Cannabis sativa affect the endocannabinoid system was gathered from the literature. SwissTarget prediction and BindingDB databases were used to anticipate the targets for the phytocannabinoids. The genes related to RA were retrieved from the DisGeNET and GeneCards databases. Protein–protein interactions (high confidence > 0.7) were carried out with the aid of the string web server and displayed using Cytoscape. The Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathway analysis was used to perform enrichment analyses on the endocannabinoid–RA common targets. ShinyGO 0.76 was used to predict the biological processes listed in the Gene Ontology (GO) classification system. The binding affinity between the ligand and the receptors was precisely understood using molecular docking, induced-fit docking, and a molecular dynamics simulation. The network pharmacology analyses predicted that processes like response to oxygen-containing compounds and peptodyl-amino acid modification are related to the potential mechanisms of treatment for RA. These biological actions are coordinated by cancer, neuroactive ligand–receptor interaction, lipids and atherosclerosis, the calcium signaling pathway, and the Rap1 signaling pathway. According to the results of molecular docking, in the context of RA, phytocannabinoids may bind to important target proteins such PIK3CA, AKT1, MAPK9, PRKCD, BRAF, IGF1R, and NOS3. This entire study predicted the phytocannabinoids’ systemic biological characteristics. Future experimental research is needed, however, to confirm the results so far.The endocannabinoid system consists of several phytocannabinoids, cannabinoid receptors, and enzymes that aid in numerous steps necessary to manifest any pharmacological activity. It is well known that the endocannabinoid system inhibits the pathogenesis of the inflammatory and autoimmune disease rheumatoid arthritis (RA). To the best of our knowledge, no research has been done that explains the network-pharmacology-based anti-rheumatic processes by focusing on the endocannabinoid system. Therefore, the purpose of this study is to further our understanding of the signaling pathways, associated proteins, and genes underlying RA based on the abundant natural endocannabinoids. The knowledge on how the phytocannabinoids in  Although inflammation is a typical reaction to tissue damage, it can become out of control and cause further problems. Specific soluble pro-inflammatory mediators, including cytokines or chemokines, prostaglandins, and leukotrienes, are produced as a result of an inflammatory reaction . Many acCannabis sativa (family: Cannabaceae) as well as cannabinoid receptors and enzymes that aid in several steps to exhibit any pharmacological activity [Cannabis sativa (family: Cannabaceae) as a traditional medication for entheogenic and recreational uses. Cannabis sativa, however, may be useful in the treatment of pain, dysmenorrhea, muscle spasms, anorexia, etc., according to earlier research [Cannabis sativa [The endocannabinoid system consists of the numerous phytocannabinoids derived from activity ,5,6. It research ,8. Recenresearch . Furtherresearch . Cannabis sativa . The moss sativa . For a ls sativa ,14.RA is a condition with several diagnoses that is primarily characterized by chronic synovial inflammation. The etiology of this illness involves the infiltration of inflammatory cells, the growth of synoviocytes that resemble fibroblasts, elevated levels of proinflammatory cytokines, and matrix metalloproteinases. These substances encourage angiogenesis and osteoclast activity, which damage joints by altering the development of bone and cartilage . The endCannabis sativa used in this investigation were derived according to reports by Morales et al. [https://pubchem.ncbi.nlm.nih.gov/, accessed on 1 December 2022). The targets of the phytocannabinoids were found using the web servers Swiss Target Prediction and BindingDB . In addition, the canonical forms and 2D structures of the phytocannabinoids were retrieved from the PubChem database . The RA targets were derived from two databases: DisGeNET  and GeneCards . The UniProt database  was utilized to standardize the target names into official gene symbols. The keywords “Rheumatoid arthritis” and the organism “Homo sapiens” were used to seek the targets, which are verified by UniProt ID. To obtain the Venn diagram of the shared targets, Venny2.1.0  was used.The abundant phytocannabinoids of s et al. . In addihttps://string-db.org, accessed on 1 December 2022) to create a PPI network using “Homo sapiens” as the species and “highest confidence > 0.7” as the minimal interaction threshold. Additionally, the detached nodes were kept a secret. Cytoscape software was used to visualize these networks. To identify PPI subnetworks and clusters with a high degree of interconnection within the PPI network, we employed the Molecular Complex Detection (MCODE) method (version 1.5.1). As threshold settings, we set the Cytoscape plug-in MCODE’s maximum depth to 100, node score at 0.2, and K-score to 2 [The chosen targets were added to the STRING 11.0 database  was used to predict Gene Ontology (GO) biological processes. The top 20 biological pathways and processes  were selected based on fold enrichment, and they were then displayed as bubble charts with the adjusted p-values, fold enrichment, gene counts, etc. The GO analysis predicted the various biological processes, cellular components, and molecular functions. A network was constructed using the key pathways obtained from the KEGG analysis for the discovery of the important target proteins that play a significant role in the treatment effects of C. sativa [C. sativa in RA, all the information on the important targets and associated pathways was gathered through online software.The KEGG metabolic pathway analysis was used to perform enrichment analyses on Molecular docking studies of 13 phytocannabinoids were done using Glide XP (extra-precision) implemented in the Schrödinger suite. The energy-minimized structures of the ligands were docked with the crystal structure of PIK3CA , AKT1 ((Gene: Akt1) (AlphFold)), MAPK9 ((Gene: MAPK9) (AlphFold)), PRKCD ((Gene: PRKCD) (AlphFold)), BRAF (PDB ID: 6P3D), IGF1R (PDB ID: 5FXS), and NOS3 (PDB ID: 6NH5) with validated grid parameters [The Desmond module of the Schrödinger software was used to simulate the docked protein–ligand complexes in an explicit water environment. Using a system builder panel, an orthorhombic box was constructed. The simulation of the bound ligand–protein complex was performed using a simple point charge (SPC) model of water. The MD simulation was performed using a constant-temperature, constant-pressure ensemble (NPT) at 310 K and 1.013 bar as an atmospheric pressure intended to last 100 ns. The Simulation Interactions Diagram Report of the Desmond software was used to thoroughly examine the results of the molecular dynamics research ,25.Induced-fit docking was carried out on the best binding energy-containing bioactives with their respective receptors by employing the IFD panel of the Schrödinger suite . Here, tThe binding free energy was calculated from the structural information of the MM-GBSA method. These approaches employ the solvent accessibility method, molecular mechanics, and the generalized Born model to evade the free energy simulations’ convolution . The eneHere, the MM-GBSA calculation ,30 was dhttp://www.swissadme.ch/, accessed on 3 December 2022) were used to determine the physicochemical and pharmacokinetic properties of all the phytocannabinoids, which helps to determine the physicochemical significant descriptors and pharmacokinetically important properties of the phytocannabinoids.The QikProp module of Schrödinger suite   and SwisCannabis sativa, were retrieved from the literature [A total of 13 phytocannabinoids, found in terature . A totalThe phytocannabinoids and common targets were added to Cytoscape 3.9.1 for the construction of a network B,C. ThisA total of 4255 terms linked to the therapeutic effects of phytocannabinoids on RA were found as a result of GO enrichment analysis on common phytocannabinoids–RA targets. 9-tetrahydrocannabivarin , and cannabichromene  (Based on the Glidescores of the phytocannabinoids against PIK3CA (PDB ID: 4L23), the top three phytocannabinoids were discovered to be cannabigerol , Δcal/mol) . The phycal/mol) .In the case of AKT1 (Gene: AKT1), the top three phytocannabinoids were discovered to be cannabitriol , cannabichromene , and cannabielsoin . The phytocannabinoids cannabitriol and cannabichromene formed a common H-bond with Asn279. Cannabitriol formed two additional H-bonds with Lys276 and Asp292. The phytocannabinoid cannabielsoin formed H-bonds with Lys158 and Glu278. All three phytocannabinoids exhibited common polar interactions with Asn279 and Thr291. The superimposition of the three phytocannabinoids predicted that they all easily fit into the Leu156, Val164, Ala177, Met227, Tyr229, Ala230, Met281, and Phe438 residues-based shared hydrophobic binding pocket.8-tetrahydrocannabinol . Cannabinodiol formed H-bond with Ile32 and Ser155, whereas the phytocannabinoids cannabichromene and Δ8-tetrahydrocannabinol formed H-bonds with Glu109 and Met111; and Lys55, respectively. All three phytocannabinoids formed common polar interactions with Ser34, Asn114, and Ser155. Through the superimposition of all three phytocannabinoids, it was revealed that they fitted well into the common hydrophobic binding pocket consisting of Ile32, Val40, Ala53, Ile86, Met108, Leu110, Met111, Val158, and Leu168.In the case of MAPK9 (Gene: MAPK9), the top three phytocannabinoids were found to be cannabinodiol , cannabichromene , and Δ9-tetrahydrocannabinol , and Δ9-tetrahydrocannabivarin . All three phytocannabinoids formed two common H-bonds with Val174 and Gln284, while cannabitriol formed an additional H-bond with Leu279. All three phytocannabinoids formed four common polar interactions with Ser173, Hie231, Asn283, and Gln284. It was discovered via the superimposition of all three phytocannabinoids that they all fit comfortably into the same hydrophobic binding pocket that is made up of the residues Val174, Cys175, Cys264, Met266, Leu279, Cys280, Ile282, and Leu286.In the case of PRKCD (Gene: PRKCD), the top three phytocannabinoids were found to be cannabitriol , ΔIn the case of BRAF (PDB ID: 6P3D), the top three phytocannabinoids were found to be cannabigerol , cannabinodiol , cannabichromene . The phytocannabinoids cannabigerol and cannabinodiol formed H-bonds and π-π interactions with Cys532 and Phe595, respectively. All three phytocannabinoids formed two common polar interactions with Thr529 and Gln530. It was discovered via the superimposition of all three phytocannabinoids that they all fit comfortably into the same hydrophobic binding pocket made up of the residues Ile463, Val471, Ala481, Val2, Leu514, Trp531, Cys532, Phe583, and Phe595.9-tetrahydrocannabinol , Δ9-tetrahydrocannabivarin , and cannabigerol . All three phytocannabinoids formed an H-bond with Met1082. They also formed one common polar interaction with Thr1083. It was discovered by the superimposition of all three phytocannabinoids that they all fit comfortably into the same hydrophobic binding pocket made up of the residues Leu1005, Phe1010, Val1013, Ala1031, Leu1081, Met1082, Ile1156, and Met1160.In the case of IGF1R (PDB ID: 5FXS), the top three phytocannabinoids were found to be ΔIn the case of NOS3 (PDB ID: 6NH5), the top three phytocannabinoids were found to be cannabinol , cannabichromene , and cannabigerol . The phytocannabinoids cannabinol and cannabichromene formed one common π-π interaction with Trp178. Cannabichromene formed one H-bond with Ser354, while cannabigerol formed two H-bonds with Cys184 and Pro334. All three phytocannabinoids formed two common polar interactions with Ser226 and Ser354. It was discovered by the superimposition of all three phytocannabinoids that they all fit comfortably into the same hydrophobic binding pocket that is made up of the residues Trp178, Ala181, Cys184, Leu193, Ala227, Pro334, Val336, Met339, Phe353, and Phe473 .The molecular dynamics simulations of protein–ligand complexes are thought to be the best developed method for assessing the structure-to-function relationships of macromolecules in computer-aided drug design. To clarify the cannabinoid’s potency against the six proteins associated with RA, an impartial molecular dynamics investigation was conducted in the current study. The stability and viability of the best docked ligands against all the target proteins were examined using MD simulation studies, which were run for 100 ns. The root mean square deviation (RMSD), the root mean square fluctuation (RMSF), and protein–ligand interactions were generated with the production of 100 ns MD simulations, and their relevance in the solidity of the protein–ligand complexes was thoroughly examined. Protein–ligand interactions and protein–ligand RMSDs were investigated and visualized during this period.For the PIK3CA–cannabigerol complex, their RMSD for the first 50 nanoseconds was more than 2.5 Å, and from 50 nanoseconds on, the ligand started to stabilize with the protein at above 3.5 Å. It was discovered through protein–ligand contact analysis that roughly 50% of the amino acids were responsible for the hydrophobic interactions with cannabigerol. The remaining 50% were engaged in either the H-bond interactions or the water bridges. Hydrophobic interactions with cannabigerol were largely caused by the residues Met772, Pro778, Trp780, Ile800, Tyr836, Ile848, Val850, Met922, and Ile932, of which Met772 and Ile932 were the most accountable at 0.15 ns and 0.20 ns fractions of the interaction time, respectively. Glu849 and Val851 were responsible for the highest H-bond contacts at 0.10 ns and 0.25 ns fractions, while Met772, Ser919, and Asn920 were responsible for forming highest water bridge contacts at 0.20 ns and 0.35 ns fractions.For the AKT1–cannabitriol complex, their RMSD for the first 60 nanoseconds was stabilized at above 4 Å, while for the next 40 nanoseconds, both the ligand and protein RMSDs were slightly fluctuating at 4.8 Å. Through protein–ligand contact analysis, it was discovered that the residues Phe161, Val164, Ala177, Lys179, and Leu181 were highly responsible for forming hydrophobic contact with cannabitriol, among which Val164 and Lys179 were the most accountable ones at 0.3 ns and 0.6 ns fractions of the interaction time, respectively. Glu234 and Asp439 were responsible for the highest water bridge forming contacts at 0.3 ns and 0.5 ns fractions. Lys179 and Asp439 were the H-bonding residues, whereas Glu234 and Asp439 were the residues forming ionic contacts.The protein–ligand RMSD plots for the MAPK9–cannabinodiol complex showed that RMSD was greater than 4 Å for the first few nanoseconds, increased to 12 Å after a while, and then increased to 16 Å before stabilizing there. Through protein–ligand contact analysis, it was found that the residues Val40, Ala53, Leu57, Ile86, Met108, Leu110, Val158, and Leu168 were responsible for hydrophobic contacts with cannabinodiol, among which Val40, Ala53, Val158, and Leu168 were the most accountable ones at 0.40 ns, 0.30 ns, 0.35 ns, and 0.30 ns fractions of the interaction time, respectively. Ile32 and Lys55 were responsible for the highest water bridging at 0.35 ns, while, on the other hand, Ile32, Lys55, Asn114, Ser155, and Asn156 were responsible for H-bonding contacts.The RMSD for the PRKCD-cannabitriol complex was greater than 4 Å for the first few nanoseconds before increasing to 8 Å and then stabilizing there. According to the protein–ligand contact study, Ser173, Gln284, and Lys285 in H-bond interactions at 0.8, 1.0, and 0.8 ns of the interaction period, respectively, accounted for the majority of the protein–ligand connections with cannabitriol. The remaining amino acid residues were in charge of either creating water bridges or creating hydrophobic interactions.The RMSD for the BRAF–cannabigerol complex was greater than 1.6 Å for the first few nanoseconds before increasing to 2.4 Å, then 2.8 Å, and then stabilizing at that value. Trp531, Phe583, and Phe595 were determined to be primarily in charge of hydrophobic interactions with cannabigerol at 0.22 ns, 0.15 ns, and 0.14 ns fractions of the interaction time, respectively, according to the protein–ligand contact study. The majority of the residues either contributed to the formation of water bridges or H-bonds. Among these, Gln461 and Cys532 played a significant role in the formation of H-bonds. Ile463, Glu533, and Gly534 played a significant role in the formation of water bridges.9–tetrahydrocannabinol complex was more than 3.0 Å for the first few nanoseconds, but with time, it increased to 4.8 Å, where it stabilized. Based on protein–ligand contact analysis, it was found that Met1082 was responsible for the highest H-bond contact with Δ9-tetrahydrocannabinol at the 1.75 ns fraction of the interaction time. The amino acid residues Leu1005, Val1013, Ala1031, Met1142, and Ile1160 were mostly responsible for hydrophobic contacts. Leu1005 and Thr1157 were responsible for forming water bridges  to two (in IFD-generated pose). In case of IFD of NOS3 with cannabinol, the number of π-π stacking interactions increased from one  to two (in IFD-generated pose). Additionally, one H-bond interaction with Ala177 was observed from the IFD-generated pose which was absent in the normal XP-mode Glide docked pose. Furthermore, the prime MM-GBSA binding free energies of the best five docked complexes were calculated and are given in the IFD  of all tData generated by QikProp predicted that all the expected attributes for phytocannabinoids fell inside the acceptable range. The SwissADME-predicted data showed that the phytocannabinoids had significant values for the studied attributes, and based on their physicochemical features, they demonstrated drug-like qualities. All phytocannabinoids obeyed Veber’s rule. The phytocannabinoids cannabigerol and cannabichromene failed to pass/satisfy the blood–brain barrier (BBB) filter , but othThe chronic inflammatory condition known as arthritis is characterized by immune-mediated inflammatory sinusitis, which causes hyperplasia, vasculogenesis, pannus development, the deterioration of cartilage and bone, joint deformity, and functional impairment. Many phytocannabinoids, cannabinoid receptors, and enzymes that are part of the endocannabinoid system all contribute to the pharmacological activity of cannabis in different ways. It is well known that the endocannabinoid system inhibits the pathogenesis of the inflammatory and autoimmune disease RA. Due to the abundance of natural phytocannabinoids, the goal of this study was to build on the signaling pathways, associated proteins, and genes that cause RA using integrated bioinformatics and network pharmacology. The general workflow scheme of the present study is depicted in p-value of <1.0 × 10−16. As would be predicted, the proteins interacted with one another quite a bit. The results of the enrichment analysis showed that the proteins were, collectively, at least loosely physiologically interconnected. For the hub PPI, we chose nodes whose median edge count was greater than double that of all other nodes.We gathered 2723 and 4881 predicted targets for phytocannabinoids from two distinct databases, namely GeneCards and the DisGeNET database, respectively. Thirteen phytocannabinoids and 244 common targets were obtained by the intersection of RA targets and phytocannabinoids (with the aid of venny 2.1.0). The PPI enrichment analysis results were reported with 244 nodes, 699 edges, highest confidence (0.90), and the PPI enrichment The results of the PPI network and GO analysis predicted that the key proteins involved in the numerous phytocannabinoids–RA common targets were primarily those involved in the pathways for cancer, neuroactive ligand–receptor interaction, lipids and atherosclerosis, calcium signaling, and RAP signaling. In cancer, there is an abnormality in the signaling of PI3K/Akt/mTOR due to which many types of research have been done to find the inhibitors of PI3K/Akt/mTOR signaling which may act as anticancer agents. RA is an autoimmune-mediated inflammatory disease where the abnormal proliferation of immune cells, macrophages, monocytes, dendritic cells, and synovial fibroblasts may significantly overlap with the abnormal growth of cancer cells. The results of some recent studies in arthritis using PI3K signaling inhibitors suggest that small molecule inhibitor strategies directed at PI3K signaling may be useful for immune-mediated arthritis . The pre9-tetrahydrocannabivarin had the highest Glidescores and the most drug-like qualities, as well as acceptable ADME/T profiles.In light of the current findings of network pharmacology, PIK3CA, AKT1, MAPK9, PRKCD, BRAF, IGF1R, and NOS3 were chosen as the seven key target proteins closely related to the progression of RA. These proteins were further subjected to molecular docking with phytocannabinoids. Negative binding energy indicates effective binding to the target. Lower binding energy suggests a stable ligand–receptor complex better explained as <−5 kcal/mol called good binding activity and <−7 kcal/mol called strong binding activity. Except for AKT1 and PRKCD, the Glidescores of the top three compounds against the other five proteins were in the range of −8.888 to −6.751 kcal/mol. The top three active ingredients were shown to have a strong binding interaction with their respective proteins. It entails unexpected bias from the materiality, which could hasten the anonymous fallacy in in vitro or in vivo studies. However, the results of the molecular docking experiment suggest the potential therapeutic approaches and could guide trials in animals. Network pharmacology is currently used extensively for various traditional medicines, such as Indian traditional medicine, traditional Chinese medicine, Kampo, traditional Korean medicine, etc. Network pharmacology research is being conducted owing to its robust potential to investigate convoluted relationships among multiple targets. Additionally, network pharmacology accelerates the modernization of the Indian traditional system of medicine by usefully illuminating the synergistic effects of biochemical and systemic biology methodologies. Researchers may identify prospective active ingredients for upcoming in vitro and in vivo studies by using molecular docking to lay out an effective binding mode of the active ingredients with disease-related important target proteins. However, a number of the Ayurvedic formula’s constituents can have antagonistic and synergistic effects on the body while being treated because they target the same protein. It is important to consider these implications and to continue to assess them. The findings of MM-GBSA (binding free energy) and IFD support the molecular dynamics findings that the ligand is stable within the protein. One of the finest molecular dynamics results was obtained with the Akt1-cannabitriol complex, despite its Glidescore of −6.035 kcal/mol. Among the seven proteins, the phytocannabinoids cannabigerol, canabichromene, and ΔThe present study evaluated the integrated bioinformatics and network pharmacology to identify therapeutic targets and molecular processes involving the endocannabinoid system in patients RA. The work used a combined bioinformatics and network pharmacology approach to further examine signaling pathways, associated proteins, and genes that may implicate naturally occurring phytocannabinoids in the pathogenesis of RA. The current research findings suggest that the abundant phytocannabinoids can exert their curative effect through a synergistic effect of multi-components, multi-targets, and multi-pathways in the treatment of RA. However, due to the limitation of network pharmacology and molecular docking, a more complete evaluation of the role of the endocannabinoid system in the immune landscape in patients with RA and a comprehensive analysis of patients with RA and normal controls would be necessary. Hence, further evaluation of in vitro and in vivo studies needs to be carried out to validate the current findings, which is the future scope of the present study. The probable mechanisms of phytocannabinoids in rheumatoid arthritis are depicted in The network pharmacology analysis results predicted that the processes such as response to oxygen-containing compounds and peptodyl-amino acid modification are related to the potential treatment mechanisms for rheumatoid arthritis. These biological actions may be coordinated by cancer, neuroactive ligand–receptor interaction, lipids and atherosclerosis, the calcium signaling pathway, and the Rap1 signaling pathway. According to the results of molecular docking, in the context of rheumatoid arthritis, phytocannabinoids may bind to important target proteins such as PIK3CA, AKT1, MAPK9, PRKCD, BRAF, IGF1R, and NOS3. Due to a lack of experimental validation, this study has shortcomings, although it can inspire in vitro or in vivo studies. However, due to the limitations of network pharmacology and molecular docking, a more complete evaluation of the role of the endocannabinoid system in the immune landscape in patients with RA and a comprehensive analysis of patients with RA and normal controls would be necessary. This entire study predicted the phytocannabinoids’ systemic biological characteristics. Future experimental research is still needed to confirm the results so far.


P value < .01, | log2 FC | ≥ 1.0). The BisoGenet plug-in and the CytoNCA plug-in of Cytoscape were used to derive the potential core target proteins of JSD in the treatment of OPF. The JSD active ingredient target interaction network and the JSD-OPF target protein core network were constructed using the Cytoscape software. In addition, the R language Bioconductor package and clusterProfiler package were used to perform gene ontology (GO)/Kyoto Encylopedia Of Genes And Genome (KEGG) enrichment analysis on core genes to explain the biological functions and signal pathways of core proteins. Finally, molecular docking and molecular dynamics simulations were carried out through PyMOL, AutoDockTools 1.5.6, Vina, LeDock, Discovery Studio (DS) 2019, and other software to verify the binding ability of drug active ingredients and core target proteins. A total of 245 targets and 70 active components were identified. Through protein-protein interaction (PPI) network construction, 39 core targets were selected for further research. GO/KEGG enrichment analysis showed that the DNA-binding transcription factor binding, RNA polymerase II-specific DNA-binding transcription factor binding, MAPK signaling pathway, and ErbB signaling pathway were mainly involved. The results of molecular docking and molecular dynamics simulations supported the good interaction between MYC protein and Quercetin/Stigmasterol. In this study, bioinformatics, molecular docking, and molecular dynamics simulations were used for the first time to clarify the active components, molecular targets, and key biological pathways of JSD in the treatment of OPF, providing a theoretical basis for further research.Osteoporotic fracture (OPF) is a prevalent skeletal disease in the middle-aged and elderly. In clinical practice, Jianshen Decoction (JSD) has been used to treat OPFs. However, the specific effective components and mechanisms of JSD on OPF have not been explored. Therefore, this study used bioinformatics analysis combined with molecular dynamics simulation validation to explore the molecular mechanism of JSD treatment of OPF. Public databases  were used to find the effective active components and corresponding target proteins of JSD . Differentially expressed genes (DEGs) related to OPF lesions were obtained based on the gene expression omnibus database (screening conditions: adjust  One OPF occurs every 3 seconds worldwide, 50% of women and 20% of men will have their first OPF after age 50, and 50% of patients who have already had one will likely have another one; the risk of re-fracturing an osteoporotic vertebral fracture in a woman is 4 times higher than the risk of a vertebral fracture that has not already broken.–5 OPF of the hip and vertebrae shorten life expectancy and can result in death rates of up to 20% and rates of permanent impairment of up to 50% in people who are bedridden for extended periods.,7 In China, the prevalence of OP in adults over 50 in 2018 was 19.2%, with 6.0% of males and 32.1% of women affected. A total of 32.0% of adults over 65 had OP, with 10.7% of males and 51.6% of women affected. In middle-aged and elderly women, OP is a severe issue. The essential tenets of treating OPFs are displacement, immobilization, functional training, and anti-OP therapy. The effectiveness of conventional anti-OP medications, however, is not particularly good. Bisphosphonates have drawbacks as the therapy of choice for OP, including a significant prevalence of fever and flu-like side effects. And estrogen has the risk of causing endometrial cancer, breast cancer, etc. Therefore, it is essential to pick a medication that can be used for a long time and is also secure, trustworthy, and affordable. Chinese Medicine therapy may alleviate symptoms, boost bone density, and enhance the quality of life while remaining safe, according to clinical practice, and it has its benefits and peculiarities.,12In middle-aged and older persons, osteoporotic fracture (OPF) is a prevalent skeletal disease. It is a low-energy or nonviolent fracture, also known as a fragility fracture, and is the severe stage of osteoporosis (OP), marked by high morbidity, high disability, and death rates, as well as significant medical expenses. Additionally, treating postmenopausal OP patients with JSD and zoledronic acid together can greatly increase their bone density, dramatically reduce their pain, stimulate estrogen production, and encourage bone growth. The combined osteoblast transplantation of JSD-containing serum into the fracture site is also discovered to significantly raise vascular endothelial growth factor mRNA expression. The studies mentioned above show that JSD is effective at treating OP and aiding in the healing of fractures, but their potential pharmacological mechanisms of action and pathway interactions remain unclear and require further research.The Jianshen Decoction (JSD) is based on the theory that Chinese medicine warms the liver and kidneys and strengthens the muscles and bones. It is mainly used for the treatment of primary OP and OPF. The combination of JSD and Calcium Carbonate D3 Chewable Tablets (II), which has a better safety profile than alendronate tablets and Calcium Carbonate D3 Chewable Tablets (II), can increase serum E2, osteoprotegerin, and IGF-I levels, increase bone mineral density, and reduce pain in OP patients with kidney yang deficiency.,17It is common practice in the drug development process to employ molecular docking, which is based on the idea of ligand-receptor interactions. A fine depiction of biomolecules is provided by molecular dynamics (MD) simulations, which explain the dynamic behavior of biomolecules at the atomic level.We hypothesize that JSD has “multi-component, multi-target, and multi-pathway” characteristics for the treatment of OPF. This study aims to elucidate and validate the effective active ingredients, targets, and potential mechanisms of JSD in the treatment of OPF using bioinformatics, molecular docking techniques, and MD Simulation, and to provide a reference for subsequent basic research. The detailed workflow is shown in Figure https://tcmsp-e.com/). Based on the search results, screening was performed according to ADME principles oral bioavailability ≥ 30%, Drug-likeness ≥ 0.18, Drug half-life ≥ 4),–21 and the corresponding targets were obtained. For the unretrieved herb “Rhizoma Cibotii," the BATM-TCM database (http://bionet.ncpsb.org.cn/batman-tcm/) was used to retrieve the active ingredients with Change Score cutoff ≥ 20 and Adjusted P value cutoff ≥ .05, and the corresponding target sites were predicted. The UniProt database (https://www.uniprot.org) was used to import all of the target protein names identified throughout the screening, and target proteins without corresponding gene names were removed.JSD is composed of 12 herbs: Eucommiae Cortex, Rhizoma Cibotii, Lycii Fructus, Rosae Laevigatae Fructus, Chaenomeles Sinensis (Thouin) Koehne, Achyranthis Bidentatae Radix, Flemingia Prostrata Roxb, Herba Taxilli, Cornus Officinalis Sieb. Et Zucc, Rehmanniae Radix Praeparata, Radix Clematidis, and Dipsaci Radix. The chemical composition of the above 12 herbs was searched using the TCMSP database , and the screening conditions were: OPFs; human and whole genome sequencing data meeting the screening criteria were obtained. The microarray matrix file numbered GSE156508 and the GPL16686 microarray gene annotation file were obtained, which contained 12 femoral head samples, including 6 patients with OPF and 6 controls. FastQC software was used for quality assessment and quality control processing of the raw data, and the clean data obtained after quality control processing was further compared and analyzed using Hisat2 software to select the reference genome, and the HTSeq Python package was used for quantitative analysis of the number of gene reads to obtain the counts file. The collated counts data were filtered by Variance filter, Low abundance, and normalized using the Log2 Transformation. Differential analysis was performed using the DESeq2 package in R language to calculate the differential genes between the 2 groups, setting P < .05, adjusted P value < .01, and expression change greater than or equal to twofold (|log2FC|≥1.0) as the criteria for screening differential genes, where log2FC ≥ 1.0 represents gene expression up-regulation, log2FC ≤ −1.0 represents gene expression down-regulation, and finally the DEGs in the OPF group and the control group were derived, that is, DEGs for OPF. Heat map and cluster analysis of the screened differential genes were performed using the online analysis website ClustVis (https://biit.cs.ut.ee/clustvis/), and the P value of the processed data was -log10 transformed, and -log10  was grouped according to log2 FC for grouping , and import the processed data into bioinformatics tool to draw volcano map.High-throughput sequencing data of genes associated with OPF were downloaded from the gene expression omnibus database ( in Cytoscape 3.7.2 software was used to construct the protein-protein interaction (PPI) network of targets of JSD and the PPI network of DEGs of OPF, respectively. Using the “Networkanalyze” function in Cytoscape 3.7.2 software, the 2 PPI networks generated were intersected to obtain the PPI network shared by the targets of JSD and DEGs of OPF. The CytoNCA plug-in in Cytoscape 3.7.2 software was then used to calculate the multicenter network topology of the obtained shared PPIs and to filter them according to the median values of Degree, BetweennessCentrality , ClosenessCentrality, and local average connectivity values to derive the core target genes of active ingredients of JSD acting on OPF.The BisoGenet plug-inhttps://string-db.org/) online analysis website was applied to construct a potential core target gene interaction network for the core target genes by setting the protein species to “Homo sapiens,” settings minimum required interaction score to “medium confidence (0.400),” and hiding the unrelated targets. Count. R plug-in was applied to obtain the frequency of common protein targets. Further, the PPI network model of potential target genes was obtained by Cytoscape 3.7.2 software, and the cytoHubba plug-in was used to filter the hub genes, and the top 20 hub genes were ranked using the Degree algorithm, the PPI networks of the top 10 hub genes based on Degree, Betweenness, and Closeness were constructed separately.The STRING (https://www.r-project.org/) software. The KEGG Pathway enrichment analysis was performed with the clusterProfilerKEGG.R package, and the corresponding signaling pathways were mapped with the path view package. The core pathway enrichment was analyzed according to the enrichment factor values.The GO analysis was performed separately for the core target genes by applying the clusterProfilerGO.R package in R language (https://pubchem.ncbi.nlm.nih.gov/), and the corresponding 3D structures were made and exported to mol*2 format by Chem3D software, then the core protein were downloaded from the PDB database (http://www.rcsb.org/) structure domain in pdb format, and dehydration and dephosphorylation of the protein were performed using PyMOL software. AutoDockTools 1.5.6 software was used to convert the drug active ingredient and core protein gene files in pdb format to pdbqt format and to find the active pockets, and finally Vina script and LeDock software were run for molecular binding energy calculation and molecular docking results presentation, while Discovery Studio (DS) 2019 software to find docking sites and calculate the LibDockScore for flexible binding.,34 The molecular docking results output from Vina software were imported into PyMOL software for molecular docking confirmation display. If the binding energy is <0, it means that the ligand and the receptor can bind spontaneously, and when the binding energy of Vina is ≤−5.0 kcal mol−1 and DS can find the docking site and LibDockScore ≥ 100, it means that the 2 forms stable docking, and the molecular docking results of ligand-receptor complexes are displayed in 3D and 2D to evaluate the bioinformatics Reliability of the predictions of the analysis.The structural formulas of the active ingredient were downloaded from the PubChem database  and root mean square fluctuations between different conformations, and to detect non-bonded interactions formed between peptides and proteins.Eucommia ulmoides Oliv.), 5 species of Rhizoma Cibotii[Cibotium barometz(L.) J.Sm.], 33 species of Lycii Fructus(Lycium chinense Mill.), 6 species of Rosae Laevigatae Fructus(Rosa laevigata Michx.), 3 species of Chaenomeles Sinensis (Thouin) Koehne[Pseudocydonia sinensis (Thouin) C. K. Schneid.], 15 species of Achyranthis Bidentatae Radix(Achyranthes bidentata Bl.), 1 specie of Flemingia Prostrata Roxb(Flemingia prostrate Roxb. f. ex Roxb.), 2 species of Herba Taxilli[Taxillus sutchuenensis (Lecomte) Danser], 10 species of Cornus Officinalis Sieb. Et Zucc, 2 species of Rehmanniae Radix Praeparata[Rehmannia glutinosa (Gaert.) Libosch. ex Fisch. et Mey.], 2 species of Radix Clematidis(Clematis chinensis Osbeck), and 4 species of Dipsaci Radix, and there were 9 ingredients common to multiple herbs . The results were imported into the bioinformatics tool to draw volcano maps  were selected for screening and validated by molecular docking simulations with the related core proteins  in that order, and the results showed that all active ingredients could form docking models with the core proteins in the docking results of the Vina script. In the LeDock software docking results, the binding energies of Beta-sitosterol with JUN, BRCA1, and Stigmasterol with SRC, and BRCA1 were greater than −5 kcal-molls Table . Comprehls Table , and DS ls Table .The optimal conformation resulting from the docking of MYC with Quercetin/Stigmasterol molecule was selected for subsequent molecular dynamics simulations and solved using an explicit periodic boundary solventized water model. In addition, for the analysis of the physiological environment, MYC-Quercetin complex was added with 711 water, 19 Sodium, and 25 Chloride, and MYC-Stigmasterol complex was added with 6495 water, 17 Sodium, and 30 Chloride. To investigate the structural stability of protein-ligand complexes during MD simulations, the RMSD values of the complexes were calculated for the complexes formed during docking for 50 ns during MD simulations  is a broad-ranging transcription factor that regulates cell differentiation and proliferation programs through a variety of mechanisms. In the RANKL-induced transcription program, MYC is a crucial transcription factor and is connected to osteoclastogenesis in vitro.There are however several limitations of this study. First, the results of this study lack cellular and animal experimental validation. Another limitation is the false negative due to targets from different databases, which may have biased effects due to different experimental conditions.In summary, the active ingredients of JSD and their molecular targets in OPF were successfully revealed through bioinformatics, molecular docking, and molecular dynamics simulations. We identified a total of 70 potential key components and 39 core targets. JSD was found to play a role in the treatment of OPF possibly through the regulation of MAPK and ErbB signaling pathways. The interaction between Quercetin/Stigmasterol and MYC is important for the mechanism of OPF treatment by JSD, but further in vivo/in vitro trials are needed. This study provided an overall view of the potential pharmacological mechanisms of JSD against OPF, however, there was no corresponding experimental validation of this study, which is something we need to do in the future.Grateful Acknowledgments is made to my supervisor Professor Huo Liwei and Wang haibin who gave me considerable help means of suggestions, comments, and criticism. In addition, I deeply appreciate the contribution to this thesis made in various ways by my friends and classmates.Conceptualization: Weinian Liu.Data curation: Weinian Liu, Mengting Hu.Formal analysis: Weinian Liu, Yuanhao Hu.Methodology: Weinian Liu, Weijian Chen, Guangwei Wang.Software: Weinian Liu, Yidong Xu, Jun Tan.Supervision: Weinian Liu, Guangwei Wang, Qi He, Haibin Wang, Liwei Huo.Validation: Weinian Liu, Weijian Chen, Mengting Hu, Qi He, Yidong Xu, Jun Tan.Visualization: Weinian Liu, Weijian Chen, Yuanhao Hu.Writing – original draft: Weinian Liu.Writing – review & editing: Weinian Liu, Haibin Wang, Liwei Huo.


Introduction: While targeted investigation of key toxicity pathways has been instrumental for biomarker discovery, unbiased and holistic analysis of transcriptomic data provides a complementary systems-level perspective. However, in a systematic context, this approach has yet to receive comprehensive and methodical implementation.Methods: Here, we took an integrated bioinformatic approach by re-analyzing publicly available MCF7 cell TempO-seq data for 44 ToxCast chemicals using an alternative pipeline to demonstrate the power of this approach. The original study has focused on analyzing the gene signature approach and comparing them to in vitro biological pathway altering concentrations determined from ToxCast HTS assays. Our workflow, in comparison, involves sequential differential expression, gene set enrichment, benchmark dose modeling, and identification of commonly perturbed pathways by network visualization.Results: Using this approach, we identified dose-responsive molecular changes, biological pathways, and points of departure in an untargeted manner. Critically, benchmark dose modeling based on pathways recapitulated points of departure for apical endpoints, while also revealing additional perturbed mechanisms missed by single endpoint analyses.Discussion: This systems-toxicology approach provides multifaceted insights into the complex effects of chemical exposures. Our work highlights the importance of unbiased data-driven techniques, alongside targeted methods, for comprehensively evaluating molecular initiating events, dose-response relationships, and toxicity pathways. Overall, integrating omics assays with robust bioinformatics holds promise for improving chemical risk assessment and advancing new approach methodologies (NAMs). Transcriptomic data has proven to be highly informative in toxicology for determining cellular modes of action (MOA) and points of departure (POD) for hazard assessment and the comparative potency of congeners. MOA refers to a sequence of biological events and processes that occur in an organism upon exposure to a chemical or substance that leads to a particular toxic effect or adverse outcome. POD, on the other hand, is the reference point or a dose level on a dose-response curve at which a specific adverse effect or toxic response begins to occur. Community standards for best practices in analyzing transcriptomic data have observed significant convergence in principles for statistical analyses, use of benchmark dose (BMD) model fitting, and POD derivation . Analysiin vitro studies, to derive PODs as a means of reducing costs and time to completion for toxicology applications. With increasing interest in using transcriptomic data for the read-across of the MOA and potency of chemicals in cellular response to in vivo exposure, the use of standard transcriptomic analyses and relational databases for comparative analyses becomes essential  pathway enrichment . Our staWhile past studies have provided important insights by focusing on particular exposure-relevant processes like estrogen-receptor alpha (ERα) signaling pathway, our work aims to complement this understanding through a holistic systems biology approach. Therefore, in this study, we sought to ask how an unbiased and holistic analysis of transcriptomic data using integrated bioinformatic approaches could enhance our understanding of chemical toxicity, dose-response relationships, and toxicity pathways, and how this approach compares to traditional targeted methods in chemical risk assessment. By broadly profiling transcriptomic changes, we revealed additional pathways related to nitrogen metabolic processes that warrant further investigation. This unbiased, systemic perspective allows us to more fully appreciate the complex interconnectivity and breadth of biological impacts induced by chemical exposure. Furthermore, BMD modeling based on pathways rather than individual endpoints integrates signals across multiple affected systems to improve dose-response characterization. Overall, our unbiased system-wide analysis provides a multifaceted complement to enrich current knowledge of the diverse processes disrupted by these chemicals. By combining targeted mechanistic investigation with global omics profiling will lead to a comprehensive understanding of exposure effects across biological scales.https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE162855).The processed gene count data  was downp-value corrections. The final output of DESeq2 is a table of estimated Log2 fold change, p-values for the defined contrasts tested, as well as Benjamini–Hochberg corrected false discovery p-values (FDR) In our analyses, we use as a statistical criterion, the Benjamini and Hochberg step-up false discovery rate corrected p-value of less than 0.05 (FDR<0.05) or less than 0.01 (FDR<0.01), and a change in expression , up or downregulated genes in the compound-treated samples relative to controls of 1.5-fold (|FC|>1.5) or 2-fold (|FC|>2).Counted reads for each annotated transcript feature were analyzed for differential expression in R (v.3.5.3) using the BioConductor library DESeq2 (v.1.20.0) . DESeq2 2-transformed expression values are computed for each gene in an RNA-Seq experiment. We have used the raw gene counts, which is the recommended data type to be used with DeSeq2  for the selected best polynomial model is compared with the AIC for the power and non-excluded Hill models to select the best overall model, which is then used to calculate a BMD and benchmark dose lower confidence limit (BMDL). To avoid model extrapolation, genes with a BMD value greater than the highest concentration used in the experiment were removed from further analysis, as were poor-fitting best overall models with a goodness of fit p-value less than or equal to 0.1. The power parameter was restricted to >1 as values less than 1 have the potential to create models fit with a slope approaching infinity. We used 1 standard deviation (1 SD) BMR factor. A BMDU/BMDL ratio of greater than 40 is also grounds for the rejection of a best-fitting model, as this indicates an increasingly large 95% CI about the estimate. The final selection of genes with the best models passing these quality control thresholds can then be matched to elements in an ontology enrichment using the publicly available Gene Ontology Pathway database, including all the BP, CC and MF components . The following criteria were used for filtering the BMD functional category results for all 47 chemicals tested: FDR<0.05; minimum number of genes observed in a pathway >5; GO category level = 2. The color scale of the heatmap in https://github.com/rasimbarutcu/EPA_Harrill_etal_BMD_Reanalysis_Codes.git. GO analyses were performed by using the FuncAssociate 3.0 tool (https://github.com/UBrau/GOplotTools) represent the FDR value and the number of genes identified for each pathway. All the figures in the manuscript were generated by using the R-ggplot2 package  . Detailsh DeSeq2 , as an imponents . Various3.0 tool . The bub package .In order to interpret MOA as well as POD values derived from differential gene expression, BMD, and ontology pathway enrichment analyses, we re-analyzed the data from p-adjusted) value of less than 0.05, and a more stringent threshold of |FC| > 2 and FDR <0.01 were considered for a gene to be significantly differentially expressed (see Methods). A non-supervised hierarchical clustering of the chemicals based on the total number of DEGs for the two thresholds  yielded highly comparable results with the published data, indicating the reproducibility of our pipelines  greater than 1.5, and a false discovery rate (FDR)  that are unique to each chemical, or common between multiple chemicals . Only thTo assess the molecular, cellular, and biological pathways associated with drug-responsive genes, we implemented the BMDexpress workflow to derive the significant GO pathways from the transcriptomic data of each chemical (see Methods). Harril et al. have previously developed a gene expression signature-based concentration-response modeling approach, primarily focusing on pathways related to estrogen receptor bioactivity. As a complementary approach, we performed a series of systems-based analyses to assess pathway-based, as well as gene-based BMD and POD values in an unbiased way.p-value <0.05. This analysis demonstrated that 8 chemicals—4-Hydroxytamoxifen, Clomiphene citrate (1-1), Thiram, Ziram, Amiodarone hydrochloride, Cycloheximide, and Maneb—regulated significant numbers of BMD pathways  modeling analysis, that were commonly shared among all the cell type vs. chemical conditions and identified that, consistent with the DEG overlap analyses , Ziram, By visualizing the pathways based on the thresholds mentioned above, and by further filtering the data to include only GO category level 11 and above pathways and have a more stringent value FDR value <0.01, we identified that genes that are dose-responsive to thiram, ziram, Clomiphene citrate (1-1) and 4-Hydroxytamoxifen, which is a selective estrogen receptor modulator (SERM) and acts as an agonist or antagonist depending on the target tissues , shared versus pathway levels). The ranking  of only a subset of the chemicals remains similar based on both our and Harrill et al. BMD approaches. Since our and Harrill et al. classification from DGE and BMD steps have some similarities, it is the final derivation of POD based on ontogeny which likely contributed to the different final quantified outcomes.Moreover, to gather information about the BMD values that are related to different biological pathways, we plotted the lower BMD (BMDL), BMD and upper BMD (BMDU) values derived from the average of the significant GO pathways that were shared among the chemicals . FurtherNext, among the overlapping pathways, we have decided to focus on the “nitrogen metabolic processing” pathway (GO:0006807), since this process plays an extensive role in several carcinogenic and immune-related processes . In addiWe first plotted the gene-level median BMD values for each gene in the nitrogen metabolic pathway for each chemical and have identified that different sets of genes within this pathway harbor varying levels of median BMD values. The result indicates that although different chemicals lead to downregulation of the “metabolic nitrogen processing” pathway, it does so by regulating different sets of genes from the same pathway .We next plotted the average gene expression profile of the genes within this pathway as a function of dose for the chemicals with the lowest 3 median BMD values, Thiram, Ziram and Cycloheximide, and identified a consistent downregulation of these genes . By seleWe also adopted two additional strategies to interpret the functional categories with regards to BMD values. In the first, the mean BMD of each pathway is plotted along the lowest 20, 10, or 5 BMD GO pathways. The second method, which we have previously implemented in Toxicogenomics is emerging as a valuable systems science approach to inform chemical risk assessment across diverse applications. By providing insights into molecular mechanisms of biological effects, toxicogenomic profiling can enhance hazard characterization and derive points of departure to support safety determinations. Furthermore, the increasing interest in new approach methodologies (NAMs) has motivated efforts to establish robust and reproducible computational pipelines to analyze these complex genomics datasets. However, the field currently lacks consensus regarding optimal bioinformatics strategies, especially for benchmark dose modeling and derivation of points of departure from transcriptomics data. To address this need, we leveraged a recently published toxicogenomics dataset assessing dose-responses for 44 chemicals in MCF7 cells . By perfOur re-analysis of the TempO-seq dataset from Harrill et al. provides important insights into the diversity of transcriptional responses induced by different chemical exposures. While some compounds like the herbicides atrazine and simazine elicited minimal gene expression changes, others including the dithiocarbamate fungicides thiram and ziram  dysregulWe have identified that tens to hundreds of differentially expressed genes that are associated with “mitotic cell cycle process,” “phosphatase regulator activity” and “protein kinase binding” overlap across the chemicals at the highest 100 µM dose . It is iOur unbiased BMD modeling of pathways reveals both shared and unique dose-responsive profiles across diverse chemical exposures. The striking perturbation of numerous pathways enriched for processes like cell cycle regulation highlights the power of omics data to capture system-wide effects missed by individual endpoint analysis. Furthermore, the chemical-specific pathway modulation underscores the potential to leverage transcriptomics to delineate mechanisms of action and address the long-standing challenge of heterogeneity in dose-response. By objectively integrating signals across the inter-connectome, toxicogenomic BMD analyses offer a robust data-driven approach to chemical safety evaluation.By performing an in-depth analysis of the nitrogen metabolic processing pathway, our study demonstrates the value of toxicogenomics for elucidating chemical mechanisms of action. While this fundamental pathway was perturbed across several compounds, our gene-level modeling and expression plots revealed chemical-specific BMD values and transcriptomic signatures. These nuances underscore the complexity hidden within shared perturbed pathways, and the need for systems-level perspectives.in vitro or in vivo research to establish concrete risk assessments (It is important to note that in toxicity risk assessment, bioinformatics approaches play a crucial role , howeveressments . For insessments  has certessments . TherefoOverall, coupling unbiased pathway analysis with targeted follow-up of key processes provides multilayered insights into the molecular initiating events and dose-dependencies of adverse effects. Our integrated approach therefore leverages the depth of omics data to link pathway perturbations to underlying genomic biomarkers and biological mechanisms. Looking forward, systematic application of these bioinformatics workflows will add enhanced resolution of pathway dose-response, improving chemical risk assessment and advancing mechanistic toxicology.


This study aimed to elucidate the molecular determinants influencing the response of cancer cells to alkylating agents, a major class of chemotherapeutic drugs used in cancer treatment. The study utilized data from the National Cancer Institute (NCI)-60 cell line screening program and employed a comprehensive multi-omics approach integrating transcriptomic, proteomic, metabolomic, and SNP data. Through integrated pathway analysis, the study identified key metabolic pathways, such as cysteine and methionine metabolism, starch and sucrose metabolism, pyrimidine metabolism, and purine metabolism, that differentiate drug-sensitive and drug-resistant cancer cells. The analysis also revealed potential druggable targets within these pathways. Furthermore, copy number variant (CNV) analysis, derived from SNP data, between sensitive and resistant cells identified notable differences in genes associated with metabolic changes , protein trafficking , and miRNAs . The findings of this study provide a holistic view of the molecular landscape and dysregulated pathways underlying the response of cancer cells to alkylating agents. The insights gained from this research can contribute to the development of more effective therapeutic strategies and personalized treatment approaches, ultimately improving patient outcomes in cancer treatment. Alkylating agents represent a pivotal class of chemotherapeutic agents extensively employed in the treatment of various cancers. These compounds exert their therapeutic effects by inducing DNA damage through the covalent modification of DNA bases, leading to DNA damage and subsequent cell death . AlthougSeveral mechanisms have been identified that contribute to cancer cells acquiring or their inherent resistance to alkylating agents. Alkylating agents can be metabolized by various enzymes, including glutathione S-transferases (GSTs) and aldehyde dehydrogenases (ALDHs), which can inactivate or detoxify the drugs. The increased expression or activity of these enzymes can lead to reduced intracellular drug concentrations, thereby limiting their cytotoxic effects . Cancer In addition to these processes, cancer endogenous metabolism plays a crucial role in drug resistance, and understanding its significance is essential for the development of effective therapeutic strategies. Metabolic reprogramming is a hallmark of cancer, characterized by altered nutrient utilization, energy production, and biosynthesis ,17,18. TTo address this critical knowledge gap, the current study was conducted to elucidate the molecular determinants that influence the response of cancer cells to alkylating agents using data from the National Cancer Institute (NCI)-60 cell line screening program. The NCI-60 cell line screen program tests the response of 60 cancer cell lines to thousands of test compounds that span numerous mechanisms of action . AdditioMining NCI-60 cell line treatments revealed 51 chemical agents with an alkylating mechanism as defined by Cellminer. This included agents broadly defined as alkylating agents, as well as those that were specifically characterized as alkylating N-2, O-6, and N-7 positions of guanine residues . After cp-values were calculated for all metabolites, transcripts, and proteins between Q1 and Q4 to identify the differentiators of cell lines that are resistant or sensitive to alkylating agents  protein interactions (tight junction (p = 8.91 × 10−11), cell adhesion molecules) p = 4.45 × 10−6), and endocytosis (p = 8.65 × 10−7)) and the Hippo signaling pathway (4.00 × 10−6) , and adherens junction (p = 1.75 × 10−5)) and the sphingolipid signaling pathway (p = 1.04 × 10−5) , focal adhesion , and tight junction (4.31 × 10−4)  (a full list of pathway results for p = 4.36 × 10−14), pyrimidine metabolism (p = 5.02 × 10−14), starch and sucrose metabolism (p = 1.3 × 10−12), and purine metabolism (p = 1.34 × 10−12). EGFR tyrosine kinase inhibitor resistance was the top pathway differentiating Q1 and Q4 (p = 4.24 × 10−73)  having the highest overall z-score rank  and the first quartile (Q1) as having the lowest overall z-score rank  . Using tg agents . This an × 10−6) A. Joint- × 10−5) B. To addpatterns . Ten alkse drugs  were inp × 10−4) C. This o × 10−4) . This ne× 10−73) .Multivariate analysis was performed using DIABLO to identify the complex relationship between the omics datasets and identify the strongest combination of predictors between Q1 and Q4. In addition to the metabolomics, transcriptomics, and proteomics datasets, SNP variant data were also uploaded to identify SNP variants that combined with the other omics datasets to differentiate Q1 and Q4. DIABLO analysis showed a clear separation between Q1 and Q4, with the first component of the model showing the strongest separation between the two groups A,B. Usinp < 0.1. Visualization showed decreases in the expression of enzymes in Q4 samples that control flux away from purine metabolism into other pathways, including the pentose phosphate pathway and amino acid pathways. Additionally, an increased expression of enzymes in the Q4 samples surrounding the base pair conversions was observed, and metabolites guanosine, xanthine, hypoxanthine, and inosine were also seen as increased in the Q4 samples in agreement with this observation , homologous recombination (2.00 × 10−24), nucleotide excision repair (2.77 × 10−9), and non-homologous end-joining (1.04 × 10−7)  Open Repository of CRISPR Screens (ORCS) . Twelve  × 10−7) . The current study provides valuable insights into the molecular determinants of response to alkylating agents. Using a multi-omics approach of NCI-60 cell line data, we were able to assign multiple metabolic features that differentiated cancer cells that are sensitive or resistant to this class of anticancer agents. While pathways related to amino acid and carbohydrate metabolism were found to be significantly different, nucleotide metabolism—specifically purine metabolism—was found to be the largest metabolic differentiator between cells sensitive or resistant to alkylating agents. Overall, gene/protein expression and metabolite levels were increased in this pathway for resistant cells, especially for the purine salvage reactions involving the interconversion of guanosine, xanthine, hypoxanthine, and inosine. Additionally, our findings of several drug resistance pathways  provide validity to our analysis approach and support the linkage between these metabolic disruptions and drug resistance. The purine salvage pathway provides the majority of the cellular requirements for purines through the recycling of degraded bases to replenish the purine pool . InteresAn analysis of CNV data derived from SNP analysis identified differences in 15 genes between sensitive and resistant cells. Included in these genes was WWOX, a tumor suppressor gene that has been shown to regulate multiple metabolic processes, including glucose and lipid metabolism ,47,48,4950 values and to identify their associated pathways. These identified pathways had a high degree of overlap with the NCI-60 results initially identified in the joint-pathway analysis of metabolites, transcripts, and proteins, with p < 0.05 between Q1 and Q4. The second method was the use of metabolites + transcripts and metabolites + proteins as separate joint-pathway analyses, both with the univariate and multivariate-selected molecules. Using this approach, agreement could be found between the two methods to identify pathways that were consistently identified as significant across the omics datasets. The third method was the combination of univariate and multivariate approaches, as well as the combination of pathway (metabolites + transcripts and metabolites + proteins) and network analysis approaches in the multi-omics analysis. Using these orthogonal approaches, commonalities in key pathways/molecules could be identified, allowing the results to be narrowed down. Lastly, additional validation was gained from analyzing data from BioGRID ORCS. CRISPR screens are an emerging method that allow for the identification of genes that significantly determine cancer cells’ ability to survive under a given selection pressure  [Multiple measures were taken to validate the results in this study. The first method involved the use of the CREAMMIST database to identify genes correlated with alkylating agent ICeatment) . An invehttps://www.ebi.ac.uk/pride/archive/projects/PXD013615 (accessed on 25 May 2023) [https://www.ncbi.nlm.nih.gov/sites/GDSbrowser?acc=GDS4296 (accessed on 24 May 2023) [Publicly available omics datasets of baseline molecular characteristics of cell lines in the NCI-60 cell line panel were obtained. Metabolomics data (average of triplicate experiments) were downloaded from NCI’s Development Therapeutics Program (DTP) website. Proteomics data were downloaded from ay 2023) ,63. Copyay 2023) . Two cel50 values and then dividing by the standard deviation of a given test compound across all cell lines [Drug activities for compounds tested in the NCI-60 cell line panel were downloaded from CellMiner in the form of z-scores—a transformed value that is calculated by taking mean-centered GIll lines . Compounll lines . p-values were calculated between Q1 and Q4 using Students’ t-test. Metabolites, proteins, and transcripts that had a p-value < 0.05 between Q1 and Q4 of the total rank order of z-scores were used for joint pathway analysis in MetaboAnalyst 5.0 [https://creammist.mtms.dev/ (accessed on 26 July 2023) [p < 0.05 between Q1 and Q4 were input into Omicsnet [Fold changes were calculated between cell lines in the top (Q4) and bottom (Q1) quartile of the total rank order of z-scores using group averages for each variable. lyst 5.0 . PathwayOmicsnet . MetabolMetabolomics, transcriptomics, proteomics, and SNP datasets were imported into Omicsanalyst for integrated analysis . Due to CNV markers that had an absolute loadings value > 0.6 for the first DIABLO principal component were aligned with associated genes indicated by CellMiner. To determine genes with increased or decreased CNV intensity in resistant cells, average intensity values were averaged for each marker in Q1 and Q4 samples, and all markers mapped to the same gene were summed. Genes with a summed intensity that was higher in Q4 were defined as increased whereas genes with a summed intensity that were lower in Q4 were defined as decreased. Frequency of mapped CNV markers for each gene was calculated to determine genes that had the greatest number of CNV markers that differentiated Q1 and Q4.p < 0.1 were input into the analysis. hsa-Homo sapiens was chosen for the species database, and positive fold changes indicate an increase in Q4 of total rank order of z-scores (most resistant to drug treatment). KEGG’s disease gene and drug targets database was analyzed to determine druggable targets that overlapped with Pathview data [To visualize pathway perturbations using multi-omic data, metabolites, proteins, and transcripts were input into Pathview with average values in Q1 and Q4 of total rank order of z-scores ,70. Onlyiew data .https://orcs.thebiogrid.org/ (accessed on 26 July 2023)) [Multi-omics analysis results were validated by searching for gene hits that determine alkyalting agent sensitivity in the Biological General Repository for Interaction Datasets (BioGRID) Open Repository of CRISPR Screens (ORCS) (y 2023)) . A totalIn conclusion, we have taken an unbiased, multi-omics approach to identify molecular networks that differentiate cancer cells that are sensitive or resistant to alkylating agents. Our analysis revealed several metabolic processes that differentiated sensitive and resistant cells, with purine metabolism emerging as the major metabolic differentiator, as determined by a combination of univariate and multivariate approaches. Additionally, CNV analysis revealed a collection of genes which may play a role in establishing these metabolic phenotypes that differentiate response status to alkylating agents. It is currently unclear if these metabolic processes play a role in cancer cell response to other drug classes. Therefore, future research should be performed to use multi-omics approaches to identify the cellular pathways associated with drug response to other classes in an unbiased manner. Notably, our findings did not indicate that cancer cell type was a major driver of sensitivity to this drug class, suggesting that omics profiles would be a better indicator of drug responsiveness. Overall, this information provides new insights into the cellular processes that may be targeted to potentially prevent or overcome resistance to alkylating agents. Due to the metabolic nature of these differences, targeting these processes should be investigated pharmacologically and nutritionally. Indeed, nutritional factors have been shown to have anticancer activity, as well as chemosensitization properties, through targeted cancer cell metabolism, such as fatty acid beta oxidation, glucose metabolism, polyunsaturated fatty acid metabolism, or nucleotide metabolism, to name a few ,73,74,75


This study aimed to investigate the pharmacological effects and molecular mechanisms of Dahuang-Gancao Decoction (DHGC) on acute kidney injury (AKI). Network pharmacology was utilized to analyze the key targets of DHGC against AKI. These targets were used to construct a protein-protein interaction (PPI) network, which was analyzed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment to predict the mechanism of action. Based on the network pharmacological analysis, Sirtuin 3 (SIRT3) was identified as a key target, and apoptosis was suggested as a mechanism of DHGC for AKI treatment. Subsequently, an AKI mouse model was induced using lipopolysaccharide (LPS), and the study demonstrated that DHGC gradient intervention significantly reduced plasma urea and creatinine levels in AKI mice, ameliorated renal pathological changes, reduced apoptosis, and lowered serum inflammatory factors. The mechanism of DHGC’s anti-AKI effect may lie in the activation of the SIRT3/NRF2/HO-1 signaling pathway, which plays an antiapoptotic role in renal cells. In summary, DHGC improved LPS-induced AKI in mice by activating the SIRT3/NRF2/HO-1 signaling pathway. These findings shed light on the potential clinical application of DHGC for the treatment of nephropathy. Acute kidney injury (AKI) is a clinical condition characterized by a rapid decline in renal excretory function, accumulation of nitrogen metabolites, reduced urine output, metabolic acidosis, and elevated potassium and phosphorus concentrations , 2. ReceRheum palmatum L (rhubarb) and Radix glycyrrhiza (licorice), is a well-known Chinese medicine formula used to treat kidney disease. DHGC was first recorded in a famous ancient medical book "Treatise on Cold Pathogenic and Miscellaneous Diseases" in the Han Dynasty, and it can be used for the treatment of constipation, excess heat stagnation of the gastrointestinal tract, and related symptoms [Traditional Chinese Medicine (TCM) has been widely used for thousands of years in China to treat kidney disease and its complications . Among tsymptoms . Accordisymptoms , 8. For symptoms , 9. Licosymptoms . This prWith advances in bioinformatics, systems biology, and pharmacology, network pharmacology has emerged as a new strategy to elucidate the active ingredients and potential mechanisms of herbal compounds . NetworkThe traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) was utilized to identify compounds and targets related to DHGC. Candidate compounds were selected based on drug-likeness (DL) values ≥0.18 and oral bioavailability (OB) ≥30%. Targets of the candidate rhubarb and licorice compounds were obtained from the TCMSP database, and translated into corresponding gene names using the protein database UniProt.The GeneCards, OMIM, and DrugBank databases were searched using the keywords “Acute kidney injury” to identify human genes associated with AKI. Repetitive targets were removed, and all target genes were converted to human genes using the UniProt database. The DHGC-related targets and AKI-related targets were analyzed using Omicshare to generate a Venn diagram to determine the intersected targets.The “DHGC-Active Ingredient-Target” network was constructed using Cytoscape 3.8.2 to elucidate the pharmacological mechanisms of action of DHGC. The intersected targets were imported into the STRING 11.0 database, and a PPI network model was constructed using “Homo sapiens” as the biological species set, with a confidence score >0.4 and unconnected nodes hidden. The resulting proteins were introduced into Cytoscape 3.8.2 software to construct the PPI networks, and node connectivity was analyzed to identify core targets.P < 0.05. DHGC was found to interfere with the biological pathway of AKI. Bubble plots were generated to display the top 20 items of GO analysis and the top 20 items of KEGG analysis.GO functional enrichment analysis and KEGG pathway enrichment analysis were conducted for the common targets of DHGC and AKI using R software and the DAVID database, with a threshold value of Rheum palmatum L) and 10 g of licorice species (Radix glycyrrhizae) added to 500 ml of boiling water. The herbs were subjected to reflux extraction with water for 1.5 h twice to obtain the water decoction and concentrate. The DHGC decoction was then dried in a vacuum drier to calculate the extraction rate, and the ratio of DHGC decoction powder to raw herbs was 15.61%. The resulting dry extract was stored at −20°C until use.Rhubarb and licorice were purchased from Hubei Tianji Chinese Medicine Decoction Company . The primary antibody against SIRT3 was obtained from Cell Signaling Technology Inc., , while other antibodies, including NRF-2 (16396-1-AP), HO-1 (10701-1-AP), and β-actin (81115-1-RR), were procured from Proteintech Group, Inc. . DHGC was prepared in accordance with the guidelines set forth in the “Treatise on Cold Pathogenic and Miscellaneous Diseases.” In brief, the drug-solvent ratio was 1:10, with 40 g of Rhubarb species (n = 8): Ctrl group, AKI group, AKI+ low dose group (Low group), AKI+ medium dose group (Medium group), and AKI+ high dose group (High group). DHGC water extract  was administered to the Low, Medium, and High groups for three days before LPS administration. Then, the AKI group, Low group, Medium group, and High group were injected with LPS , while the Ctrl group received an equal volume of saline (i.p.). After 24 hours, all mice were euthanized, and the kidney and serum were collected. Kidney tissues were photographed, and all samples were stored at −80°C for further experiments. The animal experiments were performed in accordance with the guidelines set forth by the Animal Care and Use Committee of the animal facility at the Hubei University of Chinese Medicine (No: HUCMS202302007).Male C57BL/6 mice  were obtained from Hubei Center for Disease Control and Prevention . Mice were adaptively housed for one week under a 12 h light/dark cycle  with free access to food and water. The mice were randomly divided into five groups .Kidney tissues were fixed with 4% paraformaldehyde, dehydrated, paraffin-embedded, and sectioned into 5 μm-thick sections. Deparaffinization was performed using xylene and diluted ethanol. Hematoxylin and eosin (H&E) staining was applied, and the TUNEL Staining Kit  was employed to assess apoptosis levels. Images were captured using a Leica DFC310 FX digital camera coupled with a Leica DMI4000B light microscope .−ΔΔCT) method. The list of PCR primers used in this study is shown in Based on the manufacturer’s protocol, total RNA of kidney was extracted using Trizol reagent and reversely transcribed to cDNA with a first-strand cDNA synthesis kit . The relative mRNA levels of target genes were measured by RT-qPCR using a SYBR QPCR mixture  at the ABI 7500 Real-Time Fluorescence Quantitative PCR instrument. The thermal cycle condition was as follows: pre-denaturing at 95°C for 10 min; 40 cycles of denaturation at 95°C for 10 s, annealing/extension at 60°C for 30 s. Target gene expressions were normalized against that of β-actin, and fold changes were calculated using a 2, along with a protease inhibitor cocktail . The protein concentration was determined using a bicinchoninic acid (BCA) protein assay kit . The protein samples were separated on sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) gels and transferred onto polyvinylidene difluoride (PVDF) membranes. After blocking with 5% skim milk in Tris-buffered saline tween-20 (TBST) for 1 hour, the membranes were incubated separately with primary antibodies at 4°C overnight, including SIRT3, NRF2, HO-1, and β-actin. After washing with TBST, the membranes were incubated with a secondary antibody conjugated with horseradish peroxidase (HRP) for 1.5 hours. The protein signals were finally visualized using an ECL Protein Detection kit.t-test was used to compare the difference between the two groups, while one-way ANOVA was used to assess differences among multiple groups. P < 0.05 was considered statistically significant. GraphPad Prism  was utilized for regular analysis.The data are expressed as the mean ± SD. An unpaired two-tailed Student’s The data used to support the findings of this study are available from the corresponding author upon request.In the screening process, a total of 108 chemical components were identified in DHGC. The top 20 components are listed in In order to elucidate the mechanism and pharmacodynamics of DHGC, we extracted a total of 7,828 target genes associated with AKI from GeneCard, OMIM, and DrugBank databases. By performing a Venn diagram analysis, we identified 161 unique targets that are common to both DHGC and AKI, as depicted in To investigate the association between active ingredients, potential targets, and AKI, we constructed an ingredient-target-disease network using Cytoscape 3.8.2 software. The network consisted of 10 nodes and 32 edges, as shown in To identify the core proteins targeted by DHGC for the intervention of AKI, we constructed a protein-protein interaction (PPI) network consisting of 110 nodes and 1008 edges using the STRING database . After rP-value < 0.05). The top entries included RNA polymerase II-specific DNA-binding transcription activator activity, DNA-binding transcription factor binding, DNA-binding transcription activator activity, RNA polymerase II-specific DNA-binding transcription factor binding, ubiquitin-like protein ligase binding, and nuclear receptor activity, among others  and rrelated .P < 0.01, vs. AKI group)  , 5D.P < 0.01, vs. AKI group)   . AdditioI group) .Elevated levels of proinflammatory cytokines are a hallmark of acute kidney injury . To inveP < 0.01 and 0.05, vs. AKI group)  .Acute kidney injury (AKI) is a critical clinical condition primarily managed through symptomatic therapy, as specific treatment strategies have yet to be identified. Nonetheless, herbal formulations have a long-standing history of aiding in the treatment of AKI . In thisThrough basic network pharmacological analysis, we identified the main targets of DHGC against AKI to be EGFR, ESR1, MAPK8, AR, CCND1, NCOA1, MDM2, RB1, APP, MYC, and SIRT3. Some of these targets facilitate AKI, such as EGFR, which accelerates renal cell apoptosis and caspase-3 activation during AKI progression , 15. On To validate the results predicted in network pharmacology, we subsequently induced a mouse AKI model using LPS. LPS is a recognized model drug that triggers cytokine synthesis, secretion, and subsequent inflammatory processes . It is ain vitro and in vivo [Consistent with previously reported findings, histological analysis of hematoxylin and eosin-stained sections demonstrated that mice treated with lipopolysaccharide (LPS) exhibited renal pathological injury, as evidenced by necrosis, degeneration, and cell swelling of renal tubular epithelial cells . Concomi in vivo , 32. LikTo elucidate the mechanism by which DHGC inhibits apoptosis, we investigated the SIRT3/NRF2/HO-1 signaling pathway based on network pharmacology analysis. Recent evidence has demonstrated that curbing excessive production of reactive oxygen species (ROS) in mitochondria can attenuate renal injury, decrease cytokine release, and suppress renal inflammation . MoreoveIn summary, this study demonstrated that network pharmacological analysis confirms the broad anti-AKI mechanism of DHGC, which is potentially related to its anti-inflammatory and anti-apoptotic effects on renal cells. Experimental validation confirmed that DHGC alleviates the progression of AKI in mice, as indicated by the reduction of serum inflammatory factors and renal cell apoptosis. DHGC’s therapeutic effect in attenuating apoptosis may be attributed to the activation of the SIRT3/NRF2/HO-1 signaling pathway. These findings provide insight into the potential clinical application of DHGC in the treatment of nephropathy.