EPA/600/R-09/028F | September 2009 | www.epa.gov

An Approach to Using Toxicogenomic Data in 

U.S. EPA Human Health Risk Assessments: 

A Dibutyl Phthalate Case Study
Document cover:

The document cover was designed by Katherine Loizos of SRA International, Creative Services, 

Cincinnati, OH. Permission was obtained to use the following images: 

Human chromosome image: Elsevier Press. The source is the article published in Genomics 

9(4) by Ward, DC and Baldini, A in the article, in situ hybridization banding of human 

chromosomes with Alu-PCR products: a simultaneous karyotype for gene mapping studies, pp. 

770-774. Copyright Elsevier (1991).

Microarray experiment output image: Poirazi Laboratory, Institute of Molecular Biology and 

Biotechnology, Foundation for Research and Technology - Hellas, Crete, Greece.

Gene network image: Justen Andrews, Indiana University. The data for the gene network 

image are from the paper: Costello JC, Dalkilic MM, Beason SM, et al. (2009). Gene networks 

in Drosophila melanogaster: integrating experimental data to predict gene function. Genome 

Biol 2009 Sep 16;10(9):R97. [Epub ahead of print]
EPA/600/R-09/028F

September 2009

An Approach to Using Toxicogenomic Data

In U.S. EPA Human Health Risk Assessments:

A Dibutyl Phthalate Case Study

National Center for Environmental Assessment

Office of Research and Development

U.S. Environmental Protection Agency

Washington, DC 20460
ii

DISCLAIMER

This document has been reviewed in accordance with U.S. Environmental Protection 

Agency policy and approved for publication. Mention of trade names or commercial products 

does not constitute endorsement or recommendation for use. 

Preferred citation:

U.S. Environmental Protection Agency (EPA). (2009) An approach to using toxicogenomic data 

in U.S. EPA human health risk assessments: a dibutyl phthalate case study. National Center for 

Environmental Assessment, Washington, DC; EPA/600/R-09/028F. Available from the National 

Technical Information Service, Springfield, VA, and online at http://www.epa.gov/ncea.
iii

CONTENTS

LIST OF TABLES........................................................................................................................ vii

LIST OF FIGURES ....................................................................................................................... ix

LIST OF ABBREVIATIONS AND ACRONYMS ...................................................................... xi

PREFACE.................................................................................................................................... xiv

AUTHORS, CONTRIBUTORS, AND REVIEWERS ................................................................ xv

ACKNOWLEDGMENTS ........................................................................................................... xvi

1. EXECUTIVE SUMMARY .................................................................................................. 1-1

1.1. APPROACH................................................................................................................ 1-1

1.2. DBP CASE STUDY.................................................................................................... 1-2

1.3. RECOMMENDATIONS ............................................................................................ 1-5

1.4. RESEARCH NEEDS .................................................................................................. 1-7

2. INTRODUCTION ................................................................................................................ 2-1

2.1. PURPOSE ................................................................................................................... 2-1

2.2. REPORT OVERVIEW ............................................................................................... 2-3

2.3. USE OF TOXICOGENOMICS IN RISK ASSESSMENT ........................................ 2-4

2.3.1. Definitions....................................................................................................... 2-4

2.3.2. Current Efforts to Utilize Toxicogenomic Data in Risk Assessment.............. 2-7

2.3.2.1. Toxicogenomics Informs TD ........................................................... 2-7

2.3.2.2. Toxicogenomics Informs Dose-Response........................................ 2-9

2.3.2.3. Toxicogenomics Informs Interspecies Extrapolations................... 2-10

2.3.2.4. Toxicogenomics Informs Intraspecies Variability ......................... 2-11

2.3.2.5. TK/TD Linkages Informed by Toxicogenomic Data ..................... 2-11

2.3.2.6. Toxicogenomic Activities at the U.S. Food and Drug 

Administration (FDA).................................................................... 2-12

2.3.2.7. Toxicogenomic Activities at EPA.................................................. 2-14

2.3.2.8. Toxicogenomic Activities at Other Agencies and Institutions....... 2-16

2.3.3. Current Challenges and Limitations of Toxicogenomic Technologies......... 2-18

2.4. INTRODUCTION TO THE CASE STUDY ............................................................ 2-19

2.4.1. Project Team.................................................................................................. 2-19

2.4.2. Chemical Selection........................................................................................ 2-19

2.4.2.1. Six Candidate Chemicals................................................................ 2-20

2.4.2.2. Selection of the Case-Study Chemical ........................................... 2-21

2.4.3. Case-Study Scope.......................................................................................... 2-23

3. DBP CASE-STUDY APPROACH AND EXERCISE......................................................... 3-1

3.1. EVALUATING THE EXTERNAL REVIEW DRAFT OF THE IRIS 

TOXICOLOGICAL REVIEW (TOX REVIEW) OF DBP ........................................ 3-1

3.2. CONSIDERATION OF RISK ASSESSMENT ASPECTS THAT 

TOXICOGENOMIC DATA MAY ADDRESS.......................................................... 3-3

3.2.1. Informing TK .................................................................................................. 3-7
iv

CONTENTS (continued)

3.2.1.1. Identification of Potential Metabolic and Clearance 

Pathways........................................................................................... 3-7

3.2.1.2. Selection of Appropriate Dose Metrics............................................ 3-8

3.2.1.3. Intra- and Interspecies Differences in Metabolism .......................... 3-8

3.2.1.4. TK/TD Linkages and Feedback ....................................................... 3-9

3.2.1.5. Research Needs for Toxicogenomic Studies to Inform TK ............. 3-9

3.2.1.6. DBP Case Study: Do the Available Toxicogenomic Data 

Inform TK?..................................................................................... 3-10

3.2.2. Informing Dose-Response............................................................................. 3-13

3.2.2.1. DBP Case Study: Do the Toxicogenomic Data Inform DoseResponse?....................................................................................... 3-13

3.2.3. Informing TD ................................................................................................ 3-14

3.2.3.1. General Considerations: TD Portion of Mechanisms of 

Action and MOAs .......................................................................... 3-14

3.2.3.2. DBP Case Study: MOAs for Male Reproductive 

Developmental Effects................................................................... 3-14

3.3. IDENTIFYING AND SELECTING QUESTIONS TO FOCUS THE DBP 

CASE STUDY .......................................................................................................... 3-17

4. EVALUATION OF THE REPRODUCTIVE DEVELOPMENTAL TOXICITY 

DATA SET FOR DBP.......................................................................................................... 4-1

4.1. CRITERIA AND RATIONALE FOR INCLUSION OF TOXICOLOGY 

STUDIES IN THE EVALUATION............................................................................ 4-2

4.2. REVIEW OF THE TOXICOLOGY DATA SET ..................................................... 4-10

4.3. UNEXPLAINED MOAs FOR DBP MALE REPRODUCTIVE TOXICITY 

OUTCOMES............................................................................................................. 4-21

4.4. CONCLUSIONS ABOUT THE TOXICITY DATA SET EVALUATION: 

DECISIONS AND RATIONALE............................................................................. 4-26

5. EVALUATION OF THE DBP TOXICOGENOMIC DATA SET...................................... 5-1

5.1. METHODS FOR ANALYSIS OF GENE EXPRESSION: DESCRIPTION OF 

MICROARRAY TECHNIQUES AND SEMI-QUANTITATIVE RT-PCR ............. 5-1

5.1.1. Microarray Technology................................................................................... 5-1

5.1.2. Reverse Transcription-Polymerase Chain Reaction (RT-PCR)...................... 5-2

5.2. REVIEW OF THE PUBLISHED DBP TOXICOGENOMIC STUDIES................... 5-3

5.2.1. Overview of the Toxicogenomic Studies........................................................ 5-3

5.2.2. Microarray Studies.......................................................................................... 5-3

5.2.2.1. Shultz et al. (2001) ........................................................................... 5-3

5.2.2.2. Bowman et al. (2005)....................................................................... 5-7

5.2.2.3. Liu et al. (2005)................................................................................ 5-8

5.2.2.4. Thompson et al. (2005) .................................................................. 5-10

5.2.2.5. Plummer et al. (2007)..................................................................... 5-12
v

CONTENTS (continued)

5.2.3. RT-PCR Studies............................................................................................ 5-14

5.2.3.1. Barlow et al. (2003)........................................................................ 5-14

5.2.3.2. Lehmann et al. (2004) .................................................................... 5-15

5.2.3.3. Thompson et al. (2004) .................................................................. 5-16

5.2.3.4. Wilson et al. (2004)........................................................................ 5-17

5.2.4. Study Comparisons........................................................................................ 5-18

5.2.4.1. Microarray Study Methods Comparison ........................................ 5-18

5.2.4.2. RT-PCR Study Methods Comparison ............................................ 5-19

5.3. CONSISTENCY OF FINDINGS.............................................................................. 5-22

5.3.1. Microarray Study Findings............................................................................ 5-22

5.3.2. RT-PCR Gene Expression Findings.............................................................. 5-25

5.3.3. Protein Study Findings.................................................................................. 5-25

5.3.4. DBP Toxicogenomic Data Set Evaluation: Consistency of Findings 

Summary ....................................................................................................... 5-26

5.4. DATA GAPS AND RESEARCH NEEDS ............................................................... 5-30

5.5. PATHWAY ANALYSIS OF DBP MICROARRAY DATA ................................... 5-31

5.5.1. Objective of the Reanalysis of the Liu et al. (2005) Study ........................... 5-31

5.5.2. Pathway Analysis of Liu et al. (2005) Utilizing Two Different Methods 

to Generate Hypotheses for MOAs Underlying the Unexplained Testes 

Endpoints....................................................................................................... 5-32

5.5.2.1. Two Methods for Identifying Differentially Expressed 

Genes (DEGs)................................................................................. 5-34

5.5.2.2. Pathway Analysis........................................................................... 5-37

5.6. CONCLUSIONS....................................................................................................... 5-46

6. EXPLORATORY METHODS DEVELOPMENT FOR ANALYSIS OF GENOMIC 

DATA FOR APPLICATION TO RISK ASSESSMENT .................................................... 6-1

6.1. OBJECTIVES AND INTRODUCTION..................................................................... 6-1

6.2. PATHWAY ANALYSIS AND GENE INTERACTIONS AFTER IN UTERO

DBP EXPOSURE........................................................................................................ 6-2

6.2.1. Pathway Activity Approach ............................................................................ 6-2

6.2.1.1. Significance Analysis of Pathway Activity Levels.......................... 6-3

6.2.1.2. Pathway Activity Analysis............................................................... 6-4

6.2.2. Developing a Temporal Gene Network Model ............................................... 6-9

6.3. EXPLORATORY METHODS: MEASURES OF INTERSPECIES (RAT-TOHUMAN) DIFFERENCES IN TOXICODYNAMICS ............................................ 6-12

6.4. CONCLUSIONS....................................................................................................... 6-19

7. CONCLUSIONS .................................................................................................................. 7-1

7.1. APPROACH FOR EVALUATING TOXICOGENOMIC DATA IN 

CHEMICAL ASSESSMENTS ................................................................................... 7-1

7.2. DBP CASE-STUDY FINDINGS................................................................................ 7-4
vi

CONTENTS (continued)

7.2.1. MOA Case Study Question: Do the DBP Genomic Data Inform 

Mechanism(s) of Action and MOA(s)?........................................................... 7-5

7.2.2. Interspecies MOA Case Study Question: Do the DBP Genomic Data 

Inform Interspecies Differences in TD?.......................................................... 7-8

7.2.3. Application of Genomic Data to Risk Assessment: Exploratory 

Methods and Preliminary Results.................................................................... 7-9

7.2.4. Application of Genomic Data to Risk Assessment: Using Data 

Quantitatively ................................................................................................ 7-10

7.3. LESSONS LEARNED.............................................................................................. 7-12

7.3.1. Research Needs ............................................................................................. 7-13

7.3.1.1. Data Gaps and Research Needs: DBP............................................ 7-13

7.3.1.2. Research Needs for Toxicity and Toxicogenomic Studies for 

Use in Risk Assessment ................................................................. 7-14

7.3.2. Recommendations......................................................................................... 7-16

7.3.3. Application of Genomic Data to Risk Assessment: Future 

Considerations............................................................................................... 7-18

APPENDIX A: SUPPORTING TABLES FOR CHAPTER 5.................................................. A-1

APPENDIX B: SUPPORTING TABLES AND FIGURES FOR CHAPTER 6........................B-1

APPENDIX C: QUALITY CONTROL AND ASSURANCE...................................................C-1

GLOSSARY ............................................................................................................................... G-1

REFERENCES ............................................................................................................................R-1
vii

LIST OF TABLES

2-1. Information available July 2005 on the selection criteria for the six candidate 

chemicals affecting the androgen-mediated male reproductive developmental 

toxicity pathway............................................................................................................. 2-22

4-1. Studies with exposures during development that have male reproductive 

outcomes (limited to reproductive organs and/or reproductive function) and 

were considered adequate for reference value determination.......................................... 4-4

4-2. Reporting and study size characteristics of male reproductive studies following 

in utero exposure to DBP............................................................................................... 4-12

4-3. Life stage at observation for various male reproductive system outcomes 

assessed in studies of developmental exposure to DBP................................................. 4-15

4-4. Age of assessment for individual endpoints across studies of the male 

reproductive system following developmental exposure to DBP.................................. 4-16

4-5. Incidence of gross pathology in F1 male reproductive organs in one continuous 

breeding study with DBP............................................................................................... 4-22

4-6. Evidence for MOAs for the observed effects in the male reproductive system after 

in utero DBP exposure................................................................................................... 4-24

5-1. Study comparisons for the toxicogenomic data set from male tissues after in utero

DBP exposure .................................................................................................................. 5-4

5-2. Lehmann et al. (2004) DBP dose-response gene expression data measured by RTPCR showing statistically significant changes from control ......................................... 5-16

5-3. Method comparisons for DBP microarray studies......................................................... 5-19

5-4. Method comparisons among the RT-PCR DBP studies................................................ 5-20

5-5. Evaluation of the published protein studies after DBP in utero exposure (testes 

only)............................................................................................................................... 5-27

5-6. Common pathways between the REM and SNR analyses of differentially 

expressed genes (DEGs) after in utero DBP exposure from the Liu et al. (2005) 

data................................................................................................................................. 5-39

5-7. Genes involved in cholesterol biosynthesis/metabolism that were identified by 

both the REM and SNR analyses of Liu et al. (2005) ................................................... 5-42

6-1. The KEGG pathways ordered based on their p-value for pathway activity .................... 6-7
viii

LIST OF TABLES (continued)

6-2. The amino acid sequence similarity of the enzymes in the steroidogenesis 

pathway between rat and human.................................................................................... 6-17

7-1. DBP dose-response progression of statistically significant events illustrated with 

a subset of precursor event data (steroidogenesis gene expression, T expression) 

and in vivo endpoints with the reduced T MOA............................................................ 7-11

7-2. Research needs for toxicogenomic studies to be used in risk assessment ..................... 7-15

7-3. Research needs for toxicity studies for utilizing toxicogenomic and toxicity data 

together in risk assessment............................................................................................. 7-17
ix

LIST OF FIGURES

2-1. The relationship between the project process, goals, and products for the 

development of an approach and case study for the use of toxicogenomic data in 

risk assessment................................................................................................................. 2-2

2-2. Androgen-mediated male reproductive developmental toxicity pathway ..................... 2-20

3-1. DBP case-study approach for evaluating toxicogenomic data for a health 

assessment........................................................................................................................ 3-2

3-2. Exposure response array for candidate endpoints and PODs for RfD derivation

presented in the external review draft IRIS Tox Review for DBP (U.S. EPA, 

2006a) .............................................................................................................................. 3-4

3-3. Potential uses of toxicogenomic data in chemical screening and risk assessment .......... 3-5

3-4. Potential uses of toxicogenomic data in understanding mechanisms of action............... 3-6

3-5. The fetal Leydig cell in the fetal testis........................................................................... 3-12

3-6. Approach to utilizing toxicity and toxicogenomic data for identifying affected 

pathways and candidate modes and mechanisms of action ........................................... 3-15

3-7. The proposed DBP mechanism of action for the male reproductive developmental 

effects............................................................................................................................. 3-16

4-1. The process for evaluating the male reproductive developmental toxicity data set 

for low-dose and low-incidence findings......................................................................... 4-3

4-2. The process for evaluating the MOA for individual male reproductive system 

outcomes following developmental DBP exposure....................................................... 4-23

5-1. Venn diagram illustrating similarities and differences in significant gene 

expression changes observed in three recent microarray studies of the testes:

Thompson et al. (2005), Plummer et al. (2007), and Liu et al. (2005).......................... 5-24

5-2. Summary of DBP-induced changes in fetal gene and protein expression..................... 5-29

5-3. Schematic of the two analysis methods (REM and SNR) for identifying 

differentially expressed genes and subsequent pathway analysis using GeneGo.......... 5-33

5-4. Heat map of 1,577 DEGs from SNR analysis method................................................... 5-37

5-5. Mapping the Liu et al. (2005) data set onto the canonical androstenedione and 

testosterone (T) biosynthesis and metabolism pathway in MetaCore (GeneGo) .......... 5-43
x

LIST OF FIGURES (continued)

6-1. An illustration of the adapted version of pathway activity level analysis for the 

tryptophan metabolism pathway, a nonactive pathway for DBP..................................... 6-5

6-2. Metabolic pathway network for DBP (Liu et al., 2005 data) using the pathway 

activity method and the KEGG database......................................................................... 6-8

6-3. The relationship between differential expression of individual genes and 

pathway activity using the Liu et al. (2005) DBP data .................................................. 6-10

6-4. A gene network for DBP data of Liu et al. (2005) generated using Ingenuity 

Pathway Analysis (IPA)................................................................................................. 6-11

6-5. A temporal gene network model created by IPA from the informative 

gene list based on time-course data after in utero DBP exposure ................................. 6-13

6-6. The phylogenetic relations among eight organisms based on enzyme presence, for the 

biosynthesis of steroids pathway, and based on information available on the NCBI 

taxonomy website (Sayers et al., 2008) ......................................................................... 6-15

7-1. Approach for evaluating and incorporating genomic data into future chemical 

assessments...................................................................................................................... 7-2
xi

LIST OF ABBREVIATIONS AND ACRONYMS

Please note that most gene and protein name abbreviations are not included in this list 

because of the large number of genes and proteins described in the report. The gene and protein 

names have been standardized using information from the Rat Genome Project.

ADH alcohol dehydrogenase

ADME absorption, distribution, metabolism, and excretion

AGD anogenital distance

AMH anti-mullerian hormone

ANOVA analysis of variance

AR androgen receptor

BBDR biologically based dose-response

BBP butyl benzyl phthalate

BMD benchmark dose

BMDL benchmark dose lower confidence limit

BPA bisphenol A

cDNA complementary DNA

CNPs copy-number polymorphisms

DBP dibutyl phthalate

DEG differentially expressed gene

DEHP di-(2-ethylhexyl) phthalate

DEP diethyl phthalate

DMP dimethyl phthalate

DOTP diocytyl tere-phthalate

DPP dipentyl phthalate 

EDC endocrine disrupting chemical

EPA Environmental Protection Agency

ER estrogen receptor

ESTs expressed sequence tags

FDA Food and Drug Administration

GAPDH glyceraldehyde-3-phosphate dehydrogenase

GD gestation day

GO Gene Ontology

GSH glutathione
xii

LIST OF ABBREVIATIONS AND ACRONYMS (continued)

HESI Health and Environmental Sciences Institute

ILSI International Life Sciences Institute

IPA Ingenuity® Pathway Analysis

IPCS International Programme on Chemical Safety 

IRIS Integrated Risk Information System

KEGG Kyoto Encyclopedia of Genes and Genomes

LC Leydig cell

LMW low molecular weight

LOAEL lowest-observed-adverse-effect level

LOEL lowest-observed-effect level

MAPK/ERK mitogen-activated protein kinase/extracellular signal-regulated kinase

MAQC MicroArray Quality Control

MAS microarray suite

MBP monobutylphthalate

MMP matrix metalloproteinase

MOA mode of action

mRNA messenger RNA

NCCT National Center for Computational Toxicology

NCEA National Center for Environmental Assessment

NIEHS National Institute of Environmental Health Sciences

NOAEL no-observed-adverse-effect level

NOEL no-observed-effect level

NRC National Research Council

NTP National Toxicology Program

PA pathway activity

PBPK physiologically-based pharmacokinetic

PCA principal component analysis

PCR polymerase chain reaction

PFOA perfluorooctanoic acid

PND postnatal day

POD point of departure

PPAR peroxisome proliferator-activated receptor

PPS preputial separation
xiii

LIST OF ABBREVIATIONS AND ACRONYMS (continued)

RA risk assessment

RACB reproductive assessment by continuous breeding

RfD reference dose

RT-PCR reverse transcription-polymerase chain reaction

SD Sprague-Dawley

SLR signal log ratio

SNPs single nucleotide polymorphisms

SNR signal-to-noise ratio

SPC Science Policy Council

STAR Science to Achieve Results

T testosterone

TD toxicodynamics

TF transcription factor

TK toxicokinetics 

Tox Review Toxicological Review

UFH intraspecies uncertainty factor

UMDNJ University of Medicine and Dentistry of New Jersey

VLI valine, leucine, isoleucine

WD Wolffian duct

WOE weight-of-evidence
xiv

PREFACE

The U.S. Environmental Protection Agency (EPA) is interested in developing methods to 

use genomic data most effectively in risk assessments performed at EPA. The National Center 

for Environmental Assessment (NCEA) within the Office of Research and Development (ORD) 

prepared this document for the purpose of describing and illustrating an approach for using 

toxicogenomic data in risk assessment. The approach and dibutyl phthalate (DBP) case study 

described in this document were developed by a team of scientists at EPA laboratories and 

centers, and outside organizations including The Hamner Institutes for Health Sciences, the 

National Institute of Environmental Health Sciences (NIEHS), and the EPA National Center for 

Environmental Research (NCER) Science to Achieve Results (STAR) Environmental 

Bioinformatics and Computational Toxicology (Comp Tox) Center at the University of Medicine 

and Dentistry of New Jersey (UMDNJ) and Rutgers University. The intended audience for this 

document includes risk assessors as well as scientists with expertise in genomics, bioinformatics, 

toxicology, and statistics. The approach outlined in this document is expected to be useful to 

EPA risk assessors in the Integrated Risk Information System (IRIS) Program and other program 

offices and regions, as well as the scientific community at large. The review of the literature on 

the use of genomic data in risk assessment, as well as discussions of issues, recommendations, 

and methods for evaluating and analyzing toxicogenomic data, could be useful to scientists and 

risk assessors within and outside of EPA. The research needs identified in this document will be 

useful to scientists performing toxicology and toxicogenomic research studies for application to 

risk assessment. The DBP case study presented in this document is a separate activity from the 

IRIS DBP health assessment. The review of the literature included in this document was last 

updated in July 2007.
xv

AUTHORS, CONTRIBUTORS, AND REVIEWERS

AUTHORS

Susan Euling, NCEA-W, U.S. EPA, Washington, DC (Lead)

Ioannis Androulakis, NCER STAR Bioinformatics Center, ebCTC, Rutgers/UMDNJ, 

Piscataway, NJ 

Bob Benson, Region 8, U.S. EPA, Denver, CO 

Weihsueh Chiu, NCEA-W, U.S. EPA, Washington, DC 

Paul Foster, NIEHS, Research Triangle Park (RTP), NC 

Kevin Gaido, U.S. FDA, Rockville, MD; formerly of *The Hamner Institutes for Health 

Sciences, RTP, NC

L. Earl Gray Jr., NHEERL, U.S. EPA, RTP, NC 

Susan Hester, NHEERL, U.S. EPA, RTP, NC 

Channa Keshava, IRIS, U.S. EPA, Washington, DC

Nagalakshmi Keshava, NCEA-W, U.S. EPA, Washington, DC 

Andrea Kim, Allergan, Inc., Irvine, CA; formerly of *NCEA-W, U.S. EPA, Washington, DC 

Susan Makris, NCEA-W, U.S. EPA, Washington, DC

Meric Ovacik, NCER STAR Bioinformatics Center, ebCTC, Rutgers/UMDNJ, Piscataway, NJ

Banalata Sen, NIEHS; formerly of *NCEA-RTP, U.S. EPA, RTP, NC

Chad Thompson, ToxStrategies, Katy, TX; formerly of *NCEA-W, U.S. EPA, Washington, DC 

Lori White, *NIEHS; formerly of *NCEA-RTP, U.S. EPA, RTP, NC 

Vickie Wilson, NHEERL, U.S. EPA, RTP, NC

CONTRIBUTORS

Stan Barone, NCEA-W, U.S. EPA, Washington, DC 

Marianthi Ierapetritou, NCER STAR Bioinformatics Center, ebCTC, Rutgers/UMDNJ, 

Piscataway, NJ 

____________________

*Affiliation at the time of work on this project for scientists with more than one affiliation listed.
xvi

AUTHORS, CONTRIBUTORS, AND REVIEWERS (CONTINUED)

REVIEWERS

Internal 

Maureen R. Gwinn, NCEA-W, ORD, Washington, DC

Michael Hemmer, NHEERL, ORD, Gulf Breeze, FL

Nancy McCarroll, Office of Pesticide Programs (OPP), Washington, DC

Gregory Miller, Office of Children's Health Protection and Environmental Education 

(OCHPEE), Washington, DC

Marian Olsen, Region 2, New York, NY

Santhini Ramasamy, Office of Water (OW), Washington, DC

Jennifer Seed, Office of Pollution Prevention and Toxics (OPPT), Washington, DC

Imran Shah, National Center for Computational Toxicology (NCCT), ORD, RTP, NC

Jamie Strong, IRIS, ORD, Washington, DC

Dan Villeneuve, NHEERL, ORD, Duluth, MN

External 

Jeanne Manson (chair), Exponent, Inc.

Robert Chapin, Pfizer, Inc.

Julia Gohlke, NIEHS

Hisham Hamadeh, Amgen, Inc.

Poorni Iyer, California EPA (Note: Dr. Iyer conducted the peer review as a private consultant 

and not as a representative of California EPA)

ACKNOWLEDGMENTS

This project was funded by NCEA and the EPA NCCT’s Research Program under their 

new starts grants. We thank the outside partners, NIEHS and The Hamner Institutes for Health 

Sciences, for allowing team members at these institutions to work on this project. Some of the

work described was performed at the STAR Bioinformatics Center at UMDNJ and Rutgers 

University that is supported by the grant R832721 from the EPA’s STAR program. We 

gratefully acknowledge Dr. Kevin Gaido for providing the data from the Liu et al. (2005) study 

performed in his laboratory at The Hamner Institutes for Health Sciences, Terri Konoza of 

NCEA for her detailed editorial contribution to this document, and Sarah Burgess-Herbert for her 

thoughtful review of Chapter 6.
1-1

1. EXECUTIVE SUMMARY

We developed a systematic approach for evaluating and utilizing toxicogenomic data in 

health assessment. This report describes this approach and a case study conducted for dibutyl 

phthalate (DBP) to illustrate the approach. As a result of the case-study exercise, we refined the 

initial case-study approach for general use in new chemical assessments. In this report, we 

reviewed some of the recent and ongoing activities regarding the use of genomic data in risk 

assessment, inside and outside of the U.S. Environmental Protection Agency (EPA). We also 

identified research needs, recommendations, and issues for future consideration when using 

genomic data in risk assessments.

Toxicogenomics is the application of genomic technologies (e.g., transcriptomics, 

proteomics, metabolomics, genome sequence analysis) to study the effects of environmental 

chemicals on human health and the environment. The EPA Interim Genomics Policy (U.S. EPA, 

2002a) encourages the use of genomic data, on a case-by-case basis, in a weight-of-evidence 

(WOE) approach. Currently, EPA provides no guidance for incorporating genomic data into risk 

assessments of environmental agents. However, EPA’s Science Policy Council (SPC) has 

developed interim guidance regarding other aspects of the use of microarray data at EPA, 

entitled Interim Guidance for Microarray-Based Assays: Data Submission, Quality, Analysis, 

Management, and Training Considerations (U.S. EPA, 2006b). 

DBP was selected for the case study because it has a relatively large genomic data set and 

phenotypic anchoring of certain gene expression data to some male reproductive developmental 

outcomes. The scope of the case study was limited to the male reproductive developmental 

outcomes of DBP, and this effort was limited to evaluating the available published toxicity and 

toxicogenomic data for the DBP case study. The DBP case study is a separate endeavor with 

distinct goals from EPA’s Integrated Risk Information System (IRIS) assessment of DBP.

1.1. APPROACH

Genomic data have the potential to inform toxicodynamics (TD), toxicokinetics (TK), 

inter- and intraspecies differences in TD and TK, exposure assessment, and dose-response 

assessment. Our strategy was to design an approach for evaluating genomic data for risk 

assessment that is both systematic and flexible enough to accommodate different health and risk 
1-2

assessment practices. The first step of the approach is to evaluate the available genomic data set 

for its application to a broad range of information types (e.g., TD, TK, intra-and interspecies TD 

and TK differences) that are useful to risk assessment as well as the steps of health assessment 

(e.g., hazard characterization, dose-response assessment). Through this iterative process, the 

potential use of the available genomic data is determined. As part of the scoping step, the 

available human, toxicology, and genomics studies are reviewed to determine their use to the 

genomic data set evaluation. For instance, the toxicity, human, and toxicogenomic data sets are 

considered together to determine the relationship (i.e., degree of phenotypic anchoring) between 

gene and pathway changes to health or toxicity outcomes. As a result of the scoping step, 

questions are posed to direct and focus the evaluation of the genomic data set.

The next steps include detailed evaluations directed by the formulated questions of the 

toxicity and/or epidemiological data sets and the toxicogenomic data set. For example, when 

genomic data are available to inform mechanisms of action or modes of action (MOAs), the 

toxicogenomic and toxicity data sets can be evaluated together, relating the affected endpoints 

(identified in the toxicity data set evaluation) to the genes and/or pathways (identified in the 

toxicogenomic data set evaluation) to establish or formulate hypotheses about an MOA. In 

addition to informing the mechanisms of action and the MOAs (TD and TK steps), genomic data 

also have the potential to inform inter- and intraspecies TD differences, and dose-response 

assessment, depending on the genomic study design (e.g., species, organ, single dose vs. multiple 

doses, genomic method) of the available data. The approach also includes new analyses of the 

genomic data for the purpose of risk assessment when data are available and such new analyses 

may address questions that are relevant to the risk assessment.

1.2. DBP CASE STUDY

For the DBP case-study example, we utilized the data set summaries and data gaps 

identified in the external review draft IRIS Tox Review for DBP (U.S. EPA, 2006a) and asked 

whether the genomic data set could inform any of these data gaps. In parallel, the DBP genomic 

data set was considered, in light of all risk assessment aspects that these data might inform. As a 

result of following these two processes, we formulated two specific case-study questions that the 

available genomic data for DBP had the potential to inform:
1-3

• Do the toxicogenomic data inform the mechanisms of action and/or MOAs for DBP?

• Do the toxicogenomic data inform interspecies differences in TD?

The team considered it highly likely that the DBP toxicogenomic data set could inform the 

modes or mechanisms of action. The team considered it possible, but less certain, that the crossspecies differences in one or more DBP MOAs could be informed by evaluating genomic data 

(e.g., DNA sequence data).

Additional questions were excluded because appropriate data were lacking. For example, 

one question of great interest is, Do the toxicogenomic data inform dose-response? However, 

this question could not be addressed in this case study because there were no dose-response 

genomic data for DBP. Few chemicals have available dose-response genomic data and DBP is 

not unusual in this respect. The evaluation of the one available DBP dose-response gene 

expression study, although not global, is discussed in the report. As a result of the DBP genomic 

data set limitations, the case study focuses on the qualitative application of genomic data to risk 

assessment. In addition, exposure assessment was not considered in this approach because the 

case study was performed using the IRIS chemical assessment model, which only includes 

hazard identification and dose-response steps of the risk assessment paradigm.

We found that the DBP toxicogenomic data did inform the mechanism of action, and 

generated hypotheses about possible additional MOAs, for DBP and male reproductive 

developmental outcomes. There is substantial evidence in the published literature that a number 

of the gene expression changes observed in genomic studies are phenotypically anchored for a 

number of the male reproductive developmental outcomes observed after in utero DBP exposure 

in the rat. The available genomic and other gene expression data, hormone level data, and 

toxicity data for DBP are instrumental in the establishment of two MOAs: (1) a decrease in fetal 

testicular testosterone (T); and (2) a decrease in Insulin-like 3 (Insl3) expression. A decrease in 

fetal testicular T is a well-established MOA for a number of the male reproductive 

developmental effects observed in the male rat after in utero DBP exposure. The genomic and 

single gene expression data, after in utero DBP exposure, identified changes in genes involved in 

steroidogenesis and cholesterol transport, consistent with the observed decrease in fetal testicular 

T. Decreased Insl3 expression is a second well-established MOA responsible, in conjunction 

with reduced T, for the undescended testis effect observed following in utero DBP exposure. 
1-4

Reverse transcription-polymerase chain reaction (RT-PCR) and in vivo toxicology study results

support the role of Insl3 in one of the two steps of testis descent.

Evaluating genomic and toxicity data together also provides information on putative 

novel MOAs. A number of the DBP toxicity and toxicogenomic studies were performed in the 

same strain of rat using similar doses and exposure intervals that allowed for comparisons across 

studies. In this case study, rodent reproductive developmental toxicity studies were evaluated for 

low incidence and low-dose findings and for the male reproductive developmental effects that 

currently do not have an explained MOA (termed “unexplained endpoints”). In the case study, 

we focused on the outcomes in the testes because all, but one, of the DBP toxicogenomic studies 

were performed on testes. We identified five testicular endpoints without a known MOA that 

were pursued further in the evaluation of the toxicogenomic data set. 

The nine published RT-PCR and microarray studies in the rat were evaluated as part of 

the toxicogenomic and associated gene expression data set to identify genes and pathways 

affected after in utero DBP exposure. Both the microarray data set alone and the entire gene 

expression data set (including all gene expression studies including microarray studies) were 

evaluated for consistency of findings. At the gene level, the findings from the DBP genomic 

studies (i.e., microarray, RT-PCR, and protein expression) were relatively highly correlated with 

one another in both the identification of differentially expressed genes (DEGs) and their 

direction of effect. The evaluation of the published toxicity and toxicogenomic studies 

corroborates the two known MOAs for DBP.

The published microarray studies for DBP focused primarily on pathways related to the 

reduced fetal testicular T MOA, such as the steroidogenesis pathway. We performed new 

analyses of the data from one rat testes microarray study in order to identify all possible 

pathways significantly affected by in utero DBP exposure. Using two different analytical 

methods, pathways associated with the two well-established MOAs (decreased Insl3 and fetal 

testicular T), as well as new processes (e.g., growth and differentiation, transcription, cell 

adhesion) and pathways (e.g., Wnt signaling, cytoskeleton remodeling) not associated with either 

Insl3 or steroidogenesis pathways, were identified. The newly identified putative pathways may 

play a role in the regulation of steroidogenesis (i.e., related to a known MOA for DBP) or, 

alternatively, may inform additional MOAs for one or more unexplained outcomes in the testes. 
1-5

The new analyses and the approach allowed us to develop hypotheses about possible DBP 

MOAs for some male reproductive developmental outcomes. 

To address the question of whether the available genomic data for DBP could inform the

interspecies TD part of the interspecies uncertainty factor, genomic data were evaluated to 

inform interspecies differences in the steroidogenesis pathway, relevant to the decreased fetal 

testicular T MOA. We explored the development of new methods to evaluate interspecies TD 

differences. To evaluate cross-species similarity metrics for the steroidogenesis pathway 

between rats and humans, we explored two approaches: protein sequence similarity and enzyme 

presence. Preliminary results from applying each method suggest that steroidogenesis genes are 

relatively highly conserved between rats and humans. However, we do not recommend utilizing 

these data to inform interspecies uncertainty for DBP because it is difficult to make unequivocal 

conclusions regarding a “high” versus “low” degree of conservation for the genes in this pathway 

based on these data alone. With further refinement and improved data sources, these methods 

could potentially be applied to other chemical assessments.

New methods for evaluating microarray data for the purposes of risk assessment were 

explored and developed during the DBP case study. A new pathway analysis method, the 

pathway activity level method, was developed and tested with two DBP study data sets. The 

pathway activity level method determines pathway level changes as the initial step as opposed to 

standard pathway analysis methods in which DEGs are first identified, followed by mapping of 

the DEGs to pathways, as a second step. Further, the pathway activity level method was used to 

evaluate time-course microarray data. A preliminary gene network model for DBP, based on the 

results from one time-course study, identified a temporal sequence of gene expression and 

pathway interactions that occur over an 18-hour interval within the critical window of exposure 

for DBP and testicular development effects.

1.3. RECOMMENDATIONS

In addition to following the principles of the approach (i.e., systematically consider all 

types of information with respect to the steps of risk assessment, identify questions to direct the 

evaluation, and evaluate genomic data and toxicity data together), several specific 

methodological recommendations arose from the DBP case-study experience. The first two 

recommendations are straightforward and could reasonably be performed by a risk assessor with 
1-6

basic training in genomics data evaluation and interpretation. The third recommendation 

requires expertise in genomic data analysis methods for implementation. The recommendations 

are presented below:

1. Evaluate the genomic and other gene expression data for consistency of findings across 

studies to provide a WOE evaluation of the affected gene expression and pathways. 

Some simple methods, such as using Venn diagrams and gene expression compilation 

approaches, can be applied to risk assessment. When evaluating the consistency of 

toxicogenomic data findings, it is advantageous to include all available gene expression 

data (single gene, global gene expression, protein, RNA) because single gene expression 

techniques have been traditionally used to confirm the results of global gene expression 

studies and because single gene expression data add to the database.

2. Perform benchmark dose (BMD) modeling on high-quality RT-PCR dose-response 

studies of genes known to be in the causal pathway of an MOA or outcome of interest. 

Obtaining a BMD and benchmark dose lower confidence limit (BMDL) is a useful 

starting point for both linear low-dose extrapolation and reference value approaches. We 

are not indicating which approach is appropriate to take for making predictions about the 

potential risk below the BMD or BMDL. “High quality” is defined in this context as a 

well-conducted study that assessed enough animals and litters for sufficient statistical 

power for characterizing the mean responses and the variability (interlitter and intralitter). 

3. Perform new analysis of toxicogenomic data in cases when the new analysis is likely to 

yield new information that would be useful to the risk assessment. Examples include:

• Perform a new pathway analysis in order to identify all affected pathways or other 

risk assessment applications. When the available published microarray studies 

have been conducted for purposes (e.g., basic science, pharmaceutical 

development) other than risk assessment, it may be useful to reanalyze the raw 

data for risk assessment purposes. Information about all affected pathways may 

contribute to an understanding of the mechanisms and MOAs.

• Identify the genes and pathways affected over a critical window of exposure if 

global gene expression time-course data are available. Specifically, by 

developing a gene network over time, it may be possible to identify the earliest 

affected genes and/or pathways, which in turn may represent the earlier or 

initiating events for the outcome of interest.

Based on these recommendations, we refined our initial case-study approach to produce a 

generalizable approach that can be used to evaluate genomic data in new chemical assessments.
1-7

1.4. RESEARCH NEEDS

The following research needs could potentially improve the utility of genomic data in risk 

assessment:

• Perform parallel toxicity and toxicogenomic studies with similar design characteristics 

(i.e., dose, timing of exposure, organ/tissue evaluated) in order to obtain comparable 

results which would aid our understanding of the relationship between gene expression 

changes and phenotypic outcomes.

• Test multiple doses, with increased numbers of animals, in microarray and toxicity 

studies (see bullet above) in order to relate the dose to the gene expression and pathway 

response, and to the in vivo response.

• Perform a time-course global gene expression study over a relevant exposure interval 

(e.g., critical window of development) in order to identify the earlier and possibly, 

initiating gene expression events.

• Generate TK data in an appropriate study (e.g., time, dose, tissue), and obtain a relevant 

internal dose measure to derive the best internal dose metric.

• Further develop bioinformatic methods for analyzing genomic data for the purpose of use 

in risk assessment.

As a result of considering how to best use genomic data in risk assessment, we identified 

a number of issues for future consideration. As more and various types of genomic studies are 

performed, genomic data will likely inform multiple steps of the risk assessment process beyond 

MOA. To facilitate the advancement of the use of genomics in risk assessment, first, we need 

approaches to utilize genomic data quantitatively, specifically, the application of genomic data to 

dose-response, intraspecies variability, and TK. Second, analytical methods tailored to use in 

risk assessment are needed. Bioinformatics methods development work, some initiated in this 

project, continues to evolve. The goal is to develop and/or adapt existing bioinformatic tools 

currently used for hypothesis generation to the express purpose of utilizing genomic data for risk 

assessment. The pathway activity level method presented in this report is a promising approach 

for application to risk assessment. However, continued efforts, with input from both statistical 

modeling and biology experts, is required to validate, test, and refine these methods. Third, 

training risk assessors in genomic data analysis methods would assist EPA in the evaluation and 

interpretion of complex, high-density data sets and in performing new analyses when necessary.
1-8

Finally, some of the issues in utilizing genomic data in health and risk assessment are not 

unique to genomic data but apply to precursor event information in general. Two of these issues 

are (1) defining adversity and (2) establishing biological significance of gene expression changes 

or pattern. The design and performance of appropriate studies, with both genomic and toxicity 

components, may help to address the scientific aspects of these two important issues (see 

research needs above). 

To the best of our knowledge, this is the first systematic approach for using genomic data 

in health assessment at EPA. We believe that this report can be used by risk assessors when 

considering a large range of potential applications, issues, and methods to analyze genomic data 

for future assessments. This approach advances efforts in the regulatory and scientific 

communities to devise strategies for using genomic data in risk assessment, and it is consistent 

with the pathway-based risk assessment vision outlined in the National Research Council’s 

(NRC’s) report, Toxicity Testing in the 21st Century. We also anticipate that the research needs 

and future considerations described herein will advance the design of future toxicogenomic 

studies for application to risk assessment, and as a result, benefit the bioinformatic, 

toxicogenomic, and risk assessment communities.
3-1

3. DBP CASE-STUDY APPROACH AND EXERCISE

This chapter presents a description of the approach used to evaluate toxicogenomic data 

in risk assessment, and a description of the first three steps of the DBP case study. Our strategy 

for evaluating genomic data for risk assessment was to design a flexible yet systematic approach 

that would provide a thorough evaluation of the genomic data set for a particular chemical, while 

still accommodating different risk assessment practices. The discussion includes both 

(1) general (i.e., not chemical-specific) considerations for evaluating a genomic data set, and 

(2) consideration of the DBP genomic data set as part of the DBP case study.

3.1. EVALUATING THE EXTERNAL REVIEW DRAFT OF THE IRIS 

TOXICOLOGICAL REVIEW (TOX REVIEW) OF DBP

The case-study approach begins with an evaluation of the 2006 external review draft IRIS 

Tox Review for DBP (see Figure 3-1). Use of this draft assessment as the starting point allowed 

us to take advantage of (1) the compilation of the toxicity and human data sets, allowing us to 

focus on the toxicogenomic data set evaluation, and (2) data gaps that were identified, thus, 

providing possible questions that the toxicogenomic data may be able to address. 

The IRIS assessment of DBP was in progress and the internal review draft was available 

when the DBP toxicogenomic case study project was initiated in 2005. The external review draft 

of the Tox Review for DBP and IRIS Summary were released for public comment and peer 

review on June 27, 2006 (U.S. EPA, 2006a; 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=155707). The External Review Peer 

Review Panel meeting was held July 28, 2006. 

There are extensive studies documenting developmental toxicity of DBP and its primary 

metabolite, monobutyl phthalate, in rodents (Barlow et al., 2004; Barlow and Foster, 2003; 

Mylchreest et al., 2002, 2000, 1999, 1998; Ema and Miyawaki, 2001a, b; Ema et al., 2000a, b 

1998, 1997, 1996, 1995, 1994, 1993; see Chapter 4 for further details). DBP exposure to the 

developing male rat fetus during a critical window of development in late gestation causes a 

variety of structural malformations of the reproductive tract (e.g., hypospadias); a decrease in 

anogenital distance (AGD); delayed preputial separation (PPS); agenesis of the prostate, 

epididymis, and vas deferens; degeneration of the seminiferous epithelium; interstitial cell 

hyperplasia of the testis; and retention of thoracic areolas and/or nipples (Bowman et al., 2005; 
3-2

Figure 3-1. DBP case-study approach for evaluating toxicogenomic data for 

a health assessment. Evaluation steps in the case-study process are shown in 

rectangles. Findings or products of the case study are shown in ovals. ERD, 

external review draft. Numbers in parentheses indicate report chapters where the 

case-study step is described. 

Evaluate 

DBP IRIS 

Assessment ERD

(Chapter 3)

Identify Case Study Questions

(Chapter 3)

Toxicity Data Set 

Evaluation

(Chapter 4) 

Genomic Data Set 

Evaluation

(Chapter 5)

Genomic Data Set 

New Analyses

(Chapters 5 & 6)

Consider RA Aspects 

that Genomic Data Set 

may Address

(Chapter 3)

Case Study Findings 

(Chapter 7)

Do the genomic data inform

(1) Mode or mechanism of action for male repro dev effects? 

(2) Interspecies differences in MOA?

1) Putative additional pathways to further understand MOA

2) Cross-species conservation information for

steroidogenesis pathway

Application to RA 

(Chapter 7)

•Generic Approach

•Research Needs

•Recommendations

Evaluate 

DBP IRIS 

Assessment ERD

(Chapter 3)

Identify Case Study Questions

(Chapter 3)

Toxicity Data Set 

Evaluation

(Chapter 4) 

Genomic Data Set 

Evaluation

(Chapter 5)

Genomic Data Set 

New Analyses

(Chapters 5 & 6)

Consider RA Aspects 

that Genomic Data Set 

may Address

(Chapter 3)

Case Study Findings 

(Chapter 7)

Do the genomic data inform

(1) Mode or mechanism of action for male repro dev effects? 

(2) Interspecies differences in MOA?

1) Putative additional pathways to further understand MOA

2) Cross-species conservation information for

steroidogenesis pathway

Application to RA 

(Chapter 7)

•Generic Approach

•Research Needs

•Recommendations
3-3

Kleymenova et al., 2005a; Barlow et al., 2004; Kim et al., 2004b; Barlow and Foster, 2003;

Fisher et al., 2003; Higuchi et al., 2003; Mylchreest et al., 2002, 2000, 1999, 1998; Ema et al., 

2000b, 1998, 1997, 1994; Saillenfait et al., 1998). 

 Figure 3-2 shows the studies that were candidates for the development of the reference 

dose (RfD) presented in the 2006 external review draft IRIS Tox Review for DBP (U.S. EPA, 

2006a). The point of departure (POD) selected for derivation of the RfD for all exposure 

durations (acute, short-term, subchronic, and chronic) was the no-observed-adverse-effect level 

(NOAEL) of 30 mg/kg-d for reduced fetal testicular T (Lehmann et al., 2004). In this study, a 

statistically significant decrease in T concentration in the fetal testis was detected at 50 mg/kg-d. 

The reduction in fetal testicular T is one of the well-characterized MOAs for DBP that occurs 

after in utero DBP exposure (during the critical window), initiating the cascade of events for a 

number of malformations in the developing male reproductive tract. Studies using 

radioimmunoassay of T levels in fetal testes and studies using RT-PCR, microarrays, and/or 

immunochemical staining found a decrease in the expression of protein and mRNA for several 

enzymes in the biochemical pathways for cholesterol metabolism, cholesterol transport, and 

T biosynthesis (also called steroidogenesis more generally) in the fetus (Plummer et al., 2005; 

Thompson et al., 2004, 2005; Lehmann et al., 2004; Liu et al., 2005; Barlow et al., 2003; Fisher 

et al., 2003; Shultz et al., 2001). Collectively, these studies document that exposure to DBP 

disrupts T synthesis in the fetal testis. Thompson et al. (2004) established that following in utero 

exposure to 500 mg/kg-d, the T levels in the testes return to normal after the metabolites of DBP 

are cleared from the circulation. However, the malformations induced by 500 mg/kg-d exposure 

persist into adulthood (Barlow et al., 2004; Barlow and Foster, 2003). Thus, although the 

inhibition of T synthesis can be reversed, the biological effects resulting from the decrease in T 

during the critical developmental window are irreversible.

3.2. CONSIDERATION OF RISK ASSESSMENT ASPECTS THAT 

TOXICOGENOMIC DATA MAY ADDRESS

While microarray and RT-PCR data have been used to inform the MOAs of a chemical, 

the many types of -omic data have the potential to inform TK, dose-response, interspecies and 

intraspecies differences in TK or TD, and be utilized as biomarkers of exposure or effect (see 

Figure 3-3). 
3-4 

Figure 3-2. Exposure response array for candidate endpoints and PODs for 

RfD derivation presented in the external review draft IRIS Tox Review for 

DBP (U.S. EPA, 2006a). The studies are arrayed by toxicological endpoint 

category. All studies were performed in the rat using the oral route of exposure 

(although method of oral dosing varied). The study DBP exposure interval and 

reference are shown on the X axis. The dose (Y axis) is shown in a logarithmic 

scale. See the articles for additional study details. The vertical line indicates the 

dose range tested. The lowest and highest doses tested in the study are indicated 

by the horizontal lines. The green circle indicates the study NOAEL (noobserved-adverse-effect level) and the blue triangle indicates the study LOAEL 

(lowest-observed-adverse-effect level). Neuro, neurological; GD, gestation day; 

PND, postnatal day. 
3-5 

Figure 3-3. Potential uses of toxicogenomic data in chemical screening and 

risk assessment. -Omic data from appropriately designed studies have the ability 

to inform multiple types of information and in turn, steps in screening and 

prioritization, and risk assessment. Arrows with TGx data indicate the types of 

information these data can provide. Shaded boxes indicate some of the types of 

information that are useful in risk assessment. TGx, toxicogenomic.

Risk Assessment

Risk Characterization

Risk Communication/Management

Screening & Prioritization Programs

Hazard Identification

(Screening & Characterization)

Dose-Response Assessment

Exposure Assessment

Selection Process

Intra & interspecies 

uncertainty and variability

MOA

Exposure 

biomarker

TK 

TGx data

Effects 

biomarker

D-R

TGx data TGx data

TGx data

TGx data TGx data

TGx data

Intra- & 

interspecies 

differences

TGx data

Risk Assessment

Risk Characterization

Risk Communication/Management

Screening & Prioritization Programs

Hazard Identification

(Screening & Characterization)

Dose-Response Assessment

Exposure Assessment

Selection Process

Intra & interspecies 

uncertainty and variability

MOA

Exposure 

biomarker

TK 

TGx data

Effects 

biomarker

D-R

TGx data TGx data

TGx data

TGx data TGx data

TGx data

Intra- & 

interspecies 

differences

TGx data
3-6 

However, in this case study, chemical screening and exposure assessment were not 

considered. Instead, we considered the many types of information useful to hazard 

characterization, dose-response, and risk characterization. Toxicogenomic data have been 

successful in providing information about the molecular events altered in the mechanisms of 

action, and, at times, information about the TD or TK key events of a MOA (see Figure 3-4; see 

Chapter 2). Further, data from appropriately designed toxicogenomic studies could be used to 

inform intra- and interspecies differences in molecular responses.

Figure 3-4. Potential uses of toxicogenomic data in understanding 

mechanisms of action. The process from exposure to outcome encompasses all 

of the steps of a mechanism of action, including both TK and TD steps. Available 

TGx data, such as microarray data and other gene expression data, can provide 

information about altered molecular events, at the gene expression level. In turn, 

appropriate TGx data can be used to inform intra- and interspecies differences in 

molecular responses. Appropriate TGx data could also inform internal dose and 

intra- and interspecies differences in internal dose. ADME, absorption, 

distribution, metabolism, and excretion.

Exposure

TK TD

Observed

outcome/

endpoint

Observed

outcome/

endpoint

Altered 

molecular

events

Human Rat

Intra & Interspecies 

differences TGx data

TGx data

TGx data

TGx data

Target 

tissue

dose

Biol.

effective

dose

Altered 

cellular

events

Altered 

tissue

Altered 

organ

A

D

M

ETGx data

Exposure

Exposure

TK TD

Observed

outcome/

endpoint

Observed

outcome/

endpoint

Altered 

molecular

events

Human Rat

Intra & Interspecies 

differences TGx data

TGx data

TGx data

TGx data

Target 

tissue

dose

Biol.

effective

dose

Altered 

cellular

events

Altered 

tissue

Altered 

organ

A

D

M

ETGx data

Exposure
3-7 

3.2.1. Informing TK

Characterizing the absorption, distribution, metabolism, and excretion (ADME) of 

environmental toxicants is important for both the understanding and application of MOA 

information in predicting toxicity. Differences in TK across species, individuals, and exposure 

patterns (routes, level, duration, frequency) can lead to different biological effects for the same 

total exposure to a chemical. It is well-established that a quantitative understanding of chemical 

TK (e.g., using PBPK models) can be useful in analyzing dose-response data and extrapolating 

across species, individuals, and exposure patterns (U.S. EPA, 2006e). The principles of these 

uses for TK are the same regardless of the types of response data utilized (i.e., in vivo toxicity 

endpoints [e.g., pup weight] or molecular precursor events [e.g., toxicogenomic changes]), and 

will not be reviewed here. However, the inverse question of how toxicogenomic data can inform 

TK has not been fully explored. Here we consider whether toxicogenomic data could be useful 

for understanding four aspects of a chemical’s TK: (1) identification of potential metabolic and 

clearance pathways; (2) selection of an appropriate dose metric; (3) intra- and interspecies 

differences in metabolism; and (4) TK/TD linkages and feedback. Each of these applications is 

discussed below. Finally, the available toxicogenomic data for DBP are evaluated for use in 

informing TK. 

3.2.1.1. Identification of Potential Metabolic and Clearance Pathways

While TK studies, themselves, are designed to help understand the pathways for 

metabolism and clearance of xenobiotics, toxicogenomic data may provide important 

complementary information as to what enzymes and tissues may be involved in metabolism. For 

example, many xenobiotics induce the expression of the Phase I and II enzymes that are 

responsible for their clearance. Thus, toxicogenomic data showing changes in the expression of 

genes, such as cytochrome P450s in a particular tissue, may implicate their involvement in 

metabolizing the compound. While such toxicogenomic data may confirm the major sources of 

metabolism or clearance, they may also identify minor TK pathways relevant for inducing 

toxicity. However, data indicating gene expression changes alone are insufficient to conclude 

that there is a corresponding increase in a protein levels or activity, or are relevant to the ADME 

of the chemical of interest. Ultimately, toxicogenomic data may be most useful for generating 
3-8 

hypotheses about metabolism and clearance pathways that can be tested with additional TK 

studies.

3.2.1.2. Selection of Appropriate Dose Metrics

Due to inherent differences in TK across species, individuals, and exposure patterns, 

dose-response relationships are best established based on an internal measure of a biologically 

effective dose as opposed to an external or applied dose. However, an understanding of TK 

alone may provide multiple options for the internal “dose metric,” such as blood or tissue 

concentrations of the parent or metabolites, or rates of formation of reactive compounds. Thus, a 

key question in utilizing TK data for dose-response analyses and extrapolation is dose-metric 

selection, which depends on the determination of the active chemical species and the MOAs of 

toxicity. There often may be more than one biologically plausible choice of dose metric, which 

contributes to the uncertainty in the dose-response analysis. The potential utility of 

toxicogenomic data is that gene expression data may demonstrate earlier biological effects, and, 

thus, are closer both spatially and temporally to the interaction between the active chemical 

species and endogenous cellular molecules than traditional toxicological outcomes (see 

Figure 3-4). Thus, toxicogenomic data can, in principle, provide biological support for the 

choice dose metric. Different predictions for internal dose can be statistically analyzed along 

with toxicogenomic changes that inform TD to determine the dose metric that is best correlated.

3.2.1.3. Intra- and Interspecies Differences in Metabolism

 Data from polymorphisms is one type of genomic data that can be extremely useful to 

informing intraspecies differences. Across species, data on differential expression of different 

isozyme genes may be indicative of differences in overall metabolizing capacity and affinity. In 

addition, toxicogenomic data may be informative about whether the tissue distribution of 

metabolizing enzymes may be different across species. Within species, interindividual 

variability in metabolizing capacity and/or affinity due to differences in enzyme expression or 

genetic polymorphism can greatly influence the overall TK of a chemical. For example, genetic 

polymorphisms in aldehyde dehydrogenase-2 (ALDH2) can result in an increase in blood 

acetaldehyde levels following alcohol consumption, thereby leading to overt health effects 

(Ginsberg et al., 2002). Similarly, data on CNPs can provide information (Buckley et al., 2005) 
3-9 

that directly informs TK. For example, some individuals possess different copy numbers of 

CYP2D6 that influence their response to pharmaceuticals (Bodin et al., 2005). When the 

impacts of gene expression levels and polymorphisms on enzyme levels and function have been 

established (i.e., preferably confirmed by measurement of enzyme level), this information can 

either be used to characterize the difference in a predicted dose metric for a subpopulation 

relative to the most common alleles, or it can be used in probabilistic (e.g., Monte Carlo) 

analyses to characterize the impact on population variability.

3.2.1.4. TK/TD Linkages and Feedback

Ultimately, toxicogenomic data may be useful for linking together TK and TD models 

into more comprehensive biologically based dose-response (BBDR) models (Daston, 2007). 

With an appropriate dose metric, one can link the TK predictions for a chemical (e.g., tissue 

concentration of a metabolite) with toxicogenomic changes (e.g., change in mRNA level) that, in 

turn, are linked through a TD model to alterations in cellular constituents and, ultimately, frank 

effects. Furthermore, toxicogenomic data may be useful in providing the link by which the TD 

feedback of gene and protein expression changes on TK (e.g., enzyme induction) can be 

modeled. 

3.2.1.5. Research Needs for Toxicogenomic Studies to Inform TK

 Changes in gene expression can be highly labile and vary as a function of dose and time. 

Thus, identification of appropriate dose metrics involves detection of relevant gene changes as 

well as the moiety that caused the changes. Therefore, simultaneous data collection of 

toxicogenomic data and tissue concentrations of the relevant chemical species would be 

beneficial. In order to inform interspecies extrapolation, it is important to mine toxicogenomic 

data for potential indicators of species differences in metabolism. For intraspecies variability, it 

is important to assess the potential impact of polymorphisms in Phase I and II enzymes. 

Microarray data may also be useful for identifying life stage and gender differences in relative 

expression of enzymes involved in the TK of the chemical of interest. 
3-10 

3.2.1.6. DBP Case Study: Do the Available Toxicogenomic Data Inform TK?

We considered whether the available toxicogenomic data set for DBP informs TK. A 

greater level of detail is presented for TK in this chapter than for TD because the latter subject is 

considered in greater detail in the subsequent chapters. This chapter also provides examples of 

considerations that may be helpful to risk assessors examining whether the available 

toxicogenomic data can inform TK for their chemical of interest. 

The TK of DBP is reviewed in U.S. EPA, 2006a, and is summarized briefly here for 

context. Following ingestion, DBP is primarily hydrolyzed to monobutylphthalate (MBP) in the 

gastrointestinal tract and enters systemic circulation through the portal blood. MBP undergoes 

glucuronidation in the liver, and both free and glucuronidated MBP circulate in serum and are 

subsequently excreted in urine. While there are a number of TK studies in rats, little human TK 

data are available, particularly for known exposures to DBP. The available data suggest that free 

MBP is responsible for the effects on T biosynthesis. In terms of TK pathways, the data set did 

not lead to the identification of alternative metabolic pathways for DBP. 

Toxicogenomic data could inform dose-metric selection in two broad ways: relating the 

metabolite to the gene expression or using gene expression as the dose metric. In a more 

traditional approach, changes in the expression for genes of interest could be related to a 

chemical moiety in a target tissue of relevance (or convenience). For example, Lehmann et al. 

(2004) provides a dose-response analysis of gene expression following DBP exposure. 

However, these data are limited for use in extrapolation without TK data (e.g., tissue 

concentrations of MBP). Ideally, TK data could be collected at various time points following 

various doses, but this would require a large number of fetuses. In the absence of such empirical 

data, analyses could be performed using physiologically based TK modeling, but none have yet 

been attempted. Such an approach might utilize the available published TK studies for DBP and 

attempt to reconstruct the exposure scenarios in the toxicogenomic studies with the intent to 

predict the MBP concentration in a target tissue (or blood) at the developmental time points 

where toxicogenomic samples were obtained. 

A second and more complex approach might be to use a toxicogenomic change as a 

dosimeter (or “biomarker”), which may obviate the need for TK data altogether. For example, 

the microarray study of Wyde et al. (2005) reports changes in maternal liver Cyp2b1 and 

Cyp3a1, and estrogen sulfotransferase mRNA levels following DBP exposure. Not only do 
3-11 

these gene expression changes serve as potential biomarkers, but they also suggest that there may 

be related changes in metabolic biomarkers (i.e., metabonomics) because these enzymes have 

roles in lipid and hormone synthesis, in addition to xenobiotic metabolism. Although it is not 

clear whether these changes have a relationship to a toxic endpoint of interest, it may be possible 

to establish, for instance, that an increase in a specific maternal liver mRNA is correlated with a 

decrease in a specific mRNA in the fetal testis. Indeed, Wyde et al. (2005) show that maternal 

liver estrogen sulfotransferase gene expression increases in a dose-dependent manner from 

10−500 mg/kg-d, while Lehmann et al. (2004) observed a dose-dependent decrease in Scarb1, 

Star, Cyp11a1, Hsd3b, or Cyp17a1 mRNA levels in fetal testes from 0.1–500 mg/kg-d. 

With respect to interspecies extrapolation and interindividual variability, the lack of 

adequate human TK data precludes quantitative extrapolation, a situation that cannot be 

remedied with the available toxicogenomic data (unless, as discussed above, a 

toxicogenomic-based dosimeter/biomarker was developed). For instance, available blood 

measurements of MBP in humans were taken from spot samples in the general population where 

the individual exposure patterns were unknown. Although differences were observed in the ratio 

of free to conjugated MBP in human serum as compared to the rat, these data are insufficient for 

quantitative interspecies extrapolation, because in order to replace administered dose as a dose 

metric, it is necessary to determine the absolute, not the relative, level of free MBP in serum as a 

function of exposure. The Wyde et al. (2005) study suggests that DBP-induced enzyme 

induction occurred. Specifically, this study reported that exposure to 50 and 500 mg/kg-d DBP 

leads to an increase in rat liver UDP glucuronsyltransferase 2B1 (Ugt2b1) mRNA levels. More 

TK analysis would be required to ascertain whether this induction in rats occurs at levels that are 

relevant to low-dose exposures. This enzyme induction may occur in humans and such a 

response may increase interindividual sensitivity to DBP toxicity. With regard to human TK, 

none of the available toxicogenomic data on DBP were performed in humans and thus, do not 

provide any information on DBP interindividual TK variability. For example, there are no 

available data on polymorphisms in glucuronyltransferases responsible for metabolizing MBP. 

Finally, we considered the potential for TK/TD linkages with the available data and concluded 

that in order for TK and toxicogenomic data to be integrated for use in quantitative 

dose-response analysis, more sophisticated BBDR models are needed. Using such an approach, 
3-12 

it may be feasible to relate changes in expression of genes involved in T production to quantify 

testicular T levels (see Figure 3-5). 

Figure 3-5. The fetal Leydig cell in the fetal testis. The boxes represent genes 

involved in the biosynthesis of T; the percentages (%) represent % control gene 

expression in fetal testis of dams treated with 500 mg/kg-d DBP. 

Source: Adapted from Barlow et al. (2003). 

The male reproductive developmental effects of DBP appear to be mediated by MBP, 

which causes a down-regulation of cholesterol transporters across the cell membrane (SCARB1) 

and mitochondrial inner membrane (STAR), as well as the down-regulation of two enzymes 

involved in converting cholesterol to T, CYP11A1, and CYP17A1 (Liu et al., 2005; Lehmann et 

al., 2004; Barlow et al., 2003; Shultz et al., 2001). Thus, it may be possible to relate DBP and/or 

MBP levels to reductions in cholesterol transporter (e.g., SCARB1 and STAR) and the levels of 

steroidogenic enzymes (e.g., CYP11A1 and CYP17A1) at the mRNA, protein, and/or activity 
3-13 

levels. Changes in these parameters may then be modeled to predict changes in testicular T 

levels, which may subsequently be correlated to developmental toxicity endpoints.

3.2.2. Informing Dose-Response

Toxicogenomic data that informs TK can be useful for informing or improving 

dose-response analysis because it may improve the dose metric selection among alternative dose 

metrics. However, use of toxicogenomic data as an endpoint in dose-response analysis has not 

been extensively explored. For example, BMD analysis of some dose-response studies 

determined PODs based on the GO categorization of gene expression changes (based on an 

approach of Yu et al., 2006) as a function of dose (Andersen et al., 2008; Thomas et al., 2007). 

3.2.2.1. DBP Case Study: Do the Toxicogenomic Data Inform Dose-Response?

Unfortunately, there are currently no available dose-response microarray studies to assess 

the genome-wide gene expression over a dose range. However, there is one available 

dose-response gene expression study for DBP. Specifically, Lehmann et al. (2004) conducted a 

dose-response study evaluating testicular T, RT-PCR and protein expression for a subset of 

genes thought to underlie the male reproductive developmental outcomes. This study reported a 

significant reduction in fetal testicular T at 50 mg/kg-d DBP or higher. Western analysis found 

that steroidogenic acute regulatory protein (STAR) and scavenger receptor class B, member 1 

(SCARB1) were significantly decreased at 50 mg/kg-d while cytochrome P450, family 11, 

subfamily a, polypeptide 1 (CYP11A1) was only reduced at 500 mg/kg-d. Further, RT-PCR 

analysis findings confirmed that the mRNA of these three genes was statistically significantly 

reduced at 50 mg/kg-d. The results of this study support the role of steroidogenesis enzymes and 

cholesterol transport proteins in the decreased testicular T MOA after in utero DBP exposure. 

However, without first establishing the biologically significant level of change in gene 

expression and the critical subset of genes that constitute a well-established precursor event, it is 

difficult to use these data in a dose-response assessment (see Chapter 7). It would be helpful to 

have dose-response microarray or proteomic studies to assess mRNA and protein expression on a 

genome-wide level. 
3-14 

3.2.3. Informing TD

There are numerous examples where toxicogenomic data have been used to inform the 

TD steps within mechanisms of action or MOAs for a chemical, and there are a small number of 

examples where such data have been used corroboratively for risk assessment decisions (see 

Chapter 2). 

3.2.3.1. General Considerations: TD Portion of Mechanisms of Action and MOAs

One feature of the approach (see Figure 3-1) is the evaluation of the toxicity and 

toxicogenomic data sets in conjunction in order to consider the relevance of gene expression 

changes with respect to specific endpoints of interest identified in the toxicity data set. In this 

manner, data on affected pathways may generate hypotheses and inform the mechanisms of 

action for a chemical for specific endpoints. In addition, using this approach could provide 

connections between affected pathways (identified from the toxicogenomic data set) and 

endpoints (identified from the toxicity data set), which may, in turn, inform modes or the 

mechanisms of action, as illustrated by Figure 3-6. Chapter 2 and the glossary describe the 

distinction between the definitions for mechanisms of action and MOAs. 

This approach is best suited to instances where comparable study designs between the 

toxicity/epidemiology and toxicogenomic data sets are available. For example, toxicogenomic 

and toxicity studies performed in the same species, using similar doses, similar exposure 

intervals, and assessing the same organ or tissue would be ideal for utilizing this approach. For 

the DBP toxicity (see Chapter 4) and toxicogenomic data sets (see Chapter 5), there is some 

comparability across some of the studies—i.e., some toxicity and toxicogenomic studies were 

performed at the same doses with similar exposure intervals, in the same species and strain, and 

assessed some of the same organs (e.g., testis). However, no two studies are the same for all 

study-design aspects, such as precise timing of exposure and time of assessment.

3.2.3.2. DBP Case Study: MOAs for Male Reproductive Developmental Effects

Developmental toxicity studies (reviewed in Chapter 4) and toxicogenomic studies 

(reviewed in Chapter 5) have contributed to a good understanding of DBP as a chemical that has 

multiple MOAs. Two well-characterized MOAs: a reduction in fetal testicular T and a reduction 

in Insl3 signaling activity, explain a number of the observed male reproductive developmental 
3-15 

Figure 3-6. Approach to utilizing toxicity and toxicogenomic data for 

identifying affected pathways and candidate modes and mechanisms of 

action. Toxicogenomic data can be analyzed for differentially expressed genes 

(DEGs) and, in turn, grouped into affected pathways. Toxicity data can provide 

information about affected endpoints. Evaluating toxicogenomic and toxicity data 

together can provide a level of phenotypic anchoring between gene and pathway 

changes, and in vivo outcomes. The identification of affected pathways can 

inform mechanisms of action, including MOAs, for a chemical. Such an 

approach requires similar study parameters (e.g., dose, species, duration of 

exposure) for the toxicity and toxicogenomic studies. TGx, toxicogenomic.

abnormalities. Some other observed abnormalities are not explained by these two MOAs, 

suggesting that there are additional MOAs for DBP. Acknowledging that there are additional 

data not presented in Figure 3-7, this figure attempts to show where there is agreement in the 

scientific community, based on reproducibility of microarray and RT-PCR studies, about 

affected pathways and the well-characterized MOAs for DBP. There are some endpoints and 

pathways that need further characterization and, as a result, we were interested in determining 

whether the toxicogenomic data could be used to associate the DBP MOAs and endpoints, and/or 

form hypotheses about additional MOAs for DBP.

Endpoints MOAs Pathways Genes

TGx

Comparable study design characteristics, e.g., DATA SET

-Species -Organ

-Strain -T of exposure

-Dose

TOX

DATA SET

Endpoints MOAs Pathways Genes

TGx

Comparable study design characteristics, e.g., DATA SET

-Species -Organ

-Strain -T of exposure

-Dose

TOX

DATA SET
3-16

Figure 3-7. The proposed DBP mechanism of action for the male reproductive developmental effects. The 

mechanism of action is defined as all of the steps between chemical exposure at the target tissue to expression of the 

outcome. The steps shown are based on male reproductive developmental toxicity and toxicogenomic studies. Some 

of the affected pathways and individual genes whose expression was significantly affected after DBP exposure in 

multiple studies are included. By contrast, MOAs are shown in purple letters including two well-characterized MOAs 

and one example of an unidentified MOA. 

Source: Figure adapted from Liu et al. (2005), Thompson et al. (2004), Wilson et al. (2004), Barlow et al. (2003), and 

Shultz et al. (2001).

StAR

P450

3β-HSD

P450c17

Fetal Leydig Cell

Male developmental

reproductive effects:

Sertoli Cell

Undescended testes Altered repro tract dev Multinucleated gonocyte

SCF

Disrupted

Sertoli Cell

Development

C-KIT

Gonocyte

Testosterone

α inhibition

Steroidogenesis

Cholesterol

Transport

Cholesterogenesis

Lipogenesis

Cholesterol

Insl3 ?

StAR

P450

3β-HSD

P450c17

Fetal Leydig Cell

Male developmental

reproductive effects:

Sertoli Cell

Undescended testes Altered repro tract dev Multinucleated gonocyte

SCF

Disrupted

Sertoli Cell

Development

C-KIT

Gonocyte

Testosterone

α inhibition

Steroidogenesis

Cholesterol

Transport

Cholesterogenesis

Lipogenesis

Cholesterol

Insl3 ?
3-17 

3.3. IDENTIFYING AND SELECTING QUESTIONS TO FOCUS THE DBP CASE 

STUDY

In reviewing the external review draft IRIS Tox Review for DBP, data gaps in the 

assessment were noted. Then the DBP toxicogenomic data set was evaluated for these data 

could potentially address any of the gaps (see Figure 3-1). The identified data gaps led to 

formulation of questions of interest whose answers may be able to contribute valuable 

information to a risk assessment. The following questions were identified:

Can the DBP toxicogenomic data set inform the 

• biologically significant level of reduction in fetal T? As the external review draft IRIS 

Tox Review for DBP used a reduction in fetal testicular T as the critical effect, we 

considered whether the toxicogenomic data set could aid in determining the biologically 

meaningful level of T reduction for the male reproductive developmental effects. 

• dose-response assessment in risk assessment? The microarray and RT-PCR studies 

have identified genes and pathways associated with the reduced fetal testicular T. Thus, 

there is the potential for evaluating these genes and pathways in a dose-response 

assessment.

• modes and mechanisms of action for male reproductive developmental outcomes? 

Not all of the male reproductive developmental outcomes after in utero DBP exposure 

are a consequence of reduced fetal testicular T or reduced Insl3 expression. Therefore, 

additional MOAs for these endpoints may be identified from pathway analysis of the 

microarray data.

• interspecies (rat-to-human) differences in MOAs that could, in turn, inform the TD 

part of the UFH? There is evidence from toxicogenomic studies that a reduction in gene 

expression of some of the steroidogenesis genes underlies the reduction in fetal testicular 

T observed after in utero DBP exposure. Unfortunately, there are no genomic studies in 

appropriate human in vitro cell systems to make comparisons to in vivo rat MOA 

findings. Using available DNA sequence data and other methods, we would like to 

assess the rat-to-human conservation of the steroidogenesis pathway genes.

The existing genomic data for DBP had the potential to inform two of the questions: 

informing modes and mechanisms of action and interspecies differences for the reduced T MOA 

(see above). It was highly likely that the DBP toxicogenomic data set could aid in hypothesis 

generation of DBP modes or mechanisms of action. Using genomic data, such as DNA sequence 

data, it may be possible, but less likely, to inform cross-species differences in TD for the reduced 

T MOA. Although the other two questions (see list above) were of great interest, the available 

genomic data were not considered appropriate to address them. 
3-18 

The remaining steps of the DBP case study are presented in the subsequent chapters. The 

evaluations of the toxicity data set for the male reproductive developmental effects after 

developmental exposure to DBP (see Chapter 4) and the toxicogenomic data set including new 

analyses of one microarray study (see Chapter 5). Exploration of pathway analysis methods 

development for applying microarray data to risk assessment and the use of available methods to 

evaluate rat-to-human differences for the reduced T MOA are presented in Chapter 6. Chapter 4 

follows with an in-depth evaluation of the DBP toxicity data set.
4-1

4. EVALUATION OF THE REPRODUCTIVE DEVELOPMENTAL TOXICITY

DATA SET FOR DBP

This chapter presents the evaluation of the available toxicity data for the development of 

the male reproductive system following DBP exposure and the MOA(s) (see Chapter 2 and 

glossary for definition) that contribute to these outcomes. We used the compilation of the male 

reproductive toxicology literature cited in the 2006 external peer review draft IRIS Tox Review 

for DBP (U.S. EPA, 2006a) as a starting point for our toxicology literature review for this case 

study. Each toxicology study was examined for the lowest dose and low-incidence effects in 

order to identify the full spectrum of male reproductive developmental effects. In a second 

evaluation, we used available mechanistic information for each endpoint to identify potential 

MOAs. Endpoints with MOA information have support for phenotypic anchoring to some of the 

observed DBP gene expression changes (further discussed in Chapter 5). Endpoints with 

unexplained MOAs were used to identify and focus future research needs to study the 

mechanisms that underlie those endpoints using genomics and other techniques.

An extensive toxicological data set exists for DBP that includes acute and subchronic 

studies in multiple species, multigeneration reproduction studies in rodents, and studies that 

assess developmental outcomes following in utero or perinatal/postnatal exposures. Following 

DBP exposure during the critical stages of development, the male reproductive system 

development is perturbed in rodent studies (Gray et al., 1999, 2001; Mylchreest et al., 1998, 

1999, 2000). Two MOAs of DBP, for a number of these outcomes, have been well established 

(David, 2006; Foster, 2005). The 2006 external draft IRIS Tox Review for DBP (U.S. EPA, 

2006a) selected reduced fetal testicular T levels, observed in Lehmann et al. (2004), as the 

critical effect for the derivation of acute, short-term, subchronic, and chronic reference values for 

DBP. This case study evaluated information from genomic and other gene expression studies to 

target and further elucidate the molecular events underlying these developmental outcomes (see 

Chapter 5). The intent of performing this evaluation of the toxicology studies was to examine 

the usefulness of the toxicogenomic data in characterization of the MOA(s) that contribute to the 

adverse outcomes. We also examined the data for low-dose or low-incidence findings because 

such data may aid the interpretation of toxicological outcomes that can be misinterpreted as 

transient (e.g., AGD), or nonadverse due to low incidence or magnitude (e.g., not statistically 
4-2

significant incidences of gross pathology findings in male offspring reproductive organs, or 

alteration of fetal T levels).

4.1. CRITERIA AND RATIONALE FOR INCLUSION OF TOXICOLOGY STUDIES

IN THE EVALUATION

Figure 4-1 illustrates the process for evaluating the DBP toxicology data set for the case 

study (Section 4.2 discusses the later steps of the evaluation process in more detail). The first 

step in the process was the identification of studies to be included for consideration in the case 

study. We identified a number of study selection criteria in Step 1. One criterion of prime 

importance was that the studies should include exposures to DBP during sensitive periods of 

male reproductive system development. Secondly, a no-observed-effect level (NOEL), 

lowest-observed-effect level (LOEL), or BMDL would need to be identified for presumably 

adverse outcomes in the reproductive organs and/or function of male offspring. Additionally, the 

studies would need to be of adequate quality in order to establish confidence in the study 

conduct, methods, and results. These criteria, taken together, define a subset of the available 

toxicology studies that were considered possible candidates for determining the POD for 

derivation of reference values of various exposure durations in the 2006 external peer review 

draft Tox Review for DBP (see Tables 4-1, 4-2, and 4-3 in U.S. EPA, 2006a). These candidate 

study lists were considered during the external peer review of the IRIS document, conducted in 

July 2006, thereby providing a measure of confidence in their inclusiveness and veracity for the 

purpose of this case study. Though there are observable adverse effects on male reproductive 

system development in multiple species, the only available and relevant genomic studies with 

DBP (i.e., those that addressed effects on male reproductive system development following 

prenatal exposures) were conducted in rats. Table 4-1 lists the studies that were identified for 

inclusion as of July 2006. For each study, the following information was summarized: a 

description of the dose and exposure paradigm, the treatment-related outcomes observed at each 

dose level, and the experimentally derived reproductive NOEL and/or LOEL. The terms 

NOAEL and LOAEL are not used in this case-study report, although these terms are commonly 

used in risk assessment, because some study reports do not address the issue of adversity of 

observed study outcomes. In addition, some study reports do not specifically define NOELs or 

LOELs. For that reason, Table 4-1 presents those outcomes that could be considered biomarkers 
4-3

of effects on the male reproductive system that were reported by the study authors, without 

specific consideration or judgment of adversity.

Figure 4-1. The process for evaluating the male reproductive developmental 

toxicity data set for low-dose and low-incidence findings. IRIS assessment, the 

2006 external peer review draft IRIS Tox Review for DBP.

STEP 2: For each study, identify NOEL/LOEL 

and examine data for low incidence and 

low dose findings

Problems with 

quality of 

reported data; 

individual data 

not available

STOP

Individual data 

available

STEP 3: Examine individual data for each male repro 

outcome/endpoint

Issues in 

evaluating data

Thorough 

assessment of 

data

Study or 

reporting 

deficits prevent 

further analysis 

of data

STOP

STOP

STEP 1: Evaluate toxicology studies cited in IRIS 

assessment, for inclusion in case study

Data at NOEL 

appear to be 

biologically 

relevant

Interpret or 

corroborate 

study outcomes 

& endpoints

STEP 2: For each study, identify NOEL/LOEL 

and examine data for low incidence and 

low dose findings

Problems with 

quality of 

reported data; 

individual data 

not available

STOP

Individual data 

available

STEP 3: Examine individual data for each male repro 

outcome/endpoint

Issues in 

evaluating data

Thorough 

assessment of 

data

Study or 

reporting 

deficits prevent 

further analysis 

of data

STOP

STOP

STEP 1: Evaluate toxicology studies cited in IRIS 

assessment, for inclusion in case study

Data at NOEL 

appear to be 

biologically 

relevant

Interpret or 

corroborate 

study outcomes 

& endpoints
4-4

Table 4-1. Studies with exposures during development that have male reproductive outcomes (limited to 

reproductive organs and/or reproductive function) and were considered adequate for reference value 

determination

Studya

Species (strain), duration, 

and exposure Reproductive system effects

NOEL

mg/kg-d

LOEL

mg/kg-d

Barlow and 

Foster, 2003

Rat (SD); GDs 12−21; 0 or 

500 mg/kg-d 

Large aggregates of Leydig cells, multinucleated gonocytes, and an 

increased number of gonocytes in fetal testes; a decreased number 

of spermatocytes on PNDs 16 and 21; epididymal lesions 

(decreased coiling of the epididymal duct, progressing to mild 

[PND 45], and then severe [PND 70] seminiferous epithelial 

degeneration).

500

Barlow et al., 

2003

Rat (SD); GDs 12−19; 

500 mg/kg-d 

Large aggregates of Leydig cells with lipid vacuoles. 500

Barlow et al., 

2004

Rat (SD); GDs 12−21; 0, 100, 

or 500 mg/kg-d 

Testicular dysgenesis (proliferating Leydig cells and aberrant 

tubules); decreased AGD; areolae retention; small incidence of 

Leydig cell adenomas.

100 500

Bowman et al., 

2005

Rat (SD); GDs 12−19 or 21; 0 

or 500 mg/kg-d

Marked underdevelopment of the Wolffian ducts (characterized by 

decreased coiling).

500

Carruthers and 

Foster, 2005

Rat (SD); GDs 14−15, 15−16, 

16−17, 17−18, 18−19, 19−20; 0 

or 500 mg/kg-d 

Decreased AGD; retained areolae and nipples; reduced epididymal 

weights, increased testes weight due to edema; malformations of 

the seminal vesicles, agenesis of various regions of the epididymis, 

small or flaccid testes; malformation of the coagulating gland.

500

Ema et al., 1998 Rat (Wistar); GDs 11−21; 

0, 331, 555, or 661 mg/kg-d

At 555 and 661 mg/kg-d, increased incidences of cryptorchidism 

and decreased AGD.

331 555

Ema et al., 2000b Rat (Wistar); GDs 15−17; 

0, 500, 1,000, or 1,500 mg/kg-d 

At 1,500 mg/kg-d, cryptorchidism observed in 80% of litters. 

At 500, 1,000, and 1,500 mg/kg-d, decreased AGD.

500

Rat (Wistar); GDs 12−14, or 

GD 20; 0, 1,000, or 

1,500 mg/kg-d

At 1,500 mg/kg-d (GDs 12−14), cryptorchidism observed in 50% 

of litters.

At 1,000 and 1,500 mg/kg-d, decreased AGD.

1,000

Ferrara et al., 

2006

Rat (Wistar); GDs 13.5−21.5; 

0 or 500 mg/kg-d 

Delayed entry of gonocytes into quiescence, increase in gonocyte 

apoptosis, and subsequent early postnatal decrease in gonocyte 

numbers (exposures: GDs 13.5−17.5); >10% increase in 

multinucleated gonocytes (exposures: GDs 19.5−21.5).

500
4-5

Table 4-1. (continued)

Studya

Species (strain), duration, 

and exposure Reproductive system effects

NOEL

mg/kg-d

LOEL

mg/kg-d

Fisher et al., 

2003

Rat (Wistar); GDs 13−21; 0 or 

500 mg/kg-d 

Cryptorchidism, hypospadias, infertility, and testis abnormalities 

similar to human testicular dysgenesis syndrome; abnormal Sertoli 

cell-gonocyte interaction.

500

Gray et al., 1999 Rat (Long-Evans) (P0); PND 

21—adult; 0, 250, 500, or 

1,000 mg/kg-d

At 250, 500, and 1,000 mg/kg-d, delayed puberty.

At 500 and 1,000 mg/kg-d, reduced fertility related to testicular 

atrophy and reduced cauda epididymal sperm numbers.

250

Rat (Long-Evans) (F1); 

GD 0−PND 21; 0, 250, 500, or 

1,000 mg/kg-d

At 250 and 500 mg/kg-d, reproductive malformations (low 

incidences of hypospadias, testicular nondescent, and uterus 

unicornous); reduced fecundity.

250

Rat (Long-Evans) (F1); GD 14 

to PND 3; 0 or 500 mg/kg-d

Reduced AGD, retained nipples, permanently reduced 

androgen-dependent tissue weights.

500

Kim et al., 2004

Ab

Rat (SD); GDs 10−19; 0, 250, 

500, or 700 mg/kg-d

Decreased testes and accessory sex organ weight; delayed testis 

descent; increased expression of estrogen receptor in testes.

250

(presumed) b

Kleymenova et 

al., 2004 Ab

Rat (strain not specified); 

GDs 12−17, 19, 20; 0 or 

500 mg/kg-d

Altered proliferation of Sertoli and peritubular cells; multinucleated 

gonocytes; changes in Sertoli cell-gonocyte interactions.

500

Kleymenova et 

al., 2005a Ab

Rat (SD); GDs 12−20; 0, 0.1, 1, 

10, 30, 100, or 500 mg/kg-d 

At 30 and 50 mg/kg-d, disruption of Sertoli-germ cell contact.

At 50 mg/kg-d, Sertoli cell hypertrophy, decreased total cell 

number and number of seminiferous tubules.

At 100 mg/kg-d, increased multinucleated gonocytes.

10 30

Kleymenova et 

al., 2005b

Rat (SD); GDs 12−21; 0 or 

500 mg/kg-d 

Cytoplasmic changes in Sertoli cells with abnormal cell-cell contact 

with gonocytes, clustering of gonocytes in the middle of the 

tubules, altered morphometry of seminiferous tubules, clusters of 

interstitial cells, decreased number of tubular cross sections per 

testicular section; increased number of multinucleated gonocytes.

500
4-6

Table 4-1. (continued)

Studya

Species (strain), duration, 

and exposure Reproductive system effects

NOEL

mg/kg-d

LOEL

mg/kg-d

Lee et al., 2004 Rat (SD); GD 15 to PND 21; 0, 

1.5, 14.4, 148, or 712 mg/kg-d 

(converted from 0, 20, 200, 

2,000, and 10,000 ppm DBP in 

diet)

At 712 mg/kg-d, decreased percent males; decreased AGD and 

retained nipples, decreased relative testis weight.

At 1.5, 14.4, 148, and 712 mg/kg-d, on PND 21, reduction in 

spermatocyte development, increased foci of aggregated Leydig 

cells, and decreased epididymal ductular cross section.

At 148 and 712 mg/kg-d, at wk 11, loss of germ cell development.

At 1.5 mg/kg-d, degeneration and atrophy of mammary gland 

alveoli in males at 8−11 wks of age.

1.5

Lehmann et al., 

2004

Rat (SD); GDs 12−19; 0, 0.1, 1, 

10, 30, 50, 100, or 500 mg/kg-d 

At ≥50 mg/kg-d, decreased fetal T concentration.

At 500 mg/kg-d, a reduction in oil red O staining of lipids in fetal 

testes.

30 50

Liu et al., 2005 Rat (SD); GDs 12−19; 0, 

500 mg/kg-d

Significant reduction in AGD at GD 19. 500

Mahood et al., 

2005

Rat (Wistar); GDs 13.5−20.5; 0 

or 500 mg/kg-d 

Aggregation of fetal Leydig cells; reduced Leydig cell size; reduced 

T levels at GDs 19.5 and 21.5 (early event in testicular dysgenesis); 

cryptorchidism; partial absence of epididymis at PND 90.

500

Mylchreest et al., 

1998

Rat (SD); GD 30 to PND 20; 

0, 250, 500, or 750 mg/kg-d 

At 500 and 750 mg/kg-d, decreased AGD.

At 250, 500, and 750 mg/kg-d, absent or underdeveloped 

epididymis, associated with testicular atrophy and germ cell loss, 

hypospadias, ectopic or absent testes.

At 500 and 750 mg/kg-d, absent prostate and seminal vesicles, 

small testes, and seminal vesicles.

250

Mylchreest et al., 

1999

Rat (SD); GDs 12−21; 0, 100, 

250, or 500 mg/kg-d 

At 500 mg/kg-d, hypospadias; cryptorchidism; agenesis of the 

prostate, epididymis, and vas deferens; degeneration of the 

seminiferous epithelium; interstitial cell hyperplasia and adenoma; 

decreased weight of prostate, seminal vesicles, epididymis, and 

testes.

At 250 and 500 mg/kg-d, retained areolae or thoracic nipples, 

decreased AGD.

At 100 mg/kg-d, delayed preputial separation (attributed to highly 

affected litter, and not repeated in subsequent study). 

100 250
4-7

Table 4-1. (continued)

Studya

Species (strain), duration, 

and exposure Reproductive system effects

NOEL

mg/kg-d

LOEL

mg/kg-d

Mylchreest et al., 

2000

Rat (SD); GDs 12−21; 0, 0.5, 5, 

50, 100, or 500 mg/kg-d 

At 500 mg/kg-d, decreased AGD, hypospadias, cryptorchidism, 

absent or partially developed epididymis, vas deferens, seminal 

vesicles, and ventral prostate; decreased weights of testes, 

epididymis, dorsolateral and ventral prostates, seminal vesicles, and 

levator anibulbocavernosus muscle; seminiferous tubule 

degeneration, focal Leydig cell hyperplasia, and Leydig cell 

adenoma.

At 100 and 500 mg/kg-d, retained thoracic areolae or nipples in 

male pups.

50 100

Mylchreest et al., 

2002

Rat (SD); GDs 12−21; 0 or 

500 mg/kg-d 

In GDs 18 and 21 fetuses, testicular atrophy, Leydig cell 

hyperplasia, enlarged seminiferous cords with multinucleated 

gonocytes; decreased testicular T; fewer epididymal ducts.

500

NTP, 1991 Rat (SD); continuous breeding 

(16 wks) (gestation and 

lactation); 0, 80, 385, or 

794 mg/kg-d in dams (converted 

from 0.1, 0.5, and 1.0 % DBP in 

feed)

F1 adults: 

At 80, 385, and 794 mg/kg-d: Increased incidence of absent, poorly 

developed, or atrophic testis and underdeveloped or absent 

epididymis.

At 385 and 794 mg/kg-d: Increased incidence of seminiferous 

tubule degeneration.

At 794 mg/k-d: Decreased mating, pregnancy, and fertility indices; 

decreased epididymal, prostate, seminal vesicle and testis weights; 

decreased cauda epididymal sperm concentration; decreased 

average spermatid count, total spermatid heads/testis or total 

spermatid heads/g testis; increased incidence of absent, 

small/underdeveloped/poorly developed, or atrophic penis, seminal 

vesicles, epididymis, and prostate; interstitial/Leydig cell 

hyperplasia; delayed testicular descent or cryptorchidism. 

80

NTP, 1995 (some 

of this is also 

reported in Wine 

et al., 1997)

Rat (SD); continuous breeding 

(16 wks) (gestation and 

lactation); 0, 80, 385, or 

794 mg/kg-d in dams (converted 

from 0.1, 0.5, and 1.0% DBP in 

feed)

At 794 mg/k-d: Decreased mating, pregnancy, and fertility indices; 

decreased epididymal, prostate, seminal vesicle, and testis weights.

385 794
4-8

Table 4-1. (continued)

Studya

Species (strain), duration, 

and exposure Reproductive system effects

NOEL

mg/kg-d

LOEL

mg/kg-d

NTP, 1995 Rat (Fischer 344); perinatal and 

lactation plus 17 wks; 0, 138, 

279, 571, 1,262, or 

2,495 mg/kg-d in dam 

(converted from 0 or 

10,000 ppm during gestation 

and lactation; 0, 1,250, 2,500, 

5,000, 7500, 10,000, 

20,000 ppm for 4 wks PN; 

0, 2,500, 5,000,10,000, 20,000, 

and 40,000 for last 13 wks PN)

At 571, 1,262, and 2,495 mg/kg-d: Degeneration of germinal 

epithelium.

At 1,262 and 2,495 mg/kg-d: Decreased testes and epididymal 

weights, fewer sperm heads per testis, and decreased epididymal 

sperm concentration.

279 571c

NTP, 1995 Rat (Fischer 344); perinatal and 

lactation plus 4 wks; 0, 143, 

284, 579, 879, or 1,115 mg/kg-d 

in dam (converted from 0, 

1,250, 2,500, 5,000, 7,500, 

10,000, and 20,000 ppm)

At 879 and 1,115 mg/kg-d: Moderate epididymal hypospermia in 

all males.

At 579 mg/kg-d, mild epididymal hypospermia in 2 of 10 males.

284 579d

Plummer et al., 

2005 Ab

Rat (strain not specified); 

gestation; 0 or 500 mg/kg-d

Decreased fetal T levels. 500

Shultz et al., 

2001

Rat (SD), GDs 12−21; 0 or 

500 mg/kg-d

Decreased fetal testicular T and androstenedione; increased 

progesterone.

500

Thompson et al., 

2004a

Rat (SD); GDs 12−17, 12−18, 

or 12−19; 0 or 500 mg/kg-d

Decreased fetal T. 500

Thompson et al., 

2005

Rat (SD); GD 19; 0 or 

500 mg/kg-d

Decreased fetal T. 500

Wilson et al., 

2004

Rat (SD); GDs 14−18; 0 or 

1,000 mg/kg-d 

Decreased fetal T, expression of Insl3. 1,000
4-9

Table 4-1. (continued)

Studya

Species (strain), duration, 

and exposure Reproductive system effects

NOEL

mg/kg-d

LOEL

mg/kg-d

Zhang et al., 

2004

Rat (SD); GD 1 to PND 21; 0, 

50, 250, or 500 mg/kg-d 

At 250 and 500 mg/kg-d, decreased AGD; underdeveloped 

epididymides; decreased epididymis or prostate weight at PND 70; 

decreased percent motile sperm and total sperm heads;

degeneration of the seminiferous epithelium. 

At 500 mg/kg-d, cryptorchidism, absent epididymides, decreased 

total number of sperm.

50 250

Ab, Abstract only; AGD, anogenital distance; GD, gestation day; PND, postnatal day; LOEL, lowest-observed-effect level for male reproductive system 

outcomes found in the study; NOEL, no-observed-effect level for male reproductive system outcomes; SD, Sprague-Dawley; T, testosterone. Note: These terms 

are used solely in a descriptive manner in this table, they may not reflect the terminology of the source study, and they are not intended to convey any regulatory 

implication.

a

All studies used an oral route of exposure. Lee et al. (2004) and NTP (1995, 1991) administered DBP in the diet. All other studies used oral gavage. b

The abstract states that the effects were “dose dependent,” but a LOEL is not specifically indicated.

c

Overall, the study NOEL and LOEL are lower based on liver peroxisome activity.

d

Overall, the study NOEL and LOEL are lower based on increased liver weight.
4-10

It is also noted that although BMDL values were calculated for specific developmental 

endpoints identified by Lehmann et al. (2004), Mylchreest et al. (2000), and the NTP (1995) (see 

Table 4-4 of the 2006 external peer review draft Tox Review for DBP), these values were not 

used as a POD for reference value derivation.

4.2. REVIEW OF THE TOXICOLOGY DATA SET

Figure 4-1 illustrates the stepwise approach taken in the evaluation of the toxicity studies, 

focusing on low-dose and low-incidence outcomes. First, for each toxicology study, we 

examined the data at the lowest dose levels (as defined by the study NOELs and LOELs) 

(Step 2). If there was any indication of insurmountable problems with the quality of the reported 

data (e.g., excessive variability, critical methodological concerns, lack of peer review as with 

abstracts, etc.), or if there were no individual animal data reported (as is often the case for poster 

abstracts as well as for many published studies which only contain extracted summary data), the 

review of that study would be terminated. However, if individual data were available, the review 

could proceed (Step 3). The individual animal data were examined for evidence of reproductive 

system outcomes in the males. Although for most studies the exposures were only administered 

during the perinatal developmental period, we recognized that an adverse treatment-related 

outcome might be identified at any life stage that was assessed in the study. There were three 

possible courses that the data review could take from this point forward. In cases where 

problems were identified in the data, we attempted to analyze the extent of the issues and 

determine the ability to move forward with the study analysis. In some cases, the analysis 

stopped at this point, due to deficits in the study data or to inadequate reporting of individual 

animal data. However, if the data in the report appeared to be thoroughly assessed, then the 

study outcomes and endpoints were examined. Alternatively, in some cases where adequate 

individual study data were available for analysis (NTP, 1991, 1995), further examination of the 

study could identify effects at the lowest dose levels that had been considered biologically 

irrelevant in the original review, but it might require further consideration. At any point in this 

stepwise process where data were deemed insufficient to proceed further, we identified research 

needs (discussed in Chapter 7).

To begin the characterization and evaluation of the published studies according to this 

stepwise model, important aspects of each study protocol, conduct, and reporting were 
4-11

summarized (see Table 4-2). Examination of this table demonstrates that approximately half of 

the studies that were selected for analysis (i.e., 14 of 29) were limited to a single dose group, 

which eliminated them from further examination for lower-dose level effects. It is also 

important to note that individual animal data were reported in only 2 of the 29 studies, thereby 

severely limiting, and in some cases even preventing, more rigorous evaluation of the study 

findings. These two characteristics alone tend to overshadow any of the other listed study 

attributes that might contribute to confidence in study findings (i.e., evidence that the study was 

conducted according to quality laboratory standards, description of statistical analysis of the data, 

and/or specific information regarding the number of litters and offspring assessed, which would 

provide an indicator of statistical power). Of the studies listed, only those conducted by 

NTP (1995, 1991) were considered suitable for extended examination.

In order to create a profile of outcomes to the male reproductive system following 

developmental exposures, which might then serve as a baseline for further comparison and 

analysis of toxicological findings across the studies, a list of observed effects was compiled (see 

Table 4-3). The content of this list is very clearly defined by the study protocols, both in terms 

of what endpoints were examined in each study and when (i.e., at what life stage) they were 

examined. For some endpoints, the precise GD or postnatal day (PND) of evaluation may even 

be critical. For example, fetal T should peak at approximately GD 18, so assessments made at 

earlier or later time points may be less sensitive in detecting adverse outcomes, and the effects 

will not be directly comparable across fetal ages. Decreases in T levels may not be observed 

postnatally unless treatment is continued or if testicular malformations disrupt T level (which is a 

different mechanism of perturbation than alterations to the steroidogenesis pathway). In 

neonates, examination for nipple retention is generally conducted at around PND 13 when the 

structure is readily visible but before it is obscured by hair growth. Cryptorchidism, even though 

present at birth, may not be readily observable in neonates until they reach the age of 

PND 16−21 (and of course, it should be detectable at postweaning ages and in adults). Preputial 

separation (PPS) delays can only be observed at the time of sexual maturation, which, in the 

male Sprague-Dawley (SD) rat, occurs at approximately PND 42; therefore, this effect cannot be 

detected at an earlier life stage, nor will it be observed in sexually mature adults. On the other 

hand, sperm alterations (count, morphology, or motility) and perturbations in male fertility can 

only be assessed in adult males, not in immature individuals at earlier life stages
4-12

Table 4-2. Reporting and study size characteristics of male reproductive studies following in utero exposure to 

DBP

Study

> One 

high 

dose

Individual 

data 

publicly 

available

Statistical

analysis 

method 

reported

Study 

conduct 

level 

reported

Number evaluated/group

Litters Offspring

Barlow and Foster, 2003 ✓ ✓ 1−9a 7−60a

Barlow et al., 2003 ✓subsetb ✓ ✓ NR 3

Barlow et al., 2004 ✓ ✓ ✓ 8−11a 35−74a,c

Bowman et al., 2005 ✓ ✓ 18 All male fetuses

Carruthers and Foster, 2005 ✓ ✓ 1−14d 1−91d

Ema et al., 1998 ✓ ✓ 11 DBP treated AGD: NR; crypt.: 144

Ema et al., 2000b ✓ ✓ 73 DBP treated ~770e

Ferrara et al., 2006 ✓ ✓ “in most instances” ~3−6 1−3/litter

Fisher et al., 2003 ✓ ✓ NR Testis wt: 5−10 animals/age group (4); 

hyp. and crypt.: 10 adults

Gray et al., 1999 ✓

PPS 

only

✓ 4 (LE); 8 (SD) LE: 30 male pups; 13 adult males

SD: 48 male pups; 17 adult malesf

Kim et al., 2004 Ab ✓ NR NR

Kleymenova et al., 2004 Ab ✓ NR NR

Kleymenova et al., 2005a 

Ab

✓ NR NR

Kleymenova et al., 2005b ✓ ✓ 3 14−21 pups/evaluation
4-13

Table 4-2. (continued)

Study

> One 

high 

dose

Individual 

data 

publicly 

available

Stat 

analysis 

method 

reported

Study 

conduct 

level 

reported

Number evaluated/group

Litters Offspring

Lee et al., 2004 ✓ ✓ 6−8 11−20 adults

Lehmann et al., 2004 ✓ ✓ ✓ 1−4 3−4 fetuses/group

Liu et al., 2005 ✓ ✓ 3 3 fetuses/litter

Mahood et al., 2005 ✓ ✓ 2−7 NR

Mylchreest et al., 1998 ✓ ✓ ✓ 7−10 All males/litter

Mylchreest et al., 1999 ✓ ✓ ✓ 10 All males/litter

Mylchreest et al., 2000 ✓ ✓ ✓ 11−20 All males/litter

Mylchreest et al., 2002 ✓ ✓ 5−6 23−49 fetuses

NTP, 1995, 1991 ✓ ✓ ✓ ✓ 20 All pups/litter in-life thru necropsy; 

histopath: 10/selected groups 

Plummer et al., 2005 Ab NR NR

Shultz et al., 2001 ✓ ✓ 3 1 male/litter

Thompson et al., 2004a ✓ ✓ 4 1 male/litter

Thompson et al., 2005 ✓ ✓ 4 3 fetuses/litter

Wilson et al., 2004 ✓ ✓ 3 All males/litter

Zhang et al., 2004 ✓ ✓ 14−16 20 pups/group
4-14

Table 4-2. (continued)

Ab, Abstract only; LE, Long Evans; NR, Not reported; PPS, preputial separation; ✓, present. a

Litters and pup numbers not reported for AGD and areolae retention.

b

Data for three individual animals were reported for LC and Sertoli cell staining. The other results are not reported in this table because they were from 

toxicogenomic studies (see Chapter 5). c

57−100% of these pups survived to necropsy so for malformations that required necropsy, the number of pups is less than shown. d

Litters for AGD were the statistical unit; neither litter nor pup numbers for AGD were reported. e

Number derived from the mean number of live fetuses/litter. f

In some cases, data from two experiments were combined.
4-15

Table 4-3. Life stage at observation for various male reproductive system 

outcomes assessed in studies of developmental exposure to DBP

Findings

Life stage of animals (rats) at observation

Fetus

Neonate through 

puberty Adult

Decreased T ✓ ✓ ✓

Malformations ✓ ✓ ✓

Decreased AGD ✓ ✓

Hypospadias ✓ ✓

Retained nipples/areolae ✓ ✓

Cryptorchidism ✓ ✓

Delayed PPS ✓

Organ weights ✓ ✓

Histopathology of male 

reproductive organs

✓ ✓ ✓

Abnormal sperm ✓

Decreased fertility ✓

T, Testosterone; AGD, Anogenital distance; PPS, Preputial separation.

Using the list in Table 4-3 as a guide, a more extended analysis was conducted for each 

of the selected studies. Table 4-4 presents the detailed results. In this table, the various observed 

outcomes are arrayed across three general life stage categories: prenatal (i.e., observations 

conducted in fetuses), neonatal through puberty (i.e., observations conducted in pups), and adult 

(i.e., observations conducted in young, sexually mature animals). These life stage categories do 

not represent the period of exposure for the study. While all studies include exposures during 

late gestation (i.e., during the critical window of male reproductive system development), some 

studies also maintained exposures during later life stages. For reference, Table 4-1 provides 

general descriptions of exposure durations.
4-16

Table 4-4. Age of assessment for individual endpoints across studies of the male reproductive system following 

developmental exposure to DBP 

Fetus Neonate through puberty Adult

↓ 

Ta

Histopathb↓ 

AGD Hyp

Ret.

nip/ 

areolae Cryptc

Del. 

PPSd

↓ 

Org 

wt

Histopathb↓ 

Ta Malf

↓ 

Org 

wt

HistopathbAb. 

Sperm ↓ Fert Hyp

Ret.

nip/ 

areolae Crypt

∆

AGD

↓ 

Ta

Barlow and Foster, 2003 ✓ ✓ ✓ ✓ ✓ ✓— ✓ ✓ ✓e ✓ ✓ ✓ ✓ 

Barlow et al., 2003 ✓

Barlow et al., 2004 ✓ ✓ ✓ ✓f ✓ ✓g ✓ ✓ ✓↓

Bowman et al., 2005 ✓h

Carruthers and Foster, 2005 ✓i ✓j ✓ ✓ ✓ — ✓k — ✓↑

Ema et al., 1998 ✓ ✓l ✓l

Ema et al., 2000b ✓l ✓l

Fisher et al., 2003 ✓ ✓ ✓ ✓/—m ✓ ✓ ✓ ✓ ✓ ✓ ✓ —m

Gray et al., 1999b ✓ ✓ ✓n ✓ ✓ ✓ ✓o ✓p ✓ ✓ ✓ ✓p

Kim et al., 2004 Ab ✓ ✓ ✓ ✓q

Kleymenova et al., 2004 Ab ✓

Kleymenova et al., 2005a 

Ab

✓

Kleymenova et al., 2005b ✓ — — ✓

Lee et al., 2004 ✓ —r

✓ —r — — ✓ —r✓ ✓

Lehmann et al., 2004 ✓

Liu et al., 2005 ✓s

Mahood et al., 2005 ✓ ✓ ✓t NR ✓ ✓ NR ✓ NR ✓

Mylchreest et al., 1998 ✓ ✓ ✓ — ✓ ✓ —u

✓ ✓
4-17

Table 4-4. (continued)

Fetus Neonate through puberty Adult

↓ 

Ta

Histopathb↓ 

AGD Hyp

Ret.

nip/ 

areolae Cryptc

Del. 

PPSd

↓ 

Org 

wt

Histopathb ↓ Ta Malf

↓ 

Org 

wt

HistopathbAb. 

Sperm ↓ Fert Hyp

Ret.

nip/ 

areolae Crypt

∆ 

AGD

↓ 

Ta

Mylchreest et al., 1999 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

Mylchreest et al., 2000 ✓ ✓ ✓ ✓ — ✓ ✓ ✓ ✓ NR

Mylchreest et al., 2002 ✓ ✓ NRs

NTP, 1991 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

Plummer et al., 2005 ✓ ✓v

Shultz et al., 2001 ✓ NRs

Thompson et al., 2004a ✓

Thompson et al., 2005 ✓

Wilson et al., 2004 ✓

Zhang et al., 2004 ✓ — ✓ — ✓ ✓ ✓ ✓u — ✓

✓ Observed; —, Not observed; white box, Not evaluated; shaded box, Evaluated; NR, Not reported, although the study indicates that the endpoint was evaluated; 

Ab, Abstract only; PPS, preputial separation; AGD, anogenital distance; Hyp, hypospadias; ∆, change; Ret. nip/areolae, retained nipples and/or areolae; Cryp, 

cryptorchidism; Del, delay; Org wt, organ weight decrease (absolute or relative) in at least one reproductive organ; Malf, malformations including 

ventral/dorsal/lateral prostate, seminal vesicles, androgen dependent muscles, (accessory sex organs) epididymis, vas deferens external genitalia, cryptorchidism, 

small or flaccid testes; Fert, fertility; Ab. Sperm, abnormal changes in sperm count, motility, and/or morphology.

a

Decreased testicular testosterone (T) was measured in the fetus; Decreased serum T was measured postnatally and in adults.

b

Histological changes—Leydig cell hyperplasia (aggregation); multinucleated gonocytes; Wolffian duct increased coiling (can be measured in fetus, neonate 

through puberty, or adult).

c

Cryptorchidism can be observed between PNDs 16−21 and older. d

Delayed preputial separation normally observed ~PND 42.

e

Enlargement of the seminiferous cords was observed at PNDs 19−21. f

In addition to the observed decreases and absences of male reproductive organs, “occasional enlargement” of the testes was observed only in the 500 mg/kg-d 

group. 
4-18

Table 4-4. (continued)

g

Assessed in adult animals at PNDs 180, 370, and 540. Hypospadias only observed in the 500-mg/kg-d group. h

Wolffian ducts smaller, more fragile, adipose tissue surrounding duct was more gelatinous, and decreased coiling.

i

Assessed at PNDs 1 and 13. Reduction in AGD observed in animals exposed to DBP on GDs 16 and 17, GDs 17 and 18, or GDs 19 and 20; no change in AGD 

in animals exposed GDs 14 and 15.

j

Assessed on PND 13; assessed on an individual animal basis, significant increase in nipple retention was observed after dosing on GDs 15−16; 16−17; 17−18; or 

19−20. k

Assessed at PND 90; significant increase in nipple retention only for males dosed GDs 16−17 (individual animal basis). l

AGD and cryptorchidism were assessed in fetuses on GD 21. Exposed pregnant dams were sacrificed on day 21, and live fetuses were removed. m Blood plasma T levels significantly reduced on PND 25 but not on PNDs 4, 10, or in adult.

n Delayed PPS only reported for parental generation (P0) males exposed from weaning through to puberty.

o Reduced epididymal sperm numbers; not necessarily abnormal sperm. p In P0 males.

q Evaluated T levels at 31 and 42 days (not fetus) and found decreased at 42 days.

r

It is presumed that specific malformations would have been observed if present based on the study design and methods. s

Examined in GDs 19 or 21 fetuses.

t Observed at PNDs 25 and 90; nonscrotal testes were not evaluated histopathologically.

u Only motility was evaluated in Mylchreest et al. (1998); in Zhang et al. (2004), sperm number, motility, and morphology were evaluated, but only count was 

affected.

v Study mentions that adult cryptorchidism was observed, but study methods do not indicate that offspring were retained until adult age.
4-19

Table 4-4 summarizes the outcomes and presents a broad representation of positive and 

negative observations in a manner that demonstrates that not all relevant endpoints were 

evaluated at all life stages or even in each study. To facilitate summarization of the myriad 

individual study findings, information was often combined by category (e.g., “histopathology” 

includes a broad variety of outcomes in various reproductive organs), and for the sake of brevity, 

the minute details and nuances of the study design and observations, although quite interesting, 

are not typically presented. In a few cases, negative outcomes presented in the table are 

extrapolations based on the presumption that specific findings would have been observed if they 

were present. For example, with methods that include detailed external and internal 

(macropathology) examination of pups and/or adults, the absence of reported malformations at 

either of these life stages was presumed to indicate that no gross malformations were observed 

because they should have been readily detectable (Lee et al., 2004).

Tables 4-1, 4-3, and 4-4 clearly illustrate that the study protocols varied quite extensively. 

In general, with the exception of the NTP studies, the protocols were not designed to conform to 

a particular regulatory guideline. Rather, the majority of the studies were focused research 

efforts that were verifying and/or expanding upon previously observed outcomes; therefore, the 

differences across study methods are understandable. As a result, the apparent lack of 

consistency in male reproductive system observations across studies is generally attributable to 

differences in protocol design and implementation. Some examples are discussed in detail as 

follows:

• Although all of these studies used exposures during late gestation (i.e., a critical period of 

male reproductive system development in the rat), the specific endpoints that were 

assessed and/or the life stages at which endpoints were examined varied extensively 

across the studies. Obviously, treatment-related alterations of life-stage-specific events 

require examination during the most appropriate or optimal life stage (e.g., increased 

multinucleated gonocytes can only be observed in fetal testes, delays in PPS can only be 

observed in juvenile animals at the time of sexual maturation, and disturbances in 

reproductive function can only be observed in sexually mature adults). Other permanent 

structural abnormalities may be detected across multiple life stages (e.g., hypospadias or 

cryptorchidism could theoretically be observed in late gestation fetuses, in adolescents, 

and in adults). For some outcomes, it is difficult to predict a priori the optimal time point 

for evaluation. For example, DBP-related increases in the estrogen receptor (ER) were 

observed at 31 days but not at 42 days (Kim et al., 2004).
4-20

• It is important to realize that not all available offspring are evaluated in every study; 

therefore, identification of adverse outcomes may rely, in part, on sampling protocols and 

the statistical power of the sample size for detection of rare or low-incidence events. 

Calculations of statistical power are rarely provided in study reports.

• In some cases, apparent differences in studies may result because the report contains an 

insufficient level of detail on a particular endpoint or life stage—often because the

emphasis of the scientific review lies in a slightly different direction. For example, if 

high doses of DBP are administered during sensitive periods of male reproductive system 

development, and the males are maintained and terminated as adults, at which time 

histopathological evaluation is performed, it might be assumed that various male 

reproductive system malformations and/or cryptorchidism would have been present in 

some of the males at necropsy. Yet, these findings may not be reported because the 

histopathological findings are the primary focus of the investigation and/or the 

publication (e.g., Lee et al., 2004).

• In other situations, the description of the findings at various life stages may vary. For 

example, evidence of cryptorchidism may be described as “testis located high in the 

abdomen” in a fetus, as “undescended testis(es)” in an adolescent rat, or as “unilateral 

testis” upon noninvasive clinical examination of an adult. To some extent, this lack of 

consistency in terminology may result from laboratory Standard Operating Procedures 

that direct technical staff to avoid the use of diagnostic terminology.

Overall, in spite of numerous differences in the study designs, the toxicity data set for 

DBP clearly demonstrates that exposure to DBP during critical stages of male reproductive 

system development can result in adverse structural and functional reproductive outcomes. 

When specific critical aspects of study design and implementation were similar, consistent 

outcomes were almost universally observed. The WOE embodied by the data described above is 

further supported by studies in rats that demonstrated similar incidences of cryptorchidism and 

decreased AGD in male pups of dams treated with either DBP or MBP, the metabolite of DBP 

(Ema and Miyawaki, 2001a). The ability of MBP to cross the placenta and reach the fetus has 

also been conclusively demonstrated (Fennell et al., 2004; Saillenfait et al., 1998), and these two 

TK events (metabolism and placental transport) are key to the MOA of reduced fetal testicular T 

(David, 2006). Available toxicogenomic data, described elsewhere in this case-study report, 

further elucidate the MOA(s) of DBP in producing adverse effects on male reproductive system 

development and are an important consideration in the WOE analysis of the toxicological data 

set.
4-21

In the selected DBP toxicology study data set, the presentation of extensive individual 

offspring data was limited to the NTP (1991) study conducted as a reproductive assessment by 

continuous breeding (RACB) in SD rats. The individual data from this study were carefully 

examined in order to confirm the NOEL and LOEL described in the study report. This analysis 

was conducted under the presumption that statistical and/or biological significance noted in the 

summary compilations of male reproductive system outcomes might not identify low incidence 

effects in individual offspring at lower dose levels. To further aid the identification of treatmentrelated outcomes, the male reproductive system outcomes were grouped by organ instead of 

individual animal. This analysis revealed apparently treatment-related findings in the testis and 

epididymis of F1 male offspring, as summarized in Table 4-5. At the highest dose tested 

(794 mg/kg-d, equivalent to 1.0% DBP in the diet), additional findings in the male reproductive 

organs of F1 offspring included single incidences of (1) underdeveloped prepuce; (2) mild 

secretion and severe vesiculitis of the prostate; (3) a mass on the testis; and (4) a focal granuloma 

with fluid and cellular degeneration in the epididymis; none of these findings were observed at 

the lower dose levels. Understandably, the findings at the low- and mid-dose groups were 

originally interpreted as not being treatment-related (Wine et al., 1997; NTP, 1991). However, 

consideration of MOA information for DBP, including toxicogenomic data, resulted in a more 

conservative interpretation of the toxicity data both by NTP researchers (conference call in 2008 

between Paul Foster [NTP/NIEHS], Susan Makris [EPA/NCEA], and Susan Euling 

[EPA/NCEA]) and by the EPA IRIS program (U.S. EPA, 2006a). Consequently, further analysis 

of individual offspring data in the current case study did not identify any additional sensitive 

toxicological outcomes; the study LOEL was confirmed to be the lowest treatment level tested in 

the NTP RACB study (80 mg/kg-d).

4.3. UNEXPLAINED MOAs FOR DBP MALE REPRODUCTIVE TOXICITY 

OUTCOMES 

Figure 3-6 illustrates the broad conceptual approach for consideration and interpretation 

of toxicogenomic and toxicology data to inform MOA. The toxicogenomic data can be 

evaluated to identify altered genes, gene products, and pathways; this information can lead to a 

more complete understanding of the mechanism of action or MOA(s) for the chemical toxicity. 

From the opposite perspective, the toxicity data can provide information critical to identifying 
4-22

Table 4-5. Incidence of gross pathology in F1 male reproductive organs in 

one continuous breeding study with DBPa

Gross findingb

Dose (% in diet)

0 0.1 0.5 1.0

Testis: absent, poorly developed, atrophic, 

undescended

0/20 1/20 1/20 6/20

Penis: small/underdeveloped 0/20 0/20 0/20 4/20

Epididymis: underdeveloped/absent 0/20 1/20 1/20 12/20

a

Incidences were compiled from reported individual animal macroscopic pathology data; statistical analysis was not 

performed.

b

Some animals have more than one type of malformation, and these animals were counted separately for each of the 

three outcome categories.

Source: (NTP, 1991).

the relevant MOA(s) involved in the toxicological outcomes, and thereby inform the 

interpretation of gene alterations and relevant pathways. 

Each male reproductive system outcome was evaluated for consistency with either or 

both of the two well-established MOAs using expert judgment based on the available published 

literature for DBP (see Figure 4-2). This exercise helped to identify the unexplained endpoints 

for which the evaluation of the toxicogenomic data set may suggest potential MOAs (see Chapter 

5). For the DBP case study, Table 4-6 presents a compendium of the specific findings noted in 

the male reproductive system following exposures at critical windows of development. While 

reduced fetal testicular T and reduced Insl3 signaling can be linked to some of the observed 

outcomes on the basis of available data, potential key events cannot specifically be identified for 

other outcomes. The unexplained MOAs are good candidates for further study, both in 

toxicology and toxicogenomic studies, to elucidate the underlying mechanism of action.
4-23

Figure 4-2. The process for evaluating the MOA for individual 

male reproductive system outcomes following developmental 

DBP exposure. The available data for MOA for each male 

reproductive outcome following developmental DBP exposure 

were evaluated by a team of experts. For each outcome, the 

current WOE of the data either support the MOA (“YES”), support 

that this is not the MOA ("NO"), or are inconclusive for the MOA, 

i.e., either unlikely or unclear (“?”). “Unexplained MOAs” include 

both "?" and "NO" conclusions.

Each male reproductive 

system outcome 

following developmental 

DBP exposure

Explained by ↓T? Explained by ↓Insl3?

Consider MOA evidence

YES

↓T MOA ↓Insl3

MOA

unexplained

MOA

? NO NO ? YES

Each male reproductive 

system outcome 

following developmental 

DBP exposure

Explained by ↓T? Explained by ↓Insl3?

Consider MOA evidence

YES

↓T MOA ↓Insl3

MOA

unexplained

MOA

? NO NO ? YES
4-24

Table 4-6. Evidence for MOAs for the observed effects in the male 

reproductive system after in utero DBP exposure

Organ/

Function Effect

MOA

Reduced 

fetal 

testicular 

T

Reduced 

Insl3

signaling

Testes Multinucleated gonocytes; increased number of 

gonocytes in fetal testes

?

b ?c

Altered proliferation of Sertoli and peritubular 

cells; fewer Sertoli cells

?

b ?c

Gonocyte apoptosis increase; early postnatal 

decrease in gonocyte number

?

b ?c

Abnormal Sertoli cell-gonocyte interaction ?

b ?c

Small incidence of Leydig cell adenomas, 

aggregates, and hyperplasia

✓ ?

c

Decreased number of spermatocytes or cauda 

epididymal sperm concentration.

✓ ✓d

Small or flaccid; other abnormalities; decreased 

weight

✓ ✓

Increased weight due to edema ?

e ?

Decreased number or degeneration of 

seminiferous cords/tubules; altered 

morphology; degeneration of the epithelium; 

enlarged cords/tubules

?

b ?c

Testes descent: none (cryptorchid) or delayed ✓f ✓f

Gubernacular 

ligament

Gubernacular ligament development effects: 

agenesis or elongation

X ✓

Epididymis Lesions and agenesis; partial to complete 

absence; decreased epididymal ductular cross 

section

✓ X

Reduced weights ✓ ✓

Mammary gland Nipple and/or areolae retention in males ✓ X

Degeneration and atrophy of alveoli in males ?

b X
4-25

Table 4-6. (continued)

Organ/

Function Effect

MOA

Reduced 

fetal 

testicular 

T

Reduced 

Insl3

signaling

Wolffian ducts Underdeveloped ✓ X

Seminal vesicles Malformations or absent; decreased weight ✓ X

Coagulating gland Malformations ✓ X

Penis Small, underdeveloped ✓ X

Hypospadias ✓ X

Delayed preputial separation ✓ X

Accessory sex 

organ

Decreased weight ✓ X

Prostate Decreased wt or absent ✓ X

Vas deferens Agenesis ✓ X

Levator 

anibulbocavernosus 

muscle

Decreased weight ✓ ?

c

Male/female ratio Decreased % male offspring as determined by 

AGD at birth 

✓ X

Perineum Decreased AGD ✓ X

Repro function Infertility ✓ ✓d

AGD, anogenital distance; ?, Current data indicate that it is unlikely the MOA; ✓, Current weight of evidence of the 

data support this MOA leading to the effect; X, Current weight of evidence of the data indicate that this MOA is not 

the MOA for this outcome.

a

MOA is defined as one or a sequence of key events upon which the outcome is dependent (see glossary). b

Reduced fetal testicular T may play a role, but current data indicate that reduced T is not solely responsible for this 

outcome.

c

The Insl3 knockout mouse phenotype suggests that Insl3 is specifically required for gubernacular ligament 

development and, therefore, testis descent in mice since these mice do not have other defects. d

Decreased fertility in males is a result of reduced Insl3 signaling since reduced Insl3 signaling leads to undescended 

testes, which, in turn, reduces sperm count (presumably by increasing the temperature) and can cause infertility.
4-26

Table 4-6. (continued)

e

In some animals, increased weight, due to edema, can result in animals that have epididymal agenesis, which is a 

consequence of reduced testosterone (T). f

Insl3 signaling is required for development of the gubernacular ligament and through this mechanism—the 1st stage 

of testis descent from the kidney region to the inguinal region. Testosterone is required for the 2nd stage of testis 

descent, from the inguinal region to the scrotum (reviewed in Klonisch et al., 2004). After in utero DBP exposure, 

the cryptorchid phenotype resembles the Insl3 knockout. A delay in testis descent can result from reduced Insl3

and T.

4.4. CONCLUSIONS ABOUT THE TOXICITY DATA SET EVALUATION: 

DECISIONS AND RATIONALE

The review of the toxicology data set identified a number of issues and limitations that 

are evident in the study descriptions and endpoint summaries presented in this chapter. These 

include the following:

• Lack of dose-response information: A number of studies conducted with DBP used a 

single high-dose treatment level (often at 500 mg/kg-d) in order to produce readily 

observable adverse outcomes to male reproductive system development that could be 

examined. In such studies, the absence of lower dose levels prevents the evaluation of 

dose-dependent responses and does not allow the identification of study-specific NOELs 

or LOELs. While this approach is useful for hazard characterization, it does not facilitate 

other aspects of risk assessment (e.g., dose-response assessment or risk characterization). 

Thus, studies utilizing a single high-dose level may provide important information for a 

WOE assessment of the toxicology profile, but they have diminished usefulness in 

identifying outcomes for use in risk calculations at environmentally relevant doses.

• Insufficient information on study methods: Even though every study report includes a 

section on study methods, there can be a great deal of unevenness in the amount of 

detailed information provided. Consequently, important questions may arise during study 

review that cannot be readily resolved. In some cases, this can have an impact on 

individual study interpretation or on conclusions that rely on a thorough WOE evaluation 

of the data set.

• Unavailable individual outcome data: A full range of individual animal data is seldom 

included in studies published in the open literature and is almost never available when the 

only available publication is a presentation abstract. Conversely, individual animal data 

are generally included in toxicology reports generated in response to a regulatory 

mandate or conducted by a federal agency (e.g., NTP). The availability of individual 

animal data can be quite important in interpreting the study findings, because it can 

reveal problems or inadequacies in the data, but it can also help identify low incidence 

adverse outcomes. In the case of DBP, the individual offspring data presented in the 

NTP study report (1991) include alterations in the reproductive system of F1 males that 
4-27

had been exposed during development. These findings are similar to outcomes identified 

at higher dose levels, are consistent with the proposed MOA, and, consequently, are used 

to establish a LOEL for the study.

• Protocol limitations: Unless studies are designed to meet the recommendations of a 

standardized testing protocol (e.g., NTP or U.S. EPA/Office of Prevention, Pesticides and 

Toxic Substances reproductive toxicity study guidelines), there may be a high degree of 

variability among the protocols used for testing any one chemical. Between two studies, 

there can be differences in the treatment regimen or in the assessment of outcomes that 

render them incomparable. DBP provides a good example of a chemical that targets a 

very specific critical prenatal window of reproductive system development in males, and 

results in adverse outcomes that could go unidentified if the appropriate endpoint(s) are 

not assessed at the optimal life stage or time point.

• Specific study’s limitations: Even when a study design optimizes the detection of adverse 

outcomes from chemical treatment, there may be challenges in study analysis and 

interpretation. Such is the case with the NTP study on DBP, which was conducted in 

several phases and reported both in the open literature (Wine et al., 1997) and by the 

Institute that conducted the experiments (NTP, 1995, 1991).

The analysis of the toxicology data in this chapter has provided a firm basis for expanded 

consideration of the toxicogenomic data for DBP as depicted in Figure 3-6. The extensive 

analysis of the toxicology data set and consideration of MOA(s) provide a source of information 

for use in phenotypic linking of known and potential MOAs. This chapter also provides an 

example of steps one can take to develop a toxicological data source, in particular, examining 

(1) the individual toxicity studies; (2) the WOE for the studies; (3) potential low incidence and 

low-dose effects; and (4) the MOA for the affected endpoints. All of these steps are useful 

exercises for evaluating toxicogenomic data in future risk assessments. The evaluations of both 

the toxicity and toxicogenomic data sets (detailed in Chapters 4 and 5) provide strong support for 

phenotypic anchoring for a number of gene expression changes occurring after in utero DBP 

exposure for several of the male reproductive outcomes. The available toxicogenomic studies 

for DBP are evaluated in Chapter 5.
5-1

5. EVALUATION OF THE DBP TOXICOGENOMIC

DATA SET

This chapter presents an evaluation of the DBP toxicogenomic data set from the 

published literature and some new analyses of one of the microarray studies for DBP. The 

toxicogenomic studies include nine published RT-PCR and microarray studies in the rat after in 

utero DBP exposure. First, we evaluated the toxicogenomic data set from the published 

literature for the consistency of findings. Second, evaluating the published literature and 

performing new pathway analyses of the Liu et al. (2005) data set, we generated hypotheses 

about pathways/mechanism affected by DBP in utero exposure that may explain the testis 

endpoints for which there is no established MOA (these “unexplained” endpoints were identified 

in Chapter 4). The DBP genomic data set includes nine papers published through July 2007. 

The microarray studies all exposed animals to DBP doses of 500–1,000 mg/kg-d during the 

critical window for male reproductive development, which is during late gestation and correlates 

with the time of peak T production. The chapter first discusses the methodologies utilized in the 

nine studies and provides a brief overview of each study. The chapter then presents an 

evaluation of the consistency of the findings for the microarray, RT-PCR, and protein studies 

performed in the rat testes. The findings of the Lehmann et al. (2004) study, the one available 

dose-response RT-PCR study for DBP, are discussed. In addition, the pathway reanalysis of the 

Liu et al. (2005) study is presented, and data gaps and research needs are identified. 

5.1. METHODS FOR ANALYSIS OF GENE EXPRESSION: DESCRIPTION OF 

MICROARRAY TECHNIQUES AND SEMI-QUANTITATIVE RT-PCR

5.1.1. Microarray Technology 

Microarray technology allows for analysis of genome-wide expression of thousands of 

genes from the organ or tissue of interest. In principle, there are two main types of microarrays: 

the cDNA microarray and the oligonucleotide array. The cDNA microarray contains DNA from 

each open reading frame spotted onto glass microscope slides or nylon membranes. These 

probes are used to detect cDNA, which is DNA synthesized from a mature, fully spliced mRNA 

transcript. For example, Clontech’s Atlas Arrays contain DNA sequences from thousands of 

genes immobilized on nylon membrane or glass slides. Each gene found on these arrays is wellcharacterized. These arrays use a radiolabelled detection system for analyzing the changes in 
5-2

gene expression and have been optimized for high-quality expression profiling using a limited 

set of genes. Moreover, such arrays allow for the use of 32P, and, therefore, offer a sensitive 

measure of gene expression. The second type of microarray is the oligonucleotide array. Here, 

short DNA sequences or oligonucleotides (oligos) are synthesized directly onto the glass slide 

via a number of different methods. For example, Affymetrix® uses ‘Photolithographic’ 

technology, where probes are directly synthesized onto the arrays. Briefly, the slide is coated 

with a light-sensitive chemical compound that prevents the formation of a bond between the slide 

and the first nucleotide of the DNA probe being created. Then, chromium masks are used to 

either block or transmit light onto specific locations on the surface of the slide. A solution 

containing thymine, adenine, cytosine, or guanine is poured over the slide, and a chemical bond 

is formed in areas of the array that are not protected by the mask (exposed to light). This process 

is repeated 100 times in order to synthesize probes that are 25 nucleotides long. This method 

allows for high-probe density on a slide. 

Affymetrix uses an antibody detection system with horseradish peroxidase and 

streptavidin conjugates, and a 2-dye system (Cy3- and Cy5- labeled fluorescein dyes), which is 

unique to this platform. The Agilent scanner detects the relative intensities of the red and green 

labels and gives a relative measure of the gene expression changes between the control and 

treated samples. In the case of Affymetrix and Clontech, the detection system measures the 

absolute intensity of the individual probes of the treated and control samples. These values are 

then used to calculate the relative gene expression change between the treated and control 

samples.

5.1.2. Reverse Transcription-Polymerase Chain Reaction (RT-PCR)

Polymerase Chain Reaction (PCR) is a method that allows exponential amplification of 

short DNA sequences within a longer double stranded DNA molecule using a thermo-stable 

DNA polymerase called Taq polymerase. RT-PCR is a semiquantitative technique for detection 

of expressed gene transcripts or mRNA. Over the last several years, the development of novel 

chemistries and instrumentation platforms enabling detection of PCR products on a real-time 

basis has led to widespread adoption of real-time RT-PCR as the method of choice for 

quantitating changes in gene expression. Real-time RT-PCR is a kinetic approach in which the 

reaction is observed in the early, linear stages. Furthermore, real-time RT-PCR has become the 
5-3

preferred method for confirming results obtained from microarray analyses and other techniques 

that evaluate gene expression changes on a global scale.

5.2. REVIEW OF THE PUBLISHED DBP TOXICOGENOMIC STUDIES

5.2.1. Overview of the Toxicogenomic Studies

We evaluated nine studies published prior to July 2007 that characterized altered gene 

expression in rats following prenatal DBP exposure. Among these nine studies, four are based 

on the analysis of preselected genes by real-time RT-PCR, while the other five are based on the 

analysis of global gene expression by microarray technology. Table 5-1 summarizes general 

information (e.g., DBP dose, exposure route, exposure window, tissue type) for these nine 

studies, and each study is briefly reviewed. Section 5.2.3.2 presents information about the 

similarities and differences among these studies.

5.2.2. Microarray Studies

5.2.2.1. Shultz et al. (2001) 

Six SD rats per group were treated by gavage with corn oil, DBP (500 mg/kg-d), or 

flutamide (reference antiandrogen, 50 mg/kg-d) from GDs 12–16, GDs 12–19, or GDs 12–21. 

Testes were then isolated on GD 16, 19, or 21. Global changes in gene expression were 

determined by Clontech cDNA expression array (588 genes). Shultz et al. (2001) isolated total 

RNA from testis of control and treated animals. Reverse transcription reactions were performed 

using total RNA, [32P]-dATP, and superscript II MMLV-RT. Following purification, the probes 

were counted, and equal numbers of counts per minute were added to each rat gene cDNA 

expression array. The arrays were hybridized with cDNA using 1 fetus per dam. Hybridization 

and washing were performed according to manufacturer’s instructions. Digital images were 

collected on a BioRad phosphorimager and analyzed using Clontech’s Atlas Image software. 

Eight genes were further examined by real-time RT-PCR. Total RNA was isolated from both 

testes using RNA STAT60, and then the RNA was treated with DNase I in the presence of 

RNasin. cDNA was then synthesized using random primers and TaqMan reverse transcription 

reagents. Quality of RT reactions was confirmed by comparison of RT versus no enzyme control 

for each RNA sample using the glyceraldehyde-3-phosphate dehydrogenase (GAPDH) primer 
5-4

Table 5-1. Study comparisons for the toxicogenomic data set from male tissues after in utero DBP exposure

Studya

Strain and 

species DBP doses Treatment intervalb

Toxicogenomic method

Tissues 

collected

Microarray

(Platform) RT-PCR

Barlow et 

al., 2003

SD rat 500 mg/kg-d GDs 12−19 No Yes Testis

Bowman et 

al., 2005

SD rat 500 mg/kg-d GDs 12−19 or 19−21 Yes (Clontech 

cDNA arrays)

Yes Wolffian 

ducts

Lehmann et 

al., 2004 

SD rat 0.1, 1.0, 10, 50, 

100, or 

500 mg/kg-d

GDs 12−19 No Yes Testis

Liu et al., 

2005c

SD rat 500 mg/kg-d GDs 12−19 Yes 

(Affymetrix®

GeneChip®

oligo arrays)

Yes Testis

Plummer et 

al., 2007d

Wistar rat 500 mg/kg-d GDs 12.5−15.5; 

12.5−17.5, or 12.5−19.5

Yes (Agilent 

22K and 44K 

oligo arrays) 

Yes Testis: whole, 

seminiferous 

cord, and 

interstitial 

regions

Shultz et al., 

2001

SD rat 500 mg/kg-d GDs 12−16, 12−19, or 

12−21

Yes (Clontech 

cDNA arrays)

Yes Testis

Thompson 

et al., 2004 

SD rat 500 mg/kg-d GDs 12−17, 18, or 19; 

13−19, 14−19, 15−19, 

16−19, 17−19, 18−19, or 

19 

No Yes Testis
5-5

Table 5-1. (continued)

Studya

Strain and 

species DBP doses Treatment intervalb

Toxicogenomic method

Tissues 

collected

Microarray

(Platform) RT-PCR

Thompson 

et al., 2005 

SD rat 500 mg/kg-d 0.5−24 hrs on GDs 

18−19 or GD 19

Yes 

(Affymetrix 

GeneChip oligo 

arrays)

Yes Testis

Wilson et 

al., 2004e

Rat, SD 1,000 mg/kg-d GDs 13−17 No Yes Testis

a

In all studies, oral gavage was the route of exposure.

b

GD 0 = sperm positive.

c

Study assessed 7 different phthalates.

d

Plummer et al. (2007) reported dosing intervals spanning GDs 12.5−19.5, which is comparable to GDs 12−19 in the other studies due to differences in reporting of 

GD and sperm positive at GD 0.5.

e

Wilson et al. (2004) reported a dosing interval of GDs 14−18, which is comparable to GDs 13−17 in the other studies due to differences in reporting of GD 

and sperm positive at GD 1.
5-6

set. Fourteen rat-specific primer sets were used for analyses. The ABI PRISM 7700 and the 

ABI PRISM 7900HT Sequence Detection System was used for RT-PCR with the SYBR Green 

PCR and TaqMan Universal PCR Master Mix reagents. GAPDH was used as an on-plate 

internal calibrator for all RT-PCR reactions. 

Genes analyzed by real-time RT-PCR include clusterin (Clu), cytochrome P450, 

family 11, subfamily a, polypeptide 1 (Cyp11a1), myristoylated alanine-rich C-kinase substrate 

(Marcks), proliferating cell nuclear antigen (Pcna), cytochrome P450, family 17, subfamily a, 

polypeptide 1 (Cyp17a1), steroidogenic acute regulatory protein (Star), scavenger receptor class 

B, member 1 (Scarb1), and v-kit Hardy Zuckerman 4 feline sarcoma viral oncogene homolog 

(Kit). Radioimmunoassay of steroid hormones and immunocytochemical analysis of certain 

proteins (i.e., CLU and b-cell leukemia/lymphoma 2 [BCL2]) in the fetal testes were also 

performed. 

Of the 588 genes examined, ~45 genes had at least a 2-fold change in the average 

expression values in DBP-treated rats relative to the average values in control rats. DBP 

exposure led to a reduced expression of steroidogenic enzymes at GD 19, such as Cyp11a1, 

Cyp17a1, Scarb1, and Star. These genes were upregulated at GD 19 following flutamide 

exposure, suggesting that DBP does not act as an androgen antagonist at this time point. 

Flutamide and DBP demonstrate patterns of gene expression that overlap, though both have 

distinctly expressed genes. This suggests to Shultz et al. (2001) that there are both common and 

distinct molecular pathways within the developing fetal testes.

Other genes affected after DBP exposure were Clu (upregulated) and Kit

(downregulated). Using immunocytochemical staining of CLU and BCL2 protein in the fetal 

testes, increased amounts of both proteins were observed in the Leydig and Sertoli cells of 

GD 21 testes. Decreases in testicular T and androstenedione in testes isolated on GDs 19 and 21 

were observed, while increases in progesterone in testes isolated on GD 19 in DBP-exposed 

testis were observed.

Shultz et al. (2001) suggest that the antiandrogenic effects of DBP are due to decreased 

T synthesis. Furthermore, enhanced expression of cell survival proteins, such as CLU and 

BCL2, may be involved in DBP-induced Leydig cell (LC) hyperplasia, while downregulation of 

Kit may play a role in gonocyte degeneration. 
5-7

5.2.2.2. Bowman et al. (2005) 

Four to seven SD rats per group were treated by gavage with corn oil or DBP at 

500 mg/kg-d from GDs 12–19 or GDs 12–21. The animals were sacrificed on GD 19 or 21, and 

Wolffian ducts (WD) were pooled from three to four fetuses (to obtain enough RNA for 

analysis) within the same litter for gene expression analysis. Global changes in gene expression 

were determined by Clontech Atlas Rat Toxicology 1.2 cDNA expression array (1,185 genes). 

Images were collected using a Phosphorimager and then imported into AtlasImage 2.01 and 

GeneSpring 4.2 for analysis. Selected genes were further examined by real-time quantitative 

RT-PCR using the GeneAmp 5700 Sequence Detection System. Total RNA was isolated, 

DNAse-treated, and reverse-transcribed using TaqMan reagents. Twenty-three primer sets were 

used for RT-PCR analysis. Reactions were standardized using GAPDH-specific primers. The 

genes analyzed by RT-PCR include those in the insulin-like growth factor (Igf) pathway, the 

matrix metalloproteinase (Mmp) family, the extracellular matrix, and other developmentally 

conserved signaling pathways: bone morphogenetic protein 4 (Bmp4), collagen, delta like (Dlk), 

mitogen-activated protein kinase 12 (Map3k12), epidermal growth factor receptor (Egfr), 

fibroblast growth factor 10 (Fgf10), FGF receptor 2 (Fgfr2), fibronectin, insulin-like growth 

factor 1 (Igf1), insulin-like growth factor 2 (Igf2), insulin-like growth factor 1 receptor (Igf1r), 

insulin-like growth factor binding protein 5 (Igfbp5), integrinA5, integrinB1, matrix Gla protein 

(Mgp), matrix metallopeptidase 2 (Mmp2), matrix metallopeptidase 14 (Mmp14), matrix 

metallopeptidase 16 (Mmp16), Notch2 receptor (Notch2), and tissue inhibitors of MMPs (Timp1, 

Timp2, and Timp3). Immunohistochemistry was also performed to evaluate changes in 

localization and/or intensity of IGFLRβ and androgen receptor (AR) protein expression. 

Microarray data were not presented due to considerable variability in gene expression 

levels within the treatment group at each age. Based on real-time RT-PCR analysis, compared 

with controls, prenatal exposure to DBP from GDs 12–19 or GDs 12–21 increased mRNA 

expression of different members of the IGF family including Igf1 (on GDs 19 and 21), Igf 2(on 

GD 19), Igfr1r (on GD 19), and Igfbp5 (on GD 21) in the developing WD, while Egfr was 

unchanged on GDs 19 and 21. Additionally, mRNA expression of Ar, Bmp4, integrinA5, Mmp2, 

and Map3k12 was increased on GD 19; mRNA expression of Fgf10, Fgfr2, Notch2, Mmp2, 

Timp1, and Mgp was increased on GD 21. IGFLRβ immunostaining was higher in the cytoplasm 

of the ductal epithelial cells and increased in the cytoplasm of mesenchymal cells in 
5-8

DBP-exposed fetuses compared with that in controls. In general, reduction of AR 

immunostaining in the nuclei of ductal epithelial cells of DBP-exposed WD was observed on 

GD 19. Compared with controls, WDs dissected from GD 19 DBP-exposed fetuses were slightly 

smaller in size (underdeveloped) and appeared to be more fragile. By GD 21, control fetus WDs 

were markedly coiled, while those from the exposed fetuses exhibited less coiling. 

Prenatal DBP exposure appears to alter the mesenchyme-epithelial signaling of growth 

factors (e.g., IGFs) and other developmentally conserved pathways (e.g., BMP4) in WDs. 

Bowman et al. (2005) contend that the effect of DBP on WD differentiation is likely a 

consequence of decreased fetal testicular T, although direct effects of DBP on the developing 

WD independent of T are also possible. 

5.2.2.3. Liu et al. (2005) 

Five to ten SD rats per group were treated by gavage with corn oil, DBP (500 mg/kg-d), 

or one of six other phthalate esters (500 mg/kg-d) daily from GDs 12–19. The six other 

phthalate esters include diethyl phthalate (DEP), dimethyl phthalate (DMP), diocytyl terephthalate (DOTP), diethylhexyl phthalate (DEHP), dipentyl phthalate (DPP), and butyl benzyl 

phthalate (BBP). Testes were collected on GD 19, homogenized, and then total RNA was 

isolated. RNA integrity was assessed using an Agilent 2100 Bioanalyzer. cDNA was 

synthesized from 2.5 μg total RNA and purified using RiboAmp OA. The BioArray High-Yield 

RNA Transcript Labeling Kit was used for cRNA amplification and biotin labeling. Affymetrix 

GeneChip Sample Cleanup Module was used for purifying and fragmenting the cRNA. The 

Complete GeneChip® Instrument System was then used to hybridize, wash, stain, and scan the 

GeneChip arrays (RAE230A and RAE230B; ~30,000 genes). The data were analyzed using 

analysis of variance (ANOVA [one-way, two-way, nested one-way]), Dunnett test (post hoc), 

Tukey test, and Bonferroni adjustment. 

Image files obtained from the scanner were analyzed with the Affymetrix Microarray 

Suite (MAS) 5.0 software and normalized by global scaling. Absolute analysis was performed 

for each array prior to comparative analysis. To identify differentially expressed transcripts, 

pair-wise comparison analyses were carried out with MAS 5.0 (Affymetrix). The p-values were 

determined by the Wilcoxon’s signed rank test and denoted as “increase,” “decrease,” or “no 

change.” A transcript is considered significantly altered in relative abundance when p < 0.05. 

Analysis using MAS 5.0 provides a signal log ratio (SLR), which estimates the magnitude and 
5-9

direction of change of a transcript when two arrays are compared (experimental versus control). 

The SLR output was converted into “fold-change” as recommended by Affymetrix. 

Furthermore, stringent criteria were used to identify robust signals as follows: (1) software call 

of “present,” and (2) ≥ 2-fold change or SLR 1.0, in both replicates. Average and standard 

deviations were calculated for all the fold-change values. In general, only transcripts induced or 

suppressed by ≥ 2-fold were considered as differentially expressed.

Selected genes were further examined by real-time quantitative RT-PCR using 18 primer 

sets. The genes analyzed by RT-PCR include epididymal secretory protein 1 (re1), low-density 

lipoprotein receptor (Ldlr), 17β-hydroxysteroid dehydrogenase 3 (Hsd17b3), 17β-hydroxysteroid 

dehydrogenase 7 (Hsd17b7), luteinizing hormone/choriogonadotropin receptor (Lhcgr), 

CCAAT/enhancer-binding protein (C/EBP), beta (Cebpb), early growth response 1 (Egr1), 

nuclear receptor subfamily 4, group A, member 1 (Nr4a1), nuclear factor, interleukin 3, 

regulated (Nfil3), nuclear receptor subfamily 0, group B, member 1 (Nr0b1), transcription factor 

1 (Tcf1), insulin-induced gene 1 (Insig1), protein kinase C-binding protein (Prkcbp1), decayaccelerating factor (Daf), dopa decarboxylase (Ddc), seminal vesicle secretion 5 (Svs5), and 

testis-derived transcript (Testin). Anogenital distance (AGD) was measured and 

immunohistochemistry was performed for NR0B1, TESTIN, GEB14, DDC, and CEBPB 

proteins. 

 Of ~30,000 genes examined, 391 were statistically significantly altered following 

exposure to the four developmentally toxic phthalates (DBP, BBP, DPP, and DEHP) relative to 

the controls. While the four developmentally toxic phthalates were indistinguishable in their 

effects on global gene expression, no significant changes in gene expression were detected in the 

phthalates that do not lead to developmental effects (DMP, DEP, and DOTP). Of the 391 genes 

altered by the developmentally toxic phthalates, 225 were unknown and uncharacterized 

transcribed sequences. Of the remaining 166 genes, the largest GO classification (31 genes) was 

of genes related to lipid, sterol, and cholesterol homeostasis. Additional GO classification 

groups include genes involved in lipid, sterol, and cholesterol transport (10 genes); 

steroidogenesis (12 genes); transcription factors (9 genes); signal transduction (22 genes); 

oxidative stress (11 genes); and cytoskeleton-related (13 genes). RT-PCR results indicated that 

the developmentally toxic phthalates reduced the mRNA levels of Hsd17b7, Lhcgr, Ldlr, re1, 

Svs5, Insig1, and Ddc. Additionally, the RT-PCR results indicated that the developmentally 
5-10

toxic phthalates induced the mRNA levels of Grb14, Prkcbp1, and Testin. RT-PCR results also 

indicated that gene expression of several transcription factors including Dax-1, Cebpb, Nfil3, 

Nr4a1, and Tcf1 were significantly changed by at least one of the toxic phthalates. Based on 

immunohistochemical analysis, DAX-1 expression was reduced in the gonocyte population of 

DBP-treated testis compared with that of controls. Additionally, the expression of nuclear 

CEBPB, GRB14, and DDC proteins was reduced in interstitial cells of DBP-treated testis, while 

TESTIN and GRB14 expression levels were increased in Sertoli cells of DBP-treated testis. An 

AGD reduction was observed in male fetuses exposed to any of the developmentally toxic 

phthalates. 

This study showed that the four phthalates (DBP, DEHP, BBP, and DPP) that have 

similar effects on the developing male rat reproductive tract are indistinguishable in their 

genomic signature for the developing fetal testis. These phthalates targeted pathways in LC 

production of T and other pathways that are important for normal interaction and development 

between Sertoli cells and gonocytes. By contrast, a different genomic signature was observed in 

animals exposed to any of the four phthalates that do not exhibit developmental toxicity. 

5.2.2.4. Thompson et al. (2005) 

Four SD rats per group were gavaged with corn oil or DBP daily at 500 mg/kg-d. In the 

first study, DBP treatment was 30 minutes, 1 hour, 2 hours, 3 hours, 6 hours, 12 hours, 18 hours, 

or 24 hours before sacrifice on GD 19. Global changes in gene expression were determined by 

Affymetrix GeneChips (the specific GeneChips used in the study were not reported). The 

methods were similar to Liu et al. (2005)—with the exception of the statistical analysis. 

Thompson et al. (2005) used JMP statistical software to perform Student t-tests or one-way 

ANOVAs with Tukey post hoc analysis. Selected genes were further examined by real-time 

quantitative RT-PCR. An ABI Prism 7900HT Detection System, the SYBR Green PCR Master 

Mix, and 30 primer pairs were used for analysis of DBP-induced changes in gene expression. 

The genes analyzed by RT-PCR included Cyp11a1, Scarb1, Star, Cyp17a1, Egr1, Egr2, Nr4a1, 

Nfil3, Tcf1, serum/glucocorticoid regulated kinase (Sgk), tumor necrosis factor receptor 

superfamily, member 12a (Tnfrsf12a), sclerostin domain containing 1 (Sostdc1), Wnt oncogene 

homolog 4 (Wnt4), B-cell translocation gene 2, antiproliferative (Btg2), C/EBP, delta (Cebpd), 

FBJ murine osteosarcoma viral oncogene homolog (Fos), dual specificity phosphatase 6 
5-11

(Dusp6), Hes6_predicted, interferon-regulated developmental regulator (Ifrd1), Ldlr, nuclear 

receptor subfamily 4, group A, member 3 (Nr4a3), Pawr, Nr0b1, Jun-B oncogene (Junb), 

endothelial differentiation sphingolipid G-protein-coupled receptor 3 (Edg3), thrombospondin 1 

(Tsp1), and stanniocalcin 1 (Stc1). Immunoblotting by SDS-PAGE was performed for SCARB1, 

CYP11a1, STAR, and CYP17a1. Fetal testicular T concentration was measured by 

radioimmunoassay.

 Based on microarray analysis, 106 genes in the DBP-treated groups were significantly 

different from time-matched controls. Six genes were significantly elevated within 1 hour after 

DBP exposure. An additional 43 genes were upregulated, and five genes were downregulated 

3 hours after DBP exposure. The rapid induction of these genes at 1 hour was a transient effect; 

none of the genes that were upregulated after 1 hour of DBP treatment remained significantly 

different than the controls 6 hours after treatment. Only nine genes showed significant changes 

from the control group between the 3- and 6-hour time points. After 1 and 3 hour DBP 

exposures, the majority of the changes in expression had reflected increased transcription. At 

6 hours after exposure, 19 genes were downregulated and 17 were upregulated. Based on 

RT-PCR analysis, the immediate early gene, Fos, and the putative mRNA destabilizing gene, 

zinc finger protein 36 (Zfp36), were at peak expression level 1 hour after DBP exposure. Other 

immediate early genes were at peak expression at 2 hours after DBP exposure. At 3 hours after 

exposure, the expression of Cebpd, Cxcl1, and Nr4a3 increased rapidly, while other genes 

showed a more gradual increase. Tsp1 expression was increased 25-fold at 3 hours after 

exposure and returned to baseline at 6 hours after exposure. Genes involved in testicular 

steroidogenesis were first noticeably affected 2 hours after DBP exposure. Inhibition of Star

transcription was detected ~2 hours after DBP exposure. Scarb1, Cyp11a1, and Cyp17a1

showed a significant decrease in expression at about 6 hours after DBP exposure. At 6 hours 

after exposure, the T concentration dropped to approximately the level observed after long-term 

DBP treatment. At 12 hours after exposure, steroidogenesis-associated genes, Nr0b1 and Nr4a1,

were elevated. Tcf1 and Sgk were downregulated soon after DBP exposure, but values returned 

to control levels by 3 hours after DBP exposure. Sostdc1 and Hes6_predicted returned to control 

levels at 6 hours after exposure. Based on radioimmunoassay, a decrease in fetal testicular T up 

to 50% was observed within an hour after DBP exposure. 
5-12

In a second experiment to compare the effect of DBP on steroidogenesis in the fetal 

adrenal gland, DBP treatment at GDs 12−19 was followed by analysis of gene expression in this 

tissue. A decrease (but not statistically significant) of corticosterone after GDs 12−19 DBP 

exposure was observed in the fetal adrenal. The expression of genes involved in steroidogenesis 

was less affected in the adrenal (males and females) than in the testes. This study indicates that 

the effect of DBP exposure on steroidogenesis gene expression is specific to the fetal testis and 

not in other steroidogenic organs. 

 Rapid transcriptional changes after DBP exposure in a number of genes could be 

responsible for the reduction in steroidogenesis. Peroxisome proliferator-activated receptors

(PPAR) activation is ruled out since changes in expression of genes targeted by PPAR α and γ 

are not observed until 3 hours after DBP treatment. Many of the genes whose upregulation was 

detected within the first hour after treatment were “immediate early genes,” meaning genes 

involved in cell growth and differentiation. One possible mechanism for DBP’s repression of 

steroidogenesis is that DBP may initially stimulate the mitogen-activated protein 

kinase/extracellular signal-regulated kinase (MAPK/ERK) pathway in the fetal testis. Increased 

expression of Egr1 and Zfp36 could, in turn, lead to degradation of the transcripts involved in 

testicular steroidogenesis. Consistent with this possibility, the Star mRNA contains the AU-rich 

element, which are regions with many A and U bases that target the RNA for degradation, in 

target transcripts of Zfp36. 

5.2.2.5. Plummer et al. (2007) 

Five Wistar rats per group were gavaged with corn oil or DBP at 500 mg/kg-d from 

GD 12 until the day prior to sacrifice. Animals were sacrificed on GD 15, 17, or 19 and used for 

immunolocalization, Western analysis, or RNA quantification (of whole testes, seminiferous 

cord, or interstitial regions using laser capture microdissection). Samples for laser capture 

microdetection were collected from sections of single testes from GD 19 animals. RNA samples 

from three treated litters were compared to a pool of RNA samples from control animals to 

lessen errors due to biological variation. The Agilent 22K rat and 44K whole-rat oligonucleotide 

arrays were used for analysis of the whole-fetal testes and microdissected tissue, respectively. 

RNA was isolated from the homogenized whole-fetal testes using the RNeasy mini kit (Qiagen) 

and from laser capture microdissected samples using RNeasy micro kit (Qiagen). Isolated RNA 
5-13

was labeled using the Agilent Low Input Linear Amplification Labeling kit according to the 

manufacturer’s instructions. Specific activity of the labeled cRNA was measured using the 

microarray analysis program on a NanoDrop ND1000 spectrophotometer (Montchanin, USA). 

Microarray analysis with whole-fetal testis RNA was performed using Agilent 22K rat 

oligonucleotide arrays (Agilent #G4110A). Regional microarray analysis on RNA isolated from 

laser capture microdissected fetal testis tissue was performed using Agilent 44K whole-rat 

genome oligonucleotide microarrays (Agilent #G4131A). Microarray data analysis was 

conducted using Agilent feature extraction (v7.1) and Rosetta Luminator software (Rosetta 

Biosoftware, Kirkland, USA) to generate “signature” lists, defined as significantly (p < 0.01) 

different. The compare biosets function in Luminator was used to compare signature lists from 

different fetal testis regions. Pathway analysis used Ingenuity Pathways Analysis software.

DBP induced statistically significant changes in gene expression at all three time points. 

At GD 15 in whole testes, expression of genes regulating lipid metabolism, redox homeostasis, 

cell proliferation, and apoptosis were altered. At GDs 17 and 19, these four main gene clusters 

were altered: steroidogenesis (e.g., Cyp17a1, Cyp11a1), lipid metabolism, cholesterol (e.g., Star, 

Scarb1), and redox homeostasis. In laser capture microdissection studies of GD 19 tissue, both 

regions demonstrated altered expression of genes associated with steroidogenesis (e.g., 

Cyp17a1), cholesterol transport (e.g., Scarb1), cell/tissue assembly, and cellular metabolism. In 

the interstitial regions only, genes involved in fatty acid oxidation, testes morphogenesis, and 

descent (e.g., Insl3) were altered. In the cord samples, genes associated with stress responses, 

chromatin bending, and phagocytosis were altered. 

RT-PCR analysis was performed on RNA from GD 19 testes from five rats/group using 

sequence-specific primers for the orphan nuclear receptor, nuclear receptor subfamily 5, group 

A, member 1 (Nr5a1; also known as steroidogenic factor 1 [Sf1]), Star, Cyp11a, and Insl3. The 

data were analyzed using a one-way ANOVA, followed by the Bonferroni post-test using 

GraphPad Prism. These studies showed a statistically significant reduction in the expression of 

Star, Cyp11a1, and Insl3 but not Nr5a1.

Analysis of protein expression at GD 19 showed DBP-induced reduction in levels of 

CYP11A, inhibin-α, cellular retinoic acid binding protein 2 (CRABP2), and 

phosphatidylethanolamine binding protein (PEBP) in LCs, and no change in Sertoli 

cells/seminiferous cords. These data correlated with microarray data for the genes coding for 
5-14

these proteins. Immunoreactivity for antimullerian hormone (AMH) was slightly increased in 

Sertoli cells following DBP treatment. Western blot analysis and immunolocalization of NR5A1 

demonstrated no effects of DBP on protein expression in Sertoli or LCs. Using time plots to 

assess time-dependent changes in gene expression, a coordinate down-regulation of Inhibin-α, 

Scarb1, Star, and Cyp11a1 was observed between GDs 15 and 19. 

This study confirms other study results, showing down-regulation of Scarb1, Star, 

Cyp11a1, and Cyp17a1. The authors suggest that DBP induces LC dysfunction indirectly 

through sequestration of cofactors used in key signaling pathways and not through decreases in 

NR5A1 protein expression. They further state that the use of Wistar rats could be important, as 

Wistar rats may be more susceptible than SD rats to testicular effects of DBP. 

5.2.3. RT-PCR Studies

5.2.3.1. Barlow et al. (2003) 

Six to seven SD rats per group were gavaged with corn oil or DBP at 500 mg/kg-d from 

GDs 12–19. Testicular RNA was then isolated from three randomly selected male fetuses per 

litter. RT-PCR studies were performed as described in Shultz et al. (2001). 

The mRNA of 13 preselected genes in the steroid biosynthetic pathway was analyzed by 

real-time RT-PCR; immunohistochemical and oil red O histochemical analyses were performed to 

further confirm mRNA changes. The 13 genes analyzed were Scarb1, Star, Cyp11a1, 

hydroxyl-delta-5-steroid dehydrogenase, 3 beta- and steroid delta-isomerase 1 (Hsd3b), Cyp17a1, 

hydroxysteroid (17-beta) dehydrogenase 3 (Hsd17b3), Ar, luteinizing hormone receptor (Lhr), 

follicle-stimulating hormone receptor (Fshr), Kit, stem cell factor (Scf), Pcna, and Clu.

Compared with controls, mRNA expression was downregulated for Scarb1, Star, Cyp11a1, 

Hsd3b, Cyp17a1, and Kit in DBP-treated testes; mRNA expression was upregulated for Clu following 

DBP exposure. These changes in mRNA expression were supported by immunohistochemical 

localization of selected proteins and by staining for lipids. 

The results in the study of Barlow et al. (2003) confirm the gene expression changes 

observed in a previous study (Shultz et al., 2001). Furthermore, the data support alterations in 

cholesterol synthesis, transport, and storage that likely play a role in decreased T production by 

fetal LCs. The decreased level of mRNA expression for P450scc indicates another possible 

contributor, as P450scc conversion of cholesterol to pregnenolone is the rate-limiting enzymatic 

step in T biosynthesis.
5-15

5.2.3.2. Lehmann et al. (2004) 

To date, Lehmann et al. (2004) is the only dose-response gene expression study on the 

testis performed with DBP. The other studies used a single high dose shown to affect male 

reproductive system development. 

Five to seven SD rats per group were treated by gavage with corn oil or DBP at 0.1, 1.0, 

10, 50, 100, or 500 mg/kg-d from GDs 12−19. Testes were then isolated on GD 19, and changes 

in gene and protein expression were measured by real-time RT-PCR (as described in Shultz et 

al., 2001) and Western analysis. Ten preselected genes in the steroid biosynthetic pathway were 

analyzed by RT-PCR: Scarb, Star, Cyp11a1, Hsd3b1, Cyp17a1, Kit, benzodiazepine receptor,

peripheral (Bzrp), Insl3, Clu, and sterol regulatory element binding factor 1 (Srebf1). Fetal 

testicular T concentration was determined by radioimmunoassay in a separate group of animals 

using doses of 0.1, 1.0, 10, 30, 50, 100, or 500 mg/kg-d. 

The aim of this study was to determine the DBP doses at which statistically significant 

alterations in the expression of a subset of genes and a reduction in fetal testicular T occur. As 

summarized in Table 5-2, Lehmann et al. (2004) established 50 mg /kg-d as an LOEL and 

10 mg/kg-d as an NOEL for reductions in genes and proteins associated with T production as 

well as genes associated with other MOAs (e.g., Kit, Insl3) together with reductions in 

intratesticular T. The Lehmann et al. (2004) study demonstrated a decrease in Hsd3b (also called

3β-HSD) gene expression involved in T synthesis was detected at levels as low as 0.1 mg/kg-d. 

DBP exposure resulted in a dose-dependent decline in expression of the genes involved 

in cholesterol transport and steroidogenesis: Scarb1, Star, Cyp11a1, Hsd3b, Cyp17a1, and Insl3. 

Expression of Bzrp and Clu were increased in response to DBP. Furthermore, fetal testicular T 

was significantly reduced at DBP doses ≥ 50 mg/kg-d and reduced by 26% at 30 mg/kg-d. This 

study reported a LOEL of 50 mg DBP/kg-d and a NOEL of 10 mg DBP/kg-d for reductions in 

genes and proteins associated with T production together with reductions in intratesticular T. It 

demonstrates the coordinated reduction in genes and corresponding proteins involved in 

steroidogenesis and cholesterol transport, concurrent with a decrease in testicular T. 

Importantly, the study results identify changes in T concentration and gene expression at DBP 

doses lower than the observed effects on male reproductive development in toxicology studies 

reviewed in this report (see Chapter 4).
5-16

Table 5-2. Lehmann et al. (2004) DBP dose-response gene expression data 

measured by RT-PCR showing statistically significant changes from control 

Gene Symbol

(reported gene name)

Dose (mg/kg-d)

0.1 1 10 50 100 500

Scarb1 (Sr-B1) NC ↓0.6 NC ↓0.5 ↓0.3 ↓0.2

Star NC NC NC ↓0.4 ↓0.3 ↓0.1

Cyp11a1 (P450ssc) NC NC NC ↓0.6 ↓0.7 ↓0.2

Cyp17a1 NC NC NC NC NC ↓0.3

Hsd3b (3β-HSD) ↓0.3 ↓0.4 NC ↓0.5 ↓0.3 ↓0.5

Bzrp (PBR) NC NC NC NC NC ↑2.0

Trpm2 NC NC NC NC NC ↑1.6

Kit (c-Kit) ↓0.3 ↓0.5 NC ↓0.3 ↓0.5 ↓0.1

Insl3 NC NC NC NC NC ↓0.3

NC, no statistically significant change. Gene expression values are from DBP-exposed testes expressed

relative to control values and are the statistically significant (p < 0.05)averages from five separate rat fetuses

from different dams per treatment group.

For Scarb1, Hsd3b, and Kit, significant reductions in mRNA levels were observed at 

DBP doses that approach 0.1 mg/kg-d. Thus, alterations in the expression of Scarb1, Hsd3b, and 

Kit are at least sensitive indicators of DBP exposure. However, it is not clear whether alterations 

in any one of these three genes alone or together can cause one or more reproductive 

developmental effects of DBP.

5.2.3.3. Thompson et al. (2004) 

Four to five SD rats per group were gavaged with corn oil or DBP at 500 mg/kg-d from 

GDs 12−19. Testes were isolated on GD 17, 18, or 19. Testes mRNA was isolated, and four 

preselected genes (Scarbl, Star, Cyp11a1, and Cyp17a1) in the cholesterol and steroidogenesis 

pathways were analyzed by real-time RT-PCR as described by Shultz et al. (2001). 

Immunoblotting was performed using the total protein extracted from paired testis, and the 
5-17

expressed protein levels were quantified using FluorChem. Fetal testicular T concentration was 

determined by radioimmunoassay, and whole-cell cholesterol uptake assessment was performed 

on overnight cultures.

A significant decrease in fetal testicular T concentration was observed as early as GD 17 

after in utero exposure to DBP. On GD 18, the decrease in T levels, as measured by the percent 

difference in testicular T between treated and control testes, was much higher (17.8% of control 

T levels) than on GD 17 (46.6% of control T levels). Furthermore, significant decreases in 

mRNA expression of Scarbl, Star, Cyp11a1, and Cyp17a1 were observed as early as GD 17. In 

agreement with T levels, the percentage difference of gene expression between control and 

treated testes was higher on GD 18 than on GD 17. The suppression of the transcription by DBP 

was a reversible effect, as the mRNA levels for all genes returned to control levels 48 hours after 

DBP withdrawal. When protein expression was analyzed, results similar to the gene expression 

data were obtained (i.e., strong expression in controls, decreased expression in treated animals 

with 24-hour DBP withdrawal, and rising expression after the 48-hr DBP withdrawal). 

Additionally, there was a significant decrease in the amount of cholesterol transported across the 

mitochondrial membrane in the testes from DBP-treated fetuses as assayed in overnight cultures 

of testis explants. This observation indicates that the decrease in Star mRNA correlated with 

diminished protein function as transport of cholesterol from the outer to the inner mitochondrial 

membrane by the STAR protein is a rate-limiting steps of steroidogenesis (Miller, 2008). 

The results of this study demonstrate that DBP-induced suppression of T production in 

the fetal testis correlates with diminished transcription of several genes in the cholesterol 

transport and steroidogenesis pathways as early as GD 17. This diminished effect was 

reversible, suggesting that DBP directly interferes with the signaling processes necessary for 

maintenance of steroidogenesis or with the transcriptional regulators required to maintain 

coordinate expression of the genes involved in cholesterol transport and T biosynthesis. 

5.2.3.4. Wilson et al. (2004) 

In the study by Wilson et al. (2004), SD rats were treated by gavage with corn oil or a 

developmental toxicant daily from GDs 14−18 in two separate experiments. In the first 

experiment, five rats were treated with DEHP at 750 mg/kg-d and five rats were treated with 

vehicle. In the second experiment, three rats were treated with one of six chemicals, each known 
5-18

to induce male reproductive malformations and three rats were treated with vehicle. The 

chemicals used for the second study were three AR antagonists (vinclozolin [200 mg/kg-d], 

linuron [100 mg/kg-d], and prochloraz [250 mg/kg-d]) and three phthalate esters (DEHP 

[1 g/kg-d], DBP [1 g/kg-d], and BBP [1 g/kg-d]). Dams were sacrificed on GD 18, and testes 

were removed and pooled by litter. In the first study, RNA was prepared to quantify expression 

of one preselected gene, Insl3, by real-time RT-PCR. In the second study, both steroid hormone 

production (ex vivo incubation) and Insl3 expression were assessed. Total RNA was isolated 

using Trizol, digested using Dnase I, and quantitated with RiboGreen. ImProm-II Reverse 

Transcriptase was used for RT, followed by amplification using Taq1. They completed RT-PCR 

for Insl3 using a Bio-Rad iCycler.

 In the first study, the mRNA expression of Insl3 was reduced by ~80% in DEHP litters 

compared with that in control litters. In the second study, among the six chemicals tested, only 

phthalate esters (DEHP, DBP, or BBP) reduced mRNA levels in the fetal testis, with DBP and 

BBP being more effective than DEHP. In contrast, prochloraz, linuron, DEHP, DBP, or BBP 

significantly reduced ex vivo T production. 

In a previous study with antiandrogenic chemicals that alter male sexual differentiation 

(Gray et al., 2000), phthalate esters were the only class that produced agenesis of the 

gubernacular ligaments; some of the phthalate ester-exposed rats had a phenotype similar to that 

seen in the Insl3 knock-out mouse. The study of Wilson et al. (2004) confirms this hypothesis 

since only the three phthalates reduced Insl3 gene expression. The authors proposed that the 

effects of DEHP, DBP, or BBP on Insl3 mRNA and T production result from a delay in 

maturation of fetal LCs, resulting in hyperplasia as they continue to proliferate rather than 

differentiate. 

5.2.4. Study Comparisons

5.2.4.1. Microarray Study Methods Comparison

Table 5-3 compares the study design and method of determining statistical significance 

across the five microarray studies. Because the Bowman et al. (2005) paper assessed changes in 

gene expression in WD rather than testis, and because the microarray data were not presented in 

the paper, the discussions will focus on the four other microarray studies. The Plummer et al. 

(2007) study pooled control tissue and used the Agilent platform, which differed from the 
5-19

platforms used in the other studies. Liu et al. (2005), Schutz et al. (2001), and Thompson et al. 

(2005) all assessed mRNA levels in rat testis—but with somewhat differing significance criteria. 

All studies included vehicle-treated controls.

Table 5-3. Method comparisons for DBP microarray studies

Study

Tissue 

collected Significance criteria Individual animals (n) used?

Bowman et 

al., 2005

Wolffian 

ducts

ND (microarray data not 

presented)

No, pooled (3−4 fetuses/litter; 

67 dams/treatment group)

Liu et al., 

2005 

Testis p < 0.05 compared to 

control by either 1-way 

ANOVA, post hoc 

Dunnett test, or Tukey 

test

Yes, (6 fetuses/litter; 

6 dams/treatment group) 

Plummer et 

al., 2007 

Testis (whole, 

laser captured 

interstitial 

tissue, or laser 

captured 

seminiferous 

cord tissue)

p < 0.01 using Agilent 

feature extraction 

software and then 

Rosetta Luminator 

software by performing 

one-way ANOVA on log 

fold change in the 

replicates

Yes for DBP-treated (3 pups 

from 3 different dams); Control 

RNAs were pooled

Shultz et al., 

2001 

Testis 2-fold change in average 

expression value 

compared to control 

GDs 19 and 21 time points:

Yes, 1 fetus/litter; 

3 dams/treatment group. 

GD 16 time point: pooled RNA 

from 5 fetuses/1 litter; 3 arrays 

hybridized/treatment group.

Thompson et 

al., 2005 

Testis p < 0.05 multiple 

comparison using 

Bonferroni correction

Yes (NR)

ANOVA, analysis of variance; NR, not detected.

5.2.4.2. RT-PCR Study Methods Comparison 

Table 5-4 compares the RT-PCR methods across the nine toxicogenomic published 

studies. There were many similarities among the studies. With the exception of Bowman et al. 

(2005), all groups extracted RNA from testis. All studies used a vehicle-treated control. 
5-20

Table 5-4. Method comparisons among the RT-PCR DBP studies

Study

Tissue 

collected

Significance criteria

(p values) Individual animals (n) used?

Barlow et al., 

2003

Testis p < 0.05 compared to 

control

Yes (3 fetuses/litter; 

5 dams/treatment group)

Bowman et 

al., 2005

Wolffian ducts p < 0.05 compared to 

control

No, pooled (3−4 fetuses/litter; 

6−7 dams/treatment group)

Lehmann et 

al., 2004

Testis p < 0.05 compared to 

control

Yes (5 fetuses/litter; 

4−5 litters/treatment group)

Liu et al., 

2005 

Testis p < 0.05 compared to 

control by either 2-way 

nested ANOVA or 

Dunnett

Yes (control: 6 fetuses/dam; 

6 dams for control. Treated: 

3 fetuses/dam; 3 dams)

Plummer et 

al., 2007 

Testis (whole, 

laser-captured 

interstitial 

tissue, or 

laser-captured 

seminiferous 

cord tissue)

p < 0.05 compared to 

control, normalized to 

1.0. Expressed as mean 

+/– SEM; one-way 

ANOVA followed by 

Bonferroni post test using 

GraphPad Prism software

NDa; assessed GD 19.5 fetal 

testes

Shultz et al., 

2001 

Testis p < 0.05 compared to 

control 

GDs 19 and 21 time points: 

Yes, 1 fetus/litter; 

3 dams/treatment group. 

GD 16 time point: pooled 

RNA from 5 fetuses/1 litter; 

3 arrays hybridized/treatment 

group.

Thompson et 

al., 2004 

Testis p < 0.05 compared to 

control (Student’s t-test 

or 1-way ANOVA)

ND

Thompson et 

al., 2005 

Testis p < 0.05 normalized mean 

of 3−5 fetuses/treatment 

group relative to control

Yes, 3−5 fetuses/litter

Wilson et al., 

2004 

Testis p < 0.01 compared to 

control (means on a litter 

basis)

No, pooled for each litter 

(3 dams/treatment group) 

a

Not clear from the Materials and Methods.

ANOVA, analysis of variance; ND, not detected.
5-21

Most of the studies used the same significance criteria (p < 0.05). There were some differences 

in the number of fetuses used per experiment while some studies pooled tissues. 

There were also important similarities among the nine toxicogenomic studies. Eight of 

the studies used the same strain of rat (SD), all purchased from the same vendor (Charles River, 

Raleigh, NC). All studies described dissolving the DBP in corn oil, using a corn oil vehicle 

control, and using oral gavage as the route of exposure. Six of the studies (Plummer et al., 2007; 

Bowman et al., 2005; Liu et al., 2005; Thompson et al., 2004; Barlow et al., 2003; Shultz et al., 

2001) treated the animals by gavage with 500 mg/kg-d from GDs 12−19. This dose has been 

shown to adversely affect male reproductive development without causing maternal toxicity or 

fetal death. Lehmann et al. (2004) completed a dose-response during the GDs 12−19 period, 

using 0, 0.1, 1.0, 10, 50, 100, or 500 mg/kg-d. Bowman et al. (2005) and Shultz et al. (2001) 

included an additional exposure duration of GDs 12−21. Wilson et al. (2004) exposed for a 

slightly shorter duration (GDs 13−17) and at a higher dose (1,000 mg/kg-d). This paper reports 

exposures on GDs 14−18; however, these authors consider GD 1 as the day a sperm-positive 

smear was identified in dams, whereas the other studies consider the sperm-positive day as 

GD 0. Therefore, to be comparable with the other reports, we are reporting the exposure period 

as GDs 13−17. Similarly, Plummer et al. (2007) reports exposures ranging from GDs 12.5–19.5, 

which are equivalent to GDs 12−19 as the authors consider GD 0.5 to be the sperm-positive day, 

adjusted to facilitate comparison.

All of the other selected studies collected testes for RNA extraction, with the exception of 

Bowman et al. (2005), which collected WDs. Bowman et al. (2005) focused on the WD because 

they were interested in characterizing the mechanisms responsible for prenatal DBP-induced 

epididymal malformations. WD tissue from three to four fetuses was obtained to ensure enough 

RNA for analyses (see Table 5-3). Since WDs are the precursor of the vas deferens, epididymis, 

and seminal vesicles, the tissue assayed by Bowman et al. (2005) is different from the tissue 

evaluated in the other seven studies (RNA from the testes of 1−3 fetuses). The studies used a 

variety of toxicogenomic methodologies to assess changes in gene expression. General 

descriptions of these methods utilized by the studies were presented in Section 5.1. 

An important consideration is the reliability of the data being generated and compared in 

these nine DBP studies. As discussed, the MAQC project (Shi et al., 2006) has recently 

completed a large study evaluating inter- and intraplatform reproducibility of gene expression 
5-22

measurements (see Chapter 2). Six commercially available microarray platforms and three 

alternative gene expression platforms were tested. Both Affymetrix microarrays and RT-PCR 

assays were included in the MAQC testing. Affymetrix and the other one-color platforms 

showed similar coefficients of variation of quantitative signal values (5−15%) when used to 

detect 8,000 to 12,000 genes. When comparing variation within and between test sites, the 

one-color assays demonstrated 80−95% agreement. 

Although it is difficult to compare expression values generated on different platforms 

because of differences in labeling methods and probe sequences, MAQC was able to show good 

agreement between the Affymetrix platform and the other platforms. This was particularly true 

when using the same biological sample (and, thus, removing variability introduced by the sample 

or sample preparation method). It is worth noting that Affymetrix displayed high correlation 

values with RT-PCR based on comparisons of ~500 genes. The results of the MAQC report 

suggest that the comparisons made in this case study are valid due to the reliability of the data. 

Additionally, since seven out of the nine experiments in the case study were performed in the 

same laboratory, interlaboratory variability is not an issue with these studies. 

5.3. CONSISTENCY OF FINDINGS

In the assessment of consistency of findings, a potential source of incongruence is the 

decreased sensitivity for low-expression genes in the microarray platforms as compared to the 

gene expression technologies and differences in probe location. 

5.3.1. Microarray Study Findings

An evaluation of the consistency across the four microarray studies of the testis was 

performed. Bowman et al. (2005) is not included because the microarray study results were not 

reported. In order to enhance comparability, the data from the whole testis microarray study of 

Plummer et al. (2007) are included in the evaluation, but the data from the microdissected 

regions of the testis are excluded because the lack of comparison to any other study. 

Three of the four microarray studies used the same strain, SD, and all nine used the same 

species (rat). Plummer et al. (2007) was the only study to use the Wistar rat strain because it is 

considered more susceptible to effects on the testis than SD. Table A-1 in Appendix A includes 

those genes whose expression was reported to be significantly altered, as reported by Shultz et al. 

(2001), Thompson et al. (2005), Plummer et al. (2007) (for the whole testis only), or Liu et al. 
5-23

(2005). Also presented in Table A-1 are the official gene names, exposure times, and directional 

response changes. It should be noted that some differences are to be expected in these 

comparisons because no two studies had identical study designs or platforms, or applied the 

same statistical cut-offs. For example, Thompson et al. (2005) used a very short duration of 

exposure, whereas the other three studies had longer exposure durations. In addition, the 

Affymetrix microarray platform was used only by Thompson et al. (2005) and Liu et al. (2005). 

The three testis microarray studies (Plummer et al., 2007; Liu et al., 2005; Thompson et 

al., 2005) that used the “second generation chips” containing a much larger number of probes 

(therefore, covering many more genes) than the Clontech platform were compared. The Venn 

diagram, developed for these three studies, shows some unique gene expression changes for each 

study as well as a number of common gene expression changes (see Figure 5-1). Nevertheless, 

significant corroboration in the direction of effect among the common genes was observed in 

these three studies (see Appendix A). Additionally, most of the common genes were 

downregulated after in utero DBP exposure. Further, two genes in the steroidogenesis pathway, 

Cyp11a1, and Scarb1, are common among all four microarray studies. These findings indicate 

that the microarray data set for DBP is relatively consistent and findings are reproducible.

A number of genes involved in steroidogenesis (Cyp11a1, Scarb1, Star, and Cyp17a1) 

were found to be downregulated by DBP in all three studies (see Figure 5-1). Other genes 

significantly altered include a downregulation of the serotonin and catecholamine pathway 

enzyme, Ddc, and the myosin, heavy polypeptide 6, cardiac muscle, alpha (Myh6), and the 

androgen-regulated structural protein, Svs5. 

Other genes were significantly altered in two of the three studies. For example, in 

comparing the results of the two studies that utilized the same platform (Affymetrix), the Liu et 

al. (2005) and Thompson et al. (2005) studies observed a downregulation of the steroidogenesis 

genes Sqle and Hsd3b1_predicted, cyclin-dependent protein kinase inhibitor (Cdkn1c), the 

cellular retinoic acid binding protein 2 (Crabp2), the FGF receptor activating protein 1 (Frag1), 

and the fatty acid binding protein (Fabp3). These same two studies found upregulation of the 

steroidogenesis gene Nr4a1.

There are a number of genes for which the different studies found a similar significant 

alteration but the direction of effect varied. For example, GSH S-transferase, mu 2 (Gstm2), a 

gene involved in xenobiotic metabolism, was found to be significantly downregulated by Liu et 
5-24

Figure 5-1. Venn diagram illustrating similarities and differences in 

significant gene expression changes observed in three recent microarray 

studies of the testes: Thompson et al. (2005), Plummer et al. (2007), and Liu 

et al. (2005). Numbers within each circle indicate genes whose expression was 

statistically significantly altered and that are unique to the study (i.e., not 

replicated by either of the other two studies). Gene numbers do not include 

expressed sequence tags (ESTs). The red circle indicates the Thompson et al. 

(2005) study; the green circle indicates the Plummer et al. (2007) study; and the 

blue circle indicates the Liu et al. (2005) study; Black arrows indicate the 

direction of effect, which was the same for all three of these studies.

al. (2005) and Thompson et al. (2005) and significantly upregulated by Shultz et al. (2001). The 

microsomal GSH S-transferase 1 gene (Mgst1) was downregulated in Liu et al. (2005) and 

upregulated in Shultz et al. (2001). Appendix A presents a table of the statistically significant 

gene expression changes in the Thompson et al. (2005), Shultz et al. (2001), Liu et al. (2005), 

Thompson et al. (2005) Plummer et al. (2007)

Liu et al. (2005)

36

65

93

↓ Alas1

↓ Aldh2

↓ Dbi

↓ Fads1

↓ Fdft1

↓ Fdps

↓ Fdxr

↓ Gsta3

↓ Hmgcr

↓ Hmgcs1

↓ Idi1

↓ Inha

↓ Me1

↓ Nr0b1 

↓ Pebp1

↓ Por

↓ Sc4mol

↓ Scp2

↓ Stc2

↓ Cyp11a1

↓ Cyp17a1

↓ Scarb1

↓ Ddc

↓ Fdx1

↓ Myh6

↓ Prdx3

↓ Star

↓ Svs5 ↓ Idh1

↓ Lhgcr

↑ Nr4a1

↓ Sqle

↑ Stc1

↑ Tpm1

↓ Cyp51

↓ Cdkn1c

↓ Crabp2

↓ Ddit4

↓ Dhcr7

↑ Dusp6

↓ Fabp3

↓ Frag1

↓ Fthfd

↑ Grb14

↓ Gstm2

↓ Hsd3b1_predicted

↓ Fabp3

Thompson et al. (2005) Plummer et al. (2007)

Liu et al. (2005)

36

65

93

↓ Alas1

↓ Aldh2

↓ Dbi

↓ Fads1

↓ Fdft1

↓ Fdps

↓ Fdxr

↓ Gsta3

↓ Hmgcr

↓ Hmgcs1

↓ Idi1

↓ Inha

↓ Me1

↓ Nr0b1 

↓ Pebp1

↓ Por

↓ Sc4mol

↓ Scp2

↓ Stc2

↓ Cyp11a1

↓ Cyp17a1

↓ Scarb1

↓ Ddc

↓ Fdx1

↓ Myh6

↓ Prdx3

↓ Star

↓ Svs5 ↓ Idh1

↓ Lhgcr

↑ Nr4a1

↓ Sqle

↑ Stc1

↑ Tpm1

↓ Cyp51

↓ Cdkn1c

↓ Crabp2

↓ Ddit4

↓ Dhcr7

↑ Dusp6

↓ Fabp3

↓ Frag1

↓ Fthfd

↑ Grb14

↓ Gstm2

↓ Hsd3b1_predicted

↓ Fabp3
5-25

and Plummer et al. (2007) studies. These differences in microarray results can be explained by a 

number of factors including study differences (e.g., duration of exposure, platform, and rat 

strain) and/or variability of microarray study results.

Overall, the data indicate that there are some unique gene expression changes for each 

study as well as a number of common gene expression changes. Significant corroboration in the 

direction of effect among the common genes was observed in at least three studies. In addition, 

most of the common genes among these three studies were downregulated after in utero DBP 

exposure. These findings indicate that the microarray data set for DBP is very consistent and 

reliable although certain uncertainties remain when comparing data from different platforms with 

different study design. 

5.3.2. RT-PCR Gene Expression Findings

Comparisons were also made of RT-PCR data (see Table A-2; Appendix A). All nine 

studies performed RT-PCR, and in the case of Liu et al. (2005), Shultz et al. (2001), Plummer et 

al. (2007), and Thompson et al. (2005), RT-PCR was performed following identification of the 

genes of interest from microarray studies. A number of genes were found to be similarly up- or 

downregulated by in utero DBP exposure. In the steroidogenesis pathway, five genes (Cyp11a1, 

Cyp17a1, Hsd17b3, Scarb1, and Star) were found to be downregulated by more than one 

laboratory. Some commonalities were also observed in altered gene regulation of transcription 

factors. Egr1, Nfil3, and Nr4a1 were shown in two different studies to be upregulated. Two 

studies reported similar downregulation of Nr0b1 and Tcf1. 

Three studies (Plummer et al., 2007; Lehmann et al., 2004; Wilson et al., 2004) observed 

reduced Insl3 gene expression. As discussed, Insl3 has a role in sexual differentiation and testis 

descent. Reduced fetal Insl3 has been shown to produce agenesis of the gubernacular ligaments. 

Two other genes have been shown to have DBP-induced altered expressions as assessed by 

RT-PCR in two laboratories: Clu (upregulated) and Kit (downregulated). 

5.3.3. Protein Study Findings

All nine studies completed either Western analysis (immunoblotting) or 

immunohistochemistry to characterize fetal DBP-induced changes in protein expression. 

Usually, protein analysis was conducted for proteins that had demonstrated changes in mRNA 
5-26

expression. However, up- or downregulation of genes and proteins does not always occur 

simultaneously, so a disparity between these two experimental results is quite common. 

Table 5-5 presents the protein-expression data from these studies. 

Four proteins in the steroidogenesis pathway were shown to be downregulated by DBP 

exposure. These findings are fairly consistent with the gene expression data presented earlier. 

STAR was shown to be downregulated by Western blotting in three separate experiments, and by 

immunolocalization in another experiment. STAR expression was found only in LCs in both the 

control and DBP-treated testes, with the DBP-treated testes having decreased staining intensity 

(Barlow et al., 2003). Quantitatively, three experiments demonstrated reduced SCARB1 protein 

levels in DBP-treated fetal testes; however, immunolocalization showed DBP-induced increased 

staining of Sertoli cells and decreased staining of LCs. Both CYP11a1 and CYP17a1 protein 

levels were shown in several separate experiments to be reduced following DBP exposure, which 

correlated with microarray and PCR findings. Immunolocalization was completed for CYP11a1 

and found to be downregulated in LCs (Plummer et al., 2007). Using immunolocalization, CLU 

was found to be increased in Sertoli cells and LCs. One study has shown that DBP lowers 

INSL3 protein immunoexpression levels in the fetal testis (McKinnell et al., 2005). The 

expression of NR5A1/SF1 was unchanged in Wistar rats, however, four proteins regulated by 

NR5A1 (CYP11a1, INHA, CRABP2, and PEBP) and AMH were reduced in LCs following DBP 

exposure (Plummer et al., 2007). 

5.3.4. DBP Toxicogenomic Data Set Evaluation: Consistency of Findings Summary 

A comprehensive summary of the DBP toxicogenomic data set assessed in this case 

study, including all microarray, RT-PCR, and protein data from the nine studies, is presented in 

Figure 5-2. The genes and protein included in the figure are limited to those for which two or 

more studies detected statistically significant results. In many cases, when comparing across 

RT-PCR and microarray studies, a DEG is found in one or even several studies that is not 

identified in another study. For example, Kit was downregulated in the Barlow et al. (2003),
5-27

Table 5-5. Evaluation of the published protein studies after DBP in utero exposure (testes only)

Official 

Gene 

Symbol

Protein Name 

Reported in 

Paper

Exposure 

Interval

Dose 

(mg/kg-d) Method Used

Change in Protein Expression Compared 

to Controls Reference

Amh AMH GDs 12−19 500 Immunolocalization ↑ slightly in Sertoli cells Plummer et al., 2007

Bcl2 bcl-2 GDs 12−21 500 Immunolocalization ↑ in Sertoli and Leydig cells Shultz et al., 2001

Bzrp PBR GDs 12−19 500 Immunolocalization ↓ in interstitial cells Lehmann et al., 2004

Cebpb CEBPB GDs 12−19 500 Immunolocalization ↓ in interstitial cells Liu et al., 2005

Crabp2 CRABP2

PEBP

GDs 12−19 500 Immunolocalization ↓ in Leydig cells Plummer et al., 2007

Clu TRPM-2 GDs 12−19

GDs 12−21 500 Immunolocalization ↑ in Sertoli and Leydig cells Shultz et al., 2001

Clu TRPM-2 GDs 12−19 500 Immunolocalization ↑ in Sertoli cells Barlow et al., 2003

Cyp11a1 CYP11a1 GD 18 for 18 hrs 500 Western analysis ↓ (0.6 of control) Thompson et al., 2005

Cyp11a1 P450ssc GDs 12−19 500 Western analysis ↓ (0.5 of control) Lehmann et al., 2004

Cyp11a1 P450ssc GDs 12−17 or 18 500 Western analysis ↓ (0.15 at 24 hrs; 0.5 at 48 hrs) Thompson et al., 2004

Cyp17a1 CYP17a1 GD 18 for 18 hrs 500 Western analysis ↓ (0.6 of control) Thompson et al., 2005

Cyp17a1 CYP17 GDs 12−17 or 18 500 Western analysis ↓ (ND at 24 hrs; 0.4 of control at 48 hrs) Thompson et al., 2004

Cyp17a1 cyp17 GDs 12−19 500 Western analysis ↓ (0.2 of control) Lehmann et al., 2004

Ddc Dopa 

decarboxylase

GDs 12−19 500 Immunolocalization ↓ in interstitial cells Liu et al., 2005

Grb14 GRB14 GDs 12−19 500 Immunolocalization ↓ in interstitial cells and ↑ in Sertoli cells Liu et al., 2005

Inha INHA GDs 12−19 500 Immunolocalization ↓ in Leydig cells Plummer et al., 2007

Insl3 Insl3 GDs 12−19 500 Immunolocalization ↓ in interstitial cells Lehmann et al., 2004

Kit c-kit GDs12−19 500 Immunolocalization ↓ in gonocytes Barlow et al., 2003

Kitl SCF GDs 12−19 500 Immunolocalization ↑ in Sertoli cells Barlow et al., 2003
5-28

Table 5-5. (continued)

Official 

Gene 

Symbol

Protein Name 

Reported in 

Paper

Exposure 

Interval

Dose 

(mg/kg-d) Method Used

Change in Protein Expression Compared 

to Controls Reference

Nr0b1 DAX-1 GDs 12−19 500 Immunolocalization ↓ in gonocytes Liu et al., 2005

Pebp PEBP GDs 12−19 500 Immunolocalization ↓ in Leydig cells Plummer et al., 2007

Scarb1 SCARB1 GD 19 for 6 hrs or 

GD 18 for 18 hrs 500 Western analysis ↓ (0.3 of control) Thompson et al., 2005

Scarb1 SR-B1 GDs 12−17 or 18 500 Western analysis ↓ (0.15 at 24 hrs; (0.7 of control at 48 hrs) Thompson et al., 2004

Scarb1 SR-B1 GDs 12−19 50, 100, 500 Western analysis ↓ (0.6, 0.5, and 0.1 of control) Lehmann et al., 2004

Scarb1 SRB1 GDs 12−19 500 Immunolocalization ↓ in Leydig; ↑ in Sertoli cells Barlow et al., 2003

Star STAR GD 18 for 18 hrs 500 Western analysis ↓ (0.4 of control) Thompson et al., 2005

Star STAR GDs 12−17 or 18 500 Western analysis ↓ (ND at 24 hrs; 0.4 of control at 48 hrs Thompson et al., 2004

Star STAR GDs 12−19 50, 100, 500 Western analysis ↓ (0.1, 0.2, 0.1 of control) Lehmann et al., 2004

Star STAR GDs 12−19 500 Immunolocalization ↓ in Leydig cells Barlow et al., 2003

Testin testin GDs 12−19 500 Immunolocalization ↑ in Sertoli cells and gonocytes Liu et al., 2005

ND, not detected.
5-29

Figure 5-2. Summary of DBP-induced changes in fetal gene and protein expression. M = microarray; R = 

RT-PCR; P = protein. Red indicates upregulation; green indicates downregulation. Genes and protein included in the 

figure are limited to genes that were statistically significantly altered in two or more studies. Gene symbols are 

indicated at the top of the figure. The pathway or function of each gene is listed on the bottom of the figure. This 

information has been taken from the case-study articles or from the DAVID (Database for Annotation, Visualization 

and Integrated Discovery http://david.abcc.ncifcrf.gov/list.jsp) entry for that gene.

M

M

M

M

M

M

M

M

M

M

R

R

R

P

M

R

R

M M

M

M

M

Scp2Dbi

M

M

M

M

Ezr (Vil2) M

MMicrovillar skeleton

Rpl36a M

MComponent of 60S ribosomal subunit

Hmgcr Mevalonate synthesisPebp1 PLipid bindingHmgcs1 Hydroxymethylglutaryl-CoA synthase activityFads1 Desaturation of fatty acids

Alas1 Glycine, serine, and threonine metabolism

Aldh2 Aldehyde dehydrogenase

Fdxr Electron transfer for CYPsGsta3 Glutathione metabolismInha PInhibits secretion of FSHPor Electron transportSerpine1 Complement and coagulation cascades

Stc2 Calcium and phosphate homeostasis

Tuba1a(Tuba1) Microtubule cytoskeleton

Me1 M

M

M

M

M

M

M

M

M

M

M

M

M

M

M

M

M

M

M

M

M

M

M

M

M

M

M

M

Pyruvate metabolism/PPAR signaling

Insig1Zfp36FosEgr2

(Insig-1) R R R R

M M M M

R

R

R

RClu P

P

M

M

Pawr

Ddc

M

M

M

M

R

PSerotonin and catecholamine pathway enzyme

Kitlg (Kitl) P

RLigand for c-kit

Ifrd1 R

MSignal transduction/muscle differentiation

Testin P

R

MSertoli cell secretory protein

Dusp6 R

M

MRegulates mitogen-activated protein kinases

Fdx1 M

M

MPrecursor of adrenodoxin

Tpm1 M

MMuscle contraction/cytoskeleton

Crabp2 M

M

PIntracellular lipid carrier protein

Grb14 R

M

M

PInhibits insulin-mediated pathways

Timp1 R

MInhibits metalloprotease activity

Insl3 M

R

R

PDifferentiation of gubernacular ligament

Prkcbp1 R

MCytoskeleton

Dhcr7 M

MCholesterol biosynthesis

Tnfrsf12a R

MCell proliferation, adhesion, and angiogenesis

Igf1r P

RInsulin-like growth factor receptor

Idh1 M

MCatalyzes oxidative decarboxylation of citrate

Mgst1 M

MTransfers glutathione onto hydrophobic substrates

Stc1 R

M

MCalcium/phosphate-regulating protein

Mgp R

MVitamin k-dependent protein/bone matrix

Key

R = RT-PCR

M = microarray

P = protein

Pathway/Function

Steroidogenesis

Gene

= downregulation

= upregulation

Lipid/sterol/

cholesterol

transport

Transcription

Immediate

early gene/

growth/

differentiation

Apoptosis

PPAR

signaling

Bzrp R

P

Npc2 M

R

Ldlr M

M

R

Star R

R

R

R

R

P

P

P

M

M

M

Idi1Cyp51FdpsFdft1Sc4mol M

M

M

M

M

M

M

M

M

M

Cyp11a1 M

R

R

R

R

R

P

P

P

Cyp17a1 R

R

R

R

P

P

P

Hsd3b1Hsd17b7

R

Lhcgr R

Nr4a3 R

Scarb1

M

M

R

R

R

R

P

P

P

P

Sqle

M

M

M

M

M

M

M

R

M

M

R

M

M

M

M

M

M

M

M

Cebpd R

Cebpb

M

M

R

Egr1 R

R

M

Nfil3 R

R

M

Nr0b1 R

M

M

R

P

Nr4a1 R

R

M

Hnf1a

M

(Tcf1) M

R

R

Fabp3 Binds fatty acids/fatty acid metabolism

Btg2 Anti-proliferative protein

Svs5 Androgen-regulated structural protein

Ar Androgen pathway

Gstm2 Xenobiotic metabolism

Mmp2 Matrix metalloproteinase

Kit Transmembrane receptor

Cdkn1c Cyclin-dependent protein kinase inhibitor

Myh6 Muscle contraction

Pmp22 Myelin protein

Sgk1 (Sgk) Memory consolidation/learning

Prdx3 Thioredoxin-dependent protease

Frag1 Receptor activating

Aldh1/1(Fthfd) Catalyzes NADP-dependent oxidation

Ddit4 HIF-1 responsive gene

M

M

M

M

M

M

M

M

M

R

M

M

M

R

M

P

M R

M
5-30

Lehmann et al. (2004), and Schultz et al. (2001) studies; by contrast, it was not altered 

significantly in the Liu et al. (2005) study even though it is represented on the Affymetrix array. 

Data from the Bowman et al. (2005) paper were not included because it evaluated 

changes in DBP-induced gene expression in the WD rather than testes. There are no other WD 

studies for comparisons. If an increase or decrease was reported at any time point, it was 

included. Multiple time points from the Thompson et al. (2005) study were not all included; if 

several time points showed a change, then it was recorded as one study showing a change. For 

protein data, descriptions of immunohistochemical studies suggesting an increase, though 

without real quantitation, were still counted. For the dose-response study (Lehmann et al., 

2004), data from only the 500 mg/kg-d dosing were used to allow better comparisons with the 

other studies. 

Figure 5-2 presents a summary of the changes in gene and protein expression following 

in utero DBP exposure across studies. What is most striking is the consistency of evidence for 

the DBP-induced downregulation of the steroidogenesis pathway. Both microarray and RT-PCR 

analysis show consistent downregulation of Cyp11a1, Cyp17a1, Star, and Scarb1 mRNA 

expression. Protein expression of Cyp11a1, Cyp17a1, Star, and Scarb1 is concurrently 

downregulated. Downregulation of both Hsd3b and Lhcgr mRNA expression is demonstrated 

consistently. Significantly, two genes involved in lipid/sterol/cholesterol transport, Npc2 and 

Ldlr, also show downregulation. Three transcription factors (Nfil3, Egr1, and Nr4a1) 

demonstrate DBP-induced upregulation, while two genes (Nr0b1 and Tcf1) show downregulation 

in a number of experiments. Three immediate early genes (Fos, Egr2, and Zfp36) are 

upregulated by DBP exposure. Interestingly, Clu (also known as T repressed prostate 

message-2) is upregulated, as shown by two microarray, two RT-PCR, and two protein assays. 

5.4. DATA GAPS AND RESEARCH NEEDS

Based on the evaluation of the nine toxicogenomic studies, a number of research needs 

became apparent. There are genomic data gaps for many environmental chemicals. For DBP, 

confirmatory RT-PCR studies for all of the genes identified from microarray studies, would give 

additional credence to the microarray results. Similarly, additional protein analysis, with 

quantitation by Western blotting and with immunolocalization, could further characterize 

DBP-induced effects on the male reproductive system. Looking at DBP-induced changes in 
5-31

gene expression in additional reproductive and nonreproductive (Thompson et al., 2005) tissues 

could also add information about mechanism(s) of action and tissue specificity. As testes are 

comprised of a number of cell types, evaluating additional homogeneous cell populations within 

the testes, as Plummer et al. (2007) reported, could be useful. 

In order to fully consider the question about informing the modes or mechanism of action 

(see Chapters 1 and 3), using the toxicogenomic data to determine whether there are other MOAs 

responsible for some of the male reproductive developmental effects, we decided that it would be 

helpful to analyze the raw data to assess all affected pathways. The published studies, while all 

of excellent quality, focused their pathway analyses and descriptions on particular pathways of 

interest to basic science. The following section describes efforts to reanalyze some of the DBP 

microarray studies with this goal in mind. 

5.5. PATHWAY ANALYSIS OF DBP MICROARRAY DATA

We determined that it would be advantageous to reanalyze the raw data utilizing multiple 

analytical approaches (see Figure 3-1) because most of the DBP microarray studies in the 

published literature were focused on further delineation of the mechanism of action relevant to 

one MOA, the reduction in fetal testicular T. In fact, it was the microarray and RT-PCR study 

results that identified the modulation of the steroidogenesis pathway as leading to reduced fetal 

testicular T, one of the DBP MOAs, and then, leading to a number of the male reproductive 

developmental effects. Further, a second DBP MOA of reduced Insl3 gene expression has also 

been identified (Wilson et al., 2004; see Chapter 3) leading to testis descent defects. Not all 

pathways for the identified DEGs were discussed (or presented) in detail in the published studies 

because of this focus. Therefore, a reanalysis that looks more broadly to define all pathways 

affected by DBP may inform additional pathways related to MOAs that could be linked to the 

unexplained male reproductive developmental outcomes identified in Chapter 4. Thus, the 

purpose of this reanalysis of the existing data set was to identify and characterize additional 

molecular pathways affected by DBP, beyond a reduction in fetal T and Insl3 gene expression. 

5.5.1. Objective of the Reanalysis of the Liu et al. (2005) Study

The goal was to reanalyze DBP microarray data to address the Case Study Question: Do 

the genomic data inform DBP additional MOAs and the mechanism of action for the male 
5-32

reproductive developmental effects? The purpose for the reanalysis of the existing data sets is to 

identify and characterize additional molecular pathways affected by DBP, beyond the effects on 

the androgen-mediated male reproductive developmental toxicity pathways. This exercise was 

designed to generate hypotheses about mechanisms/pathways that could underlie the unexplained 

testicular endpoints after in utero DBP exposure (see Chapter 4). 

The Liu et al. (2005) study was selected for reanalysis because the data set had a 

comprehensive exposure scenario that covered the critical window for developmental exposure 

to DBP (GDs 12−19). The Affymetrix chip was used (compatible with the proprietary and free 

software programs used for pathway-level analysis), and the data were provided by Dr. Kevin 

Gaido, a collaborator on this project. Some limitations of the Liu et al. (2005) data set are the 

small number of samples (i.e., 3 controls and 3 DBP-treated) and the lack of characterization of 

variance for treated and control. This study was a comparative analysis of six phthalate esters. 

However, only the DBP treatment and vehicle control data were used for this analysis. The 

Liu et al. (2005) study investigated global gene expression in the fetal testis following in utero

exposure to a series of phthalate esters, including both developmentally toxic phthalates (DBP, 

BBP, DPP, and DEHP) and nondevelopmentally toxic phthalates (DMP, DEP, and DOTP) 

(Liu et al., 2005). The original analysis was based on a two-way nested ANOVA model using 

Bonferroni correction that identified 391 significantly expressed genes from the control out of 

the approximately 30,000 genes queried. In their analysis, two classes of phthalate esters were 

distinguished based on the gene expression profiles. The authors also showed that 

developmentally toxic phthalates targeted gene pathways associated with steroidogenesis, lipid 

and cholesterol homeostasis, insulin signaling, transcriptional regulation, and oxidative stress. 

We can assume that the differentially expressed genes in common among the “developmental 

phthalates” assessed in the Liu et al. (2005) study are due to phthalate exposure and not general 

toxicity, providing internal positive controls. 

5.5.2. Pathway Analysis of Liu et al. (2005) Utilizing Two Different Methods to Generate 

Hypotheses for MOAs Underlying the Unexplained Testes Endpoints

Pathway analysis methods and software have been previously developed for analysis of 

microarray data for basic and applied research. Pathway-level analysis mainly depends on the 

definition of the pathways (database) and significance level uses to measure the differential 

expressions. Using these validated methods, a pathway analysis was performed. Differentially 
5-33

expressed genes that were input into the pathway analysis (GeneGo) were identified by two 

different methods, Signal-to-Noise Ratio (SNR) and Rosetta Error Model (REM). By assessing 

the intersection of the pathways identified by each approach provides a more conservative list of 

pathways than using one approach. The overall process for generating hypotheses about 

pathways that may be relevant to the testis endpoints using pathway analysis is illustrated in 

Figure 5-3. 

Figure 5-3. Schematic of the two analysis methods (REM and SNR) for 

identifying differentially expressed genes and subsequent pathway analysis 

using GeneGo. Two separate analyses, REM and SNR statistical filters, were 

performed to identify common and unique genes from the Liu et al. (2005) data. 

The two separate filtered gene lists were input into GeneGo to identify 

statistically significantly affected pathways. Common and unique pathway lists 

were generated. 

Rosetta 

Error Model

Signal to 

Noise Ratio

Input filtered 

gene list into 

GeneGo

Input filtered 

gene list into 

GeneGo

common

Significant 

Pathways 

Significant 

Pathways unique unique

unique common unique

Differentially

Expressed 

Genes

Differentially

Expressed 

Genes

Liu et al. (2005) DBP data

Rosetta 

Error Model

Signal to 

Noise Ratio

Input filtered 

gene list into 

GeneGo

Input filtered 

gene list into 

GeneGo

common

Significant 

Pathways 

Significant 

Pathways unique unique

unique common unique

Differentially

Expressed 

Genes

Differentially

Expressed 

Genes

Liu et al. (2005) DBP data
5-34

5.5.2.1. Two Methods for Identifying Differentially Expressed Genes (DEGs) 

5.5.2.1.1. Rosetta error model (REM)

The data set for the vehicle-treated and DBP-treated samples were input into the 

proprietary software, Rosetta Resolver. A principal component analysis (PCA) of the entire data 

set shows a distinct treatment response (i.e., the control and treated samples are clearly separated 

into two distinct groups) but also demonstrates the variance in the data set between similarly 

treated samples.

Next, the gene expression data were normalized using error-model algorithm in Rosetta 

Resolver®, in part, because this software was available, but more importantly, because we 

performed an internal evaluation of this algorithm compared to four other normalization 

methods. The REM is a method for identifying DEGs that takes into account the variance of the 

color intensity outputs from microarray studies. The error model conservatively estimates 

intensity error and uses this approach to decrease the likelihood of identifying a change in gene 

expression that is the result of intensity variance. When the results of REM were compared to ttest and fold-change methods, the REM provided higher detection power (Weng et al., 2006). 

The Rosetta Resolver system is a comprehensive gene expression analysis solution that 

incorporates analysis tools with a robust, scalable database. Using the reference microarray data 

set, Choe et al. (2005) compared a number of normalization methods including the quantile, 

constant, invariant set, Loess, and error models. Receiver-operator characteristic curves were 

generated to evaluate the sensitivity and specificity. Results showed that the REM identified 40-

50% more true positives compared to the other four methods (personal communication on June 

2009 between Bill Ward [EPA/NHEERL] and Susan Hester [EPA/NHEERL]). 

The annotated genes of the rat genome on the Affymetrix gene chip, ~30,000 genes, were 

input into the significance analysis using the Benjamini and Hochberg false discovery rate (FDR) 

for multiple testing correction applied at p < 0.01, a relatively stringent statistical cut-off. Of the 

~30,000 genes, the analysis passed 118 genes as being significantly altered following DBP 

exposure. Of these, 17,496 genes did not pass the statistical filter and 13,428 genes were not 

affected by the treatment. One possible reason that only 118 genes passed the multiple-testing 

correction filter is that there is a high variance between individual samples, as demonstrated by 

the PCA.
5-35

 Using the error-model algorithm analysis, the filtering criterion was changed to p < 0.05 

without applying an FDR because so few genes passed the p < 0.01 plus FDR filter which would 

be limiting for pathway-analysis purposes. It is often the case that after correcting for multiple 

hypothesis testing, few or no genes pass the threshold of statistical significance because the 

biological variances are modest relative to the noise inherent in a microarray experiment 

(Tomfohr et al., 2005). In performing DEG and pathway analysis, professional judgment is 

required to determine when to use a highly stringent statistical significance filter and when to 

focus on the available information regarding the biological significance of gene expression 

changes. We considered it appriopriate to use a p < 0.05 without applying an FDR in order 

obtain a greater number of genes because the objective was to perform a pathway analysis in 

order to gain new information about DBP toxicity. The DEGs identified using the REM are 

shown in Table A-3 in Appendix A. 

The set of 1,977 genes was deemed suitable to perform a comprehensive pathway-level 

analysis because about one third of the DEGs (999) did not meet the statistical cut-off criteria (a 

p-value ≤ 0.05). The list of 1,977 genes was input into the data analysis software program, 

GeneGo, for pathway analysis. MetaCore’sTM analytical tools enable the identification and 

prioritization of the most relevant pathways, networks, and cellular processes affected by a given 

treatment. 

5.5.2.1.2. Signal-to-noise ratio (SNR) 

We also identified DEGs by analyzing the Liu et al. (2005) data via SNR (Golub et al., 

1999), a method that differentiates between gene expression levels of two sample groups relative 

to the standard deviation within each group. Consequently, a high SNR indicates that the two 

sample groups are statistically more distinct whereas a low SNR indicates that the two sample 

groups are less statistically distinct.

For a given gene, gi, i g SNR is evaluated as in Eq. 5-1 

( ) ( )

( ) ( )

,1 ,2

,1 ,2

i

i i

g

i i

g g

SNR

g g

μ μ

σ σ

− = +

(5-1) 
5-36

where the means and standard deviations of the expression levels of gene gi are evaluated for the 

samples in group 1, gi,1 (control), and group 2, gi,2 (DBP treated). 

 SNR is used in quantitative noise analysis for microarray experiments (Tu et al., 2002) 

and feature selection in classification gene expression studies (Goh et al., 2004; Shipp et al., 

2002). Here, SNR enables us to rank genes based on the assumption that genes whose 

expression is related to DBP treatment should exhibit higher SNR values than genes whose 

expression is unaffected by DBP. In order to identify DEGs, we evaluated a permutation test. 

The multiple testing of ~30,000 gene expressions poses a problem as the probability of Type I 

errors increases with the number of hypotheses (Dudoit et al., 2003). To address this issue, we 

executed thousands of comparisons by randomly permuting the gene expression levels from the 

chip for each gene expression. Following this randomization process, p-values were obtained as 

the fraction of the randomized SNR values that are higher than the actual SNR. The genes that 

were assigned a p-value < 0.05 were characterized as DEGs (see Appendix A; the algorithm for 

selecting DEGs [Figure A-1] and the list of identified DEGs [Table A-4]). 1,559 probe sets were 

identified as DEGs. The heat map (see Figure 5-4) illustrates the distinction between the control 

and DBP treated samples. 
5-37

Figure 5-4. Heat map of 1,577 DEGs from SNR analysis method. The three lanes 

on the left are vehicle treated and the three lanes on the right are DBP treated. Data 

used for analysis from Liu et al. (2005). Control 1-3 lanes correspond to three replicate 

control samples. DBP 1-3 lanes correspond to three replicate DBP-treated samples. 

Rows represent the different 1,577 DEGs. The color red represents upregulation of 

gene expression, and green represents downregulation of gene expression. 

5.5.2.2. Pathway Analysis 

Analysis of DBP toxicogenomic studies was carried out using many proprietary 

databases and software packages with enhanced bioinformatic capabilities for pathway and 

functional level analysis (Rosetta Resolver, MetaCore GeneGo, Ingenuity® Pathway 

Knowledgebase). These software tools accept lists of genes of interest and then, using their 

database of knowledge about these gene elements, map them to cellular pathways known to exist 

from experimental literature. The advantage of trying to understand groups of genes acting in 

the same cellular process, such as the cell cycle, is that effects on a pathway or biological process 

likely provide meaningful biological information. In contrast, information about effects on 

expression of one gene does not necessarily capture the relationship of the exposure to a 

High

Low

Control 1 Control 2 Control 3 DBP 1 DBP 2 DBP 3
5-38

chemical on a biological process or pathway. The rationale behind the exercise was that 

interrogation of multiple databases would result in a more complete mining of the microarray 

data sets, which may provide an understanding of all of the potential DBP MOAs underlying the 

testes reproductive developmental effects. Analysis using different statistical tools provides 

information about the similarities and differences in results. 

The GeneGo analysis normalized data set revealed that 131 biological processes 

(p < 0.05) were associated with the 1,977 DEGs. The pathways with a p < 0.05 using the Rosetta 

Error Model (REM) are listed in Appendix A (see Table A-5). Comparisons made on the level 

of gene lists obtained by different statistical methods often do not converge (Manoli et al., 2006). 

We decided to perform a comparison of methods based on the assumption that biologically 

related groups of genes, such as metabolic or signaling pathways, may be more valid if identified 

using different microarray analysis methods. Towards this effort, we input the gene list 

(1,559 genes) using SNR to a pathway-level analysis using GeneGo, similar to the analysis 

performed on the REM results. The pathway-analysis results of significant genes identified by 

SNR are listed in Table A-6 of Appendix A. Table 5-6 lists the common pathways when two 

different statistical filters for DEGs were conducted using the GeneGo pathway analysis (i.e., the 

union of the two separate pathway lists; see Tables A-5 and A-6). In addition to the already 

established changes in the steroidogenesis pathway, this analysis highlights biological processes 

and pathways that are affected by DBP exposure to fetal testis. An assessment of linkages 

between the unique pathways and processes identified to the DBP-induced male reproductive 

toxicity outcomes can be made by querying the published literature. 
5-39

Table 5-6. Common pathways between the REM and SNR analyses of 

differentially expressed genes (DEGs) after in utero DBP exposure from the 

Liu et al. (2005) dataa,b,c

Biological Process Pathways

Cell adhesion Cytoskeleton remodeling

ECM remodeling 

Endothelial cell contacts by junctional mechanisms 

Ephrins signaling 

Integrin inside-out signaling 

Integrin outside-in signaling 

Integrin-mediated cell adhesion 

Reverse signaling by ephrin B

Cell signaling* Activation of PKC via G-Protein coupled receptor

CCR3 signaling in eosinophils

ChREBP regulation pathway

G-Protein beta/gamma signaling cascades

G-Proteins mediated regulation p. 38 and JNK signaling

Leptin signaling via JAK/STAT and MAPK cascades2

Regulation of actin cytoskeleton by Rho GTPases

Role PKA in cytoskeleton reorganization

Disease NF-AT signaling in cardiac hypertrophy

NTS activation of IL-8 in colonocytes

Growth and differentiation Angiotensin activation of ERK 

Angiotensin signaling via STATs 

EPO-induced Jak-STAT pathway

MAG-dependent inhibition of neurite outgrowth

Regulation of acetyl-CoA carboxylase 2 activity in muscle

WNT signaling pathway

Hormones Ligand-dependent activation of the ESR1/SP pathway

Immune response MIF - the neuroendocrine-macrophage connector

CXCR4 signaling pathway
5-40

Table 5-6. (continued) 

Biological Process Pathways

Metabolism* Androstenedione and testosterone biosynthesis and metabolism p.12

Cholesterol biosynthesis2

Cholesterol metabolism2

dATP/dITP metabolism 

dGTP metabolism 

Estrone metabolism 

Fructose metabolism 

G-alpha(q) regulation of lipid metabolism 

Gamma-aminobutyrate (GABA) biosynthesis and metabolism 

Glutathione metabolism 

Glycolysis and gluconeogenesis (short map) 

Glycolysis and gluconeogenesis p. 1 

Glycolysis and gluconeogenesis p. 2 

Histamine metabolism 

Histidine-glutamate-glutamine and proline metabolism 

Leucine, isoleucine and valine metabolism p. 2

Lysine metabolism

Mitochondrial ketone bodies biosynthesis and metabolism 

Mitochondrial long chain fatty acid beta-oxidation 

Mitochondrial unsaturated fatty acid beta-oxidation 

Peroxisomal branched chain fatty acid oxidation 

Metabolism* Phenylalanine metabolism 

PPAR regulation of lipid metabolism2

Propionate metabolism p.12

Propionate metabolism p.22

Regulation of fatty acid synthesis: NLTP and EHHADH 

Regulation of lipid metabolism by niacin and isoprenaline 

Regulation of lipid metabolism via LXR, NF-Y, and SREBP2

Regulation of lipid metabolism via PPAR, RXR, and VDR2

Serotonin—melatonin biosynthesis and metabolism 

TCA 

Triacylglycerol metabolism p.1 

Tryptophan metabolism 
5-41

Table 5-6. (continued) 

Biological Process Pathways

Transcription* Brca1 as transcription regulator

Role of VDR in regulation of genes involved in osteoporosis

Transcription factor Tubby signaling pathways

a

Statistically significant gene lists from SNR and REM methods were input into the GeneGo pathway 

analysis program (www.genego.com). The Gene ontology process/pathway list was generated using a 

cut-off of p < 0.05 for each analysis. From those lists, the common pathway list was generated.

b

Pathways that are part of, or overlap with, the testosterone synthesis pathways are indicated by bold italics. 

These pathways were identified by performing a PubMed literature search 

(http://www.ncbi.nlm.nih.gov/sites/entrez?db=PubMed) for “testosterone” and the name of each pathway 

(listed in the table).

c

Entrez Gene indicates that Insl3 is the ligand for the LGR8 receptor, but the Insl3 pathway is not fully 

defined 

(http://www.ncbi.nlm.nih.gov/sites/entrez?Db=gene&Cmd=ShowDetailView&TermToSearch=114215&or

dinalpos=3&itool=EntrezSystem2.PEntrez.Gene.Gene_ResultsPanel.Gene_RVDocSum). 

*Biological processes identified in Liu et al. (2005).

 Functions shown to be related to the Insl3 pathway are G-protein-coupled receptor binding and hormone 

activity. Processes identified are G-protein signaling, adenylate cyclase inhibiting pathway, gonad 

development, in utero embryonic development, male gonad development, negative regulation of apoptosis, 

negative regulation of cell proliferation, oocyte maturation, positive regulation of cAMP biosynthetic 

process, and positive regulation of cell proliferation. While a number of G-protein pathways were 

identified in this analysis, none are considered exclusive to Insl3 and are, therefore, not listed in bold 

italics.

Cholesterol biosynthesis/metabolism and associated pathways underlie one of the MOAs 

of DBP. To determine a metric for statistical analysis protocols of toxicogenomic data, we chose 

to compare the genes that are involved in the cholesterol biosynthesis/metabolism as identified 

by the three independent analysis methods (described herein) and the published data set from Liu 

et al. (2005) (see Table 5-7). These results show that there is a high degree of overlap in the 

most biologically relevant pathway/process involved in DBP toxicity, even when different 

statistical procedures are used for analysis of the same data set. These are in agreement with the 

published literature, giving the approaches used in this exercise biological confidence. 

By utilizing databases such as GeneGo, additional canonical pathways and biological 

processes were identified that may play an important role in DBP male reproductive 

developmental toxicity. Regulation of steroidogenesis requires multiple signaling pathways and 

growth factors (Stocco et al., 2005). Signaling pathways, like the protein kinase C pathway, 

arachidonic acid metabolism, growth factors, chloride ion, and the calcium messenger system are 
5-42

Table 5-7. Genes involved in cholesterol biosynthesis/metabolism that were 

identified by both the REM and SNR analyses of Liu et al. (2005)

REM (GeneGo) SNR (GeneGo) SNR (KEGG)

Acat1 Acat1

Cyp27a1

Cyp51a1 Cyp51a1

Cyp7b1

Dhcr7 Dhcr7 Dhcr7

Dhcr24

Ebp Ebp

Fdft1 Fdft1

Fdps Fdps

Hmgcr Hmgcr Hmgcr

Hmgcs1 Hmgcs1 Hmgcs1

Hsd11b1

Hsd3b1

Idi1 Idi1 Idi1

Mvd Mvd

Nsdhl

Sqle Sqle Sqle

Sc4mol Sc4mol

Soat1

Tm7sf2

capable of regulating/modulating steroid hormone biosynthesis. It is possible that some of the 

pathways and processes identified by the two methods may play a role in the regulation of 

steroidogenesis, a pathway that underlies one of the well-established MOAs by DBP. Another 

scenario could be that these pathways and processes have yet to be associated with DBP-induced 

toxicity. The androstenedione and T biosynthesis and metabolism pathway was one of the 

common pathways in the GeneGo analysis of the two different methods gene list (see 

Figure 5-5). 
5-43

Figure 5-5. Mapping the Liu et al. (2005) data set onto the canonical 

androstenedione and testosterone (T) biosynthesis and metabolism pathway

in MetaCore (GeneGo). Key enzymes activated by DBP are identified by red 

thermometers. 

It has been reported in the literature (MAQC-I, see Chapter 2) that the results of 

microarray experiments often depend on the data analysis protocol and the biological pathwayanalysis tools available to interpret the list of statistically significant genes. Dissimilar sets of 

gene expression signatures with distinct biological contexts can be generated from the same raw 

data by different data analysis protocols. Distinct biological contexts can also be generated from 
5-44

the same gene expression signatures by different biological pathway protocols. Therefore, it 

becomes important to determine and understand the relationship between the gene expression 

and pathway changes and a biological outcome of interest. 

In order to do a thorough investigation, it is necessary to use many sources of gene and 

pathway annotation. The intent of using multiple sources is to gain an enriched analysis. In 

practice, analysis is carried out with the suite of tools available to the analyst. In this case, the 

STAR Center primarily used KEGG (a resource rich in enzymatic and metabolic reactions but 

weak in signaling pathways); whereas the EPA used Rosetta Resolver, GeneGo, and Ingenuity 

Pathway Analysis, resources that are populated with signaling as well as metabolic pathways. 

This exercise demonstrates that multiple approaches to microarray data analysis can yield 

similar biologically relevant outcomes and some differences. The differences observed in the 

results could be due to a number of factors including (1) the different data normalization 

procedures used in the two separate analyses; and (2) different data interpretation tools such as 

the software for pathway analyses. However, it cannot be ruled out that the differences may 

reflect differences in biological significance (i.e., one approach is superior).

We performed a number of reanalyses of the Liu et al. (2005) data because the pathway 

analysis presented in the article was not performed for risk assessment purposes. While the 

authors of this and other microarray studies support two MOAs for DBP, a reduction of fetal 

testicular T via affects on steroidogenesis and cholesterol transport genes, not all pathways 

associated with the differentially expressed genes were discussed in detail. 

Two different bioinformatics tools to analyze the same data were utilized. Each analysis 

used multiple statistical filters to parse the noise from the signal in the microarray data set and to 

assess the quality of the data set. Ideally, for a high-quality study data set, there would be a 

minimum of variance between similarly treated samples, and the variance would lie between the 

control and treated sample data. PCA shows the quality of the Liu et al. (2005) data set to be of 

moderate quality based on the observed variance among similarly treated data sets (control and 

treated groups). One analysis utilized multiple proprietary software packages (GeneGo, Rosetta 

Resolver). The rationale for looking at the effect of DBP on the pathway level, as opposed to a 

cluster of genes, is that DBP is most likely affecting multiple pathways within a cellular 

environment. This exercise allowed us to generate a list of affected common pathways between 

the two methods, and in this way, provided more confidence about these pathways. 
5-45

The results of the new pathway analyses both corroborate the previously identified two 

MOAs for DBP male reproductive development toxicity, and provide putative novel pathways 

affected by in utero DBP exposure that may play a role in DBP-mediated toxicity. The results of 

the new pathway analyses provide hypotheses for MOA that could be tested in new experimental 

studies. Future research could investigate the role of these pathways in DBP-induced toxicity. 

In addition, a gene network was developed for DBP based on the Liu et al. (2005) data. The 

GeneGo analysis corroborated prior findings for the role of the steroidogenesis pathway and 

identified the modulation in Cyp17 and Ar that are involved in the androgen biosynthetic 

process. This is a new hypothesis that requires follow-up with new studies to confirm this 

observation. Performing new analyses was useful for the purposes to further our understanding 

of the DBP mechanism of action. 

Analyzing any given data set multiple ways and arriving at the same conclusion provides 

confidence in the analytical approach; however, there is no “gold standard” analytical method. 

Applying stringent statistical filters in pathway analysis (e.g., p < 0.05, Benjamini Hochberg 

multiple testing correction) can limit the number of genes that are identified. Interpretation of 

the biology of the system using only a limited gene set is restrictive. It is important to remember 

that the genes that do not pass the statistical stringency cut-off that may be crucial for 

understanding the biology of the system, as statistical significance and biological significance are 

not necessarily the same. Therefore, it becomes incumbent upon the researcher to analyze the 

data in multiple ways in order to maximize the benefits of microarray data. 

In summary, by identifying differentially expressed genes by two different approaches, 

performing pathway analysis, and compiling a list of common pathways between the two 

approaches, a list of corroborated pathways has been identified. The pathways (see Table 5-6) 

and processes identified have some overlap with those presented in the Liu et al. (2005) article as 

well as some differences. Comparisons of our results to those of Liu et al. (2005) are difficult 

because they presented differentially expressed genes and their associated process, not pathways. 

In Liu et al., 2005, oxidative stress and cytoskeleton processes were unique findings. Our results 

identified cell adhesion, disease, immune response, hormone, and growth and differentiation 

processes as unique findings. In addition, the reanalysis of the Liu et al. (2005) study identified 

common and unique pathways (see Table 5-6) with the tabulation of affected pathways from the 

published literature that we performed including all of the DBP gene expression studies (see 
5-46

Table 5-2). This exercise has generated hypotheses about mechanisms/pathways that could 

underlie the unexplained testicular endpoints after in utero DBP exposure (see Chapter 4) that 

need to be tested in additional studies. 

5.6. CONCLUSIONS 

In this chapter, evaluations of the published studies and a reanalysis of pathways from 

one microarray study was performed. Nine toxicogenomic studies from the published literature 

were evaluated for study comparability and study result consistency. This was done by utilizing 

Venn diagrams and a visual method for looking at the consistency across all of the gene 

expression studies (see Figure 5-2). These methods could be applied in a new assessment for a 

chemical with genomic data.

The reanalysis of the Liu et al. (2005) data set provides some examples of methods for 

identifying differentially expressed genes and performing pathway analysis using either 

proprietary or publicly available methods and databases. In performing the reanalysis, 

hypotheses were generated about possible pathways underlying some of the known and unknown 

MOAs for the testes outcomes observed after in utero DBP exposure. 
6-1

6. EXPLORATORY METHODS DEVELOPMENT FOR ANALYSIS OF GENOMIC 

DATA FOR APPLICATION TO RISK ASSESSMENT

6.1. OBJECTIVES AND INTRODUCTION

The overall goal of this chapter is to describe exploratory methods developed for analyzing 

and applying toxicogenomic data in risk assessment. The three objectives of the methods 

development projects were to 

1. Explore the development of new methods to analyze microarray data for application to 

risk assessment.

The motivation was to develop methods for performing gene expression analyses of 

microarray data for use in risk assessment. Microarray studies for basic research 

purposes do not necessarily require as high a level of stringency as for risk assessment 

purposes because the analyses are often performed to generate hypotheses (e.g., for 

developing MOA hypotheses) that are subsequently tested in additional studies. 

2. Utilize existing DBP genomic data to develop a temporal gene network model for use in 

risk assessment.

We asked whether there are data to understand gene expression changes over time. By 

modeling the gene and pathway interactions across the critical window of exposure to 

DBP, it may be possible to understand the relationships among genes and pathways over 

time, and possibly, to identify the initiating event(s) for the decreases in fetal testicular T 

or Insl3 expression. Identifying the initiating event would be very useful to risk 

assessment, as this would provide a biologically significant gene whose expression is 

critical to the outcome. 

3. Utilize genomic and other molecular data to address the Case Study Question: Do the 

toxicogenomic data inform interspecies differences in TD?

We utilized the available gene sequence data, protein sequence, and pathway crossspecies data to assess the rat-to-human conservation of the genes involved in the 

steroidogenesis pathway that underlie the reduced fetal testicular T MOA for DBP. 

The work to address the objectives of this chapter is the result of a collaborative effort 

among scientists at the STAR Bioinformatics Center at UMDNJ and Rutgers, and the EPA. The 

analyses were performed at Rutgers University.

The work presented in this chapter is highly technical and thus, is intended to be 

beneficial to scientists with expertise in bioinformatics. The technical details of the analyses are 
6-2

provided in order that scientists could apply these methods to their work. Such an approach will 

allow the risk assessor proficient in microarray analysis methodology an opportunity to apply 

these methods. The last section of this chapter (Section 6.4) summarizes the findings for a 

scientific audience that does not have a strong background in microarray analysis methods. 

6.2. PATHWAY ANALYSIS AND GENE INTERACTIONS AFTER IN UTERO DBP 

EXPOSURE

6.2.1. Pathway Activity Approach 

Usually, to identify significant biological pathways from transcriptional data, pathway 

analysis is performed after determining the DEGs using a statistical filter. Two examples of this 

approach are described in Chapter 5 (Section 5.5). An alternative approach is the use of 

“pathway scoring” methods, which begin with projecting gene expression changes onto 

pathways (Rahnenfuhrer et al., 2004; Moothaet al., 2003; Hanisch et al., 2002). The main 

advantage of applying pathway scoring methods to microarray data is that changes can be 

identified at the pathway level that may not be detected by first identifying individual DEGs. 

Most of these methods calculate the average correlation between pairs of genes within pathways 

(Rahnenfuhrer et al., 2004; Sohler et al., 2004; Hanisch et al., 2002; Zien et al., 2000). Another 

pathway scoring method tests for association between gene expression and a phenotype (e.g., 

Gene Set Enrichment Analysis [GSEA]; Mootha et al., 2003). In GSEA, all genes are ranked 

with respect to some measure that quantifies the gene expression associated with a phenotype 

(i.e., differentiation between healthy vs. disease samples). Tomfohr et al. (2005) introduced a 

pathway-based approach that is similar in spirit to GSEA. Their method translates the overall 

gene expression levels within a pathway to a “pathway activity level,” which is derived from 

singular value decomposition (SVD), described below. Hence, pathway activity levels can be 

used in the same kinds of applications as gene expression levels (Tomfohr et al., 2005). 

Tomfohr et al. (2005) compared their pathway activity method to GSEA using expression data 

from two different studies, one that studied Type 2 diabetes and one that studied the influence of 

cigarette smoke on gene expression in airway epithelia. They found similar results to those 

obtained using GSEA in the diabetes set, and further, improved results for identifying 

differentially expressed pathways in the cigarette smoke data. 

We applied a pathway activity level approach to DBP microarray data. Since pathway 

activity levels are a reduced form of the overall gene expression matrix (represented by the 
6-3

largest deviation in the overall gene expressions within a pathway) Alter et al. (2000) and 

Cangelosi (2007) raised the critical issue that pathway activity levels (represented by the largest 

deviation in the overall gene expressions within a pathway) may be attributed to random 

deviations in the data. Therefore, we use a significance analysis to distinguish the information 

captured by pathway activity levels from random deviation. 

6.2.1.1. Significance Analysis of Pathway Activity Levels

 The procedure begins with mapping genes to the KEGG pathway database. The entire 

gene set represented by the Liu et al. (2005) data set (i.e., using the Affymetrix RAE230 A and 

B chips) maps to 199 pathways in the KEGG database with 4,772 associated genes. 

Pathway activity formulation starts with SVD of the gene expression matrix of a given 

pathway. SVD involves a mathematical procedure that transforms a number of possibly 

correlated variables into a smaller number of uncorrelated variables. It mathematically 

transforms the data to a new coordinate system such that the greatest variance by any projection 

of the data lies on the first coordinate (called the eigenvector), the second greatest variance on 

the second coordinate, and so on. Associated with each of these coordinate eigenvectors is a 

weight term (called the eigenvalue) that represents the variance in the data. The eigenvalues are 

normalized such that they express the fraction of the variance along their corresponding 

eigenvector. In this study, SVD is used to calculate pathway activity levels for each 

experimental condition where each pathway activity level represents the most significant gene 

expression pattern within each pathway. The details of SVD analysis are as follows:

Using Eq. 6-1, let Ξp(k,t) be the gene expression data associated with a given pathway, p, 

composed of k genes measured at t different conditions (time, treatment, dose, etc.), normalized 

(i.e., to a mean of zero mean and unit standard deviation). The SVD of Ξp(k,t) is given as 

follows: 

( ,) ( , ) ( ,) (,)I

p p pp

Ξ= × × kt U kk S kt V tt (6-1) 

Eq. 6-1states that the columns of the matrix Up(k,k) are the orthonormal eigenvectors of Ξp(k,t). 

Sp(k,t) is a diagonal matrix containing the associated eigenvalues, and the columns of the matrix 

Vp(t,t) are projections of the associated eigenvectors of Ξp(k,t). As the elements of Sp(k,t) are 
6-4

sorted from the highest to the lowest, the first row of Vp(t,t) represents the most significant 

pattern within a pathway across different samples. Hence, PALp is mathematically defined as the 

first vector of the Vp(t,t) (given in Eq. 6-2 ). 

(,)I PAL V n 1 p p = (6-2) 

The fraction of the overall gene expression that is captured by PALp is evaluated through Eq. 6-3. 

 

( )

( )

2

2

1

,

p

P L

p

g

S 1,1 f

S LL

=

=

∑

(6-3) 

An additional analysis is needed to evaluate whether PALp represents significant 

information about the pathway. As a standard procedure for evaluating significance of 

microarray data, random sampling is used. For each pathway, an equal number of gene 

expression values are permutated 1,000 times. The p-value is computed as the permutated fp that 

exceeded the actual fp (p-value < 0.05). Next, the pathways are filtered based on the associated 

p-value of their fp value. 

We illustrate the importance of the significance analysis of PALp in Figure 6-1 using the 

gene expression matrix for the tryptophan metabolism pathway. Panel A of Figure 6-1 depicts 

both the fraction of the overall gene expression captured by each eigenvector, fp , and the average 

fraction of the overall gene expression captured by each eigenvector of the randomized data. We 

observe that the fp value captured by the PALp of the tryptophan metabolism pathway can be 

retrieved with a randomly selected gene set and thus, the tryptophan metabolism pathway is not 

significantly affected by DBP exposure. We applied a significance analysis of PALp to improve 

the confidence of Tomfohr’s pathway activity level formulation for further calculations. 

6.2.1.2. Pathway Activity Analysis

The main goal of pathway analysis is to identify significantly affected pathways, based 

on gene expression data, due to DBP exposure. For this purpose, as described above,
6-5

Figure 6-1. An illustration of the adapted version of pathway activity level 

analysis for the tryptophan metabolism pathway, a nonactive pathway for 

DBP. In panel A, the boxes indicate the variability in the actual gene expression 

data, associated with the tryptophan metabolism for each individual eigenvector. 

For comparison, the solid line represents the fraction of data variability captured 

by the corresponding eigenvectors when randomly generated data were used. No 

apparent distinction between the actual data and randomly generated data was 

identified, as quantified by the calculated p-value of 0.25. In panel B, the 

projection of the gene expression on each eigenvector is depicted for each sample 

of the control (C) and DBP-treated (T) groups. PALp is the first vector that 

corresponds to the largest variation in the data. 

overall gene expressions within a pathway are reduced to PALp. The differentiation between 

PALp of different samples is denoted as pathway activity and is determined through a process 

analogous to SNR analysis. 
6-6

If n1 samples are associated with vehicle treatment (control) and n2 samples with 

chemical treatment (DBP), then the activity levels associated with treatment groups are given in 

Eqs. 6-4 and 6-5, respectively. 

( ,) 1 I PAL V n 1 p p1 = (6-4)

( ,) 2 I PAL V n 1 p p2 = (6-5) 

Pathway activity is calculated using Eq. 6-6 where μ and σ represent the mean and 

standard deviation respectively.

( ) ( )

( ) ( )

1 2

1 2

p p

p

p p

PAL PAL

PA

PAL PAL

μ μ

σ σ

− = + (6-6)

 A high pathway activity represents a better differentiation between control and treated 

pathway activity levels. The statistical significance of pathway activity is determined using the 

randomization process. For each pathway, an equal number of genes within a given pathway are 

randomly assigned and gene expression changes are generated (from the chip) 10,000 times. The 

p-value of the pathway activity is computed as the fraction of the randomized pathway activity 

that exceeded the actual pathway activity. In this analysis, the pathways that have both 

statistically significant (p-value < 0.05) pathway activity and pathway activity level are defined 

as “active” pathways. 

“Active” pathways are those for which the overall change in gene expression in a 

pathway of treated samples compared to control samples was statistically significant. For 

example, an active pathway could be one for which gene expression was downregulated or 

turned off after DBP exposure. Alternatively, a pathway that is not identified as active would 

still have gene expression occurring, but might not exhibit a significant difference in gene 

expression following DBP exposure compared to the control samples. Thus, the term active does 

not refer to gene expression from a particular pathway. The algorithm for selecting active 

pathways using the pathway activity method is shown in Appendix B, Figure B-1. 
6-7

We identified 15 active pathways from querying the KEGG database (see Table 6-1). 

The pathway activity method identified pathways such as biosynthesis of steroids (C21 Steroid 

hormone metabolism pathways known to be biologically relevant to T levels) as well as other 

pathways including butanoate metabolism, pyruvate metabolism, and biosynthesis of unsaturated 

fatty acids (PPAR signaling pathway and fatty acid metabolism). 

Table 6-1. The KEGG pathways ordered based on their p-value for pathway 

activitya 

Pathway name

p-value of 

PAb 

p-value of 

PALc 

Reductive carboxylate cycle (CO2 fixation) <0.001 0.002

Valine, leucine and isoleucine degradation <0.001 <0.001

Biosynthesis of steroids 0.001 <0.001

Citrate cycle (TCA cycle) 0.002 <0.001

Glutathione metabolism 0.002 0.006

Tryptophan metabolism┼ 0.002 0.250

Pentose phosphate pathway 0.002 <0.001

Glycolysis / Gluconeogenesis 0.003 <0.001

Butanoate metabolism 0.004 0.006

Pyruvate metabolism 0.004 <0.001

C21Steroid hormone metabolism 0.006 0.048

Glyoxylate and dicarboxylate metabolism┼ 0.012 0.480

Biosynthesis of unsaturated fatty acids 0.012 0.048

Fatty acid metabolism 0.020 0.030

Nicotinate and nicotinamide metabolism 0.028 0.068

Propanoate metabolism 0.030 0.018

Cyanoamino acid metabolism┼ 0.032 0.074

PPAR signaling pathway 0.042 <0.001

a

Pathway activity quantifies the difference between control and DBP-treated samples from Liu et al. (2005) 

(see Eq. 6-6). PAL is the pathway activity level for both the control and treated samples (see Eq. 6-2). 

The statistical significance of PA and PAL values are evaluated through a randomization procedure. The 

p-value of PAL is used as an additional filtering process to eliminate potentially nonactive pathways. 

b

The p-value of the PA is computed as the fraction of the randomized PA that exceeded the actual PA. In 

the event that the PA of the randomly generated matrices exceeds the actual PA by more than 5 % of the

randomization process, then the actual PA is attributed to a random variable (p-value < 0.05). c

The p-value of PAL quantifies the significance of fraction of the overall gene expression captured by 

PAL. It is computed as the fraction of the randomized fp exceeding the actual fp. In the event that the PA 

of the randomly generated matrices exceed the actual PA by more than 5 % of the randomization process, 

then the actual PA is attributed to a random variable (p-value < 0.05).

PA, pathway activity; PAL, pathway activity level.
6-8

To explore the biological significance of the active pathways, a metabolic pathway 

network of the active pathways illustrating their connections via metabolites was built 

(Figure 6-2). This process includes the integration of the statistical outcome of the pathway 

activity analysis and the relationships among these pathways by querying the KEGG database. 

After DBP in utero exposure, the pathways related to cholesterol biosynthesis exhibit more 

significant changes in their gene expression compared to the rest of the active pathways. This 

finding is consistent with the hypothesis that an early decrease in T level might be due to 

cholesterol unavailability (Thompson et al., 2005). 

Figure 6-2. Metabolic pathway network for DBP (Liu et al., 2005 data) using 

the pathway activity method and the KEGG database. Active pathways 

connected to each other via metabolites are ordered from the most active pathway 

(top of the figure) to the less active pathways (bottom of the figure). The 

connections between the active pathways were retrieved from KEGG (Kanehisa 

and Goto, 2000). 
6-9

We explored the contribution of DEGs to the pathway activity for a given pathway 

(Figure 6-3 A, B, C, and D). The pathway activity of each pathway is calculated by adding 

genes one-by-one starting with the gene with the highest SNR and adding genes sequentially in 

the order of their SNR until all genes in the pathway have been added. Figure 6-3 A and B 

illustrate examples of active pathways, whereas Figure 6-3 C and D are examples of pathways 

that were not identified as active in our analysis. For pathways that were identified as active or 

not active, the cumulative pathway activity value undergoes a decrease as genes of lower SNRs 

are added. Yet for the active pathways, the cumulative pathway activity remains high enough to 

be statistically significant. For pathways identified as not active, the cumulative pathway 

activity reaches a low level when all of the genes are added. Accordingly, their pathway activity 

value is not statistically significant. The four pathways are composed of a similar number of 

genes; therefore, the number of genes in the pathway is not an issue in this comparison. We 

hypothesize that there is a subset of genes that maintain the pathway activity value high enough 

within active pathways, even when all genes are added. The cumulative behavior of this subset 

enables us to differentiate the active and nonactive pathways. Differentially expressed genes in 

active pathways are defined as “informative genes” (see Table B-1). We identified a relatively 

small number of genes as informative, and these may represent genes that DBP has most greatly 

affected. 

One of our goals was to utilize existing DBP genomic data to develop a gene network 

model useful to risk assessment. Gene network models illustrate interactions between genes and 

their products (e.g., mRNA, proteins). We used IPA software to construct a gene network model 

after DBP in utero exposure. IPA adds nodes (i.e., genes) to the input gene list (i.e., informative 

genes) and then, builds edges (i.e., relations) based on the literature. The interactions among the 

informative genes from the Liu et al. (2005) data were retrieved using IPA, and the resulting 

preliminary gene network model is shown in Figure 6-4. 

6.2.2. Developing a Temporal Gene Network Model

The Thompson et al. (2005) study was selected to develop a temporal gene network 

because it was the only available time-course study. The study had the advantages of using the 

rat Affymetrix chip, which has ~30,000 gene transcripts represented, and availability of the data 

(i.e., kindly provided by Dr. Kevin Gaido). In the study, animals were exposed to DBP for 0.5, 
6-10

Figure 6-3. The relationship between differential expression of individual genes and 

pathway activity using the Liu et al. (2005) DBP data. The pathway activity of a given 

pathway is first evaluated using the gene that has the highest SNR. Subsequently, the 

genes are added in the order of their SNR, from highest to lowest. Pathways identified as 

active for DBP, such as biosynthethis of steroids (A) and butanoate metabolism (B), 

maintain high pathway activity values even when all genes in the pathway are added. 

Alternatively, pathways not identified as active for DBP such as pentose and glucuronate 

interconversions (C) and ether lipid metabolism (D), exhibit a decrease in pathway 

activity as the less discriminating genes (i.e., those with a lower SNR value) are added. 
6-11

Figure 6-4. A gene network for DBP data of Liu et al. (2005) generated using Ingenuity Pathway Analysis (IPA). 

The figure illustrates the interactions among informative genes following in utero DBP exposure in the rat testis from 

Liu et al. (2005). Interactions among genes (shown in Appendix B, Table B-1) are derived from the Ingenuity 

database. Genes or gene products are represented as nodes. Diamonds, enzymes; Horizontal ovals, transcription 

regulators; Squares, cytokines; Rectangles, nuclear receptors. Solid lines represent direct relationships between nodes 

(i.e., molecules that make physical contact with each other, such as binding or phosphorylation). Dashed lines 

represent indirect interactions (i.e., not requiring physical contact between the two molecules, such as signaling events). 

CP, Canonical pathway.

E,PD,T (6)

NFIC

CYP7A1

IDH1

DHCR7

IDI1

SREBF2

ASS

LEP

HMGCR

ALDOAALDOC

FDFT1

SQLE

GSTA1

FASN

ACAT1

ESRRA

FDPS

GSTA3

PPARGC1B

ACSL4

GSTM2

GAPDH

TPI1

NOS1

ACO2

MVD NR5A1

SREBF1

INSIG1

ALAS1

ACADS

EBP

ALDH2

ACO1

MIF

ACADM

GSTA2

FGF4

CYP17A1

CYP11A1

HMGCS1

CP: Sterol Biosynthesis

CP: Glycolysis/Gluconeogenesis

CP: Glutathione Metabolism

CP: Valine, Leucine and Isoleucine Degradation

CP: Fatty Acid Metabolism

CP: Xenobiotic Metabolism Signaling

CP: Citrate Cycle

CP: C21-Steroid Hormone Metabolism

CP: Valine, Leucine, and Isoleucine Degradation CP: Glycolysis/Gluconegenesis CP: Citrate Cycle

CP: Glutathione Metabolism

CP: Xenobiotic Metabolism Signaling

CP: C21-Steroid Hormone Metabolism

CP: Sterol Biosynthesis

CP: Fatty Acid Metabolism

E,PD,T (6)

NFIC

CYP7A1

IDH1

DHCR7

IDI1

SREBF2

ASS

LEP

HMGCR

ALDOAALDOC

FDFT1

SQLE

GSTA1

FASN

ACAT1

ESRRA

FDPS

GSTA3

PPARGC1B

ACSL4

GSTM2

GAPDH

TPI1

NOS1

ACO2

MVD NR5A1

SREBF1

INSIG1

ALAS1

ACADS

EBP

ALDH2

ACO1

MIF

ACADM

GSTA2

FGF4

CYP17A1

CYP11A1

HMGCS1

CP: Sterol Biosynthesis

CP: Glycolysis/Gluconeogenesis

CP: Glutathione Metabolism

CP: Valine, Leucine and Isoleucine Degradation

CP: Fatty Acid Metabolism

CP: Xenobiotic Metabolism Signaling

CP: Citrate Cycle

CP: C21-Steroid Hormone Metabolism

CP: Valine, Leucine Leucine, and Isoleucine Degradation Isoleucine Degradation CP: Glycolysis/Gluconegenesis CP: Citrate Cycle

CP: Glutathione Metabolism

CP: Xenobiotic Metabolism Signaling

CP: C21-Steroid Hormone Metabolism

CP: Sterol Biosynthesis

CP: Fatty Acid Metabolism
6-12

1, 2, 3, 6, 12, 18, or 24 hours before sacrifice on GD 19. The limitations of the Thompson et al. 

(2005) study include (1) the dosing was initiated on GD 18, late in the critical window, and (2) 

the shortest duration exposure (30 minutes) began at the latest developmental time (i.e., GD 19); 

thus, developmental stage and duration of exposure do not coincide (see Chapter 5). Given this 

caveat, we utilized the available to test algorithms to build a prototype of a temporal gene 

network model. 

We used the pathway activity level method described earlier to identify biologically 

active pathways at each time point. We evaluated the informative genes at each time point and 

the resulting preliminary temporal gene network, based on the Thompson et al. (2005) data, is 

shown in Figure 6-5. The analysis showed a preponderance of signaling pathways such as 

JAK/STAT, PPAR, and MAPK perturbed at the earlier exposure durations. After the longest 

DBP exposures (18 hours), the metabolic pathways, including amino acid metabolism, lipid 

metabolism, and carbohydrate metabolism, were affected. Thompson et al. (2005) hypothesized 

that the decrease in T level after a short duration of DBP exposure might be due to cholesterol 

unavailability and their findings support this hypothesis. To have a complete understanding of 

the temporal sequence of gene expression and pathway affect events after in utero DBP 

exposure, data from an exposure-duration series across the entire critical window of exposure are 

needed. 

6.3. EXPLORATORY METHODS: MEASURES OF INTERSPECIES (RAT-TOHUMAN) DIFFERENCES IN TOXICODYNAMICS

The goal of this section is to address whether genomic and mechanistic data could inform 

the interspecies (rat-to-human) differences TD for one of the DBP MOAs reduced fetal testicular 

T (one of the DBP case-study questions). Although progress has been made in understanding the 

MOAs of chemical toxicants, it is important to evaluate the mechanistic relevance of these 

MOAs to humans. The genomic data set for DBP does not include human genomic data of any 

type, including studies from in vitro cell lines. Even if such data were available, extrapolation of 

in vivo data (rat genomic) to in vitro data (human genomic) may confound the ability to generate 

accurate interspecies comparison. In the absence of DBP genomic data in human cell lines, we 

considered genetic sequence data and other data from rats and humans for making species

comparisons. It is significant that the role of T in male reproductive development during sexual 
6-13

Figure 6-5. A temporal gene network model created by IPA from the informative gene list based on time-course 

data after in utero DBP exposure (Thompson et al., 2005). The informative genes were evaluated at each time point 

and mapped onto a global molecular network using the Ingenuity Pathways Knowledge Base. Diamonds, enzymes; 

Horizontal ovals, transcription regulators; Squares, cytokines; Rectangles, nuclear receptors. Solid lines represent 

direct relationships (also called edges) between nodes (i.e., molecules that make physical contact with each other, such 

as binding or phosphorylation). Dashed lines represent indirect interactions (i.e., not requiring physical contact 

between the two molecules, such as signaling events). CP, Canonical pathway. Low (green), downregulated 

expression with respect to control. High (red), upregulated expression with respect to control.

- 18 hour - 6 Hour -3 Hour -1 Hour Exposure Time

Added nodes by Ingenuity

High

Low

CP: Steroid Hormone Metabolism CP: Xenobiotic Metabolism

CP: MAPK Signaling

CP: PPAR Signaling

CP: Sterol Metabolism

CP: VLI degradation

CP: Fatty Acid Metabolism

CP: Glutathione Metabolism

CP: Glycolysis
6-14 

differentiation is conserved among vertebrates, thus providing a measure of human relevance of 

the reduced fetal testicular T observed in the rat after in utero DBP exposure. 

Phylogenetic analysis, the reconstruction of evolutionary relations, is based on shared, 

derived characters presumed to have a common origin. Taxonomy of organisms is one method 

for determining species relatedness. However, since DBP perturbs the activity of the 

steroidogenesis pathway and leads to the decreased T MOA for DBP, we were interested in 

developing metrics by comparing this pathway between the rat (for which we have data) and 

human. Previous phylogenetic analyses of individual pathways have included assessing: the 

number of common enzymes and their conservation across different species (Forst, 2002; Forst 

and Schulten, 1999); the topology of the underlying enzyme-enzyme relational graphs including 

their sequence conservation (Heymans and Singh, 2003); and the intersection of compounds, 

reactions, and enzymes across species (Clemente et al., 2005). 

We reconstructed the phylogenetic relationships for biosynthesis of steroids among eight 

species based on enzyme presence (Forst and Schulten, 1999; see Figure 6-6). The enzyme 

presence method is based on information available in the KEGG database about the presence of 

an enzyme (defined as catalyzing a specific reaction) in the pathway for a given species. As a 

result, a pathway topology can be represented and compared across species. In this 

representation of pathways, a vector containing binary information (where “1” is for presence, 

“0” is for absence of the enzyme) is created for a given pathway. Then, the similarity between 

pathways for two different species is defined as the ratio of the number of common enzymes to 

the number of unique enzymes. The results suggest that the steroidogenesis pathway is quite 

similar between rat and human. Further, we found that the species differences based on enzyme 

presence were different from those based on the NCBI taxonomy (Sayers et al., 2008) of the 

organisms, which is not surprising based on previous findings (Searls, 2003). In order to utilize 

more complete information about a pathway, cross-species pathway comparisons should include 

other biologically relevant information such as gene regulatory information and pathway 

interactions. 

Sequencing of the human, mouse, and rat genomes and their comparison has increased 

our understanding of cross-species similarities and differences in genes and proteins. Coexpressed genes across multiple species are most likely to have a conserved function. The rat 

genome project reported that almost all human genes known to be associated with disease have 
. 

6-15

A) Enzyme Presence for the Biosynthesis of Steroids Pathway B) Phylogenetic and Taxonomic Knowledge

Figure 6-6. The phylogenetic relations among eight organisms based on enzyme presence, for the biosynthesis of 

steroids pathway, and based on information available on the NCBI taxonomy website (Sayers et al., 2008). Panel 

A shows the results of evaluating the phylogenetic relations for the biosynthesis of steroids pathway, based on enzyme 

presence (KEGG database), among eight model species (hsa, Homo sapiens; mmu, Mouse; rno, Rat; dre, Zebra fish; 

ath, Arabidopsis; cel, C. elegans, sce, Yeast; eco, E. coli). Panel B shows the phylogenetic relations among the same 

eight organisms based on taxonomic and phylogenetic information retrieved from the NCBI taxonomy database 

(http://www.ncbi.nlm.nih.gov/Taxonomy/taxonomyhome.html/index.cgi).
6-16

orthologous genes in the rat genome, and that the human, mouse, and rat genomes are 

approximately 90% homologous (Gibbs et al., 2004). While the function of certain genes and 

their involvement in disease might not be conserved across species, the function of a pathway is 

likely to be more highly conserved among species that perform similar functions (Fang et al., 

2005). Thus, cross-species pathway conservation metrics may be more useful.

Similarity among species can be investigated by phylogenomics analysis that involves a 

comparison of genes and gene products across a number of species, characterizing homologues 

and seeking further insights about evolutionary relationships. Analyzing the similarities between 

phylogenetic gene trees and their associated protein trees can reveal additional information. For 

example, a reconstruction of the CYP2A family of cytochrome P450 enzymes indicates that the 

rat liver isoform (CYP2A1) has diverged significantly from the human (CYP2A6) and mouse 

(CYP2A4) enzymes, having a distinct branch of the tree rooted outside the rest of the family 

(Searls, 2003). This considerable deviation is associated with a well-known functional shift that 

the rat enzyme causes the coumarin to be metabolized to a hepatotoxic epoxide, whereas the 

human and mouse enzymes act on the same substrate by way of a more harmless hydroxylation. 

The same principles can be extended to amino acid sequence comparisons for the genes 

that make up a pathway. Utilizing the predicted amino acid sequence information for genes in 

the steroidogenesis pathway from rats and humans, we evaluated the similarity among this set of 

genes. Preliminary results suggest that proteins involved in the biosynthesis of steroids are 

highly conserved across rats and humans, with the average sequence similarity of enzymes 

between human and rat being ~87% as presented in Table 6-2. However, it is difficult to 

unequivocally determine a “high” versus “low” degree of conservation for the genes in this 

pathway—especially in light of the fact that events important to the effect of DBP on 

steroidogenesis are not well-understood. For example, initiating event after DBP exposure is not 

known. Additionally, there are likely differences between identifying a gene that is statistically 

highly conserved versus understanding whether or not the biologically meaningful regions of the 

predicted protein sequence, active sites, are conserved. However, endocrinological, 

developmental, and genetic studies in many vertebrate species indicate that the role of androgens 

is highly conserved across vertebrates, as androgens are critical for sexual differentiation in the 

male. Thus, taken together, this information suggests a high conservation of steroidogenesis and 

androgen synthesis in rats and humans. 
. 

6-17

Table 6-2. The amino acid sequence similarity of the enzymes in the steroidogenesis pathway between rat and 

human.

Gene 

symbol

Entrez gene 

ID mRNA and protein IDs

Human 

homolog IDs Identitiesa Positivesb Gapsc 

Dhcr7 64191 NM_022389.2→NP_071784.1 Q9UBM7 412/475 (86%) 443/475 (93%) 4/475 (0%)

Idi1 89784 NM_053539.1→NP_445991.1 AF003835 196/227 (86%) 215/227 (94%) 0/227 (0%)

Fdps 83791 NM_031840.1→NP_114028.1 M34477 301/353 (85%) 326/353 (92%) 0/353 (0%)

Fdft1 29580 NM_019238.2→NP_062111.1 AAP36671 356/413 (86%) 393/413 (95%) 0/413 (0%)

Hmgcr 25675 NM_013134.2→NP_037266.2 AAH33692 738/890 (82%) 768/890 (86%) 58/890 (6%)

Mvd 81726 NM_031062.1→NP_112324.1 AAP36301 338/398 (84%) 357/398 (89%) 1/398 (0%)

Sqle 29230 NM_017136.1→NP_058832.1 NP_003120 481/574 (83%) 528/574 (91%) 1/574 (0%)

Ebp 117278 NM_057137.1→NP_476478.1 NP_002331 618/732 (84%) 673/732 (91%) 1/732 (0%)

Lss 81681 NM_031049.1→NP_112311.1 NP_002331 618/732 (84%) 673/732 (91%) 1/732 (0%)

Sc5d 114100 NM_053642.2→NP_446094.1 NP_008849 246/299 (82%) 275/299 (91%) 0/299 (0%)

Mvk 81727 NM_031063.1→NP_112325.1 BAD92959 323/393 (82%) 355/393 (90%) 0/393 (0%)

Cyp27b1 114700 NM_053763.1→NP_446215.1 NP_000776 413/508 (81%) 453/508 (89%) 7/508 (1%)

Nqo1 24314 NM_017000.2→NP_058696.2 NP_000894 234/274 (85%) 250/274 (91%) 0/274 (0%)

Vkorc1 309004 NM_203335.2→NP_976080.1 AAQ13668 83/94 (88%) 88/94 (93%) 0/94 (0%)

Average similarity scores 84% 94.14%
. 

6-18

Table 6-2. (continued)

a

Identities, The number and fraction of total residues in the HSP which are identical.

b

Positives, The number and fraction of residues for which the alignment scores have positive values.

c

Gap, A space introduced into an alignment to compensate for insertions and deletions in one sequence relative to another. To prevent the accumulation 

of too many gaps in an alignment, introduction of a gap causes the deduction of a fixed amount (the gap score) from the alignment score. Extension of 

the gap to encompass additional nucleotides or amino acid is also penalized in the scoring of an alignment. 

The HSP (high-scoring segment pair) is the fundamental unit of BLAST algorithm output. Alignment: The process of lining up two or more sequences 

to achieve maximal levels of identity (and conservation, in the case of amino acid sequences) for the purpose of assessing the degree of similarity and 

the possibility of homology.

Source: http://searchlauncher.bcm.tmc.edu/help/BLASToutput.html#anchor14684156.
6-19

The same principles can be extended from amino acid sequence comparisons to 

structures, pathways, and expression patterns. 

6.4. CONCLUSIONS

The exploratory projects presented in this chapter include efforts to develop methods for 

analyzing genomic data for use in risk assessment and examples of genomic data analyses 

available to the risk assessor with expertise in bioinformatics. These methods include pathway 

level analysis (including the newly described pathway activity method), gene network analysis, 

and tools to assess cross-species similarities in pathways. A summary for a less technical reader 

is presented below, grouped by the three objectives for the work. 

1. Explore the development of new methods for pathway analysis of microarray data for 

application to risk assessment. 

Quality-control requirements for microarray study analysis for use in risk assessment are 

distinct from basic research. In traditional pathway level analysis, differentially 

expressed genes are first identified and then mapped to their respective pathways. 

Depending on the number of genes that map to a given pathway, the role of the pathway 

can be over- or underestimated. To overcome this problem, we used the pathway activity 

method. This method scores pathways based on the expression level of all genes in a 

given pathway. 

The pathway activity analysis identified valine, leucine, isoleucine (VL1) degradation, 

sterol biosynthesis, citrate cycle, and fatty acid metabolism as the most active pathways 

following DBP exposure. These findings support the hypothesis of Thompson et al. 

(2005), that an early decrease in T levels may be a result of cholesterol unavailability. 

However, for this approach to be useful, knowledge of tissue-specific pathways is 

required. For example, even though bile acid biosynthesis does not take place in the 

testis, a pathway related to bile acid biosynthesis was identified as statistically significant 

in this analysis. This method shows promise for use in risk assessment.

2. Utilize existing DBP genomic data to develop a gene network model for use in risk 

assessment.

Determining a sequence of gene expression changes and pathway level effects over time 

can be very useful for understanding the temporal sequence of critical biological events 

perturbed after chemical exposure, and thus, useful to a risk assessment. We developed a 

method for developing a gene network model for DBP based on the available data. The 

availability of time-course data (Thompson et al., 2005) enabled our group to model the 

series of events that occurred between exposure to DBP and the onset of toxic 

reproductive outcomes. However, given the limitations of the Thompson et al. (2005) 

study design, we did not draw conclusions about genes and pathways affected over time 
6-20

for DBP. Instead, the Thompson et al. (2005) data was used to build a prototype of a 

temporal gene network model and thus, the exercise allowed us to develop methods for 

analyzing time-course data.

3. Utilize genomic and other molecular data to address the Case Study Question: Do the 

toxicogenomic data inform interspecies differences in TD?

Extrapolation from animal-to-human data is critical for establishing human relevance of 

MOA(s) in risk assessment. Co-expressed genes across multiple species could have a 

conserved function. The human, mouse, and rat genomes have been reported to be 90% 

homologous (Gibbs et al., 2004). However, because it is not certain whether the function 

of a specific gene is conserved across species, conservation of pathways across species 

can be one important factor in establishing cross species concordance of one or more 

MOAs. In addition, a common critical role of androgens in both rodent and human male 

development of reproductive organs has been well-established. 

Using the available DNA, sequence, and protein similarity data for the steroidogenesis 

pathway, we used three different methods to assess rat-to-human conservation as metrics 

that may inform the interspecies differences in TD for one MOA, the reduced fetal 

testicular T. The pathways for the biosynthesis of steroids have similarity between 

human and rat. Comparing the predicted amino acid sequences for the steroidogenesis 

pathway genes, we found that the average sequence similarity between rat and human is 

~87%, and the average promoter region similarity of genes is 52%. Some of the 

challenges in using similarity scores to estimate the cross-species relevance of a MOA 

are described (see Section 6.3).

In summary, the preliminary analytical efforts described in this chapter address and raise 

a number of issues about the best approaches for analyzing microarray and other genomic data 

for risk assessment purposes. Traditional pathway analysis methods, while useful, also restrict 

the incorporation of all genes for determining relevant pathways that are affected by DBP. There 

is a substantial amount of background noise generated in a typical microarray experiment (i.e., 

gene expression variability even among the controls; see Smith, 2001). For use in risk 

assessment, it is important to be able to identify and separate the signal from the noise. 

Innovative approaches, such as the pathway activity method described in this chapter, may 

provide more confidence when evaluating microarray data for use in risk assessment. These 

efforts reveal some of the promises and challenges of analyzing and interpreting genomic data 

for application to risk assessment.
7-1

7. CONCLUSIONS

This chapter describes the general approach for systematically evaluating genomic data 

for risk assessment. This general approach is a result of refining the DBP case-study approach 

(see Figure 3-1). In addition, conclusions from the DBP case study, recommendations, research 

needs, and future considerations for applying genomic data to risk assessment are described. 

7.1. APPROACH FOR EVALUATING TOXICOGENOMIC DATA IN CHEMICAL 

ASSESSMENTS

There were two goals of this project (see Chapter 2): 

• Develop a systematic approach that allows the risk assessor to utilize the available 

toxicogenomic data in chemical-specific health risk assessments performed.

• Perform a case study to illustrate the approach. 

The first goal was to develop an approach for evaluating toxicogenomic data in future 

assessments. In the DBP case study, we had the benefit of the 2006 external peer-review draft 

IRIS Tox Review of DBP, including data summaries and gaps. Additionally, DBP has a more 

extensive toxicological and toxicogenomic database than most chemicals. The DBP published 

literature and the draft Tox Review provided a focus to the case study on one set of endpoints 

(the male reproductive developmental endpoints), that occur in the lower dose range. The casestudy approach (see Figure 3-1) needed refinement because the case-study chemical and process 

had some differences from that of a new assessment. A generalized approach (Figure 7-1) was 

developed for use in future chemical assessments. 

The steps of the approach are 

• STEP 1: Compile the available epidemiologic, animal toxicology, toxicogenomic, and 

other studies.
7-2

Figure 7-1. Approach for evaluating and incorporating genomic data into 

future chemical assessments. “Toxicity Data Set Evaluation” may include 

evaluation of animal toxicity data and/or human outcome data, depending on the 

available data for the chemical.
7-3

• STEP 2: Consider the quantitative and qualitative aspects of the risk assessment that 

these data may address. 

A thorough and systematic consideration of the types of information, in light of the 

available genomic data, will identify the potential utility of the genomic data and whether 

these data can be used quantitatively or qualitatively (see Section 3.2). The genomic data 

set is considered in light of whether these data could inform any risk assessment 

components (e.g., dose-response) and information (e.g., MOA information, interspecies 

TK differences) useful to risk assessment. The type of information that these data will 

provide to a risk assessment depends in part on the type of the available genomic studies 

(e.g., species, organ, design, and method). This step helps focus the genomic data 

evaluation and ensure that an important application is not overlooked. 

• STEP 3: Formulate questions to direct the toxicogenomic data set evaluation. 

Questions are formulated that can direct and focus the genomic data evaluation, and thus, 

improve efficiency. This step is similar to a scoping exercise performed in ecological 

and cumulative risk assessment. Some examples of questions considered in the DBP case 

study were: Do the data inform the MOAs for multiple outcomes (e.g., male and female 

reproductive outcomes)? Do the data inform dose-response? For example, if microarray 

data are available, then one of the questions will likely include whether the genomic data 

can inform the mechanisms and/or MOAs for the chemical as microarray studies 

typically inform the mechanism of action of a chemical. The DBP case study describes 

some examples and considerations for determining the risk assessment components that 

may be informed by a particular genomic data set (see Section 3.3). 

• STEPS 4 and 5: Evaluate the toxicity and/or human outcome and genomic data sets. 

The approach includes an integrated assessment of the toxicogenomic and toxicity data 

set to relate the affected endpoints (identified in the toxicity data set evaluation) to the 

pathways (identified in the toxicogenomic data set evaluation) as a method for 

Determining the level of support for phenotypic anchoring of genomic changes to 

in vivo outcomes is critical for appropriate interpretation of genomic data for use 

in risk assessment. In particular, determining whether gene expression changes 

are associated with or in the causal pathway for an outcome of interest.

Phenotypic Anchoring

Depending on the type of assessment performed, risk assessors may want to 

utilize aspects of the approach defined herein along with the MOA Framework in 

the EPA Cancer Guidelines (U.S. EPA, 2005a) and/or other risk assessment 

decision-logic frameworks for establishing MOAs. 

Informing the Mechanisms of Action/MOAs
7-4

Another principle of the approach is comparing toxicity and toxicogenomic data 

study designs in order to identify a set of comparable studies. It is important to 

compare the study designs among studies. Study design aspects include the time 

of exposure (in light of critical windows), dose, species, strain, and time of 

assessment. As a result of assessing study comparability for a given data set, one 

can select studies for the best comparisons across the outcome and toxicogenomic 

genomic data sets. For example, in the DBP case study, all toxicogenomic studies 

were performed in the rat, and, in most cases, the testis. Therefore, the genomic 

data set was compared with the rat toxicity data and focused on effects in the 

testis. Broadening beyond the DBP example, the available toxicogenomic data 

are best considered in light of the toxicity or epidemiologic study data that share 

study design similarities with the toxicogenomic data. For example, if 

toxicogenomic data from human tissue or cells are available, then these data are 

best considered with the human epidemiologic outcome data for the chemical. 

However, even in the absence of comparable data in the same species, the 

genomic data may still be used, but with less confidence. See Chapters 4 and 5 

for further details of the DBP case-study toxicity and toxicogenomic data set 

evaluations. 

Study Comparability

Chapter 5 includes a number of simple methods for assessing the consistency 

across the toxicogenomic studies. Venn diagrams have been used for illustrating 

the similarities and differences of DEG findings across genomic studies (see 

Figure 5-1). Figure 5-2 provides an excellent example of another method for 

assessing the consistency of findings across all types of gene expression data.

New analyses of toxicogenomic data may be valuable for the assessment 

depending on the questions asked and the nature of the analyses presented in the 

published studies. However, new analyses of the original data are not always 

needed. For instance, reanalysis may not be needed when available published 

data have been analyzed for application to risk assessment questions of interest. 

See Section 5.5 for more details of the new case study analyses methods and 

results, and Chapter 6 for exploratory methods development. 

New Analyses

• STEP 6: Describe results of evaluations and analyses to answer the questions posed in 

Step 3. 

• STEP 7: Summarize the conclusions of the evaluation in the assessment. 

7.2. DBP CASE-STUDY FINDINGS

The second goal of the project was to develop a case study. The case-study findings are 

summarized below and the details of the case-study evaluation and analyses are presented in 
7-5

Chapters 4−6 (with supplemental material in Appendices A and B). Three advantages to using 

DBP as the case-study chemical are as follows:

1. The temporal aspects (e.g., time of dosing and time of evaluation) could be considered 

because a number of well-designed studies exist. 

2. A causal connection (i.e., a high degree of phenotypic anchoring) between gene 

expression changes for some of the steroidogenesis pathway genes with a number of the 

male reproductive developmental effects has been well-established.

3. Two well-established MOAs for DBP have been defined at the molecular level. A 

number of endpoints resulting from in utero DBP exposure have MOAs that have not 

been identified or established, thus allowing for a query of the genomic data for possible 

additional MOAs. 

7.2.1. MOA Case Study Question: Do the DBP Genomic Data Inform Mechanism(s) of 

Action and MOA(s)?

In the DBP case study, we found that toxicogenomic data did inform the TD steps of the 

mechanisms of action and MOAs. The available genomic and other gene expression data, 

hormone measurement data, and toxicity data for DBP were instrumental in establishing two of 

its MOAs: (1) a decrease in fetal testicular T, and (2) a decrease in Insl3 expression. A decrease 

in fetal testicular T is the MOA responsible for a number of the male reproductive developmental 

effects in the rat. The genomic and other gene expression data identified changes in genes 

involved in steroidogenesis and cholesterol transport, providing evidence for the underlying basis 

for the observed decrease in fetal testicular T. A decrease in Insl3 expression is one of the two 

MOAs responsible for the undescended testis effect, and this MOA is well-established from the 

results of RT-PCR and in vivo toxicology studies. RT-PCR studies identified reduced Insl3

expression after in utero DBP exposure (Wilson et al., 2004) as an MOA for agenesis or 

abnormalities in the gubernaculum, effects that are not seen after exposure to anti-androgens 

(i.e., chemicals that affect T synthesis or activity). These results provided support for the Insl3

MOA as a second well-established MOA for the male reproductive developmental effects of 

DBP.

The rodent reproductive developmental toxicity data set is robust, having a high quantity 

and relatively high quality of studies. Additionally, there are a number of rodent toxicity studies 

that used similar study designs (e.g., dose, species, strain, timing of exposure) as some of the 
7-6

toxicogenomic studies. This aspect of the DBP data set is exceptional for the case study, 

allowing for the establishment of the relationship between dose, pathways, and outcomes. We 

evaluated the rodent reproductive toxicity data set for low incidence and low-dose findings but 

due to data limitations (see Chapter 4), no new findings were identified. We also evaluated the 

male reproductive developmental toxicity data set for effects that currently do not have a wellestablished MOA (see Chapter 4). The testes outcomes were the focus of the case study because 

the DBP toxicogenomic studies were all performed on testicular tissue. Five effects in the testes 

effects associated with DBP exposure that do not have well-defined MOAs were identified in 

this evaluation. 

The toxicogenomic and other gene expression studies, including nine published RT-PCR 

and microarray studies in the rat after in utero DBP exposure (Plummer et al., 2007; Bowman et 

al., 2005; Liu et al., 2005; Thompson et al., 2005; Lehmann et al., 2004; Thompson et al., 2004; 

Wilson et al., 2004; Barlow et al., 2003; Shultz et al., 2001), were evaluated. The review of the 

toxicogenomic data set focused on an evaluation of the consistency of findings from the 

published studies, both across microarray studies and all gene expression data, and on whether 

any additional pathways may illuminate the unexplained endpoints. The evaluation of the 

published literature found that the gene level findings from the DBP genomic studies (i.e., 

microarray, RT-PCR, and protein expression) were highly consistent in both the identification of 

DEGs and their direction of effect.

New analyses of the Liu et al. (2005) microarray study were performed because we were 

interested in performing a complete pathway analysis of these data (which had not been the 

purpose of the published study). These evaluations (see Chapter 5) indicate that there are a 

number of pathways affected after in utero DBP exposure; some of these pathways are related to 

new MOAs that are distinct from the reduced fetal testicular T or the Insl3 signaling MOAs. The 

Liu et al. (2005) DBP data set was reanalyzed using two different methods, the SNR and REM, 

both using a statistical cut-off of p < 0.05. Each method identified the steroidogenesis and 

cholesterol transport pathways, corroborating prior study conclusions. Each analysis also 

identified putative new pathways and processes that are not associated with either Insl3 or 

steroidogenesis pathways; some were similar across analytical methods and some were different. 

The common pathways identified between the two methods (see Table 6-3) fall into eight 

processes (characterized by Ingenuity): cell signaling, growth and differentiation, metabolism, 
7-7

transcription, immune response, cell adhesion, hormones, and disease. Among these, 54 putative 

new pathways that are not related to the two known MOAs, reduced T or Insl3 expression, were 

identified. Further, a subset of pathways (e.g., WNT signaling and cytoskeleton remodeling) was 

identified in our analysis that had not previously been identified in the published literature for 

DBP. One or more of these putative new pathways may be related to the toxicity endpoints 

without identified MOAs in the rat testes, but additional hypothesis testing studies are needed. 

Evaluating the genomic and toxicity data sets together provided information on potential, 

heretofore unexplored, MOAs. 

There are a number of possible reasons for the differences in findings between our 

reanalysis and the published analysis of the Liu et al. (2005) data. These include but are not 

limited to

• The analyses had different purposes. Liu et al. (2005) was interested in determining 

whether there is a developmental phthalate genomic signature. The purpose of our 

analysis was to identify all affected pathways.

• In the four years since the study was published, gene and pathway annotation has 

increased.

Repeated identification of DEGs and pathways via different analysis methods provides an 

additional level of confidence regarding the importance of “common” DEGs and pathways. 

However, it is important to note that the lack of repeated identification of a gene or pathway does 

not necessarily indicate a lack of biological importance for these genes or pathways.

We also asked whether there were appropriate data to develop a temporal gene network 

model, a sequence of the gene and pathway interactions over time, for DBP. Using the data from 

Thompson et al. (2005), the only time-course study available at the time of the project, changes 

in gene expression and pathways were modeled (see Figure 6-5). Two limitations of these data 

are that (1) the exposure interval was at the tail end of the critical window of exposure, GD 18, a 

time that most consider too late to induce the full spectrum of male reproductive developmental 

effects; and (2) the duration of exposure and developmental time were not aligned because all 

animals were sacrificed on GD 19 (i.e., the 1 hr time point was the latest in development; see 

Sections 5.3.1 and 6.2.2 for more discussion). The more recent study of Plummer et al. (2007) 

could provide more appropriate data for building a temporal and spatial network model as both 
7-8

time-course of exposure over the critical window of development and microdissection of the 

testis cell types were employed in their study. 

7.2.2. Interspecies MOA Case Study Question: Do the DBP Genomic Data Inform 

Interspecies Differences in TD?

Human gene expression data are not available for DBP. Therefore, the case study used 

information on interspecies similarities of the affected pathways from other available data and 

methodologies. We explored the interspecies (rat-to-human) differences in the TD part of MOA, 

focusing on the steroidogenesis pathway underlying one of the DBP MOAs, the decrease in fetal 

testicular T MOA. Comparisons of the steroidogenesis genes and pathway were performed to 

evaluate cross-species similarity metrics using three approaches: (1) protein sequence similarity; 

(2) pathway network similarities; and (3) promoter-region conservation (see Chapter 6). Results 

from all three approaches indicate that steroidogenesis pathways are relatively highly conserved 

across rats and humans and, thus, qualitatively, the rat and human mechanisms for 

steroidogenesis are highly similar. 

These results further corroborate what is known about the similar roles for androgens 

during normal male development in both rats and humans. However, the data sources used for 

all three approaches have gaps in the knowledge bases. The pathway network diagramming data 

source is not of high enough quality or comprehensive enough to utilize quantitatively. In fact, it 

is difficult to use any of the three new lines of evidence to quantitatively inform the relative 

sensitivity to DBP across species. It is possible that the small differences across species have a 

strong penetrance, leading to significant differences in the specific enzymes that may become 

more sensitive to DBP and thus, affecting T production. We further considered whether some 

steroidogenesis genes are of higher relative importance and, thus, should be weighted higher in a 

cross-species assessment of the steroidogenesis pathway. The initiating event for DBP action in 

the male reproductive developmental outcomes has not been established. However, some 

information about the rate-limiting steps for steroidogenesis, in the unperturbed scenario, is 

available (reviewed in Miller, 2008). Some studies have identified CYP11A1 (also called 

P450SCC) as a limiting enzymatic step for T production (Omura and Morohashi, 1995; Miller, 

1988). However, the available information on kinetics reflects the unperturbed state because the 

rate-limiting step was defined in assays without DBP exposure. Additionally, the rate-limiting 

step information is limited in scope to steroidogenic enzymes and not all upstream activities 
7-9

leading to T production, such as STAR, a protein that impacts the availability of cholesterol (by 

transporting cholesterol to the inner mitochondrial membrane for cleavage by P450SCC) for T 

production. Thus, there is no a priori knowledge to argue for placing more weight on a 

particular gene leading to T production. 

Because there are some questions as to the reliability of the data used to generate the 

pathway comparisons used for each species and the relative importance of individual 

steroidogenesis enzymes, there is no basis on which to transform a measure of conservation to a 

quantitative measure of sensitivity. While the confidence in these cross-species comparisons of 

the steroidogenesis pathway were not high enough to use the findings quantitatively, for the DBP 

example, the findings do add to the WOE suggesting that the role of T in male fetal development 

in rats and humans is well-conserved. These methods, however, when based on high-quality 

data, could be applied quantitatively to future chemical assessments. Further, the exploratory 

methods for developing metrics for cross-species pathway similarities described in this document 

(see Chapter 6) could be developed further and validated in the future for quantitative use in risk 

assessment. 

7.2.3. Application of Genomic Data to Risk Assessment: Exploratory Methods and 

Preliminary Results

Chapter 6 describes exploratory methods and preliminary results for analyzing genomic 

data for risk assessment application, developing a DBP gene network model, and measuring 

cross-species differences for a given pathway. 

None of the DBP genomic studies were designed with the application to risk assessment 

in mind. Methods for analyzing microarray and other -omic data were originally developed for 

screening purposes (i.e., designed to err on the side of false positives over false negatives). For 

risk assessment application, genomic analytical tools are needed that are different from those 

used in screening that can reliably separate signal from noise. In traditional pathway level 

analysis, first, DEGs are identified by a statistical filter, and second, significant genes are 

mapped to their respective pathways. Typically, the presence of three affected genes (DEGs) 

within a pathway is the cut-off for identifying a particular pathway. Depending on the number of 

genes that map to any given pathway, the role of the pathway can be over- or underestimated. 

To overcome this problem, we explored using the pathway activity level method (calculating 

PALp) that identifies affected pathways in the single step. This method ranks pathways based on 
7-10

the expression level of all genes in a given pathway and shows promise for use in risk 

assessment and further validation is underway. 

Gene network models can be very useful for understanding the temporal sequence of 

critical biological events perturbed after chemical exposure, and thus, useful to a risk assessment. 

We developed a method for developing a gene network model for DBP based on the available 

data. The availability of one time-course study (Thompson et al., 2005) enabled our group to 

model the series of events that occurred between exposure to DBP and the onset of reproductive 

outcomes. However, given the limitations of the Thompson et al. (2005) study design, we could 

not determine the genes and pathways affected by DBP exposure earliest in the critical window 

from this study. However, the exercise allowed us to develop methods for analyzing time-course 

data for use in gene network modeling. 

We also explored the use of three different methods to assess rat-to-human conservation 

as metrics that may inform the interspecies differences for one MOA, reduced fetal testicular T 

(Section 7.2.2). More work in the area of cross-species metrics is needed. Efforts to address the 

challenges in using similarity scores to quantitatively estimate the human relevance of an MOA 

are ongoing (Section 6.3). 

7.2.4. Application of Genomic Data to Risk Assessment: Using Data Quantitatively

This case study was limited to qualitative uses of genomics in risk assessment due 

to the absence of dose-response, global gene expression studies (i.e., microarray studies) 

for DBP. EPA and the larger scientific community working with genomics are interested 

in methods to use genomic data quantitatively in risk assessment. There is one doseresponse RT-PCR study that, although not a genomic (i.e., not global) study, was 

considered for use quantitatively in risk assessment (Lehmann et al., 2004; see 

Table 7-1). Some strengths of the Lehmann et al. (2004) study include the following: 

• The study includes multiple doses ranging from low to high. 

• Some of the genes assessed in this study were first identified in microarray studies, 

providing a relatively high level of confidence in the connection between the expression 

of some of the genes and particular outcomes, as well as demonstrating reproducibility 

across studies. For example, findings for Star gene expression are reproduced across 

protein expression, RT-PCR, and microarray studies. 
7-11

Table 7-1. DBP dose-response progression of statistically significant events illustrated with a subset of precursor 

event data (steroidogenesis gene expression, T expression) and in vivo endpoints with the reduced T MOA

0.1 mg/kg-d 1 mg/kg-d 10 mg/kg-d 30 mg/kg-d 50 mg/kg-d 80 mg/kg-d 100 mg/kg-d

Precursor eventa ↓ Hsd3b ↓ Hsd3b

↓Scarb1

NC in gene exp.

NC in [T] 

ND for gene 

exp.

NC in [T]

↓Scarb1

↓Hsd3b

↓Star

↓Cyp11a1

↓[T]

ND for gene exp. ↓Scarb1

↓3β-HSD

↓StAR

↓P450scc

↓[T]

In vivo endpoint ↑ incidence of absent, 

poorly developed, or 

atrophic testis and 

underdeveloped or 

absent epididymisb

Retained nipples 

and areolaec

NC, no statistically significant change; ND = not determined (Lehmann et al. [2004] did not test 80 mg/kg-d). 

a

Lehmann et al. (2004).

b

NTP (1991).

c

Mylchreest et al. (2000).
7-12

However, there are a number of issues in applying these dose-response RT-PCR 

data with confidence to BMD modeling. These limitations include the following: 

• Some of the gene expression changes are not reproducible. For example, Kit was 

observed to be significantly altered in the Lehmann et al. (2004) study but was not 

observed to be significantly reduced after in utero DBP exposure in a microarray study 

(Liu et al., 2005) utilizing the Affymetrix gene chip, yet Kit is on the Affymetrix rat chip. 

• The relationship between statistical and biological significance is not known for these 

gene expression data. For example, the expression of Hsd3b mRNA is statistically 

significantly altered at lower doses than a statistically significant T decrease was 

observed. Thus, Lehmann et al. (2004) argued that the changes in Hsd3b at 0.1 and 

1.0 mg/kg-d were not biologically significant. Alternatively, Hsd3b gene expression 

changes could be a precursor to T level changes in time and thus, be a valid precursor 

event. It is also not known whether changes in the expression of a single or multiple 

steroidogenesis genes would lead to a significant alteration in T and the phenotype. 

• Interlitter variability could not be characterized from the Lehmann et al. (2004) data 

because the RT-PCR data were collected on five individual pups representing four to 

five litters per treatment group (i.e., ~1 pup/litter). In order to have appropriate data for 

BMD modeling, litter mean values calculated from a study with a greater sample size and 

multiple litters are needed to allow characterization of intra- and interlitter variability. 

The use of the litter as the statistical unit is generally agreed upon because of the high 

variability in gene expression for pups within one litter (Barlow et al., 2003). 

We concluded that the available dose-response RT-PCR data for DBP are not of sufficient 

quality due to the lack of information about interspecies variability. Additionally, there is not 

sufficient knowledge about the biological significance of a gene expression change (and the level 

of change that is biologically significant), for one or a subset of genes, that would invariably lead 

to a reduction in T and in turn, lead to the observed male reproductive developmental outcomes. 

7.3. LESSONS LEARNED

The lessons learned from the case study are grouped by research needs that are useful to 

research scientists and recommendations that are useful to risk assessors.
7-13

7.3.1. Research Needs

7.3.1.1. Data Gaps and Research Needs: DBP

There are some research needs that would be very useful, specifically for a DBP risk 

assessment including the following: 

1. Develop a gene network model for DBP using the Plummer et al. (2007) data. This data 

set would be an excellent source of temporal and spatial gene expression information 

because one of its studies includes three time intervals, thus covering the entire critical 

window for male reproductive outcomes, and a second study used microdissection of the 

cord and interstitial cells of the testis. This study was not modeled because it was not 

published until after the modeling work had been completed, and we had not obtained the 

data. By comparing gene expression, Plummer et al. (2007) hypothesized the MOA 

underlying the gonocyte and LC effects. 

2. Perform microarray studies in male reproductive tissues, other than the testis, affected by 

DBP in order to understand the similarities and differences in DBP-affected pathways 

across reproductive organs and tissues in the male rat. Bowman et al. (2005) performed 

such a study in the WDs, but studies in other male tissues are needed.

3. Perform microarray studies in human tissues (either cell lines or from aborted male fetal 

tissue), along with parallel in vitro and in vivo studies in rats for validation and 

comparison. Such data would provide valuable information about interspecies 

differences in TD sensitivity. Some human studies found an association between in utero

phthalate exposure and newborn male reproductive developmental measures (Main et al., 

2006; Swan et al., 2005) that indicate human relevance for some of the DBP effects 

observed in male rat studies. 

4. Perform well-designed proteomic and metabolomic studies to understand the effects of in 

utero DBP exposure on the function of expressed proteins and on cellular metabolites. 

These data may provide complementary data to the available transcriptomic data, which 

could yield some new insights. 

5. Perform genomic studies to identify early, critical, upstream events as a means to identify 

the initiating event for DBP’s action in the testis. This would require performing studies 

much earlier in gestation, at the beginning of sexual differentiation. In addition, such 

studies may require greater sensitivity regarding gene expression change identification 

because a statistically significant change may be greater than a biologically significant 

change. If identified, the initiating event could be utilized in the risk assessment, thereby 

reducing uncertainty. 

6. Perform genomic studies to understand whether the female reproductive tract 

malformations after DBP exposure have common or different MOAs with the male 

development reproductive effects. This line of research would identify pathways affected 

in the developing female reproductive tracts after early gestational DBP exposure. 
7-14

7. Compare the affected DEGs and pathways between the phthalates with and without 

developmental effects could be useful for a cumulative risk assessment of the 

developmental phthalates. All of the data from the Liu et al. (2005) data set could be 

utilized to evaluate this issue. Further, evaluating consistency of findings across 

chemicals in the same MOA class that do and do not produce the same set of effects 

could be useful for improving specificity of the pathway and MOA findings for DBP. 

8. Studies to distinguish affected genes and pathways that may be compensatory vs. those in 

the causal pathway for DBP-toxicity. 

7.3.1.2. Research Needs for Toxicity and Toxicogenomic Studies for Use in Risk Assessment 

EPA and the larger scientific community are interested in methods to use genomic data 

quantitatively in risk assessment. This case study was limited to qualitative uses of genomics in 

risk assessment due to the absence of dose-response global gene expression studies for DBP. 

This is the case for many chemicals as multiple dose studies are very costly. However, multiple 

dose microarray or other global gene expression studies are needed (see Table 7-2). Such studies 

need to be designed properly such that the identification and interpretation of lower dose effects 

is possible. Gene expression changes at the lower dose may not be affected in every organ, 

tissue or cell sample assessed. High single dose microarray studies have been performed such 

that all organs are affected and one can assess a smaller sample size than for a dose-response 

study. In a dose-response study including low- to high doses, the sample size per dose group 

would need to be high enough to increase statistical power (i.e., the detection of gene expression 

changes when only a few animals are affected). For example, if an endpoint is affected in 20% 

of the animals at lower doses, then the sample size for microarray studies must be large enough 

to identify the affected animals (with affected gene expression). Perhaps the highest priority 

study is one that assesses global gene expression and toxicity endpoints of interest as 

components of the same experiment; the organ or tissue of interest would be collected at the 

appropriate age in one group of animals and a second group would be followed through to 

evaluation of the endpoint of interest. In this manner, such a study would generate data that 

could define the relationships between dose, time of exposure, gene expression level changes, 

pathway level changes, and in vivo changes. 

Table 7-2 describes some of the priority research needs for toxicogenomic studies for 

developmentally toxic chemicals, including DBP. First, appropriate time-course gene expression 

data over the critical window, using a small subset of genes whose altered expression is linked to 
7-15

Table 7-2. Research needs for toxicogenomic studies to be used in risk 

assessment

Purpose Study needed

1) Develop a gene network model Exposure time-course microarray data.

2) Improve pathway analysis statistical 

power

Number of replicates increased.

3) Use of toxicogenomic data to inform 

toxicokinetics in dose-response 

analysis 

Genomic and toxicity studies with same study 

design: Generate TK data in relevant study (time, 

dose, tissue), and obtain relevant internal dose 

measure to derive best internal dose metric.

4) Use of toxicogenomic data in doseresponse analysisMultiple doses in microarray studies in parallel 

with toxicity studies for phenotypic anchoring.

5) Determining the degree of phenotypic 

anchoring; informing MOAs (see 

Figure 3-4)

Similar study design characteristics for genomic 

and toxicity studies (i.e., dose, timing of 

exposure, organ/tissue evaluated). This includes 

assessing whether genes and pathways are due to 

compensatory mechanisms and/or general toxic 

responses.

6) Assess intraspecies differences A study assessing multiple doses across rat 

strains (e.g., Wistar vs. SD); endpoint and 

microarray component of the study.

7) Assess interspecies differences A study to assess whether different species with 

similar pathways (genes and sequence of steps) 

have a similar sensitivity to a given chemical. 

The findings could potentially enhance the utility 

of toxicogenomic data to aid species 

extrapolation in risk assessments. 

8) Appropriate statistical pathway 

analysis methods for use in risk 

assessment

Further comparisons and evaluations of different 

methods. 

9) Screening and categorizing chemicals 

by MOA in risk assessment (e.g., 

cumulative risk assessment) 

Genomic (transcriptomic, proteomic, and/or 

metabolomic) signatures can be particularly 

useful for screening and categorizing chemicals 

by MOA in risk assessment.

the outcome of interest, would be very relevant for developing a regulatory network model. 

These studies need to be carefully designed based on the information on the critical window of 

exposure and the relationship to the particular outcome of concern. Second, the statistical power 

of pathway-analysis methods for global expression techniques, including microarrays, 
7-16

proteomics and metabolomics, could be improved by designing and performing studies with 

more replicates. Thus, variability would be better characterized. Third, it would be helpful to 

design genomic studies that could inform both TK and dose-response (see Table 7-2, #3 and #4). 

Performing genomic and toxicity studies with similar designs would provide useful 

information. These studies would be designed at the most relevant time of exposure, include low 

to high doses, and assess the relevant tissues. Relevant internal dose measurements could be 

obtained on which to base the internal dose metric. These studies, employing genomic and 

toxicity studies of comparable designs, would provide information about the relationship of dose, 

gene expression, and outcome, and thus, could potentially be used in dose-response analysis. 

Studies with both a toxicity and toxicogenomic component would obviously require assessment 

of a large sample size to be informative. These same studies could be used to inform MOAs 

(Table 7-2, #5) and could be adapted to comparing species (Table 7-2, #6). Regarding 

quantitative measures of intraspecies and interspecies differences, it should be noted that the 

same information which is necessary for quantitative assessment of interspecies differences 

(Section 7.2.2) may be useful for characterizing intraspecies variability, and vice versa. In 

particular, factors that explain or predict interstrain differences in rodent sensitivity to DBP, such 

as those noted between Wistar and SD rats, may be hypothesized to contribute to human 

variability. Further, toxicologically important interstrain differences identified from the 

toxicogenomic data could be an excellent data source for investigating whether they are also 

important for modulating interspecies sensitivity.

Finally, further development and comparison studies to identify appropriate statistical 

pathway analysis methods for use in risk assessment are needed (Table 7-2, #8). It is important 

to note that such studies require research funding and laboratories with expertise in both 

genomics and toxicology. Research needs for toxicity studies that would improve the utility in 

risk assessment are also described in Table 7-3. As was noted for the DBP case (see Chapter 4), 

complete reporting is necessary for studies that are intended for use in risk assessment. 

7.3.2. Recommendations

Based on the lessons learned from performing the DBP case-study exercise, we 

developed some recommendations or best practices for evaluating genomic data in new 

assessments. The approach includes systematic consideration of
7-17

Table 7-3. Research needs for toxicity studies for utilizing toxicogenomic and 

toxicity data together in risk assessment

Study aspect Research need

Study design Exposing animals during optimal 

developmental stage/time (i.e., for the 

critical window).

Assessing outcome at optimum 

developmental stage/time for that outcome.

Parallel study design characteristics with 

toxicogenomic studies (i.e., dose, timing of 

exposure, organ/tissue evaluated) to obtain 

comparable toxicity and toxicogenomic 

studies to aid in determining relationships 

between gene expression changes and 

outcomes.

Reporting Individual animal data to aid identification 

of low incidence effects, correlate gene 

expression changes and outcomes, and 

characterize intraspecies variability.

All endpoints that were evaluated 

(independent of whether the outcome was 

positive or negative).

 

the genomic data for whether they could inform risk assessment steps, identification of questions 

to direct the evaluation, and evaluation of the genomic data and toxicity data to assess 

phenotypic anchoring. In addition, we have some specific recommendations. The first two 

recommendations are straightforward and could reasonably be performed by a risk assessor with 

basic training in genomics data evaluation and interpretation while the third recommendation 

requires expertise in genomic data analysis methods for implementation. The recommendations 

are presented below:

1. Evaluate the genomic and other gene expression data for consistency of findings across 

studies to provide a WOE evaluation of the affected gene expression and pathways. 

Some simple methods, such as using Venn diagrams and gene expression compilation 

approaches, can be applied to risk assessment. When evaluating the consistency of 

toxicogenomic data findings, it is advantageous to include all available gene expression 

data (single gene, global gene expression, protein, RNA) because single gene expression 
7-18

techniques have been traditionally used to confirm the results of global gene expression 

studies and because single gene expression data add to the database.

2. Perform BMD modeling on high-quality RT-PCR dose-response studies of genes known 

to be in the causal pathway of an MOA or outcome of interest. Obtaining a BMD and 

BMDL is a useful starting point for both linear low-dose extrapolation and reference 

value approaches. We are not indicating which approach is appropriate to take for 

making predictions about the potential risk below the BMD or BMDL. “High quality” is 

defined in this context as a well-conducted study that assessed enough animals and litters 

for sufficient statistical power for characterizing the mean responses and the variability 

(interlitter and intralitter).

3. Perform new analysis of toxicogenomic data in cases when the new analysis is likely to 

yield new information that would be useful to the risk assessment. Examples include: 

• Perform a new pathway analysis in order to identify all affected pathways or other 

risk assessment applications. When the available published microarray studies 

have been conducted for purposes (e.g., basic science, pharmaceutical 

development) other than risk assessment, it may be useful to reanalyze the data 

for risk assessment purposes. Information about all affected pathways may 

contribute to an understanding of the mechanisms and MOAs. 

• Identify the genes and pathways affected over a critical window of exposure if 

global gene expression time-course data are available. Specifically, by 

developing a gene network over time, it may be possible to identify the earliest 

affected genes and/or pathways, which in turn may represent the earlier or 

initiating events for the outcome of interest. 

7.3.3. Application of Genomic Data to Risk Assessment: Future Considerations

A number of the issues that emerged in evaluating the DBP genomic data set are relevant 

to using genomic data in risk assessment in general. Some issues regarding the use of genomic 

data are to the same as for the use of precursor information in risk assessment, regardless of the 

technique used to gather the information. Two outstanding questions are 

• How is the biologically significant level of change in a precursor marker determined? 

And, specifically for toxicogenomic data, what are the key genes (i.e., a key gene, a 

handful of genes associated with the outcome of interest, a genomic signature) whose 

altered expression leads to an adverse outcome? Currently, decisions about the degree of 

change of a precursor event tend to be based on statistical significance because data to 

address biological significance are typically lacking (as is the case for T levels and male 

development of the testis). Genes are identified as DEGs in microarray studies based on 

statistical-significance criteria that may not reflect biological significant changes (i.e., 

identified genes may not be biologically meaningful while unidentified genes may be 
7-19

meaningful). This point is also relevant to the question: What pathway analysis methods 

are most appropriate for risk assessment? As noted in Section 5.5, it is difficult to know 

whether one has identified the biologically relevant DEGs and pathways. Statistically 

significant changes and repeated findings of the same genes and pathways across studies 

and using different analytical methods, while providing corroboration, do not necessarily 

provide a greater confidence regarding biological significance of these genes and 

pathways over other genes and pathways. Further, there is a bias towards the wellannotated genes as biologically significant when, in fact, the unannotated genes could be 

of greater importance.

• What are the requirements for linkage of precursor events to in vivo endpoints? Studies 

to assess the relationship between the gene expression and outcomes are needed to 

establish a causal connection. It is important to note that DBP has two well-established 

MOAs and strong phenotypic anchoring of some gene expression changes, which is not 

typical.

There are also a number of technical issues in utilizing microarray data in EPA risk

assessments that have not fully been surmounted. The primary technical issue is the validation 

of the reproducibility of microarray study results. Reproducibility depends on biological sample 

preparation, interlaboratory (presumably related to operator and protocol differences), 

intralaboratory (presumably related to operator differences), and batch and platform variability. 

The results of the MAQC-I project (see Chapters 2 and 5) revealed that reproducibility was 

achieved when using the same biological sample. This is very encouraging for using microarray 

data in risk assessment. However, biological sample variability still needs to be addressed in 

order that protocols and details of the underlying reasons for the variability can be understood. 

MACQ-II and III projects are underway to address additional technical issues (see Chapter 2).

A number of the issues stem from the complexity of the data output from the global 

expression techniques (e.g., microarrays, proteomics, and metabolomics). This is in part a 

training issue. To address the training needs, the EPA Risk Assessment Forum held introductory 

and intermediate level training in genomics in 2007, and the FDA held genomics training 

(http://www.fda.gov/cder/genomics/Default.htm). However, it would be advantageous for 

organizations that perform risk assessments to embark on further training of risk assessors to 

enable them to perform analyses of microarray and other genomic data analysis techniques, and 

to understand the issues in applying traditional analytical methods to risk assessment. 

If additional case studies are performed using the approach outlined in Figure 7-1, then 

we recommend a chemical whose exposure leads to both cancer and noncancer outcomes to 
7-20

explore use of these data for multiple outcomes, as well as the impacts on the different risk 

assessment paradigms and processes (e.g., cancer vs. noncancer). Further, performing case 

studies on data-rich and data-poor chemicals would aid in further evaluating the approach 

described herein. For instance, performing a case study on a chemical with dose-response data 

and on a chemical with human polymorphism data would address issues in evaluating these types 

of data for risk assessment, allowing further refinement of the approach.

The approach for utilizing toxicogenomic data in risk assessment outlined in this 

document may be applied to other chemical assessments. This document advances the effort to 

devise strategies for using genomic data in risk assessment by defining an approach, performing 

a case study, and defining critical issues that need to be addressed to better utilize these data in 

risk assessment. This case study serves as an example of the considerations and methods for 

using genomic data in future risk assessments. 
A-1

APPENDIX A.

SUPPORTING TABLES FOR CHAPTER 5

Appendix A contains additional tables that support the work shown in Chapter 5.
A-2

Table A-1. Weight of evidence (WOE) for statistically significant gene expression changes after in utero 

exposure to dibutyl phthalate (DBP) from the whole rat testis microarray studiesa as reported in Thompson et 

al. (2005)b

, Shultz et al. (2001)b, Liu et al. (2005)c,d, and Plummer et al. (2007)e

Official 

gene 

symbol Official gene namef

Exposure 

window

Up or 

down

Fold 

change Cutoff used (method) Reference

Aacs Acetoacetyl-CoA synthetase GDs 12−19 Down −0.37 log2 p < 0.05 (ANOVA) Liu et al., 2005

Aadat Aminoadipate aminotransferase GDs 12−19 Down −0.38 log2 p < 0.05 (ANOVA) Liu et al., 2005

Abcg1 ATP-binding cassette, sub-family G (WHITE), 

member 1

GDs 12−19 Up 0.38 log2 p < 0.05 (ANOVA) Liu et al., 2005

Acaa1 Acetyl-Coenzyme A acyltransferase 1 GDs 12−19 Down −0.37 log2 p < 0.05 (ANOVA) Liu et al., 2005

Acaca Acetyl-Coenzyme A carboxylase alpha GDs 12−19 Down at 

GD 19

> 2 2-fold Shultz et al., 2001

Acadl Acetyl-Coenzyme A dehydrogenase, long-chain GDs 12−19 Down at 

GD 19

> 2 2-fold Shultz et al., 2001

Acads Acyl-Coenzyme A dehydrogenase, short chain GDs 12.5−15.5 Up 1.50 p < 0.01 (ANOVA) Plummer et al., 

2007

Acsl4 Acyl-CoA synthetase long-chain family member 4 GDs 12−19 Down −0.60 log2 p < 0.05 (ANOVA) Liu et al., 2005

Adam15 A disintegrin and metallopeptidase domain 15 

(metargidin) 

GDs 12.5−17.5 Up 1.20 p < 0.01 (ANOVA) Plummer et al.,

2007

Adamts1 A disintegrin-like and metallopeptidase (reprolysin 

type) with thrombospondin type 1 motif, 1 

GDs 12.5−19.5 Down −1.35 p < 0.01 (ANOVA) Plummer et al., 

2007

Admr Adrenomedullin receptor GDs 12−19 Down −0.90 log2 p < 0.05 (ANOVA) Liu et al., 2005

Adra1b Adrenergic receptor, alpha 1b GDs 12−19 Down −0.30 log2 p < 0.05 (ANOVA) Liu et al., 2005

Akt2 Thymoma viral proto-oncogene 2 GDs 12−21 Down at 

GD 21

> 2 2-fold Shultz et al., 2001

Alas1 Aminolevulinic acid synthase 1 GDs 12−19 Down −1.01 log2 p < 0.05 (ANOVA) Liu et al., 2005

Alas1 Aminolevulinic acid synthase 1 GDs 12.5−17.5 Down −1.33 p < 0.01 (ANOVA) Plummer et al., 

2007
A-3

Table A-1 (continued)

Official 

gene 

symbol Official gene namef

Exposure 

window

Up or 

down Fold change Cutoff used (method) Reference

Alas1 Aminolevulinic acid synthase 1 GDs 12.5−19.5 Down −1.44 p < 0.01 (ANOVA) Plummer et al., 

2007

Aldh1a3 Aldehyde dehydrogenase family 1, subfamily A3 GDs 12−19 Down −0.43 log2 p < 0.05 (ANOVA) Liu et al., 2005

Aldh2 Aldehyde dehydrogenase 2 GDs 12−19 Down −0.82 log2 p < 0.05 (ANOVA) Liu et al., 2005

Aldh2 Aldehyde dehydrogenase 2 GDs 12.5−17.5 Down −1.50 p < 0.01 (ANOVA) Plummer et al., 

2007

Aldh2 Aldehyde dehydrogenase 2 GDs 12.5−19.5 Down −1.91 p < 0.01 (ANOVA) Plummer et al., 

2007

Aldoa Aldolase A, fructose-bisphosphate GDs 12.5−19.5 Down −1.24 p < 0.01 (ANOVA) Plummer et al., 

2007

Aldoc Aldolase C GDs 12−19 Down −0.44 log2 p < 0.05 (ANOVA) Liu et al., 2005

Anxa5 Annexin A5 GDs 12.5−19.5 Down −1.20 p < 0.01 (ANOVA) Plummer et al., 

2007

Aox1 Aldehyde oxidase 1 GDs 12−19 Down −0.50 log2 p < 0.05 (ANOVA) Liu et al., 2005

Aqp1 Aquaporin 1 GDs 12.5−15.5 Down −1.29 p < 0.01 (ANOVA) Plummer et al., 

2007

Arf3 ADP-ribosylation factor 3 GDs 12.5−17.5 Down −1.23 p < 0.01 (ANOVA) Plummer et al., 

2007

Arrb2 Arrestin, beta 2 GDs 12−21 Down at 

GD 21

> 2 2-fold Shultz et al., 2001

Asns Asparagine synthetase GDs 12−19 Down −0.24 log2 p < 0.05 (ANOVA) Liu et al., 2005

Ass Argininosuccinate synthetase GDs 12−19 Down −0.82 log2 p < 0.05 (ANOVA) Liu et al., 2005

Atf2 Activating transcription factor 2 GDs 12−21 Up at 

GD 21

> 2 2-fold Shultz et al., 2001
A-4

Table A-1 (continued)

Official 

gene 

symbol Official gene namef

Exposure 

window

Up or 

down Fold change Cutoff used (method) Reference

Atf4 Activating transcription factor 4 GD 19 for 3 hr Up after 

3 hr

0.67 p < 0.05 (ANOVA) Thompson et al., 

2005

Atp1b1 ATPase, Na+/K+ transporting, beta 1 polypeptide GDs 12−19 Down −0.24 log2 p < 0.05 (ANOVA) Liu et al., 2005

Atp4b ATPase, H+/K+ exchanging, beta polypeptide GDs 12−19 Down −0.60 log2 p < 0.05 (ANOVA) Liu et al., 2005

Atp5f1 ATP synthase, H+ transporting, mitochondrial F0 

complex, subunit B1

GDs 12.5−15.5 Up 1.22 p < 0.01 (ANOVA) Plummer et al., 

2007

Baiap2 Brain-specific angiogenesis inhibitor 1-associated 

protein 2

GDs 12−19 Down −0.22 log2 p < 0.05 (ANOVA) Liu et al., 2005

Bhlhb2 Bhlhb2 basic helix-loop-helix domain containing, 

class B2

GD 19 for 3 hr Up after 

3 hr

0.88 p < 0.05 (ANOVA) Thompson et al., 

2005

Bhmt Betaine-homocysteine methyltransferase GDs 12−19 Down −0.24 log2 p < 0.05 (ANOVA) Liu et al., 2005

Birc5 Baculoviral IAP repeat-containing 5 GDs 12.5−15.5 Up 1.68 p < 0.01 (ANOVA) Plummer et al., 

2007

Btg2 B-cell translocation gene 2, anti-proliferative GD 19 for 1 hr Up 

after1 hr

1.30 p < 0.05 (ANOVA) Thompson et al., 

2005

Btg2 B-cell translocation gene 2, anti-proliferative GD 19 for 3 hr Up after 

3 hr

1.88 p < 0.05 (ANOVA) Thompson et al., 

2005

C4a Complement component 4a GD 19 for 6 hr Down after 

6 hr

−0.77 p < 0.05 (ANOVA) Thompson et al., 

2005

Cadps Ca2+-dependent secretion activator GDs 12−19 Up 0.31 log2 p < 0.05 (ANOVA) Liu et al., 2005

Calb2 Calbindin 2 GDs 12−19 Down −0.77 log2 p < 0.05 (ANOVA) Liu et al., 2005

Cd63 CD63 antigen GDs 12.5−19.5 Down −1.36 p < 0.01 (ANOVA) Plummer et al., 

2007

Cdkn1c Cyclin-dependent kinase inhibitor 1C (P57) GDs 12−19 Down −0.81 log2 p < 0.05 (ANOVA) Liu et al., 2005
A-5

Table A-1 (continued)

Official 

gene 

symbol Official gene namef

Exposure 

window

Up or 

down Fold change Cutoff used (method) Reference

Cdkn1c Cyclin-dependent kinase inhibitor 1C (P57) GD 19 for 6 hr Down after 

6 hr

−1.08 p < 0.05 (ANOVA) Thompson et al., 

2005

Cdkn1c Cyclin-dependent kinase inhibitor 1C (P57) GDs 18−19 for 

18 hr

Down after 

18 hr

1.63 p < 0.05 (ANOVA) Thompson et al., 

2005

Cebpb CCAAT/enhancer binding protein (C/EBP), beta GDs 12−19 Down −0.6 log2 p < 0.05 (ANOVA) Liu et al., 2005

Cebpd CCAAT/enhancer binding protein (C/EBP), delta GD 19 for 3 hr Up after 

3 hr

1.62 p < 0.05 (ANOVA) Thompson et al., 

2005

Clu Clusterin GDs 12−21 Up at 

GD 21

> 2 2-fold Shultz et al., 2001

Clu Clusterin GD 18 for 18 hr Up after 

18 hr

1.03 p < 0.05 (ANOVA) Thompson et al., 

2005

Cmklr1 Chemokine-like receptor 1 GDs 12.5−19.5 Down −1.17 p < 0.01 (ANOVA) Plummer et al., 

2007

Cnr1 Cannabinoid receptor 1 (brain) GD 19 for 3 hr Up after 

3 hr

0.99 p < 0.05 (ANOVA) Thompson et al., 

2005

Cnbp Cellular nucleic acid binding protein GDs 12.5−19.5 Down −1.29 p < 0.01 (ANOVA) Plummer et al., 

2007

Cpa1 Carboxypeptidase A1 GDs 12.5−17.5 Down −1.73 p < 0.01 (ANOVA) Plummer et al., 

2007

Cpa1 Carboxypeptidase A1 GDs 12.5−19.5 Down −2.33 p < 0.01 (ANOVA) Plummer et al., 

2007

Cpd Carboxypeptidase D GDs 12−21 Up at 

GD 21

> 2 2-fold Shultz et al., 2001

Cpe Carboxypeptidase E GDs 12−19 Up 0.59 log2 p < 0.05 (ANOVA) Liu et al., 2005

Cpt1a Carnitine palmitoyltransferase 1a, liver GDs 12−19 Down at 

GD 19

−> 2 2-fold Shultz et al., 2001
A-6

Table A-1 (continued)

Official 

gene 

symbol Official gene namef

Exposure 

window

Up or 

down Fold change Cutoff used (method) Reference

Cpt1a Carnitine palmitoyltransferase 1a, liver GDs 12−21 Down at 

GD 21

−> 2 2-fold Shultz et al., 2001

Cpt1b Cpt1b carnitine palmitoyltrans-ferase1b, muscle GDs 12−19 Up 0.23 log2 p < 0.05 (ANOVA) Liu et al., 2005

Cpz Carboxypeptidase Z GDs 12−19 Up 0.21 log2 p < 0.05 (ANOVA) Liu et al., 2005

Crabp2 Cellular retinoic acid binding protein 2 GDs 12−19 Down −0.31 log2 p < 0.05 (ANOVA) Liu et al., 2005

Crabp2 Cellular retinoic acid binding protein 2 GD 19 for 6 hr Down after 

6 hr

−1.24 p < 0.05 (ANOVA) Thompson et al., 

2005

Crem cAMP responsive element modulator GD 19 for 3 hr Up after 

3 hr

0.58 p < 0.05 (ANOVA) Thompson et al., 

2005

Crispld2 Cysteine-rich secretory protein LCCL domain 

containing 2

GDs 12−19 Down −0.27 log2 p < 0.05 (ANOVA) Liu et al., 2005

Cryab Crystallin, alpha B GDs 12−19 Up 0.22 log2 p < 0.05 (ANOVA) Liu et al., 2005

Ctgf Connective tissue growth factor GD 19 for 3 hr Up after 

3 hr

2.10 p < 0.05 (ANOVA) Thompson et al., 

2005

Ctgf Connective tissue growth factor GD 19 for 6 hr Up after 

6 hr

2.37 p < 0.05 (ANOVA) Thompson et al., 

2005

Ctsb Cathepsin B GDs 12.5−15.5 Up 1.53 p < 0.01 (ANOVA) Plummer et al., 

2007

Ctsd Cathepsin D GDs 12.5−19.5 Down −1.22 p < 0.01 (ANOVA) Plummer et al., 

2007

Cxcl10 Chemokine (C-X-C motif) ligand 10 GD 19 for 3 hr Up after 

3 hr

2.07 p < 0.05 (ANOVA) Thompson et al., 

2005

Cyb5 Cytochrome b-5 GDs 12−19 Down −0.30 log2 p < 0.05 (ANOVA) Liu et al., 2005

Cyp11a1 Cytochrome P450, family 11, subfamily a, 

polypeptide 1

GDs 12−19 Down −1.07 log2 p < 0.05 (ANOVA) Liu et al., 2005
A-7

Table A-1 (continued)

Official 

gene 

symbol Official gene namef

Exposure 

window

Up or 

down Fold change Cutoff used (method) Reference

Cyp11a1 Cytochrome P450, family 11, subfamily a, 

polypeptide 1

GDs 12−19 Down at 

GD 19

−> 2 2-fold Shultz et al., 2001

Cyp11a1 Cytochrome P450, family 11, subfamily a, 

polypeptide 1

GD 18 for 18 hr Down after 

18 hr

−1.93 p < 0.05 (ANOVA) Thompson et al., 

2005

Cyp11a1 Cytochrome P450, family 11, subfamily a, 

polypeptide 1

GDs 12.5−17.5 Down −1.71 p < 0.01 (ANOVA) Plummer et al., 

2007

Cyp11a1 Cytochrome P450, family 11, subfamily a, 

polypeptide 1

GDs 12.5−19.5 Down −2.85 p < 0.01 (ANOVA) Plummer et al., 

2007

Cyp11b1 Cytochrome P450, subfamily 11B, polypeptide 1 GD 18 for 18 hr Down after 

18 hr

−1.63 p < 0.05 (ANOVA) Thompson et al., 

2005

Cyp17a1 Cytochrome P450, family 17, subfamily a, 

polypeptide 1

GDs 12−19 Down −1.76 log2 p < 0.05 (ANOVA) Liu et al., 2005

Cyp17a1 Cytochrome P450, family 17, subfamily a, 

polypeptide 1 

GD 18 for 18 hr Down after 

18 hr

−2.1 p < 0.05 (ANOVA) Thompson et al., 

2005

Cyp17a1 Cytochrome P450, family 17, subfamily a, 

polypeptide 1 

GDs 12.5−17.5 Down −2.15 p < 0.01 (ANOVA) Plummer et al., 

2007

Cyp17a1 Cytochrome P450, family 17, subfamily a, 

polypeptide 1 

GDs 12.5−19.5 Down −3.08 p < 0.01 (ANOVA) Plummer et al., 

2007

Cyp51 Cytochrome P450, subfamily 51 GD 18 for 18 hr Down after 

18 hr

−1.06 p < 0.05 (ANOVA) Thompson et al., 

2005

Cyp51 Cytochrome P450, subfamily 51 GDs 12.5−17.5 Down −1.59 p < 0.01 (ANOVA) Plummer et al., 

2007

Cyp51 Cytochrome P450, subfamily 51 GDs 12.5−19.5 Down −1.81 p < 0.01 (ANOVA) Plummer et al., 

2007

Dab2 Disabled homolog 2 (Drosophila) GDs 12−19 Up 0.27 log2 p < 0.05 (ANOVA) Liu et al., 2005

Daf1 Decay accelerating factor 1 GDs 12−19 Up 0.19 log2 p < 0.05 (ANOVA) Liu et al., 2005
A-8

Table A-1 (continued)

Official 

gene 

symbol Official gene namef

Exposure 

window

Up or 

down Fold change Cutoff used (method) Reference

Dbi Diazepam binding inhibitor GDs 12−19 Down −0.38 log2 p < 0.05 (ANOVA) Liu et al., 2005

Dbi Diazepam binding inhibitor GDs 12.5−19.5 Down −1.28 p < 0.01 (ANOVA) Plummer et al., 

2007

Dcc Deleted in colorectal carcinoma GDs 12−19 Down at 

GD 19

−> 2 2-fold Shultz et al., 2001

Ddc Dopa decarboxylase GDs 12−19 Down −1.14 log2 p < 0.05 (ANOVA) Liu et al., 2005

Ddc Dopa decarboxylase GD 18 for 18 hr Down after 

18 hr

−1.38 p < 0.05 (ANOVA) Thompson et al., 

2005

Ddc Dopa decarboxylase GDs 12.5−19.5 Down −1.44 p < 0.01 (ANOVA) Plummer et al., 

2007

Ddit4 DNA-damage-inducible transcript 4 GDs 12−19 Down −1.02 log2 p < 0.05 (ANOVA) Liu et al., 2005

Ddit4 DNA-damage-inducible transcript 4 GD 18 for 18 hr Down after 

18 hr

−1.57 p < 0.05 (ANOVA) Thompson et al., 

2005

Ddt D-dopachrome tautomerase GDs 12.5−19.5 Down −1.22 p < 0.01 (ANOVA) Plummer et al., 

2007

Decr1 2,4-dienoyl CoA reductase 1, mitochondrial GDs 12−19 Down −0.21 log2 p < 0.05 (ANOVA) Liu et al., 2005

Dhcr7 7-dehydrocholesterol reductase GDs 12−19 Down −0.73 log2 p < 0.05 (ANOVA) Liu et al., 2005

Dhcr7 7-dehydrocholesterol reductase GD 19 for 6 hr Down after 

6 hr

−1.34 p < 0.05 (ANOVA) Thompson et al., 

2005

Dhcr7 7-dehydrocholesterol reductase GDs 18−19 for 

18 hr

Down after 

18 hr

−1.18 p < 0.05 (ANOVA) Thompson et al., 

2005

Dnm3 Dynamin 3 GDs 12−19 Down −0.27 log2 p < 0.05 (ANOVA) Liu et al., 2005

Dusp1 Dual specificity phosphatase 1 GD 19 for 3 hr Up after 

3 hr

0.91 p < 0.05 (ANOVA) Thompson et al., 

2005

Dusp6 Dual specificity phosphatase 6 GDs 12−19 Up 0.39 log2 p < 0.05 (ANOVA) Liu et al., 2005
A-9

Table A-1 (continued)

Official 

gene 

symbol Official gene namef

Exposure 

window

Up or 

down Fold change Cutoff used (method) Reference

Dusp6 Dual specificity phosphatase 6 GD 19 for 3 hr Up after 

3 hr

1.28 p < 0.05 (ANOVA) Thompson et al., 

2005

Ebp Phenylalkylamine Ca2+ antagonist (emopamil) 

binding protein

GDs 12−19 Down −0.64 log2 p < 0.05 (ANOVA) Liu et al., 2005

Echs1 Enoyl Coenzyme A hydratase, short chain 1, 

mitochondrial

GDs 12−19 Down −0.18 log2 p < 0.05 (ANOVA) Liu et al., 2005

Egr1 Early growth response 1 GDs 12−19 Up 0.77 log2 p < 0.05 (ANOVA) Liu et al., 2005

Egr2 Early growth response 2 GD 19 for 1 hr Up after 

1 hr

1.93 p < 0.05 (ANOVA) Thompson et al., 

2005

Egr2 Early growth response 2 GD 19 for 3 hr Up after 

3 hr

1.53 p < 0.05 (ANOVA) Thompson et al., 

2005

Elovl5 ELOVL family member 5, elongation of long chain 

fatty acids (yeast)

GDs 12−19 Down −0.17 log2 p < 0.05 (ANOVA) Liu et al., 2005

Elovl6 ELOVL family member 6, elongation of long chain 

fatty acids (yeast)

GDs 12−19 Down −0.40 log2 p < 0.05 (ANOVA) Liu et al., 2005

Emp3 Epithelial membrane protein 3 GDs 12.5−19.5 Down −1.24 p < 0.01 (ANOVA) Plummer et al., 

2007

Eno1 Enolase 1, alpha non-neuron GDs 12.5−19.5 Down −1.63 p < 0.01 (ANOVA) Plummer et al., 

2007

Enpep Glutamyl aminopeptidase GDs 12−19 Up 0.48 log2 p < 0.05 (ANOVA) Liu et al., 2005

Entpd5 Ectonucleoside triphosphate diphosphohydrolase 5 GDs 12−19 Down −0.52 log2 p < 0.05 (ANOVA) Liu et al., 2005

Epas1 Endothelial PAS domain protein 1 GDs 12−19 Down −0.21 log2 p < 0.05 (ANOVA) Liu et al., 2005

Ephx1 Epoxide hydrolase 1, microsomal GDs 12−19 Down −0.57 log2 p < 0.05 (ANOVA) Liu et al., 2005

Erbb2 v-erb-b2 erythroblastic leukemia viral oncogene 

homolog 2, neuro/glioblastoma derived oncogene 

homolog (avian)

GDs 12.5−17.5 Up 1.26 p < 0.01 (ANOVA) Plummer et al., 

2007
A-10

Table A-1 (continued)

Official 

gene 

symbol Official gene namef

Exposure 

window

Up or 

down Fold change Cutoff used (method) Reference

Etfdh Electron-transferring-flavoprotein dehydrogenase GDs 12−19 Down −0.39 log2 p < 0.05 (ANOVA) Liu et al., 2005

Ezr Ezrin GDs 12−19 Up 0.20 log2 p < 0.05 (ANOVA) Liu et al., 2005

Ezr Ezrin GDs 12−19 Down at 

GD 19

−> 2 2-fold Shultz et al., 2001

F10 Coagulation factor X GDs 12−19 Down −0.51 log2 p < 0.05 (ANOVA) Liu et al., 2005

Fabp3 Fatty acid binding protein 3 GDs 12−19 Down −0.49 log2 p < 0.05 (ANOVA) Liu et al., 2005

Fabp3 Fatty acid binding protein 3 GDs 12−19 Down at 

GD 19

−> 2 2-fold Shultz et al., 2001

Fabp3 Fatty acid binding protein 3 GD 19 for 3 hr Down after 

3 hr

−0.78 p < 0.05 (ANOVA) Thompson et al., 

2005

Fabp3 Fatty acid binding protein 3 GD 19 for 6 hr Down after 

6 hr

−1.68 p < 0.05 (ANOVA) Thompson et al., 

2005

Fabp3 Fatty acid binding protein 3 GDs 18−19 for 

18 hr

Down after 

18 hr

−1.09 p < 0.05 (ANOVA) Thompson et al., 

2005

Fabp5 Fatty acid binding protein 5, epidermal GDs 12−19 Down at 

GD 19

−> 2 2-fold Shultz et al., 2001

Fabp6 Fatty acid binding protein 6, ileal (gastrotropin) GDs 12−19 Down −0.23 log2 p < 0.05 (ANOVA) Liu et al., 2005

Fads1 Fatty acid desaturase 1 GDs 12−19 Down −0.80 log2 p < 0.05 (ANOVA) Liu et al., 2005

Fads1 Fatty acid desaturase 1 GDs 12.5−15.5 Up 1.42 p < 0.01 (ANOVA) Plummer et al., 

2007

Fads1 Fatty acid desaturase 1 GDs 12.5−19.5 Down 1.47 p < 0.01 (ANOVA) Plummer et al., 

2007

Fads2 Fatty acid desaturase 2 GDs 12−19 Down −0.42 log2 p < 0.05 (ANOVA) Liu et al., 2005

Fat1 FAT tumor suppressor homolog 1 (Drosophila) GDs 12.5−15.5 Down −1.32 p < 0.01 (ANOVA) Plummer et al., 

2007
A-11

Table A-1 (continued)

Official 

gene 

symbol Official gene namef

Exposure 

window

Up or 

down Fold change Cutoff used (method) Reference

Fbp2 Fructose-1,6-bisphosphatase 2 GDs 12−19 Up 0.28 log2 p < 0.05 (ANOVA) Liu et al., 2005

Fdft1 Farnesyl diphosphate farnesyl transferase 1 GDs 12−19 Down −0.58 log2 p < 0.05 (ANOVA) Liu et al., 2005

Fdft1 Farnesyl diphosphate farnesyl transferase 1 GDs 12.5−19.5 Down −1.40 p < 0.01 (ANOVA) Plummer et al.,

2007

Fdps Farensyl diphosphate synthase GDs 12−19 Down −0.73 log2 p < 0.05 (ANOVA) Liu et al., 2005

Fdps Farensyl diphosphate synthase GDs 12.5−17.5 Down −1.49 p < 0.01 (ANOVA) Plummer et al., 

2007

Fdps Farensyl diphosphate synthase GDs 12.5−19.5 Down −1.41 p < 0.01 (ANOVA) Plummer et al., 

2007

Fdx1 Ferredoxin 1 GDs 12−19 Down −1.65 log2 p < 0.05 (ANOVA) Liu et al., 2005

Fdx1 Ferredoxin 1 GD 18 for 18 hr Down after 

18 hr

−2.53 p < 0.05 (ANOVA) Thompson et al., 

2005

Fdx1 Ferredoxin 1 GDs 12.5−17.5 Down −2.06 p < 0.01 (ANOVA) Plummer et al., 

2007

Fdx1 Ferredoxin 1 GDs 12.5−19.5 Down −2.97 p < 0.01 (ANOVA) Plummer et al., 

2007

Fdxr Ferredoxin reductase GDs 12−19 Down −0.37 log2 p < 0.05 (ANOVA) Liu et al., 2005

Fdxr Ferredoxin reductase GDs 12.5−17.5 Down −1.41 p < 0.01 (ANOVA) Plummer et al., 

2007

Fgfr4 Fibroblast growth factor receptor 4 GDs 12−19 Down −0.19 log2 p < 0.05 (ANOVA) Liu et al., 2005

Folr1 Folate receptor 1 (adult) GDs 12−19 Down −0.48 log2 p < 0.05 (ANOVA) Liu et al., 2005

Fos FBJ murine osteosarcoma viral oncogene homolog GD 19 for 1 hr Up after 

1 hr

3.28 p < 0.05 (ANOVA) Thompson et al., 

2005

Fos FBJ murine osteosarcoma viral oncogene homolog GD 19 for 3 hr Up after 

3 hr

2.70 p < 0.05 (ANOVA) Thompson et al., 

2005
A-12

Table A-1 (continued)

Official 

gene 

symbol Official gene namef

Exposure 

window

Up or 

down Fold change Cutoff used (method) Reference

Frag1 FGF receptor activating protein 1 GDs 12−19 Down −0.48 log2 p < 0.05 (ANOVA) Liu et al., 2005

Frag1 FGF receptor activating protein 1 GD 18 for 18 hr Down after 

18 hr

−0.65 p < 0.05 (ANOVA) Thompson et al., 

2005

Fthfd Formyltetrahydro-folate dehydrogenase GDs 12−19 Down −1.03 log2 p < 0.05 (ANOVA) Liu et al., 2005

Fthfd Formyltetrahydro-folate dehydrogenase GD 19 for 6 hr Down after 

6 hr

−0.98 p < 0.05 (ANOVA) Thompson et al., 

2005

Fthfd Formyltetrahydro-folate dehydrogenase GDs 18−19 for 

18 hr

Down after 

18 hr

−0.83 p < 0.05 (ANOVA) Thompson et al., 

2005

Fzd2 Frizzled homolog 2 (Drosophila) GD 19 for 3 hr Down after 

3 hr

−0.7 p < 0.05 (ANOVA) Thompson et al., 

2005

Gaa Glucosidase, alpha, acid GDs 12−19 Down −0.30 log2 p < 0.05 (ANOVA) Liu et al., 2005

Ggtl3 Gamma-glutamyltransferase-like 3 GDs 12−19 Down −0.32 log2 p < 0.05 (ANOVA) Liu et al., 2005

Gja1 Gap junction membrane channel protein alpha 1 GDs 12−19 Down −0.36 log2 p < 0.05 (ANOVA) Liu et al., 2005

Glrx1 Glutaredoxin 1 (thioltransferase) GDs 12−19 Down −0.20 log2 p < 0.05 (ANOVA) Liu et al., 2005

Gnrhr Gonadotropin releasing hormone receptor GD 19 for 3 hr Up after 

3 hr

1.38 p < 0.05 (ANOVA) Thompson et al., 

2005

Gnrhr Gonadotropin releasing hormone receptor GD 19 for 6 hr Up after 

6 hr

2.03 p < 0.05 (ANOVA) Thompson et al., 

2005

Gpsn2 Glycoprotein, synaptic 2 GDs 12−19 Down −0.42 log2 p < 0.05 (ANOVA) Liu et al., 2005

Grb14 Growth factor receptor bound protein 14 GDs 12−19 Up 0.68 log2 p < 0.05 (ANOVA) Liu et al., 2005

Grb14 Growth factor receptor bound protein 14 GD 19 for 6 hr Up after 

6 hr

1.78 p < 0.05 (ANOVA) Thompson et al., 

2005

Grb14 Growth factor receptor bound protein 14 GDs 18−19 for 

18 hr

Up after 

18 hr

0.93 p < 0.05 (ANOVA) Thompson et al., 

2005
A-13

Table A-1 (continued)

Official 

gene 

symbol Official gene namef

Exposure 

window

Up or 

down Fold change Cutoff used (method) Reference

Grina Glutamate receptor, ionotropic, N-methyl 

D-aspartate-associated protein 1 (glutamate 

binding)

GDs 12.5−15.5 Up 1.59 p < 0.01 (ANOVA) Plummer et al., 

2007

Gsta2 Glutathione-S-transferase, alpha type2 GDs 12.5−17.5 Down −1.48 p < 0.01 (ANOVA) Plummer et al., 

2007

Gsta2 Glutathione-S-transferase, alpha type2 GDs 12.5−19.5 Down −2.23 p < 0.01 (ANOVA) Plummer et al., 

2007

Gsta3 Glutathione S-transferase A3 GDs 12−19 Down −0.96 log2 p < 0.05 (ANOVA) Liu et al., 2005

Gsta3 Glutathione S-transferase A3 GDs 12.5−17.5 Down −1.75 p < 0.01 (ANOVA) Plummer et al., 

2007

Gsta3 Glutathione S-transferase A3 GDs 12.5−19.5 Down −2.63 p < 0.01 (ANOVA) Plummer et al., 

2007

Gstm2 Glutathione S-transferase, mu 2 GDs 12−19 Down −0.42 log2 p < 0.05 (ANOVA) Liu et al., 2005

Gstm2 Glutathione S-transferase, mu 2 GDs 12−21 Up at 

GD 21

> 2 2-fold Shultz et al., 2001

Gstm2 Glutathione S-transferase, mu 2 GDs 18−19 for 

18 hr

Down after 

18 hr

−0.47 p < 0.05 (ANOVA) Thompson et al., 

2005

Gsto1 Glutathione S-transferase omega 1 GDs 12−19 Down −0.42 log2 p < 0.05 (ANOVA) Liu et al., 2005

Gstp1 Glutathione-S-transferase, pi 1 GDs 12.5−15.5 Up 1.34 p < 0.01 (ANOVA) Plummer et al., 

2007

Hao2 Hydroxyacid oxidase 2 (long chain) GDs 12−19 Down −0.58 log2 p < 0.05 (ANOVA) Liu et al., 2005

Hmgcr 3-hydroxy-3-methylglutaryl-Coenzyme A reductase GDs 12−19 Down −0.47 log2 p < 0.05 (ANOVA) Liu et al., 2005

Hmgcr 3-hydroxy-3-methylglutaryl-Coenzyme A reductase GDs 12.5−19.5 Down −1.83 p < 0.01 (ANOVA) Plummer et al., 

2007 

Hmgcs1 3-hydroxy-3-methylglutaryl-Coenzyme A 

synthase 1

GDs 12−19 Down −1.03 log2 p < 0.05 (ANOVA) Liu et al., 2005
A-14

Table A-1 (continued)

Official 

gene 

symbol Official gene namef

Exposure 

window

Up or 

down Fold change Cutoff used (method) Reference

Hmgcs1 3-hydroxy-3-methylglutaryl-Coenzyme A 

synthase 1

GDs 12.5−17.5 Down −1.72 p < 0.01 (ANOVA) Plummer et al., 

2007

Hmgcs1 3-hydroxy-3-methylglutaryl-Coenzyme A 

synthase 1

GDs 12.5−19.5 Down −1.87 p < 0.01 (ANOVA) Plummer et al., 

2007

Hmox1 Heme oxygenase (decycling) 1 GDs 12−19 Down −0.27 log2 p < 0.05 (ANOVA) Liu et al., 2005

Hpgd Hydroxyprostaglandin dehydrogenase 15 (NAD) GDs 12−19 Down −0.46 log2 p < 0.05 (ANOVA) Liu et al., 2005

Hprt Hypoxanthine guanine phosphoribosyl transferase GDs 12−19 Down at 

GD 19

−> 2 2-fold Shultz et al., 2001

Hrasls3 HRAS like suppressor 3 GDs 12−19 Down −0.45 log2 p < 0.05 (ANOVA) Liu et al., 2005

Hsd11b2 Hydroxysteroid (11-beta) dehydrogenase 2 GD 19 for 6 hr Down after 

6 hr

−1.16 p < 0.05 (ANOVA) Thompson et al., 

2005

Hsd17b3 Hydroxysteroid (17-beta) dehydrogenase 3 GDs 12−19 Up 0.28 log2 p < 0.05 (ANOVA) Liu et al., 2005

Hsd17b7 Hydroxysteroid (17-beta) dehydrogenase 7 GDs 12−19 Down −0.32 log2 p < 0.05 (ANOVA) Liu et al., 2005

Hsd3b1_

predicted

Hydroxysteroid dehydrogenase-1, delta< 5 >-3-beta 

(predicted)

GDs 12−19 Down −0.50 log2 p < 0.05 (ANOVA) Liu et al., 2005

Hsd3b1_

predicted

Hsd3b1_predicted hydroxysteroid dehydrogenase-1, 

delta< 5 >-3-beta (predicted) 

GD 18 for 18 hr Down after 

18 hr

−0.7 p < 0.05 (ANOVA) Thompson et al., 

2005

Hspb7 Heat shock 27kD protein family, member 7 

(cardiovascular)

GDs 12−19 Up 0.41 log2 p < 0.05 (ANOVA) Liu et al., 2005

Idh1 Isocitrate dehydrogenase 1 (NADP+), soluble GDs 12−19 Down −0.52 log2 p < 0.05 (ANOVA) Liu et al., 2005

Idh1 Isocitrate dehydrogenase 1 (NADP+), soluble GD 18 for 18 hr Down after 

18 hr

−0.67 p < 0.05 (ANOVA) Thompson et al., 

2005

Idi1 Isopentenyl-diphosphate delta isomerase GDs 12−19 Down −0.85 log2 p < 0.05 (ANOVA) Liu et al., 2005

Idi1 Isopentenyl-diphosphate delta isomerase GDs 12.5−17.5 Down −1.57 p < 0.01 (ANOVA) Plummer et al., 

2007
A-15

Table A-1 (continued)

Official 

gene 

symbol Official gene namef

Exposure 

window

Up or 

down Fold change Cutoff used (method) Reference

Igfbp2 Insulin-like growth factor binding protein 2 GDs 12−19 Down −0.39 log2 p < 0.05 (ANOVA) Liu et al., 2005

Igfbp3 Insulin-like growth factor binding protein 3 GDs 12−21 Up at 

GD 21

> 2 2-fold Shultz et al., 2001

Il6st Interleukin 6 signal transducer GDs 12−21 Down at 

GD 21

−> 2 2-fold Shultz et al., 2001

Ifitm2 Interferon induced transmembrane protein 2 GDs 12.5−17.5 Down −1.11 p < 0.01 (ANOVA) Plummer et al., 

2007

Inha Inhibin alpha GDs 12−19 Down −1.00 log2 p < 0.05 (ANOVA) Liu et al., 2005

Inha Inhibin alpha GDs 12.5−19.5 Down −1.64 p < 0.01 (ANOVA) Plummer et al., 

2007

Insig1 Insulin induced gene 1 GDs 12−19 Down −0.77 log2 p < 0.05 (ANOVA) Liu et al., 2005

Insl3 Insulin-like 3 GDs 12−19 Down −1.56 log2 p < 0.05 (ANOVA) Liu et al., 2005

interim 

symbol: 

Loc31432

3

Interim full name: transporter GDs 12−19 Down −0.35 log2 p < 0.05 (ANOVA) Liu et al., 2005

interim 

symbol:

Ratsg2

Interim name: Ratsg2 GDs 12−19 Down −0.13 log2 p < 0.05 (ANOVA) Liu et al., 2005

Kcnj8 Potassium inwardly-rectifying channel, subfamily J, 

member 8

GDs 12−21 Down at 

GD 21

−> 2 2-fold Shultz et al., 2001

Khk Ketohexokinase GDs 12.5−17.5 Up 1.30 p < 0.01 (ANOVA) Plummer et al., 

2007

Kit V-kit Hardy-Zuckerman 4 feline sarcoma viral 

oncogene homolog

GDs 12−21 Down at 

GD 21

−> 2 2-fold Shultz et al., 2001

Krt2-8 Keratin complex 2, basic, gene 8 GDs 12−19 Up 0.28 log2 p < 0.05 (ANOVA) Liu et al., 2005
A-16

Table A-1 (continued)

Official 

gene 

symbol Official gene namef

Exposure 

window

Up or 

down Fold change Cutoff used (method) Reference

Ldha Lactate dehydro-genase A GDs 12.5−19.5 Down −1.30 p < 0.01 (ANOVA) Plummer et al., 

2007

Ldlr Low density lipoprotein receptor GDs 12−19 Down −0.79 log2 p < 0.05 (ANOVA) Liu et al., 2005

Ldlr Low density lipoprotein receptor GDs 12−19 Down at 

GD 19

−> 2 2-fold Shultz et al., 2001

Lhcgr Luteinizing hormone/choriogonadotropin receptor GDs 12−21 Down at 

GD 21

−> 2 2-fold Shultz et al., 2001

Lhcgr Luteinizing hormone/choriogonadotropin receptor GD 19 for 6 hr Down after 

6 hr

−1.00 p < 0.05 (ANOVA) Thompson et al., 

2005

Lhcgr Luteinizing hormone/choriogonadotropin receptor GDs 18−19 for 

18 hr

Down after 

18 hr

−1.51 p < 0.05 (ANOVA) Thompson et al., 

2005

Lhcgr Luteinizing hormone/choriogonadotropin receptor GDs 12−19 Down −1.39 log2 p < 0.05 (ANOVA) Liu et al., 2005

Limk1 LIM motif-containing protein kinase 1 GDs 12−21 Down at 

GD 21

−> 2 2-fold Shultz et al., 2001

Lnk Linker of T-cell receptor pathways GD 19 for 3 hr Up after 

3 hr

1.17 p < 0.05 (ANOVA) Thompson et al., 

2005

Lr8 LR8 protein GDs 12−19 Down −0.45 log2 p < 0.05 (ANOVA) Liu et al., 2005

Lss Lanosterol synthase GDs 12−19 Down −0.48 log2 p < 0.05 (ANOVA) Liu et al., 2005

Mapk1 Mitogen activated protein kinase 1 GDs 12−21 Up at 

GD 21

> 2 2-fold Shultz et al., 2001

Marcks Myristoylated alanine rich protein kinase C 

substrate

GDs 12−19 Up at 

GD 19

> 2 2-fold Shultz et al., 2001

Mdk Midkine GDs 12−19 Up 0.20 log2 p < 0.05 (ANOVA) Liu et al., 2005

Me1 Malic enzyme 1, NADP(+)-dependent, cytosolic GDs 12−19 Down −0.67 log2 p < 0.05 (ANOVA) Liu et al., 2005
A-17

Table A-1 (continued)

Official 

gene 

symbol Official gene namef

Exposure 

window

Up or 

down Fold change Cutoff used (method) Reference

Me1 Malic enzyme 1, NADP(+)-dependent, cytosolic GDs 12.5−17.5 Down −1.36 p < 0.01 (ANOVA) Plummer et al., 

2007

Men1 Multiple endocrine neoplasia 1 GDs 12.5−15.5 Down −1.17 p < 0.01 (ANOVA) Plummer et al., 

2007

Mgat1 Mannoside acetylglucosaminyltransferase 1 GDs 12−19 Up 0.28 log2 p < 0.05 (ANOVA) Liu et al., 2005

Mgp Matrix Gla protein GD 19 for 6 hr Up after 

6 hr

1.66 p < 0.05 (ANOVA) Thompson et al., 

2005

Mgst1 Microsomal glutathione S-transferase 1 GDs 12−19 Down −0.36 log2 p < 0.05 (ANOVA) Liu et al., 2005

Mgst1 Microsomal glutathione S-transferase 1 GDs 12−21 Up at 

GD 21

> 2 2-fold Shultz et al., 2001

Mir16 Membrane interacting protein of RGS16 GDs 12−19 Down −0.56 log2 p < 0.05 (ANOVA) Liu et al., 2005

Mlxipl MLX interacting protein-like GDs 12−19 Down −0.31 log2 p < 0.05 (ANOVA) Liu et al., 2005

Mmp2 Matrix metallopeptidase 2 GDs 12−21 Up at 

GD 21

> 2 2-fold Shultz et al., 2001

Mtus1 Mitochondrial tumor suppressor 1 GD 19 for 3 hr Up after 

3 hr

0.67 p < 0.05 (ANOVA) Thompson et al., 

2005

Mtus1 Mitochondrial tumor suppressor 1 GD 19 for 6 hr Up after 

6 hr

0.55 p < 0.05 (ANOVA) Thompson et al., 

2005

Mvd Mevalonate (diphospho) decarboxylase GDs 12−19 Down −0.41 log2 p < 0.05 (ANOVA) Liu et al., 2005

Myd116 Myeloid differentiation primary response gene 116 GD 19 for 3 hr Up after 

3 hr

0.58 p < 0.05 (ANOVA) Thompson et al., 

2005

Myh6 Myosin, heavy polypeptide 6, cardiac muscle, alpha GDs 12−19 Down −0.72 log2 p < 0.05 (ANOVA) Liu et al., 2005

Myh6 Myosin, heavy polypeptide 6, cardiac muscle, alpha GDs 18−19 for 

18 hr

Down after 

18 hr

−1.52 p < 0.05 (ANOVA) Thompson et al., 

2005
A-18

Table A-1 (continued)

Official 

gene 

symbol Official gene namef

Exposure 

window

Up or 

down Fold change Cutoff used (method) Reference

Myh6 Myosin, heavy polypeptide 6, cardiac muscle, alpha GDs 12.5−19.5 Down −1.64 p < 0.01 (ANOVA) Plummer et al., 

2007

Myom2 Myomesin 2 GDs 12−19 Up 0.64 log2 p < 0.05 (ANOVA) Liu et al., 2005

Myrip Myosin VIIA and Rab interacting protein GDs 12−19 Down −0.27 log2 p < 0.05 (ANOVA) Liu et al., 2005

Nalp6 NACHT, leucine rich repeat and PYD containing 6 GDs 12−19 Up 0.45 log2 p < 0.05 (ANOVA) Liu et al., 2005

Nexn Nexilin GDs 12−19 Up 0.26 log2 p < 0.05 (ANOVA) Liu et al., 2005

Nf1 Neurofibromatosis 1 GDs 12−21 Down at 

GD 21

−> 2 2-fold Shultz et al., 2001

Nfil3 Nuclear factor, interleukin 3 regulated GDs 12−19 Up 0.31 log2 p < 0.05 (ANOVA) Liu et al., 2005

Nfkbia Nuclear factor of kappa light chain gene enhancer 

in B-cells inhibitor, alpha

GD 19 for 3 hr Up after 

3 hr

0.79 p < 0.05 (ANOVA) Thompson et al., 

2005

Npc2 Niemann pick type C2 GDs 12−19 Down −0.26 log2 p < 0.05 (ANOVA) Liu et al., 2005

Nppc Natriuretic peptide precursor type C GDs 12−19 Down −0.56 log2 p < 0.05 (ANOVA) Liu et al., 2005

Nr0b1 Nuclear receptor subfamily 0, group B, member 1 GDs 12−19 Down −0.37 log2 p < 0.05 (ANOVA) Liu et al., 2005

Nr0b1 Nuclear receptor subfamily 0, group B, member 1 GDs 12.5−19.5 Down −1.15 p < 0.01 (ANOVA) Plummer et al., 

2007

Nr4a1 Nuclear receptor subfamily 4, group A, member 1 GDs 12−19 Up 0.3 log2 p < 0.05 (ANOVA) Liu et al., 2005

Nr4a1 Nuclear receptor subfamily 4, group A, member 1 GD 19 for 3 hr Up after 

3 hr

1.83 p < 0.05 (ANOVA) Thompson et al., 

2005

Nr4a3 Nuclear receptor subfamily 4, group A, member 3 GD 19 for 3 hr Up after 

3 hr

2.25 p < 0.05 (ANOVA) Thompson et al., 

2005

Nr5a1 Nr5a1 nuclear receptor subfamily 5, group A, 

member 1

GDs 12.5−19.5 Down −1.18 p < 0.01 (ANOVA) Plummer et al., 

2007
A-19

Table A-1 (continued)

Official 

gene 

symbol Official gene namef

Exposure 

window

Up or 

down Fold change Cutoff used (method) Reference

Ntf3 Neurotrophin 3 GDs 12.5−17.5 Up 1.34 p < 0.01 (ANOVA) Plummer et al., 

2007

Okl38 Pregnancy-induced growth inhibitor GDs 12−19 Down −0.33 log2 p < 0.05 (ANOVA) Liu et al., 2005

Olfm1 Olfactomedin 1 GDs 12−19 Down −0.14 log2 p < 0.05 (ANOVA) Liu et al., 2005

P2ry14 Purinergic receptor P2Y, G-protein coupled, 14 GDs 12−19 Down −0.37 log2 p < 0.05 (ANOVA) Liu et al., 2005

Park7 Parkinson disease (autosomal recessive, early 

onset) 7

GDs 12.5−17.5 Down −1.32 p < 0.01 (ANOVA) Plummer et al., 

2007

Pawr PRKC, apoptosis, WT1, regulator GD 19 for 3 hr Up after 

3 hr

1.02 p < 0.05 (ANOVA) Thompson et al., 

2005

Pcna Proliferating cell nuclear antigen GDs 12−21 Up at 

GD 21

> 2 2-fold Shultz et al., 2001

Pcyt2 Phosphate cytidylyltransferase 2, ethanolamine GDs 12−19 Down −0.20 log2 p < 0.05 (ANOVA) Liu et al., 2005

Pdap1 PDGFA associated protein 1 GDs 12−21 Up at 

GD 21

> 2 2-fold Shultz et al., 2001

Pdyn Prodynorphin GDs 12−19 Down −1.06 log2 p < 0.05 (ANOVA) Liu et al., 2005

Pebp1 Phosphatidylethanolamine binding protein 1 GDs 12−19 Down −0.36 log2 p < 0.05 (ANOVA) Liu et al., 2005

Pebp1 Phosphatidylethanolamine binding protein 1 GDs 12.5−19.5 Down −1.67 p < 0.01 (ANOVA) Plummer et al., 

2007

Penk1 Proenkephalin 1 GDs 12.5−17.5 Down −1.41 p < 0.01 (ANOVA) Plummer et al., 

2007

Penk1 Proenkephalin 1 GDs 12.5−19.5 Down −1.86 p < 0.01 (ANOVA) Plummer et al., 

2007

Pfkp Phosphofructokinase, platelet GDs 12.5−19.5 Down −1.41 p < 0.01 (ANOVA) Plummer et al., 

2007
A-20

Table A-1 (continued)

Official 

gene 

symbol Official gene namef

Exposure 

window

Up or 

down Fold change Cutoff used (method) Reference

Pgam1 Phosphoglycerate mutase 1 GDs 12.5−19.5 Down −1.26 p < 0.01 (ANOVA) Plummer et al., 

2007

Pgk1 Phosphoglycerate kinase 1 GDs 12.5−19.5 Down −1.25 p < 0.01 (ANOVA) Plummer et al., 

2007

Phb Prohibitin GDs 12−21 Down at 

GD 21

−> 2 2-fold Shultz et al., 2001

Phb Prohibitin GDs 12−19 Down at 

GD 19

−> 2 2-fold Shultz et al., 2001

Phyh Phytanoyl-CoA hydroxylase GD 19 for 6 hr Down after 

6 hr

−1.02 p < 0.05 (ANOVA) Thompson et al., 

2005

Plat Plasminogen activator, tissue GDs 12−19 Up at 

GD 19

> 2 2-fold Shultz et al., 2001

Plaur Plasminogen activator, urokinase receptor GD 19 for 3 hr Up after 

3 hr

0.86 p < 0.05 (ANOVA) Thompson et al., 

2005

Pmp22 Peripheral myelin protein 22 GDs 12−19 Up at 

GD 19

> 2 2-fold Shultz et al., 2001

Pmp22 Peripheral myelin protein 22 GD 19 for 3 hr Down after 

3 hr

−0.75 p < 0.05 (ANOVA) Thompson et al., 

2005

Pmp22 Peripheral myelin protein 22 GD 19 for 6 hr Down after 

6 hr

−0.59 p < 0.05 (ANOVA) Thompson et al., 

2005

Pnliprp2 Pancreatic lipase-related protein 2 GDs 12−19 Down −0.28 log2 p < 0.05 (ANOVA) Liu et al., 2005

Por P450 (cytochrome) oxidoreductase GDs 12−19 Down −0.64 log2 p < 0.05 (ANOVA) Liu et al., 2005

Por P450 (cytochrome) oxidoreductase GDs 12.5−19.5 Down −1.39 p < 0.01 (ANOVA) Plummer et al., 

2007

Ppib Peptidylprolyl isomerase B GDs 12.5−17.5 Down −1.21 p < 0.01 (ANOVA) Plummer et al., 

2007
A-21

Table A-1 (continued)

Official 

gene 

symbol Official gene namef

Exposure 

window

Up or 

down Fold change Cutoff used (method) Reference

Ppp1cb Protein phosphatase 1, catalytic subunit, 

beta isoform

GDs 12.5−17.5 Down −1.37 p < 0.01 (ANOVA) Plummer et al., 

2007

Prdx3 Peroxiredoxin 3 GDs 12−19 Down −0.53 log2 p < 0.05 (ANOVA) Liu et al., 2005

Prdx3 Peroxiredoxin 3 GDs 18−19 for 

18 hr

Down after 

18 hr

−0.86 p < 0.05 (ANOVA) Thompson et al., 

2005

Prdx3 Peroxiredoxin 3 GDs 12.5−19.5 Down −1.63 p < 0.01 (ANOVA) Plummer et al., 

2007

Prg1 Plasticity related gene 1 GDs 12−19 Down −0.97 log2 p < 0.05 (ANOVA) Liu et al., 2005

Prkar2b Protein kinase, cAMP dependent regulatory, 

type II beta

GDs 12−19 Down −0.33 log2 p < 0.05 (ANOVA) Liu et al., 2005

Prkcbp1 Protein kinase C binding protein 1 GDs 12−19 Up 0.32 log2 p < 0.05 (ANOVA) Liu et al., 2005

Prlr Prolactin receptor GDs 12−19 Down −1.02 log2 p < 0.05 (ANOVA) Liu et al., 2005

Ptma Prothymosin alpha GDs 12−19 Down at 

GD 19

−> 2 2-fold Shultz et al., 2001

Ptp4a1 Protein tyrosine phosphatase 4a1 GDs 12−21 Up at 

GD 21

> 2 2-fold Shultz et al., 2001

PVR Poliovirus receptor GD 19 for 3 hr Up after 

3 hr

1.26 p < 0.05 (ANOVA) Thompson et al., 

2005

PVR Poliovirus receptor GD 19 for 6 hr Up after 

6 hr

0.92 p < 0.05 (ANOVA) Thompson et al., 

2005

Rabep2 Rabaptin, RAB GTPase binding effector protein 2 GD 19 for 3 hr Down after 

3 hr

−0.48 p < 0.05 (ANOVA) Thompson et al., 

2005

Rasd1 RAS, dexamethasone-induced 1 GDs 12−19 Down −0.52 log2 p < 0.05 (ANOVA) Liu et al., 2005

Rln1 Relaxin 1 GDs 12−19 Down −0.36 log2 p < 0.05 (ANOVA) Liu et al., 2005
A-22

Table A-1 (continued)

Official 

gene 

symbol Official gene namef

Exposure 

window

Up or 

down Fold change Cutoff used (method) Reference

Rnh1 Ribonuclease/angiogenin inhibitor 1 GDs 12.5−17.5 Down −1.20 p < 0.01 (ANOVA) Plummer et al., 

2007

Rpa2 Replication protein A2 GDs 12−21 Down at 

GD 21

−> 2 2-fold Shultz et al., 2001

Rpl13 Ribosomal protein L13 GDs 12.5−15.5 Up 1.17 p < 0.01 (ANOVA) Plummer et al., 

2007

Rpl32 Ribosomal protein L32 GDs 12.5−19.5 Up 1.13 p < 0.01 (ANOVA) Plummer et al., 

2007

Rpl37 Ribosomal protein L37 GDs 12.5−19.5 Up 1.13 p < 0.01 (ANOVA) Plummer et al., 

2007

Rpl36a Large subunit ribosomal protein L36a GDs 12−19 Down at 

GD 19

−> 2 2-fold Shultz et al., 2001

Rpl36a Large subunit ribosomal protein L36a GDs 12.5−15.5 Up 1.22 p < 0.01 (ANOVA) Plummer et al., 

2007

Rpn2 Ribophorin II GDs 12.5−19.5 Down −1.19 p < 0.01 (ANOVA) Plummer et al., 

2007

Rps13 Ribosomal protein S13 GDs 12.5−15.5 Up 1.30 p < 0.01 (ANOVA) Plummer et al., 

2007

Rps17 Ribosomal protein S17 GDs 12.5−19.5 Up 1.25 p < 0.01 (ANOVA) Plummer et al., 

2007

Rps19 Ribosomal protein S19 GDs 12.5−17.5 Up 1.25 p < 0.01 (ANOVA) Plummer et al., 

2007

Rps29 Ribosomal protein S29 GDs 12.5−19.5 Down −1.13 p < 0.01 (ANOVA) Plummer et al., 

2007

Sc4mol Sterol-C4-methyl oxidase-like GDs 12−19 Down −1.02 log2 p < 0.05 (ANOVA) Liu et al., 2005
A-23

Table A-1 (continued)

Official 

gene 

symbol Official gene namef

Exposure 

window

Up or 

down Fold change Cutoff used (method) Reference

Sc4mol Sterol-C4-methyl oxidase-like GDs 12.5−17.5 Down −1.82 p < 0.01 (ANOVA) Plummer et al., 

2007

Sc4mol Sterol-C4-methyl oxidase-like GDs 12.5−19.5 Down −2.36 p < 0.01 (ANOVA) Plummer et al., 

2007

Sc5d Sterol-C5-desaturase (fungal ERG3, 

delta-5-desaturase) homolog (S. cerevisae)

GDs 12−19 Down −0.32 log2 p < 0.05 (ANOVA) Liu et al., 2005

Scarb1 Scavenger receptor class B, member 1 GDs 12−19 Down −1.91 log2 p < 0.05 (ANOVA) Liu et al., 2005

Scarb1 Scavenger receptor class B, member 1 GDs 12−19 Down at 

GD 19

−> 2 2-fold Shultz et al., 2001

Scarb1 Scavenger receptor class B, member 1 GD 19 for 6 hr Down after 

6 hr

−1.60 p < 0.05 (ANOVA) Thompson et al., 

2005

Scarb1 Scavenger receptor class B, member 1 GDs 18−19 for 

18 hr

Down after 

18 hr

−2.72 p < 0.05 (ANOVA) Thompson et al., 

2005

Scarb1 Scavenger receptor class B, member 1 GDs 12.5−17.5 Down −2.23 p < 0.01 (ANOVA) Plummer et al., 

2007

Scarb1 Scavenger receptor class B, member 1 GDs 12.5−19.5 Down −2.85 p < 0.01 (ANOVA) Plummer et al., 

2007

Scd1 Stearoyl-Coenzyme A desaturase 1 GDs 12−19 Down −0.58 log2 p < 0.05 (ANOVA) Liu et al., 2005

Scn3b Sodium channel, voltage-gated, type III, beta GD 19 for 6 hr Up after 

6 hr

1.49 p < 0.05 (ANOVA) Thompson et al., 

2005

Scp2 Sterol carrier protein 2 GDs 12−19 Down −0.17 log2 p < 0.05 (ANOVA) Liu et al., 2005

Scp2 Sterol carrier protein 2 GDs 12.5−19.5 Down −1.24 p < 0.01 (ANOVA) Plummer et al., 

2007

Sdf4 Stromal cell derived factor 4 GDs 12−19 Down −0.27 log2 p < 0.05 (ANOVA) Liu et al., 2005

Sepp1 Selenoprotein P, plasma, 1 GDs 12−19 Down −0.45 log2 p < 0.05 (ANOVA) Liu et al., 2005
A-24

Table A-1 (continued)

Official 

gene 

symbol Official gene namef

Exposure 

window

Up or 

down Fold change Cutoff used (method) Reference

Serpine1 Serine (or cysteine) peptidase inhibitor, clade E, 

member 1

GD 19 for 3 hr Up after 

3 hr

1.56 p < 0.05 (ANOVA) Thompson et al., 

2005

Serpinh1 Serine (or cysteine) peptidase inhibitor, clade H, 

member 1

GDs 12.5−15.5 Down −1.32 p < 0.01 (ANOVA) Plummer et al., 

2007

Sgk Serum/glucocorticoid regulated kinase GDs 12−19 Down −0.45 log2 p < 0.05 (ANOVA) Liu et al., 2005

Slc3a2 Solute carrier family 3 (activators of dibasic and 

neutral amino acid transport), member 2

GDs 12−19 Down −0.48 log2 p < 0.05 (ANOVA) Liu et al., 2005

Slc12a2 Solute carrier family 12 (sodium/potassium/chloride 

transporters), member 2

GDs 12.5−17.5 Down −1.39 p < 0.01 (ANOVA) Plummer et al., 

2007

Slc16a6 Solute carrier family 16 (monocarboxylic acid 

transporters), member 6

GDs 12−19 Down −0.38 log2 p < 0.05 (ANOVA) Liu et al., 2005

Slc25a1 Solute carrier family 25, member 1 GDs 12−19 Down −0.27 log2 p < 0.05 (ANOVA) Liu et al., 2005

Slc25a20 Solute carrier family 25 (mitochondrial 

carnitine/acylcarnitine translocase), member 20

GDs 12−19 Down −0.23 log2 p < 0.05 (ANOVA) Liu et al., 2005

Slc7a8 Solute carrier family 7 (cationic amino acid 

transporter, y+ system), member 8

GDs 12.5−17.5 Down −1.82 p < 0.01 (ANOVA) Plummer et al., 

2007

Slc7a8 Solute carrier family 7 (cationic amino acid 

transporter, y+ system), member 8

GDs 12.5−19.5 Down −2.18 p < 0.01 (ANOVA) Plummer et al., 

2007

Smpx Small muscle protein, X-linked GDs 12−19 Up 0.21 log2 p < 0.05 (ANOVA) Liu et al., 2005

Sod2 Superoxide dismutase 2, mitochondrial GDs 12−19 Down −0.51 log2 p < 0.05 (ANOVA) Liu et al., 2005

Sod3 Superoxide dismutase 3, extracellular GDs 12−19 Down −0.33 log2 p < 0.05 (ANOVA) Liu et al., 2005

Sqle Squalene epoxidase GDs 12−19 Down −0.59 log2 p < 0.05 (ANOVA) Liu et al., 2005

Sqle Squalene epoxidase GD 18 for 18 hr Down after 

18 hr

−1.26 p < 0.05 (ANOVA) Thompson et al., 

2005

Ssr4 Signal sequence receptor 4 GDs 12−19 Down −0.23 log2 p < 0.05 (ANOVA) Liu et al., 2005
A-25

Table A-1 (continued)

Official 

gene 

symbol Official gene namef

Exposure 

window

Up or 

down Fold change Cutoff used (method) Reference

Ssrp1 Structure specific recognition protein 1 GDs 12−19 Down at 

GD 19

−> 2 2-fold Shultz et al., 2001

Star Steroidogenic acute regulatory protein GDs 12−19 Down −2.45 log2 p < 0.05 (ANOVA) Liu et al., 2005

Star Steroidogenic acute regulatory protein GDs 18−19 for 

18 hr

Down after 

18 hr

−2.33 p < 0.05 (ANOVA) Thompson et al., 

2005

Star Steroidogenic acute regulatory protein GDs 12.5−17.5 Down −2.19 p < 0.01 (ANOVA) Plummer et al., 

2007

Star Steroidogenic acute regulatory protein GDs 12.5−19.5 Down −2.53 p < 0.01 (ANOVA) Plummer et al., 

2007

Stc1 Stanniocalcin 1 GDs 12−19 Up 0.98 log2 p < 0.05 (ANOVA) Liu et al., 2005

Stc1 Stanniocalcin 1 GD 19 for 6 hr Up after 

6 hr

1.61 p < 0.05 (ANOVA) Thompson et al., 

2005

Stc2 Stanniocalcin 2 GDs 12−19 Down −1.18 log2 p < 0.05 (ANOVA) Liu et al., 2005

Stc2 Stanniocalcin 2 GDs 12.5−19.5 Down −1.59 p < 0.01 (ANOVA) Plummer et al., 

2007

Sts Steroid sulfatase GDs 12−19 Down at 

GD 19

−> 2 2-fold Shultz et al., 2001

Suclg1 Succinate-CoA ligase, GDP-forming, alpha subunit GDs 12.5−19.5 Down −1.21 p < 0.01 (ANOVA) Plummer et al., 

2007

Svs5 Seminal vesicle secretion 5 GDs 12−19 Down −3.75 log2 p < 0.05 (ANOVA) Liu et al., 2005

Svs5 Seminal vesicle secretion 5 GDs 18−19 for 

18 hr

Down after 

18 hr

−3.36 p < 0.05 (ANOVA) Thompson et al., 

2005

Svs5 Seminal vesicle secretion 5 GDs 12.5−17.5 Down −5.89 p < 0.01 (ANOVA) Plummer et al., 

2007

Svs5 Seminal vesicle secretion 5 GDs 12.5−19.5 Down −3.75 p < 0.01 (ANOVA) Plummer et al., 

2007
A-26

Table A-1 (continued)

Official 

gene 

symbol Official gene namef

Exposure 

window

Up or 

down Fold change Cutoff used (method) Reference

Syngr1 Synaptogyrin 1 GDs 12−19 Down −0.16 log2 p < 0.05 (ANOVA) Liu et al., 2005

Tcf1 Transcription factor 1 GDs 12−19 Down −0.14 log2 p < 0.05 (ANOVA) Liu et al., 2005

Tcf21 Transcription factor 21 GDs 12−19 Up 0.17 log2 p < 0.05 (ANOVA) Liu et al., 2005

Tec Tec protein tyrosine kinase GD 19 for 3 hr Up after 

3 hr

0.69 p < 0.05 (ANOVA) Thompson et al., 

2005

Testin Testin gene GDs 12−19 Up 0.59 log2 p < 0.05 (ANOVA) Liu et al., 2005

Tfrc Transferrin receptor GDs 12−19 Down −0.23 log2 p < 0.05 (ANOVA) Liu et al., 2005

Tgfb3 Transforming growth factor, beta 3 GDs 12−19 Down at 

GD 19

−> 2 2-fold Shultz et al., 2001

Timp1 Tissue inhibitor of metallopeptidase 1 GD 19 for 6 hr Up after 

6 hr

1.04 p < 0.05 (ANOVA) Thompson et al., 

2005

Timp3 Tissue inhibitor of metalloproteinase 3 (Sorsby 

fundus dystrophy, pseudoinflammatory)

GDs 12−21 Down at 

GD 21

−> 2 2-fold Shultz et al., 2001

Tkt Transketolase GDs 12.5−17.5 Down −1.19 p < 0.01 (ANOVA) Plummer et al., 

2007

Tkt Transketolase GDs 12.5−19.5 Down −1.28 p < 0.01 (ANOVA) Plummer et al., 

2007

Tmed10 Transmembrane emp24-like trafficking 

protein 10 (yeast)

GDs 12.5−19.5 Down −1.20 p < 0.01 (ANOVA) Plummer et al., 

2007

Tnfrsf12a Tumor necrosis factor receptor superfamily, 

member 12a

GD 19 for 6 hr Up after 

6 hr

1.34 p < 0.05 (ANOVA) Thompson et al., 

2005

Tnni1 Troponin I, skeletal, slow 1 GDs 12−19 Up 0.33 log2 p < 0.05 (ANOVA) Liu et al., 2005

Tnni3 Troponin I type 3 (cardiac) GDs 12−19 Up 0.26 log2 p < 0.05 (ANOVA) Liu et al., 2005

Tnnt2 Troponin T2, cardiac GDs 12−19 Up 0.77 log2 p < 0.05 (ANOVA) Liu et al., 2005
A-27

Table A-1 (continued)

Official 

gene 

symbol Official gene namef

Exposure 

window

Up or 

down Fold change Cutoff used (method) Reference

Tpi1 Triosephosphate isomerase 1 GDs 12−19 Down −0.24 log2 p < 0.05 (ANOVA) Liu et al., 2005

Tpm1 Tropomyosin 1, alpha GDs 12−19 Up 0.36 log2 p < 0.05 (ANOVA) Liu et al., 2005

Tpm1 Tropomyosin 1, alpha GD 19 for 6 hr Up after 

6 hr

1.04 p < 0.05 (ANOVA) Thompson et al., 

2005

Tsc22d1 TSC22 domain family, member 1 GDs 12.5−19.5 Down −1.34 p < 0.01 (ANOVA) Plummer et al., 

2007

Tsn Translin GDs 12.5−17.5 Up 1.54 p < 0.01 (ANOVA) Plummer et al., 

2007

Tst Thiosulfate sulfurtransferase GDs 12−19 Down −0.33 log2 p < 0.05 (ANOVA) Liu et al., 2005

Tuba1 Tubulin, alpha 1 GDs 12−21 Down at 

GD 21

−> 2 2-fold Shultz et al., 2001

Tuba1 Tubulin, alpha 1 GDs 12.5−19.5 Down −1.26 p < 0.01 (ANOVA) Plummer et al., 

2007

Txn1 Thioredoxin 1 GD 18 for 18 hr Down after 

18 hr

−0.62 p < 0.05 (ANOVA) Thompson et al., 

2005

Txnl1 Thioredoxin-like 1 GDs 12.5−15.5 Up 1.20 p < 0.01 (ANOVA) Plummer et al., 

2007

Uba52 Ubiquitin A-52 residue ribosomal protein fusion 

product 1

GDs 12.5−19.5 Up 1.10 p < 0.01 (ANOVA) Plummer et al., 

2007

Unc5b Unc-5 homolog B (C. elegans) GDs 12−21 Down at 

GD 21

−> 2 2-fold Shultz et al., 2001

Vapa VAMP (vesicle-associated membrane protein)-

associated protein A

GDs 12.5−19.5 Down −1.37 p < 0.01 (ANOVA) Plummer et al., 

2007

Vcam1 Vascular cell adhesion molecule 1 GDs 12−19 Down −0.63 log2 p < 0.05 (ANOVA) Liu et al., 2005

Vdac1 Voltage-dependent anion channel 1 GDs 12.5−19.5 Down −1.13 p < 0.01 (ANOVA) Plummer et al., 

2007
A-28

Table A-1 (continued)

Official 

gene 

symbol Official gene namef

Exposure 

window

Up or 

down Fold change Cutoff used (method) Reference

Vim Vimentin GDs 12.5−19.5 Down −1.60 p < 0.01 (ANOVA) Plummer et al., 

2007

Vnn11 Vanin 1 GDs 12−19 Down −0.32 log2 p < 0.05 (ANOVA) Liu et al., 2005

Vsnl1 Visinin-like 1 GDs 12−19 Down −0.62 log2 p < 0.05 (ANOVA) Liu et al., 2005

Ywhae Tyrosine 3-monooxygenase/tryptophan 

5-monooxygenase activation protein, epsilon 

polypeptide

GDs 12.5−19.5 Down −1.37 p < 0.01 (ANOVA) Plummer et al., 

2007

Zfp36 Zinc finger protein 36 GD 19 for 1 hr Up after 

1 hr

1.79 p < 0.05 (ANOVA) Thompson et al., 

2005

Zyx Zyxin GD 19 for 3 hr Up after 

3 hr

1.03 p < 0.05 (ANOVA) Thompson et al., 

2005

Not found Listed as “Tppc” and 289920_Rn in article, and 

Genbank #BF400584 (Plummer, personal 

communication) does not match a gene name.

GDs 12.5−17.5 Down −1.39 p < 0.01 (ANOVA) Plummer et al., 

2007

Not found Listed as “Similar to mouse IAP-binding protein” 

and 205510_Rn in article, and Genbank 

#:BG378907 (Plummer, personal communication) 

does not match a gene name.

GDs 12.5−15.5 Up 1.26 p < 0.01 (ANOVA) Plummer et al., 

2007

Not found LOC499942 similar to WAP four-disulfide core 

domain protein 8 precursor (Putative protease 

inhibitor WAP8) (Rattus norvegicus).

GDs 12−19 Down −0.25 log2 p < 0.05 (ANOVA) Liu et al., 2005

Not found LOC497726 hypothetical gene supported by 

NM_138518 (Rattus norvegicus). This record was 

discontinued.

GDs 12−19 Down −0.27 log2 p < 0.05 (ANOVA) Liu et al., 2005

a

The four studies dosed at 500 mg/kg-d DBP in the Sprague-Dawley (SD) rat. b

Thompson et al. (2005) and Shultz et al. (2001) dosed with DBP alone; gene expression changes for DBP were relative to vehicle control expression.
A-29

Table A-1. (continued)

c

Liu et al. (2005) presented microarray data for all five developmental phthalates, including DBP, since they did not find any differences in statistical significance among 

the five phthalates. Thus, we present the data for all five phthalates, which should be the same as for DBP. d

The Affy ID 1387057_at was found to be significantly down-regulated by Liu et al. (2005). This Affy ID was listed as the gene Slc7a8 (solute carrier family 7 [cationic 

amino acid transporter, y+ system], member 8) at the time of their publication. As of January 2007, Affy now lists both Slc7a8 and Syngap1. This probeset is apparently 

capable of hybridizing with two different genes. Thus, this Affy ID was not incorporated in the case study evaluation since it is not clear which gene was altered after 

DBP in utero exposure.

e

The Plummer et al. (2007) data from the whole testis are included in this table. The data from microdissection of testicular regions are not presented since no other 

studies were comparable. Plummer et al. (2007) performed their study in the Wistar rat whereas the other three microarray studies were performed in the SD rat. f

Gene function and pathway information was gathered from GeneGo (www.genego.com).

ANOVA, analysis of variance; GD, gestation day; hr, hour. 
A-30

Table A-2. WOE for statistically significant gene expression changes after in utero exposure to DBP from 

whole-rat testis reverse transcription-polymerase chain reaction (RT-PCR) studies

Official 

gene 

symbol Official gene name* Dose Exposure window Up or down Statistical analysis method Reference

Ar Androgen receptor 500 mg/kg-d GDs 12−19 Up t-test, p < 0.05 Bowman et 

al., 2005

Bmp4 Bone morphogenetic protein 4 500 mg/kg-d GDs 12−19 Up t-test, p < 0.05 Bowman et 

al., 2005

Btg2 B-cell translocation gene 2, 

anti-proliferative

500 mg/kg-d GD 19 for 30 min to 6 hr 

timepoints and GD 18 for 

12, 18, and 24 hr time 

points

Up after 

~1−6 hr 

(peak ~2 hr)

Relative expression determined 

using mean Ct; triplicate 

samples; GADPH control; SE; 

but p value not calculated

Thompson 

et al., 2005

Bzrp Benzodiazepine receptor, 

peripheral 

500 mg/kg-d GDs 12−19 Up Dunnett's test, ANOVA (one 

way), p < 0.05

Lehmann et 

al., 2004

Cebpb CCAAT/enhancer binding 

protein (C/EBP), beta 

500 mg/kg-d GDs 12−19 Down One way and two-way nested 

ANOVA; p < 0.05

Liu et al., 

2005

Cebpd CCAAT/enhancer binding 

protein (C/EBP), delta

500 mg/kg-d GD 19 for 30 min to 6 hr 

timepoints and GD 18 for 

12, 18, and 24 hr time 

points

Up after 

~1−6 hr 

(peak ~3 hr)

Relative expression determined 

using mean Ct; triplicate 

samples; GADPH control; SE; 

but p value not calculated 

Thompson 

et al., 2005

Clu Clusterin 500 mg/kg-d GDs 12−19 Up ANOVA, nested design, 

p < 0.05

Barlow et 

al., 2003

Clu Clusterin 500 mg/kg-d GDs 12−19 Up Dunnett's test, ANOVA (one 

way), p < 0.05

Lehmann et 

al., 2004

Clu Clusterin 500 mg/kg-d GDs 12−16, 12−19, or

12−21

Up p < 0.05 Shultz et 

al., 2001

Cxcl1 Chemokine (C-X-C motif) 

ligand 1

500 mg/kg-d GD 19 for 30 min to 6 hr 

timepoints and GD 18 for 

12, 18, and 24 hr time 

points

Up after 

~1−12 hr 

(peak at 

~3 hr)

Relative expression determined 

using mean Ct; triplicate 

samples; GADPH control; SE; 

but p value not calculated

Thompson 

et al., 2005
A-31

Table A-2. (continued)

Official 

gene symbol Official gene name* Dose Exposure window Up or down Statistical analysis method Reference

Cyp11a1 Cytochrome P450, 

family 11, subfamily a, 

polypeptide 1

500 mg/kg-d GDs 12−19 Down ANOVA, nested design, 

p < 0.05

Barlow et 

al., 2003

Cyp11a1 Cytochrome P450, 

family 11, subfamily a, 

polypeptide 1

50 mg/kg-d GDs 12−19 Down Dunnett's test, ANOVA (one 

way), p < 0.05

Lehmann et 

al., 2004

Cyp11a1 Cytochrome P450, 

family 11, subfamily a, 

polypeptide 1

100 mg/kg-d GDs 12−19 Down Dunnett's test, ANOVA (one 

way), p < 0.05

Lehmann et 

al., 2004

Cyp11a1 Cytochrome P450, 

family 11, subfamily a, 

polypeptide 1

500 mg/kg-d GDs 12−19 Down Dunnett's test, ANOVA (one 

way), p < 0.05

Lehmann et 

al., 2004

Cyp11a1 Cytochrome P450, 

family 11, subfamily a, 

polypeptide 1

500 mg/kg-d GDs 12−16, 12−19, or 

12−21

Down p < 0.05 Shultz et 

al., 2001

Cyp11a1 Cytochrome P450, 

family 11, subfamily a, 

polypeptide 1

500 mg/kg-d GDs 12−17 and 12−18 Down at 

GD 18

t-test, ANOVA (one-way) with 

Tukey post hoc analysis; 

p < 0.05 

Thompson 

et al., 2004

Cyp11a1 Cytochrome P450, 

family 11, subfamily a, 

polypeptide 1

500 mg/kg-d GDs 12.5−19.5 Down One-way ANOVA followed by 

Bonferroni post test using 

GraphPad Prism; p < 0.05

Plummer et 

al., 2007

Cyp17a1 Cytochrome P450, 

family 17, subfamily a, 

polypeptide 1

500 mg/kg-d GDs 12−19 Down Repeated measure ANOVA, 

nested design, p < 0.05

Barlow et 

al., 2003

Cyp17a1 Cytochrome P450, 

family 17, subfamily a, 

polypeptide 1

500 mg/kg-d GDs 12−19 Down Dunnett's test, ANOVA (one 

way), p < 0.05

Lehmann et 

al., 2004
A-32

Table A-2. (continued)

Official 

gene symbol Official gene name* Dose Exposure window Up or down Statistical analysis method Reference

Cyp17a1 Cytochrome P450, 

family 17, subfamily a, 

polypeptide 1

500 mg/kg-d GDs 12−16, 12−19, or 

12−21

Down at 

GD 19

p < 0.05 Shultz et 

al., 2001

Cyp17a1 Cytochrome P450, 

family 17, subfamily a, 

polypeptide 1

500 mg/kg-d GDs 12−17 and 12−18 Down at 

GD 17 and 

18

t-test, ANOVA (one-way) with 

Tukey post hoc analysis; 

p < 0.05 

Thompson 

et al., 2004

Daf1 Decay accelerating factor 1 500 mg/kg-d GDs 12−19 Up One way and two-way nested 

ANOVA, p < 0.05

Liu et al., 

2005

Ddc Dopa decarboxylase 500 mg/kg-d GDs 12−19 Down One way and two-way nested 

ANOVA, p < 0.05

Liu et al., 

2005

Dusp6 Dual specificity 

phosphatase 6

500 mg/kg-d GD 19 for 30 min to 6 hr 

timepoints and GD 18 for 

12, 18, and 24 hr time 

points

Up after 

~1−12 hr 

(peak at ~3 

hr)

Relative expression determined 

using mean Ct; triplicate 

samples; GADPH control; SE; 

but p value not calculated

Thompson 

et al., 2005

Edg3 Endothelial differentiation 

sphingolipid 

G-protein-coupled 

receptor 3

500 mg/kg-d GD 19 for 30 min to 6 hr 

timepoints and GD 18 for 

12, 18, and 24 hr time 

points

Up after 

~1−6 and 

18 hr (peak 

~3 hr)

Relative expression determined 

using mean Ct; triplicate 

samples; GADPH control; SE; 

but p value not calculated

Thompson 

et al., 2005

Egfr Epidermal growth factor 

receptor 

500 mg/kg-d GDs 12−19 and 12−21 Un-changed t-test, p < 0.05 Bowman et 

al., 2005

Egr1 Early growth response 1 500 mg/kg-d GD 19 for 30 min to 6 hr 

timepoints and GD 18 for 

12, 18, and 24 hr time

points

Up after 

~1−7 hr 

(peak ~2 hr)

Relative expression determined 

using mean Ct; triplicate 

samples; GADPH control; SE; 

but p value not calculated

Thompson 

et al., 2005

Egr1 Early growth response 1 500 mg/kg-d GDs 12−19 Up One way and two-way nested 

ANOVA; p < 0.05

Liu et al., 

2005
A-33

Table A-2. (continued)

Official 

gene symbol Official gene name* Dose Exposure window Up or down Statistical analysis method Reference

Egr2 Early growth response 2 500 mg/kg-d GD 19 for 30 min to 6 hr 

timepoints and GD 18 for 

12, 18, and 24 hr time 

points

Up after 

~1−12 hr 

(peak ~2 hr)

Relative expression determined 

using mean Ct; triplicate 

samples; GADPH control; SE; 

but p value not calculated

Thompson 

et al., 2005

Fgf10 Fibroblast growth factor 10 500 mg/kg-d GDs 12−21 Up t-test, p < 0.05 Bowman et 

al., 2005

Fgfr2 Fibroblast growth factor 

receptor 2

500 mg/kg-d GDs 12−19 and 12−21 No stat. 

change

t-test, p < 0.05 Bowman et 

al., 2005

Fos FBJ murine osteosarcoma 

viral oncogene homolog

500 mg/kg-d GD 19 for 30 min to 6 hr 

timepoints and GD 18 for 

12, 18, and 24 hr time 

points

Up after 

30 min and 

6 hr (peak at 

1 hr)

Relative expression determined 

using mean Ct; triplicate 

samples; GADPH control; SE; 

but p value not calculated

Thompson 

et al., 2005

Grb14 Growth factor receptor 

bound protein 14 

500 mg/kg-d GDs 12−19 Up One way and two-way nested 

ANOVA, p < 0.05

Liu et al., 

2005

Hes6 Hairy and enhancer of 

split 6 (Drosophila)

500 mg/kg-d GD 19 for 30 min to 6 hr 

timepoints and GD 18 for 

12, 18, and 24 hr time 

points

Down after 

1−3 hr (peak 

at 3 hr)

Relative expression determined 

using mean Ct; triplicate 

samples; GADPH control; SE; 

but p value not calculated

Thompson 

et al., 2005

Hsd17b3 Hydroxysteroid (17-beta) 

dehydrogenase 3

500 mg/kg-d GDs 12−19 Up One way and two-way nested 

ANOVA, p < 0.05

Liu et al., 

2005

Hsd17b7 Hydroxysteroid (17-beta) 

dehydrogenase 7

500 mg/kg-d GDs 12−19 Down One way and two-way nested 

ANOVA, p < 0.05

Liu et al., 

2005

Hsd3b1_

predicted

Hydroxysteroid 

dehydrogenase-1, 

delta< 5 >-3-beta 

(predicted)

500 mg/kg-d GDs 12−19 Down ANOVA, nested design, 

p < 0.05

Barlow et 

al., 2003
A-34

Table A-2. (continued)

Official 

gene symbol Official gene name* Dose Exposure window Up or down Statistical analysis method Reference

Hsd3b1_

predicted

Hsd3b1_predicted 

hydroxysteroid 

dehydrogenase-1, 

delta< 5 >-3-beta 

(predicted) or Hsd3b1 

hydroxy-delta-5-steroid 

dehydrogenase, 3 beta- and 

steroid delta-isomerase 1 

0.1 mg/kg-d GDs 12−19 Down Dunnett's test, ANOVA (one 

way), p < 0.05

Lehmann et 

al., 2004

Hsd3b1_

predicted

Hsd3b1_predicted 

hydroxysteroid 

dehydrogenase-1, 

delta< 5 >-3-beta 

(predicted) or Hsd3b1 

hydroxy-delta-5-steroid 

dehydrogenase, 3 beta- and 

steroid delta-isomerase 1 

1 mg/kg-d GDs 12−19 Down Dunnett's test, ANOVA (one 

way), p < 0.05

Lehmann et 

al., 2004

Hsd3b1_

predicted

Hsd3b1_predicted 

hydroxysteroid 

dehydrogenase-1, 

delta< 5 >-3-beta 

(predicted) or Hsd3b1 

hydroxy-delta-5-steroid 

dehydrogenase, 3 beta- and 

steroid delta-isomerase 1 

10 mg/kg-d GDs 12−19 Down Dunnett's test, ANOVA (one 

way), p < 0.05

Lehmann et 

al., 2004

Hsd3b1_

predicted

Hsd3b1_predicted 

hydroxysteroid 

dehydrogenase-1, 

delta< 5 >-3-beta 

(predicted) or Hsd3b1 

hydroxy-delta-5-steroid 

dehydrogenase, 3 beta- and 

steroid delta-isomerase 1 

50 mg/kg-d GDs 12−19 Down Dunnett's test, ANOVA (one 

way), p < 0.05

Lehmann et 

al., 2004
A-35

Table A-2. (continued)

Official 

gene symbol Official gene name* Dose Exposure window Up or down Statistical analysis method Reference

Hsd3b1_

predicted

Hsd3b1_predicted 

hydroxysteroid 

dehydrogenase-1, 

delta< 5 >-3-beta 

(predicted) or Hsd3b1 

hydroxy-delta-5-steroid 

dehydrogenase, 3 beta- and 

steroid delta-isomerase 1 

100 mg/kg-d GDs 12−19 Down Dunnett's test, ANOVA (one 

way), p < 0.05

Lehmann et 

al., 2004

Hsd3b1_

predicted

Hsd3b1_predicted 

hydroxysteroid 

dehydrogenase-1, 

delta< 5 >-3-beta 

(predicted) or Hsd3b1 

hydroxy-delta-5-steroid 

dehydrogenase, 3 beta- and 

steroid delta-isomerase 1 

500 mg/kg-d GDs 12−19 Down Dunnett's test, ANOVA (one 

way), p < 0.05

Lehmann et 

al., 2004

Ier3 Immediate early response 3 500 mg/kg-d GD 19 for 30 min to 6 hr 

timepoints and GD 18 for 

12, 18, and 24 hr time 

points

Up after 

1−12 hr 

(peak ~2 hr)

Relative expression determined 

using mean Ct; triplicate 

samples; GADPH control; SE; 

but p value not calculated

Thompson 

et al., 2005

Ifrd1 Interferon-related 

developmental regulator 1

500 mg/kg-d GD 19 for 30 min to 6 hr 

timepoints and GD 18 for 

12, 18, and 24 hr time 

points

Up after 

~1−6 and 

18 hr (peak 

~3 hr)

Relative expression determined 

using mean Ct; triplicate 

samples; GADPH control; SE; 

but p value not calculated

Thompson 

et al., 2005

Igf1 Insulin-like growth factor 1 500 mg/kg-d GDs 12−21 Up t-test, p < 0.05 Bowman et 

al., 2005

Igf1 Insulin-like growth factor 1 500 mg/kg-d GDs 12−19 Up t-test, p < 0.05 Bowman et 

al., 2005

Igf1r Insulin-like growth factor 1 

receptor 

500 mg/kg-d GDs 12−19 Up t-test, p < 0.05 Bowman et 

al., 2005
A-36

Table A-2. (continued)

Official 

gene symbol Official gene name* Dose Exposure window Up or down Statistical analysis method Reference

Igf2 Insulin-like growth factor 2 500 mg/kg-d GDs 12−19 Up t-test, p < 0.05 Bowman et 

al., 2005

Igfbp5 Insulin-like growth factor 

binding protein 5

500 mg/kg-d GDs 12−21 Up t-test, p < 0.05 Bowman et 

al., 2005

Insig1 Insulin induced gene 1 500 mg/kg-d GDs 12−19 Down One way; and two-way nested 

ANOVA, p < 0.05

Liu et al., 

2005

Insl3 Insulin-like 3 500 mg/kg-d GDs 12−19 Down Dunnett's test, ANOVA (one 

way), p < 0.05

Lehmann et 

al., 2004

Insl3 Insulin-like 3 1000 

mg/kg-d

GDs 13−17 (GDs 14−18 in 

Wilson et al., 2004 was 

changed to GDs 13−17 to 

make the GD comparable to 

the other 7 studies)

Down ANOVA followed by 

LSMEANS, p < 0.01 or less

Wilson et 

al., 2004

Insl3 Insulin-like 3 500 mg/kg-d GDs 12.5−19.5 Down One-way ANOVA followed by 

Bonferroni post test using 

GraphPad Prism; p < 0.05

Plummer et 

al., 2007

Itgav Integrin alpha V 500 mg/kg-d GDs 12−19 Up t-test, p < 0.05 Bowman et 

al., 2005

Junb Jun-B oncogene 500 mg/kg-d GD 19 for 30 min to 6 hr 

timepoints and GD 18 for 

12, 18, and 24 hr time 

points

UP after 

~1−12 hr 

(peak 

~2−3 hr)

Relative expression determined 

using mean Ct; triplicate 

samples; GADPH control; SE; 

but p value not calculated

Thompson 

et al., 2005

Kit v-kit Hardy-Zuckerman 

4 feline sarcoma viral 

oncogene homolog

500 mg/kg-d GDs 12−19 Down ANOVA, nested design, 

p < 0.05

Barlow et 

al., 2003

Kit v-kit Hardy-Zuckerman 

4 feline sarcoma viral 

oncogene homolog

0.1 mg/kg-d GDs 12−19 Down Dunnett's test, ANOVA (one 

way), p < 0.05

Lehmann et 

al., 2004
A-37

Table A-2. (continued)

Official 

gene symbol Official gene name* Dose Exposure window Up or down Statistical analysis method Reference

Kit v-kit Hardy-Zuckerman 

4 feline sarcoma viral 

oncogene homolog

1 mg/kg-d GDs 12−19 Down Dunnett's test, ANOVA (one 

way), p < 0.05

Lehmann et 

al., 2004

Kit v-kit Hardy-Zuckerman 4 

feline sarcoma viral 

oncogene homolog 

50 mg/kg-d GDs 12−19 Down Dunnett's test, ANOVA (one 

way), p < 0.05

Lehmann et 

al., 2004

Kit v-kit Hardy-Zuckerman 

4 feline sarcoma viral 

oncogene homolog

100 mg/kg-d GDs 12−19 Down Dunnett's test, ANOVA (one 

way), p < 0.05

Lehmann et 

al., 2004

Kit v-kit Hardy-Zuckerman 

4 feline sarcoma viral 

oncogene homolog

500 mg/kg-d GDs 12−19 Down Dunnett's test, ANOVA (one 

way), p < 0.05

Lehmann et 

al., 2004

Kit v-kit Hardy-Zuckerman 

4 feline sarcoma viral 

oncogene homolog

500 mg/kg-d GDs 12-19 Down at 

GD 19

p < 0.05 Shultz et 

al., 2001

Kitl Kit ligand 500 mg/kg-d GDs 12−19 Down ANOVA, nested design, 

p < 0.05

Barlow et 

al., 2003

Ldlr Low density lipoprotein 

receptor 

500 mg/kg-d GDs 12−19 Down One way and two-way nested 

ANOVA, p < 0.05 

Liu et al., 

2005

Lhcgr Luteinizing 

hormone/choriogonadotropi

n receptor 

500 mg/kg-d GDs 12−19 Down One way and two-way nested 

ANOVA, p < 0.05 

Liu et al., 

2005

Map3k12 Mitogen activated protein 

kinase kinase kinase 12

500 mg/kg-d GDs 12−19 Up t-test, p < 0.05 Bowman et 

al., 2005

Marcks Myristoylated alanine rich 

protein kinase C substrate 

500 mg/kg-d GDs 12−16, 12−19, or 

12−21

No stat. 

Change

p < 0.05 Shultz et 

al., 2001

Mgp Matrix Gla protein 500 mg/kg-d GDs 12−21 Up t-test, p < 0.05 Bowman et 

al., 2005
A-38

Table A-2. (continued)

Official 

gene symbol Official gene name* Dose Exposure window Up or down Statistical analysis method Reference

Mmp2 Matrix metallopeptidase 2 500 mg/kg-d GDs 12−19 Up t-test, p < 0.05 Bowman et 

al., 2005

Mmp2 Matrix metallopeptidase 2 500 mg/kg-d GDs 12−21 Up t-test, p < 0.05 Bowman et 

al., 2005

Nfil3 Nuclear factor, interleukin 3 

regulated 

500 mg/kg-d GDs 12−19 Up One way and two-way nested 

ANOVA, p < 0.05 

Liu et al., 

2005

Nfil3 Nuclear factor, interleukin 3 

regulated

500 mg/kg-d GD 19 for 30 min to 6 hr 

timepoints and GD 18 for 

12, 18, and 24 hr time 

points

Up after 

~2−24 hr 

(peak ~6 hr)

Relative expression determined 

using mean Ct; triplicate 

samples; GADPH control; SE; 

but p value not calculated

Thompson 

et al., 2005

Notch2 Notch gene homolog 2 

(Drosophila)

500 mg/kg-d GDs 12−21 Up t-test, p < 0.05 Bowman et 

al., 2005

Npc2 Niemann Pick type C2 500 mg/kg-d GDs 12−19 Down One way and two-way nested 

ANOVA, p < 0.05 

Liu et al., 

2005

Nr0b1 Nuclear receptor 

subfamily 0, group B, 

member 1

500 mg/kg-d GDs 12−19 Down One way and two-way nested 

ANOVA, p < 0.05 

Liu et al., 

2005

Nr0b1 Nuclear receptor 

subfamily 0, group B, 

member 1

500 mg/kg-d GD 19 for 30 min to 6 hr 

timepoints and GD 18 for 

12, 18, and 24 hr time 

points

Down at 

2 hr, Up 

12 hr (peak 

at 12 hr) 

Relative expression determined 

using mean Ct; triplicate 

samples; GADPH control; SE; 

but p value not calculated

Thompson 

et al., 2005

Nr4a1 Nuclear receptor 

subfamily 4, group A, 

member 1

500 mg/kg-d GDs 12−19 Up One way and two-way nested 

ANOVA, p < 0.05 

Liu et al., 

2005

Nr4a1 Nuclear receptor 

subfamily 4, group A, 

member 1

500 mg/kg-d GD 19 for 30 min to 6 hr 

timepoints and GD 18 for 

12, 18, and 24 hr time 

points

Up after 

~6 and 18 hr 

(peak at 

12 hr)

Relative expression determined 

using mean Ct; triplicate 

samples; GADPH control; SE; 

but p value not calculated

Thompson 

et al., 2005
A-39

Table A-2. (continued)

Official 

gene symbol Official gene name* Dose Exposure window Up or down Statistical analysis method Reference

Nr4a3 Nuclear receptor 

subfamily 4, group A, 

member 3

500 mg/kg-d GD 19 for 30 min to 6 hr 

timepoints and GD 18 for 

12, 18, and 24 hr time 

points

Up after 

~1−12 hr 

(peak at 

~3−6 hr)

Relative expression determined 

using mean Ct; triplicate 

samples; GADPH control; SE; 

but p value not calculated

Thompson 

et al., 2005

Pawr PRKC, apoptosis, WT1,

regulator

500 mg/kg-d GD 19 for 30 min to 6 hr 

timepoints and GD 18 for 

12, 18, and 24 hr time 

points

Up after 

~2−24 hr 

(peak ~6 hr)

Relative expression determined 

using mean Ct; triplicate 

samples; GADPH control; SE; 

but p value not calculated

Thompson 

et al., 2005

Pcna Proliferating cell nuclear 

antigen

500 mg/kg-d GDs 12−16, 12−19, or 

12−21

No stat. 

change

p < 0.05 Shultz et 

al., 2001

Prkcbp1 Protein kinase C binding 

protein 1 

500 mg/kg-d GDs 12−19 Up One way and two-way nested 

ANOVA, p < 0.05 

Liu et al., 

2005

Scarb1 Scavenger receptor class B, 

member 1

500 mg/kg-d GDs 12−19 Down ANOVA, nested design, 

p < 0.05

Barlow et 

al., 2003

Scarb1 Scavenger receptor class B, 

member 1

1 mg/kg-d GDs 12−19 Down Dunnett's test, ANOVA (one 

way), p < 0.05

Lehmann et 

al., 2004

Scarb1 Scavenger receptor class B, 

member 1

50 mg/kg-d GDs 12−19 Down Dunnett's test, ANOVA (one 

way), p < 0.05

Lehmann et 

al., 2004

Scarb1 Scavenger receptor class B, 

member 1

100 mg/kg-d GDs 12−19 Down Dunnett's test, ANOVA (one 

way), p < 0.05

Lehmann et 

al., 2004

Scarb1 Scavenger receptor class B, 

member 1

500 mg/kg-d GDs 12−19 Down Dunnett's test, ANOVA (one 

way), p < 0.05

Lehmann et 

al., 2004

Scarb1 Scavenger receptor class B, 

member 1

500 mg/kg-d GDs 12−16, 12−19, or 

12−21

Down p < 0.05 Shultz et 

al., 2001

Scarb1 Scavenger receptor class B, 

member 1

500 mg/kg-d GDs 12−17 and 12−18 Down at 

GDs 17 and 

18

t-test, ANOVA (one-way) with 

Tukey post hoc analysis, 

p < 0.05 

Thompson 

et al., 2004
A-40

Table A-2. (continued)

Official 

gene symbol Official gene name* Dose Exposure window Up or down Statistical analysis method Reference

Sgk Serum/glucocorticoid 

regulated kinase

500 mg/kg-d GD 19 for 30 min to 6 hr 

timepoints and GD 18 for 

12, 18, and 24 hr time 

points

Down and 

Up; Down 

after 2 hr; 

Up after 4 

and 10 hr 

(peak at 

6 hr)

Relative expression determined 

using mean Ct; triplicate 

samples; GADPH control; SE; 

but p value not calculated

Thompson 

et al., 2005

Sostdc1 Sclerostin domain 

containing 1

500 mg/kg-d GD 19 for 30 min to 6 hr 

timepoints and GD 18 for 

12, 18, and 24 hr time 

points

Down after 

2−6 hr; Up 

at 18 hr 

(peak)

Relative expression determined 

using mean Ct; triplicate

samples; GADPH control; SE; 

but p value not calculated

Thompson 

et al., 2005

Star Steroidogenic acute 

regulatory protein

500 mg/kg-d GDs 12−19 Down Repeated measure ANOVA, 

nested design, p < 0.05

Barlow et 

al., 2003

Star Steroidogenic acute 

regulatory protein

50 mg/kg-d GDs 12−19 Down Dunnett's test, ANOVA (one 

way), p < 0.05

Lehmann et 

al., 2004

Star Steroidogenic acute 

regulatory protein

100 mg/kg-d GDs 12−19 Down Dunnett's test, ANOVA (one 

way), p < 0.05

Lehmann et 

al., 2004

Star Steroidogenic acute

regulatory protein

500 mg/kg-d GDs 12−19 Down Dunnett's test, ANOVA (one 

way), p < 0.05

Lehmann et 

al., 2004

Star Steroidogenic acute 

regulatory protein

500 mg/kg-d GDs 12−16, 12−19, or 

12−21

Down at 

GDs 16, 19, 

and 21

p < 0.05 Shultz et 

al., 2001

Star Steroidogenic acute 

regulatory protein

500 mg/kg-d GDs 12−17 and 12−18 Down at 

GDs 17 and 

18

t-test, ANOVA (one-way) with 

Tukey post hoc analysis; 

p < 0.05 

Thompson 

et al., 2004

Star Steroidogenic acute 

regulatory protein

500 mg/kg-d GDs 12.5−19.5 Down One-way ANOVA followed by 

Bonferroni post test using 

GraphPad Prism; p < 0.05

Plummer et 

al., 2007
A-41

Table A-2. (continued)

Official 

gene symbol Official gene name* Dose Exposure window Up or down Statistical analysis method Reference

Stc1 Stanniocalcin 1 500 mg/kg-d GD 19 for 30 min to 6 hr 

timepoints and GD 18 for 

12, 18, and 24 hr time 

points

Up after 

~3−24 hr 

(peak ~6 hr)

Relative expression determined 

using mean Ct; triplicate 

samples; GADPH control; SE; 

but p value not calculated

Thompson 

et al., 2005

Svs5 Seminal vesicle secretion 5 500 mg/kg-d GDs 12−19 Down One way and two-way nested 

ANOVA, p < 0.05 

Liu et al., 

2005

Tcf1 Transcription factor 1 500 mg/kg-d GDs 12−19 Down One way and two-way nested 

ANOVA, p < 0.05 

Liu et al., 

2005

Tcf1 Transcription factor 1 500 mg/kg-d GD 19 for 30 min to 6 hr 

timepoints and GD 18 for 

12, 18, and 24 hr time 

points

Down after 

1−3 hr (peak 

at 1 hr)

Relative expression determined 

using mean Ct; triplicate 

samples; GADPH control; SE; 

but p value not calculated

Thompson 

et al., 2005

Testin Testin gene 500 mg/kg-d GDs 12−19 Up One way and two-way nested 

ANOVA, p < 0.05 

Liu et al., 

2005

Thbs1 Thrombospondin 1 500 mg/kg-d GD 19 for 30 min to 6 hr 

timepoints and GD 18 for 

12, 18, and 24 hr time 

points

Up after 

2−4 hr (peak 

~3 hr)

Relative expression determined 

using mean Ct; triplicate 

samples; GADPH control; SE; 

but p value not calculated

Thompson 

et al., 2005

Timp1 Tissue inhibitor of 

metalloproteinase 1

500 mg/kg-d GDs 12−21 Up t-test, p < 0.05 Bowman et 

al., 2005

Tnfrsf12a Tumor necrosis factor 

receptor superfamily, 

member 12a

500 mg/kg-d GD 19 for 30 min to 6 hr 

timepoints and GD 18 for 

12, 18, and 24 hr time 

points

Up after 

~1−12 hr 

(peak at 

~3 hr)

Relative expression determined 

using mean Ct; triplicate 

samples; GADPH control; SE; 

but p value not calculated

Thompson 

et al., 2005

Wnt4 Wingless-related MMTV 

integration site 4

500 mg/kg-d GD 19 for 30 min to 6 hr 

timepoints and GD 18 for 

12, 18, and 24 hr time 

points

Up after 

~12 and 

18 hr (peak 

12 hr)

Relative expression determined 

using mean Ct; triplicate 

samples; GADPH control; SE; 

but p value not calculated

Thompson 

et al., 2005
A-42

Table A-2. (continued)

Official 

gene symbol Official gene name* Dose Exposure window Up or down Statistical analysis method Reference

Zfp36 Zinc finger protein 36 500 mg/kg-d GD 19 for 30 min to 6 hr 

timepoints and GD 18 for 

12, 18, and 24 hr time 

points

Up after 

30 min and 

6 hr and 

15 and 20 hr 

(peak at 

1 hr)

Relative expression determined 

using mean Ct; triplicate 

samples; GADPH control; SE; 

but p value not calculated

Thompson 

et al., 2005

*

Gene function and pathway information was gathered from GeneGo (www.genego.com).
A-43

Table A-3. Genes identified using the Rosetta Error Model statistical filter 

and mapping to the five most significant biochemical functions and /or 

pathways using Ingenuity

Gene symbol Gene name

Genes mapped to integrin pathway

F2r Coagulation factor II (thrombin) receptor

Src Rous sarcoma oncogene

Gng5 Guanine nucleotide binding protein (G protein), gamma 5 subunit

Gnai3 Guanine nucleotide binding protein, alpha inhibiting 3

Gng7 Guanine nucleotide binding protein, gamma 7

Mapk3 Mitogen activated protein kinase 3

Gnao1 Guanine nucleotide binding protein, alpha o

Actc1 Actin alpha cardiac 1

Camk2d Calcium/calmodulin-dependent protein kinase II, delta

Gnaq Guanine nucleotide binding protein

Cxcl12 Chemokine (C-X-C motif) ligand 12

Prkce Protein kinase C, epsilon

Genes mapped to cholesterol biosynthesis/metabolism

Hmgcs1 3-Hydroxy-3-methylglutaryl-Coenzyme A synthase 1 

Hsd3b1 Hydroxyl-delta-5-steroid dehydrogenase

Dhcr7 7-Dehydrocholesterol reductase

Sqle Squalene epoxidase 

Soat1 Sterol O-acyltransferase 1

Cyp51a1 Cytochrome P450, family 51, subfamily a, polypeptide 1

Cyp27a1 Cytochrome P450, family 27, subfamily a, polypeptide 1

Hsd11b1 Hydroxysteroid 11-beta dehydrogenase 1

Hmgcr 3-Hydroxy-3-methylglutaryl-Coenzyme A reductase

Idi1 Osopentenyl-diphosphate delta isomerase 

Sc4mol Sterol-C4-methyl oxidase-like

Cyp7b1 Cytochrome P450, family 7, subfamily b, polypeptide 1
A-44

Table A-3. (continued)

Gene symbol Gene name

Genes mapped to chemokine mediated signaling

Src Rous sarcoma oncogene

Gng5 Guanine nucleotide binding protein (G protein), gamma 5 subunit

Hmgcs1 3-Hydroxy-3-methylglutaryl-Coenzyme A synthase 1

Serpine2 Serine (or cysteine) proteinase inhibitor, clade E, member 2 

Itgb5 Integrin, beta 5

Dhcr7 7-Dehydrocholesterol reductase 

Gnai3 Guanine nucleotide binding protein, alpha inhibiting 3

Gng7 Guanine nucleotide binding protein, gamma 7

Sqle Squalene epoxidase 

Mapk3 Mitogen activated protein kinase 3

Gnao1 Guanine nucleotide binding protein, alpha o

Actn1 Actinin, alpha 1

Actc1 Actin alpha cardiac 1

Cav2 Caveolin 2

Cyp51a1 Cytochrome P450, family 51, subfamily a, polypeptide 1

Genes mapped to chemokine mediated signaling

Col1a2 Procollagen, type I, alpha 2

Cfl1 Cofilin 1, non-muscle

Cav1 Caveolin 2

Hmgcr 3-Hydroxy-3-methylglutaryl-Coenzyme A reductase 

Mmp2 Matrix metallopeptidase 2

Msn Moesin

Gsk3b Glycogen synthase kinase 3 beta 

Idi1 Isopentenyl-diphosphate delta isomerase 

Plat Plasminogen activator, tissue

Sdc2 Syndecan 2

Sc4mol Sterol-C4-methyl oxidase-like 

Lef1 Lymphoid enhancer binding factor 1
A-45

Table A-3. (continued)

Gene symbol Gene name

Vegf Vascular endothelial growth factor

Genes mapped to glycolysis/gluconeogenesis

Pgk1 Phosphoglycerate kinase 1

Hmgcs1 3-Hydroxy-3-methylglutaryl-Coenzyme A synthase 1 

Tpi1 Triosephosphate isomerase 1

Fbp2 Fructose-1,6-bisphosphatase 2

Dhcr7 7-Dehydrocholesterol reductase

Pfkm Phosphofructokinase, muscle

Pfkp Phosphofructokinase, platelet

Mdh1 Malate dehydrogenase 1, NAD (soluble)

Sqle Squalene epoxidase 

Pgam1 Phosphoglycerate mutase 1

Aldoa Aldolase A

Cyp51a1 Cytochrome P450, family 51, subfamily a, polypeptide 1

Hmgcr 3-Hydroxy-3-methylglutaryl-Coenzyme A reductase

Hk1 Hexokinase 1

Gpi Glucose phosphate isomerase

Gapdh Glyceraldehyde-3-phosphate dehydrogenase

Idi1 Isopentenyl-diphosphate delta isomerase 

Sc4mol Sterol-C4-methyl oxidase-like

Pfkl Phosphofructokinase, liver
A-46

Table A-4. Differentially expressed genes that mapped to statistically 

significant pathways identified using the Signal to Noise Ratio (SNR) 

statistical filter

Gene 

symbol

Entrez 

gene ID Gene name

Aadat 29416 Aminoadipate aminotransferase

Acadm 24158 Acetyl-Coenzyme A dehydrogenase, medium chain

Acads 64304 Acyl-Coenzyme A dehydrogenase, short chain

Acat1 25014 Acetyl-Coenzyme A acetyltransferase 1

Aco2 79250 Aconitase 2, mitochondrial

Acsl4 113976 Acyl-CoA synthetase long-chain family member 4

Akr1b4 24192 Aldo-keto reductase family 1, member B4 (aldose reductase)

Alas1 65155 Aminolevulinic acid synthase 1

Aldh1a4 29651 Aldehyde dehydrogenase family 1, subfamily A4

Aldh2 29539 Aldehyde dehydrogenase 2

Aldh6a1 81708 Aldehyde dehydrogenase family 6, subfamily A1

Aldoa 24189 Aldolase A

Aldoc 24191 Aldolase C, fructose-biphosphate

Ass 25698 Arginosuccinate synthetase

Bhmt 81508 Betaine-homocysteine methyltransferase

Chkb 29367 Choline kinase beta

Cyp11a1 29680 Cytochrome P450, family 11, subfamily a, polypeptide 1

Cyp17a1 25146 Cytochrome P450, family 17, subfamily a, polypeptide 1

Dcxr 171408 Dicarbonyl L-xylulose reductase

Ddc 24311 Dopa decarboxylase

Dhcr7 64191 7-dehydrocholesterol reductase

Ebp 117278 Phenylalkylamine Ca2+ antagonist (emopamil) binding protein

Ephx1 25315 Epoxide hydrolase 1

Fbp2 114508 Fructose-1,6-bisphosphatase 2

Fdft1 29580 Farnesyl diphosphate farnesyl transferase 1

Fdps 83791 Farnesyl diphosphate synthase

Fh1 24368 Fumarate hydratase 1
A-47

Table A-4. (continued)

Gene 

symbol

Entrez 

gene ID Gene name

G6pdx 24377 Glucose-6-phosphate dehydrogenase

Gad2 24380 Glutamate decarboxylase 2

Gapdh 24383 Glyceraldehyde-3-phosphate dehydrogenase

Gatm 81660 Glycine amidinotransferase (L-arginine:glycine amidinotransferase)

Ggtl3 156275 Gamma-glutamyltransferase-like 3

Gsta2 24422 Glutathione-S-transferase, alpha type2

Gsta3 24421 Glutathione S-transferase A5

Gstm2 24424 Glutathione S-transferase, mu 2

Gstm3 81869 Glutathione S-transferase, mu type 3

Hmgcr 25675 3-hydroxy-3-methylglutaryl-Coenzyme A reductase

Hmgcs1 29637 3-hydroxy-3-methylglutaryl-Coenzyme A synthase 1

Idh1 24479 Isocitrate dehydrogenase 1 (NADP+

), soluble

Me1 24552 Malic enzyme 1

Mgst1 171341 Microsomal glutathione S-transferase 1

Mif 81683 Macrophage migration inhibitory factor

Mvd 81726 Mevalonate (diphospho) decarboxylase

Nos1 24598 Nitric oxide synthase 1, neuronal

Pycr2 364064 Pyrroline-5-carboxylate reductase family, member 2 (predicted)

Sqle 29230 Squalene epoxidase

Suclg1 114597 Succinate-CoA ligase, GDP-forming, alpha subunit

Tpi1 24849 Tpi1 protein
A-48

Table A-5. GeneGo pathway analysis of significant genes identified by REM

Pathway Biological process p-Valuea

No. of 

genesb,c

NF-AT signaling in cardiac hypertrophy Disease 2.23E−04 19/90

MIF—the neuroendocrine-macrophage 

connector

Immune response 3.00E−04 19/92

Lysine metabolism Amino acid metabolism 3.05E−04 9/27

Cholesterol metabolism Steroid metabolism 6.95E−04 6/14

Glycolysis and gluconeogenesis (short 

map)

Carbohydrates metabolism 7.40E−04 10/36

Integrin-mediated cell adhesion Cell adhesion 8.44E−04 18/92

Tryptophan metabolism Amino acid metabolism 9.56E−04 9/31

Cholesterol biosynthesis Steroid metabolism 1.44E−03 7/21

ECM remodeling Cell adhesion 1.64E−03 13/60

Regulation of lipid metabolism via 

PPAR, RXR, and VDR

Transcription 1.96E−03 7/22

Propionate metabolism p.2 Carbohydrates metabolism 1.96E−03 7/22

PPAR regulation of lipid metabolism Regulation of lipid metabolism 2.04E−03 8/28

Mitochondrial long chain fatty acid 

beta-oxidation

Lipid metabolism 2.28E−03 6/17

Role of VDR in regulation of genes 

involved in osteoporosis

Transcription 3.16E−03 12/57

ChREBP regulation pathway G-protein coupled receptor 

signaling

3.82E−03 10/44

Androstenedione and testosterone 

biosynthesis and metabolism p.1 

Steroid metabolism 4.30E−03 6/19

Arginine metabolism Amino acid metabolism 4.45E−03 9/38

Regulation of fatty acid synthesis: 

NLTP and EHHADH 

Regulation of lipid metabolism 5.02E−03 4/9

Angiotensin signaling via STATs Growth and differentiation 5.18E−03 11/53

Cytoskeleton remodeling Cell adhesion 5.19E−03 26/176

dGTP metabolism Nucleotide metabolism 5.34E−03 9/39

TCA Amino acid metabolism 5.70E−03 6/20

Glycolysis and gluconeogenesis p. 1 Carbohydrates metabolism 5.70E−03 6/20

Peroxisomal branched chain fatty acid 

oxidation 

Lipid metabolism 5.70E−03 6/20
A-49

Table A-5. (continued)

Pathway Biological process p-valuea

No. of 

genesbc

Gamma-aminobutyrate (GABA) 

biosynthesis and metabolism 

Metabolism of mediators 5.70E−03 6/20

Ligand-dependent activation of the 

ESR1/SP pathway 

Response to hormone stimulus 6.38E−03 9/40

Integrin inside-out signaling Cell adhesion 6.85E−03 14/78

Reverse signaling by ephrin B Cell adhesion 6.86E−03 15/86

G-protein beta/gamma signaling 

cascades 

G-protein coupled receptor 

protein signaling pathway 

6.94E−03 11/55

Activation of PKC via G-Protein 

coupled receptor 

G-protein coupled receptor 

protein signaling pathway 

7.65E−03 15/87

Gap junctions Cell adhesion 8.51E−03 10/49

WNT signaling pathway Proteolysis 8.59E−03 7/28

Angiotensin activation of ERK G-protein coupled receptor 

protein signaling pathway

9.12E−03 11/57

Role of Akt in hypoxia induced HIF1 

activation 

Proteolysis 9.83E−03 10/50

Regulation of actin cytoskeleton by Rho 

GTPases 

Small GTPase mediated signal 

transduction 

1.18E−02 11/59

CCR3 signaling in eosinophils Immune response 1.22E−02 18/117

MAG-dependent inhibition of neurite 

outgrowth 

Response to extracellular 

stimulus 

1.47E−02 10/53

Endothelial cell contacts by junctional 

mechanisms 

Cell adhesion 1.80E−02 7/32

Fructose metabolism Carbohydrates metabolism 1.80E−02 7/32

Regulation of lipid metabolism via LXR, 

NF-Y and SREBP 

Transcription 1.80E−02 7/32

CXCR4 signaling pathway Cytokine and chemokine 

mediated signaling pathway

1.89E−02 10/55

Serotonin-melatonin biosynthesis and 

metabolism 

Metabolism of mediators 2.04E−02 5/19

Glycolysis and gluconeogenesis p. 2 Carbohydrates metabolism 2.15E−02 4/13

Oxidative phosphorylation Energy metabolism 2.37E−02 15/99

Urea cycle Amino acid metabolism 2.58E−02 6/27

G-proteins mediated regulation p.38 and 

JNK signaling 

G-protein coupled receptor 

protein signaling pathway 

2.60E−02 11/66
A-50

Table A-5. (continued)

Pathway Biological process p-valuea

No. of 

genesbc

Transcription factor tubby signaling 

pathways 

Transcription 2.63E−02 8/42

Role PKA in cytoskeleton reorganization Protein kinase cascade 2.64E−02 13/83

Ephrins signaling Cell adhesion 2.66E−02 10/58

Propionate metabolism p.1 Carbohydrates metabolism 2.81E−02 4/14

Estrone metabolism Steroid metabolism 2.81E−02 4/14

Regulation of acetyl-CoA carboxylase 2 

activity in muscle 

Response to extracellular 

stimulus 

2.81E−02 4/14

Chemokines and adhesion Cytokine and chemokine 

mediated signaling pathway

2.82E−02 23/174

Arachidonic acid production Lipid metabolism 2.87E−02 7/35

dCTP/dUTP metabolism Nucleotide metabolism 2.99E−02 8/43

Regulation of lipid metabolism by niacin 

and isoprenaline 

Regulation of lipid metabolism 3.01E−02 9/51

Ubiquinone metabolism Vitamin and cofactor 

metabolism 

3.01E−02 9/51

Phenylalanine metabolism Amino acid metabolism 3.05E−02 6/28

Leptin signaling via JAK/STAT and 

MAPK cascades 

Response to hormone stimulus 3.57E−02 6/29

IMP biosynthesis Nucleotide metabolism 3.70E−02 3/9

EPO-induced Jak-STAT pathway Response to extracellular 

stimulus 

3.78E−02 7/37

Integrin outside-in signaling Cell adhesion 3.95E−02 12/79

Brca1 as transcription regulator Cell cycle 4.15E−02 6/30

P53 signaling pathway Transcription regulation 4.28E−02 8/46

Bile acid biosynthesis Steroid metabolism 4.43E−02 5/23

Histidine-glutamate-glutamine and 

proline metabolism 

Amino acid metabolism 4.79E−02 8/47

NTS activation of IL-8 in colonocytes Immune response 4.85E−02 10/64

a

Ordered from most significant (lowest p-value) to less significant. b

Number of genes from the DBP-exposed gene list mapping to the GeneGo pathway. c

Total number of genes in the GeneGo pathway. 
A-51

Table A-6. Significant biological pathways corresponding to differentially expressed genes (DEGs) obtained 

from SNR analysis input into GeneGo

Pathway Biological Process p-Valuea No. of genesbc

Cholesterol Biosynthesis Steroid metabolism 1.81E−09 15/21

Propionate metabolism p.2 Carbohydrates metabolism 5.54E−06 12/22

MIF—the neuroendocrine-macrophage connector Immune response 3.22E−04 25/92

Tryptophan metabolism Amino acid metabolism 3.78E−04 12/31

Lysine metabolism Amino acid metabolism 3.93E−04 11/27

Cholesterol metabolism Steroid metabolism 1.09E−03 7/14

NF-AT signaling in cardiac hypertrophy Disease 1.38E−03 23/90

Glycolysis and gluconeogenesis (short map) Carbohydrates metabolism 1.77E−03 12/36

G-alpha(q) regulation of lipid metabolism Regulation of lipid metabolism 1.93E−03 13/41

Activation of PKC via G-protein coupled receptor G-proteins/GPCR 2.00E−03 22/87

Fructose metabolism Carbohydrates metabolism 2.06E−03 11/32

Regulation of lipid metabolism by niacin and isoprenaline Regulation of lipid metabolism 2.08E−03 15/51

ATP metabolism Nucleotide metabolism 2.09E−03 16/56

Angiotensin activation of ERK Growth and differentiation 2.55E−03 16/57

NTS activation of IL-8 in colonocytes Immune response 3.60E−03 17/64

Leucine, isoleucine, and valine metabolism.p.2 Amino acid metabolism 3.64E−03 9/25
A-52

Table A-6. (continued)

Pathway Biological Process p-Valuea No. of genesbc

Reverse signaling by ephrin B Cell adhesion 3.92E−03 21/86

Cortisone biosynthesis and metabolism Steroid metabolism 4.31E−03 7/17

CXCR4 signaling pathway Immune response 4.63E−03 15/55

G-Protein beta/gamma signaling cascades G-proteins/GPCR 4.63E−03 15/55

Glutathione metabolism Vitamin and cofactor metabolism 5.77E−03 11/36

Mitochondrial ketone bodies biosynthesis and metabolism Lipid metabolism 5.96E−03 5/10

Integrin inside-out signaling Cell adhesion 6.07E−03 19/78

Propionate metabolism p.1 Carbohydrates metabolism 6.51E−03 6/14

Role of VDR in regulation of genes involved in osteoporosis Transcription factors 6.63E−03 15/57

Endothelial cell contacts by junctional mechanisms Cell adhesion 7.02E−03 10/32

EPO-induced Jak-STAT pathway Cell survival 7.24E−03 11/37

A3 receptor signaling G-proteins/GPCR 8.08E−03 19/80

Angiotensin signaling via STATs Growth and differentiation 8.28E−03 14/53

MAG-dependent inhibition of neurite outgrowth Growth and differentiation 8.28E−03 14/53

Phenylalanine metabolism Amino acid metabolism 8.48E−03 9/28

Androstenedione and testosterone biosynthesis and metabolism p.1 Steroid metabolism 8.76E−03 7/19

Cytoskeleton remodeling Cell adhesion 9.69E−03 35/176
A-53

Table A-6. (continued)

Pathway Biological Process p-Valuea No. of genesbc

ChREBP regulation pathway Regulation of transcription 1.08E−02 12/44

Leptin signaling via JAK/STAT and MAPK cascades Growth and differentiation 1.09E−02 9/29

dGTP metabolism Nucleotide metabolism 1.10E−02 11/39

TCA Amino acid metabolism 1.20E−02 7/20

Glycolysis and gluconeogenesis p. 1 Carbohydrates metabolism 1.20E−02 7/20

Gamma-aminobutyrate (GABA) biosynthesis and metabolism Metabolism of mediators 1.20E−02 7/20

BAD phosphorylation Apoptosis 1.21E−02 19/83

Ligand-dependent activation of the ESR1/SP pathway Hormones 1.34E−02 11/40

RAB5A regulation pathway G-proteins/RAS-group 1.49E−02 5/12

Integrin outside-in signaling Cell adhesion 1.50E−02 18/79

Hedgehog and PTH signaling pathways participation in bone and 

cartilage development Growth and differentiation 1.62E−02 11/41

G-Proteins mediated regulation MARK-ERK signaling G-proteins/GPCR 1.64E−02 17/74

Integrin-mediated cell adhesion Cell adhesion 1.78E−02 20/92

Mitochondrial long chain fatty acid beta-oxidation Lipid metabolism 1.88E−02 6/17

CCR3 signaling in eosinophils Immune response 2.02E−02 24/117

Regulation of lipid metabolism via PPAR, RXR, and VDR Transcription factors 2.07E−02 7/22
A-54

Table A-6. (continued)

Pathway Biological Process p-Valuea No. of genesbc

Glycolysis and gluconeogenesis p. 2 Carbohydrates metabolism 2.16E−02 5/13

Regulation of fatty acid synthesis: NLTP and EHHADH Regulation of lipid metabolism 2.30E−02 4/9

Role PKA in cytoskeleton reorganization Kinases 2.43E−02 18/83

Arginine metabolism Amino acid metabolism 2.44E−02 10/38

ECM remodeling Cell adhesion 2.45E−02 14/60

Ca (2+)-dependent NF-AT signaling in cardiac hypertrophy Disease 2.55E−02 15/66

WNT signaling pathway Growth and differentiation 2.64E−02 8/28

PPAR regulation of lipid metabolism Regulation of lipid metabolism 2.64E−02 8/28

Insulin regulation of the protein synthesis Translation regulation 2.67E−02 13/55

CXCR4 signaling via second messenger Immune response 2.67E−02 13/55

Angiotensin signaling via beta-Arrestin Growth and differentiation 2.71E−02 11/44

Estrone metabolism Steroid metabolism 2.99E−02 5/14

Regulation of acetyl-CoA carboxylase 2 activity in muscle Growth and differentiation 2.99E−02 5/14

Prolactin receptor signaling Growth factors 3.19E−02 14/62

Triacylglycerol metabolism p.1 Lipid metabolism 3.23E−02 8/29

Serotonin-melatonin biosynthesis and metabolism Metabolism of mediators 3.27E−02 6/19

Angiotensin signaling via PYK2 Growth and differentiation 3.32E−02 16/74
A-55

Table A-6. (continued)

Pathway Biological Process p-Valuea No. of genesbc

G-Protein alpha-i signaling cascades G-proteins/GPCR 3.36E−02 12/51

dATP/dITP metabolism Nucleotide metabolism 3.86E−02 12/52

Brca1 as transcription regulator Cell-cycle control 3.90E−02 8/30

Ephrins signaling Cell adhesion 3.99E−02 13/58

Mitochondrial unsaturated fatty acid beta-oxidation Lipid metabolism 4.01E−02 5/15

GDNF signaling Growth and differentiation 4.08E−02 7/25

Aspartate and asparagine metabolism Amino acid metabolism 4.15E−02 6/20

Peroxisomal branched chain fatty acid oxidation Lipid metabolism 4.15E−02 6/20

Histidine-glutamate-glutamine and proline metabolism Amino acid metabolism 4.24E−02 11/47

TGF-beta receptor signaling Growth and differentiation 4.51E−02 13/59

Regulation of actin cytoskeleton by Rho GTPases G-proteins/RAS-group 4.51E−02 13/59

G-Protein alpha-s signaling cascades G-proteins/GPCR 4.51E−02 13/59

A1 receptor signaling G-proteins/GPCR 4.61E−02 16/77

Membrane-bound ESR1: interaction with growth factors signaling Growth and differentiation 4.64E−02 10/42

Transcription factor Tubby signaling pathways Regulation of transcription 4.64E−02 10/42

Histamine metabolism Metabolism of mediators 4.83E−02 4/11

PPAR pathway Transcription factors 4.86E−02 11/48
A-56

Table A-6. (continued)

Pathway Biological Process p-Valuea No. of genesbc

Cross-talk VEGF and angiopoietin 1 signaling Growth and differentiation 5.08E−02 9/37

EPO-induced MAPK pathway Growth and differentiation 5.08E−02 13/60

a

Ordered from most significant (lowest p-value) to less significant. b

Number of genes from the DBP exposed gene list mapping to the GeneGo pathway.

c

Total number of genes in the GeneGo pathway.
A-57

Figure A-1. Algorithm for selecting differentially expressed genes 

(DEGs) using signal-to-noise ration (SNR). 1,000 random gene 

expressions were generated for each probe set, and then, SNRs were 

calculated. The ratio of the randomly generated SNR that was higher 

than the actual SNR determined whether individual probe set’s 

expression was discriminating or not.

For a given gene 

expression

Evaluate SNR between 

control and treated 

samples

Select 

random gene 

expression

Evaluate SNR for randomly selected 

gene expression

Random SNR > SNR

Random Expression = random Expression +1

i = 1000

Statistical Significance = (Random Expression/1000)*100

Statistical Significance < 0.05 Given gene expression 

is random 

Given gene expression is 

statistically significant

No

No

Yes

Yes

Yes

No

For a given gene 

expression

Evaluate SNR between 

control and treated 

samples

Select 

random gene 

expression

Evaluate SNR for randomly selected 

gene expression

Random SNR > SNR

Random Expression = random Expression +1

i = 1000

Statistical Significance = (Random Expression/1000)*100

Statistical Significance < 0.05 Given gene expression 

is random 

Given gene expression is 

statistically significant

No

No

Yes

Yes

Yes

No
B-1

APPENDIX B.

SUPPORTING TABLES AND FIGURES FOR CHAPTER 6

Appendix B contains additional tables and figures supportive of the work described in 

Chapter 6.

Table B-1. Nodes added by using Ingenuity® Pathway Analysis (IPA) 

software in developing the gene network model for DBP

Gene 

symbol Gene name

Aco1 Aconitase 1, soluble

Esrra Estrogen-related receptor alpha

Fgf4 Fibroblast growth factor 4 

Insig1 Insulin induced gene 1

Kcnj11 Potassium inwardly-rectifying channel, subfamily J, member 11

Lep Leptin 

Lnpep Leucyl/cystinyl aminopeptidase

Nfic Nuclear factor I/C (CCAAT-binding transcription factor)

Nme1 Non-metastatic cells 1, protein (NM23A) expressed in

Nr2f1 Nuclear receptor subfamily 2, group F, member 1

Nr5a1 Nuclear receptor subfamily 5, group A, member 1

Pld2 Phospholipase D2

Ppargc1b Peroxisome proliferative activated receptor, gamma, coactivator 1, beta

Srebf1 Sterol regulatory element binding transcription factor 1

Srebf2 Sterol regulatory element binding transcription factor 2

Zdhhc23 Zinc finger, DHHC-type containing 23
B-2

Figure B-1. Algorithm for selecting significant pathways using the pathway 

activity method. 1,000 random sets of gene expressions were generated for each 

pathway, then pathway activity, PAp, was evaluated. The p-value of each PAp is 

computed as the fraction of the randomized PAp that exceeded the actual PAp.
C-1

APPENDIX C.

QUALITY CONTROL AND ASSURANCE

Appendix C contains quality assurance/quality control (QA/QC) information for the work 

described in Chapters 5 and 6. The work described in this Appendix (C) is secondary data 

analysis. The studies include exploratory studies using new methods for analyzing genomic data 

for risk assessment purposes as well as some preliminary analyses using well-established of the 

raw data from two published studies. 

Three projects were performed: 

(1) A qualitative analysis and presentation of the 9 toxicogenomic DBP studies. No 

statistical analyses were performed by members of our team. 

(2) In-house analysis of the raw data from Liu et al. (2005) study performed at both 

NHEERL, US EPA by Drs. Susan Hester and Banalata Sen, and by by collaborators, Dr. 

Ioannis Androulakis and Meric Ovacik, STAR Grantees at the STAR Bioinformatics 

Center at Rutgers/UMDNJ.

(3) New analyses of Thompson et al. (2005) data performed by collaborators, Dr. Ioannis 

Androulakis and Meric Ovacik, STAR Grantees at the STAR Bioinformatics Center at 

Rutgers/UMDNJ.

C.1. PROJECT 1

The data presented in 9 published toxicogenomic studies for DBP were compared. No 

additional analyses were performed. Data were entered directly into an excel spreadsheet from 

the published literature. Study descriptions in tables and figures were developed. The data entry 

process included team members entering in the data from the published articles into tables for 

differentially expressed genes and pathways affected. One person entered the data for a subset of 

genes. A second person checked the results in the table against the articles.

C.2. PROJECT 2

The data source was the DBP treatment only data from the Liu et al. (2005) study. The 

Liu et al. (2005) data were kindly provided by Dr. Kevin Gaido, a collaborator on this project. 

The study was performed in his laboratory at The Hamner Institutes for Health Sciences 

(formerly CIIT). His QA statement for the collection and analysis of the data is provided below.
C-2

C.3. PROJECT 3

The data source was the Thompson et al. (2005) study. The Thompson et al. (2005) data 

were kindly provided by Dr. Kevin Gaido, a collaborator on this project. The study was 

performed in his laboratory at The Hamner Institutes for Health Sciences (formerly CIIT). His 

QA statement for the collection and analysis of the data is provided below.

C.4. PROJECTS 2 AND 3: DATA SOURCES 

The sources of the data used in the secondary analyses were the Liu et al. (2005) and 

Thompson et al. (2005) studies. Both of these studies were performed in the laboratory of Dr. 

Kevin Gaido. The QA details for the two studies are presented below. The Hamner Institute’s 

Quality Assurance Director is Patricia O. Pomerleau, M.S., RQAP (pomerleau@thehamner.org).

C.4.1. Sample Handling Procedures

Virgin female SD outbred CD rats, 8 weeks old, were time mated. Dams were assigned 

to a treatment group by randomization using Provantis NT 2000 and subsequently be identified 

by an ear tag and cage card. Dams were kept in the Association for Assessment and 

Accreditation of Laboratory Animal Care International accredited animal facility at The Hamner 

Institute (at the time of the two studies, The Hmaner was named CIIT) in a humidity- and 

temperature-controlled, high-efficiency particulate-air-filtered, mass air-displacement room.

Dams were treated by gavage daily from gestation day (GD) 12−19 with corn oil (vehicle 

control) and dibutyl phthalate. Body weights were recorded daily before dosing (GDs 12−19). 

The oral treatments were administered on a mg/kg-body weight basis and adjusted daily for 

weight changes. Animal doses were calculated through Provantis NT 2000. All calculations 

were checked by a second individual and recorded in the investigators’ The Hamner Institute 

notebooks. Analytical support staff confirmed appropriate dose solutions at the beginning of the 

dosing period. Body weights and doses administered were recorded each day in Provantis NT 

2000. Pups and dams were euthanized by carbon dioxide asphyxiation. 

Fetal tissues for RIA’s and RNA isolation were snap frozen in liquid nitrogen and stored 

at −80o

C. The remaining tissues were either be embedded in optical coherence tomography and 

frozen or fixed in formalin for 6 to 24 hours followed by 70% ethanol and then processed and 

embedded in paraffin for histological examination within 48 hours. The embedded tissues were 
C-3

sectioned at approximately 5 microns and stained with hematoxylin and eosin. The study 

pathologist in consultation with the histology staff determined the gross trim, orientation, and 

embedding procedure for each tissue. RNA were isolated from the frozen male reproductive 

tract, and changes in gene expression were identified by real-time reverse 

transcription-polymerase chain reaction (RT-PCR) analysis (following manufacturer’s protocols 

P/N 402876 and P/N 4304965, Applied Biosystems, Foster City, CA) and in some cases, by 

complementary DNA (cDNA) microarray (following manufacturers protocol PT3140, Clontech, 

Palo Alto, CA).

Total RNA were treated with DNase I at 37°C for 30 minutes in the presence of RNasin 

to remove DNA contamination before cDNA synthesis, followed by heat inactivation at 75°C for 

5 minutes. Primer pairs were selected using the program Primer Express and optimized for use 

prior to quantification. cDNA were synthesized using random hexamers and TaqMan Reverse 

Transcription Reagents according to the manufacturer’s suggested protocol. Real-time PCR 

(TaqMan) were performed on a Perkin-Elmer/Applied Biosystems 7500 Prism using TaqMan 

probe chemistry according to the manufacturer's instructions for quantification of relative gene 

expression. Relative differences among treatment groups were determined using the CT method 

as outlined in the Applied Biosystems protocol for reverse transcriptase(RT)-PCR. A CT value 

was calculated for each sample using the CT value for glyceraldehyde-3-phosphate 

dehydrogenase (or an appropriate housekeeping gene) to account for loading differences in the 

RT-PCRs.

C.4.2. Microarray Hybridization

Testes from individual fetuses were homogenized in RNA Stat 60 reagent (Tel-Test, Inc., 

Friendswood, TX) and RNA was isolated using the RNeasy Mini Kit (Qiagen, Valencia, CA) 

following manufacturer’s protocol. RNA integrity was assessed using the Agilent 2100 

Bioanalyzer (Agilent Technologies, Palo Alto, CA), and optical density was measured on a 

NanoDrop ND 1000 (NanoDrop Technologies, Wilmington, DE). cDNA was synthesized from 

2.5 or 3 μg total RNA and purified using the Affymetrix® One-Cycle Target Labeling and 

control reagents kit (Affymetrix, Santa Clara, CA) according to manufacturer’s protocol. Equal 

amounts of purified cDNA per sample were used as the template for subsequent in vitro 

transcription reactions for complementary RNA (cRNA) amplification and biotin labeling using 
C-4

the Affymetrix GeneChip® IVT labeling kit (Affymetrix) included in the One-Cycle Target 

Labeling kit (Affymetrix). cRNA was purified and fragmented according to the protocol 

provided with the GeneChip® Sample Cleanup module (Affymetrix). All GeneChip® arrays 

were hybridized, washed, stained, and scanned using the Complete GeneChip® Instrument 

System according to the Affymetrix Technical Manual.

For immunocytochemistry, tissues were rapidly removed, immersed in 10% (v/v) 

neutral-buffered formalin for 24−48 hours, and then stored in ethanol 70% (v/v) until processed. 

The reproductive tissues were embedded in paraffin, sectioned at 5 μ, and processed for 

immunohistochemistry or stained with hematoxylin and eosin. 

Experimental notes and data were entered into uniquely numbered Hamner Institute 

laboratory notebooks and three-ring binders along with descriptions of procedures used, 

according to SOP# QUA-007. Specimens (RNA and frozen tissue) were retained until analysis 

or discarded after a maximum of 1 year after collection. Formalin-fixed tissues, blocks, and 

slides were archived at the end of the study. Retention of these materials will be reassessed after 

5 years.

C.4.3. Quality Assurance

Both QA and QC procedures are integral parts of our research program. The research 

was conducted under the The Hamner Institute Research Quality Standards program. These 

standards include (1) scientifically reviewed protocols that are administratively approved for 

meeting requirements in data quality, animal care, and safety regulations; (2) standardized 

laboratory notebooks and data recording procedures; (3) documented methods or standard 

operating procedures for all experimental procedures—including calibration of instruments; (4) a 

central managed archive for specimens and documentation; and (5) internal peer review for 

scientific quality of abstracts and manuscripts. The Hamner Institute QA and QC processes 

assessing overall study performance and records ensure that conduct of the proposed research 

satisfies the intended project objectives.

C.4.4. Statistical Analysis

RT-PCR data were analyzed using JMP statistical analysis software (SAS Institute, Cary, 

NC). RNA were isolated from at least 3 pups from 3 different dams for each treatment group. 
C-5

PCR reactions, radioimmunoassays, and protein analysis were repeated 3−5 times for each 

sample. Based on our experience, the number of animal replicates has the statistical power to 

detect a significant change in gene expression >20% at p < 0.05. The effect of treatment was 

analyzed using a general-linear model regression analysis. Posthoc tests were conducted when 

the overall analysis of variance is significant at the p < 0.05 level using the LS-means procedure 

and adjusted for multiple comparisons by Dunnett's method.

Microarray data were analyzed by a linear mixed model with SAS Microarray Solution 

software. Perfect-match only data were normalized to a common mean on a log2 scale, and a 

linear mixed model was then applied for each probe set. Restricted maximum likelihood was 

used for estimating the parameters for both the fixed and random effects. Significance was 

determined using mixed-model based F-tests (p < 0.05).

C.4.5. Procedures used to Evaluate Success

Uniquely numbered written protocols were prepared and reviewed internally prior to the 

start of this study. The content of a protocol includes study design, materials, laboratory 

methods, sample collection, handling and custody, record keeping, data analysis and statistical 

procedures, animal care requirements, and safety measures. Numbered standardized laboratory 

notebooks and guidelines for date recording ensures completeness of data and the ability to 

reconstruct the study. An independent QA department manages the overall research data quality. 

Manuscripts describing the results of our study were prepared at the completion of each stage of 

this study. All manuscripts undergo a rigorous internal peer review that includes review by all 

authors, at least two additional PhD- level scientists, the science editor, the division manager, 

and the vice president for research.

C.5. PROJECT 2: DATA REVIEW, VERIFICATION, AND VALIDATION

Banalata Sen received the Liu et al. (2005) raw data files from Dr. Kevin Gaido. Two 

team members, Dr. Banalata Sen (National Center for Environmental Assessment, Research 

Triangle Park [NCEA-RTP]) and Dr. Susan Hester (National Health and Environmental Effects 

Research Laboratory [NHEERL]) performed the data analysis at NHEERL, RTP. Barbara 

Collins (collins.barbara@epa.gov) at NHEERL-RTP has agreed to serve as the Quality 

Assurance Manager (QAM) for the project. Dr. Hester and Sen performed analyses of the “DBP 
C-6

only” data that is a subset of the data presented in Liu et al. (2005). The analyses at NHEERL 

included statistical filtering to identify of differentially expressed genes and pathway analysis.

C.5.1. Verification of Data upon Receipt

Upon receiving data from Kevin Gaido at the Hamner Institute, EPA NHEERL scientisits 

conducted a QA review of the data by gross inspection of the cel files to confirm that the data 

had been transmitted successfully. The scientists at the STAR Bioinformatics Center/Rutgers 

received the data files from Susan Euling at EPA NCEA who had received the data from Kevin 

Gaido at the Hamner Institute. Kevin Gaido gave permission to Susan Euling to provide the data 

for these analyses. A review of the data was performed by inspection of the txt files and the 

published data to confirm that the data had been transmitted successfully. 

C.5.2. Verification of Data Analysis Calculations

EPA NHEERL used a principal component analysis (PCA) to evaluate the within-group 

and across-group variance of the six samples. PCA elucidates the separation of different 

treatment groups and provides information about whether the data contain significant 

information. This was conducted using the raw data cel files in Rosetta Resolver Software. The 

analyses were in silico without functional validation (RT-PCR of individual genes). 

The Star Bioinformatics Center also performed a principal component analysis (PCA) 

and displayed a 3-D plot to evaluate the within-group and across-group variance of the samples. 

This was conducted using the txt files in MATLAB® Software. This was an in silico analysis. 

The data were normalized to a zero mean and a unity standard deviation over samples. They 

assessed the degree of separation for Liu et al. (2005) data. A regular regular t-test and ANOVA 

analyses of the data were performed. The filtered data were visualized in a heatmap to determine 

the statistically significant subset of genes to provide a differentially expressed gene (DEG) list.

Drs. Susan Hester and Banalata Sen also performed some comparative analyses between 

the two outpus (above). The two independent analyses of the same dataset were contrasted with 

one another. Correlation plots comparing the Log10 average intensities of control samples vs. 

DBP treated samples was performed in order to determine the noise in both groups. Average 

background signal and scaling factors will be applied based on the vendor recommendations. 

QC plots will be made to determine the relationship between light intensity and each genechip. 
C-7

C.6. PROJECT 3: DATA REVIEW, VERIFICATION, AND VALIDATION

This project analyzed the time-course data from Thompson et al. (2005) dataset to then 

build a regulatory network model. The STAR Center’s internal QA/QC procedures are 

implemented and monitored by a QA official, Clifford Weisel (weisel@eohsi.rutgers.edu), at 

Rutgers University that reports to the National Center for Environmental Research (NCER), the 

granting organization for the STAR program.

C.6.1. Verification of Data upon Receipt

Data were received from Susan Euling at EPA who had received the data from Kevin 

Gaido at the Hamner Institute. Kevin Gaido gave permission to Susan Euling to provide the data 

for these analyses. A review of the data was performed by inspection of the txt files and the 

published data to confirm that the data had been transmitted successfully. 

C.6.2. Verification of Data Analysis Calculations

A principal component analysis (PCA) was performed and a 3-D plot was displayed to 

evaluate the within-group and across-group variance of the samples. This was conducted using 

the txt files in MATLAB® Software. This was an in silico analysis. The data were normalized 

to a zero mean and a unity standard deviation over samples. They assessed the degree of 

separation for the Thompson et al. (2005) data. A regular regular t-test and ANOVA analyses of 

the data were performed. The filtered data will be visualized in a heatmap to determine the 

statistically significant subset of genes to provide a differentially expressed gene (DEG) list.
G-1

GLOSSARY

Amplified Fragment Length Polymorphism Polymerase Chain Reaction (AFLP-PCR or 

AFLP): A PCR-based DNA fingerprinting tool that is a highly sensitive method for detecting 

DNA polymorphisms.

Benchmark Dose (BMD) or Concentration (BMC): A dose or concentration that produces a 

predetermined change in response rate of an adverse effect (called the benchmark response or 

BMR) compared to background.

Complementary DNA (cDNA): DNA synthesized from a mature mRNA template in a reaction 

catalyzed by the enzyme reverse transcriptase.

Copy Number Polymorphism (CNP): The normal variation in the number of copies of a gene 

or of sequences of DNA in the genome of an individual.

Exposure: Contact made between a chemical, physical, or biological agent and the outer 

boundary of an organism. Exposure is quantified as the amount of an agent available at the 

exchange boundaries of the organism (e.g., skin, lungs, gut). 

Exposure Assessment: An identification and evaluation of the human population exposed to a 

toxic agent, describing its composition and size, as well as the type, magnitude, frequency, route 

and duration of exposure. 

Expressed Sequence Tag (EST): A short subsequence of a transcribed cDNA sequence, 

produced by sequencing of a cloned mRNA representing portions of expressed genes, which can 

be used to identify gene transcripts.

Gene Network: An illustration of the interactions between genes and gene products based on 

gene expression and other molecular information curated from the published literature.
G-2

Gene Ontology (GO): A bioinformatics initiative of the GO Consortium with the goal of 

standardizing terminology for describing gene and gene product characteristics across species 

and databases. The GO has developed three structured vocabularies (ontologies), independent of 

species, to describe gene products in terms of their associated: 1) biological processes; 2) cellular 

components; and 3) molecular functions. The GO also provides tools to access and process these 

data. 

Genomics: The study of the structure and function of the whole genome. This term can also 

refer to “genomic technologies,” defined as methods to study the genome at the level of DNA 

(including genome sequencing and genotype analysis). Sometimes this term refers more 

generally to all of the methods to study the genome (see –omics).

Genomic Technologies: Methods to study the genome including genome sequencing 

technologies and genotype analysis. 

Hazard Assessment: The process of determining whether exposure to an agent can cause an 

increase in the incidence of a particular adverse health effect (e.g., cancer, birth defect) and 

whether the adverse health effect is likely to occur in humans. 

Hazard Characterization: A description of the potential adverse health effects attributable to a 

specific environmental agent, the mechanisms by which agents exert their toxic effects, and the 

associated dose, route, duration, and timing of exposure. 

Human Health Risk Assessment: The evaluation of scientific information on the hazardous 

properties of environmental agents (hazard characterization), the dose-response relationship 

(dose-response assessment), and the extent of human exposure to those agents (exposure 

assessment). The product of the risk assessment is a statement regarding the probability that 

populations or individuals so exposed will be harmed and to what degree (risk characterization).

Key Event: An empirically observable precursor step that is, itself, a necessary element of the 

mode of action or is a biologically based marker for such an element.
G-3

Lowest Observed Adverse Effect Level (LOAEL): The lowest exposure level at which there 

are biologically significant increases in the frequency or severity of adverse effects between the 

exposed population and its appropriate control group.

Lowest Observed Effect Level (LOEL): In a study, the lowest dose or exposure level at which 

a statistically or biologically significant effect is observed in the exposed population compared 

with an appropriate unexposed control group. 

Mechanism of Action: The complete molecular sequence of events between the interaction of 

the chemical with the target site and observation of the outcome. Thus, the mechanism of action 

can include toxicokinetic and/or toxicodynamic steps. 

Metabolic Pathway Network: An illustration of interactions between metabolites derived from 

pathway information curated from the published literature.

Metabolomics: The analysis of collections of small molecule metabolic intermediates and 

products of diverse biologic processes. 

Microarray: A transcriptomics tool for analyzing gene expression that consists of a small 

membrane or glass slide containing samples of many genes arranged in a regular pattern.

Microarray Quality Control (MAQC): An FDA project that was developed to provide 

quality-control tools, guidelines, and standard operating procedures (SOPs) to the microarray 

community in order to avoid procedural failures. To facilitate this effort, the MAQC has 

provided the public with large reference data sets and reference RNA samples. 

Mode of Action (MOA): One or a sequence of key events, that a particular outcome is 

dependent upon (i.e., part of the causal pathway and not a coincident event).

No Observed Adverse Effect Level (NOAEL): The highest exposure level at which there are 

no biologically significant increases in the frequency or severity of adverse effect between the 
G-4

exposed population and its appropriate control; some effects may be produced at this level, but 

they are not considered adverse or precursors of adverse effects.

No Observed Effect Level (NOEL): An exposure level at which there are no statistically or 

biologically significant increases in the frequency or severity of any effect between the exposed 

population and its appropriate control.

-omics: A suffix that is used as a general term for the genome-wide study of biological 

information objects (or “omes”), such as toxicogenome, proteome, and metabolome; a term 

referring to all of the methods for assessing the genome including genomics, metabolomics, 

proteomics, and transcriptomics.

Physiologically Based Pharmacokinetic (PBPK) Model: A model that estimates the dose to a 

target tissue or organ by taking into account the rate of absorption into the body, distribution 

among target organs and tissues, metabolism, and excretion. 

Principal Component Analysis (PCA): A technique for analysis of multivariate data involving 

a mathematical procedure that transforms a number of possibly correlated variables into a 

smaller number of uncorrelated variables, called principal components. 

Proteomics: The study of the protein complement of the genome of an organism. 

Reverse Transcription-Polymerase Chain Reaction (RT-PCR): A two-step process for 

converting mRNA to cDNA, using the enzyme reverse transcriptase, and the subsequent PCR 

amplification of the reversely transcribed DNA using the enzyme DNA polymerase.

Serial Analysis of Gene Expression (SAGE): A technique based on sequencing strings of short 

expressed sequence tags (ESTs) representing both the identity and the frequency of occurrence 

of specific sequences within the transcriptome. This method allows the entire collection of 

transcripts to be catalogued without assumptions about which transcripts are actually expressed.
G-5

Single-Nucleotide Polymorphism (SNP): A DNA sequence variation occurring when a single 

nucleotide in the genome (or other shared sequence) differs between members of a species or 

between paired chromosomes in an individual.

Singular Value Decomposition (SVD): A technique for the analysis of multivariate data where 

a rectangular, real or complex matrix, is factorized. SVD has been extensively used in 

microarray data analysis in order to achieve a linear projection of the data and represent these 

data in a reduced dimensionality space which further enables clustering and visualization of gene 

expression data patterns.

Toxicogenomics: The application of genomic technologies to study the adverse effects of 

environmental and pharmaceutical chemicals on human health and the environment.

Transcriptomics: A set of techniques to measure genome-wide mRNA expression that are used 

to understand the expression of genes and pathways involved in biological processes; also called 

“gene expression profiling.”
R-1

REFERENCES

Aardema, MJ; MacGregor, JT. (2002) Toxicology and genetic toxicology in the new era of “toxicogenomics”: 

impact of “-omics” technologies. Mutat Res 499:13–25.

Alter, O; Brown, PO; Botstein, D. (2000) Singular value decomposition for genome-wide expression data 

processing and modeling. Proc Natl Acad Sci U S A, 97(18):10101-10106.

Andersen, ME; Thomas, RS; Gaido, KW; et al. (2005) Dose-response modeling in reproductive toxicology in the 

systems biology era. Reprod Toxicol 19(3):327–337.

Andersen, ME; Clewell, HJ 3rd; Bermudez, E; et al. (2008) Genomic signatures and dose-dependent transitions in 

nasal epithelial responses to inhaled formaldehyde in the rat. Toxicol Sci. 105(2):368-383.

Anderson, NL; Anderson, NG. (1998) Proteome and proteomics: new technologies, new concepts, and new words. 

Electrophoresis 19(11):1853–1861.

Ball, CA; Brazma, A; Causton, H; et al. (2004) Submission of microarray data to public repositories. PLoS Biol 

2(9):E317.

Barlow, NJ; Foster, PM. (2003) Pathogenesis of male reproductive tract lesions from gestation through adulthood 

following in utero exposure to di(n-butyl) phthalate. Toxicol Pathol 31(4):397–410.

Barlow, NJ; Phillips, SL; Wallace, DG; et al. (2003) Quantitative changes in gene expression in fetal rat testes 

following exposure to di(n-butyl) phthalate. Toxicol Sci 73(2):431–441.

Barlow, NJ; McIntyre, BS; Foster, PM. (2004) Male reproductive tract lesions at 6, 12, and 18 months of age 

following in utero exposure to di(n-butyl) phthalate. Toxicol Pathol 32:79–90.

BASF. (1992) Study on the oral toxicity of dibutyl phthalate in Wistar rats. Administration via the diet over 

3 months. Prepared by BASF Corporation, Ludwigshafen, Germany for Eastman Kodak Company; 

Project No. 31S0449//89020; Submitted under TSCA Section 8D; EPA Document No. 86-920000903; 

NTIS OTS0535640 (As cited in NTP-CERHR, 2000).

Bodin, L; Beaune, PH; Loriot, MA. (2005) Determination of cytochrome P450 2D6 (CYP2D6) gene copy number 

by real-time quantitative PCR. J Biomed Biotechnol 2005(3):248–253.

Boverhof, DR; Zacharewski, TR. (2006) Toxicogenomics in risk assessment: applications and needs. Toxicol Sci 

89(2):352–360.

Bowman, CJ; Turner, KJ; Sar, M; et al. (2005) Altered gene expression during rat Wolffian duct development 

following di(n-butyl) phthalate exposure. Toxicol Sci 86:161–174.

Brazma, A; Hingamp, P; Quackenbush, J; et al. (2001) Minimum information about a microarray experiment 

(MIAME)—toward standards for microarray data. Nat Genet 29(4):365–371. 

Buckley, PG; Mantripragada, KK; Piotrowski, A; et al. (2005) Copy-number polymorphisms: mining the tip of an 

iceberg. Trends Genet 21(6):315–317.

Canales, RD; Luo, Y; Willey, JC; et al. (2006) Evaluation of DNA microarray results with quantitative gene 

expression platforms. Nat Biotechnol 24(9):1115–1122. doi:10.1038/nbt1236. Available online at 

http://www.nature.com/nbt/journal/v24/n9/pdf/nbt1236.pdf.

Cangelosi, R; Goriely, A. (2007) Component retention in principal component analysis with application to cDNA 

microarray data. Biol Direct 2:2.
R-2

Carruthers, CM; Foster, P. (2005) Critical window of male reproductive tract development in rats following 

gestational exposure to di-n-butyl phthalate. Birth Defects Res B Dev Reprod Toxicol 74(3):277–285.

Casanova, M; Morgan, KT; Gross, EA; et al. (1994) DNA-protein cross-links and cell replication at specific sites in 

the nose of F344 rats exposed subchronically to formaldehyde. Fundam Appl Toxicol 23(4):525−536. 

Casciano, DA; Woodcock, J. (2006) Empowering microarrays in the regulatory setting (forward). Nat Biotechnol 

24(9):1103.

Ceder, R; Merne, M; Staab, C; et al. (2007) The application of normal, SV40 T-antigen-immortalized and tumorderived oral keratinocytes, under serum-free conditions, to the study of the probability of cancer progression as a 

result of environmental exposure to chemicals. Altern Lab Anim 35(6):621−639.

Chan, VS; Theilade, MD. (2005) The use of toxicogenomic data in risk assessment: a regulatory perspective. Clin 

Toxicol 43(2):121–126.

Choe, SE; Boutros, M; Michelson, AM; et al. (2005) Preferred analysis methods for Affymetrix GeneChips revealed 

by a wholly defined control dataset. Genome Biol 6(2):R16. 

Clemente, JC; Satou, K; Valiente, G. (2005) Reconstruction of phylogenetic relationships from metabolic pathways 

based on the enzyme hierarchy and the gene ontology. Genome Inform 16(2):45–55.

Coen, M; Lenz, EM; Nicholson, JK; et al. (2003) An integrated metabonomic investigation of acetaminophen 

toxicity in the mouse using NMR spectroscopy. Chem Res Toxicol 16(3):295–303.

Cunningham, ML; Bogdanffy, MS; Zacharewski, T; et al. (2003) Workshop overview: use of genomic data in risk 

assessment. Toxicol Sci 73(2):209–215. 

Daston, GP. (2007) Genomics and developmental risk assessment. Birth Defects Res A Clin Mol Teratol 79:1–7.

Daston, GP. (2008) Gene expression, dose-response, and phenotypic anchoring: applications for toxicogenomics in 

risk assessment. Toxicol Sci. 105(2):233-234.

David, RM. (2006) Proposed mode of action for in utero effects of some phthalate esters on the developing male 

reproductive tract. Toxicol Pathol 34(3):209–19. Available online at 

http://tpx.sagepub.com/cgi/reprint/34/3/209.pdf.

Dix, DJ; Gallagher, K; Benson, WH; et al. (2006) A framework for the use of genomics data at the EPA. Nat 

Biotechnol 24(9):1108–1111. Available online at http://www.nature.com/nbt/journal/v24/n9/pdf/nbt0906-1108.pdf.

Donato, MT; Lahoz, A; Castell, JV; et al. (2008) Cell lines: a tool for in vitro drug metabolism studies. Curr Drug 

Metab 9(1):1−11.

Dudoit, S; Shaffer, JP; Boldrick, JC. (2003) Multiple hypothesis testing in microarray experiments. Stat Sci 

18(1):71–103.

Ekins, S; Nikolsky, Y; Nikolskaya, T. (2005) Techniques: application of systems biology to absorption, distribution, 

metabolism, excretion and toxicity. Trends Pharmacol Sci 26(4):202–209.

Ellinger-Ziegelbauer, H; Stuart, B; Wahle, B; et al. (2005) Comparison of the expression profiles induced by 

genotoxic and nongenotoxic carcinogens in rat liver. Mutat Res 575(1–2):61–84.

El-Masri, H; Constan, AA; Ramsdell, HS; et al. (1996) Physiologically based pharmacodynamic modeling of an 

interaction threshold between trichloroethylene and 1,1-dichloroethylene in Fischer 344 rats. Toxicol Appl 

Pharmacol 141:124–132.

El-Masri, H; Bell, DA; Portier, CJ. (1999) Effects of glutathione transferase theta polymorphism on the risk 

estimates of dichloromethane to humans. Toxicol Appl Pharmacol 158(3):221–230.
R-3

Ema, M; Miyawaki, E. (2001a) Adverse effects on development of the reproductive system in male offspring of rats 

given monobutyl phthalate, a metabolite of dibutyl phthalate, during late pregnancy. Reprod Toxicol

15(2):189−194.

Ema, M; Miyawaki, E. (2001b) Effects of monobutyl phthalate on reproductive function in pregnant and 

pseudopregnant rats. Reprod Toxicol 15(3):261−267.

Ema, M; Amano, H; Itami, T; et al. (1993) Teratogenic evaluation of di-n-butyl phthalate in rats. Toxicol Lett

69(2):197−203.

Ema, M; Amano, H; Ogawa, Y. (1994) Characterization of the developmental toxicity of di-n-butyl phthalate in rats. 

Toxicology 86(3):163−174.

Ema, M; Kurosaka, R; Amano, H; et al. (1995) Developmental toxicity evaluation of mono-n-butyl phthalate in rats. 

Toxicol Lett 78(2):101−106.

Ema, M; Kurosaka, R; Harazono, A; et al. (1996) Phase specificity of developmental toxicity after oral 

administration of mono-n-butyl phthalate in rats. Arch Environ Contam Toxicol 31(2):170−176.

Ema, M; Harazono, A; Miyawaki, E; et al. (1997) Developmental effects of di-n-butyl phthalate after a single 

administration in rats. J Appl Toxicol 17(4):223−229.

Ema, M; Miyawaki, E; Kawashima, K. (1998) Further evaluation of developmental toxicity of di-n-butyl phthalate 

following administration during later pregnancy in rats. Toxicol Lett 98(1−2):87−93.

Ema, M; Miyawaki, E; Kawashima, K. (2000a) Effects of dibutyl phthalate on reproductive function in pregnant and 

pseudopregnant rats. Reprod Toxicol 14(1):13−19.

Ema, M; Miyawaki, E; Kawashima, K. (2000b) Critical period for adverse effects on development of reproductive 

system in male offspring of rats given di-n-butyl phthalate during later pregnancy. Toxicol Lett 111(3):271−278.

Fang, H; Tong, W; Perkins, R; et al. (2005) Bioinformatics approaches for cross-species liver cancer analysis based 

on microarray gene expression profiling. BMC Bioinformatics 6(Suppl 2):S6.

Fennell, TR; Krol, WL; Sumner, SC; et al. (2004) Pharmacokinetics of dibutylphthalate in pregnant rats. Toxicol 

Sci 82(2):407−418. 

Ferrara, D; Hallmark, N; Scott, H; et al. (2006) Acute and long-term effects of in utero exposure of rats to

di(n-butyl) phthalate on testicular germ cell development and proliferation. Endocrinology 147(11):5352–5362. 

Fisher, JS; Macpherson, S; Marchetti, N; et al. (2003) Human ‘testicular dysgenesis syndrome’: a possible model 

using in-utero exposure of the rat to dibutyl phthalate. Hum Reprod 18(7):1389–1394.

Forst, CV. (2002) Network genomics--a novel approach for the analysis of biological systems in the post-genomic 

era. Mol Biol Rep 29(3):265−280.

Forst, CV; Schulten, K. (1999) Evolution of metabolisms: a new method for the comparison of metabolic pathways 

using genomics information. J Comput Biol 6(3−4):343−360.

Foster, PMD. (2005) Mode of action: impaired fetal Leydig cell function—effects on male reproductive 

development produced by certain phthalate esters. Crit Rev Toxicol 35(8−9):713–719.

Frueh, FW. (2006) Impact of microarray data quality on genomic data submissions to the FDA. Nature Biotechnol 

24(9):1105−1107. Available online at http://www.nature.com/nbt/journal/v24/n9/pdf/nbt0906-1105.pdf.

Frueh, FW; Huang, SM; Lesko, LJ. (2004) Regulatory acceptance of toxicogenomics data. Environ Health Perspect 

112(12):A663−A664. 
R-4

Geng, J; Strobel, HW. (1995) Identification of inducible mixed function oxidase system in rat glioma C6 cell line. J 

Neurochem 65(2):554−563. 

Gibbs, RA; Weinstock, GM; Metzker, ML; et al. (2004) Genome sequence of the Brown Norway Rat yields insights 

into mammalian evolution. Nature 428(6982):493-521.

Ginsberg, G; Smolenski, S; Hattis, D; et al. (2002) Population distribution of aldehyde dehydrogenase-2 genetic 

polymorphism: implications for risk assessment. Regul Toxicol Pharmacol 36(3):297–309.

Golub, TR; Slonim, DK; Tamayo, P; et al. (1999) Molecular classification of cancer: class discovery and class 

prediction by gene expression monitoring. Science 286(5439):531−537.

Gray, LE, Jr; Wolf, C; Lambright, C; et al. (1999) Administration of potentially antiandrogenic pesticides 

(procymidone, linuron, chlozolinate, p,p’-DDE, and ketoconazole) and toxic substances (dibutyl- and diethylhexyl 

phthalate, PCB 169, and ethane dimethane sulphonate) during sexual differentiation produces diverse profiles of 

reproductive malformations in the male rat. Toxicol Indust Health 15(1−2):94−118.

Gray, LE, Jr; Ostby, J; Furr, J; et al. (2000) Perinatal exposure to the phthalates DEHP, BBP, and DINP, but not 

DEP, DMP, or DOTP, alters sexual differentiation of the male rat. Toxicol Sci 85(2):350−365.

Gray, LE, Jr.; Ostby, J; Furr, J; et al. (2001) Effects of environmental antiandrogens on reproductive development in 

experimental animals. Hum Reprod Update 7(3):248−264.

Guerreiro, N; Staedtler, F; Grenet, O; et al. (2003) Toxicogenomics in drug development. Toxicol Pathol 

31(5):471−479.

Guo, L; Lobenhofer, EK; Wang, C; et al. (2006) Rat toxicogenomic study reveals analytical consistency across 

microarray platforms. Nat Biotechnol 24(9):1162−1169. Available online at 

http://www.nature.com/nbt/journal/v24/n9/pdf/nbt1238.pdf. 

Hackett, JL; Lesko, LJ. (2003) Microarray data—the US FDA, industry, and academia. Nat Biotechnol 

21(7):742−743.

Hamadeh, HK; Bushel, PR; Jayadev, S; et al. (2002a) Gene expression analysis reveals chemical-specific profiles. 

Toxicol Sci 67(2):219−231.

Hamadeh, HK; Bushel, PR; Jayadev, S; et al. (2002b) Prediction of compound signature using high density gene 

expression profiling. Toxicol Sci 67(2):232−240. 

Hanisch, D; Zien, A; Zimmer, R; et al. (2002) Co-clustering of biological networks and gene expression data. 

Bioinformatics 18 Suppl 1:S145−S154.

Hedberg, JJ; Grafstrom, RC; Vondracek, M; et al. (2001) Micro-array chip analysis of carbonyl-metabolizing 

enzymes in normal, immortalized and malignant human oral keratinocytes. Cell Mol Life Sci 58(11):1719−1726.

Heymans, M; Singh, AK. (2003) Deriving phylogenetic trees from the similarity analysis of metabolic pathways. 

Bioinformatics 19 (Suppl 1):i138-i146.

Higuchi, TT; Palmer, JS; Gray, LE, Jr; et al. (2003) Effects of dibutyl phthalate in male rabbits following in utero, 

adolescent, or postpubertal exposure. Toxicol Sci 72(2):301−313. 

Ji, H; Davis, RW. (2006) Data quality in genomics and microarrays. Nat Biotechnol 24(9):1112−1113. Available 

online at http://www.nature.com/nbt/journal/v24/n9/pdf/nbt0906-1112.pdf.

Kanehisa, M; Goto, S. (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28(1):27−30.

Kavlock, R; Ankley, GT; Collette, T; et al. (2005) Computational toxicology: framework, partnerships, and program 

development: September 29−30, 2003, Research Triangle Park, North Carolina. Reprod Toxicol 19(3):265−280. 
R-5

Kennedy, S. (2002) The role of proteomics in toxicology: identification of biomarkers of toxicity by protein 

expression analysis. Biomarkers 7(4):269−290.

Kienhuis, AS; van de Poll, MCG; Wortelboer, H; et al. (2009) Parallelogram approach using rat-human in vitro and 

rat in vivo toxicogenomics predits acetaminophen-induced hepatotoxicity in humans. Toxicol Sci 107(2):544−552.

Kim, T; Shin, J; Lee, S; et al. (2004) Effects of in utero exposure of diethylstilbestrol and dibutyl phthalate on the 

testis descent in rat offspring. Abstract # 574. Toxicologist 78:118.

Kleymenova, E; Dei Rossi, E; Liu, K; et al. (2004) Developmental exposure to di-n-butyl phthalate affects cord 

organization and Sertoli cell-gonocyte interactions in the fetal rat testis. Abstract # 926. Toxicologist 78:190.

Kleymenova, E; Swanson, C; Gaido, KW. (2005a) Low dose cellular responses in the fetal rat testis in utero 

exposed to di-n-butyl phthalate. Abstract #1372. Toxicologist 79:281.

Kleymenova, E; Swanson, C; Boekelheide, K; et al. (2005b) Exposure in utero to di(n-butyl) phthalate alters the 

vimentin cytoskeleton of fetal rat Sertoli cells and disrupts Sertoli cell-gonocyte contact. Biol Reprod 

73(3):482−490. 

Klonisch, T; Fowler, PA; Hombach-Klonisch, S. (2004) Molecular and genetic regulation of testis descent and 

external genitalia development. Dev Biol 270:1−18.

Lee, K-Y; Shibutani, M; Takagi, H; et al. (2004) Diverse developmental toxicity of di-n-butyl phthalate in both 

sexes of rat offspring after maternal exposure during the period from late gestation through lactation. Toxicology 

203(1−3):221−238.

Lehmann, KP; Phillips, S; Sar, M; et al. (2004) Dose-dependent alterations in gene expression and testosterone 

synthesis in the fetal testes of male rats exposed to di (n-butyl) phthalate. Toxicol Sci 81(1):60−68.

Leighton, JK. (2005) Application of emerging technologies in toxicology and safety assessment: regulatory 

perspectives. Int J Toxicol 24(3):153−155. 

Liu, K; Lehmann, KP; Sar, M; et al. (2005) Gene expression profiling following in utero exposure to phthalate esters 

reveals new gene targets in the etiology of testicular dysgenesis. Biol Reprod 73(1):180−192.

Lobenhofer, EK; Bushel, PR; Afshari, CA; et al. (2001) Progress in the application of DNA microarrays. Environ 

Health Perspect 109(9):881−891.

Mahood, IK; Hallmark, N; McKinnell, C; et al. (2005) Abnormal Leydig cell aggregation in the fetal testis of rats 

exposed to di (n-butyl) phthalate and its possible role in testicular dysgenesis. Endocrinol 146(2):613−623.

Main, KM; Mortensen, GK; Kaleva, MM; et al. (2006) Human breast milk contamination with phthalates and 

alterations of endogenous reproductive hormones in infants three months of age. Environ Health Perspect

114(2):270−276.

Manoli, T; Gretz, N; Gröne, HJ; et al. (2006) Group testing for pathway analysis improves comparability of different 

microarray datasets. Bioinformatics 22(20):2500-2506. 

McKinnell, C; Sharpe, RM; Mahood, K; et al. (2005) Expression of insulin-like factor 3 protein in the rat testis 

during fetal and postnatal development and in relation to cryptorchidism induced by in utero exposure to di (nButyl) phthalate. Endocrinology 146(10):4536−4544.

Miller, WL. (1988) Molecular biology of steroid hormone synthesis. Endocr Rev 9(3):295−318. 

Miller, WL. (2008) Steroidogenic enzymes. Endocr Dev 13:1-18. 

Moggs, JG. (2005) Molecular responses to xenoestrogens: mechanistic insights from toxicogenomics. Toxicology 

213(3):177−193.
R-6

Moggs, JG; Tinwell, H; Spurway, T; et al. (2004) Phenotypic anchoring of gene expression changes during 

estrogen-induced uterine growth. Environ Health Perspect 112(16):1589−1606.

Mootha, VK; Lindgren, CM; Eriksson, KF; et al. (2003) PGC-1alpha-responsive genes involved in oxidative 

phosphorylation are coordinately downregulated in human diabetes. Nat Genet 34(3):267−73.

Mylchreest, E; Cattley, RC; Foster, PMD. (1998) Male reproductive tract malformations in rats following 

gestational and lactational exposure to di(n-butyl) phthalate: an antiandrogenic mechanism? Toxicol Sci 43:47−60.

Mylchreest, E; Sar, M; Cattley, RD; et al. (1999) Disruption of androgen-regulated male reproductive development 

by di(n-butyl) phthalate during late gestation in rats is different from flutamide. Toxicol Appl Pharmacol 

156(2):81−95.

Mylchreest, E; Wallace, DG; Cattley, RC; et al. (2000) Dose-dependent alterations in androgen-regulated male 

reproductive development in rats exposed to di(n-butyl) phthalate during late gestation. Toxicol Sci 55:143−151.

Mylchreest, E; Sar, M; Wallace, DG; et al. (2002) Fetal testosterone insufficiency and abnormal proliferation of 

Leydig cells and gonocytes in rats exposed to di(n-butyl) phthalate. Repro Toxicol 16:19−28.

Naciff, JM; Jump, ML; Torontali SM; et al. (2002) Gene expression profile induced by 17alpha-ethynyl estradiol, 

bisphenol A, and genistein in the developing female reproductive system of the rat. Toxicol Sci 68(1):184−199.

Naciff, JM; Overmann, GJ; Torontali, SM; et al. (2003) Gene expression profile induced by 17 alpha-ethynyl 

estradiol in the prepubertal female reproductive system of the rat. Toxicol Sci 72(2):314−330. 

Naciff, JM; Hess, KA; Overmann, GJ; et al. (2005) Gene expression changes induced in the testis by transplacental 

exposure to high and low doses of 17α-ethynyl estradiol, genistein, or bisphenol A. Toxicol Sci 86(2):396−416. 

NRC (National Research Council) (2007a) Applications of toxicogenomic technologies to predictive toxicology and 

risk assessment. Washington, DC: National Academy Press.

NRC (National Research Council). (2007b) Toxicity testing in the 21st century. Washington, DC: National 

Academy Press.

NTP (National Toxicology Program). (1991) Final report on the reproductive toxicity of di-n-butyl phthalate (Cas. 

No. 84-74-3) in Sprague-Dawley rats. U.S. Department of Health and Human Services, Public Health Service, 

National Institutes of Health, National Toxicity Program. Research Triangle Park, NC. NTP Report # RACB89012. 

NTIS # PB92-111996.

NTP (National Toxicology Program). (1995) NTP technical report on toxicity studies of dibutyl phthalate 

administered in feed to F344/N rats and B6C3F1 mice. Toxicity Report Series Number 30. U.S. Department of 

Health and Human Services, Public Health Service, National Institutes of Health, National Toxicity Program. 

Research Triangle Park, NC. Available online at http://ntp.niehs.nih.gov/ntp/htdocs/ST_rpts/tox030.pdf.

NTP-CERHR (National Toxicology Program – Center for the Evaluation of Risks to Human Reproduction). (2000) 

NTP-CERHR monograph on the potential human reproductive and developmental effects of di-n-butyl phthalate 

(DBP). National Toxicology Program, U.S. Department of Health and Human Services, Research Triangle Park, 

NC. Available online at http://cerhr.niehs.nih.gov/chemicals/phthalates/dbp/DBP_Monograph_Final.pdf.

Oberemm, A; Onyon, L; Gundert-Remy, U. (2005) How can toxicogenomics inform risk assessment? Toxicol Appl 

Pharmacol 207(2 Suppl):592−598. 

Omura, T; Morohashi, K. (1995) Gene regulation of steroidogenesis. J Steroid Biochem Mol Biol 53(1−6):19−25.

Pandey, A; Mann, M. (2000) Proteomics to study genes and genomes. Nature 405(6788):837 −846.
R-7

Patterson, TA; Lobenhofer, EK; Fulmer-Smentek, FB; et al. (2006) Performance comparison of one-color and twocolor platforms within the Microarray Quality Control (MAQC) project. Nat Biotechnol 24(9):1140 −1150. 

Available online at http://www.nature.com/nbt/journal/v24/n9/pdf/nbt1242.pdf. 

Pennie, WD; Pettit, SD; Lord, PG. (2004) Toxicogenomics in risk assessment – An overview of an ILSI HESI 

collaborative research program. Environ Health Perspect 112(4):417−419.

Pettit, SD; Lord, PG; Pennie, WD. (2003) The application of genomics to mechanism-based risk assessment—

overview of an ILSI HESI collaborative research program. J Toxicol Sci 28(4):220 −221. 

Plummer, S; Hallmark, N; Sharpe, R; et al. (2005) Identification of gene clusters and signaling pathways affected by 

dibutyl phthalate—nuclear receptor interactions in fetal rat testes. Abstract #2258. Toxicologist 79:463.

Plummer, S; Sharpe, RM; Hallmark, N; et al. (2007) Time-dependent and compartment-specific effects of in utero 

exposure to di(n-butyl) phthalate on gene/protein expression in the fetal rat testis as revealed by transcription 

profiling and laser capture microdissection. Toxicol Sci 97(2):520−352.

Rahnenführer, J; Domingues, FS; Maydt, J; et al. (2004) Calculating the statistical significance of changes in 

pathway activity from gene expression data. Stat Appl Genet Mol Biol 3:Article 16.

Raunio, H; Hakkola, J; Hukkanen, J; et al. (1999) Expression of xenobiotic-metabolizing CYPs in human pulmonary 

tissue. Exp Toxicol Pathol 51(4−5):412−417.

Reynolds, VL. (2005) Applications of emerging technologies in toxicology and safety assessment. Int J Toxicol 

24(3):135−137. 

Robinson, DE; Pettit, SD; Morgan, DG. (2003) Use of genomics in mechanism based risk assessment. In: Inoue, T; 

Pennie, WD; eds., Toxicogenomics. Tokyo: Springer-Verlag, pp. 194−203. 

Saillenfait, AM; Payan, JP; Fabry, JP; et al. (1998) Assessment of the developmental toxicity, metabolism, and 

placental transfer of di-n-butyl phthalate administered to pregnant rats. Toxicol Sci 45(2):212−224.

Sayers, EW; Barrett, T; Benson, DA; et al. (2008) Database resources of the National Center for Biotechnology 

Information. Nucleic Acids Res. 37(9):3124. 

Schlosser, PM; Lilly, PD; Conolly, RB; et al. (2003) Benchmark dose risk assessment for formaldehyde using 

airflow modeling and a single-compartment, DNA-protein cross-link dosimetry model to estimate human equivalent 

doses. Risk Anal 23(3):473−487.

Searls, DB. (2003) Pharmacophylogenomics: genes, evolution and drug targets. Nat Rev Drug Discov. 2(8):613-

623.

Shastry, BS. (2006) Pharmacogenetics and the concept of individualized medicine. Pharmacogenomics J 6:16−21.

Shi, L; Tong, W; Goodsaid, F; et al. (2004) QA/QC: challenges and pitfalls facing the microarray community and 

regulatory agency. Expert Rev Mol Diagn 4(6):761−777.

Shi, L; Reid, LH; Jones, WD; et al. (2006) The MicroArray Quality Control (MAQC) project shows inter- and 

intraplatform reproducibility of gene expression measurements. Nat Biotechnol 24(9):1151−1161. 

Shippy, R; Fulmer-Smentek, S; Jensen, RV; et al. (2006) Using RNA sample titrations to assess microarray platform 

performance and normalization techniques. Nat Biotechnol 24(9):1123−1131. Available online at 

http://www.nature.com/nbt/journal/v24/n9/abs/nbt1241.html.

Shultz, VD; Phillips, S; Sar, M; et al. (2001) Altered gene profiles in fetal rat testes after in utero exposure to 

di(n-butyl) phthalate. Toxicol Sci 64(2):233−242.
R-8

Sladek, R; Bader, JA; Giguere, V. (1997) The orphan nuclear receptor estrogen-related receptor alpha is a 

transcriptional regulator of the human medium-chain acyl coenzyme A dehydrogenase gene. Mol Cell Biol

17(9):5400−5409.

Simmons, PT; Portier, CJ. (2002) Toxicogenomics: the new frontier in risk analysis. Carcinogenesis 23(6):903–905. 

Sohler, F; Hanisch, D; Zimmer, R. (2004) New methods for joint analysis of biological networks and expression 

data. Bioinformatics 20(10):1517−1521.

Smith, LL. (2001) Key challenges for toxicologists in the 21st century. Trends Pharmacol Sci 22(6):281−285. 

Srivastava, SP; Srivastava, S; Saxena, DK; et al. (1990) Testicular effects of di-n-butyl phthalate (DBP): 

biochemical and histopathological alterations. Arch Toxicol 64:148-152. 

Staab, CA; Ceder, R; Roberg, K; et al. (2008) Serum-responsive expression of carbonyl-metabolizing enzymes in 

normal and transformed human buccal keratinocytes. Cell Mol Life Sci 65(22):3653−3663.

Stocco, DM; Wang, X; Jo, Y; et al. (2005) Multiple signaling pathways regulating steroidogenesis and steroidogenic 

acute regulatory protein expression: More complicated than we thought. Mol Endocrinol 19(11): 2647-2659.

Swan, SH; Main, KM; Liu, F; et al. (2005) Decrease in anogenital distance among male infants with prenatal 

phthalate exposure. Environ Health Perspect 113(8):1056−1061. Erratum in: Environ Health Perspect 

113(9):A583. 

Swanson, HI. (2004) Cytochrome P450 expression in human keratinocytes: an aryl hydrocarbon receptor 

perspective. Chem Biol Interact 149(2–3):69−79. 

Thomas, RS; Allen, BC; Nong, A; et al. (2007) A method to integrate benchmark dose estimates with genomic data 

to assess the functional effects of chemical exposure. Toxicol Sci 98(1):240−248. 

Thompson, CJ; Ross, SM; Gaido, KW. (2004) Di(n-butyl) phthalate impairs cholesterol transport and 

steroidogenesis in the fetal rat testis through a rapid and reversible mechanism. Endocrinology 145(3):1227–1237. 

Thompson, CJ; Ross, SM; Hensley, J; et al. (2005) Differential steroidogenic gene expression in the fetal adrenal 

versus the testis and rapid and dynamic response of the fetal testis to di(n-butyl) phthalate. Biol Reprod 

73(5):908−917.

Thybaud, V; Le Fevre, A; Boitier, E. (2007) Application of toxicogenomics to genetic toxicology risk assessment. 

Environ Mol Mutagen 48:369−379.

Tilton, SC; Orner, GA; Benninghoff, AD; et al. (2008) Genomic profiling reveals an alternate mechanism for 

hepatic tumor promotion by perfluorooctanoic acid in rainbow trout. Environ Health Perspect 116(8):1047−1055. 

Available online at http://www.ehponline.org/docs/2008/11190/abstract.html.

Tomfohr, J; Lu, J; Kepler, TB. (2005) Pathway level analysis of gene expression using singular value 

decomposition. BMC Bioinformatics 6:225.

Tong, W; Lucas, AB; Shippy, R; et al. (2006) Evaluation of external RNA controls for the assessment of microarray 

performance. Nat Biotechnol 24(9):1132–1139. Available online at 

http://www.nature.com/nbt/journal/v24/n9/pdf/nbt1237.pdf.

U.S. EPA (Environmental Protection Agency). (1995) Reregistration eligibility decision (RED): Linuron. Office of 

Prevention, Pesticides and Toxic Substances, Washington, DC. EPA 738-R-95-003. Available from the National 

Technical Information Service, Springfield, VA, and online at http://www.epa.gov/oppsrrd1/REDs/0047.pdf.

U.S. EPA (Environmental Protection Agency). (1998) Reregistration eligibility decision (RED): Iprodione. Office of 

Prevention, Pesticides and Toxic Substances, Washington, DC. EPA 738-R-98-017. Available from the National 
R-9

Technical Information Service, Springfield, VA, and online at 

http://www.epa.gov/oppsrrd1/REDs/factsheets/2335fact.pdf.

U.S. EPA (Environmental Protection Agency). (2000a) Reregistration Eligibility Decision (RED): Vinclozolin. 

Office of Prevention, Pesticides and Toxic Substances, Washington, DC. EPA 738-R-00-023. Available from the 

National Technical Information Service, Springfield, VA, and online at 

http://www.epa.gov/opp00001/reregistration/REDs/2740red.pdf.

U.S. EPA (Environmental Protection Agency). (2002a) Interim genomics policy. Prepared by the Science Policy 

Council, Washington, DC. Available from the National Technical Information Service, Springfield, VA, and online 

at http://www.epa.gov/osa/spc/genomics.htm.

U.S. EPA (Environmental Protection Agency). (2002b) Tolerance reassessment progress and risk management 

decision (TRED) for linuron. Office of Prevention, Pesticides and Toxic Substances, Washington, DC. Available 

online at http://www.epa.gov/oppsrrd1/REDs/linuron_tred.pdf.

U.S. EPA (Environmental Protection Agency). (2004a) A framework for a computational toxicology research 

program. Office of Research and Development, National Center for Computational Toxicology, Washington, DC. 

Available online at http://www.epa.gov/comptox/comptox_framework.html..

U.S. EPA (Environmental Protection Agency). (2004b) Potential implications of genomics for regulatory and risk 

assessment applications at EPA. Genomics Task Force Workgroup, Science Policy Council, Washington, DC; EPA 

100/B-04/002. Available online at http://www.epa.gov/OSA/pdfs/EPA-Genomics-White-Paper.pdf.

U.S. EPA (Environmental Protection Agency). (2004c) Final Report for Acetochlor. Part 2:

U.S. EPA (Environmental Protection Agency). (2005a) 

Mode of Action 

Assessment Document: Evaluation of the Mode of Action of Acetochlor for Nasal Olfactory Epithelium Tumors in 

Rats and its Relevance to Human Cancer Risk Assessment. August 31, 2004. TXR No. 0052727. Cancer 

Assessment Review Committee, Health Effects Division, Office of Pesticide Programs, Washington, DC. Available 

online at http://www.epa.gov/opp00001/foia/reviews/121601/121601-2004-08-31a.pdf.

Supplemental guidance for assessing susceptibility from 

early-life exposure to carcinogens. Risk Assessment Forum, Washington, DC; EPA/630/R-03/003F. Available 

from the National Technical Information Service, Springfield, VA, and online at 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=160003.

U.S. EPA (Environmental Protection Agency). (2005b) Tolerance reassessment progress and risk management 

Decision (TRED) for procymidone. Office of Prevention, Pesticides and Toxic Substances, Washington, DC. 

Available online at http://www.epa.gov/oppsrrd1/REDs/procymidone_tred.pdf.

U.S. EPA (Environmental Protection Agency). (2006a) Toxicological review of dibutyl phthalate (di-n-butyl 

phthalate). [External review draft]. Integrated Risk Information System, Washington, DC; NCEA-S-1755. 

Available online at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=155707.

U.S. EPA (Environmental Protection Agency). (2006b) Interim guidance for microarray-based assays: data 

submission, quality, analysis, management and training considerations. [External review draft]. Genomics Task 

Force Workgroup, Science Policy Council, Washington, DC. Available online at 

http://www.epa.gov/osa/spc/genomicsguidance.htm.

U.S. EPA (Environmental Protection Agency). (2006c) Summary of the NCEA colloquium on current use and 

future needs of genomics in ecological and human health risk assessment (final report). National Center for 

Environmental Assessment, Washington, DC; EPA/600/R-04/039. Available from the National Technical 

Information Service, Springfield, VA, and online at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=149984.

U.S. EPA (Environmental Protection Agency). (2006d) Revised reregistration eligibility decision document (RED)

for MSMA; DSMA, and CAMA and cacodylic acid. Office of Prevention, Pesticides and Toxic Substances, 

Washington, DC; EPA/738/R-06/021. Available online at from the National Technical Information Service, 

Springfield, VA, and online at http://www.epa.gov/oppsrrd1/REDs/organic_arsenicals_red.pdf.
R-10

U.S. EPA (Environmental Protection Agency). (2006e) Approaches for the application of physiologically based 

pharmacokinetic (PBPK) models and supporting data in risk assessment. National Center for Environmental 

Assessment, Washington, DC; EPA/600/R-05/043F. Available from the National Technical Information Service, 

Springfield, VA, and online at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=157668.

U.S. FDA (Food and Drug Administration). (2003) Guidance for industry. Pharmacogenomic data submissions. 

Draft guidance. Washington, DC. Available online at www.fda.gov/cder/guidance/5900dft.pdf [accessed 26 July 

2004].

Vondracek, M; Xi, Z; Larsson, P; et al. (2001) Cytochrome P450 expression and related metabolism in human 

buccal mucosa. Carcinogenesis 22(3):481−488.

Vondracek, M; Weaver, DA; Sarang, Z; et al. (2002) Transcript profiling of enzymes involved in detoxification of 

xenobiotics and reactive oxygen in human normal and simian virus 40 T antigen-immortalized oral keratinocytes. 

Int J Cancer 99(6):776−782.

Waters, MD; Fostel, JM. (2004) Toxicogenomics and systems toxicology: aims and prospects. Nat Rev Genet 

5(12):936–948. Available online at 

http://www.cmbi.ru.nl/edu/bioinf4/articles/pdf/tox_ReviewToxicogenomicsNaturedec2004.pdf. 

Weng, L; Dai, H; Zhan, Y; et al. (2006) Rosetta error model for gene expression analysis. Bioinformatics 

22(9):1111−1121.

Wilson, VS; Lambright, C; Furr, J; et al. (2004) Phthalate ester-induced gubernacular lesions are associated with 

reduced insl3 gene expression in the fetal rat testis. Toxicol Lett 146(3):207−215.

Wine, RN; Li, L-H; Barnes, LH; et al. (1997) Reproductive toxicity of di-n-butylphthalate in a continuous breeding 

protocol in Sprague-Dawley rats. Environ Health Perspect 105:102−107.

Wu, W; Hu, W; Kavanagh, JJ. (2002) Proteomics in cancer research. Int J Gynecol Cancer 12(5):409−423.

Wyde, ME; Kirwan, SE; Zhang, F; et al. (2005) Di-n-butyl phthalate activates constitutive androstane receptor and 

pregnane X receptor and enhances the expression of steroid-metabolizing enzymes in the liver of rat fetuses. 

Toxicol Sci 86(2):281−290. 

Yu, X; Griffith, WC; Hanspers, K; et al. (2006) A system-based approach to interpret dose- and time-dependent 

microarray data: quantitative integration of gene ontology analysis for risk assessment. Toxicol Sci 92(2):560 −577.

Zhang, Y; Jiang, X; Chen, B. (2004) Reproductive and developmental toxicity in F1 Sprague-Dawley male rats 

exposed to di-n-butyl phthalate in utero and during lactation and determination of its NOAEL. Reprod Toxicol 

18(5):669−676. 

Zhou, T; Chou, J; Watkins, PB; et al. (2009) Toxicogenomics: transcription profiling for toxicology assessment. 

Mol Cli Envi Tox 1:325−366

Zien, A; Kuffner, R; Zimmer, R; et al. (2000) Analysis of gene expression data with pathway scores. Proc Int Conf 

Intell Syst Mol Biol 8:407−417.

URLS AND WEBSITES:

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=149984

http://www.niehs.nih.gov/research/supported/centers/trc/

http://cerhr.niehs.nih.gov/news/phthalates/report.html
R-11

http://david.abcc.ncifcrf.gov/list.jsp

http://intranet.epa.gov/ncea/pdfs/qmp/ncea_qmp.pdf

http://searchlauncher.bcm.tmc.edu/help/BLASToutput.html#anchor14684156

http://www.ehponline.org/txg/docs/admin/txg-n-press.html?section=toxicogenomics

http://www.genome.jp/kegg

http://www.hesiglobal.org/Committees/ TechnicalCommittees/Genomics/EBI+Toxicogenomics.htm

http://www.ncbi.nlm.nih.gov/sites/entrez?db=gene

www.epa.gov/iris/whatsnewarchive.htm

http://www.epa.gov/ncea/iris/subst/0170.htm

http://www.epa.gov/ncea/iris/subst/0378.htm

http://www.epa.gov/ncea/iris/subst/0126.htm

www.genego.com

www.omics.org

http://www.fda.gov/ScienceReserch/BioinformaticsTools/MicroarrayQualityControlProject/default.htm
 Official Business

Penalty for Private Use

$300

PRESORTED STANDARD

POSTAGE & FEES PAID

EPA

PERMIT NO. G-35

National Center for Environmental Assessment

Office of Research and Development

Washington, DC 20460