Integrating network pharmacology and in silico analysis deciphers Withaferin-A’s anti-breast cancer potential via hedgehog pathway and target network interplay
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PROVIDER: S-EPMC10917074 | biostudies-literature | 2024 Mar
REPOSITORIES: biostudies-literature
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Project description:AimThis study aimed to predict the key targets and endocrine mechanisms of Guizhi Fuling Wan (GZFLW) in treating adenomyosis (AM) through network pharmacology, molecular docking, and animal experiment verification.MethodsThe related ingredients and targets of GZFLW in treating AM were screened out using TCMSP, BATMAN-TCM, SwissTargetPrediction, and PubChem Database. Then, the protein-protein interaction (PPI) analysis and the network of compound-hub targets were constructed. At the same time, the key targets were uploaded to the Metascape Database for KEGG pathway enrichment analysis. After that, the molecular docking technology of the main active components and hub targets was performed. Furthermore, animal experiments were used to verify the results of network pharmacology analysis.ResultsA total of 55 active ingredients of GZFLW and 44 overlapping targets of GZFLW in treating AM were obtained. After screening, 25 hub targets were collected, including ESR1, EGF, and EGFR. Then, the KEGG pathway enrichment analysis results indicated that the endocrine therapeutic mechanism of GZFLW against AM is mainly associated with the estrogen signaling pathway, endocrine resistance, and an EGFR tyrosine kinase signaling pathway. Then, molecular docking showed that the significant compounds of GZFLW had a strong binding ability with ERα and EGFR. More importantly, the animal experiments confirmed that the GZFLW could downregulate the abnormal infiltration of the endometrial epithelium into the myometrium and had no interference with the normal sexual cycle. This effect may be directly related to intervening the local estrogen signaling pathway of the endometrial myometrial interface (EMI). It may also be associated with the myometrium cells' estrogen resistance via GPER/EGFR signaling pathway.ConclusionThe endocrine mechanism of GZFLW in treating AM was explored based on network pharmacology, molecular docking, and animal experiments, which provided a theoretical basis for the clinical application of GZFLW.
| S-EPMC8563128 | biostudies-literature
Project description:Huangqi Guizhi Wuwu Decoction (HGWD), as a classic Chinese herbal decoction, has been widely used in treating various diseases for hundreds of years. However, systematically elucidating its mechanisms of action remains a great challenge to the field. In this study, taking advantage of the network pharmacology approach, we discovered a potential new use of HGWD for patients with colon cancer (CC). Our in vivo result showed that orally administered HGWD markedly inhibited the growth of CC xenografts in mice. The subsequent enrichment analyses for the core therapeutic targets revealed that HGWD could affect multiple biological processes involving CC growth, such as metabolic reprogramming, apoptosis and immune regulation, through inhibiting multiple cell survival-related signalings, including MAPK and PI3K-AKT pathways. Notably, these in silico analysis results were most experimentally verified by a series of in vitro assays. Furthermore, our results based on serum metabolomics showed that the lipid metabolic pathways, including fatty acid biosynthesis and cholesterol metabolism, play key roles in delivery of the anti-CC effect of HGWD on tumor-bearing mice, and that cytochrome P450 family 2 subfamily E member 1 (CYP2E1) is a potential therapeutic target. Together, our integrated approach reveals a therapeutic effect of HGWD on CC, providing a valuable insight into developing strategies to predict and interpret the mechanisms of action for Chinese herbal decoctions.
| S-EPMC9607907 | biostudies-literature
Project description:Elucidating the relation between the medicines: targets, targets: diseases and diseases: diseases are of fundamental significance as-is for societal benefit. Hypertension is one of the dangerous health conditions prevalent in society, is a risk factor for several other diseases if left untreated and anti-hypertensives (AHs) are the approved drugs to treat it. The goal of the study is to decipher the connection between hypertension with other health conditions, however, is challenging due to the large interactome. To fulfill the aim, the strategy involves prior clustering of the AHs into groups as per our previous method, followed by the analyzing functional association of the target coding-genes (tc-genes) and health conditions for each group. Following our recently published work where the AHs are clustered into six groups such that molecules having similar patterns come together, here, the distribution of molecular functions and the cellular components adopted by the tc-genes of each group are analyzed. The analyses indicate that kidney, heart, brain or lung related ailments are commonly associated with the tc-genes. The association of selective tc-genes to health conditions suggests a preference for certain health conditions despite many possibilities. Analyses of experimentally validated drug-drug combinations indicate the trend in successful AHs combinations. Clinically validated combinations bind different targets. Our study provides a promising methodology in a network-based approach that considers the influence of structural diversity of AHs to the functional perspective of tc-genes concerning the health conditions. The method could be extended to explore disease-disease relationships.
| S-EPMC7677320 | biostudies-literature
Project description:Hyperuricemia (HUA), a common metabolic disease, is treated as the second-largest metabolic disease after diabetes in China. Cortex Phellodendri (CP) is one of the most frequently used herbal medicines for treating gout or HUA. However, the mechanism underlying the anti-HUA effect of CP is still unrevealed. Hence, this study aimed to explore the pharmacological mechanism of CP against HUA using network pharmacology coupled with in vivo experimental validation. Active compounds and potential targets of CP, as well as the potential targets related to HUA, were retrieved from multiple open-source databases. The drug-disease overlapping targets were obtained by Venn diagram analysis and used to construct the herb-component-target (HCT), protein-protein-interaction (PPI), and component-target-pathway (CTP) networks. The functional enrichment analysis was also performed for further study. Furthermore, a HUA mouse model was induced by a combination of intraperitoneal injection of potassium oxonate (PO, 300 mg/kg) and intragastric administration of hypoxanthine (HX, 300 mg/kg) daily for 10 days. Different dosages of CP (200, 400, and 800 mg/kg) were orally given to mice 1 h after modeling. The results showed that 12 bioactive compounds and 122 drug-disease overlapping targets were obtained by matching 415 CP-related targets and 679 HUA-related targets, and berberine was one of the most important compounds with the highest degree value. The core targets of CP for treating HUA were TP53, MAPK8, MAPK3, IL-6, c-Jun, AKT1, xanthine oxidase (XOD), and ATP-binding cassette subfamily G member 2 (ABCG2). The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment results showed that the anti-HUA effect of CP mainly involved the pathways of inflammation and apoptosis, such as PI3K/Akt, TNF, MAPK, TLR, AMPK, NF-κB, and NLRP3 signaling pathways. In vivo animal experiment further confirmed the hypouricemic effect of CP in a HUA mouse model, as evidenced by significantly restored kidney histological deteriorations, and considerably decreased levels of serum uric acid (sUA), creatinine (Cre), blood urea nitrogen (BUN), and hepatic UA. Furthermore, the hypouricemic action of CP in vivo might be attributed to its suppression of XOD activity in the liver, rather than ABCG2 in the kidney. Real-time qPCR (RT-qPCR) and Western blot analysis also confirmed the key roles of the hub genes in CP against HUA. In conclusion, CP exhibited therapeutic effect against HUA via multi-compounds, multi-targets, and multi-pathways. It possessed anti-HUA and nephroprotective effects via suppressing XOD activity, and reversed the progression of renal injury by exerting anti-inflammatory and anti-apoptotic effects.
| S-EPMC9692208 | biostudies-literature
Project description:Introduction: Pycnogenol (PYC), a standardized extract from French maritime pine, has traditionally been used to treat inflammation. However, its primary active components and their mechanisms of action have not yet been determined. Methods: This study employed UPLC-MS/MS (Ultra-high performance liquid chromatography-tandem mass spectrometry) and network pharmacology to identify the potential active components of PYC and elucidate their anti-inflammatory mechanisms by cell experiments. Results: 768 PYC compounds were identified and 19 anti-inflammatory compounds were screened with 85 target proteins directly involved in the inflammation. PPI (protein-protein interaction) analysis identified IL6, TNF, MMP9, IL1B, AKT1, IFNG, CXCL8, NFKB1, CCL2, IL10, and PTGS2 as core targets. KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis suggested that the compound in PYC might exert anti-inflammatory effects through the IL17 and TNF signal pathways. Cell experiments determined that PYC treatment can reduce the expression of IL6 and IL1β to relieve inflammation in LPS (lipopolysaccharide)-induced BV2 cells. Conclusion: PYC could affect inflammation via multi-components, -targets, and -mechanisms.
| S-EPMC11233470 | biostudies-literature
Project description:Eugenol as a natural product is the source of isoniazid, and purified eugenol is extensively used in the cosmetics industry and the productive processes of edible spices. Accumulating evidence suggested that eugenol exerted potent anti-microorganism and anti-inflammation effects. Application of eugenol effectively reduced the risk of atherosclerosis, arterial embolism, and Type 2 diabetes. A previous study confirmed that treatment with eugenol attenuated lung inflammation and improved heart functions in SARS-CoV-2 spike S1-intoxicated mice. In addition to the study, based on a series of public datasets, computational analyses were conducted to characterize the acting targets of eugenol and the functional roles of these targets in COVID-19. The binding capacities of eugenol to conservative sites of SARS-CoV-2 like RNA-dependent RNA polymerase (RdRp) and mutable site as spike (S) protein, were calculated by using molecular docking following the molecular dynamics simulation with RMSD, RMSF, and MM-GBSA methods. The results of network pharmacology indicated that six targets, including PLAT, HMOX1, NUP88, CTSL, ITGB1 andTMPRSS2 were eugenol-SARS-CoV-2 interacting proteins. The omics results of in-silico study further implicated that eugenol increased the expression of SCARB1, HMOX1 and GDF15, especially HMOX1, which were confirmed the potential interacting targets between eugenol and SARS-CoV-2 antigens. Enrichment analyses indicated that eugenol exerted extensive biological effects such as regulating immune infiltration of macrophage, lipid localization, monooxyenase activity, iron ion binding and PPAR signaling. The results of the integrated analysis of eugenol targets and immunotranscription profile of COVID-19 cases shows that eugenol also plays an important role in strengthen of immunologic functions and regulating cytokine signaling. As a complement to the integrated analysis, the results of molecular docking indicated the potential binding interactions between eugenol and four proteins relating to cytokine production/release and the function of T type lymphocytes, including human TLR-4, TCR, NF-κB, JNK and AP-1. Furthermore, results of molecular docking and molecular dynamics (100ns) simulations implicated that stimulated modification of eugenol to the SARS-CoV-2 Omicron Spike-ACE2 complex, especially for human ACE2, and the molecular interaction of eugenol to SARS-CoV-2 RdRp, were no less favorable than two positive controls, molnupiravir and nilotinib. Dynamics (200ns) simulations indicated that the binding capacities and stabilities of eugenol to finger subdomain of RdRp is no less than molnupiravir. However, the simulated binding capacity of eugenol to SARS-CoV-2 wild type RBD and Omicron mutant RBD were less than nilotinib. Eugenol was predicted to have more favor LD50 value and lower cytotoxicity than two positive controls, and eugenol can pass through the blood-brain barrier (BBB). In a brief, eugenol is helpful for attenuating systemic inflammation induced by SARS-CoV-2 infection, due to the direct interaction of eugenol to SARS-CoV-2 proteins and extensive bio-manipulation of pro-inflammatory factors. This study carefully suggests eugenol is a candidate compound of developing drugs and supplement agents against SARS-CoV-2 and its Omicron variants.
| S-EPMC9937729 | biostudies-literature
Project description:BackgroundOsteoporosis (OP) is a major and growing public health problem characterized by decreased bone mineral density and destroyed bone microarchitecture. Previous studies found that Lycium Chinense Mill (LC) has a potent role in inhibiting bone loss. Kukoamine A (KuA), a bioactive compound extract from LC was responsible for the anti-osteoporosis effect. This study aimed to investigate the anti-osteoporosis effect of KuA isolated from LC in treating OP and its potential molecular mechanism.MethodIn this study, network pharmacology and molecular docking were investigated firstly to find the active ingredients of LC such as KuA, and the target genes of OP by the TCMSP platform. The LC-OP-potential Target gene network was constructed by the STRING database and network maps were built by Cytoscape software. And then, the anti-osteoporotic effect of KuA in OVX-induced osteoporosis mice and MC3T3-E1 cell lines were investigated and the potential molecular mechanism including inflammation level, cell apoptosis, and oxidative stress was analyzed by dual-energy X-ray absorptiometry (DXA), micro-CT, ELISA, RT-PCR, and Western Blotting.ResultA total of 22 active compounds were screened, and we found KuA was identified as the highest active ingredient. Glycogen Phosphorylase (PYGM) was the target gene associated with a maximum number of active ingredients of LC and regulated KuA. In vivo, KuA treatment significantly increased the bone mineral density and improve bone microarchitecture for example increased BV/TV, Tb.N and Tb.Th but reduced Tb.Sp in tibia and lumber 4. Furthermore, KuA increased mRNA expression of osteoblastic differentiation-related genes in OVX mice and protects against OVX-induced cell apoptosis, oxidative stress level and inflammation level. In vitro, KuA significantly improves osteogenic differentiation and mineralization in cells experiment. In addition, KuA also attenuated inflammation levels, cell apoptosis, and oxidative stress level.ConclusionThe results suggest that KuA could protect against the development of OP in osteoblast cells and ovariectomized OP model mice and these found to provide a better understanding of the pharmacological activities of KuA again bone loss.
| S-EPMC10029210 | biostudies-literature
Project description:Aberrant activation of sonic Hegdehog (SHH) signaling has been found to disrupt cellular differentiation in many human cancers and to increase proliferation. The SHH pathway is known to cross-talk with EGFR dependent signaling. Recent studies experimentally addressed this interplay in Daoy cells, which are presumable a model system for medulloblastoma, a highly malignant brain tumor that predominately occurs in children. Currently ongoing are several clinical trials for different solid cancers, which are designed to validate the clinical benefits of targeting the SHH in combination with other pathways. This has motivated us to investigate interactions between EGFR and SHH dependent signaling in greater depth. To our knowledge, there is no mathematical model describing the interplay between EGFR and SHH dependent signaling in medulloblastoma so far. Here we come up with a fully probabilistic approach using Dynamic Bayesian Networks (DBNs). To build our model, we made use of literature based knowledge describing SHH and EGFR signaling and integrated gene expression (Illumina) and cellular location dependent time series protein expression data (Reverse Phase Protein Arrays). We validated our model by sub-sampling training data and making Bayesian predictions on the left out test data. Our predictions focusing on key transcription factors and p70S6K, showed a high level of concordance with experimental data. Furthermore, the stability of our model was tested by a parametric bootstrap approach. Stable network features were in agreement with published data. Altogether we believe that our model improved our understanding of the interplay between two highly oncogenic signaling pathways in Daoy cells. This may open new perspectives for the future therapy of Hedghog/EGF-dependent solid tumors.
| S-EPMC4646463 | biostudies-literature
Project description:Listeria monocytogenes (L. monocytogenes) is a serious food-borne pathogen that can cause listeriosis, an illness caused by eating food contaminated with this pathogen. Currently, the treatment or prevention of listeriosis is a global challenge due to the resistance of bacteria against multiple commonly used antibiotics, thus necessitating the development of novel green antimicrobials. Scientists are increasingly interested in microbial surfactants, commonly known as "biosurfactants", due to their antimicrobial properties and eco-friendly nature, which make them an ideal candidate to combat a variety of bacterial infections. Therefore, the present study was designed to use a network pharmacology approach to uncover the active biosurfactants and their potential targets, as well as the signaling pathway(s) involved in listeriosis treatment. In the framework of this study, 15 biosurfactants were screened out for subsequent studies. Among 546 putative targets of biosurfactants and 244 targets of disease, 37 targets were identified as potential targets for treatment of L. monocytogenes infection, and these 37 targets were significantly enriched in a Gene Ontology (GO) analysis, which aims to identify those biological processes, cellular locations, and molecular functions that are impacted in the condition studied. The obtained results revealed several important biological processes, such as positive regulation of MAP kinase activity, protein kinase B signaling, ERK1 and ERK2 cascade, ERBB signaling pathway, positive regulation of protein serine/threonine kinase activity, and regulation of caveolin-mediated endocytosis. Several important KEGG pathways, such as the ERBBB signaling pathway, TH17 cell differentiation, HIF-1 signaling pathway, Yersinia infection, Shigellosis, and C-type lectin receptor signaling pathways, were identified. The protein-protein interaction analysis yielded 10 core targets (IL2, MAPK1, EGFR, PTPRC, TNF, ITGB1, IL1B, ERBB2, SRC, and mTOR). Molecular docking was used in the latter part of the study to verify the effectiveness of the active biosurfactants against the potential targets. Lastly, we found that a few highly active biosurfactants, namely lichenysin, iturin, surfactin, rhamnolipid, subtilisin, and polymyxin, had high binding affinities towards IL2, MAPK1, EGFR, PTPRC, TNF, ITGB1, IL1B, ERBB2, SRC, and mTOR, which may act as potential therapeutic targets for listeriosis. Overall, based on the integrated network pharmacology and docking analysis, we found that biosurfactants possess promising anti-listeriosis properties and explored the pharmacological mechanisms behind their effect, laying the groundwork for further research and development.
| S-EPMC9854906 | biostudies-literature
Project description:The abundance of publicly available life science databases offers a wealth of information that can support interpretation of experimentally derived data and greatly enhance hypothesis generation. Protein interaction and functional networks are not simply new renditions of existing data: they provide the opportunity to gain insights into the specific physical and functional role a protein plays as part of the biological system. In this chapter, we describe different in silico tools that can quickly and conveniently retrieve data from existing data repositories and we discuss how the available tools are best utilized for different purposes. While emphasizing protein-protein interaction databases (e.g., BioGrid and IntAct), we also introduce metasearch platforms such as STRING and GeneMANIA, pathway databases (e.g., BioCarta and Pathway Commons), text mining approaches (e.g., PubMed and Chilibot), and resources for drug-protein interactions, genetic information for model organisms and gene expression information based on microarray data mining. Furthermore, we provide a simple step-by-step protocol for building customized protein-protein interaction networks in Cytoscape, a powerful network assembly and visualization program, integrating data retrieved from these various databases. As we illustrate, generation of composite interaction networks enables investigators to extract significantly more information about a given biological system than utilization of a single database or sole reliance on primary literature.
| S-EPMC3831179 | biostudies-literature