Introduction

Polygonum plants have been traditionally utilized in traditional medicine for their heat-clearing, detoxifying, and pain-relieving properties, as well as their anti-tumor, antioxidant, and anti-inflammatory effects1,2. P. runcinata var. sinensis is from the Persicaria genus in the Polygonaceae family. It is used in traditional medicine for its pungent and slightly cold properties. It is known for clearing heat-fire, detoxifying, promoting blood circulation, and reducing swelling. Common uses include treating blood heat-related ailments, headaches, and dysentery, as well as pain relief3,4,5. In the Guizhou province region in China, which is known for its expertise in utilizing Miao ethnomedicine, traditional healers have been observed employing P. runcinata var. sinensis for the treatment of arthritis. The present study aimed to establish DNA barcodes and perform metabolomics and network pharmacology analyses of P. runcinata var. sinensis.

Studies have shown that Persicaria runcinata, a Miao ethnomedicine, exhibits significant antibacterial effects against Salmonella typhi6 and Shigella dysenteriae7as well as potent antioxidant properties, ranking second only to Ethylenediaminetetraacetic acid in antioxidant activity8. A Miao ethnomedicine Jingushang spray contains Persicaria runcinate and demonstrates effectiveness in relaxing tendons, improving blood circulation, reducing swelling, and relieving pain9. Clinical studies show comparable efficacy to that of Yunnan Baiyao aerosol in treating acute soft tissue injuries, particularly in reducing swelling10. Research on the therapeutic effects of P. runcinata var. sinensis on arthritis is limited despite partial demonstrations of its pharmacological effects in single-herb or compound formulations.

Metabolomics is a dynamic branch of systems biology that provides systematic insights into metabolic changes in living organisms11 and offers a theoretical foundation for developing and utilizing plant parts rich in bioactive components. Alongside traditional measurement techniques, metabolomics technology assesses pharmacological effects and investigates the chemical basis and specific mechanisms of drug-related effects in traditional Chinese medicine research12aligning with holistic principles13,14. It serves as a reliable tool for exploring the components, targets, and metabolic pathways of traditional Chinese medicine and ethnic remedies, contributing to in-depth metabolite research and scientific understanding of their mechanisms of action15. It is vital for advancing traditional Chinese medicine and addressing key issues in ethnic remedies16.

The shift towards a network-target, multi-component therapeutic approach in drug discovery aims to improve efficacy by moving away from the traditional one-target, one-drug model17,18. Network pharmacology is rooted in network theory and systems biology18,19 and provides a comprehensive understanding of biological systems, drugs, and diseases. It is particularly beneficial for studying Miao ethnomedicine.

In the present study, widely targeted metabolomics technique of ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) identified chemical components of Miao ethnomedicine P. runcinata var. sinensis. Molecular identification via ITS2 gene technique provided a foundation for recognizing Miao ethnomedicines in China. Analysis of 716 metabolites revealed active substances, including catechin, gallic acid derivatives, dibutyl phthalate, and indole alkaloids. These compounds are likely to contribute to the efficacy of arthritis treatment. Network pharmacology has elucidated the interactions between the P. runcinata var. sinensis components and arthritis, providing support for further research.

Materials and methods

Sample collection

The root part of P. runcinata var. sinensis was selected due to its higher concentration of bioactive compounds (e.g., flavonoids and phenolic acids) compared to aerial parts. This aligns with traditional usage in Guizhou medicine, where root parts are preferentially employed for anti-arthritic effects. Samples of its root part were collected from Majiang County (26°45′42″N, 107°72′43″E), Kaili City, Qiandongnan Miao and Dong Autonomous Prefecture, Guizhou Province, China. Samples were prepared and sent to Wuhan Metware Biotechnology Co., Ltd. for comprehensive targeted metabolic analysis. Additional samples were used in the first attempt to determine the DNA barcode for P. runcinata var. sinensis.

Establishment of DNA barcode

Extraction of plant genome DNA

Plant DNA was extracted using the conventional CTAB method. First, 0.2 g of the root part was ground in liquid nitrogen and then plant lysis buffer (2% CTAB, 1% PVP, 100 mM pH8.0 Tris-HCl, 20 mM pH8.0 EDTA, 1.4 M NaCl and 0.2% β-mercapturyl alcohol) was added and incubated at 65 ℃ for 1 h. They were purified with a mixture of phenol-chloroform-isoamyl alcohol (25:24:1) and then purified with a solution of chloroform-isoamyl alcohol (24:1). DNA was subsequently precipitated with absolute ethanol, washed with 70% ethanol (v/v), and dissolved in sterilized double distilled water. The extraction process included centrifugation at 12,000 rpm at 4 °C for 10 min. DNA concentration and purity (A260/A280 ratio 1.8–2.0) were determined using a NanoDrop Lite spectrophotometer (Thermo Fisher Scientific, USA) by measuring absorbance at 260 nm and 280 nm. The extracted DNA was then used as template for polymerase chain reaction (PCR) amplification prior to sequencing.

PCR amplification of ITS2 sequence primers

The P. runcinata var. sinensis DNA served as the template for PCR amplification. The primer sequences were designed according to the specifications of Chinese Pharmacopoeia (ChP 2020). PCR reactions were performed in 25 µL volumes containing: 1× PCR buffer, 2.0 mM MgCl₂, 0.2 mM dNTPs, 0.1 µM each of primers ITS2-F (5’-ATGCGATACTTGGTGTGAAT-3’) and ITS3-R (5’-GACGCTTCTCCAGACTACAAT-3’), 1.0 U Taq DNA polymerase (TransGen Biotech, Beijing, China), and approximately 50 ng template DNA, with sterile ddH₂O completing the volume. Amplification was conducted in a C1000 Touch Thermal Cycler (Bio-Rad Laboratories, USA) under the following program: initial denaturation at 94 °C for 5 min; 35 cycles of 94 °C for 30 s, 56 °C for 30 s, and 72 °C for 45 s; followed by a final extension at 72 °C for 10 min. PCR products (10 µL aliquots) were analyzed by 1% agarose gel electrophoresis using DL2000 DNA Marker (TransGen Biotech, Beijing, China) as molecular weight standard. DNA bands were visualized under UV illumination with a ChemiDoc™ MP Imaging System (Bio-Rad Laboratories, USA).

Gene sequence alignment

The PCR products were amplified using the ITS2F–ITS3R primers pair. The amplified products were subsequently submitted to Sangon Biotech Co., Ltd. (Shanghai, China) for sequencing. The complete gene sequence was obtained after removing low-quality regions with weak signals at both ends based on the sequencing chromatogram. Subsequently, a sequence homology comparison analysis was conducted using the National Center for Biotechnology Information database in the United States. The resulting sequence was sequenced utilizing the Blast database (https://blast.ncbi.nlm.nih.gov/Blast.cgi) for comparison, and the relevant reference sequences were downloaded based on the following comparison results: JN235099, JF816399, JX144672, JN407515, JN407514, JN407513, JN407512, JF816398, GQ396672, FJ648806, FJ503010, DQ406628, MN852309, and MH711338.

Phylogenetic tree construction

In order to conduct sequence alignment analysis and generate a phylogenetic tree, highly similar sequences for the corresponding species were selected and downloaded from the GenBank database (https://www.ncbi.nlm.nih.gov/genbank/). Sequence alignment was performed in MEGA6 (v6.0) with ClustalW under default settings. The Kimura-2-Parameter model was employed to calculate intra- and interspecific genetic distances. A Neighbor-Joining (NJ) phylogenetic tree was constructed to evaluate differences between bases. The Bootstrap method was used for clustering analysis to ensure accuracy and the process was repeated 1,000 times.

Widely targeted metabolomic profiling

Sample preparation and extraction

The Miao ethnomedicine P. runcinata var. sinensis plant samples were prepared for analysis. The samples are freeze-dried by vacuum freeze-dryer (Scientz-100 F). The freeze-dried sample was crushed using a mixer mill (MM 400, Retsch) with a zirconia bead for 1.5 min at 30 Hz. Dissolve 50 mg of lyophilized powder with 1.2 mL 70% methanol solution, vortex 30 s every 30 min for 6 times in total. Following centrifugation at 12,000 rpm for 3 min, the extracts were filtrated (SCAA-104, 0.22 μm pore size; ANPEL, Shanghai, China, http://www.anpel.com.cn/) before UPLC-MS/MS analysis.

UPLC conditions

The sample extracts were analyzed using an UPLC-ESI-MS/MS system (UPLC, SHIMADZU Nexera X2, https://www.shimadzu.com.cn/; MS, Applied Biosystems 4500 Q TRAP, https://www.thermofisher.cn/cn/zh/home/brands/applied-biosystems.html). The analytical conditions20 were as follows, UPLC: column, Agilent SB-C18 (1.8 μm, 2.1 mm * 100 mm); The mobile phase was consisted of solvent A, pure water with 0.1% formic acid, and solvent B, acetonitrile with 0.1% formic acid. Sample measurements were performed with a gradient program that employed the starting conditions of 95% A, 5% B. Within 9 min, a linear gradient to 5% A, 95% B was programmed, and a composition of 5% A, 95% B was kept for 1 min. Subsequently, a composition of 95% A, 5.0% B was adjusted within 1.1 min and kept for 2.9 min. The flow velocity was set as 0.35 mL per minute; The column oven was set to 40 °C; The injection volume was 4 µL. The effluent was alternatively connected to an ESI-triple quadrupole-linear ion trap (QTRAP)-MS.

ESI-Q TRAP-MS/MS

The ESI source operation parameters were as follows: source temperature 550 °C; ion spray voltage (IS) 5500 V (positive ion mode)/−4500 V (negative ion mode); ion source gas I (GSI), gas II(GSII), curtain gas (CUR) were set at 50, 60, and 25 psi, respectively; the collision-activated dissociation(CAD) was high. Mass spectrometry analysis was performed using a Sciex Triple Quad™ 6500 + system (Sciex, Framingham, MA, USA). Instrument tuning and mass calibration were performed with 10 and 100 µmol/L polypropylene glycol solutions in Triple Quadrupole Mass Spectrometer (QQQ) and Linear Ion Trap (LIT) modes, respectively. QQQ scans were acquired as Declustering Potential (DP) experiments with collision gas (nitrogen) set to medium. MS detection was performed in multiple reaction monitoring (MRM) mode with a mass range of m/z 50–1500. Collision energy was optimized from 10 to 70 eV based on the stability of precursor-product ion transitions. Declustering Potential (DP) and Collision Energy (CE) for individual MRM transitions was done with further Declustering Potential (DP) and CE optimization. A specific set of MRM transitions were monitored for each period according to the metabolites eluted within this period.

Statistical analysis of metabolite data

The acquired mass spectrometry data were analyzed using MetaDNA v2.0 (Metware Biotech, Wuhan, China) and compared against the Metware Database (MWDB version 2023.1), an in-house metabolite repository containing 150,000 + annotated spectra, to perform qualitative analysis of metabolites. Quantitative profiling was conducted in MRM mode on a Triple Quadrupole Mass Spectrometer (Sciex, Framingham, MA, USA), where chromatographic peaks were integrated via MultiQuant v3.0.3 with optimized dwell time (20 ms/transition) and collision energy parameters. Data normalization included retention time alignment (max 0.5% run time deviation) and Gaussian peak shape correction (R²>0.98) to ensure cross-sample comparability of metabolite abundances. The relative content of each metabolite is calculated based on the ratio of chromatographic peak area or peak height, reflecting the relative abundance of the metabolite in a sample.

A pie chart was created based on the primary classification and the number of enriched metabolites following analysis of the selected data. To present the metabolite data more clearly and intuitively, a presentation table was generated based on the statistical primary classification, secondary classification, and main enriched metabolites. The main enriched metabolites were selected from the top-ranking enriched metabolites to obtain a more concise statistical summary of the metabolite data.

Network pharmacology analysis

Target collection

The metabolites with high content in each secondary category of untargeted metabolomics results were selected as the dominant metabolites. The Canonical SMILES of the dominant metabolites were searched using the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and then entered into the Swiss TargetPrediction database (http://www.swisstargetprediction.ch/) for prediction of the corresponding target proteins. The dominant metabolite targets were identified after the relevant target proteins were sorted and repetitive targets were eliminated. Arthritis-related targets were collected from the GeneCards website (https://www.genecards.org/) and the Comparative Toxicogenomics Database (https://ctdbase.com/).The potential arthritis-associated targets for P. runcinata var. sinensis were determined by analyzing the intersections between the retrieved active ingredient and arthritis-related targets using the Venny2.1.0 website(https://bioinfogp.cnb.csic.es/tools/venny/), resulting in the generation of a Venn diagram.

Protein-protein interaction network

The PPI (Protein-Protein Interaction) network analysis was conducted using the STRING website (https://cn.string-db.org/). A total of 187 targets were utilized to construct the PPI network, which facilitated further investigation into its interactions. Network topology analysis was performed using Cytoscape software (v3.10.1), with a focus on three centrality measures of degree, betweenness, and closeness. Subsequently, core targets were identified through a screening process and visually examined using Cytoscape software (v3.7.0).

Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis

The DAVID database (https://davidbioinformatics.nih.gov/) was utilized to conduct GO functional enrichment and KEGG pathway enrichment analyses for the intersecting targets. Pathway analysis performed using KEGG (Kanehisa Laboratories, www.kegg.jp). Biological processes (BPs), cellular components (CCs), and molecular functions (MFs) were mentioned in the GO analysis. The results of enrichment analysis are available on the following website: http://www.bioinformatics.com.cn/.

Molecular docking

The core targets were collected in the PPI network and their corresponding components were obtained. The structure file for the PDB database (https://www.rcsb.org/)21 targets was acquired and three-dimensional structures of the components from PubChem were obtained. The chemical components corresponding to the core targets were retrieved from PubChem (https://pubchem.ncbi.nlm.nih.gov/) in SDF format. AutoDock software (v1.5.7)22 was used for molecular docking to determine the lowest binding energy. PDB files were prepared in AutoDock Tools by removing water molecules, adding polar hydrogens, and assigning Kollman charges. Grid boxes were centered on the active site with dimensions adjusted to encompass all potential binding residues. In molecular docking, the binding affinity of a ligand to its target protein correlates with its potency. The conformation with the lowest binding energy (ΔG, kcal/mol) from each cluster was selected for analysis. A ΔG value < − 5.6 kcal/mol is generally considered indicative of strong binding affinity23. Docking poses were visualized in PyMOL software (v3.0.4) to analyze hydrogen bonds and hydrophobic contacts.

Results

DNA barcode establishment

The following DNA sequence was obtained after analyzing the sequencing results for the purified PCR product of P. runcinata var. sinensis and showed in supplementary file Table S1. The gene sequence for P. runcinata var. sinensis, combined with highly similar sequences downloaded from the GenBank database, underwent alignment and clustering analysis using MEGA6 software to obtain the NJ tree as shown Fig. 1. Based on this phylogenetic tree, the gene homology of P. runcinata var. sinensis samples is closely related to Persicaria chinensis, Polygonum chinense var. paradoxum, Persicaria nepalensis, Persicaria sphaerocephala, and the original subspecies Persicaria runcinata. This phylogenetic analysis provided valuable insights into the genetic relationship of this Miao ethnomedicine with its closely related species.

Fig. 1
figure 1

Phylogenetic tree of ITS2 nucleotide sequences.

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Widely targeted metabolomics analysis

Qualitative and quantitative analyses of metabolites

The qualitative analysis of primary and secondary mass spectrometry data was performed against the Metware Database (MWDB version 2023.1), an in-house metabolite repository containing 150,000 + annotated spectra, using MetaDNA v2.0 software (Metware Biotech, Wuhan, China). The analysis produced a multi-peak MRM metabolite detection chromatogram and a total ion current spectrum of multiple substances (X ion current), MRM metabolite detection multipeak chromatogram of the sample was shown in Fig. 2A, and the identified metabolites list was shown in the supplementary file 1. Based on this, the obtained mass spectrometry data were compared with the local metabolite database for qualitative and quantitative analyses of the detected metabolites.

Distribution of metabolite types

Screened data for P. runcinata var. sinensis showed 716 detected metabolites across 12 categories. Flavonoids, phenolic acids, and lipid substances were the most abundant, with over 100 enriched metabolites (supplementary file 2). A pie chart was generated for the primary classification of metabolites and enriched metabolites (Fig. 2B). The pharmacological effects of specific metabolites were analyzed in order to present the data on metabolites with anti-inflammatory effects in P. runcinata var. sinensis (Table 1). The Appendix shows the identification numbers, integration values, and corresponding metabolite names of the obtained partial metabolites.

Fig. 2
figure 2

MRM metabolite detection multipeak chromatogram and pie chart of enriched metabolite classification: (A) MRM metabolite detection multipeak chromatogram. The x-axis represents the retention time (Rt) of metabolites, and the y-axis indicates ion current intensity in counts per second (cps). (B) The pie chart of enriched metabolite classification.

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Table 1 Active anti-inflammatory ingredients of P. runcinata var. sinensis.
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Network pharmacology analysis

Venn diagram analysis

The Venny2.1.0 website was employed to analyze the active ingredient targets of P. runcinata var. sinensis and arthritis-related targets. A total of 504 drug targets and 2,052 disease targets were obtained after eliminating duplicate values from both datasets. The Venny2.1.0 platform identified 187 intersection targets (Fig. 3A).

Interaction network analysis

The organized data file was imported into Cytoscape software (version 3.10.1) for processing to carry out network analysis of “P. runcinata var. sinensis – active ingredient - intersection targets”(Fig. 3B). The resulting interaction network diagram consisted of 218 nodes and 421 edges, where the red arrow represent P. runcinata var. sinensis, the yellow circle denote its active ingredient, and the green prismatic shape show its intersection target of arthritis. Notably, larger node degree values indicated stronger correlations between interactions, with these highly connected targets or compounds playing pivotal roles in the overall network.

Analysis of the interaction network diagram revealed an average of 3.862 neighbors, indicating a phenomenon where multiple compounds act on multiple targets simultaneously. Consequently, several closely related compounds were identified through screening. These included naringenin chalcone (2’,4,4’,6’-tetrahydroxychalcone), N-feruloyltyramine, dibutyl phthalate, 7-methoxy-3-[1-(3-pyridyl)methylidene]−4-chromanone, 3-O-methylellagic acid, ellagic acid, 2- formyl-3-hydroxy-A(1)-norlup-20(29)-en-28-oic acid (colubrinic acid), 3, 3’-O-dimethylellagic acid, frambinone, and octadeca-11E, 13E, 15Z-trienoic acid. The target genes closely associated with these compounds include arachidonate 5-lipoxygenase, prostaglandin-endoperoxide synthase 2 (PTGS2), cytochrome P450 family 19 subfamily A member 1, epidermal growth factor receptor (EGFR), monoamine oxidase A, protein tyrosine phosphatase non-receptor type 1, insulin-like growth factor 1 receptor, SRC proto-oncogene, non-receptor tyrosine kinase (SRC), acetylcholinesterase, and glycogen synthase kinase 3 beta (GSK3B), all of which are the top 10 compounds and target genes in terms of degree value.

Fig. 3
figure 3

Diagrams of Venn analysis and interaction network analysis: (A) Venn diagram of intersection targets; (B) Interaction network of drug-active ingredient-drug disease intersection target.

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PPI network analysis

A PPI network diagram was generated after importing P. runcinata var. sinensis and arthritis target data into the STRING database (Fig. 4A). Subsequently, core target proteins were filtered using Cytoscape (v3.10.1), leveraging its enhanced computational stability for large-network analysis, and visualized in v3.7.0 (Fig. 4B). In this core target diagram of P. runcinata var. sinensis for arthritis treatment, node size and color saturation represent protein importance (degree centrality). This version-specific workflow ensured optimal processing efficiency (v3.10.1) while maintaining precise visualization control (v3.7.0). The total network diagram of PPI contained 187 nodes and 2,430 edges and had an average node value of 26. After analysis with the CentiScaPe2.2 topology module, Closeness unDir > 0.002635, Betweenness unDir > 201.10160, and Degree unDir > 25.98930 data were used to screen the core targets of P. runcinata var. sinensis for arthritis treatment. The study findings revealed that the top 10 core targets consisted of threonine kinase 1 (AKT1), caspase-3 (CASP3), B-cell lymphoma-2 (BCL2), PTGS2, tumor necrosis factor (TNF), EGFR, peroxisome proliferator-activated receptor gamma (PPARG), heat shock protein HSP90-alpha (HSP90 AA1), GSK3B, and hypoxia-inducible factor 1-alpha (HIF1 A). These results underscored the significance of these proteins in the above potential of P. runcinata var. sinensis for arthritis treatment.

Fig. 4
figure 4

PPI network analysis diagrams: (A) Protein-protein interaction network diagram of the intersection targets; (B) Diagram of the core P. runcinata var. sinensis targets in arthritis treatment.

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GO functional and KEGG pathway enrichment analysis

By utilizing the DAVID database to analyze the intersection target genes of P. runcinata var. sinensis and arthritis, a total of 631 BP, 88 CC, and 177 MF terms were retrieved. The P-value in KEGG/GO analysis indicates the statistical significance of enrichment, representing the probability that the observed association between genes/metabolites and a pathway or functional term occurs by random chance (with lower P-values suggesting more biologically meaningful enrichment). The top 10 most significant terms (selected by lowest p-values) were selected for visualization, illustrating various BPs, CCs, and MFs associated with the therapeutic mechanism of P. runcinata var. sinensis in arthritis(Fig. 5A). Additionally, KEGG pathway enrichment analysis revealed significant pathways closely linked to its mode of action in treating arthritis. The BPs involve a diverse array of functions, including negative regulation of the apoptotic process, protein phosphorylation, response to xenobiotic stimulus, positive regulation of Mitogen-activated protein kinase(MAPK) cascade, and peptidyl-serine phosphorylation. Similarly, the CC term pertains to gene products located within various CCs, such as cytosol, macromolecular complexes, mitochondria, extracellular exosomes, and plasma membranes. The MF term refers to gene products involved in activities, such as protein serine/threonine/tyrosine kinase activity, protein kinase activity, enzyme binding, ATP binding, and protein tyrosine kinase activity at the molecular level.

A total of 168 signaling pathways were extracted from the DAVID database and KEGG signaling pathway enrichment analysis was conducted on the top 20 pathways (Fig. 5B). The analysis results revealed that several signaling pathways, such as those in cancer, lipids, atherosclerosis, endocrine resistance, prostate cancer, chemical carcinogenesis-receptor activation, EGFR tyrosine kinase inhibitor resistance, apoptosis, cancer proteoglycans, HIF-1 signaling, AGE-RAGE signaling in diabetic complications, and other related pathways, were found to be significantly associated with the mechanism of action of P. runcinata var. sinensis in arthritis treatment. These findings revealed potential therapeutic targets for P. runcinata var. sinensis in arthritis treatment.

Fig. 5
figure 5

GO function and KEGG pathway enrichment analysis diagrams: (A) Enrichment analysis results for GO terms related to BP, CC, and MF; (B) Bubble map of KEGG-pathway enrichment results.

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Molecular docking results

Stronger binding affinity of a ligand to its protein target indicates higher potency. Thus, binding affinity data can help to select appropriate ligand-target pairs from each functional module for experimental validation of compounds aimed at treating illnesses and achieving therapeutic outcomes. The top ten lowest binding energies of molecular docking results are shown in Table 2, among which 1,8-dihydroxy-2,6-dimethylxanthen-9-one demonstrating the strongest affinity for PTGS2 at −7.3 kcal/mol. This suggested that the active compounds in P. runcinata var. sinensis may effectively treat arthritis through multiple targets. Thus, it was assumed that the active compounds of P. runcinata var. sinensis can effectively treat arthritis via multiple targets. The complexes the molecular docking model simulated in the study showed that the components and receptors bound well (Fig. 6). This demonstrated that the core target obtained during network pharmacology research was effectively combined with its corresponding active components.

Table 2 Molecular Docking binding affinities (kcal/mol) of core targets and their ligands.
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.

Fig. 6
figure 6

2D and 3D diagrams of the ten docking complexes with low binding energy: (A) PTGS2-1,8-dihydroxy-2,6-dimethylxanthen-9-one; (B) AKT1-3,3’-Di-O-methylellagic acid; (C) SRC-4-hydroxysphinganine; (D) PTGS2-ellagic acid; (E) PTGS2-octadeca-11E,13E,15Z-trienoic acid; (F) HSP90 AA1-N-feruloyltyramine; (G) PTGS2-N-feruloyltyramine; (H) PTGS2-naringenin chalcone; (I) AKT1-ellagic acid; (J) SRC − 3,3’-Di-O-methylellagic acid.

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Discussion

Research on P. runcinata var. sinensis, a key treatment for arthritis, holds significant value for advancing traditional Miao ethnomedicine. Targeted metabolomics is known for its precision and efficiency and is increasingly favored in studies on traditional Chinese and ethnic medicines. The present study employed widely targeted metabolomics to identify and quantify primary and secondary metabolites in P. runcinata var. sinensis, revealing key compounds, like catechin gallate, ellagic acid, and quercetin, with anti-inflammatory and analgesic properties. These findings offer crucial insights for the future development of treatments based on P. runcinata var. sinensis.

Network pharmacology is a prominent modern method for studying drug mechanisms and development. Various databases and tools support network pharmacology research in Miao ethnomedicine. By integrating metabolomics data with network databases, the present study identified key targets in P. runcinata var. sinensis used for arthritis treatment. Targets, such as AKT1, CASP3, BCL2, PTGS2, TNF, EGFR, PPARG, HSP90 AA1, GSK3B, and HIF1 A, regulated processes like apoptosis, protein phosphorylation, and kinase activities. These targets were involved in pathways related to cancer, atherosclerosis, hormone resistance, and apoptosis, providing a molecular basis for the application of P. runcinata var. sinensis.

In addition, the study analyzed the homology and systematic evolution of the ITS2 gene in P. runcinata var. sinensis, for the first time establishing its DNA barcode. The research methodology involved DNA extraction using the Cetyltrimethylammonium Bromide(CTAB) method, PCR amplification with ITS2 sequence primers, and subsequent imaging. Molecular identification of the ITS2 gene was conducted through DNA extraction, PCR amplification, sequence alignment, and phylogenetic tree analysis. These findings provided a foundation for standardizing medicinal materials of ethnic groups. By establishing the DNA barcode through sequence alignment and phylogenetic analysis, the present study enhanced the medicinal plant DNA barcode database, aided in plant identification, and contributed to the development of traditional Chinese medicine. The rigorous analysis of the plant’s DNA sequence and comparison with known species elucidated its genetic make-up and evolutionary relationships, offering valuable insights for the medicinal plant industry.

In summary, the present research studied the material composition of P. runcinata var. sinensis utilizing extensive targeted metabolomics technology and identified many of its key chemical substances that exerted anti-inflammatory effects. The findings have important implications for pharmacological research on Miao ethnomedicine and development of anti-inflammatory drugs for arthritis and other conditions.

Conclusion

The present study established DNA barcoding for P. runcinata var. sinensis using sequence alignment and phylogenetic analysis. Metabolomics techniques were used to identify 716 compounds, predominantly flavonoids, lipids, and phenolic acids. Active substances found in P. runcinata var. sinensis included catechin gallate and ellagic acid, forming the basis for its pharmacological effects. Network pharmacology was utilized to explore the therapeutic potential of these compounds for arthritis. Network pharmacology analysis highlighted functions and pathways relevant to arthritis treatment, showcasing potential for various arthritis types. Further research into these components, functions, and pathways may elucidate the pharmacological effects of this ethnomedicine.