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1 individual study findings, information was often combined by category (e.g., "histopathology"
2 includes a broad variety of outcomes in various reproductive organs), and for the sake of brevity,
3 the minute details and nuances of the study design and observations, although quite interesting,
4 are not typically presented. In a few cases, negative outcomes presented in the table are
5 extrapolations based upon the presumption that specific findings would have been observed if
6 they were present. For example, with methods that include detailed external and internal
7 (macropathology) examination of pups and/or adults, the absence of reported malformations at
8 either of these life stages was presumed to indicate that no gross malformations were observed
9 because they should have been readily detectable (e.g., Lee et al., 2004).
10 Tables 4-1, 4-3, and 4-4 clearly illustrate that the study protocols varied quite extensively.
11 In general, with the exception of the NTP studies, the protocols were not designed to conform to
12 a particular regulatory guideline. Rather, the majority of the studies were focused research
13 efforts that were verifying and/or expanding upon previously observed outcomes; therefore, the
14 differences across study methods are understandable. As a result, the apparent lack of
15 consistency in male reproductive system observations across studies is generally attributable to
16 differences in protocol design and implementation. Some examples are discussed in detail as
17 follows:
18
19 • Although these studies all utilized exposures during late gestation (i.e., a critical period of
20 male reproductive system development in the rat), the specific endpoints that were
21 assessed and/or the life stages at which endpoints were examined varied extensively
22 across the studies. Obviously, treatment-related alterations of life-stage-specific events
23 require examination during the most appropriate or optimal life stage (for example,
24 increased multinucleated gonocytes can only be observed in fetal testes, delays in PPS
25 can only be observed in juvenile animals at the time of sexual maturation, and
26 disturbances in reproductive function can only be observed in sexually mature adults).
27 Other permanent structural abnormalities may be detected across multiple life stages
28 (e.g., hypospadias or cryptorchidism could theoretically be observed in late gestation
29 fetuses, in adolescents, and in adults). For some outcomes, it is difficult to predict the
30 optimal time point for evaluation. For example, DBF-related decreases in the ER were
31 observed at 31 days but not at 42 days (Kim et al., 2004).
32
33 • It is important to realize that not all available offspring are evaluated in every study;
34 therefore, identification of adverse outcomes may rely in part on sampling protocols and
35 the statistical power of the sample size for detection of rare or low-incidence events.
36 Calculations of statistical power are rarely provided in study reports.
37
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1 • In some cases, apparent differences in studies may result because the report contains an
2 insufficient level of detail on a particular endpoint or life stage—often because the
3 emphasis of the scientific review lies in a slightly different direction. For example, if
4 high doses of DBF are administered during sensitive periods of male reproductive system
5 development, and the males are maintained on study and terminated as adults, at which
6 time histopathological evaluation is performed, it might be assumed that various male
7 reproductive system malformations and/or cryptorchidism would have been present in
8 some of the males at necropsy. Yet, these findings may not be reported because the
9 histopathological findings are the primary focus of the investigation and/or the
10 publication (e.g., Lee et al., 2004).
11
12 • In other situations, the description of the findings at various life stages may vary. For
13 example, evidence of cryptorchidism may be described as "testis located high in the
14 abdomen" in a fetus, as "undescended testis(es)" in an adolescent rat, or as "unilateral
15 testis" upon noninvasive clinical examination of an adult. To some extent, this lack of
16 consistency in terminology may result from laboratory Standard Operating Procedures
17 that direct technical staff to avoid the use of diagnostic terminology.
18
19 Overall, in spite of numerous differences in the study designs, the toxicological profile
20 for DBF clearly demonstrates that exposure to DBF during critical stages of male reproductive
21 system development can result in adverse structural and functional reproductive outcomes.
22 When specific critical aspects of study design and implementation were similar, consistent
23 outcomes were almost universally observed. The WOE embodied by the data described above is
24 further supported by studies in rats that demonstrated similar incidences of cryptorchidism and
25 decreased AGD in male pups of dams treated with either DBF or MBP, the metabolite of DBF
26 (Ema and Miyawaki, 2001). The ability of MBP to cross the placenta and reach the fetus has
27 also been conclusively demonstrated (Fennell, 2004; Saillenfait et al., 1998), and these two TK
28 events (metabolism and placental transport) are key to the MOA of reduced fetal testicular T
29 (David, 2006). Available toxicogenomic data, described elsewhere in this case study document,
30 further elucidate the MOA(s) of DBF in producing adverse effects on male reproductive system
31 development and are an important consideration in the WOE analysis of the toxicological profile.
32 In the selected DBF toxicology study data set, the presentation of extensive individual
33 offspring data was limited to the NTP (1991) study conducted as a reproductive assessment by
34 continuous breeding (RACE) in SD rats. The individual data from this study were carefully
35 examined in order to confirm the NOEL and LOEL described in the study report. This analysis
36 was conducted under the presumption that statistical and/or biological significance noted in the
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1 summary compilations of male reproductive system outcomes might not identify low incidence
2 effects in individual offspring at lower dose levels. To further aid the identification of
3 treatment-related outcomes, the male reproductive system outcomes were grouped by organ
4 instead of individual animal. This analysis revealed apparently treatment-related findings in the
5 testis and epididymis of Fl male offspring, as summarized in Table 4-5. At the highest dose
6 tested (794 mg/kg-d, equivalent to 1.0% DBF in the diet), additional findings in the male
7 reproductive organs of Fl offspring included single incidences of (1) underdeveloped prepuce;
8 (2) mild secretion and severe vesiculitis of the prostate; (3) a mass on the testis; and (4) a focal
9 granuloma with fluid and cellular degeneration in the epididymis; these findings were not
10 observed at the lower dose levels. Understandably, the findings at the low- and mid-dose groups
11 were not originally interpreted as being treatment related (Wine et al., 1997; NTP, 1991).
12 However, consideration of MO A information for DBF, including toxicogenomic data, has
13 resulted in a more conservative interpretation of the data both by NTP researchers (Paul Foster,
14 personal communication, 2008) and by the U.S. EPA IRIS program (U.S. EPA, 2006a).
15 Consequently, further analysis of individual offspring data in the current case study did not
16 identify any additional sensitive toxicological outcomes; the study LOEL was confirmed to be
17 the lowest treatment level tested in the NTP RACE study (80 mg/kg-d).
18
19 4.3. UNEXPLAINED MODES OF ACTION (MOAS) FOR DBF MALE
20 REPRODUCTIVE TOXICITY OUTCOMES
21 Figure 3-6 illustrates the broad conceptual approach for consideration and interpretation
22 of toxicogenomic and toxicology data to inform MO A. The toxicogenomic data can be
23 evaluated to identify altered genes, gene products, and pathways; this information can lead to a
24 more complete understanding of the mechanism of action or MOA(s) for the chemical toxicity.
25 From the opposite perspective, the toxicity data can provide information
26
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1
2
3
Table 4-5. Incidence of gross pathology in Fl male reproductive organs in one
continuous breeding study with DBPa
Gross finding"1
Testis: absent, poorly developed, atrophic,
undescended
Penis: small/underdeveloped
Epididymis: underdeveloped/absent
Dose (% in Diet)
0
0/20
0/20
0/20
0.1
1/20
0/20
1/20
0.5
1/20
0/20
1/20
1.0
6/20
4/20
12/20
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Incidences were compiled from reported individual animal macroscopic pathology data;
statistical analysis was not performed.
bSome animals have more than one type of malformation, and these animals were counted
separately for each of the three outcome categories.
Source: (NTP, 1991).
critical to identifying the relevant MOA(s) involved in the toxicological outcomes, and thereby
inform the interpretation of gene alterations and relevant pathways.
Consideration of the MO A for each outcome, in conjunction with pathways identified in
the toxicogenomic data set, may either help to corroborate known or hypothesized MO As or
suggest the existence of other potential MO As (see Figure 4-2). For the DBF 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. Each outcome is associated with
specific known MOAs. 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 be specifically identified for other outcomes.
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Identify MOAs that explain each male
reproductive system outcome following
developmental exposures
Explained Endpoints;
Consider pathways identified in
TG dataset evaluation
(Chapters 5&6) and
whether new info
corroborates MOA
1
2
3
4
Unexplained Endpoints;
Consider pathways identified in
TG dataset evaluation
(Chapters 5&6) for
potential MOAs
Figure 4-2. The process for evaluating the MOA for
individual male reproductive developmental outcomes.
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1
2
Table 4-6. Effects in the male reproductive system after in utero DBF
exposure, and MOAsa that explain the affected endpoints
Organ/
Function
Testes
Gubernacular
ligament
Epididymis
Effect
Multinucleated gonocytes; increased number of
gonocytes in fetal testes
Altered proliferation of Sertoli and peritubular
cells; fewer Sertoli cells
Gonocyte apoptosis increase; early postnatal
decrease in gonocyte number
Abnormal Sertoli cell-gonocyte interaction
Small incidence of Ley dig cell adenomas,
aggregates, and hyperplasia
Decreased number of spermatocytes or cauda
epididymal sperm concentration.
Small or flaccid; other abnormalities; decreased
weight
Increased weight due to edema
Decreased number or degeneration of
seminiferous cords/tubules; altered
morphology; degeneration of the epithelium;
enlarged cords/tubules
Testes descent: none (cryptorchid) or delayed
Gubernacular ligament development effects:
agenesis or elongation
Lesions and agenesis; partial to complete
absence; decreased epididymal ductular cross
section
Reduced weights
MOA
Reduced
fetal
testicular
T
?b
?b
?b
?b
^
^
^
9e
•
?b
s*
X
^
^
Reduced
InslS
signaling
?c
9c
•
?c
9c
•
?c
^d
^
9
9c
•
s<
s
X
^
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1
2
Table 4-6. (continued)
Organ/
Function
Mammary gland
Wolffian ducts
Seminal vesicles
Coagulating gland
Penis
Accessory sex
organ
Prostate
Vas deferens
Levator
anibulbocavernosus
muscle
Male/female ratio
Perineum
Repro function
Effect
Nipple and/or areolae retention in males
Degeneration and atrophy of alveoli in males
Underdeveloped
Malformations or absent; decreased weight
Malformations
Small, underdeveloped
Hypospadias
Delayed preputial separation
Decreased weight
Decreased wt or absent
Agenesis
Decreased weight
Decreased % male offspring as determined by
AGO at birth
Decreased AGO
Infertility
MOA
Reduced
fetal
testicular
T
^
?b
^
^
^
^
S
^
^
^
^
S
^
S
^
Reduced
InslS
signaling
X
X
X
X
X
X
X
X
X
X
X
?c
X
X
Y"
3
4
5
6
7
8
9
10
11
12
AGO, anogenital distance; ?, Current data indicate that it is unlikely the MOA; Y, 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.
aMOA is defined as one or a sequence of key events that the outcome is dependent upon (see
glossary).
bReduced fetal testicular T may play a role, but current data indicate that reduced T is not solely
responsible for this outcome.
cThe 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.
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1 Table 4-6. (continued)
2
3 dDecreased fertility in males is a result of reduced Insl3 signaling since reduced Insl3 signaling
4 leads to undescended testes, which, in turn, reduces sperm count (presumably by increasing the
5 temperature) and can cause infertility.
6 eln some animals, increased weight, due to edema, can result in animals that have epididymal
7 agenesis, which is a consequence of reduced testosterone (T).
8 flns!3 signaling is required for development of the gubernacular ligament and through this
9 mechanism—the 1st stage of testis descent from the kidney region to the inguinal region.
10 Testosterone is required for the 2nd stage of testis descent, from the inguinal region to the
11 scrotum (reviewed in Klonisch et al., 2004). After in utero DBF exposure, the cryptorchid
12 phenotype resembles the Insl3 knockout. A delay in testis descent can result from reduced Insl3
13 andT.
14
15 4.4. CONCLUSIONS ABOUT THE TOXICITY DATA SET EVALUATION:
16 DECISIONS AND RATIONALE
17 The review of the toxicology data set identified a number of issues and limitations that are
18 evident in the study descriptions and endpoint summaries presented in this chapter. These
19 include the following:
20
21 • Lack of dose-response information: A number of studies conducted with DBF used a
22 single high-dose treatment level (often at 500 mg/kg-d) in order to produce readily
23 observable adverse outcomes to male reproductive system development that could be
24 examined. In such studies, the absence of lower-dose levels prevents the evaluation of
25 dose-dependent responses and does not allow the identification of study-specific NOELs
26 or LOELs. While this approach is useful for hazard characterization, it does not facilitate
27 other aspects of risk assessment (e.g., dose-response assessment or risk characterization).
28 Thus, studies utilizing a single high-dose level may provide important information for a
29 WOE assessment of the toxicology profile, but they have diminished usefulness in
30 identifying outcomes for use in risk calculations at environmentally relevant doses.
31
32 • Insufficient information on study methods: Even though every study report includes a
33 section on study methods, there can be a great deal of unevenness in the amount of
34 detailed information provided. Consequently, important questions may arise during study
35 review that cannot be readily resolved. In some cases, this can have an impact on
36 individual study interpretation or on conclusions that rely upon a thorough WOE
37 evaluation of the data set.
38
39 • Unavailable individual outcome data. A full range of individual animal data is seldom
40 included in studies published in the open literature and is almost never available when the
41 only available publication is a presentation abstract. Conversely, individual animal data
42 are generally included in toxicology reports generated in response to a regulatory
43 mandate or conducted by a federal agency (e.g., NTP). The availability of individual
44 animal data can be quite important in interpreting the study findings, because it can
45 reveal problems or inadequacies in the data, but it can also help identify low incidence
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1 adverse outcomes. In the case of DBF, the individual offspring data presented in the
2 NTP study report (1991) include alterations in the reproductive system of Fl males that
3 had been exposed during development. These findings are similar to outcomes identified
4 at higher-dose levels, are consistent with the proposed MO A, and, consequently, are used
5 to establish a LOEL for the study.
6
7 • Protocol limitations: Unless studies are designed to meet the recommendations of a
8 standardized testing protocol (e.g., NTP or U.S. EPA/Office of Prevention, Pesticides and
9 Toxic Substances reproductive toxicity study guidelines), there may be a high degree of
10 variability among the protocols used for testing any one chemical. Between two studies,
11 there can be differences in the treatment regimen or in the assessment of outcomes that
12 render them incomparable. DBF provides a good example of a chemical that targets a
13 very specific critical prenatal window of reproductive system development in males, and
14 results in adverse outcomes that could go unidentified if the appropriate endpoint(s) are
15 not assessed at the optimal life stage or time point.
16
17 • Specific study's limitations: Even when a study design optimizes the detection of adverse
18 outcomes from chemical treatment, there may be challenges in study analysis and
19 interpretation. Such is the case with the NTP study (1995, 1991) on DBF, which was
20 conducted in several phases and reported both in the open literature (Wine et al., 1997)
21 and by the Institute that conducted the experiments.
22
23 The analysis of the toxicology data in this chapter has provided a firm basis for expanded
24 consideration of the toxicogenomic data for DBF as depicted in Figure 3-6. The extensive
25 analysis of the toxicology data set and consideration of MOA(s) provide a source of information
26 for use in phenotypic linking of known and potential MO As. The available toxicogenomic
27 studies for DBF are evaluated in Chapter 5. The genes and pathways underlying the endpoints
28 with well established or unexplained MO As are utilized in Chapter 5, where consistency of
29 findings for altered genes and pathways are evaluated, and, in Chapter 6, where the new pathway
30 analyses are presented.
31
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1 5. EVALUATION OF THE DBF TOXICOGENOMIC
2 DATA SET FROM THE PUBLISHED LITERATURE
O
4
5 This chapter presents an evaluation of the DBF toxicogenomic data set from the
6 published literature. The toxicogenomic studies include nine published RT-PCR and microarray
7 studies in the rat after in utero DBF exposure. We evaluated the toxicogenomic data set for (1)
8 the consistency of findings from the published studies, and (2) whether additional pathways
9 affected by DBF in utero exposure could possibly explain the testis endpoints for which there is
10 not an established MOA (these "unexplained" endpoints were identified in Chapter 4). The DBF
11 genomics data set includes nine papers published through July 2008. The microarray studies all
12 reported DBF doses of 500 to 1000 mg/kg-d during the critical window for male reproductive
13 development, which is during late gestation and correlates with the time period of maximal T
14 production. The chapter first discusses the methodologies utilized in the nine studies and
15 provides a brief overview of each study. The chapter then presents an evaluation of the
16 consistency of the findings or WOE for the microarray, RT-PCR, and protein studies performed
17 in the rat testes. The findings of one DBF dose-response RT-PCR study of Lehmann et al.
18 (2004) are discussed. The chapter closes with a brief discussion of data gaps and research needs.
19
20 5.1. METHODS FOR ANALYSIS OF GENE EXPRESSION: DESCRIPTION OF
21 MICROARRAY TECHNIQUES AND SEMI-QUANTITATIVE REVERSE
22 TRANSCRIPTION-POLYMERASE CHAIN REACTION (RT-PCR)
23 5.1.1. Microarray Technology
24 Microarray is a technology that allows for simultaneous analysis of expression of
25 thousands of genes from the organ or tissue of interest. In principle, there are two main types of
26 microarrays: the cDNA microarray and the oligonucleotide array. The cDNA microarray
27 contains DNA from each open reading frame spotted on to glass microscope slides or nylon
28 membranes. These probes are used to detect cDNA, which is DNA synthesized from a mature,
29 fully spliced mRNA transcript. For example, Clontech's Atlas Arrays contain DNA sequences
30 from thousands of genes immobilized on nylon membrane or glass slides. Each gene found on
31 these arrays is well characterized. These arrays, which use a radiolabelled detection system for
32 analyzing the changes in gene expression, have been optimized for high-quality expression
33 profiling using a limited set of genes. Moreover, they allow for the use of 32P, and, therefore,
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1 offer a sensitive measure of gene expression available. The second type of microarray is the
2 oligonucleotide array. Here, short DNA sequences or oligonucleotides (oligos) are synthesized
3 directly onto the glass slide via a number of different methods. For example, Affymetrix® uses a
4 technique called 'Photolithographic' technology, wherein probes are directly synthesized on to
5 the arrays. Briefly, the slide is coated with a light-sensitive chemical compound that prevents the
6 formation of a bond between the slide and the first nucleotide of the DNA probe being created.
7 Chromium masks are then used to either block or transmit light onto specific locations on the
8 surface of the slide. A solution containing thymine, adenine, cytosine, or guanine is poured over
9 the slide, and a chemical bond is formed in areas of the array that are not protected by the mask
10 (exposed to light). This process is repeated 100 times in order to synthesize probes that are 25
11 nucleotides long. This method allows for high probe density on a slide.
12 Affymetrix® uses an antibody detection system with horseradish peroxidase and
13 streptavidin conjugates, and a 2-dye system (Cy3- and Cy5- labeled fluorescein dyes), which is
14 unique to this platform. The Agilent scanner detects the relative intensities of the red and green
15 labels and gives a relative measure of the gene expression changes between the control and
16 treated samples. In the case of Affymetrix® and Clontech, the detection system measures the
17 absolute intensity of the individual probes of the treated and control samples. These values are
18 then used to calculate the relative gene expression change between the treated and control
19 samples.
20
21 5.1.2. Reverse Transcription-Polymerase Chain Reaction (RT-PCR)
22 Polymerase Chain Reaction (PCR) is a method that allows exponential amplification of
23 short DNA sequences within a longer double stranded DNA molecule using a thermo-stable
24 DNA polymerase called Taq polymerase. RT-PCR is a semi quantitative technique for detection
25 of expressed gene transcripts or mRNA. Over the last several years, the development of novel
26 chemistries and instrumentation platforms enabling detection of PCR products on a real-time
27 basis has led to widespread adoption of real-time RT-PCR as the method of choice for
28 quantitating changes in gene expression. Real-time PCR is a kinetic approach in which the
29 reaction is observed in the early, linear stages. Furthermore, real-time RT-PCR has become the
30 preferred method for confirming results obtained from microarray analyses and other techniques
31 that evaluate gene expression changes on a global scale.
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1
2 5.2. REVIEW OF THE PUBLISHED DBF TOXICOGENOMIC STUDIES
3 5.2.1. Overview of the Toxicogenomic Studies
4 We evaluated nine studies published prior to July 2008 that characterized altered gene
5 expression in rats following prenatal DBF exposure. Among these nine studies, four are based
6 on the analysis of preselected genes by real-time RT-PCR, while the other five are based on the
7 analysis of global gene expression by microarray technology. Table 5-1 summarizes general
8 information (e.g., DBF dose, exposure route, exposure window, and tissue type) for these nine
9 studies, and brief descriptions of each study are provided. Section 5.2.3.2 presents information
10 about the similarities and differences among these studies.
11
12 5.2.2 Microarray studies
13 5.2.2.1. Shultzetal (2001)
14 Six SD rats per group were treated by gavage with corn oil, DBF (500 mg/kg), or
15 flutamide (reference antiandrogen, 50 mg/kg-d) from GD 12 - 16, GD 12 - 19, or GD 12-21.
16 Testes were then isolated on GD 16, 19, or 21. Global changes in gene expression were
17 determined by Clontech cDNA expression array (588 genes). Shultz et al. (2001) isolated total
18 RNA from testis of control and treated animals. Reverse transcription reactions were performed
19 using total RNA, [32P]-dATP, and superscript IIMMLV-RT. Following purification, the probes
20 were counted, and equal numbers of counts per minute were added to each rat gene cDNA
21 expression array. The arrays were hybridized with cDNA using 1 fetus per dam. Hybridization
22 and washing were performed according to manufacturer's instructions. Digital images were
23 collected on a BioRad phosphorimager and analyzed using Clontech's Atlas Image software.
24 Eight genes were further examined by real-time RT-PCR. Total RNA was isolated from both
25 testes using RNA STAT60, and then treated the RNA with DNase I with RNasin. cDNA was
26 then synthesized using random primers and TaqMan reverse transcription reagents. Quality of
27 RT reactions was confirmed by comparison of RT versus no enzyme control for each RNA
28 sample using the glyceraldehyde-3-phosphate dehydrogenase (GAPDH) primer set. Fourteen
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Table 5-1. Study comparisons for the toxicogenomic data set from male tissues after in utero DBF exposure
o <*>'
U
O 58
S § .
g.S3-
TO
Study3
Barlow et
al., 2003
Bowman et
al., 2005
Lehmann et
al., 2004
Liu et al.,
2005C
Plummer et
al., 2007d
Shultz et al.,
2001
Strain and
species
SDrat
SDrat
SDrat
SDrat
Wistar rat
SDrat
DBF doses
500 mg/kg-d
500 mg/kg-d
0.1, 1.0, 10,50,
100, or
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
Treatment intervalb
GD 12-19
GD 12-19 or \9~2l
GD 12-19
GD 12-19
GD 12.5-15.5;
12.5-17.5, or 12.5-19.5
GD 12-16, 12-19, or
12-21
Toxicogenomic method
Microarray
(Platform)
No
Yes (Clontech
cDNA arrays)
No
Yes
(Affymetrix®
GeneChip®
oligo arrays)
Yes (Agilent
22K and 44K
oligo arrays)
Yes (Clontech
cDNA arrays)
RT-PCR
Yes
Yes
Yes
Yes
Yes
Yes
Tissues
collected
Testis
Wolffian
ducts
Testis
Testis
Testis:
whole,
seminiferous
cord, and
interstitial
regions
Testis
-------
Table 5. (continued)
o
o
S
§
s
rS"
Study3
Thompson
et al., 2004
Thompson
et al., 2005
Wilson et
al., 2004e
Strain and
species
SDrat
SDrat
Rat, SD
DBF doses
500 mg/kg-d
500 mg/kg-d
1,000 mg/kg-d
Treatment interval11
GD 12-17, 18, or 19;
13-19, 14-19, 15-19,
16-19, 17-19, 18-19, or
19
0.5-24 hr on GD 18-19
or GD 19
GD 13-17
Toxicogenomic method
Microarray
(Platform)
No
Yes
(Affymetrix®
GeneChip®
oligo arrays)
No
RT-PCR
Yes
Yes
Yes
Tissues
collected
Testis
Testis
Testis
aln all studies, oral gavage was the route of exposure.
bGD 0 = sperm positive.
cStudy assessed 7 different phthalates.
dPlummer et al. (2007) reported dosing intervals spanning GD 12.5-19.5, which is comparable to GD 12-19 in the other studies due to
differences in reporting of GD and sperm positive at GD 0.5.
eWilson et al. (2004) reported a dosing interval of GD 14-18, which is comparable to GD 13-17 in the other studies due to
differences in reporting of GD and sperm positive at GD 1.
-------
1 rat-specific primer sets were used for analyses. The ABI PRISM 7700 and the ABI PRISM
2 7900HT Sequence Detection System was used for RT-PCR, with the SYBR Green PCR and
3 TaqMan Universal PCR Master Mix reagents. GAPDH was used as an on-plate internal
4 calibrator for all RT-PCR reactions.
5 Genes analyzed by real-time RT-PCR include clusterin (Clu), cytochrome P450,
6 family 11, subfamily a, polypeptide 1 (Cypllal), myristoylated alanine-rich C-kinase substrate
7 (Marcks), proliferating cell nuclear antigen (Pcna), cytochrome P450, family 17, subfamily a,
8 polypeptide 1 (Cypl7al}, steroidogenic acute regulatory protein (Star), scavenger receptor class
9 B, member 1 (Scarbl), and v-kit Hardy Zuckerman 4 feline sarcoma viral oncogene homolog
10 (Kit). Radioimmunoassay of steroid hormones and immunocytochemical analysis of certain
11 proteins (i.e., CLU and b-cell leukemia/lymphoma 2 [BCL2]) in the fetal testes were also
12 performed.
13 Of the 588 genes examined, -45 genes had at least a 2-fold change in the average
14 expression values in DBF-treated rats relative to the average values in control rats. DBF
15 exposure led to a reduced expression of steroidogenic enzymes at GD 19, such as Cypllal,
16 Cypl7al, ScarbJ, and Star. These genes were upregulated at GD 19 following flutamide
17 exposure, suggesting that DBF does not act as an androgen antagonist at this time point.
18 Flutamide and DBF demonstrate patterns of gene expression that overlap, though both have
19 distinctly expressed genes. This suggests to Shultz et al. (2001) that there are both common and
20 distinct molecular pathways within the developing fetal testes.
21 Other genes affected after DBF exposure were Clu (upregulated) and Kit
22 (downregulated). Using immunocytochemical staining of CLU and BCL2 protein in the fetal
23 testes, increased amounts of both proteins were observed in the Ley dig and Sertoli cells of
24 GD 21 testes. Decreases in testicular T and androstenedione in testes isolated on GD 19 and 21
25 were observed, while increases in progesterone in testes isolated on GD 19 in DBF-exposed
26 testis were observed.
27 Shultz et al. (2001) suggest that the antiandrogenic effects of DBF are due to decreased
28 T synthesis. Furthermore, enhanced expression of cell survival proteins, such as CLU and
29 BCL2, may be involved in DBF-induced LC hyperplasia, while downregulation of c-KIT may
30 play a role in gonocyte degeneration.
31
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1 5.2.2.2. Bowman et al (2005)
2 Four to seven SD rats per group were treated by gavage with corn oil or DBF at
3 500 mg/kg-d from GD 12 to 19 or GD 12 to 21. The animals were sacrificed on GD 19 or 21,
4 and Wolffian ducts (WD) were pooled from three to four fetuses (to obtain enough RNA for
5 analysis) within the same litter for gene expression analysis. Global changes in gene expression
6 were determined by Clontech Atlas Rat Toxicology 1.2 cDNA expression array (1,185 genes).
7 Images were collected using a Phorpshorlmager and then imported into Atlaslmage 2.01 and
8 GeneSpring 4.2 for analysis. Selected genes were further examined by real-time quantitative
9 RT-PCR using the GeneAmp 5700 Sequence Detection System. Total RNA was isolated,
10 DNAse-treated, and reverse-transcribed using TaqMan reagents. Twenty-three primer sets were
11 used for RT-PCR analysis. Reactions were standardized using GAPDH-specific primers. The
12 genes analyzed by RT-PCR include those in the insulin-like growth factor (Igf) pathway, the
13 matrix metalloproteinase (Mmp) family, the extracellular matrix, and in other developmentally
14 conserved signaling pathways: bone morphogenetic protein 4 (Bmp4), collagen, delta like
15 (Map3kl2), epidermal growth factor receptor (Egfr), fibroblast growth factor 10 (FgflO), FGF
16 receptor 2 (Fgfr2\ fibronectin, insulin-like growth factor 1 (Igfl\ insulin-like growth factor 2
17 (/g/2), insulin-like growth factor 1 receptor (Igflr\ insulin-like growth factor binding protein
18 5(/g/&p5), integrinAS, integrinBl, matrix Gla protein (Mgp\ matrix metalopeptidase 2 (Mmp2),
19 matrix metalopeptidase 14 (Mmpl4), matrix metalopeptidase 16 (Mmpl6), Notch2 receptor
20 (Notch2), and tissue inhibitors of MMPs (Timpl, Timp2, and Timp3). Immunohistochemistry
21 was also performed to evaluate changes in localization and/or intensity of IGFLRp and androgen
22 receptor (AR) protein expression.
23 Microarray data were not presented due to considerable variability in gene expression
24 levels within the treatment group at each age. Based on real-time PCR analysis, compared with
25 controls, prenatal exposure to DBF from GD 12 to 19 or GD 12 to 21 increased mRNA
26 expression of different members of the IGF family including Igfl (on GD 19 and 21), Igf2(on
27 GD 19), Igfrlr (on GD 19), and IgfbpS (on GD 21) in the developing WD, while Egfr was
28 unchanged on GD 19 and GD 21. Additionally, mRNA expression of^4r, Bmp4, integrinAS,
29 Mmp2, andMap3k!2 was increased on GD 19; mRNA expression ofFgflO, Fgfr2, Notch2,
30 Mmp2, Timpl, andMgp was increased on GD 21. IGFLRP immunostaining was higher in the
31 cytoplasm of the ductal epithelial cells and increased in the cytoplasm of mesenchymal cells in
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1 DBF-exposed fetuses compared with that in controls. In general, reduction of AR
2 immunostaining in the nuclei of ductal epithelial cells of DBF-exposed WD was observed on
3 GD 19. Compared with controls, WDs dissected from GD 19 DBF-exposed fetuses were slightly
4 smaller in size (underdeveloped) and appeared to be more fragile. By GD 21, control fetus WDs
5 were markedly coiled, while those from the exposed fetuses exhibited less coiling.
6 Prenatal DBF exposure appears to alter the mesenchyme-epithelial signaling of growth
7 factors (e.g., IGFs) and other developmentally conserved pathways (e.g., BMP4) in WDs.
8 Bowman et al. (2005) contend that the effect of DBF on WD differentiation is likely a
9 consequence of decreased fetal testicular T, although direct effects of DBF on the developing
10 WD independent of T are also possible.
11
12 5.2.2.3. Liu et al (2005)
13 Five to ten SD rats per group were treated by gavage with corn oil, DBF (500 mg/kg-d),
14 or one of six other phthalate esters (500 mg/kg-d) daily from GD 12 to 19. The six other
15 phthalate esters include diethyl phthalate (DEP), dimethyl phthalate (DMP), diocytyl
16 tere-phthalate (DOTP), diethylhexyl phthalate (DEHP), dipentyl phthalate (DPP), and butyl
17 benzyl phthalate (BBP). Testes were collected on GD 19, homogenized, and then, total RNA
18 was isolated. RNA integrity was assessed using an Agilent 2100 Bioanalyzer. cDNAwas
19 synthesized from 2.5 ug total RNA, and purified using RiboAmp OA. The BioArray High-Yield
20 RNA Transcript Labeling Kit was used for cRNA amplification and biotin labeling. Affymetrix®
21 GeneChip Sample Cleanup Module was used for purifying and fragmenting the cRNA. The
22 Complete GeneChip® Instrument System was then used to hybridize, wash, stain, and scan the
23 GeneChip® arrays (RAE230A and RAE230B; -30,000 genes). The data were analyzed using
24 analysis of variance (ANOVA [one-way, two-way, nested one-way]), Dunnett test (post hoc),
25 Tukey test, and Bonferroni adjustment.
26 Image files obtained from the scanner were analyzed with the Affymetrix® Microarray
27 Suite (MAS) 5.0 software and normalized by global scaling. Absolute analysis was performed
28 for each array prior to comparative analysis. To identify differentially expressed transcripts,
29 pair-wise comparison analyses were carried out with MAS 5.0 (Affymetrix®). P-values were
30 determined by the Wilcoxon's signed rank test and denoted as "increase", "decrease", or "no
31 change". A transcript is considered significantly altered in relative abundance when p < 0.05.
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1 Analysis using MAS 5.0 provides a signal log ratio (SLR), which estimates the magnitude and
2 direction of change of a transcript when two arrays are compared (experimental versus control).
3 The SLR output was converted into "fold-change" as recommended by Affymetrix®.
4 Furthermore, stringent criteria were used to identify robust signals as follows: (1) software call
5 of "present", (2) >2.0-fold change or SLR 1.0, in both replicates. Average and standard
6 deviations were calculated for all the fold-change values. In general, only transcripts induced or
7 suppressed by >2-fold were considered as differentially expressed.
8 Selected genes were further examined by real-time quantitative RT-PCR using 18 primer
9 sets. The genes analyzed by RT-PCR include epididymal secretory protein 1 (re7), low-density
10 lipoprotein receptor (Ldlr), l?p-hydroxysteroid dehydrogenase 3 (Hsdl7b3}, l?p-hydroxysteroid
11 dehydrogenase 7 (HsdJ7b7), luteinizing hormone/choriogonadotropin receptor (Lhcgr\
12 CCAAT/enhancer-binding protein (C/EBP), beta (Cebpb), early growth response 1 (Egrl),
13 nuclear receptor subfamily 4, group A, member 1 (Nr4al), nuclear factor, interleukin 3,
14 regulated (NfilS), nuclear receptor subfamily 0, group B, member 1 (NrObl), transcription factor
15 1 (Tcfl\ insulin-induced gene 1 (Insigl), protein kinase C-binding protein (Prkcbpl), decay-
16 accelerating factor (Daf), dopa decarboxylase (Ddc\ seminal vesicle secretion 5 (SVs5), and
17 testis-derived transcript (Testiri). AGD was measured and immunohistochemistry was performed
18 forNROBl, TESTIN, GEB14, DDC, and CEBPB proteins.
19 Of-30,000 genes examined, 391 were statistically significantly altered following
20 exposure to the four developmentally toxic phthalates (DBF, BBP, DPP, and DEHP) relative to
21 the controls. While the four developmentally toxic phthalates were indistinguishable in their
22 effects on global gene expression, no significant changes in gene expression were detected in the
23 phthalates that do not lead to developmental effects (DMP, DEP, and DOTP). Of the 391 genes
24 altered by the developmentally toxic phthalates, 225 were unknown and uncharacterized
25 transcribed sequences. Of the remaining 166 genes, the largest GO classification (31 genes) was
26 of genes related to lipid, sterol, and cholesterol homeostasis. Additional GO classification
27 groups include genes involved in lipid, sterol, and cholesterol transport (10 genes);
28 steroidogenesis (12 genes); transcription factors (9 genes); signal transduction (22 genes);
29 oxidative stress (11 genes); and cytoskeleton-related (13 genes). RT-PCR results indicated that
30 the developmentally toxic phthalates reduced the mRNA levels ofHsd!7b7, Lhcgr, Ldlr, rel,
31 Svs5, Insigl, and Ddc. Additionally, the RT-PCR results indicated that the developmentally
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1 toxic phthalates induced the mRNA levels ofGrb!4, Prkcbpl, and Testin. RT-PCR results also
2 indicated that gene expression of several transcription factors including Dax-1, Cebpb, Nfil3,
3 Nr4al, and Tcfl were significantly changed by at least one of the toxic phthalates. Based on
4 immunohistochemical analysis, DAX-1 expression was reduced in the gonocyte population of
5 DBF-treated testis compared with that of controls. Additionally, the expression of nuclear
6 CEBPB, GRB14, and DDC proteins was reduced in interstitial cells of DBF treated testis, while
7 TESTIN and GRB14 expression levels were increased in Sertoli cells of DBF treated testis. An
8 AGO reduction was observed in male fetuses exposed to any of the developmentally toxic
9 phthalates.
10 This study showed that the four phthalates (DBF, DEHP, BBP, and DPP) that have
11 similar effects on the developing male rat reproductive tract are indistinguishable in their
12 genomic signature for the developing fetal testis. These phthalates targeted pathways in Leydig
13 cell production of T and other pathways that are important for normal interaction and
14 development between Sertoli cells and gonocytes. By contrast, in animals exposed to any of the
15 four phthalates that have not exhibited developmental toxicity (the "nondevelopment"
16 phthalates) did not have the same genomic signature.
17
18 5.2.2.4. Thompson et al (2005)
19 Four SD rats per group were gavaged with corn oil or DBF at 500 mg/kg-d daily. In the
20 first study, the treatment was performed on GD 18 or GD 19, followed by animal sacrifice
21 30 min, 1 hr, 2 hr, 3 hr, 6 hr, 12 hr, 18 hr, or 24 hr after the treatment on GD 19. Global changes
22 in gene expression were determined by Affymetrix® GeneChips® (GeneChips® used in the study
23 were not reported). The methods were similar to Liu et al. (2005)—with the exception of the
24 statistical analysis. Thompson et al. (2005) used JMP statistical software to perform Student
25 t-tests or one-way ANOVAs with Tukey post hoc analysis. Selected genes were further
26 examined by real-time quantitative RT-PCR. An ABI Prism 7900HT Detection System, the
27 SYBR Green PCR Master Mix, and 30 primer pairs were used for analysis of DBF-induced
28 changes in gene expression. The genes analyzed by RT-PCR included Cypllal, Scarbl, Star,
29 Cypl7al, Egrl, Egr2, Nr4al, Nfil3, Tcfl, serum/glucocorticoid regulated kinase (Sgk), tumor
30 necrosis factor receptor superfamily, member 12a (Tnfrsfl2a), sclerostin domain containing 1
31 (Sostdcl), Wnt oncogene homolog 4 (Wnt4), B-cell translocation gene 2, antiproliferative (Btg2),
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1 C/EBP, delta (Cebpd), FBI murine osteosarcoma viral oncogene homolog (Fos\ dual specificity
2 phosphatase 6 (Dusp6\ Hes6_predicted, interferon-regulated developmental regulator (Ifrdl\
3 Ldlr, nuclear receptor subfamily 4, group A, member 3 (Nr4a3\ Pawr, NrObl, Jun-B oncogene
4 (Junb), endothelial differentiation sphingolipid G-protein-coupled receptor 3 (EdgS),
5 thrombospondin 1 (Tspl), and stanniocalcin 1 (Stcl). Immunoblotting by SDS-PAGE was
6 performed for SCARE 1, CYPHal, STAR, and CYP17al. Fetal testicular T concentration was
7 determined by radioimmunoassay.
8 Based on microarray analysis, there were 106 genes in the DBF-treated groups that were
9 significantly different from time-matched controls. Six genes were significantly elevated within
10 1 hour of DBF exposure. An additional 43 genes were upregulated, and 5 genes were
11 downregulated 3 hours after DBF exposure. The rapid induction of these genes was a transient
12 effect; none of the genes upregulated 1 hour after DBF treatment were still significantly different
13 than the control group 6 hours after treatment. Only nine genes showed significant changes from
14 the control group between the 3- and 6-hour time points. Prior to 6 hours after DBF exposure,
15 the majority of the changes in expression had reflected increased transcription. At 6 hours,
16 19 genes were significantly decreased, and 17 were increased in expression. Based on RT-PCR
17 analysis, the immediate early gene Fos and the putative mRNA destabilizing gene zinc finger
18 protein 36 (Zfp36) were at peak expression level 1 hour after DBF exposure. Other immediate
19 early genes were at peak expression at 2 hours after DBF exposure. At 3 hours after exposure,
20 the expression of Cebpd, Cxcll, and Nr4a3 increased rapidly, while other genes showed a more
21 gradual increase. Tspl expression was increased 25-fold at 3 hours and returned to baseline at
22 6 hours. Genes involved in testicular steroidogenesis were first noticeably affected 2 hours after
23 DBF exposure: Inhibition of Star transcription was detected ~2 hours after DBF exposure.
24 Scarbl, Cypllal, and Cypl7al showed a significant decrease in expression at about 6 hours
25 after DBF exposure. Also, after 6 hours, the T concentration dropped to approximately the level
26 observed after long term DBF treatment. After 12 hours of exposure, steroidogenesis-associated
27 genes NrObl and Nr4al were elevated. Tcfl and Sgk were downregulated soon after DBF
28 exposure, but values returned to control levels by 3 hours after DBF exposure. Sostdcl and
29 Hes6_predicted returned to control levels at 6 hours after exposure. Based on
30 radioimmunoassay, a decrease in fetal testicular T to 50% was observed within an hour after
31 DBF exposure. In a second experiment to compare the effect of DBF on steroidogenesis in the
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1 fetal adrenal gland, DBF treatment at GD 12-19 was followed by analysis of gene expression in
2 this tissue. A decrease (but not statistically significant) of corticosterone after GD 12-19 DBF
3 exposure was observed in the fetal adrenal. The expression of genes involved in steroidogenesis
4 was less affected in the adrenal (males and females) than in the testes. This study indicates that
5 the effect of DBF exposure on steroidogenesis gene expression is specific to the fetal testis and
6 not in other steroidogenic organs.
7 Rapid transcript!onal changes after DBF exposure in a number of genes could be
8 responsible for the reduction in steroidogenesis. Peroxisome proliferator-activated receptors
9 (PPAR) activation is ruled out since changes in expression of genes targeted by PPAR a and y
10 are not observed until 3 hours after DBF treatment. Many of the genes whose upregulation was
11 detected within the first hour after treatment were "immediate early genes," meaning genes
12 involved in cell growth and differentiation. One possible mechanism for DBF's repression of
13 steroidogenesis is that DBF may initially stimulate the mitogen-activated protein
14 kinase/extracellular signal-regulated kinase (MAPK/ERK) pathway in the fetal testis. Increased
15 expression ofEgrl and Zfp36 could, in turn, lead to degradation of the transcripts involved in
16 testicular steroidogenesis. Consistent with this possibility, the Star mRNA contains the AU-rich
17 element, which are regions with many A and U bases that target the RNA for degradation, in
18 target transcripts of ZJp36.
19
20 5.2.2.5. Plummer et al (2007)
21 Five Wistar rats per group were gavaged with corn oil or DBF at 500 mg/kg-d from
22 GD 12 until the day prior to sacrifice. Animals were sacrificed on GD 15, 17, or 19 and used for
23 immunolocalization, Western analysis, or RNA quantification (of whole testes, seminiferous
24 cord, or interstitial regions using laser capture microdetection). Samples for laser capture
25 microdetection were collected from sections of single testes from GD 19 animals. RNA samples
26 from three treated litters were compared to a pool of RNA samples from control animals to
27 lessen errors due to biological variation. The Agilent 22K rat and 44K whole-rat oligonucleotide
28 arrays were used for analysis of the whole-fetal testes and microdissected tissue, respectively.
29 RNA was isolated from the homogenized whole-fetal testes using the RNeasy mini kit (Qiagen)
30 and from laser capture microdissected samples using RNeasy micro kit (Qiagen). Isolated RNA
31 was labeled using the Agilent Low Input Linear Amplification Labelling kit according to the
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1 manufacturer's instructions. Specific activity of the labeled cRNA was measured using the
2 microarray analysis program on a NanoDrop ND1000 spectrophotometer (Montchanin, USA).
3 Microarray analysis with whole-fetal testis RNA was performed using Agilent 22K rat
4 oligonucleotide arrays (Agilent #G4110A). Regional microarray analysis on RNA isolated from
5 laser capture microdissected fetal testis tissue was performed using Agilent 44K whole-rat
6 genome oligonucleotide microarrays (Agilent #G4131 A). Microarray data analysis was
7 conducted using Agilent feature extraction (v7.1) and Rosetta Luminator software (Rosetta
8 Biosoftware, Kirkland, USA) to generate "signature" lists, defined as significantly (p < 0.01)
9 different. The compare biosets function in Luminator was used to compare signature lists from
10 different fetal testis regions. Pathway analysis used Ingenuity Pathways Analysis software.
11 DBF induced statistically significant changes in gene expression at all three time points.
12 At GD 15 in whole testes, expression of genes regulating lipid metabolism, redox homeostasis,
13 cell proliferation, and apoptosis were altered. At GD 17 and 19, these four main gene clusters
14 were altered: steroidogenesis (e.g., Cypl7al, Cypllal), lipid metabolism, cholesterol (e.g., Star,
15 Scarbl), and redox homeostasis. In laser- capture microdissection studies of GD 19 tissue, both
16 regions demonstrated altered expression of genes associated with steroidogenesis (e.g.,
17 CypJ7aJ\ cholesterol transport (e.g., ScarbJ), cell/tissue assembly, and cellular metabolism. In
18 the interstitial regions only, genes involved in fatty acid oxidation, testes morphogenesis, and
19 descent (e.g., InsI3) were altered. In the cord samples, gene associated with stress responses,
20 chromatin bending, and phagocytosis were altered.
21 RT-PCR analysis was performed on RNA from GD 19 testes from five rats/group using
22 sequence specific primers for the orphan nuclear receptor, steroidogenic factor 1 (Sf-1), Star,
23 Cyplla, and Insl3. The data were analyzed using a one-way ANOVA followed by the
24 Bonferroni post-test, using GraphPad Prism. These studies showed a statistically significant
25 reduction in the expression of Star, Cypllal, and Insl3 but not Sf-L
26 Analysis of protein expression at GD 19 showed DBF-induced reduction in levels of
27 CYP11 A, inhibin-a, cellular retinoic acid binding protein 2 (CRABP2), and
28 phosphatidylethanolamine binding protein (PEBP) in Leydig cells, and no change in Sertoli
29 cells/seminiferous cords. These data correlated with microarray data for the genes coding for
30 these proteins. Immunoreactivity for antimullerian hormone (AMH) was slightly increased in
31 Sertoli cells following DBF treatment. Western blot analysis and immunolocalization of SF1
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1 demonstrated no effects of DBF on protein expression in Sertoli or LCs. Using time plots to
2 assess time-dependent changes in gene expression, a coordinate down-regulation of inhibin-a,
3 Scarbl, Star, and Cypl lalAl was observed between GD 15 and 19.
4 This study confirms other study results, showing down-regulation of Scarbl, Star,
5 Cypl lal, and Cypl7al. The authors suggest that DBF induces LC dysfunction indirectly
6 through sequestration of cofactors used in key signaling pathway and not through decreases in
7 SF1 protein expression. They further state that the use of Wistar rats could be important, as
8 Wistar rats may be more susceptible than SD rats to testicular effects of DBF.
9
10 5.2.3. Real-Time Reverse Transcription-Polymerase Chain Reaction (RT-PCR) Studies
11 5.2.3.1. Barlow et al (2003)
12 Six to seven SD rats per group were gavaged with corn oil or DBF at 500 mg/kg-d from
13 GD 12 to 19. Testicular RNA was then isolated from three randomly selected male fetuses per
14 litter. RT-PCR studies were performed as described in Shultz et al. (2001).
15 mRNA of 13 preselected genes in the steroid biosynthetic pathway was analyzed by
16 real-time RT-PCR; immunohistochemical and oil red O histochemical analyses were performed
17 to further confirm mRNA changes. The 13 genes analyzed were Scarbl, Star, Cypl lal,
18 hydroxyl-delta-5-steroid dehydrogenase, 3 beta- and steroid delta-isomerase 1 (Hsd3b),
19 Cypl7al, hydroxysteroid (17-beta) dehydrogenase 3 (Hsdl7b3), Ar, luteinizing hormone
20 receptor (Lhr), follicle-stimulating hormone receptor (Fshr), Kit, stem cell factor (Scj), Pcna, and
21 Clu.
22 Compared with controls, mRNA expression was downregulated for Scarbl, Star, Cypl lal,
23 Hsd3b, Cypl7al, and Kit in DBF-treated testes; mRNA expression was upregulated for Clu following
24 DBF exposure. These changes in mRNA expression were supported by immunohistochemical
25 localization of selected proteins and by staining for lipids.
26 The results in the study of Barlow et al. (2003) confirm the gene expression changes
27 observed in a previous study (Shultz et al., 2001). Furthermore, the data support alterations in
28 cholesterol synthesis, transport, and storage that likely play a role in decreased T production by
29 fetal LCs. The decreased level of mRNA expression for P450scc indicates another possible
30 contributor, as P450scc conversion of cholesterol to pregnenolone is the limiting enzymatic step
31 in T biosynthesis.
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1 5.2.3.2. Lehmann et al (2004)
2 To date, Lehmann et al. (2004) is the only dose-response gene expression study on the
3 testis performed with DBF. The other studies used a single high dose shown to affect male
4 reproductive system development.
5 Five to seven SD rats per group were treated by gavage with corn oil or DBF at 0.1, 1.0,
6 10, 50, 100, or 500 mg/kg-d from GD 12-19. Testes were then isolated on GD 19, and changes
7 in gene and protein expression were measured by real-time RT-PCR (as described in Shultz et
8 al., 2001) and Western analysis. Ten preselected genes in the steroid biosynthetic pathway were
9 analyzed by RT-PCR: Scarb, Star, Cypllal, Hsd3bl, Cypl7al, Kit, benzodiazepine receptor,
10 peripheral (Bzrp), insulin-like 3 (Insl3), Clu, and sterol regulatory element binding factor 1
11 (Srebfl). Fetal testicular T concentration was determined by radioimmunoassay in a separate
12 group of animals using doses of 0.1, 1.0, 10, 30, 50, 100, or 500 mg/kg-d.
13 The aim of this study was to determine the DBF doses at which statistically significant
14 alterations in the expression of a subset of genes and a reduction in fetal testicular T occur. As
15 summarized in Table 5-2, Lehmann et al. (2004) established 50 mg DBP/kg-d as a LOEL and
16 10 mg DBP/kg-d as a NOEL for reductions in genes and proteins associated with T production as
17 well as genes associated with other MO As (e.g., Kit, Insl3) together with reductions in
18 intratesticular T. The Lehmann et al. (2004) study demonstrated thatffsdSb (also called
19 3J3-HSD) gene expression involved in T synthesis was detected at levels as low as 0.1 mg/kg-d.
20 DBF exposure resulted in a dose-dependent decline in expression of the genes involved
21 in cholesterol transport and steroidogenesis: Scarb 1, Star, Cypllal, HsdSb, Cypl7al, andlnslS.
22 Expression of Bzrp and Clu were increased in response to DBF. Furthermore, fetal testicular T
23 was significantly reduced at DBF doses >50 mg/kg-d and reduced by 26% at 30 mg/kg-d. This
24 study reported a LOEL of 50 mg DBP/kg-d and a NOEL of 10 mg DBP/kg-d for reductions in
25 genes and proteins associated with T production together with reductions in intratesticular T. It
26 demonstrates the coordinate reduction in genes and corresponding proteins involved in
27 steroidogenesis and cholesterol transport, concurrent with a decrease in intratesticular T.
28 Importantly, it shows effects on the male reproductive system at lower doses than are used in the
29 other DBF studies in this review.
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1
2
Table 5-2. Lehmann et al. (2004) dose-response gene expression change data1
measured by RT-PCR showing statistically significant changes (p < 0.05).
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Gene Symbol
(reported gene name)
Scarbl (Sr-Bl)
Star
Cypllal (P450ssc)
Cypl7al
Hsd3b (3p-HSD)
Bzrp (PER)
Trpm2
Kit (c-Kit)
Iml3
Dose (mg/kg-d)
0.1
2
|0.3
—
—
|0.3
—
1
40.6
—
—
—
40.4
—
—
40.5
—
10
—
—
—
—
—
—
—
—
—
50
40.5
40.4
40.6
—
40.5
—
—
40.3
—
100
40.3
40.3
40.7
—
40.3
—
—
40.5
—
500
40.2
40.1
40.2
40.3
40.5
T2.0
tl.6
40.1
40.3
expression values are from DBF-exposed testes expressed relative to control values.
They are the statistically significant averages from five separate rat fetuses from different
dams per treatment group.
2 — = no statistically significant change.
Estimates for human exposure to DBF range from 0.84 to 113 |ig/kg-d (0.00084 to
0.113 mg/kg-d). For Scarbl, Hsd3b, and Kit, significant reductions in mRNA levels were
observed at DBF doses that approach maximal human exposure levels, 0.1 mg/kg-d. Alterations
in the expression of Scarbl, Hsd3b, and Kit may be sensitive indicators of DBF exposure, but
they are not necessarily of adverse consequences to DBF.
In another dose response study, Mylchreest et al. (2000) exposed pregnant SD rats to 0-,
0.5-, 5.0-, 50-, 100-, or 500-mg/kg-d DBF from GD 12-21. They found hypospadias and absent
or partially developed ventral prostate, seminal vesicles, vas deferens, and epididymis at the
500 mg/kg-d dose. They reported aNOAEL and LOAEL of 50 and 100 mg/kg-d, respectively.
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1 5.2.3.3. Thompson et al (2004)
2 Four to five SD rats per group were gavaged with corn oil or DBF at 500 mg/kg-d from
3 GD 12-19. Testes were isolated on GD 17, 18, or 19. Testes mRNA was isolated and four
4 preselected genes (Scarbl, Star, Cypllal, and Cypl7al) in cholesterol and steroidogenesis
5 pathways was analyzed by real-time RT-PCR as described in Shultz et al. (2001).
6 Immunoblotting was performed using total protein extracted from paired testis, and
7 quantification of the expressed protein levels was done using FluorChem. Fetal testicular
8 T concentration was determined by radioimmunoassay, and whole-cell cholesterol uptake
9 assessment was performed on overnight cultures.
10 A significant decrease in fetal testicular T concentration was observed as early as GD 17
11 after in utero exposure of fetuses to DBF. The percent difference in testicular T between control
12 and treated testes was much higher on GD 18 (17.8% of that seen in the control samples) than on
13 GD 17 (46.6%). Furthermore, significant decreases in mRNA expression of Scarbl, Star,
14 Cypllal, and Cypl7al were observed as early as GD 17. In agreement with T levels, the
15 percentage difference of gene expression between control and treated testes was higher on GD 18
16 than on GD 17. The suppression of the transcription by DBF was a reversible effect, as the
17 mRNA levels for all the genes returned to control levels 48 hr after DBF withdrawal. When
18 protein expression was analyzed, results similar to the gene expression data were obtained (i.e.,
19 strong expression in controls, decreased expression in treated animals with 24-hr DBF
20 withdrawal, and rising expression after the 48-hr DBF withdrawal). Additionally, there was a
21 significant decrease in the amount of cholesterol transported across the mitochondrial membrane
22 in the testes from DBF treated fetuses as assayed in overnight cultures of testis explants. This
23 observation indicates that the decrease in Star mRNA correlated with diminished protein
24 function (transport of cholesterol from the outer to the inner mitochondrial membrane by the
25 StAR protein is one of the rate-limiting steps of steroidogenesis).
26 The results of this study demonstrate that DBF-induced suppression of T production in
27 the fetal testis correlate with diminished transcription of several genes in the cholesterol transport
28 and steroidogenesis pathways as early as GD 17. This diminished effect was reversible,
29 suggesting that DBF directly interferes with the signaling processes necessary for maintenance of
30 steroidogenesis or with the transcriptional regulators required to maintain coordinate expression
31 of the genes involved in cholesterol transport and T biosynthesis.
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1 5.2.3.4. Wilson et al (2004)
2 In the studies of Wilson et al. (2004) three to five SD rats per group were treated by
3 gavage with corn oil or a developmental toxicant daily from GD 14-18 for two separate
4 experiments. In the first experiment, the rats were treated with DEHP at 750 mg/kg-d. In the
5 second experiment, the rats were treated with one of six chemicals, each known to induce male
6 reproductive malformations. The chemicals used for the second study were three AR antagonists
7 (vinclozolin [200 mg/kg-d], linuron [100 mg/kg-d], and prochloraz [250 mg/kg-d]) and three
8 phthalate esters (DEHP [1 g/kg-d], DBF [1 g/kg-d], and BBP [1 g/kg-d]). Dams were killed on
9 GD 18, and testes were removed and pooled by litter. In the first study, RNA was prepared to
10 quantify expression of one preselected gene, Insl3, by real-time RT-PCR. In the second study,
11 both steroid hormone production (ex vivo incubation) and Insl3 expression were assessed. Total
12 RNA was isolated using Trizol, digested using Dnase I, and quantitated with RiboGreen.
13 ImProm-II Reverse Transcriptase was used for RT, followed by amplification using Taql. They
14 completed RT-PCR for Insl3 using a Bio-Rad iCycler.
15 In the first study, the mRNA expression oflnslS was reduced by -80% in DEHP litters
16 compared with that in control litters. In the second study, among the six chemicals tested, only
17 phthalate esters (DEHP, DBF, or BBP) reduced mRNA levels in the fetal testis, with DBF and
18 BBP being more effective than DEHP. In contrast, prochloraz or linuron as well as any of the
19 three phthalate esters significantly reduced ex vivo T production.
20 In a previous study with antiandrogenic chemicals that alter male sexual differentiation
21 (Gray, et al. 2000), phthalate esters were the only class that produced agenesis of the
22 gubernacular ligaments; some of the phthalate ester-exposed rats had a phenotype similar to that
23 seen in the Insl3 knock-out mouse. The study of Wilson et al. (2004) confirms this hypothesis
24 since only the three phthalates reduced Insl3 gene expression. The authors proposed that the
25 effects of DEHP, DBF, or BBP on Insl3 mRNA and T production result from a delay in
26 maturation of fetal LCs, resulting in hyperplasia as they continue to proliferate rather than to
27 differentiate.
28
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1 5.2.4. Study Comparisons
2 5.2.4.1. Microarray Study Methods Comparison
3 Table 5-3 compares the study design and method of determining statistical significance
4 across the five microarray studies used in the case study. Because the Bowman et al. (2005)
5 paper assessed changes in gene expression in WD rather than testis, and because the microarray
6 data were not presented in the paper, the discussions will focus on the three other microarray
7 studies. The Plummer et al. (2007) study pooled control tissue and used the Agilent platform,
8 which differed from the platforms used in the other studies. Liu et al. (2005), Schutz et al.
9 (2001), and Thompson et al. (2005) all assessed mRNA levels in rat testis—but with somewhat
10 differing significance criteria. All studies included vehicle-treated controls.
11
12 5.2.4.2. Reverse Transcription-Polymerase Chain Reaction (RT-PCR) Study Methods
13 Comparison
14 Table 5-4 compares the RT-PCR methods used in the nine toxicogenomic articles. There
15 were many similarities among the studies. All the groups—except Bowman et al. (2005)—
16 extracted RNA from testis. All studies used a vehicle-treated control. Most of the studies used
17 the same significance criteria (p < 0.05). There were some differences in the number of fetuses
18 used per experiment while some studies pooled tissues.
19 More important, however, were the significant similarities among the nine toxicogenomic
20 studies. Eight of the studies used the same strain of rat (SD), all purchased from the same vendor
21 (Charles River, Raleigh, NC). All of the studies described dissolving the DBF in corn oil, using
22 a corn oil vehicle control, and using oral gavage as the route of exposure. Six of the studies
23 (Barlow et al., 2003; Bowman et al., 2005; Liu et al., 2005; Shultz et al., 2001; Plummer et al.,
24 2007; Thompson et al., 2004) treated the animals by gavage to 500 mg/kg-d from GD 12-19.
25 This dose has been shown to adversely affect male reproductive development without causing
26 maternal toxicity or fetal death. Lehmann et al. (2004) completed a dose response during the
27 GD 12-19 period, using 0.1, 1.0, 10, 50, 100, or 500 mg/kg-d. Bowman et al. (2005) and Shultz
28 et al. (2001) included an additional exposure duration of GD 12-21. Wilson et al. (2004)
29 exposed for a slightly shorter duration (GD 13-17) and at a higher dose (1000 mg/kg-d). This
30 paper reports exposures on GD 14-18; however, these authors consider GD 1 as the day a
31 sperm-positive smear was identified in dams, whereas the other studies consider the
32
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1
2
Table 5-3. Method comparisons for DBF microarray studies
Study
Bowman et
al., 2005
Liu et al.,
2005
Plummer et
al., 2007
Shultz et al.,
2001
Thompson et
al., 2005
Tissue
collected
Wolffian
ducts
Testis
Testis (whole,
laser captured
interstitial
tissue, or laser
captured
seminiferous
cord tissue)
Testis
Testis
Significance criteria
ND (microarray data not
presented)
p < 0.05 compared to
control by either 1-way
ANOVA, post hoc
Dunnett test, or Tukey
test
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
2-fold change in average
expression value
compared to control
p < 0.05 multiple
comparison using
Bonferroni correction
Individual animals («) used?
No, pooled (3-4 fetuses/litter;
67 dams/treatment group)
Yes, (6 fetuses/litter;
6 dams/treatment group)
Yes for DBF-treated (3 pups
from 3 different dams); Control
RNAs were pooled
GD 19 and GD 21 time points:
Yes, 1 fetus/litter;
3 dams/treatment group.
GD 16 timepoint: pooled RNA
from 5 fetuses/1 litter; 3 arrays
hybridized/treatment group.
Yes (ND)
4
5
ANOVA, analysis of variance; ND, not detected.
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1
2
Table 5-4. Method comparisons among the reverse transcription-polymerase
chain reaction (RT-PCR) DBF studies
Study
Barlow et al.,
2003
Bowman et
al., 2005
Lehmann et
al., 2004
Liu et al.,
2005
Plummer et
al., 2007
Shultz et al.,
2001
Thompson et
al., 2004
Thompson et
al., 2005
Wilson et al.,
2004
Tissue
collected
Testis
Wolffian ducts
Testis
Testis
Testis (whole,
laser-captured
interstitial
tissue, or
laser-captured
seminiferous
cord tissue)
Testis
Testis
Testis
Testis
Significance criteria
(p values)
p < 0.05 compared to
control
p < 0.05 compared to
control
p < 0.05 compared to
control
p < 0.05 compared to
control by either 2-way
nested ANOVA or
Dunnett
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
p < 0.05 compared to
control
p < 0.05 compared to
control (Student's t-test
or 1-way ANOVA)
p < 0.05 normalized mean
of 3-5 fetuses/treatment
group relative to control
p < 0.01 compared to
control (means on a litter
basis)
Individual animals («) used?
Yes (3 fetuses/litter;
5 dams/treatment group)
No, pooled (3-4 fetuses/litter;
6-7 dams/treatment group)
Yes (5 fetuses/litter;
4-5 litters/treatment group)
Yes (control: 6 fetuses/dam;
6 dams for control. Treated:
3 fetuses/dam; 3 dams)
NDa; assessed GD 19.5 fetal
testes
GD 19 and GD 21 timepoints:
Yes, 1 fetus/litter;
3 dams/treatment group.
GD 16 timepoint: pooled
RNA from 5 fetuses/ 1 litter;
3 arrays hybridized/treatment
group.
ND
Yes, 3-5 fetuses/litter
No, pooled for each litter
(3 dams/treatment group)
4
5
6
7
aNot clear from the Materials and Methods.
ANOVA, analysis of variance; ND, not detected
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1 sperm-positive day as GD 0. Therefore, to be consistent with the other groups, we are reporting
2 the exposure period as GD 13-17. Similarly, Plummer et al. (2007) reports exposures ranging
3 from GD 12.5 to GD 19.5, which are equivalent to GD 12-19 as the authors consider GD 0.5 to
4 be the sperm positive day, have been adapted, too.
5 All of the other selected studies collected testes for RNA extraction, with the exception of
6 Bowman et al. (2005), which collected WDs. Bowman et al. (2005) focused on the WD because
7 they were interested in characterizing the mechanisms responsible for prenatal DBF-induced
8 epididymal malformations. WD tissue from 3-4 fetuses was obtained to ensure enough RNA for
9 analyses (Table 5-3). Since WDs are the precursor of the vas deferens, epididymis, and seminal
10 vesicles, the tissue assayed by Bowman et al. (2005) is different from the tissue evaluated in the
11 other seven studies (RNA from the testes of 1-3 fetuses). The studies used a variety of
12 toxicogenomic methodologies to assess changes in gene expression. General descriptions of
13 these methods utilized by the studies were presented in Section 5.1.
14 An important consideration is the reliability of the data being generated and compared in
15 these nine DBF studies. As discussed, the MAQC project (MAQC Consortium et al., 2006) has
16 recently completed a large study evaluating inter- and intraplatform reproducibility of gene
17 expression measurements (see Chapter 2). Six commercially available microarray platforms and
18 three alternative gene expression platforms were tested. Both Affymetrix® microarrays and
19 RT-PCR assays were included in the MAQC testing. Affymetrix® and the other one color
20 platforms showed similar coefficients of variation of quantitative signal values (5-15%) when
21 used to detect 8,000 to 12,000 genes. When comparing variation within and between test sites,
22 the one-color assays demonstrated 80-95% agreement.
23 Although it is difficult to compare expression values generated on different platforms
24 because of differences in labeling methods and probe sequences, MAQC was able to show good
25 agreement between the Affymetrix® platform and the other platforms. This was particularly true
26 when using the same biological sample (and, thus, removing variability introduced by the sample
27 or sample preparation method). It is worth noting that Affymetrix® displayed high correlation
28 values with RT-PCR based on comparisons of-500 genes. The results of the MAQC report
29 suggest that the comparisons made in this case study are valid due to the reliability of the data.
30 Additionally, since seven out of the nine experiments in the case study were performed in the
31 same laboratory, interlaboratory variability is not an issue with these studies. In the assessment
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1 of consistency of findings described next, a potential source of incongruence is the decreased
2 sensitivity for low-expression genes in the microarray platforms as compared to the
3 gene-expression technologies and differences in probe location.
4
5 5.3. CONSISTENCY OF FINDINGS
6 5.3.1. Microarray Studies
7 An evaluation of the consistency across the four microarray studies of the testis was
8 performed. Bowman et al. (2005) is not included because the microarray study results were not
9 reported. In order to enhance comparability, the data from the whole testis microarray study of
10 Plummer et al. (2007) are included in the evaluation, but the data from the microdissected
11 regions of the testis are excluded because the lack comparison to any other study.
12 Eight of the nine toxicogenomic studies used the same strain, SD, and all nine used the
13 same species (rat). Plummer et al. (2007) was the only study to use the Wistar rat strain because
14 it is considered more susceptible to effects on the testis than SD. Table A-l in the Appendix A
15 includes those genes whose expression was reported to be significantly altered, as reported by
16 Shultz et al. (2001), Thompson et al. (2005), Plummer et al. (2007) (for the whole testis only), or
17 Liu et al. (2005) studies. Also presented in Table A-l are the official gene names, exposure
18 times, and directional response changes. It should be noted that some differences are to be
19 expected in these comparisons because no two studies had identical study designs or platform, or
20 applied the same statistical cut-offs. For example, Thompson et al. (2005) used a very short
21 duration of exposure, whereas the other three studies had longer exposure durations. In addition,
22 the Affymetrix®microarray platform was used only by Thompson et al. (2005) and Liu et al.
23 (2005).
24 The three testis microarray studies (Thompson et al. [2005], Plummer et al. [2007], and
25 Liu et al. [2005]) that used the "second generation chips" containing a much larger number of
26 probes (therefore, covering many more genes) than the Clontech platform were compared. The
27 Venn diagram, developed for these three studies, shows some unique gene expression changes
28 for each study as well as a number of common gene expression changes (Figure 5-1).
29 Nevertheless, significant corroboration in the direction of effect among the common genes were
30 observed in three studies (but not with the Shultz et al. [2001, Appendix A]). Additionally, most
31 of the genes in common were downregulated after in utero DBF exposure. Further, two genes in
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Thompson et al. (2005)
Plummer et al. (2007)
4 CypSl
36
/
/
/
/
f
[Idhl
I Lhgcr
t Nr4al
ISqle
}Stcl
\Tpml
X^ /
X /
4 Cdknlc I
| Cmbp2 \
I Ddit4
{ Dhcr? \
t Dusp6 \
| Fabp3 \
[Fragl \
| Fthfd \
t Grbl4 \
| Gstm2 N
J, Hsd3bl_predicted
{ Fabp3
{ Cypllal
{ CyplJal
{ Scarbl
[Ddc
[Fdxl
[Myh6
[Prdx3
{ Star
[Svs5
\.
XV
^
f\Por
| Sc4mol
[Scp2
[Stc2
{ Alasl
[ Aldh2
[Dbi
[Fadsl
[Fdftl
[Fdps
[Fdxr
{ Gsta3
[ Hmgcr
J, Hmgcsl
[Mil
[Inha
{ NrObl
{ Pebpl
65
93
Liu et al. (2005)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Figure 5-1. Venn diagram illustrating similarities and differences in significant gene
expression changes for three of the microarray studies in the testes for three recent
microarray studies: Thompson et al. (2005), Plummer et al. (2007), and Liu et al.
(2005). Numbers within each circle indicate genes whose expression wasstatistically
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.
the steroidogenesis pathway, Cypllal, and Scarbl, are in common between all four microarray
studies. These findings indicate that the microarray data set for DBF is relatively consistent and
findings are reproducible.
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1 A number of genes involved in steroidogenesis were found to be downregulated by DBF
2 in all three studies (Figure 5-1). These include Cyp Hal, Scarbl, Star, and Cyp 17al. Other
3 genes significantly altered include a downregulation of the serotonin and catecholamine pathway
4 enzyme Ddc, the myosin, heavy polypeptide 6, cardiac muscle, alpha (Myh6), the
5 androgen-regulated structural protein Svs5, and the cellular retinoic acid-binding protein 2
6 (Crabp2).
1 Other genes were significantly altered in two of the three studies. For example, in
8 comparing the results of the two studies that utilized the same platform (Affymetrix®), the Liu et
9 al. (2005) and Thompson et al. (2005) studies observed a downregulation of the steroidogenesis
10 genes Sqle and Hsd3bl_predicted, cyclin-dependent protein kinase inhibitor (Cdknlc), the
11 cellular retinoic acid binding protein 2 (Crabp2\ the FGF receptor activating protein 1 (Fragl),
12 and fatty acid binding protein (Fabp3). These same two studies found upregulation of the
13 steroi dogene si s gene Nr4a3.
14 There are a number of genes for which the different studies found a similar significant
15 alteration but the direction of effect varied. For example, GSH S-transferase, mu 2 (Gstm2), a
16 gene involved in xenobiotic metabolism, was found to be significantly downregulated by Liu et
17 al. (2005) and Thompson et al. (2005) and significantly upregulated by Shultz et al. (2001). The
18 microsomal GSH S-transferase 1 gene (MgstJ) was downregulated in Liu et al. (2005) and
19 upregulated in Shultz et al. (2001). Appendix A presents a table of the significantly significant
20 gene expression changes in the Thompson et al. (2005), Shultz et al. (2001), Liu et al. (2005),
21 and Plummer et al. 2007 studies. These differences in microarray results can be explained by a
22 number of factors including study differences (e.g., duration of exposure, platform, rat strain)
23 and/or variability of microarray study results.
24 Overall, the data indicate that there are some unique gene expression changes for each
25 study as well as a number of common gene expression changes. Significant corroboration in the
26 direction of effect among the common genes was observed in at least three studies. In addition,
27 most of the genes in common among these three studies were downregulated after in utero DBF
28 exposure. These findings indicate that the microarray data set for DBF is very consistent and
29 reliable although certain uncertainties remain when comparing data from different platforms with
30 different study design.
31
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1 5.3.2. Reverse Transcription-Polymerase Chain Reaction (RT-PCR) Gene Expression Findings
2 Comparisons were also made of RT-PCR data (Table A-2; Appendix A). All nine studies
3 performed RT-PCR, and in the case of the Liu et al. (2005), Shultz et al. (2001), Plummer et al.
4 (2007), and Thompson et al. (2005), the RT-PCR was performed following identification of the
5 genes of interest in microarray studies. A number of genes were found to be similarly up- or
6 downregulated by in utero DBF exposure. In the steroidogenesis pathway, 5 genes (Cypllal,
1 Cypl7al, Hsdl7b3, Scarbl, and Star} were found to be downregulated by more than one
8 laboratory. Some commonalities were also observed in altered gene regulation of transcription
9 factors. Egrl, Nfil3, and Nr4al were shown in two different studies to be upregulated. Two
10 studies reported similar downregulation ofNrObJ and Tcfl.
11 Three studies (Wilson et al. [2004], Plummer et al. [2007], and Lehmann et al. [2004])
12 observed reduced Insl3 gene expression. As discussed, Insl3 has a role in sexual differentiation
13 and testis descent. Reduced fetal InslS has been shown to produce agenesis of the gubernacular
14 ligaments. Two other genes have been shown to have DBF-induced altered expressions as
15 assessed by RT-PCR in two laboratories: Clu (upregulated) and Kit (downregulated).
16
17 5.3.3. Protein Study Findings
18 All nine studies completed either Western analysis (immunoblotting) or
19 immunohistochemistry to characterize fetal DBF-induced changes in protein expression.
20 Usually, protein analysis was conducted for proteins that had demonstrated changes in mRNA
21 expression. However, up- or downregulation of genes and proteins does not always occur
22 simultaneously, so a disparity between these two experimental results is quite common.
23 Table 5-5 presents the protein expression data.
24 Four proteins in the steroidogenesis pathway were shown to be downregulated by DBF
25 exposure. These findings align well with the gene expression data presented earlier. STAR was
26 shown to be downregulated by Western blotting in three separate experiments, and by
27 immunolocalization in another experiment. STAR expression was found only in LCs in both the
28 control and DBF-treated testes, with the DBF-treated testes having decreased staining intensity
29 (Barlow et al., 2003). Quantitatively, three experiments demonstrated reduced SCARE 1 protein
30 levels in DBF-treated in fetal testes; however, immunolocalization showed
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Table 5-5. Evaluation of the published protein studies after DBF in utero exposure (testes only)
Official
Gene
Symbol
Amh
Bcl2
Bzrp
Cebpb
Crabp2
Clu
Clu
Cypllal
Cypllal
Cypllal
Cypl7al
Cypl7al
Cypl7al
Ddc
Grbl4
Inha
Insl3
Kit
KM
Protein Name
Reported in
Paper
AMH
bcl-2
PER
CEBPB
CRABP2
PEBP
TRPM-2
TRPM-2
CYPllal
P450ssc
P450ssc
CYP17al
CYP17
cypl?
Dopa
decarboxylase
GRB14
INHA
InslS
c-kit
SCF
Exposure
Interval
GD 12-19
GD 12-21
GD 12-19
GD 12-19
GD 12-19
GD 12-19
GD 12-21
GD 12-19
GD 18 for 18 hrs
GD 12-19
GD 12-17 or 18
GD 18 for 18 hrs
GD 12-17 or 18
GD 12-19
GD 12-19
GD 12-19
GD 12-19
GD 12-19
GD 12-19
GD 12-19
Dose
(mg/kg-d)
500
500
500
500
500
500
500
500
500
500
500
500
500
500
500
500
500
500
500
Method Used
Immunolocalization
Immunolocalization
Immunolocalization
Immunolocalization
Immunolocalization
Immunolocalization
Immunolocalization
Western analysis
Western analysis
Western analysis
Western analysis
Western analysis
Western analysis
Immunolocalization
Immunolocalization
Immunolocalization
Immunolocalization
Immunolocalization
Immunolocalization
Change in Protein Expression Compared
to Controls
t slightly in Sertoli cells
t in Sertoli and Leydig cells
I in interstitial cells
J, in interstitial cells
J, in Leydig cells
t in Sertoli and Leydig cells
t in Sertoli cells
I (0.6 of control)
|(0.5 of control)
I (0.15at24hr;0.5at48hr)
\, (0.6 of control)
\, (ND at 24 hr; 0.4 of control at 48 hr)
|(0.2 of control)
J, in interstitial cells
J, in interstitial cells and t in Sertoli cells
J, in Leydig cells
J, in interstitial cells
I in gonocytes
t in Sertoli cells
Reference
Plummeretal., 2007
Shultzetal.,2001
Lehmann et al., 2004
Liu etal., 2005
Plummeretal., 2007
Shultzetal.,2001
Barlow etal., 2003
Thompson et al., 2005
Lehmann et al., 2004
Thompson et al., 2004
Thompson etal., 2005
Thompson etal., 2004
Lehmann et al., 2004
Liu etal., 2005
Liu etal., 2005
Plummer et al., 2007
Lehmann et al., 2004
Barlow etal., 2003
Barlow etal., 2003
o <*>'
U
O 58
S a
1
§ 3.
s
rS"
to
-------
Table 5-5. (continued)
Official
Gene
Symbol
NrObl
Pebp
Scarbl
Scarbl
Scarbl
Scarbl
Star
Star
Star
Star
Testin
Protein Name
Reported in
Paper
DAX-1
PEBP
SCARE 1
SR-B1
SR-B1
SRB1
STAR
StAR
StAR
StAR
testin
Exposure
Interval
GD 12-19
GD 12-19
GD 19 for 6 hrs or
GD 18 for 18 hrs
GD 12-17 or 18
GD 12-19
GD 12-19
GD 18 for 18 hrs
GD 12-17 or 18
GD 12-19
GD 12-19
GD 12-19
Dose
(mg/kg-d)
500
500
500
500
50, 100, 500
500
500
500
50, 100, 500
500
500
Method Used
Immunolocalization
Immunolocalization
Western analysis
Western analysis
Western analysis
Immunolocalization
Western analysis
Western analysis
Western analysis
Immunolocalization
Immunolocalization
Change in Protein Expression Compared
to Controls
4 in gonocytes
I in Leydig cells
4 (0.3 of control)
1(0.15 at 24 hr; (0.7 of control at 48 hr)
I (0.6, 0.5, and 0.1 of control)
1 in Leydig; t in Sertoli cells
I (0.4 of control)
1 (ND at 24 hr; 0.4 of control at 48 hr)
4 (0.1,0.2, 0.1 of control)
J, in Leydig cells
t in Sertoli cells and gonocytes
Reference
Liu et al., 2005
Plummeretal., 2007
Thompson et al., 2005
Thompson et al., 2004
Lehmann et al., 2004
Barlow et al., 2003
Thompson et al., 2005
Thompson et al., 2004
Lehmann et al., 2004
Barlow et al., 2003
Liu et al., 2005
o
o
S
§
s
rS"
ND, not detected
to
oo
-------
1 DBF-induced increased staining of Sertoli cells and decreased staining of Ley dig cells. Both
2 CYP1 lal and CYP17al protein levels were shown in several separate experiments to be reduced
3 following DBF exposure, which correlated with microarray and PCR findings.
4 Immunolocalization was completed for CYP1 lal and found to be downregulated in Leydig cells
5 (Plummer et al., 2007). Using immunolocalization, CLU was found to be increased in Sertoli
6 cells and Leydig cells. One study has shown that DBF lowers INSL3 protein immunoexpression
7 levels in the fetal testis (McKinnell et al., 2005). The expression of SF1 was unchanged in
8 Wistar rats, however, four proteins (CYP1 lal, INHA, CRABPS, and PEBP) regulated by SF1
9 and AMH, were reduced in LCs following DBF exposure (Plummer et al., 2007).
10
11 5.3.4. DBF Toxicogenomic Data Set Evaluation: Consistency of Findings Summary
12 A comprehensive summary of the DBF toxicogenomic data set assessed in this case
13 study, including all microarray, RT-PCR, and protein data from the nine studies, is presented in
14 Figure 5-2. The genes and protein included in the figure are limited to those for which two or
15 more studies detected statistically significant results. In many cases, when comparing across
16 RT-PCR and microarray studies, a differentially expressed gene (DEG) is found in one or even
17 several studies that is not identified in another study. For example, Kit was found to be
18 downregulated in the Barlow et al. (2002), Lehmann et al. (2004), and Schultz et al. (2001)
19 studies; by contrast, it was not altered significantly in the Liu et al. (2005) study even though it is
20 represented on the Affymetrix® array.
21 Data from the Bowman et al. (2005) paper were not included because it evaluated
22 changes in DBF-induced gene expression in the WD rather than testes. There are no other WD
23 studies for comparisons. If an increase or decrease was reported at any time point, it was
24 included. Multiple time points from one paper were not included, i.e., for the Thompson et al.,
25 2005 paper studying duration of exposure, if several time points showed a change, only one was
26 recorded as a study showing a change. For protein data, descriptions of immunohistochemical
27 studies suggesting an increase, though without real quantitation, were still counted. For the
28 dose-response study (Lehmann et al., 2004), data from only the 500 mg/kg-d dosing were used to
29 allow better comparisons with the other studies.
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Gene
I-
I
~P
i
1
O
OS
os
o
g
= RT-PCR
= microarray
Pathway/Function
= protein
= upregulation
= downregulation
Figure 5-2. Summary of DBF-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.
-------
1 Figure 5-2 presents a summary of the changes in gene and protein expression following
2 in utero DBF exposure across the studies. What is most striking is the consistency of evidence
3 for the DBF-induced downregulation of the steroidogenesis pathway. Both microarray and
4 RT-PCR analysis show consistent downregulation ofCypllal, Cypl7al, Star, and Scarbl
5 mRNA expression. Protein expression of Cypllal, Cypl7al, Star, and Scarbl is concurrently
6 downregulated. Downregulation ofHsdSb and Lhcgr mRNA are also consistently demonstrated.
7 Significantly, two genes involved in lipid/sterol/cholesterol transport also show downregulation:
8 Npc2 and Ldlr. Three transcription factors (NJI13, Egrl, and Nr4al} demonstrate DBF-induced
9 upregulation, while two genes (NrObl and Tcfl) show downregulation in a number of
10 experiments. Three immediate early genes (Fos, Egr2, and Zjp36) are upregulated by DBF
11 exposure. Interestingly, C7w, also known as T repressed prostate message-2, is upregulated, as
12 shown by two microarray, two RT-PCR, and two protein assays.
13
14 5.4. DATA GAPS AND RESEARCH NEEDS
15 Based on the evaluation of nine toxicogenomic studies, a number of research needs
16 became apparent. There are genomic data gaps for many environmental chemicals. For DBF,
17 confirmatory RT-PCR studies for all of the genes identified from microarray studies, would give
18 additional credence to the microarray results. Similarly, additional protein analysis, with
19 quantitation by Western blotting and with immunolocalization, could further characterize
20 DBF-induced effects on the male reproductive system. Looking at DBF-induced changes in
21 gene expression in additional reproductive and nonreproductive (Thompson et al., 2005) tissues
22 could also add information about mechanism(s) of action and tissue specificity. As testes are
23 comprised of a number of cell types, evaluating additional homogeneous cell populations within
24 the testes, as Plummer et al. (2007) reported, will be useful.
25 In order to fully consider the Case Study Question 2 (see Chapters 1 and 3), using the
26 toxicogenomic data to determine whether there are other MO As responsible for some of the male
27 reproductive developmental effects, we decided that it would be helpful to analyze the raw data
28 to assess all affected pathways. The published studies, while all excellent quality, focused their
29 pathway analyses and descriptions on particular pathways of interest to basic science. The
30 following chapter (Chapter 6) describes efforts to reanalyze some of the DBF microarray studies
31 with this goal in mind.
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1 6. NEW ANALYSES OF DBF GENOMIC STUDIES AND EXPLORATORY
2 METHODS DEVELOPMENT FOR ANALYSIS OF GENOMIC DATA FOR RISK
3 ASSESSMENT PURPOSES
4
5
6 6.1. OBJECTIVES AND INTRODUCTION
7 The overall goal of this chapter is to describe the new analyses of genomic and other
8 molecular data that were performed to answer the two case study questions. The motivation for
9 performing these new analyses is that the published DBF microarray studies were not performed
10 for risk-assessment purposes, as is the case for the majority of the current toxicogenomic
11 literature for all chemicals. And, some of the published analyses, such as the time course data of
12 Thompson et al. (2005) and Plummer et al. (2007), have not yet been applied to risk assessment.
13 This work to address the two case study questions inevitably led to the development of some new
14 methods for analyzing genomic data for use in risk assessment. The four subobjectives of the
15 new analyses and methods development proj ects were to
16
17 • Reanalyze DBF microarray data to address the Case Study Question 1: Do the genomic
18 data inform DBF additional MOAs and the mechanism of action for the male
19 reproductive developmental effects?
20
21 We determined that it would be advantageous to reanalyze the raw data utilizing different
22 analytical approaches (see Figure 3-1) because in most of the DBF microarray studies
23 were analyzed to focused on further delineation of the mechanism of action relevant to
24 one MO A, the reduction in fetal testicular T. In fact, it was the microarray and RT-PCR
25 study results that identified the modulation of the steroidogenesis pathway as leading to
26 reduced fetal testicular T, one of the DBF MOAs, and then, leading to a number of the
27 male reproductive developmental effects. Not all pathways for the identified DEGs were
28 discussed (or presented) in detail because the focus was on specific pathways of interest.
29 A second DBF MOA of reduced Insl3 gene expression has also been identified (Wilson
30 et al., 2004; see Chapter 3) leading to testis descent defects. Therefore, a reanalysis that
31 looks more broadly to define all pathways affected by DBF may inform whether there are
32 additional pathways related to MOAs that could be linked to the unexplained male
33 reproductive developmental outcomes caused by DBF identified in Chapter 4. Thus, the
34 purpose of this reanalysis of the existing data set was to identify and characterize
35 additional molecular pathways affected by DBF, beyond a reduction in fetal T and Insl3
36 gene expression.
37
38
39
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1 • Explore the development of new methods for pathway analysis ofmicroarray data for
2 application to risk assessment.
3
4 The motivation was to develop methods for performing gene expression analysis of
5 microarray data for use in risk assessment. Microarray studies for basic research
6 purposes do not require as high a level of stringency as for risk-assessment purposes
7 because the results are usually used for hypothesis testing (e.g., for developing an MOA
8 hypothesis) and further studies in basic research.
9
10
11 • Utilize existing DBF genomic data to develop a genetic regulatory network model, and
12 methods for modeling, for use in risk assessment.
13
14 We asked whether there are data to develop a genetic regulatory network model to
15 represent the biological interactions that are functional at different times following
16 exposure to DBF. Regulatory network models encompass identified cellular signaling
17 pathways from input data and, in addition, bring in gene elements that are inferred from
18 the input data but not necessarily presented in the data. This exercise provides a more
19 biologically enriched view of the cellular interactions inherent in the data.
20
21 • Utilize genomic and other molecular data to address the Case Study Question 2: Do the
22 genomic and other molecular data inform interspecies differences in MOA ?
23
24 We utilized the available DNA, sequence, and protein similarity data to assess the
25 rat-to-human conservation of the predicted amino acid sequences of genes involved in the
26 steroidogenesis pathway.
27
28 The work to address the objectives of this chapter is the result of a collaborative effort
29 between scientists at the National Center for Environmental Research STAR Center at Rutgers
30 University and the Robert Wood Johnson Medical School UMDNJ Informatics Institute and the
31 U.S. EPA. The analyses were performed either at Rutgers University or NHEERL-U.S. EPA.
32 The work presented in this chapter is highly technical and thus, is intended to be
33 beneficial to scientists with expertise in genomic and genetic data analysis. The technical details
34 of the analyses are provided in order that scientists could apply these methods to their work.
35 Such an approach will allow the risk assessor proficient in microarray analysis methodology an
36 opportunity to apply these methods. The last section of this chapter (section 6.6.) summarizes
37 the findings for a scientific audience without a strong understanding ofmicroarray analysis
38 methods..
39
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1 6.2. REANALYSIS OF GENE EXPRESSION DATA TO IDENTIFY NEW MOAs TO
2 ELUCIDATE UNEXPLAINED TESTICULAR DEVELOPMENT ENDPOINTS
3 AFTER IN UTERO DBF EXPOSURE
4
5 6.2.1. Objective of the Reanalysis of the Liu et al. (2005) Study
6 The goal was to reanalyze DBF microarray data to address the Case Study Question 1:
7 Do the genomic data inform DBF additional MOAs and the mechanism of action for the male
8 reproductive developmental effects? Modulation of the steroidogenesis pathway, leading to
9 reduced fetal testicular T, has been identified from the microarray and RT-PCR studies as one
10 MOA for DBF's male reproductive developmental effects. The Liu et al. (2005) study focused
11 on the steroidogenesis and related pathway. Not all pathways for the identified DEGs were
12 discussed (or presented) in detail because the focus of the study was on steroidogenesis.
13 Therefore, a reanalysis that looks more broadly to define all pathways affected by DBF may
14 inform whether there are additional modes and mechanisms of action that could be linked to the
15 unexplained male reproductive developmental outcomes caused by DBF identified in Chapter 4.
16 The purpose for the reanalysis of the existing data sets is to identify and characterize additional
17 molecular pathways affected by DBF, beyond the effects on the androgen-mediated male
18 reproductive developmental toxicity pathways.
19 The Liu et al. (2005) study was selected for reanalysis because the data set had a
20 comprehensive exposure scenario that covered the critical window for developmental exposure
21 to DBF (GD 12-19), the Affymetrix® chip was used (compatible with the proprietary and free
22 software programs used for pathway level analysis), and the data were provided by Dr. Kevin
23 Gaido, a collaborator on this project. Some limitations of the Liu et al. (2005) data set are the
24 small number of samples (i.e., 3 controls and 3 DBF-treated) and the within sample variance.
25 This study was a comparative analysis of six phthalate esters. However, only the DBF treatment
26 and vehicle control data were used for this analysis.
27 The Liu et al. (2005) study investigated global gene expression in the fetal testis
28 following in utero exposure to a series of phthalate esters, including both developmentally toxic
29 phthalates (DBF, BBP, DPP, and DEHP) and non-developmentally toxic phthalates (DMP, DBF,
30 and DOTP) (Liu et al., 2005). The original analysis was based on a two-way nested ANOVA
31 model using Bonferroni correction that identified 391 significantly expressed genes from the
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1 control out of the approximately 30,000 genes queried. In their analysis, two classes of phthalate
2 esters were distinguished based on the gene expression profiles. The authors also showed that
3 developmentally toxic phthalates targeted gene pathways associated with steroidogenesis, lipid
4 and cholesterol homeostasis, insulin signaling, transcriptional regulation, and oxidative stress.
5 The common approach of interrogating a handful of genes that pass user-defined
6 statistical filtering criterion to understand the biology of a system has some limitations
7 (Tomfohr et al., 2005). These include the following:
8
9 • Often times after correcting for multiple hypothesis testing, few or no genes pass the
10 threshold of statistical significance because the biological variances are modest relative to
11 the noise inherent in a microarray experiment.
12
13 • Alternatively, one is left with a long list of statistically significant genes that have no
14 unifying biological theme. Interpretation of such a list can be daunting.
15
16 • Additionally, since cellular processes are not affected by changes in single genes, but a
17 set of genes acting in concert, single gene analysis can miss out on relevant biological
18 information.
19
20 • Often times, there is little concordance between lists of statistically significant genes
21 from similar studies conducted by two groups.
22
23 6.2.1.1. Differentially Expressed Gene (DEC) Identification: Linear Weighted
24 Normalization
25 The data set for the vehicle treated and DBF treated samples were input into the
26 proprietary software Rosetta Resolver®. A principal component analysis (PC A) of the entire data
27 set shows a distinct treatment response (i.e., the control and treated samples separate out clearly
28 into two distinct groups [see Figure 6-1]). Additionally, it demonstrates certain limitations of
29 this data set—namely the variance in the data set between similarly treated samples. This is
30 apparent from the fact that even though the two groups show separation along two different axes,
31 they are not tightly grouped together in space.
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1
2
3
4
5
6
7
9
10
11
12
13
14
0.5094
0.2547
0
0
Principal Component 2
Legend
cont
(3/3)
DBF
(3/3)
Selection
(0/0)
0.462& 0.2314 0.0-0.2314
-0.2547 0.4519
Principal Component 3
4-0.4628
•
0.2259
0.0
-0.5094
-0 2259
•
U
\
Principal Component
Figure 6-1. Principal component analysis (PCA) representation of Liu et al. (2005)
data set. PCA is a standard technique for visualization of complex data, showing the
distribution of each sample and the degree of similarity to one another. PCA shows
relationship of all six samples, DBF-treated (red) and concurrent vehicle control (blue).
Generated by Rosetta Resolver Software v 7.0.
Next, the gene expression data were normalized using a linear weighted model in Rosetta
Resolver®. The Rosetta Resolver® system is a comprehensive gene expression analysis solution
that incorporates powerful analysis tools with a robust, scalable database. The annotated genes
of the rat genome on the Affymetrix® gene chip, -30,000 genes, were input into the significance
analysis with a Benjamini Hochberg Multiple FDR correction for multiple testing applied at
15 p< 0.01, a more stringent statistical cut-off. Of the -30,000 genes, the analysis passed
16 118 genes as being significantly altered following DBF exposure. Of these, 17,496 genes did not
17 pass the statistical filter, and 13,428 genes were not affected by the treatment. One possible
18 reason that only 118 genes passed the multiple-testing correction filter is that there is a high
19 variance between individual samples as demonstrated by the PCA.
20 Using the linear-weighted normalization analysis, we relaxed the filtering criterion to
21 include more genes because the objective of this exercise was to identify additional pathways
22 affected by DBF, and starting out with 118 genes would be limiting in that regard. Additionally,
23 often times, researchers have to make a judgment call on when to put emphasis on statistical
24 significance and when to focus on the biological significance. Since the objective was to use the
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1 gene expression data to gain new information about DBF toxicity, we deemed it suitable to relax
2 the statistical filtering criteria to obtain maximum numbers of genes to upload to pathway
3 mapping software.
4 The next filtering strategy involved applying a statistical t-test ofp < 0.05 (see
5 Figure 6-2) and did no multiple testing correction (MTC) was applied because when MTC was
6 applied, no genes were identified as significant. Of the 31,000 gene probes present on the RAE
7 A and B Affymetrix® GeneChips®, 1,977 passed this filter.
9
10
11
12
13
14
15
16
13
00
o
H-l
-12 -
-16 -
8000 16000 24000
Sequences
32000
Legend
+ Signature
(1,977/1,977)
+ Invariant
(999/999)
+ Unchanged
(28,066/28,066)
^ Selection
(1,977/1,977)
"
Figure 6-2. Selection of significant genes using Rosetta Resolver". 30,000 genes
were input into the significance analysis with ap-va\ue cutoff of 0.05. No
multiple-testing correction was applied. The analysis passed 1,977 genes as being
significantly altered following DBF exposure. Of the remaining genes, 999 genes were
differentially expressed but did not pass the statistical filter, and 28,066 genes were not
affected by the treatment.
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1 The set of 1,977 genes was deemed suitable to perform a comprehensive pathway-level
2 analysis because about one third of the DEGs (999) did not meet the statistical cut-off criteria (a
3 p value cutoff < or = 0.05). To do this, the list of 1,977 genes was inputted into a second
4 software program called GeneGo. GeneGo is a leading provider of data analysis solutions in
5 systems biology. Its proprietary database MetaCore™'s sophisticated analytical tools enable the
6 identification and prioritization of the most relevant pathways, networks, and cellular processes
7 affected by a given condition.
8
9 6.2.1.2. Differentially Expressed Gene (DEC) Identification: Signal-to-Noise Ratio (SNR)
10 We also identified DEGs two independent methods, Signal-to-Noise Ratio (SNR) (Golub
1 1 et al., 1999) and a two sample t-test from the Liu et al. (2005 DBF data. SNR reflects the
12 difference between the classes relative to the standard deviation within the classes.
13 Equation 6-1 evaluates the means and standard deviations of the expression levels of
14 gene g for the samples in group 1 (vehicle control) and group 2 (DBF treated), respectively.
15 For a given gene (g) we evaluate the SNR using Equation 6- 1 :
16
17
(6-1)
18 The means and standard deviations of the expression levels of gene g are denoted with // and
19 a, respectively, for the samples in group 1 (vehicle control) and group 2 (DBF treated).
20 A high value of SNR is indicative of a strong distinction between the groups—i.e.,
21 vehicle and DBF treated. In order to identify the DEGs whose expression was altered by DBF,
22 1,000 random gene expressions were permutated from the whole data set, and their SNR was
23 computed. The ratio of the randomly generated SNR value that is higher than the actual SNR
24 value determined whether the expression of the probe set is differentially expressed or not.
25 Appendix B lists the algorithm for selecting DEGs (see Figure B-l). A list of 1,559 probe sets
26 was identified as being differentially expressed following a statistical cut-off of p < 0.05. The
27 heat map (see Figure 6-3) illustrates the distinction between the vehicle and treated samples. On
28 the other hand, Student's t-test (p < 0.05) revealed 1,876 probe sets being statistically significant.
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1
2
VVVTTT
High
Low
3
4 Figure 6-3. Heat map of 1,577 differentially expressed genes (DEGs) from
5 SNR analysis method. V = Vehicle, T = Treated samples. Data used for
6 analysis from Liu et al. (2005). Columns represent the six treatment conditions
7 (3 DBF treatments, 3 vehicle controls). Rows represent the different 1,577 DEGs.
8 Red represents up regulation of gene expression, and green represents down
9 regulation of gene expression.
10
11 Array Track was used to calculate the pathway enrichment for the two DEGs lists, SNR
12 list, and t-test list. To investigate interactions of genes at the pathway level, the Kyoto
13 Encyclopedia of Genes and Genomes (KEGG; http://www.genome.jp/kegg) database was
14 utilized as a pathway mapping tool, and a Fisher's exact test was used to compute the
15 significance. The top five enriched pathways as derived from both gene lists are common:
16 biosynthesis of steroids, terpenoid biosynthesis, GSH biosynthesis, and carbon fixation. The
17 SNR gene list maps to more pathways than the t-test gene list, even though the number of DEGs
18 was greater in the t-test generated gene list than in the SNR gene list.
19
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1 6.2.2. Pathway Analysis of Liu et al. (2005) Comparing Two Methods
2 Pathway-analysis methods and software have been previously developed for analysis of
3 microarray data for basic and applied research. Pathway-level analysis mainly depends on the
4 definition of the pathways (database) and significance level uses to measure the differential
5 expressions. Using these validated methods, a pathway analysis was performed. Further, a
6 comparison of methods between the results from using different analytical approaches, SNR and
7 linear weighted normalization, was performed.
8 Analysis of DBF toxicogenomic studies was carried out using many proprietary
9 databases and software packages that are available to the microarray community with enhanced
10 bioinformatic capabilities for pathway and functional level analysis (Rosetta Resolver®,
11 MetaCore™ GeneGo, Ingenuity® Pathway Knowledgebase. These software tools accept lists of
12 genes of interest and then using their database of knowledge about these gene elements, map
13 them to cellular pathways known to exist from experimental literature. The advantage of trying
14 to understand groups of genes acting in a similar cellular process such as cell cycle provides
15 more meaningful results as opposed to trying to understand one gene at a time, which may have
16 no relationship to other genes on a statistically filtered list. The rationale behind the exercise
17 was that interrogation of multiple databases would result in a more complete mining of the
18 microarray data sets, which may provide an understanding of all of the potential DBF MO As
19 underlying the testes reproductive developmental effects. Analysis using different statistical
20 tools provides information about the similarities and differences in results.
21 Figure 6-4 shows the schematic of the comparative analysis protocol. The GeneGo
22 analysis normalized data set revealed that 131 biological processes (p < 0.05) were associated
23 with the 1,977 DEGs. Table 6-1 lists the pathways with ap < 0.05 (Fisher exact t-test).
24 Comparisons made on the level of gene lists obtained by different statistical methods often do
25 not converge (Stocco et al., 2005). We decided to perform a comparison of methods based on
26 the assumption that biologically related groups of genes, such as metabolic or signaling
27 pathways, may be more valid if identified using different microarray analysis methods. Towards
28 this effort, we treated the gene list (1,559 genes) using SNR to a pathway level analysis using
29 GeneGo, similar to the analysis performed on the linear weighted normalization results. Table
30 6-2 lists the result of this analysis.
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1
2
Liu etal. (2005) DBF data
•
Linear Weighted
Normalization
— •
Signal to
Noise Ratio
Differentially ,/ ^x Differentially
unique Expressed (common) Expressed unique
Genes \__^-^ Genes
I
Input filtered
gene list into
GeneGo
I
Input filtered
gene list into
GeneGo
Significant
unique Patnways
Significant
Pathways
unique
3
4
5
6
1
8
9
10
11
Figure 6-4. Schematic of the two analysis methods (linear weighted normalization and
SNR) for identifying differentially expressed genes and subsequent pathway analysis using
GeneGo. Two separate analyses, linear weighted normalization 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.
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1
2
Table 6-1. GeneGo pathway analysis of significant genes affected by DBF
Pathway
NF-AT signaling in cardiac hypertrophy
MIF — the neuroendocrine-macrophage
connector
Lysine metabolism
Cholesterol metabolism
Glycolysis and gluconeogenesis (short
map)
Integrin-mediated cell adhesion
Tryptophan metabolism
Cholesterol biosynthesis
ECM remodeling
Regulation of lipid metabolism via
PPAR, RXR, and VDR
Propionate metabolism p. 2
PPAR regulation of lipid metabolism
Mitochondrial long chain fatty acid
beta-oxidation
Role of VDR in regulation of genes
involved in osteoporosis
ChREBP regulation pathway
Androstenedione and testosterone
biosynthesis and metabolism p. 1
Arginine metabolism
Regulation of fatty acid synthesis:
NLTP and EHHADH
Angiotensin signaling via STATs
Cytoskeleton remodeling
dGTP metabolism
TCA
Glycolysis and gluconeogenesis p. 1
Peroxisomal branched chain fatty acid
oxidation
Biological process
Disease
Immune response
Amino acid metabolism
Steroid metabolism
Carbohydrates metabolism
Cell adhesion
Amino acid metabolism
Steroid metabolism
Cell adhesion
Transcription
Carbohydrates metabolism
Regulation of lipid metabolism
Lipid metabolism
Transcription
G-protein coupled receptor
signaling
Steroid metabolism
Amino acid metabolism
Regulation of lipid metabolism
Growth and differentiation
Cell adhesion
Nucleotide metabolism
Amino acid metabolism
Carbohydrates metabolism
Lipid metabolism
p-Valuea
2.23E-04
3.00E-04
3.05E-04
6.95E-04
7.40E-04
8.44E-04
9.56E-04
1.44E-03
1.64E-03
1.96E-03
1.96E-03
2.04E-03
2.28E-03
3.16E-03
3.82E-03
4.30E-03
4.45E-03
5.02E-03
5.18E-03
5.19E-03
5.34E-03
5.70E-03
5.70E-03
5.70E-03
No. of
genesb'c
19/90
19/92
9/27
6/14
10/36
18/92
9/31
7/21
13/60
7/22
7/22
8/28
6/17
12/57
10/44
6/19
9/38
4/9
11/53
26/176
9/39
6/20
6/20
6/20
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Table 6-1. (continued)
Pathway
Gamma-aminobutyrate (GABA)
biosynthesis and metabolism
Ligand-dependent activation of the
ESR1/SP pathway
Integrin inside-out signaling
Reverse signaling by ephrin B
G-protein beta/gamma signaling
cascades
Activation of PKC via G-Protein
coupled receptor
Gap junctions
WNT signaling pathway
Angiotensin activation of ERK
Role of Akt in hypoxia induced HIF1
activation
Regulation of actin cytoskeleton by Rho
GTPases
CCR3 signaling in eosinophils
MAG-dependent inhibition of neurite
outgrowth
Endothelial cell contacts by junctional
mechanisms
Fructose metabolism
Regulation of lipid metabolism via LXR,
NF-Y and SREBP
CXCR4 signaling pathway
Serotonin-melatonin biosynthesis and
metabolism
Glycolysis and gluconeogenesis p. 2
Oxidative phosphorylation
Urea cycle
Biological process
Metabolism of mediators
Response to hormone stimulus
Cell adhesion
Cell adhesion
G-protein coupled receptor
protein signaling pathway
G-protein coupled receptor
protein signaling pathway
Cell adhesion
Proteolysis
G-protein coupled receptor
protein signaling pathway
Proteolysis
Small GTPase mediated signal
transduction
Immune response
Response to extracellular
stimulus
Cell adhesion
Carbohydrates metabolism
Transcription
Cytokine and chemokine
mediated signaling pathway
Metabolism of mediators
Carbohydrates metabolism
Energy metabolism
Amino acid metabolism
p-valuea
5.70E-03
6.38E-03
6.85E-03
6.86E-03
6.94E-03
7.65E-03
8.51E-03
8.59E-03
9.12E-03
9.83E-03
1.18E-02
1.22E-02
1.47E-02
1.80E-02
1.80E-02
1.80E-02
1.89E-02
2.04E-02
2.15E-02
2.37E-02
2.58E-02
No. of
genesbc
6/20
9/40
14/78
15/86
11/55
15/87
10/49
7/28
11/57
10/50
11/59
18/117
10/53
7/32
7/32
7/32
10/55
5/19
4/13
15/99
6/27
This document is a draft for review purposes only and does not constitute Agency policy.
6-12 DRAFT—DO NOT CITE OR QUOTE
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Table 6-1. (continued)
Pathway
G-proteins mediated regulation p. 3 8 and
JNK signaling
Transcription factor tubby signaling
pathways
Role PKA in cytoskeleton reorganization
Ephrins signaling
Propionate metabolism p. 1
Estrone metabolism
Regulation of acetyl-CoA carboxylase 2
activity in muscle
Chemokines and adhesion
Arachidonic acid production
dCTP/dUTP metabolism
Regulation of lipid metabolism by niacin
and isoprenaline
Ubiquinone metabolism
Phenylalanine metabolism
Leptin signaling via JAK/STAT and
MAPK cascades
IMP biosynthesis
EPO-induced Jak-STAT pathway
Integrin outside-in signaling
Brcal as transcription regulator
P53 signaling pathway
Bile acid biosynthesis
Histidine-glutamate-glutamine and
proline metabolism
NTS activation of IL-8 in colonocytes
Biological process
G-protein coupled receptor
protein signaling pathway
Transcription
Protein kinase cascade
Cell adhesion
Carbohydrates metabolism
Steroid metabolism
Response to extracellular
stimulus
Cytokine and chemokine
mediated signaling pathway
Lipid metabolism
Nucleotide metabolism
Regulation of lipid metabolism
Vitamin and cofactor
metabolism
Amino acid metabolism
Response to hormone stimulus
Nucleotide metabolism
Response to extracellular
stimulus
Cell adhesion
Cell cycle
Transcription regulation
Steroid metabolism
Amino acid metabolism
Immune response
p-valuea
2.60E-02
2.63E-02
2.64E-02
2.66E-02
2.81E-02
2.81E-02
2.81E-02
2.82E-02
2.87E-02
2.99E-02
3.01E-02
3.01E-02
3.05E-02
3.57E-02
3.70E-02
3.78E-02
3.95E-02
4.15E-02
4.28E-02
4.43E-02
4.79E-02
4.85E-02
No. of
genesbc
11/66
8/42
13/83
10/58
4/14
4/14
4/14
23/174
7/35
8/43
9/51
9/51
6/28
6/29
3/9
7/37
12/79
6/30
8/46
5/23
8/47
10/64
1 aOrdered from most significant (lowestp-valuo) to less significant.
2 bNumber of genes from the DBF-exposed gene list mapping to the GeneGo pathway.
3 °Total number of genes in the GeneGo pathway.
This document is a draft for review purposes only and does not constitute Agency policy.
6_ 13 DRAFT—DO NOT CITE OR QUOTE
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Table 6-2. Significant biological pathways corresponding to differentially expressed genes (DEGs) obtained
from SNR analysis input into GeneGo
o
o
S
§
Pathway
Cholesterol Biosynthesis
Propionate metabolism p.2
MIF — the neuroendocrine-macrophage connector
Tryptophan metabolism
Lysine metabolism
Cholesterol metabolism
NF-AT signaling in cardiac hypertrophy
Glycolysis and gluconeogenesis (short map)
G-alpha(q) regulation of lipid metabolism
Activation of PKC via G-protein coupled receptor
Fructose metabolism
Regulation of lipid metabolism by niacin and isoprenaline
ATP metabolism
Angiotensin activation of ERK
NTS activation of IL-8 in colonocytes
Leucine, isoleucine, and valine metabolism.p.2
Biological Process
Steroid metabolism
Carbohydrates metabolism
Immune response
Amino acid metabolism
Amino acid metabolism
Steroid metabolism
Disease
Carbohydrates metabolism
Regulation of lipid metabolism
G-proteins/GPCR
Carbohydrates metabolism
Regulation of lipid metabolism
Nucleotide metabolism
Growth and differentiation
Immune response
Amino acid metabolism
p-Valuea
1.81E-09
5.54E-06
3.22E-04
3.78E-04
3.93E-04
1.09E-03
1.38E-03
1.77E-03
1.93E-03
2.00E-03
2.06E-03
2.08E-03
2.09E-03
2.55E-03
3.60E-03
3.64E-03
No. ofgenesbc
15/21
12/22
25/92
12/31
11/27
7/14
23/90
12/36
13/41
22/87
11/32
15/51
16/56
16/57
17/64
9/25
-------
Table 6-2. (continued)
o
o
S
§
Pathway
Reverse signaling by ephrin B
Cortisone biosynthesis and metabolism
CXCR4 signaling pathway
G-Protein beta/gamma signaling cascades
Glutathione metabolism
Mitochondria! ketone bodies biosynthesis and metabolism
Integrin inside-out signaling
Propionate metabolism p. 1
Role of VDR in regulation of genes involved in osteoporosis
Endothelial cell contacts by junctional mechanisms
EPO-induced Jak-STAT pathway
A3 receptor signaling
Angiotensin signaling via STATs
MAG-dependent inhibition of neurite outgrowth
Phenylalanine metabolism
Androstenedione and testosterone biosynthesis and metabolism p. 1
Cytoskeleton remodeling
Biological Process
Cell adhesion
Steroid metabolism
Immune response
G-proteins/GPCR
Vitamin and cofactor metabolism
Lipid metabolism
Cell adhesion
Carbohydrates metabolism
Transcription factors
Cell adhesion
Cell survival
G-proteins/GPCR
Growth and differentiation
Growth and differentiation
Amino acid metabolism
Steroid metabolism
Cell adhesion
p-Valuea
3.92E-03
4.31E-03
4.63E-03
4.63E-03
5.77E-03
5.96E-03
6.07E-03
6.51E-03
6.63E-03
7.02E-03
7.24E-03
8.08E-03
8.28E-03
8.28E-03
8.48E-03
8.76E-03
9.69E-03
No. of genesbc
21/86
7/17
15/55
15/55
11/36
5/10
19/78
6/14
15/57
10/32
11/37
19/80
14/53
14/53
9/28
7/19
35/176
-------
Table 6-2. (continued)
o
o
S
§
Pathway
ChREBP regulation pathway
Leptin signaling via JAK/STAT and MAPK cascades
dGTP metabolism
TCA
Glycolysis and gluconeogenesis p. 1
Gamma-aminobutyrate (GABA) biosynthesis and metabolism
BAD phosphorylation
Ligand-dependent activation of the ESR1/SP pathway
RAB5A regulation pathway
Integrin outside-in signaling
Hedgehog and PTH signaling pathways participation in bone and
cartilage development
G-Proteins mediated regulation MARK-ERK signaling
Integrin-mediated cell adhesion
Mitochondria! long chain fatty acid beta-oxidation
CCR3 signaling in eosinophils
Regulation of lipid metabolism via PPAR, RXR, and VDR
Biological Process
Regulation of transcription
Growth and differentiation
Nucleotide metabolism
Amino acid metabolism
Carbohydrates metabolism
Metabolism of mediators
Apoptosis
Hormones
G-proteins/RAS-group
Cell adhesion
Growth and differentiation
G-proteins/GPCR
Cell adhesion
Lipid metabolism
Immune response
Transcription factors
p-Valuea
1.08E-02
1.09E-02
1.10E-02
1.20E-02
1.20E-02
1.20E-02
1.21E-02
1.34E-02
1.49E-02
1.50E-02
1.62E-02
1.64E-02
1.78E-02
1.88E-02
2.02E-02
2.07E-02
No. of genesbc
12/44
9/29
11/39
7/20
7/20
7/20
19/83
11/40
5/12
18/79
11/41
17/74
20/92
6/17
24/117
7/22
-------
Table 6-2. (continued)
o
o
S
§
Pathway
Glycolysis and gluconeogenesis p. 2
Regulation of fatty acid synthesis: NLTP and EHHADH
Role PKA in cytoskeleton reorganization
Arginine metabolism
ECM remodeling
Ca (2+)-dependent NF-AT signaling in cardiac hypertrophy
WNT signaling pathway
PPAR regulation of lipid metabolism
Insulin regulation of the protein synthesis
CXCR4 signaling via second messenger
Angiotensin signaling via beta-Arrestin
Estrone metabolism
Regulation of acetyl-CoA carboxylase 2 activity in muscle
Prolactin receptor signaling
Triacylglycerol metabolism p. 1
Serotonin-melatonin biosynthesis and metabolism
Angiotensin signaling via PYK.2
Biological Process
Carbohydrates metabolism
Regulation of lipid metabolism
Kinases
Amino acid metabolism
Cell adhesion
Disease
Growth and differentiation
Regulation of lipid metabolism
Translation regulation
Immune response
Growth and differentiation
Steroid metabolism
Growth and differentiation
Growth factors
Lipid metabolism
Metabolism of mediators
Growth and differentiation
p-Valuea
2.16E-02
2.30E-02
2.43E-02
2.44E-02
2.45E-02
2.55E-02
2.64E-02
2.64E-02
2.67E-02
2.67E-02
2.71E-02
2.99E-02
2.99E-02
3.19E-02
3.23E-02
3.27E-02
3.32E-02
No. of genesbc
5/13
4/9
18/83
10/38
14/60
15/66
8/28
8/28
13/55
13/55
11/44
5/14
5/14
14/62
8/29
6/19
16/74
-------
Table 6-2. (continued)
o
o
S
§
Pathway
G-Protein alpha-i signaling cascades
dATP/dlTP metabolism
Brcal as transcription regulator
Ephrins signaling
Mitochondria! unsaturated fatty acid beta-oxidation
GDNF signaling
Aspartate and asparagine metabolism
Peroxisomal branched chain fatty acid oxidation
Histidine-glutamate-glutamine and proline metabolism
TGF-beta receptor signaling
Regulation of actin cytoskeleton by Rho GTPases
G-Protein alpha-s signaling cascades
Al receptor signaling
Membrane-bound ESR1 : interaction with growth factors signaling
Transcription factor Tubby signaling pathways
Histamine metabolism
PPAR pathway
Biological Process
G-proteins/GPCR
Nucleotide metabolism
Cell-cycle control
Cell adhesion
Lipid metabolism
Growth and differentiation
Amino acid metabolism
Lipid metabolism
Amino acid metabolism
Growth and differentiation
G-proteins/RAS -group
G-proteins/GPCR
G-proteins/GPCR
Growth and differentiation
Regulation of transcription
Metabolism of mediators
Transcription factors
p-Valuea
3.36E-02
3.86E-02
3.90E-02
3.99E-02
4.01E-02
4.08E-02
4.15E-02
4.15E-02
4.24E-02
4.51E-02
4.51E-02
4.51E-02
4.61E-02
4.64E-02
4.64E-02
4.83E-02
4.86E-02
No. of genesbc
12/51
12/52
8/30
13/58
5/15
7/25
6/20
6/20
11/47
13/59
13/59
13/59
16/77
10/42
10/42
4/11
11/48
oo
-------
Table 6-2. (continued)
O «>'
i *
I, 5-
Si, S>
i §.
^
o ^
^ a
s ^
*""*•• 5.
^
-------
1 Table 6-3 lists the pathways that are in common between conducting the two different
2 analyses by using the GeneGo analysis (i.e., the union of the two separate pathway lists; see
3 Tables 6-1 and 6-2). This analysis highlights biological processes and pathways that are affected
4 by DBF exposure to fetal testis besides the already established changes in the steroidogenesis
5 pathway. An attempt to link these unique pathways and processes to the DBF-induced male
6 reproductive toxicity outcomes will be made based on the published literature.
7 Cholesterol biosynthesis/metabolism and associated pathways underlie one of the MO As
8 of DBF. To determine a metric for statistical analysis protocols of toxicogenomic data, we chose
9 to compare the genes that are involved in the cholesterol biosynthesis/metabolism as identified
10 by the three independent analysis methods (described herein) as well as the published data set
11 from Liu et al. (2005) (see Table 6-4). These results show that there is a high degree of overlap
12 in the most biologically relevant pathway/process involved in DBF toxicity, even when different
13 statistical procedures are used for analysis of the same data set. These are in agreement with the
14 published literature, giving the approaches used in this exercise biological confidence.
15 By utilizing databases such as GeneGo, additional canonical pathways and biological
16 processes were identified that may play an important role in its toxicity. Regulation of
17 steroidogenesis requires multiple signaling pathways and growth factors (Stocco et al., 2005).
18 Signaling pathways, like the protein kinase C pathway, arachidonic acid metabolism, growth
19 factors, chloride ion, and the calcium messenger system are capable of regulating/modulating
20 steroid hormone biosynthesis. It is possible that some of the pathways and processes identified
21 by the two methods may play a role in the regulation of steroidogenesis, known to be affected by
22 DBF. Another scenario could be that these pathways and processes have yet to be associated
23 with DBF-induced toxicity.
24 Previous transcriptional studies have been shown that DBF does not bind to the AR
25 unlike flutamide (Parks et al., 2000), rather, it interrupts T synthesis (Shultz et al., 2001). The
26 androstenedione and T biosynthesis and metabolism pathway was one of the common pathways
27 in the GeneGo analysis of the two different methods gene lists (see Figures 6-5 and 6-6). We
28 investigated the potential role of AR in DBF-induced toxicity by querying the GeneGo database
29 based on the transcriptional profiling data.
30
777/5 document is a draft for review purposes only and does not constitute Agency policy.
6-20 DRAFT—DO NOT CITE OR QUOTE
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1
2
3
4
Table 6-3. Common pathways between the linear weighted normalization
and SNR analyses of differentially expressed genes (DEGs) after in utero
DBF exposure from the Liu et al. (2005) dataa'b'c
Biological Process
Cell adhesion
Cell signaling
Disease
Growth and differentiation
Hormones
Immune response
Pathways
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
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/STA T and MAPK cascades2
Regulation of actin cytoskeleton by Rho GTPases
Role PKA in cytoskeleton reorganization
NF-AT signaling in cardiac hypertrophy
NTS activation of IL-8 in colonocytes
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
Ligand-dependent activation of the ESR1/SP pathway
MIF - the neuroendocrine-macrophage connector
CXCR4 signaling pathway
777/5 document is a draft for review purposes only and does not constitute Agency policy.
6-21 DRAFT—DO NOT CITE OR QUOTE
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Table 6-3. (continued)
Biological Process
Metabolism
Metabolism
Pathways
Androstenedione and testosterone biosynthesis and metabolism p. I2
Cholesterol biosynthesis2
Cholesterol metabolism2
dATP/dlTP metabolism
dGTP metabolism
Estrone metabolism
Fructose metabolism
G-alpha(q) regulation of lipid metabolism
Gamma-aminoburyrate (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
Phenylalanine metabolism
PPAR regulation of lipid metabolism2
Propionate metabolism p. I2
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
This document is a draft for review purposes only and does not constitute Agency policy.
6-22 DRAFT—DO NOT CITE OR QUOTE
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Table 6-3. (continued)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Biological Process
Transcription
Pathways
Brcal as transcription regulator
Role of VDR in regulation of genes involved in osteoporosis
Transcription factor Tubby signaling pathways
"Significant gene list from SNR and linear weighted average methods were input into 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.
bPathways 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).
°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&ordinalpo
s=3&itool=EntrezSystem2.PEntrez.Gene.Gene_ResultsPanel.Gene_RVDocSum). Functions that have been 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 InslS and
are, therefore, not listed in bold italics.
This document is a draft for review purposes only and does not constitute Agency policy.
6-23 DRAFT—DO NOT CITE OR QUOTE
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1
2
3
4
Table 6-4. Genes involved in cholesterol biosynthesis/metabolism as identified by
the two analyses (i.e., linear weighted normalization and signal to noise ratio) of Liu
et al. (2005)
Linear weighted
normalization (GeneGo)
Cyp27al
CypSlal
Cyp7bl
Dhcr?
Hmgcr
Hmgcsl
Hsdllbl
Hsd3bl
Mil
Sqle
Sc4mol
Soatl
SNR (GeneGo)
Acatl
CypSlal
Dhcr?
Dhcr24
Ebp
Fdftl
Fdps
Hmgcr
Hmgcsl
Mil
Mvd
Nsdhl
Sqle
Sc4mol
Tm7sf2
SNR (KEGG)
Acatl
Dhcr?
Ebp
Fdftl
Fdps
Hmgcr
Hmgcsl
Mil
Mvd
Sqle
This document is a draft for review purposes only and does not constitute Agency policy.
6-24 DRAFT—DO NOT CITE OR QUOTE
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.8.2.2
16 alpha-hydroxy-
dehydro-
epiandrosterone 1.14 4.1
3-sultete
16 alpha-hydro^-
dehydro-
epiandrosterone
biosynthesis
ST2A1
Homodimer
ctehydroepi andro 1.14.99.9
sterone
__^ ' *^^^ Ut
,,2* .
CYP3A7 1.1.1.1^515.3.3.1 1 1 1 1 «JS
17-alpha-hydroxy-
pregnenolone
331 1.1.1.1 IS 6.3.3.1
16 alpha-hydroxy-
andnostenect one C YP17
1
Conversion of pregnenolone and progesterone to
their 17- alpha-hydroxylated products and
subsequently to dehydroepiandrosterone (DHEA)
and androstenedione. Catalyzes both the 17-
alpha-hydroxylation and the 17j20-!yase reaction.
Involved in sexual development during fetal life
and at puberty.
Pregnenoloneand
progesterone
biosynthesis and
metabolism
17^lpha-
hydroxy progesterone
1.14.99.9
2
3
4
5
6
Figure 6-5. 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 DBF are identified by red
thermometers.
This document is a draft for review purposes only and does not constitute Agency policy.
6-25 " DRAFT—DO NOT CITE OR QUOTE
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Androgen Receptor nuclear signaling
Export to image
Experiments order
IL-6 receptor Galpha(q)-specific frizzled GPCRs Tap-beta receptor TGF-beta receptor type II IGF-1 jjeceptor ^ EGFR
type I
1
2
3
4
5
6
1
8
9
10
11
Figure 6-6. Mapping the Liu et al. (2005) data set onto the canonical
Androgen receptor (AR) nuclear signaling pathway in MetaCore™
(GeneGo). The thermometers denote input intensities of genes from our
statistical list mapped to this GeneGo pathway. Blue thermometers represent
downregulated genes present in the data and red thermometer represents
upregulated genes present in the data set that map to this pathway.
The GeneGo network connections reveal that CYP17 and AR are involved in the
androgen biosynthetic process. Based on the transcriptional profiling data, the AR is down
This document is a draft for review purposes only and does not constitute Agency policy.
6-26 ' DRAFT—DO NOT CITE OR QUOTE
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1 regulated by DBF in the fetal testes. This was a novel finding from this analysis and needs
2 further corroboration.
3 It has been reported in the literature (MAQCI, see Chapter 2) that the results of a
4 microarray experiment are heavily dependent on the data analysis protocol and the biological
5 pathway analysis tools available to interpret the list of statistically significant genes. Dissimilar
6 sets of gene expression signatures with distinct biological contexts can be generated from the
7 same raw data by different data analysis protocols. Distinct biological contexts can also be
8 generated from the same gene expression signatures by different biological pathway protocols.
9 Therefore, it becomes important to determine and understand the relationship between the gene
10 expression and pathway changes and a biological outcome of interest.
11 To do a thorough investigation it is necessary to use many sources of gene and pathway
12 annotation. The intent of using multiple sources is to gain an enriched analysis. In practice,
13 analysis is carried out with the suite of tools available to the analyst. In this case, the Star Center
14 primarily used KEGG (a resource rich in enzymatic and metabolic reactions but weak in
15 signaling pathways); whereas the U.S. EPA used Rosetta Resolver, GeneGo, and Ingenuity
16 Pathway Analysis, resources that are populated with signaling as well as metabolic pathways.
17 This exercise demonstrates that multiple approaches to microarray data analysis can yield
18 similar biologically relevant outcomes. The differences observed in the results could be due to a
19 number of factors including (1) the different data normalization procedures used in the two
20 separate analyses; (2) different data interpretation tools such as the software for pathway
21 analyses, for examples. However, it cannot be ruled out that the differences may reflect
22 differences in biological significance (i.e., one approach is better than the other).
23
24 6.2.3. Transcription Factor (TF) Analysis
25 Inspection of the regulatory elements of the informative genes would reveal important
26 information about DBF exposure on gene expression. All the informative genes demonstrated a
27 down regulation in expression, and their co-regulated genes are likely to have a similar response
28 (Turner et al., 2007). EXPANDER is used for TF enrichment analysis (Shamir, 2005).
29 TF enrichment analysis revealed six transcription factors in informative genes with a statistical
30 significance level of 0.05 (see Table 6-5). Liu et al. (2005) study states that the regulatory
31 regions of several steroidogenic genes contain Globin transcription factor 1 binding protein
777/5 document is a draft for review purposes only and does not constitute Agency policy.
6-27 DRAFT—DO NOT CITE OR QUOTE
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1 (GATA) elements, and propose that GATA factors, particularly GATA-4 and GATA-6, might
2 represent novel downstream effectors of hormonal signaling in steroidogenic tissues.
3 Interestingly, GATA-4 appears in the DEG list as up regulated, and GATA-1 is one of the
4 enriched transcription factors. Another study claims that estrogen receptor (ER) a-deficient mice
5 (ERa-/-) display higher levels oftesticular T secretion than wild-type mice from fetal day 13.5
6 onwards (Delbes et al., 2005) and that ER is expressed in the rat testis (van Pelt et al., 1999).
7 Sex determining region Y (SRY) is one of the enriched transcription factors. Although SRY is
8 known to be the major determinant for testis formation, a recent study showed that SRY is
9 expressed also in rat testis tissues (Turner et al., 2007). Nuclear factor Y (NF-Y) is another
10 putative transcription factor, and it is known as taking action in sterol regulation (Shea-Eaton et
11 al., 2001; Xiong et al., 2000).
12
13 Table 6-5. Enriched transcription factors (TFs) from Liu et al. (2005) data set
14
Transcription factor"
ER
GATA-1
AREB6
SRY
NF-Y
Nrf2
/>-Valueb
0.00297
0.00966
0.0197
0.0385
0.0407
0.0462
15
16 aPRIMA (Promoter Integration in Microarray Analysis) is used to identify transcription factors whose binding sites
17 are enriched in a given set of genes promoter regions.
18 bThe enrichment score of the transcription factors: p < 0.05 cutoff.
19
20
21 6.3. DEVELOPMENT OF A NEW METHOD FOR PATHWAY ANALYSIS AND GENE
22 INTERACTIONS: PATHWAY ACTIVITY LEVEL (PAL) APPROACH
23
24 An alternative approach to infer important biological pathways is based on the use of the
25 available knowledge of functional annotations prior to statistical analysis. Based on the
26 assumption that the expression levels of sets of genes that are functionally related follow similar
27 trajectories, due to activation or deactivation of a pathway under different environmental
777/5 document is a draft for review purposes only and does not constitute Agency policy.
6-28 DRAFT—DO NOT CITE OR QUOTE
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1 conditions or at different time points, average correlation between genes in a given pathway
2 leads to significant findings (Kurhekar et al., 2002; Pavlidis et al., 2002; Zien et al., 2000). It is
3 not required that all the genes follow the same pattern. In these pathway scoring methods, a
4 pre-defined cut-off value is applied to determine the number of genes to be included. However,
5 focusing only on genes with a pre-determined significance analysis in gene expression may
6 result in a loss of information.
7 An alternative method to define the systematic behavior of the pathway is to evaluate the
8 pathway activity level (PAL), the method suggested by (Tomfohr et al., 2005). The strength of
9 the PAL method over other pathway analyses is that the expressions of all genes within a
10 pathway are considered.
11 The procedure begins with mapping genes to the KEGG pathway database. The entire
12 gene set represented by the Liu et al. (2005) data set (i.e., using the Affymetrix® RAE230 A and
13 B chips) maps to 168 pathways in the KEGG database with 2,483 associated genes. Gene
14 expressions are z-scored before the analysis. Using Equation 6-2, let Ep(^k f) be the gene
15 expression matrix of a given pathway p of size k genes and t arrays (i.e., t-different time
16 points). Tabulate the normalized (i.e., to zero mean and a unity standard deviation) gene
17 expression data. Each element of Ep(k f) is the relative expression level of the kth gene in the tth
18 time point. The vector in the kth row of the matrix Ep(k^ iists the relative expression of the kth
19 gene across the different time points.
20
21 E ,, ., =U (k,k)xS (k,t)xV (t.t)1 (6-2)
p{K,t) p\7/p\J/p\JS \ /
22
23 Equation 6-2 states that the matrix Ep(^k f) can be decomposed to a rotation matrix,
24 U (k, k), a stretch matrix, Sp (k, t), and a second rotation matrix, Vp (t, t). Up (k, k} is an
25 orthonormal basis that spans the gene expression space of Ep , whereas Vp(t,t) is an
26 orthonormal basis spanning the sample (array) space of Ep(k f), that forms a set of new basis
27 vectors for the columns of Ep(k t}. Sp (k, t) is a diagonal matrix (i.e., eigenvalue matrix), whose
28 elements are sorted from highest to the lowest based on the magnitude of the singular values. In
29 Equation 6-3, the PAL of a given pathway is defined as the projection onto the first eigenvector
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1 that spans the sample (array) space Ep(k f) . Thus, gene expression levels are reduced to pathway
2 activity levels.
3 PALp(ri) = Vp(n,Vf (6-3)
4
5 PALp(n) is a Ixn vector, and each entry represents the pathway activity level of
6 corresponding sample. If ni samples are denoted as control experiments and n2 have undergone
7 some type of treatment then the activity levels are given in Equations 6-4 and 6-5.
',V"1>V (6_4)
10
11 PAL2 (p) = Vp (n2,1)1 (6-5)
12
13 Activity levels represent the cumulative effect of gene expressions in a given pathway
14 and therefore the relative activity. The next step is to quantify the differentiation between
15 pathway activities of the treatment groups, control and treated. Overall pathway activity (OPA)
16 denotes the change of pathway activity levels between different groups (Equation 6-6). For a
17 given pathway p:
18
1Q OPA = ±
p a(PALl) + a(PAL2) (6-6)
20
21
22 ju and a denote the mean and standard deviation of the activity levels, as evaluated
23 using Equation 6-6 for pathway/*. A higher OPA indicates a better discrimination between
24 pathway activity levels of vehicle and treated samples. To compute the statistical significance of
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1 the OPA of a given pathway, we randomly permute the gene expression data on the chip for each
2 pathway and calculate the pathway activity levels and OPA 1000 times via Equations 6-4, 6-5,
3 and 6-6. If the fraction of the artificial OPA that are higher than the actual OPA exceeds 0.05, or
4 any other appropriately defined statistical significance level, the actual OPA is attributed to
5 random variations. The pathways that exhibit statistically significant high OPA are defined as
6 "active pathways." Appendix B shows the algorithm for selecting statistically significant
7 pathways (see Figure B-2). This calculation allows us to rank, and compare, active pathways
8 based on their OPA. The term "active pathway" does not indicate any up-regulation or
9 down-regulation, but rather indicates an overall change of the pathway compared to control
10 samples. Thus, an "active" pathway can still be one that is reduced and nonfunctional following
11 chemical treatment, and an "inactive pathway" can still be functional but not exhibit significant
12 difference from the control. Of the 168 KEGG pathways that mapped to the Liu et al. (2005)
13 data set, only 32 were found to be active pathways with an OPA level of less than/? = 0.05 (see
14 Table 6-6).
15 This analysis identified valine, leucine, isoleucine (VLI) degradation, sterol biosynthesis,
16 citrate cycle, and fatty acid metabolism as the most active pathways due to DBF exposure.
17 Figure 6-7 depicts the active pathways and their connections via metabolites, from the most
18 active pathways towards the least active pathways based on OPA. The connections of the active
19 pathways are retrieved from KEGG. The statistical outcome of the pathway activity analysis and
20 the relationship between active pathways are integrated. The active pathways have connections
21 to non-active pathways; but only active pathways are included in the metabolic network. It is
22 shown that the active pathways identified in this study are linked together at the metabolite level
23 indicating biological significance.
24
25
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1
2
Table 6-6. Statistically significant pathways as derived by signal-to-noise
ratio analysis3
4
5
6
7
8
9
10
Pathwayb
Inositol metabolism
Reductive carboxylate cycle CO2 fixation
Galactose metabolism
Pentose phosphate pathway
Pyruvate metabolism
Glycolysis/gluconeogenesis
Fructose and mannose metabolism
Pentose and glucuronate interconversions
Carbon fixation
Synthesis and degradation of ketone bodies
Alanine and aspartate metabolism
Phenylalanine metabolism
Propanoate metabolism
Citrate cycle TCA cycle
Benzoate degradation via CoA ligation
C21 -Steroid hormone metabolism
Metabolism of xenobiotics by cytochrome P450
Tryptophan metabolism
Ascorbate and aldarate metabolism
Glutathione metabolism
Terpenoid biosynthesis
Lysine degradation
Fatty acid metabolism
Limonene and pinene degradation
Arginine and proline metabolism
Histidine metabolism
Glycine, serine and threonine metabolism
beta-alanine metabolism
Butanoate metabolism
Biosynthesis of steroids
Valine, leucine and isoleucine degradation
Alkaloid biosynthesis
Activity
1.6338
1.65
1.7422
1.8216
1.8747
2.0128
2.1187
2.1545
2.2202
2.2333
2.4667
2.4877
2.5783
2.6658
2.6678
2.911
3.0373
3.0424
3.1052
3.1356
3.3621
3.4557
3.4732
3.4945
3.7056
3.71
3.9578
4.1212
5.1243
5.3459
5.6232
5.6922
/7-valuec
0.0328
0.0444
0.0475
0.0467
0.0435
0.0456
0.0405
0.0315
0.0337
0.0224
0.0235
0.0212
0.0224
0.0218
0.0145
0.0136
0.0245
0.0205
0.0095
0.0182
0.0044
0.0121
0.0154
0.0072
0.011
0.0084
0.0092
0.0063
0.0023
0.0011
0.003
0.001
""Pathways: that are defined in KEGG.
bActivity: quantifies the difference between different experimental conditions (i.e., corn oil control and DBF-treated
samples).
Significance analysis of activities: p < 0.05 cutoff for significant pathways perturbed by DBF exposure.
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2
3
4
5
6
7
8
9
10
1 1
12
Non-Active Pathways
Host AtMve Pathway
Active Pathways
Valini, Uucine and Isoteucim Digredation
Biosynthesis of Steroids
Least Active Pathway
Figure 6-7. Statistically significant pathway interactions generated using the
KEGG database following overall pathway activity (OPA) analysis. The Liu
et al. (2005) data set used for analysis. CD = pathway, CZI = metabolite. Larger
oval sizes indicate relative impact on a pathway, where the larger ovals indicate a
greater effect on a pathway after DBF exposure.
The value of this approach depends on the content of the employed pathway database.
For example, some of the pathways may not be present in testes tissue. For example, even
though bile acid biosynthesis does not occur in the testis, the collection of genes related to bile
acid biosynthesis showed statistically significant change.
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1 OPA is a linear projection of gene expressions that constitute a given pathway. The
2 singular value decomposition analysis quantifies the variance between two experimental groups
3 in the context of the pathway. To examine the effect of statistical significance of gene
4 expression on a given pathway, the OPA of a pathway is calculated by adding genes one at a
5 time starting with the gene with the highest SNR. Subsequently, the next gene with the second
6 highest SNR in this pathway is identified and added, etc., until all genes in the pathway have
7 been used to determine the OPA. Then, the Equations 6-4, 6-5, and 6-6 (section 6.3.) are
8 reevaluated with two genes and so forth until all of the genes in the given pathway have been
9 included. Figure 6-8 illustrates an example of this process for determining active and inactive
10 pathways, evaluating the Liu et al. (2005) DBF data. The inactive mTOR pathway has only a
11 single gene with a high SNR. As additional genes within the pathway with much lower SNR are
12 considered, the OPA is reduced. In contrast, the active VLI degradation pathway has numerous
13 genes with high SNR, and as all genes within the pathway are considered, the OPA remains high.
14 From this analysis, we determined that there is a subset of genes with high SNR that maintain the
15 OPA score for active pathways. We define DEGs that are in active pathways as informative
16 genes (see Table B-l). The interactions between informative genes were retrieved via IP A® and
17 the resulting preliminary gene network is shown in Figure 6-9.
18
19
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30
25
^>
3 20
2
15
10
0
0
SNR
I
7
6
5
4
&
$ 3
6
2
0.
0
mTOR Signaling Pathway
10 15 20 25 30 35 40
Ranked List of Genes
Valine, Leucine, and Isoleucine Degradation
5 10 15 20 25
Ranked List of Genes
30
2
3
Figure 6-8. Overall pathway activity (OPA) of the affected pathways
calculated by adding genes according to the decreasing signal-to-noise ratio
(SNR). The Liu et al. (2005) DBF only data were evaluated using the OP A
method.
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Va\ine, Leucine, and Isoleucine Degradation
o <*>'
% §•
* 8
i §
ij
-
a
o C
~* ^
§ ^
2 >
TO
Glycolysis/Gmconegenesis
C2l-Stemid Hormone Metabolism
Xenobiotic Metabolism Signaling
Figure 6-9. Gene network after DBF exposure created by Ingenuity" Pathway Analysis (IPA) from the
informative gene list. This model is based on data from Liu et al. (2005). This model illustrates the interactions
among genes after DBF in utero exposure in the rat testis. Genes (noted in Table E-3) are added in from the Ingenuity®
knowledgebase. . Active pathways, which do not share any common metabolites with other active pathways, may
interact via added nodes and informative genes. Genes or gene products are represented as nodes. Diamonds,
enzymes; Horizontal ovals, transcription regulators; Squares, cytokines; Rectangles, nuclear receptors; Solid lines,
direct relationship between edges (i.e., 2 nodes; 2 molecules that make physical contact with each other such as binding
or phosphorylation); Dashed lines, indirect interactions (i.e., do not require physical contact between the two
molecules, such as signaling events) between edges.
-------
1
2 6.4. EXPLORING GENETIC REGULATORY NETWORK MODELING: METHODS
3 AND THE DBF CASE STUDY
4 The goal was to utilize existing DBF genomic data to develop a regulatory network
5 model useful to risk assessment. Genetic regulatory network models illustrate interactions
6 between genes and their products (e.g., mRNA, proteins). Network models encompass identified
7 pathways from input data and in addition incorporate gene elements that are inferred from the
8 input data. The availability of one time course study data enabled us to model the series of
9 events that occurred between exposure to DBF and the onset of adverse reproductive outcomes
10 by the generation of a regulatory network model. We used Ingenuity® Pathway Analysis (IPA)
11 software to identify the relationships among the informative genes. IPA adds nodes (i.e., genes)
12 to the input gene list (i.e., informative genes) and then, builds edges (i.e., relations) based on the
13 literature to develop a regulatory network (Sladek et al., 1997).
14 Time-course studies are ideal for developing regulatory network models of biological
15 processes to model the dynamic networks for formulating mechanistic explanations of dynamic
16 developmental mechanisms. The Thompson et al. (2005) study was selected because it was the
17 only study that had time-course data. Additionally the study had the advantage of using the
18 Affymetrix® chip, which has -30,000 rat genes represented, and the data were provided by Dr.
19 Kevin Gaido, one of our collaborators. Thompson et al. (2005) conducted a study where animals
20 were exposed to DBF for 30 minutes and 1, 2, 3, 6, 12, 18, and 24 hours on GD 18 and 19. The
21 limitations of the Thompson et al. (2005) study include: 1) the dosing was initiated on GD 18,
22 quite late in the critical window, and 2) the shortest duration exposure began at the latest
23 developmental time (i.e., duration and developmental stage do not coincide; see Chapter 5).
24 Given this caveat, the data were utilized because it was the only study available to test
25 algorithms to build a prototype of a regulatory network model.
26 We used the PAL method, described earlier, to identify biologically active pathways at
27 each time point. We evaluated the informative genes at each time point and the resulting
28 preliminary gene network, based on the Thompson et al. (2005) data, is shown in Figure B-3.
29 The analysis showed a preponderance of signaling pathways such as JAK/STAT, PPAR, and
30 MAPK perturbed at the earlier exposure durations with the metabolic pathways being affected
31 following longest exposures to DBF (18 hours). The majority of the active pathways at this
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1 dose-exposure time (18 Hour) are metabolic pathways such as amino acid metabolism, lipid
2 metabolism, and carbohydrate metabolism. Thompson et al. (2005) hypothesized that the
3 decrease in T level after a short duration of DBF exposure might be because of the cholesterol
4 unavailability. Their study findings support this hypothesis. To have a complete understanding
5 of the temporality of the DBF effect, data from an exposure-duration series across the entire
6 critical window of exposure are needed.
7
8 6.5. EXPLORING METHODS TO MEASURE INTERSPECIES (RAT TO HUMAN)
9 DIFFERENCES IN MOA
10 The goal was to address Case Study Question 2, whether genomic and mechanistic data
11 could inform the interspecies (rat to human) differences in MOA, was explored. Although
12 progress has been made over the past four decades in understanding the MOA of chemical
13 toxicants, it is increasingly important to determine mechanistically the relevance of these MO As
14 in humans. With the sequencing of the human, mouse, and rat genomes and knowledge of cross
15 species gene and protein homologies, the studies of differential gene expression in animal
16 models have the potential to greatly enhance our understanding of human disease. Genes
17 co-expressed across multiple species are most likely to have conserved function. The rat genome
18 project reported that almost all human genes known to be associated with disease have
19 orthologous genes in the rat genome, and that the human, mouse, and rat genomes are
20 approximately 90% homologous (Gibbs et al., 2004). Because the function of a specific gene
21 and its involvement in disease might not be conserved across species, along with structural and
22 functional homology, the conservation of function of blocks of genes—i.e., pathways—are likely
23 to be more important in cross species comparison (Fang et al., 2005).
24 In the absence of DBF genomic data in human cell lines, we considered genetic sequence
25 data as a source of genomic data for making species comparisons. Even if such data were
26 available, in vivo (rat genomic data) to in vitro (human genomic data) extrapolations may
27 confound the ability to generate an accurate interspecies comparison. Use of bioinformatic
28 approaches to examine microarray expression profiles from exposure to a chemical in an animal
29 model to elucidating genes and pathways that might be associated with exposure in humans
30 holds great promise. Similarity analysis between single gene and protein sequence analysis
31 cannot represent the complex relationships species therefore species comparison studies emerged
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1 to compare pathways to analyze a higher level organization. Attempts include reaction content
2 (Hong et al., 2004), enzyme presence (Heymans, 2003), and enzyme sequence information of the
3 enzymes in a given pathway (Forst et al., 1999, 2001). The pathways for the biosynthesis of
4 steroids have a lot of similarity between humans and rats. Protein sequence similarity,
5 cross-species pathway network similarities, and promoter region conservation cross-species
6 comparisons to evaluate cross-species similarity metrics were performed. The results from
7 comparing the predicted amino acid sequence similarities between rat and human for the
8 steroidogenesis pathway proteins are shown in Table 6-7.
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Table 6-7. The enzyme sequence similarity of the enzymes of steroidogenesis pathway between rat and human
o
o
S
§
Gene
symbol
Dhcr?
Mil
Fdps
Fdfil
Hmgcr
Mvd
Sqle
Ebp
Lss
ScSd
Mvk
Cyp27bl
Nqol
Vkorcl
Entrez gene
ID
64191
89784
83791
29580
25675
81726
29230
117278
81681
114100
81727
114700
24314
309004
mRNA and protein IDs
NM_022389.2^NP_071784. 1
NM_053539.1->NP_445991.1
NM_03 1840. 1^NP_1 14028. 1
NM_0 19238.2^NP_062 1 1 1 . 1
NM_013 134.2^NP_037266.2
NM_03 1062. 1^NP_1 12324. 1
NM_017136. 1^NP_058832. 1
NM_057137. 1^NP_476478. 1
NM_03 1049. 1->NP_1 123 11.1
NM_053642.2^NP_446094. 1
NM_03 1063. 1^NP_1 12325. 1
NM_053763. 1^NP_446215. 1
NM_017000.2^NP_058696.2
NM_203335.2^NP_976080. 1
Human
homolog IDs
Q9UBM7
AF003835
M34477
AAP36671
AAH33692
AAP36301
NP_003120
NP_002331
NP_002331
NP_008849
BAD92959
NP_000776
NP_000894
AAQ 13668
Average similarity scores
Identities"
412/475 (86%)
196/227 (86%)
301/353 (85%)
356/413 (86%)
738/890 (82%)
338/398 (84%)
481/574 (83%)
618/732 (84%)
618/732 (84%)
246/299 (82%)
323/393 (82%)
413/508 (81%)
234/274 (85%)
83/94 (88%)
84%
Positives'"
443/475 (93%)
215/227 (94%)
326/353 (92%)
393/413 (95%)
768/890 (86%)
357/398 (89%)
528/574 (91%)
673/732 (91%)
673/732 (91%)
275/299 (91%)
355/393 (90%)
453/508 (89%)
250/274 (91%)
88/94 (93%)
94.14%
Gapsc
4/475 (0%)
0/227 (0%)
0/353 (0%)
0/413 (0%)
58/890 (6%)
1/398 (0%)
1/574 (0%)
1/732 (0%)
1/732 (0%)
0/299 (0%)
0/393 (0%)
7/508 (1%)
0/274 (0%)
0/94 (0%)
a\
-k
o
-------
Table 6-7. (continued)
^
^ Identities: The number and fraction of total residues in the HSP which are identical.
^ bPositive: The number and fraction of residues for which the alignment scores have positive values.
s °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
s. 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
ff encompass additional nucleotides or amino acid is also penalized in the scoring of an alignment.
3.
^
<*>' 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
a 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
§" ofhomology.
i
o* Source: http://searchlauncher.bcm.tmc.edu/help/BLASToutput.html#anchorl4684156.
TO
-------
1 Our analysis suggests that the biosynthesis of steroids is highly conserved across humans
2 and rats, with the average sequence similarity of enzymes between human and rat being -87%
3 and the average promoter region conservation of genes at 52% (see Table 6-7). However, it is
4 difficult to unequivocally determine a "high" versus "low" degree of conservation for the genes
5 in this pathway—especially in light of the fact that the more important gene products (such as a
6 rate-limiting step) have not been identified for DBF on steroidogenesis. Additionally, there are
7 likely differences between a statistically meaningful "high" degree of conservation vs. an
8 understanding of whether the biologically meaningful regions of the predicted protein sequence
9 are conserved.
10 Cross-species pathway network comparison is a creative approach using network data
11 from publicly available databases to assess species similarities. However, uncertainties and gaps
12 in the database information at this time make conclusions difficult. Therefore, these data are not
13 described herein.
14 Development of new bioinformatic and statistical resources using data generated in
15 human cell lines, together with the information obtained from rat in vivo studies may provide
16 new, useful data to further investigate interspecies differences in response to a chemical agent.
17 To determine the viability of using such metrics to inform the interspecies concordance of
18 mechanism issue in risk assessment, homology-based analysis of genes and proteins need to be
19 conducted in systems where the concordance in mechanism across species is well established by
20 prior studies to serve as a base line for "high homology."
21
22 6.6. CONCLUSIONS
23 The projects to address the four objectives presented in this chapter serve as a broad
24 range of examples of genomic data analyses available to the risk assessor with expertise (or
25 collaborators with expertise) in bioinformatics, and in some cases, represent exploratory efforts
26 to develop methods for analyzing genomic data for use in risk assessment. These methods
27 include DEG identification, pathway level analysis (including the newly described OPA
28 method), regulatory network analysis, and tools to assess cross-species similarities in pathways.
29 A summary for a less technical audience than the remainder of this chapter is presented next,
30 grouped by the four subobjectives for the work.
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2 • Reanalyze DBF microarray data to address the Case Study Question 1: Do the genomic
3 data inform DBF additional MOAs and the mechanism of action for the male
4 reproductive developmental effects?
5
6 We performed a number of reanalyses of the Liu et al. (2005) data because the pathway
7 analysis presented in the article was not performed for risk assessment purposes. While
8 the authors of this and other microarray studies support two MOAs for DBF, a reduction
9 of fetal testicular testosterone via affects on steroidogenesis and cholesterol transport
10 genes, not all pathways associated with the differentially expressed genes were discussed
11 in detail.
12
13 Two different bioinformatics tools to analyze the same data were compared. Each
14 analysis used multiple statistical filters to parse the noise from the signal in the
15 microarray data set and to assess the quality of the data set. Ideally, for a high quality
16 study data set, there would be a minimum of variance between similarly treated samples
17 and the variance would lie between the control and treated sample data. PC A shows the
18 quality of the Liu et al. (2005) data set to be of moderate quality based on the observed
19 variance among similarly treated data sets (control and treated groups). One analysis
20 utilized multiple proprietary software packages (GeneGo, Rosetta Resolver). The
21 rationale for looking at the effect of DBF on the pathway level as opposed to a cluster of
22 genes is that DBF is most likely affecting multiple pathways within a cellular
23 environment. The methods comparison exercise allowed us to generate a list of affected
24 pathways in common between the two methods, and in this way, provided more
25 confidence focusing on these pathways.
26
27 The results of the new pathway analyses both corroborate the previously identified two
28 MOAs for DBF male reproductive development toxicity, and provide putative novel
29 pathways affected by in utero DBF exposure that may play a role in DBF-mediated
30 toxicity. The results of the new pathway analyses provide hypotheses for MOA that
31 could be tested in new experimental studies. Future research could investigate the role of
32 these pathways in DBF-induced toxicity. In addition, a gene network was developed for
33 DBF based on the Liu et al. (2005) data. The GeneGo analysis and the validating the role
34 of the steroidogenesis pathway also revealed the modulation in CYP17 and AR that are
35 involved in the androgen biosynthetic process. This is a new hypothesis that requires
36 followup with new studies to confirm this observation. Performing new analyses was
37 useful for the purposes to further our understanding of the DBF mechanism of action.
38
39
40 • Explore the development of new methods for pathway analysis of microarray data for
41 application to risk assessment.
42
43 Quality control requirements for microarray study analysis for use in risk assessment are
44 distinct from their use in basic research. In traditional pathway level analysis, significant
45 genes are mapped to their respective pathways. Depending on whether the number of
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1 genes that map to any given pathway, the role of the pathway can be over of
2 underestimated. To overcome this problem, we developed the overall pathway activity
3 (OPA) method that employs one as opposed to two steps (i.e., first, identifying DEGs and
4 second, identifying significantly affected pathways by grouping the DEGs using pathway
5 analysis programs). This method scores pathways based on the expression level of all
6 genes in a given pathway.
7
8 The OPA analysis identified valine, leucine, isoleucine (VL1) degradation, sterol
9 biosynthesis, citrate cycle, and fatty acid metabolism as the most active pathways
10 following DBF exposure. These findings support the hypothesis of Thompson et al.
11 (2005), that an early decrease in testosterone levels may be a result of cholesterol
12 unavailability. However, for this approach to be useful, knowledge of tissue-specific
13 pathways is required. For example, even though bile acid biosynthesis does not take
14 place in the testis, a pathway related to bile acid biosynthesis was identified as
15 statistically significant in this analysis. Further developed on the OPA method needs to
16 incorporate tissue-specific relevant. This method shows promise for use in risk
17 assessment.
18
19
20 • Utilize existing DBF genomic data to develop a genetic regulatory network model, and
21 methods for modeling, for use in risk assessment.
22
23 Genetic regulatory network models can be very useful for understanding the temporal
24 sequence of critical biological events perturbed after chemical exposure, and thus, useful
25 to a risk assessment. We developed a method for developing a genetic regulatory network
26 model for DBF based on the available data. The availability of a time-course data
27 (Thompson et al. [2005]) enabled our group to model the series of events that occurred
28 between exposure to DBF and the onset of toxic reproductive outcomes by the generation
29 of a regulatory network model. However, given the limitations of the Thompson et al.
30 (2005) study design, we did not draw conclusions about affected genes and pathways
31 over time for DBF from this study. Instead, the Thompson et al. (2005) data was used to
32 build a prototype of a regulatory network model and thus, the exercise allowed us to
33 develop methods for analyzing time course data for use in building a regulatory network
34 model.
35
36 • Utilize genomic and other molecular data to address the Case Study Question 2: Do the
3 7 genomic and other molecular data inform inter species differences in MO A ?
38
39 Extrapolation from animal to human data is critical for establishing human relevance of
40 an MOA in risk assessment. Genes co-expressed across multiple species could have a
41 conserved function. The human, mouse, and rat genomes have been reported to be 90%
42 homologous (Gibbs et a., 2004). However, because it is not certain whether the function
43 of a specific gene is conserved across species, conservation of pathways across species
44 can be one important factor in establishing cross species concordance of MOA. In
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1 addition, a common critical role of androgens in both rodent and human male
2 development of reproductive organs has been well established.
O
4 Using the available DNA, sequence, and protein similarity data for the steroidogenesis
5 pathway, we used three different methods to assess rat-to-human conservation as metrics
6 that may inform the interspecies differences for one MO A, the reduced fetal testicular T.
7 The pathways for the biosynthesis of steroids have similarity between humans and rats.
8 Comparing the predicted amino acid sequences for the steroidogenesis pathway genes,
9 we found that the average sequence similarity between rat and human is -87% and the
10 average promoter region similarity of genes is 52%. Some of the challenges in using
11 similarity scores to estimate the cross species relevance of a MOA are described (section
12 6.5.).
13
14
15 In summary, the preliminary analytical efforts described in this chapter address and raise
16 a number of issues about the analysis of microarray data for risk assessment purposes. First,
17 analyzing any given data set multiple ways and arriving at the same conclusion provides
18 confidence in the analytical approach—however, there is no "gold standard" analytical method.
19 Second, applying stringent statistical filters in pathway analysis (e.g., p < 0.05, Benjamini
20 Hochberg multiple testing correction) can limit the number of genes that are identified.
21 Interpretation of the biology of the system using only a limited gene set is restrictive. It is
22 important to remember that the genes that do not pass the statistical stringency cut-off may be
23 crucial for understanding the biology of the system, as statistical significance and biological
24 significance are not necessarily the same. Therefore, it becomes incumbent upon the researcher,
25 to analyze the data in multiple ways in order to maximize the benefits of this technology. Third,
26 a pathway level analysis restricts the incorporation of all genes for determining relevant
27 pathways that are affected by DBF. There is a substantial amount of background noise generated
28 in a typical microarray experiment (i.e., gene expression variability even among the controls; see
29 Smith, 2000). For use in risk assessment, it is important to be able to identify and separate the
30 signal from the noise. Innovative approaches such as the OPA method described in this chapter
31 may provide more confidence when evaluating microarray data for use in risk assessment. These
32 efforts reveal some of the promises and challenges of use of toxicogenomic data in risk
33 assessment.
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1 7. CONCLUSIONS
2
O
4 This chapter describes the approach that was refined based on performing the DBF case
5 study, summary conclusions of the DBF case study, recommendations, future considerations, and
6 research needs for applying genomic data to risk assessment.
7
8 7.1. APPROACH FOR EVALUATING TOXICOGENOMIC DATA IN CHEMICAL
9 ASSESSMENTS
10
11 To review, there were two goals of this project (see Chapter 2):
12 • Develop a systematic approach that allows the risk assessor to utilize the available
13 toxicogenomic data in chemical-specific health risk assessments performed at U.S. EPA;
14 and
15
16 • Perform a case study to illustrate the approach.
17
18 The first goal was to develop an approach for evaluating toxicogenomic data in future
19 chemical assessments. The DBF case study was unlike the process for a new risk assessment in
20 a number of ways. In the case study, we had the benefit of utilizing toxicity and human study
21 data set evaluations summarized in the IRIS DBF assessment external review draft.
22 Additionally, the information about DBF from the published literature and the IRIS assessment
23 draft allowed us to focus on one set of endpoints, the male reproductive developmental
24 endpoints. Thus, the case study approach (see Figure 3-1) needed to be refined to develop a
25 systematic approach for incorporating toxicogenomic data in a future chemical assessment
26 (Figure 7-1).
27
28
29
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STEP 1: Compile Datasets for Assessment
STEP 2: Consider Quantitative & Qualitative
Aspects that Genomic Dataset may Address
STEP 3: Identify Questions
Do the Genomic Data Inform:
1) MOA or Mechanism of Action?
2) Other Data-Dependent Questions
STEP 4:
Toxic ity
Data Set
Evaluation
phenotypic
anchoring
study
STEP 5:
Genomic
Data Set
Evaluation
targeted
analyses
comparability
STEP 7: Results of Evaluation
1) New pathways identified?
2) Other Questions Answered?
STEP 6:
Genomic
Data Set
New Analyses
i
2
3
4
5
STEP 8:
Conclusions Summarized in RA
•MOA & Other Sections
•Data Gaps
•Research Needs
^•^^H
Figure 7-1. Approach for evaluating and incorporating genomic data for health
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.
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1 The steps of the approach are:
2 • STEP 1: Compile the epidemiologic, animal toxicology, and toxicogenomic study data
3 sets.
4 • STEP 2: Consider the quantitative and qualitative aspects of the risk assessment that
5 these data may address.
6 The genomic data set is considered for whether these data could inform risk assessment
7 components (e.g., dose response) and information (e.g., MOA information, interspecies
8 TK differences) useful to risk assessment. The type of information that these data will
9 provide to a risk assessment depends in part on the type of genomic studies (e.g., species,
10 organ, design, method) that are available. A thorough and systematic consideration of the
11 types of information in light of the available genomic data will identify the potential
12 utility of the genomic data and whether these data can be used quantitatively or
13 qualitatively. See Section 3.2 for more details.
14 • STEP 3: Formulate questions to direct the toxicogenomic data set evaluation.
15 Questions are formulated that can direct the genomic data evaluation. Some examples of
16 questions considered in the DBF case study are: Do the data inform the MO As for the
17 female reproductive outcomes?; Do the data inform dose-response? For example, if
18 microarray data are available, then one of the questions will likely include whether the
19 genomic data can inform the mechanism and/or MOA for the chemical as microarray
20 data typically inform the mechanism of action of a chemical. The DBF case study
21 describes some examples and considerations for determining the risk assessment
22 components that may be informed by a particular genomic data set (See Section 3.3 for
23 more details of the considerations).
24
25 • STEPS 4 and 5: Evaluate the toxicity and/or human study and genomic data sets
26 The approach includes an integrated assessment of the toxicogenomic and toxicity data
27 set to relate the affected endpoints (identified in the toxicity data set evaluation) to the
28 pathways (identified in the toxicogenomic data set evaluation) as a method for:
29 (1) Determining the level of support for phenotypic anchoring of genomic changes to in
30 vivo outcomes.
31 (2) Informing the mechanism of action/MOA.
32 Risk assessors may want to utilize aspects of the approach defined herein along with the
33 Mode of Action Framework in the U.S. EPA Cancer Guidelines (U.S. EPA, 2005) and/or
34 other risk assessment decision-logic frameworks for establishing MO As.
35 Another principle of the approach is identifying comparable toxicity and toxicogenomic
36 data. For example, in the DBF case study, all of the toxicogenomic studies were
37 performed in the rat, and, in most cases, the testis. Therefore, the genomic data set was
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1 compared with the rat toxicity data, and focused on effects in the testis. Broadening
2 beyond the DBF example, the available toxicogenomic data are best considered in light
3 of the toxicity or epidemiologic study data with the similarities to the toxicogenomic
4 study design. For example, if toxicogenomic data from human tissue or cells are
5 available, these data are best considered with the human epidemiologic outcome data for
6 the chemical. However, even in the absence of comparable data in the same species, the
7 genomic data may still be utilized, but with less confidence. See Chapters 4 and 5 for
8 further details of the DBF case study toxicity and toxicogenomic data set evaluations.
9 Chapter 5 includes a number of simple methods for assessing the consistency of the
10 toxicogenomic data. Venn diagrams have been utilized for illustrating the similarities
11 and differences of DEG findings across genomic studies. Figure 5-2 is an example of
12 another method for assessing the consistency of findings across all types of gene
13 expression data.
14 • STEP 6: Perform new analyses of the genomic data.
15 New analyses of raw toxicogenomic data may be valuable for the assessment depending
16 on the questions asked and the nature of the analyses presented in the published studies.
17 Depending on the pathway-analysis methods used in the published genomic studies,
18 reanalysis with different pathway analysis methods may be warranted. New analyses of
19 the raw data may not be needed—for instance, in the case that the available published
20 data have been analyzed appropriately for application to the specific risk assessment
21 questions. See Chapter 6 for more details of the DBF case study new analyses.
22 • STEP 7 and 8: Describe results of evaluations and analyses. Then, summarize these
23 conclusions in the assessment.
24
25 7.2. DBF CASE STUDY FINDINGS
26 The second goal of the project was to develop a case study. The case study findings are
27 summarized here. The details of the case study evaluation and analyses are presented in
28 Chapters 4-6 (with supplemental material in Appendices A and B). Two advantages to using
29 DBF as the case study chemical are as follows:
30
31 • The temporal aspects (e.g., time of dosing and time of evaluation) could be considered
32 because a number of well designed studies exist;
33 • The expression of a number of the steroidogenesis pathway genes have a strong
34 phenotypic anchoring/association with a number of the male reproductive developmental
35 effects;
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1 • Two well established MO As for DBF have been defined at the molecular level. DBF is
2 known to affect multiple MO As allowing for a query of the genomic data for possible
3 MO As for the unexplained endpoints.
4
5 7.2.1. Case Study Question 1: Do the DBF Genomic Data Inform Mechanism of Action
6 and MOA?
7 In our case study, we found that toxicogenomic data did inform the mechanism of
8 action and MOA. The available genomic and other gene expression data, hormone
9 measurement data, and toxicity data for DBF were instrumental in establishing two of its
10 MO As: (1) a decrease in fetal testicular T, and (2) a decrease in Insl3 expression. A
11 decrease in fetal testicular T is the MOA responsible for a number of the male
12 reproductive developmental effects in the rat, and the genomic and other gene expression
13 data identified changes in genes involved in steroidogenesis and cholesterol transport,
14 which is consistent with and provides the underlying basis for the observed decrease in
15 fetal testicular T. A decrease in Insl3 expression is one of the two MO As responsible for
16 undescended testis descent, and this MOA is well established by RT-PCR and in vivo
17 toxicology data. RT-PCR studies identified reduced Insl3 expression (Wilson et al.,
18 2004) after in utero DBF exposure that was associated gubernacular agenesis or
19 abnormalities observed in toxicology studies, effects that are not seen after exposure to
20 chemicals that affect T synthesis or activity (e.g., AR binding). These results provided
21 support for the Insl3 MOA for DBF.
22 Rodent reproductive developmental toxicity studies were evaluated for low incidence and
23 low-dose findings as well as for male reproductive development effects that currently do not
24 have a known MOA (see Chapter 4). The testes outcomes were the focus of the case study
25 because the DBF toxicogenomic studies were all performed on testicular tissue. Five testes
26 effects associated with DBF exposure that do not have well described MO As were identified in
27 this evaluation.
28 The toxicogenomic and other gene expression studies, including nine published RT-PCR
29 and microarray studies in the rat after in utero DBF exposure (Shultz et al., 2001; Barlow et al.,
30 2003; Lehmann et al., 2004; Wilson et al., 2004; Bowman et al., 2005; Thompson et al., 2004;
31 Thompson et al., 2005; Liu et al., 2005; Plummer et al., 2007), were evaluated. The review of
32 the toxicogenomic data set focused on an evaluation of the consistency of findings from the
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1 published studies, and on whether additional pathways may illuminate the unexplained
2 endpoints. This evaluation found that the gene-level findings from the DBF genomic studies
3 (i.e., microarray, RT-PCR, and protein expression) were highly consistent in both the
4 identification of DEGs and their direction of effect.
5 New analyses of the Liu et al. (2005) microarray study were performed. These
6 evaluations (see Chapter 5) indicate that there are a number of pathways affected after in utero
7 DBF exposure; some of these pathways are related to new MO As because they are not related to
8 either the reduced fetal testicular T or the Insl3 signaling MOAs. The Liu et al. (2005) DBF raw
9 data set was re-analyzed using two different methods, the SNR and the weighted-linear model,
10 both using a statistical cutoff of p < 0.05. Each method identified the steroidogenesis and
11 cholesterol transport pathways, thus, corroborating prior study conclusions. Each analysis also
12 identified putative new pathways and processes that are not associated with either Insl3 or
13 steroidogenesis pathways; some were similar across analytical methods and some were different.
14 The pathways identified that were in common between the two methods (Table 6-3) fall into
15 eight processes (characterized by Ingenuity®): cell signaling, growth and differentiation,
16 metabolism, transcription, immune response, cell adhesion, hormones, and disease. There were
17 54 pathways, not related to reduced T or Insl3 expression, including a subset (e.g., WNT
18 signaling and cytoskeleton remodeling) that were not previously identified in the published
19 literature for DBF. One or more of these additional pathways may provide information about the
20 MOAs for the unexplained toxicity endpoints in the rat testes, but this remains to be determined.
21 Evaluating the genomic and toxicity data sets together provided information on potential,
22 heretofore unexplored, MOAs.
23 There are many possible reasons for the differences in findings between the reanalysis
24 and the published analysis of the Liu et al. (2005) data. These include but are not limited to
25 (1) The analyses had different purposes. Liu et al. (2005) was interested in determining
26 whether there is a developmental phthalate genomic signature. This work was interested
27 in identifying all affected pathways;
28 (2) In the 3 years since the study was published, gene and pathway annotation has
29 increased. Further, repeated identification of DEGs and pathways provides an additional
30 level of confidence regarding the importance of "in common" DEGs and pathways but by
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1 no means indicate a lack of importance for the genes and pathways that were not
2 repeatedly identified.
3
4 We also asked whether there are appropriate data to develop a regulatory network model
5 for DBF. Using the raw data from Thompson et al. (2005), the only time-course study available
6 at the time of the project, changes in gene expression and pathways were modeled (Figure B-3).
7 Two limitations of these data are that (1) the exposure interval was at the tail end of the critical
8 window of exposure, GD 18, a time that most consider too late to induce the full spectrum of
9 male reproductive developmental effects; and (2) the duration of exposure and developmental
10 time were not aligned because all animals were sacrificed on GD 19 (i.e., the 1 hour time point
11 was the latest in development; see Chapter 6 for more discussion). The more recent study of
12 Plummer et al. (2007) may be more appropriate data to use to build a regulatory network model
13 as both time-course of exposure over the critical window of development and microdissection of
14 the testis cell types were employed in their study. Use of these data would allow for a regulatory
15 network model to incorporate both temporal and spatial aspects of DBF's effects on pathways
16 and endpoints.
17
18 7.2.2. Case Study Question 2: Do the DBF Genomic Data Inform Interspecies Differences
19 in the TD part of the MOA?
20 Human gene expression data are not available for DBF. Therefore, the case study used
21 information on interspecies similarities of the affected pathways from other data and methods.
22 We explored the interspecies (rat to human) differences in the TD part of the MOA, focusing on
23 the steroidogenesis pathway underlying the decrease in fetal testicular testosterone MOA. The
24 similarities between genes and protein sequences of genes in the biosynthesis of steroid pathway
25 suggest similarities in the pathway across humans and rats. Comparisons of the steroidogenesis
26 genes and pathway were performed to evaluate cross-species similarity metrics (see Chapter 6)
27 using three approaches: (1) protein sequence similarity; (2) pathway network similarities; and
28 (3) promoter-region conservation. Results from all three approaches indicate that
29 steroidogenesis pathways are relatively highly conserved across rats and humans and, thus,
30 qualitatively, the rat and human mechanisms for steroidogenesis share many similarities.
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1 These results further corroborate what is known about the similar roles for androgens
2 during normal male development in both rat and human. However, the data sources used for all
3 three approaches have gaps in the knowledge bases. For the pathway network diagramming,
4 there is a data quality concern. Due to data quality caveats, it is difficult to use these new lines
5 of evidence to quantitatively inform the relative sensitivity to DBF across species. It is possible
6 that the small differences across species have a strong penetrance, leading to significant
7 differences in what proteins may be more sensitive to DBF for T production. Because there are
8 some questions as to the reliability of the data used to generate the pathway comparisons used for
9 each species, there is no basis on which to transform a measure of conservation to a quantitative
10 measure of sensitivity. Thus, we do not recommend utilizing these data to inform interspecies
11 uncertainty in the case of DBF because it is difficult to make unequivocal conclusions regarding
12 a "high" versus "low" degree of conservation for the genes in this pathway based on these data
13 alone. These methods, however, when based on high quality data, could be applied
14 quantitatively to future chemical assessments.
15 We further considered whether some steroidogenesis genes are of higher relative
16 importance and, thus, should be weighted higher in a cross-species assessment of the
17 steroidogenesis pathway. The initiating event for DBF action in the male reproductive
18 developmental outcomes has not been established. Some knowledge of the rate-limiting steps
19 for steroidogenesis, in the unperturbed scenario, is available. P450scc has been identified in
20 some studies as a limiting enzymatic step for T production (Miller, 1988; Omura and Morohashi,
21 1995). However, the information on kinetics reflects the unperturbed state because the
22 rate-limiting step was defined in assays without DBF exposure. Additionally, the rate-limiting
23 step information is limited in scope to steroidogenic enzymes and not all upstream activities
24 leading to T production, such as STAR, a protein that impacts the availability of cholesterol (by
25 transporting cholesterol to the inner mitochondrial membrane for cleavage by P450scc) for T
26 production. Thus, there is no a priori knowledge to argue for placing more weight on a particular
27 gene leading to T production.
28 While the confidence in the cross species comparisons of the steroidogenesis pathway
29 were not high enough to utilize the findings quantitatively, the findings do add to the weight-of-
30 evidence suggesting that the role of T in male fetal development in rats and humans is well
31 conserved. Further, the exploratory methods for developing metrics for cross-species pathway
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1 similarities described in this document (see Chapter 6) may be developed and validated in the
2 future for quantitative use in risk assessment.
3
4
5 7.2.3. Application of Genomic Data to Risk Assessment: New Methods
6 None of the DBF genomic studies were designed with the application to risk assessment
7 in mind. Microarray and other 'omic data analytical methods were originally developed for
8 screening purposes (i.e., designed to err on the side of false positives over false negatives). For
9 risk-assessment application, different genomic analytical tools are needed that do not err on the
10 side of false positives (i.e., do not detecting a change in gene expression by chance) and reliably
11 separate signal from noise. In traditional pathway level analysis, significant genes are mapped to
12 their respective pathways. Depending on whether the number of genes that map to any given
13 pathway, the role of the pathway can be over of underestimated. To overcome this problem, we
14 developed the overall pathway activity (OPA) method that employs one as opposed to two steps
15 (i.e., first, identifying DEGs and second, identifying significantly affected pathways by grouping
16 the DEGs using pathway analysis programs). This method, that ranks pathways based on the
17 expression level of all genes in a given pathway, shows promise for use in risk assessment but
18 needs to be further validated.
19 Chapter 6 describes exploratory methods for developing a genetic regulatory network
20 model and measuring cross-species differences for a given pathway. Genetic regulatory network
21 models can be very useful for understanding the temporal sequence of critical biological events
22 perturbed after chemical exposure, and thus, useful to a risk assessment. We developed a method
23 for developing a genetic regulatory network model for DBF based on the available data. The
24 availability of a time-course data (Thompson et al. [2005]) enabled our group to model the series
25 of events that occurred between exposure to DBF and the onset of toxic reproductive outcomes
26 by the generation of a regulatory network model. However, given the limitations of the
27 Thompson et al. (2005) study design, we did not draw conclusions about affected genes and
28 pathways over time for DBF from this study. Given the limitations of the Thompson et al.
29 (2005) data (see Chapter 6), the exercise allowed us to develop methods for analyzing time
30 course data for use in building a regulatory network model. We used three different methods to
31 assess rat-to-human conservation as metrics that may inform the interspecies differences for one
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1 MO A, the reduced fetal testicular T. However, there are a number of challenges in using
2 similarity scores to quantitatively estimate the human relevance of a MOA (section 6.5.).
3
4 7.2.4. Application of Genomic Data to Risk Assessment: Using Data Quantitatively
5 This case study was limited to qualitative uses of genomics in risk assessment.
6 U.S. EPA and the larger scientific community working with genomics are interested in
7 methods to use genomic data quantitatively in risk assessment. Genomic data were not
8 assessed quantitatively in this case study due to the absence of dose-response global gene
9 expression studies (i.e., microarray studies) for DBF. There is one dose-response
10 RT-PCR study that, although not a genomic (i.e., not global) study, was considered for
11 use quantitatively in risk assessment (Lehmann et al., 2004; Table 7-1). Strengths of the
12 Lehmann et al. (2004) study include the following:
13
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Table 7-1. DBF 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
o <*>'
U
O 58
S a
1
§ 3.
Precursor event3
in vivo endpoint
0.1 mg/kg-d
| HsdSb
1 mg/kg-d
| HsdSb
I Scarbl
10 mg/kg-d
NC in gene exp.
NC in [T]
30 mg/kg-d
ND for gene
exp.
NC in [T]
50 mg/kg-d
[Scarbl
IHsdSb
IStAR
[Cypllal
80 mg/kg-d
ND for gene
exp.
t incidence of
absent, poorly
developed, or
atrophic testis
and
underdeveloped
or absent
epididymisb
100 mg/kg-d
[Scarbl
[3/3-HSD
[StAR
\P450scc
Retained nipples
and areolae0
TO
NC, no statistically significant change; ND = not determined (Lehmann et al. (2004) did not test 80 mg/kg-d).
Sources: "Lehmann et al. (2004); bNTP, 1991; TVIylchreest et al., 2000.
-------
1 • The study includes low to high doses.
2
3 • Some of the genes assessed in this study were first identified in microarray studies,
4 providing a level of connection between the gene and particular outcomes as well as
5 demonstrating reproducibility across studies. For example, findings for Star gene
6 expression are reproduced across protein expression, RT-PCR, and microarray studies.
7
8 However, there are a number of issues in utilizing these dose-response RT-PCR data.
9 These limitations include the following:
10
11 • Some of the gene expression changes are not reproducible. For example, Kit was
12 observed to be significantly altered in the Lehmann et al. (2004) study but was not
13 observed to be significantly reduced after in utero DBF exposure in a microarray study
14 (Liu et al., 2005) utilizing the Affymetrix® gene chip, yet Kit is on the Affymetrix® rat
15 chip.
16
17 • The relationship between statistical significance and biological significance is not known
18 for genomic data. For example, the expression ofHsdSb mRNA is statistically
19 significantly altered at lower doses than a statistically significant [T] decrease was
20 observed. Thus, Lehmann et al. (2004) argued that the changes in Hsd3b at 0.1 and
21 1.0 mg/kg-d were not biologically significant. It is also not known whether changes in
22 the expression of a single or multiple steroidogenesis genes would lead to a significant
23 alteration in [T] and the phenotype.
24
25 • Inter-litter variability could not be characterized from the Lehmann et al. (2004) data
26 because the RT-PCR data were collected on five individual pups representing four to
27 five litters per treatment group (i.e., ~1 pup/litter). In order to have appropriate data for
28 BMD modeling, litter mean values calculated from a study with a greater sample size and
29 multiple litters are needed to allow characterization of inter-litter variability.
30
31
32 Regarding quantitative measures of intraspecies and interspecies differences, it should be
33 noted that the same information which is necessary for quantitative assessment of interspecies
34 differences (Section 7.2.2) may be useful for characterizing intraspecies variability, and vice
35 versa. In particular, factors that explain or predict interstrain differences in rodent sensitivity to
36 DBF, such as those noted between Wistar and SD rats, may be hypothesized to contribute to
37 human variability. Further, lexicologically important interstrain differences identified from the
38 toxicogenomic data could be an excellent data source for investigating whether they are also
39 important for modulating interspecies sensitivity.
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1
2 7.3. LESSONS LEARNED
3 The lessons learned from the case study are grouped by research needs and
4 recommendations that are useful to research scientists and those who work on risk assessments.
5 7.3.1. Research Needs
6 7.3.1.1. Data Gaps and Research Needs: DBF
1 There are some research needs that would be very useful to a DBF risk assessment.
8 Research needs for DBF include the following:
9
10 (1) Developing a genetic regulatory network model using the Plummer et al. (2007) data.
11 This data set would be an excellent source of temporal and spatial gene expression
12 information because one of its studies includes three time intervals, thus covering the
13 entire critical window for male reproductive outcomes; and a second study used
14 microdissection of the cord and interstitial cells of the testis. This study was not modeled
15 because it was not published until after the modeling work was performed. By
16 comparing gene expression, they hypothesized the MO A underlying the gonocyte and LC
17 effects. These data could be used to develop a regulatory network for DBF in utero
18 exposure and effects on the rat testis;
19 2) Performing microarray studies in male reproductive tissues, other than the testis,
20 affected by DBF in order to understand the similarities and differences in DBF-affected
21 pathways in across reproductive organs and tissues in the male rat. Bowman et al.
22 (2005) performed such a study in the WDs, but studies in other male tissues are needed;
23 3) Performing microarray studies in human tissues (either cell lines or from aborted male
24 fetal tissue), along with parallel in vitro and in vivo studies in rats for validation and
25 comparison. Such data would provide critical information for the IRIS DBF assessment
26 on qualitative, and possibly quantitative, interspecies differences in TDs sensitivity.
27 Some human studies found an association between in utero phthalate exposure and
28 newborn male reproductive developmental measures (Swan et al., 2005; Main et al.,
29 2006) that indicate human relevance for some of the DBF effects observed in male rat
30 studies;
31 4) Performing well designedproteomic and metabolomic studies to understand the affect of
32 in utero DBF exposure on the function of expressed proteins, and on cellular metabolites.
33 These data may provide complementary data to the available transcriptomic data, which
34 could yield some new insights;
35 5) Performing genomic studies to identify early, critical, upstream events as a means to
36 identify the initiating event for DBF's action in the testis. This would require performing
37 studies much earlier in gestation, at the beginning of sexual differentiation. In addition,
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1 such studies may require greater sensitivity regarding gene expression change
2 identification because a statistically significant change may be greater than a biologically
3 significant change. If identified, the initiating event could be utilized in the risk
4 assessment, thereby reducing uncertainty;
5 6) Performing genomic studies to under stand whether the female reproductive tract
6 malformations after DBF exposure share a common MO A with the male development
1 reproductive effects. This line of research would identify pathways affected in the
8 developing female reproductive tracts after early gestational DBF exposure.
9 7) Comparing the affectedDEGs and pathways between the phthalates with and without
10 developmental effects could be useful for a cumulative risk assessment of the
11 developmental phthalates. All of the data from the Liu et al. (2005) data set could be
12 utilized to evaluate this issue. Further, evaluating consistency of findings across
13 chemicals in the same MOA class that do and do not produce the same set of effects
14 could be useful for improving specificity of the MOA findings.
15
16 7.3.1.2. Research Needs for Toxicity and Toxicogenomic Studies for Use in Risk Assessment:
17 Future Chemical Assessments
18 The U.S. EPA and the larger scientific community are interested in methods to use
19 genomic data quantitatively in risk assessment. This case study was limited to qualitative uses of
20 genomics in risk assessment due to the absence of dose-response global gene expression studies
21 (i.e., microarray studies) for DBF. Thus, multiple dose microarray studies are needed
22 (Table 7-2). Such studies are very costly and without proper design and power can be difficult to
23 interpret because the lower doses may not affect gene expression in every organ assessed,
24 leading to the need for increased sample size. For example, 500 mg/kg-d DBF was used as the
25 single dose in the published microarray studies because exposure during the critical window at
26 this dose leads to the maximum reproductive developmental effects (i.e., almost all animals are
27 affected in every male pup) without effects on maternal toxicity. In a dose-response study
28 including low to high doses, the sample size per dose group would need to be high enough to
29 increase statistical power (i.e., the detection of gene expression changes when only a few animals
30 are affected). For example, if an endpoint is affected in 20% of the animals at lower doses, then
31 the sample size for microarray studies must be large enough to identify the affected animals
32 (with affected gene expression). Perhaps the highest priority study is one that assesses global
33 gene expression and toxicity endpoints of interest; the testis would be collected at GD 19 in one
34 group of animals but a second group would be followed through to evaluation of the
35 developmental endpoint of interest.
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1
2
3
4
5
6
7
Table 7-2 describes some of the priority research needs for toxicogenomic studies for
developmental toxic chemicals, including DBF. First, appropriate time-course gene-expression
data over the critical window, using a small subset of genes whose altered expression is linked to
the outcome of interest, would be very relevant for developing a regulatory network model.
Table 7-2. Research needs for toxicogenomic studies to be used in risk
assessment
Purpose
1) Develop a regulatory network model
2) Improve pathway analysis statistical
power
3) Use of toxicogenomic data to inform
toxicokinetics in dose-response
analysis
4) Use of toxicogenomic data in
dose-response analysis
5) Phenotypic anchoring; informing MOA
(Figure 3-4)
6) Assess intraspecies differences
7) Assess interspecies differences
8) Appropriate statistical pathway
analysis methods for use in risk
assessment
9) Screening and categorizing chemicals
by MOA in risk assessment (e.g.,
cumulative risk assessment)
Study Needed
Exposure time-course microarray data.
Number of replicates increased.
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.
Multiple doses in microarray studies in parallel
with phenotypic anchoring.
Similar study design characteristics for genomic
and toxicity studies (i.e., dose, timing of
exposure, organ/tissue evaluated).
A study assessing multiple doses across rat
strains (e.g., Wistar vs. SD); endpoint and
microarray component of the study.
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 TgX data to aid species extrapolation in risk
assessments.
Further comparisons and evaluations of different
methods.
Genomic (transcriptomic, proteomic, and/or
metabolomic) signatures can be particularly
useful for screening and categorizing chemicals
by MOA in risk assessment.
This document is a draft for review purposes only and does not constitute Agency policy.
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1 These studies need to be carefully designed based on the information on the critical window of
2 exposure and the relationship to the particular outcome of concern. Second, the statistical power
3 of pathway-analysis methods for global expression techniques, including microarrays,
4 proteomics and metabolomics, could be improved by designing and performing studies with
5 more replicates. Thus, variability would be better characterized. Third, it would be helpful to
6 design genomic studies that could inform both TKs and dose response (#3 and #4, Table 7-2).
7 Performing genomic and toxicity studies with similar designs would provide useful
8 information. These studies would be designed at the most relevant time of exposure, include low
9 to high doses, and assess the relevant tissues. Relevant internal dose measurements could be
10 obtained on which to base the internal dose metric. These studies, employing genomic and
11 toxicity studies of comparable designs, would allow for phenotypic anchoring of dose, gene
12 expression, and outcome, and thus, could potentially be used in dose-response analysis. Studies
13 with both a toxicity and toxicogenomic component would obviously require assessment of a
14 large sample size to be informative. These same studies could be used to inform MO A (#5) and
15 could be adapted to comparing species (#6). Finally, further development and comparison
16 studies to identify appropriate statistical pathway analysis methods for use in risk assessment are
17 needed (#8). It is important to note that such studies require research funding and laboratories
18 with expertise in both genomics and toxicology.
19 Research needs for toxicity studies that would improve the utility in risk assessment are
20 described in Table 7-3. As was noted for the DBF case (Chapter 4), complete reporting is
21 necessary for studies that are intended for use in risk assessment.
22
23 7.3.2. Recommendations
24 Based on the lessons learned from performing the DBF case study exercise, we
25 developed some recommendations or best practices for performing assessments for
26 chemicals having available genomic data. We recommend following the principles of the
27 approach described herein, to thoroughly consider the available genomic data for whether
28 it can inform every information type useful to risk assessment, and to evaluate genomic
29
This document is a draft for review purposes only and does not constitute Agency policy.
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Table 7-3. Research needs for toxicity studies for utilizing toxicogenomic and
toxicity data together in risk assessment
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Study Aspect
Study design
Reporting
Research Need
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 connections between gene
expression changes and outcomes.
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).
data and toxicity data together to assess phenotypic anchoring. In addition, we recommend four
specific methods for evaluating genomic data that arose from the DBF case study. Two of these
recommendations are straightforward and could reasonably be performed by a risk assessor with
basic genomics training:
1) Evaluate the genomic and other gene expression data for consistency of findings across
studies to provide aweight-of-the-evidence (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 was advantageous to include all of the
available gene expression data (single gene, global gene expression, protein, RNA)
because the single gene expression techniques have been traditionally used to confirm the
results of global gene expression studies.
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1 2) Perform benchmark dose (BMD) modeling on high-quality RT-PCR dose-response
2 studies for genes known to be in the causal pathway of a MO A or outcome of interest.
3 Obtaining a BMD and BMDL (benchmark dose lower confidence limit) is a useful
4 starting point for both linear low-dose extrapolation and reference value approaches. We
5 are not indicating which approach is appropriate to take for making predictions about the
6 potential risk below the BMD or BMDL. "High quality" is defined in this context as a
7 well conducted study that assessed enough animals and litters for sufficient statistical
8 power for characterizing the mean responses and the variability (interlitter and intralitter
9 variability).
10
11 Two additional recommendations require expertise in genomic data analysis methods to
12 implement:
13 3) Perform new analysis of toxicogenomic raw data in order to identify all affected
14 pathways or for other risk assessment applications. Most often, microarray studies are
15 conducted for different purposes (e.g., basic science, pharmaceutical development). In
16 these cases, new pathway analysis of microarray data can be potentially useful.
17 4) Develop a genetic regulatory network model for the chemical of interest to define the
18 system of interacting regulatory DNA sequences, expression of genes, and pathways for
19 one or more outcomes of interest. Genetic regulatory network model methods,
20 developed as part of this case study, could be used in a risk assessment. If time-course
21 genomic data are available, the temporal sequence of mechanistic events after chemical
22 exposure can be defined, and the earliest affected genes and pathways, that may be define
23 the initiating event, may be identified.
24
25
26 7.3.3. Application of Genomic Data to Risk Assessment: Future Considerations
27 A number of the issues that emerged in evaluating the DBF genomic data set are relevant
28 to using genomic data in risk assessment in general. Some issues regarding the use of genomic
29 data are to the same as for the use of precursor information in risk assessment, regardless of the
30 technique used to gather the information. Two outstanding questions are
31
32 • How is the biologically significant level of change in a precursor marker determined?
33 And, specifically for toxicogenomic data, what are the key genes (i.e., a key gene, a
34 handful of genes associated with the outcome of interest, a genomic signature) whose
35 altered expression leads to an adverse outcome? Currently, decisions about the degree of
36 change of a precursor event tend to be based on statistical significance because data to
37 address biological significance are typically lacking (as is the case for T levels and male
38 development of the testis). Genes are identified as DEGs in microarray studies based on
39 statistical-significance criteria that may not reflect biological significant changes (i.e.,
40 identified genes may not be biologically meaningful while unidentified genes may be
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1 meaningful). This point is also relevant to the question: What pathway analysis methods
2 are most appropriate for risk assessment? As noted in Chapter 6, it is difficult to know
3 whether one has identified the biologically relevant DEGs and pathways. Statistically
4 significant changes and repeated findings of the same genes and pathways across studies
5 and using different analytical methods does not necessarily provide a greater confidence
6 regarding biological significance of these genes and pathways over other genes and
7 pathways. Further, there is a bias towards the well annotated genes as biologically
8 significant when, in fact, the unannotated genes could be of greater importance.
9
10 • What are the requirements for linkage of precursor events to in vivo endpoints? Studies
11 to assess the relationship between the gene expression and outcomes are needed to
12 establish a causal connection.
13
14 There are also a number of technical issues in utilizing microarray data in U.S. EPA risk
15 assessments that have not fully been surmounted. The primary technical issue is the validation
16 of the reproducibility of microarray study results. Reproducibility depends on biological sample
17 preparation, interlaboratory (presumably related to operator and protocol differences),
18 intralaboratory (presumably related to operator differences), and platform variability. The results
19 of the MAQC project (see Chapters 2 and 5) revealed that reproducibility was achieved when
20 using the same biological sample. This is very encouraging for using microarray data in risk
21 assessment. However, biological sample variability still needs to be addressed in order that
22 protocols and details of the underlying reasons for the variability can be understood.
23 A number of the issues stem from the complexity of the data output from the global
24 expression techniques (e.g., microarrays, proteomics, metabolomics). This is in part a training
25 issue. To address the training needs, the U.S. EPA Risk Assessment Forum held introductory
26 and intermediate level training in genomics in 2007. The FDA has also held genomics training
27 (http://www.fda.gov/cder/genomics/Default.htm). However, it would be advantageous for U.S.
28 EPA to embark on further training of risk assessors to enable them to perform analyses of
29 microarray and other genomic data analysis techniques,and to understand the issues in applying
30 traditional analytical methods to risk assessment.
31
32
33 If additional case studies are performed using the approach outlined in Figure 7-1, we
34 recommend a chemical whose exposure leads to both cancer and noncancer outcomes to explore
35 use of these data for multiple outcomes as well as the impacts on the different risk assessment
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1 paradigms and processes (e.g., cancer versus noncancer). In fact, one of the phthalates might be
2 a good candidate chemical for such a case study. Further, performing case studies on data-rich
3 and data-poor chemicals would aid in further evaluating the approach described herein.
4 The approach for utilizing toxicogenomic data in risk assessment outlined in this
5 document may be applied to other chemical assessments. This document advances the effort to
6 devise strategies for using genomic data in risk assessment by defining an approach, performing
7 a case study, and defining critical issues that need to be addressed to better utilize these data in
8 risk assessment. This case study serves as an example of the considerations and methods for
9 using genomic data in future risk assessments for environmental agents.
This document is a draft for review purposes only and does not constitute Agency policy.
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777/5 document is a draft for review purposes only and does not constitute Agency policy.
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777/5 document is a draft for review purposes only and does not constitute Agency policy.
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777/5 document is a draft for review purposes only and does not constitute Agency policy.
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777/5 document is a draft for review purposes only and does not constitute Agency policy.
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URLS AND WEBSITES:
http://cfpub.epa. gov/ncea/cfm/recordisplav.cfm?deid=149984
http://cerhr.niehs.nih.gov/news/phthalates/DEHP-final.pdf
http://cerhr.niehs.nih.gov/news/phthalates/report.html
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#anchorl4684156.
http://www.ehponline.org/txg/docs/admin/txg-n-press.html?section=toxicogenomics
777/5 document is a draft for review purposes only and does not constitute Agency policy.
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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&Cmd=ShowDetailView&TermToSearch=114215&ordinalpos=
3&itool=EntrezSvstem2.PEntrez.Gene.Gene ResultsPanel.Gene RVDocSum
http://www.ncbi.nlm. nih.gov/sites/entrez?db=PubMed
www.epa.gov/iris/whatsnewarchive.htm
www.genego.com
www.omics.org
This document is a draft for review purposes only and does not constitute Agency policy.
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1 9. GLOSSARY
2
O
4 Amplified Fragment Length Polymorphism PCR (AFLP-PCR or AFLP): A PCR-based tool
5 used in genetics research, DNA fingerprinting, and in the practice of genetic engineering.
6
7 Benchmark Dose (BMD) or Concentration (BMC): A dose or concentration that produces a
8 predetermined change in response rate of an adverse effect (called the benchmark response or
9 BMR) compared to background.
10
11 Copy Number Polymorphism (CNP): Normal variation in the number of copies of a sequence
12 within the DNA.
13
14 Complementary DNA (cDNA): A double stranded DNA version of an mRNA molecule.
15 Exposure: Contact made between a chemical, physical, or biological agent and the outer
16 boundary of an organism. Exposure is quantified as the amount of an agent available at the
17 exchange boundaries of the organism (e.g., skin, lungs, gut).
18 Exposure Assessment: An identification and evaluation of the human population exposed to a
19 toxic agent, describing its composition and size, as well as the type, magnitude, frequency, route
20 and duration of exposure.
21 Expressed Sequence Tag (EST): A short subsequence of a transcribed cDNA sequence.
22 Gene Ontology (GO): A collaborative project of the Gene Ontology Consortium that has
23 developed three structured controlled vocabularies (ontologies) that describe gene products in
24 terms of their associated biological processes, cellular components and molecular functions in a
25 species-independent manner. There are three separate aspects to this effort: first, the
26 development and maintenance of the ontologies themselves; second, the annotation of gene
27 products, which entails making associations between the ontologies and the genes and gene
28 products in the collaborating databases; and third, development of tools that facilitate the
29 creation, maintenance and use of ontologies.
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1
2 Gene Regulatory Network (GRN) Model: A representation of the regulation (e.g., positive or
3 negative regulation) of genes and their expression (e.g., RNAs, proteins, metabolites) of a system
4 (e.g., cell, tissue), and their relations. A GRN model can be expressed at the genomic and
5 metabolic level. Genes can be viewed as nodes in the network, with input being proteins (e.g.,
6 transcription factors), and outputs being the level of gene expression. Further, GRNs can
7 describe changes over time or space if based on time course or spatial compartment data.
8
9 Genomics: The study of the genome and include genome sequencing and genotype analysis
10 techniques (e.g., polymorphism identification).
11
12 Hazard Assessment: The process of determining whether exposure to an agent can cause an
13 increase in the incidence of a particular adverse health effect (e.g., cancer, birth defect) and
14 whether the adverse health effect is likely to occur in humans.
15
16 Hazard Characterization: A description of the potential adverse health effects attributable to a
17 specific environmental agent, the mechanisms by which agents exert their toxic effects, and the
18 associated dose, route, duration, and timing of exposure.
19
20 Key Event: An empirically observable precursor step that is, itself, a necessary element of the
21 mode of action or is a biologically based marker for such an element (U.S. EPA, 2005).
22
23 Lowest Observed Adverse Effect Level (LOAEL): The lowest exposure level at which there
24 are biologically significant increases in frequency or severity of adverse effects between the
25 exposed population and its appropriate control group.
26
27 Lowest Observed Effect Level (LOEL): In a study, the lowest dose or exposure level at which
28 a statistically or biologically significant effect is observed in the exposed population compared
29 with an appropriate unexposed control group.
30
This document is a draft for review purposes only and does not constitute Agency policy.
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1 Microarray Quality Control (MAQC): A project that was developed to provide quality-
2 control tools to the microarray community in order to avoid procedural failures and to develop
3 guidelines for microarray data analysis by providing the public with large reference data sets
4 along with readily accessible reference RNA samples.
5
6 Metabolomics: Metabolomics is the study of low-molecular-weight metabolic products.
7
8 Microarray: A microarray is a tool for analyzing gene expression that consists of a small
9 membrane or glass slide containing samples of many genes arranged in a regular pattern.
10
11 Mechanism of Action: The complete molecular sequence of events between the interaction of
12 the chemical with the target site and observation of the outcome. Thus, the mechanism of action
13 can include toxicokinetic and/or toxicodynamic steps.
14
15 Mode of Action (MOA): One event, or a sequence of key events, that the outcome is dependent
16 upon (i.e., part of the causal pathway and not a coincident event).
17
18 No Observed Adverse Effect Level (NOAEL): The highest exposure level at which there are
19 no biologically significant increases in the frequency or severity of adverse effect between the
20 exposed population and its appropriate control; some effects may be produced at this level, but
21 they are not considered adverse or precursors of adverse effects.
22
23 No Observed Effect Level (NOEL): An exposure level at which there are no statistically or
24 biologically significant increases in the frequency or severity of any effect between the exposed
25 population and its appropriate control.
26
27 Omics: Omics is a general term for a broad discipline of science and engineering for analyzing
28 the interactions of biological information objects in various 'omes' such as toxicogenome,
29 proteome, and metabolome.
30
This document is a draft for review purposes only and does not constitute Agency policy.
G-3 DRAFT—DO NOT CITE OR QUOTE
-------
1 Physiologically Based Pharmacokinetic (PBPK) Model: A model that estimates the dose to a
2 target tissue or organ by taking into account the rate of absorption into the body, distribution
3 among target organs and tissues, metabolism, and excretion.
4
5 Principal Component Analysis (PCA): A technique for analysis of multivariate data that is
6 similar to SVD (see below). There is a direct relation between PCA and SVD in the case where
7 principal components are calculated from the covariance matrix. Compared to PCA, SVD is
8 more fundamental because SVD simultaneously provides the PC As in both row and column
9 spaces.
10
11 Proteomics: The study of proteins in an organism.
12
13 Reverse Transcription Polymerase Chain Reaction (RT-PCR): A two-step process for
14 converting RNA to DNA and the subsequent PCR amplification of the reversely transcribed
15 DNA.
16
17 Human Health Risk Assessment: The evaluation of scientific information on the hazardous
18 properties of environmental agents (hazard characterization), the dose-response relationship
19 (dose-response assessment), and the extent of human exposure to those agents (exposure
20 assessment). The product of the risk assessment is a statement regarding the probability that
21 populations or individuals so exposed will be harmed and to what degree (risk characterization).
22
23 Serial Analysis of Gene Expression (SAGE): A powerful tool that allows the analysis of
24 overall gene expression patterns with digital analysis.
25
26 Single-Nucleotide Polymorphism (SNP): A DNA sequence variation occurring when a single
27 nucleotide — A, T, C, or G — in the genome (or other shared sequence) differs between
28 members of a species (or between paired chromosomes in an individual).
29
30 Singular value decomposition (SVD): A technique for analysis of multivariate data. This
31 method describes a system of high number of correlated variables by uncorrelated reduced
This document is a draft for review purposes only and does not constitute Agency policy.
G-4 DRAFT—DO NOT CITE OR QUOTE
-------
1 number of variables. For analysis of microarray data, SVD provides a linear projection of the
2 gene expression data from the genes * samples space to a noise reduced space and thus,
3 differentiates underlying signals from the noise. Noise reduced space approximates the data with
4 a fraction of the overall expression.
5
6 Toxicogenomics: A set of technologies for assessing the genome, transcriptome, proteome, and
7 metabolome gene products after toxic agent exposure.
8
9 Transcriptomics: A set of techniques to measure global mRNA expression; it is a tool used to
10 understand specific the expression of genes and pathways involved in biological processes.
This document is a draft for review purposes only and does not constitute Agency policy.
G-5 DRAFT—DO NOT CITE OR QUOTE
-------
1 APPENDIX A
2
3 SUPPORTING TABLES FOR CHAPTER 5
4
5
6 Appendix A contains additional tables that support the work shown in Chapter 5.
This document is a draft for review purposes only and does not constitute Agency policy.
A-l DRAFT—DO NOT CITE OR QUOTE
-------
This document is a draft for review A-2
purposes only and does not constitute
Asencv volicv
Table A-l. Weight of evidence (WOE) for statistically significant gene expression changes after in utero
exposure to dibutyl phthalate (DBF) from the whole rat testis microarray studies3 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
Aacs
Aadat
Abcgl
Acaal
Acaca
Acadl
Acads
AcsU
AdamlS
Adamtsl
Admr
Adralb
Akt2
Alasl
Alasl
Official gene namef
Acetoacetyl-CoA synthetase
Aminoadipate aminotransferase
ATP-binding cassette, sub-family G (WHITE),
member 1
Acetyl-Coenzyme A acyltransferase 1
Acetyl-Coenzyme A carboxylase alpha
Acetyl-Coenzyme A dehydrogenase, long-chain
Acyl-Coenzyme A dehydrogenase, short chain
Acyl-CoA synthetase long-chain family member 4
A disintegrin and metallopeptidase domain 15
(metargidin)
A disintegrin-like and metallopeptidase (reprolysin
type) with thrombospondin type 1 motif, 1
Adrenomedullin receptor
Adrenergic receptor, alpha Ib
Thymoma viral proto-oncogene 2
Aminolevulinic acid synthase 1
Aminolevulinic acid synthase 1
Exposure
window
GD 12-19
GD 12-19
GD 12-19
GD 12-19
GD 12-19
GD 12-19
GD 12.5-15.5
GD 12-19
GD 12.5-17.5
GD 12.5-19.5
GD 12-19
GD 12-19
GD 12-21
GD 12-19
GD 12.5-17.5
Up or
down
Down
Down
Up
Down
Down at
GD 19
Down at
GD 19
Up
Down
Up
Down
Down
Down
Down at
GD21
Down
Down
Fold
change
-0.37 Iog2
-0.38 Iog2
0.38 Iog2
-0.37 Iog2
>2
>2
1.50
-0.60 Iog2
1.20
-1.35
-0.90 Iog2
-0.30 Iog2
>2
-1.01 Iog2
-1.33
Cutoff used (method)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
2-fold
2-fold
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
2-fold
p < 0.05 (ANOVA)
p < 0.01 (ANOVA)
Reference
Liu etal., 2005
Liu etal., 2005
Liu etal., 2005
Liu etal., 2005
Shultz etal., 2001
Shultz etal., 2001
Plummer etal.,
2007
Liu etal., 2005
Plummer etal.,
2007
Plummer etal.,
2007
Liu etal., 2005
Liu etal., 2005
Shultz etal., 2001
Liu etal., 2005
Plummer etal.,
2007
-------
purp
Table A-l (continued)
" document is a draft for review A- 3
?ses only and does not constitute
Asencv volicv
Official
gene
symbol
Alasl
Aldhla3
Aldh2
Aldh2
Aldh2
Aldoa
Aldoc
AnxaS
Aoxl
Aqpl
Arfl
Arrb2
Asns
Ass
At/2
Official gene namef
Aminolevulinic acid synthase 1
Aldehyde dehydrogenase family 1, subfamily A3
Aldehyde dehydrogenase 2
Aldehyde dehydrogenase 2
Aldehyde dehydrogenase 2
Aldolase A, fructose-bisphosphate
Aldolase C
Annexin A5
Aldehyde oxidase 1
Aquaporin 1
ADP-ribosylation factor 3
Arrestin, beta 2
Asparagine synthetase
Argininosuccinate synthetase
Activating transcription factor 2
Exposure
window
GD 12.5-19.5
GD 12-19
GD 12-19
GD 12.5-17.5
GD 12.5-19.5
GD 12.5-19.5
GD 12-19
GD 12.5-19.5
GD 12-19
GD 12.5-15.5
GD 12.5-17.5
GD 12-21
GD 12-19
GD 12-19
GD 12-21
Up or
down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down at
GD21
Down
Down
Up at
GD21
Fold change
-1.44
-0.43 Iog2
-0.82 Iog2
-1.50
-1.91
-1.24
-0.44 Iog2
-1.20
-0.50 Iog2
-1.29
-1.23
>2
-0.24 Iog2
-0.82 Iog2
>2
Cutoff used (method)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
2-fold
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
2-fold
Reference
Plummer et al.,
2007
Liu et al., 2005
Liu et al., 2005
Plummer et al.,
2007
Plummer et al.,
2007
Plummer et al.,
2007
Liu et al., 2005
Plummer et al.,
2007
Liu et al., 2005
Plummer et al.,
2007
Plummer et al.,
2007
Shultzetal.,2001
Liu et al., 2005
Liu et al., 2005
Shultzetal.,2001
-------
^3
S K
iis document is a d
voses only and dot
Asencv vo
raft for review A-4
>,s not constitute
Hcv
Official
gene
symbol
Atf4
Atplbl
Atp4b
AtpSfl
Baiap2
Bhlhb2
Bhmt
BircS
Btg2
Btg2
C4a
Cadps
Calb2
Cd63
Cdknlc
Official gene namef
Activating transcription factor 4
ATPase, Na+/K+ transporting, beta 1 polypeptide
ATPase, R+/K+ exchanging, beta polypeptide
ATP synthase, H+ transporting, mitochondrial FO
complex, subunit B 1
Brain-specific angiogenesis inhibitor 1 -associated
protein 2
Bhlhb2 basic helix-loop-helix domain containing,
class B2
Betaine-homocysteine methyltransferase
Baculoviral IAP repeat-containing 5
B-cell translocation gene 2, anti-proliferative
B-cell translocation gene 2, anti-proliferative
Complement component 4a
Ca2+-dependent secretion activator
Calbindin 2
CD63 antigen
Cyclin-dependent kinase inhibitor 1C (P57)
Exposure
window
GD 19 for 3 hr
GD 12-19
GD 12-19
GD 12.5-15.5
GD 12-19
GD 19 for 3 hr
GD 12-19
GD 12.5-15.5
GD 19 for 1 hr
GD 19 for 3 hr
GD 19 for 6 hr
GD 12-19
GD 12-19
GD 12.5-19.5
GD 12-19
Up or
down
Up after
3hr
Down
Down
Up
Down
Up after
3hr
Down
Up
Up
afterl hr
Up after
3hr
Down after
6 hr
Up
Down
Down
Down
Fold change
0.67
-0.24 Iog2
-0.60 Iog2
1.22
-0.22 Iog2
0.88
-0.24 Iog2
1.68
1.30
1.88
-0.77
0.311og2
-0.77 Iog2
-1.36
-0.81 Iog2
Cutoff used (method)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
Reference
Thompson et al.,
2005
Liu et al., 2005
Liu et al., 2005
Plummer et al.,
2007
Liu et al., 2005
Thompson et al.,
2005
Liu et al., 2005
Plummer et al.,
2007
Thompson et al.,
2005
Thompson et al.,
2005
Thompson et al.,
2005
Liu et al., 2005
Liu et al., 2005
Plummer et al.,
2007
Liu et al., 2005
-------
Table A-l (continued)
S K
lis document is a draft for review A- 5
V oses only and does not constitute
Asencv volicv
Official
gene
symbol
Cdknlc
Cdknlc
Cebpb
Cebpd
Clu
Clu
Cmklrl
Cnrl
Cnbp
Cpal
Cpal
Cpd
Cpe
Cptla
Official gene namef
Cyclin-dependent kinase inhibitor 1C (P57)
Cyclin-dependent kinase inhibitor 1C (P57)
CCAAT/enhancer binding protein (C/EBP), beta
CCAAT/enhancer binding protein (C/EBP), delta
Clusterin
Clusterin
Chemokine-like receptor 1
Cannabinoid receptor 1 (brain)
Cellular nucleic acid binding protein
Carboxypeptidase Al
Carboxypeptidase Al
Carboxypeptidase D
Carboxypeptidase E
Carnitine palmitoyltransferase la, liver
Exposure
window
GD 19 for 6 hr
GD 18-19 for
18 hr
GD 12-19
GD 19 for 3 hr
GD 12-21
GD 18 for 18 hr
GD 12.5-19.5
GD 19 for 3 hr
GD 12.5-19.5
GD 12.5-17.5
GD 12.5-19.5
GD 12-21
GD 12-19
GD 12-19
Up or
down
Down after
6hr
Down after
18 hr
Down
Up after
3hr
Up at
GD21
Up after
18 hr
Down
Up after
3hr
Down
Down
Down
Up at
GD21
Up
Down at
GD 19
Fold change
-1.08
1.63
-0.6 Iog2
1.62
>2
1.03
-1.17
0.99
-1.29
-1.73
-2.33
>2
0.59 Iog2
->2
Cutoff used (method)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
2-fold
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
2-fold
p < 0.05 (ANOVA)
2-fold
Reference
Thompson et al.,
2005
Thompson et al.,
2005
Liu et al., 2005
Thompson et al.,
2005
Shultzetal., 2001
Thompson et al.,
2005
Plummer et al.,
2007
Thompson et al.,
2005
Plummer et al.,
2007
Plummer et al.,
2007
Plummer et al.,
2007
Shultzetal., 2001
Liu et al., 2005
Shultzetal., 2001
-------
^3
S K
iis document is a d
voses only and dot
Asencv vo
raft for review A-6
>,s not constitute
Hcv
Official
gene
symbol
Cptla
Cptlb
Cpz
Crabp2
Cmbp2
Crem
Crispld2
Cryab
Ctgf
Ctgf
Ctsb
Ctsd
CxcllO
Cyb5
Cypllal
Official gene namef
Carnitine palmitoyltransferase la, liver
Cptlb carnitine palmitoyltrans-feraselb, muscle
Carboxypeptidase Z
Cellular retinoic acid binding protein 2
Cellular retinoic acid binding protein 2
cAMP responsive element modulator
Cysteine-rich secretory protein LCCL domain
containing 2
Crystallin, alpha B
Connective tissue growth factor
Connective tissue growth factor
Cathepsin B
Cathepsin D
Chemokine (C-X-C motif) ligand 10
Cytochrome b-5
Cytochrome P450, family 11, subfamily a,
polypeptide 1
Exposure
window
GD 12-21
GD 12-19
GD 12-19
GD 12-19
GD 19 for 6 hr
GD 19 for 3 hr
GD 12-19
GD 12-19
GD 19 for 3 hr
GD 19 for 6 hr
GD 12.5-15.5
GD 12.5-19.5
GD 19 for 3 hr
GD 12-19
GD 12-19
Up or
down
Down at
GD21
Up
Up
Down
Down after
6hr
Up after
3hr
Down
Up
Up after
3hr
Up after
6hr
Up
Down
Up after
3hr
Down
Down
Fold change
->2
0.23 Iog2
0.21 Iog2
-0.31 Iog2
-1.24
0.58
-0.27 Iog2
0.22 Iog2
2.10
2.37
1.53
-1.22
2.07
-0.30 Iog2
-1.07 Iog2
Cutoff used (method)
2-fold
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
Reference
Shultzetal, 2001
Liu et al., 2005
Liu et al., 2005
Liu et al., 2005
Thompson et al.,
2005
Thompson et al.,
2005
Liu et al., 2005
Liu et al., 2005
Thompson et al.,
2005
Thompson et al.,
2005
Plummer et al.,
2007
Plummer et al.,
2007
Thompson et al.,
2005
Liu et al., 2005
Liu et al., 2005
-------
Table A-l (continued)
S K
lis document is a draft for review A-7
V oses only and does not constitute
Asencv volicv
Official
gene
symbol
Cypllal
Cypllal
Cypllal
Cypllal
Cypllbl
Cypl7al
Cypl7al
Cypl7al
Cypl7al
CypSl
CypSl
CypSl
Dab2
Dafl
Official gene namef
Cytochrome P450, family 11, subfamily a,
polypeptide 1
Cytochrome P450, family 11, subfamily a,
polypeptide 1
Cytochrome P450, family 11, subfamily a,
polypeptide 1
Cytochrome P450, family 11, subfamily a,
polypeptide 1
Cytochrome P450, subfamily 1 IB, polypeptide 1
Cytochrome P450, family 17, subfamily a,
polypeptide 1
Cytochrome P450, family 17, subfamily a,
polypeptide 1
Cytochrome P450, family 17, subfamily a,
polypeptide 1
Cytochrome P450, family 17, subfamily a,
polypeptide 1
Cytochrome P450, subfamily 5 1
Cytochrome P450, subfamily 5 1
Cytochrome P450, subfamily 5 1
Disabled homolog 2 (Drosophila)
Decay accelerating factor 1
Exposure
window
GD 12-19
GD 18 for 18 hr
GD 12.5-17.5
GD 12.5-19.5
GD 18 for 18 hr
GD 12-19
GD 18 for 18 hr
GD 12.5-17.5
GD 12.5-19.5
GD 18 for 18 hr
GD 12.5-17.5
GD 12.5-19.5
GD 12-19
GD 12-19
Up or
down
Down at
GD19
Down after
18 hr
Down
Down
Down after
18 hr
Down
Down after
18 hr
Down
Down
Down after
18 hr
Down
Down
Up
Up
Fold change
->2
-1.93
-1.71
-2.85
-1.63
-1.76 Iog2
-2.1
-2.15
-3.08
-1.06
-1.59
-1.81
0.27 Iog2
0.191og2
Cutoff used (method)
2-fold
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
Reference
Shultzetal, 2001
Thompson et al.,
2005
Plummer et al.,
2007
Plummer et al.,
2007
Thompson et al.,
2005
Liu et al., 2005
Thompson et al.,
2005
Plummer et al.,
2007
Plummer et al.,
2007
Thompson et al.,
2005
Plummer et al.,
2007
Plummer et al.,
2007
Liu et al., 2005
Liu et al., 2005
-------
^3
S K
iis document is a d
voses only and dot
Asencv vo
raft for review A-8
>,s not constitute
Hcv
Official
gene
symbol
Dbi
Dbi
Dec
Ddc
Ddc
Ddc
Ddit4
Ddit4
Ddt
Decrl
Dhcr?
Dhcr?
Dhcr?
Dnm3
Duspl
Dusp6
Official gene namef
Diazepam binding inhibitor
Diazepam binding inhibitor
Deleted in colorectal carcinoma
Dopa decarboxylase
Dopa decarboxylase
Dopa decarboxylase
DNA-damage-inducible transcript 4
DNA-damage-inducible transcript 4
D-dopachrome tautomerase
2,4-dienoyl Co A reductase 1, mitochondria!
7-dehydrocholesterol reductase
7-dehydrocholesterol reductase
7-dehydrocholesterol reductase
Dynamin 3
Dual specificity phosphatase 1
Dual specificity phosphatase 6
Exposure
window
GD 12-19
GD 12.5-19.5
GD 12-19
GD 12-19
GD 18 for 18 hr
GD 12.5-19.5
GD 12-19
GD 18 for 18 hr
GD 12.5-19.5
GD 12-19
GD 12-19
GD 19 for 6 hr
GD 18-19 for
18 hr
GD 12-19
GD 19 for 3 hr
GD 12-19
Up or
down
Down
Down
Down at
GD 19
Down
Down after
18 hr
Down
Down
Down after
18 hr
Down
Down
Down
Down after
6hr
Down after
18 hr
Down
Up after
3hr
Up
Fold change
-0.38 Iog2
-1.28
->2
-1.141og2
-1.38
-1.44
-1.02 Iog2
-1.57
-1.22
-0.21 Iog2
-0.73 Iog2
-1.34
-1.18
-0.27 Iog2
0.91
0.39 Iog2
Cutoff used (method)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
2-fold
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
Reference
Liu et al., 2005
Plummer et al.,
2007
Shultzetal, 2001
Liu et al., 2005
Thompson etal.,
2005
Plummer et al.,
2007
Liu et al., 2005
Thompson et al.,
2005
Plummer et al.,
2007
Liu et al., 2005
Liu et al., 2005
Thompson et al.,
2005
Thompson et al.,
2005
Liu et al., 2005
Thompson et al.,
2005
Liu et al., 2005
-------
Table A-l (continued)
S H
J*s >4
its document is a draft for review A-9
f oses only and does not constitute
Asencv volicv
Official
gene
symbol
Dusp6
Ebp
Echsl
Egrl
Egr2
Egr2
ElovlS
Elovl6
Emp3
Enol
Enpep
EntpdS
Epasl
Ephxl
Erbb2
Official gene namef
Dual specificity phosphatase 6
Phenylalkylamine Ca2+ antagonist (emopamil)
binding protein
Enoyl Coenzyme A hydratase, short chain 1,
mitochondria!
Early growth response 1
Early growth response 2
Early growth response 2
ELOVL family member 5, elongation of long chain
fatty acids (yeast)
ELOVL family member 6, elongation of long chain
fatty acids (yeast)
Epithelial membrane protein 3
Enolase 1, alpha non-neuron
Glutamyl aminopeptidase
Ectonucleoside triphosphate diphosphohydrolase 5
Endothelial PAS domain protein 1
Epoxide hydrolase 1, microsomal
v-erb-b2 erythroblastic leukemia viral oncogene
homolog 2, neuro/glioblastoma derived oncogene
homolog (avian)
Exposure
window
GD 19 for 3 hr
GD 12-19
GD 12-19
GD 12-19
GD 19 for 1 hr
GD 19 for 3 hr
GD 12-19
GD 12-19
GD 12.5-19.5
GD 12.5-19.5
GD 12-19
GD 12-19
GD 12-19
GD 12-19
GD 12.5-17.5
Up or
down
Up after
3hr
Down
Down
Up
Up after
Ihr
Up after
3hr
Down
Down
Down
Down
Up
Down
Down
Down
Up
Fold change
1.28
-0.64 Iog2
-0.181og2
0.77 Iog2
1.93
1.53
-0.171og2
-0.40 Iog2
-1.24
-1.63
0.48 Iog2
-0.52 Iog2
-0.21 Iog2
-0.57 Iog2
1.26
Cutoff used (method)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
Reference
Thompson et al.,
2005
Liu et al., 2005
Liu et al., 2005
Liu et al., 2005
Thompson et al.,
2005
Thompson et al.,
2005
Liu et al., 2005
Liu et al., 2005
Plummer et al.,
2007
Plummer et al.,
2007
Liu et al., 2005
Liu et al., 2005
Liu et al., 2005
Liu et al., 2005
Plummer et al.,
2007
-------
Table A-l (continued)
S H
J*s >4
its document is a draft for review A-10
f oses only and does not constitute
Asencv volicv
Official
gene
symbol
Etfdh
Ezr
Ezr
F10
FabpS
Fabp3
FabpS
Fabp3
FabpS
FabpS
Fabp6
Fadsl
Fadsl
Fadsl
Fads2
Fall
Official gene namef
Electron-transferring-flavoproteindehydrogenase
Ezrin
Ezrin
Coagulation factor X
Fatty acid binding protein 3
Fatty acid binding protein 3
Fatty acid binding protein 3
Fatty acid binding protein 3
Fatty acid binding protein 3
Fatty acid binding protein 5, epidermal
Fatty acid binding protein 6, ileal (gastrotropin)
Fatty acid desaturase 1
Fatty acid desaturase 1
Fatty acid desaturase 1
Fatty acid desaturase 2
FAT tumor suppressor homolog 1 (Drosophila)
Exposure
window
GD 12-19
GD 12-19
GD 12-19
GD 12-19
GD 12-19
GD 12-19
GD 19 for 3 hr
GD 19 for 6 hr
GD 18-19 for
18 hr
GD 12-19
GD 12-19
GD 12-19
GD 12.5-15.5
GD 12.5-19.5
GD 12-19
GD 12.5-15.5
Up or
down
Down
Up
Down at
GD 19
Down
Down
Down at
GD 19
Down after
3hr
Down after
6hr
Down after
18 hr
Down at
GD 19
Down
Down
Up
Down
Down
Down
Fold change
-0.39 Iog2
0.20 Iog2
~>2
-0.51 Iog2
-0.49 Iog2
->2
-0.78
-1.68
-1.09
->2
-0.23 Iog2
-0.80 Iog2
1.42
1.47
-0.42 Iog2
-1.32
Cutoff used (method)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
2-fold
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
2-fold
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
2-fold
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
Reference
Liu et al., 2005
Liu et al., 2005
Shultzetal.,2001
Liu et al., 2005
Liu et al., 2005
Shultzetal.,2001
Thompson et al.,
2005
Thompson et al.,
2005
Thompson et al.,
2005
Shultzetal.,2001
Liu et al., 2005
Liu et al., 2005
Plummer et al.,
2007
Plummer et al.,
2007
Liu et al., 2005
Plummer et al.,
2007
-------
Table A-l (continued)
S K
lis document is a draft for review A-11
V oses only and does not constitute
Asencv volicv
Official
gene
symbol
Fbp2
Fdftl
Fdftl
Fdps
Fdps
Fdps
Fdxl
Fdxl
Fdxl
Fdxl
Fdxr
Fdxr
Fgfr4
Folrl
Fos
Fos
Official gene namef
Fructose-l,6-bisphosphatase 2
Farnesyl diphosphate farnesyl transferase 1
Farnesyl diphosphate farnesyl transferase 1
Farensyl diphosphate synthase
Farensyl diphosphate synthase
Farensyl diphosphate synthase
Ferredoxin 1
Ferredoxin 1
Ferredoxin 1
Ferredoxin 1
Ferredoxin reductase
Ferredoxin reductase
Fibroblast growth factor receptor 4
Folate receptor 1 (adult)
FB J murine osteosarcoma viral oncogene homolog
FB J murine osteosarcoma viral oncogene homolog
Exposure
window
GD 12-19
GD 12-19
GD 12.5-19.5
GD 12-19
GD 12.5-17.5
GD 12.5-19.5
GD 12-19
GD 18 for 18 hr
GD 12.5-17.5
GD 12.5-19.5
GD 12-19
GD 12.5-17.5
GD 12-19
GD 12-19
GD 19 for 1 hr
GD 19 for 3 hr
Up or
down
Up
Down
Down
Down
Down
Down
Down
Down after
18 hr
Down
Down
Down
Down
Down
Down
Up after
Ihr
Up after
3hr
Fold change
0.28 Iog2
-0.58 Iog2
-1.40
-0.73 Iog2
-1.49
-1.41
-1.65 Iog2
-2.53
-2.06
-2.97
-0.37 Iog2
-1.41
-0.191og2
-0.48 Iog2
3.28
2.70
Cutoff used (method)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
Reference
Liu et al., 2005
Liu et al., 2005
Plummer et al.,
2007
Liu et al., 2005
Plummer et al.,
2007
Plummer et al.,
2007
Liu et al., 2005
Thompson et al.,
2005
Plummer et al.,
2007
Plummer et al.,
2007
Liu et al., 2005
Plummer et al.,
2007
Liu et al., 2005
Liu et al., 2005
Thompson et al.,
2005
Thompson et al.,
2005
-------
^3
S K
iis document is a d
voses only and dot
Asencv vo
raft for review A-1
>,s not constitute
Hcv
Official
gene
symbol
Fragl
Fragl
Fthfd
Fthfd
Fthfd
Fzd2
Gaa
Ggtl3
Gjal
Glrxl
Gnrhr
Gnrhr
Gpsn2
Grbl4
Grbl4
Grbl4
Official gene namef
FGF receptor activating protein 1
FGF receptor activating protein 1
Formyltetrahydro-folate dehydrogenase
Formyltetrahydro-folate dehydrogenase
Formyltetrahydro-folate dehydrogenase
Frizzled homolog 2 (Drosophila)
Glucosidase, alpha, acid
Gamma-glutamyltransferase-like 3
Gap junction membrane channel protein alpha 1
Glutaredoxin 1 (thioltransferase)
Gonadotropin releasing hormone receptor
Gonadotropin releasing hormone receptor
Glycoprotein, synaptic 2
Growth factor receptor bound protein 14
Growth factor receptor bound protein 14
Growth factor receptor bound protein 14
Exposure
window
GD 12-19
GD 18 for 18 hr
GD 12-19
GD 19 for 6 hr
GD 18-19 for
18 hr
GD 19 for 3 hr
GD 12-19
GD 12-19
GD 12-19
GD 12-19
GD 19 for 3 hr
GD 19 for 6 hr
GD 12-19
GD 12-19
GD 19 for 6 hr
GD 18-19 for
18 hr
Up or
down
Down
Down after
18 hr
Down
Down after
6hr
Down after
18 hr
Down after
3hr
Down
Down
Down
Down
Up after
3hr
Up after
6hr
Down
Up
Up after
6hr
Up after
18 hr
Fold change
-0.48 Iog2
-0.65
-1.03 Iog2
-0.98
-0.83
-0.7
-0.30 Iog2
-0.32 Iog2
-0.36 Iog2
-0.20 Iog2
1.38
2.03
-0.42 Iog2
0.68 Iog2
1.78
0.93
Cutoff used (method)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
Reference
Liu et al., 2005
Thompson et al.,
2005
Liu et al., 2005
Thompson et al.,
2005
Thompson et al.,
2005
Thompson et al.,
2005
Liu et al., 2005
Liu et al., 2005
Liu et al., 2005
Liu et al., 2005
Thompson et al.,
2005
Thompson et al.,
2005
Liu et al., 2005
Liu et al., 2005
Thompson et al.,
2005
Thompson et al.,
2005
-------
Table A-l (continued)
S K
lis document is a draft j
voses only and does no
Asencv volicv
or review /
t constitute
^
Official
gene
symbol
Grina
Gsta2
Gsta2
Gsta3
GstaS
Gsta3
Gstm2
Gstm2
Gstm2
Gstol
Gstpl
Hao2
Hmgcr
Hmgcr
Hmgcsl
Official gene namef
Glutamate receptor, ionotropic, N-methyl
D-aspartate-associated protein 1 (glutamate
binding)
Glutathione-S-transferase, alpha type2
Glutathione-S-transferase, alpha type2
Glutathione S-transferase A3
Glutathione S-transferase A3
Glutathione S-transferase A3
Glutathione S-transferase, mu 2
Glutathione S-transferase, mu 2
Glutathione S-transferase, mu 2
Glutathione S-transferase omega 1
Glutathione-S-transferase, pi 1
Hydroxyacid oxidase 2 (long chain)
3-hydroxy-3-methylglutaryl-Coenzyme A reductase
3-hydroxy-3-methylglutaryl-Coenzyme A reductase
3-hydroxy-3-methylglutaryl-Coenzyme A
synthase 1
Exposure
window
GD 12.5-15.5
GD 12.5-17.5
GD 12.5-19.5
GD 12-19
GD 12.5-17.5
GD 12.5-19.5
GD 12-19
GD 12-21
GD 18-19 for
18 hr
GD 12-19
GD 12.5-15.5
GD 12-19
GD 12-19
GD 12.5-19.5
GD 12-19
Up or
down
Up
Down
Down
Down
Down
Down
Down
Up at
GD21
Down after
18 hr
Down
Up
Down
Down
Down
Down
Fold change
1.59
-1.48
-2.23
-0.96 Iog2
-1.75
-2.63
-0.42 Iog2
>2
-0.47
-0.42 Iog2
1.34
-0.58 Iog2
-0.47 Iog2
-1.83
-1.03 Iog2
Cutoff used (method)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
2-fold
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
Reference
Plummer et al.,
2007
Plummer et al.,
2007
Plummer et al.,
2007
Liu et al., 2005
Plummer et al.,
2007
Plummer et al.,
2007
Liu et al., 2005
Shultzetal, 2001
Thompson et al.,
2005
Liu et al., 2005
Plummer et al.,
2007
Liu et al., 2005
Liu et al., 2005
Plummer et al.,
2007
Liu et al., 2005
-------
Table A-l (continued)
S H
J*s >4
its document is a draft for review A-14
f oses only and does not constitute
Asencv volicv
Official
gene
symbol
Hmgcsl
Hmgcsl
Hmoxl
Hpgd
Hprt
Hrasls3
Hsdllb2
Hsdl7b3
Hsdl7b7
Hsd3bl_
predicted
Hsd3bl_
predicted
Hspb?
Idhl
Idhl
Mil
Idil
Official gene namef
3-hydroxy-3-methylglutaryl-Coenzyme A
synthase 1
3-hydroxy-3-methylglutaryl-Coenzyme A
synthase 1
Heme oxygenase (decycling) 1
Hydroxyprostaglandin dehydrogenase 15 (NAD)
Hypoxanthine guanine phosphoribosyl transferase
HRAS like suppressor 3
Hydroxy steroid (11-beta) dehydrogenase 2
Hydroxysteroid (17-beta) dehydrogenase 3
Hydroxysteroid (17-beta) dehydrogenase 7
Hydroxysteroid dehydrogenase- 1, delta< 5 >-3-beta
(predicted)
Hsd3bl_predicted hydroxy steroid dehydrogenase- 1,
delta< 5 >-3-beta (predicted)
Heat shock 27kD protein family, member 7
(cardiovascular)
Isocitrate dehydrogenase 1 (NADP+), soluble
Isocitrate dehydrogenase 1 (NADP+), soluble
Isopentenyl-diphosphate delta isomerase
Isopentenyl-diphosphate delta isomerase
Exposure
window
GD 12.5-17.5
GD 12.5-19.5
GD 12-19
GD 12-19
GD 12-19
GD 12-19
GD 19 for 6 hr
GD 12-19
GD 12-19
GD 12-19
GD 18 for 18 hr
GD 12-19
GD 12-19
GD 18 for 18 hr
GD 12-19
GD 12.5-17.5
Up or
down
Down
Down
Down
Down
Down at
GD 19
Down
Down after
6hr
Up
Down
Down
Down after
18 hr
Up
Down
Down after
18 hr
Down
Down
Fold change
-1.72
-1.87
-0.27 Iog2
-0.46 Iog2
->2
-0.45 Iog2
-1.16
0.28 Iog2
-0.32 Iog2
-0.50 Iog2
-0.7
0.41 Iog2
-0.52 Iog2
-0.67
-0.85 Iog2
-1.57
Cutoff used (method)
;?< 0.01 (ANOVA)
;?< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
2-fold
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
Reference
Plummer et al.,
2007
Plummer et al.,
2007
Liu et al., 2005
Liu et al., 2005
Shultzetal.,2001
Liu et al., 2005
Thompson et al.,
2005
Liu et al., 2005
Liu et al., 2005
Liu et al., 2005
Thompson et al.,
2005
Liu et al., 2005
Liu et al., 2005
Thompson et al.,
2005
Liu et al., 2005
Plummer et al.,
2007
-------
Table A-l (continued)
S K
lis document is a d
voses only and dot
Asencv vo
raft for review A-15
>,s not constitute
Hcv
Official
gene
symbol
Igfbp2
Igfbp3
Il6st
Ifitm2
Inha
Inha
Insigl
Insl3
interim
symbol:
Loc31432
3
interim
symbol:
Ratsg2
Kcnj'8
Khk
Kit
Krt2-8
Official gene namef
Insulin-like growth factor binding protein 2
Insulin-like growth factor binding protein 3
Interleukin 6 signal transducer
Interferon induced transmembrane protein 2
Inhibin alpha
Inhibin alpha
Insulin induced gene 1
Insulin-like 3
Interim full name: transporter
Interim name: Ratsg2
Potassium inwardly -rectifying channel, subfamily J,
member 8
Ketohexokinase
V-kit Hardy -Zuckerman 4 feline sarcoma viral
oncogene homolog
Keratin complex 2, basic, gene 8
Exposure
window
GD 12-19
GD 12-21
GD 12-21
GD 12.5-17.5
GD 12-19
GD 12.5-19.5
GD 12-19
GD 12-19
GD 12-19
GD 12-19
GD 12-21
GD 12.5-17.5
GD 12-21
GD 12-19
Up or
down
Down
Up at
GD21
Down at
GD21
Down
Down
Down
Down
Down
Down
Down
Down at
GD21
Up
Down at
GD21
Up
Fold change
-0.39 Iog2
>2
->2
-1.11
-1.00 Iog2
-1.64
-0.77 Iog2
-1.56 Iog2
-0.35 Iog2
-0.131og2
->2
1.30
->2
0.28 Iog2
Cutoff used (method)
p < 0.05 (ANOVA)
2-fold
2-fold
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
2-fold
p< 0.01 (ANOVA)
2-fold
p < 0.05 (ANOVA)
Reference
Liu et al., 2005
Shultzetal.,2001
Shultzetal.,2001
Plummer et al.,
2007
Liu et al., 2005
Plummer et al.,
2007
Liu et al., 2005
Liu et al., 2005
Liu et al., 2005
Liu et al., 2005
Shultzetal.,2001
Plummer et al.,
2007
Shultzetal.,2001
Liu et al., 2005
-------
^3
S K
iis document is a d
voses only and dot
Asencv vo
raft for review A-
>,s not constitute
Hcv
Official
gene
symbol
Ldha
Ldlr
Ldlr
Lhcgr
Lhcgr
Lhcgr
Lhcgr
Limkl
Lnk
Lr8
Lss
Mapkl
Marcks
Mdk
Mel
Official gene namef
Lactate dehydro-genase A
Low density lipoprotein receptor
Low density lipoprotein receptor
Luteinizing hormone/choriogonadotropin receptor
Luteinizing hormone/choriogonadotropin receptor
Luteinizing hormone/choriogonadotropin receptor
Luteinizing hormone/choriogonadotropin receptor
LIM motif -containing protein kinase 1
Linker of T-cell receptor pathways
LR8 protein
Lanosterol synthase
Mitogen activated protein kinase 1
Myristoylated alanine rich protein kinase C
substrate
Midkine
Malic enzyme 1, NADP(+)-dependent, cytosolic
Exposure
window
GD 12.5-19.5
GD 12-19
GD 12-19
GD 12-21
GD 19 for 6 hr
GD 18-19 for
18 hr
GD 12-19
GD 12-21
GD 19 for 3 hr
GD 12-19
GD 12-19
GD 12-21
GD 12-19
GD 12-19
GD 12-19
Up or
down
Down
Down
Down at
GD 19
Down at
GD21
Down after
6hr
Down after
18 hr
Down
Down at
GD21
Up after
3hr
Down
Down
Up at
GD21
Up at
GD19
Up
Down
Fold change
-1.30
-0.79 Iog2
->2
->2
-1.00
-1.51
-1.39 Iog2
->2
1.17
-0.45 Iog2
-0.48 Iog2
>2
>2
0.20 Iog2
-0.67 Iog2
Cutoff used (method)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
2-fold
2-fold
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
2-fold
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
2-fold
2-fold
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
Reference
Plummer et al.,
2007
Liu et al., 2005
Shultzetal., 2001
Shultzetal., 2001
Thompson etal.,
2005
Thompson et al.,
2005
Liu et al., 2005
Shultzetal., 2001
Thompson et al.,
2005
Liu et al., 2005
Liu et al., 2005
Shultzetal., 2001
Shultzetal., 2001
Liu et al., 2005
Liu et al., 2005
-------
^3
S K
iis document is a draft for review A-
V oses only and does not constitute
Asencv volicv
Official
gene
symbol
Mel
Menl
Mgatl
Mgp
Mgstl
Mgstl
Mirl6
Mlxipl
Mmp2
Mtusl
Mtusl
Mvd
Mydll6
Myh6
Myh6
Official gene namef
Malic enzyme 1, NADP(+)-dependent, cytosolic
Multiple endocrine neoplasia 1
Mannoside acetylglucosaminyltransferase 1
Matrix Gla protein
Microsomal glutathione S-transferase 1
Microsomal glutathione S-transferase 1
Membrane interacting protein of RGS16
MLX interacting protein-like
Matrix metallopeptidase 2
Mitochondria! tumor suppressor 1
Mitochondria! tumor suppressor 1
Mevalonate (diphospho) decarboxylase
Myeloid differentiation primary response gene 116
Myosin, heavy polypeptide 6, cardiac muscle, alpha
Myosin, heavy polypeptide 6, cardiac muscle, alpha
Exposure
window
GD 12.5-17.5
GD 12.5-15.5
GD 12-19
GD 19 for 6 hr
GD 12-19
GD 12-21
GD 12-19
GD 12-19
GD 12-21
GD 19 for 3 hr
GD 19 for 6 hr
GD 12-19
GD 19 for 3 hr
GD 12-19
GD 18-19 for
18 hr
Up or
down
Down
Down
Up
Up after
6hr
Down
Up at
GD21
Down
Down
Up at
GD21
Up after
3hr
Up after
6hr
Down
Up after
3hr
Down
Down after
18 hr
Fold change
-1.36
-1.17
0.28 Iog2
1.66
-0.36 Iog2
>2
-0.56 Iog2
-0.31 Iog2
>2
0.67
0.55
-0.41 Iog2
0.58
-0.72 Iog2
-1.52
Cutoff used (method)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
2-fold
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
2-fold
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
Reference
Plummer et al.,
2007
Plummer et al.,
2007
Liu et al., 2005
Thompson et al.,
2005
Liu et al., 2005
Shultzetal.,2001
Liu et al., 2005
Liu et al., 2005
Shultzetal.,2001
Thompson et al.,
2005
Thompson et al.,
2005
Liu et al., 2005
Thompson etal.,
2005
Liu et al., 2005
Thompson et al.,
2005
-------
^3
S K
iis document is a d
voses only and dot
Asencv vo
raft for review A
>,s not constitute
Hcv
Official
gene
symbol
Myh6
Myom2
Myrip
Nalp6
Nexn
Nfl
Nfil3
Nfkbia
Npc2
Nppc
NrObl
NrObl
Nr4al
Nr4al
Nr4a3
NrSal
Official gene namef
Myosin, heavy polypeptide 6, cardiac muscle, alpha
Myomesin 2
Myosin VIIA and Rab interacting protein
NACHT, leucine rich repeat and PYD containing 6
Nexilin
Neurofibromatosis 1
Nuclear factor, interleukin 3 regulated
Nuclear factor of kappa light chain gene enhancer
in B -cells inhibitor, alpha
Niemann pick type C2
Natriuretic peptide precursor type C
Nuclear receptor subfamily 0, group B, member 1
Nuclear receptor subfamily 0, group B, member 1
Nuclear receptor subfamily 4, group A, member 1
Nuclear receptor subfamily 4, group A, member 1
Nuclear receptor subfamily 4, group A, member 3
Nr5al nuclear receptor subfamily 5, group A,
member 1
Exposure
window
GD 12.5-19.5
GD 12-19
GD 12-19
GD 12-19
GD 12-19
GD 12-21
GD 12-19
GD 19 for 3 hr
GD 12-19
GD 12-19
GD 12-19
GD 12.5-19.5
GD 12-19
GD 19 for 3 hr
GD 19 for 3 hr
GD 12.5-19.5
Up or
down
Down
Up
Down
Up
Up
Down at
GD21
Up
Up after
3hr
Down
Down
Down
Down
Up
Up after
3hr
Up after
3hr
Down
Fold change
-1.64
0.64 Iog2
-0.27 Iog2
0.45 Iog2
0.26 Iog2
->2
0.311og2
0.79
-0.26 Iog2
-0.56 Iog2
-0.37 Iog2
-1.15
0.3 Iog2
1.83
2.25
-1.18
Cutoff used (method)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
2-fold
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
Reference
Plummer et al.,
2007
Liu et al., 2005
Liu et al., 2005
Liu et al., 2005
Liu et al., 2005
Shultzetal, 2001
Liu et al., 2005
Thompson et al.,
2005
Liu et al., 2005
Liu et al., 2005
Liu et al., 2005
Plummer et al.,
2007
Liu et al., 2005
Thompson et al.,
2005
Thompson et al.,
2005
Plummer et al.,
2007
oo
-------
^3
S K
iis document is a d
voses only and dot
Asencv vo
raft for review A-
>,s not constitute
Hcv
Official
gene
symbol
Nt/3
Okl38
Olfml
P2ryl4
Park?
Pawr
Pcna
Pcyt2
Pdapl
Pdyn
Pebpl
Pebpl
Penkl
Penkl
Pjkp
Official gene namef
Neurotrophin 3
Pregnancy -induced growth inhibitor
Olfactomedin 1
Purinergic receptor P2Y, G-protein coupled, 14
Parkinson disease (autosomal recessive, early
onset) 7
PRKC, apoptosis, WT1, regulator
Proliferating cell nuclear antigen
Phosphate cytidylyltransferase 2, ethanolamine
PDGFA associated protein 1
Prodynorphin
Phosphatidylethanolamine binding protein 1
Phosphatidylethanolamine binding protein 1
Proenkephalin 1
Proenkephalin 1
Phosphofructokinase, platelet
Exposure
window
GD 12.5-17.5
GD 12-19
GD 12-19
GD 12-19
GD 12.5-17.5
GD 19 for 3 hr
GD 12-21
GD 12-19
GD 12-21
GD 12-19
GD 12-19
GD 12.5-19.5
GD 12.5-17.5
GD 12.5-19.5
GD 12.5-19.5
Up or
down
Up
Down
Down
Down
Down
Up after
3hr
Up at
GD21
Down
Up at
GD21
Down
Down
Down
Down
Down
Down
Fold change
1.34
-0.33 Iog2
-0.141og2
-0.37 Iog2
-1.32
1.02
>2
-0.20 Iog2
>2
-1.06 Iog2
-0.36 Iog2
-1.67
-1.41
-1.86
-1.41
Cutoff used (method)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
2-fold
p < 0.05 (ANOVA)
2-fold
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
Reference
Plummer et al.,
2007
Liu et al., 2005
Liu et al., 2005
Liu et al., 2005
Plummer et al.,
2007
Thompson et al.,
2005
Shultzetal.,2001
Liu et al., 2005
Shultzetal.,2001
Liu et al., 2005
Liu et al., 2005
Plummer et al.,
2007
Plummer et al.,
2007
Plummer et al.,
2007
Plummer et al.,
2007
-------
Table A-l (continued)
S K
lis document is a draft for review A-20
V oses only and does not constitute
Asencv volicv
Official
gene
symbol
Pgaml
Pgkl
Phb
Phb
Phyh
Plat
Plaur
Pmp22
Pmp22
Pmp22
Pnliprp2
For
For
Ppib
Official gene namef
Phosphoglycerate mutase 1
Phosphoglycerate kinase 1
Prohibitin
Prohibitin
Phytanoyl-CoA hydroxylase
Plasminogen activator, tissue
Plasminogen activator, urokinase receptor
Peripheral myelin protein 22
Peripheral myelin protein 22
Peripheral myelin protein 22
Pancreatic lipase-related protein 2
P450 (cytochrome) oxidoreductase
P450 (cytochrome) oxidoreductase
Peptidylprolyl isomerase B
Exposure
window
GD 12.5-19.5
GD 12.5-19.5
GD 12-21
GD 12-19
GD 19 for 6 hr
GD 12-19
GD 19 for 3 hr
GD 12-19
GD 19 for 3 hr
GD 19 for 6 hr
GD 12-19
GD 12-19
GD 12.5-19.5
GD 12.5-17.5
Up or
down
Down
Down
Down at
GD21
Down at
GD 19
Down after
6hr
Up at
GD 19
Up after
3hr
Up at
GD 19
Down after
3hr
Down after
6hr
Down
Down
Down
Down
Fold change
-1.26
-1.25
->2
->2
-1.02
>2
0.86
>2
-0.75
-0.59
-0.28 Iog2
-0.64 Iog2
-1.39
-1.21
Cutoff used (method)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
2-fold
2-fold
p < 0.05 (ANOVA)
2-fold
p < 0.05 (ANOVA)
2-fold
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
Reference
Plummer et al.,
2007
Plummer et al.,
2007
Shultzetal., 2001
Shultzetal., 2001
Thompson et al.,
2005
Shultzetal., 2001
Thompson et al.,
2005
Shultzetal., 2001
Thompson et al.,
2005
Thompson et al.,
2005
Liu et al., 2005
Liu et al., 2005
Plummer et al.,
2007
Plummer et al.,
2007
-------
Table A-l (continued)
S K
lis document is a d
voses only and dot
Asencv vo
raft for review A-2
>,s not constitute
Hcv
Official
gene
symbol
Ppplcb
Prdx3
Prdx3
Prdx3
Prgl
Prkar2b
Prkcbpl
Prlr
Ptma
Ptp4al
PVR
PVR
Rabep2
Rasdl
Rlnl
Official gene namef
Protein phosphatase 1, catalytic subunit,
beta isoform
Peroxiredoxin 3
Peroxiredoxin 3
Peroxiredoxin 3
Plasticity related gene 1
Protein kinase, cAMP dependent regulatory,
type II beta
Protein kinase C binding protein 1
Prolactin receptor
Prothymosin alpha
Protein tyrosine phosphatase 4al
Poliovirus receptor
Poliovirus receptor
Rabaptin, RAB GTPase binding effector protein 2
RAS, dexamethasone-induced 1
Relaxin 1
Exposure
window
GD 12.5-17.5
GD 12-19
GD 18-19 for
18 hr
GD 12.5-19.5
GD 12-19
GD 12-19
GD 12-19
GD 12-19
GD 12-19
GD 12-21
GD 19 for 3 hr
GD 19 for 6 hr
GD 19 for 3 hr
GD 12-19
GD 12-19
Up or
down
Down
Down
Down after
18 hr
Down
Down
Down
Up
Down
Down at
GD 19
Up at
GD21
Up after
3hr
Up after
6hr
Down after
3hr
Down
Down
Fold change
-1.37
-0.53 Iog2
-0.86
-1.63
-0.97 Iog2
-0.33 Iog2
0.32 Iog2
-1.02 Iog2
->2
>2
1.26
0.92
-0.48
-0.52 Iog2
-0.36 Iog2
Cutoff used (method)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
2-fold
2-fold
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
Reference
Plummer et al.,
2007
Liu et al., 2005
Thompson et al.,
2005
Plummer et al.,
2007
Liu et al., 2005
Liu et al., 2005
Liu et al., 2005
Liu et al., 2005
Shultzetal., 2001
Shultzetal., 2001
Thompson et al.,
2005
Thompson et al.,
2005
Thompson et al.,
2005
Liu et al., 2005
Liu et al., 2005
-------
Table A-l (continued)
S K
lis document is a draft for review A
V oses only and does not constitute
Asencv volicv
Official
gene
symbol
Rnhl
Rpa2
Rpll3
Rpl32
Rpl37
Rpl36a
Rpl36a
Rpn2
Rpsl3
Rpsl7
Rpsl9
Rps29
Sc4mol
Official gene namef
Ribonuclease/angiogenin inhibitor 1
Replication protein A2
Ribosomal protein L 13
Ribosomal protein L32
Ribosomal protein L37
Large subunit ribosomal protein L36a
Large subunit ribosomal protein L36a
Ribophorin II
Ribosomal protein S13
Ribosomal protein S17
Ribosomal protein S19
Ribosomal protein S29
Sterol-C4-methyl oxidase-like
Exposure
window
GD 12.5-17.5
GD 12-21
GD 12.5-15.5
GD 12.5-19.5
GD 12.5-19.5
GD 12-19
GD 12.5-15.5
GD 12.5-19.5
GD 12.5-15.5
GD 12.5-19.5
GD 12.5-17.5
GD 12.5-19.5
GD 12-19
Up or
down
Down
Down at
GD21
Up
Up
Up
Down at
GD 19
Up
Down
Up
Up
Up
Down
Down
Fold change
-1.20
->2
1.17
1.13
1.13
->2
1.22
-1.19
1.30
1.25
1.25
-1.13
-1.02 Iog2
Cutoff used (method)
^<0.01(ANOVA)
2-fold
^<0.01(ANOVA)
^<0.01(ANOVA)
^<0.01(ANOVA)
2-fold
^<0.01(ANOVA)
^<0.01(ANOVA)
^<0.01(ANOVA)
^<0.01(ANOVA)
^<0.01(ANOVA)
^<0.01(ANOVA)
p < 0.05 (ANOVA)
Reference
Plummer et al.,
2007
Shultzetal., 2001
Plummer et al.,
2007
Plummer et al.,
2007
Plummer et al.,
2007
Shultzetal., 2001
Plummer et al.,
2007
Plummer et al.,
2007
Plummer et al.,
2007
Plummer et al.,
2007
Plummer et al.,
2007
Plummer et al.,
2007
Liu et al., 2005
to
to
-------
Table A-l (continued)
S K
lis document is a draft for review /
V oses only and does not constitute
Asencv volicv
^
to
Official
gene
symbol
Sc4mol
Sc4mol
ScSd
Scarbl
Scarbl
Scarbl
Scarbl
Scarbl
Scarbl
Scdl
Scn3b
Scp2
Scp2
Sdf4
Seppl
Official gene namef
Sterol-C4-methyl oxidase-like
Sterol-C4-methyl oxidase-like
Sterol-C5-desaturase (fungal ERGS,
delta-5-desaturase) homolog (S. cerevisae)
Scavenger receptor class B, member 1
Scavenger receptor class B, member 1
Scavenger receptor class B, member 1
Scavenger receptor class B, member 1
Scavenger receptor class B, member 1
Scavenger receptor class B, member 1
Stearoyl-Coenzyme A desaturase 1
Sodium channel, voltage-gated, type III, beta
Sterol carrier protein 2
Sterol carrier protein 2
Stromal cell derived factor 4
Selenoprotein P, plasma, 1
Exposure
window
GD 12.5-17.5
GD 12.5-19.5
GD 12-19
GD 12-19
GD 12-19
GD 19 for 6 hr
GD 18-19 for
18 hr
GD 12.5-17.5
GD 12.5-19.5
GD 12-19
GD 19 for 6 hr
GD 12-19
GD 12.5-19.5
GD 12-19
GD 12-19
Up or
down
Down
Down
Down
Down
Down at
GD 19
Down after
6hr
Down after
18 hr
Down
Down
Down
Up after
6hr
Down
Down
Down
Down
Fold change
-1.82
-2.36
-0.32 Iog2
-1.91 Iog2
->2
-1.60
-2.72
-2.23
-2.85
-0.58 Iog2
1.49
-0.171og2
-1.24
-0.27 Iog2
-0.45 Iog2
Cutoff used (method)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
2-fold
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
Reference
Plummer et al.,
2007
Plummer et al.,
2007
Liu et al., 2005
Liu et al., 2005
Shultzetal.,2001
Thompson et al.,
2005
Thompson et al.,
2005
Plummer et al.,
2007
Plummer et al.,
2007
Liu et al., 2005
Thompson et al.,
2005
Liu et al., 2005
Plummer et al.,
2007
Liu et al., 2005
Liu et al., 2005
-------
^3
S K
iis document is a draft for review A-24
voses only and does not constitute
Asencv volicv
Official
gene
symbol
Serpinel
Serpinhl
Sgk
Slc3a2
Slcl2a2
Slcl6a6
Slc25al
Slc25a20
Slc7a8
Slc7a8
Smpx
Sod2
Sod3
Sqle
Sqle
Ssr4
Official gene namef
Serine (or cysteine) peptidase inhibitor, clade E,
member 1
Serine (or cysteine) peptidase inhibitor, clade H,
member 1
Serum/glucocorticoid regulated kinase
Solute carrier family 3 (activators of dibasic and
neutral amino acid transport), member 2
Solute carrier family 12 (sodium/potassium/chloride
transporters), member 2
Solute carrier family 16 (monocarboxylic acid
transporters), member 6
Solute carrier family 25, member 1
Solute carrier family 25 (mitochondrial
carnitine/acylcarnitine translocase), member 20
Solute carrier family 7 (cationic amino acid
transporter, y+ system), member 8
Solute carrier family 7 (cationic amino acid
transporter, y+ system), member 8
Small muscle protein, X-linked
Superoxide dismutase 2, mitochondrial
Superoxide dismutase 3, extracellular
Squalene epoxidase
Squalene epoxidase
Signal sequence receptor 4
Exposure
window
GD19for3hr
GD 12.5-15.5
GD 12-19
GD 12-19
GD 12.5-17.5
GD 12-19
GD 12-19
GD 12-19
GD 12.5-17.5
GD 12.5-19.5
GD 12-19
GD 12-19
GD 12-19
GD 12-19
GD 18 for 18 hr
GD 12-19
Up or
down
Up after
3hr
Down
Down
Down
Down
Down
Down
Down
Down
Down
Up
Down
Down
Down
Down after
18 hr
Down
Fold change
1.56
-1.32
-0.45 Iog2
-0.48 Iog2
-1.39
-0.38 Iog2
-0.27 Iog2
-0.23 Iog2
-1.82
-2.18
0.21 Iog2
-0.51 Iog2
-0.33 Iog2
-0.59 Iog2
-1.26
-0.23 Iog2
Cutoff used (method)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
Reference
Thompson et al.,
2005
Plummer et al.,
2007
Liu et al., 2005
Liu et al., 2005
Plummer et al.,
2007
Liu et al., 2005
Liu et al., 2005
Liu et al., 2005
Plummer et al.,
2007
Plummer et al.,
2007
Liu et al., 2005
Liu et al., 2005
Liu et al., 2005
Liu et al., 2005
Thompson et al.,
2005
Liu et al., 2005
-------
Table A-l (continued)
S K
lis document is a d
voses only and dot
Asencv vo
raft for review /
>,s not constitute
Hcv
^
to
Official
gene
symbol
Ssrpl
Star
Star
Star
Star
Stcl
Stcl
Stc2
Stc2
Sts
Suclgl
Svs5
Svs5
Svs5
Svs5
Official gene namef
Structure specific recognition protein 1
Steroidogenic acute regulatory protein
Steroidogenic acute regulatory protein
Steroidogenic acute regulatory protein
Steroidogenic acute regulatory protein
Stanniocalcin 1
Stanniocalcin 1
Stanniocalcin 2
Stanniocalcin 2
Steroid sulfatase
Succinate-CoA ligase, GDP-forming, alpha subunit
Seminal vesicle secretion 5
Seminal vesicle secretion 5
Seminal vesicle secretion 5
Seminal vesicle secretion 5
Exposure
window
GD 12-19
GD 12-19
GD 18-19 for
18 hr
GD 12.5-17.5
GD 12.5-19.5
GD 12-19
GD 19 for 6 hr
GD 12-19
GD 12.5-19.5
GD 12-19
GD 12.5-19.5
GD 12-19
GD 18-19 for
18 hr
GD 12.5-17.5
GD 12.5-19.5
Up or
down
Down at
GD19
Down
Down after
18 hr
Down
Down
Up
Up after
6hr
Down
Down
Down at
GD 19
Down
Down
Down after
18 hr
Down
Down
Fold change
->2
-2.45 Iog2
-2.33
-2.19
-2.53
0.98 Iog2
1.61
-1.181og2
-1.59
->2
-1.21
-3.75 Iog2
-3.36
-5.89
-3.75
Cutoff used (method)
2-fold
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
2-fold
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
Reference
Shultzetal.,2001
Liu et al., 2005
Thompson et al.,
2005
Plummer et al.,
2007
Plummer et al.,
2007
Liu et al., 2005
Thompson et al.,
2005
Liu et al., 2005
Plummer et al.,
2007
Shultzetal.,2001
Plummer et al.,
2007
Liu et al., 2005
Thompson et al.,
2005
Plummer et al.,
2007
Plummer et al.,
2007
-------
Table A-l (continued)
S H
J*s >4
its document is a draft for review A-2
f oses only and does not constitute
Asencv volicv
Official
gene
symbol
Syngrl
Tcfl
Tc/21
Tec
Testin
Tfrc
TgfbS
Timpl
Timp3
Tkt
Tkt
TmedlO
Tnfrsfl2a
Tnnil
TnniS
Tnnt2
Official gene namef
Synaptogyrin 1
Transcription factor 1
Transcription factor 2 1
Tec protein tyrosine kinase
Testin gene
Transferrin receptor
Transforming growth factor, beta 3
Tissue inhibitor of metallopeptidase 1
Tissue inhibitor of metalloproteinase 3 (Sorsby
fundus dystrophy, pseudoinflammatory)
Transketolase
Transketolase
Transmembrane emp24-like trafficking
protein 10 (yeast)
Tumor necrosis factor receptor superfamily,
member 12a
Troponin I, skeletal, slow 1
Troponin I type 3 (cardiac)
Troponin T2, cardiac
Exposure
window
GD 12-19
GD 12-19
GD 12-19
GD 19 for 3 hr
GD 12-19
GD 12-19
GD 12-19
GD 19 for 6 hr
GD 12-21
GD 12.5-17.5
GD 12.5-19.5
GD 12.5-19.5
GD 19 for 6 hr
GD 12-19
GD 12-19
GD 12-19
Up or
down
Down
Down
Up
Up after
3hr
Up
Down
Down at
GD 19
Up after
6hr
Down at
GD21
Down
Down
Down
Up after
6hr
Up
Up
Up
Fold change
-0.161og2
-0.141og2
0.171og2
0.69
0.59 Iog2
-0.23 Iog2
->2
1.04
->2
-1.19
-1.28
-1.20
1.34
0.33 Iog2
0.26 Iog2
0.77 Iog2
Cutoff used (method)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
2-fold
p < 0.05 (ANOVA)
2-fold
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
Reference
Liu et al., 2005
Liu et al., 2005
Liu et al., 2005
Thompson et al.,
2005
Liu et al., 2005
Liu et al., 2005
Shultzetal.,2001
Thompson et al.,
2005
Shultzetal.,2001
Plummer et al.,
2007
Plummer et al.,
2007
Plummer et al.,
2007
Thompson et al.,
2005
Liu et al., 2005
Liu et al., 2005
Liu et al., 2005
-------
Table A-l (continued)
S K
lis document is a draft for review /
V oses only and does not constitute
Asencv volicv
^
to
Official
gene
symbol
Tpil
Tpml
Tpml
Tsc22dl
Tsn
Tst
Tubal
Tubal
Txnl
Txnll
Uba52
UncSb
Vapa
Vcaml
Vdacl
Official gene namef
Triosephosphate isomerase 1
Tropomyosin 1, alpha
Tropomyosin 1, alpha
TSC22 domain family, member 1
Translin
Thiosulfate sulfurtransferase
Tubulin, alpha 1
Tubulin, alpha 1
Thioredoxin 1
Thioredoxin-like 1
Ubiquitin A-52 residue ribosomal protein fusion
product 1
Unc-5 homolog B (C. elegans)
VAMP (vesicle-associated membrane protein)-
associated protein A
Vascular cell adhesion molecule 1
Voltage-dependent anion channel 1
Exposure
window
GD 12-19
GD 12-19
GD 19 for 6 hr
GD 12.5-19.5
GD 12.5-17.5
GD 12-19
GD 12-21
GD 12.5-19.5
GD 18 for 18 hr
GD 12.5-15.5
GD 12.5-19.5
GD 12-21
GD 12.5-19.5
GD 12-19
GD 12.5-19.5
Up or
down
Down
Up
Up after
6hr
Down
Up
Down
Down at
GD21
Down
Down after
18 hr
Up
Up
Down at
GD21
Down
Down
Down
Fold change
-0.24 Iog2
0.36 Iog2
1.04
-1.34
1.54
-0.33 Iog2
->2
-1.26
-0.62
1.20
1.10
->2
-1.37
-0.63 Iog2
-1.13
Cutoff used (method)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
2-fold
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
2-fold
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
Reference
Liu et al., 2005
Liu et al., 2005
Thompson et al.,
2005
Plummer et al.,
2007
Plummer et al.,
2007
Liu et al., 2005
Shultzetal., 2001
Plummer et al.,
2007
Thompson et al.,
2005
Plummer et al.,
2007
Plummer et al.,
2007
Shultzetal., 2001
Plummer et al.,
2007
Liu et al., 2005
Plummer et al.,
2007
-------
Table A-l (continued)
S K
lis document is a d
voses only and dot
Asencv vo
raft for review
>,s not constitute
Hcv
Official
gene
symbol
Vim
Vnnll
Vsnll
Ywhae
Zfp36
Zyx
Not found
Not found
Not found
Not found
Official gene namef
Vimentin
Vanin 1
visinin-like 1
Tyrosine 3-monooxygenase/tryptophan
5-monooxygenase activation protein, epsilon
polypeptide
Zinc finger protein 36
Zyxin
Listed as "Tppc" and 289920_Rn in article, and
Genbank #BF400584 (Plummer, personal
communication) does not match a gene name.
Listed as "Similar to mouse IAP -binding protein"
and 2055 10 Rn in article, and Genbank
#:BG378907 (Plummer, personal communication)
does not match a gene name.
LOC499942 similar to WAP four-disulfide core
domain protein 8 precursor (Putative protease
inhibitor WAP8) (Rattus norvegicus).
LOC497726 hypothetical gene supported by
NM 138518 (Rattus norvegicus). This record was
discontinued.
Exposure
window
GD 12.5-19.5
GD 12-19
GD 12-19
GD 12.5-19.5
GD 19 for 1 hr
GD 19 for 3 hr
GD 12.5-17.5
GD 12.5-15.5
GD 12-19
GD 12-19
Up or
down
Down
Down
Down
Down
Up after
Ihr
Up after
3hr
Down
Up
Down
Down
Fold change
-1.60
-0.32 Iog2
-0.62 Iog2
-1.37
1.79
1.03
-1.39
1.26
-0.25 Iog2
-0.27 Iog2
Cutoff used (method)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
p< 0.01 (ANOVA)
p< 0.01 (ANOVA)
p < 0.05 (ANOVA)
p < 0.05 (ANOVA)
Reference
Plummer et al.,
2007
Liu et al., 2005
Liu et al., 2005
Plummer et al.,
2007
Thompson et al.,
2005
Thompson et al.,
2005
Plummer et al.,
2007
Plummer et al.,
2007
Liu et al., 2005
Liu et al., 2005
aThe four studies dosed at 500 mg/kg-d DBF in the Sprague-Dawley (SD) rat.
7 bThompson et al. (2005) and Shultz et al. (2001) dosed with DBF alone; gene expression changes for DBF were relative to vehicle control expression.
to
oo
-------
°Liu et al. (2005) presented microarray data for all five developmental phthalates, including DBF, 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 DBF.
dThe 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
o <•"' amino acid transporter, y+ system], member 8) at the time of their publication. As of January 2007, Affy now lists both Slc7a8 and Syngapl. 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
o 58 DBF in utero exposure.
"*^ "^
^ <> jf eThe 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
TO a js 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.
<"i IsL £3' fGene function and pathway information was gathered from GeneGo (www.genego.com).
g^ TO ^- ANOVA, analysis of variance; GD, gestation day; hr, hour.
o ^
^ c^
rS"
K)
VO
-------
Table A-2. WOE for statistically significant gene expression changes after in utero exposure to DBF from
whole-rat testis reverse transcription-polymerase chain reaction (RT-PCR) studies
o <*>'
U
O 58
S a
1
§ 3.
Official
gene
symbol
Ar
Bmp4
Btg2
Bzrp
Cebpb
Cebpd
Clu
Clu
Clu
Cxcll
Official gene name*
Androgen receptor
Bone morphogenetic protein 4
B-cell translocation gene 2,
anti-proliferative
Benzodiazepine receptor,
peripheral
CCAAT/enhancer binding
protein (C/EBP), beta
CCAAT/enhancer binding
protein (C/EBP), delta
Clusterin
Clusterin
Clusterin
Chemokine (C-X-C motif)
ligand 1
Dose
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
Exposure window
GD 12-19
GD 12-19
GD 19for30minto6hr
timepoints and GD 18 for
12, 18, and 24 hr time
points
GD 12-19
GD 12-19
GD 19for30minto6hr
timepoints and GD 18 for
12, 18, and 24 hr time
points
GD 12-19
GD 12-19
GD 12-16, 12-19, or
12-21
GD 19for30minto6hr
timepoints and GD 18 for
12, 18, and 24 hr time
points
Up or down
Up
Up
Up after
-1-6 hr
(peak ~2 hr)
Up
Down
Up after
-1-6 hr
(peak ~3 hr)
Up
Up
Up
Up after
-1-12 hr
(peak at
~3hr)
Statistical analysis method
t-test,/?<0.05
t-test,/?<0.05
Relative expression determined
using mean Ct; triplicate
samples; GADPH control; SE;
but/) value not calculated
Dunnett's test, ANOVA (one
way), p< 0.05
One way and two-way nested
ANOVA;^<0.05
Relative expression determined
using mean Ct; triplicate
samples; GADPH control; SE;
but/) value not calculated
ANOVA, nested design,
^<0.05
Dunnett's test, ANOVA (one
way), p< 0.05
^<0.05
Relative expression determined
using mean Ct; triplicate
samples; GADPH control; SE;
but/) value not calculated
Reference
Bowman et
al., 2005
Bowman et
al., 2005
Thompson
et al., 2005
Lehmann et
al., 2004
Liu et al.,
2005
Thompson
et al., 2005
Barlow et
al., 2003
Lehmann et
al., 2004
Shultz et
al., 2001
Thompson
et al., 2005
s
rS"
-------
Table \-2. (continued)
o
o
S
§
Official
gene symbol
Cypllal
Cypllal
Cypllal
Cypllal
Cypllal
Cypllal
Cypllal
Cypl7al
Cypl7al
Official gene name*
Cytochrome P450,
family 11, subfamily a,
polypeptide 1
Cytochrome P450,
family 11, subfamily a,
polypeptide 1
Cytochrome P450,
family 11, subfamily a,
polypeptide 1
Cytochrome P450,
family 11, subfamily a,
polypeptide 1
Cytochrome P450,
family 11, subfamily a,
polypeptide 1
Cytochrome P450,
family 11, subfamily a,
polypeptide 1
Cytochrome P450,
family 11, subfamily a,
polypeptide 1
Cytochrome P450,
family 17, subfamily a,
polypeptide 1
Cytochrome P450,
family 17, subfamily a,
polypeptide 1
Dose
500 mg/kg-d
50 mg/kg-d
100 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
Exposure window
GD 12-19
GD 12-19
GD 12-19
GD 12-19
GD 12-16, 12-19, or
12-21
GD 12-11 and 12-18
GD 12.5-19.5
GD 12-19
GD 12-19
Up or down
Down
Down
Down
Down
Down
Down at
GD 18
Down
Down
Down
Statistical analysis method
ANOVA, nested design,
^<0.05
Dunnett's test, ANOVA (one
way), p< 0.05
Dunnett's test, ANOVA (one
way), p< 0.05
Dunnett's test, ANOVA (one
way), p< 0.05
^<0.05
t-test, ANOVA (one-way) with
Tukey post hoc analysis;
^<0.05
One-way ANOVA followed by
Bonferroni post test using
GraphPad Prism; p < 0.05
Repeated measure ANOVA,
nested design, p < 0.05
Dunnett's test, ANOVA (one
way), p< 0.05
Reference
Barlow et
al, 2003
Lehmann et
al., 2004
Lehmann et
al., 2004
Lehmann et
al., 2004
Shultz et
al., 2001
Thompson
et al., 2004
Plummer et
al., 2007
Barlow et
al., 2003
Lehmann et
al., 2004
s
rS"
>
-------
Table A-2. (continued)
Official
gene symbol
Cypl7al
Cypl7al
Dafl
Ddc
Dusp6
Edg3
Egfr
Egrl
Egrl
Official gene name*
Cytochrome P450,
family 17, subfamily a,
polypeptide 1
Cytochrome P450,
family 17, subfamily a,
polypeptide 1
Decay accelerating factor 1
Dopa decarboxylase
Dual specificity
phosphatase 6
Endothelial differentiation
sphingolipid
G-protein-coupled
receptor 3
Epidermal growth factor
receptor
Early growth response 1
Early growth response 1
Dose
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
Exposure window
GD 12-16, 12-19, or
12-21
GD 12-17 and 12-18
GD 12-19
GD 12-19
GD 19for30minto6hr
timepoints and GD 18 for
12, 18, and 24 hr time
points
GD 19for30minto6hr
timepoints and GD 18 for
12, 18, and 24 hr time
points
GD 12-19 and 12-21
GD 19for30minto6hr
timepoints and GD 18 for
12, 18, and 24 hr time
points
GD 12-19
Up or down
Down at
GD 19
Down at
GD 17 and
18
Up
Down
Up after
-1-12 hr
(peak at ~3
hr)
Up after
-1-6 and
18 hr (peak
~3hr)
Un-changed
Up after
-1-7 hr
(peak -2 hr)
Up
Statistical analysis method
^<0.05
t-test, ANOVA (one-way) with
Tukey post hoc analysis;
^<0.05
One way and two-way nested
ANOVA,^<0.05
One way and two-way nested
ANOVA,^<0.05
Relative expression determined
using mean Ct; triplicate
samples; GADPH control; SE;
but/) value not calculated
Relative expression determined
using mean Ct; triplicate
samples; GADPH control; SE;
but/) value not calculated
t-test, p< 0.05
Relative expression determined
using mean Ct; triplicate
samples; GADPH control; SE;
but/) value not calculated
One way and two-way nested
ANOVA;^<0.05
Reference
Shultz et
al., 2001
Thompson
et al., 2004
Liu et al.,
2005
Liu et al.,
2005
Thompson
et al., 2005
Thompson
et al., 2005
Bowman et
al., 2005
Thompson
et al., 2005
Liu et al.,
2005
o <*>'
U
O 58
S § .
g.S3-
s
rS"
to
-------
Table A-2. (continued)
Official
gene symbol
Egr2
FgflO
Fgfr2
Fos
Grbl4
Hes6
Hsdl7b3
Hsdl7b7
Hsd3bl_
predicted
Official gene name*
Early growth response 1
Fibroblast growth factor 10
Fibroblast growth factor
receptor 1
FBJ murine osteosarcoma
viral oncogene homolog
Growth factor receptor
bound protein 14
Hairy and enhancer of
split 6 (Drosophila)
Hydroxy steroid (17-beta)
dehydrogenase 3
Hydroxy steroid (17-beta)
dehydrogenase 7
Hydroxy steroid
dehydrogenase-1,
delta< 5 >-3-beta
(predicted)
Dose
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
Exposure window
GD 19for30minto6hr
timepoints and GD 18 for
12, 18, and 24 hr time
points
GD 12-21
GD 12-19 and 12-21
GD 19for30minto6hr
timepoints and GD 18 for
12, 18, and 24 hr time
points
GD 12-19
GD 19for30minto6hr
timepoints and GD 18 for
12, 18, and 24 hr time
points
GD 12-19
GD 12-19
GD 12-19
Up or down
Up after
-1-12 hr
(peak ~2 hr)
Up
No stat.
change
Up after
30 min and
6 hr (peak at
Ihr)
Up
Down after
1-3 hr (peak
at3hr)
Up
Down
Down
Statistical analysis method
Relative expression determined
using mean Ct; triplicate
samples; GADPH control; SE;
but/) value not calculated
t-test,/?<0.05
t-test,/?<0.05
Relative expression determined
using mean Ct; triplicate
samples; GADPH control; SE;
but/) value not calculated
One way and two-way nested
ANOVA,^<0.05
Relative expression determined
using mean Ct; triplicate
samples; GADPH control; SE;
but/) value not calculated
One way and two-way nested
ANOVA,^<0.05
One way and two-way nested
ANOVA,^<0.05
ANOVA, nested design,
^<0.05
Reference
Thompson
et al., 2005
Bowman et
al., 2005
Bowman et
al., 2005
Thompson
et al., 2005
Liu et al.,
2005
Thompson
et al., 2005
Liu et al.,
2005
Liu et al.,
2005
Barlow et
al., 2003
o <*>'
U
O 58
S § .
g.S3-
s
rS"
-------
Table A-2. (continued)
Official
gene symbol
Hsd3bl_
predicted
Hsd3bl_
predicted
Hsd3bl_
predicted
Hsd3bl_
predicted
Official gene name*
Hsd3b l_predicted
hydroxy steroid
dehydrogenase-1,
delta< 5 >-3-beta
(predicted) orHsdSbl
hy droxy-delta-5 -steroid
dehydrogenase, 3 beta- and
steroid delta-isomerase 1
Hsd3b l_predicted
hydroxy steroid
dehydrogenase-1,
delta< 5 >-3-beta
(predicted) orHsdSbl
hy droxy-delta-5 -steroid
dehydrogenase, 3 beta- and
steroid delta-isomerase 1
Hsd3b l_predicted
hydroxy steroid
dehydrogenase-1,
delta< 5 >-3-beta
(predicted) orHsdSbl
hy droxy-delta-5 -steroid
dehydrogenase, 3 beta- and
steroid delta-isomerase 1
Hsd3b l_predicted
hydroxy steroid
dehydrogenase-1,
delta< 5 >-3-beta
(predicted) orHsdSbl
hy droxy-delta-5 -steroid
dehydrogenase, 3 beta- and
steroid delta-isomerase 1
Dose
0.1 mg/kg-d
1 mg/kg-d
10 mg/kg-d
50 mg/kg-d
Exposure window
GD 12-19
GD 12-19
GD 12-19
GD 12-19
Up or down
Down
Down
Down
Down
Statistical analysis method
Dunnett's test, ANOVA (one
way), p< 0.05
Dunnett's test, ANOVA (one
way), p< 0.05
Dunnett's test, ANOVA (one
way), p< 0.05
Dunnett's test, ANOVA (one
way), p< 0.05
Reference
Lehmann et
al., 2004
Lehmann et
al., 2004
Lehmann et
al., 2004
Lehmann et
al., 2004
o <*>'
U
O 58
S § .
g.S3-
s
rS"
-------
Table A-2. (continued)
Official
gene symbol
Hsd3bl_
predicted
Hsd3bl_
predicted
Ier3
Ifrdl
Igfl
Igfl
Igflr
Official gene name*
Hsd3b l_predicted
hydroxy steroid
dehydrogenase-1,
delta< 5 >-3-beta
(predicted) orHsdSbl
hy droxy-delta-5 -steroid
dehydrogenase, 3 beta- and
steroid delta-isomerase 1
Hsd3b l_predicted
hydroxy steroid
dehydrogenase-1,
delta< 5 >-3-beta
(predicted) orHsdSbl
hy droxy-delta-5 -steroid
dehydrogenase, 3 beta- and
steroid delta-isomerase 1
Immediate early response 3
Interferon-related
developmental regulator 1
Insulin-like growth factor 1
Insulin-like growth factor 1
Insulin-like growth factor 1
receptor
Dose
100 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
Exposure window
GD 12-19
GD 12-19
GD 19for30minto6hr
timepoints and GD 18 for
12, 18, and 24 hr time
points
GD 19for30minto6hr
timepoints and GD 18 for
12, 18, and 24 hr time
points
GD 12-21
GD 12-19
GD 12-19
Up or down
Down
Down
Up after
1-12 hr
(peak ~2 hr)
Up after
~l-6 and
18hr(peak
~3hr)
Up
Up
Up
Statistical analysis method
Dunnett's test, ANOVA (one
way), p< 0.05
Dunnett's test, ANOVA (one
way), p< 0.05
Relative expression determined
using mean Ct; triplicate
samples; GADPH control; SE;
but/) value not calculated
Relative expression determined
using mean Ct; triplicate
samples; GADPH control; SE;
but/) value not calculated
t-test,/?<0.05
t-test,/?<0.05
t-test,/?<0.05
Reference
Lehmann et
al., 2004
Lehmann et
al., 2004
Thompson
et al., 2005
Thompson
et al., 2005
Bowman et
al., 2005
Bowman et
al., 2005
Bowman et
al., 2005
o <*>'
U
O 58
S § .
g.S3-
s
rS"
-------
Table A-2. (continued)
Official
gene symbol
IgP
Ig/bpS
Insigl
Insl3
Insl3
Insl3
Itgav
Junb
Kit
Kit
Official gene name*
Insulin-like growth factor 1
Insulin-like growth factor
binding protein 5
Insulin induced gene 1
Insulin-like 3
Insulin-like 3
Insulin-like 3
Integrin alpha V
Jun-B oncogene
v-kit Hardy -Zuckerman
4 feline sarcoma viral
oncogene homolog
v-kit Hardy -Zuckerman
4 feline sarcoma viral
oncogene homolog
Dose
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
1000
mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
0.1 mg/kg-d
Exposure window
GD 12-19
GD 12-21
GD 12-19
GD 12-19
GD 13-17 (GD 14-18 in
Wilson et al., 2004 was
changed to GD 13-17 to
make the GD comparable to
the other 7 studies)
GD 12.5-19.5
GD 12-19
GD 19for30minto6hr
timepoints and GD 18 for
12, 18, and 24 hr time
points
GD 12-19
GD 12-19
Up or down
Up
Up
Down
Down
Down
Down
Up
UP after
-1-12 hr
(peak
-2-3 hr)
Down
Down
Statistical analysis method
t-test,/?<0.05
t-test,/?<0.05
One way; and two-way nested
ANOVA,^<0.05
Dunnett's test, ANOVA (one
way), p< 0.05
ANOVA followed by
LSMEANS, p < 0.01 or less
One-way ANOVA followed by
Bonferroni post test using
GraphPad Prism; p < 0.05
t-test,/?<0.05
Relative expression determined
using mean Ct; triplicate
samples; GADPH control; SE;
but/) value not calculated
ANOVA, nested design,
^<0.05
Dunnett's test, ANOVA (one
way), p< 0.05
Reference
Bowman et
al., 2005
Bowman et
al., 2005
Liu et al.,
2005
Lehmann et
al., 2004
Wilson et
al., 2004
Plummer et
al., 2007
Bowman et
al., 2005
Thompson
et al., 2005
Barlow et
al., 2003
Lehmann et
al., 2004
o <*>'
U
O 58
S § .
g.S3-
s
rS"
-------
Table A-2. (continued)
Official
gene symbol
Kit
Kit
Kit
Kit
Kit
KM
Ldlr
Lhcgr
Map3kl2
Marcks
Mgp
Official gene name*
v-kit Hardy -Zuckerman
4 feline sarcoma viral
oncogene homolog
v-kit Hardy -Zuckerman 4
feline sarcoma viral
oncogene homolog
v-kit Hardy -Zuckerman
4 feline sarcoma viral
oncogene homolog
v-kit Hardy -Zuckerman
4 feline sarcoma viral
oncogene homolog
v-kit Hardy -Zuckerman
4 feline sarcoma viral
oncogene homolog
Kit ligand
Low density lipoprotein
receptor
Luteinizing
hormone/choriogonadotropi
n receptor
Mitogen activated protein
kinase kinase kinase 12
Myristoylated alanine rich
protein kinase C substrate
Matrix Gla protein
Dose
1 mg/kg-d
50 mg/kg-d
100 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
Exposure window
GD 12-19
GD 12-19
GD 12-19
GD 12-19
GD 12-19
GD 12-19
GD 12-19
GD 12-19
GD 12-19
GD 12-16, 12-19, or
12-21
GD 12-21
Up or down
Down
Down
Down
Down
Down at
GD 19
Down
Down
Down
Up
No stat.
Change
Up
Statistical analysis method
Dunnett's test, ANOVA (one
way), p< 0.05
Dunnett's test, ANOVA (one
way), p< 0.05
Dunnett's test, ANOVA (one
way), p< 0.05
Dunnett's test, ANOVA (one
way), p< 0.05
p<0.05
ANOVA, nested design,
p<0.05
One way and two-way nested
ANOVA,^<0.05
One way and two-way nested
ANOVA,^<0.05
t-test,/?<0.05
^<0.05
t-test,/?<0.05
Reference
Lehmann et
al., 2004
Lehmann et
al., 2004
Lehmann et
al., 2004
Lehmann et
al., 2004
Shultz et
al., 2001
Barlow et
al., 2003
Liu et al.,
2005
Liu et al.,
2005
Bowman et
al., 2005
Shultz et
al., 2001
Bowman et
al., 2005
o <*>'
U
O 58
S § .
g.S3-
s
rS"
-------
Table A-2. (continued)
Official
gene symbol
Mmp2
Mmp2
Nfil3
Nfil3
Notch2
Npc2
NrObl
NrObl
Nr4al
Nr4al
Official gene name*
Matrix metallopeptidase 1
Matrix metallopeptidase 1
Nuclear factor, interleukin 3
regulated
Nuclear factor, interleukin 3
regulated
Notch gene homolog 1
(Drosophila)
Niemann Pick type C2
Nuclear receptor
subfamily 0, group B,
member 1
Nuclear receptor
subfamily 0, group B,
member 1
Nuclear receptor
subfamily 4, group A,
member 1
Nuclear receptor
subfamily 4, group A,
member 1
Dose
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
Exposure window
GD 12-19
GD 12-21
GD 12-19
GD 19for30minto6hr
timepoints and GD 18 for
12, 18, and 24 hr time
points
GD 12-21
GD 12-19
GD 12-19
GD 19for30minto6hr
timepoints and GD 18 for
12, 18, and 24 hr time
points
GD 12-19
GD 19for30minto6hr
timepoints and GD 18 for
12, 18, and 24 hr time
points
Up or down
Up
Up
Up
Up after
-2-24 hr
(peak ~6 hr)
Up
Down
Down
Down at
2hr,Up
12 hr (peak
at 12 hr)
Up
Up after
~6 and 18 hr
(peak at
12 hr)
Statistical analysis method
t-test,/?<0.05
t-test,/?<0.05
One way and two-way nested
ANOVA,^<0.05
Relative expression determined
using mean Ct; triplicate
samples; GADPH control; SE;
but/) value not calculated
t-test,/?<0.05
One way and two-way nested
ANOVA,^<0.05
One way and two-way nested
ANOVA,^<0.05
Relative expression determined
using mean Ct; triplicate
samples; GADPH control; SE;
but/) value not calculated
One way and two-way nested
ANOVA,^<0.05
Relative expression determined
using mean Ct; triplicate
samples; GADPH control; SE;
but/) value not calculated
Reference
Bowman et
al., 2005
Bowman et
al., 2005
Liu et al.,
2005
Thompson
et al., 2005
Bowman et
al., 2005
Liu et al.,
2005
Liu et al.,
2005
Thompson
et al., 2005
Liu et al.,
2005
Thompson
et al., 2005
o <*>'
U
O 58
S § .
g.S3-
s
rS"
oo
-------
Table A-2. (continued)
Official
gene symbol
Nr4a3
Pawr
Pcna
Prkcbpl
Scarbl
Scarbl
Scarbl
Scarbl
Scarbl
Scarbl
Scarbl
Official gene name*
Nuclear receptor
subfamily 4, group A,
member 3
PRKC, apoptosis, WT1,
regulator
Proliferating cell nuclear
antigen
Protein kinase C binding
protein 1
Scavenger receptor class B,
member 1
Scavenger receptor class B,
member 1
Scavenger receptor class B,
member 1
Scavenger receptor class B,
member 1
Scavenger receptor class B,
member 1
Scavenger receptor class B,
member 1
Scavenger receptor class B,
member 1
Dose
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
1 mg/kg-d
50 mg/kg-d
100 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
Exposure window
GD 19for30minto6hr
timepoints and GD 18 for
12, 18, and 24 hr time
points
GD 19for30minto6hr
timepoints and GD 18 for
12, 18, and 24 hr time
points
GD 12-16, 12-19, or
12-21
GD 12-19
GD 12-19
GD 12-19
GD 12-19
GD 12-19
GD 12-19
GD 12-16, 12-19, or
12-21
GD 12-17 and 12-18
Up or down
Up after
-1-12 hr
(peak at
-3-6 hr)
Up after
-2-24 hr
(peak -6 hr)
No stat.
change
Up
Down
Down
Down
Down
Down
Down
Down at
GD 17 and
18
Statistical analysis method
Relative expression determined
using mean Ct; triplicate
samples; GADPH control; SE;
but/) value not calculated
Relative expression determined
using mean Ct; triplicate
samples; GADPH control; SE;
but/) value not calculated
p<0.05
One way and two-way nested
ANOVA,^<0.05
ANOVA, nested design,
p<0.05
Dunnett's test, ANOVA (one
way), p< 0.05
Dunnett's test, ANOVA (one
way), p< 0.05
Dunnett's test, ANOVA (one
way), p< 0.05
Dunnett's test, ANOVA (one
way), p< 0.05
^<0.05
t-test, ANOVA (one-way) with
Tukey post hoc analysis,
^<0.05
Reference
Thompson
et al., 2005
Thompson
et al., 2005
Shultz et
al., 2001
Liu et al.,
2005
Barlow et
al., 2003
Lehmann et
al., 2004
Lehmann et
al., 2004
Lehmann et
al., 2004
Lehmann et
al., 2004
Shultz et
al., 2001
Thompson
et al., 2004
o <*>'
U
O 58
S § .
g.S3-
s
rS"
VO
-------
Table A-2. (continued)
Official
gene symbol
Sgk
Sostdcl
Star
Star
Star
Star
Star
Star
Star
Official gene name*
Serum/glucocorticoid
regulated kinase
Sclerostin domain
containing 1
Steroidogenic acute
regulatory protein
Steroidogenic acute
regulatory protein
Steroidogenic acute
regulatory protein
Steroidogenic acute
regulatory protein
Steroidogenic acute
regulatory protein
Steroidogenic acute
regulatory protein
Steroidogenic acute
regulatory protein
Dose
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
50 mg/kg-d
100 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
Exposure window
GD 19for30minto6hr
timepoints and GD 18 for
12, 18, and 24 hr time
points
GD 19for30minto6hr
timepoints and GD 18 for
12, 18, and 24 hr time
points
GD 12-19
GD 12-19
GD 12-19
GD 12-19
GD 12-16, 12-19, or
12-21
GD 12-17 and 12-18
GD 12.5-19.5
Up or down
Down and
Up; Down
after 2 hr;
Up after 4
andlOhr
(peak at
6hr)
Down after
2-6 hr; Up
atlShr
(peak)
Down
Down
Down
Down
Down at
GD 16, 19,
and 21
Down at
GD 17 and
18
Down
Statistical analysis method
Relative expression determined
using mean Ct; triplicate
samples; GADPH control; SE;
but/) value not calculated
Relative expression determined
using mean Ct; triplicate
samples; GADPH control; SE;
but/) value not calculated
Repeated measure ANOVA,
nested design, p < 0.05
Dunnett's test, ANOVA (one
way), p< 0.05
Dunnett's test, ANOVA (one
way), p< 0.05
Dunnett's test, ANOVA (one
way), p< 0.05
p<0.05
t-test, ANOVA (one-way) with
Tukey post hoc analysis;
p<0.05
One-way ANOVA followed by
Bonferroni post test using
GraphPad Prism; p < 0.05
Reference
Thompson
et al., 2005
Thompson
et al., 2005
Barlow et
al., 2003
Lehmann et
al., 2004
Lehmann et
al., 2004
Lehmann et
al., 2004
Shultz et
al., 2001
Thompson
et al., 2004
Plummer et
al., 2007
o <*>'
U
O 58
S § .
g.S3-
s
rS"
-------
Table A-2. (continued)
Official
gene symbol
Stcl
Svs5
Tcfl
Tcfl
Testin
Thbsl
Timpl
Tnfrsfl2a
Wnt4
Official gene name*
Stanniocalcin I
Seminal vesicle secretion 5
Transcription factor 1
Transcription factor 1
Testin gene
Thrombospondin 1
Tissue inhibitor of
metalloproteinase 1
Tumor necrosis factor
receptor superfamily,
member 12a
Wingless-related MMTV
integration site 4
Dose
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
500 mg/kg-d
Exposure window
GD 19 for 30 min to 6 hr
timepoints and GD 18 for
12, 18, and 24 hr time
points
GD 12-19
GD 12-19
GD 19 for 30 min to 6 hr
timepoints and GD 18 for
12, 18, and 24 hr time
points
GD 12-19
GD 19 for 30 min to 6 hr
timepoints and GD 18 for
12, 18, and 24 hr time
points
GD 12-21
GD 19 for 30 min to 6 hr
timepoints and GD 18 for
12, 18, and 24 hr time
points
GD 19 for 30 min to 6 hr
timepoints and GD 18 for
12, 18, and 24 hr time
points
Up or down
Up after
-3-24 hr
(peak ~6 hr)
Down
Down
Down after
1-3 hr (peak
atlhr)
Up
Up after
2-4 hr (peak
~3hr)
Up
Up after
-1-12 hr
(peak at
~3hr)
Up after
-12 and
18 hr(peak
12 hr)
Statistical analysis method
Relative expression determined
using mean Ct; triplicate
samples; GADPH control; SE;
but/) value not calculated
One way and two-way nested
ANOVA,^<0.05
One way and two-way nested
ANOVA,^<0.05
Relative expression determined
using mean Ct; triplicate
samples; GADPH control; SE;
but/) value not calculated
One way and two-way nested
ANOVA,^<0.05
Relative expression determined
using mean Ct; triplicate
samples; GADPH control; SE;
but/) value not calculated
t-test,/?<0.05
Relative expression determined
using mean Ct; triplicate
samples; GADPH control; SE;
but/) value not calculated
Relative expression determined
using mean Ct; triplicate
samples; GADPH control; SE;
but/) value not calculated
Reference
Thompson
et al., 2005
Liu et al.,
2005
Liu et al.,
2005
Thompson
et al., 2005
Liu et al.,
2005
Thompson
et al., 2005
Bowman et
al., 2005
Thompson
et al., 2005
Thompson
et al., 2005
o <*>'
U
O 58
S § .
g.S3-
s
rS"
>
-------
TO
Table A-2. (continued)
Official
gene symbol
Zfp36
Official gene name*
Zinc finger protein 36
Dose
500 mg/kg-d
Exposure window
GD 19for30minto6hr
timepoints and GD 18 for
12, 18, and 24 hr time
points
Up or down
Up after
30 min and
6 brand
15 and 20 hr
(peak at
Ihr)
Statistical analysis method
Relative expression determined
using mean Ct; triplicate
samples; GADPH control; SE;
butp value not calculated
Reference
Thompson
et al, 2005
o <*>'
U
O 58
S § .
g.S3-
*Gene function and pathway information was gathered from GeneGo (www.genego.comX
to
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1 APPENDIX B
2
3 SUPPORTING TABLES AND FIGURES FOR CHAPTER 6
4
5
6 Appendix B contains additional tables and figures supportive of the work described in
7 Chapter 6.
This document is a draft for review purposes only and does not constitute Agency policy.
DRAFT—DO NOT CITE OR QUOTE
B-l
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1
2
Table B-l. Differentially expressed genes that mapped to statistically significant
pathways identified using the Signal to Noise Ratio (SNR) statistical filter
Gene
symbol
Aadat
Acadm
Acads
Acatl
Aco2
Acsl4
Akrlb4
Alasl
Aldhla4
Aldh2
Aldh6al
Aldoa
Aldoc
Ass
Bhmt
Chkb
Cypllal
CyplJal
Dcxr
Ddc
Dhcr?
Ebp
Ephxl
Fbp2
Fdftl
Fdps
Fhl
Entrez
gene ID
29416
24158
64304
25014
79250
113976
24192
65155
29651
29539
81708
24189
24191
25698
81508
29367
29680
25146
171408
24311
64191
117278
25315
114508
29580
83791
24368
Gene name
Aminoadipate aminotransferase
Acetyl-Coenzyme A dehydrogenase, medium chain
Acyl-Coenzyme A dehydrogenase, short chain
Acetyl-Coenzyme A acetyltransferase 1
Aconitase 2, mitochondrial
Acyl-CoA synthetase long-chain family member 4
Aldo-keto reductase family 1, member B4 (aldose reductase)
Aminolevulinic acid synthase 1
Aldehyde dehydrogenase family 1, subfamily A4
Aldehyde dehydrogenase 2
Aldehyde dehydrogenase family 6, subfamily Al
Aldolase A
Aldolase C, fructose-biphosphate
Arginosuccinate synthetase
Betaine-homocysteine methyltransferase
Choline kinase beta
Cytochrome P450, family 11, subfamily a, polypeptide 1
Cytochrome P450, family 17, subfamily a, polypeptide 1
Dicarbonyl L-xylulose reductase
Dopa decarboxylase
7-dehydrocholesterol reductase
Phenylalkylamine Ca2+ antagonist (emopamil) binding protein
Epoxide hydrolase 1
Fructose- 1,6-bisphosphatase 2
Farnesyl diphosphate farnesyl transferase 1
Farnesyl diphosphate synthase
Fumarate hydratase 1
This document is a draft for review purposes only and does not constitute Agency policy.
DRAFT—DO NOT CITE OR QUOTE
B-2
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Table B-l. (continued)
Gene
symbol
G6pdx
Gad2
Gapdh
Gatm
Ggtl3
Gsta2
Gsta3
Gstm2
Gstm3
Hmgcr
Hmgcsl
Idhl
Mel
Mgstl
Mif
Mvd
Nosl
Pycr2
Sqle
Suclgl
Tpil
Entrez
gene ID
24377
24380
24383
81660
156275
24422
24421
24424
81869
25675
29637
24479
24552
171341
81683
81726
24598
364064
29230
114597
24849
Gene name
Glucose-6-phosphate dehydrogenase
Glutamate decarboxylase 2
Glyceraldehyde-3 -phosphate dehydrogenase
Glycine amidinotransferase (L-arginine:glycine amidinotransferase)
Gamma-glutamyltransferase-like 3
Glutathione-S-transferase, alpha type2
Glutathione S-transferase A5
Glutathione S-transferase, mu 2
Glutathione S-transferase, mu type 3
3-hydroxy-3-methylglutaryl-Coenzyme A reductase
3-hydroxy-3-methylglutaryl-Coenzyme A synthase 1
Isocitrate dehydrogenase 1 (NADP+), soluble
Malic enzyme 1
Microsomal glutathione S-transferase 1
Macrophage migration inhibitory factor
Mevalonate (diphospho) decarboxylase
Nitric oxide synthase 1, neuronal
Pyrroline-5-carboxylate reductase family, member 2 (predicted)
Squalene epoxidase
Succinate-CoA ligase, GDP -forming, alpha subunit
Tpil protein
This document is a draft for review purposes only and does not constitute Agency policy.
DRAFT—DO NOT CITE OR QUOTE
B-3
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1
2
3
4
Table B-2. Genes identified using the Linear-Weighted Normalization
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
Src
GngS
Gnai3
Gng7
Mapk3
Gnaol
Actcl
Camk2d
Gnaq
CxclJ2
Prkce
Coagulation factor II (thrombin) receptor
Rous sarcoma oncogene
Guanine nucleotide binding protein (G protein), gamma 5 subunit
Guanine nucleotide binding protein, alpha inhibiting 3
Guanine nucleotide binding protein, gamma 7
Mitogen activated protein kinase 3
Guanine nucleotide binding protein, alpha o
Actin alpha cardiac 1
Calcium/calmodulin-dependent protein kinase II, delta
Guanine nucleotide binding protein
Chemokine (C-X-C motif) ligand 12
Protein kinase C, epsilon
Genes mapped to cholesterol biosynthesis/metabolism
Hmgcsl
HsdSbl
Dhcr?
Sqle
Soatl
CypSlal
Cyp27aJ
Hsdllbl
Hmgcr
Idil
Sc4mol
Cyp7bl
3-Hydroxy-3-methylglutaryl-Coenzyme A synthase 1
Hydroxyl-delta-5-steroid dehydrogenase
7-Dehydrocholesterol reductase
Squalene epoxidase
Sterol O-acyltransferase 1
Cytochrome P450, family 51, subfamily a, polypeptide 1
Cytochrome P450, family 27, subfamily a, polypeptide 1
Hydroxy steroid 11 -beta dehydrogenase 1
3-Hydroxy-3-methylglutaryl-Coenzyme A reductase
Osopentenyl-diphosphate delta isomerase
Sterol-C4-methyl oxidase-like
Cytochrome P450, family 7, subfamily b, polypeptide 1
This document is a draft for review purposes only and does not constitute Agency policy.
DRAFT—DO NOT CITE OR QUOTE
B-4
-------
Table B-2. (continued)
Gene symbol
Gene name
Genes mapped to chemokine mediated signaling
Src
Gng5
Hmgcsl
Serpine2
ItgbS
Dhcr?
Gnai3
Gng7
Sqle
Mapk3
Gnaol
Actnl
Actcl
Cav2
CypSlal
Rous sarcoma oncogene
Guanine nucleotide binding protein (G protein), gamma 5 subunit
3-Hydroxy-3-methylglutaryl-Coenzyme A synthase 1
Serine (or cysteine) proteinase inhibitor, clade E, member 2
Integrin, beta 5
7-Dehydrocholesterol reductase
Guanine nucleotide binding protein, alpha inhibiting 3
Guanine nucleotide binding protein, gamma 7
Squalene epoxidase
Mitogen activated protein kinase 3
Guanine nucleotide binding protein, alpha o
Actinin, alpha 1
Actin alpha cardiac 1
Caveolin 2
Cytochrome P450, family 51, subfamily a, polypeptide 1
Genes mapped to chemokine mediated signaling
Colla2
Cfll
Cavl
Hmgcr
Mmp2
Msn
Gsk3b
Mil
Plat
Sdc2
Sc4mol
Procollagen, type I, alpha 2
Cofilin 1, non-muscle
Caveolin 2
3-Hydroxy-3-methylglutaryl-Coenzyme A reductase
Matrix metallopeptidase 2
Moesin
Glycogen synthase kinase 3 beta
Isopentenyl-diphosphate delta isomerase
Plasminogen activator, tissue
Syndecan 2
Sterol-C4-methyl oxidase-like
This document is a draft for review purposes only and does not constitute Agency policy.
DRAFT—DO NOT CITE OR QUOTE
B-5
-------
Table B-2. (continued)
Gene symbol
Lefl
Vegf
Gene name
Lymphoid enhancer binding factor 1
Vascular endothelial growth factor
Genes mapped to glycolysis/gluconeogenesis
Pgkl
Hmgcsl
Tpil
Fbp2
Dhcr?
P/km
Pfkp
Mdhl
Sqle
Pgaml
Aldoa
CypSlal
Hmgcr
Hkl
Gpi
Gapdh
Mil
Sc4mol
Pfkl
Phosphoglycerate kinase 1
3-Hydroxy-3-methylglutaryl-Coenzyme A synthase 1
Triosephosphate isomerase 1
Fructose- 1,6-bisphosphatase 2
7-Dehydrocholesterol reductase
Phosphofructokinase, muscle
Phosphofructokinase, platelet
Malate dehydrogenase 1, NAD (soluble)
Squalene epoxidase
Phosphoglycerate mutase 1
Aldolase A
Cytochrome P450, family 51, subfamily a, polypeptide 1
3-Hydroxy-3-methylglutaryl-Coenzyme A reductase
Hexokinase 1
Glucose phosphate isomerase
Glyceraldehyde-3 -phosphate dehydrogenase
Isopentenyl-diphosphate delta isomerase
Sterol-C4-methyl oxidase-like
Phosphofructokinase, liver
This document is a draft for review purposes only and does not constitute Agency policy.
DRAFT—DO NOT CITE OR QUOTE
B-6
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1
2
Table B-3. Nodes added by using Ingenuity® Pathway Analysis (IPA)
software in developing the gene regulatory network for DBP
Gene
symbol
Acol
Esrra
Fgf4
Insigl
Kcnjll
Lep
Lnpep
Nfic
Nmel
Nr2fl
NrSal
Pld2
Ppargclb
Srebfl
Srebf2
Zdhhc23
Gene name
Aconitase 1, soluble
Estrogen-related receptor alpha
Fibroblast growth factor 4
Insulin induced gene 1
Potassium inwardly-rectifying channel, subfamily J, member 1 1
Leptin
Leucyl/cystinyl aminopeptidase
Nuclear factor I/C (CCAAT -binding transcription factor)
Non-metastatic cells 1, protein (NM23A) expressed in
Nuclear receptor subfamily 2, group F, member 1
Nuclear receptor subfamily 5, group A, member 1
Phospholipase D2
Peroxisome proliferative activated receptor, gamma, coactivator 1, beta
Sterol regulatory element binding transcription factor 1
Sterol regulatory element binding transcription factor 2
Zinc finger, DHHC-type containing 23
This document is a draft for review purposes only and does not constitute Agency policy.
DRAFT—DO NOT CITE OR QUOTE
B-7
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For a given gene
expression
Evaluate SNR between
control and treated
samples
v
/Select ~7
random gene K.—
expression /
Evaluate SNR for randomly selected
gene expression
No
Random Expression = random Expression +1
No
Statistical Significance = (Random Expression/1000)* 100
No /
Given gene expression
is random
1
2
3
4
5
6
7
Given gene expression is
statistically significant
Figure B-l. Algorithm for selecting differentially expressed genes (DEGs).
1,000 random gene expressions were generated for each probe set, and then,
Signal to Noise ratios (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.
This document is a draft for review purposes only and does not constitute Agency policy.
DRAFT—DO NOT CITE OR QUOTE
B-8
-------
For a given pathway
A.
Evaluate OPA
Select a random
set of gene
expression
Evaluate OPA for randomly selected
set of genes
No
Random Pathway Activity = Random Pathway Activity +1
Statistical Significance = (Random Pathway Activity/1000)* 100
Given pathway
activity is random
7
1
2
3
4
5
6
Given pathway activity is
statistically significant
Figure B-2. Algorithm for selecting active pathways. 1,000 random sets of
gene expressions were generated for each pathway, then its overall pathway
activity (OPA) was calculated. The ratio of the randomly generated OPA that was
higher than the actual OPA determined whether pathway activity was statistically
significant.
This document is a draft for review purposes only and does not constitute Agency policy.
DRAFT—DO NOT CITE OR QUOTE
B-9
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1
2
3
4
5
Steroid Hormone Metabolism
Glutathione Metabolism
. Added nodes by Ingenuity
Exposure Time
-18 hour
- 6 Hour
-3 Hour
-IHour
Figure B-3. Genetic regulatory network after DBF exposure created by Ingenuity" Pathway Analysis (IPA)
from the informative gene list based on data from Thompson et al. (2005) The informative genes of Thompson et
al. (2005) were evaluated at each time point and mapped onto a global molecular network developed from information
contained in the Ingenuity® Pathways Knowledge Base.
This document is a draft for review purposes only and does not constitute Agency policy.
DRAFT—DO NOT CITE OR QUOTE
B-10
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1 APPENDIX C
2
3 QUALITY CONTROL AND ASSURANCE
4
5
6 Appendix C contains quality assurance/quality control (QA/QC) information for the work
7 described in Chapters 5 and 6. The work described in this Appendix (C) is secondary data
8 analysis. The studies include exploratory studies using new methods for analyzing genomic data
9 for risk assessment purposes as well as some preliminary analyses using well-established of the
10 raw data from two published studies.
11
12 Three projects were performed:
13 (1) A qualitative analysis and presentation of the 9 toxicogenomic DBF studies. No
14 statistical analyses were performed by members of our team.
15 (2) In-house analysis of the raw data from Liu et al. (2005) study performed at both
16 NHEERL, US EPA by Drs. Susan Hester and Banalata Sen, and by by collaborators, Dr.
17 loannis Androulakis and Meric Ovacik, STAR Grantees at the STAR Bioinformatics
18 Center at Rutgers/UMDNJ.
19 (3) New analyses of Thompson et al. (2005) data performed by collaborators, Dr. loannis
20 Androulakis and Meric Ovacik, STAR Grantees at the STAR Bioinformatics Center at
21 Rutgers/UMDNJ.
22
23 PROJECT 1
24 The data presented in 9 published toxicogenomic studies for DBF were compared. No
25 additional analyses were performed. Data were entered directly into an excel spreadsheet from
26 the published literature. Study descriptions in tables and figures were developed. The data entry
27 process included team members entering in the data from the published articles into tables for
28 differentially expressed genes and pathways affected. One person entered the data for a subset of
29 genes. A second person checked the results in the table against the articles.
30
31 PROJECT!
32 The data source was the DBF treatment only data from the Liu et al. (2005) study. The
33 Liu et al. (2005) data were kindly provided by Dr. Kevin Gaido, a collaborator on this project.
777/5 document is a draft for review purposes only and does not constitute Agency policy.
C-l DRAFT—DO NOT CITE OR QUOTE
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1 The study was performed in his laboratory at The Hamner Institutes for Health Sciences
2 (formerly CUT). His QA statement for the collection and analysis of the data is provided below.
3
4 PROJECT 3
5 The data source was the Thompson et al. (2005) study. The Thompson et al. (2005) data
6 were kindly provided by Dr. Kevin Gaido, a collaborator on this project. The study was
7 performed in his laboratory at The Hamner Institutes for Health Sciences (formerly CUT). His
8 QA statement for the collection and analysis of the data is provided below.
9
10 PROJECTS 2 and 3: DATA SOURCES
11 The sources of the data used in the secondary analyses were the Liu et al. (2005) and
12 Thompson et al. (2005) studies. Both of these studies were performed in the laboratory of Dr.
13 Kevin Gaido. The QA details for the two studies are presented below. The Hamner Institute's
14 Quality Assurance Director is Patricia O. Pomerleau, M.S., RQAP (pomerleau@thehamner.org).
15
16 A. Sample Handling Procedures
17 Virgin female SD outbred CD rats, 8 weeks old, were time mated. Dams were assigned
18 to a treatment group by randomization using Provantis NT 2000 and subsequently be identified
19 by an ear tag and cage card. Dams were kept in the Association for Assessment and
20 Accreditation of Laboratory Animal Care International accredited animal facility at The Hamner
21 Institute (at the time of the two studies, The Hmaner was named CUT) in a humidity- and
22 temperature-controlled, high-efficiency particulate-air-filtered, mass air-displacement room.
23 Dams were treated by gavage daily from gestation day (GD) 12-19 with corn oil (vehicle
24 control) and dibutyl phthalate. Body weights were recorded daily before dosing (GD 12-19).
25 The oral treatments were administered on a mg/kg-body weight basis and adjusted daily for
26 weight changes. Animal doses were calculated through Provantis NT 2000. All calculations
27 were checked by a second individual and recorded in the investigators' The Hamner Institute
28 notebooks. Analytical support staff confirmed appropriate dose solutions at the beginning of the
29 dosing period. Body weights and doses administered were recorded each day in Provantis NT
30 2000. Pups and dams were euthanized by carbon dioxide asphyxiation.
777/5 document is a draft for review purposes only and does not constitute Agency policy.
C-2 DRAFT—DO NOT CITE OR QUOTE
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1 Fetal tissues for RIA's and RNA isolation were snap frozen in liquid nitrogen and stored
2 at -80°C. The remaining tissues were either be embedded in optical coherence tomography and
3 frozen or fixed in formalin for 6 to 24 hours followed by 70% ethanol and then processed and
4 embedded in paraffin for histological examination within 48 hours. The embedded tissues were
5 sectioned at approximately 5 microns and stained with hematoxylin and eosin. The study
6 pathologist in consultation with the histology staff determined the gross trim, orientation, and
7 embedding procedure for each tissue. RNA were isolated from the frozen male reproductive
8 tract, and changes in gene expression were identified by real-time reverse
9 transcript!on-polymerase chain reaction (RT-PCR) analysis (following manufacturer's protocols
10 P/N 402876 and P/N 4304965, Applied Biosystems, Foster City, CA) and in some cases, by
11 complementary DNA (cDNA) microarray (following manufacturers protocol PT3140, Clontech,
12 Palo Alto, CA).
13 Total RNA were treated with DNase I at 37°C for 30 minutes in the presence of RNasin
14 to remove DNA contamination before cDNA synthesis, followed by heat inactivation at 75°C for
15 5 minutes. Primer pairs were selected using the program Primer Express and optimized for use
16 prior to quantification. cDNA were synthesized using random hexamers and TaqMan Reverse
17 Transcription Reagents according to the manufacturer's suggested protocol. Real-time PCR
18 (TaqMan) were performed on a Perkin-Elmer/Applied Biosystems 7500 Prism using TaqMan
19 probe chemistry according to the manufacturer's instructions for quantification of relative gene
20 expression. Relative differences among treatment groups were determined using the CT method
21 as outlined in the Applied Biosystems protocol for reverse transcriptase(RT)-PCR. A CT value
22 was calculated for each sample using the CT value for glyceraldehyde-3-phosphate
23 dehydrogenase (or an appropriate housekeeping gene) to account for loading differences in the
24 RT-PCRs.
25
26 B. Microarray Hybridization
27 Testes from individual fetuses were homogenized in RNA Stat 60 reagent (Tel-Test, Inc.,
28 Friendswood, TX) and RNA was isolated using the RNeasy Mini Kit (Qiagen, Valencia, CA)
29 following manufacturer's protocol. RNA integrity was assessed using the Agilent 2100
30 Bioanalyzer (Agilent Technologies, Palo Alto, CA), and optical density was measured on a
31 NanoDrop ND 1000 (NanoDrop Technologies, Wilmington, DE). cDNA was synthesized from
This document is a draft for review purposes only and does not constitute Agency policy.
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1 2.5 or 3 jig total RNA and purified using the Affymetrix® One-Cycle Target Labeling and
2 control reagents kit (Affymetrix, Santa Clara, CA) according to manufacturer's protocol. Equal
3 amounts of purified cDNA per sample were used as the template for subsequent in vitro
4 transcription reactions for complementary RNA (cRNA) amplification and biotin labeling using
5 the Affymetrix GeneChip® IVT labeling kit (Affymetrix) included in the One-Cycle Target
6 Labeling kit (Affymetrix). cRNA was purified and fragmented according to the protocol
7 provided with the GeneChip® Sample Cleanup module (Affymetrix). All GeneChip® arrays
8 were hybridized, washed, stained, and scanned using the Complete GeneChip® Instrument
9 System according to the Affymetrix Technical Manual.
10 For immunocytochemistry, tissues were rapidly removed, immersed in 10% (v/v)
11 neutral-buffered formalin for 24-48 hours, and then stored in ethanol 70% (v/v) until processed.
12 The reproductive tissues were embedded in paraffin, sectioned at 5 \i, and processed for
13 immunohistochemistry or stained with hematoxylin and eosin.
14 Experimental notes and data were entered into uniquely numbered Hamner Institute
15 laboratory notebooks and three-ring binders along with descriptions of procedures used,
16 according to SOP# QUA-007. Specimens (RNA and frozen tissue) were retained until analysis
17 or discarded after a maximum of 1 year after collection. Formalin-fixed tissues, blocks, and
18 slides were archived at the end of the study. Retention of these materials will be reassessed after
19 5 years.
20
This document is a draft for review purposes only and does not constitute Agency policy.
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1 C. Quality Assurance
2 Both QA and QC procedures are integral parts of our research program. The research
3 was conducted under the The Hamner Institute Research Quality Standards program. These
4 standards include (1) scientifically reviewed protocols that are administratively approved for
5 meeting requirements in data quality, animal care, and safety regulations; (2) standardized
6 laboratory notebooks and data recording procedures; (3) documented methods or standard
7 operating procedures for all experimental procedures—including calibration of instruments; (4) a
8 central managed archive for specimens and documentation; and (5) internal peer review for
9 scientific quality of abstracts and manuscripts. The Hamner Institute QA and QC processes
10 assessing overall study performance and records ensure that conduct of the proposed research
11 satisfies the intended project objectives.
12
13 D. Statistical Analysis
14 RT-PCR data were analyzed using JMP statistical analysis software (SAS Institute, Gary,
15 NC). RNA were isolated from at least 3 pups from 3 different dams for each treatment group.
16 PCR reactions, radioimmunoassays, and protein analysis were repeated 3-5 times for each
17 sample. Based on our experience, the number of animal replicates has the statistical power to
18 detect a significant change in gene expression >20% at/? < 0.05. The effect of treatment was
19 analyzed using a general-linear model regression analysis. Posthoc tests were conducted when
20 the overall analysis of variance is significant at the/? < 0.05 level using the LS-means procedure
21 and adjusted for multiple comparisons by Dunnett's method.
22 Microarray data were analyzed by a linear mixed model with SAS Microarray Solution
23 software. Perfect-match only data were normalized to a common mean on a Iog2 scale, and a
24 linear mixed model was then applied for each probe set. Restricted maximum likelihood was
25 used for estimating the parameters for both the fixed and random effects. Significance was
26 determined using mixed-model based F-tests (p < 0.05).
27
28 E. Procedures used to Evaluate Success
29 Uniquely numbered written protocols were prepared and reviewed internally prior to the
30 start of this study. The content of a protocol includes study design, materials, laboratory
31 methods, sample collection, handling and custody, record keeping, data analysis and statistical
This document is a draft for review purposes only and does not constitute Agency policy.
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1 procedures, animal care requirements, and safety measures. Numbered standardized laboratory
2 notebooks and guidelines for date recording ensures completeness of data and the ability to
3 reconstruct the study. An independent QA department manages the overall research data quality.
4 Manuscripts describing the results of our study were prepared at the completion of each stage of
5 this study. All manuscripts undergo a rigorous internal peer review that includes review by all
6 authors, at least two additional PhD- level scientists, the science editor, the division manager,
7 and the vice president for research.
8
9 PROJECT 2: DATA REVIEW, VERIFICATION, AND VALIDATION
10 Banalata Sen received the Liu et al. (2005) raw data files from Dr. Kevin Gaido. Two
11 team members, Dr. Banalata Sen (National Center for Environmental Assessment, Research
12 Triangle Park [NCEA-RTP]) and Dr. Susan Hester (National Health and Environmental Effects
13 Research Laboratory [NHEERL]) performed the data analysis at NHEERL, RTF. Barbara
14 Collins (collins.barbara@epa.gov) at NHEERL-RTP has agreed to serve as the Quality
15 Assurance Manager (QAM) for the project. Dr. Hester and Sen performed analyses of the "DBF
16 only" data that is a subset of the data presented in Liu et al. (2005). The analyses at NHEERL
17 included statistical filtering to identify of differentially expressed genes and pathway analysis.
18
19 A. VERIFICATION OF DATA UPON RECEIPT
20 Upon receiving data from Kevin Gaido at the Hamner Institute, EPA NHEERL scientisits
21 conducted a QA review of the data by gross inspection of the eel files to confirm that the data
22 had been transmitted successfully. The scientists at the STAR Bioinformatics Center/Rutgers
23 received the data files from Susan Euling at EPA NCEA who had received the data from Kevin
24 Gaido at the Hamner Institute. Kevin Gaido gave permission to Susan Euling to provide the data
25 for these analyses. A review of the data was performed by inspection of the txt files and the
26 published data to confirm that the data had been transmitted successfully.
27
28
29 B. VERIFICATION OF DATA ANALYSIS CALCULATIONS
777/5 document is a draft for review purposes only and does not constitute Agency policy.
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1 EPA NHEERL used a principal component analysis (PCA) to evaluate the within-group
2 and across-group variance of the six samples. PCA elucidates the separation of different
3 treatment groups and provides information about whether the data contain significant
4 information. This was conducted using the raw data eel files in Rosetta Resolver Software. The
5 analyses were in silico without functional validation (RT-PCR of individual genes).
6 The Star Bioinformatics Center also performed a principal component analysis (PCA)
7 and displayed a 3-D plot to evaluate the within-group and across-group variance of the samples.
8 This was conducted using the txt files in MATLAB® Software. This was an in silico analysis.
9 The data were normalized to a zero mean and a unity standard deviation over samples. They
10 assessed the degree of separation for Liu et al. (2005) data. A regular regular t-test and ANOVA
11 analyses of the data were performed. The filtered data were visualized in a heatmap to determine
12 the statistically significant subset of genes to provide a differentially expressed gene (DEG) list.
13 Drs. Susan Hester and Banalata Sen also performed some comparative analyses between
14 the two outpus (above). The two independent analyses of the same dataset were contrasted with
15 one another. Correlation plots comparing the LoglO average intensities of control samples vs.
16 DBF treated samples was performed in order to determine the noise in both groups. Average
17 background signal and scaling factors will be applied based on the vendor recommendations.
18 QC plots will be made to determine the relationship between light intensity and each genechip.
19
20 PROJECT 3: DATA REVIEW, VERIFICATION, AND VALIDATION
21 This project analyzed the time-course data from Thompson et al. (2005) dataset to then build a
22 regulatory network model. The STAR Center's internal QA/QC procedures are implemented
23 and monitored by a QA official, Clifford Weisel (weisel@eohsi.rutgers.edu), at Rutgers
24 University that reports to the National Center for Environmental Research (NCER), the granting
25 organization for the STAR program.
26
27 A. VERIFICATION OF DATA UPON RECEIPT
28 Data were received from Susan Euling at EPA who had received the data from Kevin
29 Gaido at the Hamner Institute. Kevin Gaido gave permission to Susan Euling to provide the data
30 for these analyses. A review of the data was performed by inspection of the txt files and the
31 published data to confirm that the data had been transmitted successfully.
777/5 document is a draft for review purposes only and does not constitute Agency policy.
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1
2 B. VERIFICATION OF DATA ANALYSIS CALCULATIONS
3 A principal component analysis (PCA) was performed and a 3-D plot was displayed to
4 evaluate the within-group and across-group variance of the samples. This was conducted using
5 the txt files in MATLAB® Software. This was an in silico analysis. The data were normalized to
6 a zero mean and a unity standard deviation over samples. They assessed the degree of separation
7 for the Thompson et al. (2005) data. A regular regular t-test and ANOVA analyses of the data
8 were performed. The filtered data will be visualized in a heatmap to determine the statistically
9 significant subset of genes to provide a differentially expressed gene (DEG) list.
10
11
This document is a draft for review purposes only and does not constitute Agency policy.
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