 2004. 14(3): p. 647-70.

212

40.

Vandesompele, J., et al., Accurate normalization of real-time quantitative RT-PCR data
by geometric averaging of multiple internal control genes. Genome Biol, 2002. 3(7): p.
RESEARCH0034.

41.

Sun, Y.V., et al., Comparative analysis of dioxin response elements in human, mouse and
rat genomic sequences. Nucleic Acids Res, 2004. 32(15): p. 4512-23.

42.

Dere, E., et al., Genome-Wide Computational Analysis of Dioxin Response Element
Location and Distribution in the Human, Mouse and Rat Genomes. submitted
manuscript.

43.

Phillips, J.M., L.D. Burgoon, and J.I. Goodman, Phenobarbital (PB) Elicits Unique,
Early Changes in the Expression of Hepatic Genes That Affect Critical Pathways in
Tumor-Prone B6C3F1 Mice. Toxicol Sci, 2009.

44.

Dennis, G., Jr., et al., DAVID: Database for Annotation, Visualization, and Integrated
Discovery. Genome Biol, 2003. 4(5): p. P3.

45.

Boverhof, D.R., et al., Comparative toxicogenomic analysis of the hepatotoxic effects of
TCDD in Sprague Dawley rats and C57BL/6 mice. Toxicol Sci, 2006. 94(2): p. 398-416.

46.

NTP, NTP technical report on the toxicology and carcinogenesis studies of 2,3,7,8tetrachlorodibenzo-p-dioxin (TCDD) (CAS No. 1746-01-6) in female Harlan SpragueDawley rats (Gavage Studies). Natl Toxicol Program Tech Rep Ser, 2006(521): p. 4-232.

47.

van Birgelen, A.P., et al., Interactive effects between 2,3,7,8-tetrachlorodibenzo-p-dioxin
and 2,2',4,4',5,5'-hexachlorobiphenyl in female B6C3F1 mice: tissue distribution and
tissue-specific enzyme induction. Fundam Appl Toxicol, 1996. 34(1): p. 118-31.

48.

De Jongh, J., et al., The effects of 2,2',4,4',5,5'-hexachlorobiphenyl cotreatment on the
disposition of 2,3,7,8-tetrachlorodibenzo-p-dioxin in mice. Toxicol Lett, 1995. 80(1-3): p.
131-7.

49.

FDA, Dose Selection for Carcinogenicity Studies, U.S.D.o.H.a.H. Services, Editor. 2008. p.
1-10.

50.

Zhai, Y., et al., A Functional Crosstalk Between LXRalpha and CAR Links Lipogenesis
and Xenobiotic Responses. Mol Pharm, 2010.

51.

Maglich, J.M., et al., Nuclear pregnane x receptor and constitutive androstane receptor
regulate overlapping but distinct sets of genes involved in xenobiotic detoxification. Mol
Pharmacol, 2002. 62(3): p. 638-46.

52.

Nebert, D.W., D.D. Petersen, and A.J. Fornace, Jr., Cellular responses to oxidative
stress: the [Ah] gene battery as a paradigm. Environ Health Perspect, 1990. 88: p. 13-25.

213

53.

Gennings, C., et al., Statistical analysis of interactive cytotoxicity in human epidermal
keratinocytes following exposure to a mixture of four metals. Journal of Agricultural
Biological and Environmental Statistics, 2002. 7(1): p. 58-73.

54.

Van der Kolk, J., et al., Interactions of 2,2',4,4',5,5'-hexachlorobiphenyl and 2,3,7,8tetrachlorodibenzo-p-dioxin in a subchronic feeding study in the rat. Chemosphere, 1992.
25(12): p. 2023-2027.

55.

De Jongh, J., et al., Toxicokinetic interactions in the liver of the C57BL/6J mouse after
administration of a single oral dose of a binary mixture of some PCBs. Chemosphere,
1992. 25: p. 1165-1170.

56.

DeVito, M.J., et al., Dose-response relationships for disposition and hepatic
sequestration of polyhalogenated dibenzo-p-dioxins, dibenzofurans, and biphenyls
following subchronic treatment in mice. Toxicol Sci, 1998. 46(2): p. 223-34.

57.

Diliberto, J.J., D.E. Burgin, and L.S. Birnbaum, Effects of CYP1A2 on disposition of
2,3,7,
8-tetrachlorodibenzo-p-dioxin,
2,3,4,7,8-pentachlorodibenzofuran,
and
2,2',4,4',5,5'-hexachlorobiphenyl in CYP1A2 knockout and parental (C57BL/6N and
129/Sv) strains of mice. Toxicol Appl Pharmacol, 1999. 159(1): p. 52-64.

58.

Yeager, R.L., et al., Introducing the "TCDD-inducible AhR-Nrf2 gene battery". Toxicol
Sci, 2009. 111(2): p. 238-46.

59.

Klaassen, C.D. and S.A. Reisman, Nrf2 the rescue: effects of the
antioxidative/electrophilic response on the liver. Toxicol Appl Pharmacol, 2010. 244(1):
p. 57-65.

60.

Ma, Q., et al., Induction of murine NAD(P)H:quinone oxidoreductase by 2,3,7,8tetrachlorodibenzo-p-dioxin requires the CNC (cap 'n' collar) basic leucine zipper
transcription factor Nrf2 (nuclear factor erythroid 2-related factor 2): cross-interaction
between AhR (aryl hydrocarbon receptor) and Nrf2 signal transduction. Biochem J,
2004. 377(Pt 1): p. 205-13.

61.

Park, J.W., et al., Deglutathionylation of 2-Cys peroxiredoxin is specifically catalyzed by
sulfiredoxin. J Biol Chem, 2009. 284(35): p. 23364-74.

62.

Singh, A., et al., Nrf2-dependent sulfiredoxin-1 expression protects against cigarette
smoke-induced oxidative stress in lungs. Free Radic Biol Med, 2009. 46(3): p. 376-86.

63.

Liu, M.J., et al., The aldo-keto reductase Akr1b7 gene is a common transcriptional target
of xenobiotic receptors pregnane X receptor and constitutive androstane receptor. Mol
Pharmacol, 2009. 76(3): p. 604-11.

64.

Matsumoto, K., et al., Enzymatic properties of a member (AKR1C20) of the aldo-keto
reductase family. Biol Pharm Bull, 2006. 29(3): p. 539-42.

214

65.

Lee, J.H., et al., A Novel Role for the Dioxin Receptor in Fatty Acid Metabolism and
Hepatic Steatosis. Gastroenterology, 2010. 139(2): p. 653-63.

66.

Greco, D., et al., Gene expression in human NAFLD. Am J Physiol Gastrointest Liver
Physiol, 2008. 294(5): p. G1281-7.

67.

Tijet, N., et al., Aryl hydrocarbon receptor regulates distinct dioxin-dependent and
dioxin-independent gene batteries. Mol Pharmacol, 2006. 69(1): p. 140-53.

68.

Moon, Y.A., R.E. Hammer, and J.D. Horton, Deletion of ELOVL5 leads to fatty liver
through activation of SREBP-1c in mice. J Lipid Res, 2008.

69.

Takaishi, K., et al., Hepatic insig-1 or -2 overexpression reduces lipogenesis in obese
Zucker diabetic fatty rats and in fasted/refed normal rats. Proc Natl Acad Sci U S A,
2004. 101(18): p. 7106-11.

70.

Roth, A., et al., Regulatory cross-talk between drug metabolism and lipid homeostasis:
constitutive androstane receptor and pregnane X receptor increase Insig-1 expression.
Mol Pharmacol, 2008. 73(4): p. 1282-9.

71.

Gelb, M.H., et al., Cloning and recombinant expression of a structurally novel human
secreted phospholipase A2. J Biol Chem, 2000. 275(51): p. 39823-6.

72.

Murakami, M., et al., Cellular arachidonate-releasing function of novel classes of
secretory phospholipase A2s (groups III and XII). J Biol Chem, 2003. 278(12): p. 1065767.

73.

Viluksela, M., B.U. Stahl, and K.K. Rozman, Tissue-specific effects of 2,3,7,8Tetrachlorodibenzo-p-dioxin (TCDD) on the activity of phosphoenolpyruvate
carboxykinase (PEPCK) in rats. Toxicol Appl Pharmacol, 1995. 135(2): p. 308-15.

74.

Venkatesha, V.A., et al., PCB-153 exposure coordinates cell cycle progression and
cellular metabolism in human mammary epithelial cells. Toxicol Lett, 2010. 196: p. 110116.

75.

Diani-Moore, S., et al., Identification of the aryl hydrocarbon receptor target gene
Tiparp as a mediator of suppression of hepatic gluconeogenesis by 2,3,7,8tetrachlorodibenzo-p-dioxin and of nicotinamide as a corrective agent for this effect. J
Biol Chem, 2010.

76.

Tharappel, J.C., et al., Regulation of cell proliferation, apoptosis, and transcription factor
activities during the promotion of liver carcinogenesis by polychlorinated biphenyls.
Toxicol Appl Pharmacol, 2002. 179(3): p. 172-84.

77.

Lu, Z., et al., Effect of 2,2',4,4',5,5'-hexachlorobiphenyl (PCB-153) on hepatocyte
proliferation and apoptosis in mice deficient in the p50 subunit of the transcription factor
NF-kappaB. Toxicol Sci, 2004. 81(1): p. 35-42.
215

78.

Blanco-Bose, W.E., et al., C-Myc and its target FoxM1 are critical downstream effectors
of constitutive androstane receptor (CAR) mediated direct liver hyperplasia. Hepatology,
2008. 48(4): p. 1302-11.

79.

Staudinger, J.L., et al., Regulation of drug transporter gene expression by nuclear
receptors. Drug Metab Dispos, 2003. 31(5): p. 523-7.

80.

Wei, P., et al., The nuclear receptor CAR mediates specific xenobiotic induction of drug
metabolism. Nature, 2000. 407(6806): p. 920-3.

81.

Tracey, K.J. and A. Cerami, Tumor necrosis factor: a pleiotropic cytokine and
therapeutic target. Annu Rev Med, 1994. 45: p. 491-503.

82.

Shi, M., et al., Tip30-induced apoptosis requires translocation of Bax and involves
mitochondrial release of cytochrome c and Smac/DIABLO in hepatocellular carcinoma
cells. Biochim Biophys Acta, 2008. 1783(2): p. 263-74.

83.

Seitz, S.J., et al., Chemotherapy-induced apoptosis in hepatocellular carcinoma involves
the p53 family and is mediated via the extrinsic and the intrinsic pathway. Int J Cancer,
2010. 126(9): p. 2049-66.

84.

Dang, C.V., c-Myc target genes involved in cell growth, apoptosis, and metabolism. Mol
Cell Biol, 1999. 19(1): p. 1-11.

85.

Wu, M.X., Roles of the stress-induced gene IEX-1 in regulation of cell death and
oncogenesis. Apoptosis, 2003. 8(1): p. 11-8.

86.

Gottstein, B., et al., Hepatic gene expression profile in mice perorally infected with
Echinococcus multilocularis eggs. PLoS One, 2010. 5(4): p. e9779.

87.

Scott, F.L., et al., SerpinB6 is an inhibitor of kallikrein-8 in keratinocytes. J Biochem,
2007. 142(4): p. 435-42.

88.

Strik, M.C., et al., Intracellular serpin SERPINB6 (PI6) is abundantly expressed by
human mast cells and forms complexes with beta-tryptase monomers. Blood, 2004.
103(7): p. 2710-7.

89.

Huss, J.M. and C.B. Kasper, Two-stage glucocorticoid induction of CYP3A23 through
both the glucocorticoid and pregnane X receptors. Mol Pharmacol, 2000. 58(1): p. 48-57.

90.

Edgar, A.J., et al., Cloning of dexamethasone-induced transcript: a novel glucocorticoidinduced gene that is upregulated in emphysema. Am J Respir Cell Mol Biol, 2001. 25(1):
p. 119-24.

91.

Noti, M., et al., TNF suppresses acute intestinal inflammation by inducing local
glucocorticoid synthesis. J Exp Med, 2010. 207(5): p. 1057-66.

216

92.

Rawat, R., et al., Inflammasome up-regulation and activation in dysferlin-deficient
skeletal muscle. Am J Pathol, 2010. 176(6): p. 2891-900.

93.

Therrien, C., et al., Characterization of lipid binding specificities of dysferlin C2 domains
reveals novel interactions with phosphoinositides. Biochemistry, 2009. 48(11): p. 237784.

94.

Di Paolo, G. and P. De Camilli, Phosphoinositides in cell regulation and membrane
dynamics. Nature, 2006. 443(7112): p. 651-7.

95.

Boesze-Battaglia, K. and R. Schimmel, Cell membrane lipid composition and
distribution: implications for cell function and lessons learned from photoreceptors and
platelets. J Exp Biol, 1997. 200(Pt 23): p. 2927-36.

96.

Li, Z., L.B. Agellon, and D.E. Vance, Phosphatidylcholine homeostasis and liver failure.
J Biol Chem, 2005. 280(45): p. 37798-802.

97.

Borgert, C.J., et al., Evaluating chemical interaction studies for mixture risk assessment.
Human and Ecological Risk Assessment, 2001. 7(2): p. 259-305.

217

CHAPTER 7

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CHAPTER 7
CONCLUSIONS AND FUTURE RESEARCH
The preceding studies utilized comprehensive gene expression analyses with phenotypic
anchoring to characterize non-additive interactions between dioxin-like and non-dioxin-like
chemicals on hepatic toxicity in C57BL/6 mice. Comprehensive assessment of hepatic responses
elicited by dioxin-like PCB126 (Chapter 3 and 4) and non-dioxin-like PCB153 (Chapter 5) was
first performed independently to obtain a quantitative baseline of toxicity. Thorough evaluation
of PCB126 hepatic effects confirmed its dioxin-like toxicity, albeit at lower efficacy when
compared to equipotent doses of TCDD. Examination of a reconstituted mixture of dioxin and
non-dioxin-like PCB153 was therefore performed with TCDD, instead of PCB126, to reflect
greater affinity for the AhR and to better illustrate the non-additive effects on the dioxin-like
toxicity.
Individually, TCDD and PCB153 elicited unique and complex temporal and dosedependent gene expression responses, which could be related to physiological outcomes.
However, co-administration of both chemicals in a mixture identified a subset of dosedependent, synergistically induced genes that exhibited greater expression than the sum of
TCDD- and PCB153-elicited effects alone. The synergistic gene expression changes at 24 h
preceded reported changes in relative liver weights, histopathology, lipid profiles, and
pharmacokinetics.
These data have expanded the knowledge on the diversity of gene expression and
physiological responses mediated by PCB126, TCDD and PCB153 alone. In addition, the
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acquired in vivo data can provide insight into the non-additive gene expression underlying nonadditive changes at later time points. The data generated in these studies have also opened new
research questions, which should be explored to further increase the current understanding of the
toxic effects elicited by mixtures of AhR and non-AhR ligands. Future considerations and
suggested experiments and approaches are outlined below.

CONSIDERATIONS FOR CROSS-SPECIES COMPARISONS
Multiple reports and previous research from this lab has demonstrated that TCDD
mediates its toxic effects in a species-specific manner, which can have significant implications
for cross-species mixture evaluation. TCDD exposure in Sprague-Dawley rats has revealed that
these rodents are more sensitive to dioxin-mediated toxicity and do not exhibit the same
spectrum of toxicological responses when compared to C57BL/6 mice [1]. In addition, important
considerations for the evaluation of CAR and PXR-mediated responses have been highlighted in
Chapter 5, as species-specific ligand preferences have been reported, particularly for PXR and it
is therefore important to investigate species-specific activation by PCB153 [2]. Establishment of
permanently transfected cell lines containing human, mouse, and rat CAR and PXR chimeric
receptors coupled with luciferase reporter gene systems in collaborations will address the
receptor activation by PCB153 across species. Comprehensive assessment of TCDD and
PCB153 mixture effects in models other than the one presented in this dissertation may require
adjustments in dose spectrum and will require preliminary pilot studies to determine the
optimum dose combinations without exerting over toxicity. Future cross-species comparative
work utilizing microarray technology should also consider the immaturity of the rat genome
annotation compared to either human or mouse that may limit the functional comparisons
between the species.
220

TOXICOGENOMIC EVALUATION OF NON-ADDITIVE GENE EXPRESSION
The major advantage of utilizing toxicogenomics is the ability to simultaneously evaluate
thousands of transcripts at the same time. To date, results presented in Chapter 6 are the first to
utilize whole-genome mouse microarray analysis to examine non-additive gene expression. Data
analysis revealed that hundreds of genes were differentially regulated in response to TCDD,
PCB153, and to a mixture of both chemicals at one or more time points, however only a small
subset displayed putative non-additive expression that was further investigated in a doseresponse study. In addition, all of the identified mixture non-additive responses confirmed dosedependent synergistic effects compared to TCDD and PCB153 alone. Microarray analysis did
not identify candidate genes that displayed antagonistic effects in the mixtures, instead examples
of synergistic suppression were observed for some of the down-regulated genes. In addition, the
magnitude of gene repression was smaller compared to up-regulation which can further impair
statistical modeling and identification of antagonistic responses. Additional dose-response
studies at different time points in addition to the 24-h experiments, might reveal time-dependent
effects on the non-additive interactions.
Previous studies exploring mixtures of TCDD or tamoxifen with ethynyl estradiol
confirmed the lack of “global” non-additive gene expression effects. [3, 4]. These reports further
support the findings from this dissertation, suggesting that the majority of genes are not
significantly affected by the co-treatment. Instead, mixture gene expression effects are
comparable to the gene expression profiles elicited by either of the chemicals alone. Additional
studies, with different ratios of the individual chemicals might be required to determine a
threshold for non-additive responses. Results from earlier studies investigating mixtures of

221

TCDD and PCB153 report the lack of consistency of the antagonistic effects, suggesting that a
“window of antagonism” exists, which is dependent on the dose and target tissue [5, 6].
Multiple studies have reported PCB153-mediated antagonism of TCDD-induced Cyp1a1
mRNA levels or protein activity (EROD assay), in addition to other changes in physiological
endpoints. In contrast to previous reports, the work embodied in this dissertation has focused on
non-additive gene expression responses that could be linked to the observable changes in
phenotype. Results by Staal et al. [7] investigating binary mixtures of polyaromatic
hydrocarbons revealed that gene expression changes were generally additive or slightly
antagonistic, but DNA adduct formation exhibited synergistic effects in human hepatoma
(HepG2) cells, further complicating evaluation of carcinogenic potencies of the mixtures.

IN VITRO CONSIDERATIONS FOR MIXTURE EVALUATION
Studies utilizing in vivo designs for mixture evaluation contribute significantly more to
risk assessment, however the associated high cost does not permit for factorial evaluation of
binary and potentially ternary mixtures, including all possible dose-combinations allowing for
the determination of the response surfaces [8]. Utilization of in vitro models is favored, as it is
expected to reduce, refine and replace the use of animal models in toxicology and can in theory
permit the extrapolation of the responses to the whole organism. To further expand the work
described in this dissertation, toxicogenomic mixture evaluation could be performed in human,
mouse and rat primary hepatocytes and compared to the in vivo rodent data to evaluate the nonadditive responses in a human model. These cross-model (in vitro-in vivo) and cross-species
(mouse, rat, human) comparisons of mixture effects would significantly expand mixture risk
assessment and assess the ability of cost-effective in vitro approaches to contribute to risk
assessment practices.
222

REFERENCES

223

REFERENCES
1.

Boverhof, D.R., et al., Comparative toxicogenomic analysis of the hepatotoxic effects of
TCDD in Sprague Dawley rats and C57BL/6 mice. Toxicol Sci, 2006. 94(2): p. 398-416.

2.

Moore, J.T., et al., Functional and structural comparison of PXR and CAR. Biochim
Biophys Acta, 2003. 1619(3): p. 235-8.

3.

Boverhof, D.R., et al., Inhibition of estrogen-mediated uterine gene expression responses
by dioxin. Mol Pharmacol, 2008. 73(1): p. 82-93.

4.

Fong, C.J., et al., Effects of tamoxifen and ethynylestradiol cotreatment on uterine gene
expression in immature, ovariectomized mice. J Mol Endocrinol, 2010. 45(3): p. 161-73.

5.

Smialowicz, R.J., et al., Opposite effects of 2,2',4,4',5,5'-hexachlorobiphenyl and 2,3,7,8tetrachlorodibenzo-p-dioxin on the antibody response to sheep erythrocytes in mice.
Fundam Appl Toxicol, 1997. 37(2): p. 141-9.

6.

Bannister, R. and S. Safe, Synergistic interactions of 2,3,7,8-TCDD and 2,2',4,4',5,5'hexachlorobiphenyl in C57BL/6J and DBA/2J mice: role of the Ah receptor. Toxicology,
1987. 44(2): p. 159-69.

7.

Staal, Y.C., et al., Binary PAH mixtures cause additive or antagonistic effects on gene
expression but synergistic effects on DNA adduct formation. Carcinogenesis, 2007.
28(12): p. 2632-40.

8.

Bermudez, D.S., L.E. Gray, Jr., and V.S. Wilson, Modeling the interaction of binary and
ternary mixtures of estradiol with bisphenol A and bisphenol A F in an in vitro estrogen
mediated transcriptional activation assay (T47D-KBluc). Toxicol Sci, 2010. 116(2): p.
447-487.

224