Selective Modulation of Aryl Hydrocarbon Receptor by Coptisine in MAFLD

Selective Modulation of Aryl Hydrocarbon Receptor by Coptisine in MAFLD | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Selective Modulation of Aryl Hydrocarbon Receptor by Coptisine in MAFLD Xiliang Zhu, Qi Liu, Zhaoyun Cheng, Yi Luo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5201468/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Metabolic-associated fatty liver disease (MAFLD) is a multifaceted condition driven by disrupted lipid metabolism and chronic inflammation, influenced by genetic, environmental, and dietary factors. The Aryl Hydrocarbon Receptor (AHR) has emerged as a critical regulator in this context, mediating responses to various environmental and dietary signals. The dual role of AHR in MAFLD is complex, with some ligands exacerbating liver damage while others confer protective effects, suggesting that AHR’s impact may be highly context-dependent. Methods: This study analyzed single-cell RNA sequencing (scRNA-seq) data to explore the metabolic and transcriptional heterogeneity of hepatocyte subpopulations in a high-sucrose, high-fat diet (HSDFD)-induced MAFLD model. Virtual screening identified potential AHR-targeting compounds, leading to the selection of CPT for further study. The efficacy of CPT was evaluated through in vivo and in vitro assays, including Cellular Thermal Shift Assay (CETSA), Drug Affinity Responsive Target Stability (DARTS), Western blotting, immunohistochemistry (IHC), immunofluorescence, and Bodipy staining. These methods were employed to elucidate the molecular interactions between AHR and its ligands, and to assess CPT’s impact on lipid accumulation and AHR-mediated transcriptional activity. Results: Our findings reveal significant alterations in hepatocyte subpopulation dynamics under HSDFD conditions, with subpopulations such as Rasd1(hi), Galnt17(hi), and Lpin1(-) displaying enhanced metabolic activity. Transcriptional regulation analysis identified a reorganization of the M1 regulon module, with differential AHR activity across subpopulations. Notably, CPT emerged as a potent AHR-targeting compound, effectively reducing lipid accumulation and restoring CYP1A1 expression in MAFLD models. Structural and dynamic analyses demonstrated that CPT induces specific conformational changes in AHR, leading to a transcriptional environment that favors lipid metabolism and oxidative stress management. Conclusion: This study highlights the complex role of AHR in MAFLD and underscores the therapeutic potential of CPT in modulating AHR activity to mitigate lipid dysregulation. The findings provide valuable insights for developing targeted therapies that leverage the AHR/CYP1A1 pathway to treat MAFLD. Biological sciences/Drug discovery/Drug screening Biological sciences/Drug discovery/Pharmacology Biological sciences/Drug discovery/Target identification Biological sciences/Drug discovery/Target validation Metabolic-associated fatty liver disease Aryl Hydrocarbon Receptor single-cell RNA sequencing high-sucrose high-fat diet Coptisine CYP1A1 transcriptional regulation lipid metabolism. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Metabolic-associated fatty liver disease (MAFLD) is a complex condition characterized by dysregulated lipid metabolism and inflammation, which is influenced by various genetic, environmental, and metabolic factors[ 1 – 7 ]. Among these, the transcriptional regulation mediated by nuclear receptors plays a critical role in the pathogenesis of MAFLD[ 8 , 9 ]. A key nuclear receptor in this context is the Aryl Hydrocarbon Receptor (AHR), which acts as a sensor for environmental and dietary signals[ 10 – 12 ]. Given its ability to perceive and integrate these cues, AHR has been increasingly recognized as a pivotal player in MAFLD development. However, the role of AHR in MAFLD remains controversial. On one hand, certain known environmental small molecules, such as 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) and polychlorinated biphenyls (PCBs), that act as AHR agonists have been shown to exacerbate MAFLD by promoting liver inflammation and fibrosis[ 13 – 18 ]. On the other hand, natural molecules, including indigo and resveratrol, which act as either AHR agonists or antagonists, have demonstrated protective effects against metabolic dysfunction and inflammation[ 19 – 22 ]. This dichotomy suggests that the diverse effects of AHR in MAFLD may be attributed to the heterogeneity of AHR responses within different hepatocyte subpopulations, spatial and temporal dynamics, and ligand-induced conformational changes at distinct action sites. Moreover, MAFLD is often accompanied by alterations in fatty acid metabolism, with enzymes like CYP1A1 playing a role in oxidizing various structurally unrelated compounds, including steroids, fatty acids, and xenobiotics[ 23 – 26 ]. This enzymatic activity suggests a potential role for CYP1A1 in MAFLD progression, further complicating the understanding of AHR's involvement in the disease. Given these complexities, our study aims to elucidate the metabolic and transcriptional heterogeneity among hepatocyte subpopulations in an HSDFD-induced MAFLD model, with a particular focus on identifying the differential AHR activity in response to dietary environmental stimuli. Additionally, we sought to identify natural molecules targeting AHR through virtual screening, evaluate their effects in a mouse model of MAFLD, and elucidate their potential structural basis and molecular signaling pathways. Methods 1. Animal Model and Diet Protocol Eight-week-old male C57BL/6 mice (SLAC Laboratory Animal, Shanghai, China) were maintained under specific pathogen-free conditions with a controlled 12-hour light/dark cycle. The animals had unrestricted access to food and water throughout the experimental period. To investigate the effects of dietary interventions and coptisine treatment on the development of metabolic-associated fatty liver disease (MAFLD), the mice (n = 10 per group) were randomly allocated to one of four dietary regimens: the control group received a standard chow diet, the chow diet plus coptisine (CHOW+CPT), the high-fat diet (35 kcal% fat) tailored to induce MAFLD, or the high-fat diet plus coptisine (HSDFD+CPT). The specialized high-fat diet (KL11606, KangLang, Shanghai, China) comprised 66.5% standard rodent chow, supplemented with 10% lard, 20% sucrose, 2.5% cholesterol, and 1% sodium cholate, designed specifically to model type 2 diabetes mellitus (T2DM) and MAFLD. Coptisine (CPT, B20560, Yuanye, Shanghai,200 µg/g) was administered to the CHOW+CPT and HSDFD+CPT groups via oral gavage three times per week. The dietary intervention was conducted over a 12-week period, during which body weights were meticulously recorded on a weekly basis, and food consumption was closely monitored. At the conclusion of the intervention, the mice were first anesthetized with 2% isoflurane inhalation, followed by euthanasia through cervical dislocation. After confirmation of death, liver tissues were harvested for subsequent analyses, including histological evaluation, immunohistochemical staining, and various molecular assays. All experimental procedures adhered to the highest standards of animal care and were rigorously approved by the Institutional Animal Care and Use Committee (IACUC). This study was conducted and reported in accordance with the ARRIVE (Animal Research: Reporting of In Vivo Experiments) guidelines. All methods were carried out in accordance with relevant guidelines and regulations. This well-established protocol provided a robust framework for inducing MAFLD in this experimental model, ensuring consistency and reproducibility across subsequent analyses. 2. Cell Culture and Treatment AML12 mouse hepatocyte cells were cultured in DMEM/F-12 medium supplemented with fetal bovine serum, insulin-transferrin-selenium, and dexamethasone at 37°C with 5% CO2. For lipid accumulation, cells were treated with palmitic acid conjugated to bovine serum albumin for 24 hours. Control cells received BSA only. To assess the protective effects of coptisine, cells were co-treated with palmitic acid and varying concentrations of coptisine (100 nM, 1 µM, and 10 µM) for 24 hours. Post-treatment, cells were harvested for lipid accumulation assays, Western blotting, and immunofluorescence studies. 3. High-Throughput Virtual Screening and Post-Screening Analysis A high-throughput virtual screening was conducted using the TCMSP database to identify small molecule ligands targeting the Aryl Hydrocarbon Receptor (AHR, PDB ID: 8qmo). Molecular structures were processed with the RDKit toolkit for docking simulations. AutoDock Vina was employed for docking, focusing on the AHR binding site. Binding affinities were analyzed using RDKit, DeepChem, and Biopython, applying Lipinski's Rule of Five to filter compounds. This approach yielded high-affinity AHR ligands prioritized for experimental validation, facilitating the identification of potential therapeutic compounds. 4. Histological Examination and Analysis of Liver Tissues Liver samples were collected and fixed in 10% neutral-buffered formalin for 24 hours. Tissues underwent dehydration, clearing, and embedding in paraffin, followed by sectioning. Hematoxylin and eosin staining was performed, and histopathological analysis was conducted under a light microscope. Images were captured from multiple fields and analyzed using ImageJ software to assess hepatic steatosis and inflammation. Statistical analysis involved evaluating at least five sections per animal across three animals per group, with data presented as mean ± SEM and significance determined by Student's t-test (p < 0.05). 5. Histological Assessment of Liver Fibrosis and Lipid Accumulation Liver tissues were fixed in 10% neutral-buffered formalin for 24 hours, dehydrated, cleared in xylene, and embedded in paraffin. Sections (4 μm thick) were subjected to Masson’s trichrome staining for fibrosis evaluation, using Weigert’s iron hematoxylin for nuclei and Biebrich scarlet-acid fuchsin for cytoplasm, with aniline blue highlighting collagen fibers. Microscopic examination at 200× magnification captured images from five random fields, and collagen deposition was quantified using ImageJ software. Sirius Red staining further assessed fibrosis, with sections stained and analyzed similarly. For lipid quantification, liver tissues were frozen, sectioned (8 μm thick), and stained with Oil Red O to visualize neutral lipids. Lipid accumulation was quantified as a percentage of Oil Red O-positive regions relative to total tissue area. Statistical analyses included at least five sections per animal, with results expressed as mean ± SEM and significance determined by Student’s t-test (p < 0.05). 6. Immunohistochemistry for AHR Detection in Liver Tissue Liver tissues were fixed in 10% neutral buffered formalin for 24 hours and processed for paraffin embedding. Sections were deparaffinized, rehydrated, and subjected to antigen retrieval using heat-induced epitope retrieval in citrate buffer. Endogenous peroxidase activity was blocked, and sections were incubated with a primary antibody specific for AHR, followed by a biotinylated secondary antibody. Visualization was achieved using a diaminobenzidine (DAB) substrate, and sections were counterstained with hematoxylin. Examination at 200× magnification allowed for the capture of images from five random fields. AHR immunoreactivity was quantified using ImageJ software, calculating the percentage of positive staining relative to total tissue area. At least five sections per liver were analyzed, with data expressed as mean ± SEM. Statistical significance was assessed using Student’s t-test, with p < 0.05 considered significant. 7. Liver Function and Lipid Profile Analysis in Mice To evaluate liver function and lipid profiles, blood samples were collected via retro-orbital bleeding after a 12-hour fasting period. Serum was obtained and stored at -80°C. Liver function was assessed by measuring ALT and AST levels using colorimetric assays. Lipid profiles, including total cholesterol (TC) and triglycerides (TG), were measured enzymatically. HDL-C levels were determined after precipitating LDL and VLDL fractions, while LDL-C concentration was calculated using the Friedewald equation. All measurements were conducted in duplicate, with data reported as mean ± SEM. Statistical comparisons were performed using one-way ANOVA, with significance defined as p < 0.05. 8. Biochemical Assessment of Hepatic Lipid Levels in Experimental Mice Liver tissues were homogenized for analysis of total cholesterol (TC) and triglyceride (TG) levels using commercial assay kits. Absorbance for TC and TG was measured at 500 nm and 510 nm, respectively. Lipid content was quantified and expressed as milligrams of lipid per gram of liver tissue (mg/g). Statistical analyses were performed to evaluate differences between treatment groups, ensuring rigorous comparison of hepatic lipid levels. 9. Cellular Thermal Shift Assay (CESTA) The Cellular Thermal Shift Assay (CESTA) was used to investigate how Coptisine (CPT) affects the thermal stability of the Aryl Hydrocarbon Receptor (AHR). AML12 cells were treated with varying concentrations of CPT, lysed, and subjected to temperatures from 37°C to 70°C. After rapid freezing and centrifugation, Western blot analysis was performed on soluble protein fractions to assess AHR levels. The melting temperature (Tm) was derived from the thermal stability curve, revealing how CPT modulates AHR stability. Differences in Tm between treated and control samples indicated CPT’s potential stabilizing effects on the receptor. 10. Drug Affinity Responsive Target Stability (DARTS) Assay The Drug Affinity Responsive Target Stability (DARTS) assay assessed the interaction between Coptisine (CPT), TCDD, and AHR by examining their effects on AHR's proteolytic stability. AML12 cells were treated with CPT and TCDD, lysed, and incubated with the compounds before limited proteolysis with pronase. Western blotting analyzed AHR levels, revealing that proteins bound by ligands exhibit increased resistance to proteolysis. The extent of AHR's stability in CPT- and TCDD-treated samples was compared to untreated controls, providing insights into the direct interactions and enhanced stability of AHR in the presence of these compounds. 11. Immunofluorescence Staining of AHR in AML12 Cells AML12 cells were cultured on coverslips and treated with vehicle, Coptisine (CPT), palmitic acid (PA), or PA + CPT. After fixation with paraformaldehyde, cells were permeabilized and blocked to minimize non-specific binding. They were then incubated with a primary antibody against AHR, followed by a fluorescently-labeled secondary antibody. After counterstaining with DAPI, fluorescence microscopy was used to visualize AHR localization and expression. Images were captured consistently across treatment groups, and fluorescence intensity was quantified using ImageJ software to compare AHR expression across different conditions. 12. BODIPY Staining for Lipid Droplets in AML12 Cells AML12 cells were treated with vehicle, Coptisine (CPT), palmitic acid (PA), or PA + CPT, then fixed and stained with BODIPY 493/503 dye to visualize lipid droplets. After rinsing and counterstaining with DAPI, fluorescent images were captured using a confocal microscope. The BODIPY dye indicated lipid droplets with green fluorescence, while DAPI stained nuclei purple. Lipid droplet content was quantitatively analyzed using ImageJ software, measuring relative fluorescence intensity. Statistical analyses compared lipid accumulation across treatment groups, illustrating CPT's effect on reducing PA-induced lipid accumulation in AML12 cells. 13. Western Blotting Analysis of AHR and CYP1A1 Expression in AML12 Cells Western blotting was performed to evaluate AHR and CYP1A1 protein expression in AML12 cells treated with vehicle, Coptisine (CPT), palmitic acid (PA), or PA + CPT. Cells were lysed in RIPA buffer, and protein concentrations were measured. Equal amounts of protein were separated by SDS-PAGE and transferred to PVDF membranes, which were blocked and incubated with primary antibodies against AHR, CYP1A1, and GAPDH. After washing, HRP-conjugated secondary antibodies were applied, and protein bands were detected using an ECL system. Band intensities were quantified with ImageJ, normalizing against GAPDH, and statistical analyses assessed expression differences across treatment groups. The original uncropped Western blot images are provided in Supplementary Material 1. 14. Luciferase Reporter Assay Luciferase reporter assays were conducted using AML12 cells transfected with an AHR-responsive luciferase reporter plasmid or constructs of the AHR binding motif linked to luciferase. Transfections were performed with Lipofectamine 3000, followed by treatment with varying concentrations of Coptisine (CPT) or palmitic acid (PA). Luciferase activity was measured using the Dual-Luciferase Reporter Assay System, normalizing luminescence values to a co-transfected Renilla luciferase plasmid. Relative luciferase units (RLU) were calculated, and statistical comparisons evaluated the effects of treatments on AHR and CYP1A1 activity, with results expressed as mean ± SEM. 15. Molecular Dynamics Simulation Molecular dynamics (MD) simulations were performed to explore the conformational dynamics of AHR in its apo state and when bound to Coptisine (CPT). Using GROMACS software, the system was solvated and neutralized, with energy minimization conducted. The system was equilibrated in NVT and NPT ensembles before a 100 ns production MD simulation. Key structural analyses, including RMSD, RMSF, SASA, and hydrogen bond calculations, were performed to evaluate stability and conformational changes. The dynamics of the binding pocket were analyzed, and the Gibbs free energy landscape was calculated to identify stable conformational states, revealing significant differences between apo and CPT-bound AHR. 16. Single-Cell Data Analysis Single-cell RNA sequencing (scRNA-seq) data were sourced from the Gene Expression Omnibus (GEO) (GSE182365), encompassing liver cells from mice on standard chow and high-fat, high-sucrose diets. Isolated hepatocytes and stellate cells were processed into single-cell suspensions for RNA sequencing. The data underwent preprocessing to construct a single-cell atlas of the liver, with dimensionality reduction (UMAP) applied to visualize cellular diversity. Differential expression analysis revealed dietary impacts on hepatic cell populations, highlighting alterations in metabolic pathways and transcriptional networks linked to diet-induced liver disease, supported by visualizations and statistical analyses. Results 1.Hepatocyte Subpopulation Dynamics and Functional Heterogeneity in Response to HSDFD Diet Single-cell RNA sequencing (scRNA-seq) was employed to explore hepatocyte subpopulations in the liver under Chow and HSDFD dietary conditions. UMAP visualization (Figure 1A) reveals distinct liver cell populations, with HSDFD significantly altering hepatocyte composition. Most subpopulations cluster well, but Igfbp7(-) hepatocytes exhibit an isolated distribution, suggesting a unique adaptive response to HSDFD. The HSDFD diet also impacts the spatial distribution of hepatocyte subpopulations, as density plots show a reorganization of these populations. Under Chow conditions (Figure 1B, left), hepatocyte subpopulations are concentrated with clear density peaks. In contrast, HSDFD (Figure 1B, right) leads to new outlier clusters, indicating novel subpopulations formed in response to metabolic stress. UMAP visualization (Figure 1C) highlights the contraction of certain subpopulations, such as those marked by Ptgds and Rbm8a2, under HSDFD, suggesting selective pressure that may reduce their functional capacity. The Circos plot (Figure 1D) illustrates gene expression patterns specific to each subpopulation, reinforcing distinctions driven by the HSDFD diet. Biological processes (BP) and molecular functions (MF) analysis (Figure 1E) reveals significant heterogeneity in gene expression profiles under HSDFD. Hepatocytes show enrichment in nutrient response, extracellular stimulus response, and starvation, indicating active adaptation to metabolic challenges. Other subpopulations respond to oxidative stress and detoxification, suggesting a protective role against oxidative damage from the HSDFD diet. Immune-related processes, such as interleukin-4 response, are also enriched, indicating the diet's influence on inflammatory pathways. Molecular function analysis shows enrichment in antioxidant and peroxidase activities, highlighting roles in oxidative stress management. Additionally, ubiquitin-dependent protein binding and DNA-binding transcription repressor activity enrichment suggest involvement in protein quality control and gene regulation, crucial for maintaining liver function under HSDFD conditions. Figure 1: Single-cell RNA sequencing reveals cell type-specific responses in the liver under Chow and HSDFD conditions A) UMAP of liver cell populations, color-coded by cell type with key gene markers annotated, showing the diversity of liver cell types under study.B) Density plots comparing the distribution of a specific cell population between Chow (left) and HSDFD (right) conditions, highlighting changes in population density under different dietary conditions.C) UMAP showing the distribution and proportion of different liver cell types between Chow and HSDFD groups, illustrating the impact of diet on cell type composition.D) Circos plot displaying marker genes for different hepatocyte subpopulations, indicating gene expression patterns specific to each subpopulation.E) Biological Process (BP) and Molecular Function (MF) enrichment analysis of marker genes associated with different hepatocyte subpopulations, providing insights into the functional roles of these subpopulations in response to dietary changes. 2.Metabolic Pathway Activity in Hepatocyte Subpopulations under HSDFD Diet The metabolic landscape of hepatocyte subpopulations under the HSDFD diet shows significant variability, indicating different responses to dietary-induced stress. Box plots (Figure 2A) reveal that subpopulations such as Rasd1(hi), Galnt17(hi), and Lpin1(-) have the highest pathway activity scores, suggesting their crucial role in liver adaptation to the HSDFD diet. The heatmap (Figure 2B) compares metabolic pathway activity across hepatocyte subpopulations, highlighting active pathways like starch and sucrose metabolism and ascorbate and aldarate metabolism, essential for processing the HSDFD diet's high carbohydrate and fat content. These subpopulations also show elevated activity in fatty acid metabolism, underscoring their versatility in sustaining liver function. UMAP plots (Figure 2C) illustrate that the HSDFD diet enhances the activity of fatty acid biosynthesis, degradation, and elongation pathways, reflecting the liver's response to excess lipids. Notably, the Galnt17(hi) and Lpin1(-) subpopulations are enriched in pathways associated with non-alcoholic fatty liver disease (NAFLD) (Figure 2D), implicating them in diet-induced liver disease. The UMAP projection onto the Human Protein Atlas (HPA) liver cell atlas (Figure 2E) shows a shift in cellular identity post-HSDFD exposure, with Chow-fed hepatocytes aligning with Hepatocyte-3, while HSDFD exposure shifts alignment towards Hepatocyte-1 and Hepatocyte-2 (Figure 2E). The active Galnt17(hi) and Lpin1(-) subpopulations drive this shift (Figure 2F), emphasizing their relevance in fatty acid metabolism (Supplementary Figure 1). Figure 2: Pathway activity and disease association in liver cell subpopulations under Chow and HSDFD conditions A) Box plots illustrating the metabolic activity across various hepatocyte subpopulations in mice, highlighting differences among subpopulations. B) Heatmap showing the activity levels of distinct metabolic pathways across all hepatocyte subpopulations. C) UMAP plots comparing the activity of fatty acid synthesis, elongation, and degradation pathways in hepatocyte subpopulations under Chow (left) and HSDFD (right) conditions. D) KEGG pathway-based disease enrichment analysis, indicating the association of specific hepatocyte subpopulation marker genes with non-alcoholic fatty liver disease (NAFLD). E) UMAP projection of total hepatocytes from Chow and HSDFD groups onto the Human Protein Atlas (HPA) liver cell atlas, highlighting the alignment with human liver cell types. F) UMAP projection of individual mouse hepatocyte subpopulations onto the HPA human liver cell atlas, providing insights into the correspondence between mouse and human hepatocyte subpopulations. 3.Transcriptional Regulation Landscape in Hepatocyte Subpopulations under HSDFD Diet The transcriptional regulatory landscape of hepatocyte subpopulations under the HSDFD diet reveals significant heterogeneity, with distinct regulatory patterns emerging among certain subpopulations. UMAP plots of hepatocyte subpopulations(Figure 3A) highlight that Galnt17(hi), Top2a(hi), Col4a1(-), and Igfbp7(-) exhibit unique transcriptional regulatory profiles. These differences suggest that these subpopulations might be governed by specific regulatory networks, potentially leading to specialized roles within the liver under the HSDFD diet (Figure 3A). Contrastingly, the Lpin1(-) subpopulation follows a regulatory pattern akin to that of most other hepatocyte subpopulations (Figure 3B), indicating shared regulatory mechanisms across the majority of liver cells. A significant finding from this analysis is the identification of the M1 regulon module as the predominant transcriptional regulatory module in hepatocytes (Figure 3C). The activity of the M1 module undergoes substantial changes under HSDFD conditions, reflecting a diet-induced reorganization of hepatocyte subpopulations. This reorganization is evident in the differential transcriptional regulation of the M1 module between Chow and HSDFD conditions (Figure 3D). The alterations in M1 activity correspond to shifts in gene expression profiles and the functional outputs of the liver, driven by the HSDFD diet.Enrichment analysis of downstream genes in the identified modules reveals that M1 is involved in key pathways such as unfolded protein response ,MYC targets and adipogenesis, while M4 and M5 show significant involvement in metabolic pathways, including adipogenesis and xenobiotic metabolism(Figure 3E). Further examination of the M1 module reveals that, except for Atf3 and Cebpb, the transcription factors associated with this module are expressed at low levels across hepatocyte subpopulations (Figure 3F). This minimal expression is consistent with the roles of these transcription factors, which can exert regulatory influence even at low expression levels. Additionally, the regulons within the M1 module exhibit heightened transcriptional regulatory activity in subpopulations such as Kbtbd11(hi), Ptgds(hi), Rbm8a2(hi), and Fut11(lo) (Figure 3G), indicating their importance in the liver’s response to the HSDFD diet (Figure 3G). The AHR regulon within the M1 module reveals distinct reactivity across different hepatocyte subpopulations. It shows high transcriptional activity in the metabolically active Galnt17(hi) subpopulation while being suppressed in the Lpin1(-) subpopulation (Figures 3H,I). This differential regulation underscores AHR's pivotal role in modulating metabolic and regulatory responses across hepatocyte subpopulations under the HSDFD diet. Finally, differential analysis of the Lpin1(-) subpopulation reveals a significant downregulation of Ahr expression following HSDFD exposure, demonstrating the HSDFD diet's regulatory effect on AHR abundance in specific hepatocyte subpopulations. This reduction in Ahr expression likely contributes to the altered metabolic and transcriptional landscape observed in this subpopulation, highlighting the importance of AHR regulation in maintaining hepatic homeostasis under dietary stress(Figures 3J). Figure 3: Clustering and Transcriptional Regulation Analysis of Liver Cell Subpopulations Under Chow and HSDFD ConditionsA) UMAP plots show hepatocyte subpopulations based on gene expression (left) and transcriptional regulatory activity (right), revealing distinct clusters.B) Correlation matrix illustrates relationships between hepatocyte subpopulations based on regulatory activity.C) Identification of key transcriptional regulatory modules in mouse hepatocytes.D) UMAP plots display RAS regulatory activity of the M1 module under Chow and HSDFD conditions.E) Hallmark pathway enrichment analysis highlights significant pathways in MAFLD.F) Dot plot shows expression abundance of M1-related transcription factors.G) Heatmap depicts M1 module regulon activity across subpopulations.H) UMAP plots compare AHR regulon activity between conditions.I) Violin plots indicate variations in AHR activity.J) Scatter plot shows downregulation of Ahr in Lpin1(-) subpopulation after HSDFD feeding. Virtual Screening and Analysis of AHR-Targeting Small Molecules from the TCMSP Database A systematic approach was employed to virtually screen small molecules from the TCMSP database to identify potential AHR-targeting compounds. The initial phase of this study involved hierarchical clustering of the entire molecular library using Morgan molecular fingerprints, which facilitated the organization of molecules based on structural similarities (Figure 4A). This hierarchical clustering provided a broad overview of the molecular diversity within the TCMSP database and allowed for the identification of distinct clusters that might contain potential AHR modulators. The virtual screening identified coptisine as a molecule with a notably high binding affinity for AHR (Figure 4B). This compound was selected from a pool of high-affinity molecules, further validated through clustering analyses using multiple fingerprinting methods—Daylight, MACCS, and ECFP4 (Figure 4C). The clustering analysis revealed that coptisine occupies a central position within its cluster, indicating its structural congruence with other high-affinity AHR-targeting molecules. To delve deeper into coptisine's molecular properties, a molecular map was constructed, focusing on the structural alignment between coptisine and a representative center molecule within its cluster. The molecular structure of the cluster’s center molecule shows key chemical groups, with significant structural overlap between coptisine and this center molecule, underscoring the chemical features contributing to coptisine’s high affinity for AHR (Figure 4D). Subsequent pharmacokinetic assessments provided insights into coptisine's absorption, distribution, metabolism, and toxicity (ADMET) profile. The absorption characteristics show poor permeability across MDCK and Caco-2 assays, with negative values (−4.66 and −4.96), indicating limited absorption potential through these cell models. However, moderate permeability in the PAMPA assay (0.35) suggests that coptisine may still possess passive diffusion capabilities. Additionally, coptisine interacts with P-glycoprotein (P-gp), acting as both a substrate (0.999) and an inhibitor (0.0036), which could influence its bioavailability by modulating drug transport. The low human intestinal absorption (HIA) score (0.0041) points to poor absorption in vivo, raising concerns about its overall bioavailability. These results reflect a mixed absorption profile for coptisine, warranting further study to fully understand its pharmacokinetic behavior (Figure 4E).The distribution analysis based on the provided data reveals that coptisine exhibits a high degree of plasma protein binding (PPB) with a value of 0.774, indicating that a significant portion of the compound is likely to bind to plasma proteins in circulation. Coptisine also shows moderate permeability across the blood-brain barrier (BBB) with a value of 0.385, suggesting potential distribution into the central nervous system. Additionally, coptisine demonstrates significant interaction with bile salt export pump (BSEP) transporters (0.999), indicating potential biliary excretion. The moderate volume of distribution (logVDss = 0.258) further supports its widespread distribution across various tissues, including the liver and other peripheral tissues. These findings highlight coptisine's potential for extensive distribution in vivo, with implications for its pharmacokinetics and therapeutic action (Figure 4F). Metabolic analysis of coptisine’s interaction with key cytochrome P450 (CYP) enzymes reveals that it is likely metabolized primarily by CYP1A2 and CYP2D6, with both substrate and inhibitor interactions showing near-maximal values (0.999). CYP3A4 also plays a significant role as both an inhibitor (0.9998) and a substrate (0.00017), suggesting that coptisine may undergo extensive hepatic metabolism. Additionally, CYP2C9 and CYP2B6 show moderate involvement, with coptisine acting as both a substrate and inhibitor for these enzymes. The presence of multiple CYP interactions indicates that coptisine’s metabolic profile could significantly influence its pharmacokinetics and potential for drug-drug interactions (Figure 4G).The Tox21 profile of coptisine reveals significant interactions with various nuclear receptors and stress response pathways. Notably, coptisine shows strong activity towards the AHR pathway (0.999), supporting its potential as an AHR modulator. It also demonstrates interactions with AR-LBD (0.906), Aromatase (0.726), and SR-ARE (0.968), indicating possible effects on hormone-related pathways and oxidative stress response. Additional interactions with stress response proteins such as SR-MMP (0.996) and SR-p53 (0.998) suggest coptisine may influence cellular stress mechanisms and DNA damage response, further underscoring its multifaceted biological activity (Figure 4H). Additionally, the toxicity analysis of coptisine involved assessments of several key parameters, including hepatotoxicity (H−HT=0.150), neurotoxicity (0.079), and nephrotoxicity-DI (0.120). These evaluations provide a preliminary risk assessment of potential adverse effects, which are crucial for guiding further preclinical studies. The radar plot reveals a moderate risk for hepatotoxicity and nephrotoxicity, while neurotoxicity remains a lower concern. These findings help outline the safety profile of coptisine and highlight areas that warrant further investigation to ensure a balanced evaluation of its therapeutic potential and safety (Figures 4I). Figure 4: Virtual screening and analysis of small molecules targeting AHR from the TCMSP database A) Hierarchical clustering of small molecules in the TCMSP database based on Morgan molecular fingerprints.B) Virtual screening of small molecules targeting AHR, with coptisine highlighted for its high binding affinity.C) Clustering analysis of high-affinity small molecules using different fingerprint methods (Daylight, MACCS, ECFP4).D) Molecular map of the center molecule in the cluster containing coptisine (top), and the matched structure between coptisine and the center molecule (bottom).E) Absorption analysis of coptisine, including metrics like MDCK, Caco-2, PAMPA permeability, and P-gp substrate/inhibitor potential. F) Distribution analysis of coptisine, showing factors such as plasma protein binding (PPB), blood-brain barrier (BBB) permeability, and volume of distribution (Vd).G) Metabolism analysis of coptisine, focusing on its interaction with key cytochrome P450 enzymes (CYPs).H) Tox21 profile of coptisine, showing interactions with nuclear receptors and stress response pathways.I) Toxicity analysis of coptisine, covering various endpoints including hepatotoxicity, cardiotoxicity, and general cytotoxicity. Impact of CPT on Liver Pathology and Serum Lipid Profiles in HSDFD-Fed Mice The liver histopathology and serum lipid profiles were thoroughly examined across the four experimental groups: Chow, Chow+CPT, HSDFD, and HSDFD+CPT. Additionally, liver function did not show significant changes under these conditions, indicating that despite dietary intervention and CPT treatment, overall hepatic function remained stable across all groups. This suggests that the treatments did not induce overt liver toxicity(Supplementary Figure2). Hematoxylin and eosin (HE) staining (Figure 5A) revealed abnormal hepatic lipid deposition in the HSDFD group, characterized by an increase in the presence and size of lipid vacuoles within hepatocytes. This lipid accumulation was significantly ameliorated in the HSDFD+CPT group, where CPT administration resulted in a notable reduction in lipid vacuoles, reflecting an improvement in liver morphology and lipid distribution. Masson’s trichrome staining (Figure 5B) and Sirius Red (SR) staining (Figure 5C) were employed to evaluate the degree of fibrosis within the liver tissues. Both staining techniques consistently showed minimal differences in collagen deposition among all groups, suggesting that CPT treatment under the conditions tested did not significantly impact liver fibrosis. The Oil Red O staining (Figure 5D) further corroborated the HE staining results by showing substantial lipid accumulation in the livers of HSDFD-fed mice. CPT treatment resulted in a dramatic decrease in hepatic lipid content, aligning the HSDFD+CPT group more closely with the lipid profiles observed in the Chow-fed groups, which exhibited minimal steatosis. Immunohistochemical analysis for AHR expression (Figure 5E) demonstrated that the HSDFD diet caused a significant downregulation of AHR levels in liver tissues. Notably, CPT treatment partially restored AHR expression, indicating that CPT may exert a regulatory effect on AHR under the stress of a high-fat, high-sucrose diet. Biochemical analyses of serum lipids reflected these histological findings. The HSDFD group displayed significantly reduced levels of HDL-C (Figure 5F) compared to the Chow group, a decline that was partially reversed by CPT treatment. Conversely, LDL-C levels (Figure 5G) were significantly elevated in the HSDFD group but were reduced following CPT administration. Additionally, the serum levels of total cholesterol (TC) and triglycerides (TG) (Figures 5H and 5I) were markedly increased in the HSDFD group, yet these increases were significantly mitigated by CPT treatment. In liver tissue homogenates, similar trends were observed, with both TC and TG levels (Figures 5J and 5K) showing significant elevation in the HSDFD group and substantial reduction upon CPT treatment. Figure 5: Histological and Biochemical Analysis of Liver Tissues and Serum Under Different Dietary Conditions with and without CPT TreatmentA) HE staining shows significant hepatic steatosis in the HSDFD group, reduced by CPT.B) Masson’s trichrome staining indicates no significant collagen differences, suggesting CPT does not affect fibrosis.C) Sirius Red staining confirms no significant fibrosis changes.D) Oil Red O staining reveals substantial lipid accumulation in HSDFD, reduced by CPT.E) Immunohistochemical staining shows reduced AHR expression in HSDFD, partially restored by CPT.F) Serum HDL-C is lower in HSDFD, with partial restoration from CPT.G) LDL-C, total cholesterol, and triglyceride levels are elevated in HSDFD but significantly reduced by CPT.H) Hepatic cholesterol and triglyceride levels are elevated in HSDFD and reduced after CPT treatment.I) Serum triglyceride levels are elevated in HSDFD and decrease following CPT treatment.J) Hepatic total cholesterol levels increase in HSDFD and significantly decrease with CPT.K) Hepatic triglyceride levels are elevated in HSDFD and reduced after CPT treatment. The Role of CPT in Modulating AHR-Driven Cyp1a1 Expression in Mouse Liver Cells To elucidate the role of CPT in the regulation of AHR-mediated gene expression within mouse hepatocytes, we employed an integrated approach that combined ChIP-seq analysis, transcriptional assays, and protein expression studies. Initial ChIP-seq analysis from the CISTROME database highlighted significant AHR binding across various transcription start site (TSS) regions in murine liver cells, with a particularly strong enrichment at the Cyp1a1 locus. This observation suggests a critical role for AHR in regulating Cyp1a1 expression (Figure 6A). Further supporting this, detailed track plots showed prominent AHR binding peaks at the Cyp1a1 gene, reinforcing its position as a key regulatory target under AHR control (Figure 6B). Subsequent luciferase reporter assays were conducted to explore the impact of CPT on AHR-mediated transcription. The results demonstrated a clear dose-dependent activation of AHR in response to increasing concentrations of CPT, indicating that CPT serves as a potent enhancer of AHR's transcriptional activity (Figure 6C). Parallel assessments of Cyp1a1 transcription revealed similar dose-dependent upregulation, directly linking CPT to the activation of Cyp1a1 via AHR (Figure 6D).The study also examined the effects of palmitic acid (PA) on AHR and Cyp1a1 activity. PA treatment resulted in a marked suppression of both AHR and Cyp1a1 transcriptional activities compared to the vehicle control (Figure 6E, F). Notably, the introduction of CPT in conjunction with PA reversed this suppression, leading to significant activation of both AHR and Cyp1a1, particularly in the PA+CPT co-treatment group (Figure 6E, F). These findings suggest that CPT effectively counteracts the inhibitory effects of PA, thereby enhancing AHR-mediated transcriptional activation. To gain structural insights, we employed AF3-based modeling, which predicted a specific binding interaction between AHR and the Cyp1a1 promoter that could underlie the observed regulatory effects (Figure 6G). Comparative analyses of AHR binding sites across species, including humans, mice, and rats, revealed a combination of conserved and species-specific elements within the Cyp1a1 promoter, underscoring the evolutionary importance of the AHR-Cyp1a1 interaction (Figure 6H). Further validation was provided by luciferase assays utilizing wild-type (WT), mutant (MUT), and deletion (DEL) constructs of the Cyp1a1 transcriptional regulatory region, specifically focusing on the identified AHR binding motif. The assays revealed that AHR overexpression significantly enhanced transcriptional activity in WT constructs, while both MUT and DEL constructs exhibited a notable reduction in activity. This confirms the critical role of the AHR binding motif in the regulation of Cyp1a1 expression (Figure 6I). Immunofluorescence staining experiments revealed differential expression patterns of AHR across various treatment groups. AHR levels were markedly elevated in cells treated with CPT, especially in the PA+CPT group, indicating a synergistic effect of CPT in enhancing AHR expression under metabolic stress conditions (Figure 6J). Additionally, Bodipy staining showed substantial lipid accumulation in hepatocytes treated with PA alone, which was significantly reduced by the addition of CPT, demonstrating CPT's protective role against PA-induced lipotoxicity (Figure 6K). Protein expression analyses further corroborated these findings, with observed increases in both AHR and CYP1A1 protein levels in response to CPT treatment(Supplementary Figure3). The elevation was most pronounced in the PA+CPT co-treatment group, aligning with the transcriptional and immunofluorescence data. These results collectively underscore the role of CPT as a modulator of AHR activity, particularly in enhancing the expression of Cyp1a1 under conditions of lipid-induced stress, suggesting its potential therapeutic value in liver disease contexts. Figure 6: AHR binding and transcriptional activity analysis in mouse liver cells A) TSS region heatmaps from the CISTROME database showing AHR ChIP-seq peaks in mouse liver cells under different conditions.B) Track plot illustrating the binding peaks of Cyp1a1 across different samples, highlighting AHR interaction sites. C) Dose-dependent AHR transcriptional activity relative to CPT concentrations, demonstrating a clear response pattern.D) Dose-dependent transcriptional activity of Cyp1a1 relative to CPT concentrations, showing modulation by CPT.E) AHR transcriptional activity under various treatment conditions (VEH, VEH+CPT, PA, PA+CPT), with significant changes observed.F) Transcriptional activity of Cyp1a1 under different treatment conditions, indicating the effect of CPT and PA on Cyp1a1 expression.G) Predicted structure of mouse AHR bound to the Cyp1a1 motif, as modeled by AF3, providing insights into the binding conformation.H) Comparison of AHR binding sites in the promoter regions of Cyp1a1 across different species (human, mouse, rat), showing conserved and divergent regions.I) Changes in Cyp1a1 transcriptional activity in WT, MUT, and DEL constructs after AHR overexpression, with clear differences observed between constructs.J) Immunofluorescence staining showing AHR expression in liver cells under different conditions (VEH, VEH+CPT, PA, PA+CPT), with CPT treatment notably enhancing AHR levels.K) Bodipy staining showing lipid accumulation in liver cells under different conditions, with and without CPT treatment, indicating the effect on lipid metabolism. Distinct Binding Mechanisms and Functional Effects of AHR Ligands TCDD and CPT To elucidate the molecular interactions between AHR and its ligands TCDD and CPT, a combination of structural modeling and functional assays was employed, revealing distinctive binding patterns and their potential implications for receptor function. In analyzing the AHR binding pocket (Figure 7A), critical regions responsible for ligand interaction were identified, providing insight into the structural basis for AHR's ligand specificity. TCDD, a well-characterized AHR agonist, engages the receptor through a π-H interaction with Ile325 and forms a hydrogen bond with Gln383. These interactions are integral to securing TCDD within the binding pocket, ensuring the stability of the AHR-TCDD complex (Figure 7B, C). The binding configuration highlights the importance of both hydrophobic interactions and hydrogen bonding, which collectively anchor TCDD in a position that likely influences downstream signaling cascades. CPT, a different ligand, exhibits a unique mode of interaction within the same AHR binding site. Unlike TCDD, CPT primarily interacts through π-π stacking with Phe295, supplemented by a π-H interaction with Ile325, and forms a hydrogen bond with Ser336 (Figure 7D, E). This distinct binding approach suggests that CPT modulates AHR function differently, potentially leading to alternative receptor activation states. The divergence in binding strategies between TCDD and CPT underscores the adaptability of the AHR binding pocket, capable of accommodating structurally diverse ligands while maintaining functional relevance. The MM-PBSA analysis revealed a total binding energy of −115.6773 kJ/mol, indicating a strong and favorable interaction. The entropy correction value of 14.0819 kJ/mol further highlights the thermodynamic contributions to the system’s stability. The van der Waals (vdW) interactions provided a significant negative contribution, indicating strong stabilization, while Coulombic interactions were near neutral. The molecular mechanics (MM) term also contributed negatively to stability. However, the Poisson-Boltzmann (dPB) energy term was positive, reflecting its destabilizing effect, while the surface area (dSA) energy was slightly negative, contributing to overall stability(Supplementary Figure 4A,B).Residue decomposition analysis indicated several key regions of energetic contributions. For the molecular mechanics (MM) components, significant energy contributions were observed within the residue ranges of 0–20, 40, 60–80, and around residue 100. In contrast, the Poisson-Boltzmann (PB) energy decomposition showed positive values, particularly within residues 0–20, around 80, and near residue 100, suggesting that these regions contribute to destabilizing the interaction. Surface area (SA) energy decomposition indicated that residues 0–20, 60–80, and around residue 100 contributed negatively, enhancing the stability of the ligand-receptor binding. These findings suggest that while some regions promote binding stability, other regions—reflected by the positive PB values—contribute to an overall balancing of interaction forces(Supplementary Figure 4C-E). To further explore the functional consequences of these interactions, the stabilization effect of CPT on AHR was assessed using the Cellular Thermal Shift Assay (CETSA). The results indicated that CPT binding significantly enhanced AHR stability, as evidenced by a notable temperature-dependent retention of AHR protein levels (Figure 7F). This stabilization suggests that CPT not only binds effectively to AHR but also confers a protective effect against thermal denaturation, thereby potentially influencing the receptor's ability to transduce signals. Moreover, the Drug Affinity Responsive Target Stability (DARTS) assay provided additional validation of CPT's impact on AHR. This assay revealed that CPT, akin to TCDD, confers increased proteolytic resistance to AHR, indicating that CPT stabilizes the receptor in a manner that may affect its degradation pathway (Figure 7G). The enhanced stability observed in the presence of CPT points to a ligand-specific modulation of AHR, which could lead to differential gene expression outcomes depending on the ligand involved. Figure 7: Structural analysis and functional validation of AHR binding with TCDD and CPT A) Visualization of the AHR pocket structure, highlighting key regions involved in ligand binding. B) Structural model showing the interaction between TCDD and AHR, with a detailed view of the binding pocket, illustrating the specific residues involved. C) 2D interaction diagram of TCDD binding within the AHR pocket, detailing the key residues and their interactions. D) Structural model showing the interaction between CPT and AHR, with a detailed view of the binding pocket, emphasizing the specific binding interactions. E) 2D interaction diagram of CPT binding within the AHR pocket, highlighting the crucial residues involved in the interaction. F) Cellular Thermal Shift Assay (CETSA) analysis demonstrating the stabilization of AHR by CPT, showing a temperature-dependent shift in protein expression levels compared to control. G) Drug Affinity Responsive Target Stability (DARTS) assay indicating the proteolytic stability of AHR in the presence of TCDD and CPT, with a quantification of relative protein expression levels. CPT-Induced Modulation of AHR Stability, Conformational Flexibility, and Residue Network Reorganization By conducting a detailed structural and dynamic analysis, the interaction between AHR in both its apo form and when bound to CPT has revealed critical insights into the protein's stability and conformational behavior. The RMSD values for the backbone atoms of AHR quickly stabilize in both states, with the apo form showing slightly higher fluctuations compared to the CPT-bound form, suggesting that CPT binding enhances the overall structural stability of AHR (Figure 8A). The RMSF analysis shows that the most pronounced differences between the apo and CPT-bound states occur beyond residue 400, where increased fluctuations are observed in the CPT-bound form. This suggests that CPT may introduce additional flexibility or localized destabilization in specific regions, particularly in the C-terminal region, rather than uniformly stabilizing the protein (Figure 8B). SASA analysis supports the observation of a more compact structure over time in both conditions, with a marked reduction in solvent exposure particularly evident in the apo form between 90 and 100 ns, potentially reflecting additional structural compression (Figure 8C). The hydrogen bond analysis revealed that the number of hydrogen bonds fluctuated more in the CPT-bound state compared to the apo state, indicating that CPT binding may introduce additional flexibility or alter the dynamics of hydrogen bond formation within the AHR protein (Figure 8D). The radius of gyration measurements align with these findings, showing rapid compaction early in the simulation for both forms. The apo form, however, undergoes a temporary expansion phase between 40 and 80 ns before stabilizing, contrasting with the consistently compact structure observed when CPT is bound, reinforcing CPT’s stabilizing role (Figure 8E). Examination of secondary structure content using DSSP analysis shows consistent patterns across the simulation, with minimal shifts in secondary structure elements between the apo and CPT-bound forms, suggesting that CPT binding does not drastically alter the overall secondary structure distribution but may impact localized regions (Figure 8F). Examination of pi-cation interaction distances reveals distinct differences in stability, particularly in the CPT-bound form. For example, interactions such as PHE2-ILE1_NH3 and TYR25-ARG33 maintain more consistent distances in the presence of CPT, suggesting a role for CPT in stabilizing these specific interactions. In contrast, interactions like TYR25-LYS71 and PHE39-ARG33 in the apo form exhibit significant fluctuations in the latter stages of the simulation, highlighting potential instability without CPT (Figure 8G). The salt bridge occupancy analysis provides further evidence of structural stability conferred by CPT. Key interactions such as ARG113-ASP44 and ARG67-ASP44 maintain full occupancy in both forms, underscoring their importance. Interestingly, the LYS116-GLU50 salt bridge, present in the apo form, is disrupted upon CPT binding, indicating localized structural changes. Additionally, salt bridges like ASP103-ARG74 show increased occupancy with CPT, suggesting enhanced stability in these regions (Figure 8H). Snapshots of CPT within the AHR binding pocket at different time points demonstrate CPT's stable positioning, with a periodic oscillatory movement indicating robust interaction with AHR. The analysis of pocket shape characteristics throughout the simulation shows minimal changes, reinforcing the notion of a stable binding pocket that remains largely unaltered by CPT binding(Figure 8I). Through the calculation of the relative distance between the centroid of CPT and the pocket, we observed a periodic movement of CPT within the pocket, accompanied by minimal fluctuations in pocket shape characteristics (linear, planar, isotropic), which remained at a relatively consistent level throughout the simulation(Figure 8J,K). This suggests that CPT binding does not significantly alter the structural integrity of the binding pocket, further supporting its stable interaction with AHR. The structural and functional dynamics of AHR interacting with coptisine (CPT) were investigated through molecular dynamics simulations. The Tanimoto similarity matrix reveals distinct modules, indicating regions of high structural similarity throughout the 100-ns trajectory, suggesting that certain segments of the AHR-CPT complex maintain stable interactions over time(Supplementary Figure 5A). The weighted interaction diagram identifies key residues of AHR, such as VAL381, VAL363, and Ile325, as major contributors to these stable interactions with CPT(Supplementary Figure 5B). Further, time-resolved interactions between specific AHR residues and CPT are categorized into hydrophobic, hydrogen bond, and Van der Waals contacts, showing that residues like Phe295, Ile325, and LEU353 persistently interact with CPT(Supplementary Figure 5C). A word cloud derived from Phignet predictions highlights biological processes associated with AHR activity, including "metabolic process" and "biosynthetic regulation"(Supplementary Figure 5D). Finally, a structural visualization of AHR reveals regions involved in cellular aromatic compound metabolic processes (colored in red), residues interacting with CPT during the simulation (colored in green), and overlapping regions where both functionalities converge (colored in blue), indicating a potential mechanistic role of AHR in regulating the metabolic response to CPT(Supplementary Figure 5E). Dimensionality reduction techniques, including UMAP and t-SNE(Figure 9A,B), reveal distinct clustering patterns in the conformational landscape of AHR, indicating the influence of CPT on its dynamic behavior. The Gibbs free energy landscape analysis further differentiates the two states, with the apo form displaying a primary conformational cluster that includes two energy traps, with the lowest energy states occurring early in the simulation. In contrast, the CPT-bound form shows a broader distribution of conformational clusters with a dominant energy trap occurring around 50 ns, suggesting that CPT binding stabilizes specific conformational states of AHR and may influence its functional dynamics during the simulation (Figure 9C,D). Further analysis based on the Residue Distance Contact Matrix (RDCM) reinforces these observations. While the apo and CPT-bound forms exhibit similar overall residue-residue distance patterns, CPT binding results in increased distances between N-terminal residues relative to C-terminal residues, suggesting structural shifts (Supplementary Figure 6A). The time occupancy of the residue contact matrix shows that while the overall contact pattern remains largely unchanged, specific local adjustments occur in the CPT-bound state, particularly between residues 40-60 and C-terminal residues (Supplementary Figure 6B). Additionally, the Dynamic Cross-Correlation Matrix (DCCM) indicates that CPT binding enhances antagonistic relationships within the 50-100 residue segment while reducing cooperative relationships across other segments of AHR(Supplementary Figure 6C). The Pearson correlation matrix over time further supports this, showing strengthened positive correlations between certain residue pairs following CPT binding(Supplementary Figure 6D). Hierarchical clustering based on RDCM reveals a reshuffling of residue clusters in response to CPT binding, reflecting changes in intramolecular interactions(Supplementary Figure 6E). Finally, Principal Component Analysis (PCA) of RDCM highlights a more diverse conformational clustering in the CPT-bound form, with the emergence of new conformational states later in the simulation (Supplementary Figure 6F). Upon binding CPT, the residue network within AHR undergoes extensive reorganization, characterized by an expansion of both the shortest and second-shortest pathway networks (SPN) (Figure 10A,B). This reconfiguration is particularly evident in the C-terminal region, where new communication pathways between the final two helices are established and reinforced. Interestingly, both in the presence and absence of CPT, a consistent connection exists between the penultimate helix and the central β-sheet. However, the introduction of CPT not only modifies these interactions but also leads to a reshuffling of residue communities, indicating a substantial impact on the overall residue connectivity within AHR(Figure 10A,B). Furthermore, analysis of residue dynamics through the Dynamic Cross-Correlation Matrix (DCCM) underscores the influence of CPT on AHR’s structural coherence(Figure 10C,D). The enhanced cooperative interactions observed among adjacent residues along the protein's main axis suggest a stabilization of certain regions, while increased correlations, both cooperative and antagonistic, between residues across spatially distinct secondary structures, point to a nuanced modulation of AHR's conformational flexibility(Figure 10C,D). This intricate balance of stability and adaptability, induced by CPT binding, could have significant implications for AHR's functional capabilities, particularly in terms of its transcriptional regulation and interaction with other molecular partners. Figure 8: Molecular dynamics analysis of AHR in apo form and with CPT binding A) Root mean square deviation (RMSD) of AHR backbone atoms in apo form and with CPT binding over time, showing the structural stability under different conditions. B) Root mean square fluctuation (RMSF) of AHR residues in apo form and with CPT binding, highlighting regions of increased flexibility or stability upon ligand binding. C) Solvent accessible surface area (SASA) of AHR in apo form and with CPT binding, indicating changes in the protein's exposure to the solvent environment. D) Number of hydrogen bonds (H-bonds) in AHR over time, comparing the apo form and CPT-bound form, illustrating the role of hydrogen bonds in structural integrity. E) Radius of gyration analysis of AHR, indicating the overall compactness of the protein structure in the apo form (left) and with CPT binding (right). F) Secondary structure content analysis (DSSP) of AHR over time, comparing the secondary structure elements in the apo form and with CPT binding. G) Analysis of the distances between key residues involved in pi-cation interactions within AHR, comparing apo and CPT-bound conditions, showing how CPT binding affects these interactions. H) Salt bridge occupancy analysis, showing the stability and frequency of salt bridges within AHR in both apo and CPT-bound forms.I) Structural snapshots of CPT within the AHR pocket at 0 ns, 25 ns, 50 ns, 75 ns, and 100 ns, showing the dynamic positioning of CPT within the binding pocket over time.J) Time series of the distance between CPT and the center of the AHR binding pocket, showing the periodic motion and stability of CPT within the pocket.K) Evolution of the pocket shape characteristics (linear, planar, isotropic) over the simulation time, comparing the apo form and CPT binding, indicating the conformational changes induced by CPT. Figure 9: Dimensionality reduction and energy landscape analysis of AHR in apo form and with CPT binding A) UMAP plots based on atomic coordinates, comparing the conformational space of AHR in apo form (left) and with CPT binding (right) over time. The color gradient represents the progression of time from blue (0 ns) to red (100 ns). B) t-SNE plots based on atomic coordinates, illustrating the conformational clustering of AHR in apo form (left) and with CPT binding (right) over time. The time progression is indicated by the color gradient from blue to red.C) Gibbs free energy landscape (FEL) of AHR in its apo form, depicting the distribution of energy minima across the conformational space, with deeper blue regions indicating lower energy states.D) Gibbs free energy landscape (FEL) of AHR with CPT binding, showing the distribution of energy minima in the conformational space. The scatter plot highlights specific energy minima, with annotations indicating the corresponding time points. Figure 10: Network and dynamic cross-correlation analysis of AHR in apo form and with CPT binding A) Shortest Pathway Network (SPN) analysis of AHR, comparing the apo form (left) and CPT-bound state (right). The network illustrates the most efficient communication pathways within the protein, with key residues highlighted. B) Second Shortest Pathway Network (second SPN) analysis of AHR, comparing the apo form (left) and CPT-bound state (right). This analysis reveals alternative communication routes within the protein structure. C) Dynamic Cross-Correlation Matrix (DCCM) analysis for the apo form of AHR. The panel includes the DCCM matrix (left), which shows correlated motions between residues, and 3D representations of residue interactions at different correlation levels (right). D) Dynamic Cross-Correlation Matrix (DCCM) analysis for AHR with CPT binding. This panel also includes the DCCM matrix (left) and 3D representations (right), highlighting the changes in residue correlations upon CPT binding. Discussion Metabolic-associated fatty liver disease (MAFLD) is a multifactorial condition, rooted in complex molecular interactions that involve disrupted lipid metabolism, chronic inflammation, and a range of genetic and environmental influences[ 23 – 26 ]. Central to these processes is the Aryl Hydrocarbon Receptor (AHR), a nuclear receptor that plays a critical role in sensing and responding to environmental and dietary cues[ 10 , 11 , 28 ]. The involvement of AHR in MAFLD is paradoxical; on one side, environmental toxins like 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) exacerbate the disease by promoting hepatic inflammation and fibrosis, while on the other side, natural compounds such as indigo and resveratrol offer protective benefits, reducing liver damage and improving metabolic function[ 13 , 29 – 32 ]. This apparent contradiction likely stems from the heterogeneity in how different hepatocyte subpopulations respond to AHR activation, as well as the diverse binding affinities and conformational changes induced by various ligands at distinct receptor sites[ 30 ]. Our study uniquely addresses the metabolic and transcriptional diversity within hepatocyte subpopulations in an experimental MAFLD model induced by a high sucrose and fat diet (HSDFD). This research highlights how specific hepatocyte subpopulations, particularly those marked by Rasd1(hi), Galnt17(hi), and Lpin1(-), show heightened metabolic activity in response to HSDFD, potentially contributing to the disease's pathogenesis. Such metabolic reprogramming emphasizes the pivotal role of these subpopulations in liver adaptation to dietary-induced metabolic stress, suggesting that targeting these metabolic pathways might offer novel therapeutic strategies for MAFLD[ 33 ]. Beyond metabolic heterogeneity, our findings reveal substantial variability in the transcriptional regulatory networks across hepatocyte subpopulations. The M1 regulon module, which is prominently reorganized under HSDFD conditions, stands out as a critical player in this context. Notably, AHR activity within the M1 module exhibits differential responses across subpopulations, with a marked reduction in the Lpin1(-) subpopulation following HSDFD exposure. This decrease in AHR activity could signify a shift in the regulatory landscape, contributing to the altered metabolic and transcriptional profiles within this subpopulation. These insights underscore the importance of AHR in maintaining liver homeostasis during metabolic stress and suggest its potential as a therapeutic target in MAFLD. The differential effects of TCDD and CPT on MAFLD underscore the complex and context-dependent nature of AHR signaling, which is not merely a function of ligand binding but is profoundly influenced by the specific conformational changes induced by each ligand[01,1]. TCDD, a well-known environmental pollutant, is a potent AHR agonist that has been extensively studied for its toxicological effects[ 15 , 32 ]. Its ability to exacerbate MAFLD is likely rooted in its strong and prolonged activation of AHR, which leads to sustained transcription of pro-inflammatory and pro-fibrotic genes[47]. TCDD's binding to AHR stabilizes the receptor in a conformation that promotes interactions with co-activators and the transcriptional machinery responsible for driving the expression of genes that contribute to liver inflammation, oxidative stress, and fibrosis. In contrast, CPT, while also binding to AHR, induces a markedly different conformational state of the receptor. This conformational difference is critical, as it appears to selectively modulate AHR's interaction with its co-factors, leading to a transcriptional program that favors lipid metabolism over inflammation and fibrosis. CPT's ability to restore CYP1A1 expression, a key enzyme in detoxifying reactive oxygen species and metabolizing xenobiotics, further highlights its role in mitigating oxidative stress and lipid peroxidation—two pivotal processes in MAFLD pathogenesis[ 23 , 25 , 26 ]. This suggests that CPT promotes a protective transcriptional environment within hepatocytes, counteracting the deleterious effects of a high-fat, high-sucrose diet. A deeper understanding of these differential effects might be found in the ligand-induced conformational flexibility of AHR, which dictates its interaction with various co-regulatory proteins. TCDD likely locks AHR into a rigid, persistent active state, leading to a pathological overactivation of AHR target genes. In contrast, CPT might allow for a more dynamic or flexible receptor conformation, enabling a balanced activation of protective metabolic pathways while avoiding the overexpression of genes that drive inflammation and fibrosis. This ligand-specific flexibility could be a key factor in determining whether AHR activation leads to disease exacerbation or protection. Additionally, the epigenetic landscape of hepatocytes might play a significant role in these differential outcomes. TCDD-induced AHR activation could potentially lead to more stable changes in chromatin structure, facilitating prolonged gene expression changes that contribute to MAFLD progression[01,1,1]. On the other hand, CPT might influence AHR's interaction with chromatin in a way that results in transient or more regulated gene expression changes, which could explain its protective effects against MAFLD. In light of these findings, we utilized virtual screening to identify potential AHR modulators, leading to the discovery of CPT as a candidate compound. Subsequent in vivo and in vitro experiments demonstrated that CPT effectively reduces lipid accumulation induced by HSDFD or palmitic acid (PA), thus offering a protective effect against MAFLD. However, CPT did not significantly affect fibrosis, indicating a selective impact on lipid metabolism. The distinct effects of CPT and TCDD on MAFLD likely arise from their different modes of interaction with AHR, as suggested by our structural analyses. These findings highlight the complex and nuanced role of AHR ligands in MAFLD and point to the importance of ligand-specific actions in modulating AHR function[ 35 ]. Furthermore, our research suggests that CPT exerts its protective effects through the AHR/CYP1A1 signaling pathway. CYP1A1, a cytochrome P450 monooxygenase, plays a crucial role in metabolizing various endogenous substrates, including fatty acids, steroid hormones, and vitamins[ 25 , 26 , 37 – 39 ]. In MAFLD and PA-treated models, the suppression of CYP1A1 may impair the liver's ability to process these substrates, exacerbating metabolic stress and contributing to disease progression. By activating AHR and restoring CYP1A1 expression, CPT may enhance the liver's capacity to manage lipid metabolism, offering a new therapeutic avenue for MAFLD treatment. Despite these promising results, several key scientific questions remain. First, the precise mechanisms by which different AHR ligands produce divergent effects on MAFLD require further investigation, particularly in terms of ligand-induced conformational changes and subsequent signaling pathways. Second, the extent to which subpopulation-specific responses to AHR activation influence the overall progression of MAFLD needs to be more clearly defined. Finally, the interaction between AHR and other metabolic regulators within the liver deserves deeper exploration to uncover additional therapeutic targets[ 40 ]. The limitations of our study must also be acknowledged. Our research primarily focuses on the short-term effects of CPT on lipid metabolism, without considering the long-term impact on disease progression, particularly concerning fibrosis and inflammation. Moreover, while our study highlights CYP1A1's role in mediating CPT's effects, the broader implications of modulating cytochrome P450 enzymes in liver disease contexts remain to be fully understood. In summary, our study offers new insights into AHR's role in MAFLD, particularly concerning hepatocyte subpopulation heterogeneity and the differential effects of AHR ligands. CPT emerges as a promising therapeutic agent that targets the AHR/CYP1A1 signaling pathway to ameliorate lipid dysregulation in MAFLD. These findings lay a foundation for future research aimed at developing more targeted and effective therapies for MAFLD, addressing the significant clinical challenges posed by this widespread liver disease. Declarations Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the National Natural Science Foundation of China Grant (No. 82200434). Author Contribution X.Z. and Q.L. contributed equally to the study. X.Z. and Z.C. designed the research. Q.L. and Y.L. conducted the experiments. X.Z. and Y.L. analyzed the data. Z.C. and Y.L. wrote the manuscript. All authors reviewed and approved the final version of the manuscript. Data Availability The single-cell RNA sequencing data used in this study are available in the Gene Expression Omnibus (GEO) under accession number GSE182365. The protein structure data are available in RCSB Protein Data Bank (PDB ID: 8qmo). The chemical structure of indirubin can be accessed through PubChem (Compound CID: 72322). References Gofton, C., Upendran, Y., Zheng, M. H. & George, J. How is it different from NAFLD? Clin. Mol. Hepatol. 29 (Suppl), S17–S31. 10.3350/cmh.2022.0367 (2023). 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Use of natural AhR ligands as potential therapeutic modalities against inflammatory disorders. Nutr. Rev. 71 (6), 353–369. 10.1111/nure.12024 (2013). Nault, R. et al. Single-cell transcriptomics shows dose-dependent disruption of hepatic zonation by TCDD in mice. Toxicol. Sci. 191 (1), 135–148. 10.1093/toxsci/kfac109 (2023). Bock, K. W. Functions of aryl hydrocarbon receptor (AHR) and CD38 in NAD metabolism and nonalcoholic steatohepatitis (NASH). Biochem. Pharmacol. 169 , 113620. 10.1016/j.bcp.2019.08.022 (2019). Shan, Q., Huang, F., Wang, J. & Du, Y. Effects of co-exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin and polychlorinated biphenyls on nonalcoholic fatty liver disease in mice. Environ. Toxicol. 30 (12), 1364–1374. 10.1002/tox.22006 (2015). Liu, W. et al. Single-cell transcriptome analysis of liver immune microenvironment changes induced by microplastics in mice with non-alcoholic fatty liver. Sci. Total Environ. 912 (168308). 10.1016/j.scitotenv.2023.168308 (2024). Cho, S. H. et al. Regulation of CYP1a1 and Inflammatory Cytokine by NCOA7 Isoform 4 in Response to Dioxin Induced Airway Inflammation, Tuberc. Respir Dis. 78 (2), 99–105. 10.4046/trd.2015.78.2.99 (2015). Deierlein, A. L., Rock, S. & Park, S. Persistent Endocrine-Disrupting Chemicals and Fatty Liver Disease. Curr. Environ. Health Rep. 4 (4), 439–449. 10.1007/s40572-017-0166-8 (2017). Patil, N. Y., Friedman, J. E. & Joshi, A. D. Role of Hepatic Aryl Hydrocarbon Receptor in Non-Alcoholic Fatty Liver Disease. Receptors (Basel) . 2 (1), 1–15. 10.3390/receptors2010001 (2023). Chen, H., Howald, W. N. & Juchau, M. R. Biosynthesis of all-trans-retinoic acid from all-trans-retinol: catalysis of all-trans-retinol oxidation by human P-450 cytochromes. Drug Metab. Dispos. 28 (3), 315–322 (2000). Badawi, A. F., Cavalieri, E. L. & Rogan, E. G. Role of human cytochrome P450 1a1, 1a2, 1B1, and 3a4 in the 2-, 4-, and 16alpha-hydroxylation of 17beta-estradiol. Metabolism . 50 (9), 1001–1003. 10.1053/meta.2001.25592 (2001). Mesaros, C., Lee, S. H. & Blair, I. A. Analysis of epoxyeicosatrienoic acids by chiral liquid chromatography/electron capture atmospheric pressure chemical ionization mass spectrometry using [13C]-analog internal standards. Rapid Commun. Mass. Spectrom. 24 (22), 3237–3247. 10.1002/rcm.4760 (2010). Rakateli, L., Huchzermeier, R. & van der Vorst, E. AhR, PXR and CAR: From Xenobiotic Receptors to Metabolic Sensors. Cells . 12 (23). 10.3390/cells12232752 (2023). Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigures.docx supplementarymaterial1.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5201468","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":377981868,"identity":"ef056033-839d-4144-819e-e7310e53c27c","order_by":0,"name":"Xiliang Zhu","email":"","orcid":"","institution":"Fuwai Central China Cardiovascular Hospital, Central China Fuwai Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Xiliang","middleName":"","lastName":"Zhu","suffix":""},{"id":377981871,"identity":"e4423dd3-10ad-4a29-9053-208ba5730a8f","order_by":1,"name":"Qi Liu","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Liu","suffix":""},{"id":377981872,"identity":"489e0f89-8282-4375-9910-4c2cf4a13477","order_by":2,"name":"Zhaoyun Cheng","email":"","orcid":"","institution":"Fuwai Central China Cardiovascular Hospital, Central China Fuwai Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Zhaoyun","middleName":"","lastName":"Cheng","suffix":""},{"id":377981873,"identity":"7b0771fa-ce99-4702-bfd0-efbd2f510889","order_by":3,"name":"Yi Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYFCCA4wHgCQPP4idUECcFgawFskGkBYDYu0BEQYQkgjl8o1nDA7zVByWMT6/OvHDAwMGeX6xA/i1MDaAtJw5zGN24+1mCaDDDGfOTsCvhZkBqIW3DaTl7AaQlgSD2wS0sMG0GM84u/kHUVp4YFoM+Hu3EWeLBMOxgoNzzqTzSNzg3WaRYCBB2C/yMw5vfPCmwtqev//s5ps/Kmzk+aUJaGGQOAAim4GMBIithAF/A4isAzIOEKF6FIyCUTAKRiQAAIy9Rx9Nm7pUAAAAAElFTkSuQmCC","orcid":"","institution":"Zhejiang University","correspondingAuthor":true,"prefix":"","firstName":"Yi","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2024-10-04 04:53:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5201468/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5201468/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70220724,"identity":"b218337f-0e50-4e92-8a98-8cb3afe2411b","added_by":"auto","created_at":"2024-11-29 16:41:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3060675,"visible":true,"origin":"","legend":"\u003cp\u003eSingle-cell RNA sequencing reveals cell type-specific responses in the liver under Chow and HSDFD conditions A) UMAP of liver cell populations, color-coded by cell type with key gene markers annotated, showing the diversity of liver cell types under study.B) Density plots comparing the distribution of a specific cell population between Chow (left) and HSDFD (right) conditions, highlighting changes in population density under different dietary conditions.C) UMAP showing the distribution and proportion of different liver cell types between Chow and HSDFD groups, illustrating the impact of diet on cell type composition.D) Circos plot displaying marker genes for different hepatocyte subpopulations, indicating gene expression patterns specific to each subpopulation.E) Biological Process (BP) and Molecular Function (MF) enrichment analysis of marker genes associated with different hepatocyte subpopulations, providing insights into the functional roles of these subpopulations in response to dietary changes.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5201468/v1/63ba42bd692b3392ea5894b1.png"},{"id":70220865,"identity":"966cd24e-ab78-4f5c-bbb3-1acfb9a6c93d","added_by":"auto","created_at":"2024-11-29 16:49:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5649803,"visible":true,"origin":"","legend":"\u003cp\u003ePathway activity and disease association in liver cell subpopulations under Chow and HSDFD conditions A) Box plots illustrating the metabolic activity across various hepatocyte subpopulations in mice, highlighting differences among subpopulations. B) Heatmap showing the activity levels of distinct metabolic pathways across all hepatocyte subpopulations. C) UMAP plots comparing the activity of fatty acid synthesis, elongation, and degradation pathways in hepatocyte subpopulations under Chow (left) and HSDFD (right) conditions. D) KEGG pathway-based disease enrichment analysis, indicating the association of specific hepatocyte subpopulation marker genes with non-alcoholic fatty liver disease (NAFLD). E) UMAP projection of total hepatocytes from Chow and HSDFD groups onto the Human Protein Atlas (HPA) liver cell atlas, highlighting the alignment with human liver cell types. F) UMAP projection of individual mouse hepatocyte subpopulations onto the HPA human liver cell atlas, providing insights into the correspondence between mouse and human hepatocyte subpopulations.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5201468/v1/e8f9e9db710c6e3c7082e021.png"},{"id":70220863,"identity":"e9ca90d3-ac01-45b0-ad30-867aef14d467","added_by":"auto","created_at":"2024-11-29 16:49:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5703483,"visible":true,"origin":"","legend":"\u003cp\u003eClustering and Transcriptional Regulation Analysis of Liver Cell Subpopulations Under Chow and HSDFD ConditionsA) UMAP plots show hepatocyte subpopulations based on gene expression (left) and transcriptional regulatory activity (right), revealing distinct clusters.B) Correlation matrix illustrates relationships between hepatocyte subpopulations based on regulatory activity.C) Identification of key transcriptional regulatory modules in mouse hepatocytes.D) UMAP plots display RAS regulatory activity of the M1 module under Chow and HSDFD conditions.E) Hallmark pathway enrichment analysis highlights significant pathways in MAFLD.F) Dot plot shows expression abundance of M1-related transcription factors.G) Heatmap depicts M1 module regulon activity across subpopulations.H) UMAP plots compare AHR regulon activity between conditions.I) Violin plots indicate variations in AHR activity.J) Scatter plot shows downregulation of Ahr in Lpin1(-) subpopulation after HSDFD feeding.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5201468/v1/b3fce04642bf9c4af83124e7.png"},{"id":70220861,"identity":"4ed84ceb-d957-479a-ac1b-1f328a2eabdd","added_by":"auto","created_at":"2024-11-29 16:49:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2417460,"visible":true,"origin":"","legend":"\u003cp\u003eVirtual screening and analysis of small molecules targeting AHR from the TCMSP database A) Hierarchical clustering of small molecules in the TCMSP database based on Morgan molecular fingerprints.B) Virtual screening of small molecules targeting AHR, with coptisine highlighted for its high binding affinity.C) Clustering analysis of high-affinity small molecules using different fingerprint methods (Daylight, MACCS, ECFP4).D) Molecular map of the center molecule in the cluster containing coptisine (top), and the matched structure between coptisine and the center molecule (bottom).E) Absorption analysis of coptisine, including metrics like MDCK, Caco-2, PAMPA permeability, and P-gp substrate/inhibitor potential.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5201468/v1/31908785a45b23eaee2c75de.png"},{"id":70220730,"identity":"13b22f13-42c5-43c1-8363-fdffda29088c","added_by":"auto","created_at":"2024-11-29 16:41:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2566416,"visible":true,"origin":"","legend":"\u003cp\u003eHistological and Biochemical Analysis of Liver Tissues and Serum Under Different Dietary Conditions with and without CPT TreatmentA) HE staining shows significant hepatic steatosis in the HSDFD group, reduced by CPT.B) Masson’s trichrome staining indicates no significant collagen differences, suggesting CPT does not affect fibrosis.C) Sirius Red staining confirms no significant fibrosis changes.D) Oil Red O staining reveals substantial lipid accumulation in HSDFD, reduced by CPT.E) Immunohistochemical staining shows reduced AHR expression in HSDFD, partially restored by CPT.F) Serum HDL-C is lower in HSDFD, with partial restoration from CPT.G) LDL-C, total cholesterol, and triglyceride levels are elevated in HSDFD but significantly reduced by CPT.H) Hepatic cholesterol and triglyceride levels are elevated in HSDFD and reduced after CPT treatment.I) Serum triglyceride levels are elevated in HSDFD and decrease following CPT treatment.J) Hepatic total cholesterol levels increase in HSDFD and significantly decrease with CPT.K) Hepatic triglyceride levels are elevated in HSDFD and reduced after CPT treatment.\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5201468/v1/6d41e1188db3a9a1f339bbfd.png"},{"id":70220862,"identity":"8447f0fa-d63f-4c9b-88d0-24021276f534","added_by":"auto","created_at":"2024-11-29 16:49:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2616143,"visible":true,"origin":"","legend":"\u003cp\u003eAHR binding and transcriptional activity analysis in mouse liver cells A) TSS region heatmaps from the CISTROME database showing AHR ChIP-seq peaks in mouse liver cells under different conditions.B) Track plot illustrating the binding peaks of Cyp1a1 across different samples, highlighting AHR interaction sites. C) Dose-dependent AHR transcriptional activity relative to CPT concentrations, demonstrating a clear response pattern.D) Dose-dependent transcriptional activity of Cyp1a1 relative to CPT concentrations, showing modulation by CPT.E) AHR transcriptional activity under various treatment conditions (VEH, VEH+CPT, PA, PA+CPT), with significant changes observed.F) Transcriptional activity of Cyp1a1 under different treatment conditions, indicating the effect of CPT and PA on Cyp1a1 expression.G) Predicted structure of mouse AHR bound to the Cyp1a1 motif, as modeled by AF3, providing insights into the binding conformation.H) Comparison of AHR binding sites in the promoter regions of Cyp1a1 across different species (human, mouse, rat), showing conserved and divergent regions.I) Changes in Cyp1a1 transcriptional activity in WT, MUT, and DEL constructs after AHR overexpression, with clear differences observed between constructs.J) Immunofluorescence staining showing AHR expression in liver cells under different conditions (VEH, VEH+CPT, PA, PA+CPT), with CPT treatment notably enhancing AHR levels.K) Bodipy staining showing lipid accumulation in liver cells under different conditions, with and without CPT treatment, indicating the effect on lipid metabolism.\u003c/p\u003e","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5201468/v1/af7b7496637705c7bca57045.png"},{"id":70220726,"identity":"4e2e1aad-2f40-4de7-8cf3-9b8748a2cd4b","added_by":"auto","created_at":"2024-11-29 16:41:53","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3070384,"visible":true,"origin":"","legend":"\u003cp\u003eStructural analysis and functional validation of AHR binding with TCDD and CPT A) Visualization of the AHR pocket structure, highlighting key regions involved in ligand binding. B) Structural model showing the interaction between TCDD and AHR, with a detailed view of the binding pocket, illustrating the specific residues involved. C) 2D interaction diagram of TCDD binding within the AHR pocket, detailing the key residues and their interactions. D) Structural model showing the interaction between CPT and AHR, with a detailed view of the binding pocket, emphasizing the specific binding interactions. E) 2D interaction diagram of CPT binding within the AHR pocket, highlighting the crucial residues involved in the interaction. F) Cellular Thermal Shift Assay (CETSA) analysis demonstrating the stabilization of AHR by CPT, showing a temperature-dependent shift in protein expression levels compared to control. G) Drug Affinity Responsive Target Stability (DARTS) assay indicating the proteolytic stability of AHR in the presence of TCDD and CPT, with a quantification of relative protein expression levels.\u003c/p\u003e","description":"","filename":"figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-5201468/v1/3ac22e2abc3347576e55ac01.png"},{"id":70220735,"identity":"8b3b2a02-24ce-4cec-ab66-c88c67349637","added_by":"auto","created_at":"2024-11-29 16:41:53","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2686402,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular dynamics analysis of AHR in apo form and with CPT binding A) Root mean square deviation (RMSD) of AHR backbone atoms in apo form and with CPT binding over time, showing the structural stability under different conditions. B) Root mean square fluctuation (RMSF) of AHR residues in apo form and with CPT binding, highlighting regions of increased flexibility or stability upon ligand binding. C) Solvent accessible surface area (SASA) of AHR in apo form and with CPT binding, indicating changes in the protein's exposure to the solvent environment. D) Number of hydrogen bonds (H-bonds) in AHR over time, comparing the apo form and CPT-bound form, illustrating the role of hydrogen bonds in structural integrity. E) Radius of gyration analysis of AHR, indicating the overall compactness of the protein structure in the apo form (left) and with CPT binding (right). F) Secondary structure content analysis (DSSP) of AHR over time, comparing the secondary structure elements in the apo form and with CPT binding. G) Analysis of the distances between key residues involved in pi-cation interactions within AHR, comparing apo and CPT-bound conditions, showing how CPT binding affects these interactions. H) Salt bridge occupancy analysis, showing the stability and frequency of salt bridges within AHR in both apo and CPT-bound forms.I) Structural snapshots of CPT within the AHR pocket at 0 ns, 25 ns, 50 ns, 75 ns, and 100 ns, showing the dynamic positioning of CPT within the binding pocket over time.J) Time series of the distance between CPT and the center of the AHR binding pocket, showing the periodic motion and stability of CPT within the pocket.K) Evolution of the pocket shape characteristics (linear, planar, isotropic) over the simulation time, comparing the apo form and CPT binding, indicating the conformational changes induced by CPT.\u003c/p\u003e","description":"","filename":"figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-5201468/v1/e98ac8f8f92e5f03460303c2.png"},{"id":70221901,"identity":"3fdb43b3-3b5d-4621-bf14-c62082d8a19b","added_by":"auto","created_at":"2024-11-29 16:57:53","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":2400853,"visible":true,"origin":"","legend":"\u003cp\u003eDimensionality reduction and energy landscape analysis of AHR in apo form and with CPT binding A) UMAP plots based on atomic coordinates, comparing the conformational space of AHR in apo form (left) and with CPT binding (right) over time. The color gradient represents the progression of time from blue (0 ns) to red (100 ns). B) t-SNE plots based on atomic coordinates, illustrating the conformational clustering of AHR in apo form (left) and with CPT binding (right) over time. The time progression is indicated by the color gradient from blue to red.C) Gibbs free energy landscape (FEL) of AHR in its apo form, depicting the distribution of energy minima across the conformational space, with deeper blue regions indicating lower energy states.D) Gibbs free energy landscape (FEL) of AHR with CPT binding, showing the distribution of energy minima in the conformational space. The scatter plot highlights specific energy minima, with annotations indicating the corresponding time points.\u003c/p\u003e","description":"","filename":"figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-5201468/v1/d1fbdb12ca48faa68cdeb29a.png"},{"id":70220728,"identity":"3fbe4419-ef7b-473c-938c-43c49a99b424","added_by":"auto","created_at":"2024-11-29 16:41:53","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":3106798,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork and dynamic cross-correlation analysis of AHR in apo form and with CPT binding A) Shortest Pathway Network (SPN) analysis of AHR, comparing the apo form (left) and CPT-bound state (right). The network illustrates the most efficient communication pathways within the protein, with key residues highlighted. B) Second Shortest Pathway Network (second SPN) analysis of AHR, comparing the apo form (left) and CPT-bound state (right). This analysis reveals alternative communication routes within the protein structure. C) Dynamic Cross-Correlation Matrix (DCCM) analysis for the apo form of AHR. The panel includes the DCCM matrix (left), which shows correlated motions between residues, and 3D representations of residue interactions at different correlation levels (right). D) Dynamic Cross-Correlation Matrix (DCCM) analysis for AHR with CPT binding. This panel also includes the DCCM matrix (left) and 3D representations (right), highlighting the changes in residue correlations upon CPT binding.\u003c/p\u003e","description":"","filename":"figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-5201468/v1/010f77bc93105479cdc693b0.png"},{"id":95802162,"identity":"63851324-5e3d-4419-a6ce-a46b540c689e","added_by":"auto","created_at":"2025-11-13 08:27:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":35976041,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5201468/v1/eae840b7-79da-4d40-bf57-0509c0ddcd69.pdf"},{"id":70220739,"identity":"c9a3d2cf-4545-4bbf-b88b-cb1679d6f1ec","added_by":"auto","created_at":"2024-11-29 16:41:54","extension":"docx","order_by":18,"title":"","display":"","copyAsset":false,"role":"supplement","size":68473157,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-5201468/v1/abca6e842d4eb00a65e35063.docx"},{"id":70220734,"identity":"b4d574ab-aacd-4e7a-9a90-fad3aa12fdc0","added_by":"auto","created_at":"2024-11-29 16:41:53","extension":"pdf","order_by":19,"title":"","display":"","copyAsset":false,"role":"supplement","size":394910,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterial1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5201468/v1/c0abbf4549ccb1fe8e0011e8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Selective Modulation of Aryl Hydrocarbon Receptor by Coptisine in MAFLD","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMetabolic-associated fatty liver disease (MAFLD) is a complex condition characterized by dysregulated lipid metabolism and inflammation, which is influenced by various genetic, environmental, and metabolic factors[\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Among these, the transcriptional regulation mediated by nuclear receptors plays a critical role in the pathogenesis of MAFLD[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A key nuclear receptor in this context is the Aryl Hydrocarbon Receptor (AHR), which acts as a sensor for environmental and dietary signals[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Given its ability to perceive and integrate these cues, AHR has been increasingly recognized as a pivotal player in MAFLD development.\u003c/p\u003e \u003cp\u003eHowever, the role of AHR in MAFLD remains controversial. On one hand, certain known environmental small molecules, such as 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) and polychlorinated biphenyls (PCBs), that act as AHR agonists have been shown to exacerbate MAFLD by promoting liver inflammation and fibrosis[\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. On the other hand, natural molecules, including indigo and resveratrol, which act as either AHR agonists or antagonists, have demonstrated protective effects against metabolic dysfunction and inflammation[\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This dichotomy suggests that the diverse effects of AHR in MAFLD may be attributed to the heterogeneity of AHR responses within different hepatocyte subpopulations, spatial and temporal dynamics, and ligand-induced conformational changes at distinct action sites.\u003c/p\u003e \u003cp\u003eMoreover, MAFLD is often accompanied by alterations in fatty acid metabolism, with enzymes like CYP1A1 playing a role in oxidizing various structurally unrelated compounds, including steroids, fatty acids, and xenobiotics[\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This enzymatic activity suggests a potential role for CYP1A1 in MAFLD progression, further complicating the understanding of AHR's involvement in the disease.\u003c/p\u003e \u003cp\u003eGiven these complexities, our study aims to elucidate the metabolic and transcriptional heterogeneity among hepatocyte subpopulations in an HSDFD-induced MAFLD model, with a particular focus on identifying the differential AHR activity in response to dietary environmental stimuli. Additionally, we sought to identify natural molecules targeting AHR through virtual screening, evaluate their effects in a mouse model of MAFLD, and elucidate their potential structural basis and molecular signaling pathways.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003e1. Animal Model and Diet Protocol\u003c/p\u003e\n\u003cp\u003eEight-week-old male C57BL/6 mice (SLAC Laboratory Animal, Shanghai, China) were maintained under specific pathogen-free conditions with a controlled 12-hour light/dark cycle. The animals had unrestricted access to food and water throughout the experimental period. To investigate the effects of dietary interventions and coptisine treatment on the development of metabolic-associated fatty liver disease (MAFLD), the mice (n = 10 per group) were randomly allocated to one of four dietary regimens: the control group received a standard chow diet, the chow diet plus coptisine (CHOW+CPT), the high-fat diet (35 kcal% fat) tailored to induce MAFLD, or the high-fat diet plus coptisine (HSDFD+CPT). The specialized high-fat diet (KL11606, KangLang, Shanghai, China) comprised 66.5% standard rodent chow, supplemented with 10% lard, 20% sucrose, 2.5% cholesterol, and 1% sodium cholate, designed specifically to model type 2 diabetes mellitus (T2DM) and MAFLD. Coptisine (CPT, B20560, Yuanye, Shanghai,200 \u0026micro;g/g) was administered to the CHOW+CPT and HSDFD+CPT groups via oral gavage three times per week.\u003c/p\u003e\n\u003cp\u003eThe dietary intervention was conducted over a 12-week period, during which body weights were meticulously recorded on a weekly basis, and food consumption was closely monitored. At the conclusion of the intervention, the mice were first anesthetized with 2% isoflurane inhalation, followed by euthanasia through cervical dislocation. After confirmation of death, liver tissues were harvested for subsequent analyses, including histological evaluation, immunohistochemical staining, and various molecular assays.\u003c/p\u003e\n\u003cp\u003eAll experimental procedures adhered to the highest standards of animal care and were rigorously approved by the Institutional Animal Care and Use Committee (IACUC). This study was conducted and reported in accordance with the ARRIVE (Animal Research: Reporting of In Vivo Experiments) guidelines. All methods were carried out in accordance with relevant guidelines and regulations. This well-established protocol provided a robust framework for inducing MAFLD in this experimental model, ensuring consistency and reproducibility across subsequent analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Cell Culture and Treatment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAML12 mouse hepatocyte cells were cultured in DMEM/F-12 medium supplemented with fetal bovine serum, insulin-transferrin-selenium, and dexamethasone at 37\u0026deg;C with 5% CO2. For lipid accumulation, cells were treated with palmitic acid conjugated to bovine serum albumin for 24 hours. Control cells received BSA only. To assess the protective effects of coptisine, cells were co-treated with palmitic acid and varying concentrations of coptisine (100 nM, 1 \u0026micro;M, and 10 \u0026micro;M) for 24 hours. Post-treatment, cells were harvested for lipid accumulation assays, Western blotting, and immunofluorescence studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. High-Throughput Virtual Screening and Post-Screening Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA high-throughput virtual screening was conducted using the TCMSP database to identify small molecule ligands targeting the Aryl Hydrocarbon Receptor (AHR, PDB ID: 8qmo). Molecular structures were processed with the RDKit toolkit for docking simulations. AutoDock Vina was employed for docking, focusing on the AHR binding site. Binding affinities were analyzed using RDKit, DeepChem, and Biopython, applying Lipinski\u0026apos;s Rule of Five to filter compounds. This approach yielded high-affinity AHR ligands prioritized for experimental validation, facilitating the identification of potential therapeutic compounds.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Histological Examination and Analysis of Liver Tissues\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLiver samples were collected and fixed in 10% neutral-buffered formalin for 24 hours. Tissues underwent dehydration, clearing, and embedding in paraffin, followed by sectioning. Hematoxylin and eosin staining was performed, and histopathological analysis was conducted under a light microscope. Images were captured from multiple fields and analyzed using ImageJ software to assess hepatic steatosis and inflammation. Statistical analysis involved evaluating at least five sections per animal across three animals per group, with data presented as mean\u0026nbsp;\u0026plusmn;\u0026nbsp;SEM and significance determined by Student\u0026apos;s t-test (p \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. Histological Assessment of Liver Fibrosis and Lipid Accumulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLiver tissues were fixed in 10% neutral-buffered formalin for 24 hours, dehydrated, cleared in xylene, and embedded in paraffin. Sections (4\u0026nbsp;\u0026mu;m thick) were subjected to Masson\u0026rsquo;s trichrome staining for fibrosis evaluation, using Weigert\u0026rsquo;s iron hematoxylin for nuclei and Biebrich scarlet-acid fuchsin for cytoplasm, with aniline blue highlighting collagen fibers. Microscopic examination at 200\u0026times;\u0026nbsp;magnification captured images from five random fields, and collagen deposition was quantified using ImageJ software. Sirius Red staining further assessed fibrosis, with sections stained and analyzed similarly. For lipid quantification, liver tissues were frozen, sectioned (8\u0026nbsp;\u0026mu;m thick), and stained with Oil Red O to visualize neutral lipids. Lipid accumulation was quantified as a percentage of Oil Red O-positive regions relative to total tissue area. Statistical analyses included at least five sections per animal, with results expressed as mean\u0026nbsp;\u0026plusmn;\u0026nbsp;SEM and significance determined by Student\u0026rsquo;s t-test (p \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6. Immunohistochemistry for AHR Detection in Liver Tissue\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLiver tissues were fixed in 10% neutral buffered formalin for 24 hours and processed for paraffin embedding. Sections were deparaffinized, rehydrated, and subjected to antigen retrieval using heat-induced epitope retrieval in citrate buffer. Endogenous peroxidase activity was blocked, and sections were incubated with a primary antibody specific for AHR, followed by a biotinylated secondary antibody. Visualization was achieved using a diaminobenzidine (DAB) substrate, and sections were counterstained with hematoxylin. Examination at 200\u0026times;\u0026nbsp;magnification allowed for the capture of images from five random fields. AHR immunoreactivity was quantified using ImageJ software, calculating the percentage of positive staining relative to total tissue area. At least five sections per liver were analyzed, with data expressed as mean\u0026nbsp;\u0026plusmn;\u0026nbsp;SEM. Statistical significance was assessed using Student\u0026rsquo;s t-test, with p \u0026lt; 0.05 considered significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7. Liver Function and Lipid Profile Analysis in Mice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate liver function and lipid profiles, blood samples were collected via retro-orbital bleeding after a 12-hour fasting period. Serum was obtained and stored at -80\u0026deg;C. Liver function was assessed by measuring ALT and AST levels using colorimetric assays. Lipid profiles, including total cholesterol (TC) and triglycerides (TG), were measured enzymatically. HDL-C levels were determined after precipitating LDL and VLDL fractions, while LDL-C concentration was calculated using the Friedewald equation. All measurements were conducted in duplicate, with data reported as mean\u0026nbsp;\u0026plusmn;\u0026nbsp;SEM. Statistical comparisons were performed using one-way ANOVA, with significance defined as p \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8. Biochemical Assessment of Hepatic Lipid Levels in Experimental Mice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLiver tissues were homogenized for analysis of total cholesterol (TC) and triglyceride (TG) levels using commercial assay kits. Absorbance for TC and TG was measured at 500 nm and 510 nm, respectively. Lipid content was quantified and expressed as milligrams of lipid per gram of liver tissue (mg/g). Statistical analyses were performed to evaluate differences between treatment groups, ensuring rigorous comparison of hepatic lipid levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e9. Cellular Thermal Shift Assay (CESTA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Cellular Thermal Shift Assay (CESTA) was used to investigate how Coptisine (CPT) affects the thermal stability of the Aryl Hydrocarbon Receptor (AHR). AML12 cells were treated with varying concentrations of CPT, lysed, and subjected to temperatures from 37\u0026deg;C to 70\u0026deg;C. After rapid freezing and centrifugation, Western blot analysis was performed on soluble protein fractions to assess AHR levels. The melting temperature (Tm) was derived from the thermal stability curve, revealing how CPT modulates AHR stability. Differences in Tm between treated and control samples indicated CPT\u0026rsquo;s potential stabilizing effects on the receptor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e10. Drug Affinity Responsive Target Stability (DARTS) Assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Drug Affinity Responsive Target Stability (DARTS) assay assessed the interaction between Coptisine (CPT), TCDD, and AHR by examining their effects on AHR\u0026apos;s proteolytic stability. AML12 cells were treated with CPT and TCDD, lysed, and incubated with the compounds before limited proteolysis with pronase. Western blotting analyzed AHR levels, revealing that proteins bound by ligands exhibit increased resistance to proteolysis. The extent of AHR\u0026apos;s stability in CPT- and TCDD-treated samples was compared to untreated controls, providing insights into the direct interactions and enhanced stability of AHR in the presence of these compounds.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e11. Immunofluorescence Staining of AHR in AML12 Cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAML12 cells were cultured on coverslips and treated with vehicle, Coptisine (CPT), palmitic acid (PA), or PA + CPT. After fixation with paraformaldehyde, cells were permeabilized and blocked to minimize non-specific binding. They were then incubated with a primary antibody against AHR, followed by a fluorescently-labeled secondary antibody. After counterstaining with DAPI, fluorescence microscopy was used to visualize AHR localization and expression. Images were captured consistently across treatment groups, and fluorescence intensity was quantified using ImageJ software to compare AHR expression across different conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e12. BODIPY Staining for Lipid Droplets in AML12 Cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAML12 cells were treated with vehicle, Coptisine (CPT), palmitic acid (PA), or PA + CPT, then fixed and stained with BODIPY 493/503 dye to visualize lipid droplets. After rinsing and counterstaining with DAPI, fluorescent images were captured using a confocal microscope. The BODIPY dye indicated lipid droplets with green fluorescence, while DAPI stained nuclei purple. Lipid droplet content was quantitatively analyzed using ImageJ software, measuring relative fluorescence intensity. Statistical analyses compared lipid accumulation across treatment groups, illustrating CPT\u0026apos;s effect on reducing PA-induced lipid accumulation in AML12 cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e13. Western Blotting Analysis of AHR and CYP1A1 Expression in AML12 Cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWestern blotting was performed to evaluate AHR and CYP1A1 protein expression in AML12 cells treated with vehicle, Coptisine (CPT), palmitic acid (PA), or PA + CPT. Cells were lysed in RIPA buffer, and protein concentrations were measured. Equal amounts of protein were separated by SDS-PAGE and transferred to PVDF membranes, which were blocked and incubated with primary antibodies against AHR, CYP1A1, and GAPDH. After washing, HRP-conjugated secondary antibodies were applied, and protein bands were detected using an ECL system. Band intensities were quantified with ImageJ, normalizing against GAPDH, and statistical analyses assessed expression differences across treatment groups.\u0026nbsp;The original uncropped Western blot images are provided in Supplementary Material 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e14. Luciferase Reporter Assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLuciferase reporter assays were conducted using AML12 cells transfected with an AHR-responsive luciferase reporter plasmid or constructs of the AHR binding motif linked to luciferase. Transfections were performed with Lipofectamine 3000, followed by treatment with varying concentrations of Coptisine (CPT) or palmitic acid (PA). Luciferase activity was measured using the Dual-Luciferase Reporter Assay System, normalizing luminescence values to a co-transfected Renilla luciferase plasmid. Relative luciferase units (RLU) were calculated, and statistical comparisons evaluated the effects of treatments on AHR and CYP1A1 activity, with results expressed as mean\u0026nbsp;\u0026plusmn;\u0026nbsp;SEM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e15. Molecular Dynamics Simulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMolecular dynamics (MD) simulations were performed to explore the conformational dynamics of AHR in its apo state and when bound to Coptisine (CPT). Using GROMACS software, the system was solvated and neutralized, with energy minimization conducted. The system was equilibrated in NVT and NPT ensembles before a 100 ns production MD simulation. Key structural analyses, including RMSD, RMSF, SASA, and hydrogen bond calculations, were performed to evaluate stability and conformational changes. The dynamics of the binding pocket were analyzed, and the Gibbs free energy landscape was calculated to identify stable conformational states, revealing significant differences between apo and CPT-bound AHR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e16. Single-Cell Data Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle-cell RNA sequencing (scRNA-seq) data were sourced from the Gene Expression Omnibus (GEO) (GSE182365), encompassing liver cells from mice on standard chow and high-fat, high-sucrose diets. Isolated hepatocytes and stellate cells were processed into single-cell suspensions for RNA sequencing. The data underwent preprocessing to construct a single-cell atlas of the liver, with dimensionality reduction (UMAP) applied to visualize cellular diversity. Differential expression analysis revealed dietary impacts on hepatic cell populations, highlighting alterations in metabolic pathways and transcriptional networks linked to diet-induced liver disease, supported by visualizations and statistical analyses.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e1.Hepatocyte Subpopulation Dynamics and Functional Heterogeneity in Response to HSDFD Diet\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle-cell RNA sequencing (scRNA-seq) was employed to explore hepatocyte subpopulations in the liver under Chow and HSDFD dietary conditions. UMAP visualization (Figure 1A) reveals distinct liver cell populations, with HSDFD significantly altering hepatocyte composition. Most subpopulations cluster well, but Igfbp7(-) hepatocytes exhibit an isolated distribution, suggesting a unique adaptive response to HSDFD.\u003c/p\u003e\n\u003cp\u003eThe HSDFD diet also impacts the spatial distribution of hepatocyte subpopulations, as density plots show a reorganization of these populations. Under Chow conditions (Figure 1B, left), hepatocyte subpopulations are concentrated with clear density peaks. In contrast, HSDFD (Figure 1B, right) leads to new outlier clusters, indicating novel subpopulations formed in response to metabolic stress.\u003c/p\u003e\n\u003cp\u003eUMAP visualization (Figure 1C) highlights the contraction of certain subpopulations, such as those marked by Ptgds and Rbm8a2, under HSDFD, suggesting selective pressure that may reduce their functional capacity. The Circos plot (Figure 1D) illustrates gene expression patterns specific to each subpopulation, reinforcing distinctions driven by the HSDFD diet.\u003c/p\u003e\n\u003cp\u003eBiological processes (BP) and molecular functions (MF) analysis (Figure 1E) reveals significant heterogeneity in gene expression profiles under HSDFD. Hepatocytes show enrichment in nutrient response, extracellular stimulus response, and starvation, indicating active adaptation to metabolic challenges. Other subpopulations respond to oxidative stress and detoxification, suggesting a protective role against oxidative damage from the HSDFD diet. Immune-related processes, such as interleukin-4 response, are also enriched, indicating the diet's influence on inflammatory pathways.\u003c/p\u003e\n\u003cp\u003eMolecular function analysis shows enrichment in antioxidant and peroxidase activities, highlighting roles in oxidative stress management. Additionally, ubiquitin-dependent protein binding and DNA-binding transcription repressor activity enrichment suggest involvement in protein quality control and gene regulation, crucial for maintaining liver function under HSDFD conditions.\u003c/p\u003e\n\u003cp\u003eFigure 1: Single-cell RNA sequencing reveals cell type-specific responses in the liver under Chow and HSDFD conditions A) UMAP of liver cell populations, color-coded by cell type with key gene markers annotated, showing the diversity of liver cell types under study.B) Density plots comparing the distribution of a specific cell population between Chow (left) and HSDFD (right) conditions, highlighting changes in population density under different dietary conditions.C) UMAP showing the distribution and proportion of different liver cell types between Chow and HSDFD groups, illustrating the impact of diet on cell type composition.D) Circos plot displaying marker genes for different hepatocyte subpopulations, indicating gene expression patterns specific to each subpopulation.E) Biological Process (BP) and Molecular Function (MF) enrichment analysis of marker genes associated with different hepatocyte subpopulations, providing insights into the functional roles of these subpopulations in response to dietary changes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.Metabolic Pathway Activity in Hepatocyte Subpopulations under HSDFD Diet\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe metabolic landscape of hepatocyte subpopulations under the HSDFD diet shows significant variability, indicating different responses to dietary-induced stress. Box plots (Figure 2A) reveal that subpopulations such as Rasd1(hi), Galnt17(hi), and Lpin1(-) have the highest pathway activity scores, suggesting their crucial role in liver adaptation to the HSDFD diet.\u003c/p\u003e\n\u003cp\u003eThe heatmap (Figure 2B) compares metabolic pathway activity across hepatocyte subpopulations, highlighting active pathways like starch and sucrose metabolism and ascorbate and aldarate metabolism, essential for processing the HSDFD diet's high carbohydrate and fat content. These subpopulations also show elevated activity in fatty acid metabolism, underscoring their versatility in sustaining liver function.\u003c/p\u003e\n\u003cp\u003eUMAP plots (Figure 2C) illustrate that the HSDFD diet enhances the activity of fatty acid biosynthesis, degradation, and elongation pathways, reflecting the liver's response to excess lipids.\u003c/p\u003e\n\u003cp\u003eNotably, the Galnt17(hi) and Lpin1(-) subpopulations are enriched in pathways associated with non-alcoholic fatty liver disease (NAFLD) (Figure 2D), implicating them in diet-induced liver disease.\u003c/p\u003e\n\u003cp\u003eThe UMAP projection onto the Human Protein Atlas (HPA) liver cell atlas (Figure 2E) shows a shift in cellular identity post-HSDFD exposure, with Chow-fed hepatocytes aligning with Hepatocyte-3, while HSDFD exposure shifts alignment towards Hepatocyte-1 and Hepatocyte-2 (Figure 2E). The active Galnt17(hi) and Lpin1(-) subpopulations drive this shift (Figure 2F), emphasizing their relevance in fatty acid metabolism (Supplementary Figure 1).\u003c/p\u003e\n\u003cp\u003eFigure 2: Pathway activity and disease association in liver cell subpopulations under Chow and HSDFD conditions A) Box plots illustrating the metabolic activity across various hepatocyte subpopulations in mice, highlighting differences among subpopulations. B) Heatmap showing the activity levels of distinct metabolic pathways across all hepatocyte subpopulations. C) UMAP plots comparing the activity of fatty acid synthesis, elongation, and degradation pathways in hepatocyte subpopulations under Chow (left) and HSDFD (right) conditions. D) KEGG pathway-based disease enrichment analysis, indicating the association of specific hepatocyte subpopulation marker genes with non-alcoholic fatty liver disease (NAFLD). E) UMAP projection of total hepatocytes from Chow and HSDFD groups onto the Human Protein Atlas (HPA) liver cell atlas, highlighting the alignment with human liver cell types. F) UMAP projection of individual mouse hepatocyte subpopulations onto the HPA human liver cell atlas, providing insights into the correspondence between mouse and human hepatocyte subpopulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.Transcriptional Regulation Landscape in Hepatocyte Subpopulations under HSDFD Diet\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe transcriptional regulatory landscape of hepatocyte subpopulations under the HSDFD diet reveals significant heterogeneity, with distinct regulatory patterns emerging among certain subpopulations. UMAP plots of hepatocyte subpopulations(Figure 3A) highlight that Galnt17(hi), Top2a(hi), Col4a1(-), and Igfbp7(-) exhibit unique transcriptional regulatory profiles. These differences suggest that these subpopulations might be governed by specific regulatory networks, potentially leading to specialized roles within the liver under the HSDFD diet (Figure 3A).\u003c/p\u003e\n\u003cp\u003eContrastingly, the Lpin1(-) subpopulation follows a regulatory pattern akin to that of most other hepatocyte subpopulations (Figure 3B), indicating shared regulatory mechanisms across the majority of liver cells.\u003c/p\u003e\n\u003cp\u003eA significant finding from this analysis is the identification of the M1 regulon module as the predominant transcriptional regulatory module in hepatocytes (Figure 3C). The activity of the M1 module undergoes substantial changes under HSDFD conditions, reflecting a diet-induced reorganization of hepatocyte subpopulations. This reorganization is evident in the differential transcriptional regulation of the M1 module between Chow and HSDFD conditions (Figure 3D). The alterations in M1 activity correspond to shifts in gene expression profiles and the functional outputs of the liver, driven by the HSDFD diet.Enrichment analysis of downstream genes in the identified modules reveals that M1 is involved in key pathways such as unfolded protein response ,MYC targets and adipogenesis, while M4 and M5 show significant involvement in metabolic pathways, including adipogenesis and xenobiotic metabolism(Figure 3E).\u003c/p\u003e\n\u003cp\u003eFurther examination of the M1 module reveals that, except for Atf3 and Cebpb, the transcription factors associated with this module are expressed at low levels across hepatocyte subpopulations (Figure 3F). This minimal expression is consistent with the roles of these transcription factors, which can exert regulatory influence even at low expression levels. Additionally, the regulons within the M1 module exhibit heightened transcriptional regulatory activity in subpopulations such as Kbtbd11(hi), Ptgds(hi), Rbm8a2(hi), and Fut11(lo) (Figure 3G), indicating their importance in the liver\u0026rsquo;s response to the HSDFD diet (Figure 3G).\u003c/p\u003e\n\u003cp\u003eThe AHR regulon within the M1 module reveals distinct reactivity across different hepatocyte subpopulations. It shows high transcriptional activity in the metabolically active Galnt17(hi) subpopulation while being suppressed in the Lpin1(-) subpopulation (Figures 3H,I). This differential regulation underscores AHR's pivotal role in modulating metabolic and regulatory responses across hepatocyte subpopulations under the HSDFD diet.\u003c/p\u003e\n\u003cp\u003eFinally, differential analysis of the Lpin1(-) subpopulation reveals a significant downregulation of Ahr expression following HSDFD exposure, demonstrating the HSDFD diet's regulatory effect on AHR abundance in specific hepatocyte subpopulations. This reduction in Ahr expression likely contributes to the altered metabolic and transcriptional landscape observed in this subpopulation, highlighting the importance of AHR regulation in maintaining hepatic homeostasis under dietary stress(Figures 3J).\u003c/p\u003e\n\u003cp\u003eFigure 3: Clustering and Transcriptional Regulation Analysis of Liver Cell Subpopulations Under Chow and HSDFD ConditionsA) UMAP plots show hepatocyte subpopulations based on gene expression (left) and transcriptional regulatory activity (right), revealing distinct clusters.B) Correlation matrix illustrates relationships between hepatocyte subpopulations based on regulatory activity.C) Identification of key transcriptional regulatory modules in mouse hepatocytes.D) UMAP plots display RAS regulatory activity of the M1 module under Chow and HSDFD conditions.E) Hallmark pathway enrichment analysis highlights significant pathways in MAFLD.F) Dot plot shows expression abundance of M1-related transcription factors.G) Heatmap depicts M1 module regulon activity across subpopulations.H) UMAP plots compare AHR regulon activity between conditions.I) Violin plots indicate variations in AHR activity.J) Scatter plot shows downregulation of Ahr in Lpin1(-) subpopulation after HSDFD feeding.\u003c/p\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003e\u003cstrong\u003eVirtual Screening and Analysis of AHR-Targeting Small Molecules from the TCMSP Database\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eA systematic approach was employed to virtually screen small molecules from the TCMSP database to identify potential AHR-targeting compounds. The initial phase of this study involved hierarchical clustering of the entire molecular library using Morgan molecular fingerprints, which facilitated the organization of molecules based on structural similarities (Figure 4A). This hierarchical clustering provided a broad overview of the molecular diversity within the TCMSP database and allowed for the identification of distinct clusters that might contain potential AHR modulators.\u003c/p\u003e\n\u003cp\u003eThe virtual screening identified coptisine as a molecule with a notably high binding affinity for AHR (Figure 4B). This compound was selected from a pool of high-affinity molecules, further validated through clustering analyses using multiple fingerprinting methods\u0026mdash;Daylight, MACCS, and ECFP4 (Figure 4C). The clustering analysis revealed that coptisine occupies a central position within its cluster, indicating its structural congruence with other high-affinity AHR-targeting molecules.\u003c/p\u003e\n\u003cp\u003eTo delve deeper into coptisine's molecular properties, a molecular map was constructed, focusing on the structural alignment between coptisine and a representative center molecule within its cluster. The molecular structure of the cluster\u0026rsquo;s center molecule shows key chemical groups, with significant structural overlap between coptisine and this center molecule, underscoring the chemical features contributing to coptisine\u0026rsquo;s high affinity for AHR (Figure 4D).\u003c/p\u003e\n\u003cp\u003eSubsequent pharmacokinetic assessments provided insights into coptisine's absorption, distribution, metabolism, and toxicity (ADMET) profile. The absorption characteristics show poor permeability across MDCK and Caco-2 assays, with negative values (\u0026minus;4.66 and \u0026minus;4.96), indicating limited absorption potential through these cell models. However, moderate permeability in the PAMPA assay (0.35) suggests that coptisine may still possess passive diffusion capabilities. Additionally, coptisine interacts with P-glycoprotein (P-gp), acting as both a substrate (0.999) and an inhibitor (0.0036), which could influence its bioavailability by modulating drug transport. The low human intestinal absorption (HIA) score (0.0041) points to poor absorption in vivo, raising concerns about its overall bioavailability. These results reflect a mixed absorption profile for coptisine, warranting further study to fully understand its pharmacokinetic behavior (Figure 4E).The distribution analysis based on the provided data reveals that coptisine exhibits a high degree of plasma protein binding (PPB) with a value of 0.774, indicating that a significant portion of the compound is likely to bind to plasma proteins in circulation. Coptisine also shows moderate permeability across the blood-brain barrier (BBB) with a value of 0.385, suggesting potential distribution into the central nervous system. Additionally, coptisine demonstrates significant interaction with bile salt export pump (BSEP) transporters (0.999), indicating potential biliary excretion. The moderate volume of distribution (logVDss = 0.258) further supports its widespread distribution across various tissues, including the liver and other peripheral tissues. These findings highlight coptisine's potential for extensive distribution in vivo, with implications for its pharmacokinetics and therapeutic action (Figure 4F). Metabolic analysis of coptisine\u0026rsquo;s interaction with key cytochrome P450 (CYP) enzymes reveals that it is likely metabolized primarily by CYP1A2 and CYP2D6, with both substrate and inhibitor interactions showing near-maximal values (0.999). CYP3A4 also plays a significant role as both an inhibitor (0.9998) and a substrate (0.00017), suggesting that coptisine may undergo extensive hepatic metabolism. Additionally, CYP2C9 and CYP2B6 show moderate involvement, with coptisine acting as both a substrate and inhibitor for these enzymes. The presence of multiple CYP interactions indicates that coptisine\u0026rsquo;s metabolic profile could significantly influence its pharmacokinetics and potential for drug-drug interactions (Figure 4G).The Tox21 profile of coptisine reveals significant interactions with various nuclear receptors and stress response pathways. Notably, coptisine shows strong activity towards the AHR pathway (0.999), supporting its potential as an AHR modulator. It also demonstrates interactions with AR-LBD (0.906), Aromatase (0.726), and SR-ARE (0.968), indicating possible effects on hormone-related pathways and oxidative stress response. Additional interactions with stress response proteins such as SR-MMP (0.996) and SR-p53 (0.998) suggest coptisine may influence cellular stress mechanisms and DNA damage response, further underscoring its multifaceted biological activity (Figure 4H). Additionally, the toxicity analysis of coptisine involved assessments of several key parameters, including hepatotoxicity (H\u0026minus;HT=0.150), neurotoxicity (0.079), and nephrotoxicity-DI (0.120). These evaluations provide a preliminary risk assessment of potential adverse effects, which are crucial for guiding further preclinical studies. The radar plot reveals a moderate risk for hepatotoxicity and nephrotoxicity, while neurotoxicity remains a lower concern. These findings help outline the safety profile of coptisine and highlight areas that warrant further investigation to ensure a balanced evaluation of its therapeutic potential and safety (Figures 4I).\u003c/p\u003e\n\u003cp\u003eFigure 4: Virtual screening and analysis of small molecules targeting AHR from the TCMSP database A) Hierarchical clustering of small molecules in the TCMSP database based on Morgan molecular fingerprints.B) Virtual screening of small molecules targeting AHR, with coptisine highlighted for its high binding affinity.C) Clustering analysis of high-affinity small molecules using different fingerprint methods (Daylight, MACCS, ECFP4).D) Molecular map of the center molecule in the cluster containing coptisine (top), and the matched structure between coptisine and the center molecule (bottom).E) Absorption analysis of coptisine, including metrics like MDCK, Caco-2, PAMPA permeability, and P-gp substrate/inhibitor potential.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eF) Distribution analysis of coptisine, showing factors such as plasma protein binding (PPB), blood-brain barrier (BBB) permeability, and volume of distribution (Vd).G) Metabolism analysis of coptisine, focusing on its interaction with key cytochrome P450 enzymes (CYPs).H) Tox21 profile of coptisine, showing interactions with nuclear receptors and stress response pathways.I) Toxicity analysis of coptisine, covering various endpoints including hepatotoxicity, cardiotoxicity, and general cytotoxicity.\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003e\u003cstrong\u003e Impact of CPT on Liver Pathology and Serum Lipid Profiles in HSDFD-Fed Mice\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe liver histopathology and serum lipid profiles were thoroughly examined across the four experimental groups: Chow, Chow+CPT, HSDFD, and HSDFD+CPT. Additionally, liver function did not show significant changes under these conditions, indicating that despite dietary intervention and CPT treatment, overall hepatic function remained stable across all groups. This suggests that the treatments did not induce overt liver toxicity(Supplementary Figure2). Hematoxylin and eosin (HE) staining (Figure 5A) revealed abnormal hepatic lipid deposition in the HSDFD group, characterized by an increase in the presence and size of lipid vacuoles within hepatocytes. This lipid accumulation was significantly ameliorated in the HSDFD+CPT group, where CPT administration resulted in a notable reduction in lipid vacuoles, reflecting an improvement in liver morphology and lipid distribution.\u003c/p\u003e\n\u003cp\u003eMasson\u0026rsquo;s trichrome staining (Figure 5B) and Sirius Red (SR) staining (Figure 5C) were employed to evaluate the degree of fibrosis within the liver tissues. Both staining techniques consistently showed minimal differences in collagen deposition among all groups, suggesting that CPT treatment under the conditions tested did not significantly impact liver fibrosis.\u003c/p\u003e\n\u003cp\u003eThe Oil Red O staining (Figure 5D) further corroborated the HE staining results by showing substantial lipid accumulation in the livers of HSDFD-fed mice. CPT treatment resulted in a dramatic decrease in hepatic lipid content, aligning the HSDFD+CPT group more closely with the lipid profiles observed in the Chow-fed groups, which exhibited minimal steatosis.\u003c/p\u003e\n\u003cp\u003eImmunohistochemical analysis for AHR expression (Figure 5E) demonstrated that the HSDFD diet caused a significant downregulation of AHR levels in liver tissues. Notably, CPT treatment partially restored AHR expression, indicating that CPT may exert a regulatory effect on AHR under the stress of a high-fat, high-sucrose diet.\u003c/p\u003e\n\u003cp\u003eBiochemical analyses of serum lipids reflected these histological findings. The HSDFD group displayed significantly reduced levels of HDL-C (Figure 5F) compared to the Chow group, a decline that was partially reversed by CPT treatment. Conversely, LDL-C levels (Figure 5G) were significantly elevated in the HSDFD group but were reduced following CPT administration. Additionally, the serum levels of total cholesterol (TC) and triglycerides (TG) (Figures 5H and 5I) were markedly increased in the HSDFD group, yet these increases were significantly mitigated by CPT treatment. In liver tissue homogenates, similar trends were observed, with both TC and TG levels (Figures 5J and 5K) showing significant elevation in the HSDFD group and substantial reduction upon CPT treatment.\u003c/p\u003e\n\u003cp\u003eFigure 5: Histological and Biochemical Analysis of Liver Tissues and Serum Under Different Dietary Conditions with and without CPT TreatmentA) HE staining shows significant hepatic steatosis in the HSDFD group, reduced by CPT.B) Masson\u0026rsquo;s trichrome staining indicates no significant collagen differences, suggesting CPT does not affect fibrosis.C) Sirius Red staining confirms no significant fibrosis changes.D) Oil Red O staining reveals substantial lipid accumulation in HSDFD, reduced by CPT.E) Immunohistochemical staining shows reduced AHR expression in HSDFD, partially restored by CPT.F) Serum HDL-C is lower in HSDFD, with partial restoration from CPT.G) LDL-C, total cholesterol, and triglyceride levels are elevated in HSDFD but significantly reduced by CPT.H) Hepatic cholesterol and triglyceride levels are elevated in HSDFD and reduced after CPT treatment.I) Serum triglyceride levels are elevated in HSDFD and decrease following CPT treatment.J) Hepatic total cholesterol levels increase in HSDFD and significantly decrease with CPT.K) Hepatic triglyceride levels are elevated in HSDFD and reduced after CPT treatment.\u003c/p\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003e\u003cstrong\u003e The Role of CPT in Modulating AHR-Driven Cyp1a1 Expression in Mouse Liver Cells\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo elucidate the role of CPT in the regulation of AHR-mediated gene expression within mouse hepatocytes, we employed an integrated approach that combined ChIP-seq analysis, transcriptional assays, and protein expression studies.\u003c/p\u003e\n\u003cp\u003eInitial ChIP-seq analysis from the CISTROME database highlighted significant AHR binding across various transcription start site (TSS) regions in murine liver cells, with a particularly strong enrichment at the Cyp1a1 locus. This observation suggests a critical role for AHR in regulating Cyp1a1 expression (Figure 6A). Further supporting this, detailed track plots showed prominent AHR binding peaks at the Cyp1a1 gene, reinforcing its position as a key regulatory target under AHR control (Figure 6B).\u003c/p\u003e\n\u003cp\u003eSubsequent luciferase reporter assays were conducted to explore the impact of CPT on AHR-mediated transcription. The results demonstrated a clear dose-dependent activation of AHR in response to increasing concentrations of CPT, indicating that CPT serves as a potent enhancer of AHR's transcriptional activity (Figure 6C). Parallel assessments of Cyp1a1 transcription revealed similar dose-dependent upregulation, directly linking CPT to the activation of Cyp1a1 via AHR (Figure 6D).The study also examined the effects of palmitic acid (PA) on AHR and Cyp1a1 activity. PA treatment resulted in a marked suppression of both AHR and Cyp1a1 transcriptional activities compared to the vehicle control (Figure 6E, F). Notably, the introduction of CPT in conjunction with PA reversed this suppression, leading to significant activation of both AHR and Cyp1a1, particularly in the PA+CPT co-treatment group (Figure 6E, F). These findings suggest that CPT effectively counteracts the inhibitory effects of PA, thereby enhancing AHR-mediated transcriptional activation.\u003c/p\u003e\n\u003cp\u003eTo gain structural insights, we employed AF3-based modeling, which predicted a specific binding interaction between AHR and the Cyp1a1 promoter that could underlie the observed regulatory effects (Figure 6G). Comparative analyses of AHR binding sites across species, including humans, mice, and rats, revealed a combination of conserved and species-specific elements within the Cyp1a1 promoter, underscoring the evolutionary importance of the AHR-Cyp1a1 interaction (Figure 6H). Further validation was provided by luciferase assays utilizing wild-type (WT), mutant (MUT), and deletion (DEL) constructs of the Cyp1a1 transcriptional regulatory region, specifically focusing on the identified AHR binding motif. The assays revealed that AHR overexpression significantly enhanced transcriptional activity in WT constructs, while both MUT and DEL constructs exhibited a notable reduction in activity. This confirms the critical role of the AHR binding motif in the regulation of Cyp1a1 expression (Figure 6I).\u003c/p\u003e\n\u003cp\u003eImmunofluorescence staining experiments revealed differential expression patterns of AHR across various treatment groups. AHR levels were markedly elevated in cells treated with CPT, especially in the PA+CPT group, indicating a synergistic effect of CPT in enhancing AHR expression under metabolic stress conditions (Figure 6J). Additionally, Bodipy staining showed substantial lipid accumulation in hepatocytes treated with PA alone, which was significantly reduced by the addition of CPT, demonstrating CPT's protective role against PA-induced lipotoxicity (Figure 6K).\u003c/p\u003e\n\u003cp\u003eProtein expression analyses further corroborated these findings, with observed increases in both AHR and CYP1A1 protein levels in response to CPT treatment(Supplementary Figure3). The elevation was most pronounced in the PA+CPT co-treatment group, aligning with the transcriptional and immunofluorescence data. These results collectively underscore the role of CPT as a modulator of AHR activity, particularly in enhancing the expression of Cyp1a1 under conditions of lipid-induced stress, suggesting its potential therapeutic value in liver disease contexts.\u003c/p\u003e\n\u003cp\u003eFigure 6: AHR binding and transcriptional activity analysis in mouse liver cells A) TSS region heatmaps from the CISTROME database showing AHR ChIP-seq peaks in mouse liver cells under different conditions.B) Track plot illustrating the binding peaks of Cyp1a1 across different samples, highlighting AHR interaction sites. C) Dose-dependent AHR transcriptional activity relative to CPT concentrations, demonstrating a clear response pattern.D) Dose-dependent transcriptional activity of Cyp1a1 relative to CPT concentrations, showing modulation by CPT.E) AHR transcriptional activity under various treatment conditions (VEH, VEH+CPT, PA, PA+CPT), with significant changes observed.F) Transcriptional activity of Cyp1a1 under different treatment conditions, indicating the effect of CPT and PA on Cyp1a1 expression.G) Predicted structure of mouse AHR bound to the Cyp1a1 motif, as modeled by AF3, providing insights into the binding conformation.H) Comparison of AHR binding sites in the promoter regions of Cyp1a1 across different species (human, mouse, rat), showing conserved and divergent regions.I) Changes in Cyp1a1 transcriptional activity in WT, MUT, and DEL constructs after AHR overexpression, with clear differences observed between constructs.J) Immunofluorescence staining showing AHR expression in liver cells under different conditions (VEH, VEH+CPT, PA, PA+CPT), with CPT treatment notably enhancing AHR levels.K) Bodipy staining showing lipid accumulation in liver cells under different conditions, with and without CPT treatment, indicating the effect on lipid metabolism.\u003c/p\u003e\n\u003col start=\"7\"\u003e\n\u003cli\u003e\u003cstrong\u003e Distinct Binding Mechanisms and Functional Effects of AHR Ligands TCDD and CPT\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo elucidate the molecular interactions between AHR and its ligands TCDD and CPT, a combination of structural modeling and functional assays was employed, revealing distinctive binding patterns and their potential implications for receptor function.\u003c/p\u003e\n\u003cp\u003eIn analyzing the AHR binding pocket (Figure 7A), critical regions responsible for ligand interaction were identified, providing insight into the structural basis for AHR's ligand specificity. TCDD, a well-characterized AHR agonist, engages the receptor through a \u0026pi;-H interaction with Ile325 and forms a hydrogen bond with Gln383. These interactions are integral to securing TCDD within the binding pocket, ensuring the stability of the AHR-TCDD complex (Figure 7B, C). The binding configuration highlights the importance of both hydrophobic interactions and hydrogen bonding, which collectively anchor TCDD in a position that likely influences downstream signaling cascades.\u003c/p\u003e\n\u003cp\u003eCPT, a different ligand, exhibits a unique mode of interaction within the same AHR binding site. Unlike TCDD, CPT primarily interacts through \u0026pi;-\u0026pi; stacking with Phe295, supplemented by a \u0026pi;-H interaction with Ile325, and forms a hydrogen bond with Ser336 (Figure 7D, E). This distinct binding approach suggests that CPT modulates AHR function differently, potentially leading to alternative receptor activation states. The divergence in binding strategies between TCDD and CPT underscores the adaptability of the AHR binding pocket, capable of accommodating structurally diverse ligands while maintaining functional relevance.\u003c/p\u003e\n\u003cp\u003eThe MM-PBSA analysis revealed a total binding energy of \u0026minus;115.6773 kJ/mol, indicating a strong and favorable interaction. The entropy correction value of 14.0819 kJ/mol further highlights the thermodynamic contributions to the system\u0026rsquo;s stability. The van der Waals (vdW) interactions provided a significant negative contribution, indicating strong stabilization, while Coulombic interactions were near neutral. The molecular mechanics (MM) term also contributed negatively to stability. However, the Poisson-Boltzmann (dPB) energy term was positive, reflecting its destabilizing effect, while the surface area (dSA) energy was slightly negative, contributing to overall stability(Supplementary Figure 4A,B).Residue decomposition analysis indicated several key regions of energetic contributions. For the molecular mechanics (MM) components, significant energy contributions were observed within the residue ranges of 0\u0026ndash;20, 40, 60\u0026ndash;80, and around residue 100. In contrast, the Poisson-Boltzmann (PB) energy decomposition showed positive values, particularly within residues 0\u0026ndash;20, around 80, and near residue 100, suggesting that these regions contribute to destabilizing the interaction. Surface area (SA) energy decomposition indicated that residues 0\u0026ndash;20, 60\u0026ndash;80, and around residue 100 contributed negatively, enhancing the stability of the ligand-receptor binding. These findings suggest that while some regions promote binding stability, other regions\u0026mdash;reflected by the positive PB values\u0026mdash;contribute to an overall balancing of interaction forces(Supplementary Figure 4C-E).\u003c/p\u003e\n\u003cp\u003eTo further explore the functional consequences of these interactions, the stabilization effect of CPT on AHR was assessed using the Cellular Thermal Shift Assay (CETSA). The results indicated that CPT binding significantly enhanced AHR stability, as evidenced by a notable temperature-dependent retention of AHR protein levels (Figure 7F). This stabilization suggests that CPT not only binds effectively to AHR but also confers a protective effect against thermal denaturation, thereby potentially influencing the receptor's ability to transduce signals.\u003c/p\u003e\n\u003cp\u003eMoreover, the Drug Affinity Responsive Target Stability (DARTS) assay provided additional validation of CPT's impact on AHR. This assay revealed that CPT, akin to TCDD, confers increased proteolytic resistance to AHR, indicating that CPT stabilizes the receptor in a manner that may affect its degradation pathway (Figure 7G). The enhanced stability observed in the presence of CPT points to a ligand-specific modulation of AHR, which could lead to differential gene expression outcomes depending on the ligand involved.\u003c/p\u003e\n\u003cp\u003eFigure 7: Structural analysis and functional validation of AHR binding with TCDD and CPT A) Visualization of the AHR pocket structure, highlighting key regions involved in ligand binding. B) Structural model showing the interaction between TCDD and AHR, with a detailed view of the binding pocket, illustrating the specific residues involved. C) 2D interaction diagram of TCDD binding within the AHR pocket, detailing the key residues and their interactions. D) Structural model showing the interaction between CPT and AHR, with a detailed view of the binding pocket, emphasizing the specific binding interactions. E) 2D interaction diagram of CPT binding within the AHR pocket, highlighting the crucial residues involved in the interaction. F) Cellular Thermal Shift Assay (CETSA) analysis demonstrating the stabilization of AHR by CPT, showing a temperature-dependent shift in protein expression levels compared to control. G) Drug Affinity Responsive Target Stability (DARTS) assay indicating the proteolytic stability of AHR in the presence of TCDD and CPT, with a quantification of relative protein expression levels.\u003c/p\u003e\n\u003col start=\"8\"\u003e\n\u003cli\u003e\u003cstrong\u003eCPT-Induced Modulation of AHR Stability, Conformational Flexibility, and Residue Network Reorganization\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eBy conducting a detailed structural and dynamic analysis, the interaction between AHR in both its apo form and when bound to CPT has revealed critical insights into the protein's stability and conformational behavior. The RMSD values for the backbone atoms of AHR quickly stabilize in both states, with the apo form showing slightly higher fluctuations compared to the CPT-bound form, suggesting that CPT binding enhances the overall structural stability of AHR (Figure 8A).\u003c/p\u003e\n\u003cp\u003eThe RMSF analysis shows that the most pronounced differences between the apo and CPT-bound states occur beyond residue 400, where increased fluctuations are observed in the CPT-bound form. This suggests that CPT may introduce additional flexibility or localized destabilization in specific regions, particularly in the C-terminal region, rather than uniformly stabilizing the protein (Figure 8B).\u003c/p\u003e\n\u003cp\u003eSASA analysis supports the observation of a more compact structure over time in both conditions, with a marked reduction in solvent exposure particularly evident in the apo form between 90 and 100 ns, potentially reflecting additional structural compression (Figure 8C). The hydrogen bond analysis revealed that the number of hydrogen bonds fluctuated more in the CPT-bound state compared to the apo state, indicating that CPT binding may introduce additional flexibility or alter the dynamics of hydrogen bond formation within the AHR protein (Figure 8D).\u003c/p\u003e\n\u003cp\u003eThe radius of gyration measurements align with these findings, showing rapid compaction early in the simulation for both forms. The apo form, however, undergoes a temporary expansion phase between 40 and 80 ns before stabilizing, contrasting with the consistently compact structure observed when CPT is bound, reinforcing CPT\u0026rsquo;s stabilizing role (Figure 8E).\u003c/p\u003e\n\u003cp\u003eExamination of secondary structure content using DSSP analysis shows consistent patterns across the simulation, with minimal shifts in secondary structure elements between the apo and CPT-bound forms, suggesting that CPT binding does not drastically alter the overall secondary structure distribution but may impact localized regions (Figure 8F).\u003c/p\u003e\n\u003cp\u003eExamination of pi-cation interaction distances reveals distinct differences in stability, particularly in the CPT-bound form. For example, interactions such as PHE2-ILE1_NH3 and TYR25-ARG33 maintain more consistent distances in the presence of CPT, suggesting a role for CPT in stabilizing these specific interactions. In contrast, interactions like TYR25-LYS71 and PHE39-ARG33 in the apo form exhibit significant fluctuations in the latter stages of the simulation, highlighting potential instability without CPT (Figure 8G).\u003c/p\u003e\n\u003cp\u003eThe salt bridge occupancy analysis provides further evidence of structural stability conferred by CPT. Key interactions such as ARG113-ASP44 and ARG67-ASP44 maintain full occupancy in both forms, underscoring their importance. Interestingly, the LYS116-GLU50 salt bridge, present in the apo form, is disrupted upon CPT binding, indicating localized structural changes. Additionally, salt bridges like ASP103-ARG74 show increased occupancy with CPT, suggesting enhanced stability in these regions (Figure 8H).\u003c/p\u003e\n\u003cp\u003eSnapshots of CPT within the AHR binding pocket at different time points demonstrate CPT's stable positioning, with a periodic oscillatory movement indicating robust interaction with AHR. The analysis of pocket shape characteristics throughout the simulation shows minimal changes, reinforcing the notion of a stable binding pocket that remains largely unaltered by CPT binding(Figure 8I). Through the calculation of the relative distance between the centroid of CPT and the pocket, we observed a periodic movement of CPT within the pocket, accompanied by minimal fluctuations in pocket shape characteristics (linear, planar, isotropic), which remained at a relatively consistent level throughout the simulation(Figure 8J,K). This suggests that CPT binding does not significantly alter the structural integrity of the binding pocket, further supporting its stable interaction with AHR.\u003c/p\u003e\n\u003cp\u003eThe structural and functional dynamics of AHR interacting with coptisine (CPT) were investigated through molecular dynamics simulations. The Tanimoto similarity matrix reveals distinct modules, indicating regions of high structural similarity throughout the 100-ns trajectory, suggesting that certain segments of the AHR-CPT complex maintain stable interactions over time(Supplementary Figure 5A). The weighted interaction diagram identifies key residues of AHR, such as VAL381, VAL363, and Ile325, as major contributors to these stable interactions with CPT(Supplementary Figure 5B). Further, time-resolved interactions between specific AHR residues and CPT are categorized into hydrophobic, hydrogen bond, and Van der Waals contacts, showing that residues like Phe295, Ile325, and LEU353 persistently interact with CPT(Supplementary Figure 5C). A word cloud derived from Phignet predictions highlights biological processes associated with AHR activity, including \"metabolic process\" and \"biosynthetic regulation\"(Supplementary Figure 5D). Finally, a structural visualization of AHR reveals regions involved in cellular aromatic compound metabolic processes (colored in red), residues interacting with CPT during the simulation (colored in green), and overlapping regions where both functionalities converge (colored in blue), indicating a potential mechanistic role of AHR in regulating the metabolic response to CPT(Supplementary Figure 5E).\u003c/p\u003e\n\u003cp\u003eDimensionality reduction techniques, including UMAP and t-SNE(Figure 9A,B), reveal distinct clustering patterns in the conformational landscape of AHR, indicating the influence of CPT on its dynamic behavior. The Gibbs free energy landscape analysis further differentiates the two states, with the apo form displaying a primary conformational cluster that includes two energy traps, with the lowest energy states occurring early in the simulation. In contrast, the CPT-bound form shows a broader distribution of conformational clusters with a dominant energy trap occurring around 50 ns, suggesting that CPT binding stabilizes specific conformational states of AHR and may influence its functional dynamics during the simulation (Figure 9C,D).\u003c/p\u003e\n\u003cp\u003eFurther analysis based on the Residue Distance Contact Matrix (RDCM) reinforces these observations. While the apo and CPT-bound forms exhibit similar overall residue-residue distance patterns, CPT binding results in increased distances between N-terminal residues relative to C-terminal residues, suggesting structural shifts (Supplementary Figure 6A). The time occupancy of the residue contact matrix shows that while the overall contact pattern remains largely unchanged, specific local adjustments occur in the CPT-bound state, particularly between residues 40-60 and C-terminal residues (Supplementary Figure 6B). Additionally, the Dynamic Cross-Correlation Matrix (DCCM) indicates that CPT binding enhances antagonistic relationships within the 50-100 residue segment while reducing cooperative relationships across other segments of AHR(Supplementary Figure 6C). The Pearson correlation matrix over time further supports this, showing strengthened positive correlations between certain residue pairs following CPT binding(Supplementary Figure 6D). Hierarchical clustering based on RDCM reveals a reshuffling of residue clusters in response to CPT binding, reflecting changes in intramolecular interactions(Supplementary Figure 6E). Finally, Principal Component Analysis (PCA) of RDCM highlights a more diverse conformational clustering in the CPT-bound form, with the emergence of new conformational states later in the simulation (Supplementary Figure 6F).\u003c/p\u003e\n\u003cp\u003eUpon binding CPT, the residue network within AHR undergoes extensive reorganization, characterized by an expansion of both the shortest and second-shortest pathway networks (SPN) (Figure 10A,B). This reconfiguration is particularly evident in the C-terminal region, where new communication pathways between the final two helices are established and reinforced. Interestingly, both in the presence and absence of CPT, a consistent connection exists between the penultimate helix and the central \u0026beta;-sheet. However, the introduction of CPT not only modifies these interactions but also leads to a reshuffling of residue communities, indicating a substantial impact on the overall residue connectivity within AHR(Figure 10A,B). Furthermore, analysis of residue dynamics through the Dynamic Cross-Correlation Matrix (DCCM) underscores the influence of CPT on AHR\u0026rsquo;s structural coherence(Figure 10C,D). The enhanced cooperative interactions observed among adjacent residues along the protein's main axis suggest a stabilization of certain regions, while increased correlations, both cooperative and antagonistic, between residues across spatially distinct secondary structures, point to a nuanced modulation of AHR's conformational flexibility(Figure 10C,D). This intricate balance of stability and adaptability, induced by CPT binding, could have significant implications for AHR's functional capabilities, particularly in terms of its transcriptional regulation and interaction with other molecular partners.\u003c/p\u003e\n\u003cp\u003eFigure 8: Molecular dynamics analysis of AHR in apo form and with CPT binding A) Root mean square deviation (RMSD) of AHR backbone atoms in apo form and with CPT binding over time, showing the structural stability under different conditions. B) Root mean square fluctuation (RMSF) of AHR residues in apo form and with CPT binding, highlighting regions of increased flexibility or stability upon ligand binding. C) Solvent accessible surface area (SASA) of AHR in apo form and with CPT binding, indicating changes in the protein's exposure to the solvent environment. D) Number of hydrogen bonds (H-bonds) in AHR over time, comparing the apo form and CPT-bound form, illustrating the role of hydrogen bonds in structural integrity. E) Radius of gyration analysis of AHR, indicating the overall compactness of the protein structure in the apo form (left) and with CPT binding (right). F) Secondary structure content analysis (DSSP) of AHR over time, comparing the secondary structure elements in the apo form and with CPT binding. G) Analysis of the distances between key residues involved in pi-cation interactions within AHR, comparing apo and CPT-bound conditions, showing how CPT binding affects these interactions. H) Salt bridge occupancy analysis, showing the stability and frequency of salt bridges within AHR in both apo and CPT-bound forms.I) Structural snapshots of CPT within the AHR pocket at 0 ns, 25 ns, 50 ns, 75 ns, and 100 ns, showing the dynamic positioning of CPT within the binding pocket over time.J) Time series of the distance between CPT and the center of the AHR binding pocket, showing the periodic motion and stability of CPT within the pocket.K) Evolution of the pocket shape characteristics (linear, planar, isotropic) over the simulation time, comparing the apo form and CPT binding, indicating the conformational changes induced by CPT.\u003c/p\u003e\n\u003cp\u003eFigure 9: Dimensionality reduction and energy landscape analysis of AHR in apo form and with CPT binding A) UMAP plots based on atomic coordinates, comparing the conformational space of AHR in apo form (left) and with CPT binding (right) over time. The color gradient represents the progression of time from blue (0 ns) to red (100 ns). B) t-SNE plots based on atomic coordinates, illustrating the conformational clustering of AHR in apo form (left) and with CPT binding (right) over time. The time progression is indicated by the color gradient from blue to red.C) Gibbs free energy landscape (FEL) of AHR in its apo form, depicting the distribution of energy minima across the conformational space, with deeper blue regions indicating lower energy states.D) Gibbs free energy landscape (FEL) of AHR with CPT binding, showing the distribution of energy minima in the conformational space. The scatter plot highlights specific energy minima, with annotations indicating the corresponding time points.\u003c/p\u003e\n\u003cp\u003eFigure 10: Network and dynamic cross-correlation analysis of AHR in apo form and with CPT binding A) Shortest Pathway Network (SPN) analysis of AHR, comparing the apo form (left) and CPT-bound state (right). The network illustrates the most efficient communication pathways within the protein, with key residues highlighted. B) Second Shortest Pathway Network (second SPN) analysis of AHR, comparing the apo form (left) and CPT-bound state (right). This analysis reveals alternative communication routes within the protein structure. C) Dynamic Cross-Correlation Matrix (DCCM) analysis for the apo form of AHR. The panel includes the DCCM matrix (left), which shows correlated motions between residues, and 3D representations of residue interactions at different correlation levels (right). D) Dynamic Cross-Correlation Matrix (DCCM) analysis for AHR with CPT binding. This panel also includes the DCCM matrix (left) and 3D representations (right), highlighting the changes in residue correlations upon CPT binding.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMetabolic-associated fatty liver disease (MAFLD) is a multifactorial condition, rooted in complex molecular interactions that involve disrupted lipid metabolism, chronic inflammation, and a range of genetic and environmental influences[\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Central to these processes is the Aryl Hydrocarbon Receptor (AHR), a nuclear receptor that plays a critical role in sensing and responding to environmental and dietary cues[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The involvement of AHR in MAFLD is paradoxical; on one side, environmental toxins like 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) exacerbate the disease by promoting hepatic inflammation and fibrosis, while on the other side, natural compounds such as indigo and resveratrol offer protective benefits, reducing liver damage and improving metabolic function[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This apparent contradiction likely stems from the heterogeneity in how different hepatocyte subpopulations respond to AHR activation, as well as the diverse binding affinities and conformational changes induced by various ligands at distinct receptor sites[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study uniquely addresses the metabolic and transcriptional diversity within hepatocyte subpopulations in an experimental MAFLD model induced by a high sucrose and fat diet (HSDFD). This research highlights how specific hepatocyte subpopulations, particularly those marked by Rasd1(hi), Galnt17(hi), and Lpin1(-), show heightened metabolic activity in response to HSDFD, potentially contributing to the disease's pathogenesis. Such metabolic reprogramming emphasizes the pivotal role of these subpopulations in liver adaptation to dietary-induced metabolic stress, suggesting that targeting these metabolic pathways might offer novel therapeutic strategies for MAFLD[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond metabolic heterogeneity, our findings reveal substantial variability in the transcriptional regulatory networks across hepatocyte subpopulations. The M1 regulon module, which is prominently reorganized under HSDFD conditions, stands out as a critical player in this context. Notably, AHR activity within the M1 module exhibits differential responses across subpopulations, with a marked reduction in the Lpin1(-) subpopulation following HSDFD exposure. This decrease in AHR activity could signify a shift in the regulatory landscape, contributing to the altered metabolic and transcriptional profiles within this subpopulation. These insights underscore the importance of AHR in maintaining liver homeostasis during metabolic stress and suggest its potential as a therapeutic target in MAFLD.\u003c/p\u003e \u003cp\u003eThe differential effects of TCDD and CPT on MAFLD underscore the complex and context-dependent nature of AHR signaling, which is not merely a function of ligand binding but is profoundly influenced by the specific conformational changes induced by each ligand[01,1]. TCDD, a well-known environmental pollutant, is a potent AHR agonist that has been extensively studied for its toxicological effects[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Its ability to exacerbate MAFLD is likely rooted in its strong and prolonged activation of AHR, which leads to sustained transcription of pro-inflammatory and pro-fibrotic genes[47]. TCDD's binding to AHR stabilizes the receptor in a conformation that promotes interactions with co-activators and the transcriptional machinery responsible for driving the expression of genes that contribute to liver inflammation, oxidative stress, and fibrosis.\u003c/p\u003e \u003cp\u003eIn contrast, CPT, while also binding to AHR, induces a markedly different conformational state of the receptor. This conformational difference is critical, as it appears to selectively modulate AHR's interaction with its co-factors, leading to a transcriptional program that favors lipid metabolism over inflammation and fibrosis. CPT's ability to restore CYP1A1 expression, a key enzyme in detoxifying reactive oxygen species and metabolizing xenobiotics, further highlights its role in mitigating oxidative stress and lipid peroxidation\u0026mdash;two pivotal processes in MAFLD pathogenesis[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This suggests that CPT promotes a protective transcriptional environment within hepatocytes, counteracting the deleterious effects of a high-fat, high-sucrose diet.\u003c/p\u003e \u003cp\u003eA deeper understanding of these differential effects might be found in the ligand-induced conformational flexibility of AHR, which dictates its interaction with various co-regulatory proteins. TCDD likely locks AHR into a rigid, persistent active state, leading to a pathological overactivation of AHR target genes. In contrast, CPT might allow for a more dynamic or flexible receptor conformation, enabling a balanced activation of protective metabolic pathways while avoiding the overexpression of genes that drive inflammation and fibrosis. This ligand-specific flexibility could be a key factor in determining whether AHR activation leads to disease exacerbation or protection.\u003c/p\u003e \u003cp\u003eAdditionally, the epigenetic landscape of hepatocytes might play a significant role in these differential outcomes. TCDD-induced AHR activation could potentially lead to more stable changes in chromatin structure, facilitating prolonged gene expression changes that contribute to MAFLD progression[01,1,1]. On the other hand, CPT might influence AHR's interaction with chromatin in a way that results in transient or more regulated gene expression changes, which could explain its protective effects against MAFLD.\u003c/p\u003e \u003cp\u003eIn light of these findings, we utilized virtual screening to identify potential AHR modulators, leading to the discovery of CPT as a candidate compound. Subsequent in vivo and in vitro experiments demonstrated that CPT effectively reduces lipid accumulation induced by HSDFD or palmitic acid (PA), thus offering a protective effect against MAFLD. However, CPT did not significantly affect fibrosis, indicating a selective impact on lipid metabolism. The distinct effects of CPT and TCDD on MAFLD likely arise from their different modes of interaction with AHR, as suggested by our structural analyses. These findings highlight the complex and nuanced role of AHR ligands in MAFLD and point to the importance of ligand-specific actions in modulating AHR function[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, our research suggests that CPT exerts its protective effects through the AHR/CYP1A1 signaling pathway. CYP1A1, a cytochrome P450 monooxygenase, plays a crucial role in metabolizing various endogenous substrates, including fatty acids, steroid hormones, and vitamins[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In MAFLD and PA-treated models, the suppression of CYP1A1 may impair the liver's ability to process these substrates, exacerbating metabolic stress and contributing to disease progression. By activating AHR and restoring CYP1A1 expression, CPT may enhance the liver's capacity to manage lipid metabolism, offering a new therapeutic avenue for MAFLD treatment.\u003c/p\u003e \u003cp\u003eDespite these promising results, several key scientific questions remain. First, the precise mechanisms by which different AHR ligands produce divergent effects on MAFLD require further investigation, particularly in terms of ligand-induced conformational changes and subsequent signaling pathways. Second, the extent to which subpopulation-specific responses to AHR activation influence the overall progression of MAFLD needs to be more clearly defined. Finally, the interaction between AHR and other metabolic regulators within the liver deserves deeper exploration to uncover additional therapeutic targets[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe limitations of our study must also be acknowledged. Our research primarily focuses on the short-term effects of CPT on lipid metabolism, without considering the long-term impact on disease progression, particularly concerning fibrosis and inflammation. Moreover, while our study highlights CYP1A1's role in mediating CPT's effects, the broader implications of modulating cytochrome P450 enzymes in liver disease contexts remain to be fully understood.\u003c/p\u003e \u003cp\u003eIn summary, our study offers new insights into AHR's role in MAFLD, particularly concerning hepatocyte subpopulation heterogeneity and the differential effects of AHR ligands. CPT emerges as a promising therapeutic agent that targets the AHR/CYP1A1 signaling pathway to ameliorate lipid dysregulation in MAFLD. These findings lay a foundation for future research aimed at developing more targeted and effective therapies for MAFLD, addressing the significant clinical challenges posed by this widespread liver disease.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Natural Science Foundation of China Grant (No. 82200434).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eX.Z. and Q.L. contributed equally to the study. X.Z. and Z.C. designed the research. Q.L. and Y.L. conducted the experiments. X.Z. and Y.L. analyzed the data. Z.C. and Y.L. wrote the manuscript. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe single-cell RNA sequencing data used in this study are available in the Gene Expression Omnibus (GEO) under accession number GSE182365. The protein structure data are available in RCSB Protein Data Bank (PDB ID: 8qmo). The chemical structure of indirubin can be accessed through PubChem (Compound CID: 72322).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGofton, C., Upendran, Y., Zheng, M. H. \u0026amp; George, J. How is it different from NAFLD? Clin. \u003cem\u003eMol. 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AhR, PXR and CAR: From Xenobiotic Receptors to Metabolic Sensors. \u003cem\u003eCells\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e (23). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cells12232752\u003c/span\u003e\u003cspan address=\"10.3390/cells12232752\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Metabolic-associated fatty liver disease, Aryl Hydrocarbon Receptor, single-cell RNA sequencing, high-sucrose, high-fat diet, Coptisine, CYP1A1, transcriptional regulation, lipid metabolism.","lastPublishedDoi":"10.21203/rs.3.rs-5201468/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5201468/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eMetabolic-associated fatty liver disease (MAFLD) is a multifaceted condition driven by disrupted lipid metabolism and chronic inflammation, influenced by genetic, environmental, and dietary factors. The Aryl Hydrocarbon Receptor (AHR) has emerged as a critical regulator in this context, mediating responses to various environmental and dietary signals. The dual role of AHR in MAFLD is complex, with some ligands exacerbating liver damage while others confer protective effects, suggesting that AHR\u0026rsquo;s impact may be highly context-dependent.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eThis study analyzed single-cell RNA sequencing (scRNA-seq) data to explore the metabolic and transcriptional heterogeneity of hepatocyte subpopulations in a high-sucrose, high-fat diet (HSDFD)-induced MAFLD model. Virtual screening identified potential AHR-targeting compounds, leading to the selection of CPT for further study. The efficacy of CPT was evaluated through in vivo and in vitro assays, including Cellular Thermal Shift Assay (CETSA), Drug Affinity Responsive Target Stability (DARTS), Western blotting, immunohistochemistry (IHC), immunofluorescence, and Bodipy staining. These methods were employed to elucidate the molecular interactions between AHR and its ligands, and to assess CPT\u0026rsquo;s impact on lipid accumulation and AHR-mediated transcriptional activity.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eOur findings reveal significant alterations in hepatocyte subpopulation dynamics under HSDFD conditions, with subpopulations such as Rasd1(hi), Galnt17(hi), and Lpin1(-) displaying enhanced metabolic activity. Transcriptional regulation analysis identified a reorganization of the M1 regulon module, with differential AHR activity across subpopulations. Notably, CPT emerged as a potent AHR-targeting compound, effectively reducing lipid accumulation and restoring CYP1A1 expression in MAFLD models. Structural and dynamic analyses demonstrated that CPT induces specific conformational changes in AHR, leading to a transcriptional environment that favors lipid metabolism and oxidative stress management.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eThis study highlights the complex role of AHR in MAFLD and underscores the therapeutic potential of CPT in modulating AHR activity to mitigate lipid dysregulation. The findings provide valuable insights for developing targeted therapies that leverage the AHR/CYP1A1 pathway to treat MAFLD.\u003c/p\u003e","manuscriptTitle":"Selective Modulation of Aryl Hydrocarbon Receptor by Coptisine in MAFLD","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-29 16:41:48","doi":"10.21203/rs.3.rs-5201468/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0d501546-f338-492a-b4e7-53c2e4312f7c","owner":[],"postedDate":"November 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40247141,"name":"Biological sciences/Drug discovery/Drug screening"},{"id":40247142,"name":"Biological sciences/Drug discovery/Pharmacology"},{"id":40247143,"name":"Biological sciences/Drug discovery/Target identification"},{"id":40247144,"name":"Biological sciences/Drug discovery/Target validation"}],"tags":[],"updatedAt":"2025-11-13T07:23:44+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-29 16:41:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5201468","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5201468","identity":"rs-5201468","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}