Panax ginseng has been used to treat T2DM in traditional medicine, with previous in vitro, in vivo, and clinical trial studies demonstrating its efficacy. This study aimed to determine the mechanism of P. ginseng in treating T2DM by network pharmacology.  Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder with a high global incidence. P. ginseng and corresponding targets of P. ginseng-T2DM were retrieved across multiple databases. The protein-protein interaction network was established using the STRING database and topological analysis helped identify the core target. Using the DAVID tool, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Finally, we checked the binding of core targets and bioactive compounds using molecular docking.  The bioactive compounds of P. ginseng-T2DM networks mainly contained 22 bioactive compounds and 314 overlapping targets. The five most significant core targets were SRC, STAT3, MAPK1, AKT1, and PIK3R1. There were 244 GO terms and 95 KEGG pathways (adjusted p < 0.01) that were strongly correlated with diabetes-related signaling pathways, such as insulin resistance, the HIF-1 signaling pathway, the PI3K/Akt signaling pathway, the prolactin signaling pathway, the Rap1 signaling pathway, the Ras signaling pathway, the calcium signaling pathway, and the FoxO signaling pathway. Molecular docking results revealed that the top five core targets had a high binding affinity with the bioactive compounds of P. ginseng.  The P. ginseng ameliorate T2DM by regulating insulin resistance and multiple signaling pathways. The bioactive compounds and targets in  Type 2 diabetes mellitus (T2DM) is a type of diabetes marked by elevated blood glucose levels resulting from defective insulin secretion and insulin resistance . T2DM coPanax ginseng holds a prominent place in the oriental pharmacopeia. Traditionally, P. ginseng has long been used as an aphrodisiac, adaptogen, and nourishing stimulant as well as in the treatment of sexual dysfunction and T2DM [ginseng-related therapies increase insulin sensitivity in patients with impaired glucose tolerance or T2DM [P. ginseng, have been verified to have antidiabetic properties because of their antihyperglycemic, antiinflammatory, and antioxidant actions [P. ginseng compounds in the treatment of diabetes has concentrated on isolating compounds and studying their hypoglycemic effects in vitro and in vivo [P. ginseng in T2DM treatment have not been comprehensively revealed.and T2DM . Recentl or T2DM . Ginseno actions . Current in vivo –11. HoweP. ginseng systemically.Along with the advancement of bioinformatics, the integration of network pharmacology and molecular docking has been applied in recent years to study herbal compounds and traditional medicine mechanisms . NetworkP. ginseng on the effectiveness of T2DM treatment. Initially, we screened the bioactive compounds of P. ginseng and overlapping targets of P. ginseng and T2DM. Then, the protein-protein interaction (PPI) and core targets were established based on network topological structure analysis. Subsequently, we used Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis to determine the functions and pathways of the overlapping targets. Lastly, we used molecular docking to check compound-target binding affinity based on network pharmacology results. The workflow is shown in In the current study, a systematic method was implemented to determine the therapeutic impact of P. ginseng were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) version 2.3 [https://pubchem.ncbi.nlm.nih.gov/) to acquire the compounds' structures for target prediction and molecular docking.The compounds of sion 2.3 . Based osion 2.3 , these cHomo sapiens targets associated with bioactive compounds were predicted using Swiss target prediction, in which we set the probability filter above zero [https://www.uniprot.org/) was used to identify the target names.The ove zero . FurtherP. ginseng compounds and T2DM were visualized using VENNY 2.1 [The disease targets of T2DM were obtained using DisGeNET version 7.0 with “diabetes mellitus, non-insulin-dependent” (CUI: C0011860) as the keywords, and all proposed disease targets have been selected . The oveENNY 2.1 .H. sapiens,” and the highest confidence level as 0.9; the unconnected proteins were then removed [To evaluate the protein interactions among the overlapping targets, the PPI network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database version 11.5 by setting the species as “ removed . Followi removed . The corP. ginseng-related to the treatment of T2DM, we used the Database for Annotation, Visualization, and Integrated Discovery (DAVID) version 6.8 with an adjusted p value <0.01 (after Benjamini's correction) to process GO and KEGG pathway enrichment [P. ginseng [To explore the detailed molecular mechanism of richment . GO anal ginseng .https://pubchem.ncbi.nlm.nih.gov/) and Protein Data Bank (https://www.rcsb.org/), respectively. The PyMOL 2.5.2 software was used to create the chemical structures [Molecular docking was used to calculate docking energy between the top five core targets and their corresponding bioactive compounds. The 3D structures of ligands (compounds) and protein receptors (targets) were downloaded from the PubChem database , including 173 biological process terms, 44 molecular functions, and 27 cell compositions. The top five terms of biological process, ranked through the adjusted p value were as follows: response to the drug (GO: 0042493), positive regulation of the ERK1 and ERK2 cascade (GO: 0070374), steroid hormone-mediated signaling pathway (GO: 0043401), negative regulation of the apoptotic process (GO: 0043066), and positive regulation of MAP kinase activity (GO: 0043406). The top five terms in molecular functions were as follows: steroid hormone receptor activity (GO: 0003707), RNA polymerase II transcription factor activity, ligand-activated sequence-specific DNA binding (GO: 0004879), protein kinase activity (GO: 0004672), protein tyrosine kinase activity (GO: 0004713), and drug binding (GO: 0008144). Moreover, these processes occurred mainly in the plasma membrane (GO: 0005886), integral compounds of the plasma membrane (GO: 0005887), cytosol (GO: 0005829), receptor complex (GO: 0043235), and membrane raft (GO: 0045121). The top 15 GO terms of the three categories are visualized in The GO enrichment analysis of 314 targets resulted in 244 GO terms (adjusted p < 0.01), mainly involved pathways in cancer (hsa05200), neuroactive ligand-receptor interaction (hsa04080), the PI3K/Akt signaling pathway (hsa04151), proteoglycans in cancer (hsa05205), the Rap1 signaling pathway (hsa04015), and the Ras signaling pathway (hsa04014). The top 20 pathways are visualized in P. ginseng in the insulin resistance pathway are shown in KEGG pathway enrichment analysis obtained 95 signaling pathways  and the corresponding bioactive compounds of P. ginseng, a Qi-tonifying herb, is used in combination with other T2DM treating herbs, such as “white tiger plus ginseng,” to invigorate the Qi [P. ginseng has also been demonstrated by previous in vitro, in vivo, and clinical trial studies [P. ginseng against T2DM in multiple signaling pathways.In traditional medicine, diabetes is classified as “Xiaoke.” The primary pathogenesis of “Xiaoke” is spleen deficiency, the root pathogen is a lack of Qi spirit, and blood stasis is implicated during the entire course of diabetes. In clinical treatment, e the Qi , 28. The studies . Using aP. ginseng was discovered to be associated with 22 bioactive compounds  signaling pathway, suggesting that it might be a therapeutic target for diabetic nephropathy [β, a negative regulator of insulin [P. ginseng have significant antidiabetic effects.Based on the multicompound and multitarget features of network pharmacology, 314 overlapping targets were found between the herb and disease. As seen in the PPI topological network Figures , 27 corehropathy . STAT3 ihropathy . STAT3 h insulin . MAPK1 i insulin . MAPK1 p insulin ,43. AKT1 insulin . PIK3R1  insulin , 46. PIK insulin . In addi2+ signaling and its dysregulation have been linked to diabetes development, and it plays a vital role in Cd-induced β-cell dysfunction and apoptosis [2+/cAMP signaling regulates insulin release from pancreatic β-cells and contribute to β-cells homeostasis by stimulating cell proliferation and differentiation [P. ginseng acting through multitarget and multipathway.In the KEGG enrichment analysis, insulin resistance and many diabetes-related signaling pathways were identified, such as the HIF-1, PI3K/Akt, prolactin, Rap1, Ras, calcium, and FoxO signaling pathways . The inspoptosis . Ca2+/cAntiation . In addintiation –57. In sThe combination of network pharmacology and molecular docking can provide compelling evidence of the molecular mechanisms of herbal medicine. However, there are also several limitations to our study. First, more comprehensive input data are required to make the analysis outcomes more reliable. Second, the computational tools of network pharmacology and molecular docking for predicting and validating the mechanisms of herbal medicine still have drawbacks. For example, SwissTargetPrediction uses ligand-based approaches to target prediction based on the assumption that if the molecule is active, which means the molecule will likely bind to some protein. For molecules with unknown bioactivity, predicted targets are false positives . TherefoP. ginseng for treating T2DM by using network pharmacology. The outcomes showed that 22 bioactive compounds and 314 corresponding targets significantly contributed to the antidiabetic effects of P. ginseng via regulating diabetes-related signaling pathways and biological processes, such as insulin resistance, the HIF-1 signaling pathway, the PI3K/Akt signaling pathway, the prolactin signaling pathway, the Rap1 signaling pathway, the Ras signaling pathway, the calcium signaling pathway, and the FoxO signaling pathway. Moreover, molecular docking successfully checked the binding action of the top five core targets and their corresponding bioactive compounds.This study revealed the “multicompound, multitarget, and multipathway” mechanisms of


Background: Diabetic kidney disease (DKD) becomes the leading cause of death for end-stage renal disease, whereas the potential mechanism is unclear and effective therapy is still rare. Our study was designed to investigate the cellular mechanism of Forsythiaside against DKD.Materials and Methods: The targets of Forsythiaside and the DKD-related targets were obtained from databases. The overlapping targets in these two sets were regarded as potential targets for alleviation of DKD by Forsythiaside. The targets of diabetic podocytopathy and tubulopathy were also detected to clarify the mechanism of Forsythiaside ameliorating DKD from the cellular level.Results: Our results explored that PRKCA and RHOA were regarded as key therapeutic targets of Forsythiaside with excellent binding affinity for treating DKD podocytopathy. Enrichment analysis suggested the underlying mechanism was mainly focused on the oxidative stress and mTOR signaling pathway. The alleviated effects of Forsythiaside on the reactive oxidative species accumulation and PRKCA and RHOA proteins upregulation in podocytes were also confirmed.Conclusion: The present study elucidates that Forsythiaside exerts potential treatment against DKD which may act directly RHOA and PRKCA target by suppressing the oxidative stress pathway in podocytes. And Forsythiaside could be regarded as one of the candidate drugs dealing with DKD in future experimental or clinical researches. As the population who suffers from diabetes surges rapidly, the prevalence rate of diabetic kidney disease (DKD) significantly increases and occurs in approximately 40% of patients with type 2 diabetes mellitus . DKD as A variety of cells were involved in the pathogenesis of DKD and the clinical effects and mechanism of DKD occurrence and development have been preliminarily elucidated. Studies showed that glomerular changes were undoubtedly major characteristics of DKD . TraditiTraditional Chinese Medicine (TCM) shows remarkable therapeutic effect on chronic diseases, and has been widely supplemented to intervene DKD in China and other Asian countries . ChineseForsythia suspensa (Thunb.) Vahl, has long been thought to have benefits involving clearing heat and detoxifying, dispelling carbuncle and is commonly used in treating infections and pyrexia , the PubChem database (http://pubchem.ncbi.nlm.nih.gov), the Swiss Target Prediction database (http://www.swisstargetprediction.ch/), the Binding database (https://www.bindingdb.org/bind/index.jsp), and the PharmMapper database (http://lilab-ecust.cn/pharmmapper/index.html). All potential targets were summarized and their names standardized using the universal Protein Resource site (RRID: SCR_002380) (http://www.uniprot.org/).The drug-like properties of Forsythiaside are summarized in database  (http://database  (http://database  (https:/database  (http://http://bidd.nus.edu.sg/group/cjttd/), GeneCards (https://www.genecards.org/), DisGeNET (RRID: SCR_006178) (https://www.disgenet.org/) (https://go.drugbank.com/) and Online Mendelian Inheritance in Man  (https://omim.org/) (The target proteins involved in the pathogenesis of DKD were predicted and acquired from a variety of databases. The terms “Diabetic kidney disease” and “Diabetic nephropathy” were used as keywords across multiple databases. We used Therapeutic Target  (http://eneCards  (https:/et.org/) , DrugBanet.org/)  (https:/im.org/)  databasevia which Forsythiaside could intervene in DKD, the predicted targets of Forsythiaside were mapped onto the DKD-related proteins and overlapping targets were extracted as key genes. Overlapping targets were visualized using a Venn diagram.To clarify the candidate target molecules https://string-db.org/), a useful tool for the discovery of functional protein interactions and further generation of a PPI network.Once mapped, overlapping target molecules between Forsythiaside and DKD were inserted into the STRING website  (https:/https://cytoscape.org/), an open source software platform for visualizing intricate networks and integrating networks with attribute data   to carry out KEGG enrichment analysis. Bubble diagrams of KEGG enrichment analysis were drawn using R Project for Statistical Computing (RRID: SCR_001905) (https://www.r-project.org/).To further clarify the potential role of Forsythiaside in alleviating DKD, overlapping targets were inserted into the Database for Annotation, Visualization and Integrated Discovery site   following the methods described in Upon target mapping, overlapping target molecules between Forsythiaside and diabetic podocytopathy or tubulopathy were used to generate a PPI network using the STRING database (RRID: SCR_005223) (https://cytoscape.org/). The values of degree were calculated using the Cytoscape plug-in Network Analyzer, and the nodes were ranked by these values.Furthermore, PPI networks were developed for the following: diabetic podocytopathy and targets; diabetic tubulopathy and targets; overlapping targets between Forsythiaside and diabetic podocytopathy; overlapping targets between Forsythiaside and diabetic tubulopathy; and overlapping target-pathway network were constructed using Cytoscape software (RRID: SCR_003032) (https://david.ncifcrf.gov/) and the Metascape database (RRID: SCR_016620) (http://metascape.org/)  . The topUpon three enrichment analyses, the key pathways through which Forsythiaside alleviates DKD, diabetic podocytopathy, and tubulopathy, respectively were identified. The same pathways were extracted as key ones that Forsythiaside might alter to treat DKD, by ameliorating targets in diabetic podocytopathy or tubulopathy. The overlapping Forsythiaside and diabetic podocytopathy or tubulopathy target proteins that were identified as critical proteins through which Forsythiaside may intervene in order to treat DKD.http://www.pdb.org/), and PDB codes of the protein receptors were summarized in http://mgltools.scripps.edu/documentation/links/autodock) were used to prepare the appropriate protein receptor by adding hydrogen and computing charges. The protein macromolecule was saved in pdbqt format.The crystal structures of critical proteins were acquired from RCSB Protein Data Bank database  (http://http://lsp.nwu.edu.cn/tcmsp.php)  , which whttps://pymol.org/2/), and Discovery Studio Visualizer software (version 21.1.0.20298) was applied to transform the 3D structure of the complex to 2D.Upon the preparation of ligand and receptor, Autodock software (version 4.2.6) was used to evaluate and confirm the binding affinity between ligand and receptor and further construct the binding model. Binding models were visualized using PyMOL software (RRID: SCR_000305)  was purchased from MedChemExpress , and dissolved in DMSO at various concentrations . Conditionally immortalized human podocytes were kindly supplied by Dr. Yi Fan  and cultured in RPMI-1640 medium  supplemented with 10% fetal bovine serum , 100 units/mL penicillin and 100 mg/mL streptomycin  until cells became confluent.Podocytes were incubated in various media containing 5.5 mM D-glucose (NG), 5.5 mM D-glucose +34.5 mM D-mannitol (MG) , 40 mM D4 cells/cm2 in RPMI 1640 medium on 96-well plates. After cell adherence, RPMI-1640 medium was replaced with other media, including NG, MG, HG, or HG + FA at concentrations of 2.5, 5, or 10 μg/mL. Cells were subsequently incubated for 48 h. Cell viability was detected using the CCK-8 assay kit  according to the manufacturer’s instructions. The absorbance of each sample was measured at a wavelength of 450 nm.Human podocytes were seeded at a density 3×10β-actin  was used as a reference.Proteins were isolated from cultured podocytes after various treatments. Protein levels were detected using a BCA Protein Assay Kit . Proteins were subsequently separated by SDS-PAGE electrophoresis. Antibodies against protein kinase C alpha (PRKCA)  and RHOA  were used for western blotting. An antibody against ROS were measured in podocytes using a ROS detection kit  according to the manufacturer’s instructions. Briefly, podocytes were seeded on sterile glass coverslips in a 6-well plate and incubated with different media for 48 h. Thereafter, slides were incubated with a 10 μM DCFH-DA working solution for 30 min at 37°C. After washing three times with warm PBS, ROS intensity was detected under a fluorescent microscope (Olympus FSX100).https://www.ibm.com/products/spss-statistics) and presented as means ± standard error of the mean. Student’s t-test was used to evaluate the significance of difference between two groups. One-way ANOVA and two-way ANOVA tests were used for multiple statistic comparisons. p < .05 was considered as statistically significant.Technical and biological replicates were performed three times. Data were processed using SPSS software (RRID: SCR_002865) . Cell viability was further assessed in the HG + FA group. Results showed that the viability of podocytes in this group were not significantly different when compared with podocytes in the NG group . However, treatment with 10 μg/mL Forsythiaside did not show significant increase of cell viability when compared with HG group (p > .05).To investigate the direct effect and drug toxicity of Forsythiaside on podocytes, we performed a CCK-8 assay. Firstly, cell viability of podocytes was examined after high glucose stimulation. As shown in p > .05, , indicatTo clarify a potential mechanism for the renal protective effect of Forsythiaside, potential changes in ROS were measured with a reagent solution, DCFH-DA, which fluoresced upon oxidation by ROS. As shown in p < .05), while treatment with Forsythiaside in HG medium visibly suppressed the expression of PRKCA (p < .05). Additionally, western blotting results showed that the expression of RHOA was significantly upregulated in podocytes in the HG group compared with those in the NG and MG groups (p < .05), which was in accordance with the results of PRKCA protein. It is worth noting that 2.5 μg/mL Forsythiaside treatment markedly inhibited the expression of RHOA protein compared with the HG group  belongs to the family of Small GTPase. The primary function of RHOA is mainly related with cytoskeleton organization when binding to a variety of effector proteins, and then is responsible for control of cellular responses . ResearcProtein kinase C alpha (PRKCA), another target of Forsythiaside we acquired from network analysis and molecular docking in this study, is a serine/threonine-protein kinase which is involved in the regulation of a variety of cellular processes by activating signaling cascade involving mTOR pathway . Previouvia controlling RHOA signaling pathway (via intervening RHOA and PRKCA.The targets that Forsythiaside bound and intervened were primarily identified, whereas the underlying mechanism is still unclear. Further Go enrichment analysis was performed and the results showed that Forsythiaside could relieve oxidative stress and improve kinase activity in DKD podocytes. Dysfunction of podocytes has been verified as a critical driving factor for DKD occurrence and development . Podocyt pathway . Additio pathway , suggestFurthermore, our study clarified that Forsythiaside alleviated the detrimental effect of hyperglycemia on podocytes in diabetic podocytopathy possibly by inhibiting the mTOR signaling pathway. Research revealed that Everolimus, an inhibitor of mTOR remodeled aberrant podocyte behavior by substantial increase of RHOA activity and enhanced actin stress fibers , suggestIn conclusion, by using network analysis, we found that Forsythiaside intervened DKD by mainly targeting diabetic podocytopathy. Furthermore, our study elucidated that Forsythiaside exerted potential treatment against DKD which may act directly RHOA and PRKCA target by suppressing the oxidative stress pathway in podocytes, which preliminarily reflected the characteristics of multi-target and multi-action pathway of Forsythiaside in treating DKD from the point of view of cellular precision therapy. Our findings could offer a reference for investigation of therapeutic benefits of Forsythiaside in DKD from the cellular facet. However, systematic studies of Forsythiaside intervention on DKD are still insufficient. Furthermore, limitations existed in the study. The lack of comprehensiveness in current network information, and the delayed update of database may become the inevitable limitations. The explanation of data should be more careful. Therefore, it is of great clinical significance to study the exact mechanism of Forsythiaside against DKD podocytopathy and to further develop related pharmaceutical preparations in future, and future research study should focus on the therapeutic interventions of Forsythiaside for DKD patients in clinic.


This study aimed to explore the mechanisms of Baishi tablets (BSTs) in the treatment of vitiligo through network pharmacology-based identification and experimental validation.  In brief, the compounds and related targets of BST were extracted from the TCMSP database, and disease information was obtained from the OMIM, GeneCards, PharmGkb, TTD, and DrugBank databases. A Venn diagram was generated to visualize the common targets of BST and vitiligo. GO and KEGG analyses were performed to explore the potential biological processes and signaling pathways. The PPI network and core gene subnetwork were constructed using STRING and Cytoscape software. In addition, the measurement of apoptosis in PIG1 cells and intracellular reactive oxygen species were measured using quercetin (QU), luteolin (LU), and kaempferol (KA) to protect melanocytes from oxidative stress. HMOX1, CXCL8, CCL2, IL6, MAPK8, CASP3, PTGS2, AKT1, IL1B, MYC, TP53, IFNG, and IL2. Furthermore, a molecular docking analysis was conducted to simulate the combination of compounds and gene proteins, reflecting that QU, LU, and KA can strongly bind the core genes. Through a series of experimental validations, we found that QU, LU, and KA could attenuate H2O2-induced apoptosis in melanocytes. Further evidence revealed that QU, LU, and KA could enhance the scavenging of intracellular reactive oxygen species (ROS).  A total of 55 compounds with 236 targets and 1205 vitiligo-related genes were obtained from the TCMSP database. GO and KEGG analyses were performed to explore the potential biological processes and signaling pathways, revealing that BST may cure vitiligo by influencing the biological processes of cellular oxidative stress and related signaling pathways. A critical subnetwork was obtained with 13 core genes by analyzing the PPI network, which includes  Based on the results of network pharmacology analysis and experimental verification, QA, LU, and KA can be utilized to protect PIG1 cells by inhibiting oxidative stress and reducing the intracellular level of ROS. This may explain the underlying mechanism of BST therapy and provide a novel strategy for the treatment of vitiligo. Vitiligo is an acquired, chronic depigmenting disorder of the skin. It results from the selective destruction of melanocytes . ApproxiManagement of vitiligo includes the topical use of glucocorticoids, calcineurin inhibitors, and phototherapy. A small number of patients who meet the indications can also choose surgical transplantation of pigments or decolorization treatment . HoweverTraditional Chinese medicine (TCM) is a powerful and far-reaching system of medical treatment. From the viewpoint of compatibility of TCM and the interaction of various traditional Chinese medicines, TCM formulations have relatively low toxicity and few side effects and have shown curative effects in the treatment of several diseases . StudiesBaishi tablets (BSTs) consist primarily of Chaihu (bupleurum), Chishao (red peony), Zhixiangfu (rhizome cyperi), Baishao (white peony), and Zhishi (citrus aurantium). BST can replenish qi, promote stagnation, aid blood circulation and detoxification, and dispel wind. The dermatology department of our institution has used BST to treat vitiligo for many years, and excellent outcomes have been achieved. Our research group conducted a clinical randomized trial in 2015, and the Vitiligo Disease Activity (VIDA) score of the patients treated with BST was significantly reduced . NeverthNetwork pharmacology is a promising method that combines pharmacology and computer science to construct and visualize the interaction network of multiple genes, targets, and signaling pathways. It is highly suitable for researching drugs with complex ingredients  and is a cost-effective method of drug development –12.in vitro experiments on immortalized human melanocytes. The flowchart of our study is shown in Herein, we applied network pharmacology to identify the active ingredients and examine the core targets and signaling pathways of BST for vitiligo treatment. We also undertook molecular docking studies to ascertain how BST binds to its predicted targets. Subsequently, we conducted a series of http://tcmspw.com/tcmsp.php/) [via the UniProt database (http://www.uniprot.org/) by limiting the species to Homo sapiens for further analyses [The compounds Chaihu, Chishao, Xiangfu, Baishao, and Zhishi were searched and obtained from the Traditional Chinese Medicine System Pharmacology Database  , as weresp.php/) . Moreoveanalyses .https://omim.org/) [http://www.genecards.org/) [http://www.pharmgkb.org/) [http://db.idrblab.net/ttd/) [http://www.drugbank.ca/) [Vitiligo-related targets were extracted by screening the Online Mendelian Inheritance in Man  , Genecards.org/) , PharmGkkb.org/) , Therapeet/ttd/)  and Drugank.ca/)  databasehttps://cytoscape.org/) to show the relationship among vitiligo, BST, and the related gene targets [Having prepared two sets of target lists for the gene targets of compounds and vitiligo-related targets, screening for drug–disease crossover was carried out. A Venn diagram was generated with R  using the Venn Diagram package to show the intersection sets. A compound–target–disease network diagram was established using Cytoscape 3.8.0 ( targets .http://geneontology.org/) and Kyoto Encyclopedia of Genes and Genomes  databases, respectively. The “clusterprofile” and “bioconductor” packages within R were employed to assess the biological process (BP), cellular component (CC), molecular function (MF), and key signaling pathways. Significantly enriched terms were identified, and p < 0.05 and q < 0.05 indicated a strong association with related BPs [Subsequently, analyses of functional enrichment and enrichment of signaling pathways were undertaken using the gene ontology  database [The Search Tool for the retrieval of interacting genes/proteins (STRING) (database  was useddatabase . Eligiblhttps://pubchem.ncbi.nlm.nih.gov/) [via ChemBio 3D (http://www.adeptscience.co.uk/products/lab/chembio3d/). Moreover, the 3D structure of the receptor proteins encoded by the core genes was searched in the UniProt database and downloaded from the Research Collaboratory for Structural Bioinformatics Protein Database (https://www.rcsb.org/) [Compounds with the top-three highest numbers of related critical genes and their common core genes were selected for molecular docking. After downloading the two-dimensional (2D) molecular structure of ligands from the PubChem database (ih.gov/) , the 3D sb.org/) .https://pymol.org/2/). AutoDock (https://autodock.scripps.edu/) was utilized to modify the receptor protein as well as carry out the hydrogenation and charging calculations of proteins [via Vina within AutoDock [After preparing the files for the 3D structure, the receptor proteins were dehydrated, and ligands were removed using PyMOL  was purchased from the American Type Cell Collection . PIG1 cells were cultured in Dulbecco's modified Eagle's medium  supplemented with 10% fetal bovine serum (Gibco). PIG1 cells were cultured in a 37°C incubator in an atmosphere of 5% COμmol/L QU, LU, or KA for 24 h. Then, H2O2  was added to each well, and incubation was undertaken for an additional 2 h. Simultaneously, we set up simple compound-treatment groups and a control group (without any treatment). After the previous treatment, we digested PIG1 cells with EDTA-free trypsin, collected them in tubes, washed them twice with phosphate-buffered saline (PBS), and resuspended them in PBS. According to the protocol, PIG1 cells were stained with an Annexin V-FITC Apoptosis Detection Kit  and detected by flow cytometry. FlowJo (http://www.flowjo.com/) was used to measure the percentage of apoptotic cells.PIG1 cells were cultured in 60 mm petri dishes after treatment with 50 μmol/L) of QU, LU, and KA for 24 h. H2O2 was added  to each well and incubated for an additional 2 h. After the corresponding treatment, we washed cells twice with serum-free medium. According to product instructions, cells were stained with dichlorodihydrofluorescein diacetate using a reactive oxygen species kit  and photographed under an inverted fluorescence microscope .QU, LU, and KA were purchased from Aladdin . QU, LU, and KA were dissolved in dimethyl sulfoxide  for further use. PIG1 cells were cultured in six-well plates after being treated with different concentrations  or SPSS 22.0 . The Student's t- test or one-way analysis of variance was used for multiple group comparisons. The experiments were repeated at least three times. ), was obtained , CALM1 (20 compounds), DPP4 (18 compounds), F2 (18 compounds), and PRSS1 (16 compounds).A compound–disease–target interaction network  was visup < 0.05 and q < 0.05 as criteria, 2112 significantly enriched GO terms were obtained: 1975 BPs, 26 CCs, and 111 MFs. The top 10 terms is shown in Analyses of functional enrichment using the GO database revealed the underlying BPs, CCs, and MFs of the 71 target genes. Using The top 10 BPs were responses to metal ions; nutrient levels; lipopolysaccharide; molecules of “bacterial origin,” “radiation,” “oxidative stress,” “aging,” “antibiotics,” “drugs,” and “reactive to oxygen species.”The top 10 CCs were “cyclin-dependent protein kinase holoenzyme complex,” “membrane raft,” “membrane microdomain,” “membrane region,” “caveola,” “serine/threonine-protein kinase complex,” “transcription factor complex,” “nuclear chromatin,” “mitochondrial outer membrane,” and “plasma membrane raft.”The top 10 MFs were “cytokine activity,” “cytokine receptor binding,” “receptor ligand activity,” “phosphatase binding,” “transcription cofactor binding,” “RNA polymerase II basal transcription factor binding,” “heme binding,” “protein phosphatase binding,” “tetrapyrrole binding,” and “transcription coactivator binding.”p < 0.05 and q < 0.05 as criteria, analyses of signaling-pathway enrichment using the KEGG database were performed. We found that 165 potential signaling pathways were enriched , LU affected eight core genes  and KA regulated five core genes . Subsequently, an additional calculation was made to simulate the molecular docking of three compounds with four common protein receptors: PTGS2 (protein database (PDB) code: 5KIR), AKT1 (PDB code: 6HHG), CASP3 (PDB code: 3PD0) and HMOX1 (PDB code: 1N45). The results of molecular docking and affinity values are listed in Referring to the results of the core gene network, we selected the top-three compounds  that influenced most vitiligo-related genes as ligands. Then, we conducted molecular docking on these core genes. According to our previous analyses, QU influenced 12 core genes   .μmol/L) of QU, LU, or KA. Subsequently, PIG1 cells were treated with H2O2 to simulate the environment of cellular oxidative stress, and we measured ROS production by fluorescence staining. Compared with PIG1 cells in the pure-H2O2 environment, ROS levels in PIG1 cells decreased in all three groups that were pretreated with 25 or 50 μmol/L of compounds. ROS levels decreased in the same treatment group with increasing concentrations of the compound, respectively  cells and attenuate ultraviolet B-induced COX-2 release and activator protein-1 transcriptional activity [KA is a natural flavonoid found in tea, fruits, and vegetables. Lee and colleagues found that KA could inhibit ultraviolet B-induced expression of cyclooxygenase-2 (COX-2) release in mouse skin epidermal (JB6Pactivity . KA can activity . Our exp2O2 use), melanocyte apoptosis and ROS production were reduced under pretreatment by QU, LU, or KA. Therefore, BST may have a specific protective role in the oxidative stress pathway of melanocytes.Hence, QU, LU, and KA are flavonoids that have anti-inflammatory and antioxidant effects. Therefore, we speculated that they could affect the oxidative stress of melanocytes. We conducted experiments to verify that under oxidative stress E2 and is made by epidermal keratinocytes in response to ultraviolet radiation [COX2 affect the risk of vitiligo [2O2-treated PIG1 cells. HMOX1 has been demonstrated to protect human melanocytes from oxidative damage via the E2-related factor 2 (Nrf2)-antioxidant response element (ARE) pathway [β-catenin, thereby activating the microphthalmia-associated transcription factor and tyrosinase family, eventually leading to melanogenesis of melanocytes [In the PPI network analysis of BST, adiation , 50. PGEvitiligo . Heme ox pathway . AKT1 isanocytes . IL-6 isanocytes . These rOur study had three main limitations. First, experimentally validated targets were the predicted results of network pharmacology, but there were certain deviations compared with the actual targets. Second, we revealed that the main compounds QU, LU, and KA could protect melanocytes from oxidative stress; however, how they regulate targets and affect downstream signaling pathways to have a role in vitiligo treatment was not tested. Third, our study was based on network pharmacology, so the compounds with the most targets were selected for experimental verification according to the results of network pharmacology, but their concentration in BST could not be determined.in vivo experiments, QA, LU, and KA can be utilized to protect PIG1 cells. This phenomenon was achieved thanks to the inhibition of oxidative stress by reducing the intracellular level of ROS. This may explain the underlying mechanism of action of BST therapy and could provide a novel strategy for the treatment of vitiligo.Under the prediction obtained using network pharmacology, we clarified the active compounds in BST and their main targets in vitiligo treatment. Based on network pharmacology and


Firmicutes, Fusobacteria, the Toll-like receptor signaling pathway, mitogen-activated protein kinase 1, and phenylacetylglutamine are notable components of NAFLD and therefore to understanding its processes and possible therapeutic approaches. The key components and potential mechanisms of metabolites from gut microbiota against NAFLD were explored utilizing network pharmacology analyses. This study provides scientific evidence to support the therapeutic efficacy of metabolites for NAFLD and suggests holistic insights on which to base further research.The metabolites of gut microbiota show favorable therapeutic effects on nonalcoholic fatty liver disease (NAFLD), but the active metabolites and mechanisms against NAFLD have not been documented. The aim of the study was to investigate the active metabolites and mechanisms of gut microbiota against NAFLD by network pharmacology. We obtained a total of 208 metabolites from the gutMgene database and retrieved 1256 targets from similarity ensemble approach (SEA) and 947 targets from the SwissTargetPrediction (STP) database. In the SEA and STP databases, we identified 668 overlapping targets and obtained 237 targets for NAFLD. Thirty-eight targets were identified out of those 237 and 223 targets retrieved from the gutMgene database, and were considered the final NAFLD targets of metabolites from the microbiome. The results of molecular docking tests suggest that, of the 38 targets, mitogen-activated protein kinase 8-compound K and glycogen synthase kinase-3 beta-myricetin complexes might inhibit the Wnt signaling pathway. The microbiota-signaling pathways-targets-metabolites network analysis reveals that  The gut microbiota is a significant element in human health and disease and variations in its diversity are associated with an unhealthy diet, medicines, and pathogenic infections as well as chronic kidney disease3. Notably, genetically engineered gut bacteria are significant therapeutic resources capable of producing beneficial metabolites for the treatment of chronic diseases such as cancer, autoimmune disorders, metabolic diseases, and beyond NAFLD4. An imbalance in gut microbiota can lead to the progression of some diseases, such as cancer, atherosclerosis, type 1 diabetes, and even nonalcoholic fatty liver disease (NAFLD)6. It has been suggested to have relatively stable and diverse distributions with a communal crucial microbiota, including the Firmicutes and Bacteroidetes phyla, as the key dominants7. The microbiota products are related to the occurrence and development of liver complications via diverse mechanisms, such as differential intestinal permeability, persistent inflammatory responses, and secretion of some short-chain fatty acids8. The microbiota products are related to the occurrence and development of liver complications via diverse mechanisms, such as differential intestinal permeability, persistent inflammatory responses, and secretion of some short-chain fatty acids9.The community of microorganisms inhabiting the human gut  is defined as the microbiota, which is estimated to be 100 trillion, including bacteria, viruses, fungi, and protozoa10. These benefits of the cross-talk between microbiota and the gut can be exerted locally as well as in distant organs due to the systemic circulation of metabolites produced in the intestine11. Furthermore, the gut-liver axis is critical for understanding the mechanism of diverse liver diseases, such as NAFLD, nonalcoholic steatohepatitis (NASH), and the development and occurrence of cirrhosis12. For instance, the progression of NAFLD is related to lipopolysaccharide (LPS) produced by gram-negative bacteria inhabiting the gut13. Likewise, the gut microbiota converts choline into trimethylamine oxide, which exacerbates liver inflammation and damage15. This implies that the gut microbiota is critically related to liver diseases caused by inflammation. Over the past few years, the gut microbiota has been an increasingly significant therapeutic strategy for relieving NAFLD due to its great efficacy and low adverse effects16. The metabolites produced by gut microbiota are effective agents for the treatment of NAFLD17. Some microbiota-associated metabolites have been examined to determine either positive or negative effects on the development of NAFLD, even though the number of metabolites of gut microbiota is not completely clear18. Furthermore, the active metabolites of gut microbiota and their pharmacological mechanisms against NAFLD have not yet been reported. Hence, studies on active metabolites transformed by substrates and their mechanism of action should be better defined prior to clinical trials of proposed NAFLD treatments.In particular, the gut-related microbiota converts exogenous and endogenous compounds into metabolites via the microbiota and nervous system19. The development and occurrence of NAFLD are dependent on multiple factors that involve inherited characteristics as well as inconsistent microbiota distribution20. Therefore, network pharmacology would seem to be a very effective technology to explore the function of microbiota-related metabolites against diseases.We suggest that the systematic methodology of network pharmacology can be used to unravel interactions of multiple components, for gut microbiota analysis, such as microbiota, signaling pathways, targets, and metabolites. Most recently, a report demonstrated that the gut microbiota have anti-fatigue effects by analyzing multiple targets via network pharmacologyIn this study, network pharmacology was utilized to investigate the analysis of a multi-factorial and very complex process, including key microbiota, signaling pathways, targets, and metabolites, in NAFLD. In parallel, we determined the key signaling pathways, targets, and metabolites to alleviate NAFLD. First, metabolites produced by the gut microbiome were identified utilizing a microbiome database, and metabolite-related targets were identified using cheminformatics. Then, NAFLD-related targets were retrieved via a bioinformatics database, and we identified the final targets among the metabolite-related targets and NAFLD targets. Second, we conducted a protein–protein interaction (PPI) network analysis, Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis, and gene ontology (GO) analysis. In key signaling pathways, we performed molecular docking test (MDT) to verify the most stable metabolites, which were identified by drug-likeness and toxicity in the in silico platform. Finally, we analyzed the microbiota-signaling pathways-targets-metabolites (MSTM) networks to identify the most significant components, microbiota, signaling pathways, targets, and metabolites from a holistic perspective. The workflow is represented in Fig. http://bio-annotation.cn/gutmgene/) (Accessed on 2 April 2022). The Simplified Molecular Input Line Entry System (SMILES) formats of each metabolite were identified by PubChem (https://pubchem.ncbi.nlm.nih.gov/) (accessed on 3 April 2022).The metabolites and targets of gut microbiota were retrieved by gutMGene v1.0 (http://sea.bkslab.org/) (accessed on 4 April 2022)21 and SwissTargetPrediction (STP) (http://www.swisstargetprediction.ch/) (accessed on 4 April 2022)22 with the “Homo sapiens” setting. The overlapping targets between the SEA and STP databases were considered to be important targets for further analysis. In addition, NAFLD targets were obtained by DisGeNET (https://www.disgenet.org/) (accessed on 4 April 2022)23 and OMIM (accessed on 5 April 2022)24. Significant targets were identified among the metabolite-related targets and NAFLD targets. Then, the core targets were recognized between the significant targets and the gutMGene database.The targets related to metabolites were identified through both similarity ensemble approach (SEA) (https://string-db.org/) (accessed on 6 April 2022). A target with the highest degree value in the PPI networks was considered a hub target to control the PPI network against NAFLD.The PPI network was constructed using R package and was based on final targets in STRING analysis , biological function (BF), and cellular component (CC) analyses. The KEGG pathway enrichment analysis was used to understand the potential signaling pathways related to the final targets against NAFLD. The bubble plots are based on a rich factor defined as the gene ratio expressed differentially to the total target number in a signaling pathwayhttps://www.rcsb.org/) (accessed on 6 April 2022). The.pdb format obtained by RCSB was converted into .pdbqt format by using AutoDock (http://autodock.scripps.edu/) (accessed on 6 April 2022).The metabolites associated with the key target were converted from the .sdf format from PubChem to.pdb format using PyMOL, and we obtained the .pdbqt format via AutoDock. The key target was identified in STRING through RCSB  (accessed on 7th April 2022)27. The threshold value of MDT was – 6.0 kcal/mol28 and a core metabolite with the lowest Gibbs free energy was selected on the metabolite-target complex in PyMOL.The metabolites were docked with the key target utilizing AutoDock 4 by setting up 4 energy ranges and 8 exhaustiveness values as the defaults to acquire 10 different poses of the metabolites29 and the literature. Commonly, metabolites have hydrophilic properties and have low bioavailability; therefore, we identified their physicochemical properties through an in silico strategy.The drug-likeness properties of the three metabolites were evaluated using SwissAMDE30 and cause human hepatotoxicity31, Ames mutagenicity32, Skin sensitization33, Lethal Dose 50 (LD50) of acute toxicity34, and Drug Induced Liver Injury (DILI)35. Thus, we confirmed the six parameters by using ADMETlab platform36.One of key reason for failure of drug development is the lack of safety caused by some adverse effects: hERG blockers obstruct potassium channelsThe MSTM networks were constructed as a size plot based on the degree value of each node. In the network plot, yellow circles (nodes) describe the gut microbiota; pink circles (nodes) display the signaling pathways; orange circles (nodes) represent the targets; and violet circles (nodes) represent the metabolites. The size of the yellow circles represents the total number of relationships with signaling pathways, metabolites, and targets; the size of pink circles represents the number of correlations with gut microbiota; the size of orange circles depicts the number of interactions with signaling pathways; and the size of violet circles describes the number of relationships with targets. The merged network was built using R Package.We obtained 208 metabolites from the gutMgene microbiome database. The obtained targets and metabolites were considered significant components to analyze the therapeutic effects of the gut microbiota.A total of 208 metabolites were analyzed to search for their targets in silico in the SEA and STP databases. We identified 1256 targets from SEA and 947 targets from STP  linked to the Wnt signaling pathway were identified via KEGG pathway enrichment analysis. MDT was performed to verify the binding affinity of each complex at the molecular level. AutoDockTools-1.5.6 software was used for MDT analysis; the docking scores are displayed in Supplementary Tables 28. Between the 53 metabolites and 3 targets, the most stable complexes were JUN-platycodin D , MAPK8-Compound K , and GSK3B-myricetin  , which can exert its efficacy on the targetol) Fig. .Figure 42). The other two metabolites (Compound K and myricetin) had acceptable drug-likeness properties  were identified by the ADME parameters in silico. Platycodin D violated the drug-likeness properties characterized by Lipinski’s rule, including the topological polar surface area (TPSA)  and 1047 edges of the network. The green circles represent the gut microbiota, the pink circles represent the signaling pathways, the orange circles depict the targets, and the violet circles describe the metabolites Fig. . The conFirmicutes and Fusobacteria are the most significant microbiota, with 586 degrees of value each, the Toll-like receptor signaling pathway is the most significant effector mechanism, with 33 degrees of value, MAPK1 is the uppermost target, with 34 degrees of value, and phenylacetylglutamine is the highest metabolite, with 10 degrees of value. The 4 components exhibited more relationships, suggesting that these components might be the most significant hallmarks in NAFLD.We discovered that 38. This study demonstrated that the relevant microbiome-derived metabolites might be detected by using network-based systems pharmacology, and the results of our study support the power of this approach.We investigated the interaction between metabolites and gut microbiota via data-driven analysis. Previous research has suggested the use of gut microbiota in NAFLD treatment, but the details of the relevant metabolites and their targets remain unclear. Recently, network-based systems pharmacology has been used for diagnosis of various diseases and identification of target substances39. The IL6 level was markedly increased in NAFLD patients, which can exacerbate its severity40. This implies that inactivation of IL6 might be a therapeutic strategy to alleviate NAFLD. Additionally, a study demonstrated that upregulation of PPARG can accelerate the progression of adipogenic hepatic steatosis41. In the NAFLD cellular sample, the expression of JUN was considerably elevated, suggesting that miR-139-5p overexpression is an indirect approach to dampen the JUN expression level42. An animal test suggested that epidermal growth factor receptor (EGFR) activation exacerbates the severity of NAFLD due to dysfunction of lipid metabolism43. Therefore, the five targets may be promising key targets for the treatment of NAFLD via gut microbiota metabolites.In the PPI networks, AKT1, IL6, PPARG, JUN, and EGFR were defined as important targets. AKT inactivation attenuated NAFLD progression and liver tumorigenesis in mouse experimentsThe GO enrichment analysis results suggest that NAFLD targets of metabolites from gut microbiota are mainly related to bile acid receptor activity, vitamin D 24-hydroxylase activity, the Sin3 complex, nucleosome remodeling and the deacetylase (NuRD) complex, the neutrophil apoptotic process, alkaloid catabolic process, dibenzo-p-dioxin metabolic process, and fungiform papilla formation to relieve NAFLD. This analysis sheds light on the functions of metabolites in the treatment of NAFLD.44. Advanced glycation end-products–receptor advanced glycation end-products (AGE-RAGE) signaling pathway: The upregulation of advanced glycation end-products (AGEs) accelerates the detrimental effects  of NAFLD; therefore, a restrictive regime of AGEs might be a therapeutic strategy to relieve NAFLD45. C-type lectin receptor signaling pathway: C-type lectin is a hallmark to identify the stage of chronic liver disease, which is commonly upregulated in nonalcoholic steatohepatitis (NASH)46. It has been postulated that the overexpression of C-type lectin might induce excessive inflammation in hepatocytes. Tumor necrosis factor (TNF) signaling pathway: the expression level of TNF-α was increased in serum samples of NAFLD patients; in contrast, mice with deleted TNF receptors showed attenuated inflammation, steatosis, and fibrosis47. Toll-like receptor 7 (TLR7) dampened the development of NAFLD, and might be a potential treatment48. T-cell receptor signaling pathway: The dysregulation of T cells leads to the development of NAFLD, which results in cirrhosis and hepatocellular carcinoma49. Epithelial cell signaling in Helicobacter pylori infection: Helicobacter pylori infection might lead to NAFLD due to excessive inflammatory responses and insulin resistance50. NOD-like receptor signaling pathway: NLR induces the innate immune response to defend against foreign bodies, such as microbes or toxic chemicals, and the silencing of NLR can protect against cytokines51. Neurotrophin signaling pathway: The synthesis of brain-derived neurotrophic factor in the central nervous system indirectly enhances NAFLD via adiponectin52. Prolactin signaling pathway: Prolactin decreases lipid accumulation in hepatocytes, which ameliorates inflammation in the liver53. The rich factor (gene-ratio) results in our analysis showed the Wnt signaling pathway to have the lowest rich factor, indicating that the pathway might function as an inhibitive mechanism against NAFLD. Consistent with this result, Wnt antagonists have been shown to be a significant target for inhibiting the progression of NAFLD54.The results of the KEGG enrichment analysis indicate enrichment in inflammatory-related pathways, such as the IL-17 signaling pathway, AGE-RAGE signaling pathway, C-type lectin receptor signaling pathway, TNF signaling pathway, Toll-like receptor signaling pathway, T-cell receptor signaling pathway, epithelial cell signaling in Helicobacter pylori infection, the NOD-like receptor signaling pathway, neurotrophin signaling pathway, and prolactin signaling pathway. The targets of the key metabolites of gut microbiota associated with NAFLD are also related to inflammation. The relationships of the 10 significant pathways according to the FDR  are briefly discussed. IL-17 signaling pathway: IL-17 signaling aggravated the severity of NAFLD in mouse experiments due to the causal contribution of gut microbiota driving IL-17 production in damaged hepatocytes55. Myricetin is a metabolite of myricitrin, which is transformed by Escherichia sp. 12, Escherichia sp. 33, and Enterococcus sp.4556. Furthermore, these metabolites have stable physicochemical properties in common in the systemic circulation and have low toxicity."57.Our study shows that Compound K and myricetin are promising antagonists that bind stably to MAPK8 and GSK3B in the Wnt signaling pathway, respectively. Compound K is a major metabolite of ginsenoside Rb1, which is converted by the gut microbiotaFirmicutes phyla play significant roles in repressing the growth of pathogenic microbes, maintaining a constant immune system58. A group who consumed red wine combined with polyphenols had increased levels of Fusobacteria and Firmicutes, suggesting that the gut microbes might be significant players against cirrhosis59. Moreover, polyphenols play important roles in inhibiting hepatic fat accumulation, which has been confirmed by several in vitro experiments, in vivo tests, and clinical trials60. A finding which has been confirmed that both Firmicutes and Fusobacteria might exert desirable effects on NAFLD. MAPK inhibition attenuates obesity, insulin resistance, and steatosis in NAFLD61. With the highest degree of value of the metabolites of the gut microbiota, phenylacetylglutamine might be a biomarker to sign hepatic dysfunction62.According to the MSTM networks, the results suggest that 41 microbiota constituents, 41 signaling pathways, 23 targets, and 125 metabolites might exert therapeutic efficacy against NAFLD. The 63. Moreover, the associations and interactions between microbiota and complex chronic diseases can be better understood/elucidated utilizing network pharmacology concepts64.Our results in this study show that a holistic-based analysis, as integrated science, is a powerful tool for unraveling complex diseases and targets, as concluded by othersFirmicutes and Fusobacteria, the Toll-like receptor signaling pathway, MAPK1, and phenylacetylglutamine might be important key components and distinctive features of NAFLD in MSTM networks. Thus, we suggest that a systemic approach to the analysis of metabolites of gut microbiota can be an effective methodology to screen therapeutic agents.In summary, this study investigated the key metabolites of gut microbiota in treating NAFLD via a network pharmacology-based study. We revealed that Compound K and myricetin can function as antagonists of the Wnt signaling pathway by docking stably to MAPK8  and GSK3B. Our study provides crucial evidence that Compound K converted from ginsenoside Rb1 and myricetin converted from myricitrin can be administered orally as a therapeutic strategy against NAFLD. From a holistic viewpoint, Supplementary Tables.


Pan Ji Sheng Formula is a Chinese medicine formula that enables heat-free detoxification as well as anti-inflammatory and immune-boosting properties. This formula contains eight herbs. Its underlying mechanism is unknown. The bioactive ingredients were screened in our work, and the mechanism of this formula was investigated.  Using traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP), ingredients in Pan Ji Sheng Chinese medicine formula were screened, and we selected the main bioactive ingredients for web-based research. The targets of bioactive ingredients are primarily obtained from the SwissTargetPrediction and TCMSP databases, and the text mining method is used. STRING and Cytoscape were then used to examine the protein-protein interaction (PPI) networks. To explore the biological function and related pathways, functional annotation and pathway analysis were performed.  This research discovered 96 bioactive ingredients. Then, 215 potential targets of bioactive ingredients were screened. Through the analysis of the PPI network, we discovered 25 key target genes, which can be described as hub target genes regulated by bioactive ingredients. Bioactive ingredients primarily regulate CASP3, AKT1, JUN, and other proteins. The formula works synergistically to enhance immune response and antiinfection by regulating immune-related pathways, TNF signaling pathways, and apoptosis.  A variety of bioactive ingredients in the formula could play roles in regulating CASP3, AKT1, and other genes in immune, infection, apoptosis, and tumor-related signaling pathways. Our data point the way forward for future studies on the mechanism of action of this formula. The climate in China's Lingnan region is standard subtropical. Summers are hot, rainy, as well as wet . FurtherInflammation is a pathological defense response and it is also the most important protective response . In modeMicroctis Folium (the leaves of Microcos paniculata), Polygonum chinense (creeping smartweed), Ecliptae Herba , Perilla Frutescens (the leaves of Beefsteak Plant), Isatidis Radix (the dried roots of the plant Isatis indigotica Fort or Isatis tinctoria L.), Chrysanthemi Flos (the flower of Chrysanthemum indicum Linne or Chrysanthemum morifolium Ramatuelle), Glycyrrhiza uralensis , and Chimonanthus salicifolius (wintersweet). All of these herbs are commonly used to treat diseases by clinicians. According to published research, these Chinese herbal medicines can prevent and treat diseases by utilizing a wide range of chemical components and multiple targets [The ancestors attempted to collect herbs for clearing heat and detoxification, and boiling water for drinking to eliminate the “heat” in order to get rid of dampness and heat and adapt to the environment. Since this type of herbal medicine was safe to drink, it gradually spread among the people , 13. Peo targets –21. For  targets ; at the  targets , and pol targets .There is, however, no systematic research report on the specific formula and network mechanism of the formula's effects of clearing heat, detoxifying, anti-inflammatory, and enhancing immune response. Now, researchers have realized the “one key, one lock” model is insufficient for deciphering drug effects, particularly in complex diseases . NetworkMicroctis Folium, Ecliptae Herba, Perilla Frutescens, Isatidis Radix, Chrysanthemi Flos, and Glycyrrhiza uralensis) from the TCMSP database. There is no information about Polygonum chinense and Chimonanthus salicifolia in the TCMSP database, so we search the literature for bioactive ingredients of these two herbals, then test OB ≥ 30% and DL ≥ 0.18 in TCMSP to determine the active ingredients.Through TCMSP, we search the relevant information about the bioactive ingredients in eight herbals in Pan Ji Sheng formula and screen the qualified compounds as the formula's active ingredients. The screening conditions are oral bioavailability (OB) ≥ 30%, number of hydrogen bond donors (Hdon) < 5, lipid water partition coefficient (Alogp) < 5, number of hydrogen bond receptors (HACC) < 10, intestinal epithelial permeability (Caco-2) > 0, drug class (DL) ≥ 0.18, and drug half-life (HL) ≥ 4. We obtained bioactive ingredients of six herbals ” and use a confidence level of 0.9 to build the target interaction network (PPI). We hide the discrete points in the network, then export the results to a TSV file and import it to Cytoscape 3.9.1 [We import target genes into STRING  and set pe 3.9.1 . CytoscaThen, in Cytoscape, the MCODE and Cytohubba plug-ins were used to extract the functional modules and top 25 hub genes of the PPI network, respectively.All screened target genes were entered into the Metascape platform for enrichment analysis .The hub targets were imported into the David database to clarify their function and role in signal transduction. GO biological process enrichment analysis and KEGG signal pathway analysis are carried out. The enrichment analysis results are enhanced with the R program package and displayed in the form of a bubble diagram.Cytoscape 3.9.1 software was used to build the bioactive ingredients-hub target network. In this network, nodes represent bioactive ingredients and hub targets.Use Cytoscape 3.9.1 to build the network model. Nodes represent hub targets, pathways, and diseases, and edges represent interactions between these nodes.Polygonum chinense and Chimonanthus salicifolia, we obtained the active components of these two herbals through literature retrieval and then tested whether they meet the standards of oral bioavailability (OB) ≥ 30 percent and drug class (DL) ≥ 0.18 in TCMSP. We obtained the active components of the other six herbals from TCMSP. In total, this study screened 96 active ingredients from eight herbals in the Pan Ji Sheng formula , and multipathways . These findings could serve as guidelines for future research into this formula. Based on the present study, functional experiments can be performed on animal models or human cells to validate the pharmacological mechanisms of the Pan Ji Sheng formula in the future. This research has theoretical significance for the TCM pharmacology and has application value for the development and utilization of TCMs.


Increasing evidence indicates that psoriasis (PSO) and periodontitis (PD) are likely to occur together, however, the underlying mechanism remains unclear.The expression profiles of PSO  and PD  were downloaded from the GEO database. First, we investigated the common differentially expressed genes (DEGs) of PSO and PD. Then, GO and KEGG enrichment analysis, protein interaction network (PPI) construction, and hub gene identification analysis were carried out. Finally, GO and KEGG enrichment analysis, miRNA interaction analysis, and transcription factors (TFs) interaction analysis for hub genes were performed.Eighteen DEGs were identified for further analysis, including 15 up-regulated genes and 3 down-regulated genes. 9 hub genes were then identified via Cytohubba, including IL1B, CXCL1, CXCL8, MMP12, CCL18, SELL, CXCL13, FCGR3B, and SELE. Their functions are mainly enriched in two aspects: neutrophil chemotaxis and migration, chemokine activation and interaction. The enriched signaling pathways includes three categories: host defense, inflammation-related signaling pathways, and disease-related pathways. 9 common miRNAs based on experimental evidence and 10 common TFs were further identified in both PSO and PD.Our study revealed possible comorbidity mechanisms in PSO and PD from the perspective of bioinformatics tentatively. The data can present new insight for joint prevention and treatment of in PSO and PD, as well as provide data support for further prospective studies.The online version contains supplementary material available at 10.1186/s41065-023-00266-z. Psoriasis (PSO) is an immune-mediated inflammatory skin disease that affects more than 60 million adults and children worldwide . It’s dePeriodontitis (PD), affecting roughly 10%-15% people worldwide, is a chronic and destructive periodontal disease that results from several dynamic interactions . InflammThe above contents suggest that both PSO and PD are related to amounts of systemic diseases and inflammatory response. Meanwhile, more and more evidence indicates that there is a bidirectional association between PSO and PD from the perspective of clinical research. Several observational researches have shown that individuals with PSO have significantly more tooth loss, more plaque and bleeding sites on probing, and a significantly higher prevalence of PD than controls –14. ReseDespite the strong clinical correlation between PSO and PD, the underlying mechanisms remain unclear. Based on previous studies, we speculated that there are similar mechanisms in the pathogenesis of PSO and PD, such as some common DEGs and pathways. To verify this hypothesis, Bioinformatics is an ideal approach and perspective. It’s a tool designed to understand biological phenomena using information science and statistical methods, which is widely used in deep sequencing, imaging and mass spectrometry analysis, and plays an important role in the study of molecular mechanisms such as tumors, systemic diseases and rare diseases .Hence, the purpose of this study is to understand the correlation between PSO and PD, and explore the underlying comorbidity mechanism through bioinformatics analysis. So as to provide certain data support and breakthrough point for the joint prevention and treatment of PSO and PD.Based on the criteria we established before, GSE30999, GSE14905, GSE16134 and GSE10334 were obtained. The information of these datasets was shown in , neutrophil migration (P.adjust = 5.48784E-06), chemokine activity (P.adjust = 5.25019E-06), chemokine receptor binding (P.adjust = 8.84745E-06), CXCR chemokine receptor binding (P.adjust = 0.000475041) Fig. A. AccordCytoscape was used to build PPI networks containing 14 nodes and 33 interactions for the common DEGs Fig. . 9 hub gAccording to the co-expression network of hub genes, we obtained the top 7 related functions and different weight of interaction, including co-expression of 46.46%, shared protein domains 21.24%, physical interactions of 14.35%, co-localization of 13.92%, predicted of 4.03% , Aging , Granulopoiesis , Innate Immunity , Cell Proliferation , Immune Response , Latent Virus Replication   The external environment stimulates keratinocytes to produce antibacterial peptides, growth factors, chemokines, triggering the initial response of immune system to produce pro-inflammatory factors. (ii) Helper T cell type 17 (Th17) produces IL-17 and IL-22 by activating pathways such as JAK-STAT and NF-κB signaling pathways to amplify inflammation, causing excessive proliferation of keratinocytes and dynamic inflammatory response , 25, 26.Consistent with the above researches, our results also reveal the important roles of IL-17 signaling pathway, Cytokine-cytokine receptor interaction, Chemokine signaling pathway, NF-κB signaling pathway from the perspective of bioinformatics in PSO and PD. Moreover, the remaining top 10 signaling pathways can be divided into two categories, one is the response of viruses, malaria parasites, legionella, and amoebas after infection, showing the role of host defense pathways in PSO and PD , 35. TheAnother point worth emphasizing is that we select experimentally validated data as the basis for both screening common miRNAs and screening genes that interact with miRNAs. The HMDD database collects proven miRNA data from diseases , and in In summary, our study is the first to directly identify common DEGs, hub genes, miRNAs, TFs, and their enriched pathways in PSO and PD from the perspective of bioinformatics. These results may help to reveal the comorbidity mechanisms of PSO and PD, although there is no further experimental verification. Meanwhile, it can provide a theoretical framework and basis for future research. Next, well-designed clinical prospective studies based on our data are necessary to directly detect the changes occurring in patients with both PSO and PD. In this process, confounding factors such as smoking and drinking can be adjusted to further improve the reliability and clinical promotion value of the study.Our study revealed possible comorbidity mechanisms in PSO and PD from the perspective of bioinformatics tentatively. The data can present new insight for joint prevention and treatment of PSO and PD, as well as provide data support for further prospective studies.http://www.ncbi.nlm.nih.gov/geo) contains millions of microarray datasets and high-throughput sequences submitted by researchers worldwide. We used the key word “Psoriasis” or “Periodontitis” to search gene expression datasets. Inclusion and exclusion criteria:(i) the test specimens should be Homo sapiens (ii) the gene expression profiles should include cases as well as controls. (iii) The samples of both diseases should be local pathological tissues. The PSO are skin tissues, and the PD are gum tissues. (iv) Preferentially select the datasets with large sample size (v) The sequencing platform should be consistent. (vi) Patients receiving clinical intervention are excluded. As a result, the GEO datasets: GSE30999, GSE14905, GSE16134, and GSE10334 were selected. For GSE30999 and GSE14095, we used dataset full soft file for further analysis. For GSE16134, GSE10334, we matched the gene symbols and probes based on platform file and performed log2 transformed to acquire gene matrix for the final analysis,The GEO database (R package “limma” was used to identify DEGs. R package “Impute” was used to replenish the missing expression data. Multiple probes corresponding to a single gene symbol were averaged and probes without a corresponding gene symbol were removed. The criteria of DEGs are and |logFC (fold change)|≥ 1 and adjusted P-value < 0.05. The Venn diagram drew by R package “ggplot2” was used to obtain their common DEGs. R version is 3.6.3.R Package “clusterProfiler” was used to perform GO and KEGG pathway enrichment analysis. The GO analysis contains three terms: biological process, cellular component, and molecular function. R Package “ggplot2” was used for visualization.http://string-db.org) was used to explore the complex regulatory relationships among proteins of interest. The combined score over 0.4 was set as statistically significant. Seven different algorithms of Cytohubba in Cytoscape were used to explore hub genes.String (http://www.genemania.org) is an effective tool for identifying the interconnectedness of gene sets, through which we established the co-expression network of these hub genes.R Package “clusterProfiler” was used to perform GO and KEGG pathway enrichment analysis same as before. GeneMANIA (http://www.cuilab.cn/hmdd), which is full of experimental evidence related to miRNAs and disease. The PSO-associated miRNA and the PD-associated miRNA were obtained and intersected, in addition, the mature miRNA was searched in the miRDB database (http://mirdb.org) to conduct further analysis. TAM 2.0 (http://www.lirmed.com/tam2) was used to perform miRNA function analysis. The terms ranked by p-values and p-value < 0.05 were identified as significant.The miRNAs exert gene regulation by degrading mRNA or inhibiting its function. Here, we searched for miRNAs associated with two diseases based on the HMDD database (https://mirtarbase.cuhk.edu.cn), which included amounts of miRNAs and target genes supported by experimental evidence. Taking intersection of common DEGs and target genes of predicted consensus miRNAs in PSO and PD. The miRNAs–mRNAs regulated network was established.MiRTarbase is an experimentally validated miRNA-target interactions database (https://www.grnpedia.org/trrust) includes the target genes of common TFs and the regulatory relationship between TFs, which is usually used for the prediction of transcriptional regulatory networks. A total of 8,444 and 6,552 TFs target regulatory relationships is recorded in this database. TFs that regulate the hub genes were searched from TRRUST, and P.value < 0.05 was included in the analysis.TRRUST  of hub genes.


MTOR, TSC1, LAMTOR2, RPS6K1 and RICTOR from peripheral blood mononuclear cells isolated from venous blood of children suffering from different forms of malnutrition and compared them with those from healthy children. Significant reduction in the phosphorylation of mTORC1 was noted, as well as a decrease in expression of LAMTOR2 gene and increase in TSC1 gene expression were observed between malnourished children in comparison to the healthy children. The deregulation in the activity of the TSC1 and LAMTOR2 gene was significantly associated with all forms of childhood malnutrition. Our findings provide key insights into possible down-modulation in the overall activity of the mTOR pathway in childhood malnutrition. Further studies focusing on the analysis of a multitude of components involved in the mTOR pathway both at the gene and protein expression levels are required for conclusive evidence for the aforementioned proposition.Dysregulations in the mammalian target of rapamycin (mTOR) pathway are associated with several human anomalies. We aimed to elucidate possible implications for potential aberrations in the mTOR pathway with childhood malnutrition. We analyzed the activity of phospho-mTORC1 and the expressions of several mTOR pathway genes, namely:  Childhood malnutrition has been strongly associated with a number of acute illnesses and damages to a number of physiological activities, including restricted mental growth and development, compromised host immunity and contributes to about 45% of cases of worldwide childhood mortality . StuntinRecent evidence indicates that human growth is controlled by the master growth regulation pathway, known as the mammalian target of the rapamycin (mTOR) pathway . The mTOTSC complex subunit 1 (TSC1), which is involved in the negative regulation of the pathway [MTOR, which encodes the crucial subunit component of mTORC1 [LAMTOR2, which is reported to be involved in the positive regulation of the mTORC1 pathway [RPS6K1 gene, which encodes a downstream modulator of the mTOR pathway [RICTOR, a gene encoding a crucial structural sub-unit of mTORC2 [No comprehensive study has been conducted until now, which has envisaged establishing a possible association between the mTOR pathway with the various forms of childhood malnutrition. In this study, we aimed to explore a potential aberration in the mTOR pathway with the phenotype of childhood malnutrition. We attempted to compare the activity of several mTOR pathway components among malnourished children  in comparison to healthy children. These selected mTOR pathway components included: phospho-mTORC1/the activated form of mTORC1 ; TSC com pathway ; MTOR, wf mTORC1 ; LAMTOR2 pathway ; RPS6K1  pathway  and RICTf mTORC2 . Stable f mTORC2 . We hypoThis study was a cross-sectional study conducted in a cross-sectional manner in the slum areas and adjacent localities of Bauniabadh, Mirpur, Dhaka and in the Dhaka Hospital of icddr,b between October 2018 to February 2019. The study participants enrolled from the community were aged between 8 and 18 months of either sex recruited into two malnourished cohorts comprising of: stunted (LAZ < −2) and children at risk of stunting (LAZ < −1 to −2). Healthy children  of identical age groups were recruited from the same demographic area in the community and served as the controls. Age and sex matched children with SAM  were enrolled exclusively from the Dhaka Hospital of icddr,b, following acute phase management and prior to the start of nutritional rehabilitation.The study participants were selected for enrolment into either of the four study cohorts based upon the anthropometric indices . Individuals with a history of cardiovascular illness, tuberculosis, cancer, impaired neurological and cognitive development, congenital anomalies and any known chronic illness were excluded upon primary screening. Moreover, participants screened with an ongoing episode of diarrhea, history of persistent diarrhea in the past month or history of acute diarrhea in the past 7 days, incidences of recent viral hepatitis, acute respiratory infections and asthma also served as exclusion criteria, since these physiological conditions have been reported to perturb the of the activity of the mTOR pathway ,14,15,16Measurement of length (to the nearest 0.1 cm) and weight (to the nearest 0.1 kg) of the study participants was conducted by using a portable infantometer and weighing scales . Head circumference (to the nearest 0.1 cm) was measured using measuring tape, while mild-upper arm circumference (to the nearest cm) was measured using MUAC tape . All anthropometric measurements were conducted by trained research assistants and standardized protocols for anthropometric measurements described elsewhere were followed all throughout the study . EvaluatFollowing enrolment of the study participants, our research assistants interviewed the parents or legal guardians of the participants to collect data on socio-demographic factors and feeding practices in structured questionnaires.Owing to the very limited number of published studies addressing the potential aberration in the activity of the mTOR signaling pathway in human anomalies, the sample size for this proposed study was calculated using the data from a study performed on children of the age group of 4.2–10.8 years of either sex suffering from asthma . In this2 × (Standard deviation)2/(Absolute Error)2 [1-α is fixed at 1.96 at an absolute error of 5 pg/mL. Using this formula, the sample size was calculated to 31 samples per study arm, thus making up a total of 124 participants distributed equally across the four study groups.Sample size =  Error)2 , where tAfter enrolment of the participants in the study, 5 mL of venous blood was extracted following appropriate aseptic techniques in EDTA tubes  by trained clinical staff and then transported to the laboratory under maintenance of proper cold chain. For the participants enrolled from the community, blood was collected following overnight fasting in an attempt to avoid potential post-prandial effects . Blood wIn the laboratory, peripheral blood mononuclear cells (PBMC) were isolated from the blood by ficoll-hypaque density gradient centrifugation . Followig for 20 min and the supernatant (cell lysate) was collected and stored at −80 °C temperature until subsequent analysis.Thawed PBMC samples were lysed using commercially available lysis buffer , reconstituted with protease and phosphatase inhibitors . The volume of lysis buffer that was added to the thawed cells was adjusted according to the viable cell count obtained after thawing of PBMC samples, following which cells were incubated at 4 °C for 30 min with gentle shaking. After this incubation, centrifugation was performed at 10,000× Cell lysates stored at −80 °C temperature was thawed on ice and were subject to 1:5 dilution using assay diluent supplied by the manufacturer prior to being run in duplicate wells of a pre-coated phospho-mTORC1 ELISA plate . Subsequent procedures for the ELISA were conducted as per the instructions and guidelines from the manufacturer. Optical density (OD) readings were taken at 450 nm and the mean OD value for each sample at 450 nm was calculated and used for the subsequent analyses, as described elsewhere .Extraction of RNA from thawed PBMC was performed using the RNeasy Mini kit , according to the manufacturer’s instructions with minor adjustments. The concentration and purity of RNA were checked using Thermo Scientific NanoDrop ND-1000 . The RNA samples with 260/280 wavelength ratios between 1.9 and 2.1 and 260/230 wavelength ratios more than 2.0 were considered as good quality samples . The RNAMTOR, TSC1, LAMTOR2, RPS6K1 and RICTOR. The primer sequences used for the gene expression studies have been shown in GAPDH) was used as the reference gene and the amplification protocol followed was the same as that used for the expression analysis of mTOR pathway genes. Results were quantified using CFX connect software  and the 2−ΔΔCt method [GAPDH) in each of the four study cohorts.Real-time PCR was performed in a 96-well CFX 96 Touch™ Real Time Detection System  with iTaq Universal SYBR Green Supermix  for the assessment of the expression of the selected mTOR pathway genes in the four cohorts , for gene expression analysis of each selected mTOR pathway genes, namely: t method  was usedp-value of less than 0.05 was considered to be statistically significant.All statistical analyses were conducted using STATA 13.0  and GraphPad Prism version 8.0 . Owing to the overall skewed nature of the data, non-parametric tests were used to compare the variables between the study groups. Mann–Whitney U test was used to compare the optical density for p-mTORC1 and gene expression data of the aforementioned genes from each of the three malnourished groups  with that of the healthy control group. Spearman Rank Correlation was conducted to assess the correlation between the anthropometric indices of LAZ, WAZ and WLZ with the activity of phospho-mTORC1 and gene expression data of the selected mTOR pathway genes. A p-value was expressed for each of the analyzed explanatory variables  in order to compare between the outcome variables, constituting the three malnourished groups. All variables with a p-value less than 0.2 from the bivariate analysis were adjusted in the final multivariate logistic regression model and other variables that are already reported to be associated with childhood malnutrition [Multivariate logistic regression was performed to quantify the association of the optical density for phospho-mTORC1 and gene expression data of the selected mTOR pathway genes with the three malnourished phenotypes of stunting, at risk of stunting and SAM while using the healthy control group as reference. Odds ratio with the 95% CI along with the utrition ,28.A total of 124 participants equally distributed across the four study groups were enrolled. The proportion of male participants was the highest in the “at risk of stunting” group and lowest in the healthy control group. The stunted children were comparatively more underweight (WAZ: −2.42 vs. −1.17) and wasted (WLZ: −1.57 vs. −0.45) compared to the children at risk of stunting. Among the SAM children enrolled from the Dhaka Hospital of icddr,b, only one participant had bilateral pedal edema. The duration of exclusive breastfeeding among the children at risk of stunting was comparatively lower than that in the other three study groups, while the proportion of children currently breastfeeding in the SAM cohort was comparatively lower than that in the other groups. The proportion of children currently being formula fed, fed with rice powder/suji and given cow/goat’s milk was also higher in the SAM group, compared to that reported in the other three groups.The activity of phospho-mTORC1 as indicated by the OD values at 450 nm, was higher in the healthy control group compared to that in the malnourished groups. The optical density (OD) values for phospho-mTORC1 were higher for the participants in the healthy control group in comparison to that obtained from the PBMC samples of participants in each of the three malnourished arms . Figure The OD values for p-mTORC1 at 450 nm for the SAM group were found to be significantly lowered in comparison to that from the healthy control group. No statistically significant difference in OD values for p-mTORC1 at 450 nm for the stunted and for the “at risk of stunting” group was observed in comparison to the healthy control group. Moreover, the activity of p-mTORC1  was found to have significant statistical correlations with all the anthropometric indices  as shown in MTOR and LAMTOR2 was found to be significantly lower, while the expression of the TSC1 gene was found to be significantly higher in comparison to that of the healthy control group. Consequently, the expression of the RPS6K1 gene was found to be increased and that of the RICTOR gene was reduced for the stunted group in comparison to the healthy control group, though no statistical significance was found for the difference in the expression of these genes among the aforementioned groups.MTOR and LAMTOR2 gene was found to be significantly reduced and that of TSC1 was found to be significantly elevated for the “at risk of stunting” group in comparison to the expression of these genes in the healthy control group. Additionally, no statistical significance was found in the expression of the RPS6K1 gene between the children from “at risk of stunting” group compared to the children from the healthy control group. However, the expression of the RICTOR gene was found to be significantly reduced in “at risk of stunting” group in comparison to the healthy control group. In the SAM group, the expression of the LAMTOR2 gene was found to be significantly lowered in comparison to that in the healthy control group. Subsequently, the expression of the TSC1 gene was found to be significantly elevated in the SAM group in comparison to the healthy control group.Similar to the stunted group, the expression of the LAMTOR2 gene with the anthropometric measurements of LAZ, WAZ and WLZ. A significant negative correlation was observed between the expression of the TSC1 gene and the anthropometric measurement of LAZ. Consequently, correlation analyses revealed a significant positive correlation between the expression of the The association between the analyzed mTOR pathway components with the different forms of childhood malnutrition, as determined from multivariate logistic regression analysis has been shown in LAMTOR2 gene expression and a statistically significant positive association with TSC1 gene expression. The expression of the MTOR gene was found to be negatively associated with childhood stunting but was not found to be statistically significant. Statistically significant negative associations were found between the expression of the MTOR gene and LAMTOR2 gene with the nutritional status of “at risk stunting”. On the other hand, a statistically significant positive association was observed between the expression of the TSC1 gene and the phenotype of at risk of stunting. Negative associations were found between p-mTORC1 expression and RICTOR gene expression; however, these associations were not statistically significant. Severe acute malnutrition (SAM) was found to have statistically significant negative associations with phospho-mTORC1 protein expression and with TSC1 and LAMTOR2 gene expression. Negative associations were observed between the expression of the MTOR gene and RICTOR gene with SAM; although not statistically significant.Stunting was found to have statistically significant negative associations with p-mTORC1 protein expression and MTOR, TSC1 and LAMTOR2 among malnourished children compared to that observed among healthy children. In addition, significant correlations were observed between the activity of phospho-mTORC1, LAMTOR2 and TSC1 with the anthropometric indices of LAZ, WAZ and WLZ. The expression of several of the mTOR pathway components analyzed in this study has been found to have a statistical association with the different forms of childhood malnutrition. To the best of our knowledge, this is the first study that has aimed to study the mTOR pathway, with extensive focus on the expressions of different key mTOR pathway components in the context of childhood malnutrition.Despite, a plethora of attempts and extensive intervention strategies being undertaken, the prevalence of some of the most chronic forms of childhood malnutrition, such as stunting, has not reduced at the desired rate . SeveralChronic forms of malnutrition, such as stunting, are associated with environmental enteric dysfunction (EED), which is characterized by blunting of the intestinal villi and inflammation of the lamina propria . The useZea mays) based stable diet, which has been reported to be a poor source of the essential amino acid tryptophan [Consequently, elevation in the levels of pro-inflammatory cytokines, such as IL-6, TNF-α and IL-1β has been observed among malnourished children ,39, leadyptophan . Low ciryptophan , whereasyptophan . On the yptophan ,47.MTOR gene (TSC1 gene, known to code for a well reported negative modulator of the mTORC1 pathway [LAMTOR2, reported to be a code for a vital positive modulator of the mTORC1 pathway, [Concurrent studies have reported the mTOR pathway to be exclusively sensitive to the availability of essential amino acids . DeficieTOR gene . Subsequ pathway  was founpathway,  was founTSC1 gene with LAZ, thus corroborate with such previous reports of disruptions in bone growth due to deregulations in the mTOR pathway, ultimately leading to linear growth faltering among the children. On the other hand, the mTOR pathway has been reported to control the processes of protein synthesis and lipogenesis [LAMTOR2 with the anthropometric index of WAZ, indicating that a downregulation of these components of the mTOR pathway could result in reduced protein and lipid synthesis, resulting in reduced muscle mass and reduced ponderal growth.Recent works have indicated potential deregulation in the mTORC1 pathway with childhood stunting, possibly through disruptions in the chondral plate development and subsequent perturbations in bone growth . Our stuogenesis ,6. In ouRPS6K1, a gene known to encode a downstream modulator of mTORC1 [RPS6K1 gene as determined from our analysis (RICTOR gene among the malnourished group with respect to that among the healthy control group indicate a potential role of the mTORC2 pathway with childhood malnutrition. The mTORC2 pathway has been implicated to exhibit crucial roles in the maintenance of cytoskeletal organization and in the mediation of autophagy, in a manner that is independent of the mTORC1 pathway [The activity of f mTORC1  was founf mTORC1 . Such inanalysis . On the  pathway . Althoug pathway . HencefoDespite the findings of the study, that associate certain key components of the mTOR pathway with childhood malnutrition, there are a number of limitations to our findings. The use of biopsy samples from the upper GI tract or the epiphyseal plate could have been a more suitable representative to study the potential deregulations in the mTOR pathway in childhood malnutrition. However, in our study, we could not obtain such biopsy samples since that would have required the use of invasive protocols. We could not obtain blood from the SAM participants following overnight fasting, due to strict hospital guidelines regarding the management of diet for the SAM children; thereby, the issue of post-prandial effects may have been involved for the SAM children. Consequently, the mTOR pathway is an intricate intracellular signaling cascade involving the activity of a plethora of genes, transcription factors and upstream and downstream modulators. In our study, we had only investigated the expression of a limited number of mTOR pathway components, whereas components of other intracellular pathways known to be connected with the mTOR pathway were not studied. Lack of data regarding the protein expression hinder us from having conclusive evidence regarding possible deregulations of the mTOR pathway in childhood malnutrition, since post-transcriptional and post-translational modifications of the products of the gene expression may occur. Our study did not involve investigations into the known upstream modulators of the mTOR pathway, including IGF-1, growth hormone, pro-inflammatory cytokines, biomarkers of systemic inflammation and oxidative stress and levels of essential amino acids and branched chain amino acids from the biological samples from our study participants. Additionally, the findings of this study have been obtained from the pediatric Bangladeshi population and may not necessarily be extrapolated for other pediatric populations, owing to differences in epigenetic influences on gene expression.Despite the aforementioned limitations, this study nonetheless provides a cornerstone for establishing a potential role of the mTOR pathway in childhood malnutrition. The study findings implicate an evident downregulation in the overall activity of the mTOR pathway to be linked with the pathogenesis of chronic forms of childhood malnutrition. Further studies exploring other integral mTOR pathway components, as well as components of other intracellular pathways known to be interconnected with the mTOR pathway; along with systematic investigations into the well-established upstream modulators of the mTOR pathway, need to be assessed to decipher conclusive roles of the mTOR pathway with childhood malnutrition. Although previous animal model studies have demonstrated an overall down-modulation of the mTOR pathway with reduced cell proliferation and growth, our findings confirm such propositions in humans.