Evaluation of the mechanism of the efficacy of Persicaria runcinata var. sinensis (Hemsl.) Bo Li on arthritis: an integrated widely targeted metabolomics and network pharmacology study

Evaluation of the mechanism of the efficacy of Persicaria runcinata var. sinensis (Hemsl.) Bo Li on arthritis: an integrated widely targeted metabolomics and network pharmacology study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Evaluation of the mechanism of the efficacy of Persicaria runcinata var. sinensis (Hemsl.) Bo Li on arthritis: an integrated widely targeted metabolomics and network pharmacology study Zengcai Liang, Xifeng Chen, Yunrong Xie, Feiyan He, Linlin Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6208115/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract The present study aimed to investigate the material basis of Persicaria runcinata var. sinensis (Hemsl.) Bo Li using widely targeted metabolomics and network pharmacology techniques. Efforts were also made to establish a DNA barcode for Persicaria runcinata var. sinensis (Hemsl.) Bo Li. Widely targeted metabolomics technique of Ultra performance liquid chromatography-mass spectrometry(UPLC-MS/MS) was employed to detect and analyze the metabolites in Persicaria runcinata var. sinensis (Hemsl.) Bo Li. Network pharmacology was used to screen and analyze metabolites with high content and pharmacological effects. DNA extraction, Polymerase chain reaction(PCR) amplification, sequence alignment, and phylogenetic tree construction were performed to establish the DNA barcode. A total of 716 metabolites were detected in Miao ethnomedicine Persicaria runcinata var. sinensis (Hemsl.) Bo Li, with key targets, enriched functions, and pathways identified using network pharmacology. The DNA sequence for the ITS2 primer of Persicaria runcinata var. sinensis (Hemsl.) Bo Li was determined and a phylogenetic tree was generated. Metabolite enrichment in Persicaria runcinata var. sinensis (Hemsl.) Bo Li revealed potential therapeutic compounds for arthritis. The study’s approach provided theoretical support for understanding the substance basis and therapeutic effects of Persicaria runcinata var. sinensis (Hemsl.) Bo Li. Additionally, the high homology level with various Polygonum species provided the foundation for molecular identification in ethnomedicine. Health sciences/Rheumatology/Rheumatic diseases/Osteoarthritis Biological sciences/Drug discovery/Pharmacology Arthritis DNA barcode Miao Ethnomedicine network pharmacology Persicaria runcinata var. sinensis (Hemsl.) Bo Li widely targeted metabolomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Highlights 1. Metabolomics Investigation : The study employed widely targeted metabolomics using ultra-performance liquid chromatography-mass spectrometry (UPLC-MS/MS) to identify 716 metabolites in Persicaria runcinata var. sinensis (Hemsl.) Bo Li. 2. Network Pharmacology Analysis : Key pharmacological targets, enriched functions, and metabolic pathways were determined, indicating the potential of identified metabolites as therapeutic compounds for arthritis. 3. DNA Barcode Establishment : DNA extraction, polymerase chain reaction (PCR) amplification, and phylogenetic tree construction were conducted to establish the ITS2 DNA barcode, revealing high genetic homology with various Polygonum species. 1. 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 effects [ 1 , 2 ]. Persicaria runcinata var. sinensis (Hemsl.) Bo Li 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 relief [ 3 – 5 ]. In the Guizhou province region in China, which is known for its expertise in utilizing Miao ethnomedicine, traditional healers have been observed employing Persicaria runcinata var. sinensis for the treatment of arthritis. The present study aimed to establish DNA barcodes and perform metabolomics and network pharmacology analyses of Persicaria runcinata var. sinensis . Studies have shown that Persicaria runcinata , a Miao ethnomedicine, exhibits significant antibacterial effects against Salmonella typhi [ 6 ] and Shigella dysenteriae [ 7 ], as well as potent antioxidant properties, ranking second only to Ethylene Diamine Tetraacetic Acid in antioxidant activity [ 8 ]. A Miao ethnomedicine Jingushang spray contains Persicaria runcinate and demonstrates effectiveness in relaxing tendons, improving blood circulation, reducing swelling, and relieving pain [ 9 ]. Clinical studies show comparable efficacy to that of Yunnan Baiyao aerosol in treating acute soft tissue injuries, particularly in reducing swelling [ 10 ]. Research on the therapeutic effects of Persicaria runcinata 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 organisms [ 11 ] 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 material basis and specific mechanisms of drug-related effects in traditional Chinese medicine research [ 12 ], aligning with holistic principles [ 13 , 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 action [ 15 ]. It is vital for advancing traditional Chinese medicine and addressing key issues in ethnic remedies [ 16 ]. 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 model [ 17 , 18 ]. Network pharmacology is rooted in network theory and systems biology [ 18 , 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 Persicaria 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 Persicaria runcinata var. sinensis components and arthritis, providing support for further research. 2. Materials and methods 2.1 Sample collection The herb Persicaria runcinata var. sinensis belongs to the Polygonaceae family and is widely used in traditional Guizhou medicine to treat arthritis. It also has analgesic and anti-inflammatory effects. In the present study, Persicaria runcinata var. sinensis was selected as the research subject. Samples of its root portion collected in Kaili City, Qiandongnan Miao, and Dong Autonomous Prefecture, Guizhou Province, China were prepared and sent to Wuhan Maitwell Biotechnology Co., Ltd. for comprehensive targeted metabolic analysis. Additional samples were used in the first attempt to determine the DNA barcode for Persicaria runcinata var. sinensis . 2.2 Establishment of DNA barcode 2.2.1 Extraction of plant genome DNA First, 0.2 g of the root was ground in liquid nitrogen and then purified using a phenol-chloroform-isoamyl alcohol mixture followed by chloroform-isoamyl alcohol solution. The DNA was subsequently precipitated with ethanol, washed, and dissolved in water. The extraction process included centrifugation at 12,000 rpm at 4°C. The DNA concentration was measured and the extracted DNA was used as a sample for polymerase chain reaction (PCR) amplification to facilitate sequencing. 2.2.2 PCR amplification of ITS2 sequence primers The Persicaria runcinata var. sinensis DNA served as the template for PCR amplification. The PCR reaction mixture included template DNA, forward and reverse primers, Taq polymerase, and sterile double-distilled water. This mixture was placed into a gradient PCR thermocycler. The ITS2 sequence primers (forward primer ITS2 -F: 5'-ATGCGATACTTGGTGTGAAT-3'; reverse primer ITS2 -R: 5'-GACGCTTCTCCAGACTACAAT-3') were utilized according to the 2020 Pharmacopeia guidelines of the People’s Republic of China. PCR amplification was conducted according to the specified ITS2 sequence amplification protocol. A 10-µL aliquot of the amplified product was used as a sample and subjected to 1% agarose gel electrophoresis, with the DL2000 DNA molecular weight marker serving as a reference. Post-electrophoresis visualization was achieved using a chemiluminescence gel imaging system. 2.2.3 Gene sequence alignment The PCR products were amplified using the ITS2 F– ITS3 R primers as PCR reaction primers. The amplified products were subsequently submitted to Sangon Biotech (Shanghai) Co., Ltd. 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. 2.2.4 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/ ). These sequences were then imported into the MEGA6 software, where they were analyzed using the ClustalW method. 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. 2.3 Widely targeted metabolomic profiling 2.3.1 Sample preparation and extraction The Miao ethnomedicine Persicaria runcinata var. sinensis plant samples were prepared for analysis. The samples are freeze-dried by vacuum freeze-dryer (Scientz-100F). 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 seconds every 30 minutes for 6 times in total. Following centrifugation at 12000 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. 2.3.2 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 conditions 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. 2.3.3 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. 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 MRM experiments with collision gas (nitrogen) set to medium. DP(declustering potential) and CE(collision energy) for individual MRM transitions was done with further DP and CE optimization. A specific set of MRM transitions were monitored for each period according to the metabolites eluted within this period. 2.3.4 Statistical analysis of metabolite data The resultant mass spectra were compared in the Metware Biotechnology Co., Ltd. (MWDB) database. The quantitative analysis of metabolites was performed using the MRM mode of Q mass spectrometry. After obtaining the signal intensities of each metabolite’s distinctive ions in several samples, the chromatographic peak regions of those ions were integrated to indicate the metabolite’s relative abundance. The peak areas were adjusted with retention time and peak type to make it easier to quantitatively compare the same metabolites between samples. 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. 2.4 Network pharmacology analysis 2.4.1. Target collection 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/).Th e potential arthritis-associated targets for Persicaria runcinata var. sinensis were determined by analyzing the intersections between the retrieved active ingredient and arthritis-related targets using the Venny2.1.0 website, resulting in the generation of a Venn diagram. 2.4.2. 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, 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. 2.4.3 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. 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/ . 2.4.4 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/ ) [ 20 ] targets was acquired and three-dimensional structures of the components from PubChem were obtained. AutoDock software [ 21 ] was used for molecular docking to determine the lowest binding energy. The docking results were visualized through PyMOL software. 3. Results 3.1 DNA barcode establishment The following DNA sequence was obtained after analyzing the sequencing results for the purified PCR product of Persicaria runcinata var. Sinensis and showed in supplementary file Table S1 . The gene sequence for Persicaria 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 Persicaria 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. 3.2 Widely targeted metabolomics analysis 3.2.1 Qualitative and quantitative analyses of metabolites The qualitative analysis of primary and secondary mass spectrometry data was performed based on the self-built database Metware Database(MWDB) (Metware Biotechnology Co., Ltd. Wuhan, China) and publicly available metabolite databases. The analysis produced a multi-peak MRM metabolite detection chromatogram and a total ion current spectrum of multiple substances (X ion current), as shown in the supplementary file Figure S1 . Based on this, the obtained mass spectrometry data were compared with the local metabolite database for qualitative and quantitative analyses of the detected metabolites. 3.2.2 Distribution of metabolite types Screened data for Persicaria runcinata showed 716 detected metabolites across 12 categories. Flavonoids, phenolic acids, and lipid substances were the most abundant, with over 100 enriched metabolites (supplementary file Table S2 ). A bar graph was generated for the primary classification of metabolites and enriched metabolites (Fig. 2 ). The pharmacological effects of specific metabolites were analyzed in order to present the data on metabolites with anti-inflammatory effects in Persicaria runcinata (Table 1 ). The Appendix shows the identification numbers, integration values, and corresponding metabolite names of the obtained partial metabolites. Table 1 Active anti-inflammatory ingredients of Persicaria runcinata var. sinensis Number Compounds Pharmacological effects 1 Catechin gallate Substantially reduces inflammatory damage and downregulates inflammation-related proteins[ 22 ]. 2 Ellagic acid Exhibiting antibacterial and anti-inflammatory effects, it alleviates inflammation by reducing neutrophil recruitment and infiltration, and downregulating pro-inflammatory cytokine expression[ 23 ]. 3 Phthalate Delays onset and progression of osteoarthritis[ 24 ]. 4 Indole alkaloids Demonstrating analgesic and anti-inflammatory effects[ 25 ]. 5 Proanthocyanidins Reduces activity of pro-inflammatory mediators and inhibits production of pro-inflammatory factors, decreasing inflammation-induced damage[ 26 ]. 6 Pyridoxol Reduces vascular inflammatory reactions to some extent[ 27 ]. 7 Gentiopicroside Exhibits preventive and therapeutic effects on inflammation in rat ankle joint tissues[ 28 ]. 8 Swertiamarin Offers protective effects against adjuvant-induced arthritis in rats[ 29 ]. Inhibits generation of inflammation-related factors, protects nerves, and alleviates neuropathic pain[ 30 ]. Exhibits neuroprotective and analgesic properties[ 31 ], reducing neuropathic pain by restoring the balance of inflammatory factors in diabetic rats[ 32 ]. 3.3 Network pharmacology analysis 3.3.1 Venn diagram analysis The Venny2.1.0 website was employed to analyze the active ingredient targets of Persicaria 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. 3 A). 3.3.2 Interaction network analysis The organized data file was imported into Cytoscape 3.10.1 software for processing to carry out network analysis of “ Persicaria runcinata var. sinensis – active ingredient - intersection targets”(Fig. 3 B). The resulting interaction network diagram consisted of 218 nodes and 421 edges, where the red arrow represent Persicaria 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. 3.3.3 PPI network analysis A PPI network diagram was generated after importing Persicaria runcinata var. sinensis and arthritis target data into the STRING database (Fig. 4 A). Subsequently, the core target proteins were filtered using the Cytoscape 3.10.1 software, and a concise visual analysis was performed using the Cytoscape 3.7.0 software. The core target diagram of Persicaria runcinata var. sinensis in the treatment of arthritis is shown in Fig. 4 B. The Cytoscape 3.7.0 software was employed for visualization, with the node size and color saturation indicating the degree of importance. 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 Persicaria 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 (HSP90AA1), GSK3B, and hypoxia-inducible factor 1-alpha (HIF1A). These results underscored the significance of these proteins in the above potential of Persicaria runcinata var. sinensis for arthritis treatment. 3.3.4 GO functional and KEGG pathway enrichment analysis By utilizing the DAVID database to analyze the intersection target genes of Persicaria runcinata var. sinensis and arthritis, a total of 631 BP, 88 CC, and 177 MF terms were retrieved. The top 10 terms were selected based on their P-values for visualization, illustrating various BPs, CCs, and MFs associated with the therapeutic mechanism of Persicaria runcinata var. sinensis in arthritis(Fig. 5 A). 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. 5 B). 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 Persicaria runcinata var. sinensis in arthritis treatment. These findings revealed potential therapeutic targets for Persicaria runcinata var. sinensis in arthritis treatment. 3.4 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 molecular docking results (Table 2 ) indicated that a ligand’s binding affinity to its protein target correlated with its potency, with values below − 5.6 kcal/mol suggesting strong binding. It is generally accepted that a compound with a binding energy to the receptor protein of <-5.6 kcal/mol indicates a strong binding [ 33 ], with 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 Persicaria runcinata may effectively treat arthritis through multiple targets. Thus, it was assumed that the active compounds of Persicaria runcinata 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 No. Compounds PubChem CIDs a Target names PDB IDs b Bas c 1 1,8-dihydroxy-2,6-dimethylxanthen-9-one 163195715 PTGS2 5kir -7.3 2 3,3'-O-Dimethylellagic Acid 5488919 AKT1 3o96 -6.5 3 4-Hydroxysphinganine 122121 SRC 2src -6.46 4 Ellagic acid 5281855 PTGS2 5kir -6.43 5 Octadeca-11E,13E,15Z-trienoic acid 54564607 PTGS2 5kir -6.3 6 N-Feruloyltyramine 5280537 HSP90AA1 2xjx -6.06 7 N-Feruloyltyramine 5280537 PTGS2 5kir -6.03 8 Naringenin chalcone 5280960 PTGS2 5kir -5.92 9 Ellagic acid 5281855 AKT1 3o96 -5.91 10 3,3'-O-Dimethylellagic Acid 5488919 SRC 2src -5.86 a PDB ID, protein identifier in protein data bank. b PubChem CID, compound identifier in PubChem database. c BAs, binding affinity (kcal/mol). 3. Discussion Research on Persicaria 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 Persicaria 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 Persicaria 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 Persicaria runcinata var. sinensis used for arthritis treatment. Targets, such as AKT1, CASP3, BCL2, PTGS2, TNF, EGFR, PPARG, HSP90AA1, GSK3B, and HIF1A, 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 Persicaria runcinata var. sinensis . In addition, the study analyzed the homology and systematic evolution of the ITS2 gene in Persicaria 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 Persicaria 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. 4. Conclusion The present study established DNA barcoding for Persicaria 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 Persicaria 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. Declarations Acknowledgements: We thank International Science Editing (http://www.internationalscienceediting.com ) for editing this manuscript. Author contributions : All data were generated in-house, and no paper mill was used. All authors agree to be accountable for all aspects of work ensuring integrity and accuracy. Conceptualization, Taofeng Lu, Zengcai Liang and Xiang Zhu; Funding acquisition, Xiang Zhu, and Taofeng Lu; Methodology, Zengcai Liang and Xifeng Chen; Project administration, Taofeng Lu and Xiang Zhu; Resources, Zengcai Liang and Xifeng Chen; Supervision, Xifeng Chen and Taofeng Lu; Visualization, Yunrong Xie and Feiyan He; Writing – original draft, Zengcai Liang, Yunrong Xie and Feiyan He; Writing – review & editing, Taofeng Lu, Linlin Zhang and Xifeng Chen. Funding: This research were funded by the Project of Shanghai Science and Technology Commission (24141900201), the Science and Technology Foundation of Health Commission of Guizhou Province (gzwkj 2021-245), and the Guizhou Province Science and Technology Department Support Plan (QKHJC-ZK[2021]-YB156). Conflicts of Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Supplementary materials: Supplementary material associated with this article can be found in the supplementary files, Table S.docx, Figure S.docx and Metabolomics data for Persicaria runcinata var. sinensis (Hemsl.) Bo Li.xlsx. Data Availability statement: All data generated or analyzed during this study are included in this published article and the supplementary information files. References Shen, B. et al. Research Progress of Chemical Constituents and its Pharmacological Activities from the Medicinal Plants of Genus Polygonum. J. Hunan Univ. Chin. Med. 35 , 63–70 (2015). Hang, Y. et al. Ethnopharmacological study of Polygonum plants in traditional ethnic medicine. Journal of Medicine & Pharmacy of Chinese Minorities. 25 61–63. https://doi.org10.16041/j.cnki.cn15-1175.2019.05.033. (2019). Yi, L. Polygonum runcinatum. Chin. J. Ethnomed. Ethnopharmacy . 30 , 123 (2021). Medicine, N. U. C. & Team, C. The Chinese Dictionary (Second Edition, Volume One). Shanghai: Shanghai Science and Technology Press. 1448–1449. (2006). Tingxiang, L. et al. Comparison and differentiation between Miao medicine Polygonum runcinatum and the easily confused product Polygonum chinense L. Journal of Chinese Medicinal Materials. 44 1099–1103. https://doi.org10.13863/j.issn1001-4454.2021.05.011. (2021). Lan, L. et al. Study on in vitro Bacteriostatic Activity of the Extracts from Miao Medicine Polygonum runcinatum. China Pharm. 29 , 1343–1347 (2018). Hong, X., XuYing, W. & AiQun, S. Studies on Antibacterial Action of Watery Solution of P.runcinatum Buch.-Ham.ex D.Don.Var. J. Guizhou Univ. Eng. Sci. 65–68. (2003). Xuying, W. Study on antioxidation of ethanol extract from runcinate knotweed herb. J. Jinggangshan University(Natural Science) . 31 , 58–6064 (2010). Chaofei, X. Studies on the Quality Control of the JingushangSprays of Miao Nationality Medicines (Guizhou Medical University, 2017). Qinglong, L. et al. Clinical efficacy observation of Miao Medicine Jingushang Spray in the treatment of acute soft tissue injury. Electronic Journal of Clinical Medical Literature. 4 2909–2910. https://doi.org10.16281/j.cnki.jocml.2017.15.110. (2017). Tongsheng, W. & Ming, X. Metabonomics and modern study of traditional chinese medicine. China & Foreign Medical Treatment. 27 44–45. https://doi.org10.16662/j.cnki.1674-0742.2008.05.009. (2008). Qinglin, L., Ling, W. & Hua, Y. Modern research on Chinese medicine based on the metabonomics technology. China J. Traditional Chin. Med. Pharm. 28 , 3149–3152 (2013). Yuan, M. et al. Research progress on metabolomics of ethnomedicinal plants. J. Plant. Resour. Environ. 31 , 73–81 (2022). Hongying, C. & Yongzhi, Z. The application of metabolomics in modern research of traditional Chinese medicine. Tianjin Pharm. 21 , 48–50 (2009). Qi, C., Tenglong, Y. & Zhe, F. Research progress on the mechanism of traditional Chinese medicine in promoting fracture healing. J. Guangxi Univ. Chin. Med. 17 , 85–87 (2014). GuangJi, W. et al. Metabonomics and its Application Prospect in TCM Study. Chin. J. Nat. Med. 89–97. (2008). Li, S., Zhang, B. & Zhang, N. Network target for screening synergistic drug combinations with application to traditional Chinese medicine. BMC Syst Biol. 5 Suppl 1 S10. https://doi.org10.1186/1752-0509-5-s1-s10. (2011). Li, S. et al. Network Pharmacology in Traditional Chinese Medicine. Evidence-Based Complementary and Alternative Medicine. 2014 138460. https://doi.org10.1155/2014/138460. (2014). Hopkins, A. L. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol. 4 682 – 90. https://doi.org10.1038/nchembio.118. (2008). Burley, S. K. et al. RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. Nucleic Acids Res. 49 D437-d451. https://doi.org10.1093/nar/gkaa1038. (2021). Eberhardt, J. & Santos-Martins, D. AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings. 61 3891–3898. https://doi.org10.1021/acs.jcim.1c00203. (2021). Yan, L. et al. Effects of epigallocatechin gallate against inflammatory injury in microglia based on TLT4-MyD88-TRAF6 signaling pathway. Tianjin J. Traditional Chin. Med. 36 , 296–299 (2019). Songying, L. Gallic acid ameliorates Aspergillus fumigatus keratitis through reducingfungal load and suppressing the inflammatory response. Qingdao University. https://doi.org10.27262/d.cnki.gqdau.2022.001708. (2023). Chao, F. The Effect of Diterbutyl Phthalate Extracted from Panax Notoginseng on Osteoarthritis and its Mechanism. Naval Medical University. https://doi.org10.26998/d.cnki.gjuyu.2021.000075. (2022). Dihui, X. Identification of indole alkaloids in toad venom and evaluation of its analgesic and anti-inflammatory activity based on mass spectrometry. Nanjing University of Chinese Medicine. https://doi.org10.27253/d.cnki.gnjzu.2021.000556. (2022). Shunyi, Z. et al. Meta-analysis of proanthocyanidin-mediated MAPK pathway in inhibiting inflammatory injury. Journal of Toxicology. 33 351–356. https://doi.org10.16421/j.cnki.1002-3127.2019.05.002. (2019). Ran, M. Studies of the effect and mechanism of Pyrodoxine against Atherosclerosis (Nanjing University, 2016). Zhongying, L., Shixue, C. & Mengqin, Z. Effect of Radiculoside on Reducing Uric Acid and Preventing Acute Gouty Arthritis. Shandong Chemical Industry. 50 53–55 + 57. https://doi.org10.19319/j.cnki.issn.1008-021x.2021.17.019. (2021). Xinqiang, W. et al. Therapeutic Effects of Swertiamarin on Adjuvant-Induced Arthritis Rats by Inhibiting NLRP3 InflammasomeཤNF-κB Signaling Pathways. Pharmaceutical Biotechnology. 28 123–128. https://doi.org10.19526/j.cnki.1005-8915.20210203. (2021). Junyan, W., Yeling, T. & Wenhui, Z. Anti-inflammatory Activity of Swertiamarin and Its Effect on the Expression of NF-κB Pathway-related Factors p65 and IKK-α. Chinese Journal of Modern Applied Pharmacy. 35 1817–1820. https://doi.org10.13748/j.cnki.issn1007-7693.2018.12.013. (2018). Leong, X. Y. et al. A systematic review of the protective role of swertiamarin in cardiac and metabolic diseases. Biomed Pharmacother. 84 1051–1060. https://doi.org10.1016/j.biopha.2016.10.044. (2016). Bao, W. et al. Effect of swertiamarin on inflammatory factor levels in rats with diabetic peripheral neuropathy. Acad. J. Guangzhou Med. Univ. 47 , 7–10 (2019). Hsin, K. Y. et al. systemsDock: a web server for network pharmacology-based prediction and analysis. Nucleic Acids Res. 44 (2016) W507-13. https://doi.org10.1093/nar/gkw335. Additional Declarations No competing interests reported. Supplementary Files TableS.docx FigureS.docx MetabolomicsdataforPersicariaruncinatavar.sinensisHemsl.BoLi.xlsx Cite Share Download PDF Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 07 Apr, 2025 Reviews received at journal 05 Apr, 2025 Reviews received at journal 04 Apr, 2025 Reviewers agreed at journal 24 Mar, 2025 Reviewers agreed at journal 24 Mar, 2025 Reviewers invited by journal 24 Mar, 2025 Editor assigned by journal 24 Mar, 2025 Editor invited by journal 21 Mar, 2025 Submission checks completed at journal 21 Mar, 2025 First submitted to journal 11 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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(B) Interaction network of drug-active ingredient-drug disease intersection target.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6208115/v1/118b0df53f44fbbe2cf44fd4.jpg"},{"id":79297958,"identity":"86f42d1a-fea1-4c90-8c97-cbff0ba7bd78","added_by":"auto","created_at":"2025-03-26 17:50:15","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5513056,"visible":true,"origin":"","legend":"\u003cp\u003ePPI network analysis diagrams: (A) Protein-protein interaction network diagram of the intersection targets; (B) Diagram of the core \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e targets in arthritis treatment.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6208115/v1/511f4f86a605ce2b884632bb.jpg"},{"id":79297956,"identity":"b747ac81-5990-4651-b8ae-a171f5cb6dbc","added_by":"auto","created_at":"2025-03-26 17:50:15","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1029939,"visible":true,"origin":"","legend":"\u003cp\u003eGO 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.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6208115/v1/bbf5e6f72b2cf8ffeb3d0744.jpg"},{"id":79298745,"identity":"d66b0bbd-f19f-41a1-8fc6-441b83833562","added_by":"auto","created_at":"2025-03-26 18:06:15","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":6309725,"visible":true,"origin":"","legend":"\u003cp\u003e2D 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'-O-dimethylellagic acid; (C) SRC-4-hydroxysphinganine; (D) PTGS2-ellagic acid; (E) PTGS2-octadeca-11E,13E,15Z-trienoic acid; (F) HSP90AA1-N-feruloyltyramine; (G) PTGS2-N-feruloyltyramine; (H) PTGS2-naringenin chalcone; (I) AKT1-ellagic acid; (J) SRC -3,3'-O-dimethylellagic acid.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6208115/v1/bde6dfa52150e1ee71cf2ac1.jpg"},{"id":86178959,"identity":"3febf43c-2ad0-42ef-90bd-84422cc00c13","added_by":"auto","created_at":"2025-07-07 16:12:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":19837242,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6208115/v1/c8e6262d-602f-4f6f-8830-c0e7dba51733.pdf"},{"id":79297952,"identity":"35b0f811-6d32-43f4-bc9d-d91f579bf44e","added_by":"auto","created_at":"2025-03-26 17:50:15","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":19560,"visible":true,"origin":"","legend":"","description":"","filename":"TableS.docx","url":"https://assets-eu.researchsquare.com/files/rs-6208115/v1/646edd06ebd256dfcf79ea6f.docx"},{"id":79297953,"identity":"d0a1c1a4-d9ff-457e-b587-6545fd960780","added_by":"auto","created_at":"2025-03-26 17:50:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":871625,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS.docx","url":"https://assets-eu.researchsquare.com/files/rs-6208115/v1/c92e4d80e5317809f8c0ad4e.docx"},{"id":79297950,"identity":"561ab671-23f8-4d73-96fd-2f0eff18e063","added_by":"auto","created_at":"2025-03-26 17:50:15","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":118979,"visible":true,"origin":"","legend":"","description":"","filename":"MetabolomicsdataforPersicariaruncinatavar.sinensisHemsl.BoLi.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6208115/v1/4dc7511a9521a3c12c6012f5.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of the mechanism of the efficacy of Persicaria runcinata var. sinensis (Hemsl.) Bo Li on arthritis: an integrated widely targeted metabolomics and network pharmacology study","fulltext":[{"header":"Highlights","content":"\u003cp\u003e\u003cstrong\u003e1.\u003c/strong\u003e \u003cstrong\u003eMetabolomics Investigation\u003c/strong\u003e: The study employed widely targeted metabolomics using ultra-performance liquid chromatography-mass spectrometry (UPLC-MS/MS) to identify 716 metabolites in \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e (Hemsl.) Bo Li.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Network Pharmacology Analysis\u003c/strong\u003e: Key pharmacological targets, enriched functions, and metabolic pathways were determined, indicating the potential of identified metabolites as therapeutic compounds for arthritis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. DNA Barcode Establishment\u003c/strong\u003e: DNA extraction, polymerase chain reaction (PCR) amplification, and phylogenetic tree construction were conducted to establish the ITS2 DNA barcode, revealing high genetic homology with various Polygonum species.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003e \u003cem\u003ePolygonum\u003c/em\u003e 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 effects [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e (Hemsl.) Bo Li is from the \u003cem\u003ePersicaria\u003c/em\u003e genus in the \u003cem\u003ePolygonaceae\u003c/em\u003e 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 relief [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In the Guizhou province region in China, which is known for its expertise in utilizing Miao ethnomedicine, traditional healers have been observed employing \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e for the treatment of arthritis. The present study aimed to establish DNA barcodes and perform metabolomics and network pharmacology analyses of \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eStudies have shown that \u003cem\u003ePersicaria runcinata\u003c/em\u003e, a Miao ethnomedicine, exhibits significant antibacterial effects against \u003cem\u003eSalmonella typhi\u003c/em\u003e [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and \u003cem\u003eShigella dysenteriae\u003c/em\u003e [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], as well as potent antioxidant properties, ranking second only to Ethylene Diamine Tetraacetic Acid in antioxidant activity [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A Miao ethnomedicine Jingushang spray contains \u003cem\u003ePersicaria runcinate\u003c/em\u003e and demonstrates effectiveness in relaxing tendons, improving blood circulation, reducing swelling, and relieving pain [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Clinical studies show comparable efficacy to that of Yunnan Baiyao aerosol in treating acute soft tissue injuries, particularly in reducing swelling [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Research on the therapeutic effects of \u003cem\u003ePersicaria runcinata\u003c/em\u003e on arthritis is limited despite partial demonstrations of its pharmacological effects in single-herb or compound formulations.\u003c/p\u003e \u003cp\u003eMetabolomics is a dynamic branch of systems biology that provides systematic insights into metabolic changes in living organisms [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] 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 material basis and specific mechanisms of drug-related effects in traditional Chinese medicine research [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], aligning with holistic principles [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. 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 action [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. It is vital for advancing traditional Chinese medicine and addressing key issues in ethnic remedies [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe 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 model [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Network pharmacology is rooted in network theory and systems biology [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and provides a comprehensive understanding of biological systems, drugs, and diseases. It is particularly beneficial for studying Miao ethnomedicine.\u003c/p\u003e \u003cp\u003eIn the present study, widely targeted metabolomics technique of ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) identified chemical components of Miao ethnomedicine \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e. Molecular identification via \u003cem\u003eITS2\u003c/em\u003e 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 \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e components and arthritis, providing support for further research.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Sample collection\u003c/h2\u003e \u003cp\u003eThe herb \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e belongs to the \u003cem\u003ePolygonaceae\u003c/em\u003e family and is widely used in traditional Guizhou medicine to treat arthritis. It also has analgesic and anti-inflammatory effects. In the present study, \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e was selected as the research subject. Samples of its root portion collected in Kaili City, Qiandongnan Miao, and Dong Autonomous Prefecture, Guizhou Province, China were prepared and sent to Wuhan Maitwell Biotechnology Co., Ltd. for comprehensive targeted metabolic analysis. Additional samples were used in the first attempt to determine the DNA barcode for \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Establishment of DNA barcode\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Extraction of plant genome DNA\u003c/h2\u003e \u003cp\u003eFirst, 0.2 g of the root was ground in liquid nitrogen and then purified using a phenol-chloroform-isoamyl alcohol mixture followed by chloroform-isoamyl alcohol solution. The DNA was subsequently precipitated with ethanol, washed, and dissolved in water. The extraction process included centrifugation at 12,000 rpm at 4\u0026deg;C. The DNA concentration was measured and the extracted DNA was used as a sample for polymerase chain reaction (PCR) amplification to facilitate sequencing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 PCR amplification of \u003cem\u003eITS2\u003c/em\u003e sequence primers\u003c/h2\u003e \u003cp\u003eThe \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e DNA served as the template for PCR amplification. The PCR reaction mixture included template DNA, forward and reverse primers, Taq polymerase, and sterile double-distilled water. This mixture was placed into a gradient PCR thermocycler. The \u003cem\u003eITS2\u003c/em\u003e sequence primers (forward primer \u003cem\u003eITS2\u003c/em\u003e-F: 5'-ATGCGATACTTGGTGTGAAT-3'; reverse primer \u003cem\u003eITS2\u003c/em\u003e-R: 5'-GACGCTTCTCCAGACTACAAT-3') were utilized according to the 2020 Pharmacopeia guidelines of the People\u0026rsquo;s Republic of China. PCR amplification was conducted according to the specified \u003cem\u003eITS2\u003c/em\u003e sequence amplification protocol. A 10-\u0026micro;L aliquot of the amplified product was used as a sample and subjected to 1% agarose gel electrophoresis, with the DL2000 DNA molecular weight marker serving as a reference. Post-electrophoresis visualization was achieved using a chemiluminescence gel imaging system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Gene sequence alignment\u003c/h2\u003e \u003cp\u003eThe PCR products were amplified using the \u003cem\u003eITS2\u003c/em\u003eF\u0026ndash;\u003cem\u003eITS3\u003c/em\u003eR primers as PCR reaction primers. The amplified products were subsequently submitted to Sangon Biotech (Shanghai) Co., Ltd. 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 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://blast.ncbi.nlm.nih.gov/Blast.cgi\u003c/span\u003e\u003cspan address=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ) 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Phylogenetic tree construction\u003c/h2\u003e \u003cp\u003eIn 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 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/genbank/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/genbank/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These sequences were then imported into the MEGA6 software, where they were analyzed using the ClustalW method. 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.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Widely targeted metabolomic profiling\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e2.3.1 Sample preparation and extraction\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe Miao ethnomedicine \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e plant samples were prepared for analysis. The samples are freeze-dried by vacuum freeze-dryer (Scientz-100F). 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 seconds every 30 minutes for 6 times in total. Following centrifugation at 12000 rpm for 3 min, the extracts were filtrated (SCAA-104, 0.22 \u0026micro;m pore size; ANPEL,Shanghai, China, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.anpel.com.cn/\u003c/span\u003e\u003cspan address=\"http://www.anpel.com.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) before UPLC-MS/MS analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 UPLC Conditions\u003c/h2\u003e \u003cp\u003eThe sample extracts were analyzed using an UPLC-ESI-MS/MS system (UPLC, SHIMADZU Nexera X2, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.shimadzu.com.cn/\u003c/span\u003e\u003cspan address=\"https://www.shimadzu.com.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; MS, Applied Biosystems 4500 Q TRAP, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.thermofisher.cn/cn/zh/home/brands/applied-biosystems.html\u003c/span\u003e\u003cspan address=\"https://www.thermofisher.cn/cn/zh/home/brands/applied-biosystems.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The analytical conditions were as follows, UPLC: column, Agilent SB-C18 (1.8 \u0026micro;m, 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\u0026deg;C; The injection volume was 4 \u0026micro;L. The effluent was alternatively connected to an ESI-triple quadrupole-linear ion trap (QTRAP)-MS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 ESI-Q TRAP-MS/MS\u003c/h2\u003e \u003cp\u003eThe ESI source operation parameters were as follows: source temperature 550\u0026deg;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. Instrument tuning and mass calibration were performed with 10 and 100 \u0026micro;mol/L polypropylene glycol solutions in Triple Quadrupole Mass Spectrometer(QQQ) and Linear Ion Trap༈LIT༉ modes, respectively. QQQ scans were acquired as MRM experiments with collision gas (nitrogen) set to medium. DP(declustering potential) and CE(collision energy) for individual MRM transitions was done with further DP and CE optimization. A specific set of MRM transitions were monitored for each period according to the metabolites eluted within this period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Statistical analysis of metabolite data\u003c/h2\u003e \u003cp\u003eThe resultant mass spectra were compared in the Metware Biotechnology Co., Ltd. (MWDB) database. The quantitative analysis of metabolites was performed using the MRM mode of Q mass spectrometry. After obtaining the signal intensities of each metabolite\u0026rsquo;s distinctive ions in several samples, the chromatographic peak regions of those ions were integrated to indicate the metabolite\u0026rsquo;s relative abundance. The peak areas were adjusted with retention time and peak type to make it easier to quantitatively compare the same metabolites between samples.\u003c/p\u003e \u003cp\u003eA 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.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Network pharmacology analysis\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. Target collection\u003c/h2\u003e \u003cp\u003eThe Canonical SMILES of the dominant metabolites were searched using the PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and then entered into the Swiss TargetPrediction database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swisstargetprediction.ch/\u003c/span\u003e\u003cspan address=\"http://www.swisstargetprediction.ch/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) 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 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the Comparative Toxicogenomics Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ctdbase.com/).Th\u003c/span\u003e\u003cspan address=\"https://ctdbase.com/).Th\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ee potential arthritis-associated targets for \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e were determined by analyzing the intersections between the retrieved active ingredient and arthritis-related targets using the Venny2.1.0 website, resulting in the generation of a Venn diagram.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2. Protein-Protein Interaction Network\u003c/h2\u003e \u003cp\u003eThe PPI (Protein-Protein Interaction) network analysis was conducted using the STRING website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). 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, 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3 Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis\u003c/h2\u003e \u003cp\u003eThe DAVID database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://davidbioinformatics.nih.gov/\u003c/span\u003e\u003cspan address=\"https://davidbioinformatics.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized to conduct GO functional enrichment and KEGG pathway enrichment analyses for the intersecting targets. 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: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioinformatics.com.cn/\u003c/span\u003e\u003cspan address=\"http://www.bioinformatics.com.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e2.4.4 Molecular docking\u003c/h2\u003e \u003cp\u003eThe core targets were collected in the PPI network and their corresponding components were obtained. The structure file for the PDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] targets was acquired and three-dimensional structures of the components from PubChem were obtained. AutoDock software [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] was used for molecular docking to determine the lowest binding energy. The docking results were visualized through PyMOL software.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.1 DNA barcode establishment\u003c/h2\u003e \u003cp\u003eThe following DNA sequence was obtained after analyzing the sequencing results for the purified PCR product of \u003cem\u003ePersicaria runcinata var. Sinensis\u003c/em\u003e and showed in supplementary file Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The gene sequence for \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e, 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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Based on this phylogenetic tree, the gene homology of \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e samples is closely related to \u003cem\u003ePersicaria chinensis\u003c/em\u003e, \u003cem\u003ePolygonum chinense\u003c/em\u003e var. \u003cem\u003eparadoxum\u003c/em\u003e, \u003cem\u003ePersicaria nepalensis\u003c/em\u003e, \u003cem\u003ePersicaria sphaerocephala\u003c/em\u003e, and the original subspecies \u003cem\u003ePersicaria runcinata\u003c/em\u003e. This phylogenetic analysis provided valuable insights into the genetic relationship of this Miao ethnomedicine with its closely related species.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Widely targeted metabolomics analysis\u003c/h2\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Qualitative and quantitative analyses of metabolites\u003c/h2\u003e \u003cp\u003eThe qualitative analysis of primary and secondary mass spectrometry data was performed based on the self-built database Metware Database(MWDB) (Metware Biotechnology Co., Ltd. Wuhan, China) and publicly available metabolite databases. The analysis produced a multi-peak MRM metabolite detection chromatogram and a total ion current spectrum of multiple substances (X ion current), as shown in the supplementary file Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Based on this, the obtained mass spectrometry data were compared with the local metabolite database for qualitative and quantitative analyses of the detected metabolites.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Distribution of metabolite types\u003c/h2\u003e \u003cp\u003eScreened data for \u003cem\u003ePersicaria runcinata\u003c/em\u003e showed 716 detected metabolites across 12 categories. Flavonoids, phenolic acids, and lipid substances were the most abundant, with over 100 enriched metabolites (supplementary file Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). A bar graph was generated for the primary classification of metabolites and enriched metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The pharmacological effects of specific metabolites were analyzed in order to present the data on metabolites with anti-inflammatory effects in \u003cem\u003ePersicaria runcinata\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The Appendix shows the identification numbers, integration values, and corresponding metabolite names of the obtained partial metabolites.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eActive anti-inflammatory ingredients of \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompounds\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePharmacological effects\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCatechin gallate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSubstantially reduces inflammatory damage and downregulates inflammation-related proteins[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEllagic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExhibiting antibacterial and anti-inflammatory effects, it alleviates inflammation by reducing neutrophil recruitment and infiltration, and downregulating pro-inflammatory cytokine expression[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhthalate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDelays onset and progression of osteoarthritis[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndole alkaloids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDemonstrating analgesic and anti-inflammatory effects[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProanthocyanidins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReduces activity of pro-inflammatory mediators and inhibits production of pro-inflammatory factors, decreasing inflammation-induced damage[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePyridoxol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReduces vascular inflammatory reactions to some extent[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGentiopicroside\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExhibits preventive and therapeutic effects on inflammation in rat ankle joint tissues[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSwertiamarin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOffers protective effects against adjuvant-induced arthritis in rats[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInhibits generation of inflammation-related factors, protects nerves, and alleviates neuropathic pain[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExhibits neuroprotective and analgesic properties[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], reducing neuropathic pain by restoring the balance of inflammatory factors in diabetic rats[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Network pharmacology analysis\u003c/h2\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Venn diagram analysis\u003c/h2\u003e \u003cp\u003eThe Venny2.1.0 website was employed to analyze the active ingredient targets of \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e 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.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Interaction network analysis\u003c/h2\u003e \u003cp\u003eThe organized data file was imported into Cytoscape 3.10.1 software for processing to carry out network analysis of \u0026ldquo;\u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e \u0026ndash; active ingredient - intersection targets\u0026rdquo;(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The resulting interaction network diagram consisted of 218 nodes and 421 edges, where the red arrow represent \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e, 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.\u003c/p\u003e \u003cp\u003eAnalysis 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.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 PPI network analysis\u003c/h2\u003e \u003cp\u003eA PPI network diagram was generated after importing \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e and arthritis target data into the STRING database (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Subsequently, the core target proteins were filtered using the Cytoscape 3.10.1 software, and a concise visual analysis was performed using the Cytoscape 3.7.0 software. The core target diagram of \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e in the treatment of arthritis is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB. The Cytoscape 3.7.0 software was employed for visualization, with the node size and color saturation indicating the degree of importance. 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\u0026thinsp;\u0026gt;\u0026thinsp;0.002635, Betweenness unDir\u0026thinsp;\u0026gt;\u0026thinsp;201.10160, and Degree unDir\u0026thinsp;\u0026gt;\u0026thinsp;25.98930 data were used to screen the core targets of \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e 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 (HSP90AA1), GSK3B, and hypoxia-inducible factor 1-alpha (HIF1A). These results underscored the significance of these proteins in the above potential of \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e for arthritis treatment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e3.3.4 GO functional and KEGG pathway enrichment analysis\u003c/h2\u003e \u003cp\u003eBy utilizing the DAVID database to analyze the intersection target genes of \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e and arthritis, a total of 631 BP, 88 CC, and 177 MF terms were retrieved. The top 10 terms were selected based on their P-values for visualization, illustrating various BPs, CCs, and MFs associated with the therapeutic mechanism of \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e in arthritis(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). 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.\u003c/p\u003e \u003cp\u003eA 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.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). 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 \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e in arthritis treatment. These findings revealed potential therapeutic targets for \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e in arthritis treatment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Molecular docking results\u003c/h2\u003e \u003cp\u003eStronger 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 molecular docking results (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) indicated that a ligand\u0026rsquo;s binding affinity to its protein target correlated with its potency, with values below \u0026minus;\u0026thinsp;5.6 kcal/mol suggesting strong binding. It is generally accepted that a compound with a binding energy to the receptor protein of \u0026lt;-5.6 kcal/mol indicates a strong binding [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], with 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 \u003cem\u003ePersicaria runcinata\u003c/em\u003e may effectively treat arthritis through multiple targets. Thus, it was assumed that the active compounds of \u003cem\u003ePersicaria runcinata\u003c/em\u003e 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.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This demonstrated that the core target obtained during network pharmacology research was effectively combined with its corresponding active components.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMolecular docking binding affinities (kcal/mol) of core targets and their ligands\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompounds\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePubChem CIDs\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTarget names\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePDB IDs\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBas\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,8-dihydroxy-2,6-dimethylxanthen-9-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e163195715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePTGS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5kir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-7.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,3'-O-Dimethylellagic Acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5488919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAKT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3o96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-6.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4-Hydroxysphinganine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e122121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2src\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-6.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEllagic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5281855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePTGS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5kir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-6.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOctadeca-11E,13E,15Z-trienoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54564607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePTGS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5kir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-6.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN-Feruloyltyramine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5280537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHSP90AA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2xjx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-6.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN-Feruloyltyramine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5280537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePTGS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5kir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-6.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNaringenin chalcone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5280960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePTGS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5kir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-5.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEllagic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5281855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAKT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3o96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-5.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,3'-O-Dimethylellagic Acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5488919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2src\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-5.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003ePDB ID, protein identifier in protein data bank. \u003csup\u003eb\u003c/sup\u003ePubChem CID, compound identifier in PubChem database. \u003csup\u003ec\u003c/sup\u003eBAs, binding affinity (kcal/mol).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eResearch on \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e, 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 \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e, 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 \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eNetwork 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 \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e used for arthritis treatment. Targets, such as AKT1, CASP3, BCL2, PTGS2, TNF, EGFR, PPARG, HSP90AA1, GSK3B, and HIF1A, 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 \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eIn addition, the study analyzed the homology and systematic evolution of the \u003cem\u003eITS2\u003c/em\u003e gene in \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e, for the first time establishing its DNA barcode. The research methodology involved DNA extraction using the Cetyltrimethylammonium Bromide(CTAB) method, PCR amplification with \u003cem\u003eITS2\u003c/em\u003e sequence primers, and subsequent imaging. Molecular identification of the \u003cem\u003eITS2\u003c/em\u003e 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.\u003c/p\u003e \u003cp\u003eIn summary, the present research studied the material composition of \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e 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.\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThe present study established DNA barcoding for \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e using sequence alignment and phylogenetic analysis. Metabolomics techniques were used to identify 716 compounds, predominantly flavonoids, lipids, and phenolic acids. Active substances found in \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e 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.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe thank International Science Editing (http://www.internationalscienceediting.com ) for editing this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eAll data were generated in-house, and no paper mill was used. All authors agree to be accountable for all aspects of work ensuring integrity and accuracy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConceptualization, Taofeng Lu, Zengcai Liang and Xiang Zhu; Funding acquisition, Xiang Zhu, and Taofeng Lu; Methodology, Zengcai Liang and Xifeng Chen; Project administration, Taofeng Lu and Xiang Zhu; Resources, Zengcai Liang and Xifeng Chen; Supervision, Xifeng Chen and Taofeng Lu; Visualization, Yunrong Xie and Feiyan He; Writing\u0026nbsp;\u0026ndash;\u0026nbsp;original draft,\u0026nbsp;Zengcai Liang,\u0026nbsp;Yunrong Xie\u0026nbsp;and\u0026nbsp;Feiyan He; Writing\u0026nbsp;\u0026ndash;\u0026nbsp;review \u0026amp; editing, Taofeng Lu, Linlin Zhang\u0026nbsp;and\u0026nbsp;Xifeng Chen.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research were funded by the Project of Shanghai Science and Technology Commission (24141900201),\u0026nbsp;the Science and Technology Foundation of Health Commission of Guizhou Province (gzwkj 2021-245), and the Guizhou Province Science and Technology Department Support Plan (QKHJC-ZK[2021]-YB156).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary materials:\u0026nbsp;\u003c/strong\u003eSupplementary material associated with this article can be found in the supplementary files, Table S.docx, Figure S.docx and Metabolomics data for Persicaria runcinata var. sinensis (Hemsl.) Bo Li.xlsx.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability statement:\u003c/strong\u003e All data generated or analyzed during this study are included in this published article and the supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eShen, B. et al. Research Progress of Chemical Constituents and its Pharmacological Activities from the Medicinal Plants of Genus Polygonum. \u003cem\u003eJ. Hunan Univ. Chin. Med.\u003c/em\u003e \u003cb\u003e35\u003c/b\u003e, 63\u0026ndash;70 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHang, Y. et al. Ethnopharmacological study of Polygonum plants in traditional ethnic medicine. Journal of Medicine \u0026amp; Pharmacy of Chinese Minorities. 25 61\u0026ndash;63. https://doi.org10.16041/j.cnki.cn15-1175.2019.05.033. (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYi, L. Polygonum runcinatum. \u003cem\u003eChin. J. 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Effect of swertiamarin on inflammatory factor levels in rats with diabetic peripheral neuropathy. \u003cem\u003eAcad. J. Guangzhou Med. Univ.\u003c/em\u003e \u003cb\u003e47\u003c/b\u003e, 7\u0026ndash;10 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHsin, K. Y. et al. systemsDock: a web server for network pharmacology-based prediction and analysis. Nucleic Acids Res. 44 (2016) W507-13. https://doi.org10.1093/nar/gkw335.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Arthritis, DNA barcode, Miao Ethnomedicine, network pharmacology, Persicaria runcinata var. sinensis (Hemsl.) Bo Li, widely targeted metabolomics","lastPublishedDoi":"10.21203/rs.3.rs-6208115/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6208115/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe present study aimed to investigate the material basis of \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e (Hemsl.) Bo Li using widely targeted metabolomics and network pharmacology techniques. Efforts were also made to establish a DNA barcode for \u003cem\u003ePersicaria runcinata var. sinensis \u003c/em\u003e(Hemsl.) Bo Li. Widely targeted metabolomics technique of Ultra performance liquid chromatography-mass spectrometry(UPLC-MS/MS) was employed to detect and analyze the metabolites in \u003cem\u003ePersicaria runcinata var. sinensis \u003c/em\u003e(Hemsl.) Bo Li. Network pharmacology was used to screen and analyze metabolites with high content and pharmacological effects. DNA extraction, Polymerase chain reaction(PCR) amplification, sequence alignment, and phylogenetic tree construction were performed to establish the DNA barcode. A total of 716 metabolites were detected in Miao ethnomedicine \u003cem\u003ePersicaria runcinata var. sinensis \u003c/em\u003e(Hemsl.) Bo Li, with key targets, enriched functions, and pathways identified using network pharmacology. The DNA sequence for the \u003cem\u003eITS2\u003c/em\u003e primer of \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e(Hemsl.) Bo Li was determined and a phylogenetic tree was generated. Metabolite enrichment in \u003cem\u003ePersicaria runcinata var. sinensis\u003c/em\u003e (Hemsl.) Bo Li revealed potential therapeutic compounds for arthritis. The study’s approach provided theoretical support for understanding the substance basis and therapeutic effects of \u003cem\u003ePersicaria runcinata var. sinensis \u003c/em\u003e(Hemsl.) Bo Li. Additionally, the high homology level with various \u003cem\u003ePolygonum\u003c/em\u003e species provided the foundation for molecular identification in ethnomedicine.\u003c/p\u003e","manuscriptTitle":"Evaluation of the mechanism of the efficacy of Persicaria runcinata var. sinensis (Hemsl.) 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