Differentially expressed genes
Agilent 4 × 44k Chicken Gene Expression microarrays were used to identify changes in gene transcription in CEH exposed for 24 h to different isomeric mixtures of PFOS relative to a vehicle control group. Array hybridizations were performed using RNA from CEH exposed to 10 µM L-PFOS, 40 µM L-PFOS, and 10 µM T-PFOS (henceforth referred to as L10, L40, and T10, respectively) and the DMSO solvent control (n = 5 per dose group). Cells exposed to 40 µM T-PFOS had a statistically significant decrease in total RNA yield (identified by ANOVA, p < 0.05) and could therefore not be used for the microarray analysis. This was not observed in the independent culture used to investigate dose–response relationships. Therefore, it is unclear whether this effect was due to T-PFOS exposure.
Microarray data were submitted to the National Center for Biotechnical Information Gene Expression Omnibus following MIAME protocols. A total of 447 probe sets showed differential expression >1.5-fold (FDR p < 0.05) in CEH following exposure to either L-PFOS or T-PFOS relative to the DMSO control group. Among these, 334 genes were upregulated and 113 were downregulated. One hundred seven genes were dysregulated (up- or downregulated) by L-PFOS only (both doses), 178 affected only by T-PFOS, and 162 affected by both L-PFOS and T-PFOS (Fig. 1). The numbers of probes up- and downregulated in each treatment are summarized in Table 1. Perfluorooctane sulfonate T10 altered the expression of approximately 2.5 times as many probes as L10 and 1.5 times as many probes as L40. A detailed list of the probes differentially expressed by PFOS-treated cells is presented in Supplemental Data, Table S2.
Figure 1. Venn diagram illustrating the numbers of genes that were dysregulated (fold change >1.5, p < 0.05) by either 10 or 40 µM linear perfluorooctane sulfonate (L10 and L40, respectively) or 10 µM technical-grade perfluorooctane sulfonate (T10).
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Table 1. Number of genes differentially expressed (fold change >1.5, p < 0.05) in chicken embryonic hepatocytes exposed to 10 or 40 µM linear or 10 µM technical-grade perfluorooctane sulfonate (L10, L40, or T10, respectively)
|Treatment||No. of genes|
The highest increase in expression was for myosin heavy chain 6 (Myh6), which was greater than threefold in all treatment groups. Typically, Myh6 is expressed only in ventricular and extraocular muscle 30. However, one EST, ChEST143b11, a possible splice variant consisting of Myh6 intron and exon sequence, has been detected in chicken liver tissue 31. The probe for this variant also showed differential expression, although significantly only in T10 (1.7-fold increase, gene ID CR353427). These results indicate that PFOS interferes with the transcriptional regulation of this gene locus. The role of Myh6 in the response to PFOS is unclear. It is a type II myosin usually associated with contractile activity. It may be involved in the contraction of bile canaliculi 32, which, if improperly regulated, can lead to intrahepatic cholestasis and interfere with lipid metabolism. Functional analysis revealed that several other genes associated with intrahepatic cholestasis were also dysregulated by PFOS (shown below under Functional analysis).
The largest increase in expression that was unique to L-PFOS was a 2.2-fold increase in the abundance of a transcript identified as LOC770996. This transcript encodes a protein that has 80% sequence identity to rat and mouse L-gulonolactone oxidase (GULO), an enzyme involved in L-ascorbate (vitamin C) production. Although not typically produced in chicken liver 33, L-ascorbate plays several important biological roles, including oxidative stress relief and collagen synthesis. This coincides with observations that PFOS can increase production of reactive oxygen species and induce related genes 34, 35. Two other genes downstream of L-ascorbate production were also affected by L-PFOS and T-PFOS (Supplemental Data, Fig. S1), providing further evidence that this pathway was affected by PFOS exposure. Why expression of LOC770996 was not affected by T-PFOS in the present study is unclear. It is possible that this particular gene is affected only by higher exposures to L-PFOS, which are not reached using the impure T-PFOS, or perhaps upregulation of this gene is somehow prevented by branched PFOS isomers.
The largest unique increase to T-PFOS was a 2.2-fold increase in the expression of type II iodothyronine deiodinase (Dio2), which converts thyroxine to its more active form, tri-iodothyronine. This is consistent with observations of reduced serum thyroxine in animals exposed to PFOS 36, 37. A significant increase in expression for a gene with high sequence similarity to thyroid hormone receptor beta 2 (gene ID X62642) was also observed only in the T-PFOS dose group. The fact that Dio2 and X62642 were perturbed only by T-PFOS, and not by L-PFOS, suggests that interference with active thyroid hormone levels may be instigated by the branched isomers of PFOS. Despite the presence of thyroxine in the culture medium (1 µg/ml) and changes in Dio2 and X62642 expression, an accompanying change in expression for other thyroid hormone receptor-controlled genes was not observed.
The largest overall decrease in expression was an approximately fourfold decrease in glutathione-S-transferase α3 (Gsta3) mRNA in all treatment groups. Downregulation of several other glutathione-S-transferases (GSTs) was observed, including the α4 and ω1 isozymes (Gsta4 and Gsto1, respectively). These genes belong to a class of enzymes that detoxifies reactive electrophilic compounds by catalyzing their conjugation to reduced glutathione (GSH). Liu et al. 34 observed a decrease in total GST activity in response to PFOS exposure in cultured tilapia hepatocytes as well as a decrease in GSH levels. The authors suggested that GST activity was likely diminished to help conserve unconjugated GSH as a response to the oxidative stress resulting from increased lipid metabolism. The decrease in GST mRNA expression in the present study supports this hypothesis. In addition to its role in detoxification, the protein encoded by Gsta3 also has steroid isomerase activity crucial for steroid hormone production, which consumes cholesterol 38. Suppression of Gsta3 expression may also serve to maintain cholesterol levels, which are known to decrease following PFOS exposure 39, 40. This is supported in the present study by the upregulation of several genes involved in cholesterol synthesis, including 3-hydroxy-3-methylglutaryl-CoA reductase (Hmgcr), cytochrome P4508B1 (Cyp8B1), and isopentenyl-diphosphate δ isomerase 1 (Idi1), although these three genes were affected only by T-PFOS, whereas Gsta3 was affected by both L- and T-PFOS. Because GSTs were downregulated in all treatments, their response may be independent of the presence of branched PFOS isomers. The largest decreases in expression detected that were unique to L-PFOS or T-PFOS were 1.9-fold and 1.7-fold decreases in the uncharacterized transcripts KLHDC8B and CN236254, respectively.
Microarray results were validated by real-time RT-PCR analysis for 15 PFOS-responsive genes. In addition to the PFOS samples, the transcriptional response of cytochrome P450 isoforms 1A4 and 1A5 in CEH exposed to TCDD was included for microarray validation purposes. Validation was performed using the same RNA samples used for microarray analysis (n = 2–4). Real-time RT-PCR results were directionally consistent with microarray data for 35 of the 48 conditions (Supplemental Data, Table S3), resulting in a validation rate of 73%. To supplement the microarray results, the expression of four genes was further examined in a separate independent cell culture using a more comprehensive range of exposure concentrations (1–40 µM) to investigate dose–response relationships (n = 2–3). The genes acyl-CoA synthetase long-chain family member 1 (Acsl1), acyl-CoA synthetase bubble gum family member 2 (Acsbg2), amphiregulin (Areg), and Gsta3 were selected for analysis because results for PCR validation closely matched the microarray data both in direction and in significance (see Supplemental Data, Table S3). Both Acsl1 and Acsbg2 genes are involved in fatty acid metabolism. Areg encodes a growth factor that is often associated with liver injury 41, whereas Gsta3, as previously mentioned, is involved in the oxidative stress response. The expression profiles for all four genes were similar to those observed from the microarray analysis (Fig. 2); however, the concentration at which significant effects were detected was higher (with the exception of Gsta3). This likely is due to the variability between the independent cell cultures and the lower samples size for cultures used to examine the broader range of doses. Both L-PFOS and T-PFOS downregulated Gsta3, which is consistent with the microarray analysis, although this effect was not apparent at higher concentrations of T-PFOS. Significant dose-dependent increases in the expression Acsl1, Acsbg2 and Areg were observed in cells exposed to T-PFOS, whereas expression was not affected by L-PFOS, as predicted from the microarray results.
Figure 2. Expression of Acsl1, Acsbg2, Areg, and Gsta3 in cultured chicken embryo hepatocytes following exposure to linear or technical-grade perfluorooctane sulfonate (L-PFOS or T-PFOS, respectively). Data represent the mean and standard deviation from two or three replicates.
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PCA and clustering analysis
The expression profiles of all 20 microarray samples (five per dose group) were reduced to four principal components using the principal component analysis function in GeneSpring GX. Samples are plotted on the first three components in Figure 3. The first principal component (PC1) accounted for 57.6% of the variability among samples. A large proportion of differentially expressed transcripts (379/447), including all 74 that were affected by all three treatments, had absolute PC1 loadings greater than 0.5 (data not shown). This shows that PC1 largely characterized the difference between DMSO- and PFOS-treated samples. Accordingly, all PFOS-treated samples segregated from DMSO samples along the PC1 axis. The L10 samples were an intermediate distance from DMSO samples, whereas L40 and T10 clustered farthest from DMSO along PC 1 but were not separated, suggesting similar expression profiles between these two groups. The second principal component (PC2) accounted for 20.8% of intersample variability, and 115 transcripts had PC2 loadings greater than 0.5. Among these, 87% (100) were differentially expressed by either L-PFOS or T-PFOS, but not by both, demonstrating that PC2 describes mainly the variability between samples treated with different isomeric mixtures of PFOS. This is exemplified by the almost complete separation of the equal-dose samples, L10 and T10, along PC2. Again, L40 and T10 samples were not separated along PC2. The third and fourth principal components (PC3 and PC4) accounted for 11.1% and 10.5% of variability, respectively. However, no major trends in sample separation were observed along PC3 or PC4, suggesting that they captured residual intersample variability.
Figure 3. Expression profile of linear or technical-grade perfluorooctane sulfonate-treated (L-PFOS or T-PFOS, respectively) chicken embryo hepatocytes plotted on three principal components compared with the dimethyl sulfoxide (DMSO) control group. Analysis was based on the expression profiles of 447 dysregulated genes. [Color figure can be seen in the online version of this article, available at wileyonlinelibrary.com.]
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Hierarchical clustering using the differentially expressed gene set was also performed in GeneSpring GX. The expression profile of each sample was clustered based on gene expression and treatment (Fig. 4). Cluster analysis separated DMSO-treated CEH from all PFOS-treated samples. Similar to PCA analysis, L10 and T10 clustered separately. However, hierarchical clustering could not separate L40 samples from the other treatment groups: two L40 samples clustered with L10, whereas the other three clustered with T10. Similar conclusions can be drawn from PCA and hierarchical clustering. Both methods show that T10 and L10 have distinct expression profiles. However, PCA could not distinguish L40 from T10, whereas clustering showed L40 to be intermediate between L10 and T10.
Figure 4. Hierarchical clustering of expression profiles of chicken embryonic hepatocytes exposed to linear and technical-grade perfluorooctane sulfonate (L-PFOS and T-PFOS, respectively) and dimethyl sulfoxide (DMSO) vehicle control. Clustering was based on 447 differentially expressed genes (fold change >1.5, p < 0.05). [Color figure can be seen in the online version of this article, available at wileyonlinelibrary.com.]
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Mapping gene IDs to IPA
The DAVID Gene ID Conversion Tool was used to map microarray gene IDs to human, rat, or mouse orthologs in IPA. For all genes on the array, 12,615 of 42,034 genes (30%) were recognized by IPA as orthologs. Among all dysregulated genes, 197 of 447 (44%) were recognized as orthologs in IPA. Genes that mapped to IPA are identified in the list of differentially expressed genes (Supplemental Data, Table S2). Most unmapped IDs correspond to probes for hypothetical proteins and ESTs with unknown function. Several differentially expressed genes with known functions, however, did not map to orthologs in IPA, such as liver basic fatty acid binding protein (gene ID: LBFAPB) and thyroid hormone receptor β2 (gene ID: X62642), and therefore could not be included in downstream functional, pathway, and interactome analysis.
Ingenuity Pathway Analysis was used to perform a functional enrichment analysis. The relevant functional categories are summarized in Table 2. The specific functions that were significantly enriched in each category are shown in detail in Supplemental Data, Table S4A–C. The top functional categories were lipid metabolism, hepatic system development and disease, cellular movement, and cellular growth and proliferation. Although a similar set of functional categories was enriched for both L-PFOS and T-PFOS, in almost all cases, the number of genes affected in each category was greatest in T10 followed by L40 and then L10. In general, the functional profiles are consistent with similar gene expression studies for mammalian and avian models 37, 42–44. Changes in genes associated with lipid metabolism and cellular proliferation are generally consistent with the activation of peroxisome proliferator activated receptors (PPARs), particularly the PPARα and PPARγ isotypes 45. However, one important difference from mammalian PFOS studies is the lack of response of genes involved in the peroxisomal β-oxidation of fatty acids that are transcriptionally regulated by PPARα. It is believed that much of the toxicity of PFOS is propagated through activation of PPARα in rodents. Differential expression of the β-oxidation genes acyl-CoA oxidase (ACOX), enoyl-CoA hydratase, bifunctional enzyme (BIEN), and peroxisomal 3-ketoacyl-CoA thiolase has been reported in rat liver and cultured rat hepatoma cells following PFOS exposure 42, 43. Other avian gene expression studies, however, reported no apparent correlation between PFOS exposure and β-oxidation 15, 44, 46, although a pair of studies performed in our laboratory reported T-PFOS-induced changes in β-oxidation genes in cultured CEH 18, 24. These changes were significant only after 36 h of exposure at ≥40 µM, which is a longer exposure time and higher concentration than were used in the present study. These results indicate that activation of PPARα may occur as a secondary response or only at high concentrations of PFOS. For 24 h of exposure to 10 µM T-PFOS, the results of Cwinn et al. 24 are in accordance with the present study, including a two to threefold change in liver basic fatty acid binding protein (LBFABP), which was suggested to be independent of PPARα. In the present study, most of the lipid metabolism functions that were enriched were involved not in β-oxidation but rather in the synthesis, modification, and transport of lipids, fatty acids, terpenoids, eicosanoids, steroids. and cholesterol. The differential expression of a similar gene set was also reported for rat hepatic cells exposed to PFCs 37, 42 and may represent PPARα-independent effects on lipid metabolism. The role of PPARα in the avian hepatocyte response to PFOS exposure is addressed further below under Interaction networks and potential regulatory molecules.
Table 2. Enriched functional categories for genes differentially expressed in chicken embryonic hepatocytes due to exposure to 10 or 40 µM linear or 10 µM technical-grade perfluorooctane sulfonate (L10, L40, or T10, respectively)
|p Value range||No. of genes||p Value range||No. of genes||p Value range||No. of genes|
|2||Hepatic system development and disease||2.35E-03–2.30E-02||3||4.72E-06–1.02E-02||12||3.39E-05–1.10E-02||12|
|4||Cellular growth and proliferation||2.81E-02–2.81E-02||1||1.41E-04–1.57E-02||23||2.23E-05–1.10E-02||37|
|5||DNA replication, recombination, and repair||4.73E-03–4.18E-02||1||1.38E-03–1.57E-02||10||1.13E-04–1.10E-02||12|
|7||Cancer|| || ||6.76E-04–1.55E-02||24||1.44E-04–1.10E-02||31|
|8||Cell development and morphology||1.41E-02–1.41E-02||1||1.50E-04–8.19E-03||10||2.21E-03–1.10E-02||18|
|10||Cell-to-cell signaling and interaction||4.73E-03–2.34E-02||2||4.37E-04–7.88E-03||8||4.72E-03–1.10E-02||12|
|11||Amino acid metabolism||4.73E-03–4.63E-02||4||8.96E-04–1.57E-02||5||8.66E-03–1.10E-02||3|
|12||Tissue development and morphology||2.34E-02–2.34E-02||1||1.06E-02–1.37E-02||7||1.28E-03–3.83E-03||5|
|14||Cellular assembly and organization|| || ||7.88E-03–1.57E-02||2||2.43E-03–1.10E-02||3|
|15||Endocrine system development and function||4.73E-03–2.34E-02||3||7.88E-03–7.88E-03||2||3.22E-03–1.10E-02||3|
|16||Inflammatory response|| || ||4.52E-03–6.05E-03||10||6.75E-03–6.75E-03||7|
|17||Nucleic acid metabolism||4.73E-03–9.44E-03||2||7.88E-03–7.88E-03||3||1.10E-02–1.10E-02||4|
|18||Drug metabolism|| || ||7.88E-03–1.57E-02||2|| || |
The large number of dysregulated genes involved in cellular growth and proliferation, hepatic system disease, and DNA replication, as well as an enrichment of cancer-related genes in the L40 and T10 groups, is consistent with the hepatomegaly, hepatocellular adenoma, and hyperplasia that have been observed in mammals 10, 40, 47 and birds 12–14, 16 exposed to PFOS. Several genes in the hepatic system and disease category were also associated with intrahepatic cholestasis (Supplemental Data, Table S4A–C), which, as previously discussed, may be a result of Myh6 overexpression. Although cellular movement was a significantly enriched functional category, most genes from this group are involved in tumor cell migration and cell–cell signaling. Changes in other cell–cell interaction genes such as gap junction proteins, including gap junction protein β1 (Gjb1, also known as Cx32), cell adhesion molecule 1 (Cadm1), and other cell–cell signaling genes are in agreement with impaired gap junction intercellular communication observed in liver tissue and cultured cells 48.
Canonical pathway mapping
Ingenuity pathway analysis was used to map significantly dysregulated genes to canonical pathways in Ingenuity's Knowledgebase. Ingenuity pathway analysis revealed that exposure to L-PFOS and T-PFOS differentially regulated genes belonging to several pathways. Canonical pathways that were significantly affected (p < 0.05) are summarized in Table 3. A more detailed description of all pathways affected by each dose group and the genes that were affected is shown in Supplemental Data, Table S5A–C. Pathways that were most significantly disrupted by PFOS exposure included LPS/IL-1-mediated inhibition of RXR function, glutathione metabolism, LXR/RXR activation, and biosynthesis of steroids. Although these pathways were significantly enriched, the number of genes affected compared with the total number in each pathway is relatively low. This would suggest that neither of these pathways represents the main mechanism of action of PFOS. For example, although the LPS/IL-1-mediated inhibition of RXR function pathway was the most significantly affected, it was actually the genes downstream from this pathway, regulated by RXR's binding partners, CAR, PXR, and PPARs, that were disrupted and not genes involved in the inhibition mechanism (Supplemental Data, Fig. S2). The third most significantly enriched pathway was LXR/RXR activation. These results suggest that PFOS may exert its effect through RXR and its binding partners. However, binding studies have shown that PFOS does not activate human, rat, or mouse RXR 49. Perfluorooctane sulfonates may interfere with RXR heterodimer formation through its binding partners. Perfluorooctane sulfonate does show weak binding to both PPARα and PPARγ 49. Binding studies with CAR, PXR, and LXR would also clarify this issue.
Table 3. Significantly enriched (p < 0.05) canonical pathways for genes differentially expressed in chicken embryonic hepatocytes exposed to 10 or 40 µM linear or 10 µM technical-grade perfluorooctane sulfonate (L10, L40, or T10, respectively).
|Ingenuity canonical pathways||L10||L40||T10||No. of genes in pathway|
|p Value||No. of genes||p Value||No. of genes||p Value||No. of genes|
|LPS/IL-1-mediated inhibition of RXR function||3.16E-02||3||9.77E-04||6||1.95E-04||8||149|
|Biosynthesis of steroids|| || ||1.11E-01||1||5.13E-04||3||22|
|Metabolism of xenobiotics by cytochrome P450||8.71E-03||3||6.92E-04||5||3.16E-03||5||92|
|Aryl hydrocarbon receptor signaling||8.51E-02||2||8.51E-03||4||8.91E-04||6||105|
|Pentose and glucuronate interconversions|| || ||1.15E-03||3||3.02E-03||3||29|
|Pyruvate metabolism|| || ||4.68E-03||3||1.26E-03||4||51|
|Bladder cancer signaling|| || ||5.01E-03||3||1.38E-03||4||45|
|NRF2-mediated oxidative stress response||2.19E-02||3||3.09E-03||5||5.13E-02||4||129|
|Xenobiotic metabolism signaling||2.19E-01||2||3.16E-03||6||1.62E-02||6||188|
|Ascorbate and aldarate metabolism||5.89E-02||1||4.37E-03||2||1.33E-01||1||16|
|Fatty acid metabolism||3.77E-01||1||4.27E-02||3||4.47E-03||5||100|
|Pancreatic adenocarcinoma signaling|| || ||1.74E-02||3||7.24E-03||4||71|
|Glycine, serine, and threonine metabolism|| || ||2.86E-01||1||1.12E-02||3||50|
|Bile acid biosynthesis||1.41E-02||2||3.63E-02||2||6.76E-02||2||47|
|Ovarian cancer signaling|| || ||1.55E-02||3||3.80E-02||3||68|
|Hepatic fibrosis/hepatic stellate cell activation|| || ||1.52E-01||2||1.58E-02||4||89|
|Galactose metabolism|| || ||1.58E-02||2||3.02E-02||2||30|
|Wnt/β-catenin signaling|| || ||5.08E-01||1||1.66E-02||4||90|
|Fructose and mannose metabolism|| || ||2.57E-02||2||4.68E-02||2||35|
|Taurine and hypotaurine metabolism||2.75E-02||1||4.57E-02||1||6.31E-02||1||9|
|Nicotinate and nicotinamide metabolism||2.95E-02||2|| || ||4.72E-01||1||59|
|Airway pathology in chronic obstructive pulmonary disease|| || ||3.09E-02||1||4.27E-02||1||4|
|p53 Signaling|| || ||3.95E-01||1||3.24E-02||3||64|
|Chronic myeloid leukemia signaling|| || ||4.09E-01||1||3.63E-02||3||67|
|Lysine biosynthesis||3.72E-02||1|| || ||8.32E-02||1||8|
|Melanoma signaling|| || ||2.03E-01||1||3.98E-02||2||29|
|Synthesis and degradation of ketone bodies||4.17E-02||1|| || ||9.33E-02||1||10|
|Chondroitin sulfate biosynthesis|| || ||2.09E-01||1||4.17E-02||2||30|
|Keratan sulfate biosynthesis|| || ||2.09E-01||1||4.17E-02||2||30|
|Cell cycle: G2/M DNA damage checkpoint regulation|| || ||2.09E-01||1||4.17E-02||2||30|
|Propanoate metabolism|| || ||4.79E-02||2||8.51E-02||2||49|
Three of the four genes affected in the glutathione metabolism pathway were glutathione-S-transferases, which, as previously mentioned, may be downregulated in order to conserve unconjugated GSH. The fourth was glutaredoxin (Glrx), which was downregulated 1.7-fold in L40. Because Glrx catalyzes the reduction of disulfide bonds, consuming GSH in the process, this enzyme may also be downregulated in order to conserve GSH. We discussed above the upregulation of genes involved in cholesterol production, and pathway analysis demonstrated that all genes affected in the steroid biosynthesis pathway are upregulated and have upstream roles in the production of cholesterol. Again, these may be upregulated as a negative-feedback response to the decrease in cholesterol that often accompanies PFOS exposure. The impact on steroid biosynthesis genes was greatest in T10, suggesting that branched isomers have the greatest effect on these genes. As with the functional analysis, a general trend in the number of genes affected in each pathway was as follows: L10 < L40 < T10.
Interaction networks and potential regulatory molecules
Ingenuity pathway analysis was used to generate interaction networks with mapped orthologs of differentially expressed genes for all three treatment groups. Only direct interactions with gene products that occur in the liver were considered for interaction network construction. An example of one of these networks is shown in Figure 5. This example is one of two networks generated using T10 data. A high-resolution version of Figure 5, as well as all networks examined in the present study, can be found in Supplemental Data, Figures S3–S7.
Figure 5. One of two Ingenuity pathway analysis (IPA)-generated interaction networks for genes dysregulated by exposure to technical-grade perfluorooctane sulfonate, 10 µM. Red shapes represent genes that were significantly upregulated, and green shapes represent genes that were downregulated. Connecting lines represent interactions between genes that are documented in the Ingenuity Knowledgebase. White shapes are genes that were added to the network by IPA based on their connectivity to dysregulated genes. Shapes circled in orange appear in Table 4. [Color figure can be seen in the online version of this article, available at wileyonlinelibrary.com.]
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All network entities that had direct interactions with four or more dysregulated genes were considered to have potential regulatory roles in the PFOS response. For example, in Figure 5, the nodes labeled HNF4A and PPARG have direct interactions with greater than four red or green nodes and were therefore considered to have potential regulatory activity. Ingenuity Pathway Analysis was then used to identify all possible direct or indirect interactions between potential regulatory molecules and all dysregulated genes in each dose group. All known interactions in the Ingenuity Knowledgebase were considered regardless of tissue type (an example for TP53 is shown in Supplemental Data, Fig. S8), and these data are summarized in Table 4. A detailed list of potential regulatory molecules and the genes with which they interact can be found in Supplemental Data, Table 6. The overall trend in the number of interactions between potential regulatory molecules and genes dysregulated by PFOS was similar to the trend observed for functional and pathway enrichment analysis. All potential regulatory molecules had the highest number of interactions with dysregulated genes in T10, followed by L40, then L10. This supports our hypothesis that, although L-PFOS and T-PFOS may affect similar regulatory machinery, the effect is much more prominent following T-PFOS exposure.
Table 4. Molecules from networks generated in Ingenuity Pathway Analysis that had interactions with four or more genes that were differentially expressed in chicken embryonic hepatocytes exposed to by 10 or 40 µM linear or 10 µM technical-grade perfluorooctane sulfonate (L10, L40, or T10, respectively; italicized entries are fold change >1.5; p < 0.05; NP = probe not present on microarray)
|Molecule||Entrez gene name||Dose||Fold change||p Value||No. of interactions with DE genes|
|TP53||Tumor protein 53||L10||NP||NP||3||1||4|
| || ||L40||NP||NP||6||7||13|
| || ||T10||NP||NP||12||9||21|
|MYC||v-myc Myelocytomatosis viral oncogene homolog (avian)||L10||1.16||0.422||4||3||7|
| || ||L40||1.41||0.060||9||4||13|
| || ||T10||1.33||0.078||13||5||18|
|HNF4A||Hepatocyte nuclear factor 4α||L10||1.09||0.686||5||1||6|
| || ||L40||−1.08||0.959||13||0||13|
| || ||T10||−1.10||0.587||17||1||18|
|CTNNB1||Catenin (cadherin-associated protein), β1, 88 kDa||L10||1.99||0.096||2||1||3|
| || ||L40||1.46||0.538||4||3||7|
| || ||T10||1.12||0.722||8||7||15|
|PPARG||Peroxisome proliferator-activated receptor gamma||L10||−1.01||0.849||4||2||6|
| || ||L40||1.06||0.677||6||2||8|
| || ||T10||−1.00||0.980||10||4||14|
|SP1||Sp1 transcription factor||L10||−1.14||0.468||3||1||4|
| || ||L40||−1.06||0.964||11||1||12|
| || ||T10||1.02||0.911||11||0||11|
|SREBF1||Sterol regulatory element binding transcription factor 1||L10||1.00||0.989||3||1||4|
| || ||L40||1.07||0.871||5||1||6|
| || ||T10||−1.05||0.646||11||1||12|
|CEBPB||CCAAT/enhancer binding protein (C/EBP), β||L10||−1.01||0.953||5||2||7|
| || ||L40||−1.01||0.998||8||1||9|
| || ||T10||1.05||0.749||8||2||10|
|CEBPA||CCAAT/enhancer binding protein (C/EBP), α||L10||NP||NP||3||0||3|
| || ||L40||NP||NP||10||0||10|
| || ||T10||NP||NP||8||0||8|
|HDAC1||Histone deacetylase 1||L10||1.40||0.188||1||0||1|
| || ||L40||1.50||0.085||6||0||6|
| || ||T10||1.58||0.019||8||0||8|
|NCOR1||Nuclear receptor corepressor 1||L10||1.17||0.687||2||0||2|
| || ||L40||1.12||0.977||7||0||7|
| || ||T10||1.80||0.111||7||0||7|
|ESRRA||Estrogen-related receptor α||L10||NP||NP||1||0||1|
| || ||L40||NP||NP||4||0||4|
| || ||T10||NP||NP||6||0||6|
|MYCN||v-myc Myelocytomatosis viral related oncogene, neuroblastoma derived (avian)||L10||−1.00||0.999||1||0||1|
| || ||L40||1.31||0.119||2||1||3|
| || ||T10||1.37||0.027||5||0||5|
Many of the potential regulatory molecules identified through interactome analysis, such as tumor protein p53 (TP53), v-myc myelocytomatosis viral oncogene homolog (MYC), hepatocyte nuclear factor 4α (HNF4A), and catenin β1 (CTNNB1), play integral roles in the proper functioning of diverse cellular processes. The responsiveness of these proteins to PFOS exposure may represent secondary responses, although some evidence exists that PFOS may act more directly in some instances. Tumor suppressor protein TP53 is an important regulator of the cell cycle. It is not surprising that most PFOS-affected genes involved with cell cycle, proliferation, or cancer have direct or indirect interactions with TP53. It is unlikely that PFOS exerts its effect directly on TP53. A typical TP53 response involves differential p21/WAF1 expression, which was not observed, and is induced by DNA damage. Perfluorooctane sulfonate does not appear to be genotoxic 50–52, although Liu et al. 34 did report DNA damage at high PFOS exposures but concluded that it was likely apoptosis related. Recent evidence has shown that lipid peroxidation products can activate TP53 53, which may explain results from the present study. The fact that both Mdm2, a gene that inactivates TP53, and Cdkn2A, which modulates Mdm2 activity 54, were both overexpressed following PFOS exposure is further evidence that TP53 activity was perturbed.
The multifunctional transcription factor MYC is believed to regulate the expression of 10 to 15% of all cellular genes directly or indirectly and is one of the most frequently affected genes in a variety of cancers 55. This protein exerts effects on many cellular processes including growth, differentiation, and apoptosis in a TP53-dependent or -independent fashion 55. This being the case, many of the genes affected by PFOS that interact with MYC are also known to interact with TP53.
The transcription factor HNF4A is believed to be involved in the regulation of up to 40% of all genes expressed in the liver 56 and plays an indispensible role in hepatocyte development and lipid metabolism as evidenced in HNF4A knockdown studies ; HNF4A-null mice die during embryogenesis, whereas mature mice that lack HNF4A expression accumulate lipid in the liver and have reduced serum cholesterol and increased serum bile levels 57. Many of the genes that were dysregulated by PFOS and interact with HNF4A (see Supplemental Data, Table 6) are involved in lipid metabolism (acetyl-coenzyme A acetyltransferase 1 [Acat1]; Acsl1; aldo-keto reductase family 1, member B1 [Akr1b1]; apolipoprotein A-IV [Apoa4]; and Cyp8b1) or in cellular proliferation (cyclin G2 [Ccng2]; Gjb1; and inhibin, βA [Inhba]). Binding studies would be needed to determine whether PFOS interferes directly with HNF4A. Alternatively, HNF4A might be affected indirectly through altered fatty acyl-CoA thioester levels 58 that may result from PFOS interference in other regulatory mechanisms of lipid metabolism (for example, through interference with PPARs).
Catenin β1 is an important component of two cellular systems: Wnt signaling and adherens junctions. The Wnt signaling pathway is one of the most fundamental mechanisms that directs cell proliferation and fate during embryonic development and tissue homeostasis 59. Defects in proper Wnt signaling are often linked to birth defects and cancer and other diseases. Increased expression of one of the Frazzled Wnt receptors, Fzd4, and Wnt5B glycoprotein following PFOS exposure indicates that the Wnt signaling pathway may be disrupted by PFOS. Wnt signaling is also linked to TP53 and MYC activity, whereas Wnt5B protein has been shown to activate PPARγ 60. Catenin β1 is also a key component in the adherens junction complex, a cell–cell junction complex that mechanically attaches adjacent cells and relays signals. Interference with CTNNB1 function may be involved with impaired cell–cell communication, which has been demonstrated following PFOS exposure 48.
The hepatocyte response to PFOS may be mediated more directly through the lipid sensing PPARs. In interactome analysis, only PPARγ was present in any of the networks generated by IPA for both L-PFOS and T-PFOS. Analysis revealed that it had many direct interactions with differentially expressed genes, most of which were expression-type interactions (Fig. 6A). In contrast, PPARα did not occur in any of the interaction networks generated by IPA. When all possible interactions in the Ingenuity Knowledgebase with differentially expressed genes were considered (as was done for all potential regulatory molecules), PPARα had fewer interactions with genes that were dysregulated by PFOS than PPARγ (Fig. 6B). Only two of these were expression-type interactions, suggesting that PPARα had minimal influence on the expression profile of CEH in response to PFOS. Binding assays 49 and expression profile studies 37 support the hypothesis that PFOS may be a PPARγ agonist. These same studies, however, also suggest that PFOS is a more potent activator of PPARα in mammals. Given the lack of response of PPARα-regulated genes in the present study and other avian studies 15, 44, it is possible that PFOS is a more potent activator of PPARγ in the chicken. Binding assays using chicken PPAR isoforms would help in clarifying this issue.
Figure 6. Direct (solid line) and indirect (dashed line) interactions between genes that were differentially expressed following exposure to linear or technical-grade perfluorooctane sulfonate and peroxisome proliferator-activated receptor-γ (PPARγ; A) or peroxisome proliferator-activated receptor-α (PPARα; B). [Color figure can be seen in the online version of this article, available at wileyonlinelibrary.com.]
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Another possible mechanism for the effects of PFOS exposure is activation of SREBP1, which enhances the transcription of genes involved in fatty acid and cholesterol metabolism. Cleavage activation of SREBP1 is upregulated in response to cellular cholesterol demand 61. This may represent a secondary response to PFOS exposure, which, as previously mentioned, can result in reduced cholesterol levels.
Among all potential regulatory molecules identified, the transcription of only one was affected and only by exposure to T-PFOS: HDAC1. This may represent a molecular effect that is specific to the branched isomers of PFOS and may be responsible for the increased transcriptional activity of T-PFOS over L-PFOS. Histone deacetylase (HDAC1) is involved in chromatin packaging and coordinates how the transcriptional machinery accesses DNA 62. Perturbation of Hdac1 transcription by T-PFOS may play a major role in the increased numbers of genes affected in this treatment group via effects on chromatin structure. Real-time RT-PCR expression analysis of an independent culture showed a significant dose-dependent increase in Hdac1 expression in response T-PFOS, which did not change in the L-PFOS treatment group (Supplemental Data, Fig. S9).
Given the diversity in the effects of PFOS exposure, it is unlikely that it has a single primary mode of action. The mechanism leading to PFOS toxicity is in all likelihood a complex interplay of various molecular events, many of which are proposed here. The results from the present study represent only a snapshot of the transcriptional response to PFOS after 24 h of exposure. Time-course experiments would be required to investigate more thoroughly the proposed mechanisms to obtain a better understanding of their order of activation, how they interact, and how they are affected by different isomer mixtures.