Computer-Aided Active Ingredients Screening and Validation of Dong Medicine for Fever and Cough Exploration

Summary

Here, we constructed a multi-dimensional network of "component-target-pathway-disease", integrating network pharmacology screening, molecular docking, and LPS-induced mouse model validation. By establishing clear key parameters, it enables the efficient identification of the active components of Tong ethnic medicine, Lengyuxiao Tang, and the verification of a repeatable process.

Abstract

To systematically screen and verify the active components in the Dong ethnic medicine Lengyuxiao Tang (LYXT; Semen Pharbitidis, Verbena officinalis L., and Zingiber officinale Roscoe) for treating fever and cough, this scheme established a methodology process integrating network pharmacology prediction and experimental verification. Firstly, using the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP), with an oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18 as criteria, the key active components of LYXT were screened, and the related targets for fever and cough were collected from multiple disease databases. Subsequently, the "component-target-disease" interaction network was constructed, and, through topological analysis and gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment, the core action targets (CYP3A4, POR) and key signaling pathways (MAPK pathway) were identified. Molecular docking technology was used to verify the binding ability of key components to core targets. Finally, a chronic bronchitis mouse model was established by intranasal instillation of lipopolysaccharide (LPS), and the in vivo anti-inflammatory efficacy of the predicted main active components was verified. The results showed that components such as β-carotene and saparenol, screened by network pharmacology, exhibited varying degrees of anti-inflammatory effects in animal experiments, and their mechanisms may involve inhibition of the MAPK/p38 signaling pathway. This scheme provides a repeatable example for the screening and mechanism research of active components in traditional ethnic medicines.

Introduction

Dong medicine is an ethnomedical system within the framework of Traditional Chinese Medicine (TCM), practiced by the Dong ethnic group in Guizhou, Hunan, and Guangxi. It represents their cumulative empirical knowledge, developed over generations. This system integrates herbal medicine, acupuncture, bone-setting, massage, dietary therapy, and spiritual practices. Its distinct characteristics arise from the group's historical geographical isolation and oral tradition, which fostered a unique, practice-based pharmacopeia adapted to the local environment and cultural beliefs.

Persistent fever, a condition that develops when the body cannot control its temperature, is a sign that the body is dysfunctional and a vital function of self-protection in human cells¹. Many different methods exist for the treatment of heating medicine of the Dong nationality. Among them, fever is categorized into internal fever and external fever. Internal fever refers to heat caused by internal diseases, and external fever refers to the heat generated by cold or trauma². Exogenous fever usually affects gastrointestinal function³. Dong national doctors use cold antipyretic therapy to treat fever. Among the various Dong medicine prescriptions, Lengyuxiao Tang (LYXT) is a standard decoction used for fever and pain, comprising Semen Pharbitidis, Verbena officinalis L., and Zingiber officinale Roscoe, which are constantly pounded and mixed with water⁴. It is mainly used for fever and headache caused by a cold.

However, the active components of LYXT and their underlying molecular mechanisms remain poorly understood. To address this gap, the present study employed a network pharmacology approach to predict potential active ingredients and targets. To validate these predictions, a well-established lipopolysaccharide (LPS)-induced mouse model of lung inflammation was selected, as it mimics the key inflammatory responses associated with fever and cough, including leukocyte infiltration and pro-inflammatory cytokine production.

In the process of drug discovery, various studies have revealed that compounds can interact with multiple protein targets. Hence, the "one drug, one biological target" paradigm is shifting to a systems pharmacology paradigm. A compound that interacts with several biological target proteins of viruses or bacteria can provide useful pharmacological effects⁵. Network pharmacology analysis is a feasible and powerful way to mitigate the expensive and long drug discovery process. It can help us better comprehend interactions between drugs, predict mechanisms causing the disease, suggest alternative drug members for the intended pharmacological effects without side effects, and predict bioactivities of traditional medicine. Chemoinformatic approaches, molecular docking, and data-derived modeling have been successfully applied to predict bioactivities and analyze mechanisms. These methods facilitate the rapid discovery of antiviral and antibacterial compounds with clinical potential, and these approaches are also used to explain the efficacy and adverse effects of drugs⁶. For example, imatinib mesylate, a small molecule, is an inhibitor of the causal agent in chronic myelogenous leukemia, the fusion protein Bcr-Abl, but it also blocks the activity of several tyrosine kinases such as Abl, Kit, and PDGFR⁷. Therefore, research on identifying drugs and their targets is still crucial in potentially treating diseases, which helps us understand the mechanisms by which drug compounds work to treat diseases at the molecular level. Computational modeling and network analysis approaches can accurately screen potential drugs and targets, and multiplicative interaction analysis can provide a theoretical basis and practical guidance for experimental verification. In this paper, by utilizing search tool for the retrival of interacting genes/proteins (STRING), database for annotation, visualization, and integrated discovery (DAVID), and other databases, combined with dynamic gene ontology (GO) analysis and pathway analysis, the data regarding active ingredients and related action proteins of Dong medicine LYXT for fever and cough were collected, and the mechanism of the combination treatment of active ingredients was explored, which can provide a specific theoretical reference for further research on the Dong medicine LYXT, provides a reproducible strategy for exploring the pharmacological mechanisms of ethnomedicinal formulas and offers insights into the development of new anti-inflammatory agents.

Protocol

The animal experiment program has been reviewed by the Experimental Animal Ethics Committee of Lanzhou University (D2024-292). It conforms to the principles of animal protection, animal welfare, and ethics, as well as the relevant provisions of the national experimental animal welfare ethics.

1. Preparation of the database and software

1. Active ingredient screening and target gene collection
   1. Access the TCM system pharmacology database and analysis platform (TCMSP) (https://tcmsp-e.com/) and perform separate searches using the Latin names of each herb in the LYXT formulation, namely Semen Pharbitidis, Verbena officinalis L., and Zingiber officinale Roscoe, as keywords to retrieve all chemical compounds associated with each herb.
   2. Apply the screening criteria to filter the retrieved compounds for each herb as follows: for Verbena officinalis L., set oral bioavailability (OB) ≥ 30.1% and drug-likeness (DL) ≥ 0.12; for Zingiber officinale Roscoe, set OB ≥ 60% and DL ≥ 0.13; for Semen Pharbitidis, set OB ≥ 16.93% and DL ≥ 0.50. Then, merge the filtered compound lists from all three herbs and remove duplicate entries based on unique TCMSP identifiers to generate the final list of active compounds in LYXT.
   3. Export all target proteins associated with these active compounds from the TCMSP database as a table containing compound identifiers and target protein names, then import this target protein list into the UniProt database (https://www.uniprot.org/).
   4. Use the UniProtKB ID mapping tool to convert all target protein names to official human gene symbols with the mapping direction set from protein name to gene name, and remove any entries lacking a valid gene symbol to generate the final list of target genes associated with LYXT active components.
2. Disease target gene collection and overlap analysis
   1. Download fever- and cough-related genes from the GeneCards database (www.genecards.org) by searching separately with the keywords "fever" and "cough", retain genes with a relevance score ≥ 5, and merge the results from both searches within the database.
   2. Download fever- and cough-related genes from the therapeutic target database (TTD) (db.idrblab.net/ttd) by searching separately with the keywords "fever" and "cough", download all associated target genes, and merge the results from both searches within the database.
   3. Download fever- and cough-related genes from the PharmGkb database (www.pharmgkb.org) by searching separately with the keywords "fever" and "cough", download all associated genes, and merge the results from both searches within the database.
   4. Download fever- and cough-related genes from the Online Mendelian Inheritance in Man (OMIM) database (www.omim.org) by searching separately with the keywords "fever" and "cough", download all associated genes, and merge the results from both searches within the database.
   5. Download fever- and cough-related genes from the DrugBank database (go.drugbank.com)⁸ by searching separately with the keywords "fever" and "cough", download all associated genes, and merge the results from both searches within the database.
   6. Merge the gene lists obtained from all five databases into a single comprehensive list and remove duplicate gene symbols to generate the final list of fever- and cough-related disease target genes.
   7. Compare the disease target list from step 1.2.6 with the LYXT active component target gene list from step 1.1.4 to select the overlapping genes that are common to both lists, and export this overlapping gene list as a CSV file.
3. ID conversion and cross-section analysis
   1. Load the overlapping gene list from step 1.2.7 into the R environment and access the DAVID website (https://david.ncifcrf.gov/summary.jsp).
   2. Upload the overlapping gene list to DAVID with the species set to Homo sapiens and the list type set to gene symbol, then execute the functional annotation tool to perform cross-section analysis.
   3. Convert the symbol IDs of the intersecting proteins to ensemble IDs using the gene ID conversion function within DAVID, with the conversion direction set from gene symbol to ensemble ID.
   4. Export the converted ensemble ID list along with the original gene symbols as a CSV file for downstream enrichment analysis.

2. Screening the vital active ingredients and target proteins

1. Compound-target network construction
   1. Load the intersecting gene list from step 1.2.7 and the LYXT active component target gene list from step 1.1.4 into the R environment, then perform a comparison analysis to identify compounds that are associated with both the target genes and the intersecting genes.
   2. Import the compound-target relationship data into Cytoscape version 3.8.1 and generate the "LYXT-Fever and cough-target" network diagram to visualize the interactions between active ingredients and their associated targets.
2. Protein-protein interaction data acquisition
   1. Import the gene ID file for the intersection of LYXT active ingredient targets and fever and cough targets obtained in step 2.1.1 into the STRING database at https://stringdb.org/, select Homo sapiens as the species, and multiple proteins as the data processing method.
   2. Filter the protein-protein interactions by setting the confidence threshold to ≥ 0.90 and isolating proteins with very low correlation, then generate a TSV file containing the filtered interaction data for downstream analysis.
3. Intersection analysis confirmation
   1. Utilize the R language to analyze the intersection between the target proteins of the active ingredients and the target proteins of fever and cough, confirming the overlapping gene set identified in step 1.2.7.
   2. Prepare this confirmed overlapping gene set for subsequent protein-protein interaction (PPI) network analysis.

3. Screening of key target proteins

1. PPI network visualization
   1. Import the filtered interaction data from step 2.2.2 into Cytoscape version 3.8.1 and use the network visualization tool to draw the PPI network diagram.
   2. Install and open the CytoNCA plugin within Cytoscape, then use this plugin to perform topological analysis by calculating the following parameters for each node in the network: degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), eigenvector centrality (EC), network centrality (NC), and local average connectivity (LAC).
2. Key target protein identification using median-based screening
   1. Export the calculated topological parameter table from CytoNCA as a CSV file containing all node identifiers and their corresponding DC, BC, CC, EC, NC, and LAC values.
   2. Calculate the median value for degree centrality (DC) across all nodes in the network and identify all nodes with DC greater than twice the median value.
   3. Calculate the median values for closeness centrality (CC) and betweenness centrality (BC) across all nodes in the network, then from the nodes identified in step 3.2.2, retain only those nodes that simultaneously satisfy CC greater than the median value and BC greater than the median value, following the screening approach described by Kibble et al.⁹.
3. Final key target compilation
   1. Compile the identifiers of the retained nodes from step 3.2.3 into a list of key target proteins and export this list as a CSV file.
   2. Apply this median-based screening rule (DC greater than twice the median, CC greater than the median, and BC greater than the median) as the sole criterion for key target identification throughout the manuscript, ensuring consistency with the unified screening approach established in the protocol.

4. GO enrichment analysis and KEGG pathway analysis

1. Functional enrichment analysis using the DAVID database
   1. Following the method described by Tanabe et al.¹⁰, upload the intersecting gene list from step 1.3.4 in gene symbol format to the DAVID database at https://david.ncifcrf.gov/, set the species to Homo sapiens, and the list type to gene symbol.
   2. Perform GO enrichment analysis by selecting GOTERM_BP_DIRECT for biological process, GOTERM_CC_DIRECT for cellular component, and GOTERM_MF_DIRECT for molecular function, and perform KEGG pathway enrichment analysis by selecting KEGG_PATHWAY.
   3. Set the significance threshold to p < 0.05 and apply the Benjamini-Hochberg false discovery rate (FDR) correction for multiple testing, then download the enrichment results as CSV files containing term names, gene counts, p-values, FDR-adjusted p-values, and associated genes.
2. Bubble chart generation using the OmicShare cloud platform
   1. Import the GO enrichment results from step 4.1.3 into the OmicShare cloud platform at https://www.omicshare.com/tools and select the GO enrichment bubble chart tool.
   2. Upload the results file containing term names, gene counts, and p-values, then configure the tool to display the top 20 terms with the lowest p-values.
   3. Set the x-axis to represent the enrichment ratio, the y-axis to list the GO terms, bubble size to represent gene count, and bubble color to represent p-value.
   4. Generate the bubble chart and download it as a high-resolution image file.
3. KEGG pathway visualization using KEGG mapper
   1. Sort the KEGG pathway enrichment results by p-value in ascending order and select the top 20 pathways with the smallest p-values.
   2. For each selected pathway, access the KEGG database at https://www.kegg.jp/kegg/pathway.html to obtain the pathway diagram.
   3. Identify the target proteins from the intersecting gene list that are present in each pathway by cross-referencing with the pathway gene sets from the enrichment results.
   4. Access the KEGG mapper tool at https://www.kegg.jp/kegg/mapper/, select the Color Pathway function, input the list of intersecting gene symbols and the KEGG pathway identifier, and generate a color-coded pathway diagram with the identified target proteins highlighted in red.
   5. Compile all enrichment analysis outputs, including the GO enrichment CSV files for three categories, the KEGG pathway enrichment CSV file, the bubble chart image, and the color-coded pathway diagrams for the top 20 pathways, then export these files for downstream analysis.

5. Molecular docking verification

1. Download the 3D structures of vital proteins (such as CYP3A4 and POR identified as key targets in the network analysis) from the RCSB PDB database at https://www.rcsb.org/, selecting structures with resolution ≤ 2.5 Å from Homo sapiens that contain a co-crystallized ligand to define the active site, and download the structure data files of active compounds (including beta-carotene, spathulenol, and beta-sitosterol) from the PubChem database in SDF format.
2. Prepare the protein structures using AutoDockTools by removing water molecules and the co-crystallized ligand, adding polar hydrogens, and assigning Gasteiger charges, then save the prepared proteins in PDBQT format, convert the compound SDF files to PDB format using Open Babel, detect rotatable bonds for each ligand in AutoDockTools, and save the ligands in PDBQT format.
3. Define the docking grid box with dimensions of 60 × 60 × 60 Å centered on the coordinates of the co-crystallized ligand's binding site, then perform molecular docking using AutoDock Vina with exhaustiveness set to 8 and num_modes set to 10 to generate 10 binding poses for each ligand-protein pair.
4. Import the resulting docking files and their corresponding target files into PyMOL, identify and save the docking model with the lowest binding energy (most negative value) as the representative pose.
5. Then submit this model to the PLIP database at https://plip-tool.biotec.tu-dresden.de/ to analyze the interactions between the small molecules and proteins, including hydrogen bonds, hydrophobic interactions, π-stacking, and salt bridges.
6. Calculate and compile the binding energies for all active components in LYXT against the target proteins associated with fever and cough, then generate a summary table containing compound names, target proteins, binding energies (kcal/mol), and key interaction types for presentation in the results section.
   NOTE: The R script used for network pharmacology analysis is available in Supplementary File 1.

6. Animal experiment

1. Experimental timeline
   1. Acclimate the animals for 1 week, then perform LPS or saline administration on day 0, administer daily drug treatments from day 7 to day 20 for 2 weeks, and perform euthanasia with sample collection on day 21.
2. Animal acquisition and grouping
   1. Purchase 60 adult male C57BL/6 mice weighing 25 ±± 4 g, feed the mice for 1 week, then randomly divide them into a normal saline group (6 mice) and an LPS injured group (54 mice).
   2. On day 7 before treatment initiation, further randomly divide the LPS injured group into seven subgroups with 6 mice each: model group, positive group (dexamethasone, 50 mg·kg^-1·d^-1), LYXT treatment group (200 mg·kg^-1·d^-1), beta-carotene group (50 mg·kg^-1·d^-1), spathulenol group (50 mg·kg^-1·d^-1), and beta-sitosterol group (50 mg·kg^-1·d^-1).
      NOTE: Treatment administration and outcome assessments were blinded.
3. LPS-induced lung inflammation model
   1. Administer 2% pentobarbital sodium via intraperitoneal injection at a dose of 60 mg/kg for anesthesia. Confirm a moderate depth of anesthesia by the absence of a tail-pinch response. Finally, based on the recorded body weight, calculate the required injection volume of LPS at a concentration of 10 mg/mL.
   2. Prepare for nasal administration by drawing the precisely calculated volume of LPS solution into the pipette. Firmly hold the mouse by gripping the base of its tail with the right hand, while using the thumb and index finger of the left hand to gently secure its head by the ears.
   3. Expel the liquid slowly until a small droplet (approximately 2 µL) forms at the pipette tip. Carefully bring the droplet close to the mouse's right nostril until it makes contact. Immediately and swiftly lift the left hand upward by 30–40 cm, then gently return it and the mouse to the starting position.
      NOTE: It is necessary to pay attention to the mouse's nostrils to maintain the upward state.
   4. Use the same method to inject small liquid drops into the left nostril of the mouse, and add them to the left and right nostrils successively until the liquid infusion is completed.
   5. Then release the mouse, fix the mouse on the mouse board with tape, and keep the position with the abdomen up and the head up tilted for more than 4 h. After the mouse wakes up, put it back into the cage for observation, following the method described by Lorenz et al.¹¹.
4. Bronchoalveolar lavage fluid collection
   1. Randomly separate the LPS injured group mice into seven groups: negative control group, model group, positive group (dexamethasone, 50 mg.kg^-1.d^-1), LYXT treatment group (200 mg.kg^-1.d^-1), Beta-carotene group (50 mg.kg^-1.d^-1), spathulenol group (50 mg.kg^-1.d^-1), and beta-sitosterol group (50 mg.kg^-1.d^-1), with six mice in each group.
      NOTE: Prepare LYXT as follows: Mix Pharbitis nil (L.)  Choisy, Verbena officinalis L., and Zingiber officinale Roscoe powder in a mass ratio of 5:3:2.  Mix the powder with water at 1:30 (g/mL) and reflux at 80 °C for 2 h. Filter and concentrate the solution (30 °C, 0.01–0.08 MPa) and dissolve it in water to prepare a 2 g/mL solution. Beta-carotene, beta-sitosterol, and spathulenol were used as reference standards for experimental validation. Administer the drugs to each group once a day for 2 weeks, starting 1 week after LPS treatment. Euthanize the animals by an overdose of thiopental (100 mg/kg), according to Jin et al.¹².
   2. Intubate the ventricle with a No. 23 cannula. Then, slowly inject 10 mL of PBS into the lung tissue under a pressure of 25 cm H₂O, and repeat this lavage approximately 6 times to obtain bronchoalveolar lavage fluid (BALF), following the method described by Pinheiro et al.¹³.
5. Leukocyte and cytokine analysis in BALF and serum
   1. Centrifuge the collected BALF at 500 × g for 10 min at 4 °C. Resuspend the cell pellet in 1 mL of PBS, dilute 1:10 with trypan blue, and count total leukocytes using a hemocytometer under a light microscope at 400× magnification.
   2. Collect the BALF supernatant and serum samples from cardiac puncture.
   3. Measure TNF-α, IL-1β, and IL-18 concentrations using commercial ELISA kits according to the manufacturer's instructions, reading absorbance at 450 nm with a microplate reader, and calculating concentrations from standard curves. Determine protein concentration in BALF using the BCA assay.
6. Validation of active components by HE and WB analysis
   1. To identify active components contributing to LYXT efficacy, re-establish the LPS-induced lung inflammation model following section 6.3.
   2. Purchase 60 mice, acclimate for one week, and randomly divide into five groups (n = 12 each): model group, LYXT group (200 mg·kg^-1·d^-1), beta-carotene group (50 mg·kg^-1·d^-1), spathulenol group (50 mg·kg^-1·d^-1), and combination group (25 mg·kg^-1·d^-1 each).
   3. Administer LPS on day 0 and treatments from day 7 to day 20 as described in step 6.3. On day 21, collect lung tissue¹⁴.
7. Histological analysis
   1. Fix the left lung superior lobe in 4% paraformaldehyde for 24 h, embed in paraffin, cut 5 µm sections, and perform hematoxylin and eosin (HE) staining.
   2. Examine sections under a light microscope at 200× and 400× magnification to evaluate inflammatory cell infiltration and alveolar wall thickening. Capture representative images.
8. Western blot analysis
   1. Homogenize lung tissue in radioimmunoprecipitation assay (RIPA) buffer with protease and phosphatase inhibitors. Centrifuge at 12,000 × g for 15 min at 4 °C and determine protein concentration using the bicinchoninic acid (BCA) assay.
   2. Load 30 µg of protein per well onto sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) gels, transfer to polyvinylidene fluoride (PVDF) membranes, and block with 5% non-fat milk.
   3. Incubate with primary antibodies against p-p38, p38, TNF-α, IL-1β, IL-18, and β-actin overnight at 4 °C, then with HRP-conjugated secondary antibodies.
      NOTE: The dilution of antibodies is provided in the Table of Materials.
   4. Visualize bands using ECL substrate, capture images, and quantify intensities using ImageJ. Normalize target protein expression to β-actin or total p38.

Results

The TCMSP database was used to collect 409 active LYXT ingredients. Among them, 58 ingredients came from Verbena officinalis L., 86 from Semen Pharbitidis, and 265 active ingredients came from Zingiber officinale Roscoe. Twenty-six key active ingredients were screened according to the index, OB ≥ 30.1% and DL ≥ 0.12 for Verbena officinalis L., OB ≥ 60%, and DL ≥ 0.13 for Zingiber officinale Roscoe, OB ≥ 16.93%, and DL ≥ 0.50 for Semen Pharbitidis (Table 1).

Discussion

This paper successfully identified 26 active ingredients and 207 potential targets of LYXT, a traditional Dong medicine decoction used for fever and cough. Key findings include the identification of CYP3A4 as a critical target protein through network topology analysis, and the experimental validation of LYXT, beta-carotene and spathulenol as active compounds with significant anti-inflammatory effects in an LPS-induced lung inflammation model. These findings were achieved through an integrated approach combining network p...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Anti-IL-18	Proteintech, Wuhan, China	27095-1-AP	Rabbit polyclonal, 1:500
Anti-IL-1β	Proteintech, Wuhan, China	66737-1-Ig	Mouse monoclonal, 1:500
Anti-p38	Proteintech, Wuhan, China	14064-1-AP	Rabbit polyclonal, 1:1000
Anti-p-p38	Proteintech, Wuhan, China	28796-1-AP	Rabbit polyclonal, 1:1000
Anti-TNF-α	Proteintech, Wuhan, China	80258-6-RR	Rabbit monoclonal, 1:500
Anti-β-actin	Proteintech, Wuhan, China	66009-1-Ig	Mouse monoclonal, 1:2000
AutoDock Vina	https://vina.scripps.edu/	Version 1.5.6	Molecular docking software
AutoDockTools	https://autodock.scripps.edu/	Version 1.5.6	Molecular docking preparation tool
BCA Protein Assay Kit	Thermo Fisher Scientific, Waltham, MA, USA	23225	For protein concentration determination
Beta-carotene	Sigma-Aldrich, St. Louis, MO, USA	83-46-5	High-purity standard, >95%
Beta-sitosterol	Sigma-Aldrich, St. Louis, MO, USA	83-46-5	High-purity standard, >95%
C57BL/6 mice	medical animal center of lanzhou university	-	Adult male, 25 ± 4 g
Cytoscape	https://cytoscape.org/	Version 3.8.1	Network visualization and analysis software
Dexamethasone	Sigma-Aldrich, St. Louis, MO, USA	D4902	Positive control
ECL Substrate	MilliporeSigma, Burlington, MA, USA	WBKLS0500	For Western blot detection
ELISA Kits
GraphPad Prism	https://www.graphpad.com/	Version 8.0 or higher	Statistical analysis and graphing software
Hematoxylin and Eosin (H&E) Staining Kit	Beijing Solarbio Science & Technology Co., Ltd, Beijing, China	G1120	For lung tissue staining
Histology
HRP-conjugated anti-mouse	Proteintech, Wuhan, China	SA00001-7G	Secondary antibody, 1:5000
HRP-conjugated anti-rabbit	Proteintech, Wuhan, China	SA00001-7H	Secondary antibody, 1:5000
IL-18 ELISA Kit	Beijing Solarbio Science & Technology Co., Ltd, Beijing, China	SEKH-0016	For measuring IL-18 in BALF and serum
IL-1β ELISA Kit	Beijing Solarbio Science & Technology Co., Ltd, Beijing, China	SEKM-0004	For measuring IL-1β in BALF and serum
ImageJ	https://imagej.nih.gov/ij/	Version 1.53	Image analysis software
Lipopolysaccharide (LPS)	Sigma-Aldrich, St. Louis, MO, USA	L2630	From Escherichia coli O111:B4
LYXT Raw Materials	Huaihua University	-	Powder mixture of Pharbitis nil (L.) Choisy, Verbena officinalis L., and Zingiber officinale Roscoe (5:3:2, w/w/w)
Other Materials
Pentobarbital sodium	Sigma-Aldrich, St. Louis, MO, USA	P3761	2% solution for anesthesia
PLIP	https://plip-tool.biotec.tu-dresden.de/	-	Protein-ligand interaction profiler
PVDF membranes	MilliporeSigma, Burlington, MA, USA	IPVH00010	For Western blot transfer
PyMOL	https://pymol.org/	Version 2.5	Molecular visualization software
R	https://www.r-project.org/	Version 4.0 or higher	Statistical computing and graphics software
Spathulenol	MedChemExpress, Monmouth Junction, NJ, USA	HY-N1205	High-purity standard, >95%
STRING	https://stringdb.org/	-	Search Tool for the Retrieval of Interacting Genes/Proteins
TCMSP	https://tcmsp-e.com/	-	Traditional Chinese Medicine Systems Pharmacology Database
Thiopental	Sigma-Aldrich, St. Louis, MO, USA	T102	For euthanasia
TNF-α ELISA Kit	Beijing Solarbio Science & Technology Co., Ltd, Beijing, China	SEKM-0034	For measuring TNF-α in BALF and serum
UniProt	https://www.uniprot.org/	-	Protein sequence and annotation database