Network pharmacology approach to reveals therapeutic mech-anism of traditional plants formulation used by Malaysia in-digenous ethnics in coronaviruses infection

Network pharmacology analysis can act as a strategy to identify the pharmacological effect of plant-based bioactive compounds against coronavirus diseases. This study aimed to investigate the potential pharmacological mechanism of a local ethnomedicine (Costus speciosus, Hibiscus rosa-sinensis and Phyllanthus niruri) of Northern Borneo against coronaviruses known as CHP. Compounds in CHP were extracted from databases and screened for their oral bioavailability and drug-likeness before a compound-target network was built. Furthermore, the protein-protein interaction network and pathway enrichment were constructed and analyzed. A compound-target network consisting of 48 putative bioactive compounds targeting 587 candidate genes was identified. A total of 186 coronavirus-related genes were extracted and TP53, STAT3, HSP90AA1, STAT1, and EP300 were predicted to be the key targets. Notably, mapping of these target genes into the target-pathway network illustrated that functional enrichment was on viral infection and regulation of inflammation pathways. Urinatetralin is predicted, for the first time, as a bioactive compound that solely targets STAT3. The results from this study indicate that compounds present in CHP employ STAT3 and its connected pathways as the mechanism of action against coronaviruses. In conclusion, urinatetralin should be further investigated for its potential application against coronavirus infections.

Systematic Understanding of Mechanism of Danggui Shaoyao San against Ischemic Stroke Using a Network Pharmacology Approach

Purpose. Danggui Shaoyao San (DSS) was developed to treat the ischemic stroke (IS) in patients and animal models. The purpose of this study was to explore its active compounds and demonstrate its mechanism against IS through network pharmacology, molecular docking, and animal experiment. Methods. All the components of DSS were retrieved from the pharmacology database of TCM system. The genes corresponding to the targets were retrieved using OMIM, CTD database, and TTD database. The herb-compound-target network was constructed by Cytoscape software. The target protein-protein interaction network was built using the STRING database. The core targets of DSS were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Then, we achieved molecular docking between the hub proteins and the key active compounds. Finally, animal experiments were performed to verify the core targets. Triphenyltetrazolium chloride (TTC) staining was used to calculate the infarct size in mice. The protein expression was determined using the Western blot. Results. Compound-target network mainly contained 51 compounds and 315 corresponding targets. Key targets contained MAPK1, SRC, PIK3R1, HRAS, AKT1, RHOA, RAC1, HSP90AA1, and RXRA FN1. There were 417 GO items in GO enrichment analysis ( p < 0.05 ) and 119 signaling pathways ( p < 0.05 ) in KEGG, mainly including negative regulation of apoptosis, steroid hormone-mediated signaling pathway, neutrophil activation, cellular response to oxidative stress, and VEGF signaling pathway. MAPK1, SRC, and PIK3R1 docked with small molecule compounds. According to the Western blot, the expression of p-MAPK 1, p-AKT, and p-SRC was regulated by DSS. Conclusions. This study showed that DSS can treat IS through multiple targets and routes and provided new insights to explore the mechanisms of DSS against IS.

Integrating Network Pharmacology and Molecular Docking to Analyse the Potential Mechanism of action of Macleaya cordata (Willd.) R. Br. in the Treatment of Bovine Hoof Disease

Based on network pharmacological analysis and molecular docking techniques, the main components of M. cordata for the treatment of bovine relevant active compounds in M. cordata were searched for through previous research bases and literature databases, and then screened to identify candidate compounds based on physicochemical properties, pharmacokinetic parameters, bioavailability, and drug-like criteria. Target genes associated with hoof disease were obtained from the GeneCards database. Compound−target, compound−target−pathway−disease visualization networks, and protein−protein interaction (PPI) networks were constructed by Cytoscape. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed in R language. Molecular docking analysis was done using AutoDockTools. The visual network analysis showed that four active compounds, sanguinarine, chelerythrine, allocryptopine and protopine, were associated with the 10 target genes/proteins (SRC, MAPK3, MTOR, ESR1, PIK3CA, BCL2L1, JAK2, GSK3B, MAPK1, and AR) obtained from the screen. The enrichment analysis indicated that the cAMP, PI3K-Akt, and ErbB signaling pathways may be key signaling pathways in network pharmacology. The molecular docking results showed that sanguinarine, chelerythrine, allocryptopine, and protopine bound well to MAPK3 and JAK2. A comprehensive bioinformatics-based network topology strategy and molecular docking study has elucidated the multi-component synergistic mechanism of action of M. cordata in the treatment of bovine hoof disease, offering the possibility of developing M. cordata as a new source of drugs for hoof disease treatment.

Identification of miRNA and Their Regulatory Effects Induced by Total Flavonoids From Dracocephalum moldavica in the Treatment of Vascular Dementia

Vascular dementia (VaD) is a general term used to describe difficulties in memory, reasoning, judgment, and planning caused by a reduced blood flow to the brain and consequent brain damage, in which microRNAs (miRNAs) are involved. Dracocephalum moldavica L. (D. moldavica) is traditionally used in the treatment of cardiovascular diseases as well as VaD, but the biomolecular mechanisms underlying its therapeutic effect are obscure. In the present study, the molecular mechanisms involved in the treatment of VaD by the total flavonoids from Dracocephalum moldavica L. (TFDM) were explored by the identification of miRNA profiling using bioinformatics analysis and experimental verification. A total of 2,562 differentially expressed miRNAs (DEMs) and 3,522 differentially expressed genes (DEGs) were obtained from the GSE120584 and GSE122063 datasets, in which the gene functional enrichment and protein-protein interaction network of 93 core targets, originated from the intersection of the top DEM target genes and DEGs, were established for VaD gene profiling. One hundred and eighty-five targets interacting with 42 flavonoids in the TFDM were included in a compound-target network, subsequently found that they overlapped with potential targets for VaD. These 43 targets could be considered in the treatment of VaD by TFDM, and included CaMKII, MAPK, MAPT, PI3K, and KDR, closely associated with the vascular protective effect of TFDM, as well as anti-oxidative, anti-inflammatory, and anti-apoptotic properties. The subsequent analysis of the compound-target gene-miRNA network indicated that eight miRNAs that mediated 43 targets had a close interaction with TFDM, suggesting that the neuroprotective effects were principally due to kaempferol, apigenin, luteolin, and quercetin, which were mostly associated with the miR-3184-3p/ESR1, miR-6762-3p/CDK1, miR-6777-3p/ESRRA, and other related axes. Furthermore, the in vitro oxygen-glucose deprivation (OGD) model demonstrated that the dysregulation of miR-3184-3p and miR-6875-5p found by qRT-PCR was consistent with the changes in the bioinformatics analysis. TFDM and its active compounds involving tilianin, luteolin, and apigenin showed significant effects on the upregulation of miR-3184-3p and downregulation of miR-6875-5p in OGD-injured cells, in line with the improved cell viability. In conclusion, our findings revealed the underlying miRNA-target gene network and potential targets of TFDM in the treatment of VaD.

Protein domain-based prediction of drug/compound–target interactions and experimental validation on LIM kinases

Predictive approaches such as virtual screening have been used in drug discovery with the objective of reducing developmental time and costs. Current machine learning and network-based approaches have issues related to generalization, usability, or model interpretability, especially due to the complexity of target proteins’ structure/function, and bias in system training datasets. Here, we propose a new method “DRUIDom” (DRUg Interacting Domain prediction) to identify bio-interactions between drug candidate compounds and targets by utilizing the domain modularity of proteins, to overcome problems associated with current approaches. DRUIDom is composed of two methodological steps. First, ligands/compounds are statistically mapped to structural domains of their target proteins, with the aim of identifying their interactions. As such, other proteins containing the same mapped domain or domain pair become new candidate targets for the corresponding compounds. Next, a million-scale dataset of small molecule compounds, including those mapped to domains in the previous step, are clustered based on their molecular similarities, and their domain associations are propagated to other compounds within the same clusters. Experimentally verified bioactivity data points, obtained from public databases, are meticulously filtered to construct datasets of active/interacting and inactive/non-interacting drug/compound–target pairs (~2.9M data points), and used as training data for calculating parameters of compound–domain mappings, which led to 27,032 high-confidence associations between 250 domains and 8,165 compounds, and a finalized output of ~5 million new compound–protein interactions. DRUIDom is experimentally validated by syntheses and bioactivity analyses of compounds predicted to target LIM-kinase proteins, which play critical roles in the regulation of cell motility, cell cycle progression, and differentiation through actin filament dynamics. We showed that LIMK-inhibitor-2 and its derivatives significantly block the cancer cell migration through inhibition of LIMK phosphorylation and the downstream protein cofilin. One of the derivative compounds (LIMKi-2d) was identified as a promising candidate due to its action on resistant Mahlavu liver cancer cells. The results demonstrated that DRUIDom can be exploited to identify drug candidate compounds for intended targets and to predict new target proteins based on the defined compound–domain relationships. Datasets, results, and the source code of DRUIDom are fully-available at: https://github.com/cansyl/DRUIDom.

Identification of Key Pathways and Targets of Kai Xin San in The Treatment of Alzheimer's Disease Based On a Network Pharmacology Approach and Experimental Validation

Background: Alzheimer's disease (AD) is the commonest neurodegenerative disease characterized with a progressive loss of cognitive functions and memory decline. Kai Xin San (KXS), a traditional Chinese herbal classic prescription, has been used to ameliorate cognitive dysfunction for thousands of years. However, its specific pharmacological molecular mechanisms have not been fully clarified.Methods: The ingredients of KXS and their corresponding targets were firstly screened from ETCM database. AD-related target proteins were obtained from Malacards database and DisGeNet database. Venn diagram was used to intersect the common targets between KXS and AD. Then, key ingredients and key targets were identified from compound-target-disease network and protein-protein interaction (PPI) network analysis respectively. Moreover, the binding affinity between the key ingredients and targets were verified by molecular docking. KEGG enrichment analysis further predicted the potential key signaling pathway involved in the treatment of KXS on AD, and the predicted signaling pathway was validated via experimental approach.Results: A total of 38 ingredients and 469 corresponding targets were screened, and 264 target proteins associated with AD were obtained. Compound-target-disease network and PPI identified the key active ingredients and targets, which correlate with the treatment of KXS on AD. Molecular docking revealed a good binding affinity between key ingredients and targets. KEGG pathway analysis suggested the potential effect of KXS in treatment of AD via Aβ-GSK3β-Tau pathway. Aβ1-42-injected induced a decline in spatial learning and memory and upregulated the expression of GSK3β and CDK5 along with the downregulated PP1 and PP2 expression. However, KXS significantly improve the cognitive deficits induced by Aβ1-42, decrease the GSK3β and CDK5 levels and increase the expression of PP1 and PP2.Conclusions: Our research elucidated that KXS exerted neuroprotective effects through regulating the Aβ-GSK3β-Tau signaling pathway, which provided a novel insight into the therapeutic mechanism of KXS in treatment of AD.

Network Pharmacology-Based Study of the Underlying Mechanisms of Huangqi Sijunzi Decoction for Alzheimer’s Disease

Background. Huangqi Sijunzi decoction (HQSJZD) is a commonly used conventional Chinese herbal medicine prescription for invigorating Qi, tonifying Yang, and removing dampness. Modern pharmacology and clinical applications of HQSJZD have shown that it has a certain curative effect on Alzheimer’s disease (AD). Methods. The active components and targets of HQSJZD were searched in the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). The genes corresponding to the targets were retrieved using UniProt and GeneCard database. The herb-compound-target network and protein-protein interaction (PPI) network were constructed by Cytoscape. The core targets of HQSJZD were analysed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The main active compounds of HQSJZD were docked with acetylcholinesterase (AChE). In vitro experiments were conducted to detect the inhibitory and neuroprotective effects of AChE. Results. Compound-target network mainly contained 132 compounds and 255 corresponding targets. The main compounds contained quercetin, kaempferol, formononetin, isorhamnetin, hederagenin, and calycosin. Key targets contained AChE, PTGS2, PPARG, IL-1B, GSK3B, etc. There were 1708 GO items in GO enrichment analysis and 310 signalling pathways in KEGG, mainly including the cAMP signalling pathway, the vascular endothelial growth factor (VEGF) signalling pathway, serotonergic synapses, the calcium signalling pathway, type II diabetes mellitus, arginine and proline metabolism, and the longevity regulating pathway. Molecular docking showed that hederagenin and formononetin were the top 2 compounds of HQSJZD, which had a high affinity with AChE. And formononetin has a good neuroprotective effect, which can improve the oxidative damage of nerve cells. Conclusion. HQSJZD was found to have the potential to treat AD by targeting multiple AD-related targets. Formononetin and hederagenin in HQSJZD may regulate multiple signalling pathways through AChE, which might play a therapeutic role in AD.

Study on the Mechanism of Salvia miltiorrhiza in the Treatment of Traumatic Bone Defects

Traumatic bone defect is one of the major orthopedic diseases in clinics, and its incidence is increasing year by year. And repairing traumatic bone defects is a very difficult problem in clinics at present. The surface of medical titanium-based alloy has good biological properties, and its implant has a certain role in promoting bone in bone tissue. However, titanium-based materials are biologically inert and have no biological activity. As a traditional Chinese medicine, Salvia miltiorrhiza has the efficacy of treating bone diseases and promoting bone healing. The curative effect can be better exerted by loading the traditional Chinese medicine active compound Salvia miltiorrhiza on the surface of the titanium implant in a certain way. At present, due to the complex chemical composition of Salvia miltiorrhiza, the mechanism of its use for the treatment of traumatic bone defects is still unclear. Therefore, in this study, we mainly discussed the potential target and mechanism of Salvia miltiorrhiza in the treatment of traumatic bone defects through network pharmacology, which may provide a scientific basis for the treatment of traumatic bone defects with Salvia miltiorrhiza loaded on the surface of medical titanium-based alloy. We screened out effective compounds and targets of Salvia miltiorrhiza and targets related to traumatic bone defects with the help of relevant databases. The targets of Salvia miltiorrhiza for traumatic bone defects were analyzed by STRING and GeneCards databases, and the results were visualized by constructing a compound-target network, protein-protein interaction network, and compound-target-disease network with Cytoscape 3.7.1 analysis software. Finally, the selected core targets carried out GO and KEGG enrichment. The results showed that 60 main active components were screened from Salvia miltiorrhiza Bunge, which could act on 149 targets. There were 33 active components and 70 targets related to traumatic bone defects, respectively. The core targets of Salvia miltiorrhiza in the treatment of traumatic bone defects were MAPK1, MAPK10, MAPK14, TGFB1, and TNF. The results of enrichment analysis showed that Salvia miltiorrhiza might treat traumatic bone defects through an osteogenic differentiation pathway.

Analysing the role of Saraswatarishta in the treatment of neurological disorders based on network pharmacology

Ayurvedic medications originated centuries ago and are still prevalent today. Saraswatarishta (SWRT) is a well-known ayurvedic formulation that is often prescribed to control the manifestations of neurological illnesses and disorders such as slurred speech, anxiety, Parkinson's disease (PD) and Alzheimer's disease(AD). However, scientific research on its mode of action has not been studied extensively. Therefore, this study employs network pharmacology to understand better the neuroprotective role of Saraswatarishta (SWRT) in neurological disorders. Out of the 18 ingredients in SWRT, five were considered in this study due to their elevated therapeutic action in neurological disorders. Further, nine active phytoconstituents were chosen from the five selected ingredients. The gene targets of the active phytoconstituents were screened and selected using STITCH, SwissTargetPrediction and ChEMBL. Protein-Protein interaction and Gene Ontology (GO) enrichment analysis were carried out using STRING and g:Profiler, respectively. Cytoscape 3.7.2 was used to create three networks-the compound-target, the target-disease and the compound-target-disease network. Molinspiration and admetSAR2.0 were used to obtain the bioactivity scores and the blood-brain barrier (BBB) probability scores. The three networks indicated that all nine phytoconstituents were linked to the gene targets that encode proteins involved in the pathways of 10 major neurological disorders. This includes Parkinson's disease (PD), Alzheimer's disease (AD), dementia, Huntington disease, epilepsy, schizophrenia, spinocerebellar ataxia, amyotrophic lateral sclerosis (ALS), multiple sclerosis and attention deficit hyperactivity disorder (ADHD). The gene targets were expressed significantly in various central nervous system regions such as the cerebral cortex, cerebellum and amygdala. The bioactivity scores of the phytoconstituents were in the active range along with high BBB probability scores, indicating that the phytoconstituents can potentially cross the BBB and impart therapeutic effects.

Insights into the molecular mechanisms of Huangqi decoction on liver fibrosis via computational systems pharmacology approaches

Background The traditional Chinese medicine Huangqi decoction (HQD) consists of Radix Astragali and Radix Glycyrrhizae in a ratio of 6: 1, which has been used for the treatment of liver fibrosis. In this study, we tried to elucidate its action of mechanism (MoA) via a combination of metabolomics data, network pharmacology and molecular docking methods. Methods Firstly, we collected prototype components and metabolic products after administration of HQD from a publication. With known and predicted targets, compound-target interactions were obtained. Then, the global compound-liver fibrosis target bipartite network and the HQD-liver fibrosis protein–protein interaction network were constructed, separately. KEGG pathway analysis was applied to further understand the mechanisms related to the target proteins of HQD. Additionally, molecular docking simulation was performed to determine the binding efficiency of compounds with targets. Finally, considering the concentrations of prototype compounds and metabolites of HQD, the critical compound-liver fibrosis target bipartite network was constructed. Results 68 compounds including 17 prototype components and 51 metabolic products were collected. 540 compound-target interactions were obtained between the 68 compounds and 95 targets. Combining network analysis, molecular docking and concentration of compounds, our final results demonstrated that eight compounds (three prototype compounds and five metabolites) and eight targets (CDK1, MMP9, PPARD, PPARG, PTGS2, SERPINE1, TP53, and HIF1A) might contribute to the effects of HQD on liver fibrosis. These interactions would maintain the balance of ECM, reduce liver damage, inhibit hepatocyte apoptosis, and alleviate liver inflammation through five signaling pathways including p53, PPAR, HIF-1, IL-17, and TNF signaling pathway. Conclusions This study provides a new way to understand the MoA of HQD on liver fibrosis by considering the concentrations of components and metabolites, which might be a model for investigation of MoA of other Chinese herbs.