AHR-CYP1A1 Axis Perturbation and Necroptosis in 1,2-Dichloroethane Hepatotoxicity: Elucidation by an Integrated Network Toxicology and In Vitro Validation

TL;DR

This study explores how 1,2-dichloroethane causes liver damage by activating a specific pathway and triggering cell death.

Contribution

The study reveals a dual-phase mechanism of 1,2-DCE-induced hepatotoxicity involving the AHR-CYP1A1 axis and necroptosis.

Findings

1,2-DCE triggers rapid AHR nuclear translocation and transient CYP1A1 upregulation.

Prolonged exposure leads to CYP1A1 suppression and activation of necroptosis markers.

AHR is identified as a potential target for mitigating 1,2-DCE-induced liver injury.

Abstract

As a typical halogenated hydrocarbon environmental pollutant, 1,2-dichloroethane (1,2-DCE) exhibits clinically confirmed hepatotoxicity with incompletely understood mechanisms. This study integrated network toxicology, molecular docking, and in vitro experiments to investigate necroptosis in 1,2-DCE-induced liver injury. Computational analysis predicted involvement of the aryl hydrocarbon receptor (AHR)/cytochrome P450 1A1 (CYP1A1) pathway, and molecular docking suggested potential binding between 1,2-DCE and AHR (−6.5 kcal/mol). CCK-8 assays showed that 1,2-DCE reduced THLE-2 hepatocyte viability in a concentration-dependent manner. Notably, 1,2-DCE triggered rapid AHR nuclear translocation within 1 h and transiently upregulated CYP1A1 at both the transcriptional and protein levels (3–6 h). Further studies revealed elevated intracellular reactive oxygen species (ROS) at 24 h. After 48 h exposure, CYP1A1 expression was significantly suppressed, accompanied by activation of necroptosis markers, including increased lactate dehydrogenase (LDH) release, enhanced propidium iodide (PI) staining, and elevated phosphorylation of receptor-interacting protein kinase 3 (RIPK3) and mixed lineage kinase domain-like protein (MLKL). These findings reveal a dual-phase mechanism: an early adaptive stress response via the AHR-CYP1A1 axis, followed by pathway dysfunction and transition to necroptosis, suggesting AHR as a potential target for intervening in 1,2-DCE-induced hepatotoxicity.

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1. Introduction

Halogenated aliphatic hydrocarbons, as a class of industrially widespread solvents and ubiquitous environmental pollutants, possess potential hepatotoxicity [1]. Among them, 1,2-dichloroethane (1,2-DCE) has garnered significant attention due to its high volatility and environmental persistence [2,3]. Indeed, occupational exposure or accidental incidents can lead to severe liver injury, clinically manifested as elevated hepatic enzymes, jaundice, and even fatal acute poisoning [1,4]. Consistent with this, our prior autopsy studies confirmed that 1,2-DCE induces diffuse vacuolar degeneration and focal necrosis of hepatocytes [4]. Our recent in vivo investigations using a zebrafish embryo model further demonstrated that 1,2-DCE disrupts liver development, causing morphological alterations, disorganized hepatocellular architecture, vacuolation, and hepatic atrophy, concomitant with oxidative stress damage and downregulated expression of cytochrome P450, family 1, subfamily a (cyp1a), the homolog of mammalian CYP1A1. [5]. Although the hepatotoxic effects of 1,2-DCE are well-established, the precise molecular mechanisms linking these pathological changes remain unclear.

Necroptosis, as a strictly regulated form of programmed cell death, is distinct from apoptosis and accidental necrosis both mechanistically and morphologically [6]. Its hallmark features include plasma membrane rupture, organelle swelling, and the release of damage-associated molecular patterns (DAMPs). Traditionally, necroptosis is understood to be mediated by the receptor-interacting serine-threonine protein kinase 1/receptor-interacting serine-threonine protein kinase 3/mixed lineage kinase domain-like protein (RIPK1/RIPK3/MLKL) pathway and is implicated in the pathogenesis of various liver diseases, including metabolic dysfunction-associated steatohepatitis (MASH), alcohol-associated liver disease, liver fibrosis, and hepatocellular carcinoma [7,8]. Recent studies have revealed that the RIPK3-MLKL axis serves as the central execution hub of necroptosis, and its activation can occur independently of RIPK1 [9,10]. Previous studies have reported that the environmental toxicant cypermethrin can induce hepatocyte necroptosis via a similar mechanism [11]; however, whether halogenated hydrocarbons like 1,2-DCE are involved in this process remains unreported.

In recent years, network toxicology and molecular docking technologies have become important tools for deciphering the mechanisms of hepatotoxicity induced by xenobiotic compounds [12]. In this study, our network toxicology analysis revealed that RELA, a core component of the nuclear factor kappa B (NF-κB) inflammatory signaling pathway, is a key regulatory factor in 1,2-DCE-induced hepatotoxicity and necroptosis (Figure 1). Further protein–protein interaction (PPI) analysis revealed a functional interplay between RELA and the aryl hydrocarbon receptor (AHR), with AHR also being identified within the intersection of 1,2-DCE-targeted molecules and those associated with necroptosis (Figure 1). This discovery prompted further investigation into its downstream mechanisms.

Notably, RELA has been shown to crosstalk with the AHR pathway, a central hub for xenobiotic metabolism, and both play critical roles in liver injury [13,14,15]. It is noteworthy that AHR has been classically recognized as an upstream regulator of CYP1A1. Following its binding to xenobiotics, AHR activates the transcription of CYP1A1, thereby mediating the metabolic activation of these compounds through cytochrome P450 enzymes such as CYP1A1, a process during which reactive oxygen species (ROS) are generated [16]. Importantly, oxidative stress serves as a key link between inflammatory responses and cell death, particularly as a significant driver of necroptosis [9]; inhibition of oxidative stress has been confirmed to alleviate ethanol-induced hepatocyte necroptosis [17]. Recent evidence indicates that, in addition to interacting with AHR, RELA may also directly or cooperatively regulate the expression of CYP1A1 [15], indicating that CYP1A1 could be a critical downstream node integrating RELA-mediated inflammatory signaling and AHR-mediated xenobiotic metabolic signaling, and dysfunction of this node may contribute to necroptosis through mechanisms such as oxidative stress. Our prior zebrafish experiments provided key clues, showing that 1,2-DCE exposure induced oxidative stress and liver defects concurrently with downregulation of embryonic cyp1a expression [5], which likely reflects dysregulation of the metabolic network centered on this key node under toxic stress. However, it remains unclear whether 1,2-DCE induces hepatocyte necroptosis by perturbing the dynamic function of the AHR-CYP1A1 axis and whether this process bridges xenobiotic stress with inflammatory and oxidative injury.

Based on this background, the present study integrates network toxicology, molecular docking, and in vitro experiments to systematically investigate whether 1,2-DCE induces hepatotoxicity by regulating the necroptosis pathway, with a focus on the role of the AHR/CYP1A1 axis. This work aims to provide new insights into the toxic mechanisms of halogenated hydrocarbon pollutants.

2. Materials and Methods

2.1. Materials

1,2-Dichloroethane (CAS No. 107-06-2, purity 99.5%) was obtained from Shanghai Macklin Biochemical Technology Co., Ltd. (Shanghai, China). DMEM-F12 medium was procured from Gibco (Grand Island, NY, USA). Fetal bovine serum (FBS, Cat. No. 164210) was obtained from Procell Life Science & Technology Co., Ltd. (Wuhan, China). Dimethyl sulfoxide (DMSO) and the CCK-8 Cell Proliferation and Cytotoxicity Assay Kit (BS350A) were supplied by Biosharp (Hefei, China). DCFH-DA (85155) was acquired from Cayman Chemical Company (Ann Arbor, MI, USA). The LDH Release Assay Kit with WST-8 (C0019M), PI Staining Solution (C1352S) and PMSF were purchased from Beyotime Biotechnology (Shanghai, China). RIPA Lysis Buffer (PC102), phosphatase inhibitor cocktail (GRF102), BCA Protein Assay Kit (ZJ102), Western Blot Fast Stripping Buffer (PS107), and Multicolor Prestained Protein Ladder (WJ103) were all obtained from Epizyme Biomedical Technology (Nanjing, China). Antibodies including MLKL Monoclonal Antibody (66675-1-Ig), Phospho-MLKL (Ser358) Recombinant Antibody (82090-2-RR), RIPK3 Monoclonal Antibody (68786-2-Ig), GAPDH Recombinant Antibody (81640-5-RR), CYP1A1 Polyclonal Antibody (13241-1-AP), AHR Polyclonal antibody (28727-1-AP), Multi-rAb^®^ CoraLite^®^ Plus 488-Goat Anti-Rabbit Recombinant Secondary Antibody (H+L) (RGAR002), HRP-conjugated Goat Anti-Rabbit IgG (SA00001-2), and HRP-conjugated Goat Anti-Mouse IgG (SA00001-1) were sourced from Proteintech Group (Wuhan, China). Phospho-RIP3 (Ser227) (D6W2T) Rabbit mAb (93654) was procured from Cell Signaling Technology (Danvers, MA, USA). The PrimeScript™ RT Reagent Kit (RR047A) was purchased from Takara Bio (Kusatsu, Japan). SuperStar Universal SYBR Master Mix (CW3360) was acquired from CWBIO (Taizhou, China). The Super FastPure Cell RNA Isolation Kit (RC102) was supplied by Vazyme Biotech Co., Ltd. (Nanjing, China). All other chemicals and reagents were purchased from Beyotime Biotechnology (Shanghai, China).

2.2. Prediction and Screening of Putative Targets for 1,2-DCE and Necroptosis

Given that metabolites of 1,2-dichloroethane (1,2-DCE), such as 2-chloroacetic acid (2-CA) and chloroacetaldehyde, also exhibit hepatotoxic effects [18], a systematic investigation of their potential protein targets is crucial for comprehensively elucidating the toxicity mechanisms of 1,2-DCE. Accordingly, we employed multiple chemoinformatics and ligand-based prediction platforms—including TargetNet [19], ChEMBL [20], SwissTargetPrediction [21], and the Similarity Ensemble Approach (SEA) [22]—to systematically predict the potential protein targets of 1,2-DCE and its major metabolites (including 2-CA and chloroacetaldehyde). The resulting targets were integrated to construct a comprehensive target library for 1,2-DCE. Subsequently, Venn analysis was performed using the Venny 2.1 online tool (https://bioinfogp.cnb.csic.es/tools/venny/ (accessed on 13 August 2025)) to identify and visualize the overlapping targets between 1,2-DCE and its metabolites. The list of 94 common targets shared by 1,2-DCE and its two major metabolites is provided in Supplementary Table S1. The resulting common targets were subjected to functional enrichment analysis to elucidate the potential biological processes and pathways affected by 1,2-DCE and its metabolites.

To further explore the potential role of 1,2-DCE in inducing necroptosis, necroptosis-related genes were retrieved from the OMIM [23] and GeneCards [24] databases using the keyword “necroptosis”. A second Venn analysis was then conducted to identify and visualize the overlapping targets among the integrated 1,2-DCE target library, its metabolites, and the necroptosis-related gene set. The subsequent screening against a necroptosis gene set identified 9 core intersecting candidates, detailed in Supplementary Table S2. The common targets obtained from this intersection were selected as candidate mediators for PPI network analysis to investigate their potential roles and interactions in 1,2-DCE-induced necroptosis [25].

2.3. Functional Enrichment Analysis

Functional enrichment analysis of the overlapping targets was performed using the DAVID database (version 2021; https://david.ncifcrf.gov/ (accessed on 24 August 2025)) to identify significantly overrepresented Gene Ontology (GO) biological processes [26]. The significantly enriched terms were then ranked in descending order by the count of involved genes to identify the most prevalent biological processes. The top 30 terms based on this sorting criterion were selected for further visualization. The enrichment results were presented as a bubble plot using the online bioinformatics platform (https://www.bioinformatics.com.cn; last accessed on 10 December 2024), which effectively displays the enrichment factor [27], p-value, and gene count for each term.

2.4. PPI Network Construction and Core Target Selection

To investigate the functional associations among the putative targets, a PPI network was constructed using the STRING database (version 12.0; https://string-db.org (accessed on 24 August 2025)) [25]. The common targets identified from the Venn analysis were submitted as input. The analysis was performed with a high confidence interaction score threshold of ≥0.7 to ensure biological relevance. The resulting PPI network was then visualized and analyzed. The resulting PPI network was imported into Cytoscape (version 3.7.1) for topological analysis [28]. Centrality metrics—betweenness, closeness and degree—were calculated for each node. Nodes exceeding median centrality values were considered core targets.

2.5. Molecular Docking

Molecular docking was performed using AutoDock Vina (version 1.5.7)to evaluate the potential interaction between 1,2-DCE and AHR. The three-dimensional structure of 1,2-DCE (PubChem CID: 11) was obtained from the PubChem database and preprocessed with Molecular Operating Environment (MOE) software (version 2020.0901) for energy minimization and charge correction [29]. The crystal structure of the human AHR ligand-binding domain (UniProt ID: P35869) was retrieved from the Protein Data Bank (PDB ID: 7ZUB). The protein structure was prepared by removing water molecules, adding hydrogen atoms, and optimizing the conformation. Docking simulations were conducted in full-atom mode, and the top nine binding poses were retained for analysis. The results were evaluated based on the calculated binding free energy (in −kcal/mol) and the number of intermolecular interactions, with lower energy and a greater number of interactions indicating a more favorable binding mode. Visualization was performed using PyMOL (version 2.6.0).

2.6. Cell Culture and Chemical Exposure

The THLE-2 human normal hepatocyte cell line (CL-0833) was purchased from Procell Life Science & Technology Co., Ltd. (Wuhan, China). Cells were cultured in DMEM/F12 complete medium, supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin, and maintained at 37 °C in a humidified incubator containing 5% CO_2_. The THLE-2 human hepatocyte cell line was selected for this study due to its established utility in modeling liver-specific functions, hepatotoxicity, and its retention of key cell death pathways, including RIP3/MLKL-mediated necroptosis, relevant to compound-induced liver injury [30].

For all experiments, cells were seeded at an appropriate density and allowed to adhere for 24 h prior to chemical exposure. Immediately before each treatment, a working solution of 1,2-DCE was prepared as follows: 1,2-DCE was first mixed with a minimal volume of sterile dimethyl sulfoxide (DMSO) as a cosolvent [31]. This mixture was then added to complete DMEM/F12 medium to prepare a primary stock solution of 4000 μg/mL. Final working concentrations were obtained by further diluting an aliquot of this stock with the complete medium. This fresh preparation for each experiment was essential to minimize dose inaccuracy due to the volatility of 1,2-DCE. The stock and working solutions were protected from light throughout the procedure to minimize photodegradation. The final concentration of DMSO in all treatment groups was kept ≤0.1% (v/v) to avoid solvent-induced cytotoxicity. The concentration gradient of 1,2-DCE used for treatment and the incubation conditions (37 °C in a humidified atmosphere of 5% CO_2_) were established based on prior literature reports and our preliminary zebrafish toxicity assays [5,32].

2.7. Cell Viability Assay (CCK-8)

THLE-2 cells were seeded in 96-well plates at a density of 8 × 103 cells per well. After 24 h of incubation, the culture medium was replaced with fresh complete medium containing serial concentrations of 1,2-DCE (0, 200, 400, 800, 1200, 1600, 2000, and 4000 μg/mL) or 0.1% DMSO (vehicle control). For the determination of the half-maximal inhibitory concentration (IC_50_), cells were treated for 24 h. To evaluate the time-dependent effect, cells were exposed to 2000 μg/mL 1,2-DCE for 1, 3, or 24 h. After the respective treatment periods, the medium was removed and replaced with fresh medium containing CCK-8 reagent (BS350A, Biosharp). Following incubation at 37 °C for 1 h, the absorbance at 450 nm was measured using a microplate reader. Cell viability was calculated, and the IC_50_ value was derived from the dose–response curve by nonlinear regression in GraphPad Prism 10.0 using a four-parameter logistic model with variable slope. The 95% confidence interval (CI) for the IC_50_ was determined via the profile likelihood method. Data from the time-course experiment were subjected to statistical analysis.

2.8. Intracellular ROS Measurement (DCFH-DA Assay)

Cells were seeded in 24-well plates. After 24 h, they were treated with 1,2-DCE or 0.05% DMSO (control) for 24 h. To assess potential interference from autofluorescence, a control group without DCFH-DA working solution was included in the initial experiment; no detectable autofluorescence was observed. Subsequently, cells were washed with PBS and incubated with DCFH-DA working solution (250 μg/mL) at 37 °C in the dark for 30 min. Following two washes with PBS, serum-free basal medium was added, and fluorescence images were immediately captured within 30 min to prevent quenching. Images were acquired using an Olympus inverted fluorescence microscope under consistent parameters (including exposure time) for both experimental and control groups. Fluorescence intensity was quantified as integrated density using ImageJ software (version 1.53o), and statistical analysis was conducted with GraphPad Prism 10.0.

2.9. LDH Release Assay

Cells were seeded in 24-well or 6-well plates and cultured in DMEM-F12 complete medium supplemented with heat-inactivated fetal bovine serum (incubated at 56 °C for 30 min to eliminate intrinsic bioactive components). After treatment, the culture supernatant was collected and centrifuged at 400× g for 5 min. The supernatant was then serially diluted in a 96-well plate to appropriate concentrations. According to the manufacturer’s instructions of the Lactate Dehydrogenase Assay Kit (WST-8 method), the samples were treated with the LDH detection working solution and incubated at 37 °C for 35 min. Absorbance was measured at 450 nm using a BIO-TEK multi-mode microplate reader (Agilent Technologies Inc., Winooski, VT, USA), and LDH activity was calculated and expressed in mU/mL.

2.10. Necrotic Cell Detection (PI Staining)

Cells were grown on glass coverslips in 24-well or 6-well plates. After treatment, cells were washed with PBS and incubated with PI working solution in the dark at 37 °C for 10 min. Following a PBS wash, cells were mounted with antifade mounting medium containing DAPI. Fluorescence images were captured using an Olympus inverted fluorescence microscope after mounting. All imaging parameters, including exposure time, were kept consistent between the experimental and control groups. The number of red (PI-positive) and blue (DAPI-stained) cells in each image was separately counted using ImageJ software. Non-specific fluorescent signals were carefully excluded during counting. The ratio of PI-positive cells to total cells (DAPI-stained nuclei) was calculated in Excel. The resulting data, representing the proportion of PI-positive cells, were subjected to statistical analysis using GraphPad Prism 10.0.

2.11. Immunofluorescence Staining and Nucleo-Cytoplasmic Ratio Analysis of AHR

THLE-2 cells were seeded on glass coverslips in 24-well plates. After treatment with 800 μg/mL 1,2-DCE or 0.02% DMSO (vehicle control) for the indicated durations (1–24 h), cells were washed twice with PBS and fixed with 4% paraformaldehyde for 15 min at room temperature. Subsequently, cells were permeabilized with 0.5% Triton X-100 in PBS for 10 min and blocked with 1% IgG-free bovine serum albumin (BSA) in PBS for 30 min. The cells were then incubated with a primary AHR polyclonal antibody (diluted 1:200) at 4 °C overnight. After washing, a fluorescein-conjugated goat anti-rabbit recombinant secondary antibody (488 nm, diluted 1:200) was applied and incubated in the dark for 1 h at room temperature. Cell nuclei were counterstained with DAPI-containing mounting medium. Fluorescence images were captured using an Olympus inverted fluorescence microscope under consistent parameters (including exposure time) for both experimental and control groups.

Quantitative analysis of AHR nucleo-cytoplasmic distribution was performed using ImageJ software (National Institutes of Health) [33]. For each cell, a nuclear mask was generated from the DAPI channel to define the nuclear area. A whole-cell mask was created from the AHR (green) fluorescence channel to outline the entire cellular boundary. The integrated fluorescence intensity of AHR within the nucleus and within the whole cell was measured. The cytoplasmic fluorescence intensity was calculated by subtracting the nuclear intensity from the whole-cell intensity. The nucleo-cytoplasmic ratio for each cell was determined as follows: Nucleo-cytoplasmic Ratio = Integrated Nuclear AHR Fluorescence Intensity/Integrated Cytoplasmic AHR Fluorescence Intensity. The analysis was performed on ten random cells per image from three independent experiments, resulting in a total of 30 cells per treatment group for statistical comparison.

2.12. Quantitative Real-Time PCR (qRT-PCR)

Total RNA was extracted from cells using the Super FastPure Cell RNA Isolation Kit (RC102, Vazyme, Nanjing, China) according to the manufacturer’s instructions. RNA concentration and purity were determined using a NanoDrop spectrophotometer. Complementary DNA (cDNA) was synthesized from 1 μg of total RNA using the PrimeScript™ RT Reagent Kit (RR047A, Takara Bio, Kusatsu, Japan). Quantitative real-time PCR (qPCR) was performed using the SuperStar Universal SYBR Master Mix (CW3360, CWBIO, Taizhou, China) on a QuantStudio™ Real-Time PCR System (Applied Biosystems, Waltham, MA, USA). All gene-specific primers (sequences provided in Table 1) were synthesized by Sangon Biotech (Shanghai, China). The thermal cycling conditions consisted of an initial denaturation at 95 °C for 30 s, followed by 40 cycles of 95 °C for 5 s and 60 °C for 30 s. Melting curve analysis was performed at the end of each run to confirm amplification specificity. All reactions were carried out in triplicate. The mRNA expression levels of target genes were normalized to the geometric mean of the two reference genes, β-tubulin (TUBB) and TATA-box binding protein (TBP), which were previously identified as stable housekeeping genes for assessing mRNA expression in liver-derived cell lines, to control for sample-to-sample variation [34]. Relative quantification of gene expression was calculated using the 2^–ΔΔCt^ method.

2.13. Western Blot Analysis

Cells were seeded in 6-well plates and treated with 800 μg/mL 1,2-DCE or 0.02% DMSO (vehicle control) for 48 h when cell confluence reached 50–60%. Total proteins were extracted using RIPA lysis buffer supplemented with 1 mM PMSF and phosphatase inhibitors. Protein concentration was determined using the BCA Protein Assay Kit. Equal amounts of protein (25 μg per lane) were separated by 10% SDS-PAGE and transferred to PVDF membranes.

For detection of phosphorylated proteins, including p-MLKL (Ser358) and p-RIPK3 (Ser227), the membranes were blocked with 5% BSA in TBST for 1.5 h at room temperature before incubation with specific primary antibodies. Subsequent steps followed the standard Western blot protocol. After imaging, the membranes were stripped with Western Blot Fast Stripping Buffer until no bands were visible (approximately 30 min), followed by a 10 min wash with TBST. They were then re-blocked with 5% non-fat milk in TBST for 1.5 h at room temperature. The same procedure was repeated to detect non-phosphorylated total proteins (MLKL and RIPK3). After visualization, the stripping procedure was performed again to subsequently detect the internal reference protein (GAPDH).

For conventional Western blot, after blocking with 5% non-fat milk for 1.5 h at room temperature, the membranes were incubated overnight at 4 °C with primary antibodies against MLKL, RIPK3, CYP1A1 and GAPDH. Following primary antibody incubation, membranes were incubated with corresponding HRP-conjugated secondary antibodies for 1.5 h at room temperature. Protein bands were visualized using an enhanced chemiluminescence (ECL) detection system and imaged using a Bio-Rad ChemiDoc™ Imaging System (Bio-Rad, Hercules, CA, USA). Band intensities were quantified with ImageJ software. The ratios of p-MLKL to total MLKL and p-RIPK3 to total RIPK3 were calculated, and total protein levels were normalized to GAPDH. Statistical analysis was performed using GraphPad Prism 10.0.

2.14. Statistical Analysis

All statistical analyses were performed using GraphPad Prism software (version 10.0). Data obtained from at least three independent experiments are presented as the mean ± standard deviation (SD), unless otherwise specified. The normality of data distribution was assessed using the Shapiro–Wilk test. For comparisons between two groups, an unpaired Student’s t-test was used for normally distributed data, and the Mann–Whitney U test was used for non-normally distributed data. For comparisons among multiple groups, one-way analysis of variance (ANOVA) was applied to normally distributed data, followed by Dunnett’s post hoc test for comparisons against a single control group if a significant difference was detected (p < 0.05). The Kruskal–Wallis test, followed by Dunn’s multiple comparisons test, was used for non-normally distributed data. Specific considerations for Western blot data: Due to the typical small sample size (n = 3–5 per group) and the common practice of normalizing band intensity to the control group, which can constrain variance, all pairwise comparisons involving Western blot quantifications (e.g., treated groups vs. control at specific time points) were performed using the Mann–Whitney U test. A p-value of less than 0.05 was considered statistically significant.

Specific analysis for AHR nuclear translocation: Potential outliers were first identified using Grubbs’ test (alpha = 0.05), and none were detected in any group. The normality of distribution was assessed using the Shapiro–Wilk test. While data from the Control and 1,2-DCE 1 h groups passed all normality tests (p > 0.05), data from the 1,2-DCE 6 h and 24 h groups exhibited non-normal distributions (p < 0.05). Consequently, non-parametric methods were employed for all group comparisons of this dataset. Differences were analyzed using the Kruskal–Wallis test, followed by Dunn’s multiple comparisons test with Holm-Bonferroni correction when overall significance was detected. These data are presented as the median with interquartile range (IQR).

3. Results

3.1. Integrated Network Toxicology Analysis Reveals 1,2-DCE-Induced Toxicity and Necroptosis Mechanisms

Venn analysis and functional enrichment of 1,2-DCE and its metabolites revealed significant overlaps in their molecular targets. Enrichment analysis indicated that these shared targets were most significantly enriched in the inflammatory response. Additionally, they were involved in other key biological processes, including cholinergic signaling, hypoxia, oxidative stress, and responses to toxic substances and xenobiotics (Figure 1A,B). These findings suggest that 1,2-DCE and its metabolites disrupt essential cellular functions primarily through inflammatory pathways, along with other interrelated mechanisms.

To identify potential targets through which 1,2-DCE and its metabolites mediate necroptosis, an intersection analysis was performed using a Venn diagram (Figure 1C). Subsequent PPI network analysis revealed strong functional associations among these candidate molecules (Figure 1D). The PPI network identified nine targets, including RELA and AHR, as central players in 1,2-DCE–induced necroptosis (Figure 1D). Among them, the RELA gene (encoding the NF-κB p65 subunit) exhibited the highest connectivity, suggesting its role as a hub molecule. This was corroborated by the weighted clustering coefficient algorithm in Cytoscape, which confirmed RELA’s central position within the network (Figure 1E). In the network visualization, node size and color intensity represent degree values, while edge thickness denotes interaction strength, collectively illustrating the molecular interaction network underlying 1,2-DCE–induced necroptosis.

While the role of NF-κB–mediated inflammatory signaling, including RELA, in 1,2-DCE–induced cellular injury has been previously established [35], the involvement of AHR—although central to hepatic xenobiotic metabolism—remains poorly understood in the context of 1,2-DCE toxicity. To explore the initial molecular event that triggers the toxicity, we focused on the potential direct interaction between 1,2-DCE and its predicted hub target, AHR. Molecular docking was employed to assess this binding.

3.2. Cytotoxic Effects of 1,2-DCE on Human Hepatocytes In Vitro

To investigate the hepatotoxic effects of 1,2-DCE, an in vitro toxicity model was established in THLE-2 hepatocytes based on the dosage range used in our prior zebrafish study. Using this model, the CCK-8 assay demonstrated that 1,2-DCE significantly reduced cell viability in a concentration- and time-dependent manner, with a half-maximal inhibitory concentration (IC50) value of 2125 μg/mL (95% CI: 1994 to 2276 μg/mL) at 24 h. The dose–response curve was fitted well by a four-parameter logistic model (R^2^ = 0.9132, degrees of freedom (df) = 55, sum of squares = 4901) (Figure 2A). For the time-course experiment, data from all groups passed normality testing (Shapiro–Wilk test, all p > 0.05). One-way ANOVA revealed a statistically significant effect of treatment duration (F = 105.1, df = 3, p < 0.0001). Subsequent Dunnett’s multiple comparisons test showed that while a marked and statistically significant decrease was observed after 24 h of 1,2-DCE treatment compared to the control (p < 0.0001), the reductions at 3 h time points, though showing a downward trend, did not reach statistical significance (Figure 2B).

3.3. Molecular Docking and Cellular Validation Suggest AHR as a Direct Target of 1,2-DCE

To assess whether AHR could be a direct target of 1,2-DCE, molecular docking was performed. The results showed that 1,2-DCE could be docked into the PAS-B domain of AHR with a predicted binding free energy of −6.5 kcal/mol. The compound formed 23 intermolecular interaction bonds with key residues, including ARG 392, GLY 12, PHE 129, GLY 130, VAL 131, GLU 42, and GLY 132 (Figure 3A), suggesting a plausible binding mode.

Cellular immunofluorescence assays were used to examine the functional consequence of this potential interaction. Quantitative analysis of the AHR nucleo-cytoplasmic ratio revealed a rapid and significant increase in nuclear translocation after 1 h of 1,2-DCE treatment (p < 0.0001) (Figure 3B,C). Notably, the response at this time point exhibited considerable variability among individual cells, reflecting heterogeneity in cellular response. In contrast, no statistically significant change in AHR localization was observed at the 6 h and 24 h time points compared to the control (Figure 3B,C).

Taken together, the computational docking and time-resolved cellular imaging data are consistent with the interpretation that 1,2-DCE may directly interact with AHR, leading to its transient activation and nuclear import. This finding prompted us to investigate the effect on its downstream target, CYP1A1.

3.4. 1,2-DCE Exerts a Biphasic Effect on CYP1A1 Expression: Induction at Short-Term Exposure Versus Suppression at Prolonged Exposure

Exposure to 800 μg/mL 1,2-DCE triggered a time-dependent biphasic response in the expression of CYP1A1 in THLE-2 cells, characterized by an early induction followed by a late suppression. Notably, the initial activation phase was observed at both transcriptional and translational levels. A significant upregulation of CYP1A1 mRNA was detected after 3 and 6 h of treatment (both p < 0.0001 vs. control) (Figure 4A), which was accompanied by a concurrent increase in CYP1A1 protein expression at these early time points (p = 0.0143 and p = 0.0286 vs. control, respectively) (Figure 4C,E). However, prolonged exposure to 1,2-DCE led to a striking inhibitory phase. The inductive effect on CYP1A1 mRNA was not sustained over time (One-way ANOVA, F = 13.63, p < 0.0001). While levels at 24 h were not significantly different from the control, they declined significantly by 48 h (p = 0.0012 vs. control) (Figure 4B). This transcriptional suppression was followed at the protein level, where CYP1A1 expression was down regulated at 48 h of treatment (p = 0.0179 vs. control) (Figure 4D,F). Collectively, these results demonstrate that 1,2-DCE exerts a dual effect on CYP1A1, acting as a short-term activator but a long-term suppressor, revealing a critical time-dependent dimension to its hepatotoxic mechanism.

3.5. 1,2-DCE Triggers Oxidative Stress in Hepatocytes

DCFH-DA fluorescence analysis revealed that 1,2-DCE treatment increased intracellular ROS levels (Figure 5). The levels of ROS differed significantly among the treatment groups (Kruskal–Wallis test, H = 10.68, p = 0.0024). Post hoc Dunn’s test revealed a significant increase specifically in the 400 μg/mL 1,2-DCE group compared to the control (p = 0.0039), whereas the 800 and 2000 μg/mL 1,2-DCE groups showed no statistically significant change. It is worth noting that, although an upward trend in ROS fluorescence was observed in the 800 and 2000 μg/mL groups, the changes did not reach statistical significance. This is likely due to the cytotoxicity at higher concentrations (as shown in Figure 2A, cell viability decreased), which reduced the number of viable cells and affected the ROS measurement, along with the greater variability in the data from the high-dose groups, which may have reduced the statistical power.

3.6. 1,2-DCE Induces Necroptosis in Hepatocytes

The LDH release assay indicated that treatment with 800 μg/mL 1,2-DCE resulted in significant cell membrane damage at both 24 and 48 h, as evidenced by increased LDH activity in the culture supernatant (p = 0.0053 and p < 0.0001 vs. control, respectively) (Figure 6A). Correspondingly, PI staining after 48 h of exposure showed a significant increase in the percentage of PI-positive cells (p = 0.0159 vs. control), indicating enhanced necrotic cell death (Figure 6B,C). Western blot analysis further demonstrated that 1,2-DCE treatment for 48 h markedly up-regulated the phosphorylation levels of key necroptotic regulators RIPK3 (p = 0.0286) and MLKL (p = 0.0357), without altering their total protein expression (Figure 6D,E). Collectively, these findings indicate that prolonged (48 h) exposure to 1,2-DCE triggers necroptosis in human hepatocytes, characterized by cell membrane damage, increased cell death, and activation of the RIPK3/MLKL signaling pathway.

4. Discussion

Liver injury induced by high concentrations of 1,2-DCE represents a significant challenge in the clinical management of chemical hepatotoxicity [1], and its hepatotoxic effects have been extensively confirmed by numerous in vivo and in vitro studies [2]. In recent years, necroptosis, a form of programmed necrosis, has been identified as a key mechanism in chemical-induced liver injury [7,9]. However, whether and how 1,2-DCE induces hepatocyte necroptosis remained unclear. By integrating network toxicology prediction with in vitro experimental validation, this study suggests that the perturbation of the AHR/CYP1A1 axis is involved in this process, revealing a novel time-dependent dimension to its mechanism.

Network toxicology, by integrating multi-source biological data with systematic network analysis, serves as a powerful tool for efficiently identifying key targets and pathways related to compound toxicity and elucidating their multi-target mechanisms. Therefore, we first applied this approach to systematically analyze the toxic network of 1,2-DCE and its metabolites. The results showed that their shared targets were significantly enriched in pathways such as inflammation, oxidative stress, and xenobiotic metabolism (Figure 1A,B), which is consistent with the established toxic mechanism of 1,2-DCE—namely, its metabolism by cytochrome P450 2E1 (CYP2E1), activation of the p38 mitogen-activated protein kinase (p38 MAPK)/NF-κB signaling axis, and release of inflammatory factors [35,36]. Given that necroptosis has been confirmed as a key bridge linking cell death and inflammatory responses—a process that not only directly leads to cell death and amplifies inflammation but also exacerbates pathological changes such as oxidative stress through key factors like RIPK3, while oxidative stress itself is a core driver of necroptosis [8,9,37]—this suggests that necroptosis may be a critical integrative mechanism in 1,2-DCE toxicity. Accordingly, we further constructed a PPI network focusing on targets associated with necroptosis. This network highlighted RELA and AHR as central hub molecules (Figure 1D,E), indicating potential crosstalk between inflammatory (NF-κB) and metabolic (AHR) signaling. Although NF-κB-mediated inflammatory injury induced by 1,2-DCE has been reported [35], the role of AHR in 1,2-DCE hepatotoxicity remains unknown. Focusing on AHR as a research priority is supported by emerging evidence that highlights its central role as a key molecular sensor for a variety of environmental pollutants. For instance, network toxicology and Mendelian randomization analyses have proposed AHR as a critical bridge linking certain air pollutants (e.g., Toluene, sulfur dioxide) to male reproductive impairment, although such studies primarily rely on bioinformatic predictions and lack experimental validation [38]. Similarly, another study integrating network toxicology and experimental data indicated that AHR activation mediates oxidative stress and disrupts inflammatory responses in lung injury induced by dioxin-like compounds; however, this work also lacked direct evidence for pollutant-induced AHR activation [39]. Together, these studies emphasize the central importance of AHR in environmental toxicology, but they also reveal a common methodological limitation—namely, these studies have not provided comprehensive experimental data to substantiate ligand-receptor interactions and their functional activation. Our prior zebrafish experiments suggested that 1,2-DCE exposure induced hepatic oxidative stress and vacuolar degeneration while suppressing the expression of cyp1a (the homolog of mammalian CYP1A1), a downstream gene of AHR. Therefore, this study aims to systematically reveal the dynamic changes in the AHR/CYP1A1 axis upon 1,2-DCE exposure and its association with oxidative stress and necroptosis, in order to preliminarily elucidate its potential mode of action in 1,2-DCE-induced hepatotoxicity.

To test the hypothesis that 1,2-DCE may activate the AHR/CYP1A1 signaling pathway, we first employed molecular docking to evaluate its potential direct interaction with AHR. The results demonstrated that 1,2-DCE could form interactions with key residues (e.g., ARG 392, PHE 129) within the PAS-B domain of AHR, with a computed binding free energy of −6.5 kcal/mol, indicating potential binding (Figure 3A). Structurally, 1,2-DCE is a small halogenated hydrocarbon that shares the hydrophobicity and molecular dimensions characteristic of ligands for the AHR hydrophobic binding pocket—a site that typically accommodates planar aromatic hydrocarbons like the classic agonist and potent AHR ligand, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), a persistent organic pollutant [40].

Cellular experiments provided functional support for the computationally predicted binding. Immunofluorescence analysis revealed that 1,2-DCE induced significant AHR nuclear translocation within 1 h (Figure 3B,C), indicating receptor activation. This is consistent with the canonical AHR pathway, where ligand binding leads to nuclear translocation, ARNT heterodimerization, and xenobiotic response elements (XREs) binding to activate target genes like CYP1A1 [41,42]. Accordingly, short-term 1,2-DCE exposure (3–6 h) significantly induced CYP1A1 expression at both transcriptional and translational levels (Figure 4A,C,E). Thus, molecular docking, observed nuclear translocation, and CYP1A1 induction together provide coherent evidence that 1,2-DCE acts as an AHR ligand to transiently activate the AHR/CYP1A1 pathway.

Conventionally, the hepatic metabolism of 1,2-DCE is understood to depend primarily on the CYP2E1 and glutathione systems—a process known to generate ROS and toxic intermediates [43]. Our integrated findings from network toxicology and in vitro experiments indicate that 1,2-DCE can activate the AHR-CYP1A1 axis. Notably, activation of this axis is inherently associated with ROS production during xenobiotic metabolism [16]. We therefore hypothesized that, in addition to the classical CYP2E1 pathway, perturbation of the AHR-CYP1A1 axis by 1,2-DCE might constitute an alternative source contributing to oxidative stress in hepatocytes. To test this hypothesis, intracellular ROS levels were measured following 1,2-DCE exposure. Consistent with our previous zebrafish findings of hepatic oxidative stress injury, the results confirmed that 1,2-DCE significantly induced ROS production (Figure 5), thereby providing crucial experimental support for the proposed mechanism.

A key finding of this study is the time-dependent biphasic regulation of CYP1A1 expression by 1,2-DCE. Following an initial induction phase (3–6 h), prolonged exposure (48 h) led to a significant suppression of CYP1A1 (Figure 4). Notably, this late-phase suppression aligns with our prior observation in a zebrafish model, where 1,2-DCE exposure similarly resulted in the downregulation of cyp1a, the homolog of mammalian CYP1A1 [5]. This dynamic shift from activation to repression delineates a critical transition in the cellular response to toxic insult.

Although high concentrations of 1,2-DCE (2000 μg/mL) significantly reduced cell viability by 24 h (Figure 2), the execution of necroptosis was most prominently detected at the 48 h time point. Strikingly, the late-phase downregulation of CYP1A1 coincided temporally with the onset of necroptosis. Our data demonstrate that 48 h exposure triggered hallmark features of necroptosis: plasma membrane damage (increased LDH release), elevated necrotic cell death (PI staining), and crucial activation of the RIPK3/MLKL pathway (upregulation of p-RIPK3 and p-MLKL) (Figure 6). This temporal correlation suggests that the collapse of the adaptive AHR-CYP1A1 response may be linked to the initiation of the cell death program. Concurrently induced oxidative stress (Figure 5) likely acts as a key amplifier and connector in this cascade, as reactive oxygen species are both a potential byproduct of CYP450 metabolism and a well-established driver of necroptotic signaling. Most importantly, the activation of this necroptotic pathway (RIPK3/MLKL phosphorylation, LDH release) provides a plausible molecular mechanism for the focal necrosis observed in human tissues and the hepatic disruption in zebrafish [4,5]. This multi-level consistency strongly supports the relevance of our in vitro findings in modeling 1,2-DCE hepatotoxicity.

Based on this integrated investigation, our study systematically unveils a novel mechanism underlying 1,2-DCE-induced hepatotoxicity through a combination of network toxicology, molecular docking, and a series of cellular experiments: 1,2-DCE can act as a potential ligand for the AHR, transiently activating the AHR/CYP1A1 signaling axis and triggering an early adaptive metabolic response. However, with prolonged exposure, this axis becomes dysfunctional, as evidenced by significant suppression of CYP1A1. Notably, this suppression is temporally coupled with the execution of necroptosis, which is mediated by the activation of the RIPK3/MLKL pathway. These findings collectively establish a dynamic toxicity model of “early compensatory activation followed by late-phase functional collapse,” providing crucial temporal and mechanistic insights into the hepatotoxicity of halogenated hydrocarbons.

Nevertheless, certain limitations of this study should be noted. First, although molecular docking and nuclear translocation assays suggest a potential interaction between 1,2-DCE and AHR and its subsequent activation, direct evidence of AHR transcriptional activity across different time points—such as from reporter gene assays or electrophoretic mobility shift assays—is still lacking and would be needed to definitively confirm its functional activation. Second, the role of CYP1A1 in liver injury has been primarily demonstrated in the THLE-2 cell line; further validation in primary hepatocytes or in vivo mammalian models would strengthen the physiological and translational relevance of the findings. Furthermore, the causal relationship between CYP1A1 and necroptosis remains to be elucidated through genetic knockdown or pharmacological inhibition experiments. Finally, the upstream regulatory mechanisms responsible for the late-phase suppression of CYP1A1 remain unclear and represent an important direction for future research. While acknowledging the limitations of this study, it is also important to recognize its methodological advancements. Compared to research paradigms that rely primarily on bioinformatic predictions or infer mechanisms from downstream phenotypic correlations [38,39], the integrated strategy employed in this work—combining network toxicology prediction, molecular docking simulations, and multi-time-point experimental validation—has provided more direct and multi-layered functional evidence for elucidating the interaction between 1,2-DCE and AHR. Moving beyond the mechanistic toxicological understanding, this study reveals that the time-dependent biphasic modulation of the AHR-CYP1A1 axis by 1,2-DCE holds significant translational implications. AHR has emerged as a clinically validated drug target, with its agonist Tapinarof already approved for psoriasis treatment, and several AHR modulators currently undergoing clinical trials for cancers, autoimmune diseases, and inflammatory disorders [44]. The ligand-specific, dynamic regulatory mechanism uncovered in this work, particularly the unique “early-activation–late-suppression” pattern, provides a crucial theoretical basis for the future development of more selective AHR modulators. Moreover, it establishes an experimental foundation for scientifically assessing the potential risks of environmental chemicals that may disrupt this critical immune-metabolic node. Building on this, our next steps will utilize reporter gene assays to directly measure the temporal dynamics of AHR transcriptional activity and, through genetic or pharmacological interventions targeting CYP1A1, further clarify its regulatory role in necroptosis.

5. Conclusions

Employing an integrated strategy combining network toxicology prediction, molecular docking, and multi-time-point in vitro validation, we demonstrated for the first time that 1,2-DCE can act as a potential ligand for the AHR. We revealed a novel mechanism whereby 1,2-DCE induces a dynamic biphasic response in the AHR-CYP1A1 axis—characterized by early activation followed by late-phase suppression—which subsequently couples with an oxidative stress-necroptosis cascade. Our findings propose a new paradigm of the “metabolic adaptation-to-cell death” transition in halogenated hydrocarbon-induced hepatotoxicity, providing a crucial theoretical foundation for a deeper understanding of its time-dependent toxicological mechanisms and for the future development of intervention strategies targeting the AHR/CYP1A1 signaling axis.