Network pharmacology-based approach of novel traditional Chinese medicine formula for treatment of acute skin inflammation in silico

A Network Pharmacology and Molecular Dynamics Simulation-Based Study of Qing Run Hua Jie Decoction in Interstitial Pneumonia Treatment

Objective

This study is dedicated to revealing the potential mechanism of Qin Run Hua Jie (QRHJ) decoction in Interstitial pneumonia (IP) treatment.

Methods

The TCMSP database predicted the chemical components and targets of QRHJ decoction, and the IP-related genes were from the Genecards database. Cytoscape software was used to establish the interaction network. R package clusterProfiler was utilized for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The molecular docking analysis of target proteins and the corresponding active pharmaceutical ingredients in the core position of the interaction network was conducted. Then, molecular dynamics (MD) simulations of a potential active substance and its key targets were performed. The binding efficiency of EGFR and luteolin, HIF1A and diosgenin was detected by cellular thermal shift assay (CETSA), and protein expression was measured by Western blot. CCK-8 was used to detect cell activity.

Results

A total of 153 active ingredients, 127 targets and 362 IP-related genes were obtained. KEGG enrichment analysis identified IP-related signaling pathways including HIF-1 signaling pathway and TNF signaling pathway. The two key components luteolin and diosgenin stably bound to the key targets EGFR and HIF1A. Cell experiments further showed that EGFR and luteolin, HIF1A and diosgenin bound to exert anti-fibrotic effects.

Conclusion

As an active ingredient of QRHJ decoction, luteolin and diosgenin may exert therapeutic effect on IP through binding to the key target EGFR and HIF1A. This work initially revealed the key molecular mechanism of QRHJ decoction in IP treatment and offered theoretical evidence.

Multi-target meridians classification based on the topological structure of anti-cancer phytochemicals using deep learning

ETHNOPHARMACOLOGICAL RELEVANCE

Traditional Chinese medicine (TCM) meridian is the key theoretical guidance of prescription against tumor in clinical practice. However, there is no scientific and systematic verification of therapeutic action of herbs under meridians context. Several studies have determined the Chinese herbal medicine (CHM) phytochemicals for intrinsic attribute or meridians classification based on artificial intelligence (AI) tools. However, it is challenging to represent the complex molecular structures with large heterogeneity through the current technologies. In addition, the multiple correspondence between herbs and meridians has not been paid much attention.

AIM OF THE STUDY

We aim to develop an AI framework to classify multi-target meridians through the topological structure of phytochemicals.

MATERIALS AND METHODS

A total of 354 anti-cancer herbs, their corresponding TCM meridians and 5471 ingredient compounds were collected from public databases of CancerHSP, ETCM, and Hit 2.0. The statistical analysis of herbal and compound datasets, clustering analysis of the associated cancers, and correlational analysis of meridian tropism were preliminary conducted. Then a deep learning (DL) hybrid model named GRMC consisting of graph convolutional network (GCN) and recurrent neural network (RNN) was employed to generate the meridian multi-label sequences based on molecular graph.

RESULTS

The curing herbs against tumors have tight relationships to lung, liver, stomach, and spleen meridians. These herbs behave different properties in curing certain cancer. Certain cancer types have co-occurrence such as ovarian, bladder and cervical cancer. Compounds have multitarget meridians with characteristics of higher-order correlations. Compared with the other state-of-the-art algorithms on the datasets and previous methods dealing with conventional fixed fingerprints of herbal compounds, the proposed GRMC has superior overall performance on testing dataset with the one error of 0.183, hamming loss of 0.112, mean averaged accuracy (MAA) of 0.855, mean averaged precision (MAP) of 0.891, mean averaged recall (MAR) of 0.812, and mean averaged F1 score (MAF) of 0.849.

CONCLUSIONS

The proposed method can predict multi-targeted meridians through neural graph features in herbal compounds and outperforms several comparison methods. It could provide a basis for understanding the molecular scientific evidence of TCM meridians.

A comprehensive review of Tripterygium wilfordii hook. f. in the treatment of rheumatic and autoimmune diseases: Bioactive compounds, mechanisms of action, and future directions

Rheumatic and autoimmune diseases are a group of immune system-related disorders wherein the immune system mistakenly attacks and damages the body's tissues and organs. This excessive immune response leads to inflammation, tissue damage, and functional impairment. Therapeutic approaches typically involve medications that regulate immune responses, reduce inflammation, alleviate symptoms, and target specific damaged organs. Tripterygium wilfordii Hook. f., a traditional Chinese medicinal plant, has been widely studied in recent years for its application in the treatment of autoimmune diseases, including rheumatoid arthritis, systemic lupus erythematosus, and multiple sclerosis. Numerous studies have shown that preparations of Tripterygium wilfordii have anti-inflammatory, immunomodulatory, and immunosuppressive effects, which effectively improve the symptoms and quality of life of patients with autoimmune diseases, whereas the active metabolites of T. wilfordii have been demonstrated to inhibit immune cell activation, regulate the production of inflammatory factors, and modulate the immune system. However, although these effects contribute to reductions in inflammatory responses and the suppression of autoimmune reactions, as well as minimize tissue and organ damage, the underlying mechanisms of action require further investigation. Moreover, despite the efficacy of T. wilfordii in the treatment of autoimmune diseases, its toxicity and side effects, including its potential hepatotoxicity and nephrotoxicity, warrant a thorough assessment. Furthermore, to maximize the therapeutic benefits of this plant in the treatment of autoimmune diseases and enable more patients to utilize these benefits, efforts should be made to strengthen the regulation and standardized use of T. wilfordii.

Chinese Medicine as Supporting Therapy for Psoriasis: Past, Present, and Future

Psoriasis is a chronic skin disease and an important health concern. Western medicine and therapies are the main treatment strategies for psoriasis vulgaris (PV); however, the overall prognosis of patients with PV is still poor. Therefore, PV prevention is especially crucial. Chinese medicine (CM) has a long history of treating psoriasis, and it has unique wisdom in different cognitive angles and treatment modes from modern medicine. In this review, we first summarized the herbs and ancient CM formulas that have therapeutic effects on PV. Second, the research status and obstacles to the current development of CM in modern medicine were reviewed. Finally, the future of CM in the context of precision medicine and integrated medicine was discussed. After a detailed reading of the abundant literature, we believe that CM, through thousands of years of continuous development and clinical practice, has achieved high effectiveness and safety for PV treatment, despite its surrounding controversy. Moreover, precise analyses and systematic research methods have provided new approaches for the modernization of CM in the future. The treatment of PV with CM is worth popularizing, and we hope it can benefit more patients.

TCM-Suite: A comprehensive and holistic platform for Traditional Chinese Medicine component identification and network pharmacology analysis

DNA-based biological ingredient identification and downstream pharmacology network analysis are commonly used in research for Traditional Chinese Medicine preparations (TCM formulas). Advancements in bioinformatics tools and the accumulation of related data have become driving forces for progress in this field. However, a lack of a platform integrating biological ingredient identification and downstream pharmacology network analysis hinders the deep understanding of TCM. In this study, we developed the TCM-Suite platform composed of two sub-databases, Holmes-Suite and Watson-Suite, for TCM biological ingredient identification and network pharmacology investigation, respectively, both are among the most complete: In the Holmes-Suite, we collected and processed six types of marker gene sequences, accounting for 1,251,548 marker gene sequences. In the Watson-Suite, we curated and integrated a massive number of entries from more than 10 public databases. Importantly, we developed a comprehensive pipeline to integrate TCM biological ingredient identification and downstream network pharmacology research, allowing users to simultaneously identify components of a TCM formula and analyze its potential pharmacology mechanism. Furthermore, we designed search engines and a user-friendly platform to better search and visualize these rich resources. TCM-Suite is a comprehensive and holistic platform for TCM-based drug discovery and repurposing. TCM-Suite website: http://TCM-Suite.AImicrobiome.cn.

Protective Effect and Potential Mechanism of the Traditional Chinese Medicine Shaoyao-Gancao Decoction on Ethanol-Induced Gastric Ulcers in Rats

Background

Shaoyao-Gancao decoction (SGD) is a classic prescription in traditional Chinese medicine. SGD is effective in the treatment of gastric and duodenal ulcers. However, the biological activity and possible mechanisms of SGD in the treatment of gastric ulcers have not been fully elucidated. The purpose of this study was to scientifically evaluate the protective effect and potential mechanism of SGD against ethanol-induced gastric ulcers in rats.

Methods

A single gavage of 10 mL/kg of 75% ethanol was used to establish a rat gastric ulcer model. A histopathological examination of the gastric tissue was performed. The levels of TNF-α, EGF, PGE₂, SOD, and TBARS in gastric tissue were measured by ELISA. Cellular apoptosis in gastric tissues was assessed by TUNEL assay. The expression levels of caspase-3 and Bcl-2 were determined by immunohistochemistry. The potential mechanism of SGD in treating gastric ulcers was further studied using a network pharmacology research method.

Results

The gastric tissue of rats with ethanol-induced gastric ulcers had obvious injury throughout the mucosal layer, which was significantly weakened in rats treated with SGD. Furthermore, treatment with SGD significantly increased the levels of EGF, PGE₂, SOD, and Bcl-2 and decreased the levels of TNF-α, TBARS, and caspase-3 in the gastric tissue of rats with ethanol-induced gastric ulcers. SGD reduced ethanol-induced cell apoptosis in gastric tissue from rats with gastric ulcers. A traditional Chinese medicine-based network pharmacology study revealed that SGD exerts its anti-gastric ulcer effect by acting on multiple pathways.

Conclusions

The above results indicate that SGD can improve gastric ulcers induced by ethanol. Moreover, this study demonstrated multicomponent, multitarget, and multipathway characteristics of SGD in the treatment of gastric ulcers and provided a foundation for further drug development research.

Investigating the mechanism of Xian-ling-lian-xia-fang for inhibiting vasculogenic mimicry in triple negative breast cancer via blocking VEGF/MMPs pathway

Background

Xian-ling-lian-xia-fang (XLLXF), a Chinese medicine decoction, is widely used in the treatment of triple negative breast cancer (TNBC). However, the underlying mechanism of XLLXF in TNBC treatment has not been totally elucidated.

Methods

Here, network pharmacology and molecular docking were used to explore the mechanism of Traditional Chinese medicine in the treatment of TNBC. Then, biological experiments were integrated to verify the results of network pharmacology.

Results

Network pharmacology showed that the candidate active ingredients mainly included quercetin, kaempferol, stigmasterol, and β-sitosterol through the “XLLXF–active ingredients–targets” network. Vascular endothelial growth factor A (VEGFA) and matrix metalloproteinase (MMP) 2 were the potential therapeutic targets obtained through the protein–protein interaction (PPI) network. Molecular docking confirmed that quercetin, kaempferol, stigmasterol, and β-sitosterol could stably combine with VEGFA and MMP2. Experimental verification showed that XLLXF could inhibit proliferation, colony ability, and vasculogenic mimicry (VM) formation and promote cell apoptosis in TNBC. Laser confocal microscopy found that XLLXF impaired F-actin cytoskeleton organization and inhibited epithelial mesenchymal transition. Animal experiments also found that XLLXF could inhibit tumor growth and VM formation in TNBC xenograft model. Western blot analysis and immunohistochemical staining showed that XLLXF inhibited the protein expression of VEGFA, MMP2, MMP9, Vimentin, VE-cadherin, and Twist1 and increased that of E-cadherin, tissue inhibitors of metalloproteinase (TIMP)-1, and TIMP-3 in vitro and in vivo.

Conclusions

Integrating the analysis of network pharmacology and experimental validation revealed that XLLXF could inhibit VM formation via downregulating the VEGF/MMPs signaling pathway.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13020-022-00597-5.

Exploring the mechanism of Jianpi Qushi Huayu Formula in the treatment of chronic glomerulonephritis based on network pharmacology

This study was to explore the effective components, potential targets, and pathways of Jianpi Qushi Huayu Formula (JQHF) for the treatment of chronic glomerulonephritics (CGN). First, the Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP), GeneCards, and OMIM databases were used to collect the major active components of JQHF and potential therapeutic targets of CGN. Then, functional enrichment analysis was performed to clarify the mechanisms of the JQHF on CGN. Subsequently, molecular docking was simulated to assess the binding ability of key targets and major active components. Finally, quantitative real-time PCR and western blot were performed for experimental verification of cells in vitro. A total of 55 active ingredients contained and 220 putative identified targets were screened from JQHF, of which 112 overlapped with the targets of CGN and were considered potential therapeutic targets. Then, we found quercetin and kaempferol are two key ingredients of JQHF, which may act on the top 10 screened targets of PPI, affecting CGN through related signal transduction pathways. Subsequently, molecular docking predicted that quercetin and kaempferol bind firm with the top 10 core targets of PPI. Further experiment verified some results and showed that JQHF has protected glomerular mesangial cells from lipopolysaccharide-induced inflammation by inhibiting expressions of IL6, TNF-α, and AKT1, and activating expressions of VEGFA. Based on network pharmacology, we explored the multi-component, multi-target, and multi-pathway characteristics of JQHF in treating CGN, and found that JQHF could act on IL6, TNF-α, VEGFA, and AKT1 to exert the effect of anti-CGN, which provided new ideas and methods for further research on the mechanism of JQHF in treating CGN.

A Network Pharmacology Approach to Explore the Mechanisms of Shugan Jianpi Formula in Liver Fibrosis

Purpose

We explored the mechanism of Shugan Jianpi Formula (SGJPF) and its effective components for the treatment of liver fibrosis (LF).

Materials and Methods

We collected the active ingredients in SGJPF through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform and screened the effective components by absorption, distribution, metabolism, and excretion. Herb-associated target proteins were predicted and screened based on the Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine and Search Tool for Interactions of Chemicals databases. LF-associated target proteins were predicted and screened based on the Online Mendelian Inheritance in Man® Database and Comparative Toxicogenomics Database. Common genes with LF and herbs were selected, and Cytoscape 3.5.1 software was used to construct an herb pathway and component-LF common target network. The Search Tool for the Retrieval of Interacting Genes/Proteins was used to build a protein-protein interaction, and quantitative PCR was used to verify the related target genes. Finally, clusterProfiler was applied for the analysis of Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways.

Results

The pharmacological network contained 252 active compounds (e.g., Astragaloside A, saikosaponin, linoleic acid, and Poria acid A), 84 common target genes, and 94 significant signaling pathways. Among them, interleukin 6 (IL-6), tumor protein 53 p53 (TP53), prostaglandin-endoperoxide synthase 2 (PTGS2), AKT1, IL-1β, and the nucleotide-binding and oligomerization domain-like receptor and Janus kinase-signal transducer and activator of transcription signaling pathways were selected as the critical target gene and critical signal pathway, respectively.

Conclusion

The mechanisms of SGJPF in protecting against LF include the regulation of multiple targets such as IL-6, TP53, PTGS2, and AKT1. These target proteins affect LF through various signal transduction pathways.

Applications of Network Pharmacology in Traditional Chinese Medicine Research

Human diseases, especially infectious ones, have been evolving constantly. However, their treatment strategies are not developing quickly. Some diseases are caused by a variety of factors with very complex pathologies, and the use of a single drug cannot solve these problems. Traditional Chinese Medicine (TCM) medication is a unique treatment method in China. TCM formulae contain multiple herbs with multitarget, multichannel, and multilink characteristics. In recent years, with the flourishing development of network pharmacology, a new method for searching therapeutic drugs has emerged. The multitarget action in network pharmacology is consistent with the complex mechanisms of disease and drug action. Using network pharmacology to understand TCM is an emerging trend.

A network pharmacology approach to investigate the anti-inflammatory mechanism of effective ingredients from Salvia miltiorrhiza

Salvia miltiorrhiza, known as Danshen in Chinese, has been widely used to treat cardiovascular diseases in China. Tanshinone I (Tan I) and cryptotanshinone (CST) are the lipid-soluble and effective components from Salvia miltiorrhiza. However, the molecular mechanism of Tan I and CST for treating inflammation is still not known. Therefore, this study was designed to use network pharmacology-based strategy to predict therapeutic targets of Tan I and CST against inflammation, and further to investigate the pharmacological molecular mechanism in vitro. Inflammation targets were identified and followed by acquisition of verified targets of Tan I and CST. After constructing target-functional protein interaction network of Tan I and CST against inflammation, the core therapeutic targets of Tan I and CST against inflammation were obtained. Further, pathway enrichment analyses were performed on core therapeutic targets to evaluate key signaling pathways of Tan I and CST against inflammation. As revealed in network pharmacology analysis, 8 key hub targets for Tan I and CST against inflammation were identified, respectively: JUN, VEGFA, IL-6, TNF, MAPK8, CXCL8, and PTGS2 for Tan I, while STAT3, AKT1, CCND1, MAPK14, VEGFA, ESR1, MAPK8 and AR for CST. Pathway enrichment analysis by DAVID database indicated that Tan I and CST principally regulated the inflammation-associated pathway, such as TLR, JAK-STAT signaling pathway, focal adhesion, apoptosis, mTOR signaling pathway. In vitro, we found that both Tan I and CST exerts significantly effect on LPS stimulated NO secretion and iNOS expression in macrophages. Taken together, our data elucidate that anti-inflammatory pharmacological activities of Tan I and CST may be predominantly related to inhibition of TLR signaling pathway and regulating iNOS synthesis. These findings highlight the predicted therapeutic targets may be potential targets of Tan I and CST for anti-inflammation treatment.

Systems biology approaches in the study of Chinese herbal formulae

Systems biology is an academic field that attempts to integrate different levels of information to understand how biological systems function. It is the study of the composition of all components of a biological system and their interactions under specific conditions. The core of systems biology is holistic and systematic research, which is different from the manner of thinking and research of all other branches of biology to date. Chinese herbal formulae (CHF) are the main form of Chinese medicine and are composed of single Chinese herbal medicines (CHMs) with pharmacological and pharmacodynamic compatibility. When single CHMs are combined into CHF, the result is different from the original effect of a single drug and can be better adapted to more diseases with complex symptoms. CHF represent a complex system with multiple components, targets and effects. Therefore, the use of systems biology is conducive to revealing the complex characteristics of CHF. With the rapid development of omics technologies, systems biology has been widely and increasingly applied to the study of the basis of the pharmacological substances, action targets and mechanisms of CHF. To meet the challenges of multiomics synthesis-intensive studies and system dynamics research in CHF, this paper reviews the common techniques of genomics, transcriptomics, proteomics, metabolomics, and metagenomics and their applications in research on CHF.

Feifukang ameliorates pulmonary fibrosis by inhibiting JAK-STAT signaling pathway

Background

Feifukang (FFK) is a traditional Chinese medicine composed of herbs that protect lung function. However, difficulty arises regarding the clinical application of FFK due to the complex mechanism of Chinese medicines. This study aimed to investigate the efficacy of FFK and explore its targeted genes and pathways.

Methods

Histopathological changes and collagen deposition were measured to evaluate the effect of FFK on bleomycin-induced pulmonary fibrosis in mice. The differentially expressed targeted genes and pathways were first screened using RNA sequencing. Then network pharmacology and other experiments were conducted to confirm RNA sequencing data.

Results

FFK treatment reduced the pathological score and collagen deposition, with a decrease in α-SMA and collagen. RNA sequencing and network pharmacology results all showed that FFK can ameliorate pulmonary fibrosis through multi-genes and multi-pathways. The targeted genes in JAK-STAT signaling pathway are some of the most notable components of these multi-genes and multi-pathways. Further experiments illustrated that FFK regulated phosphorylation of SMAD3, STAT3 and JAK1, and their co-expressed lncRNAs, which all are the important genes in JAK-STAT signaling pathway.

Conclusion

FFK can ameliorate pulmonary fibrosis by inhibiting JAK-STAT signaling pathway and has potential therapeutic value for lung fibrosis treatment. Our study provides a new idea for the study of traditional Chinese medicine.

Approaches in studying the pharmacology of Chinese Medicine formulas: bottom-up, top-down-and meeting in the middle

Investigating the pharmacology is key to the modernization of Chinese Medicine (CM) formulas. However, identifying which are the active compound(s) of CM formulas, which biological entities they target, and through which signaling pathway(s) they act to modify disease symptoms, are still difficult tasks for researchers, even when equipped with an arsenal of advanced modern technologies. Multiple approaches, including network pharmacology, pharmaco-genomics, -proteomics, and -metabolomics, have been developed to study the pharmacology of CM formulas. They fall into two general categories in terms of how they tackle a problem: bottom-up and top-down. In this article, we compared these two different approaches in several dimensions by using the case of MaZiRenWan (MZRW, also known as Hemp Seed Pill), a CM herbal formula for functional constipation. Multiple hypotheses are easy to be proposed in the bottom-up approach (e.g. network pharmacology); but these hypotheses are usually false positives and hard to be tested. In contrast, it is hard to suggest hypotheses in the top-down approach (e.g. pharmacometabolomics); however, once a hypothesis is proposed, it is much easier to be tested. Merging of these two approaches could results in a powerful approach, which could be the new paradigm for the pharmacological study of CM formulas.