Elucidating the hepatoprotective mechanisms of Vernonia cinerea phytoactives in drug-induced liver injury by computational approach of network pharmacology, molecular docking and dynamic simulation studies

doi:10.21203/rs.3.rs-3237131/v1

Download PDF

Research Article

Elucidating the hepatoprotective mechanisms of Vernonia cinerea phytoactives in drug-induced liver injury by computational approach of network pharmacology, molecular docking and dynamic simulation studies

https://doi.org/10.21203/rs.3.rs-3237131/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Drug-Induced Liver Injury (DILI), also known as Hepatotoxicity, refers to unexpected liver damage triggered by common medications, which harms hepatocytes and other liver cells. This damage can either be intrinsic, appearing shortly after drug exposure, or idiosyncratic, emerging from intricate interactions of individual and environmental factors with the drug. Despite precautions taken during drug development, preventing DILI remains a daunting task due to insufficient preventive guidelines. Herbal remedies such as Vernonia cinerea, known as purple fleabane or Sahadevi, might provide a potential treatment for DILI. Traditionally, this plant has been used for ailments like hepatitis, fever, diabetes, and asthma, demonstrating a wide array of pharmacological properties. Nonetheless, traditional medicine frequently lacks adequate scientific data on these herbal remedies' composition, action mechanisms, and ADMET profiles.The present study's goal is to uncover potential phytoconstituents in V. cinerea associated with DILI or hepatotoxicity and shed light on their possible treatment mechanisms using chemo-informatic tools and databases like STRING, Cytoscape 3.6.1 for network pharmacology, PyRx 0.8, DSV 2022 for molecular docking, and GROMACS for dynamic simulation studies.Out of the twenty-six phytoactives, Feroxin A and Apocyanin B demonstrated the highest drug-likeness scores, i.e., 1.1 and 0.94, respectively. Further docking studies showed that Apocyanin B, when complexed with Epidermal growth factor receptor (EGFR), displayed the lowest binding energy (-9.6kcal/mol) and formed complex stability with 11 residues: Leu718, Val726, Ala743, Lys745, Met766, Gln791, Leu792, Met793, Arg841, Leu844, and Lys852 over a 100 ns molecular dynamic production run.In contrast, the Erlotinib-EGFR complex was used as a reference to validate the Apocyanin B protein with the EGFR complex. Erlotinib docking exhibited a binding energy of -6.7kJ/mol and created complex stability through 11 residues like Leu718, Val726, Ala743, Lys745, Thr790, Leu792, Gly796, and Leu844 during the dynamic state at a 100ns run.The hepatoprotective capability of Vernonia cinerea may be attributed to the presence of phytoactives, particularly Apocyanin B, which modulate targets such as AKT1, PIK3CA, MAPK, HRAS, and EGFR. The findings from this study illuminate the molecular mechanisms that contribute to the hepatoprotective advantages of Vernonia cinerea.

Hepatotoxicity

Apocyanin B

Vernonia cinerea. Erlotinib

Epidermal growth factor receptor

Drug-induced liver injury (DILI) or Hepatotoxicity refers to the unanticipated damage that commonly used medications can inflict on the liver, including impairment to hepatocytes and other liver cells. The liver's crucial function in the biotransformation (metabolism) of many different substances is likely the primary factor causing its susceptibility to negative drug responses, xenobiotics entering the hepatic system. Based on the assumed mechanism of action of the chemical molecule, liver damage associated to medications has traditionally been categorized into two types: intrinsic and idiosyncratic. The majority of people who are exposed to the substance, which is toxic at a specific threshold level, experience the intrinsic (direct or predictable) kind, which is dose-related and begins soon after exposure (hours to days) [1]. Most people who are exposed to the substance have hazardous effects at a specific threshold level. The idiosyncratic (indirect or unexpected) variant of DILI, on the other hand, has a longer latency time (from a few days to several months) and is governed by the interaction of host and environmental factors with the drug [2]. It typically affects one out of every 10,000 people who are exposed to the drug [3]. The DILI is determined by the cause of asymptomatic elevation of hepatic enzymes viz., alanine aminotransferase [ALT], aspartate aminotransferase [AST], and Alkaline phosphatase [ALP] [4]. Identification of a probable hepatotoxin, a pattern of abnormal liver tests that are specific to the substance, and injury onset that is temporally consistent with the offending hepatotoxin's typical profile is essential to the diagnosis of DILI [5]. Avoiding DILI should start throughout the drug development phase, even though a medicine's apparent safety in small preclinical studies does not guarantee that the drug would be safe once it is used widely [6]. Hence, still, there is no proper advice to avoid the DILI. According to the available reports, herbal remedies may treat DILI or hepatotoxicity. However, folk/traditional medicine lacks sufficient scientific data in the management of complex diseases like diabetes; be short of evidence in phytoconstituents composition, mechanism of action, and ADMET profile.

Vernonia cinerea also known as purple fleabane, Sahadevi belongs to Family Asteraceae is a plant which were used by traditional healers/ folks which is used in the treatment of hepatitis, fever, diarrhea[7], diabetes mellitus, high blood pressure, dysentery, and asthma. [8–12] and having diverse pharmacological properties like anthelmintic, antibacterial, antiviral, antifungal, anti-inflammatory, diuretic, and stomachic [13].Therefore, the current study's objectives are to discover potential phytoconstituents from V. cinerea, pinpoint their targets that are involved in DILI or hepatotoxicity, and provide the most likely mechanism for managing these conditions using network pharmacology, molecular docking, and dynamic simulation.

Genes and pathways involved Isoniazid induced Hepatoxicity

The genes associated with hepatotoxicity were identified using Genecard, an online server (https://www.genecards.org/). Genecard is a comprehensive database that provides detailed information on human genes, including annotations and predictions. The genes were assigned scores, with a cutoff of 20. Subsequently, these proteins were analyzed using STRING 11.5 (Search Tool for the Retrieval of Interacting Genes/Proteins, https://string-db.org) to explore protein-protein interactions and pathways involved. To identify enriched pathways related to hepatotoxicity, the KEGG database was utilized. In this study, a false discovery rate (FDR) p value cutoff of < 0.05 was applied to determine significant functions, processes, and pathway enrichments.

Mining of phytocompounds

The previously reported phytochemicals from Vernonia cinereawere collated from the literature, books,Dr. Duke's Databases and other publicly available repositories.

Draggability and toxicity profile of compounds

Molecular formula (MF), molecular weight (MW), hydrogen bond acceptor (HBA), hydrogen bond donor (HBD), logP value, and canonical SMILES of each molecule were retrieved from the PubChem chemical database (https://pubchem.ncbi.nlm.nih.gov/). The canonical SMILES of individual compound was queried in MolSoft online server (https://molsoft.com/mprop/) (accessed 25 January 2023) [Molsoft, L.C.C. Available online: https://molsoft.com/mprop/ ]. The druggability of compounds were considered based on the lipinski rule of five. The molsoft server provides structural data, such as MF, MW, HBA, HBD, logP, probable drug-likeness score (DLS). Further, phytocompounds with positive DLS were selected and their toxicity was predicted using the ADVERpred server [14]. We chose this server because it provides potential adverse effects such as hepatotoxicity, nephrotoxicity, arrhythmia, and myocardial infarction based on the probable activity (Pa) and probable inactivity (Pi) of unknown substances. Phytocompounds having a Pa value of ≤ 0.5 were considered for further study.

Target Identification

Canonical SMILES of the shortlisted phytocompounds were queried into SwissTargetPrediction server [15] (http://www.swisstargetprediction.ch/) for probable target protein identification and genes with p-value greater than 0 were considered.

Gene Ontology and Pathway Enrichment Analysis

To understand the molecular functions, processes of probable protein targets modulated by the phytocompounds and to retrieve the protein–protein interactions systematically, a set of retrieved gene IDs was provided as input to STRING 11.5 (Search Tool for the Retrieval of Interacting Genes/Proteins (https://string–db.org) [16] and KEGG database was utilized to identify the enriched pathways involved in hepatotoxicity. In this study, to obtain significant functions, processes, and pathway enrichment, the default false discovery rate (FDR) p value cut-off of < 0.05 was considered.

Network Construction

Cytoscape v3.6.1 software (Washington, USA) was used to build the compound-protein pathway. It is an open-source tool used to demonstration biological pathways and intricate large molecular interaction networks [17]. Edge count topological parameter was used to indicate the degree of linkages between the compound-protein pathways. Also, the network was designed using "low values to small sizes" for node size [18].

Structure Refinement, Homology Modelling, and Assessment of Active Site Residues.

Amongst the predicted targets, the bioactive phytocompound, AKT1, PIK3CA, MAPK1, HRAS, and EGFR were found to have top five hub genes with higher edge count within the network and also these genes were found to share the common pathways such as MAPK, PI3K-Akt, VEGF, TNF, AMPK, and Jak-STAT signalling pathways. Hence the above-mentioned therapeutic targets are well reported in hepatotoxicity and are considered for further virtual screening studies. For the protein AKT1 the Crystal Structure of Human AKT1 (PDB id: 3O96 with resolution of 2.70 Å) expressed in Trichoplusiani expression system) was selected from Protein Data Bank (PDB) (https://www.rcsb.org/structure/3O96) [19]. For the protein PIK3CA the Structure of PIK3CA with covalent inhibitor 19 (PDB id: 7R9V (with resolution of 2.69 Å) expressed in Spodoptera frugiperda expression system) was selected from Protein Data Bank (PDB) (https://www.rcsb.org/structure/7R9V) [20]. For the protein MAPK1 the Crystal Structure of the ERK2 (PDB id: 4G6O (with resolution of 2.20 Å) expressed in Escherichia coli expression system) was selected from Protein Data Bank (PDB) (https://www.rcsb.org/structure/4G6O) [21]. For the protein H-Ras Y137F (PDB id: 4XVR (with resolution of 2.03 Å) expressed in Escherichia coli BL21 was selected from Protein Data Bank (PDB) (https://www.rcsb.org/structure/4XVR )[22]. For the protein EGFR the Crystal structure of the wild-type EGFR kinase domain in complex with dacomitinib (PDB id: 4I23 (with resolution of 2.8 Å) expressed in Spodoptera frugiperda expression system) was selected from Protein Data Bank (PDB) (https://www.rcsb.org/structure/4I23) [23].

All the structures were visualized inDiscovery Studio Visualizer version 2021 (DSV2021) to analyse the missing amino acid residues. ClustalW (https://www.genome.jp/tools-bin/clustalw) [24] is multi-sequence alignment online server which is used to align the sequences of all structures; for AKT1 (PDB id: 3O96), PIK3CA (PDB id: 7R9V), MAPK1(PDB id: 4G6O), HRAS(PDB id: 4XVR), EGFR(PDB id: 4I23) ; identified sixty-one, eighty-three, six, five, twenty amino acid residues data were missing in their structures respectively; with the complete sequence from Uniprot with their accession numbers as follows P31749, P42336, P28482, P01112, P00533. Further, the Swiss model (https://swissmodel.expasy.org/interactive#structure) is used to rebuild the missing amino acid residues of all proteins [25]. The stereochemical check for the all-modeled structures was assessed using the PROCHECK web server. Further, the residues numbers and chain ids are aligned using Protein Data Bank File Editor by JL v 2008 [26].

Molecular docking:

Preparation of Ligand and Protein Target

Ligand preparation: The ligands Beta-Glucogallin, alpha-Copaene, 1-O-Methyl-3,5-O-dicaffeoylquinic acid methyl ester were found to interacting with the target protein AKT1; for protein PIK3CA, Apocynin B, Dendrocandin E, Elemol, Isoduartin; for target protein MAPK1, (9Z,12Z,15Z)-octadeca-9,12,15-trienoic acid, Apocynin B, Asebotin, Beta-Glucogallin, Isoduartin; for protein HRAS, Asebotin, Beta-Glucogallin, Feroxin A, Renifolin, Torachrysone-8-O-beta-D-glucopyranoside; for target protein EGFR, Apocynin B, Asebotin, Dendrocandin E, Isoduartin, Kukoamine A, Renifolin, Squalene, Torachrysone-8-O-beta-D-glucopyranoside were chosen. The ligand's 3D conformation was collated from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and converted to .pdb using Discovery Studio Visualizer v.2021. (https://discover.3ds.com/discovery-studio-visualizer-download). The ligand’s energy was minimized using the mmff94 force field [27].

Preparation of macromolecule

the above prepared protein structures (in section Structure Refinement, Homology Modelling, and Assessment of Active Site Residues) were chosen further for docking study.

Ligand–protein docking

the prioritized ligands weredocked with the selected target proteins using PyRx 0.8 software [28]. After docking, nine distinct ligand positions were found. The ligand poses with the lowest binding energies were chosen to visualize the ligand-protein interactions and the lead hit complex was subjected to MD simulation [29].

Molecular dynamic simulation

Stability of the docked complexes

The GROMACS (https://www.gromacs.org/) 2021.3 software package was used to perform MD simulations with the Amber ff99SB-ildn force field [30, 31] The partial charges of the small molecules were generated by performing quantum calculation using an antechamber with a ‘bcc’ charge model and topology parameters of the ligands and whole complex were generated using the xleap module of AmberTools (https://ambermd.org/AmberTools.php). The prepared systems were solvated using a three-site water (TIP3P) model in a rectangular box having 10.0 Å boundary conditions from the protein’s edges in all directions. The charges on the prepared systems were neutralized by adding the required number of counter ions. Steepest descent followed by conjugate gradient energy minimization method was used to obtain near-global state least energy conformations. The systems were equilibrated using “canonical (NVT) and isobaric (NPT)” ensembles for 1 ns. In NVT equilibration, a modified Berendsen thermostat algorithm was applied to maintain the constant volume and temperature (300 K). In NPT equilibration, Parrinello-Rahman barostat was applied to maintain the constant pressure of 1 bar.In addition, to compute coulomb, van der Waals, and the long-range electrostatic interactions, the Particle Mesh Ewald approximation was applied with a cut-off value of 1 nm. Similarly, the LINear Constraint Solver algorithm was used to constraint bond length.All the complexes were subjected to a production run of 100 ns and the coordinates were recorded at every 2 fs. The trajectories generated were analyzed using in-build gromacs utilities, and some other additional software packages were used for specific analysis wherever required.

Genes and pathways involved Isoniazid induced Hepatoxicity

48 genes associated with a score of 20 were identified as contributors to Isoniazid-induced Hepatotoxicity. Subsequently, analysis of these genes uncovered 99 pathways involved. However, through peer review, it was determined that only 12 pathways such as PI3K-Akt, MAPK, p53, JAK-STAT, NF-kappa B, TGF-beta, TNF signaling pathway as well as Toll-like receptor, Chemokine signaling pathway, Apoptosis, Hepatitis B, Hepatocellular carcinoma significantly influenced the development of hepatotoxicity [32, 33]. The probable gene and pathways related in Isoniazid induced Hepatotoxicity is demonstrated in Figure S1

Mining of phytocompounds

About 109 phytochemical compounds reported to present in Vernonia cinerea were collected from plant database and available literature.

Druglikeness and Toxicity Profile

Among 109 phytochemical compounds, 26 compounds showed positive drug-likeness properties. 24 compounds showed Pa ≤ 0.5 and 2 have shown Pa ≤ 0.7 for toxic properties. Table S1signifies the 26 compounds chemical information, druggability, and Table S2and Figure S2represents the toxicity profile of phytocompounds.

Target Identification

The SwissTargetPrediction was used to anticipate the potential protein targets of 23 phytocompounds. All these phytocompounds were predicted to modulate 135 protein targets. Table S3 represents the genes modulated byphytocompounds.

Gene Set Enrichment Analysis and Network Construction

The KEGG pathway (Table S4) enrichment analysis showed 135 targets to trigger overall 147 pathways. After peer reviewing, 20 pathways were associated with hepatotoxicity.

Network Analysis

Among the predicted targets, five targets AKT1, PIK3CA, MAPK1, HRAS, EGFR were identified as a hub gene’s and modulating common pathways such as Apelin, AMPK, HIF-1, PI3K-Akt, JAK-STAT, MAPK, VEGF, Sphingolipid, TNF, IL-17 signaling pathways, and also proteins were modulating Hepatitis B, Hepatitis c, Hepatocellular carcinoma, Insulin resistance, Non-alcoholic fatty liver disease, Regulation of lipolysis in adipocytes in the network. The role of top 5 hub gene’s is briefed in Table S5, and network construction is demonstrated in Figure S3

Structure Refinement, Homology Modeling, and Active Sites Assessment

As discussed above the five target proteins that are having highest edge count were selected and those structure were refined and the structure were validated and checked for stability by ERRAT score and Ramchandran’s plot for the protein’s AKT1, PIK3CA, MAPK1, HRAS, EGFR the server revealed 81.32, 92.14, 96.07, 98.65, 95.58 and 86, 89, 90, 89.3, 89.5% of residues in most favoured region respectively.

Molecular Docking Study

A. Apocynin B- Epidermal growth factor receptor (EGFR) complex.

The molecular docking of 15 phytocompounds with 5 target proteins were carried out and the corresponding binding energies (BE) and intermolecular interactions were briefed in the Table S6. Firstly, 15 phytocompounds were predicted to form interaction with the active site residue with the targeted proteins which are determined by the network analysis. The protein AKT1 was possessing interaction with phytocompounds Beta-Glucogallin, alpha-Copaene, and 1-O-Methyl-3,5-O-dicaffeoylquinic acid methyl ester. Among them, 1-O-Methyl-3,5-O-dicaffeoylquinic acid methyl ester was found to have the lowest Binding energy of -10.8 kcal/mol via forming ten hydrogen bonds, i.e., Asn54…=O, Glu85…OH, Thr211…OH (2), Asp274…OH, Gly294…=O, Cys 296…OHand four hydrophobicTrp80, Ile84, Leu210, Arg 273 interaction were found, the Figure S4.1 represents interaction between AKT1 and1-O-Methyl-3,5-O-dicaffeoylquinic acid methyl ester,. For the protein PIK3CA, phytocompounds Apocynin B, Dendrocandin E, Elemol, Isoduartin were the lead hits, in which Apocynin B showed the lowest Binding energy of -9.6 kcal/mol via forming six hydrogen bonds, i.e., Tyr836…OH, Val851…OH, Gln859…O, Ser919…OH, Ser919…O, Asp933…O and forming five hydrophobic interactions i.e., Trp780, Ile800, Ile848, Met922, Ile932 (2) residues, Figure S4.2 represents interaction betweenPIK3CA and Apocynin B. For the protein MAPK1, compounds (9Z,12Z,15Z)-octadeca-9,12,15-trienoic acid, Apocynin B, Asebotin, Beta-Glucogallin, and Isoduartin were the lead hits, in which Apocynin B was found to have the lowest Binding energy of -9.4 kcal/mol via forming the five hydrogen bonds i.e., Gln103...OH, Met106…OH, Asp109...OH (2), Lys112…O and forming four hydrophobic i.e., Ile29, Val37, Lys52, Asp109 residues, Figure S4.3 represents interaction between MAPK1 and Apocynin B. For the protein HRAS, compounds, Asebotin, Beta-Glucogallin, Feroxin A, Renifolin, Torachrysone-8-O-beta-D-glucopyranoside were the lead hits, in which Asebotinwas found to have the lowest Binding energy of -10.2 kcal/mol via forming the five hydrogen bonds i.e., Gly15…O, Val29…OH, Phe28…OH, Glu31…OH, Lys117…O and forming eleven hydrophobic bonds i.e., Gly15, Ala18 (2), Ile21, Phe28, Val29, Glu31, Asp33, Lys117, Ala146, Lys147 residues, Figure S4.4 represents interaction between HRAS andAsebotin. Finally for protein EGFR, eight phytocompound were found to have interaction i.e., Apocynin B, Asebotin, Dendrocandin E, Isoduartin, Kukoamine A, Renifolin, Squalene, Torachrysone-8-O-beta-D-glucopyranoside, in which Apocynin B was found to have lowest Binding energy of -8.9 kcal/mol via forming the two hydrogen bonds i.e., Lys745…=O, Gln791…O and forming six hydrophobic bonds i.e., Leu718, Val726, Ala743, Leu844 (2), Asp855 residues, Figure S4.5 represents interaction between EGFRand Apocynin B.

B. Erlotinib - Epidermal growth factor receptor (EGFR) complex.

The Erlotinib is well-known antagonist of Epidermal growth factor receptor (EGFR) and we considered as standard to cross verify Apocynin B protein with EGFR complex. The macromolecule i.e., 4i23 were docked with the Erlotinib the Binding energy was found to be -6.7 kJ/mol via forming one hydrogen bond i.e., MET 793…NH and forming six hydrophobic bonds i.e., Leu718, Val726, Lys745, Met766, Leu788, Leu844 residues, Figure S4.6 represents interaction between EGFR andErlotinib.

Molecular dynamic study

Apocynin B- Epidermal growth factor receptor (EGFR) complex.

A. Stability of protein -Apocynin B complex

The protein Apocynin B complex showed stable dynamics after an equilibrium period 40ns. Initially, the backbone and complex RMSD values gradually increased from ~ 1.2 Å to 5 Å and ~ 0.1 Å to 5 Å for backbone and complex respectively. The residues Thr725, Ile740, Ser921, Glu922 showed relatively fewer fluctuations as they participate in stable non-bonded interactions. Although the Ala750, Ser752; Leu1001, Met1002 showed relatively higher fluctuation (~ 5.3 Å; ~6.4 Å), in the ligand interaction which was observed in the simulation run. Further the Rg value showed the stable complex after the 40ns till that compactness were not observed in the simulation. In addition, the SASA showed initially decrease from 180 nm² to 170 nm² after that stability was observed, this ensures that stable complex formation until 100 ns. The decrease in the SASA values intimates that the ligand was getting fit into the pocket. The complex formed 7-hydrogen bonds of which 3 were consistent during the simulation period. Further, the structural was visualized into contribution of energy per residue decomposition energy. It was observed that 11 residues had significant contribution to for the complex stability i.e., Leu718, Val726, Ala743, Lys745, Met766, Gln791, Leu792, Met793, Arg841, Leu844, Lys852. Most prominently, the residues Lys745, Met793, Arg841, Leu844 showed weighty contribution in binding affinity by the score the lowest contribution energy of -5.68, -4.66, -4.58, 13.05 kJ/mol respectively.Figure S5 represents EGFR–Apocynin B complex 100 ns MD production run.3

Erlotinib - Epidermal growth factor receptor (EGFR) complex.

A. Stability of Erlotinib complex

The standard drug Erlotinib complex showed revealed that stable dynamic from 10ns – 70ns after that the fluctuation were seen till 100ns. Initially, the backbone and complex RMSD values gradually increased from ~ 0.7Å to 3 Å and ~ 0.1 Å to 3.3 Å for backbone and complex respectively. The residues 710, 714, 790, 808, 857, 863, 925 showed relatively fewer fluctuation as they participate in stable non-bonded interactions. Although the residues 724, 746, 758, 922, 934; 985, 996, 1007 showed relatively more fluctuation (~ 3.1 Å; ~4.5 Å), in the ligand interaction which was observed in the simulation run. Further the Rg value showed the stable complex till 68ns (~ 20.4 Å - ~21.1 Å) that compactness was observed in the simulation after that fewer fluctuation were seen till 100ns. In addition, the SASA showed initially increase from 166 nm² to 184 nm² that the stability was observed, this ensures that stable complex formation until 100 ns. The complex formed 3-hydrogen bonds of which 2 were consistent during the simulation period. Further, the structural was visualized into contribution of energy per residue decomposition energy. It was observed that 08 residues had significant contribution to for the complex stability i.e., Leu718, Val726, Ala743, Lys745, Thr790, Leu792, Gly796, Leu844. Most prominently, the residues Leu718, Val726, Lys745, Thr790, Leu844 showed weighty contribution in binding affinity by the score the lowest contribution energy of -6.52, -7.59, -6.24, -6.47, -8.85 kJ/mol respectively. Figure S6 EGFR– Erlotinib complex 100 ns MD production run.

The goal of the current study was to gain an understanding of the molecular mechanisms underlying the herb Vernonia cinerea activity on hepatoprotective properties used in the Indian traditional medicines by using gene set enrichment analysis, network pharmacology, in-silico docking and simulation study. To understand the relationship between phytoconstituents and protein targets, network pharmacology is the latent approach to determine traditional medicines for the management of several diseases and disorders in which hepatotoxicity is also a major component. Based on the principle that "similar compounds target similar proteins", primarily we predicted the pathways and the proteins which are involved in Isoniazid induced Hepatotoxicity, firstly the proteins which are involved in causing Hepatotoxicity data were retrieved from the Gene card online server further the obtained proteins are queried into the Swiss target prediction to determine the regulated pathways for respective genes, after peer reviewing the pathways which are related to the hepatic disorder were selected and the network were build using the Cytoscape v3.6.1 and further the plant chemical compound was collected from the all available literature and the canonical smiles were retrieved from the PubChem online server and predicted for the positive DLS in which we found 26 phytocompound and their targets were predicted by using Swiss target prediction and Additionally, using the ADVERpred an online server in these server chemical compounds were examined for their potential to cause myocardial infarction, hepatotoxicity, cardiac toxicity, and other serious adverse medication responses and side effects, out of 26 phytocompounds 17 phytocompounds data were available in ADVERpred server in that one compound named Dendrocandin E showed the Pa of 0.61 that means this might cause toxicity, rest were Pa ≤ 0.5, and further the targets were carefully selected only based on probable molecular functions, processes, and pathways of higher significance(p ≤ 0.05). STRING database was used to determine the molecular functions, biological processes, and pathways regulated by the anticipated targets, the pathways were determined by the KEGG pathway which are regulated in the Hepatotoxicity were considered such as HIF-1 signaling pathway, PI3K-Akt signaling pathway, Hepatitis B, Insulin resistance, Hepatitis C, JAK-STAT signaling pathway, Insulin signaling pathway, MAPK signaling pathway, Hepatocellular carcinoma, VEGF signaling pathway, TNF signaling pathway, Non-alcoholic fatty liver disease, Sphingolipid signaling pathway, AMPK signaling pathway, PPAR signaling pathway, Regulation of lipolysis in adipocytes, p53 signaling pathway, NF-kappa B signaling pathway, IL-17 signaling pathway. The pathways mechanism involves, HIF-1 pathway acts different mechanisms in liver damage in ALD it inhibits the protection in intestinal integrity[34, 35], Reduce fatty acid synthesis [36] and promotes lipid accumulation [37], and increases portal pressure [38], through the Akt/HIF-1α pathway [39] and in NAFLD it Inhibit excessive accumulation of liver fat [40] and promotes liver fibrosis [41] Inhibit excessive accumulation of liver fat [40] and Promote liver fibrosis [42]. And AILI acts by Inhibiting Contribute to hemostasis [43], Reduce inflammatory reaction, Improve centrilobular necrosis [44]. And in Viral hepatitis acts Increase autotaxin protein expression [45] and Facilitate HIF-1α-mediated glycolytic adaptation [46], and in HCC acts Promote migration, invasion, metastasis and angiogenesis [47–49] and according to literature, PI3K is an intracellular phosphatidyl inositol kinase involved in a number of cellular processes, including cell division, proliferation, and death as well as glucose transport [50]. The second messenger PIP3 is synthesised in the plasma membrane by PI3K. PIP3 was interacting with the intracellular signal proteins PDK1 and Akt, which led to PDK1 activating Akt. A crucial PI3K downstream effector, Akt, demonstrated antiapoptotic properties by phosphorylating a variety of target proteins and downstream pathways [51]. In certainty, stimulation of the PI3K/Akt pathway increased the transcription activity of NF-κB [52]. In order to release NFB from the cytoplasm, carry out nuclear translocation, activate its target genes, and support cell viability, Akt activated IB kinase (IKK/IB), which led to the degradation of NF-B inhibitor I-B [53]. NF-κB is a crucial transcription factor in the nucleus that plays a role in immunity and inflammation as well as controlling the production of genes that lead to apoptosis.

In the all pathways there are 36 genes were having the similar genes which are responsible for modulating the pathways in which AKT1, PIK3CA, MAPK1, HRAS, and EGFR were found to have top five hub genes with higher edge count within the network and their relation between the Pathway and compound.

After gene ontology, enrichment, and network analysis, we continued the investigation through utilizing molecular docking and MD simulation studies to look at the intermolecular interactions between phytocompounds and anticipated protein targets. As a result, the top five genes i.e., AKT1, PIK3CA, MAPK1, HRAS, and EGFR as they earned the top-scoring node with the highest edge count of 19, 18, 17, 16, and 14 respectively. Thus, these hub genes were carriedforward for the molecular docking based on the highest edge count, and as well as on secondary literature analysis, they were found to be well documented as a therapeutic target for hepatotoxicity and HCC (identified to be involved in the MAPK, PI3-Akt, and Jak-STAT signaling pathways within the network). Information on phytocompounds that target protein targets and regulated pathways is provided in Table S9 (the edge count, or the number of compounds and pathways that connect to a given protein target, was noted). Further for molecular docking the proteins Crystal Structures of AKT1, PIK3CA, MAPK1, HRAS, and EGFR were obtained from PDB having the PDB id: 3O96, 7R9V, 4G6O, 4XVR, and 4I23 respectively and the missing residues were fixed by homology modeling. The molecular docking results of all proteins were shown in table, for EGFR protein discovered to interact with the eight phytochemicals, including Apocynin B, Asebotin, Dendrocandin E, Isoduartin, Kukoamine A, Renifolin, Squalene, and Torachrysone-8-O-beta-D-glucopyranoside were, Apocynin B was shown to have the lowest binding energy of -8.9 kcal/mol by creating six hydrophobic bonds with the residues Leu718, Val726, Ala743, Leu844 (2), and Asp855 as well as two hydrogen bonds with Lys745...=O and Gln791...O.. After obtaining the results Apocynin B were compared with Erlotinib which is known EGFR antagonist [54]. Surprisingly it showed less binding affinity with the EGFR protein having Binding energy of -6.7 kJ/mol via forming one hydrogen bond i.e., MET 793…NH and forming six hydrophobic bonds i.e., LEU 718, VAL726, LYS745, MET766, LEU788, LEU844 residues. Further for molecular dynamics the chemical compound Apocynin B and Erlotinib compound selected for the molecular dynamics study with the best protein molecule EGFR (based on a greater number of chemical compounds having lowest binding energy with the protein were selected) to determine protein structure stability on ligand binding. The Apocynin B_EGFR complex showed stable dynamics after an equilibrium period 40ns, the residues Thr725, Ile740, Ser921, Glu922 showed relatively fewer fluctuations as they participate in stable non-bonded interactions. Although the Ala750, Ser752; Leu1001, Met1002 showed relatively higher fluctuation (~ 5.3 Å; ~6.4 Å), Rg value reveals the stable complex after the 40ns till 100ns, in addition, the SASA exposed initially decrease from 180 nm2 to 170 nm2 denoting decrease in the SASA values complying that the ligand was getting fit into the pocket by forming 7 hydrogen bonds in which 3 were consistent, and in concern with energy contribution revealed 11 residues had significantly contributing for the complex stability. With respect to the Erlotinib_EGFR complex showed stable dynamic from 10ns – 70ns after that the fluctuation were seen till 100ns, when the Rg value, SASA, Hydrogen bonding, and energy contribution of residues compared with the Apocynin B_EGFR complex, Apocynin B_EGFR complex were shown significant results.

The investigation through network pharmacology led to the identification of potential protein targets and pathways connected to hepatotoxicity. Following this, in-silico docking studies uncovered the connections between phytoconstituents of Vernonia cinerea and the chosen protein targets, primarily focusing on critical hub genes such as AKT1, PIK3CA, MAPK1, HRAS, and EGFR. The stability of these protein-ligand complexes, particularly the one between Apocynin B and EGFR, was further substantiated through molecular dynamics simulations. These discoveries emphasize the therapeutic potential of Vernonia cinerea for managing hepatotoxicity and hepatocellular carcinoma, primarily through its modulation of the MAPK, PI3-Akt, and Jak-STAT signaling pathways. The outcomes from this study shed light on the molecular mechanics contributing to the hepatoprotective benefits of Vernonia cinerea and establish a stepping stone for subsequent experimental explorations and the creation of innovative therapeutics addressing liver diseases.

Funding: “The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.”

Competing Interests: “The authors have no relevant financial or non-financial interests to disclose.”

Data Availability: “The data are not publicly available due to their containing information that could compromise the privacy of research, butare available on request from the author.”

Code availability: “Not applicable”

Authors Contribution: Muralidhar R Dhongadihas collected data, designed the study, and confirmed the findings. Laxmi Pattanashetti helped in manuscript writing, interpreted the results. Vishal Patilhelped in prepared figures of the numerical results.

Ethics approval: “This article does not contain any studies with human participants or animals performed by any of the authors.”

Consent to participate: “Not applicable”

Consent to publish: “Not applicable”

Acknowledgement: Thanks to the Prof.(Dr) AHM Vishwanath Swamy, Principal and Dr.S. K. Nimbal, HOD, Department of Pharmacology KLE College of Pharmacy, Hubli, (KAHER, Belagavi), India for providing necessary facilities as support.

Andrade RJ, Chalasani N, Björnsson ES, Suzuki A, Kullak-Ublick GA, Watkins PB, Devarbhavi H, Merz M, Lucena MI, Kaplowitz N, Aithal GP. Drug-induced liver injury. Nature Reviews Disease Primers. 2019 Aug 22;5(1):58.
Chen M, Suzuki A, Borlak J, Andrade RJ, Lucena MI. Drug-induced liver injury: Interactions between drug properties and host factors. Journal of hepatology. 2015 Aug 1;63(2):503-14.
Kaplowitz N. Idiosyncratic drug hepatotoxicity. Nature reviews Drug discovery. 2005 Jun 1;4(6):489-99.
Brar HS, Marathi R. Case of cholestatic drug-induced liver injury (DILI) associated with black cohosh. BMJ Case Reports CP. 2021 May 1;14(5):e240408.
Teschke R, Larrey D, Melchart D, Danan G. Traditional Chinese medicine (TCM) and herbal hepatotoxicity: RUCAM and the role of novel diagnostic biomarkers such as microRNAs. Medicines. 2016 Jul 19;3(3):18.
Regev A. Drug-induced liver injury and drug development: industry perspective. InSeminars in liver disease 2014 May (Vol. 34, No. 02, pp. 227-239). Thieme Medical Publishers.
Ueda JY, Tezuka Y, Banskota AH, Tran QL, Tran QK, Harimaya Y, Saiki I, Kadota S. 2002. Antiproliferative activity of Vietnamese medicinal plants. Biol Pharm Bull. 25:753–760.]
Chuakul W, Saralamp P, Boonpleng A. 2002. Medicinal plants used in the Kutchum district, Yasothon Province, Thailand. Thai J Phytopharm. 9:22–49.
Sayeed MSB, Ferdous FMTI. 2013. A randomized, placebo-controlled, crossover study of an herbal preparation containing Vernonia cinerea in the treatment of Type 2 diabetes. J Altern Complement Med. 19:767–771.
Aparece UB. 2006. Lunas: the ‘mother’ of all Sukdan shamans’ curing rituals. Phil Quart Cult Soc. 34:135–187.
Shubhangi P, Patil DA. 2011. Ethnomedicinal plants in Jalgaon district: current status. Curr Bot. 2:15–21.
Sambandan K, Dhatchanamoorthy N. 2012. Studies on the phytodiversity of a sacred grove and its traditional uses in Karaikal district, U.T. Puducherry. J Phytol. 4:16–21.
Verma S. Phytochemical and Pharmacological investigation of Vernonia cinerea: Asteraceae. The Pharma Innovation Journal. 2018;7(6):519-21.
Ivanov SM, Lagunin AA, Rudik AV, Filimonov DA, Poroikov VV. ADVERPred–Web service for prediction of adverse effects of drugs. Journal of Chemical Information and Modeling. 2018 Jan 22;58(1):8-11.
Gfeller D, Grosdidier A, Wirth M, Daina A, Michielin O, Zoete V. SwissTargetPrediction: a web server for target prediction of bioactive small molecules. Nucleic acids research. 2014 Jul 1;42(W1):W32-8.
Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic acids research. 2000 Jan 1;28(1):27-30.
Otasek D, Morris JH, Bouças J, Pico AR, Demchak B. Cytoscape automation: empowering workflow-based network analysis. Genome biology. 2019 Dec;20:1-5.
Patil VS, Khatib NA. Triterpene saponins from Barringtonia acutangula (L.) Gaertn as a potent inhibitor of 11β-HSD1 for type 2 diabetes mellitus, obesity, and metabolic syndrome. Clinical Phytoscience. 2020 Dec;6:1-5.
Wu WI, Voegtli WC, Sturgis HL, Dizon FP, Vigers GP, Brandhuber BJ. Crystal structure of human AKT1 with an allosteric inhibitor reveals a new mode of kinase inhibition. PloS one. 2010 Sep 23;5(9):e12913.
Borsari C, Keles E, McPhail JA, Schaefer A, Sriramaratnam R, Goch W, Schaefer T, De Pascale M, Bal W, Gstaiger M, Burke JE. Covalent proximity scanning of a distal cysteine to target PI3Kα. Journal of the American Chemical Society. 2022 Mar 30;144(14):6326-42.
Rasl J. Regulation of epithelial plasticity by ERK1 and ERK2 isoforms.
Ting PY, Johnson CW, Fang C, Cao X, Graeber TG, Mattos C, Colicelli J. Tyrosine phosphorylation of RAS by ABL allosterically enhances effector binding. The FASEB Journal. 2015 Sep;29(9):3750.
Gajiwala KS, Feng J, Ferre R, Ryan K, Brodsky O, Weinrich S, Kath JC, Stewart A. Insights into the aberrant activity of mutant EGFR kinase domain and drug recognition. Structure. 2013 Feb 5;21(2):209-19.
Thompson JD, Gibson TJ, Higgins DG. Multiple sequence alignment using ClustalW and ClustalX. Current protocols in bioinformatics. 2003 Jan(1):2-3.
Guex N, Peitsch MC. SWISS‐MODEL and the Swiss‐Pdb Viewer: an environment for comparative protein modeling. electrophoresis. 1997;18(15):2714-23.
Lee J, Kim SH. PDB editor: a user-friendly Java-based Protein Data Bank file editor with a GUI. Acta Crystallographica Section D: Biological Crystallography. 2009 Apr 1;65(4):399-402.
Halgren TA. Merck molecular force field. V. Extension of MMFF94 using experimental data, additional computational data, and empirical rules. Journal of Computational Chemistry. 1996 Apr;17(5‐6):616-41.
Dallakyan S, Olson AJ. Small-molecule library screening by docking with PyRx. Chemical biology: methods and protocols. 2015:243-50
Fu Y, Zhao J, Chen Z. Insights into the molecular mechanisms of protein-ligand interactions by molecular docking and molecular dynamics simulation: A case of oligopeptide binding protein. Computational and mathematical methods in medicine. 2018 Dec 4;2018.
D. Van Der Spoel, E. Lindahl, B. Hess, G. Groenhof, A.E. Mark, H.J. Berendsen, GROMACS: fast, flexible, and free, J. Comput. Chem. 26 (2005) 1701–1718, https://doi.org/10.1002/jcc.20291
P S.R. Dwivedi, V.S. Patil, P. Khanal, V.V. Bhandare, S. Gurav, D.R. Harish, B. M. Patil, S. Roy, System biology-based investigation of Silymarin to trace hepatoprotective effect, Comput. Biol. Med. 142 (2022), 105223, https://doi.org/ 10.1016/j.compbiomed.2022.105223.
Wu B, Sodji QH, Oyelere AK. Inflammation, fibrosis and cancer: mechanisms, therapeutic options and challenges. Cancers. 2022 Jan 22;14(3):552.
Chakraborty S, Kaur S, Guha S, Batra SK. The multifaceted roles of neutrophil gelatinase associated lipocalin (NGAL) in inflammation and cancer. Biochimica et Biophysica Acta (BBA)-Reviews on Cancer. 2012 Aug 1;1826(1):129-69
Lin H, Chen D, Du Q, Pan T, Tu H, Xu Y, Teng T, Tu J, Li J, Lin Z, Wang X. Dietary Copper Plays an Important Role in Maintaining Intestinal Barrier Integrity During Alcohol-Induced Liver Disease Through Regulation of the Intestinal HIF-1α Signaling Pathway and Oxidative Stress. Frontiers in Physiology. 2020 May 8;11:369
Wang Y, Kirpich I, Liu Y, Ma Z, Barve S, McClain CJ, Feng W. Lactobacillus rhamnosus GG treatment potentiates intestinal hypoxia-inducible factor, promotes intestinal integrity and ameliorates alcohol-induced liver injury. The American journal of pathology. 2011 Dec 1;179(6):2866-75.
Nishiyama Y, Goda N, Kanai M, Niwa D, Osanai K, Yamamoto Y, Senoo-Matsuda N, Johnson RS, Miura S, Kabe Y, Suematsu M. HIF-1α induction suppresses excessive lipid accumulation in alcoholic fatty liver in mice. Journal of hepatology. 2012 Feb 1;56(2):441-7.
Li L, Chen SH, Zhang Y, Yu CH, Li SD, Li YM. Is the hypoxia-inducible factor-1 alpha mRNA expression activated by ethanol-induced injury, the mechanism underlying alcoholic liver disease?. Hepatobiliary & pancreatic diseases international: HBPD INT. 2006 Nov 1;5(4):560-3
Guo X, Wang Y, Shen Y, Gao Y, Chang Y, Duan X. Gene expression profiles of sodium-dependent vitamin C transporters in mice after alcohol consumption. Acta Biochim Biophys Sin. 2013 Nov 1;45(11):912-20.
Wang Z, Li B, Jiang H, Ma Y, Bao Y, Zhu X, Xia H, Jin Y. IL-8 exacerbates alcohol-induced fatty liver disease via the Akt/HIF-1α pathway in human IL-8-expressing mice. Cytokine. 2021 Feb 1;138:155402.
Arai T, Tanaka M, Goda N. HIF-1-dependent lipin1 induction prevents excessive lipid accumulation in choline-deficient diet-induced fatty liver. Scientific reports. 2018 Sep 21;8(1):1-9.
Mesarwi OA, Shin MK, Bevans-Fonti S, Schlesinger C, Shaw J, Polotsky VY. Hepatocyte hypoxia inducible factor-1 mediates the development of liver fibrosis in a mouse model of nonalcoholic fatty liver disease. PLoS One. 2016 Dec 28;11(12):e0168572.
Yoo W, Noh KH, Ahn JH, Yu JH, Seo JA, Kim SG, Choi KM, Baik SH, Choi DS, Kim TW, Kim HJ. HIF‐1α Expression as a Protective Strategy of HepG2 Cells Against Fatty Acid‐Induced Toxicity. Journal of cellular biochemistry. 2014 Jun;115(6):1147-58.
Lucena MI, Sanabria J, García-Cortes M, Stephens C, Andrade RJ. Drug-induced liver injury in older people. The Lancet Gastroenterology & Hepatology. 2020 Sep 1;5(9):862-74.
Andrade RJ, Chalasani N, Björnsson ES, Suzuki A, Kullak-Ublick GA, Watkins PB, Devarbhavi H, Merz M, Lucena MI, Kaplowitz N, Aithal GP. Drug-induced liver injury. Nature Reviews Disease Primers. 2019 Aug 22;5(1):58.
Tse AP, Sze KM, Shea QT, et al. Hepatitis transactivator protein X promotes extracellular matrix modification through HIF/LOX pathway in liver cancer. Oncogenesis 2018;7:44. 10.1038/s41389-018-0052-8
Liu LP, Hu BG, Ye C, Ho RL, Chen GG, Lai PB. HBx mutants differentially affect the activation of hypoxia-inducible factor-1α in hepatocellular carcinoma. British journal of cancer. 2014 Feb;110(4):1066-73.
Shao G, Zou Y, Lucatelli P, Tsilimigras DI, Shimise S, Kawaguchi T. Chinese expert consensus on technical recommendations for the standard operation of drug-eluting beads for transvascular embolization. Annals of translational medicine. 2021 Apr;9(8).
Shah JL, Zendejas-Ruiz IR, Thornton LM, Geller BS, Grajo JR, Collinsworth A, George Jr TJ, Toskich B. Neoadjuvant transarterial radiation lobectomy for colorectal hepatic metastases: a small cohort analysis on safety, efficacy, and radiopathologic correlation. Journal of gastrointestinal oncology. 2017 Jun;8(3):E43
Niu L, Liu L, Yang S, Ren J, Lai PB, Chen GG. New insights into sorafenib resistance in hepatocellular carcinoma: Responsible mechanisms and promising strategies. Biochimica et Biophysica Acta (BBA)-Reviews on Cancer. 2017 Dec 1;1868(2):564-70
Rasul A, Ding C, Li X, Khan M, Yi F, Ali M, Ma T. Dracorhodin perchlorate inhibits PI3K/Akt and NF-κB activation, up-regulates the expression of p53, and enhances apoptosis. Apoptosis. 2012 Oct;17:1104-19.
Zheng T, Yang X, Wu D, Xing S, Bian F, Li W, Chi J, Bai X, Wu G, Chen X, Zhang Y. Salidroside ameliorates insulin resistance through activation of a mitochondria‐associated AMPK/PI3K/A kt/GSK 3β pathway. British journal of pharmacology. 2015 Jul;172(13):3284-301.
Zhang DQ, Sun P, Jin Q, Li X, Zhang Y, Zhang YJ, Wu YL, Nan JX, Lian LH. Resveratrol regulates activated hepatic stellate cells by modulating NF‐κB and the PI3K/Akt signaling pathway. Journal of food science. 2016 Jan;81(1):H240-5
Bava SV, Sreekanth CN, Thulasidasan AK, Anto NP, Cheriyan VT, Puliyappadamba VT, Menon SG, Ravichandran SD, Anto RJ. Akt is upstream and MAPKs are downstream of NF-κB in paclitaxel-induced survival signaling events, which are down-regulated by curcumin contributing to their synergism. The international journal of biochemistry & cell biology. 2011 Mar 1;43(3):331-41.
Sobhakumari A, Schickling BM, Love-Homan L, Raeburn A, Fletcher EV, Case AJ, Domann FE, Miller Jr FJ, Simons AL. NOX4 mediates cytoprotective autophagy induced by the EGFR inhibitor erlotinib in head and neck cancer cells. Toxicology and applied pharmacology. 2013 Nov 1;272(3):736-45.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Elucidating the hepatoprotective mechanisms of Vernonia cinerea phytoactives in drug-induced liver injury by computational approach of network pharmacology, molecular docking and dynamic simulation studies

Status:

Version 1

Abstract

Introduction

Materials and methods

Genes and pathways involved Isoniazid induced Hepatoxicity

Mining of phytocompounds

Draggability and toxicity profile of compounds

Target Identification

Gene Ontology and Pathway Enrichment Analysis

Network Construction

Molecular docking:

Preparation of Ligand and Protein Target

Molecular dynamic simulation

Stability of the docked complexes

Results

Genes and pathways involved Isoniazid induced Hepatoxicity

Mining of phytocompounds

Druglikeness and Toxicity Profile

Target Identification

Gene Set Enrichment Analysis and Network Construction

Network Analysis

Structure Refinement, Homology Modeling, and Active Sites Assessment

Molecular dynamic study

A. Stability of protein -Apocynin B complex

A. Stability of Erlotinib complex

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1