* Syngenta Central Toxicology Laboratory, Alderley Park, Cheshire, SK10 4TJ, United Kingdom; School of Biosciences, University of Birmingham, Birmingham, B15 2TT, United Kingdom
1 To whom correspondence should be addressed at Syngenta Central Toxicology Laboratory, Alderley Park, Cheshire, SK10 4TJ, UK. Fax: +44 (0) 1625 585715. E-mail: richard.currie{at}syngenta.com.
Received April 4, 2005; accepted May 13, 2005
![]() |
ABSTRACT |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Key Words: toxicogenomics; gene ontology; pathway mapping; non-genotoxic carcinogenesis; diethylhexylphthalate.
![]() |
INTRODUCTION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
We have examined the utility of GO and pathway mapping as an unbiased approaches for analyzing toxicogenomic data to identify holistically the biological processes and pathways affected by toxicant exposure. We chose to use the acute effects caused by the non-genotoxic carcinogen and peroxisome proliferator DEHP in the rodent liver, as these have been characterized at the molecular, cellular, and phenotypic levels, thus providing established biological endpoints from which to validate our GO-mapping approach. Previous studies have suggested that DEHP induces cancer through alterations in the control of cell growth, proliferation, and apoptosis, allowing the development and expansion of pre-neoplastic foci under the influence of paracrine signaling from non-parenchymal cells (Hasmall et al., 2000). The hepatocarcinogenic effects of DEHP in rodents are dependent on the presence of the peroxisome proliferator (PP)-activated receptor alpha PPAR
(Gonzalez, 2002
), a member of the nuclear receptor family that functions as a ligand-regulated transcription factor, which in turn controls gene expression by binding to specific response elements (PPREs) within target gene promoters (Gonzalez, 2002
). However, different responses of species to these non-genotoxic carcinogens call into question the relevance of such rodent studies for human risk assessment (Klaunig et al., 2003
). Thus, the identification of the molecular pathways through which these compounds control cell proliferation should facilitate the assessment of the likely risk posed to humans by these compounds. Our GO mapping of DEHP-responsive genes not only reveals known and novel pathways of DEHP action in the rodent liver but also provides insights into the mechanisms by which non-genotoxic carcinogens control hepatocyte growth and proliferation.
![]() |
MATERIALS AND METHODS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Histology.
Formolsaline-fixed liver samples were embedded in paraffin, and 5-µm-thick sections were cut and stained with hematoxylin and eosin (H&E) before examination by light microscopy for phenotypic features including centrilobular hepatocyte eosinophilia, indicative of the development of smooth endoplasmic reticulum (peroxisomes), and hepatocyte basophilia, indicative of protein production in the endoplasmic reticulum.
Gene expression profiling.
Gene expression levels were measured 2, 8, 24, and 72 h after first exposure using Affymetrix (High Wycombe UK) Mouse Genome 430 2.0 GeneChip arrays (45,101 probe sets). Three animals used were from both treatment and control groups for each time point. Total liver RNA was isolated with RNeasy Maxi kits (Qiagen Ltd, Crawley, West Sussex, UK), and 10 µg was converted to cDNA and amplified with Affymetrix GeneChip 3' Amplification reagents (Invitrogen for Affymetrix). Biotin-labeled complementary RNAs were synthesized with the Enzo Bioarray HighYield RNA Transcript Labeling Kit, and 15 µg was hybridized to Affymetrix Mouse Genome 430 2.0 GeneChips as described in the Affymetrix GeneChip Expression Analysis Technical Manual (http://www.affymetrix.com/support/technical/manual/expression.manual.affx). Probe arrays were scanned, and the intensities were averaged with Microarray Suite 5.0 (Affymetrix). The mean of each array was globally normalized to 500. These data are submitted to Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/). Data were imported into GeneSpring v6.0 (SiliconGenetics), and the following normalizations were applied: data transformation setting measurements less than 0.01 to 0.01; per-gene normalization, where the signal strength of each gene is normalized to the median of its signal strength in all samples. The 45,101 probe sets on the array were then analyzed by means of a series of data filters and statistical tests. First, genes were selected for further analysis on the following basis: genes that had probe sets for which the expression value was greater than 60 during at least one time point and one treatment (which in our study constitutes the average background reading of all probe sets), and that had a present flag call in half the samples (3 of 6). To detect significant changes in the expression levels, a 2-way analysis of variance (ANOVA: parametric; assuming equal variances; Benjamini and Hochberg multiple testing correction at a False Discovery rate <0.05) on time and treatment was then applied to the resulting 21,317 genes, generating three lists of probe sets changing by "time" (1863 probe sets), "treatment" (899 probe sets), and "timetreatment interaction" (76 probe sets). Ratios of changes in gene expression were calculated by normalizing each DEHP-treated sample to the median value of the three corresponding time-matched vehicle controls. Because there was a possibility that the "time" gene list may include genes whose expression levels only changed with time (i.e., changed with time similarly in both treatment and control groups), to identify only those probe sets whose expression was altered by DEHP treatment during the time course, we performed a one-way ANOVA (parametric; assuming equal variances; Benjamini and Hochberg multiple testing correction at a False Discovery rate <0.05) on time on the 1863 "time" probe sets, using the aforementioned ratios of changes in gene expression. The resultant 1084 probe sets were merged with the "treatment" and "timetreatment interaction" lists identified in the two-way ANOVA to form a final list of 1786 probe sets whose expression was significantly altered by DEHP during the 72-h time course. Theoretically, the use of the Benjamini and Hochberg multiple testing correction at a False Discovery rate <0.05 limits the number likely to have been selected by chance to 5% of the 1786 probe sets.
Gene Ontology and GenMAPP analysis of DEHP-regulated genes.
To identify the biological processes and pathways altered by DEHP, we used complementary methods (see Fig. 3C): mapping the DEHP-responsive Affymetrix probe sets to Gene Ontology (Cheng et al., 2004) and secondly to GenMAPPs (Doniger et al., 2003
). Statistically significantly (Benjamini and Hochberg False discovery rate <0.1) overrepresented Gene Ontology Biological Process and Cellular Component annotations from the 1786 differentially regulated probe sets were identified by comparison with the 21,317 probe sets identified as expressed in the liver in our experiment (hereafter called the liver transcriptome) using the Web-based tool GOStat (Beissbarth and Speed, 2004
; http://gostat.wehi.edu.au/). GOStat counts the number of appearances of each GO term for the genes inside the DEHP-altered group and for the reference (liver transcriptome) genes. Fisher's exact test is performed to judge whether the observed difference is significant. To quantify the extent to which a Gene Ontology annotation was overrepresented, an alternative complementary GO-mapping tool was employed, in which the representation index (Ri) was calculated for each annotation as described by Karpinets et al. (2004)
with one modification. In their original method they assumed that if a set of genes in a cluster was chosen at random from the chip, the percentage of genes with a given annotation would equal the percentage of genes on the array with that annotation. As not all of the probe sets on the GeneChip Mouse Genome 430 2.0 Array were found to be expressed in our study, this assumption does not hold. Therefore the "universe" of annotations for the comparison was reduced to the liver transcriptome rather than the entire group of probe sets on the chip. The Gene Ontology Biological Process and Biological Component annotations associated with the liver transcriptome were extracted via the NetAffx Gene Ontology Mining tool (Affymetrix,http://www.affymetrix.com/analysis/query/go_analysisaffix; Chen et al., 2004) and exported into Microsoft Excel, where Ri values were calculated (Karpinets et al., 2004
) for each statistically significant GO term identified using GOStat. Of the 1786 DEHP-regulated probe sets, 890 had Process annotations associated with their target genes, and 960 had Component annotations. A Ri value of +1 would indicate that all of the probe sets associated with a particular annotation are included in the DEHP-regulated gene set, whereas a value of 1 would indicate that none are included. A value of 0 indicates that the proportion of probe sets with that annotation in the DEHP-regulated gene set is the same as the proportion in the liver transcriptome. The pathway visualization and analysis tool GenMAPP/MAPPFinder (Doniger et al., 2003
) was used to search out and visualize statistically significant pathways that were altered by DEHP. For PANTHER analysis (https://panther.appliedbiosystems.com/), the Affymetrix probe set identifiers for both the "DEHP-altered" genes and the "liver transcriptome" were converted to the representative NCBI identifiers with GeneSpring and then submitted to the PANTHER "compare gene lists" tool.
|
![]() |
RESULTS AND DISCUSSION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
|
|
To confirm that the microarrays were capable of detecting known DEHP-responsive genes, we examined the expression profiles of classical PP-responsive genes. As mentioned above, members of the Cyp4a gene subfamily are direct targets of PPAR (Mandard et al., 2004
) and have been reported to be upregulated by DEHP in mouse liver (Kroetz et al., 1998
). Our data reveal that two members of the Cyp4a subfamily (Cyp4a10 and Cyp4a14) were consistently upregulated by DEHP from the 2 h time point onward, ranging from 4.6 ± 1.4-fold induction to 73.2 ± 32.9-fold induction during the 72 h time course (see Table 1 in the Supplementary Data online), changes similar in magnitude to the those observed in Cyp4a10 gene expression as measured by qPCR) (Fig. 1D). In addition we compared our data to published transcript profiling studies of a variety of peroxisome proliferators (Anderson et al. 2004b
; Hamadeh et al., 2002
; McMillian et al., 2004
; Wong and Gill, 2002
; Yadetie et al., 2003
). Comparison of gene expression data with those obtained in other studies on peroxisome proliferators necessarily has to be made with caution because the experimental conditions used (species, chemical, dose, time points) may differ. Nevertheless, in the rat, gene expression changes associated with fatty acid metabolism, cell cycle, and acute phase proteins were altered at 24 h and 2 weeks after daily dosing with peroxisome proliferators (Hamadeh et al., 2002
; McMillian et al., 2004
). Furthermore, Yadetie et al. (2003)
treated rats with ciprofibrate daily for 60 days and found changes in gene expression associated with lipid metabolism and inflammatory responses. Expression profiles were also altered in relation to cell cycle and stress responses, but these changes may reflect secondary changes associated with altered pathology at this late time point. It is important to note that in mouse hepatocytes, the vast majority of gene expression changes seen after 7 days of exposure to WY-14,643 were dependent on PPAR
(Anderson et al., 2004b
). The marked effect on expression of proteasomal genes seen in that in vitro study were reflected in our shorter term in vivo study with DEHP. Thus, our gene expression profiling data confirm a stereotypical liver cell growth and proliferation response to DEHP at the transcriptional level, as well as at the organ and histological levels (Fig. 1B and 1C).
Gene Ontology and Pathway Mapping of DEHP-Induced Gene Expression Changes
Next, we employed a supervised Gene Ontologydriven clustering approach, using both Gene Ontology and pathway mapping tools, as an unbiased method for identifying the predominant biological processes and pathways represented among the 1786 DEHP-responsive genes. The major Gene Ontology Biological Process and Cellular Component terms represented in the DEHP-regulated probe sets and their relative abundance are illustrated in Figure 3A and B, respectively. DEHP regulates the expression of genes associated with a broad range of biological processes. The largest group of genes is associated with "metabolism," while "organismal/cellular physiological processes" and "cell communication" are the next most abundant groups of gene annotations. DEHP also alters the expression of genes in a variety of cellular component locations, with "intracellular" genes being the largest single group, and with significant numbers of probe sets being associated with "membranes" and the "extracellular space." The inset pie chart in Figure 3B further illustrates the subdivision of the "intracellular" GO component annotations, showing that the predominant annotations are "mitochondrion," "endoplasmic reticulum," and "peroxisome." This rather simplistic GO analysis merely quantifies the number of probe sets with a particular annotation, and thus it may simply reflect the distribution of annotations associated with genes expressed in the liver (i.e., it does not allow us to determine which GO annotation(s) are overrepresented in the DEHP-regulated set).
To overcome this limitation, we next used a series of more advanced Gene Ontology and pathway mapping tools (Fig. 3C) to identify the biological pathways and processes targeted by DEHP. Both DEHP-regulated and liver transcriptome probe sets were submitted to GOStat (Beissbarth and Speed, 2004), to determine the most statistically significantly (Benjamini and Hochberg False Discovery Rate p < 0.1) overrepresented Cellular Component annotations (Table 1) and Biological Process annotations (Table 2). To identify the extent to which a particular term was overrepresented, we calculated the representation index (Ri; Karpinets et al., 2004
) for each annotation relative to the liver transcriptome (as described in Materials and Methods). These values are also shown in Tables 1 and 2. To identify known biological pathways that were altered by DEHP, we analyzed the DEHP-regulated and liver transcriptome gene lists, using MAPPFinder (Doniger et al., 2003
) and PANTHER (https://panther.appliedbiosystem.com). Significantly overrepresented GenMAPPs ranked by Z-score (as defined in Doniger et al., 2003
) are listed in Table 3. Significantly overrepresented PANTHER pathway and biological process groups are listed in Tables 2 and 3 in the Supplementary Data online. Importantly, the most significantly overrepresented Gene Ontology component annotation (Ri = 0.310) is "peroxisome." Given that DEHP is known to target this organelle at the molecular level, resulting in peroxisome proliferation, our data demonstrate that GO mapping of global gene expression profiling data can be used in an unbiased manner to identify the principal biological processes associated with cellular responses to a toxicant. Indeed, inspection of the gene lists associated with this term reveals genes associated with both peroxisomal organization and biogenesis, in addition to peroxisomal fatty acid ß-oxidation. This observation confirms the validity of our GO analysis method, as induction of peroxisomal genes is expected to feature prominently in the gene expression response to PPs (Mandard et al., 2004
). The significant overrepresentation of genes annotated as microsomal (Ri = 0.147) probably reflects the regulation of xenobiotic metabolism enzymes, as well as the PPAR
-induced Cyp4a genes (Cyp4a10 and Cyp4a14), which are also expressed in microsomes. The observed overrepresentation of mitochondrial genes is likely to reflect the induction of fatty acid metabolism genes involved in the PP response.
|
|
|
A comparison of the statistically significantly overrepresented GO terms in Table 1 with the Cellular Components identified, as shown in Figure 3B, implies that the majority of GO annotations identified as associated with the DEHP-regulated probe sets are not overrepresented when compared to the liver transcriptome. Although some genes that are not overrepresented may be biologically important (e.g., see DEHP-responsive genes related to epigenetic status discussed below), our analysis illustrates the utility of overrepresentation analysis, when compared to a mere listing of GO terms, for the identification of the collective effects of DEHP treatment on biological processes.
There are many tools in the public domain that perform the overrepresentation analysis of Gene Ontology described above, we chose to use GOStat because of its Web-based interface and ease of use. The analyses performed by PANTHER use equivalent statistical tools to compare two gene lists; however, Applied Biosystems (ABI) has created its own ontology as the basis for the comparative analysis. A comparison of Tables 2 and 3 with Tables 2 and 3 of the Supplementary Data online indicates the high degree of concordance among the three methods; especially with regard the identification of the overrepresentation of genes in lipid (fatty acid) metabolism, steroid metabolism, blood clotting, complement activation, and the circadian clock. PANTHER also reproduces identification of the "Defense response" genes by GOStat, under the term "Immunity and Defense." Therefore the broad conclusions drawn from these different tools are the same, and either of them could be used for an initial analysis. However, there are some differences between the outputs of these tools.
PANTHER analysis also identified as overrepresented a number of genes involved in transporting the components of those metabolic pathways identified as overrepresented by all three methodse.g., "lipid and fatty acid transport" and "small molecule transport." Also, genes involved in proteolysis and monosaccharide metabolism were identified as overrepresented by PANTHER but not by GOStat or MAPPFinder. The reason for this probably lies in the structure of the ontology used as the basis for the comparisons, because these four PANTHER biological process terms have many (GO terms), whereas only one (PANTHER term) maps in the PANTHER ontology, which may indicate that there is an advantage to using PANTHER for identifying increases in a number of related biological processes. Indeed, searching for overrepresented "molecular function" Gene Ontology terms using GOStat does indicate an overrepresentation of a number of diverse transport genes (data not shown). Clearly, an analysis involving multiple tools gives useful complementary information.
This mapping of GO processes to a single term may also be a disadvantage if the aggregated processes are, in fact, always biologically distinct. For example, the ER-overload GO term is mapped, along with a number of other specific stress responses, to a much more general "Stress Response" biological process. Given that stress-responses are discrete biological processes, the PANTHER ontology may be less useful for the identification of cellular stress through overrepresentation analysis. This problem may be inherent in the design of flat ontologies, and it demonstrates that great care should be taken in the design of ontologies for the purpose of microarray data analysis to avoid losing important biological information. It also points to the importance of using complementary data analysis tools for a thorough evaluation of genomic data.
We conclude that GO mapping of transcript profiling data can correctly identify known molecular pathways and cellular processes targeted by a PP. These include acyl-CoA and fatty acid metabolism pathways and the peroxisomal cellular component, findings consistent with previous studies of DEHP-induced liver cell growth and proliferation. In addition, our GO mapping analyses significantly extend previous observations of DEHP-dependent gene expression changes associated with a wide range of biological responses, including amino acid metabolism, hemostasis, complement activation, and steroid metabolism (Tables 2 and 3; Fig. 4), and they reveal novel insights into the mechanisms of DEHP action, including roles for the endoplasmic reticulum overload response and circadian rhythms (described in more detail below). Therefore, GO and pathway mapping are powerful approaches to generating an unbiased view of the biological processes and cellular components that are regulated by DEHP at the transcriptional level.
|
|
Steroid Metabolism
The liver is a major center for steroid hormone metabolism and is vital for the correct functioning of a number of hormonal systems. Interestingly, our GO and GenMAPP analyses (Tables 2 and 3) identified genes associated with steroid hormone metabolism as being overrepresented in the DEHP-altered gene list. Consistent with our observations, Wong and Gill (2002) demonstrated altered expression of some genes involved in steroid hormone metabolism in the livers of mice dosed for 13 weeks. Additionally Fan et al. (1998)
showed that peroxisome proliferators can increase the expression of estrogen-metabolizing enzymes. Our data extend these observations, by showing that four isoforms of 3-ß-hydroxysteroid dehydrogenase (2, 3, 5, and 6) and 3-isoforms of 17-ß-hydroxysteroid dehydrogenase are coordinately regulated by DEHP. The Hsd3b-isoforms are all downregulated from 2 h after DEHP treatment (Fig. 4B), with peak decreases seen at 8 h (see Fig 4B inset graph). Interestingly, Wong and Gill (2002)
reported large sustained alterations in Hsd3b gene expression after 13 weeks of dosing, whereas we observed only transient changes in Hsd3b isoforms. Therefore, it seems likely that any dramatic alteration in their expression occurs only with longer term dosing with DEHP.
Early Signaling Responses to DEHP
Several GO terms associated with stress, defense, and regulatory responses were identified as overrepresented in response to DEHP: for example, "endoplasmic reticulum (ER)-overload response," "response to stress," and "negative regulation of protein kinase activity" (Table 2).
ER Overload Response and Apoptosis
The ER-overload response is defined by AmiGO (www.godatabase.org), as "the series of molecular signals generated by the accumulation of normal or misfolded proteins in the endoplasmic reticulum and leading to activation of transcription by NF-kappaB." The GO cellular component analysis (Table 1) indicates alterations in the expression of many genes whose products are resident in the ER. Interestingly a specialized ER compartment has been proposed to be important for peroxisome biosynthesis (Geuze et al., 2003). Our histological studies showed increased eosin staining after 48 h (data not shown), which may be representative of increased smooth ER or peroxisome proliferation. Also, the ER-overload response has been reported to be activated by overexpression of cytochrome P450s (Szczesna-Skorupa et al., 2004
). Interestingly both Macdonald et al. (2000)
and Anderson et al. (2004a)
have shown that peroxisome proliferators induce proteins involved in proteome maintenance in a PPAR
-dependent manner and so may provide evidence for ER or other stress responses. Irrespective of the mechanism involved, activation of the ER-overload response would be expected to lead to a block in apoptosis via activation of NF-
B (nuclear factor kappaB) (Szczesna-Skorupa et al., 2004
). It is therefore possible that the DEHP-induced ER-overload response drives an anti-apoptotic mechanism that may play a role in DEHP-induced carcinogenesis by preventing the normal apoptotic clearance of tumorigenic cells (Oliver and Roberts, 2002
).
Signaling Pathways
Inspection of the genes associated with the overrepresented GO cluster "response to stress" reveals the presence of a number of known components of the TNF/IL-1 (tumor necrosis factor/interleukin-1) signaling pathways, including Irak2, Myd88, mitogen-activated protein kinase kinase kinase 7 interacting protein 2 (Map3k7ip2, TAB2), and Ikbkg (Fig. 5A). These genes have a similar time course of expression (see Table 4), with increases early (2 h) after DEHP treatment that decline at later times (24 h). Interestingly, it has been shown that, in the presence of PPs, non-parenchymal cells (probably Kupffer cells) secrete factors (e.g., TNF, IL-1) that stimulate hepatocytes to enter S phase (Hasmall et al., 2001
). For PP-induced S-phase entry, PPAR
is required in the hepatocytes but not in the non-parenchymal cells (Hasmall et al., 2001
), indicating that a PPAR
-dependent gene expression change in the hepatocytes is required to respond to the levels of cytokines secreted by the non-parenchymal cells. Additionally, there is evidence that the p38 and PPAR
signaling pathways are both involved in the proliferative response of rodent hepatocytes upon PP exposure (reviewed by Roberts et al., 2002
)
|
These alterations in the expression levels of key signaling components may therefore create a DEHP-induced sensitization of hepatocytes to the levels of cytokines prevailing in the hepatic milieu. Overall, the rapid (within 2 h) DEHP-induced alterations in the expression of various stress-response genes are consistent with the activation of NF-B and p38 signaling, leading to a pro-proliferative and anti-apoptotic phenotype in hepatocytes (Roberts et al., 2002
) that drives subsequent liver weight increases and proliferation (Fig. 1B).
Circadian Genes
A novel finding of both our Gene Ontology and GenMAPP analyses of DEHP-responsive genes was the overrepresentation of genes with a "circadian" GO annotation (Fig. 6A and Tables 2 and 3). This GO term is defined as "the specific actions or reactions of an organism that recur with a regularity of approximately 24 hours" (AmiGO, www.godatabase.org). DEHP-responsive genes with this annotation include genes that encode circadian rhythm transcriptional regulators (e.g., Per2, Cry1, Clock, and Dbp; Schibler and Sassone-Corsi, 2002; Oishi et al., 2003
) and genes that exhibit a circadian rhythm in their gene expression pattern (e.g., G0s2, Map3k7ip2; Oishi et al., 2003
; Zambon et al., 2003
). Our GO mapping data therefore suggest that DEHP regulates the circadian timing of cellular responses in the liver at the transcriptional level. Importantly, recent observations indicate a direct link between components of the circadian rhythm regulatory machinery and the timing of cell division in the liver (Matsuo et al., 2003
). Thus, the DEHP-induced hepatocyte proliferation and liver enlargement observed in the present study (Fig. 1) may be caused in part by alterations in the circadian rhythm gene pathways. Whether circadian rhythm genes are under direct control of PPAR
remains to be determined, but it is noteworthy that Ppara expression levels have been reported to be regulated with a circadian rhythm (Lemberger et al., 1996
), which in turn may regulate the circadian expression patterns of certain PPAR
-target genes (Patel et al., 2001
).
|
One of the earliest DEHP-induced alterations in circadian gene expression was observed for the G0/G1 switch gene 2 (G0s2). The mRNA levels of G0s2 have been reported to follow a circadian pattern of expression in the mouse liver and skeletal muscle (Zambon et al., 2003). Consistent with circadian regulation, we found that G0s2 gene expression levels varied (up to fivefold) with time in vehicle-treated control animals (Fig 6B). Strikingly, DEHP treatment induced a much larger upregulation in G0s2 expression that was maximal (29-fold relative to the control gene) at 8 h (Fig. 6B).
G0s2 was initially identified as an upregulated transcript in human T lymphocytes upon lectin-induced proliferation (Siderovski et al., 1990), and its expression is repressed by treatment with the immunosuppressant cyclosporin A (Cristillo et al., 1997
). Although these data point toward a role of G0s2 in the mechanism of G0 to G1 transition, the gene product has also been reported to be upregulated upon retinoic acid-mediated cell growth arrest in the human acute promyelocytic leukemia cell line, NB4 (Tamayo et al., 1999
). These apparent contradictions of function could be explained by our observations that GOs2 has a similar DEHP-induced expression pattern to that of the Cdkn1a cell cycle regulator (see Table 1 in the Supplementary Data online), and thus may have a similar function (i.e., to inhibit cell cycle progression; Ekholm and Reed, 2000
). However, we have recently shown that a transient increase in Cdkn1a precedes nuclear hormone receptordriven proliferation in vivo (Moggs et al., 2004
) and therefore increases in the expression of negative cell-cycle regulators may stall the cell cycle until the preparations for progression into S phase have been completed.
Genes associated with epigenetic status.
Our initial unbiased Gene Ontology mapping identified genes associated with the term "regulation of gene expression, epigenetic" (Fig. 3A) as being DEHP regulated, although our subsequent overrepresentation analysis (Table 2) did not highlight this biological process. Given that a growing body of evidence suggests that alterations in epigenetic status, particularly DNA methylation patterns, accompany, and may even promote, carcinogenesis induced by non-genotoxic chemicals (Bombail et al., 2004; Watson and Goodman, 2002
), we explored whether DEHP might also be associated with early alterations in the expression of genes associated with epigenetic status. Therefore, we also interrogated the 1786 DEHP-responsive genes, using both hand-edited gene lists and GO terms associated with known mechanisms of epigenetic regulation of gene expression, including "DNA methylation," "epigenetic," "chromatin," "one-carbon compound metabolism."
Using this approach, we identified methyl CpG-binding domain protein (Mbd1) as an early DEHP-responsive gene that was initially upregulated and was then repressed at later times (see Table 1 of the Supplementary Data online). Mbd1 binds to methylated DNA sequences and is involved in transcriptional repression and the epigenetic regulation of gene expression (Fujita et al., 2003). The DEHP-dependent repression of Mbd1 may thus function to activate the transcription of downstream target genes at later times. Furthermore, we also identified DEHP-induced changes in the expression of a number of genes involved in one-carbon compound metabolism, including enzymes involved in cellular methylation reactions (e.g., methionine adenosyltransferase 2A (Mat2a), serine hydroxymethyltransferase 1 and 2 (Shmt1 and Shmt2), formyltetrahydrofolate dehydrogenase (Fthfd), and S-adenosylhomocysteine hydrolyase (Ahcy). Epigenetic regulation of gene expression is intimately associated with both DNA and histone methylation, and these processes require methyl donors in the form of S-adenosyl methionine (SAM). Thus, the observed DEHP-dependent alterations in cellular methylation reactions linked to SAM synthesis may modulate the activities of DNA and histone methyltransferases, and they might lead to alterations in the epigenetic status of the genome. The functional consequences of these changes may promote carcinogenesis, as it has been demonstrated that dietary manipulation of compounds involved in one-carbon compound metabolism (and SAM production) are associated with DNA hypomethylation and can lead to cancer in rodent liver (Wilson et al., 1984
).
Our studies demonstrate that, although unsupervised GO mapping and subsequent overrepresentation analysis constitute a powerful unbiased approach for holistically defining the biological pathways and processes associated with DEHP-induced proliferation, complementary supervised GO mapping approaches, perhaps guided by the initial unsupervised analysis and our a priori knowledge of those biological pathways known to be important, will provide additional complementary insights.
![]() |
CONCLUSIONS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
In this study we have focused on early gene expression changes that may play important roles in the development of altered pathology. The persistence of critical changes that determine alteration of apoptosis and cell proliferation needs further assessment. Furthermore, the pathways identified in this study might be examined in cultured hepatocytes, including those of humans, to identify potential species differences. The application of GO-mapping approaches similar to those described here are also likely to become increasingly important for the interpretation of toxicogenomic data generated during the development, risk assessment, and regulation of novel pharmaceuticals and chemicals (Cunningham et al., 2003; Freeman, 2004
; Frueh et al., 2004
; Orphanides, 2003
; Pettit, 2004
).
![]() |
SUPPLEMENTARY MATERIAL |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
![]() |
NOTES |
---|
![]() |
ACKNOWLEDGMENTS |
---|
![]() |
REFERENCES |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Anderson, S. P., Dunn, C., Laughter, A., Yoon, L., Swanson, C., Stulnig, T. M., Steffensen, K. R., Chandraratna, R. A. S., Gustaffsson, J.-A., and Corton, J. C. (2004b). Overlapping transcriptional programs regulated by the nuclear receptors peroxisome proliferator-activated receptor a, retinoid X receptor, and liver X receptor in mouse liver. Mol. Pharmacol. 66, 14401452.
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T. et al. (2000). Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 2529.[CrossRef][ISI][Medline]
Balsalobre, A., Brown, S. A., Marcacci, L., Tronche, F., Kellendonk, C., Reichardt, H. M., Schutz, G., and Schibler, U. (2000). Resetting of circadian time in peripheral tissues by glucocorticoid signaling. Science 289, 23442347.
Beissbarth, T., and Speed, T. P. (2004). GOstat: Find statistically overrepresented gene ontologies within a group of genes. Bioinformatics 20, 14641465.[Abstract]
Bombail, V., Moggs, J. G., and Orphanides, G. (2004). Perturbation of epigenetic status by toxicants. Toxicol. Lett. 149, 5158.[CrossRef][ISI][Medline]
Cheng, J., Sun, S., Tracy, A., Hubbell, E., Morris, J., Valmeekam, V., Kimbrough, A., Cline, M. S., Liu, G., Shigeta, R. et al. (2004). NetAffx Gene Ontology Mining Tool: A visual approach for microarray data analysis. Bioinformatics 20, 14621463.[Abstract]
Corton, J. C., Fan, L.-Q., Brown, S., Anderson, S. P., Bocos, C., Cattley, R. C., Mode, A., and Gustafsson J.-A. (1998). Down-regulation of cytochrome P450 2C family members and positive acute-phase response gene expression by peroxisome proliferator chemicals. Mol. Pharmacol. 54, 463473.
Corton, J. C., Apte, U., Anderson, S. P., Limaye, P., Yoon, L., Latendresse, J., Dunn, C., Everitt, J. I., Voss, K. A., Swanson, C. et al. (2004). Mimetics of caloric restriction include agonists of lipid-activated nuclear receptors. J. Biol. Chem. 279, 4620446212.
Cristillo, A. D., Heximer, S. P., Russell, L., and Forsdyke, D. R. (1997). Cyclosporin A inhibits early mRNA expression of G0/G1 switch gene 2 (G0S2) in cultured human blood mononuclear cells. DNA Cell Biol 16, 14491458.[ISI][Medline]
Cunningham, M. L., Bogdanffy, M. S., Zacharewski, T. R., and Hines, R. N. (2003). Workshop overview: Use of genomic data in risk assessment. Toxicol. Sci. 73, 209215.
Dahlquist, K. D., Salomonis, N., Vranizan, K., Lawlor S. C., and Conklin, B. R. (2002). GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways. Nat. Genet. 31, 1920.[CrossRef][ISI][Medline]
De Smaele, E., Zazzeroni, F., Papa, S., Nguyen, D. U., Jin, R., Jones, J., Cong, R., and Franzoso, G. (2001). Induction of gadd45beta by NF-kappaB downregulates pro-apoptotic JNK signalling. Nature 414, 308313.[CrossRef][ISI][Medline]
Doniger, S. W., Salomonis, N., Dahlquist, K. D., Vranizan, K., Lawlor, S. C., and Conklin, B. R. (2003). MAPPFinder: Using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biol. 4, R7.[CrossRef][Medline]
Ekholm, S. V., and Reed, S. I. (2000). Regulation of G1 cyclin-dependent kinases in the mammalian cell cycle. Curr. Opin. Cell Biol.12, 676684.[CrossRef][ISI][Medline]
Elisaf, M. (2002). Effects of fibrates on serum metabolic parameters. Curr. Med. Res. Opin. 18, 269276.[CrossRef][ISI][Medline]
Fan, L. Q., Cattley, R. C., and Corton, J. C. (1998). Tissue-specific induction of 17 beta-hydroxysteroid dehydrogenase type IV by peroxisome proliferator chemicals is dependent on the peroxisome proliferator-activated receptor alpha. J. Endocrinol. 158, 237246.
Fielden, M. R., and Zacharewski, T. R. (2001). Challenges and limitations of gene expression profiling in mechanistic and predictive toxicology. Toxicol. Sci. 60, 610.
Freeman, K. (2004). Toxicogenomics data: The road to acceptance. Environ. Health Perspect. 112, A678A685.[ISI][Medline]
Frueh, F. W., Huang, S. M., and Lesko, L. J. (2004). Regulatory acceptance of toxicogenomics data. Environ. Health Perspect. 112, A663A664.[ISI][Medline]
Fujita, N., Watanabe, S., Ichimura, T., Tsuruzoe, S., Shinkai, Y., Tachibana, M., Chiba, T., and Nakao, M. (2003). Methyl-CpG binding domain 1 (MBD1) interacts with the Suv39h1-HP1 heterochromatic complex for DNA methylation-based transcriptional repression. J. Biol. Chem. 278, 2413224138.
Gervois, P., Vu-Dac, N., Kleemann, R., Kockx, M., Dubois, G., Laine, B., Kosykh, V., Fruchart, J. C., Kooistra, T., and Staels, B. (2001). Negative regulation of human fibrinogen gene expression by peroxisome proliferator-activated receptor alpha agonists via inhibition of CCAAT box/enhancer-binding protein beta. J. Biol. Chem. 276, 3347133477.
Geuze, H. J., Murk, J. L., Stroobants, A. K., Griffith, J. M., Kleijmeer, M. J., Koster, A. J., Verkleij, A. J., Distel, B., and Tabak, H. F. (2003). Involvement of the endoplasmic reticulum in peroxisome formation. Mol. Biol. Cell 14, 29002907.
Gonzalez, F. J. (2002). The peroxisome proliferator-activated receptor alpha (PPARalpha): Role in hepatocarcinogenesis. Mol. Cell Endocrinol. 193, 7179.[CrossRef][ISI][Medline]
Hamadeh, H. K., Bushel, P. R., Jayadev, S., Martin, K., DiSorbo, O., Sieber, S., Bennett, L., Tennant, R., Stoll, R., Barrett, J. C. et al. (2002). Gene expression analysis reveals chemical-specific profiles. Toxicol. Sci. 67, 219231.
Hara, R., Wan, K., Wakamatsu, H., Aida, R., Moriya, T., Akiyama, M., and Shibata, S. (2001). Restricted feeding entrains liver clock without participation of the suprachiasmatic nucleus. Genes Cells 6, 26978.
Hasmall, S., James, N., Hedley, K., Olsen, K., and Roberts, R. (2001). Mouse hepatocyte response to peroxisome proliferators: Dependency on hepatic nonparenchymal cells and peroxisome proliferator activated receptor alpha (PPARalpha). Arch. Toxicol. 75, 357361.[CrossRef][ISI][Medline]
Hasmall, S. C., West, D. A., Olsen, K., and Roberts, R. A. (2000). Role of hepatic non-parenchymal cells in the response of rat hepatocytes to the peroxisome proliferator nafenopin in vitro. Carcinogenesis 21, 21592165.
Heinloth, A. N., Irwin, R. D., Boorman, G. A., Nettesheim, P., Fannin, R. D., Sieber, S. O., Snell, M. L., Tucker, C. J., Li, L., Travlos, G. S. et al. (2004). Gene expression profiling of rat livers reveals indicators of potential adverse effects. Toxicol. Sci. 80, 193202.
Hurtt, M. E., Elloit, G. S., Cook, J. C., Obourn, J. D., Frame, S. R., and Biegel, L. B. (1997). Induction of coagulation effects by Wyeth-14643 in CRL:CD® BR rats. Drug Chem. Toxicol. 20, 110.[ISI][Medline]
Irwin, R. D., Boorman, G. A., Cunningham, M. L., Heinloth, A. N., Malarkey, D. E., and Paules, R. S. (2004). Application of toxicogenomics to toxicology: Basic concepts in the analysis of microarray data. Toxicol. Pathol. 32 (Suppl 1), 7283.[CrossRef]
James, N. H., Soames, A. R., and Roberts, R. A. (1998). Suppression of hepatocyte apoptosis and induction of DNA synthesis by the rat and mouse hepatocarcinogen diethylhexylphlathate (DEHP) and the mouse hepatocarcinogen 1,4-dichlorobenzene (DCB). Arch. Toxicol. 72, 78490.[CrossRef][ISI][Medline]
Janssens, S., and Beyaert, R. (2003). Functional diversity and regulation of different interleukin-1 receptor-associated kinase (IRAK) family members. Mol. Cell 11, 293302.[CrossRef][ISI][Medline]
Johnson, G. (2002). Scaffolding proteinsMore than meets the eye. Science 295, 12491250.
Karpinets, T. V., Foy, B. D., and Frazier, J. M. (2004). Tailored gene array databases: Applications in mechanistic toxicology. Bioinformatics 20, 507517.
Kersten, S., Mandard, S., Escher, P., Gonzalez, F. J., Tafuri, S., Desvergne, B., and Wahli, W. (2001). The peroxisome proliferator-activated receptor alpha regulates amino acid metabolism. FASEB J. 15, 19711978.
Klaunig, J. E., Babich, M. A., Baetcke, K. P., Cook, J. C., Corton, J. C., David, R. M., DeLuca, J. G., Lai, D. Y., McKee, R. H., Peters, J. M. et al. (2003). PPARalpha agonist-induced rodent tumors: Modes of action and human relevance. Crit. Rev. Toxicol. 33, 655780.[ISI][Medline]
Kockx, M., Gervois, P. P., Poulain, P., Derudas, B., Peters, J. M., Gonzalez, F. J., Princen, H. M., Kooistra, T., and Staels, B. (1999). Fibrates suppress fibrinogen gene expression in rodents via activation of the peroxisome proliferator-activated receptor-alpha. Blood 93, 29912998.
Kroetz, D. L., Yook, P., Costet, P., Bianchi, P., and Pineau, T. (1998). Peroxisome proliferator-activated receptor alpha controls the hepatic CYP4A induction adaptive response to starvation and diabetes. J. Biol. Chem. 273, 3158131589.
Lemberger, T., Saladin, R., Vazquez, M., Assimacopoulos, F., Staels, B., Desvergne, B., Wahli, W., and Auwerx, J. (1996). Expression of the peroxisome proliferator-activated receptor alpha gene is stimulated by stress and follows a diurnal rhythm. J. Biol. Chem. 271, 17641769.
Li, S., Becich, M. J., and Gilbertson, J. (2004). Microarray data mining using gene ontology. Medinfo 2004, 778782.
Liu, G., Loraine, A. E., Shigeta, R., Cline, M., Cheng, J., Valmeekam, V., Sun, S., Kulp, D., and Siani-Rose, M. A. (2003). NetAffx: Affymetrix probe sets and annotations. Nucleic Acids Res. 31, 8286.
Lovett, R. A. (2000). Toxicogenomics. Toxicologists brace for genomics revolution. Science 289, 536537.
Macdonald, N., Barrow, K., Tonge, R., Davidson, M., Roberts, R. A., and Chevalier, S. (2000). PPARa-dependent alterations of GRP95 expression in mouse hepatocytes. Biochem. Biophys. Res. Commun. 277, 699704.[CrossRef][ISI][Medline]
Macdonald, N., Chevalier, S., Tonge, R., Davison, M., Rowlinson, R., Young, J., Rayner, S., and Roberts, R. (2001). Quantitative proteomic analysis of mouse liver response to the peroxisome proliferator diethylhexylphthalate (DEHP). Arch. Toxicol. 75, 415424.[CrossRef][ISI][Medline]
Mandard, S., Muller, M., and Kersten, S. (2004). Peroxisome proliferator-activated receptor alpha target genes. Cell Mol. Life Sci. 61, 393416.[CrossRef][ISI][Medline]
Matsuo, T., Yamaguchi, S., Mitsui, S., Emi, A., Shimoda, F., and Okamura, H. (2003). Control mechanism of the circadian clock for timing of cell division in vivo. Science 302, 255259.
McMillian, M., Nie, A. Y., Parker, J. B., Leone, A., Kemmerer, M., Bryant, S., Herlich, J., Yieh, L., Bittner, A. et al. (2004). Inverse gene expression patterns for macrophage activating hepatotoxicants and peroxisome proliferators in rat liver. Biochem. Pharmacol. 67, 21412167.[CrossRef][ISI][Medline]
Moggs, J. G., Tinwell, H., Spurway, T., Chang, H. S., Pate, I., Lim, F. L., Moore, D. J., Soames, A., Stuckey, R. Currie, R. et al. (2004). Phenotypic anchoring of gene expression changes during estrogen-induced uterine growth. Environ. Health Perspect. 112, 15891606.[ISI][Medline]
Nuwaysir, E. F., Bittner, M., Trent, J., Barrett J. C., and Afshari, C. A. (1999). Microarrays and toxicology: The advent of toxicogenomics. Mol. Carcinog. 24, 153159.[CrossRef][ISI][Medline]
Oishi, K., Miyazaki, K., Kadota, K., Kikuno, R., Nagase, T., Atsumi, G., Ohkura, N., Azama, T., Mesaki, M., Yukimasa, S. et al. (2003). Genome-wide expression analysis of mouse liver reveals CLOCK-regulated circadian output genes. J. Biol. Chem. 278, 4151941527.
Oliver, J. D., and Roberts, R. A. (2002). Receptor-mediated hepatocarcinogenesis: role of hepatocyte proliferation and apoptosis. Pharmacol. Toxicol. 91, 17.[CrossRef][ISI][Medline]
Orphanides, G. (2003). Toxicogenomics: Challenges and opportunities. Toxicol. Lett. 140141, 145148.[ISI]
Patel, D. D., Knight, B. L., Wiggins, D., Humphreys, S. M., and Gibbons, G. F. (2001). Disturbances in the normal regulation of SREBP-sensitive genes in PPAR alpha-deficient mice. J. Lipid Res. 42, 328337.
Paules, R. (2003). Phenotypic anchoring: linking cause and effect. Environ. Health Perspect. 111, A338A339.[ISI][Medline]
Pettit, S. D. (2004). Toxicogenomics in risk assessment: communicating the challenges. Environ. Health Perspect. 112, A662.[ISI][Medline]
Roberts, R. A., Chevalier, S., Hasmall, S. C., James, N. H., Cosulich, S. C., and Macdonald, N. (2002). PPAR alpha and the regulation of cell division and apoptosis. Toxicology 181182, 167170.[ISI]
Rutter, J., Reick, M., and McKnight, S. L. (2002). Metabolism and the control of circadian rhythms. Annu. Rev. Biochem. 71, 307331.[CrossRef][ISI][Medline]
Schibler, U., and Sassone-Corsi, P. (2002). A web of circadian pacemakers. Cell 111, 919922.[CrossRef][ISI][Medline]
Schulze, P. C., Yoshioka, J., Takahashi, T., He, Z., King, G. L., and Lee, R. T. (2004). Hyperglycemia promotes oxidative stress through inhibition of thioredoxin function by thioredoxin-interacting protein. J. Biol. Chem. 279, 3036930374.
Siderovski, D. P., Blum, S., Forsdyke, R. E., and Forsdyke, D. R. (1990). A set of human putative lymphocyte G0/G1 switch genes includes genes homologous to rodent cytokine and zinc finger protein-encoding genes. DNA Cell Biol. 9, 579587.[ISI][Medline]
Szczesna-Skorupa, E., Chen, C. D., Liu, H., and Kemper, B. (2004). Gene expression changes associated with the endoplasmic reticulum stress response induced by microsomal cytochrome p450 overproduction. J. Biol. Chem. 279, 1395313961.
Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E. S., and Golub, T. R. (1999). Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. Proc. Natl. Acad. Sci. U. S. A. 96, 29072912.
Watson, R. E., and Goodman, J. I. (2002). Epigenetics and DNA methylation come of age in toxicology. Toxicol. Sci. 67, 1116.
Wilson, M. J., Shivapurkar, N., and Poirier, L. A. (1984). Hypomethylation of hepatic nuclear DNA in rats fed with a carcinogenic methyl-deficient diet. Biochem. J. 218, 987990.[ISI][Medline]
Wong, J. S., and Gill, S. S. (2002). Gene expression changes induced in mouse liver by di(2-ethylhexyl) phthalate. Toxicol. Appl. Pharmacol. 185, 180196.[CrossRef][ISI][Medline]
Yadetie, F., Laegreid, A., Bakke, I., Kusnierczyk, W., Komorowski, J., Waldum, H. L., and Sandvik, A. K. (2003). Liver gene expression in rats in response to the peroxisome proliferator-activated receptor- agonist ciprofibrate Physiol. Genomics 15, 919.
Yamazaki, K., Kuromitsu, J., and Tanaka, I. (2002). Microarray analysis of gene expression changes in mouse liver induced by peroxisome proliferator- activated receptor alpha agonists. Biochem. Biophys. Res. Commun. 290, 11141122.[CrossRef][ISI][Medline]
Zambon, A. C., McDearmon, E. L., Salomonis, N., Vranizan, K. M., Johansen, K. L., Adey, D., Takahashi, J. S., Schambelan, M., and Conklin, B. R. (2003). Time- and exercise-dependent gene regulation in human skeletal muscle. Genome Biol. 4, R61.[CrossRef][Medline]
|