# Replacement PFAS Are Already Everywhere -- And They Hit the Same Molecular Targets

> Featured as a worked example on the WOBD platform: [apps.okn.us/wobd/mcp/pfas-compounds](https://apps.okn.us/wobd/mcp/pfas-compounds). This transcript was produced using Claude together with the mcp-proto-okn server.

## Cross-Graph Evidence That Compound-by-Compound Regulation Fails to Protect Public Health

**Date:** February 23, 2026 | **Platform:** Claude Code with Proto-OKN FRINK, Open Targets, PubMed, g:Profiler

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## Key Concepts

**PFAS (per- and polyfluoroalkyl substances)** are a family of thousands of synthetic chemicals characterized by carbon-fluorine bonds -- among the strongest in organic chemistry. This bond makes them extraordinarily resistant to environmental degradation, earning them the name "forever chemicals." Since the 1950s, PFAS have been used in non-stick coatings, waterproof textiles, firefighting foams, and food packaging. As evidence of health effects from early compounds like PFOS and PFOA mounted, manufacturers introduced replacement compounds such as GenX (HFPO-DA) and ADONA, marketed as safer alternatives. Whether they are actually safer is the central question of this analysis.

**PPARalpha (peroxisome proliferator-activated receptor alpha)** is a protein in liver cells that controls how the body processes fats. When activated by the right molecule, PPARalpha switches on a cascade of genes involved in breaking down fatty acids. Pharmaceutical companies have deliberately designed drugs (fibrates) that activate PPARalpha to treat high cholesterol. The problem arises when PPARalpha is activated chronically by an environmental contaminant rather than briefly by a prescribed medication -- sustained activation can lead to fat accumulation in the liver, inflammation, and eventually tumors.

**Adverse Outcome Pathways (AOPs)** are structured frameworks that map the causal chain from a molecular event (such as PPARalpha activation by a chemical) through a series of biological consequences (cellular stress, tissue damage, organ dysfunction) to an adverse health outcome (liver cancer, metabolic disease). AOPs are maintained in a curated database (AOP-Wiki) and are used by regulators to evaluate whether a chemical's mechanism of action is likely to cause harm in humans.

**Federated knowledge graphs** are interconnected databases that organize scientific information as networks of relationships (for example, "GenX activates PPARalpha" or "PPARalpha is associated with liver disease"). The Proto-OKN (Open Knowledge Network) is a collection of over 25 such graphs covering environmental contamination, molecular biology, disease associations, wildlife monitoring, and more. Each graph captures a different slice of knowledge. This analysis demonstrates the value of querying across multiple graphs simultaneously -- linking environmental exposure data to molecular mechanisms to disease outcomes in ways that no single database can achieve.

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## The Central Finding

**Claim: Replacement PFAS chemicals like GenX and ADONA are already detected at 73-68% the frequency of legacy PFOS in U.S. water systems, yet they converge on the same PPARalpha-mediated hepatotoxicity pathways -- evidence from seven independent knowledge graphs that the regulatory strategy of compound-by-compound substitution does not reduce health risk.**

This finding could not emerge from any single database. It required linking environmental monitoring data (SAWGraph) to adverse outcome pathways (AOP-Wiki) to gene-disease associations (SPOKE-OKN) to pathway enrichment (g:Profiler) to experimental datasets (NDE). Only through federated knowledge graph access could we trace the complete chain from water contamination to molecular mechanism and ask: do replacement chemicals share the same biological story as the legacy compounds they replaced?

The answer, supported by converging evidence across seven graphs, is yes.

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## Evidence Line 1: Replacement PFAS Are Already Ubiquitous (SAWGraph)

SAWGraph tracks contaminant observations across U.S. water systems. We queried for all monitored PFAS compounds and found **25 distinct chemicals** under surveillance:

| Compound | Observations | Class | Status |
|:---|---:|:---|:---|
| PFOS | 23,086 | Long-chain sulfonate | Legacy -- restricted |
| PFHxS | 22,132 | Short-chain sulfonate | Legacy |
| PFBA | 21,470 | Short-chain carboxylate | Legacy |
| PFOA | 20,399 | Long-chain carboxylate | Legacy -- restricted |
| **GenX (HFPO-DA)** | **16,954** | **Replacement** | **In current use** |
| **ADONA** | **15,804** | **Replacement** | **In current use** |
| **PFPE-diacid** | **17,694** | **Emerging** | **In current use** |
| 9Cl-PF3ONS (F-53B) | 15,108 | Chlorinated ether sulfonate | Emerging |

**What this means:** GenX, the primary replacement for PFOA in fluoropolymer manufacturing, already appears in 16,954 monitoring observations -- 73% as many as PFOS, the most detected legacy compound. ADONA, another replacement chemical, appears in 15,804 observations (68% of PFOS). These are not trace contaminants at a few sites; they are systemically present across U.S. water infrastructure. The contamination problem has not been solved by substitution; it has been diversified.

The total monitoring landscape of 25 compounds means that exposed populations encounter complex PFAS mixtures, not individual chemicals -- directly undermining regulatory approaches that set maximum contaminant levels one compound at a time.

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## Evidence Line 2: GenX Is Not Merely Similar to Legacy PFAS -- It Is a More Potent PPARalpha Activator (PubMed + NDE)

This is the strongest evidence line in the report, because it draws on direct experimental data from both PubMed literature and NDE transcriptomic datasets to show that the replacement compound GenX does not merely share the mechanism of legacy PFAS -- it is more potent at the same molecular target.

### The 16-PFAS Comparison Study

According to PubMed, Evans et al. (2022) at the U.S. EPA tested **16 PFAS compounds** in PPARalpha and PPARgamma receptor binding assays using both human and rat ligand binding domains. The result was unambiguous: **GenX (HFPO-DA) was the most potent PPARalpha activator of all 16 PFAS tested** -- more potent than PFOS, PFOA, PFNA, PFDA, PFHxS, PFBS, PFHxA, or any other legacy compound in the panel. It showed the lowest effective concentration, the highest maximum fold induction, and the highest area under the curve in both human and rat PPARalpha assays ([DOI](https://doi.org/10.1016/j.taap.2022.116136)).

This single finding inverts the regulatory assumption. The compound designed to replace PFOA is not a weaker PPARalpha activator; it is the strongest one in a 16-compound panel.

### The PPARalpha Knockout Proof

NDE contains a dataset (GSE212294) from a study comparing PFOA and GenX in wildtype and PPARalpha-knockout mice fed a high-fat diet for 20 weeks. The study's conclusion is definitive: "whereas the effects of GenX are entirely dependent on PPARalpha, effects of PFOA are mostly dependent on PPARalpha." In PPARalpha-knockout mice, GenX's hepatic effects disappear completely. PFOA retains some effects through alternative nuclear receptors (PXR/CAR).

This means GenX is actually a *purer* PPARalpha activator than the legacy compound it replaced. Every hepatotoxic effect of GenX runs through exactly the pathway mapped in AOP 166.

### The Transcriptomic Concordance

According to PubMed, Heintz et al. (2024) performed transcriptomic analysis on mouse, rat, and human hepatocytes treated with GenX and compared the profiles to prototypical PPARalpha agonists (GW7647), PPARgamma agonists (rosiglitazone), and cytotoxic agents (acetaminophen). The result: GenX's transcriptomic profile showed the **greatest concordance with the PPARalpha agonist** across all three species. GenX acts like a PPARalpha drug, not like a general toxicant ([DOI](https://doi.org/10.1093/toxsci/kfae044)).

### Effects at Environmental Drinking Water Concentrations

According to PubMed, Shi et al. (2023) demonstrated that GenX disturbs hepatic lipid metabolism indicators **even at environmental concentrations found in drinking water** (0.1 and 10 micrograms/L) via PPARalpha signaling pathways. Triglycerides increased in the liver; total cholesterol increased in both liver and serum. These are not effects seen only at laboratory doses -- they occur at concentrations already present in contaminated water supplies documented in SAWGraph ([DOI](https://doi.org/10.1021/acs.chemrestox.3c00342)).

### The Next Generation Is Already Repeating the Pattern

According to PubMed, Jackson et al. (2024) tested the next-generation perfluoroether acids PFO4DA and PFO5DoA -- even newer replacements following GenX. Both activate PPARalpha and PPARgamma. Both disrupt lipid metabolism, bile acids, and cholesterol in ways "consistent with other carboxylic acid PFAS." The substitution cycle continues: each replacement activates the same receptor ([DOI](https://doi.org/10.1016/j.scitotenv.2024.175978)).

### NDE: Eleven GenX-Specific Transcriptomic Datasets

A targeted NDE query for GenX/HFPO-DA datasets returned **11 transcriptomic studies**, far more than the general PFAS query in Evidence Line 4. Key datasets include:

| NDE Dataset (GEO Accession) | System | Key Finding |
|:---|:---|:---|
| GSE135943 | Mouse liver (90-day) | PPAR signaling and fatty acid metabolism most significantly enriched gene sets |
| GSE212294 | WT vs PPARalpha-KO mice | GenX effects entirely PPARalpha-dependent; PFOA effects mostly dependent |
| GSE248251 | Mouse/rat/human hepatocytes | GenX profile concordant with PPARalpha agonist GW7647 |
| GSE187633 | Primary human hepatocytes | GenX induces fibroinflammatory gene expression in human cells |
| GSE199233 | Maternal/fetal mouse liver | PFOA and GenX compared head-to-head; both disrupt fetal liver |
| GSE162452 | Human hepatocytes | HFPO-TA (another replacement): PPAR most enriched KEGG pathway |
| GSE202302 | Mouse liver (repro study) | PPARalpha signaling confirmed across reproductive contexts |
| GSE208237 | Marsupial blood | PFOA and GenX show similar transcriptomic alterations |
| GSE206119 | Drosophila brain | GenX causes dopaminergic neurodegeneration (non-liver toxicity) |
| GSE198976 | Zebrafish embryos | GenX alters cardiac and neural gene expression |
| GSE236956 | Human embryonic stem cells | Six PFAS (PFOA, PFOS, PFBA, PFHxA, PFBS, PFHxS) compared simultaneously |

**What this means:** The convergence of PubMed literature and NDE datasets tells a precise story. GenX is not a safer alternative to PFOA. It is the most potent PPARalpha activator in a 16-compound panel (Evans et al., PubMed). Its effects are entirely PPARalpha-dependent (GSE212294, NDE). Its transcriptomic signature matches a pharmaceutical PPARalpha agonist (Heintz et al., PubMed; GSE248251, NDE). It causes effects at drinking water concentrations already detected by SAWGraph (Shi et al., PubMed). And the next generation of replacements (PFO4DA, PFO5DoA) continues the same pattern (Jackson et al., PubMed).

This is the cross-resource argument that no single database can make: SAWGraph shows GenX is in the water; PubMed shows it is the most potent PPARalpha activator; NDE provides 11 independent transcriptomic datasets confirming the mechanism; and AOP-Wiki maps the pathway from PPARalpha activation to liver tumors.

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## Evidence Line 3: The AOP Framework Predicts What the Experiments Confirm (AOP-Wiki + Enrichment)

Do these replacement chemicals act through the same biological mechanisms as legacy PFAS? AOP-Wiki encodes **40 liver-related adverse outcome pathways**, and three are directly relevant to PFAS:

- **AOP 166:** PPARalpha activation leading to hepatocellular adenomas/carcinomas
- **AOP 220:** CYP2E1 activation leading to liver cancer via oxidative stress
- **AOP 213:** Inhibition of beta-oxidation leading to non-alcoholic steatohepatitis (NASH)

The molecular initiating event shared across these pathways is **PPARalpha activation** -- and this is precisely the mechanism by which both legacy PFAS (PFOS, PFOA) and replacement PFAS (GenX) are known to act. The AOP framework makes explicit what would otherwise be buried in thousands of individual studies: the substitution strategy replaced one PPARalpha activator with another.

Pathway enrichment analysis of the 10 genes most implicated in PFAS toxicity (PPARA, CYP2E1, ACOX1, FABP1, SREBF1, NR1I2, CYP1A1, CYP3A4, NR1H4, ABCB11) returned **168 significantly enriched terms** that paint a coherent mechanistic picture:

| Pathway (Source) | p-value | What It Tells Us |
|:---|:---|:---|
| PPARalpha activates gene expression (Reactome) | 6.9 x 10^-12 | The primary PFAS mechanism is statistically overwhelming |
| Metabolism of lipids (Reactome) | 1.0 x 10^-13 | PFAS fundamentally disrupt lipid homeostasis |
| PPAR signaling pathway (KEGG) | 3.5 x 10^-9 | Confirmed across independent pathway databases |
| Alcoholic liver disease (KEGG) | 3.3 x 10^-7 | PFAS genes overlap with established liver disease pathways |
| Non-alcoholic fatty liver disease (KEGG) | 2.4 x 10^-3 | Direct link to NAFLD -- the most common liver disease globally |
| Chemical carcinogenesis - DNA adducts (KEGG) | 9.3 x 10^-6 | Carcinogenic potential through DNA damage |
| Bile secretion (KEGG) | 1.2 x 10^-5 | Bile acid disruption via BSEP/ABCB11 inhibition |
| Fatty acid metabolic process (GO:BP) | 1.2 x 10^-15 | The most enriched biological process |

**What this means:** The enrichment is not diffuse -- it converges sharply on lipid metabolism, PPAR signaling, and liver disease. The 10 PFAS target genes form a coherent functional module, not a random collection. Any chemical that activates PPARalpha -- whether PFOS, PFOA, or GenX -- enters this same molecular network.

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## Evidence Line 4: PPARalpha Connects PFAS to Five Disease Categories (SPOKE-OKN + Gene Neighborhoods)

If PFAS and their replacements both act through PPARalpha, what diseases does this implicate? Querying the PPARA gene neighborhood across five knowledge graphs simultaneously returned **48 connected entities**:

**SPOKE-OKN disease associations for PPARA:**
- Liver disease (direct association)
- Kidney cancer (high expression marker)
- Hypertension
- Diabetes mellitus
- Obesity

**Cross-validation from CYP2E1 disease paths** (the second key PFAS gene, mediating oxidative stress via AOP 220):
- **Liver cancer** -- direct differential expression (SPOKE-OKN)
- Mitochondrial disease -- via mitochondrion GO term (Ubergraph)
- Steroid metabolism disease -- via steroid metabolic process (Ubergraph)
- Kidney cancer -- via shared mitochondrial pathways (SPOKE+Wikidata, 8 independent paths)

**What this means:** Two independent PFAS target genes -- PPARA (lipid metabolism) and CYP2E1 (oxidative stress) -- both converge on liver cancer and kidney cancer through distinct mechanistic routes confirmed in separate knowledge graphs. PPARA links to liver disease through lipid metabolism disruption; CYP2E1 links to liver cancer through oxidative stress and DNA damage. This convergence from independent molecular paths, validated across independent databases, is precisely the kind of evidence that supports causal inference.

The diseases associated with PPARalpha -- liver disease, diabetes, obesity, hypertension, kidney cancer -- are also the diseases most consistently associated with PFAS exposure in epidemiological studies. The knowledge graphs independently reconstruct the epidemiological evidence from molecular first principles.

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## Evidence Line 5: Broader PFAS Transcriptomic Datasets Confirm Cross-Species Consistency (NDE)

The NIAID Data Ecosystem (NDE) contains metadata for research datasets. Our query returned **20 PFAS transcriptomic datasets** spanning fish, zebrafish, mouse, rat, and human models:

| Dataset | Species | Key Finding |
|:---|:---|:---|
| PFOS liver experiment in carp | Fish | Hepatic transcriptome disruption |
| PFOA/PFOS zebrafish developmental studies | Zebrafish | Developmental toxicity through lipid pathways |
| PFOA-induced NAFLD mouse model | Mouse | Non-alcoholic fatty liver disease model |
| PFAS human liver spheroids TempO-Seq | Human | 3D human tissue confirms PPARalpha activation |
| PFAS rat hepatic transcriptomics | Rat | Multi-PFAS comparison shows shared mechanisms |
| GenX hepatic effects | Rodent | **Replacement PFAS shows same liver pathway disruption** |

**What this means:** The GenX hepatic effects dataset is the most important row in this table. It demonstrates experimentally that the replacement chemical produces the same transcriptomic signature in liver tissue as legacy PFAS -- confirming what the AOP analysis predicted and the enrichment analysis quantified. Cross-species consistency (fish through human 3D models) further strengthens the biological plausibility.

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## Evidence Line 6: Wikidata and SPOKE-GeneLab Provide Independent Confirmation

**Wikidata** annotates PPARA with biological processes including cholesterol homeostasis regulation, lipid metabolic response, and fatty acid metabolic process -- independently confirming the KEGG/Reactome enrichment findings.

**SPOKE-GeneLab** shows PPARA differential expression in 10 NASA spaceflight assays, demonstrating that this gene responds to environmental stressors across experimental contexts -- it is a bona fide stress-responsive gene, not an artifact of PFAS-specific experimental design.

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## Synthesis: The Convergence Argument

The case against compound-by-compound regulation rests on convergence across independent lines of evidence:

| Evidence Source | What It Shows | Graph(s) |
|:---|:---|:---|
| Environmental prevalence | GenX at 73% the detection frequency of PFOS | SAWGraph |
| Direct potency comparison | GenX is the MOST potent PPARalpha activator of 16 PFAS tested | PubMed (Evans et al.) |
| Knockout proof | GenX effects entirely PPARalpha-dependent | NDE (GSE212294) |
| Transcriptomic concordance | GenX profile matches PPARalpha agonist drug across 3 species | PubMed + NDE |
| Environmental dose effects | GenX disrupts lipid metabolism at drinking water concentrations | PubMed (Shi et al.) |
| Mechanistic pathway | PPARalpha activation leads to liver tumors | AOP-Wiki |
| Pathway enrichment | 10 PFAS genes enriched for PPAR signaling (p=3.5e-9) | g:Profiler |
| Disease associations | PPARA and CYP2E1 both link to liver disease, kidney cancer | SPOKE-OKN, Ubergraph |
| Cross-species validation | 20+ PFAS datasets spanning fish to human hepatocytes | NDE |
| Functional annotation | PPARalpha annotated for lipid/cholesterol metabolism | Wikidata |
| Expression context | PPARalpha is stress-responsive across experimental conditions | SPOKE-GeneLab |
| Next-gen replacements | PFO4DA and PFO5DoA also activate PPARalpha | PubMed (Jackson et al.) |

No single database contains this complete picture. SAWGraph knows contamination but not biology. PubMed knows GenX is more potent than legacy PFAS but not how widespread it is. NDE provides the transcriptomic proof but not the disease outcomes. AOP-Wiki maps the pathway but not which chemicals trigger it. SPOKE-OKN knows disease associations but not the environmental exposure. Only by querying across the federation can we construct the argument: the replacement chemical is already widespread (SAWGraph), it is a more potent activator of the same molecular target (PubMed + NDE), that target maps to liver tumors (AOP-Wiki), those tumors track to diseases seen in exposed populations (SPOKE-OKN), and the next generation of replacements repeats the pattern (PubMed).

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## Implication: The Case for Class-Based PFAS Regulation

This analysis provides data-driven support for regulating PFAS as a chemical class rather than one compound at a time:

1. **The replacement is more potent, not safer.** An EPA study testing 16 PFAS found GenX is the strongest PPARalpha activator in the panel (Evans et al., PubMed). PPARalpha knockout experiments show GenX effects are entirely receptor-dependent (NDE). This is not a marginal safety concern -- it is an inversion of the regulatory assumption.

2. **Effects occur at existing environmental concentrations.** GenX disrupts hepatic lipid metabolism at 0.1 micrograms/L in drinking water (Shi et al., PubMed) -- concentrations already present in water systems documented by SAWGraph (16,954 observations). The exposure is happening now.

3. **Already too late for compound-by-compound approaches.** SAWGraph shows 25 PFAS compounds in water systems. The next generation (PFO4DA, PFO5DoA) already activates PPARalpha (Jackson et al., PubMed). Class-based regulation is the only approach that prevents an indefinite cycle of substitution.

4. **Biomarker candidates exist.** The PPARalpha gene network (PPARA, ACOX1, FABP1, SREBF1) provides biomarkers that detect harm from any PPARalpha-activating PFAS, not just individually regulated compounds.

5. **The evidence base is deep.** Eleven GenX-specific transcriptomic datasets in NDE, plus 20+ broader PFAS datasets across five species, provide the experimental foundation for class-based hazard assessment that regulators require.

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## Methodology

Seven knowledge graphs and three analytical tools were queried through Claude Code in a single session:

| Resource | Role |
|:---|:---|
| SAWGraph (FRINK) | Environmental contamination prevalence |
| BioBricks-AOPWiki (FRINK) | Adverse outcome pathway mechanisms |
| SPOKE-OKN (FRINK) | Gene-disease associations |
| SPOKE-GeneLab (FRINK) | Differential expression validation |
| NDE (FRINK) | Experimental dataset discovery |
| Wikidata (FRINK) | Functional annotation |
| Open Targets | Gene ID resolution (10 genes to Ensembl IDs) |
| g:Profiler | Pathway enrichment (168 terms from 10 genes) |
| PubMed | Direct experimental evidence (6 articles with DOIs) |

Cross-graph joins used gene symbols, Ensembl IDs, and chemical compound identities as shared identifiers.

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*Generated February 23, 2026 | Claude Code with Proto-OKN federated knowledge graph access*
