Exploration of the Potential Mechanisms of Yinqiao Powder in Treating Atopic Dermatitis: Integrating Network Pharmacology, Single-Cell, and Bulk RNA Sequencing
Jul 2026· Journal of Asthma and Allergy· Vol 19, pp. 1-25· 0 citations· 105 references
Medicine
TL;DR
It is indicated that CTNNB1, ERBB2, TP53, and HIF1A are key targets of YQP in AD treatment, with beta-carotene, aloe-emodin, and quercetin as likely active components.
Abstract
Purpose Yinqiao powder (YQP) is a traditional Chinese medicine formula that has been widely used clinically to treat infectious diseases such as respiratory tract infections, influenza, and pneumonia, exhibiting anti-inflammatory and antiviral properties. This study aimed to investigate its therapeutic potential for atopic dermatitis (AD) and to identify its key target genes and active components. Methods We performed mRNA transcriptome sequencing on blood samples collected from 15 adult patients with AD and 15 adult healthy controls. Potential targets were identified by intersecting differentially expressed genes (DEGs) with known YQP target genes. Key genes were pinpointed using protein-protein interaction networks and receiver operating characteristic (ROC) curve analysis. We estimated immune cell proportions, constructed regulatory networks, and used molecular docking to predict active ingredients. Single-cell RNA sequencing data (GSE180885) was also analyzed to identify key cell types and track gene expression. Results Fifty-two potential target genes were identified. From these, four key genes—CTNNB1, ERBB2, TP53, and HIF1A—were highlighted, potentially involved in carbon metabolism and organelle biosynthesis. A nomogram model based on these genes showed strong predictive power for AD. Immune analysis linked resting CD4+ memory T cells with TP53 and HIF1A. Molecular docking suggested beta-carotene, aloe-emodin, and quercetin as potential active ingredients binding to these key targets. Single-cell analysis identified keratinocytes as a key population, with key gene expression varying during their differentiation. Conclusion This study indicates that CTNNB1, ERBB2, TP53, and HIF1A are key targets of YQP in AD treatment, with beta-carotene, aloe-emodin, and quercetin as likely active components. These findings provide a theoretical basis for using YQP in AD therapy.
Background Alopecia areata (AA) is a common non-scarring autoimmune disease with a complex pathogenesis, high recurrence rates, and difficulty in achieving a cure. Recent years have seen an increasing focus on both clinical and basic research regarding AA. Hejie Shengfa Decoction (HSD), a traditional Chinese medicine formula, has shown certain efficacy in the clinical treatment of AA, though its underlying mechanisms remain unclear. Methods In this study, we retrieved the active ingredients of HSD and their associated targets from public databases. We then identified AA-related targets through bioinformatics analysis of publicly available AA datasets. A protein-protein interaction (PPI) network was constructed to generate the HSD-AA action network by integrating drug-specific targets and disease-related targets. Functional enrichment analysis was subsequently performed. To further investigate key genes, three machine learning algorithms—least absolute shrinkage and selection operator (LASSO) regression, support vector machine-recursive feature elimination (SVM-RFE), and Random Forest—were applied Additionally, immune cell infiltration analysis was carried out to examine the roles of these key genes in the local immune microenvironment. Molecular docking and molecular dynamics simulations were employed to assess the binding stability of the active ingredients with the core targets. Results The results revealed that HSD contains 96 active ingredients and 985 related targets. From the AA dataset, 955 differentially expressed genes and 492 co-expressed modular genes were identified, resulting in 23 intersecting genes after integration. Machine learning algorithms identified four key target genes among these 23. Immune infiltration analysis suggested that HSD could influence the immune microenvironment of AA by modulating the expression of these key targets. Molecular docking and molecular dynamics simulations confirmed strong and stable binding interactions between HSD’s main active ingredients—especially quercetin—and the core targets. Conclusion This study elucidates the potential of HSD as a treatment for AA and provides insights into its mechanisms of action, offering a novel approach for treating AA with multi-targeted traditional Chinese medicine.
INTRODUCTION
Danggui Shaoyao Powder (DSP), a classical prescription in Traditional Chinese Medicine (TCM), has garnered growing evidence for its promising therapeutic effects on ulcerative colitis (UC). Nevertheless, the specific molecular mechanisms responsible for its anti-UC activity remain poorly understood. Herein, we integrated network pharmacology with experimental validation to systematically explore and clarify the underlying mechanisms of DSP in the treatment of UC.
METHODS
Network pharmacology was utilized to identify the active components and therapeutic targets of DSP in the treatment of UC. Subsequently, a protein-protein interaction (PPI) network of the identified targets was constructed using the STRING database, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Molecular docking was performed to evaluate the binding affinity between the primary bioactive components of DSP and Th17-associated proteins. Finally, the protective efficacy of DSP in maintaining immune homeostasis and alleviating inflammation was validated in vivo using a dextran sulfate sodium (DSS)- induced UC mouse model.
RESULTS
A total of 51 active components of DSP and 103 corresponding targets were retrieved from the TCMSP database. Additionally, 4,419 potential targets associated with UC were identified from the OMIM, DisGeNET, and GeneCards databases. Sixty-three overlapping potential targets were identified as therapeutic targets of DSP against UC, which were subsequently refined to construct an optimized protein-protein interaction network containing 100 central nodes. Key hubs included IL-6, AKT1, ESR1, CASP3, PTGS2, BCL2, PPARG, HSP90AA1, TGF-β1, and JUN, and these targets were highly enriched in pathways associated with inflammatory regulation and immune signaling, with Th17 cell differentiation serving as a key biological process. Molecular docking demonstrated stable binding affinities between the DSP components Paeoniflorin and Stigmasterol and the core targets TGF-β1 and IL-6 (binding energy < -5 kcal/mol). Furthermore, in vivo experiments demonstrated that DSP ameliorated symptoms and histological changes in DSS-induced colitis by inhibiting Th17 cell differentiation.
DISCUSSION
This study provides experimental evidence supporting the protective effects of DSP against UC, with its actions likely mediated, at least in part, through the modulation of Th17 cell differentiation. These results deepen our insight into the mechanistic underpinnings of DSP activity and can serve as a theoretical basis for the further advancement of DSP-based therapeutic strategies for UC.
CONCLUSION
DSP has shown potential as a therapeutic agent for UC by acting on multiple core targets and relevant signaling pathways, with the modulation of Th17 cell differentiation serving as a key mediating mechanism.
Yanxia Huang, Q. Lin, Min Zhu et al.· Combinatorial chemistry & hi...· 0 citations
Atopic dermatitis (AD) is a chronic inflammatory skin disorder with complex pathogenesis, and current therapies face limitations in efficacy and safety. Huangqin (Scutellaria baicalensis) exhibits anti-inflammatory properties, yet its multi-target mechanisms against AD remain unclear. A systems pharmacology approach integrating multi-omics profiling was utilized to decode Huangqin anti-AD mechanisms. First, bioactive compounds and their potential targets were systematically identified, followed by constructing compound–target networks and enriching key pathways. Then, machine learning algorithms (Support Vector Machine/Recursive Feature/Least Absolute Shrinkage and Selection Operator) were applied to prioritize hub targets from network-derived candidates. Finally, molecular docking was conducted to validate ligand–receptor binding affinity. Twenty-nine bioactive compounds were identified, interacting with 55 AD-related targets. AKT1 emerged as the most central hub in the protein–protein interaction network. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis revealed Huangqin potential roles in modulating bacterial infection responses and regulating pathways such as IL-17, TNF, HIF-1α, and PI3K–AKT signaling. Machine learning algorithms were applied to prioritize key genes, which highlighted AKT1, solute carrier family 6 member 4, and chemokine ligand 2 as core targets, with molecular docking confirming strong binding between wogonin, baicalein, beta-sitosterol, and these targets. These findings suggest that Huangqin exerts multi-target effects on AD, centered on AKT1-mediated signaling crosstalk, to regulate inflammatory and immune pathways. This mechanistic insight establishes a foundation for clinical translation and AKT1-focused drug development.
INTRODUCTION
Yifei Capsule (YFC), a Traditional Chinese Medicine (TCM) formulation, has shown clinical benefit in patients with stable Chronic Obstructive Pulmonary Disease (COPD). However, its molecular mechanisms remain insufficiently understood. This study aimed to systematically investigate the active compounds, key targets, and signaling pathways underlying the therapeutic effects of YFC in stable COPD by integrating computational pharmacology with single-cell transcriptomic validation.
METHODS
An integrated workflow combining network pharmacology, molecular docking, and 100 ns Molecular Dynamics (MD) simulations was used to identify bioactive compounds, candidate therapeutic targets, and major signaling pathways of YFC. To externally validate the in silico findings, an independent public single-cell RNA sequencing (scRNA-seq) dataset (GSE249584) containing 54,821 cells from 15 human samples (7 controls and 8 COPD patients) was analyzed to characterize the cell-type-specific expression patterns of hub genes in COPD.
RESULTS
A total of 167 bioactive compounds in YFC and 977 putative targets were identified, of which 222 overlapped with COPD-related targets. Network topology analysis identified SRC, PIK3R1, and STAT3 as major hub genes, and KEGG enrichment analysis indicated that these targets were primarily enriched in the PI3K-Akt signaling pathway (hsa04151, p = 1.23E-15). Molecular docking revealed strong binding affinities between representative compounds and core targets, while MD simulations confirmed the dynamic stability of key complexes, particularly SRC-Korseveriline. scRNA-seq analysis further demonstrated cell-type-specific dysregulation of hub genes in COPD: STAT3 was significantly upregulated in monocyte/macrophage populations (adjusted p = 6.59E-08, log2FC = 0.32), PIK3R1 was downregulated in T cells (adjusted p = 2.05E-20, log2FC = -0.41), and SRC showed bimodal expression, with upregulation in endothelial cells (adjusted p = 0.002, log2FC = 0.38) but downregulation in T cells (adjusted p = 0.03, log2FC = -0.27).
DISCUSSION
These findings suggest that YFC may exert therapeutic effects in stable COPD through multi-component, multi-target regulation of the PI3K-Akt pathway and related inflammatory signaling networks. The cell-type-specific dysregulation of SRC, PIK3R1, and STAT3 supports a nuanced immunomodulatory and vascular-regulatory mechanism, highlighting the value of integrating computational prediction with single-cell transcriptomic evidence.
CONCLUSION
This multi-scale study provides a mechanistic framework for understanding the action of YFC in stable COPD and identifies SRC, PIK3R1, and STAT3 as plausible core targets. The results support the utility of combining network pharmacology, molecular simulation, and single-cell transcriptomics to dissect the mechanisms of complex herbal medicines. Nevertheless, the present findings remain correlative and require further experimental validation to establish causality.
INTRODUCTION
Antipsychotic-induced hyperprolactinemia (HPRL) is a prevalent and troubling side effect that impairs treatment adherence. Xuefu Zhuyu Pills (XFZY), a traditional Chinese medicine formula, have shown efficacy in clinical practice; however, their active components and mechanisms of action remain unknown.
METHODS
We identified the components of XFZY in human plasma by UPLC-Q-TOF-MS/MS and performed network pharmacology analysis to screen key targets and core active components through topological analysis. Intersecting targets were subjected to GO and KEGG enrichment analyses on Metascape, followed by molecular docking and molecular dynamics simulations to assess binding stability between core active components and key targets.
RESULTS
A total of 67 absorbed components were found in human plasma. Based on the network pharmacology results, we identified seven core targets (AKT1, ESR1, IL6, TNF, ERBB2, CTNNB1, and MAOA) and three potential active ingredients (Makisterone B, 3-O-beta-Dglucopyranosylplatycodigenin, and 4',5,6,7-Tetramethoxyflavone). GO and KEGG suggested that hormone level regulation, the neuroactive ligand-receptor interaction, the apelin signaling pathway, and the cGMP-PKG signaling pathway may play key roles in treating HPRL caused by antipsychotic drugs.
DISCUSSION
Molecular docking results showed that the core active ingredient binds well with the key targets, and molecular dynamics simulations further verified their stability.
CONCLUSION
This study preliminarily revealed the components in human plasma of XFZY in HPRL treatment and their potential pharmacological mechanism. These findings provide a scientific basis for clinical studies.
Mingyue Liu, Zihuan Zhang, Guanli Su et al.· Combinatorial chemistry & hi...· 0 citations
INTRODUCTION
Diabetic Cardiomyopathy (DCM) is a serious diabetic complication with limited therapies. While XuanTong BuLuo Decoction (XTBLD) benefits diabetic complications, its active components and anti-DCM mechanisms are unclear. This study aimed to predict XTBLD's active ingredients, key targets, and related pathways against DCM via network pharmacology, and verify them by animal experiments.
METHODS
Network pharmacology and molecular docking were used to screen XTBLD's active components, potential targets against DCM, and their binding affinity. Animal experiments evaluated XTBLD's therapeutic effects on DCM by assessing glycemic control, inflammation, and key protein expression in related pathways.
RESULTS
A total of 173 active ingredients were screened from XTBLD, with 217 intersecting genes obtained. Enrichment analysis revealed 125 signaling pathways, with 11 involved in XTBLD against DCM. Five main active components were identified using UPLC-MS/MS. In diabetic mice, XTBLD showed positive effects on blood glucose control, inflammatory response, and creatine kinase isoenzyme levels, as well as the expression of cyclin-dependent kinase inhibitor 1 and insulin-like growth factor binding protein 3.
DISCUSSION
This study demonstrates that XTBLD exerts cardioprotective effects against DCM via regulating its key active components, core targets (CDKN1A, IGFBP3), and related signaling pathways, providing a scientific basis for XTBLD's clinical application in DCM management.
CONCLUSION
XTBLD may exhibits a potential protective effect on glycemic control and myocardial injury by regulating signaling pathways associated with cell growth, apoptosis, and inflammatory responses, with puerarin, luteolin, formononetin, kaempferol, and quercetin proposed as its potential main active components.
Min Wang, Kaijie Xie, Hao Liu et al.· Current pharmaceutical desig...· 0 citations