Promotion of Hepatocarcinogenesis by Perfluoroalkyl Acids

All rights reserved. For Permissions, please email: [email protected] © The Author 2011. Published by Oxford University Press on behalf of the Society of Toxicology.

Promotion of Hepatocarcinogenesis by Perfluoroalkyl Acids in Rainbow Trout

Abby D. Benninghoff*, Gayle A. Orner†, Clarissa H. Buchner‡, Jerry D. Hendricks‡,

Aaron M. Duffy§ and David E. Williams†‡¶

* Department of Animal, Dairy and Veterinary Sciences and the Graduate Program in

Toxicology, Utah State University, 4815 Old Main Hill, Logan, UT, 84322, USA. † Linus Pauling Institute, Oregon State University, 307 Linus Pauling Science Center, Corvallis,

OR 97331, USA. ‡ Department of Environmental and Molecular Toxicology, Oregon State University, 1007

Agricultural and Life Sciences Building, Corvallis, OR, 97331, USA. § Department of Biology, Utah State University, 5305 Old Main Hill, Logan, UT, 84322, USA. ¶ Superfund Basic Research Center, Oregon State University, 435 Weniger Hall, Corvallis, OR

97331, USA.

Corresponding author:

Name: Abby D. Benninghoff, Ph.D.

Address: Animal, Dairy and Veterinary Sciences

Utah State University

4815 Old Main Hill

Logan, UT 84322-4815

Email: [email protected]

Phone: 435-797-8649

Fax: 435-979-2118

Short title: Promotion of hepatocarcinogenesis by PFAAs

Key words: Estradiol, hepatocarcinogenesis, perfluoroalkyl acid, perfluorooctanoic acid,

perfluorooctane sulfonate, tumor promotion, microarray, transcript profiling

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ABSTRACT

Previously, we reported that perfluorooctanoic acid (PFOA) promotes liver cancer in

manner similar to that of 17β-estradiol (E2) in rainbow trout. Also, other perfluoroalkyl acids

(PFAAs) are weakly estrogenic in trout and bind the trout liver estrogen receptor (ER). The

primary objective of this study was to determine whether multiple PFAAs enhance hepatic

tumorigenesis in trout, an animal model that represents human insensitivity to peroxisome

proliferation. A two-stage chemical carcinogenesis model was employed in trout to evaluate

PFOA, perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA), perfluorooctane

sulfonate (PFOS) and 8:2 fluorotelomer alcohol (8:2FtOH) as complete carcinogens or

promoters of aflatoxin B1 (AFB1)- and/or N-methyl-N'-nitro-N-nitrosoguanidine (MNNG)-

induced liver cancer. A custom trout DNA microarray was used to assess hepatic transcriptional

response to these dietary treatments in comparison to E2 and the classic peroxisome proliferator

clofibrate (CLOF). Incidence, multiplicity and size of liver tumors in trout fed diets containing

E2, PFOA, PFNA and PFDA were significantly higher compared to AFB1-initiated animals fed

control diet, whereas PFOS caused a minor increase in liver tumor incidence. E2 and PFOA also

enhanced MNNG-initiated hepatocarcinogenesis. Pearson correlation analyses, unsupervised

hierarchical clustering and principal components analyses showed that the hepatic gene

expression profiles for E2 and PFOA, PFNA, PFDA and PFOS were overall highly similar,

though distinct patterns of gene expression were evident for each treatment, particularly for

PFNA. Overall, these data suggest that multiple PFAAs can promote liver cancer and that the

mechanism of promotion may be similar to that for E2.

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INTRODUCTION

Polyfluorinated chemicals (PFCs) have been manufactured by either electrochemical

fluorination to produce mixtures of branched eight-carbon isomers or telomerization to

synthesize linear fluorotelomers. Perfluoroalkyl acids (PFAAs) are intermediates or by-products

formed during the production or breakdown of these fluoropolymers, widely used as surfactants,

surface protectors, paper and textile coatings, polishes and fire-retardant foams (Fromme et al.,

2009). Biotransformation of fluorotelomers, such as polyfluoroalkyl phosphate esters, used to

coat paper packaging that comes into contact with food, may also be a significant source of

human exposure to PFAAs (D'eon and Mabury, 2011). Perfluorooctanoic acid (PFOA) and

perfluorooctane sulfonate (PFOS) are members of the broader class of PFAAs, which are

structurally characterized by a hydrophobic fluorinated carbon chain of varying length with

either a carboxylic or sulfonic acid end group (Supplemental Figure 1). Blood levels of PFOA

and PFOS in U.S. residents are estimated to be about 4 and 20 ppb, respectively, though these

levels have declined in recent years (Calafat et al., 2007; Olsen et al., 2003). Other PFAAs have

also been detected in humans and wildlife worldwide, including perfluorononanoic acid (PFNA)

and perfluorodecanoic acid (PFDA) (Calafat, et al., 2007; Kannan et al., 2004; Martin et al.,

2004). The residence time of PFOA varies among species, ranging from hours in the female rat

to days in canine and rainbow trout (Hanhijarvi et al., 1988; Martin et al., 2003b). In contrast,

humans have very limited capacity for elimination of PFAAs, as the estimated half-lives of

PFOA and PFOS are 3.8 and 5.4 years, respectively (Olsen et al., 2007).

PFOA and other PFAAs are peroxisome proliferators (PPs), a class of chemicals that also

includes some plasticizers, hypolipidemic drugs, herbicides, solvents and certain long chain fatty

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acids. Many biological responses to PPs are mediated by interaction with the peroxisome

proliferator-activated receptor α (PPARα), which is highly expressed in the liver (Holden and

Tugwood, 1999). PFOA and other PPs are non-genotoxic hepatocarcinogens or promoters of

hepatocarcinogenesis in rodents (reviewed in Abdellatif et al., 1991; Lai, 2004), though

differences in susceptibility have been observed among species. Mice and rats are highly

susceptible to liver toxicity and cancer caused by peroxisome proliferating chemicals, whereas

humans and non-human primates are insensitive or non-responsive (Holden and Tugwood, 1999;

Lai, 2004). The weak response of humans to PPs has been attributed to the low level of PPARα

expression in human liver (Palmer et al., 1998). New evidence showing that the environmental

PPARα agonist di(2-ethylhexyl) phthalate (DEHP) significantly increased liver cancer incidence

in PPARα null mice (Ito et al., 2007) suggests that some PPs may act via PPARα-independent

modes of action to increase risk of hepatocarcinogenesis.

Recently, our laboratory utilized the rainbow trout (Oncorhynchus mykiss) as an animal

model that mimics human insensitivity to peroxisome proliferation to investigate alternative

mechanisms of action for PFAAs. Chronic dietary exposure to PFOA enhanced liver cancer in

trout and elicited changes in hepatic gene expression indicative of estrogen exposure, whereas

the classic peroxisome proliferator clofibrate (CLOF) was ineffective (Tilton et al., 2008). Thus,

we deduced that the cancer-enhancing effects of PFOA in trout were due to novel mechanisms

related to estrogen signaling, rather than the typical peroxisome proliferator response observed

for this chemical in rodent models. Subsequently, we reported that multiple PFAAs, including

PFOA, PFNA, PFDA and PFOS, are weakly estrogenic in rainbow trout based upon induction of

the estrogen-sensitive biomarker plasma protein vitellogenin (Vtg) and evidence for direct

interaction of these compounds with the trout liver estrogen receptor (ER) (Benninghoff et al.,

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2011). Moreover, none of these compounds elicited a typical peroxisome proliferator response

in trout liver. The estrogen-like action of these compounds is likely not restricted to trout, as

multiple PFAAs increase activity of a human ERα gene reporter and were demonstrated to

effectively dock in silico to the ligand-binding domain of the human and mouse ERα

(Benninghoff, et al., 2011).

The objective of the present study was to determine the impact of multiple PFAAs with

reported estrogen-like activity on hepatic tumorigenesis in rainbow trout, a well-established

model used for chemically induced liver cancer in humans (Bailey et al., 1996). A two-stage

chemical carcinogenesis model was employed to evaluate PFOA, PFNA, PFDA, PFOS and 8:2

fluorotelomer alcohol (8:2FtOH) as potential complete carcinogens and promoters of aflatoxin

B1 (AFB1)- and/or N-methyl-N'-nitro-N-nitrosoguanidine (MNNG)-induced liver cancer. A

toxicogenomics approach was utilized to evaluate mechanisms of chemical hepatocarcinogenesis

in PFAA-exposed trout compared to 17β-estradiol (E2) and the classic peroxisome proliferator

CLOF. We hypothesized that PFAAs, identified previously as weak xenoestrogens, would

enhance liver carcinogenesis and produce a hepatic gene expression profile indicative of an

estrogen-like transcriptional response.

METHODS

Materials

Analytical grade AFB1, E2, PFOA, PFNA, PFDA and 8:2FtOH were obtained from

Sigma-Aldrich (St. Louis, MO). PFOS and CLOF were purchased from Fluka Chemical Corp

(St. Louis, MO). MNNG was obtained from ChemService (West Chester, PA). All other

reagents were purchased from Sigma-Aldrich or other general laboratory suppliers and were of

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the highest purity available. Chemical structures for compounds tested as tumor promoters are

provided in Supplemental Figure 1.

Animals

Mt. Shasta strain rainbow trout were hatched and reared at the Sinnhuber Aquatic

Research Laboratory at Oregon State University in Corvallis, Oregon. Fish were maintained in

flow-through 375-l tanks at 12 °C with activated carbon water filtration on a 12:12 hr light:dark

cycle. All procedures for treatment, handling, maintenance and euthanasia of animals used in this

study were approved by the Oregon State University Institutional Animal Care and Use

Committee.

Tumor study, necropsy and histopathology

An overview of the study design is provided in Supplemental Figure 2. Approximately

3500 fry were initiated at 10 weeks post spawn with an aqueous exposure to 10 ppb AFB1 or

0.01% EtOH (non-initiated sham controls) for 30 min; a second cohort of about 1000 fry was

AFB1- or sham-initiated at 15 weeks of age. To determine whether the expected tumor-

promoting effects of PFOA and related compounds are carcinogen- or target organ-dependent, a

third cohort of about 1000 fry was initiated at 10 weeks post spawn with a 30-min aqueous

exposure to 35 ppm MNNG, a multi-organ carcinogen in trout (Hendricks et al., 1995), or 0.01%

DMSO (non-initiated sham control). After initiation, fry were fed Oregon Test Diet, a semi-

purified casein-based diet, for one month (Lee et al., 1991). Then, within each initiation cohort,

trout were randomly distributed into dietary treatment groups with 125 animals assigned to

duplicate tanks (250 fish/treatment) (Supplemental Figure 2). In the first cohort, fish were fed

experimental diets containing 5 ppm E2, 2000 ppm PFOA (approximately 50 mg/kg bw/day),

2000 ppm FtOH or 2000 ppm CLOF ad libitum (2.8-5.6% of body weight) five days per week

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for six months. PFNA and PFDA experimental diets were initially administered at 2000 ppm

based upon prior testing of PFOA without significant mortality (Tilton, et al., 2008; unpublished

observations). Due to an unexpected number of mortalities in the PFNA and PFDA treatment

groups early in the study, diet concentrations were reduced to 200 ppm PFDA (5 mg/kg/day) or

1000 ppm PFNA (25 mg/kg/day) for the remainder of the exposure period. In the second cohort

(AFB1 at 15 weeks), trout were fed 100 ppm PFOS (2.5 mg/kg/day); this lower test concentration

of PFOS was selected based upon observed lethal toxicity at the 2000 ppm diet level

(unpublished data). Finally, MNNG-initiated trout were fed 5 ppm E2 or 2000 ppm PFOA. All

experimental diets were prepared monthly, stored frozen at -20°C and then thawed to 4°C a few

days prior to feeding. Most test compounds were added directly to the oil portion of the OTD

diet, though 8:2FtOH was incorporated into the diet via an oil-in-water emulsification. At

conclusion of the 6-month promotion diet period, animals were once again fed standard OTD for

the remainder of the study.

At 12.5 months post spawn, juvenile trout were euthanized with an overdose (250 ppm)

of tricane methanesulfonate (MS-222) and necropsied over a one-week period. Livers, kidneys,

stomachs and swim bladders were preserved in Bouin’s solution for up to seven days for

histologic examination of tumors by hematoxylin and eosin staining. Neoplasms were classified

according to the criteria described by Hendricks et al. (1984). The effect of experimental diets

on tumor incidence was modeled by logistic regression (LOGISTIC procedure, SAS version 9.2,

SAS Institute, Cary, NC); analyses included diet treatment, sex, body weight and replicate tank

as experimental factors. Firth’s bias correction was used as the likelihood penalty when a

maximum likelihood estimate was not obtained. Some fish in this study showed symptoms of a

liver disease of unknown origin, which was characterized by pale or jaundiced livers. To

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determine whether this idiopathic disease impacted the study outcome, logistic regression

analyses were performed using two data sets: all subjects included all experimental subjects,

males and females, regardless of disease symptoms; final subjects excluded any fish that showed

symptoms of idiopathic liver disease. Data, statistical analyses and conclusions presented in this

manuscript are for the final subjects data set, unless noted otherwise, while information and

analysis of the all subjects data set is available in the supplemental materials. Tumor multiplicity

(number of tumors per tumor-bearing animal) and size data were analyzed by the Kruskal-Wallis

test with Dunnett’s with post-hoc test for multiple comparisons (GraphPad Prism 5, La Jolla,

CA).

Microarray experiment

Two weeks after the start of experimental diets, 24 fry (sex undetermined) from each of

the sham-exposed treatment groups were removed from the study (12 fish/duplicate tank),

euthanized by MS-222 and randomly distributed to create three pools of eight livers (n = 3).

Total hepatic RNA was extracted from pooled whole liver samples using TRIzol reagent (Sigma-

Aldrich), purified using the RNeasey Mini kit (Qiagen, Valencia, CA) and evaluated for quality

using the Bioanalyzer 2100 (Agilent, Palo Alto, CA). A reference RNA pool was made by

combining equal amounts of RNA from all control RNA samples. Because PFOS trout were

treated at a later age, a separate time-matched reference RNA pool was prepared for competitive

hybridization of PFOS samples.

Details on the development, manufacture and quality control assessment of the OSUrbt

version 5.0 microarray have been provided previously (Benninghoff and Williams, 2008; Tilton

et al., 2005) (Gene Expression Omnibus [GEO] platform accession ID: GPL5478). For

detection of gene expression on the OSUrbt-v5 array, the Genisphere 3DNA Array 900 kit

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(Hatfield, PA) was used according to the supplier’s protocol in a standard dye-swap, reference

sample design as previously described (Benninghoff and Williams, 2008). Note that the RNA

reference for competitive hybridization of PFOS samples was a separate, time-matched pool of

RNA obtained from sham-initiated, control-fed trout at 15 weeks. Each reverse transcription

reaction also included spiked-in mRNA corresponding to SpotReport Alien Oligo control

features (Stratagene, La Jolla, CA). Hybridization of cDNA and capture reagents to the OSUrbt

arrays was performed using the Hybex Microarray Incubation system (SciGene Corporation,

Sunnyvale, CA) as described previously (Benninghoff and Williams, 2008). Within 24 hr of

hybridization, array images at a resolution of 5 μm were obtained using the Axon GenePix Pro

4200A scanner (Molecular Devices Corp., Sunnyvale, CA) at 543 nm and 633 nm excitation

wavelengths for Cy3 and Cy5, respectively, with saturation tolerance set at 1% and laser power

set at 90%.

Array image files were processed with ratio-centering, and spot intensities were

quantified using GenePix Pro software (Molecular Devices). Protocols for the maintenance,

processing and filtering of raw data sets (technical replication and fold-change criteria) were

detailed previously (Benninghoff and Williams, 2008). All data files associated with this

experiment are available at the GEO online data repository (Accession ID: GSE31085).

Statistical analyses of gene expression were performed using the normalized, geometric mean

expression values for each biological replicate to compare each individual experimental

treatment to the control (MultiExperiment Viewer [MeV]) (Saeed et al., 2003); a statistically

significant change in gene expression was inferred when p < 0.05 (Welch’s t-test, between

subjects and assuming unequal variances). Unsupervised, bidirectional hierarchical clustering

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and principal components analyses were performed using MeV. Normalized data were also

exported to Prism 5 for pairwise Pearson correlation analyses of gene expression profiles.

Gene annotation and ontology analysis

Manual annotation of differentially regulated array features was performed as previously

described (Benninghoff and Williams, 2008). For the proteins encoded by the putative trout

homolog mRNAs, functional information was inferred from annotations in the Gene Ontology,

Online Mendelian Inheritance in Man (OMIM) and SwissProt Protein Knowledgebase databases.

Automatic annotation of the entire OSUrbt-v5 array was performed using traditional basic local

alignment search tool (BLAST) in a two-step process, as follows. First, the array 70mer oligo

sequences were queried against the NCBI expressed sequence tag (EST) databases for rainbow

trout, salmon (Salmo salar) and zebrafish (Danio rerio). Of the 1676 features on the OSUrbt-v5

array, 1384 EST matches were obtained. The resulting top EST hit (E < 10-4) for each array

feature was then used for a translated blastx search against the NCBI non-redundant protein

sequence (nr) database. The resulting top hit (E < 10-6), excluding hypothetical proteins, was

considered the best match for array feature identification; 1103 gene matches were obtained from

the NCBI nr database. NCBI accession numbers for the top hits were used to obtain gene

symbols for each array feature using BioThesaurus (Liu et al., 2006).

Gene ontology enrichment analysis was performed using High Throughput GoMiner

(Zeeberg et al., 2005). For each treatment, the list of differentially regulated genes

(Supplemental Table 5) was compared to an auto-generated list derived from gene ontologies for

rainbow trout (NCBI taxonomy ID 8022), zebrafish (ID:7095) and human (ID:9606). Because

the OSUrbt-v5 array is a medium-sized array (about 1450 genes) with probes focused on

processes involved in carcinogenesis, reproduction, toxicological response and stress physiology,

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it was necessary to automatically generate a global list of genes to avoid potential pathway bias

inherent in a targeted array. All available database resources were searched, and all evidence

levels were included in the analysis. A minimum of two genes per category was set for

generation of category statistics, and 100 randomizations were used for the enrichment analysis.

A significant effect of dietary treatment on GO term category (biological process) enrichment

was inferred when p < 0.05, as determined by a one-sided Fisher’s exact test after false discovery

rate (FDR) correction. Cluster Image Maps (CIM) for biological processes over- and under-

represented in treatment gene lists were generated using CIMminer (Weinstein, 2004) with GO

categories clustered by Euclidian distance method with average linkage. To visualize and

compare relationships among differentially regulated GO categories associated with dietary E2

and PFNA, differentially-regulated gene lists were subjected to analysis in AgriGO (Du et al.,

2010) using the singular enrichment analysis (SEA) tool against the zebrafish gene ontology

database.

Real-time qRT-PCR

To validate changes in gene expression detected on the OSUrbt array, mRNA levels of

select genes were evaluated by the quantitative real time reverse transcriptase polymerase chain

reaction (qRT-PCR) as described previously (Benninghoff and Williams, 2008), with a few

modifications. Total RNA (1 μg) was reverse transcribed (Superscript II, Invitrogen) according

to the supplier’s protocol with oligo d(T)18 primer and a final reaction volume of 50 μl. Primer

sequences are provided in Supplemental. Table 1, and qRT-PCR was performed using the

PerfeCta SYBR Green FastMix (Quanta Biosciences, Gaithersburg, MD) on a Mastercycler ep

Realplex (Eppendorf, Hauppauge, NY). PCR standards for each target gene were prepared by

gel-purification of PCR products (QIAX II, Qiagen, Valencia, CA), quantified using the

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PicoGreen dsDNA Quantification Kit (Molecular Probes, Eugene, OR) and serially diluted for

final concentrations ranging from 0.001 to 100 ng DNA. All qRT-PCR expression values were

normalized by the geometric mean fold change of four housekeeping genes (actb, gapdh, top2a

and atp5b). Then, for comparison to microarray expression values, log2 fold change ratios were

calculated for treated samples compared to the same reference pool that was utilized in the

microarray study. qRT-PCR data were analyzed by one-way ANOVA with Dunnett’s post-hoc

test for multiple comparisons, and a significant change in gene expression was inferred when

p<0.05.

RESULTS

Promotion of AFB1- or MNNG-initiated hepatocarcinogenesis by PFAAs

Initiation with 10 ppb AFB1 resulted in a moderate rate of liver tumor incidence (13%) in

12 month-old trout (Table 1; Figure 1A), whereas no tumors were observed in sham-initiated

animals. The 5 ppm E2 promotion diet markedly enhanced liver tumor incidence to 83% (p <

0.0001), increased liver tumor multiplicity (p < 0.001) and doubled the average liver tumor size

(p < 0.001) (Figure 1A,D). Post-initiation exposure to experimental diets containing PFOA,

PFNA or PFDA resulted in a hepatic tumor response similar to that of E2, and PFDA was the

most potent promoting agent tested in this study. Interestingly, 200 ppm PFDA increased liver

tumor incidence to a greater extent (26% higher) than did a 10-fold higher diet concentration of

PFOA. Dietary PFOA, PFNA and PFDA also significantly increased tumor multiplicity and size

in a manner similar to that of E2 (Figure 1D). In contrast, post-initiation dietary exposure to

8:2FtOH or the classic peroxisome proliferator compound CLOF did not change liver tumor

incidence, burden or size. Liver tumor incidence in trout initiated with AFB1 at 15 weeks was

only substantially lower at 1% (Table 1); dietary PFOS increased the liver cancer rate to 13% (p

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= 0.0014), though tumor burden and multiplicity remained unchanged compared to time-matched

controls (Figure 1B, E). Logistic regression analyses for the E2, PFOA, PFNA, PFDA and

PFOS treatment groups showed that the experimental diet was the primary factor driving tumor

response (p-values ranging from 0.0014 to <0.0001); reduced body weight was a minor factor

associated with tumor outcome, while fish sex, replicate tank or idiopathic liver disease did not

impact tumor outcome (Supplemental Table 2; Supplemental Figure 3). Dietary treatment with

E2, PFOA, PFNA, PFDA or PFOS significantly increased relative liver weight, though this

observation could be partially attributed to lower body weight in some of these treatment groups

(Supplemental Figure 4).

A third cohort of trout was initiated with 35 ppm MNNG to determine whether the

tumor-promoting effects of dietary PFOA was specific to hepatocarcinogenesis or dependent

upon the initiating carcinogen. Initiation with the multi-organ carcinogen MNNG resulted in

tumorigenesis of the liver, kidney, stomach and swim bladder (Table 2). Dietary exposure to 5

ppm E2 and 2000 ppm PFOA significantly increased liver tumor incidence (p < 0.0001),

multiplicity (p <0.001) and size (p <0.001) compared to control diet (Figure 1C, F). Kidney and

stomach carcinogenesis were not significantly affected by E2 or PFOA (Table 2), and the

apparent impact of these compounds on swim bladder tumor incidence was confounded by

significant over-dispersion among the replicate tanks (Supplemental Figure 5). Logistic

regression analyses for MNNG-initiated groups showed that experimental diet was the primary

factor impacting liver tumor outcome (p < 0.0001), and there was not a significant effect of fish

sex, replicate tank or idiopathic liver disease on liver carcinogenesis (Supplemental Table 3).

Histological evaluation of tumors in 12.5-month old trout confirmed previous

observations from our laboratory that the predominant liver tumor type in AFB1- or MNNG-

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initiated animals was mixed carcinoma (MC) with hepatocellular adenoma (HCA) and

hepatocellular carcinoma (HCC) as secondary tumor types (Tables 1-2). Tumor type profiles

were not noticeably different among the various tumor promotion diets, though cholangiocellular

tumors (adenoma and carcinoma) were more common in AFB1-initiated trout fed E2 or PFAA

promotion diets.

Perfluoroalkyl modulation of hepatic gene expression

In the present study, we used the trout OSUrbt-v5 microarray to examine hepatic

transcriptional responses to several structurally related polyfluorinated compounds in

comparison to E2 and CLOF (GEO accession GSE31085). Quality control analysis of array data

showed that intra- and inter-array variability was generally low and that hybridization was

consistent and reproducible (Supplemental Figure 6). Multiple criteria were used to reduce the

original raw data sets to a subset of array features considered significantly regulated by any one

of the experimental treatments (Supplemental Table 4). Average expression values, accession

numbers and gene annotations for select array features that passed all stringency criteria are

shown in Supplemental Table 5. The impact of E2, PFOA and CLOF on hepatic gene expression

was very similar to prior observations in our laboratory (Figure 2) (Tilton, et al., 2008). Dietary

PFOA, PFNA and PFDA commonly altered expression of 54 genes, of which many were shared

with the E2 group. Genes regulated by PFOS and FtOH were somewhat similar to E2 and the

perfluoroalkyl acids, whereas CLOF had very little effect on liver gene expression in trout.

Several analytical approaches were utilized to compare PFAA gene expression profiles to E2, a

model estrogen, and CLOF, a classic peroxisome proliferator. Pairwise Pearson correlation

analyses for significantly regulated genes revealed strong correlations among E2, PFOA, PFNA,

PFDA treatments (r ≥ 0.84), whereas the E2, PFOS and FtOH groups were modestly similar (r

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values from 0.66 to 0.83) (Figure 3A; Supplemental Table 6). Principal components analysis

(PCA) was employed to reduce the dimensionality of the data set so that general relationships

between the promotion diets could be discerned more easily (Figure 3B). Transcript profiles for

E2, PFOA and PFDA treatments were highly similar, indicated by close proximity in the PCA

plot, whereas PFOS and FtOH were moderately similar (within the same quadrant); all

treatments were distinct from CLOF and CON groups. Also, the expression profile for PFNA

was sufficiently unique to form a separate cluster distant from all other treatment groups.

Bidirectional clustering of genes differentially regulated by at least one of the experimental diets

showed distinct patterns of expression corresponding to two primary nodes in the sample tree,

with one node encompassing all polyfluorinated chemicals and E2 and the second node including

CLOF and control groups (Figure 4A). Distinct patterns of gene expression were evident for

each experimental condition, particularly for PFNA, which formed a separate sub-node within

the estrogen group. These patterns remained consistent when this analysis was applied to the

entire array dataset (Supplemental Figure 7).

Transcripts differentially regulated by the estrogen-like treatments, including E2, PFOA,

PFNA and PFDA, represent biological processes involved in cell proliferation; apoptosis; signal

transduction; transcription; protein translation, modification and transport; phase I and II

metabolism; redox regulation; and adaptive immune response (Supplemental Tables 5, 7-8;

Supplemental Figures 8-9). Overall, the estrogenic transcriptional profile observed in this study

is highly similar to previous trout experiments in our laboratory, as a similar set of estrogen

biomarker genes were differentially regulated, including vtg, ctds, esr1, rtn9-a1, sec61ab, vhsv4,

and ikk1, among others (Benninghoff and Williams, 2008; Tilton, et al., 2008). Moreover,

typical gene markers indicative of a typical transcriptional response to peroxisome proliferators,

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such as crot and acat1, were not significantly regulated by E2, the polyfluorinated compounds

tested in this study or the classic PPAR agonist CLOF; however, catalase expression was

significantly repressed by PFNA (Supplemental Table 5). Also of note, dietary exposure to all of

the fluorochemicals tested caused significant enrichment of GO categories response to estradiol

stimulus and estrogen receptor signaling pathway. Though the transcriptional profiles for E2

and the polyfluorinated chemicals examined in this study were broadly similar, some distinctions

were evident (Figure 4B; Supplemental Tables 7-8). In particular, the perfluoroalkyl carboxylic

acids significantly suppressed expression of several genes involved in regulation of the blood

coagulation cascade and the complement pathway; E2 similarly repressed genes in these

pathways, though to a lesser extent. Additionally, several genes associated with phase I and II

metabolism (gstp1, cyp3a27, mgst1 and cbr1) were differentially regulated by dietary PFOS

and/or FtOH, but not E2 or the perfluoroalkyl carboxylic acids.

Expression of select genes differentially induced or repressed was verified by qRT-PCR,

including a2m, ctsd, cyp1a, cyp2k5, hpx, pgds, tcpbp, trx and vtg. Generally, qRT-PCR values

followed a pattern similar to that acquired using the microarray (Supplemental Figure 10).

However, the magnitude of change in gene expression detected by qRT-PCR was occasionally

greater compared to the microarray data (e.g., vtg) due to saturation beyond the linear range of

detection on the array. Overall, results of these analyses confirm that our strategy for

identification of differentially regulated genes from the OSUrbt-v5 data set resulted in the

detection of meaningful changes in gene expression.

DISCUSSION

We report for the first time that multiple PFAAs enhance hepatocarcinogenesis via an

estrogen-like mechanism in rainbow trout, an animal model that recapitulates human

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insensitivity to peroxisome proliferation. Previously, we demonstrated that dietary exposure to

the ubiquitous environmental contaminant PFOA enhanced AFB1-initiated liver tumorigenesis in

trout (Tilton, et al., 2008). Subsequent in vitro and in vivo experiments showed that several

perfluoroalkyl carboxylic acids and sulfonates have weak estrogen activity, likely via direct

interaction with the ER (Benninghoff, et al., 2011); moreover, in this animal model, PFAAs did

not elicit the typical peroxisome proliferator response expected for PPARα ligands. In the

present study, we tested the hypothesis that PFAAs structurally related to PFOA would similarly

impact liver tumorigenesis. We determined that chronic exposure to three different PFAAs via

the diet, including PFOA, PFNA and PFDA, markedly increased hepatocarcinogenesis in trout in

a manner similar the prototypical estrogen, E2. Also, tumor promotion by PFOA was restricted

to the liver, but not dependent upon the initiating carcinogen. Dietary exposure to PFOS caused

a modest increase in liver tumor incidence, possibly due to the lower diet concentration selected

for this compound or the slightly older age of these fish at initiation and start of dietary

treatment.

Although the diet concentrations of PFAAs tested in this study (100 to 2000 ppm, or 2.5

to 50 mg/kg bw/day) are typical for peroxisome proliferator cancer studies in rodents, these

levels were substantially greater than would be expected from a typical human environmental

exposure (Fromme, et al., 2009). Extrapolation from a two-week dietary dose-response study in

trout with PFOA and PFDA (Benninghoff, et al., 2011) suggests that the diet concentrations

employed in this tumor promotion study result in blood levels in the micromolar range,

considerably higher than the nanomolar range reported for these compounds in human blood

(Calafat, et al., 2007; Olsen, et al., 2003). Evidence from a previous limited dose-response

tumor study with PFOA in trout suggested that a lower dietary exposure to PFAAs may not

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substantially increase liver cancer risk in animals that are insensitive to peroxisome proliferation

(Tilton, et al., 2008). However, the observation from the present study that 200 ppm PFDA

increased tumor incidence to an even greater extent than 2000 ppm PFOA (88% and 62%

incidence, respectively) points to the need for further studies utilizing a comprehensive dose-

response approach with individual PFAAs to appropriately assess cancer risk for these

compounds. Moreover, because multiple members of this chemical class are often detected in

blood and tissue samples (Calafat, et al., 2007; Lau et al., 2007), the potential for additive or

synergistic effects of PFAA mixtures in promoting liver carcinogenesis should not be ignored.

The liver gene expression profiles obtained by the trout custom DNA microarray were

highly similar among E2 and PFAA treatments, suggesting that these compounds likely act via a

common mechanism of action to promote hepatocarcinogenesis in trout. Previously, we

identified a set of 17 hepatic genes as biomarkers of estrogen exposure (Benninghoff and

Williams, 2008), of which, 13 were differentially regulated by PFAAs in trout. Although the

specific mechanism for promotion of liver cancer by estrogens in trout is not known, results of

this and previous gene expression profiling experiments (Benninghoff and Williams, 2008;

Tilton et al., 2006; Tilton, et al., 2008) point to the involvement of genes associated with cell

growth, apoptosis, cell signaling, regulation of transcription, protein stability and transport and

immune response. For example, E2- or PFAA-dependent promotion of hepatocarcinogenesis

may involve disruption of the NFκB signaling pathway (e.g., nfkb1, ikk1, ikbe) or suppression of

innate immune response (e.g., C-3, C-9, mbl) (Sun and Karin, 2008; Vainer et al., 2008).

Interestingly, the gene expression profiles for PFAAs obtained from the trout microarray are

generally similar to profiles reported by Wei et al. (2007; 2009) following aqueous exposures of

PFOA, PFOS and various mixtures of PFAAs in rare minnow (Gobiocypris rarus). In rat liver,

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the transcriptional response to an oral gavage of PFOA or PFOS was dominated by genes

associated with lipid metabolism and transport, including genes in the peroxisomal fatty acid

oxidation pathway (e.g., Acat1) (Guruge et al., 2006; Hu et al., 2005). However, few transcripts

associated with the metabolism and transport of lipids and cholesterol were significantly altered

by PFAA exposure in trout (<3% of all regulated features), and several of these were also

regulated by E2. These observations, along with the recent discovery that PFOA, PFNA, PFDA

and PFOS competitively bind to the trout ER (Benninghoff, et al., 2011), provide further

evidence that PFAAs promote hepatic cancer in this species via an estrogen-like mechanism

involving activation of the ER, rather than via interaction with PPARα and induction of

peroxisomal proliferation.

At the time liver tissues were collected for the microarray study, all three perfluoroalkyl

carboxylic acids had been administered at the same diet concentration (2000 ppm) for two-

weeks. Thus, apparent distinctions in transcriptional profiles among PFOA, PFNA and PFDA

may reflect chemical-specific responses, differences in the strength of interaction with molecular

targets mediating the transcription response or possible differences in uptake, distribution or

elimination of these chemicals in vivo. Martin et al. (2003a; 2003b) reported that values for

bioconcentration and residence time of PFAAs in trout liver generally increased with increasing

length of the fluorinated carbon chain (half-life of 5 days for PFOA compared to 14 days for

PFDA). However, the high similarity in transcriptional response to PFOA and PFDA observed

in this study did not reflect these apparent differences in chemical pharmacokinetics, most likely

due to the daily dietary exposure protocol employed. Dietary PFNA altered hepatic expression

of 175 transcripts (65 induced, 110 repressed), nearly twice the number for PFOA and PFDA;

however, many of these array features were similarly induced or repressed by all three

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carboxylic acids and E2, though to differing extent. A case in point is PFNA-induced

dysregulation of the blood coagulation pathway, a reported side-effect of pharmacological

estrogen exposure (Sherif, 1999).

Only a few definitive chemical-specific gene targets were identified in this study, most

notably st2s2 and cyp3a7 for PFOS and gstp1 for 8:2FtOH. Additionally, the modest

transcriptional response to PFOS as compared to the carboxylic acids tested should be

considered in the context of the lower dietary exposure (200 ppm). Dietary 8:2FtOH (2000 ppm)

modified relatively few transcripts, most of which were highly sensitive estrogen biomarker

genes (e.g., vtg, zrp, esr1). Previously, we determined that 8:2FtOH was not overtly estrogenic

in trout and does not interact with the ER (Benninghoff, et al., 2011); it is possible that the

transcriptional activity of this chemical observed in this study may be due to in vivo metabolism

of 8:2FtOH to PFOA or other estrogenic derivative (Brandsma et al., 2011). Other laboratories

have also reported estrogen-like activity of PFAAs and some fluorotelomers, although

inconsistencies among these reports suggest that some species are more responsive to one

compound class than the other (Ishibashi et al., 2008; Liu et al., 2007; Maras et al., 2006).

In conclusion, we report the important finding that multiple PFAAs, including PFOA,

PFNA, PFDA and PFOS enhance liver tumorigenesis in trout, an animal model that is not

responsive to peroxisome proliferation. Evidence from gene expression profiling suggests that

the mechanism of action for PFAA-dependent promotion of hepatocarcinogenesis likely involves

interaction with the hepatic ER. Finally, this study highlights the use of an alternative animal

model to reveal novel estrogen-like action of multiple PFAAs in modulating chemical

carcinogenesis.

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SUPPLEMENTARY DATA

Supplementary data are available online at www.toxsci.oupjournals.org as a single Adobe

PDF file, which includes graphical presentation of all trout morphometric and tumor data as well

the detailed results of the statistical analyses performed. Also provided are figures and tables

detailing the results of the microarray study as well as the gene ontology analyses performed.

For a complete list of figures and tables, see the supplemental data file table of contents.

FUNDING

This work was supported in part by the National Institute of Environmental Health

Sciences (P30 ES03850, T32 ES07060, P30 ES00210, P42 ES016465 and R01 ES013534) and

the Utah Agricultural Experiment Station.

ACKNOWLEDGEMENTS

The authors wish to acknowledge the assistance of Eric Johnson and Greg Gonnerman at

the Sinnhuber Aquatic Research Laboratory (SARL) for care of the animals used in this study.

The technical assistance of Marilyn Henderson, Lisbeth Siddens, Trevor Fish and Brittany

Packard is also gratefully acknowledged.

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http://www.toxsci.oupjournals.org/


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Table 1. Impact of dietary PFCs on AFB1-induced liver carcinogenesis

Tumor class (%)b

Treatment a Incidence (%) HCA HCC MA MC CCA CCC

Initiated at 10 weeks

Sham/CON 0 0 0 0 0 0 0

Sham/E2 7* 0 58 0 33 8 0

Sham/PFOA 0 0 0 0 0 0 0

Sham/PFNA 0 0 0 0 0 0 0

Sham/PFDA 5 0 0 0 100 0 0

Sham/FtOH 0 0 0 0 0 0 0

Sham/CLOF 1 0 0 0 100 0 0

AFB1/CON 13 26 23 2 47 0 2

AFB1/E2 83#### 6 22 4 65 1 2

AFB1/PFOA 62## 10 27 1 54 4 5

AFB1/PFNA 72#### 5 17 0 68 3 8

AFB1/PFDA 88#### 7 24 1 63 1 4

AFB1/FtOH 23 12 29 3 52 2 2

AFB1/CLOF 15 11 29 6 41 5 8

Initiated at 15 weeks

Sham/CON 0 0 0 0 0 0 0

Sham/PFOS 0 0 0 0 0 0 0

AFB1/CON 1 0 29 0 71 0 0

AFB1/PFOS 13†† 5 10 5 68 3 10 a Treatment groups are indicated as initiation/diet (see Methods for complete details). b Abbreviations: HCA, hepatocellular adenoma; HCC, hepatocellular carcinoma; MA, mixed

adenoma; MC, mixed carcinoma; CCA, cholangiocellular adenoma; CCC, cholangiocellular

carcinoma.

*, p < 0.05 compared to Sham/CON; ##, p < 0.01; ####, p<0.0001compared to AFB1/CON; ††,

p<0.01 compared to AFB1/CON (15 weeks) as determined by logistic regression analysis.

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Table 2. Impact of dietary PFCs on MNNG-induced multi-organ carcinogenesis

Incidence (%) Liver tumor class b

Treatment a Stomach Kidney SB Liver HCA HCC MA MCC CCA CCC

Sham/CON 0 0 0 0 0 0 0 0 0 0

MNNG/CON 99 37 45 51 25 28 3 39 2 3

MNNG/E2 99 49 51 97**** 33 13 1 51 1 1

MNNG/PFOA 99 29 34 86**** 26 11 4 55 3 1 a Treatment groups are indicated as initiation/diet (see Methods for complete details). b Abbreviations: SB, swimbladder; HCA, hepatocellular adenoma; HCC, hepatocellular

carcinoma; MA, mixed adenoma; MC, mixed carcinoma; CCA, cholangiocellular adenoma;

CCC, cholangiocellular carcinoma.

****, p<0.0001 compared to MNNG

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FIGURE LEGENDS

Figure 1. Perfluoroalkyls increase liver tumor incidence, multiplicity and size in AFB1-

and MNNG-initiated trout. (A-C) Liver tumor incidence and multiplicity (males and females).

(D-F) Average liver tumor size ± SE. Trout were initiated with 10 ppm AFB1 at 10 (A, D) or 15

weeks of age (B, E) or with 35 ppm MNNG at 10 weeks (C, F). Details on experimental diets

are provided in Methods. **, p < 0.01 and ****, p < 0.0001, significant difference in tumor

incidence compared to CON diet (within each initiation group) as determined by logistic

regression analysis (complete results in Supplemental Tables 2-3). #, p < 0.05 and ###, p <

0.001, significant difference in tumor multiplicity or size compared to CON diet (within each

initiation group) as determined by the Kruskal-Wallis test with Dunnett’s post-hoc test for

multiple comparisons. A color version of this figure is available in the online version of the

article.

Figure 2. Venn diagrams depicting overlap of differentially regulated genes among

experimental treatments. The total number of genes differentially regulated induced by the

experimental treatment is indicated for each intersection. A color version of this figure is

available in the online version of the article.

Figure 3. Dietary exposure to PFAAs induces an estrogen-like hepatic gene expression

profile in trout. (A) Pairwise correlation of hepatic gene expression profiles. Values shown are

the log2 geometric mean of fold change for each array feature ± SE (n = 3). Pearson correlation

coefficients (r) are indicated for each comparison, and overlay lines indicate results of least-

squares linear regression analysis. A color version of this figure is available in the online version

of this article. (B) Principal components analysis (PCA) on experimental condition. PC1 and

PC2 are shown and account for 57.9% and 9.6% of experiment variance, respectively. Symbols

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represent biological replicates (n = 3), and dashed circles represent overlap, or lack thereof,

among treatment groups. A color version of this figure is available in the online version of the

article.

Figure 4. Bi-directional hierarchical clustering of gene expression data and Cluster Image

Maps (CIM) showing impact of treatment diet on enrichment of biological process GO

terms. (A) Unsupervised bi-directional hierarchical cluster analysis. The heat map shows

expression data (geometric mean of Log2 values, n = 3) for genes differentially regulated two-

fold up or down (p < 0.05 by Welch’s t-test) in at least one treatment group clustered by array

feature (top tree) and treatment (left tree). (B) Gene ontology enrichment analysis was performed

using GoMiner, and unsupervised cluster analyses of GO categories were performed using

CIMminer as described above. Scale bars represent the range of FDR-corrected p-values: orange

for biological process categories induced by experimental diets, blue for those repressed and

white for unchanged. The indicated numbers for GO term categories correspond to rows in

Supplemental Tables 7-8. A significant effect of dietary treatment on enrichment of the GO term

category (biological process) was inferred p < 0.05 as determined by a one-sided Fisher’s exact

test after false discovery rate (FDR) correction.

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SUPPLEMENTAL MATERIALS

Promotion of Hepatocarcinogenesis by Perfluoroalkyl Acids in Rainbow Trout

Abby D. Benninghoff, Gayle A. Orner, Clarissa H. Buchner, Jerry D. Hendricks,

Aaron M. Duffy and David E. Williams

TABLE OF CONTENTS Page

Supplemental Tables

1. Primer set sequences for real-time RT-PCR validation of gene expression ..................... 2

2. Logistic regression analyses for experimental factors diet, sex, body

weight, replicate tank and symptoms of liver disease in AFB1-initiated trout ................. 3

3. Logistic regression analyses for experimental factors diet, sex, body weight,

replicate tank and symptoms of liver disease in MNNG-initiated trout .......................... 4

4. Summary of array data following application of selection filters for

significance, level of response and feature consistency .................................................. 5

5. Select genes differentially regulated by experimental diets in trout liver ........................ 6

6. Pearson correlation coefficients (r) for pair-wise comparisons by treatment group ....... 11

7. Over-represented Gene Ontology biological process annotations associated

with genes induced by the indicated dietary treatments ............................................... 12

8. Over-represented Gene Ontology biological process annotations associated

with genes repressed by the indicated dietary treatments .............................................. 13

Supplemental Figures

1. Chemical structures of compounds tested for promotion of hepatocarcinogenesis

in rainbow trout .......................................................................................................... 20

2. Outline of AFB1 and MNNG tumor studies in trout with dietary

polyfluorinated chemicals ........................................................................................... 21

3. Lack of effect of sex, experimental tank or idiopathic liver disease on

AFB1-induced liver carcinogenesis .............................................................................. 22

4. Impact of promotion diets on morphological parameters in trout ................................. 24

5. Lack of effect of sex, experimental tank or idiopathic liver disease on

MNNG-induced carcinogenesis .................................................................................. 25

6. Quality control analysis of array hybridization ............................................................ 26

7. Unsupervised bidirectional hierarchical cluster analysis of hepatic gene

expression profiles ....................................................................................................... 27

8. Directed acyclic graph for enriched GO terms associated with dietary E2 .................... 28

9. Directed acyclic graph (DAG) for selected enriched GO terms associated

with dietary PFNA ....................................................................................................... 29

10. Validation of treatment-induced changes in hepatic gene expression determined by

microarray analysis using qRT-PCR ........................................................................... 30

Supplemental References ....................................................................................................... 31



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Benninghoff, et al. Promotion of hepatocarcinogenesis by PFAAs

Page 2

SUPPLEMENTAL TABLES

Supplemental Table 1. Primer set sequences for real-time RT-PCR validation of gene expression

Gene name (symbol) Array feature Forward primer (5’ to 3’) Reverse primer (5’ to 3’) Size (bp) OAT (°C)f

Genes of interest

Alpha-3-macroglobulin (a2m) OmyOSU8 ACAAGGCTCGGGGAATACTT CTCCAGCATTGAAGCAGTGA 235 60

Cathepsin D (ctsd) a OmyOSU139 TCCACTATCCATCATCTACC AGATCAGTGCATTTCAACTC 272 56-58

Cytochrome P450 1A1 (cyp1a1) b,c OmyOSU396 TCAACTTACCTCTGCTGGAAGC GGTGAACGGCAGGAAGGA 85 60

Cytochrome P450 2K5 (cyp2k5) a OmyOSU1389 GTGTCAACTCTAATCTAGTGCCC CCGTCCCTGATTGAAGTGAC 368 58-60

Hemopexin (hpx) OmyOSU699 GCAGCAGAAGCAAAACATCA CAGCACATTCAGAGGGACAA 161 55

Prostaglandin D synthase (pgds) a OmyOSU1395 CATAATGGGAGTTCTGCTGTG TGGGATGTCAGTCTTCTTGG 293 57

Trout c-polysaccharide binding protein

(tcpbp) e OmyOSU1478 GGCCAAAGGAGACATCGTTT TCCCAACCTACACCCTGACC 155 62-64

Thioredoxin (thx) OmyOSU1422 TCCCAACAGCATTGCTCTAA CCATGCCTCTAAATCCTCCA 122 55

Vitellogenin (vtg1) e OmyOSU203 TTGCCTTTGCCAACATCGAC CGGACATTGACGTATGCTTT 238 54

Genes for normalization

β-actin (actb) a OmyOSU205 GTGCGGGATTATATCATTTACCCT CCACGTAGCTGTCTTTCTGG 221 58-60

Glyceraldehyde-3-phosphate

dehydrogenase (gapdh) a

OmyOSU229 CCAACCAAACGCTACCGAAC CCAGATTCCATCTCACCTT 173 60

DNA topoisomerase 2 (top2a) OmyOSU1644 CTGCAGCAGTCCCTCACTT CAAAGAAATCCCTCAGCACA 100 55

ATP synthase subunit beta (atp5b) OmyOSU1585 GCCCATGGTGGTTACTCTGT AGGTGTCGTCCTTCAGGTTG 112 55

a Benninghoff and Williams (2008).

b Primer pair does not surround the sequence for the corresponding 70mer oligonucleotide on the OSUrbt array. c Rees and Li (2004). d Mortensen et al. (2006). e Tilton et al. (2006). f Measured optimal annealing temperature (OAT).



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Supplemental Table 2. Logistic regression analyses for experimental factors diet, sex, body weight,

replicate tank and symptoms of liver disease in AFB1-initiated trout

All subjects a Final subjects

a

Treatment Factor p-Value OR (95% CI) p-Value OR (95% CI)

AFB1/E2 Diet <0.0001 34.0 (10.3 - 112) <0.0001 26.4 (7.61 - 91.6)

Sex 0.4478 0.82 (0.49 - 1.38) 0.4407 0.08 (0.44 - 1.44)

Body weight 0.0027 1.02 (1.01 - 1.03) 0.0046 1.02 (1.01 - 1.03)

Replicate tank 0.7710 1.08 (0.63 - 1.87) 0.2238 1.47 (0.79 - 2.72)

Liver disease symptoms 0.0923 0.52 (0.24 - 1.11)

AFB1/PFOA Diet <0.0001 25.1 (7.94 - 79.5) 0.0014 27. 3 (3.57 - 209) Sex 0.2148 0.73 (0.44 - 1.20) 0.5655 0.78 (0.33 - 1.84)

Body weight 0.0010 1.02 (1.01 - 1.03) 0.0384 1.01 (1.00 - 1.02)

Replicate tank 0.5498 0.86 (0.53 - 1.41) 0.3298 0.66 (0.29 - 1.52)


AFB1/PFNA Diet <0.0001 26.0 (7.09 - 95.1) <0.0001 24.2 (5.96 - 98.2)

Sex 0.6675 0.88 (0.50 - 1.57) 0.4457 0.78 (0.14 - 1.49)

Body weight 0.0003 1.02 (1.01 - 1.03) 0.0002 1.02 (1.01 - 1.03)

Replicate tank 0.8392 1.06 (0.60 - 1.87) 0.4507 1.29 (0.67 - 2.46)


AFB1/PFDA Diet <0.0001 41.7 (9.52 - 182) <0.0001 34.0 (6.89 - 168)

Sex 0.7178 0.88 (0.44 - 1.75) 0.9424 0.97 (0.45 - 2.10) Body weight 0.0102 1.01 (1.00 - 1.02) 0.0186 1.01 (1.00 - 1.03)

Replicate tank 0.7750 1.11 (0.56 - 2.20) 0.3806 1.45 (0.63 - 3.34)


AFB1/8:2FtOH Diet 0.1652 2.34 (0.70 - 7.78) 0.5649 1.51 (0.37 - 6.11)

Sex 0.9379 1.02 (0.62 - 1.69) 0.5693 1.18 (0.66 - 2.12)

Body weight <0.0001 1.02 (1.01 - 1.03) <0.0001 1.02 (1.01 - 1.03)

Replicate tank 0.8213 1.06 (0.62 - 1.82) 0.4806 1.25 (0.67 - 2.31)


AFB1/CLOF Diet 0.8866 0.92 (0.27 - 3.11) 0.3397 0.51 (0.13 - 2.05)

Sex 0.0428 0.55 (0.31 - 0.98) 0.2793 0.71 (0.38 - 1.32)

Body weight 0.0003 1.01 (1.01 - 1.02) 0.0016 1.01 (1.01 - 1.02)

Replicate tank 0.3261 1.32 (0.76 - 2.31) 0.1199 1.65 (0.88 - 3.12) Liver disease symptoms 0.0195 2.27 (1.14 - 4.53)

AFB1/PFOS

(15 wks)

Diet 0.0025 19.2 (2.83 - 131) 0.0014 27.3 (3.57 - 209)

Sex 0.9021 0.95 (0.42 - 2.12) 0.5655 0.78 (0.33 - 1.84)

Body weight 0.0559 1.01 (1.00 - 1.02) 0.0384 1.01 (1.00 - 1.02)

Replicate tank 0.3349 0.68 (0.31 - 1.50) 0.3298 0.66 (0.29 - 1.52)

Liver disease symptoms 0.9054 1.10 (0.23 - 5.17) a Logistic regression analysis was performed including all experimental subjects to determine the impact of five

experimental factors on liver tumor outcome in AFB1-initiated trout; all comparisons were made compared to the

AFB1/CON treatment group, except for the PFOS treatment which was compared to AFB1(15wk)/CON. Firth’s bias

correction was used as the likelihood penalty when a maximum likelihood estimate was not obtained. Chi-square P-

values and odds ratios (OR) with 95% confidence intervals (CI) are shown. A significant effect of the indicated

experimental factor was inferred when p<0.05. b Logistic regression analyses were performed as before, but subjects with symptoms of idiopathic liver disease were

excluded; thus, only four experimental factors were evaluated.



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Supplemental Table 3. Logistic regression analyses for experimental factors diet, sex, body weight, replicate tank and symptoms of liver disease in

MNNG-initiated trout

Liver tumors c

Kidney tumors c

Stomach tumors c

Swim bladder tumors c

Treatment

Factor p-Value OR (95% CI) p-Value OR (95% CI) p-Value OR (95% CI) p-Value OR (95% CI)

Including all subjects a

MNNG/E2 Diet <0.0001 17.7 (4.98 – 62.6) 0.1946 2.00 (0.70 - 5.70) 0.0512 190 (0.97 - ∞) 0.0134 3.76 (1.32 - 10.7)

Sex 0.0861 1.58 (0.94 – 2.68) 0.7165 1.09 (0.70 - 1.70) 0.5341 1.74 (0.30 - 9.93) 0.0079 1.84 (1.17 - 2.88) Body weight 0.1702 1.01 (1.00 - 1.01) 0.3607 1.00 (1.00 - 1.01) 0.4075 0.99 (0.97 - 1.01) 0.0044 1.01 (1.00 - 1.02) Tank 0.6588 0.88 (0.50 – 1.56) 0.3450 0.80 (0.49 - 1.28) 0.0523 0.07 (0.01 - 1.03) 0.0139 0.55 (0.34 - 0.89) Liver disease symptoms 0.1007 0.58 (0.30 – 1.11) 0.0201 0.51 (0.29 - 0.90) 0.2097 0.25 (0.03 - 2.21) 0.3785 1.28 (0.74 - 2.22)

MNNG/PFOA Diet <0.0001 17.1 (5.04 - 57.9) 0.8457 0.90 (0.30 - 2.68) 0.3915 0.13 (0.00 - 14.4) <0.0001 12.3 (3.92 -38.4) Sex 0.5145 1.18 (0.71 - 1.97) 0.8797 1.04 (0.65 - 1.66) 0.2572 3.65 (0.39 - 34.4) 0.5430 1.42 (0.88 - 2.31)

Body weight 0.1001 1.01 (1.00 - 1.01) 0.1867 1.01 (1.00 - 1.01) 0.5701 0.99 (0.97 - 1.02) 0.0005 1.01 (1.01 - 1.02) Tank 0.2723 0.75 (0.44 - 1.26) 0.8432 0.95 (0.59 - 1.55) 0.8590 1.18 (0.18 - 7.60) <0.0001 0.29 (0.17 - 0.47)

Liver disease symptoms 0.3296 0.70 (0.34 - 1.44) 0.7962 0.92 (0.48 - 1.75) 0.1657 0.18 (0.02 - 2.04) 0.1813 1.58 (0.81 - 3.06)

Final subjects b

MNNG/E2 Diet <0.0001 18.9 (4.37 – 82.2) 0.1127 2.58 (0.80 – 8.36) 0.3497 14.0 (0.06 - ∞) 0.0083 5.05 (1.52 – 16.8) Sex 0.2360 1.47 (0.78 – 2.77) 0.7898 1.07 (0.64 - 1.79) 0.7729 1.33 (0.20 – 8.94) 0.0857 1.58 (0.94 – 2.67) Body weight 0.1525 1.01 (1.00 – 1.02) 0.7820 1.00 (0.99 – 1.01) 0.5054 0.99 (0.97 - 1.02) 0.0677 1.01 (1.00 - 1.02) Tank 0.9725 0.99 (0.52 – 1.88) 0.3355 0.78 (0.46 – 1.30) 0.3316 0.28 (0.02 – 3.64) 0.0230 0.55 (0.33 – 0.92)

MNNG/PFOA Diet <0.0001 17.0 (4.47 – 64.4) 0.4424 0.63 (0.19 – 2.07) 0.3163 0.05 (0.00 – 18.5) <0.0001 12.47 (3.59 – 43.4) Sex 0.8812 0.96 (0.54 – 1.69) 0.8380 0.95 (0.56 – 1.61) 0.5522 2.11 (0.18 – 24.7) 0.5034 1.20 (0.70 – 2.07)

Body weight 0.0213 1.01 (1.00 – 1.02) 0.7580 1.00 (0.99 - 1.01) 0.2675 0.98 (0.95 - 1.01) 0.0074 1.01 (1.00 – 1.02) Tank 0.5656 0.85 (0.48 – 1.50) 0.7980 1.07 (0.63 – 1.83) 0.4622 2.56 (0.21 – 31.4) <0.0001 0.28 (0.17 – 0.50) a Logistic regression analysis was performed including all experimental subjects to determine the impact of five experimental factors on liver tumor outcome in

MNNG-initiated trout; all comparisons were made compared to the MNNG/CON treatment group. Firth’s bias correction was used as the likelihood penalty

when a maximum likelihood estimate was not obtained. b Logistic regression analyses were performed as before, but subjects with symptoms of idiopathic liver disease were excluded; thus, only four experimental

factors were evaluated. c Chi-square P-values and odds ratios (OR) with 95% confidence intervals (CI) are shown. A significant effect of the indicated experimental factor was inferred

when p<0.05.



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Supplemental Table 4. Summary of array data following application of selection filters

for significance, level of response and feature consistency

Number of features passing indicated criterion

Treatment

Welch’s

t-test a

Mean 2-fold

change b

Spot

consistency c

All

criteria d

Portion of array

differentially regulated (%)

CON N/A 3 0 0 0 E2 266 103 107 60 3.8

PFOA 205 109 118 88 5.3

PFNA 342 266 241 175 10.8 PFDA 230 139 132 91 5.6

PFOS 204 115 73 44 2.8

8:2FtOH 99 35 33 26 1.7

CLOF 101 21 21 5 0.30 a Number of array features that were identified as significantly regulated by the Welch’s t-test (p <

0.05) when comparing each experimental treatment to CON (n = 3). b Number of array features for which a minimum 2-fold change in the geometric mean of expression

values was observed. c Number of array features for which 9 out of 10 spots (including all technical and biological

replicates) were differentially regulated >1.5-fold. d Number of array features that passed all filtering criteria.



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Supplemental Table 5. Select genes differentially regulated by experimental diets in trout liver. Mean log2 fold-change in gene expression by treatment

b

Array ID DFCI ID a

Gene name (accession number; species)

Symbol CON E2 PFOA PFNA PFDA PFOS FTOH CLOF

Liver-specific proteins (vitellogenesis)

OmyOSU208 TC132491 Vitellogenin (Q92093; Oncorhynchus mykiss) c vtg1 -0.27 6.07* 6.61* 6.22* 6.07* 5.62* 5.83* -0.12

OmyOSU1552 BX306977 Vitelline Envelope Protein gamma (Q9I9M6; O. mykiss) veg -0.26 5.79* 5.78* 5.22* 5.67* 4.87* 4.40* 0.11

OmyOSU1540 TC133595 Vitelline Envelope Protein alpha (Q9I9M8; O. mykiss) vepa -0.21 5.76* 5.84* 5.27* 5.74* 5.39* 5.11* 0.49

OmyOSU1542 TC169120 Zona radiata structural protein (Q90XC3; O. mykiss) zrp -0.02 5.73* 5.38* 5.41* 5.75* 5.38* 4.92* 0.70

Cell proliferation (cell signaling, regulation of transcription, cell growth and apoptosis)

OmyOSU244 NP543968 Estrogen receptor beta (P57782; O. mykiss) esr2 -0.27 5.53* 6.41* 5.49 5.66* 3.75* 4.79* 0.01

OmyOSU1015 RTL00033 Inhibitor of NFκB subunit alpha (Q4G3H4; Danio rerio) c ikk1 -0.01 4.72* 5.15* 4.67 4.22* 3.67* 2.79* 0.30

OmyOSU127 TC146408 Nuclear factor NF-kappa-B p105 subunit (A3FJ60; Siniperca chuatsi)

nfkb1 -0.50 3.78* 3.75* 4.20* 2.78* 2.77* 2.27* 0.07

OmyOSU212 TC138144 TATA box binding protein (C0HA61; Salmo salar) tbp -0.39 2.97* 2.81* 3.48* 3.10* 1.16 1.45 -0.07

OmyOSU151 TC162795 Estrogen receptor alpha (P16058; O. mykiss) c esr1 0.20 2.46* 3.62* 2.94* 3.04* 2.18* 1.85* 0.85

OmyOSU1667 TC169305 Poly A binding protein, cytoplasmic 1 b (Q6P3L1; D. rerio) pabpc1b 0.04 1.59* 1.73* 2.01* 1.43 0.93 0.38 0.08

OmyOSU1191 TC146689 NF-kappa-B inhibitor epsilon (B5X3Y7; S. salar) ikbe 0.09 1.42* 0.83 1.31* 1.06 0.16 0.41 0.11

OmyOSU1484 TC150787 Tryptophanyl-tRNA synthetase (Q28BU4; Xenopus tropicalis)

wars -0.07 0.81 0.38 2.04* 0.85 0.22 0.24 0.41

OmyOSU1615 TC141666 Transmembrane 4 superfamily member protein (Q9DFD3; O. mykiss)

tm4sf 0.09 0.73 0.50 1.58* 0.98 0.12 -0.05 0.22

OmyOSU803 BX079929 Growth arrest and DNA-damage-inducible, beta (C1BER7; O. mykiss)

ga45b -0.12 0.43 0.39 1.92* 1.04 -0.33 -0.11 -0.18

OmyOSU1428 TC143767 Reticulon RTN9-A1 (Q6IEJ0; O. mykiss) rtn9-a1 -0.01 0.03 1.71 1.21 1.00 0.47 0.53 0.42

OmyOSU1427 TC140351 Reticulon RTN9-A2 (Q6IEI9; O. mykiss) rtn9-a2 -0.06 0.42 1.12 0.68 0.79 0.06 0.18 0.18 OmyOSU1669 NP814796 Tumor necrosis factor receptor associated factor 2 (Q7T2X2;

O. mykiss) traf2 0.08 -0.37 -1.57* -1.61* -1.67* -0.65 -0.06 -0.05

OmyOSU387 TC156633 Allograft inflammatory factor 1 (B5XGK1; S. salar) aif1 0.23 -0.70 -1.07 -2.72* -1.59* -0.12 -0.39 -0.28

OmyOSU725 CA379375 Putative hepatocyte growth factor activator (Q9DFD4; O. mykiss)

hgfac -0.01 -1.29 -0.80 -1.58* -1.19* 0.17 -0.39 -0.11

OmyOSU313 TC132515 Bone morphogenetic protein-7 (Q5BN41; O. mykiss) bmp7 0.07 -1.35* -1.34* -1.84* -0.98 -0.54 0.16 0.27

OmyOSU916 NP544392 Mixed lineage leukemia-like protein (Q9PT21; O. mykiss) mll 0.01 -1.61* -1.46* -2.52* -1.84* -0.34 -0.45 -0.16

Protein stability and transport

OmyOSU893 TC145126 Heat shock protein 90 (P87397; Oncorhynchus tshawytscha) hsp90 0.19 2.58* 3.61* 3.37* 2.37 1.20 1.17* 0.64

OmyOSU139 TC150271 Cathepsin D (P87370; O. mykiss) ctsd -0.17 1.99* 1.44* 1.88* 1.29* 0.98 0.28 0.05

OmyOSU1308 TC164544 Protein transport protein Sec61 α subunit isoform B (Q98SN8; O. mykiss) c

sec61ab 0.16 1.89* 1.14* 1.59* 1.20* 0.50 0.42 0.82

OmyOSU859 TC145413 Heat shock protein 47 (Q5DW60; O. mykiss) hsp47 0.22 1.11* 0.28 1.91* 0.38 0.39 0.34 0.31

OmyOSU1602 TC160913 Coatomer subunit epsilon (C1BG73; O. mykiss) cope 0.06 0.97 0.39 1.17* 0.71 0.02 0.34 0.26



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b

Array ID DFCI ID a



OmyOSU85 TC132785 Proteasome subunit beta type (B5XAY5; S. salar) psb7 0.32 0.52 0.74 1.43* 1.03* 0.61 0.49 0.40

OmyOSU861 TC161889 60 kDa Heat shock protein, mitochondrial (C0HBF1; S. salar) ch60 0.16 0.32 0.20 1.78* 0.52 1.14* 0.62 0.17

OmyOSU216 BX315865 40S Ribosomal protein S11 (Q9DF27; S. salar) rs11 -0.28 0.21 0.71 1.09* 0.35 -0.10 -0.15 -0.01

OmyOSU910 TC132590 Proteasome subunit beta type 9 precursor (Q9PT26; O. mykiss)

psmb9 -0.24 -0.11 0.51 1.37* 0.16 -0.20 -0.09 0.37

OmyOSU1042 BX879214 Ubiquitin B (B9EQM0; S. salar) ubb -0.17 -0.13 0.13 0.13 0.34 1.09* 0.16 1.02*

Phase I and II metabolism

OmyOSU1380 TC150232 Cytochrome P450 2k1 (Q92090; O. mykiss) c cyp2k1 0.15 1.41 3.68* 3.27* 3.04 3.11 0.25 0.81

OmyOSU754 TC146311 Microsomal glutathione S-transferase (C1BFV1; O. mykiss) mgst 0.16 -0.10 0.23 1.39 0.58 1.01* 1.89* 1.27

OmyOSU396 TC158463 Cytochrome P450 1a1 (Q92110; O. mykiss) c cyp1a1 0.43 -0.26 -1.58* -2.44* -0.59 -0.07 -0.42 -0.35

OmyOSU993 TC147595 Glutathione S-transferase Mu 3 (C1BZU6; Esox lucius) gstm3 0.22 -0.27 -0.48 -2.47* -0.89 -0.66 0.10 -0.28

OmyOSU1134 TC143007 Cytosolic sulfotransferase 2 (B5X695; S. salar) st2s2 0.04 -0.31 0.48 -0.15 0.39 1.19* 0.23 0.48

OmyOSU1203 BX306987 Microsomal glutathione S-transferase 1 (C1BM45; Osmerus mordax)

gst1 0.25 -0.34 0.01 -1.62* -0.46 0.14 0.59 0.40

OmyOSU1507 BX085479 Glutathione S-transferase (Q9W647; Oncorhynchus nerka) gstp1 0.25 -0.35 -0.32 0.05 -0.45 0.07 1.19* 0.65

OmyOSU829 BX085279 Carbonyl reductase (Q9PT38; O. mykiss) cbr1 -0.06 -0.35 0.53 0.31 0.24 1.26* 1.20* 0.94

OmyOSU115 BX310129 Glutathione S-transferase P (B5XC10; S. salar) gstp1 0.21 -0.36 -0.12 -0.63 -0.29 -0.25 3.03* 0.20

OmyOSU398 TC139996 Cytochrome P450 3a27 (O42563; O. mykiss) c cyp3a27 0.06 -0.96 0.58 0.58 -0.27 2.07* -0.05 0.69

OmyOSU356 TC150068 Cytochrome P450 2j24 (Q5TZ75; D. rerio) cyp2j24 -0.08 -1.24* -0.55 -2.32* -1.07* 0.59 -0.04 0.04

OmyOSU460 TC134882 3-Oxo-5-beta-steroid 4-dehydrogenase (B9EMZ1; S. salar) ark1d1 -0.15 -1.41 -1.94* -3.74* -1.75* -0.23 -0.18 -0.47

OmyOSU1389 TC133687 Cytochrome P450 2k5 (Q9IAT1; O. mykiss) c cyp2k5 -0.10 -1.58* 1.39* 0.15 0.76 2.82* -0.08 1.67*

OmyOSU392 CB491885 Cytochrome P450 2m1 (Q92088; O. mykiss) c cyp2m1 0.19 -1.77* -2.27* -2.61* -1.42* -0.08 0.16 0.10

Redox regulation

OmyOSU245 TC152739 Succinate dehydrogenase complex subunit A flavoprotein (B5DFZ8; S. salar)

sdha 0.22 1.98 1.88 1.88 2.62 1.26 1.15 0.36

OmyOSU238 TC147716 Glutathione peroxidase (D2CKK9; D. rerio) gpx4a 0.08 1.21 1.61 2.29 2.22 0.03 0.57 0.14

OmyOSU1490 TC135270 Cytochrome c oxidase subunit 3 (P69218; O. nerka) cox3 -0.18 1.20 1.49 1.75 1.26 0.40 0.16 0.02

OmyOSU32 TC132824 Glutathione peroxidase (B5RI90; S. salar) gpx4b -0.07 0.32 0.40 1.08 0.36 0.07 -0.11 0.16

OmyOSU55 TC143718 Peroxiredoxin-5, mitochondrial (B5X5Q6; S. salar) prdx5 0.42 0.26 0.69 1.56 0.89 0.36 0.60 0.92

OmyOSU1633 CA373161 Cytochrome C-1 (Q3B7R0; D. rerio) cyc1 0.10 0.23 0.48 1.72 0.84 0.45 0.08 0.20

OmyOSU92 BX074038 Cu/Zn-superoxide dismutase (C1BFL3; O. mykiss) sod1 0.29 0.21 0.50 1.01 0.46 0.07 -0.03 0.33

OmyOSU572 TC156955 Cytochrome c oxidase polypeptide Via (O13085; O. mykiss) coxa 0.12 0.05 0.03 -1.86 -0.46 0.16 -0.09 0.20

OmyOSU566 CU068081 Peroxisomal carnitine O-octanoyltransferase (Q503F8; D. rerio)

crot -0.14 -0.02 0.20 -0.29 -0.24 0.31 -0.18 -0.11



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b

Array ID DFCI ID a



OmyOSU325 TC146929 Acetyl-CoA acetyltransferase, mitochondrial (Q6AZA0; D. rerio)

acat1 -0.22 -0.03 -0.10 -0.20 -0.04 0.22 -0.25 -0.13

OmyOSU1422 TC147703 Thioredoxin (C1BH85; O. mykiss) trx -0.68 -0.41 -0.07 2.48 0.27 0.59 1.54 0.34

OmyOSU37 TC141467 Catalase (C0HAV1; S. salar) cat 0.04 -0.75 -0.76 -1.38 -1.00 0.02 -0.07 -0.23

Extracellular matrix and vascularization factors

OmyOSU1557 TC135220 Secreted protein acidic and rich in cysteine protein (Q9YGD9; O. mykiss)

sparc -0.03 -0.79 -0.68 -1.21* -0.18 -0.52 -0.31 -0.28

OmyOSU419 CA356156 Angiogenin (B5XAZ0; S. salar) ang1 -0.23 -1.44* -1.70* -1.95* -1.50* 0.03 -0.28 0.11

Immune response

OmyOSU1564 TC140147 VHSV4 (Q8QGB4; O. mykiss) vhsv4 0.04 5.62* 6.32* 5.57* 6.11* 4.90* 4.26* -0.13

OmyOSU1566 TC132651 VHSV6 (Q8QGB2; O. mykiss) vhsv6 0.15 3.88* 3.90* 3.36* 3.78* 1.92* 1.93 0.02

OmyOSU582 TC167667 P-selectin (B5X3V6 ; S. salar) lyam3 0.18 1.65* 0.99 0.99 0.62 0.55 -0.13 0.54

OmyOSU1590 TC132541 VHSV-induced protein 2 (Q9DD73; O. mykiss) vhsv2 -0.16 0.83 0.58 1.08* 0.30 0.13 -0.17 0.10

OmyOSU148 TC161751 Differentially regulated trout protein 1 (Q9DFD5; O. mykiss)c drtp1 -0.04 0.71 0.16 4.02* 2.55 -0.56 0.33 0.40

OmyOSU634 TC162804 CD209-like protein (Q64HY2; O. mykiss) cd209 0.09 0.67 -0.12 1.12* 0.67 0.46 0.34 0.07

OmyOSU15 BX311693 Complement component C3-3 (Q98977; O. mykiss) c3-3 0.20 -0.06 -1.24* -2.39* -0.94 -0.15 -0.07 -0.81

OmyOSU44 BX306395 Complement component C3-4 (Q9DDV9; O. mykiss) c3-4 0.08 -0.10 -1.39* -2.60* -0.92 0.08 -0.18 -0.84

OmyOSU411 BX882784 Complement C9 (Q4QZ25; O. mykiss) c9 0.14 -0.13 -0.39 -1.18* -0.20 -0.08 0.09 -0.10

OmyOSU348 TC149604 CD59-like protein (B5X604; S. salar) cd59 0.41 -0.14 -1.52* -1.61* -1.31* -2.19* -0.11 -0.94

OmyOSU371 TC159535 C1q-like adipose specific protein (Q8JI26; Salvelinus fontinalis)

c1q -0.18 -0.29 -1.54 -2.67* -1.84* -0.04 -0.25 -0.97

OmyOSU1147 TC152823 Pentraxin (P79899; O. mykiss) c ptx -0.08 -0.41 -2.97* -2.60* -1.51* -0.32 0.01 -0.13

OmyOSU638 TC137183 C-type mannose-binding lectin (Q8JJ68; O. mykiss) mbl-1 -0.17 -0.51 -1.21* -1.49* -1.56* -0.58 -0.36 -0.41

OmyOSU636 TC138355 C-type MBL-2 protein (Q4LAN6; O. mykiss) mbl 0.23 -0.54 -1.35* -1.49* -1.68 -0.88 -0.21 -0.16

OmyOSU34 TC141865 C1 inhibitor (Q70W32; O. mykiss) c1 inh 0.03 -0.58 -0.55 -1.33* -0.42 0.34 -0.13 0.11

OmyOSU76 TC132971 Complement receptor-like protein 1 (Q2PDG0; O. mykiss) crlp1 -0.05 -0.67 -0.86 -1.21* -0.75 -0.14 -0.12 0.34

OmyOSU1426 TC142005 Complement factor H protein (Q4QZ18; O. mykiss) cfh 0.11 -0.74 -0.26 -1.09* -0.76 0.23 -0.10 0.26

OmyOSU1469 TC167824 Cathepsin S (C0HDJ6; S. salar) cats -0.09 -0.96 -1.87 -4.59* -1.69* -0.08 -0.48 -0.03

OmyOSU878 CA383049 Chemotaxin (Q9DFJ1; O. mykiss) ctx -0.08 -1.16* -2.04* 4.00* -0.13 -0.71 0.48 0.76

OmyOSU866 TC148069 Hemagglutinin/amebocyte aggregation factor (B5XF94; S. salar)

haaf 0.06 -1.31* -2.00* -1.87* -1.80 -0.89 -0.41 -1.62

OmyOSU1 CA347121 CD80-like protein (A1IMH7; O. mykiss) cd80 -0.18 -1.95 -1.73* -3.75* -2.37* -0.35 -0.45 -0.71

OmyOSU1477 TC139517 Trout C-polysaccharide binding protein 1 (Q9DFE5; O. mykiss) c

tcpbp 0.55 -2.11* -2.60* -3.66* -2.48* 0.36 -0.69 0.21



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b

Array ID DFCI ID a



Blood factors and coagulation

OmyOSU723 TC138894 Plasminogen (Q5DVP8; O. mykiss) plg 0.04 -0.34 -0.16 -1.05* -0.50 0.44 -0.23 -0.06

OmyOSU90 TC157215 Beta-2-glycoprotein 1 (C0H7U1; S. salar) apoh 0.03 -0.57 -0.70 -1.11* -0.66 -0.09 -0.18 -0.30

OmyOSU649 TC145770 Coagulation factor VIII (Q804W6; Takifugu rubripes) c f8 0.11 -0.62 -0.44 -1.71* -0.93 -0.02 -0.25 0.17

OmyOSU665 TC149137 Alpha-globin I (Q98973; O. mykiss) hbaa1 0.07 -0.65 -0.86 -1.72* -0.59 -0.94 -0.20 -0.27

OmyOSU775 TC139629 Protein C (Q7T3B6; D. rerio) proc -0.25 -0.68 -0.23 -1.07* -0.51 0.41 -0.31 -0.19

OmyOSU1338 TC143725 Antithrombin (Q9PTA8; S. salar) c at 0.12 -0.70 -0.52 -1.25* -0.78 0.37 -0.29 -0.09

OmyOSU667 CU064560 Hemoglobin subunit alpha-1 (Q98974; O. mykiss) hba1 0.26 -0.72 -1.32* -2.74* -1.72* -1.08 0.09 -0.23

OmyOSU219 TC135277 Complement factor Bf-1 (Q9YGE7; O. mykiss) cfb1 -0.15 -0.72 -1.21* -1.85* -1.24* 0.20 -0.22 -0.03

OmyOSU355 TC172297 Serum albumin 1 (P21848; S. salar) c alb1 0.06 -0.76 -0.71 -3.48* -1.86* -0.07 -0.24 0.00

OmyOSU677 TC151435 Fibrinogen (Q9DFD8; O. mykiss) fgg 0.16 -1.07* -1.08* -0.49 -1.10* -0.14 -0.26 -0.16

OmyOSU8 TC164674 Alpha 2 macroglobulin (C1K6P9; Perca flavescens) c a2m 0.13 -1.19 -1.10* -2.67* -1.30* 0.05 -0.22 -0.42

OmyOSU1502 TC132862 Tissue factor (Q90W13; O. mykiss) f3a 0.68 -1.49 -1.14 -2.26* -1.75* 0.17 -0.59 0.03

Lipid and cholesterol, metabolism and transport

OmyOSU449 TC151125 Apolipoprotein B (C3UZW7; P. flavescens) apob 0.03 1.17* -0.21 0.27 0.60 0.55 0.33 -0.26

OmyOSU721 TC145590 Fatty acid binding protein, heart (O13008; O. mykiss) fabp3 0.09 1.07* 1.50* 0.40 1.21* 0.45 0.23 0.88

OmyOSU153 TC162807 Fatty acid binding protein (Q9DFE6; O. mykiss) fabp 0.27 0.88 2.53* 4.83* 3.57* -1.02 -0.48 0.35

OmyOSU904 TC132457 Lipoprotein lipase (Q9W6Y2; O. mykiss) lpl 0.00 -0.77 -0.57 -1.24* -0.87 0.00 -0.26 -0.27

OmyOSU908 TC148996 Epidermis-type lipoxygenase 3 (B5X0R4 ; S. salar) loxe3 0.04 -0.82 -1.08* -2.11* -1.36* -0.12 -0.29 -0.40

OmyOSU453 BX073289 Apolipoprotein A-I-1 (O57523; O. mykiss) apo-AI-1 -0.09 -1.05* -1.37* -0.83 -1.01 -0.02 -0.42 -0.05

OmyOSU1652 TC161762 Apolipoprotein E (Q9PT02; O. mykiss) c apoE -0.17 -1.36 -1.61* 0.00 -1.43* 0.11 -0.46 -0.40

Ion binding and transport

OmyOSU1020 TC165296 Metallothionein B (P68501; O. mykiss) c mt-b -0.32 1.18* 1.23* 3.29* 1.34* 0.88 1.33* 0.82

OmyOSU846 TC147024 Transferrin (Q9PT13; O. mykiss) c tf 0.05 -0.23 -0.98 -2.72* -0.61 0.23 0.09 0.25

OmyOSU657 TC169785 Haptoglobin 1 (Q9DFG1; O. mykiss) c hp1 -0.15 -0.41 -0.82 -1.60* -0.04 -0.04 0.06 -0.20

OmyOSU685 BX073517 Ferritin (P79823; O. mykiss) c ft -0.29 -1.20* -1.78* -1.11* -1.56* -0.96 -0.22 -0.31

OmyOSU699 TC169206 Hemopexin (P79825; O. mykiss) hpx -0.24 -1.99* -2.08* -3.28* -2.50* -0.57 -0.78 -0.90

Glycolysis and carbohydrate metabolism

OmyOSU1241 TC152596 6-Phosphofructokinase type C (C0HAA0; S. salar) k6pp -0.09 2.43* 0.91 1.72* 1.69 -0.25 -0.05 -0.02

OmyOSU875 CU072064 Glucokinase (O93314; O. mykiss) gk -0.27 2.32* 1.10 0.98 -0.81 2.30* -0.30 -0.10

OmyOSU1149 TC132998 Phosphoenolpyruvate carboxykinase (Q98T97; O. mykiss) pck 0.04 -0.12 -0.68 -1.30* -0.63 0.33 0.18 0.05

OmyOSU116 TC150193 Phosphorylase (C0PUK4; S. salar) pygm 0.31 -0.75 -0.75 -1.69* -0.76 0.35 -0.04 -0.05



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b

Array ID DFCI ID a



Nucleoside Metabolism

OmyOSU252 TC152774 Hypoxanthine phosphoribosyltransferase 1 (Q7ZV49; D. rerio)

hprt1 0.28 2.50 2.04 2.22 2.45 1.16 0.43 0.20

OmyOSU1518 TC171662 Uridine phophorylase (B5X227; S. salar) upp1 -0.04 2.53 2.32 2.23 2.08 1.01 1.08 0.03

Miscellaneous

OmyOSU343 TC141079 Biotinidase (Q8AV84; T. rubripes) btd -0.13 -2.41 -2.57 -3.58 -2.44 -0.72 -0.24 -0.04

a Manual array feature annotation was performed by querying the DFCI R.trout Gene Index (http://compbio.dfci.harvard.edu/tgi/) for the closest EST match to the array 70-mer

sequence. Matching EST sequences were then BLASTX queried in the NCBI genome database. The top hit (lowest E-score) was selected as the matching gene. If an EST had no

significant (E-value <10-6) BLASTX hit, then the most significant BLASTN hit is shown. b Log2 geometric mean fold change values are shown (N = 3) and represent background corrected, ratio-centered and Lowess-normalized signal ratios. Values in bold and marked

with an asterisk are considered statistically significant (P ≤ 0.05 by Welch’s t-test) and passed all stringency criteria. Color scales are provided to indicate visually the similarities

or differences in gene expression among treatment groups (red, induced; green, repressed; white, no change). c Unique array features targeted the same gene in some cases, though a single representative array feature is shown in this table.



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Supplemental Table 6. Pearson correlation coefficients (r) for pair-wise

comparisons by treatment group.

PFOA PFNA PFDA PFOS FtOH CLOF

Differentially regulated genes a

E2 0.93 0.84 0.93 0.78 0.83 0.30

PFOA 0.86 0.95 0.82 0.82 0.39

PFNA 0.91 0.66 0.73 0.45

PFDA 0.78 0.83 0.41

PFOS 0.83 0.42

8:2FtOH 0.30

All array features

E2 0.85 0.75 0.83 0.61 0.50 0.25

PFOA 0.73 0.83 0.62 0.68 0.27

PFNA 0.83 0.53 0.61 0.40

PFDA 0.63 0.74 0.38

PFOS 0.70 0.46

8:2FtOH 0.35

a Correlation analysis were performed using a data subset including OSUrbt array features that

were determined to be significantly differentially regulated in any one of the experimental

treatments.



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Suppl. Table 7. Over-represented Gene Ontology biological process annotations associated with genes induced by the indicated dietary treatmentsa

E2 PFOA PFNA PFDA PFOS 8:2FtOH CLOF

# b

Accession Biological Process E FDR E FDR E FDR E FDR E FDR E FDR E FDR

1 GO:0032355 response to estradiol stimulus 178.6 0.030 202.4 <0.001 86.7 0.040 151.8 <0.001 189.8 0.040 303.6 <0.001 1518.0 0.210

2 GO:0030520 estrogen receptor signaling pathway 259.8 0.020 294.4 <0.001 126.2 0.030 220.8 <0.001 276.0 <0.001 441.6 <0.001 2208.0 0.220

3 GO:0006914 autophagy 75.2 0.080 85.2 0.027 36.5 0.073 63.9 0.048 39.9 0.165 0.0 0.996 0.0 0.982

4 GO:0046651 lymphocyte proliferation 14.9 0.352 0.0 0.976 21.7 0.040 12.7 0.360 31.6 0.071 0.0 0.905 0.0 0.584

5 GO:0070661 leukocyte proliferation 14.4 0.352 0.0 0.997 21.0 0.031 12.3 0.358 30.7 0.065 0.0 1.000 0.0 0.999

6 GO:0032943 mononuclear cell proliferation 14.4 0.352 0.0 0.997 21.0 0.031 12.3 0.358 30.7 0.065 0.0 1.000 0.0 0.999

7 GO:0032602 chemokine production 0.0 0.965 0.0 0.974 40.8 0.306 0.0 0.958 178.6 0.027 0.0 0.988 0.0 0.985

8 GO:0009719 response to endogenous stimulus 18.2 0.073 13.7 0.308 8.8 0.250 15.4 0.056 19.3 0.025 20.6 0.455 102.9 0.503

9 GO:0010033 response to organic substance 11.5 0.060 6.5 0.386 5.6 0.248 7.4 0.206 12.3 0.015 9.8 0.656 49.1 0.528

10 GO:0001816 cytokine production 7.9 0.379 8.9 0.481 7.7 0.309 6.7 0.399 25.2 0.020 0.0 0.999 0.0 0.994

11 GO:0001818 negative regulation of cytokine production 47.6 0.325 54.0 0.377 23.1 0.338 40.5 0.291 101.2 0.020 0.0 0.995 0.0 0.978

12 GO:0030522 intracellular receptor-mediated signaling pathway

32.8 0.151 37.2 0.193 16.0 0.261 27.9 0.095 34.9 0.071 55.8 0.031 279.2 0.362

13 GO:0048545 response to steroid hormone stimulus 37.6 0.068 42.6 0.069 18.3 0.254 32.0 0.105 39.9 0.039 63.9 0.030 319.6 0.382

14 GO:0030518 steroid hormone receptor signaling

pathway

43.3 0.051 49.1 0.050 21.0 0.188 36.8 0.051 46.0 0.033 73.6 0.032 368.0 0.375

15 GO:0043627 response to estrogen stimulus 64.9 0.068 73.6 0.030 31.5 0.090 55.2 0.051 69.0 0.027 110.4 0.013 552.0 0.390

16 GO:0009410 response to xenobiotic stimulus 55.0 0.063 62.3 0.028 26.7 0.110 46.7 0.063 58.4 0.026 93.4 0.035 467.1 0.363

a Gene ontology enrichment analysis was performed using High Throughput GoMiner. A significant enrichment of the GO term category was inferred when p<0.05 as determined by a one-sided Fisher’s exact test after false discovery rate (FDR) correction. Significantly enriched GO terms are indicated in bold with yellow or orange highlight. b Number (#) corresponds to position in Cluster Image Map presented in Figure 4. Abbreviations: E, enrichment value; FDR, false discovery rate-corrected p-value.



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Suppl. Table 8. Over-represented Gene Ontology biological process annotations associated with genes repressed by the indicated dietary treatmentsa


#b


1 GO:0002440 production of molecular mediator of immune response 32 0.236 55 0.007 25 0.020 55 0.008 0 1.000 0 0.998 0 1.000

2 GO:0009967 positive regulation of signal transduction 12 0.172 16 0.008 10 0.010 16 0.008 0 0.999 9 0.557 0 1.000

3 GO:0010647 positive regulation of cell communication 12 0.173 15 0.009 9 0.012 15 0.010 0 1.000 8 0.548 0 1.000

4 GO:0031347 regulation of defense response 30 0.056 40 0.002 18 0.008 40 0.002 0 0.999 0 0.995 0 1.000

5 GO:0002700 regulation of production of molecular mediator of immune response

51 0.205 89 0.006 41 0.012 89 0.004 0 1.001 0 0.981 0 1.000

6 GO:0032623 interleukin-2 production 58 0.199 101 0.005 46 0.012 101 0.004 0 0.996 0 0.992 0 1.000

7 GO:0010033 response to organic substance 7 0.239 12 0.006 7 0.009 12 0.005 0 0.999 10 0.456 0 1.000

8 GO:0032663 regulation of interleukin-2 production 64 0.186 112 0.004 51 0.009 112 0.003 0 1.000 0 0.994 0 1.000

9 GO:0048583 regulation of response to stimulus 13 0.047 23 0.000 16 0.000 23 0.000 0 0.999 0 0.992 0 1.000

10 GO:0002526 acute inflammatory response 36 0.046 47 0.001 43 0.000 47 0.002 0 0.965 0 0.769 0 1.000

11 GO:0002250 adaptive immune response 32 0.045 43 0.002 26 0.000 43 0.002 0 1.000 0 0.995 0 1.000

12 GO:0002460 adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily

domains

33 0.046 43 0.002 26 0.000 43 0.002 0 1.000 0 1.000 0 1.000

13 GO:0002702 positive regulation of production of molecular mediator of immune response

173 0.130 304 0.001 139 0.002 304 0.003 0 0.985 0 0.971 0 1.000

14 GO:0048584 positive regulation of response to stimulus 16 0.132 29 0.001 20 0.000 29 0.003 0 1.000 0 0.973 0 1.000

15 GO:0002253 activation of immune response 18 0.296 48 0.001 37 0.000 48 0.002 0 1.000 0 0.997 0 1.000

16 GO:0050778 positive regulation of immune response 28 0.070 48 0.000 33 0.000 48 0.002 0 0.976 0 0.834 0 1.000

17 GO:0002252 immune effector process 19 0.112 33 0.000 22 0.000 33 0.002 0 1.000 0 0.980 0 1.000

18 GO:0045087 innate immune response 18 0.112 24 0.006 18 0.000 24 0.004 0 1.000 0 0.998 0 1.000

19 GO:0002699 positive regulation of immune effector process 53 0.205 92 0.006 63 0.000 92 0.004 0 1.000 0 0.995 0 1.000

20 GO:0002824 positive regulation of adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains

75 0.176 132 0.004 91 0.000 132 0.004 0 1.000 0 0.990 0 1.000

21 GO:0002705 positive regulation of leukocyte mediated immunity 67 0.185 117 0.004 80 0.000 117 0.003 0 1.000 0 1.000 0 1.000

22 GO:0002708 positive regulation of lymphocyte mediated immunity 67 0.185 117 0.004 80 0.000 117 0.003 0 1.000 0 1.000 0 1.000

23 GO:0006954 inflammatory response 10 0.198 17 0.003 13 0.000 21 0.003 0 1.000 0 0.998 0 1.000

24 GO:0002821 positive regulation of adaptive immune response 72 0.180 127 0.004 87 0.000 127 0.004 0 0.995 0 0.990 0 1.000

25 GO:0051605 protein maturation by peptide bond cleavage 23 0.273 40 0.010 37 0.000 40 0.011 0 1.000 0 0.999 0 1.000

26 GO:0002703 regulation of leukocyte mediated immunity 36 0.223 63 0.007 43 0.001 63 0.007 0 1.000 0 0.998 0 1.000



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#b


27 GO:0006956 complement activation 39 0.219 69 0.006 63 0.000 69 0.007 0 1.000 0 0.973 0 1.000

28 GO:0002706 regulation of lymphocyte mediated immunity 39 0.219 69 0.006 47 0.000 69 0.007 0 1.000 0 0.973 0 1.000

29 GO:0002541 activation of plasma proteins involved in acute inflammatory response

36 0.223 63 0.007 58 0.000 63 0.007 0 1.000 0 0.998 0 1.000

30 GO:0080134 regulation of response to stress 0 0.969 20 0.007 15 0.000 20 0.007 0 0.999 0 0.936 0 1.000

31 GO:0006826 iron ion transport 72 0.009 63 0.007 29 0.018 63 0.007 0 1.000 0 0.998 0 1.000

32 GO:0045088 regulation of innate immune response 89 0.007 78 0.006 36 0.015 78 0.006 0 1.000 0 1.000 0 1.000

33 GO:0050731 positive regulation of peptidyl-tyrosine phosphorylation 81 0.009 71 0.006 32 0.015 71 0.007 0 0.999 0 0.997 0 1.000

34 GO:0002682 regulation of immune system process 15 0.018 21 0.000 16 0.000 21 0.004 0 0.999 0 0.986 0 1.000

35 GO:0002697 regulation of immune effector process 42 0.018 56 0.001 34 0.000 56 0.002 0 1.000 0 1.000 0 1.000

36 GO:0006959 humoral immune response 40 0.018 53 0.001 40 0.000 53 0.002 0 0.999 0 0.992 0 1.000

37 GO:0000041 transition metal ion transport 55 0.000 48 0.001 22 0.005 48 0.002 258 0.773 0 0.997 0 1.000

38 GO:0030005 cellular di- tri-valent inorganic cation homeostasis 29 0.000 32 0.000 15 0.000 32 0.000 103 0.811 0 0.990 0 1.000

39 GO:0006879 cellular iron ion homeostasis 122 0.000 107 0.000 49 0.000 107 0.000 426 1.127 0 0.922 0 1.000

40 GO:0055072 iron ion homeostasis 0 0.976 100 0.000 46 0.000 100 0.000 398 0.880 0 0.995 0 1.000

41 GO:0002920 regulation of humoral immune response 434 0.000 380 0.001 173 0.001 380 0.003 0 1.000 0 0.980 0 1.000

42 GO:0055080 cation homeostasis 0 0.965 25 0.000 12 0.001 25 0.002 81 0.637 0 0.974 0 1.000

43 GO:0055066 di- tri-valent inorganic cation homeostasis 0 0.997 31 0.000 14 0.000 31 0.003 99 0.732 0 0.993 0 1.000

44 GO:0030003 cellular cation homeostasis 27 0.000 29 0.000 13 0.000 29 0.003 93 0.676 0 0.986 0 1.000

45 GO:0050801 ion homeostasis 17 0.005 18 0.000 8 0.004 18 0.002 58 0.591 0 0.995 0 1.000

46 GO:0006873 cellular ion homeostasis 19 0.007 20 0.000 9 0.003 20 0.003 65 0.666 0 0.986 0 1.000

47 GO:0055082 cellular chemical homeostasis 0 0.701 20 0.000 9 0.003 20 0.003 63 0.624 0 0.968 0 1.000

48 GO:0002684 positive regulation of immune system process 25 0.008 37 0.000 24 0.000 37 0.000 0 0.972 0 0.810 0 1.000

49 GO:0050776 regulation of immune response 27 0.008 40 0.000 26 0.000 40 0.000 0 0.999 0 0.970 0 1.000

50 GO:0002819 regulation of adaptive immune response 77 0.009 101 0.000 62 0.000 101 0.003 0 0.995 0 0.783 0 1.000

51 GO:0002822 regulation of adaptive immune response based on somatic

recombination of immune receptors built from immunoglobulin superfamily domains

79 0.009 104 0.000 63 0.000 104 0.003 0 1.000 0 0.973 0 1.000

52 GO:0001934 positive regulation of protein amino acid phosphorylation 43 0.018 37 0.011 17 0.062 37 0.012 0 0.987 0 0.990 0 1.000

53 GO:0050730 regulation of peptidyl-tyrosine phosphorylation 57 0.016 50 0.008 23 0.037 50 0.009 0 1.000 0 0.995 0 1.000

54 GO:0031399 regulation of protein modification process 25 0.008 22 0.006 10 0.037 22 0.006 0 0.976 0 0.992 0 1.000



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#b


55 GO:0010627 regulation of protein kinase cascade 7 0.368 13 0.066 9 0.049 13 0.070 0 0.973 10 0.525 0 1.000

56 GO:0002888 positive regulation of myeloid leukocyte mediated immunity 0 0.992 0 0.981 347 0.036 0 0.983 0 1.000 0 0.993 0 1.000

57 GO:0001796 regulation of type IIa hypersensitivity 0 0.992 0 0.981 347 0.036 0 0.983 0 1.000 0 0.993 0 1.000

58 GO:0008065 establishment of blood-nerve barrier 0 0.992 0 0.981 347 0.036 0 0.983 0 1.000 0 0.993 0 1.000

59 GO:0007597 blood coagulation intrinsic pathway 0 0.992 0 0.981 347 0.036 0 0.983 0 1.000 0 0.993 0 1.000

60 GO:0002445 type II hypersensitivity 0 0.992 0 0.981 347 0.036 0 0.983 0 1.000 0 0.993 0 1.000

61 GO:0034392 negative regulation of smooth muscle cell apoptosis 0 0.992 0 0.981 347 0.036 0 0.983 0 1.000 0 0.993 0 1.000

62 GO:0002892 regulation of type II hypersensitivity 0 0.992 0 0.981 347 0.036 0 0.983 0 1.000 0 0.993 0 1.000

63 GO:0002894 positive regulation of type II hypersensitivity 0 0.992 0 0.981 347 0.036 0 0.983 0 1.000 0 0.993 0 1.000

64 GO:0001794 type IIa hypersensitivity 0 0.992 0 0.981 347 0.036 0 0.983 0 1.000 0 0.993 0 1.000

65 GO:0001798 positive regulation of type IIa hypersensitivity 0 0.992 0 0.981 347 0.036 0 0.983 0 1.000 0 0.993 0 1.000

66 GO:0016064 immunoglobulin mediated immune response 26 0.257 23 0.112 21 0.038 23 0.118 0 0.999 0 0.997 0 1.000

67 GO:0045765 regulation of angiogenesis 0 0.947 0 0.789 22 0.037 0 0.797 0 1.000 0 0.997 0 1.000

68 GO:0051271 negative regulation of cell motion 0 0.994 25 0.108 23 0.037 25 0.113 0 1.000 0 1.000 0 1.000

69 GO:0051240 positive regulation of multicellular organismal process 10 0.341 17 0.052 12 0.017 17 0.055 0 1.000 0 0.998 0 1.000

70 GO:0010740 positive regulation of protein kinase cascade 11 0.336 19 0.050 13 0.015 19 0.052 0 0.997 0 0.834 0 1.000

71 GO:0046486 glycerolipid metabolic process 0 0.988 9 0.154 12 0.015 9 0.171 0 1.000 0 0.995 0 1.000

72 GO:0050818 regulation of coagulation 0 0.630 38 0.094 35 0.015 38 0.099 0 0.990 0 0.833 0 1.000

73 GO:0043542 endothelial cell migration 0 0.748 37 0.095 34 0.015 37 0.100 0 1.000 0 0.971 0 1.000

74 GO:0006953 acute-phase response 48 0.208 42 0.092 39 0.013 42 0.097 0 0.998 0 0.977 0 1.000

75 GO:0050819 negative regulation of coagulation 0 0.968 47 0.088 43 0.012 47 0.094 0 0.998 0 0.990 0 1.000

76 GO:0030193 regulation of blood coagulation 0 0.994 46 0.090 42 0.012 46 0.095 0 1.000 0 0.995 0 1.000

77 GO:0002455 humoral immune response mediated by circulating immunoglobulin

53 0.205 46 0.090 42 0.012 46 0.095 0 1.000 0 0.995 0 1.000

78 GO:0030336 negative regulation of cell migration 0 0.861 28 0.104 25 0.020 28 0.109 0 1.000 0 0.998 0 1.000

79 GO:0042440 pigment metabolic process 36 0.223 32 0.100 29 0.018 32 0.105 0 1.000 0 0.998 0 1.000

80 GO:0016525 negative regulation of angiogenesis 0 0.920 0 0.986 56 0.009 0 0.987 0 0.996 0 0.998 0 1.000

81 GO:0030195 negative regulation of blood coagulation 0 0.993 58 0.080 53 0.009 58 0.086 0 1.000 0 1.000 0 1.000

82 GO:0010594 regulation of endothelial cell migration 0 0.973 69 0.074 63 0.007 69 0.080 0 0.995 0 0.983 0 1.000

83 GO:0002889 regulation of immunoglobulin mediated immune response 83 0.173 72 0.073 66 0.007 72 0.079 0 1.000 0 0.995 0 1.000



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#b


84 GO:0002712 regulation of B cell mediated immunity 83 0.173 72 0.073 66 0.007 72 0.079 0 1.000 0 0.995 0 1.000

85 GO:0010596 negative regulation of endothelial cell migration 0 0.972 117 0.063 107 0.003 117 0.067 0 1.000 0 0.989 0 1.000

86 GO:0042730 fibrinolysis 0 0.970 0 0.953 77 0.004 0 0.955 0 1.000 0 0.981 0 1.000

87 GO:0042060 wound healing 8 0.359 14 0.061 13 0.003 14 0.065 0 1.000 0 0.973 0 1.000

88 GO:0002673 regulation of acute inflammatory response 108 0.159 95 0.067 87 0.003 95 0.072 0 1.000 0 0.919 0 1.000

89 GO:0020027 hemoglobin metabolic process 108 0.159 95 0.067 87 0.003 95 0.072 0 1.000 0 0.919 0 1.000

90 GO:0006957 complement activation alternative pathway 0 0.956 101 0.066 139 0.000 101 0.070 0 1.000 0 0.990 0 1.000

91 GO:0051918 negative regulation of fibrinolysis 0 0.979 0 0.945 278 0.000 0 0.948 0 0.999 0 0.986 0 1.000

92 GO:0006641 triglyceride metabolic process 0 0.943 36 0.097 50 0.000 36 0.101 0 1.000 0 0.997 0 1.000

93 GO:0051917 regulation of fibrinolysis 0 0.733 0 0.547 154 0.002 0 0.563 0 0.999 0 0.903 0 1.000

94 GO:0006639 acylglycerol metabolic process 0 0.880 32 0.100 44 0.001 32 0.104 0 0.994 0 0.997 0 1.000

95 GO:0018904 organic ether metabolic process 0 0.963 30 0.102 41 0.001 30 0.106 0 1.000 0 0.984 0 1.000

96 GO:0006638 neutral lipid metabolic process 0 0.990 32 0.100 43 0.001 32 0.105 0 1.000 0 0.998 0 1.000

97 GO:0006662 glycerol ether metabolic process 0 0.975 30 0.102 42 0.001 30 0.106 0 0.999 0 0.971 0 1.000

98 GO:0050878 regulation of body fluid levels 11 0.335 19 0.050 17 0.002 19 0.052 0 0.996 0 0.837 0 1.000

99 GO:0002714 positive regulation of B cell mediated immunity 217 0.114 190 0.052 173 0.001 190 0.055 0 1.000 0 0.980 0 1.000

100 GO:0002891 positive regulation of immunoglobulin mediated immune response

217 0.114 190 0.052 173 0.001 190 0.055 0 1.000 0 0.980 0 1.000

101 GO:0007599 hemostasis 13 0.325 23 0.042 21 0.001 23 0.044 0 1.000 0 0.972 0 1.000

102 GO:0032101 regulation of response to external stimulus 13 0.325 22 0.042 20 0.001 22 0.044 0 1.000 0 0.936 0 1.000

103 GO:0050817 coagulation 13 0.325 22 0.042 20 0.001 22 0.044 0 1.000 0 0.936 0 1.000

104 GO:0007596 blood coagulation 13 0.325 23 0.038 21 0.000 23 0.040 0 1.000 0 1.000 0 1.000

105 GO:0051604 protein maturation 14 0.326 24 0.039 28 0.000 24 0.041 0 0.999 0 0.934 0 1.000

106 GO:0002449 lymphocyte mediated immunity 17 0.304 30 0.026 21 0.006 30 0.025 0 1.000 0 0.984 0 1.000

107 GO:0016485 protein processing 15 0.316 27 0.032 31 0.000 27 0.034 0 0.999 0 0.992 0 1.000

108 GO:0002443 leukocyte mediated immunity 15 0.318 26 0.032 18 0.009 26 0.034 0 0.951 0 0.937 0 1.000

109 GO:0050727 regulation of inflammatory response 28 0.252 48 0.008 22 0.037 48 0.009 0 1.000 0 0.984 0 1.000

110 GO:0030162 regulation of proteolysis 35 0.225 62 0.007 14 0.213 62 0.007 0 0.989 0 0.994 0 1.000

111 GO:0019220 regulation of phosphate metabolic process 8 0.224 14 0.005 5 0.142 14 0.004 0 0.998 0 0.991 0 1.000

112 GO:0051174 regulation of phosphorus metabolic process 8 0.224 14 0.005 5 0.142 14 0.004 0 0.998 0 0.991 0 1.000



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#b


113 GO:0051247 positive regulation of protein metabolic process 18 0.125 24 0.006 7 0.151 24 0.004 0 0.946 0 0.984 0 1.000

114 GO:0032270 positive regulation of cellular protein metabolic process 19 0.113 25 0.005 8 0.147 25 0.004 0 1.000 0 0.996 0 1.000

115 GO:0042325 regulation of phosphorylation 8 0.220 14 0.004 5 0.135 14 0.003 0 0.998 0 0.986 0 1.000

116 GO:0050865 regulation of cell activation 11 0.330 30 0.003 9 0.123 30 0.003 0 1.000 0 0.999 0 1.000

117 GO:0031348 negative regulation of defense response 72 0.180 127 0.004 29 0.158 127 0.004 0 0.995 0 0.990 0 1.000

118 GO:0050870 positive regulation of T cell activation 26 0.259 45 0.009 20 0.050 45 0.010 0 1.000 0 0.986 0 1.000

119 GO:0051251 positive regulation of lymphocyte activation 21 0.281 37 0.010 17 0.062 37 0.013 0 1.000 0 1.000 0 1.000

120 GO:0042176 regulation of protein catabolic process 24 0.265 43 0.009 10 0.243 43 0.011 0 0.999 0 0.992 0 1.000

121 GO:0048585 negative regulation of response to stimulus 24 0.265 43 0.009 10 0.243 43 0.011 0 0.999 0 0.992 0 1.000

122 GO:0001869 negative regulation of complement activation lectin pathway 867 0.055 759 0.027 347 0.036 759 0.027 0 1.000 0 0.993 0 1.000

123 GO:0015886 heme transport 867 0.055 759 0.027 347 0.036 759 0.027 0 1.000 0 0.993 0 1.000

124 GO:0045627 positive regulation of T-helper 1 cell differentiation 867 0.055 759 0.027 347 0.036 759 0.027 0 1.000 0 0.993 0 1.000

125 GO:0060760 positive regulation of response to cytokine stimulus 0 0.997 759 0.027 347 0.036 759 0.027 0 1.000 0 0.993 0 1.000

126 GO:0001868 regulation of complement activation lectin pathway 867 0.055 759 0.027 347 0.036 759 0.027 0 1.000 0 0.993 0 1.000

127 GO:0002369 T cell cytokine production 0 0.993 253 0.048 116 0.082 253 0.049 0 1.000 0 0.992 0 1.000

128 GO:0032743 positive regulation of interleukin-2 production 0 0.993 253 0.048 116 0.082 253 0.049 0 1.000 0 0.992 0 1.000

129 GO:0010875 positive regulation of cholesterol efflux 0 0.993 253 0.048 0 0.967 253 0.049 0 1.000 0 0.992 0 1.000

130 GO:0032604 granulocyte macrophage colony-stimulating factor production 289 0.097 253 0.048 116 0.082 253 0.049 0 1.000 0 0.992 0 1.000

131 GO:0042253 granulocyte macrophage colony-stimulating factor biosynthetic process

289 0.097 253 0.048 116 0.082 253 0.049 0 1.000 0 0.992 0 1.000

132 GO:0002724 regulation of T cell cytokine production 0 0.993 253 0.048 116 0.082 253 0.049 0 1.000 0 0.992 0 1.000

133 GO:0010953 regulation of protein maturation by peptide bond cleavage 289 0.097 253 0.048 116 0.082 253 0.049 0 1.000 0 0.992 0 1.000

134 GO:0034447 very-low-density lipoprotein particle clearance 0 0.993 253 0.048 0 0.967 253 0.049 0 1.000 0 0.992 0 1.000

135 GO:0002694 regulation of leukocyte activation 12 0.328 21 0.048 10 0.120 21 0.048 0 1.000 0 0.983 0 1.000

136 GO:0009991 response to extracellular stimulus 12 0.329 21 0.048 10 0.123 21 0.048 166 0.728 0 1.000 0 1.000

137 GO:0032645 regulation of granulocyte macrophage colony-stimulating factor

production

347 0.083 304 0.042 139 0.072 304 0.044 0 0.999 0 0.986 0 1.000

138 GO:0002726 positive regulation of T cell cytokine production 0 0.979 304 0.042 139 0.072 304 0.044 0 0.999 0 0.986 0 1.000

139 GO:0045939 negative regulation of steroid metabolic process 0 0.979 304 0.042 0 0.997 304 0.044 0 0.999 0 0.986 0 1.000

140 GO:0030240 muscle thin filament assembly 0 0.979 304 0.042 139 0.072 304 0.044 0 0.999 0 0.986 0 1.000



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#b


141 GO:0031641 regulation of myelination 347 0.083 304 0.042 139 0.072 304 0.044 0 0.999 0 0.986 0 1.000

142 GO:0045423 regulation of granulocyte macrophage colony-stimulating factor

biosynthetic process

347 0.083 304 0.042 139 0.072 304 0.044 0 0.999 0 0.986 0 1.000

143 GO:0010894 negative regulation of steroid biosynthetic process 0 0.979 304 0.042 0 0.997 304 0.044 0 0.999 0 0.986 0 1.000

144 GO:0034382 chylomicron remnant clearance 0 0.979 304 0.042 0 0.997 304 0.044 0 0.999 0 0.986 0 1.000

145 GO:0034380 high-density lipoprotein particle assembly 0 0.979 304 0.042 0 0.997 304 0.044 0 0.999 0 0.986 0 1.000

146 GO:0042159 lipoprotein catabolic process 0 0.979 304 0.042 0 0.997 304 0.044 0 0.999 0 0.986 0 1.000

147 GO:0060333 interferon-gamma-mediated signaling pathway 0 0.986 304 0.042 139 0.072 304 0.044 0 0.999 0 0.986 0 1.000

148 GO:0051249 regulation of lymphocyte activation 14 0.326 24 0.039 11 0.114 24 0.041 0 1.000 0 0.991 0 1.000

149 GO:0031667 response to nutrient levels 14 0.326 24 0.038 11 0.114 24 0.040 190 0.797 0 0.799 0 1.000

150 GO:0002921 negative regulation of humoral immune response 434 0.071 380 0.038 173 0.062 380 0.040 0 0.995 0 0.968 0 1.000

151 GO:0045063 T-helper 1 cell differentiation 434 0.071 380 0.038 173 0.062 380 0.040 0 0.995 0 0.968 0 1.000

152 GO:0042508 tyrosine phosphorylation of Stat1 protein 434 0.071 380 0.038 173 0.062 380 0.040 0 0.995 0 0.968 0 1.000

153 GO:0060334 regulation of interferon-gamma-mediated signaling pathway 0 0.926 380 0.038 173 0.062 380 0.040 0 0.995 0 0.968 0 1.000

154 GO:0002825 regulation of T-helper 1 type immune response 434 0.071 380 0.038 173 0.062 380 0.040 0 0.995 0 0.968 0 1.000

155 GO:0030825 positive regulation of cGMP metabolic process 0 0.917 380 0.038 0 0.706 380 0.040 0 0.995 0 0.968 0 1.000

156 GO:0045624 positive regulation of T-helper cell differentiation 434 0.071 380 0.038 173 0.062 380 0.040 0 0.995 0 0.968 0 1.000

157 GO:0010544 negative regulation of platelet activation 0 0.917 380 0.038 0 0.706 380 0.040 0 0.995 0 0.968 0 1.000

158 GO:0045824 negative regulation of innate immune response 434 0.071 380 0.038 173 0.062 380 0.040 0 0.995 0 0.968 0 1.000

159 GO:0002923 regulation of humoral immune response mediated by circulating immunoglobulin

434 0.071 380 0.038 173 0.062 380 0.040 0 0.995 0 0.968 0 1.000

160 GO:0030828 positive regulation of cGMP biosynthetic process 0 0.917 380 0.038 0 0.706 380 0.040 0 0.995 0 0.968 0 1.000

161 GO:0002922 positive regulation of humoral immune response 578 0.057 506 0.032 231 0.050 506 0.034 0 1.000 0 0.989 0 1.000

162 GO:0045425 positive regulation of granulocyte macrophage colony-stimulating factor biosynthetic process

578 0.057 506 0.032 231 0.050 506 0.034 0 1.000 0 0.989 0 1.000

163 GO:0042511 positive regulation of tyrosine phosphorylation of Stat1 protein 578 0.057 506 0.032 231 0.050 506 0.034 0 1.000 0 0.989 0 1.000

164 GO:0010955 negative regulation of protein maturation by peptide bond cleavage

578 0.057 506 0.032 231 0.050 506 0.034 0 1.000 0 0.989 0 1.000

165 GO:0002925 positive regulation of humoral immune response mediated by circulating immunoglobulin

578 0.057 506 0.032 231 0.050 506 0.034 0 1.000 0 0.989 0 1.000

166 GO:0002639 positive regulation of immunoglobulin production 578 0.057 506 0.032 231 0.050 506 0.034 0 1.000 0 0.989 0 1.000

167 GO:0045916 negative regulation of complement activation 578 0.057 506 0.032 231 0.050 506 0.034 0 1.000 0 0.989 0 1.000



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#b


168 GO:0030449 regulation of complement activation 578 0.057 506 0.032 231 0.050 506 0.034 0 1.000 0 0.989 0 1.000

169 GO:0045625 regulation of T-helper 1 cell differentiation 578 0.057 506 0.032 231 0.050 506 0.034 0 1.000 0 0.989 0 1.000

170 GO:0042510 regulation of tyrosine phosphorylation of Stat1 protein 578 0.057 506 0.032 231 0.050 506 0.034 0 1.000 0 0.989 0 1.000

171 GO:0001961 positive regulation of cytokine-mediated signaling pathway 578 0.057 506 0.032 231 0.050 506 0.034 0 1.000 0 0.989 0 1.000

172 GO:0060759 regulation of response to cytokine stimulus 0 0.990 506 0.032 231 0.050 506 0.034 0 1.000 0 0.989 0 1.000

173 GO:0031401 positive regulation of protein modification process 31 0.057 27 0.032 12 0.091 27 0.034 0 1.001 0 0.922 0 1.000

174 GO:0050863 regulation of T cell activation 17 0.305 29 0.032 13 0.083 29 0.034 0 0.999 0 0.990 0 1.000

175 GO:0009894 regulation of catabolic process 17 0.307 29 0.032 7 0.290 29 0.034 0 0.972 0 0.981 0 1.000

176 GO:0045541 negative regulation of cholesterol biosynthetic process 0 0.986 506 0.032 0 0.899 506 0.034 0 1.000 0 0.989 0 1.000

177 GO:0032803 regulation of low-density lipoprotein receptor catabolic process 0 0.986 506 0.032 0 0.899 506 0.034 0 1.000 0 0.989 0 1.000

178 GO:0032802 low-density lipoprotein receptor catabolic process 0 0.986 506 0.032 0 0.899 506 0.034 0 1.000 0 0.989 0 1.000

179 GO:0018212 peptidyl-tyrosine modification 37 0.046 33 0.026 15 0.071 33 0.026 0 0.994 0 0.989 0 1.000

180 GO:0045937 positive regulation of phosphate metabolic process 38 0.048 33 0.026 15 0.071 33 0.026 0 1.000 0 0.998 0 1.000

181 GO:0010562 positive regulation of phosphorus metabolic process 38 0.048 33 0.026 15 0.071 33 0.026 0 1.000 0 0.998 0 1.000

182 GO:0018108 peptidyl-tyrosine phosphorylation 38 0.048 33 0.026 15 0.071 33 0.026 0 1.000 0 0.998 0 1.000

183 GO:0002696 positive regulation of leukocyte activation 19 0.294 33 0.026 15 0.071 33 0.026 0 1.000 0 0.998 0 1.000

184 GO:0031644 regulation of neurological system process 18 0.301 31 0.026 7 0.282 31 0.026 0 0.974 0 0.986 0 1.000

185 GO:0050867 positive regulation of cell activation 18 0.296 32 0.026 15 0.071 32 0.026 0 1.000 0 0.997 0 1.000

186 GO:0051969 regulation of transmission of nerve impulse 0 0.999 32 0.026 7 0.278 32 0.026 0 1.000 0 0.997 0 1.000

187 GO:0042327 positive regulation of phosphorylation 39 0.054 34 0.027 16 0.072 34 0.027 0 1.000 0 0.971 0 1.000

188 GO:0001775 cell activation 5 0.411 13 0.027 4 0.234 13 0.027 0 1.000 0 0.995 0 1.000

189 GO:0007584 response to nutrient 21 0.281 37 0.027 17 0.062 37 0.027 293 0.876 0 0.995 0 1.000

a Gene ontology enrichment analysis was performed using High Throughput GoMiner. A significant enrichment of the GO term category was inferred when p<0.05 as determined by a one-sided Fisher’s exact test after false discovery rate (FDR) correction. Significantly enriched GO terms are indicated in bold with yellow or orange highlight. b Number (#) corresponds to position in Cluster Image Map presented in Figure 4. Abbreviations: E, enrichment value; FDR, false discovery rate-corrected p-value.



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Supplemental Figure 1. Chemical structures of compounds tested for promotion of

hepatocarcinogenesis in rainbow trout.



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Supplemental Figure 2. Outline of AFB1 and MNNG tumor studies in trout with dietary

polyfluorinated chemicals. Fry were initiated at 10 weeks of age post spawn with 10 ppb AFB1

(A) or 35 ppm MNNG (C), though one cohort of animals were initiated with AFB1 at a slightly

delayed age of 14 weeks (B). Four weeks after initiation, animals were fed the indicated

experimental diets ad libitum (2.8-5.6% of body weight ration) for 6 months. PFOS was also

initially tested at a diet concentration of 2000 ppm, though this exposure level was lethal in trout

(data not shown). Consequently, a second cohort of trout fry (B) was initiated with a month

delay compared to the other initiated groups, and a much lower diet concentration of 100 ppm

PFOS was tested; as in the other treatment groups, the length of diet exposure was 6 months for

PFOS-exposed trout. Necropsies for all treatment groups were performed at 12.5 months of age

and were completed within one week.



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Supplemental Figure 3. Legend on following page.

CON E2 PFOA PFNA PFDA FtOH CLOF0

20

40

60

80

100

Treatment

Tu

mo

r in

cid

ence

(% o

f fis

h w

ith tu

mo

rs)

CON PFOS0

20

40

60

80

100

Sham - Females

Sham - Males

Sham - Total

AFB1 - Females

AFB1 - Males

AFB1 - Total

Treatment

BA


20

40

60

80

100

Treatment

Exc

lud

ed s

ub

ject

s

(% o

f to

tal)

CON PFOS0

20

40

60

80

100

Sham - Females

Sham - Males

Sham - Total

AFB1 - Females

AFB1 - Males

AFB1 - Total

Treatment

DC


20

40

60

80

100

Treatment

Tu

mo

r in

cid

ence

(% o

f fis

h w

ith tu

mo

rs)

CON PFOS0

20

40

60

80

100

Sham - Final subjects

AFB1 - Final subjects

Sham - All subjects

AFB1 - All subjects

Treatment

FE



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Supplemental Figure 3. Lack of effect of sex, experimental tank or idiopathic liver disease

on AFB1-induced liver carcinogenesis. (previous page) (A, B) Comparison of liver tumor

incidence data between male and female fish and between duplicate experimental tanks. Values

are tumor incidence in animals initiated by 10 ppm AFB1 (at 10 weeks of age for panel A, 15

weeks for panel B) following a 6-month dietary exposure to the indicated treatments. Tumor

outcome was not significantly different between male and female fish or between the duplicate

tanks as determined by logistic regression analysis. (C, D) Frequency of observed idiopathic

liver disease unrelated to experimental condition. Values shown are incidence of fish with

symptoms of liver disease from unknown origin, characterized by pale or jaundiced livers, which

were excluded from the final experimental analyses. With the exception of PFOA treatment (p <

0.05), no effect of either carcinogen or dietary treatment on the frequency of idiopathic liver

disease was observed in this study as determined by logistic regression analyses. (E, F) Liver

tumor incidence in all experimental subjects compared to those with no symptoms of idiopathic

liver disease (final subjects). No differences in tumor outcome were observed between the all

subject and final subject data sets, as determined by logistic regression analyses. For all panels,

symbols represent duplicate experimental tanks.

Supplemental Figure 4. Impact of promotion diets on morphological parameters in trout.

(next page) Animals were initiated with 10 ppb AFB1 or ethanol sham treatment at 10 weeks of

age (A, D, G and J) or at 15 weeks (B, E, H and K); some animals were initiated with 35 ppm

MNNG or DMSO sham treatment (C, F, I and L). See Methods for details on experimental diets.

(A-C) Survival at termination of the study (12.5 months post spawn) for all treatment groups; in

each figure, symbols represent duplicate tanks. (D-L) Average body weight, liver weight and

liver somatic index (LSI = [liver weight/body weight]*100) values are shown as box-whisker

plots with 10-90 percentile whisker bars. *, p < 0.05; **, p < 0.01, ***, p < 0.001 compared to

control diet (within initiation group, Sham/CON, AFB1/CON or MNNG/CON) as determined by

the Kuskal-Wallis test with Dunn’s multiple comparison test. ##, p<0.05; ###, p<0.001

compared to control diet (15wk initiation) as determined by unpaired Welch’s t-test.



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Sham/C

ON

Sham/E

2

Sham/P

FOA

Sham/P

FNA

Sham/P

FDA

Sham/8

:2FtO

H

Sham/C

LOF/C

ON

1

AFB

/E2

1

AFB /PFOA

1

AFB/P

FNA

1

AFB/P

FDA

1

AFB /8:2

FtOH

1

AFB

/CLOF

1

AFB

0

50

100

150

200

*********

**

***

Bo

dy

we

igh

t (g

)

Sham/C

ON

Sham/P

FOS/C

ON

1

AFB /PFOS

1

AFB

0

50

100

150

200

##

Sham/C

ON

Sham/E

2

Sham/P

FOA

Sham/P

FNA

Sham/P

FDA

Sham/8

:2FtO

H

Sham/C

LOF/C

ON

1

AFB

/E2

1

AFB /PFOA

1

AFB/P

FNA

1

AFB/P

FDA

1

AFB /8:2

FtOH

1

AFB

/CLOF

1

AFB

0.0

0.5

1.0

1.5

2.0

2.5

**

*

Liv

er

we

igh

t (g

)

Sham/C

ON

Sham/P

FOS/C

ON

1

AFB /PFOS

1

AFB

0.0

0.5

1.0

1.5

2.0

2.5

####

Sham/C

ON

Sham/E

2

Sham/P

FOA

Sham/P

FNA

Sham/P

FDA

Sham/8

:2FtO

H

Sham/C

LOF/C

ON

1

AFB

/E2

1

AFB /PFOA

1

AFB/P

FNA

1

AFB/P

FDA

1

AFB /8:2

FtOH

1

AFB

/CLOF

1

AFB

0

1

2

3

******

****** *** ***

***

Treatment

LS

I (%

)

Sham/C

ON

Sham/P

FOS/C

ON

1

AFB /PFOS

1

AFB

0

1

2

3

######

Treatment

D E

G H

J K

Sham/C

ON

MNNG/C

ON

MNNG/E

2

MNNG/P

FOA

0

50

100

150

200

*****

Sham/C

ON

MNNG/C

ON

MNNG/E

2

MNNG/P

FOA

0.0

0.5

1.0

1.5

2.0

2.5

*

Sham/C

ON

MNNG/C

ON

MNNG/E

2

MNNG/P

FOA

0

1

2

3

******

Treatment

F

I

L

Sham/C

ON

Sham/E

2

Sham/P

FOA

Sham/P

FNA

Sham/P

FDA

Sham/8

:2FtO

H

Sham/C

LOF/C

ON

1

AFB

/E2

1

AFB /PFOA

1

AFB/P

FNA

1

AFB/P

FDA

1

AFB /8:2

FtOH

1

AFB

/CLOF

1

AFB

0

20

40

60

80

100 ****

*** ***

***

******

Su

rviv

al

(%)

Sham/C

ON

Sham/P

FOS/C

ON

1

AFB /PFOS

1

AFB

0

20

40

60

80

100 ##

Sham/C

ON

MNNG/C

ON

MNNG/E

2

MNNG/P

FOA

0

20

40

60

80

100

****

A B C

Supplemental Figure 4. Legend on previous page.



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Sham/C

ON

MNNG/C

ON

MNNG/E

2

MNNG/P

FOA

0

20

40

60

80

100

Treatment

Liv

er tu

mo

r in

cid

ence

(% o

f fis

h w

ith tu

mo

rs)

Sham/C

ON

MNNG/C

ON

MNNG/E

2

MNNG/P

FOA

0

20

40

60

80

100

Females (all subjects)

Males (all subjects)

Total (all subjects)

Females (final subjects)

Total (final subjects)

Males (final subjects)

TreatmentS

tom

ach

tum

or

inci

den

ce

(% o

f fis

h w

ith tu

mo

rs)

A B

Sham/C

ON

MNNG/C

ON

MNNG/E

2

MNNG/P

FOA

0

20

40

60

80

100

Treatment

Kid

ney

tum

or

inci

den

ce

(% o

f fis

h w

ith tu

mo

rs)

Sham/C

ON

MNNG/C

ON

MNNG/E

2

MNNG/P

FOA

0

20

40

60

80

100

Treatment

Sw

im b

lad

der

tum

or

inci

den

ce

(% o

f fis

h w

ith tu

mo

rs)

C D

Supplemental Figure 5. Lack of effect of sex, experimental tank or idiopathic liver disease

on MNNG-induced carcinogenesis. Comparisons of tumor incidence data for liver (A),

stomach (B), kidney (C) and swim bladder (D) tumors between male and female fish and

between duplicate experimental tanks. Values are tumor incidence in animals initiated by 35

ppm MNNG following a 6-month dietary exposure to the indicated treatments (see Methods). In

each figure, symbols represent duplicate experimental tanks, and values for all experimental

subjects and final subjects (no symptoms of idiopathic liver disease) are shown. For all tumor

types, tumor outcome was not significantly different between male and female fish, between the

duplicate tanks or between the all subjects and final subjects data groups; the single exception to

this observation was for swim bladder tumors in MNNG/PFOA treatment group, where

significant over-dispersion between experimental tanks was observed (p = 0.003).



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0 2 4 60

2

4

6

Array 15R

2=0.984

y = 0.94x + 0.28

Log10 Intensity Ch1

Lo

g1

0 I

nte

ns

ity

Ch

2

1 2 3 4 5 6 7 8 9 101

2

3

4

5

Array 15

Alien oligo #

A

1 2 3 4 5 6 7 8 9 10-1.0

-0.5

0.0

0.5

1.0

Array 15

Alien oligo #

M

0 2 4 60

2

4

6

All dataR

2=0.978

y = 0.99x + 0.018

Log10 Intensity Ch1

Lo

g1

0 I

nte

ns

ity

Ch

2

1 2 3 4 5 6 7 8 9 101

2

3

4

5

All data

Alien oligo #

A

1 2 3 4 5 6 7 8 9 10-1.0

-0.5

0.0

0.5

1.0

All data

Alien oligo #

M

F

B C

D E

A

Supplemental Figure 6. Quality control analysis of array hybridization. Data for each

SpotReport Alien Oligo feature for a representative array (A-C; N = 16 spots per olio per array)

or for all arrays hybridized in this study (D-F; N = 640) are shown. Pairwise correlation analyses

of Ch1 and Ch2 intensities are shown for all SpotReport Alien features (A) or those for a

representative array (D). Box and whiskers (10-90 confidence interval) plots of values for mean

intensity (panels B and E; A = log10(sqrt(Ch1*Ch2)) and the ratio of intensities (panels C and F;

M = log2(Ch1/Ch2) are also shown for the entire array and for array 15. In summary, non-

specific hybridization to buffer spots was not detected, background fluorescence was consistently

low across the array and there was no apparent spatial bias on these arrays. As expected, a very

strong correlation between Ch1 and Ch2 intensities was observed with slopes of the linear

regression trend lines of 0.94 and 0.99 for array 15 or all arrays, respectively, indicating that the

potential problem of dye bias was eliminated by ratio-centering and Lowess-normalization of the

raw data. The quality control analysis shows that these arrays can detect changes in gene

expression across a broad range of signal intensities (three orders of magnitude) and that

hybridization to the OSUrbt array was consistent and reproducible.



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Supplemental Figure 7. Unsupervised bidirectional hierarchical cluster analysis of hepatic

gene expression profiles. Bidirectional hierarchical clustering analysis was performed using

sample data for either all array features; log2 fold change expression values are shown for each

biological replicate (n = 3). Patterns of gene expression were clustered in two directions, by

gene (left tree) and treatment (top tree), using the Euclidean distance method with average

linkage.



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Supplemental Figure 8. Directed acyclic graph for selected enriched GO terms associated with dietary E2. GO term enrichment was

performed using agriGO singular enrichment analysis tool against the zebrafish gene ontology database. The scale bar indicates level of significance

(FDR-corrected p-value) for enriched GO terms, and the inset legend indicates the type of relationship between terms as depicted by different arrow

types.

<1e-09

<0.05

FDR p value Relationship



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Supplemental Figure 9. Directed acyclic graph (DAG) for selected enriched GO terms associated with dietary PFNA. GO term enrichment

was performed using agriGO singular enrichment analysis tool against the zebrafish gene ontology database. Because of the extensive network of

GO terms associated with PFNA exposure, only a portion of the DAG is shown for comparison to the E2 DAG shown in Suppl. Fig. 8. The scale bar

indicates level of significance (FDR-corrected p-value) for enriched GO terms, and the inset legend indicates the type of relationship between terms

as depicted by different arrow types. Best viewed using Adobe PDF viewer.

<1e-09

<0.05

FDR p value Relationship



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Supplemental Figure 10. Validation of treatment-induced changes in hepatic gene

expression determined by microarray analysis using qRT-PCR. Values from qRT-PCR

(open circles) are expressed as mean fold change (log2) normalized to expression of

housekeeping genes actb, atp5b, gapdh and top2a in a reference pool of cDNA from control

treatments; values from the OSUrbt microarray (black squares) are expressed as mean fold

change (log2) compared to the control reference pool + SEM (N = 3) for select genes including

(A) a2m, (B) ctsd, (C) cyp1a, (D) cyp2k5, (E) hpx, (F) pgds, (G) tcpbp, (H) trx and (I) vtg. qRT-

PCR values for the PFOS treatment were normalized to the appropriate time-matched reference

pool to correctly reflect array hybridization conditions for this treatment group. Light gray

shading in the plot area indicates regions of two-fold induction or repression of gene expression.

Asterisks indicate that the qRT-PCR expression value is significantly different (*, p < 0.05; **, p

< 0.01; ***, p < 0.001) from control treatment (CON) as determined by the Kruskal-Wallis test

with Dunnett’s test for multiple comparisons. Results of statistical analyses of microarray data

are provided in Supplementary Table 6.



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Supplemental References

Mortensen, A. S., Tolfsen, C. C. and Arukwe, A. (2006). Gene expression patterns in estrogen

(nonylphenol) and aryl hydrocarbon receptor agonists (PCB-77) interaction using

rainbow trout (Oncorhynchus mykiss) primary hepatocyte culture. J. Toxicol. Environ.

Health A 69, 1-19.

Rees, C. B. and Li, W. (2004). Development and application of a real-time quantitative PCR

assay for determining CYP1A transcripts in three genera of salmonids. Aquat. Toxicol.

66, 357-68.

Tilton, S. C., Givan, S. A., Pereira, C. B., Bailey, G. S. and Williams, D. E. (2006).

Toxicogenomic profiling of the hepatic tumor promoters indole-3-carbinol, 17-estradiol

and -naphthoflavone in rainbow trout. Toxicol. Sci. 90, 61-72.



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