Mechanisms Underlying the Protective Effects of Obeticholic Acid-activated FXR in Valproic Acid-induced Hepatotoxicity via Network Pharmacology, Molecular Docking and Molecular Dynamics Simulations

Mechanisms Underlying the Protective Effects of Obeticholic Acid-activated FXR in Valproic Acid-induced Hepatotoxicity via Network Pharmacology, Molecular Docking and Molecular Dynamics Simulations | 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 Mechanisms Underlying the Protective Effects of Obeticholic Acid-activated FXR in Valproic Acid-induced Hepatotoxicity via Network Pharmacology, Molecular Docking and Molecular Dynamics Simulations Yanan Chen, Jingkai Zhou, Shansen Xu, Lei Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4291847/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 This study aims to comprehensively investigate the underlying mechanisms of farnesoid X receptor (FXR) activation by obeticholic acid (OCA) in the treatment of VPA-induced hepatotoxicity. Network pharmacology was performed to identified the potential targets and pathways underlying the amelioration of VPA-induced hepatotoxicity by OCA. The identified pathways were validated through GEO data analysis, and the interactions between OCA and potential targets were predicted using molecular docking as well as molecular dynamics simulations. A total of 462 targets associated with VPA-induced hepatotoxicity and 288 targets of OCA were identified, with 81 overlapping targets between VPA-induced hepatotoxicity and OCA. KEGG pathway and GO enrichment analysis indicated that the effect of OCA on VPA-induced hepatotoxicity primarily involved lipid metabolism, as well as oxidative stress and inflammation. The results of GEO data analysis, molecular docking and molecular dynamics simulations revealed a close association between bile secretion, PPAR signaling pathway, and the treatment of VPA-induced hepatotoxicity by OCA. Our findings revealed that OCA exhibits potential therapeutic efficacy against VPA-induced hepatotoxicity through multiple targets and pathways, thereby highlighting the therapeutic potential of FXR as a target for the treatment of VPA-induced hepatotoxicity. Biological sciences/Computational biology and bioinformatics Biological sciences/Drug discovery Biological sciences/Drug discovery/Drug safety Biological sciences/Drug discovery/Toxicology Valproic acid Hepatotoxicity Farnesoid X receptor Obeticholic acid Network pharmacology Molecular Docking Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Valproic acid (VPA) is a first-line antiepileptic drug; however, its clinical application has been limited due to the occurrence of side effects, with hepatotoxicity being a major concern of clinicians[1]. The mechanisms underlying VPA-induced hepatotoxicity have been extensively studied, and are generally believed to be closely related to liberation of toxic metabolites[2], carnitine and coenzyme A deprivation[3], oxidative stress[4] and dysregulation of lipid metabolism[5], which provides scientific basis for the prevention and treatment of VPA-induced hepatotoxicity. In clinical practice, non-alcoholic fatty liver disease (NAFLD) is considered to be a prominent manifestation of VPA-induced hepatotoxicity, with or without elevated aminotransferases[6, 7]. It is characterized by dysregulation of lipid metabolism, including free carnitine deprivation, long-chain fatty acid uptake and triacylglycerols synthesis[5, 8, 9]. In addition to lipid metabolism, other metabolic pathways are also involved in VPA-induced hepatotoxicity, such as glucose metabolism, amino acid metabolism and bile acid homeostasis[10]. Farnesoid X receptor (FXR), a pivotal nuclear factor that regulates cholesterol and bile acid homeostasis, as well as lipid and glucose metabolism and inflammation[11], has been implicated in VPA-induced hepatotoxicity[10], and its activation could reduce the levels of inflammatory biomarkers in the VPA-treated rats[12, 13]. Obeticholic acid (OCA), a synthetic FXR agonist approved for the treatment of primary biliary cirrhosis with promising outcomes in managing non-alcoholic steatohepatitis[14, 15], has been found to ameliorate VPA-induced hepatotoxicity by reducing steatosis and oxidative stress through activating FXR in mice[13]. These findings suggest that targeting FXR activation could be a viable strategy for treating VPA-induced hepatotoxicity; however, the underlying mechanisms remain unclear and further investigations are warranted. Network pharmacology is a comprehensive filed of systematic drug research that aims to elucidate drug-target interactions, facilitating the identification of therapeutic targets, enhancement of drug efficacy, and mitigation of side effects[16]. Molecular docking is a computational technique that simulates ligand-target interactions at molecular levels, and predicts structure-activity relationships[17]. Molecular dynamics simulation, an exceptional computational technique used to simulate the motion and interactions of particles, can be integrated with other analytical methods and experiments to accurately and efficiently elucidate the molecular mechanisms in scientific researches[18]. In this study, these combined approaches were carried out to explore the potential targets and signaling pathways of OCA in the treatment of VPA-induced hepatotoxicity; this analysis provides a basis for FXR as a potential therapeutic target for the treatment of VPA-induced hepatotoxicity. 2. Methods The study was conducted in accordance with the Basic & Clinical Pharmacology & Toxicology policy for experimental and clinical studies[19]. 2.1. Network pharmacology 2.1.1. Potential targets collection The targets of VPA-induced hepatotoxicity were retrieved from Comparative Toxicogenomics Database (CTD) database (http://ctdbase.org/). The therapeutic targets of OCA were obtained from CTD, TargetNET (http://targetnet.scbdd.com), SwissTargetPrediction (http://www.swisstargetprediction.ch), and Pharmmapper (http://lilab-ecust.cn/pharmmapper/). Subsequently, protein target names were converted into official genetic symbols by UniProt database (https://www.uniprot.org/). After removing duplicates, the remaining targets were regarded as the potential therapeutic targets of OCA. Next, the two sets of targets were combined and intersected using Draw Venn Diagram (https://bioinformatics.psb.ugent.be/webtools/Venn/). The intersecting part was the targets we studied in this article. 2.1.2. Protein-Protein interaction (PPI) network construction The intersecting targets of VPA-induced hepatotoxicity and OCA were uploaded to the STRING database (https://cn.string-db.org/) to obtain PPI information. The specific settings in this study were as follows: The species was limited to Homo sapiens , the confidence level was set to ≥ 0.7, and disconnected targets were removed. Subsequently, the PPI network of OCA in treating VPA-induced hepatotoxicity was performed by Cytoscape 3.9.1, and potential key targets were identified based on the conditions that degree, betweenness centrality and closeness centrality were greater than the median of all nodes. 2.1.3. KEGG pathway and Gene ontology (GO) analysis KEGG pathway analysis and GO enrichment analysis were carried out using DAVID database (https://david.ncifcrf.gov/), with P < 0.05 and FDR < 0.05 as cut-off values. Top 10 pathways and items were respectively imported into bioinformatics platform (http://bioinformatics.com.cn/) for visualization by making a bubble chart and a histogram. 2.1.4. Target-pathway network construction The top 10 pathways related to VPA-induced hepatotoxicity and intersecting targets were imported into Cytoscape 3.9.1 to construct a target-pathway network, facilitating the comprehensive understanding of the intricate relationships among OCA, targets, pathways, and VPA-induced hepatotoxicity. 2.2. GEO data analysis The messenger RNA (mRNA) expression profiles of OCA in the treatment of VPA-induced hepatotoxicity (GSE138810) were downloaded from Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). In GSE138810 dataset, 12 C57BL/6 mice were assigned to a chow diet or a chow diet mixed with OCA (25 mg/kg) for 4 weeks, then randomly divided into two groups: VPA and VPA+OCA, and treated with VPA (100 mg/kg) for 4 additional weeks, the hepatic gene expression profiles were acquired through the Illumina NovaSeq platform. The differentially expressed genes (DEGs) were defined as those with |log 2 fold change (FC)| > 1 and P < 0.05, and visualized by a heatmap. These DEGs were then uploaded to DAVID database for KEGG pathway and GO analysis, bubble chart and histogram were drawn to visualize the KEGG pathways and top 10 GO items in every category. 2.3. Molecular docking 2.3.1. Preparation of ligand and targets before docking The mol2 format of OCA was acquired from ZINC database (https://zinc.docking.org/), and converted to pdbqt format by AutoDockTools 1.5.6. The crystal structures of candidate targets were downloaded from Protein Data Bank (PDB) database (https://www.rcsb.org/) based on the following conditions: the source organism was Homo sapiens , the experimental method was X-ray diffraction, and the resolution was less than two. After removing redundant ligands and water molecules using PyMOL 2.5, these proteins were isolated, and subsequently optimized for molecular docking using AutoDockTools, including adding hydrogen and adjusting Grid box size. 2.3.2. Molecular docking and analysis of results Based on Lamarckian GA algorithm, the binding activity of candidate targets with OCA were evaluated by Autodock 4.2.6, and represented by free binding energy. It is generally believed that binding energy less than -4.25 kcal/mol indicated a certain binding activity, less than -5.0 kcal/mol indicated good binding activity, and less than -7.0 kcal/mol indicated strong binding activity[18]. In this study, proteins with binding energy less than -5.0 kcal/mol were selected as the targets of OCA for the treatment of VPA-induced hepatotoxicity. PyMOL 2.5 and Discovery Studio 4.5 were applied to visualize and analyze interactions as well as binding modes. 2.4. Molecular dynamics simulations To further validate the results of molecular docking, molecular dynamics simulations were performed by Gromacs 2018 Software[20]. The OPLS force field was employed for both proteins and ligand. The system was solvated in a truncated octahedral box containing explicit water molecules modeled with TIP3P, and neutralized by adding counterions. A buffer of at least 1.2 nm was maintained between the protein surface and the boundaries of the periodic box. During molecular dynamics simulations, long-range electrostatic interactions were handled using the Particle Mesh Ewald (PME) method, and the cutoff value for non-hydrogen bonding interactions was set at 1 nm. The temperature was maintained at 300 K using the V-rescale temperature coupling method, and the pressure was controlled at 1 bar through application of the Berendsen method. Following equilibrating under Isothermal-Isochoric (NVT) ensemble and the Isothermal-Isobaric (NPT) ensemble, molecular dynamics simulations were conducted for 100 ns (a total of 50,000 steps) with the trajectory data saved every 0.5 ns for subsequent analysis, including the root mean square deviation (RMSD), root mean square fluctuation (RMSF), hydrogen bonds, radius of gyration (Rg), and solvent accessible surface area (SASA). All results of molecular dynamic simulations were visualized by Origin 2021 software. 3. Results 3.1. Network pharmacology prediction 3.1.1. Potential targets identification of OCA for treating VPA-induced hepatotoxicity As shown in Figure 1A, 462 targets related to VPA-induced hepatotoxicity were obtained from CTD database. From CTD, TargetNET, SwissTarget Prediction and Pharmmapper databases, a total of 288 targets related to OCA were screened. There were 81 overlapping targets, through which OCA might ameliorate VPA-induced hepatotoxicity. In order to identify the potential key targets, the PPI information of intersecting targets was constructed by STRING database and Cytoscape 3.9.1. As shown in Figure 1B, the PPI network composed 80 nodes with 460 connections. After triplicate screenings (Degree > 7.5, betweenness centrality > 0.005553, and closeness centrality > 0.422508), 30 nodes were identified as potential key targets of OCA in the treatment of VPA-induced hepatotoxicity (Supplementary Table 1). 3.1.2. KEGG pathway and GO enrichment analysis In order to explore the possible mechanisms of OCA in the treatment of VPA-induced hepatotoxicity, the 81 intersection targets were evaluated by a KEGG pathway analysis. These targets were highly enriched in 126 pathways ( P < 0.05), and many of which were related to drug-induced liver injury. As shown in Figure 2A, the top 10 pathways related to VPA-induced hepatotoxicity were non-alcoholic fatty liver (NAFLD) disease, IL-17 signaling pathway, bile secretion, FoxO signaling pathway, PPAR signaling pathway, PI3K-Akt signaling pathway, TNF signaling pathway, AMPK signaling pathway, MAPK signaling pathway and apoptosis. GO enrichment analysis was carried out to explore the potential roles of the target genes. In this study, 81 intersection targets were enriched in 267 GO items, including 197 biological processes (BP), 49 cellular components (CC), and 21 molecular functions (MF). Based on the lowest P value, the top 5 BP items were negative regulation of apoptotic process, positive regulation of transcription from RNA polymerase II promoter, response to ethanol, positive regulation of transcription, DNA-templated, response to xenobiotic stimulus, and positive regulation of pri-miRNA transcription from RNA polymerase II promoter. The top 5 CC items were nucleoplasm, chromatin, macromolecular complex, cytoplasm and nucleus. The top 5 MF items were RNA polymerase II transcription factor activity, ligand-activated sequence-specific DNA binding, enzyme binding, sequence-specific DNA binding, zinc ion binding, and identical protein binding. The top 10 items with lowest P value in every category were shown as a histogram (Figure 2B). 3.1.3. The main target-pathway network of OCA treatment for VPA-induced hepatotoxicity According to the targets and top 10 KEGG pathways, a target-pathway network was constructed by Cytoscape 3.9.1. As shown in Figure 3, the network contained 92 nodes and 206 edges, fully revealing the multi-target mechanisms of OCA in treating VPA-induced hepatotoxicity. 3.2. GEO data analysis Dataset GSE138810 provided the information on the effect of OCA treatment on hepatic gene expression in mice treated with VPA. By comparing the hepatic gene expression profiles between OCA and VPA+OCA group, 80 DEGs were identified with 47 up-regulated and 33 down-regulated (Figure 4). As shown in Figure 5A, these DEGs were highly enriched in bile secretion, metabolic pathways, arachidonic acid metabolism, PPAR signaling pathway, retinol metabolism, thyroid cancer, endometrial cancer, gastric cancer and melanoma. Among them, bile secretion and PPAR signaling pathway were also identified in the analysis of network pharmacology, which would be further explored. As shown in Figure 5B, GO enrichment analysis revealed that many biological processes were involved in the effect of OCA on hepatic gene expression profiles of mice under chronic treatment with VPA, such as lipid metabolic process, transmembrane transport, cellular response to lithium ion, long-chain fatty acid metabolic process, fumarate transport and so on. These DEGs primarily localized in basolateral plasma membrane, apical plasma membrane, membrane, cell surface and extracellular space, and functioned as transmembrane transporter activity, heme binding, monooxygenase activity, oxidoreductase activity and oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular. 3.3. Molecular docking To validate whether bile secretion and PPAR signaling pathway were involved in the treatment of VPA-induced hepatotoxicity with OCA, the potential key targets (e.g., RXRA/RXRα, CYP7A1, PPARA/PPARα, PPARG/PPARγ, CYP3A4, NR1H4/FXR and NR0B2/SHP) involved in two signaling pathways were selected for molecular docking with OCA. As shown in Table 1, free binding energies of docking results were in the range of +4.81 to -8.05 kcal/mol. OCA exhibited strong binding activity with CYP3A4, FXR and CYP7A1, as well as good binding activity with PPARγ, RXRα, and PPARα; however, it showed poor binding activity with SHP. Therefore, except for SHP, the other six proteins were the key targets in the OCA treatment of VPA-induced hepatotoxicity. The interactions and binding modes between six proteins (CYP3A4, FXR, CYP7A1, PPARγ, RXRα, and PPARα) and OCA were shown in Figure 6A-F. According to the chemical structure of OCA, three hydroxyl groups enabled these proteins to form conventional hydrogen bond with OCA; one carboxyl group enabled these proteins which contained lysine or arginine in the binding pockets to form attractive charge and / or salt bridge with OCA. In addition to these interactions, van der Waals forces and hydrophobic interactions also contributed to the binding affinities. Table 1 Details of targets and OCA for molecular docking Target KEGG Pathway PDB ID Binding Energy (kcal/mol) CYP3A4 Bile secretion 4D6Z -8.05 FXR Bile secretion 1OSH -7.63 CYP7A1 Bile secretion, PPAR signaling pathway 3V8D -7.01 PPARγ PPAR signaling pathway 5Y2T -6.47 RXRα Bile secretion, PPAR signaling pathway 6LB4 -6.39 PPARα PPAR signaling pathway 6KB1 -6.15 SHP Bile secretion 6W9M +4.81 3.4. Molecular dynamics simulations Based on the docking results, the complexes of OCA with CYP3A4 (4D6Z), CYP7A1 (3V8D), PPARγ (5Y2T), and PPARα (6KB1) were subjected to molecular dynamics simulation analysis to assess their dynamics. RMSD measures the extent of deviation in the position of atoms relative to their initial positions, with a lower deviation suggesting greater conformational stability. As shown in Figure 7A, the RMSD curves of all complexes initially fluctuated during the simulations, and gradually converged to a stable state below 0.5 nm. However, in comparison with other complexes, the RMSD curve of PPARγ-OCA exhibited dramatic fluctuation in the 100 ns simulation trajectory. The Rg parameter quantifies the compactness of the complexes, with a lower value indicating greater levels of compaction. The Rg curves of OCA with CYP3A4, CYP7A1, PPARγ and PPARα in Figure 7B exhibited slight fluctuations while remaining stable throughout the simulations, with averages of 2.296 nm, 2.318 nm, 2.541 nm and 1.771 nm, respectively. RMSF analysis is performed to assess the flexibility of amino acid residues within a molecular system during a molecular dynamics simulation. The RMSF curves indicated that most of the protein amino acid residues in complexes of OCA with CYP3A4, CYP7A1 and PPARα exhibited slight fluctuations, measuring less than 0.5 nm, while the PPARγ-OCA complex displayed more pronounced fluctuation ranging from 0.6 to 1.2 nm (Figure 7C). The SASA parameter quantifies the surface area of a molecule exposed to the solvent molecules during simulations. The SASA plot (Supplementary Figure 1) revealed that slight fluctuations in the curves of OCA with CYP3A4, CYP7A1 and PPARα, while the curve of OCA with PPARγ fluctuated dramatically before eventually stabilizing, which was consistent with the results of RMSD, Rg and RMSF. Additionally, the hydrogen bonds information of complexes was also acquired during the simulations. As shown in Figure 7D, the number of hydrogen bonds formed between OCA and CYP3A4, CYP7A1, PPARγ and PPARα were 5, 6, 1 and 0 respectively at 100 ns. By integrating and analyzing the aforementioned results of molecular dynamics simulations, it was found that there were no significant conformational changes in the proteins during the simulations, indicating stable dynamic interactions between OCA and CYP3A4, CYP7A1, PPARγ and PPARα. 4. Discussion The objective of this study was to investigate the underlying mechanisms of FXR activated by OCA in the treatment of VPA-induced hepatotoxicity. Through network pharmacology, molecular docking and molecular dynamics simulations, multiple targets and pathways associated with OCA treatment of VPA-induced hepatotoxicity were identified. In network pharmacology analysis, the overlapping targets of VPA-induced hepatotoxicity and OCA were highly enriched in 126 pathways, many of which were associated with cancers, such as hsa05200 (pathways in cancer), hsa05215 (prostate cancer) and hsa05205 (proteoglycans in cancer). This could be explained by the inhibitory effects of VPA on histone deacetylases, making it an adjuvant drug for cancer therapy[21]. However, it is important to note that this study exclusively focused on the liver diseases-related pathways rather than those mentioned above. The top 10 pathways identified in OCA treatment for VPA-induced hepatotoxicity were primarily associated with lipid metabolism, oxidative stress and inflammation. These pathways included hsa04932 (non-alcoholic fatty liver disease), hsa04976 (bile secretion), hsa03320 (PPAR signaling pathway), hsa04151 (PI3K-Akt signaling pathway), hsa04152 (AMPK signaling pathway), hsa04068 (FoxO signaling pathway), hsa04657 (IL-17 signaling pathway) and hsa04668 (TNF signaling pathway). The disorders of lipid metabolism and oxidative stress are the primary mechanisms underlying VPA-induced hepatotoxicity. VPA and its metabolites, such as 4-ene-VPA and 2,4-diene-VPA, disrupt fatty acid β-oxidation by depleting coenzyme A and carnitine, as well as inhibiting key enzymes involved in β-oxidation, leading to hepatic fat accumulation and microvesicular liver steatosis[1]. Additionally, VPA induces long-chain fatty acid uptake and triglyceride synthesis through upregulating the expression of fatty acid transporter CD36 and diacylglycerol acyltransferase 2 (DGAT2)[8]. Oxidative stress is a hallmark feature of VPA-induced hepatotoxicity, resulting from the promotion of reactive oxygen species (ROS), lipid peroxidation and endoplasmic reticulum stress, as well as depletion of glutathione[22, 23]. Therefore, interventions targeting improved lipid metabolism using L-carnitine[24] or alleviating oxidative stress via antioxidants (such as Vitamin U[25] and taurine[26]) have shown benefits against VPA-induced hepatotoxicity. FXR is a pivotal nuclear factor that has been implicated in VPA-induced hepatotoxicity[10], and its activation can improve lipid metabolism and attenuate inflammation through multiple pathways, including PI3K/AKT/mTOR[27], AMPK-ACC-CPT1α[28], TLR4/NF-κB[29], etc. The PI3K-Akt pathway plays a critical role in regulating lipid metabolism and oxidative stress[30]. Our previous study has shown that the expression of CD36 and DGAT2 induced by VPA could be abrogated through the PI3K-Akt pathway[8]. The AMPK pathway is crucial for metabolic homeostasis, and its activation can ameliorate lipid accumulation[31], although contradictory effects have also been observed[32]. As an activator of AMPK, VPA has been shown to decrease liver mass, hepatic fat accumulation and serum glucose in obese mice[33], which contradicts the hepatic steatosis induced by VPA. It would be interesting to investigate the role of AMPK pathway in VPA-induced lipid metabolism disorders. Interleukin-17 (IL-17), a proinflammatory cytokine, potentiates oxidative stress and its associated inflammation is implicated in the pathogenesis of drug-induced liver injury, such as acetaminophen[34] and triptolide[35]. To date, limited studies have investigated the IL-17 signaling pathway in VPA-induced hepatotoxicity; only elevated IL-17 levels have been observed following exposure to VPA[36, 37]. TNF signaling is a complex signal transduction system that regulates inflammation, immunity, as well as apoptosis[38]. Khodayar et al .[26] found that VPA administration could induce oxidative stress and apoptosis in mouse liver tissue via TNF-α upregulation mediated by RIPK1/RIPK3/MLKL signaling axis. Supported by GEO data analysis, molecular docking and molecular dynamics simulations, this study highlights the involvement of bile secretion in OCA treating for VPA-induced hepatotoxicity. Bile acids are a group of endogenous metabolites synthesized in hepatocytes; they play important roles in cholesterol homeostasis, lipid metabolism, glucose metabolism, and inflammation, and are primarily mediated by FXR[39]. In our previous study, we observed an obvious shift in bile acid profiles and impaired FXR signaling in the liver of VPA-treated mice, characterized by elevated levels of most bile acids and upregulated expression of bile acid synthetases, such as CYP7A1[10]. In this study, OCA exhibited good binding activity with four targets involved in bile secretion (CYP3A4, FXR, CYP7A1, and RXRα), with binding energies ranging from -6.39 to -8.05 kcal/mol. And there were no significant conformational changes in CYP3A4 and CYP7A1 upon binding with OCA. Notably, the binding energy of OCA-FXR (-7.63 kcal/mol) closely aligns with the previously reported value of -7.6 kcal/mol[40]. Zhang et al . observed that OCA improved the hepatic bile acid profiles induced by lipopolysaccharide in pregnant mice, accompanied by the lower expression of bile acid synthase CYP7A1 and the higher expression of CYP3A[41], which was similar to our findings. An interesting finding in this study was the involvement of PPAR signaling pathway in OCA treatment for VPA-induced hepatotoxicity. PPARs are members of the nuclear receptor superfamily that play a vital role in regulating lipid homeostasis and inflammation[42]. Our previous studies have revealed that PPARα and PPARγ were implicated in VPA-induced lipid disorders by inhibiting fatty acid β-oxidation and inducing long-chain fatty acid uptake and triacylglycerols synthesis, respectively, and their activation could ameliorate VPA-induced hepatic steatosis[3, 8]. The results of docking and molecular dynamics simulations indicated that the binding of OCA with PPARα and PPARγ was stable; however, limited studies have been conducted on the effects of OCA on PPARs. Although the repression of the PPARγ pathway has been implicated in mitigating VPA-induced hepatotoxicity by OCA[13], and the activation of FXR could induce the expression of PPARα[43], further experimental verification is still needed to elucidate the role of PPAR signaling pathway in OCA treatment for VPA-induced hepatotoxicity. To the best of our knowledge, this is the first application of bioinformatics methods, including network pharmacology, GEO data analysis, molecular docking, and molecular dynamics simulations, to comprehensively investigate the underlying mechanism of OCA in VPA-induced hepatotoxicity. However, there are certain limitations associated with the approaches employed in this study. Firstly, target data were extracted from literature and databases; thus, the reliability and accuracy of predictions relied on the quality of acquired data. Secondly, this study utilized a data mining approach and computational simulation techniques, requiring experimental validation to confirm these results. 5. Conclusions In conclusion, our findings suggest that OCA treatment for VPA-induced hepatotoxicity involves multiple pathways, with a particular emphasis on bile secretion and PPAR signaling pathway. These results provide a basis for considering FXR as a potential therapeutic target in the treatment of VPA-induced hepatotoxicity. Declarations Author Contributions SSX and LW designed the study. YNC performed the data analysis and writing. JKZ conducted mapping and language revision. All the authors contributed to the article, reviewed the manuscript, and approved the submitted version. Funding This study was supported by grant from the Natural Science Foundation of Jiangsu Province (Grant No. BK20210003), the Hospital Pharmaceutical Research Fund of Nanjing Pharmaceutical Association (Grant No. 2021YX031), and the Hospital Science and Technology Development Fund (Grant No. ZJ202105). Conflict of interests SSX is an employee of Jiangsu Simcere Pharmaceutical Co., Ltd. Other authors declare that they have no conflict of interest. Data Availability Statement The mRNA expression profile data in this study are available from GEO datasets under the accession number GSE138810. 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Gastroenterology 157(2): 552-568. http://doi.org/10.1053/j.gastro.2019.04.023. Schiavo A, Maldonado C, Vázquez M et al (2023) Quantitative systems pharmacology Model to characterize valproic acid-induced hyperammonemia and the effect of L-carnitine supplementation. Eur J Pharm Sci 183: 106399. http://doi.org/10.1016/j.ejps.2023.106399. Celik E, Tunali S, Gezginci-Oktayoglu S et al (2021) Vitamin U prevents valproic acid-induced liver injury through supporting enzymatic antioxidant system and increasing hepatocyte proliferation triggered by inflammation and apoptosis. Toxicol Mech Methods 31(8): 600-608. http://doi.org/10.1080/15376516.2021.1943089. Khodayar MJ, Kalantari H, Khorsandi L et al (2021) Taurine attenuates valproic acid-induced hepatotoxicity via modulation of RIPK1/RIPK3/MLKL-mediated necroptosis signaling in mice. Mol Biol Rep 48(5): 4153-4162. http://doi.org/10.1007/s11033-021-06428-4. Xu J, Yao X, Li X et al (2022) Farnesoid X receptor regulates PI(3)K/AKT/mTOR signaling pathway, lipid metabolism, and immune response in hybrid grouper. Fish Physiol Biochem 48(6): 1521-1538. http://doi.org/10.1007/s10695-022-01130-z. Liu Y, Song A, Yang X et al (2018) Farnesoid X receptor agonist decreases lipid accumulation by promoting hepatic fatty acid oxidation in db/db mice. Int J Mol Med 42(3): 1723-1731. http://doi.org/10.3892/ijmm.2018.3715. Liu S, Kang W, Mao X et al (2022) Melatonin mitigates aflatoxin B1-induced liver injury via modulation of gut microbiota/intestinal FXR/liver TLR4 signaling axis in mice. J Pineal Res 73(2): e12812. http://doi.org/10.1111/jpi.12812. Savova MS, Mihaylova LV, Tews D et al (2023) Targeting PI3K/AKT signaling pathway in obesity. Biomed Pharmacother 159: 114244. http://doi.org/10.1016/j.biopha.2023.114244. Steinberg GR and Hardie DG (2023) New insights into activation and function of the AMPK. Nat Rev Mol Cell Biol 24(4): 255-272. http://doi.org/10.1038/s41580-022-00547-x. Choi YJ, Lee KY, Jung SH et al (2017) Activation of AMPK by berberine induces hepatic lipid accumulation by upregulation of fatty acid translocase CD36 in mice. Toxicol Appl Pharmacol 316: 74-82. http://doi.org/10.1016/j.taap.2016.12.019. Avery LB and Bumpus NN (2014) Valproic acid is a novel activator of AMP-activated protein kinase and decreases liver mass, hepatic fat accumulation, and serum glucose in obese mice. Mol Pharmacol 85(1): 1-10. http://doi.org/10.1124/mol.113.089755. Lee HC, Liao CC, Day YJ et al (2018) IL-17 deficiency attenuates acetaminophen-induced hepatotoxicity in mice. Toxicol Lett 292: 20-30. http://doi.org/10.1016/j.toxlet.2018.04.021. Wang X, Jiang Z, Xing M et al (2014) Interleukin-17 mediates triptolide-induced liver injury in mice. Food Chem Toxicol 71: 33-41. http://doi.org/10.1016/j.fct.2014.06.004. Barzegari A, Amouzad Mahdirejei H, Hanani M et al (2023) Adolescent swimming exercise following maternal valproic acid treatment improves cognition and reduces stress-related symptoms in offspring mice: Role of sex and brain cytokines. Physiol Behav 269: 114264. http://doi.org/10.1016/j.physbeh.2023.114264. A-G N, Am EB, Eh R et al (2021) Maternal Sodium Valproate Exposure Alters Neuroendocrine-Cytokines and Oxido-inflammatory Axes in Neonatal Albino Rats. Endocr Metab Immune Disord Drug Targets 21(8): 1491-1503. http://doi.org/10.2174/1871530320999200918120617. Varfolomeev E and Vucic D (2018) Intracellular regulation of TNF activity in health and disease. Cytokine 101: 26-32. http://doi.org/10.1016/j.cyto.2016.08.035. Cai J, Rimal B, Jiang C et al (2022) Bile acid metabolism and signaling, the microbiota, and metabolic disease. Pharmacol Ther 237: 108238. http://doi.org/10.1016/j.pharmthera.2022.108238. Jose S, Devi SS, Sajeev A et al (2023) Repurposing FDA-approved drugs as FXR agonists: a structure based in silico pharmacological study. Biosci Rep 43(3). http://doi.org/10.1042/bsr20212791. Zhang C, Gan Y, Lv JW et al (2020) The protective effect of obeticholic acid on lipopolysaccharide-induced disorder of maternal bile acid metabolism in pregnant mice. Int Immunopharmacol 83: 106442. http://doi.org/10.1016/j.intimp.2020.106442. Wang Y, Nakajima T, Gonzalez FJ et al (2020) PPARs as Metabolic Regulators in the Liver: Lessons from Liver-Specific PPAR-Null Mice. Int J Mol Sci 21(6). http://doi.org/10.3390/ijms21062061. Pineda Torra I, Claudel T, Duval C et al (2003) Bile acids induce the expression of the human peroxisome proliferator-activated receptor alpha gene via activation of the farnesoid X receptor. Mol Endocrinol 17(2): 259-72. http://doi.org/10.1210/me.2002-0120. Additional Declarations No competing interests reported. Supplementary Files FigureS1.tif Figure S1. SASA plot of OCA with CYP3A4, CYP7A1, PPARγ, and PPARα. SupplementaryTable1.docx 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4291847","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":296460529,"identity":"b298c215-50fd-42b1-80d5-5d705c89147e","order_by":0,"name":"Yanan Chen","email":"","orcid":"","institution":"Jiangsu Cancer Hospital \u0026 Jiangsu Institute of Cancer Research \u0026 the Affiliated Cancer Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yanan","middleName":"","lastName":"Chen","suffix":""},{"id":296460531,"identity":"809fc0bf-e83a-45cc-bc9d-ea56e6615245","order_by":1,"name":"Jingkai Zhou","email":"","orcid":"","institution":"Beckman Research Institute City of Hope National Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Jingkai","middleName":"","lastName":"Zhou","suffix":""},{"id":296460533,"identity":"ffc37cf0-e70c-46ba-a61e-151bd146fa34","order_by":2,"name":"Shansen Xu","email":"","orcid":"","institution":"Jiangsu Simcere Pharmaceutical Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Shansen","middleName":"","lastName":"Xu","suffix":""},{"id":296460535,"identity":"8ddbd339-0303-446c-b854-64d6e2721ae6","order_by":3,"name":"Lei Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIie3QoQrCUBTG8TsuXMudq2dFfIQDwjAMfBDLXVmzLwy9MHBxeW8hWIwbB0wDX2HgC8xucEaD7NgM95/PL3xHCJfrH5sJ0SRPWARSUs8jciQPu16FpUqRTbzaZsnpppfAEkiyJf8C3pm0QJHHWwZRhvwOZER+04trurNTJCKNpBWoiOYGPUscEgxvoleFRmASLdr6CICSSzaksBk6QKDxyYazJSyL+2Cy/aGqiPohj6fJZ+a3c5fL5XJ96wX+fT6sJYoUcwAAAABJRU5ErkJggg==","orcid":"","institution":"Jiangsu Cancer Hospital \u0026 Jiangsu Institute of Cancer Research \u0026 the Affiliated Cancer Hospital of Nanjing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-04-19 08:24:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4291847/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4291847/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55574450,"identity":"9081809d-1b26-4d4f-a6da-a084ed007edc","added_by":"auto","created_at":"2024-04-30 06:26:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":809738,"visible":true,"origin":"","legend":"\u003cp\u003eScreening of potential targets. The Venn diagram (A) and PPI network (B) of the overlapping targets between VPA-induced hepatotoxicity and OCA.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4291847/v1/0524f4573fafc9bdc46c0a08.png"},{"id":55574910,"identity":"f84be0ac-dcfc-43ce-b60f-1214b73052d5","added_by":"auto","created_at":"2024-04-30 06:34:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":658281,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG pathway and GO enrichment analysis of OCA on VPA-induced hepatotoxicity. (A) The bubble chart of top 10 pathways based on KEGG pathway analysis. (B) The histogram of top 10 items in BP, CC and MF based on GO enrichment analysis.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4291847/v1/cbb34a7160a1f296d7e3f8c2.png"},{"id":55574457,"identity":"09227405-8636-4a3d-b09e-9beacf51692c","added_by":"auto","created_at":"2024-04-30 06:26:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":610266,"visible":true,"origin":"","legend":"\u003cp\u003eThe target-pathway network. The violet oval represents OCA, the orange rectangle represents a potential key target, the green rectangle represents a target, and the yellow arrow represents a pathway.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4291847/v1/82c146702e102cc807f98bef.png"},{"id":55574911,"identity":"9607b427-39f9-48c7-9b22-f529302727a5","added_by":"auto","created_at":"2024-04-30 06:34:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":270998,"visible":true,"origin":"","legend":"\u003cp\u003eThe heatmap of DEGs in GEO dataset. Red represents up-regulated expression, blue represents down-regulated expression.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4291847/v1/84e5ff09de9f53dfb1066a80.png"},{"id":55574451,"identity":"feb05b04-8982-4927-959d-fd71ed442a37","added_by":"auto","created_at":"2024-04-30 06:26:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":343593,"visible":true,"origin":"","legend":"\u003cp\u003eGEO data analysis of the effect of OCA on hepatic gene expression profiles in mice treated with VPA.(A) The bubble chart of top 10 pathways based on KEGG pathway analysis. (B) The histogram of top 10 items in BP, CC and MF based on GO enrichment analysis.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4291847/v1/987a5053ea168eebc43f5d0d.png"},{"id":55574456,"identity":"73c88c17-8715-4bea-bf31-f592e87d35e6","added_by":"auto","created_at":"2024-04-30 06:26:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":867671,"visible":true,"origin":"","legend":"\u003cp\u003eDocking patterns of OCA and key targets. (A1-F1): Three dimensional patterns of OCA and CYP3A4, NR1H4, CYP7A1, PPARγ, RXRα and PPARα, respectively. (A2-F2): Two dimensional patterns of bond, respectively.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4291847/v1/b2d7ab05aa0116a46f37cbb9.png"},{"id":55574459,"identity":"8b5e492d-0115-4c9e-9a0a-c4db98a7d0ba","added_by":"auto","created_at":"2024-04-30 06:26:19","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":307990,"visible":true,"origin":"","legend":"\u003cp\u003eThe analysis plots of molecular dynamics simulations. (A) RMSD plot, (B) Rg plot, (C) RMSF plot, and (D) hydrogen bonds analysis of OCA with CYP3A4, CYP7A1, PPARγ, and PPARα.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4291847/v1/f8b05f4e32a16407a102d313.png"},{"id":57388538,"identity":"5bd248af-0fe8-4522-ac1f-584f83bbd048","added_by":"auto","created_at":"2024-05-30 04:53:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3932337,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4291847/v1/a0f4bcc2-01ca-4efa-966a-03ea0b4fcb16.pdf"},{"id":55574455,"identity":"ce038c21-1fc1-40e4-a950-7e036a45ccf5","added_by":"auto","created_at":"2024-04-30 06:26:19","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":53043,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1.\u003c/strong\u003e SASA plot of OCA with CYP3A4, CYP7A1, PPARγ, and PPARα.\u003c/p\u003e","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-4291847/v1/be1654df7161aef9c705d9a0.tif"},{"id":55574453,"identity":"99157921-ce5f-434f-a6be-7c1079fea96d","added_by":"auto","created_at":"2024-04-30 06:26:19","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19072,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4291847/v1/e19c7f49b6b79b0e578b78ac.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mechanisms Underlying the Protective Effects of Obeticholic Acid-activated FXR in Valproic Acid-induced Hepatotoxicity via Network Pharmacology, Molecular Docking and Molecular Dynamics Simulations","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eValproic acid (VPA) is a first-line antiepileptic drug; however,\u0026nbsp;its clinical application has been limited due to the occurrence of side effects, with hepatotoxicity being a major concern of clinicians[1].\u0026nbsp;The mechanisms underlying\u0026nbsp;VPA-induced hepatotoxicity have been extensively studied, and are generally believed to be closely related to liberation of toxic metabolites[2], carnitine and coenzyme A deprivation[3], oxidative stress[4]\u0026nbsp;and dysregulation of lipid metabolism[5], which provides scientific basis for the\u0026nbsp;prevention and treatment of VPA-induced hepatotoxicity.\u003c/p\u003e\n\u003cp\u003eIn clinical practice,\u0026nbsp;non-alcoholic fatty liver disease (NAFLD) is considered to be a prominent manifestation of VPA-induced hepatotoxicity, with or without elevated aminotransferases[6, 7].\u0026nbsp;It is characterized by\u0026nbsp;dysregulation of\u0026nbsp;lipid metabolism,\u0026nbsp;including free carnitine\u0026nbsp;deprivation, long-chain fatty acid uptake and triacylglycerols synthesis[5, 8, 9].\u0026nbsp;In addition to lipid metabolism, other metabolic pathways are also involved in VPA-induced hepatotoxicity, such as glucose metabolism, amino acid metabolism and bile acid homeostasis[10].\u0026nbsp;Farnesoid X receptor (FXR), a pivotal nuclear factor that regulates cholesterol and bile acid homeostasis, as well as lipid and glucose metabolism and inflammation[11], has been implicated in VPA-induced hepatotoxicity[10], and its activation could\u0026nbsp;reduce the levels of inflammatory biomarkers in the VPA-treated rats[12, 13].\u0026nbsp;Obeticholic acid\u0026nbsp;(OCA), a synthetic FXR agonist approved for the treatment of primary biliary cirrhosis with promising outcomes in managing non-alcoholic steatohepatitis[14, 15], has been found to ameliorate VPA-induced hepatotoxicity by reducing steatosis and oxidative stress through activating FXR in mice[13]. These findings suggest that targeting FXR activation could be a viable strategy for treating VPA-induced hepatotoxicity; however, the underlying mechanisms remain unclear and further investigations are warranted.\u003c/p\u003e\n\u003cp\u003eNetwork pharmacology is a comprehensive filed of systematic drug research that aims to elucidate drug-target interactions, facilitating the identification of therapeutic targets, enhancement of drug efficacy, and mitigation of side effects[16]. Molecular docking is a computational technique that simulates ligand-target interactions at molecular levels, and predicts structure-activity relationships[17]. Molecular dynamics simulation, an exceptional computational technique used to simulate the motion and interactions of particles, can be integrated with other analytical methods and experiments to accurately and efficiently elucidate the molecular mechanisms in scientific researches[18]. In this study, these combined approaches were carried out to explore the potential targets and signaling pathways of OCA in the treatment of VPA-induced hepatotoxicity; this analysis provides a basis for FXR as a potential therapeutic target for the treatment of VPA-induced hepatotoxicity.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThe study was conducted in accordance with the Basic \u0026amp; Clinical Pharmacology \u0026amp; Toxicology policy for experimental and clinical studies[19].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1. Network pharmacology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1.1. Potential targets collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe targets of VPA-induced hepatotoxicity were retrieved from Comparative Toxicogenomics Database (CTD) database (http://ctdbase.org/). The therapeutic targets of OCA were obtained from\u0026nbsp;CTD,\u0026nbsp;TargetNET\u0026nbsp;(http://targetnet.scbdd.com), SwissTargetPrediction (http://www.swisstargetprediction.ch), and\u0026nbsp;Pharmmapper (http://lilab-ecust.cn/pharmmapper/).\u0026nbsp;Subsequently, protein target names were converted into official genetic symbols by UniProt database (https://www.uniprot.org/). After removing duplicates, the remaining targets were regarded as the potential\u0026nbsp;therapeutic targets of OCA. Next,\u0026nbsp;the two sets of targets were combined and intersected using Draw Venn Diagram\u0026nbsp;(https://bioinformatics.psb.ugent.be/webtools/Venn/). The intersecting part was the targets we studied in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1.2. Protein-Protein interaction (PPI) network construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe intersecting targets of VPA-induced hepatotoxicity and OCA\u0026nbsp;were uploaded to the STRING database (https://cn.string-db.org/) to obtain PPI information. The specific settings in this study were as follows: The species was limited to \u003cem\u003eHomo sapiens\u003c/em\u003e, the confidence level was set to \u0026ge; 0.7, and disconnected targets were removed. Subsequently, the PPI network of OCA in treating VPA-induced hepatotoxicity was performed by Cytoscape 3.9.1, and potential key targets were identified based on the conditions that degree,\u0026nbsp;betweenness centrality and closeness centrality were greater than the median of all nodes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1.3. KEGG pathway and Gene ontology (GO) analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKEGG pathway analysis and GO enrichment analysis\u0026nbsp;were carried out using DAVID database (https://david.ncifcrf.gov/), with \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 and FDR \u0026lt; 0.05 as cut-off values. Top 10 pathways and items were respectively imported into bioinformatics platform (http://bioinformatics.com.cn/) for visualization by making a bubble chart and a histogram.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1.4. Target-pathway network construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe top 10 pathways related to VPA-induced hepatotoxicity and intersecting targets were imported into Cytoscape 3.9.1 to construct a target-pathway network, facilitating the comprehensive understanding of the intricate relationships among OCA, targets, pathways, and VPA-induced hepatotoxicity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. GEO data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe messenger RNA (mRNA) expression profiles of OCA in the treatment of VPA-induced hepatotoxicity (GSE138810) were downloaded from Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). In GSE138810 dataset, 12 C57BL/6 mice were assigned to a chow diet or a chow diet mixed with OCA (25 mg/kg) for 4 weeks, then randomly divided into two groups: VPA and VPA+OCA, and treated with VPA (100 mg/kg) for 4 additional weeks, the hepatic gene expression profiles were acquired through the Illumina NovaSeq platform. The differentially expressed genes (DEGs) were defined as those with |log\u003csub\u003e2\u0026nbsp;\u003c/sub\u003efold change (FC)| \u0026gt; 1 and \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05, and visualized by a heatmap. These DEGs were then uploaded to DAVID database for KEGG pathway and GO analysis, bubble chart and histogram were drawn to visualize the KEGG pathways and top 10 GO items in every category.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3. Molecular docking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.1. Preparation of ligand and targets before docking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mol2 format of OCA was acquired from\u0026nbsp;ZINC database (https://zinc.docking.org/), and converted to pdbqt format by AutoDockTools 1.5.6.\u0026nbsp;The crystal structures of candidate targets were downloaded from Protein Data Bank (PDB) database (https://www.rcsb.org/) based on the following conditions: the source organism was \u003cem\u003eHomo sapiens\u003c/em\u003e, the experimental method was X-ray diffraction, and the resolution was less than two.\u0026nbsp;After removing redundant ligands\u0026nbsp;and water molecules using PyMOL 2.5,\u0026nbsp;these proteins were\u0026nbsp;isolated, and subsequently\u0026nbsp;optimized for molecular docking using AutoDockTools, including\u0026nbsp;adding hydrogen\u0026nbsp;and adjusting Grid box size.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.2. Molecular docking and analysis of results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on Lamarckian GA algorithm, the binding activity of candidate targets with OCA were evaluated by Autodock 4.2.6, and represented by free binding energy. It is generally believed that binding energy less than -4.25 kcal/mol indicated a certain binding activity, less than -5.0 kcal/mol indicated good binding activity, and less than -7.0 kcal/mol indicated strong binding activity[18]. In this study, proteins with binding energy less than -5.0 kcal/mol were selected as the targets of OCA for the treatment of VPA-induced hepatotoxicity. PyMOL 2.5 and Discovery Studio 4.5 were applied to visualize and analyze interactions as well as binding modes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Molecular dynamics simulations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further validate the results of molecular docking, molecular dynamics simulations were performed by Gromacs 2018 Software[20]. The OPLS force field was employed for both proteins and ligand. The system was solvated in a truncated octahedral box containing explicit water molecules modeled with TIP3P, and neutralized by adding counterions. A buffer of at least 1.2 nm was maintained between the protein surface and the boundaries of the periodic box. During molecular dynamics simulations, long-range electrostatic interactions were handled using the Particle Mesh Ewald (PME) method, and the cutoff value for non-hydrogen bonding interactions was set at 1 nm. The temperature was maintained at 300 K using the V-rescale temperature coupling method, and the pressure was controlled at 1 bar through application of the Berendsen method. Following equilibrating under Isothermal-Isochoric (NVT) ensemble and the Isothermal-Isobaric (NPT) ensemble, molecular dynamics simulations were conducted for 100 ns (a total of 50,000 steps) with the trajectory data saved every 0.5 ns for subsequent analysis, including the root mean square deviation (RMSD), root mean square fluctuation (RMSF), hydrogen bonds, radius of gyration (Rg), and solvent accessible surface area (SASA). All results of molecular dynamic simulations were visualized by Origin 2021 software.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1. Network pharmacology prediction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1.1. Potential targets identification of OCA for treating VPA-induced hepatotoxicity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 1A,\u0026nbsp;462 targets related to VPA-induced hepatotoxicity\u0026nbsp;were obtained from CTD database. From CTD, TargetNET, SwissTarget Prediction and Pharmmapper databases, a total of\u0026nbsp;288 targets related to\u0026nbsp;OCA were screened. There were 81 overlapping targets, through which OCA might ameliorate VPA-induced hepatotoxicity.\u003c/p\u003e\n\u003cp\u003eIn order to identify the potential key targets, the PPI information of intersecting targets was constructed by STRING database and Cytoscape 3.9.1. As shown in Figure 1B, the PPI network composed 80 nodes with 460 connections. After triplicate screenings (Degree \u0026gt; 7.5, betweenness centrality \u0026gt; 0.005553, and closeness centrality \u0026gt; 0.422508), 30 nodes were identified as potential key targets of OCA in the treatment of VPA-induced hepatotoxicity (Supplementary Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1.2. KEGG pathway and GO enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to explore the possible mechanisms of OCA in the treatment of VPA-induced hepatotoxicity, the 81 intersection targets were evaluated by a KEGG pathway analysis. These targets were highly enriched in 126 pathways (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), and many of which were related to drug-induced liver injury. As shown in Figure 2A, the top 10 pathways related to VPA-induced hepatotoxicity were\u0026nbsp;non-alcoholic fatty liver (NAFLD) disease, IL-17 signaling pathway, bile secretion, FoxO signaling pathway, PPAR signaling pathway, PI3K-Akt signaling pathway, TNF signaling pathway, AMPK signaling pathway, MAPK signaling pathway and apoptosis.\u003c/p\u003e\n\u003cp\u003eGO enrichment analysis was carried out to explore the potential roles of the target genes. In this study, 81 intersection targets were enriched in 267 GO items, including 197 biological processes (BP), 49 cellular components (CC), and 21 molecular functions (MF). Based on the lowest \u003cem\u003eP\u003c/em\u003e value, the top 5 BP items were negative regulation of apoptotic process, positive regulation of transcription from RNA polymerase II promoter, response to ethanol, positive regulation of transcription, DNA-templated, response to xenobiotic stimulus, and positive regulation of pri-miRNA transcription from RNA polymerase II promoter. The top 5 CC items were nucleoplasm, chromatin, macromolecular complex, cytoplasm and nucleus. The top 5 MF items were RNA polymerase II transcription factor activity, ligand-activated sequence-specific DNA binding, enzyme binding, sequence-specific DNA binding, zinc ion binding, and identical protein binding. The top 10 items with lowest \u003cem\u003eP\u003c/em\u003e value in every category were shown as a histogram (Figure 2B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1.3. The main target-pathway network of OCA treatment for VPA-induced hepatotoxicity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the targets and top 10 KEGG pathways, a target-pathway network was constructed by Cytoscape 3.9.1. As shown in Figure 3,\u0026nbsp;the network contained\u0026nbsp;92 nodes and 206 edges, fully revealing the multi-target mechanisms of OCA in treating VPA-induced hepatotoxicity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. GEO data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDataset\u0026nbsp;GSE138810\u0026nbsp;provided the information on the effect of OCA treatment on hepatic gene expression in mice treated with VPA. By comparing the hepatic gene expression profiles between OCA and VPA+OCA group, 80 DEGs were identified with 47 up-regulated and 33 down-regulated (Figure 4). As shown in Figure 5A, these DEGs were highly enriched in bile secretion, metabolic pathways, arachidonic acid metabolism, PPAR signaling pathway, retinol metabolism, thyroid cancer, endometrial cancer, gastric cancer and melanoma. Among them, bile secretion and PPAR signaling pathway were also identified in the analysis of network pharmacology, which would be further explored.\u003c/p\u003e\n\u003cp\u003eAs shown in\u0026nbsp;Figure\u0026nbsp;5B, GO enrichment analysis revealed that many biological processes were involved in the effect of OCA on hepatic gene expression profiles of mice under chronic treatment with VPA, such as lipid metabolic process, transmembrane transport, cellular response to lithium ion, long-chain fatty acid metabolic process, fumarate transport and so on. These DEGs primarily localized in basolateral plasma membrane, apical plasma membrane, membrane, cell surface and extracellular space, and functioned as transmembrane transporter activity, heme binding, monooxygenase activity, oxidoreductase activity and oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. Molecular docking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo validate whether bile secretion and PPAR signaling pathway were involved in the treatment of VPA-induced hepatotoxicity with OCA, the potential key targets (e.g., RXRA/RXR\u0026alpha;, CYP7A1, PPARA/PPAR\u0026alpha;, PPARG/PPAR\u0026gamma;, CYP3A4,\u0026nbsp;NR1H4/FXR and\u0026nbsp;NR0B2/SHP) involved in two signaling pathways were selected for molecular docking with OCA. As shown in Table 1, free binding energies of docking results were in the range of +4.81 to -8.05 kcal/mol. OCA exhibited strong binding activity with CYP3A4, FXR and CYP7A1, as well as good binding activity with PPAR\u0026gamma;,\u0026nbsp;RXR\u0026alpha;, and\u0026nbsp;PPAR\u0026alpha;; however, it showed poor binding activity with SHP. Therefore, except for SHP, the other six proteins were the key targets in the OCA treatment of VPA-induced hepatotoxicity. The interactions and binding modes between six proteins (CYP3A4, FXR, CYP7A1, PPAR\u0026gamma;, RXR\u0026alpha;, and PPAR\u0026alpha;) and OCA were shown in Figure 6A-F.\u0026nbsp;According to the chemical structure of OCA, three hydroxyl groups enabled these proteins to form\u0026nbsp;conventional hydrogen bond with OCA; one\u0026nbsp;carboxyl group enabled these proteins which contained lysine or arginine in the binding pockets to form attractive charge and / or salt bridge with OCA. In addition to these interactions, van der Waals forces and hydrophobic interactions also contributed to the binding affinities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Details of targets and OCA for molecular docking\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"572\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.86013986013986%\" valign=\"top\"\u003e\n \u003cp\u003eTarget\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.35664335664335%\" valign=\"top\"\u003e\n \u003cp\u003eKEGG Pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003ePDB ID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.244755244755243%\" valign=\"top\"\u003e\n \u003cp\u003eBinding Energy (kcal/mol)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.86013986013986%\" valign=\"top\"\u003e\n \u003cp\u003eCYP3A4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.35664335664335%\" valign=\"top\"\u003e\n \u003cp\u003eBile secretion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e4D6Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.244755244755243%\" valign=\"top\"\u003e\n \u003cp\u003e-8.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.86013986013986%\" valign=\"top\"\u003e\n \u003cp\u003eFXR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.35664335664335%\" valign=\"top\"\u003e\n \u003cp\u003eBile secretion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e1OSH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.244755244755243%\" valign=\"top\"\u003e\n \u003cp\u003e-7.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.86013986013986%\" valign=\"top\"\u003e\n \u003cp\u003eCYP7A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.35664335664335%\" valign=\"top\"\u003e\n \u003cp\u003eBile secretion, PPAR signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e3V8D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.244755244755243%\" valign=\"top\"\u003e\n \u003cp\u003e-7.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.86013986013986%\" valign=\"top\"\u003e\n \u003cp\u003ePPAR\u0026gamma;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.35664335664335%\" valign=\"top\"\u003e\n \u003cp\u003ePPAR signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e5Y2T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.244755244755243%\" valign=\"top\"\u003e\n \u003cp\u003e-6.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.86013986013986%\" valign=\"top\"\u003e\n \u003cp\u003eRXR\u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.35664335664335%\" valign=\"top\"\u003e\n \u003cp\u003eBile secretion, PPAR signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e6LB4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.244755244755243%\" valign=\"top\"\u003e\n \u003cp\u003e-6.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.86013986013986%\" valign=\"top\"\u003e\n \u003cp\u003ePPAR\u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.35664335664335%\" valign=\"top\"\u003e\n \u003cp\u003ePPAR signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e6KB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.244755244755243%\" valign=\"top\"\u003e\n \u003cp\u003e-6.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.86013986013986%\" valign=\"top\"\u003e\n \u003cp\u003eSHP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.35664335664335%\" valign=\"top\"\u003e\n \u003cp\u003eBile secretion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e6W9M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.244755244755243%\" valign=\"top\"\u003e\n \u003cp\u003e+4.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.4. Molecular dynamics simulations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the docking results, the complexes of OCA with CYP3A4 (4D6Z), CYP7A1 (3V8D), PPAR\u0026gamma; (5Y2T), and PPAR\u0026alpha; (6KB1) were subjected to molecular dynamics simulation analysis to assess their dynamics.\u003c/p\u003e\n\u003cp\u003eRMSD measures the extent of deviation in the position of atoms relative to their initial positions, with a lower deviation suggesting greater conformational stability.\u0026nbsp;As shown in Figure 7A, the RMSD curves of all complexes initially fluctuated during the simulations, and gradually converged to a stable state below 0.5 nm. However, in comparison with other complexes, the RMSD curve of PPAR\u0026gamma;-OCA exhibited dramatic fluctuation in the 100 ns simulation trajectory. The Rg parameter quantifies the compactness of the complexes, with a lower value indicating greater levels of compaction. The Rg curves of OCA with CYP3A4, CYP7A1, PPAR\u0026gamma; and PPAR\u0026alpha; in Figure 7B exhibited slight fluctuations while remaining stable throughout the simulations, with averages of 2.296 nm, 2.318 nm, 2.541 nm and 1.771 nm, respectively. RMSF analysis is performed to assess the flexibility of amino acid residues within a molecular system during a molecular dynamics simulation. The RMSF curves indicated that most of the protein amino acid residues in complexes of OCA with CYP3A4, CYP7A1 and PPAR\u0026alpha; exhibited slight fluctuations, measuring less than 0.5 nm, while the PPAR\u0026gamma;-OCA complex displayed more pronounced fluctuation ranging from 0.6 to 1.2 nm (Figure 7C). The SASA parameter quantifies the surface area of a molecule exposed to the solvent molecules during simulations. The SASA plot (Supplementary Figure 1) revealed that slight fluctuations in the curves of OCA with CYP3A4, CYP7A1 and PPAR\u0026alpha;, while the curve of OCA with PPAR\u0026gamma; fluctuated dramatically before eventually stabilizing, which was consistent with the results of RMSD, Rg and RMSF. Additionally, the hydrogen bonds information of complexes was also acquired during the simulations. As shown in Figure 7D, the number of hydrogen bonds formed between OCA and CYP3A4, CYP7A1, PPAR\u0026gamma; and PPAR\u0026alpha; were 5, 6, 1 and 0 respectively at 100 ns.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy integrating and analyzing the aforementioned results of molecular dynamics simulations, it was found that there were no significant conformational changes in the proteins during the simulations, indicating stable dynamic interactions between OCA and CYP3A4, CYP7A1, PPAR\u0026gamma; and PPAR\u0026alpha;.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe objective of this study was to investigate the underlying mechanisms of FXR activated by OCA in the treatment of VPA-induced hepatotoxicity. Through\u0026nbsp;network pharmacology, molecular docking and molecular dynamics simulations, multiple targets and pathways associated with\u0026nbsp;OCA\u0026nbsp;treatment of VPA-induced hepatotoxicity were identified.\u003c/p\u003e\n\u003cp\u003eIn\u0026nbsp;network pharmacology\u0026nbsp;analysis, the overlapping targets of VPA-induced hepatotoxicity and OCA were highly enriched in 126 pathways, many of which were associated with cancers, such as hsa05200 (pathways in cancer), hsa05215 (prostate cancer) and hsa05205 (proteoglycans in cancer). This could be explained by the inhibitory effects of VPA on histone deacetylases, making it an adjuvant drug for cancer therapy[21]. However, it is important to note that this study exclusively focused on the liver diseases-related pathways rather than those mentioned above. The top 10 pathways identified in OCA treatment for VPA-induced hepatotoxicity were primarily associated with lipid metabolism, oxidative stress and inflammation. These pathways included hsa04932\u0026nbsp;(non-alcoholic fatty liver disease), hsa04976 (bile secretion), hsa03320 (PPAR signaling pathway), hsa04151 (PI3K-Akt signaling pathway), hsa04152 (AMPK signaling pathway), hsa04068 (FoxO signaling pathway), hsa04657 (IL-17 signaling pathway) and hsa04668 (TNF signaling pathway).\u003c/p\u003e\n\u003cp\u003eThe disorders of lipid metabolism and\u0026nbsp;oxidative stress are the primary mechanisms underlying VPA-induced hepatotoxicity. VPA and its\u0026nbsp;metabolites, such as 4-ene-VPA and 2,4-diene-VPA,\u0026nbsp;disrupt fatty acid \u0026beta;-oxidation by depleting\u0026nbsp;coenzyme A\u0026nbsp;and\u0026nbsp;carnitine, as well as\u0026nbsp;inhibiting key enzymes involved in \u0026beta;-oxidation, leading to hepatic fat accumulation and microvesicular liver steatosis[1]. Additionally, VPA induces long-chain fatty acid uptake and triglyceride synthesis through upregulating the expression of\u0026nbsp;fatty acid transporter CD36 and diacylglycerol acyltransferase 2 (DGAT2)[8].\u0026nbsp;Oxidative stress\u0026nbsp;is a hallmark feature of VPA-induced hepatotoxicity, resulting from the promotion of reactive oxygen species (ROS), lipid peroxidation and endoplasmic reticulum stress, as well as depletion of glutathione[22, 23]. Therefore, interventions targeting improved lipid metabolism using L-carnitine[24]\u0026nbsp;or alleviating oxidative stress via antioxidants (such as Vitamin U[25]\u0026nbsp;and taurine[26]) have\u0026nbsp;shown benefits\u0026nbsp;against VPA-induced hepatotoxicity.\u003c/p\u003e\n\u003cp\u003eFXR is a pivotal nuclear factor that\u0026nbsp;has been implicated in VPA-induced hepatotoxicity[10], and its activation can improve lipid metabolism and attenuate inflammation through multiple pathways, including PI3K/AKT/mTOR[27], AMPK-ACC-CPT1\u0026alpha;[28], TLR4/NF-\u0026kappa;B[29], etc.\u0026nbsp;The PI3K-Akt pathway plays a critical role in regulating lipid metabolism and oxidative stress[30]. Our previous study has shown that the expression of CD36 and DGAT2 induced by VPA could be abrogated through the PI3K-Akt pathway[8]. The AMPK pathway is crucial for metabolic homeostasis, and its activation can ameliorate lipid\u0026nbsp;accumulation[31], although contradictory effects have\u0026nbsp;also been observed[32]. As an activator of AMPK, VPA has been shown to decrease liver mass, hepatic fat accumulation and serum glucose in obese mice[33], which contradicts the hepatic steatosis induced by VPA. It would be interesting to investigate the role of AMPK pathway in VPA-induced lipid metabolism disorders.\u0026nbsp;Interleukin-17 (IL-17), a proinflammatory cytokine, potentiates oxidative stress and its associated inflammation is implicated in the pathogenesis of drug-induced liver injury, such as acetaminophen[34]\u0026nbsp;and triptolide[35].\u0026nbsp;To date, limited studies have investigated the IL-17 signaling pathway in VPA-induced hepatotoxicity; only elevated IL-17 levels have been observed following exposure to VPA[36, 37].\u0026nbsp;TNF\u0026nbsp;signaling is a complex signal transduction system that regulates inflammation, immunity, as well as apoptosis[38]. Khodayar \u003cem\u003eet al\u003c/em\u003e.[26]\u0026nbsp;found that VPA administration could induce oxidative stress and apoptosis in mouse liver tissue via TNF-\u0026alpha; upregulation mediated by RIPK1/RIPK3/MLKL signaling axis.\u003c/p\u003e\n\u003cp\u003eSupported by GEO data analysis, molecular docking\u0026nbsp;and molecular dynamics simulations,\u0026nbsp;this study highlights the involvement of bile secretion in OCA treating for VPA-induced hepatotoxicity. Bile acids are a group of endogenous metabolites synthesized in hepatocytes; they play important roles in cholesterol homeostasis, lipid metabolism, glucose metabolism, and inflammation, and are primarily mediated by FXR[39].\u0026nbsp;In our previous study, we observed an obvious shift in bile acid profiles\u0026nbsp;and impaired FXR signaling\u0026nbsp;in the liver of VPA-treated mice, characterized by elevated levels of\u0026nbsp;most bile acids and upregulated expression of bile acid synthetases, such as\u0026nbsp;CYP7A1[10].\u0026nbsp;In this study,\u0026nbsp;OCA\u0026nbsp;exhibited good binding activity with\u0026nbsp;four targets\u0026nbsp;involved in bile secretion\u0026nbsp;(CYP3A4, FXR, CYP7A1, and RXR\u0026alpha;), with binding energies ranging from -6.39 to\u0026nbsp;-8.05 kcal/mol. And there were no significant conformational changes in CYP3A4 and CYP7A1 upon binding with OCA.\u0026nbsp;Notably, the binding energy of OCA-FXR (-7.63 kcal/mol) closely aligns with the previously reported value of -7.6 kcal/mol[40].\u0026nbsp;Zhang \u003cem\u003eet al\u003c/em\u003e. observed that OCA improved the hepatic bile acid profiles induced by lipopolysaccharide in pregnant mice, accompanied by the lower expression of bile acid synthase CYP7A1 and the higher expression of CYP3A[41], which was similar to our findings.\u003c/p\u003e\n\u003cp\u003eAn interesting finding in this study was the involvement of PPAR\u0026nbsp;signaling pathway in\u0026nbsp;OCA treatment for VPA-induced hepatotoxicity. PPARs are members of the nuclear receptor superfamily that play a vital role in\u0026nbsp;regulating lipid homeostasis and inflammation[42]. Our previous studies have revealed that PPAR\u0026alpha; and PPAR\u0026gamma; were implicated in VPA-induced lipid disorders by inhibiting fatty acid \u0026beta;-oxidation and inducing long-chain fatty acid uptake and triacylglycerols synthesis, respectively, and their activation could ameliorate VPA-induced hepatic steatosis[3, 8]. The results of\u0026nbsp;docking\u0026nbsp;and molecular dynamics simulations indicated that the binding of OCA with PPAR\u0026alpha; and PPAR\u0026gamma; was stable;\u0026nbsp;however, limited studies have been conducted on the effects of OCA on PPARs.\u0026nbsp;Although the repression of the PPAR\u0026gamma;\u0026nbsp;pathway has been\u0026nbsp;implicated in mitigating VPA-induced hepatotoxicity by OCA[13], and\u0026nbsp;the activation\u0026nbsp;of\u0026nbsp;FXR\u0026nbsp;could induce the expression of PPAR\u0026alpha;[43],\u0026nbsp;further experimental verification is still needed to elucidate the role of PPAR signaling pathway in\u0026nbsp;OCA\u0026nbsp;treatment for VPA-induced hepatotoxicity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo the best of our knowledge, this is the first application of bioinformatics methods, including network pharmacology, GEO data analysis, molecular docking, and molecular dynamics simulations, to comprehensively investigate the underlying mechanism of OCA in VPA-induced hepatotoxicity. However, there are certain limitations associated with the approaches employed in this study. Firstly, target data were extracted from literature and databases; thus, the reliability and accuracy of predictions relied on the quality of acquired data. Secondly, this study utilized a data mining approach and computational simulation techniques, requiring experimental validation to confirm these results.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn conclusion, our findings suggest that OCA treatment for VPA-induced hepatotoxicity involves multiple pathways, with a particular emphasis on bile secretion and PPAR signaling pathway. These results provide a basis for considering FXR as a potential therapeutic target in the treatment of VPA-induced hepatotoxicity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor Contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSSX and LW designed the study. YNC performed the data analysis and writing. JKZ conducted mapping and language revision. All the authors contributed to the article, reviewed the manuscript, and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by grant from the Natural Science Foundation of Jiangsu Province (Grant No. BK20210003), the Hospital Pharmaceutical Research Fund of Nanjing Pharmaceutical Association (Grant No. 2021YX031), and the Hospital Science and Technology Development Fund (Grant No. ZJ202105).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConflict of interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSSX\u0026nbsp;is an employee of Jiangsu Simcere Pharmaceutical Co., Ltd. Other authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData Availability Statement\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mRNA expression profile data in this study are available from GEO datasets under the accession number GSE138810. All data are provided in this study and raw data can be requested from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEzhilarasan D and Mani U (2022) Valproic acid induced liver injury: An insight into molecular toxicological mechanism. \u003cem\u003eEnviron Toxicol Pharmacol\u003c/em\u003e 95: 103967. http://doi.org/10.1016/j.etap.2022.103967.\u003c/li\u003e\n\u003cli\u003eWang WJ, Zhao YT, Dai HR et al (2023) Successful LC-MS/MS assay development and validation for determination of valproic acid and its metabolites supporting proactive pharmacovigilance. \u003cem\u003eJ Pharm Biomed Anal\u003c/em\u003e 234: 115538. http://doi.org/10.1016/j.jpba.2023.115538.\u003c/li\u003e\n\u003cli\u003eMa Y, Wang M, Guo S et al (2022) The serum acylcarnitines profile in epileptic children treated with valproic acid and the protective roles of peroxisome proliferator-activated receptor a activation in valproic acid-induced liver injury. 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proliferator-activated receptor alpha gene via activation of the farnesoid X receptor. \u003cem\u003eMol Endocrinol\u003c/em\u003e 17(2): 259-72. http://doi.org/10.1210/me.2002-0120.\u003cstrong\u003e\u003cbr\u003e \u003c/strong\u003e\u003c/li\u003e\n\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":"Valproic acid, Hepatotoxicity, Farnesoid X receptor, Obeticholic acid, Network pharmacology, Molecular Docking ","lastPublishedDoi":"10.21203/rs.3.rs-4291847/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4291847/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study aims to comprehensively investigate the underlying mechanisms of farnesoid X receptor (FXR) activation by obeticholic acid (OCA) in the treatment of VPA-induced hepatotoxicity. Network pharmacology was performed to identified the potential targets and pathways underlying the amelioration of VPA-induced hepatotoxicity by OCA. The identified pathways were validated through GEO data analysis, and the interactions between OCA and potential targets were predicted using molecular docking as well as molecular dynamics simulations. A total of 462 targets associated with VPA-induced hepatotoxicity and 288 targets of OCA were identified, with 81 overlapping targets between VPA-induced hepatotoxicity and OCA. KEGG pathway and GO enrichment analysis indicated that the effect of OCA on VPA-induced hepatotoxicity primarily involved lipid metabolism, as well as oxidative stress and inflammation. The results of GEO data analysis, molecular docking and molecular dynamics simulations revealed a close association between bile secretion, PPAR signaling pathway, and the treatment of VPA-induced hepatotoxicity by OCA. Our findings revealed that OCA exhibits potential therapeutic efficacy against VPA-induced hepatotoxicity through multiple targets and pathways, thereby highlighting the therapeutic potential of FXR as a target for the treatment of VPA-induced hepatotoxicity.","manuscriptTitle":"Mechanisms Underlying the Protective Effects of Obeticholic Acid-activated FXR in Valproic Acid-induced Hepatotoxicity via Network Pharmacology, Molecular Docking and Molecular Dynamics Simulations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-30 06:26:14","doi":"10.21203/rs.3.rs-4291847/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":"e9a61687-46b0-4b48-93f7-468a87aafba5","owner":[],"postedDate":"April 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":31266187,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":31266188,"name":"Biological sciences/Drug discovery"},{"id":31266189,"name":"Biological sciences/Drug discovery/Drug safety"},{"id":31266190,"name":"Biological sciences/Drug discovery/Toxicology"}],"tags":[],"updatedAt":"2024-05-30T04:45:37+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-30 06:26:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4291847","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4291847","identity":"rs-4291847","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}