Identification of Classifiers for Increase or Decrease of Thyroid

ARTICLE pubs.acs.org/est

Identification of Classifiers for Increase or Decrease of Thyroid Peroxidase Activity in the FTC-238/hTPO Recombinant Cell Line Mee Song,†,§ Youn-Jung Kim,‡ Mi-Kyung Song,† Han-Seam Choi,† Yong-Keun Park,§ and Jae-Chun Ryu*,† †

Cellular and Molecular Toxicology Laboratory, Korea Institute of Science and Technology (KIST), Department of Applied Chemistry, Kyung Hee University, Yongin 449-701, Republic of Korea, § School of Life Sciences and Biotechnology, Korea University, Anam-Dong, Seoungbuk-Gu, Seoul, 136-701, Republic of Korea ‡

bS Supporting Information ABSTRACT:

Thyroid peroxidase (TPO) plays an important role in thyroid hormone biosynthesis, as it catalyzes all of the essential steps in iodide organification. TPO activity can be detected using the guaiacol assay; however, this assay is complex and very time-consuming. Therefore, we focused on devising a simplified method using microarrays to detect changes in TPO activity, which is a target for disruption of the thyroid hormone axis. These experiments have systematically assessed the potential utility of transcriptomic end points as enhancements to the guaiacol assay. Previously, we demonstrated that benzophenone-2, benzophenone, perfluorooctane sulfonate, bisphenol A bis ether, and vinclozolin decreased TPO activity, and that dibutyl phthalate, carbaryl, dibenzo(a,h)anthracene, benzo(a)pyrene, and methylmercury increased TPO activity. In this work, we used human oligonucleotide chips to examine changes in the gene expression profile of FTC-238 human follicular thyroid carcinoma cells expressing human recombinant TPO, after exposure of the cells to TPO activity-disrupting agents. We identified 362 classifiers that could predict the effect of the toxicants on TPO activity with about 70% accuracy. These classifiers are potential markers for predicting the effects of chemicals on thyroid hormone production.

’ INTRODUCTION The biosynthesis of thyroid hormone (TH; triiodothyronine (T3) or thyroxine (T4)) can be considered as a six-step process: (1) iodide is transported from the circulatory system into follicular cells by the sodium iodide symporter and converted to iodine; (2) thyroglobulin (Tg) is synthesized and transported to the colloidal space; (3) the tyrosine residues on Tg are converted to monoiodotyrosine (MIT) and di-iodotyrosine (DIT) by thyroid peroxidase (TPO) in the presence of hydrogen peroxide (H2O2); (4) T4 and T3 are formed by the coupling of two DITs or a DIT and MIT, respectively, on Tg; (5) Tg is endocytosed into follicular cells, degraded by hydrolytic enzymes, and reabsorbed into the cytoplasm, leaving T4, T3, DIT, and MIT in r 2011 American Chemical Society

the endosome; and (6) T4 and T3 are released into the bloodstream.1,2 TH synthesis is regulated at several points, including TPO, iodide transport, type I 50 -deiodinase (50 DI), and TH receptor (TR).3 The ability to detect changes in TPO activity is important, as TPO represents a primary factor in the TH axis. TPO is a large (∼105 kDa) heme-containing glycoprotein present in the apical cytoplasmic membrane of thyroid epithelial cells and in the Received: February 11, 2011 Accepted: August 2, 2011 Revised: August 1, 2011 Published: August 02, 2011 7906

dx.doi.org/10.1021/es200475k | Environ. Sci. Technol. 2011, 45, 7906–7914

Environmental Science & Technology endoplasmic reticulum of thyrocytes, where it functions as a cellsurface enzyme.35 The N-glycan portion of the glycoprotein in TPO is required for movement of the enzyme into cells, and inhibition of N-glycosylation which disrupts formation of all N-linked oligosaccharides on TPO leads to a decrease in TPO activity.6 As TPO is a major microsomal antigen in autoimmune thyroid disease, the presence of anti-TPO antibodies is a marker of thyroid dysfunction and a predictor of high risk for autoimmune disease.3,7 One work suggested that exposure to persistent organic pollutants (POPs) was related to increased antiTPO antibody levels, indicating a risk for autoimmune disease.7 Responses to a decrease in TPO activity are typical in the early stages of hypothyroidism, and an increase in TPO activity indicates potential hyperthyroidism 8 The guaiacol assay can be used to detect changes in TPO activity; however, this assay is complex and time-consuming. The assay requires a large amount of TPO extraction, a long reaction time with the TPO extraction and compounds, and a well-ventilated space because of the strong, characteristic smell of guaiacol. In this work, a microarray approach was used to assess the disruption of TPO activity. Technological advancements in biology and engineering, such as transcriptomic analysis, have provided increasingly sensitive, comprehensive, time-, and cost-effective microarray approaches; these allow for a more accurate assessment of the utility of microarrays for the identification of classifiers that may enhance the performance of the guaiacol assay. Predictive toxicology is important for the assessment of chemical toxicity, and the potential human health risks of toxic environmental compounds are a major issue. New techniques are needed for earlier detection and better prediction of potentially dangerous exposure to toxic environmental compounds. Many recent studies have applied a DNA microarray, which is a powerful, functional, and transcriptome-based method, as a diagnostic tool to provide a new, mechanism-based way of predicting toxic risk in humans.9,10 The toxicity of a compound is often predicted according to its classification, based on the postulate that changes in gene expression profiles associated with known toxicants represent the changes associated with other chemicals of the same toxicological class and predict their toxicological effects.9,10 Therefore, changes in gene expression data that are characteristic of exposure to an environmental toxicant can provide an early marker of toxicity, as changes in gene expression are often detectable before chemical, histopathological, or clinical indications.10 Hazardous environmental chemicals have been shown to affect TH production, although the specific targets of the chemicals are largely unknown. Environmental chemicals may affect TH systems by interfering at the receptor level, binding to transport proteins, modifying TH metabolism, altering cellular uptake, or increasing thyroid cancer potential.11 Our preliminary studies have shown that dibutyl phthalate (DBP), carbaryl (Car), methylmercury (MM), benzophenone (BP), benzo(a)pyrene [B(a)P] and dibenzo(a,h)anthracene [D(a,h)A] increase TPO activity, whereas 2,20 ,4,40 -tetrahydroxibenzophenone (BP-2), vinclozolin (Vin), methimazole (MMI), genistein (Gen), bisphenol A bis ether (BABDE), and perfluorooctane sulfonate (PFOS) decrease TPO activity. In addition, POPs and polycyclic aromatic hydrocarbons (PAHs) alter TPO activity (preliminary studies). In the present work, we focused on devising an easy method using microarrays to detect disruption of the TH axis 12 in order to identify a set of classifier genes that could be used to build a predictive model capable of accurately categorizing chemicals as TPO inducers or TPO inhibitors. Using transcriptomic analyses,

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we identified and optimized classifier genes based on changes in global gene expression profiles in human follicular thyroid carcinoma (FTC-238) cells expressing human recombinant TPO (hrTPO). The present work examines the potential utility of transcriptomic end points as enhancements to the guaiacol assay for assessing the disruption of TPO activity

’ MATERIALS AND METHODS Construction of the FTC-238/hTPO Cell Line. We adapted and modified a cell model based on hrTPO stably transfected into the human follicular thyroid carcinoma cell line FTC-238.3 The cDNA plasmid coding for human TPO was kindly provided by B. Rapport.13 The human follicular thyroid carcinoma cell line FTC-238 was purchased from the European Collection of Cell Cultures (ECACC, Porton Down, Wilts, UK). FTC-238, which does not express TPO endogenously, was stably transfected with the TPO cDNA plasmid using Lipofectamine 2000 (Invitrogen) according to the manufacturer’s instructions. Three days after transfection, the cells were incubated in a selection medium containing 300 μg/mL G418 (Sigma, St. Louis, MO, USA). Twelve single clones were isolated, seeded, and tested for TPO activity using a guaiacol assay. FTC-238/hTPO/RSK008, the clone with the highest activity level, was chosen for additional experiments. Cells were grown in Dulbecco’s modified Eagle’s medium nutrient mixture F-12 (Ham) 1 (DMEM/F-12; GIBCO, Grand Island, NY, USA) with 5% fetal bovine serum (GIBCO), 300 μg/ mL G418, and penicillin/streptomycin (20 U/mL and 20 μg/mL, respectively). The cells were maintained under a humidified atmosphere of 5% CO2 and 95% air at 37 °C; the culture medium was refreshed every two to three days. Guaiacol Assay. The guaiacol assay for measuring activity of thyroid peroxidase was performed following the modifications described by Hosoya.14 FTC-238/hTPO/RSK008 cells were grown in DMEM/F-12 containing 5% fetal bovine serum and stimulated with hematin (1 μg/mL; Sigma) for two days. Afterward, the cells were harvested, lysed by ultrasonification, and centrifuged for 90 min at 11 000 rpm and 4 °C. The precipitate was incubated for 24 h at 4 °C in 1% (wt/vol) digitonin then centrifuged twice as described. The supernatant was used for TPO assays. The protein concentrations were determined by a modified BCA assay (Pierce, Bonn, Germany). All TPO measurements were repeated at least three times. The guaiacol oxidation assay was performed in a mixture containing 100 μg of extracted protein, 50 mmol/L potassium phosphate buffer (pH 7.4), 40 mmol/L guaiacol, and 2,230 μmol/L H2O2. Increases in the concentration of the oxidation product 3,30 dimethoxy-4,40 -biphenochinone were monitored at a wavelength of 470 nm. TPO activity is given as micromoles H2O2 reduced per minute and per milligram extracted membrane protein. During the TPO inactivation assay, TPO-containing extract (325 μg) was preincubated with chemicals and H2O2 in 100 mmol/L potassium phosphate buffer (pH 7.4) in a final volume of 600 μL. The incubation was started with H2O2, and aliquots were removed after 0 and 3 min. The remaining TPO activity was then determined by a conventional guaiacol assay using 100 μLof the preincubation mixture. Cell Viability. To determine cytotoxicity and effects on cell growth, an MTT [3-(4,5-dimethylthaizol-2-yl)-2,5-diphenyltetrazolium bromide; Sigma] cell proliferation assay was performed, as modified by Mosmann.15 Briefly, FTC-238/ hTPO/RSK008 cells were seeded in 24-well culture plates 7907

dx.doi.org/10.1021/es200475k |Environ. Sci. Technol. 2011, 45, 7906–7914

Environmental Science & Technology

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Figure 1. Determination of chemical dose. (A) Cell viability determined by MTT assay. Doseresponse curve of FTC-238/hTPO/RSK008 cells treated for 48 h with each chemical. (B) Disruption of human TPO activity by chemical toxicants. BP-2, 2,20 ,4,40 -tetrahydroxybenzophenone; DBP, dibutyl phthalate; Car, carbaryl; Vin, vinclozolin; MMI, methimazole; Gen, genistein; MM, methylmercury; BP, benzophenone; B(a)P, benzo(a)pyrene; D(a,h)A, dibenzo(a,h)anthracene; BABDE, bisphenol A bis ether; PFOS, perfluorooctane sulfonates; *, p < 0.05 compared with control by one-way ANOVA with Dunnett’s test.

(BD FalconTM; Franklin Lakes, NJ, USA) at a density of 5 104 cells/mL. After reaching 80% confluence, the cells were exposed to various concentrations of BP, BP-2, DBP, B(a)P, D(a,h)A, PFOS, MM, BABDE, Gen, MMI, (Sigma), Car and Vin (Riedelde Ha€en, Hannover, Germany) for 48 h. All chemicals were dissolved in dimethylsulfoxide (DMSO) and diluted 1:100 into the medium. After exposure, the cells were incubated for 3 h with final concentration of 0.4 mg/mL MTT at 37 °C. To quench the reaction, the medium was removed and dimethyl sulfoxide (DMSO; Sigma) was added. The absorbance of each sample was measured at 540 nm. Untreated samples (1% DMSO only treatment) were used as negative controls (100% viability). The nontoxic concentration (NT) for cell proliferation was defined as concentrations of chemicals that initiated the reduction of cell viability by comparing them with the untreated control. The NT values were determined directly from the linear doseresponse

curves. The MTT assay was performed in triplicate for each sample. RNA Extraction. Total RNA was extracted from chemicalstreated FTC-238/hTPO/RSK008 cells using TRIzol (Invitrogen, Carlsbad, CA, USA) and was purified using an RNeasy mini kit (Qiagen, Valencia, CA, USA), according to the manufacturer’s instructions. Genomic DNA was removed using an RNase-free DNase Set (Qiagen). Total RNA was quantitated by measuring optical absorbance (NanoDrop ND 1000 spectrophotometer; NanoDrop Technologies, Inc., Wilmington, DE, U.S.), and its quality was evaluated by automated gel electrophoresis (Experion; Bio-Rad Laboratories, Hercules, CA, U.S.). Oligonucleotide Microarray Hybridization. Gene expression analysis was conducted using a 4 44 K and 8 60 K Whole Human Genome Microarray (Agilent Technologies, Palo Alto, CA, USA). For hybridization, RNA was labeled with either 7908



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Table 1. Study Design for the DNA Microarray Assay dose set

group

training set

increase of

chemical carbaryl

(μM) 1

TPO activity dibutyl phthalate decrease of TPO activity

test set

increase of

2,20 4,40 -tetrahydroxy-

100 1

benzophenone vinclozolin

10

methimazole genistein

10 1

methylmercury

0.1

benzo(a)pyrene

100

TPO activity

decrease of

dibenzo(a,h)anthracene

1

benzophenone

1

bisphenol A bis ether perfluorooctane sulfonates

5 5

TPO activity

cyanine 3 (Cy3)-labeled (control) or Cy5-labeled (treated samples) nucleotides. Hybridization was performed in a hybridization oven at 62 °C for 12 h. After a series of washes (2 SSC/ 0.1% SDS for 2 min at 58 °C; 1 SSC for 3 min at room temperature; and 0.2 SSC for 2 min at room temperature), the array was dried by centrifugation at 800 rpm for 3 min at room temperature. The hybridized array was scanned using a GenePix 4000B microarray scanner (Axon Instruments, Union City, CA, U.S.), and the images were analyzed using GenePix 4.1 software (Axon Instruments), to obtain gene expression ratios. The fluorescence intensity of each spot was calculated by local median background subtraction. We used the robust scatter-plot smoother LOWESS function to perform intensity-dependent normalization of gene expression. Scatter-plot analysis was conducted using Microsoft Excel 2000 (Microsoft Corp., Redmond, WA, U.S.). To determine whether changes in expression were statistically significant, a p-value was calculated for each gene, using the permutation procedure. For each permutation, two-sample t statistics were computed for each gene. Genes were considered to be differentially expressed when the logarithmic gene expression ratio in three independent hybridizations was greater than 0.666 or less than 0.666 (1.5-fold difference in expression level) and when the p-value was less than 0.05. Significance analysis of microarrays (SAM) was performed for genes exhibiting significant differential expression. Microarray data deposit in GEO series (GSE) (#GSE30961). Functional Analysis. In order to classify the selected genes into groups with a similar pattern of expression, each gene was assigned to an appropriate category according to its main cellular function. To determine significantly over-represented Gene ontology (GO) analysis, the DAVID (http://david.abcc.ncifcrf.gov) functional annotation clustering tool was used by choosing the default option.16 The threshold value of the Enrichment Score was set at above 1.0 thereby avoiding the loss of valuable information. The genes classified as the significantly overrepresented biological process group. Statistical Analysis. The data were analyzed with SigmaStat (SPSS, Inc., Chicago, IL, USA) using one-way ANOVA followed by Dunnett’s method. The treatment groups were compared

individually with the control group; p < 0.05 was considered significant.

’ RESULTS Determination of Nontoxic Values for Chemicals to be Used in FTC-238/hTPO/RSK008 Cells. The relative survival

rates of FTC-238/hTPO/RSK008 cells following exposure to each of 12 chemicals [BP, BP-2, DBP, B(a)P, D(a,h)A, PFOS, MM, BABDE, Gen, MMI, Car, and Vin] over a range of concentrations were determined by the MTT assay. The survival of chemical-treated cells is expressed relative to the survival of solventtreated control cells. Dose-dependent cell viability curves were obtained after 48 h of exposure to each chemical (Figure 1A). The nontoxic values (NT) for the chemicals were 1 μM BP-2, 100 μM DBP, 1 μM Car, 10 μM Vin, 10 μM MMI, 1 μM Gen, 0.1 μM MM, 1 μM BP, 100 μM B(a)P, 1 μM D(a,h)A, 5 μM BABDE, and 5 μM PFOS. To identify specific genes involved in the increase or decrease of TPO activity, we used concentrations that had no effect on cell viability (NT), yet disrupted TPO activity. We measured TPO activity using the guaiacol assay in the presence of each of the 12 chemicals (preliminary studies) and confirmed the TPO activity in cells exposed to the NT dose of each (Figure 1B). Significant decreases in TPO activity were observed at 1 μM BP-2, 10 μM Vin, 10 μM MMI, 1 μM Gen, 1 μM BP, 5 μM BABDE, and 5 μM PFOS, and significant increases in TPO activity were observed at 100 μM DBP, 1 μM Car, 0.1 μM MM, 100 μM B(a)P, and 1 μM D(a,h)A. Characteristic Large-Scale Differential Gene Expression Profiles Provide a Molecular Signature for Each Environmental Toxicant that Affects TPO Activity. Transcription is regulated by intracellular responses to biological stresses, and these stimuli result in characteristic gene expression profiles.11 To investigate large-scale changes in the gene expression profile in response to TPO activity-altering chemicals, the five TPO activity-increasing chemicals [Car, DBP, MM, B(a)P, and D(a,h)A] were divided into two sets: a training set (two chemicals) and a test set (three chemicals). Similarly, the seven TPO activitydecreasing chemicals [BP-2, Vin, MMI, Gen, BP, BABDE, and PFOS] were divided into a training set and a test set (Table 1). We performed unsupervised hierarchical clustering analysis using the chemicals in the training sets (Table 1). Of the 45 220 probes in the DNA microarray, 22 063 probes exceeded the minimal gene expression criterion of a 1.5-fold change in response to at least one toxicant. These genes represented the transcriptome of FTC-238/hTPO/RSK008 cells exposed to the chemicals. As shown in Figure 2, hierarchical clustering analysis of these genes gave six subclusters on a dendrogram; the clusters for MMI, Gen, DBP, BP-2, Car, and Vin each showed a distinct expression profile, representing the characteristic differential gene expression profile associated with each specific chemical (Figure 2B). These data suggest that the expression profiles are characteristic of each environmental toxicant, irrespective of its effect on TPO activity. Identification of Molecular Markers Characteristic of Environmental Toxicants Based on TPO Activity. We next identified molecular markers that could determine TPO activity. To do this, TPO activity was measured in the presence of each chemical using the guaiacol assay. To identify the maximum number of genes that specifically classify the toxicants as compounds that increase or decrease TPO activity, we performed two 7909



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Figure 2. Unsupervised hierarchical clustering analysis of expression profiles after exposure to TPO activity-disrupting chemicals. (A) Threedimensional diagram of 11 109 genes selected using minimal criteria. Rows represent gene expression profiles after exposure to TPO activity-disrupting chemicals. Columns represent probe measurements. (B) Dendrogram derived from clustering using the set of 11 109 genes. MMI, methimazole; Gen, genistein; DBP, dibutyl phthalate; BP-2, 2,20 ,4,40 -tetrahydroxybenzophenone; Car, carbaryl; Vin, vinclozolin.

different multiclassification algorithms [one-way analysis of variance (ANOVA) and class prediction] using a commercial analytical program (GeneSpring GX 7.3.1; Agilent Technologies). Classifier genes that could discriminate compounds that increase or decrease TPO activity (Figure 3A) were found by combining the genes identified by all three methods: gene filtering, one-way ANOVA, and class prediction. As mentioned above, we first identified 22 063 genes whose expression levels were affected by the toxicants. To classify the large-scale changes in gene expression (1966 genes) according to the chemicals’ effects on TPO activity, we then used a one-way ANOVA, with a Welch’s t test (cutoff value, p < 0.05) and the BenjaminiHochberg method for controlling the false discovery rate in multiple comparisons, to analyze the expression profiles associated with toxicants producing specific effects on TPO activity. With the class prediction tool and the k-nearest neighbors algorithm, using Fisher’s exact test for gene selection, 13 as the number of neighbors, and 0.2 as the decision cutoff p-value ratio, we identified 750 genes as being the maximum number that satisfied the ANOVA criteria with 100% accuracy. By combining the genes found by gene filtering, one-way ANOVA, and class prediction, 362 genes were identified as the maximum number specifying each effect on TPO activity for the environmental toxicants in the training set (Figures 3A and S1 of the Supporting Information, SI). Hierarchical clustering of the expression profiles of the 362 genes classified each effect (increase or decrease) on TPO activity in the dendrogram (Figure 3B,D). Thus, this selection criterion resulted in the identification of 362 genes as TPO activity classifier genes. The data indicate that disruption of TPO activity displays a robust transcriptomic response upon exposure to TPO activity-disrupting

chemicals and yields response numbers of comparable sensitivity to the traditional guaiacol assay. The 362 classifiers identified in the training sets were then validated using the test sets. The classification resulted in an affinity value to each of the two classes. For the classifications we chose an affinity threshold of zero. The class assignments are based on an affinity threshold > 0. The classifiers predicted the effects of the test toxicants with 66.7% accuracy (Figure 4). On the basis of gene expression profiles, MM, B(a)P, Car, D(a,h)A, and DBP, which were determined to increase TPO activity in the guaiacol assay, were classified as expected, and BP-2, Gen, and Vin were correctly classified as compounds that decrease TPO activity. However, MMI, BABDE, BP, and PFOS were judged to be false positives (FPs), as their predicted effects on TPO activity differed from their effects determined using the guaiacol assay. The presence of residual unmetabolized phenolic compounds may interfere with guaiacol oxidation through competitive or noncompetitive TPO inactivation mechanisms and may significantly affect the number of FPs in the results. In addition, the small number of genes expressed in response to exposure to the test compounds may be the reason for the FPs. Functions of the Characteristic Molecular Markers that Distinguish Toxicant Effects on TPO Activity. To gain insight into the functions of the molecular markers used to predict toxicant effects on TPO activity, we sought to identify the molecular mechanisms in which the 362 classifier genes participate by using the DAVID Bioinformatics Resource, which contains a functional annotation database with each gene categorized according to its primary function. The classifier genes were associated with functional categories related most significantly to the terms WD domain, nuclear 7910



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Figure 3. Identification of discernible molecular markers of TPO activity-disrupting chemicals. (A) Schema of the combination of gene filtering, oneway ANOVA, and class prediction analysis. (B) Dendrogram cluster of toxicants, showing two clusters according to the expression profiles of the 362 predictive molecular markers. (C) Accuracy was tested using the k-nearest neighbors algorithm method. (D) Results of a principal component analysis (PCA) for the set of 362 genes in the chemicals, presented as a two-dimensional scatterplot showing the categories of two different classes (TPO activators and TPO inhibitors). BP-2, 2,20 ,4,40 -tetrahydroxybenzophenone; Vin, vinclozolin; Gen, genistein; MMI, methimazole; DBP, dibutyl phthalate; Car, carbaryl.

Figure 4. Predictions for TPO activity-disrupting chemicals according to TPO activity class in transcription levels. The outcome of the classification is an affinity value to each of the two classes. The affinities of all samples to TPO activity classes are displayed according to the color scales on the left side of each diagram. The right side shows the assignment of each sample to a class on the basis of the affinity. Zero was set as the threshold, and samples with affinities below zero for two classes remained unclassified. MM, methylmercury; B(a)P, benzo(a)pyrene; Car, carbaryl; D(a,h)A, dibenzo(a,h)anthracene; DBP, dibutyl phthalate; MMI, methimazole; BABDE, bisphenol A bis ether; BP, benzophenone; BP-2, 2,20 ,4,40 -tetrahydroxybenzophenone; Gen, genistein; PFOS, perfluorooctane sulfonates; Vin, vinclozolin; D, decrease; I, increase; Affin., affinity; Assign, assignment. 7911



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Table 2. Functional Classification of 362 Classifier Genes in DAVID (http://david.abcc.ncifcrf.gov). category annotation cluster 1

annotation cluster 2






term

count

%

enrichment score: 3.29 UP_SEQ_FEATURE

repeat:WD 7

11

4.31

UP_SEQ_FEATURE

repeat:WD 6

12

4.71

UP_SEQ_FEATURE

repeat:WD 5

12

4.71

INTERPRO

IPR019782:WD40 repeat 2

12

4.71

INTERPRO

IPR017986:WD40 repeat, region

12

4.71

UP_SEQ_FEATURE

repeat:WD 4

12

4.71

INTERPRO UP_SEQ_FEATURE

IPR019781:WD40 repeat, subgroup repeat:WD 3

12 12

4.71 4.71

INTERPRO

IPR015943:WD40/YVTN repeat-like

13

5.10

UP_SEQ_FEATURE

repeat:WD 2

12

4.71

UP_SEQ_FEATURE

repeat:WD 1

12

4.71

SP_PIR_KEYWORDS

wd repeat

12

4.71

Enrichment Score: 2.59 GOTERM_CC_FAT

GO:0031981∼nuclear lumen

34

13.33

GOTERM_CC_FAT Enrichment Score: 2.26

GO:0031974∼membrane-enclosed lumen

40

15.69

GOTERM_BP_FAT

GO:0001701∼in utero embryonic development

9

3.53

GOTERM_BP_FAT

GO:0043009∼chordate embryonic development

12

4.71

GOTERM_BP_FAT

GO:0009792∼embryonic development ending in birth or egg hatching

12

4.71

Enrichment Score: 1.86 GOTERM_CC_FAT

GO:0043232∼intracellular nonmembrane-bounded organelle

48

18.82

GOTERM_CC_FAT

GO:0043228∼nonmembrane-bounded organelle

48

18.82

Enrichment Score: 1.54 GOTERM_BP_FAT

GO:0042981∼regulation of apoptosis

21

8.24

GOTERM_BP_FAT

GO:0043067∼regulation of programmed cell death

21

8.24

Enrichment Score: 1.46 GOTERM_MF_FAT

GO:0001883∼purine nucleoside binding

35

13.73

GOTERM_MF_FAT

GO:0001882∼nucleoside binding

35

13.73

GOTERM_MF_FAT

GO:0030554∼adenyl nucleotide binding

34

13.33

GOTERM_BP_FAT GOTERM_BP_FAT

GO:0007265∼Ras protein signal transduction GO:0007266∼Rho protein signal transduction

6 3

2.35 1.18

GOTERM_BP_FAT

GO:0007264∼small GTPase mediated signal transduction

8

3.14

Enrichment Score: 1.22

lumen, embryonic development, nonmembrane-bound organelles, apoptosis regulation, nucleoside binding, and Ras protein signal transduction (Table 2).

’ DISCUSSION With an increasing awareness of environmental health issues, there is greater concern regarding the potential human health risks posed by environmental compounds and chemical stresses. A major aim of current toxicogenomic research is to predict potential human health risks associated with toxicants, based on selective, sensitive biomarkers identified through the use of microarrays that measure the responses of biologically relevant genes to environmental toxicants.9 The application of microarray-based technology for gene expression profiling has contributed to rapid advancements in our understanding of global, coordinated cellular events in various paradigms.17 The potential use of transcriptomic tools in indicating exposure to unknown compounds has been suggested, but this possibility remains largely unsubstantiated to date. Nevertheless, environmental toxicology studies demonstrating the benefits of microarrays in

estimating and assessing exposure to environmental toxicants are steadily increasing. Transcriptomic analysis using microarrays may represent a valuable tool to enhance the assessment of TPO activity-disrupting chemicals. Data from these studies should be anchored to the traditional guaiacol assay. Therefore, we conducted transcriptomic analyses of TPO activity disruption to facilitate comparisons with results from the guaiacol assay and to establish a simplified method for detecting TPO activity at transcriptomic levels. Furthermore, early predictions from the use of transcriptomic end points suggest that these methods may be orders of magnitude more sensitive than traditional assays. Early detection of toxicant exposure is determined mainly by class prediction, which is based on the idea that changes in gene expression profiles associated with known toxicants represent the changes associated with other chemicals of the same toxicological class and predict their toxicological effects.9 The development of gene expression signatures may allow for early screening of suspected toxicants on the basis of their similarity to known toxicants.18 Classifier genes based on microarray analysis could be used for a 7912



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Table 3. Effects of Environmental Compounds on Thyroid Targetsa effects

BP-2

DBP

iodide uptake

V

V

?

V

V

?

?

TPO activity

V

?

v

?

V

V

?

?

√

?

√

TR agonist

ND

TR antagonist

ND

3,36

36

ref

Car

37

Vin

?

MMI

38

Gen

MM

BP

B(a)P

D(a,h)A

BABDE

?

?

?

v

?

?

V

√

?

?

?

?

?

?

40

40

38,39

20,41

PFOS

? ? 42

(Vinhibitory effect; , no effect; ND, not determined; ?, not reported; TR, thyroid hormone receptor; BP-2, 2,20 ,4,40 -tetrahydroxybenzophenone; DBP, dibutyl phthalate; Car, carbaryl; Vin, vinclozolin; MMI, methimazole; Gen, genistein; MM, methylmercury; BP, benzophenone; B(a)P, benzo(a)pyrene; D(a,h)A, dibenzo(a,h)anthracene; BABDE, bisphenol A bis ether). a

prediction system and as a replacement for the traditional complex guaiacol assay, which is time and labor consuming.19 Data on classifier genes must be accumulated for a sufficient number of chemicals in order to make predictions; only a few chemicals have been studied to date.10 In the present work, toxicants that increase or decrease TPO activity were studied by microarray analysis in in vitro experiments to identify classifier genes for the toxicants based on their effects on TPO activity. Many environmental compounds, including BPs, PAHs, POPs, pesticides, and phthalates, alter TH function, by either inhibiting or mimicking it.20 Environmental endocrine disruptors (EDCs) are exogenous chemical substances that can interfere with TH synthetic pathways, have deiodinase functions in peripheral tissues, transport proteins in the blood, and act as agonists or antagonists of target tissue receptors.21 TH plays a major role in the development and regulation of the reproductive system and in perinatal development of the central nervous system.3 Thus, disruption of the TH axis can severely impair mammalian brain maturation, resulting in mental retardation or neurological defects. Several studies have reported a potential relationship between the increase of neurodevelopmental disorders and the exponential increase in exposure to chemicals over the last 50 years.22,23 Polychlorinated biphenyls (PCBs), dioxins, and pesticides are reported to interfere with TH metabolism, for example, by decreasing T3 and T4 levels and increasing TSH levels.2428 Disturbance of the TH axis may cause thyroid tumors induced by cellular hypertrophy and proliferation in the thyroid gland.27 There are various toxicant targets in the TH axis, and some of the compounds used in the present work have been reported to disrupt some of these targets, including iodide uptake, TPO activity, and agonists and antagonists of TR (Table 3). In this work, we focused on TPO activity, owing to its importance for the TH axis, and introduced a gene-based prediction system using microarrays for rapid screening. We determined the gene expression profile of the FTC-238/hTPO recombinant cell line and identified discernible characteristic markers displaying differential expression in response to TPO activity-disrupting chemicals. Changes in gene expression were compound-specific, and a simple nonparametric analysis yielded a large number of genes (11 109) that could identify each compound. Using a combination of gene filtering, one-way ANOVA, and class prediction analysis, we identified 362 genes that could distinguish between TPO-increasing and TPO-decreasing toxicants in the training set with 100% accuracy. These 362 classifier genes showed 66.7% accuracy when validated using the additional toxicants in the test set. These results suggest that the gene transcription responses were TPO activityspecific and may contribute to early detection of chemical exposure. The biological processes associated with the 362 prediction markers included the WD domain, embryonic development,

apoptosis regulation, and Ras protein signal transduction. WD domains have been implicated in signal transduction and transcription regulation, and play roles in the cell cycle and apoptosis.29,30 The disruption of TPO activity in Graves’ disease is related to increased stimulation of apoptosis in thyroid follicular cells.31 Propylthiouracil-induced hypothyroidism due to the inhibition of TPO activity results in TH deficiency, as well as alterations in oxidative stress and the consequent apoptosis of cerebellum cells during neonatal brain development.32 The inactivation of TPO was also shown to induce the inhibition of thyrocyte function by deregulation of TGF-β1.33 Ras is an important marker of thyroid carcinomas induced by the disruption of TPO activity.34,35 Therefore, it can be inferred from these data that disruption of TPO activity influences apoptosis in thyroid follicular cells. Future work will examine the induction of apoptosis by TPO activity-inducing and -inhibiting toxicants. The present work identified characteristic gene expression patterns for toxicants that stimulate or inhibit TPO activity and showed that these molecular signatures could be used to determine exposure to specific environmental compounds. Moreover, these expression patterns may be useful as discernible surrogate markers for determining biological responses to toxicant exposure. Further work is required to provide mechanistic insight into the functional effects of these gene signatures.

’ ASSOCIATED CONTENT

bS

Supporting Information. S1 is the list of classifier genes for TPO activity. This information is available free of charge via the Internet at http://pubs.acs.org/.

’ AUTHOR INFORMATION Corresponding Author

*Fax: 082-02-958-5059, e-mail: [email protected].

’ ACKNOWLEDGMENT This study was supported by Korea Research Foundation Grants (to J. C. Ryu) from the Korean Ministry of the Environment as “The Eco-technopia 21 Project”, KIST Program of the Republic of Korea. ’ REFERENCES (1) Gavaret, J. M.; Cahnmann, H. J.; Nunez, J. Thyroid hormone synthesis in thyroglobulin. The mechanism of the coupling reaction. J. Biol. Chem. 1981, 256, 9167–9173. (2) Ohye, H.; Sugawara, M. Dual oxidase, hydrogen peroxide and thyroid diseases. Exp. Biol. Med. (Maywood) 2010, 235, 424–433. 7913


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Identification of Classifiers for Increase or Decrease of Thyroid

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