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Research Article

Molecular Mechanisms of Dachengqi Decoction in Acute Respiratory Distress Syndrome: A Network Pharmacology and Bioinformatics Analysis

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DOI:

10.3791/69651

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March 27th, 2026

 , 
1Department of Emergency Medicine, The Fourth Affiliated Hospital of Soochow University

In This Article

Summary

This study elucidates the molecular mechanisms of Dachengqi Decoction (DCQD) against acute respiratory distress syndrome (ARDS) using network pharmacology, bioinformatics, and molecular docking. Key targets and pathways were identified. Molecular docking validated robust ligand-target interactions, supporting DCQD's multi-target therapeutic potential for ARDS.

Abstract

Acute respiratory distress syndrome (ARDS) is a life-threatening complication of sepsis, characterized by refractory hypoxemia and pulmonary inflammation. Currently, effective pharmacological interventions remain limited. Traditional Chinese medicine (TCM), exemplified by Dachengqi Decoction (DCQD), has demonstrated therapeutic potential in modulating complex inflammatory responses. To investigate the mechanism of DCQD in ARDS treatment, active components of DCQD and their associated targets were first retrieved from public databases. ARDS-related targets were identified through bioinformatics analysis of public datasets. By intersecting drug-specific targets with disease-related targets, a DCQD-ARDS interaction network was constructed using protein-protein interaction (PPI) analysis and functional enrichment. Comprehensive analysis revealed 32 overlapping genes critical to DCQD's therapeutic efficacy in ARDS. Enrichment analysis highlighted key pathways, including the chemokine signaling pathway, NOD-like receptor signaling, and neutrophil extracellular trap (NET) formation, which are implicated in immunomodulation and inflammation. The PPI network identified HSP90AB1, MMP9, HSP90AA1, ARG1, and MYC as core targets. Molecular docking confirmed strong binding affinities between these targets and DCQD components. Our findings untangle the mechanistic basis supporting DCQD as a promising adjunctive therapy for sepsis-induced ARDS.

Introduction

Acute respiratory distress syndrome (ARDS) is a highly heterogeneous clinical syndrome caused by various pulmonary or extrapulmonary factors, primarily characterized by diffuse lung injury and refractory hypoxemia1. Since its first definition in 1967, the understanding of ARDS has continuously evolved, leading to iterative revisions of its diagnostic criteria2. Furthermore, the implementation of therapeutic strategies, including lung-protective ventilation, ex vivo support, and prone positioning, has profoundly influenced both clinical management and research progress3. Nevertheless, ARDS mortality remains alarmingly high. A global survey revealed that ARDS patients account for 10.4% of all intensive care unit (ICU) admissions. Despite significant advances in critical care over recent decades, mortality rates (both in ICU and post-discharge) persist at 35-45%4.

The pathophysiology of ARDS is complex, involving alveolar epithelial and capillary endothelial injury, pulmonary and systemic inflammatory responses, and ventilator-induced lung injury5. Notably, COVID-19-associated ARDS exhibits distinct pathophysiological features compared to classical ARDS, with COVID-19 potentially inducing more severe microvascular thrombosis and regionally heterogeneous inflammation6,7. Given the disease's intricate pathophysiology and high heterogeneity, no definitive pharmacotherapy currently exists for ARDS. Aside from corticosteroids and neuromuscular blocking agents -- which are conditionally recommended due to mortality benefits -- other drug therapies (e.g., nitric oxide [NO], prostacyclin, interferon-β, pulmonary surfactants, and activated protein C) have demonstrated no clinical efficacy. Conversely, β-agonists (e.g., albuterol) and keratinocyte growth factor have been proven harmful8,9. Consequently, the development of safe, multifunctional therapeutics remains a critical research priority.

Traditional Chinese Medicine (TCM), with a history spanning over 3,000 years, has been used to prevent and treat various respiratory diseases. Through millennia of clinical application and the modernization of TCM research, it has demonstrated potential in the management of ARDS10,11. TCM exerts its therapeutic effects through multi-component, multi-pathway, and multi-target mechanisms, particularly by mitigating inflammatory responses, modulating immune function, and protecting pulmonary tissues12. For instance, Xuebijing injection (XBJ), a TCM-derived preparation composed of Paeonia lactiflora roots, Ligusticum chuanxiong rhizomes, Salvia miltiorrhiza roots, Carthamus tinctorius flowers, and Angelica sinensis roots, contains multiple active components. It alleviates ARDS by regulating immune function, oxidative stress, and inflammation through the IL-17, HIF-1, and TNF signaling pathways13. Clinical studies have further demonstrated that patients subjected to XBJ treatment exhibited reduced mortality rates, shorter ICU stays, and lower levels of inflammatory cytokines14. During the COVID-19 pandemic, excessive immune activation triggered by severe inflammatory responses emerged as a critical factor in severe cases15,16. Additionally, certain TCM components possess antioxidant properties, enabling them to scavenge free radicals and attenuate oxidative stress-induced lung injury. ARDS is often accompanied by pulmonary microcirculatory dysfunction, which impairs gas exchange. Some TCM formulations exhibit blood-activating and stasis-resolving effects, improving microcirculation to mitigate pulmonary edema and enhance oxygenation. Furthermore, TCM can ameliorate lung injury by regulating cell death pathways12,17,18. Collectively, these findings underscore the considerable clinical potential of TCM in ARDS treatment.

Dachengqi Decoction (DCQD), an essential formula in TCM for acute conditions, is composed of four herbal components: Rhubarb, mirabilite, magnolia bark, and immature bitter orange19. As a compound formulation, DCQD exhibits multi-component and multi-target interactions, effectively inhibiting the production of various inflammatory mediators and reactive oxygen species, including tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6)20,21. High-mobility group box 1 (HMGB1), a profound inflammatory mediator, plays a pivotal role in the pathogenesis and progression of sepsis. Toll-like receptor 4 (TLR4), a receptor expressed on immune cells, activates downstream inflammatory signaling pathways, such as nuclear factor-kappa B (NF-κB) and mitogen-activated protein kinase (MAPK), upon binding HMGB1, leading to increased release of inflammatory cytokines. DCQD exerts therapeutic effects by modulating the HMGB1-TLR4 signaling axis, thereby suppressing the activation of NF-κB and p38 MAPK, reducing inflammatory cytokine production, and mitigating systemic inflammation22,23. Furthermore, DCQD constituents exert therapeutic effects by modulating pro-inflammatory signaling pathways, oxidative stress, apoptosis, and pyroptosis24,25,26,27. DCQD also plays a critical role in treating ARDS by attenuating lipopolysaccharide (LPS)-induced hyperinflammatory responses and pulmonary epithelial damage. Mechanistically, it suppresses Z-DNA-binding protein 1 (ZBP1)-receptor-interacting protein kinase 1 (RIPK1)-PANoptosome assembly (refers to the process of forming a multi-protein complex that can trigger inflammatory cell death), thereby mitigating LPS-induced PANoptosis28. Therapeutic oral administration of DCQD upregulates aquaporin-1 (AQP-1) and aquaporin-5 (AQP-5) protein expression in lung tissue while inhibiting the TLR4/NF-κB signaling pathway and inflammatory cytokine production, ultimately ameliorating ARDS-associated inflammation.

The complex composition of TCM, characterized by the synergistic effects of multiple compounds, presents both advantages and challenges in elucidating its pharmacological mechanisms. Different TCM components may exert therapeutic effects through distinct molecular targets and pathways, while potential interactions among these components further complicate the comprehensive understanding of their integrated mechanisms29,30. Specifically, the phytochemicals involved in ARDS treatment and their mechanistic pathways remain elusive. To address these knowledge gaps, bioinformatics, an emerging interdisciplinary field integrating big data analytics and artificial intelligence, holds promise for identifying active pharmaceutical ingredients and deciphering underlying drug mechanisms31,32,33. Accordingly, this study employs bioinformatics approaches, including network pharmacology and molecular docking, to systematically identify the essential phytochemicals, molecular targets, and pathways underlying DCQD's therapeutic effects in ARDS treatment.

Based on the above rationale, we hypothesize that DCQD exerts therapeutic effects on ARDS by synergistically modulating key pathological processes, including inflammatory dysregulation and immune cell activation, through its multi-component network. To test this hypothesis and move beyond the limitations of traditional target prediction, we employed an integrated bioinformatics approach. The key innovation of this strategy lies in intersecting the potential targets of DCQD with ARDS-related genes derived not only from public databases but, more importantly, from the clinical transcriptomic dataset (GSE32707) of patients with sepsis-induced ARDS. This three-tiered screening aims to enhance the clinical relevance of our findings and identify the core hub genes and pathways through which DCQD may ameliorate ARDS, thereby providing a more precise and reliable theoretical framework for its mechanism of action.

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Protocol

All materials, including databases, software, and experimental reagents, used in this study are listed in the Table of Materials. All experimental procedures were approved by the Animal Ethics Committee of the Suzhou Dushu Lake Hospital (permit number: 2410008).

Screening of active components and target acquisition of DCQD
The active components of the constituent herbs rhubarb, mirabilite, magnolia bark, and immature bitter orange in Da-Cheng-Qi Decoction (DCQD) were screened from the traditional Chinese medicine systems pharmacology (TCMSP) database using the following criteria: oral bioavailability (OB) ≥30% and drug-likeness (DL) ≥0.18. The corresponding targets of the identified active components were then collected. These thresholds are widely adopted in network pharmacology studies to filter for compounds with favorable pharmacokinetic properties and higher potential for being drug-like molecules34,35. To improve the reliability of compound selection and reduce bias from single-database screening, cross-validation was performed using the BATMAN-TCM database. BATMAN-TCM is an integrative database that stores known and predicted interactions between TCM ingredients and target proteins, aiding in the exploration of TCM pharmacological mechanisms and drug discovery. The updated version 2.0 offers a significantly expanded TTI dataset and enhanced features, making it a valuable resource for understanding TCM's molecular mechanisms and developing new treatments for complex diseases. Compounds without predicted targets or with BATMAN-TCM target scores lower than 20 were excluded. In addition, compounds with relatively low OB values but with predicted targets in BATMAN-TCM and well-documented pharmacological activities (e.g., anthraquinones and flavonoids) were retained through literature curation. To further reduce potential false-positive components, endogenous hormones and neurotransmitter-like substances (e.g., progesterone and serotonin) were manually excluded. The final compound set was used for subsequent target prediction and network construction. The detailed workflow is illustrated in Supplementary Figure 1. The final list of active components is provided in Supplementary Table 1.

The SMILES identifiers (a linear notation method that uses ASCII strings to describe the chemical structure of molecules. By employing specific symbols and rules, it encodes atoms, chemical bonds, and molecular topology into a continuous string of characters without spaces, enabling concise, unambiguous, and computer-readable storage and transmission of molecular information) of each active component were retrieved from the PubChem database and potential targets were predicted using the swiss target prediction, with the organism parameter set to 'Homo sapiens' and a probability score cut-off of >0 applied. The target sets obtained from both databases were merged, and duplicate entries were removed to generate the final candidate target set for DCQD.

It is important to note that Mirabilitum (Mangxiao, Na₂SO₄·10H₂O), a mineral drug in DCQD, is not included in the TCMSP or similar compound-target databases. Consequently, due to this database limitation, the component-target network constructed in this study does not encompass the potential contributions of Mirabilitum. The network is therefore based on the phytochemical constituents of the other three herbal components: Rheum palmatum, Magnolia officinalis,and Citrus aurantium.

Enrichment analysis of DCQD targets
DCQD targets were imported into the metascape database for enrichment analysis, with the species restricted to "Homo sapiens". Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses were performed, with the former encompassing biological processes (BP), cellular components (CC), and molecular functions (MF). The top 6 of GO enrichment results and the top 20 of KEGG enrichment results were selected. The KEGG and GO bar plots were generated using the online tool from bioinformatics. Data was imported via the DOSE package in R to construct the disease ontology (DO) bar plot.

Construction of the "Herbal Medicine-Component-Target" network
The relevant components of DCQD and their corresponding targets were imported into Cytoscape 3.10.3 to construct a network diagram illustrating the "herbal medicine-component-target" relationships. The CytoNCA plugin was employed to calculate Degree values, and the top 5 active components were screened based on ranking.

Retrieval of ARDS targets from public databases
Using "acute respiratory distress syndrome" as the keyword, ARDS-related targets were retrieved from the OMIM and GeneCards databases. In Gene Cards, targets with a relevance score >5 were selected. After merging and removing duplicates from both databases, a candidate target set for ARDS was obtained.

Bioinformatics analysis
The sepsis-ARDS-related dataset GSE32707 (GPL570 platform, n = 45), comprising RNA-seq data from peripheral blood mononuclear cells of 30 sepsis-induced ARDS patients and 15 healthy controls, was downloaded from the NCBI GEO database. Data were imported using the GEOquery package in R, and normalization was performed using the limma package to minimize inter-sample variability. Differentially expressed genes (DEGs) were identified using the thresholds |log2FC| > 1 and p < 0.05. Visualization was conducted via volcano plots and heatmaps. GO and KEGG enrichment analyses were performed for pathway analysis, with results presented as bubble plots and bar plots.

Prediction of potential targets for DCQD in ARDS treatment
A Venn diagram was generated using the bioinformatics online tool to compare the DEGs, ARDS-related targets from public databases, and DCQD drug targets, thereby identifying potential therapeutic targets of DCQD for ARDS.

Construction of the protein-protein interaction (PPI) network and screening of core targets
The overlapping targets were imported into the STRING database with the species set to "Homo sapiens" and an interaction score threshold of 0.4. The PPI network data were exported in TSV format and visualized using Cytoscape 3.10.3. The CytoHubba plugin was applied to screen the top 10 targets based on MCC, MNC, degree, EPC, betweenness, closeness, radiality, and stress scores. The intersection of these targets yielded 5 core targets.

Construction of the "Herbal Medicine-Component-Target-Disease" network
Based on the overlapping targets, corresponding DCQD components were identified. The relevant components, along with their associated targets and disease linkages, were imported into Cytoscape 3.10.3 to construct a network diagram illustrating the "herbal medicine-component-target-disease" relationships.

Enrichment analysis of core targets and construction of the "herbal medicine-component-target-disease-pathway" network
To explore the potential biological functions and key signaling pathways of DCQD in ARDS treatment, the core targets were subjected to GO and KEGG enrichment analyses in Metascape (species: "Homo sapiens"). The top 10 of GO and the top 13 of KEGG enrichment results were selected. Visualization was performed using Bioinformatics tools to generate KEGG bubble plots, GO bar plots, and Sankey bubble plots. Additionally, based on the functional enrichment results, a network diagram was constructed in Cytoscape 3.10.3 to illustrate the "herbal medicine-component-target-disease-pathway" relationships.

Molecular docking and visualization
The secondary molecular structures of compounds were retrieved from the PubChem database, while the 3D structures of core targets were obtained from the PDB database (http://www.rcsb.org/). Water molecules and amino acid residues were removed using PyMOL. Target proteins and compounds were imported into AutoDock for molecular docking to calculate binding energies. Visualization was performed using PyMOL. A binding energy heatmap was generated using the pheatmap package in R, with core targets on the x-axis, compounds on the y-axis, and a color gradient representing binding energies. Two-dimensional interaction diagrams of docking results were generated using Discovery Studio Visualizer 4.5. Molecular docking was performed to evaluate the binding feasibility between compounds and targets, rather than to predict actual inhibitory efficacy.

Construction of ARDS mouse model
This study was approved by the medical ethics committee of Suzhou Dushu Lake Hospital (2410008). Healthy male C57BL/6J mice (n = 30; 20 ± 5 g) were obtained. The mice were divided into a control group (n = 10), an LPS group (n = 10, LPS 10 mg/kg), and a DCQD group (n = 10, receiving DCQD 0.9 g/kg via oral gavage combined with LPS 10 mg/kg). Mice in the LPS group received an intraperitoneal injection of LPS. Mice in the DCQD group were administered DCQD 0.9 g/kg via oral gavage immediately following the intraperitoneal injection of LPS. Measurements were taken immediately before and after lung extraction from all mice. Subsequently, the lung tissues were stored at -80 °C.

Western blot analysis (WB)
Proteins were isolated from mouse lung tissue lysates, and their concentrations were measured using a BCA protein quantification kit. Subsequently, the proteins were denatured by boiling at 95 °C for 10 min. The denatured proteins were separated on a 10% SDS-PAGE gel prepared with a gel preparation kit, then transferred to a PVDF membrane. To prevent nonspecific binding, the PVDF membranes were blocked at room temperature for 5 min using a protein-free rapid blocking buffer. After washing with PBST, the membranes were incubated overnight at 4 °C with primary antibodies against MMP9, HSP90, MYC, and ARG1. Following another wash with PBST, the membranes were incubated for 2 h at room temperature with HRP-conjugated goat anti-rabbit IgG(H+L). Finally, the ultra-sensitive chemiluminescence detection kit substrate was applied, and chemiluminescent protein bands were detected using a BIO-RAD chemiluminescence imaging system. Grayscale analysis was performed with ImageJ. The antibodies used in this experiment are listed in Supplementary Table 2.

Enzyme-linked immunosorbent assay analysis (ELISA)
First, bronchoalveolar lavage fluid (BALF) was collected by performing lavage three times with 2 mL of saline. The resulting lavage fluids were pooled and centrifuged at 10,000 g for 10 min to remove cells. The supernatant was then aspirated and stored frozen in liquid nitrogen. Subsequently, the concentrations of target cytokines were determined following the manufacturer's instructions using the Mouse IL-6 ELISA Kit, Mouse IL-18 ELISA Kit, and Mouse TNF-alpha ELISA Kit. Next, the reaction plate was placed in a microplate reader, and the optical density (OD) value of each well was measured at a wavelength of 450 nm, with the blank control well used for zero adjustment. Finally, a standard curve was plotted based on the concentrations and corresponding OD values of the standards. The concentration of the target factor in each sample was calculated according to the standard curve equation.

Statistical analysis
Data were analyzed using GraphPad Prism 10.1.2. The significance of the differences between groups was statistically evaluated through one-way analysis of variance or Student's t-test. If the P value is less than 0.05, the difference is considered significant; if the P value is less than 0.01, it is regarded as highly significant.

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Results

Bioactivity analysis of DCQD
To untangle the molecular mechanisms underlying the bioactivity of DCQD, potential targets were first identified by screening the Swiss target prediction and TCMSP databases. A total of 16 components from rhubarb, 17 from immature bitter orange, and 2 from magnolia bark were matched, yielding 614 potential targets (Figure 1A). Subsequently, a network diagram was constructed to visualize the relationships among the three herbal medicines, thei...

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Discussion

ARDS has evolved into a life-threatening pulmonary disorder, imposing a substantial burden on global healthcare, with an annual incidence of 1.5-79 cases per 100,000 individuals and mortality rates ranging from 34.9% to 46.1% across varying severity levels37. Despite advancements in mechanical ventilation strategies, including lung-protective ventilation and prone positioning, clinical outcomes remain suboptimal due to the heterogeneity of pathophysiological processes and limited therapeutic optio...

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Disclosures

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Generative AI and AI-assisted technologies were not used in the preparation of this work.

Acknowledgements

This study was supported by the National Natural Science Foundation of China (Grant Number: 82570078).

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
Arginase-1 Polyclonal antibodyProtein Tech Group Inc., USA16001-1-AP
AutoDockThe Scripps Research InstituteNAhttp://autodock.scripps.edu/
BATMAN-TCM database Beijing University of Chinese MedicineNAhttp://bionet.ncpsb.org.cn/batman-tcm/
BCA protein quantification kitServicebioG2026
BioinformaticsBioinformaticsNAhttps://www.bioinformatics.com.cn/
BIO-RAD chemiluminescence imaging systemThermo Fisher Scientifichttps://www.bio-rad.com/en-in/category/chemidoc-imaging-systems?ID=NINJ0Z15
C57BL/6J miceHangzhou Ziyuan Laboratory Animal Technology Co., Ltd
c-MYC Polyclonal antibodyProtein Tech Group Inc., USA10828-1-AP
CytoscapeCytoscapeversion 3.10.3https://cytoscape.org/
DCQDThe Traditional Chinese Medicine Department of Suzhou Dushu Lake Hospital
Discovery Studio Visualizer Discoveryversion 4.5
GraphPad Prism Dotmaticsversion10.1.2https://www.graphpad.com/
GAPDH Polyclonal antibodyProtein Tech Group Inc., USA10494-1-AP
Gel preparation kitServicebioG2003
GeneCards  databaseCrown human genome centerNAhttps://previous.genecards.org/
GEO databaseNational Center for Biotechnology Information (NCBI)GSE32707 (GPL570 platform, n=45)
HRP-conjugated Affinipure Goat Anti-Rabbit IgG(H+L)Protein Tech Group Inc., USASA00001-2
HSP90 Polyclonal antibodyProtein Tech Group Inc., USA13171-1-AP
Lipopolysaccharides(LPS)SolarbioL8880
Metascape databaseNIHNAhttps://metascape.org/gp/index
Microplate readerBioTeKhttps://www.agilent.com/en/product/cell-analysis/microplate-readers?srs
ltid=AfmBOoqJhJhNJW6vNU
fnskFb9jjcTgXww1A1km-JVP
P1SScq1Mx78HBi
MMP-9 (N-terminal) Polyclonal antibodyProtein Tech Group Inc., USA10375-2-AP
Mouse IL-18 ELISA KitServicebioGEH0010
Mouse IL-6 ELISA KitServicebioGEM0001
Mouse TNF-alpha ELISA KitServicebioGEH0004
OMIM databaseJohns Hopkins UniversityNAhttps://www.omim.org/
Omni-ECL™ Ultra-Sensitive Chemiluminescence Detection KitEpizyme BiotechSQ201
PDB databaseRCSBNAhttp://www.rcsb.org/
Protein-free rapid blocking bufferEpizyme BiotechG2052
PubChem databaseNational Center for Biotechnology Information (NCBI)https://pubchem.ncbi.nlm.nih.gov/
PVDF membraneMerckNA
PyMOLSchrodingerversion 3.10.3
STRING databaseGlobal biodata coalition and ElixirNAhttps://cn.string-db.org/
Swiss Target Prediction databaseSwiss Institute of BioinformaticsNAhttp://www.swisstargetprediction.cn/
Traditional Chinese Medicine Systems Pharmacology (TCMSP) databaseNorthwestern Polytechnical Universityversion 2.3https://www.tcmsp-e.com/

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