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. Author manuscript; available in PMC: 2022 Dec 10.
Published in final edited form as: Toxicology. 2022 Jul 15;477:153259. doi: 10.1016/j.tox.2022.153259

GenX Induces Fibroinflammatory Gene Expression in Primary Human Hepatocytes

Dakota R Robarts a, Kaitlyn K Venneman a, Sumedha Gunewardena b, Udayan Apte a
PMCID: PMC9741548  NIHMSID: NIHMS1825800  PMID: 35850385

Abstract

The toxicity induced by the persistent organic pollutants per- and polyfluoroalkyl substances (PFAS) is dependent on the length of their polyfluorinated tail. Long-chain PFASs have significantly longer half-lives and profound toxic effects compared to their short-chain counterparts. Recently, production of a short-chain PFAS substitute called ammonium 2,3,3,3-tetrafluoro-2-(heptafluoropropoxy) propanoate, also known as GenX, has significantly increased. However, the adverse health effects of GenX are not completely known. In this study, we investigated the dose-dependent effects of GenX on primary human hepatocytes (PHH). Freshly isolated PHH were treated with either 0.1, 10, or 100 μM of GenX for 48 and 96 hours; then, global transcriptomic changes were determined using Human Clariom™ D arrays. GenX-induced transcriptional changes were similar at 0.1 and 10 μM doses but were significantly different at the 100 μM dose. Genes involved in lipid, monocarboxylic acid, and ketone metabolism were significantly altered following exposure of PHH at all doses. However, at the 100 μM dose, GenX caused changes in genes involved in cell proliferation, inflammation and fibrosis. A correlation analysis of concentration and differential gene expression revealed that 576 genes positively (R > 0.99) and 375 genes negatively (R < −0.99) correlated with GenX concentration. The upstream regulator analysis indicated HIF1α was inhibited at the lower doses but were activated at the higher dose. Additionally, VEGF, PPARα, STAT3, and SMAD4 signaling was induced at the 100 μM dose. These data indicate that at lower doses GenX can interfere with metabolic pathways and at higher doses can induce fibroinflammatory changes in human hepatocytes.

Keywords: PFAS, GenX, Toxicogenomics, Primary Human Hepatocytes, Inflammation, Proliferation

1. Introduction

Per- and polyfluoroalkyl substances (PFAS) are persistent organic pollutants with a myriad of adverse health effects, including immune, developmental, and liver toxicity (Bassler et al. 2019; Das et al. 2015; Gaballah et al. 2020; Peden-Adams et al. 2008). PFAS are anthropogenic chemicals with a prolonged half-life in humans on the magnitude of years (Stein et al. 2016; Suja et al. 2009) and exhibit significant bioaccumulation. These compounds are structurally similar to fatty acids, which both consist of a charged functional group coupled to a carbon backbone. However, fatty acids have a hydrocarbon backbone, while PFAS contains an aliphatic fluorocarbon. Due to this fluorination, PFAS are extremely stable, making them useful in a variety of products that range from food packaging, waterproof fabrics, firefighting foams, and nonstick pans (Lindstrom et al. 2011).

Studies show that the length of the fluorocarbon backbone of these compounds correlates with their half-lives and toxicity (Agency 2006; Jensen and Warming 2015). PFAS with a carboxylic acid functional group with a number of carbons ≥ 7 are considered long-chain while compounds with < 7 carbons are considered short-chain (Brendel et al. 2018). In general, long-chain PFAS have a significantly longer half-life in humans than in rodents (Li et al. 2018; Olsen et al. 2007; Wong et al. 2015; Zhang et al. 2013). Serum concentrations are inclined to be higher in older human subjects than in younger individuals. In humans, PFAS have a shorter half-life in young females (0.5 to 10 years) compared to males and older females (1.2 to 90 years) (Zhang et al. 2013). In addition, studies show there is significantly higher elimination through renal and menstrual clearance in young females, which may help explain the shorter half-life of PFAS in this population (Fu et al. 2016; Harada et al. 2005). The short-chain PFAS have less bioaccumulation and are presumed to be less toxic (Buck et al. 2011; Conder et al. 2008). This has given rise to the increased production of short-chain replacements, such as ammonium 2,3,3,3-tetrafluoro-2-(heptafluoropropoxy) propanoate (GenX; CAS number: 62037–80-3) (Fig. 1A) (Blake et al. 2020; Brendel et al. 2018; Kancharla et al. 2022). GenX was originally developed to aid in the manufacture of fluoropolymers because of its water-solubility at pH>2.84 and is now used as an oil-repellent coating. It has also become the replacement for the long-chain PFAS perfluorooctanoic acid (PFOA) (Heidari et al. 2021; Paul Douglas Brothers et al. 2009). Due to the ether residing in the backbone of GenX and its proximity to the carboxylic acid, GenX adsorption is significantly less than PFOA, and the ether does not contribute to water solubility due to steric hindrance from H2O interactions (Wang et al. 2019).

Figure 1. No cell death or morphological changes after GenX treatment.

Figure 1.

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(A) Chemical structure of GenX. (B) Phase-contrast images of PHH treated with either 0.1, 10, or 100 μM for 48 or 96 hours at 100x magnification. (C) LDH cytotoxicity assay for both 48 and 96 hours after GenX treatment indicating at these concentrations, GenX does not induce cell death (n of 4 per group). Bars represent mean ± SEM.

Like most PFAS, GenX is persistent in the environment and has been shown to cause adverse effects in animal models (Brendel et al. 2018; Chambers et al. 2021; Joerss et al. 2020). In rodents, GenX is rapidly absorbed and eliminated exclusively through the urine. Male and female rats have a beta phase elimination half-life of 72.2 and 67.4 hours, respectively. In mice, the beta phase elimination half-life is 36.9 hours for males and 24.2 hours for females (Gannon et al. 2016). Studies using rat primary brain capillary cells and human cell lines (NCI/ADR-RES and MX-MCF-7) showed that GenX inhibits p-glycoprotein (P-gp) and breast cancer resistance protein (BCRP) (Cannon et al. 2020). However, the half-life of GenX in humans remains unknown.

Recent studies have shown increasing accumulation of GenX in the environment. For example, GenX was found in the Lower Cape Fear River basin in North Carolina, the primary source of drinking water for surrounding towns, leading to human exposure (Guillette et al. 2020; Hopkins et al. 2018). In the Netherlands, GenX was found in the drinking water for multiple towns, including Zwijndrecht and Papendrecht (Gebbink and van Leeuwen 2020). Whereas significant data exist on serum concentrations of the long-chain legacy PFAS (PFAS perfluorooctanesulfonic acid (PFOS) and PFOA) in occupational workers (Beggs et al. 2016; Chang et al. 2014; Olsen et al. 2007), on limited data exist for the levels of GenX in the serum of humans. A recent report by the Chemours Company measured the concentration of GenX in 24 human plasma samples, finding concentrations ranging from 1–51.2 ng/mL (0.0029–0.15 μM) (2021b). However, there was no other information in the study about the demographics or exposure time of the participants. Further, even less is known about the health effects of GenX exposure in humans. The United States Environmental Protection Agency (US EPA) has set the oral reference doses (RfDs) for sub-chronic and chronic exposures to 0.00003 and 0.000003 mg/kg-day, respectively (2021a).

Several rodent studies indicate that the liver, specifically the hepatocytes, is a primary target of PFAS (Berthiaume and Wallace 2002; Chappell et al. 2020; Conley et al. 2019; Cui et al. 2009). Rodents exposed to GenX exhibited dose-dependent liver hypertrophy and transcriptomic changes in lipid metabolism, peroxisome processes, and xenobiotic metabolism (Chappell et al. 2020). In addition, female mice exposure to GenX during gestation caused hepatotoxicity and induction of the inflammatory pathways mediated by the toll-like receptor 4 (TLR4) in the liver, indicating that GenX is hepatotoxic and proinflammatory in rodents (Xu et al. 2022). However, adverse effects and the underlying mechanisms of GenX on human hepatocytes are not known. Here, we report the adverse effects of GenX on freshly isolated primary human hepatocytes (PHH) identified using global transcriptomic analysis.

2. Materials and Methods

2.1. Primary Human Hepatocytes Isolating and Culture

Primary Human Hepatocytes (PHH) were isolated from human liver donors by the Cell Isolation Core at the University of Kansas Medical Center, as previously described (Xie et al. 2014). All human liver tissues were obtained with informed consent in accordance with the ethical and institutional guidelines. All studies were approved by the Institutional Review Board of KUMC. PHH from four separate human liver donors were used in this study. Briefly, the livers were perfused first with EGTA (ethylene glycol-bis(β-aminoethyl ether)-N,N,N′,N′-tetraacetic acid) (1 mM) to chelate the calcium. This was followed by a collagenase (0.1 mg/ml) and protease (0.02 mg/ml) perfusion. The Glisson capsule of the liver was then severed to release the contained hepatocytes in ice-cold, phosphate-buffered saline (PBS). A 50 g centrifugation was performed on the cells to purify the PHH from the other non-parenchymal cells. The PHH were then seeded at 1 × 106 cells per well on collagen-coated, 6-well plates and were cultured with phenol red-free Williams’ Medium E media (ThermoFisher cat # A1217601), supplemented to a final concentration of 10 mM HEPES buffer (ThermoFisher cat # 15630080) and 2 mM GlutaMAX™ (ThermoFisher cat # 35050061) by using the primary hepatocyte maintenance supplement kit (ThermoFisher cat # CM4000). The cells were allowed to attach overnight in a 5% CO2 incubator at 37°C.

2.2. PHH GenX Exposure

GenX (Synquest Laboratories cat # 2122-3-09) was dissolved in the culturing media to obtain a final concentration of 100 mM. The 100 mM stock was diluted in culturing media to 0.1 μM, 10 μM, and 100 μM concentrations. PHH were treated for either 48 hours or 96 hours at 37°C with 5% CO2. The media was changed every 48 hours. Studies included 4 biological replicates (PHH from four separate human livers) with 3 technical replicates per biological replicate. The 96-hour timepoint was selected as the longest timepoint to avoid complications associated with spontaneous dedifferentiation of PHH in culture. The treatment concentrations were selected for two observations. First, a study by the Chemours Company reported human serum concentrations within the range used (0.15 μM) (2021b). Second, past studies showed that occupational workers had PFOA and PFOS serum levels of 12.3 and 7.0 μM, respectively (Beggs et al. 2016). Given these findings, GenX serum levels in humans could be between 0.1 to 100 μM.

2.3. Lactate Dehydrogenase Activity

The lactate dehydrogenase (LDH) assay is a well-established method to measure cell death in hepatocytes (McGill et al. 2011; Rowan-Carroll et al. 2021; Xie et al. 2015; Yan et al. 2010). This assay is known to provide robust and reproducible results for determining toxicity (Kaja et al. 2017; Kumar et al. 2018). Additionally, LDH has the capacity to detect low levels of cell membrane damage compared to other assays (Allen et al. 1994; McGill et al. 2011). Given these factors, an LDH assay was used to establish GenX cytotoxicity, as previously described (Bajt et al. 2004; McGill et al. 2011). LDH was measured in both cell lysate and media. Briefly, 0.154 mg of NADH was added to 1 mL of Potassium Phosphate Pyruvate Buffer (KPP Buffer; KH2PO4 9.6 mM, K2HPO4 50.4 mM, Pyruvate 0.9 mM, pH 7.5) and was prewarmed to 37°C. Then, 5 μL of hepatocyte lysate and 15 μL of media were used to determine the amount of LDH in each fraction. Each was loaded into separate wells of a 96-well microplate, and 100 μL of NADH KPP buffer was added to each well. The absorbance was measured at 340 nm for 4 minutes to measure the loss of NADH. Concentrations were determined utilizing Beer– Lambert’s Law. The ratio of LDH concentration in the media and the total LDH concentration (amount of LDH in the media and cell lysate) were then calculated to determine the percent of LDH released for each sample.

2.3. Microarray Analysis

Total RNA was isolated from untreated and GenX-exposed cells using the TRIzol method, as previously stated (Robarts et al. 2022; Sandoval et al. 2019). An assessment of the quality and quantity of the RNA was made using the Agilent TapeStation 4200. All samples had an RNA integrity number equivalent (RINe) score greater than 9.8. As previously stated, microarray analysis was performed on pooled samples from the control, 0.1, 10 and 100 μM groups 96 hours after exposure (n of 3 -biological replicates, derived from separate liver donors) using the Human Clariom™ D Array (Affymetrix, Santa Clara, CA) at the University of Kansas Medical Center Genomics Core (Borude et al. 2018). The raw microarray CEL data files were normalized and analyzed with Transcriptome Analysis Console software to produce fold change values (ThermoFisher, Version 4.0). The raw data were deposited into the Gene Expression Omnibus (GEO) database (GSE187633).

2.4. Pathway Analysis

To ascertain altered pathways, the Metascape tool (https://metascape.org/) was used on genes with a ±2-fold change cutoff (Zhou et al. 2019). This tool included the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and REACTOME databases. Enriched pathway networks were constructed using the Cytoscape software (Shannon et al. 2003). Next, these genes were uploaded into the Qiagen’s Ingenuity Pathway Analysis (IPA) where functional pathways, tox function, canonical pathways, and upstream regulator analysis were performed, as previously stated (Bhushan et al. 2020; Robarts et al. 2022). The pathways with a p-value ≤ 0.05 and an absolute z-score ≥ 2 were considered significantly altered.

2.5. Determining Linear Concentration Response

A Pearson correlation was performed across all treatment groups for each gene, using both concentration and gene fold-change in RStudio (R version 4.0.3; RStudio Team). This was only performed if one or more of the treatment groups displayed a ±2-fold change of that gene. Genes that had a correlation coefficient greater than 0.99 or less than −0.99 were considered concentration-dependent genes. The concentration-dependent genes were then uploaded into the IPA software to determine concentration-dependent altered pathways.

2.6. Venn Diagram and Heatmap Construction

Heatmaps were generated using RStudio (R version 4.0.3; RStudio Team) with the heatmap.plus (Version 1.3), RcolorBrewer (Version 1.1–2), and gplots (Version 3.1.1) packages, as previously stated (Gunewardena et al. 2022). Colors were based on either -log10(p-value), z-score, or log2(fold-change); orange indicated upregulation, and blue indicated downregulation of gene expression. Euclidean distance with Ward linkage was used on rows to determine clustering. Venn Diagrams were generated using the package VennDiagram (Version 1.6.20), and colors were generated by using the RColorBrewer package.

2.7. Quantitative Polymerase Chain Reaction

The total RNA isolated from each sample (3 biological replicates per group), that was concurrently used for the microarray, was also used for real-time quantitative polymerase chain reaction (qPCR). The RNA concentrations were determined using the Implen NanoPhotometer® NP50. Four μg of total RNA was reverse transcribed to complementary DNA (cDNA) for each sample, using the High-Capacity cDNA Reverse Transcription Kit according to the manufacturer’s protocol (ThermoFisher, cat # 4368814). PowerUp™ SYBR™ Green Master Mix was utilized to measure cDNA levels of various genes using a total of 100 ng of cDNA per reaction and a final forward and reverse primer concentration of 250 nM (ThermoFisher, cat # A25742). Integrated DNA Technologies (IDT) produced and formulated all qPCR primers. The list of forward and reverse primer sequences is shown in Table S1. A 384-well plate setup was utilized for all qPCR reactions using the BioRad CFX384. These qPCR reactions were then analyzed in the CFX Maestro Software (Version 4.1.2433.1219). All cycle threshold (Ct) values were normalized to the Ct of 18S and then to the untreated group, using the 2–ΔΔCt method as previously described in depth (Livak and Schmittgen 2001).

2.8. Protein extraction and Western Blot Analysis

Hepatocytes were lysed in 300 μL of RIPA buffer (ThermoFisher cat # 89901) containing 1x Halt™ Protease Inhibitor Cocktail (ThermoFisher cat #78438) and 1x Halt™ Phosphatase Inhibitor Cocktail (ThermoFisher cat # 78427). Protein concentration was measured using a Pierce™ Bicinchoninic Acid (BCA) Protein Assay Kit, according to the manufacturer’s protocol (ThermoFisher cat # 23225). The protein extracts were then diluted to 2 μg/μL in 1x Invitrogen™ NuPAGE™ LDS Sample Buffer (Fisher Scientific cat # NP0007) and 1x Invitrogen™ NuPAGE™ Antioxidant (Fisher Scientific cat # NP0005). The protein was loaded (20 μg total per sample) into a “NuPAGE™ 4 to 12%, Bis-Tris, 1.0–1.5 mm, Mini Protein Gels” (Fisher Scientific cat # NP0323BOX) and run at 140 volts for 1 hour and 45 minutes (3 biological replicates per group). The protein was then transferred into a MilliporeSigma™ Immobilon™-P PVDF Transfer Membrane (Millipore Sigma cat # IPVH00010) for 1 hour at 30 volts, followed by 30 minutes at 35 volts.

The primary antibodies used in this study include CDK4 (Cell Signaling cat # 12790), PCNA (Cell Signaling cat # 13110), phosphorylated-retinoblastoma (p-RB) (Cell Signaling cat # 8516), Ki67 (Cell Signaling cat # 9449), and GAPDH (Cell Signaling cat # 5174) and were used at a 1:1000 concentration diluted in 2% BSA. The secondary antibodies include horseradish peroxidase (HRP) conjugated anti-mouse (Cell Signaling cat # 7076) or anti-rabbit (Cell Signaling cat # 7074) diluted at a 1:5000 concentration in 2% BSA. The blots were imaged on a Li-Cor Odyssey FC using the SuperSignal™ West Pico PLUS Chemiluminescent Substrate, according to the manufacturer’s protocol (ThermoFisher cat # PI34578). The densitometries of the bands of the western blots were calculated using the Image Studio Software (Version 5.2). The raw densitometry values were normalized to the densitometry of their respective loading control (GAPDH) and then to the normalized values of the respective paired, untreated PHH group.

2.9. Graphing and Statistical Analysis

The bar graphs were produced in GraphPad Prism (Version 9.1.2), with bars representing the mean ± standard error of the mean (SEM). Statistical significance was tested between multiple groups using an ANOVA followed by a Tukey’s multiple comparison test. The difference between each group was considered significant if the p-value was less than 0.05.

3. Results

3.1. No cell death or morphological changes after GenX treatment

GenX exposure caused no significant morphological changes in the hepatocyte at any time point (Fig. 1B). To assess GenX cytotoxicity, we performed an LDH activity assay on the cellular fraction and the supernatant fraction after GenX exposure. There were no statistical differences between the untreated, 0.1 μM, 10 μM, and 100 μM groups at either 48 hours or 96 hours, indicating that there was no cytotoxicity at any dose (Fig. 1C). However, there was a concentration-dependent downward trend of LDH released in the GenX treatment groups (Fig. 1C).

3.2. GenX treatment caused significant gene transcriptional changes in PHH

Global transcriptomic analysis, using a Human Clariom™ D microarray array, showed significant gene expression changes 96 hours after GenX treatment (Table S2.). After a ±2-fold expression cutoff, a total of 831 genes (351 upregulated and 480 downregulated) were significantly changed in the 0.1 μM group, 1366 genes (453 upregulated and 913 downregulated) were significantly changed in the 10 μM group, and 1364 genes (826 upregulated and 538 downregulated) were significantly changed in the 100 μM groups (Table 1).

Table 1.

Number of genes significantly altered upon GenX 96-hour treatment using a ±2 fold-change cutoff relative to the untreated PHH.

Treatment Upregulated Downregulated
0.1 μM 351 480
10 μM 453 913
100 μM 826 538
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Overall, the 0.1 μM and 10 μM treatments showed similar altered gene expression profiles when compared to the 100 μM treatment, as shown by the chord plot and the Venn diagram (Fig. 2A–B). Among the downregulated genes, 166 were uniquely common between the 0.1 μM and 10 μM treatments, 24 were uniquely common between the 10 μM and 100 μM treatments, and 9 were uniquely common between the 0.1 μM and 100 μM treatments (Fig. 2C). Only two genes, Histone H2A type 1-B/E (HIST1H2AE) and UDP Glucuronosyltransferase Family 2 Member B10 (UGT2B10), were commonly downregulated between all three treatments (Fig. 2C, Table 2). Of the upregulated genes, 73 were uniquely common between 0.1 μM and 10 μM, 11 were uniquely common between 10 μM and 100 μM, and 10 were uniquely common between 0.1 μM and 100 μM (Fig. 2D). A total of 12 genes were commonly upregulated between all treatments (Fig. 2D, Table 3).

Figure 2. Significantly altered genes in PHH following 0.1, 10, and 100 μM GenX exposure.

Figure 2.

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(A) Cord plot of all genes for each group that had a ± 2-fold change (0.1 μM = red, 10 μM = blue, and 100 μM = green), where each purple line represents a shared gene between two groups. Venn diagrams of (B) all significant altered genes, (C) significantly downregulated, and (D) significantly upregulated genes using a ± 2-fold-change cutoff.

Table 2.

Common downregulated genes between 0.1, 10, and 100 μM treatments with corresponding fold change relative to the untreated PHH.

Gene Symbol 0.1μM 10 μM 100 μM
HIST1H2AE −4.86 −2.25 −2.15
UGT2B10 −2.08 −10.13 −2.17
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Table 3.

Common upregulated genes between 0.1, 10, and 100 μM treatments with corresponding fold change relative to the untreated PHH.

Gene Symbol 0.1 μM 10 μM 100 μM
DKCl 2.31 3.11 2.06
IGFBPl 2.12 3.03 3.21
LAMBl 2.89 4.19 2.51
LYVEl 2.72 2.49 2.49
MIR5580 2.25 2.25 2.32
PLIN2 2.30 2.31 2.77
RPll-436F23.1 2.11 2.07 3.45
RRAGC 2.06 2.25 2.19
SNORA33 4.11 3.43 2.24
SNORA4S 2.48 2.57 3.29
SNORD93 2.51 2.16 2.3
VPS53 2.04 2.04 2.43
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3.3. Metabolic pathway alterations after GenX treatment

To determine pathways impacted by the GenX treatment, we performed GO, REACTOME, and KEGG pathway analyses on significantly changed genes in 0.1, 10, and 100 μM-treated PHH. In all three treatment groups, “regulation of biological processes,” “metabolic processes,” and “cellular component organization” were significantly altered (Fig. 3A). “Cell proliferation” was predicted to be altered only in the 0.1 μM and 10 μM-treated PHH. Pathways impacted in only the 0.1 μM and 100 μM- treated PHH included “immune system process,” “signaling,” and “growth and biological adhesions” (Fig. 3A). Enrichment analysis revealed multiple impacted metabolic pathways (Fig. 3B). In all groups, “metabolism of lipids,” “regulation of lipid metabolic processes,” and “monocarboxylic acid metabolic process and biological oxidations” were significantly altered, indicating that GenX interferes with lipid metabolism. Pathways associated with cell proliferation, such as “cell cycle,” “phosphatidylinositol 3 kinase-protein kinase B (PI3K-Akt) signaling pathway,” and “small cell lung cancer” were enriched in the 0.1 μM and 100 μM-treated PHH. Further, genes involved in “mitotic prometaphase” were enriched in the 10 μM treatment group. The 0.1 μM and 10 μM groups had significant changes in “drug metabolism,” “positive regulation of locomotion,” and “SUMOylation” (Fig. 3B). To visualize the relationship between these enriched pathways, a network was constructed using the number of significantly impacted genes within the pathways and the similarity score produced by Cytoscape. Clusters were determined by using the MCODE algorithm to group pathways relative to their function. This revealed a large cluster across all three treatments for cell cycle, cell proliferation, and metabolism (Fig. 3C–D, Fig. S1A). Taken together, these data demonstrate that GenX at all doses impacts lipid metabolism and cell proliferation.

Figure 3. Gene Ontology (GO) analysis revealed metabolic changes in PHH treated with GenX.

Figure 3.

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(A) Biological pathways and (B) top enriched pathways in 0.1, 10, and 100 μM treated PHH. Euclidean distance and ward linkage was used to generate clustering. Color represents log10(p-value) for the impacted pathway with grey indicating no p-value. (C) Network analysis of altered pathways and (D) pie chart network of which treatment group altered within that pathway. The size of the dot represents the number of genes within the pathway that was significantly altered, where larger diameter equates to more impacted genes. The edges signify the similarity score produced by Cytoscape, where bolder means higher similarity score. Orange and green ovals represent metabolic and cell proliferation clusters, respectively.

3.4. 100 μM treatment caused significant alterations in inflammatory and fibrosis upstream regulators

We performed an upstream regulator analysis using the IPA software, which predicts the major signaling nodes activated based on the gene expression data provided. This analysis predicted that retinoblastoma transcriptional corepressor 1 (RB1) was significantly activated in the 0.1 μM treatment (Fig. 4A). Conversely, the regulators ribonucleoprotein 1 translational regulator (LARP1), Raf-1 Proto-Oncogene (RAF1), hypoxia inducible factor 1 subunit alpha (HIF1A), and Cyclin D1 (CCND1) were inhibited (Fig. 4A). In the 10 μM dose, Musculin (MSC), homeobox B13 (HOXB13), hepatocyte nuclear factor-1 alpha (HNF1A), HIF1A, hypoxia-inducible factor 2-alpha (EPAS1/HIF2A), and D-box binding PAR BZIP transcription factor (DBP) showed a substantial inhibition, accompanied with a significant increase in the predicted activity of lysine-specific demethylase, KDM5B (Fig. 4B). These data indicate that in the lower GenX concentrations, stress proteins and proliferation pathways are inactivated.

Figure 4. Ingenuity Pathway Analysis (IPA) revealed extensive upstream regulator alterations.

Figure 4.

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Dot plots of (A) 0.1 μM, (B) 10 μM, and (C) 100 μM altered upstream regulators, exported from the IPA software using a z-score of a ± 2 cutoff. The red dash indicates a Z-Score of 0. The size of the dot indicates the number of altered genes within that pathway. Color represents the p-value.

Compared to the 0.1 μM and 10 μM-treated PHH, the 100 μM treatment had more altered pathways. Pro-proliferative growth pathways such as hepatocyte growth factor (HGF), vascular endothelial growth factor (VEGF), proto-oncogene tyrosine-protein kinase Src (SRC), and mitogen-activated protein kinase 1/2 (MAP2K1/2) were significantly activated (Fig. 4C). To further investigate the effects of GenX on proliferation, qPCR was used to measure the gene expression of promitogenic genes such as Cyclin A2 (CCNA2), Cyclin B1 (CCNB1), Cyclin D1 (CCND1), Cyclin E1 (CCNE1), and Cyclin Dependent Kinase 4 (CDK4) (Fig. 5A). qPCR analysis showed significant induction of proliferation in the 100 μM treatment group and an increasing trend in the 0.1 μM and 10 μM treatment groups. Western blot analysis of the promitogenic proteins, including proliferating cell nuclear antigen (PCNA), Ki67, CDK4, and phosphorylated-retinoblastoma (p-RB), showed an induction of these proliferative markers upon treatment, particularly PCNA, CDK4, and Ki67 (Fig. 5B–C). Conversely, Hepatocyte Nuclear Factor 4 Alpha (HNF4α), a nuclear receptor critical for hepatocyte differentiation and inhibition of proliferation (Huck et al. 2019; Walesky and Apte 2015), was decreased upon GenX exposure (Fig. 5B–C).

Figure 5. GenX-induced changes in genes involved in proliferation, inflammation and fibrosis.

Figure 5.

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(A) qPCR of genes related to cell proliferation, including Cyclin A1, B1, D1, E1, and CDK4. (B) Western blot analysis of cell proliferation regulators, derived from the three liver donors, for PCNA, Ki67, CDK4, p-RB, HNF4α, and GAPDH. (C) Bar graphs showing densitometric analysis of the western blots. In the bar graphs, green circles, orange triangles, and blue squares represent the liver donors 1, 2, and 3, respectively. The gray bar represents the mean of the normalized densitometry for each treatment group. qPCR of (D) inflammatory genes (SKG1, FN1, VEGFA, WTAP, JAK1, and POLRC3) and (E) fibrosis genes (HEXIM1, CITED2, ANKEL2, WNT11, and PNO1) identified in the IPA analysis. For qPCR, genes were normalized to the housekeeping gene 18s. All groups (n of 3 per group) were normalized to the untreated group. Bars represent mean ± SEM. ANOVA was performed followed by a Tukey’s post hoc test. * p < 0.05, ** p < 0.01 and *** p < 0.001.

Activation of the inflammatory pathways, such as signal transducer and activator of transcription 3 (STAT3), were predicted to be significantly activated (Fig. 4C). Genes regulated by the proinflammatory upstream regulators STAT3 and Tumor Necrosis Factor (TNF) were identified in the IPA analysis. To validate this finding, representative genes contributing to the predicted activation of these proinflammatory pathways were measured by qPCR. GenX treatment resulted in dose-dependent induction of Serum/Glucocorticoid Regulated Kinase 1 (SGK1), fibronectin-1 (FN1), Vascular Endothelial Growth Factor A (VEGFA), RNA Polymerase III Subunit C 3 (POLRC3), Janus Kinase 1 (JAK1), and WT1 Associated Protein (WTAP) (Fig. 5D). Additionally, the fibrosis pathways transforming growth factor beta 1 (TGFB1) and mothers against decapentaplegic homolog 4 (SMAD4) were activated in the 100 μM-treated PHH (Fig. 4C). To validate this finding, qPCR was performed (Fig. 5E). HEXIM P-TEFb Complex Subunit 1 (HEXIM1), Cbp/P300 Interacting Transactivator with Glu/Asp Rich Carboxy-Terminal Domain 2 (CITED2), Ankyrin Repeat and LEM Domain Containing 2 (ANKEL2), Wnt Family Member 11 (WNT11), and Partner of NOB1 Homolog (PNO1) were significantly increased and followed a dose-response (Fig. 5E). Accompanying this fibroinflammatory response, peroxisome proliferator activated receptor alpha (PPARα), a known fatty acid metabolism regulator, was also activated in the 100 μM-treated PHH (Fig. 4C).

3.5. Inflammatory pathways followed a GenX concentration-dependent activation

To determine if transcriptional changes were occurring in a linear, concentration-dependent manner, we performed Pearson correlation analyses using the GenX concentrations and the fold changes of the genes (Fig. 6A). A total of 576 genes had at least one gene that had a ±2-fold change across any of the PHH treatment groups and had a correlation coefficient below −0.99 or above 0.99 (Fig. S2A–C, Table S3). These genes were then uploaded into the IPA software to determine dose-dependent pathway alterations. IPA analysis of these 576 genes showed that with increasing doses, GenX induced genes involved in “viral infection,” “inflammation,” and “cell survival/viability” (Fig. 6B). Similarly, genes involved in lipid metabolism and cell transformation exhibited a negative correlation with increasing doses of GenX (Fig. 6C). Furthermore, canonical pathways and upstream regulators related to inflammation were activated in a concentration-dependent manner, including interleukin 3 (IL3), nuclear factor kappa B (NFκB), Interferon Gamma (IFNG), STAT3, interleukin 6 (IL6), and Signal Transducer and Activator of Transcription 1 (STAT1) (Fig. 6D, F). The pathways such as phosphatase and tensin homolog (PTEN), sirtuin signaling, and nuclear protein 1 (NUPR1) showed a negative correlation with the concentration of GenX (Fig. 6E, G). Representative genes identified in the dose-dependent IPA analysis were used to confirm the activities of the inflammatory pathways STAT3 (DTX3, VIM, and PGK1) and IL6 (THBS1, MERTK, and LY96). Lastly, qPCR analysis was performed on these representative genes and confirmed a dose-dependent trend (Fig. S2DE). Collectively, this indicates that GenX induces transcriptional changes associated with inflammation in a concentration-dependent manner.

Figure 6. GenX alters inflammatory pathways in a concentration-dependent manner.

Figure 6.

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Altered genes induced by GenX treatment that were either positively or negatively correlated were uploaded into IPA to determine concentration-dependent pathways. (A) Heatmaps of positively and (B) negatively correlated phenotypes that resemble those transcriptional profiles. (C) Heatmaps of positively and (D) negatively correlated canonical pathways. Upstream regulators that were (E) positively and (F) negatively correlated. Color represents Z-Score with Euclidean distance and ward linkage clustering.

4. Discussion

4.1. Cytotoxicity of GenX

Our objective in these studies was to define molecular hepatotoxicity of GenX over a broad dose range using human hepatocytes. One of the major concerns was if GenX would be overtly cytotoxic and lethal at any of the doses. Previous studies in NCI/ADR-RES, MX-MCF-7, and A549 cell lines had found that GenX was not cytotoxic at a 10 μM dose (Cannon et al. 2020; Jabeen et al. 2020). In other studies, GenX was shown to be cytotoxic at concentrations ranging from 0.003 to 2.9 μM in the Fischer rat thyroid cell line 5 (FRTL-5) (Coperchini et al. 2020). However, no data existed on the cytotoxicity on primary human hepatocytes. Our studies are the first to determine cytotoxicity of GenX on freshly isolated human hepatocytes. Our results are consistent with previous studies in NCI/ADR-RES and MX-MCF-7 cell lines, where no cytotoxicity and lethality were exhibited at 0.1, 10, and 100 μM for 48 and 96 hours.

4.2. Evidence that GenX activates PPARα

Activation of PPARα is a common mode of action for many PFAS, including GenX. Mice exposed to 1 and 5 mg/kg of GenX exhibited increased liver weight and hepatocellular hypertrophy (MacKenzie 2010; Wang et al. 2017). RNA sequencing of these livers revealed PPARα activation with increased fatty acid oxidation and lipogenesis in both male and female mice (Chappell et al. 2020). Consistent with this, our results also show that GenX induces concentration-dependent PPARα activation in PHH. GO analysis showed altered lipid metabolism at all three doses, which was confirmed by the IPA analysis. Metabolism of monocarboxylic acid, also regulated by PPARα, was also changed in all three treatment groups (Konig et al. 2010; Konig et al. 2008). Taken together, these data indicate that PPARα is activated by GenX. Further, these data suggests that a large metabolic remodeling occurs in PHH after GenX exposure. The relevance of GenX-induced PPARα is not clear given the relatively low level of PPARα expression in human livers, and the current thinking that it is a non-human relevant mechanism of toxicity (Choudhury et al. 2000; Hall et al. 2012; Palmer et al. 1998).

4.3. Inflammatory and Profibrotic Pathways are Activated by GenX

One of the most striking observations of our studies is the prominent activation of genes involved in both inflammation and fibrosis following high dose GenX exposure. While lower concentrations of GenX mainly perturbed lipid metabolism, the highest dose of GenX resulted in induction of several proinflammatory and profibrotic pathways, including IL3, IL6, IFNG, NF-κB and TGFβ.

The role of the TGFβ signaling pathway in inducing pro-fibrotic gene expression is well known (Fabregat and Caballero-Diaz 2018). The canonical pathway of TGFβ signaling involves binding TGFβ to the TGFβ2 receptor which heterodimerizes and activates the TGFβ1 receptor. TGFβ1 then phosphorylates SMAD2 and SMAD3, which then forms a complex with SMAD4. This complex translocates to the nucleus to upregulate its target genes (Xu et al. 2020). In addition, the a proinflammatory cytokine IL6 binds to the IL6 receptor, activating the STAT3 signaling pathway in cell proliferation and epithelial-mesenchymal transition (EMT) (Liu et al. 2014). Once activated, STAT3 promotes the expression of its target gene, Twist Family Basic Helix-Loop-Helix Transcription Factor 1 (TWIST1), a transcription factor with a significant role in the EMT (Cho et al. 2013). We observed a concentration-dependent activation in TGFβ1, IL6, STAT3, and TWIST1 signaling, indicating that at high concentrations GenX may promote fibrosis, inflammation, and EMT. Previous studies have shown that GenX induces inflammation through TLR4 by promoting lipopolysaccharides (LPS) leaking from the intestine (Xu et al. 2022). Our data indicate that GenX causes the activation of inflammatory pathways in PHH independent of LPS. Further studies are needed to determine the mechanism by which GenX induces TGFβ1 and IL6 signaling. These signaling pathways play a vital role in liver inflammation and fibrosis in chronic liver diseases, including nonalcoholic steatohepatitis (NASH) and alcohol-associated steatohepatitis (ASH) (Gressner et al. 2002). While, there are no reports on the association between GenX and liver inflammatory diseases at this time, other PFAS, including PFOA, are known to cause liver inflammation and promote NASH (Jin et al. 2020; Marques et al. 2020; Sen et al. 2022; Yang et al. 2014). Our data warrant further studies on the role of GenX in fibroinflammatory liver diseases.

4.4. GenX Induces Cell Proliferation

Induing hepatocyte proliferation in rodents is a hallmark of PFAS. Several PFAS, including PFOA, PFOS, and perfluorononanoic acid (PFNA) promote hepatocyte proliferation (Beggs et al. 2016; Buhrke et al. 2015). Rat studies have demonstrated that GenX increases mitotic figures in hepatocytes, indicating initiation of proliferation (Thompson et al. 2019). GenX also induced proliferation in human lung carcinoma cell lines at concentrations greater than 50 μM (Jabeen et al. 2020). In our studies, GenX induced pro-mitogenic pathways such as HGF, VEGF, SRC, MAP2K1/2, and Fibroblast Growth Factor 2 (FGF2) at the highest tested concentration (100 μM). HGF is a primary mitogen for hepatocytes, that activates multiple signaling pathways, including phosphatidylinositol 3-kinase (PI3K), SRC, phospholipase C-gamma (PLCγ), STAT3, and others via its receptor MET. Both HGF and MET are induced and activated in hepatocellular carcinoma (HCC) (Block et al. 1996; Bowen et al. 2014; Cecchi et al. 2011; Michalopoulos and Bhushan 2021; Wang et al. 2020). Altogether, our findings show that GenX induces hepatocyte proliferation at high doses and corroborate previous data (Chappell et al. 2020; Jabeen et al. 2020). Further, we observed a dose dependent decrease in protein levels of HNF4α, the master regulator of hepatic differentiation (Walesky and Apte 2015; Walesky et al. 2013). Studies from our laboratory have shown that HNF4α inhibits hepatocyte proliferation, and the loss of HNF4α is a key feature in chronic liver diseases, including HCC (Gunewardena et al. 2022). These data indicate that at concentrations, GenX may stimulate promitogenic pathways and may inhibit anti-mitogenic and pro-differentiation pathways in hepatocytes.

5. Conclusion

In summary, our data have highlighted the molecular effects of GenX on PHH over a broad range of doses. We found that GenX disrupts lipid metabolism at lower concentrations and can induce fibroinflammatory signaling in hepatocytes. Our data shows that GenX could have major implications in liver diseases that involve significant inflammation and proliferation, such as NAFLD and hepatocellular carcinoma. As human exposure to GenX increases, it is critical to understand the effects on human health. Our studies highlight the critical need to determine the effects of GenX on human hepatocytes. Given the history of liver toxicity of previous PFAS (including PFOA, PFOS, and others), it is important to proactively investigate adverse effects of GenX on the liver and identify the underlying mechanisms.

Supplementary Material

Supplement Info

Financial Support:

These studies were supported by NIH-COBRE (P20 RR021940–03, P30 GM118247, P30 GM122731), NIH S10OD021743, NIH R01 DK0198414 and NIH R56 DK112768

Footnotes

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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