Medicinal plant exosome anti-inflammatory regeneration mechanism identification method

Abstract

The invention relates to the cross technical field of biomedical technology, bioinformatics and molecular biology, and discloses a medicinal plant exosome anti-inflammatory regeneration mechanism identification method, which comprises the following steps: separating a high-purity exosome from medicinal plant tissue, and characterizing the high-purity exosome to be qualified; carrying out transcriptome and metabolome data acquisition on qualified samples; inputting the standardized data into a bioinformatics analysis process, integrating multiple omics data to identify a key regulation and control module and a path, and proposing a mechanism hypothesis; and finally, designing in vitro and in vivo function verification experiments based on a prediction mechanism. According to the method disclosed by the invention, through in vivo and in vitro linkage verification, the regulation effect of the exosome on inflammatory factors and pathway gene expression is verified at a cellular level in vitro so as to clarify a molecular mechanism; the promotion effect of the exosome on the wound healing rate and the tissue regeneration physiological effect is verified by using an animal model in vivo to form a complete evidence chain, the repeatability is ensured by strictly standardizing technical details, and a scientific basis is provided for development and application of the medicinal plant exosome.

Description

Technical Field

[0001] This invention relates to the interdisciplinary fields of biomedical technology, bioinformatics and molecular biology, specifically a method for identifying the anti-inflammatory and regenerative mechanisms of exosomes in medicinal plants. Background Technology

[0002] Exosomes are nanoscale membrane vesicles secreted by cells, widely distributed in various biological fluids, and play a crucial role in intercellular communication, substance transport, and physiological function regulation. In recent years, plant-derived exosomes (PDEs) have attracted widespread attention due to their unique biological activities. Studies have shown that PDEs possess anti-inflammatory and tissue regeneration potential, and are expected to become important resources in the development of natural drugs, regenerative medicine, medical aesthetic products, and agricultural biotechnology. However, the molecular mechanisms by which PDEs contribute to anti-inflammation and tissue regeneration remain unclear, and systematic and comprehensive identification methods are lacking. Traditional research methods often focus on single-omics analyses or limited functional verification, making it difficult to deeply elucidate the complex composition and regulatory networks of PDEs. Therefore, there is an urgent need for a method that integrates multi-omics data, utilizes advanced bioinformatics tools, and combines in vitro and in vivo functional verification to comprehensively reveal the anti-inflammatory and regenerative molecular mechanisms of plant exosomes, and promote their application and development in related fields. Summary of the Invention

[0003] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a method for identifying the anti-inflammatory and regenerative mechanisms of exosomes in medicinal plants. This method has the advantages of being systematic, multi-dimensional, and precise, and solves the problems of traditional methods being unable to analyze the complex regulatory networks of PDEs and lacking systematic mechanism identification methods.

[0004] (II) Technical Solution

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for identifying the anti-inflammatory and regenerative mechanism of exosomes from medicinal plants, comprising the following steps: Step 1: Isolate high-purity exosomes from medicinal plant tissues and transfer qualified exosome samples to the next step; Step 2: Collect transcriptomic and metabolomic data from qualified exosome samples; Step 3: Input standardized transcriptomic and metabolomic data into the bioinformatics analysis process, integrate multi-omics data through algorithmic tools, identify key regulatory modules and pathways, and propose mechanistic hypotheses; Step 4: Based on the regulatory mechanism predicted in Step 3, design in vitro and in vivo functional verification experiments.

[0006] Preferably, the sample preprocessing in step one includes: S1.1.1 Sample processing: Select fresh medicinal plant tissues, flash freeze them in liquid nitrogen, grind them into powder, suspend them in pre-cooled PBS buffer, and let them stand for 25-30 minutes to allow the cell contents to be fully released; S1.1.2 Differential centrifugation purification: First, place the suspension in a centrifuge and centrifuge at 300×g at 3-5℃ for 10-15 minutes. After centrifugation, transfer the supernatant to a new centrifuge tube and centrifuge at 10,000×g at 3-5℃ for 30-45 minutes. After centrifugation, transfer the supernatant to an ultracentrifuge tube and centrifuge at 100,000×g at 3-5℃ for 65-70 minutes using a Beckman Optima XPN-80 centrifuge. Finally, collect the exosome precipitate. S1.1.3. Sample collection: The collected exosome precipitate was resuspended in a 20%–50% sucrose gradient solution and centrifuged at 100,000×g for 2–2.5 hours to collect high-purity exosomes at a density layer of 30%–40%.

[0007] Preferably, the exosome sample characterization process in step one includes: S1.2.1 Transmission electron microscopy characterization: Exosome samples were negatively stained with 1.95% to 2% phosphotungstic acid and the morphology of exosomes was observed by transmission electron microscopy. Typical exosomes should present a cup-shaped structure with a diameter of 30-150 nm. S1.2.2 Next, the particle size distribution of exosomes was determined using a Malvern NanoSight NS300. S1.2.3. Perform Western blot analysis on exosome samples to detect exosome marker proteins TET8 and Calnexin; S1.2.4 Finally, the exosome protein content was determined by the BCA method, with a standard curve range of 0.1-2 mg / mL.

[0008] Preferably, in step one, when performing transmission electron microscopy characterization, the negative staining time of the exosome sample is controlled at 3-5 minutes, and after staining, excess staining solution should be absorbed with filter paper, and the sample should be air-dried before being observed under an electron microscope; when determining the particle size distribution through nanoparticle tracking analysis, the sample dilution factor needs to be adjusted according to the exosome concentration.

[0009] Preferably, the transcriptome sequencing process in step two is as follows: S2.1.1 Library construction and sequencing: Total RNA was extracted from exosomal tissues using the TRIzol method. RNA integrity was confirmed by Agilent 2100 assay. Small RNA libraries were constructed and sequenced using NovaSeq 6000 (150bp paired ends, ≥20M reads / sample). S2.1.2 Data Analysis: After FastQC quality control and Trimmomatic purification, the raw data were compared with the miRBase database to identify known miRNAs, and miRDeep2 was used to predict new miRNAs to generate a list of differentially expressed genes.

[0010] Preferably, the metabolomics analysis process in step two is as follows: S2.2.1 Chromatographic separation: Metabolites were separated by elution with an ACQUITY UPLC BEH C18 column using a gradient elution of 0.1% to 0.0105% formic acid-acetonitrile. S2.2.2 Mass spectrometry detection: Switch to electrospray ionization positive and negative ion mode, scan the range of 50-1500 m / z, and obtain the characteristic peaks of metabolites; S2.2.3 Data preprocessing: Peak extraction, alignment and normalization were performed using Progenesis QI software, and the data were compared with the HMDB and KNApSAcK databases to generate a metabolite abundance matrix; S2.2.4 Output: Standardized transcriptomic and metabolomic data.

[0011] Preferably, the bioinformatics analysis and mechanism prediction process in step three is as follows: S3.1 Data Integration and Differential Analysis: Using the "PlantExoAnalyzer" tool, input the standardized data from step two, and use the DESeq2 algorithm to screen differentially expressed genes and identify differentially expressed miRNAs and mRNAs; S3.2 Conserved miRNA screening: Align differential miRNA sequences with the miRBase database, analyze cross-species conservation, and generate a candidate list of conserved miRNAs; S3.3 Pathway Enrichment and Network Construction: ClusterProfiler was used to enrich differentially expressed genes using the KEGG / GO pathway, and Cytoscape was used to construct a "miRNA-target gene-pathway" interaction network to identify core regulatory modules; S3.4 Algorithm-assisted prediction: Automated analysis is achieved through R language code, improving analysis efficiency and identifying key pathways and molecules related to anti-inflammatory regeneration; S3.5, Output: Mechanism Hypothesis.

[0012] Preferably, in step three, when performing bioinformatics analysis using the "PlantExoAnalyzer" tool, low-expression genes will be filtered in the differential expression analysis module. When constructing the "miRNA-target gene-pathway" interaction network, the node size is set to be proportional to the connectivity, and the edge color is distinguished according to the regulatory relationship, using Cytoscape software.

[0013] Preferably, the in vitro inflammation model validation process in step four is as follows: S4.1.1 Cell treatment: RAW264.7 macrophages were treated with LPS to induce inflammation for 24-26 hours, and then exosomes at a concentration of 10-100 μg / mL were added for 48-49 hours. S4.1.2 Detection indicators: The concentrations of TNF-α and IL-6 inflammatory factors were measured by ELISA, and the expression of TLR4 and NF-κB p65 inflammation-related genes was detected by qPCR.

[0014] Preferably, the animal regeneration model verification in step four includes: S4.2.1 Model Construction: An 8mm diameter full-thickness skin defect was created on the back of C57BL / 6 mice, and hyaluronic acid gel containing exosomes was applied locally. S4.2.2 Using wound healing rate: ImageJ analyzes the wound area recorded in digital photographs; S4.2.3, Histopathology: H&E staining was used to observe epidermal regeneration, and Masson staining was used to assess collagen deposition; S4.2.4 Immunohistochemistry: Detection of Ki-67 and α-SMA; S4.2.5 Output: Experimental data verify the mechanism hypothesis.

[0015] Compared with the prior art, the present invention provides a method for identifying the anti-inflammatory and regenerative mechanism of exosomes in medicinal plants, which has the following beneficial effects: 1. This invention achieves the beneficial effect of comprehensively revealing the anti-inflammatory and regenerative mechanism of exosomes in medicinal plants through a bottom-up exosome isolation and multi-omics analysis. In particular, the bottom-up isolation process from plant tissue to exosomes ensures that subsequent analysis is based on real exosome components, thereby avoiding interference from impurities and providing a reliable foundation for subsequent mechanism research. Combined with transcriptomic and metabolomic data, the active components and regulatory information of exosomes are comprehensively captured at the gene expression and metabolite levels to construct a "data panorama" and provide multi-dimensional clues and evidence for in-depth analysis of the anti-inflammatory and regenerative mechanism.

[0016] 2. This invention achieves the beneficial effect of efficiently mining key targets and regulatory pathways through bioinformatics analysis and algorithm-assisted prediction. Using tools such as "PlantExoAnalyzer" and algorithms such as DESeq2 and ClusterProfiler, multi-omics data are integrated and analyzed to quickly screen differentially expressed genes and conserved miRNAs, thereby identifying key regulatory modules and pathways. By constructing a "miRNA-target gene-pathway" interaction network, the regulatory relationship is presented intuitively, locking in the core targets and pathways related to anti-inflammatory regeneration, greatly improving the efficiency of experimental research, and thus reducing the blind spots in research.

[0017] 3. This invention achieves the beneficial effect of confirming the complete mechanism from molecular mechanism to physiological effect through in vitro and in vivo verification. In vitro experiments verify the regulatory role of exosomes on inflammatory factors and pathway gene expression at the cellular level to clarify the molecular mechanism. In vivo experiments use animal models to verify the promoting effect of exosomes on physiological effects such as wound healing rate and tissue regeneration, linking molecular mechanism with physiological function to form a complete chain of evidence. Strict standardized technical details and experimental procedures ensure the reproducibility of experiments and the reliability of results, thereby providing a scientific basis for the development and application of medicinal plant exosomes. Attached Figure Description

[0018] Figure 1 This is a diagram for the functional verification test of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figure 1 A method for identifying the anti-inflammatory and regenerative mechanism of exosomes from medicinal plants, comprising the following steps: Step 1: Isolate high-purity exosomes from medicinal plant tissues and transfer qualified exosome samples to the next step. This "bottom-up" separation process from plant tissues to exosomes ensures that subsequent analyses are based on real exosome components. Step 2: Collect transcriptomic and metabolomic data from qualified exosome samples to ensure that the omics analysis is based on real and effective exosome components, so as to comprehensively capture the gene expression profile (transcriptome) and small molecule metabolites (metabolome) of exosomes, providing multidimensional data for mechanism analysis in the next step. Multi-omics data (transcriptome + metabolome) provide a "data panorama" for mechanism prediction, and bioinformatics analysis realizes "data dimensionality reduction" and key target discovery. Step 3: Input standardized transcriptomic and metabolomic data (such as differentially expressed gene lists and metabolite abundance matrices) into the bioinformatics analysis process, integrate multi-omics data through algorithmic tools, identify key regulatory modules and pathways, and propose mechanistic hypotheses. Step 4: Based on the regulatory mechanisms predicted in Step 3 (such as miR159 / NF-κB axis regulation of inflammation), design in vitro and in vivo functional verification experiments. The functional experiments verify the "in vitro molecular mechanism" to the "in vivo physiological effect" layer by layer, forming a complete research chain of "hypothesis proposal → experimental verification → mechanism confirmation".

[0021] Specifically, in step one, sample preprocessing includes: S1.1.1 Sample processing: Select fresh medicinal plant tissues (such as ginseng rhizomes and aloe vera leaves), grind them into powder after quick freezing with liquid nitrogen, suspend them in pre-cooled PBS buffer (containing EDTA and BSA), and let them stand for 25-30 minutes to allow the cell contents to be fully released; S1.1.2 Differential centrifugation purification: First, place the suspension in a centrifuge and centrifuge at 300×g at 3-5℃ for 10-15 minutes to remove cell debris. After centrifugation, transfer the supernatant to a new centrifuge tube and centrifuge at 10,000×g at 3-5℃ for 30-45 minutes to remove large particulate impurities. After centrifugation, transfer the supernatant to an ultracentrifuge tube and centrifuge at 100,000×g at 3-5℃ for 65-70 minutes using a Beckman Optima XPN-80 centrifuge. Finally, collect the exosome precipitate. S1.1.3. Sample collection: The collected exosome precipitate was resuspended in a 20%–50% sucrose gradient solution and centrifuged at 100,000×g for 2–2.5 hours to collect high-purity exosomes at a density layer of 30%–40%.

[0022] Specifically, in step one, the exosome sample characterization process includes: S1.2.1 Transmission Electron Microscopy (TEM) Characterization: Exosome samples were negatively stained with 1.95%–2% phosphotungstic acid. The morphology of exosomes was observed by transmission electron microscopy. Typical exosomes should present a cup-shaped structure with a diameter of 30–150 nm. S1.2.2 Next, use Malvern NanoSight NS300 to determine the particle size distribution of exosomes to ensure that their particle size range conforms to the characteristics of exosomes; S1.2.3. Perform Western blot analysis on the exosome samples to detect the exosome marker proteins TET8 (positive) and Calnexin (negative control) to confirm the purity of the samples and the presence of exosomes; S1.2.4 Finally, the exosome protein content was determined using the BCA method, with a standard curve range of 0.1-2 mg / mL, to ensure that the concentration of the exosome sample was suitable for subsequent experiments.

[0023] Specifically, in step one, during transmission electron microscopy (TEM) characterization, to ensure observation results, the negative staining time of the exosome sample should be controlled at 3-5 minutes. After staining, excess stain should be absorbed with filter paper, and the sample should be allowed to air dry before being placed under the electron microscope for observation. When determining the particle size distribution using nanoparticle tracking analysis (NTA), the sample dilution factor needs to be adjusted according to the exosome concentration to ensure that the particle concentration is within the optimal detection range of the instrument during the detection process (50-200 particles per frame).

[0024] Specifically, in step two, the transcriptome sequencing process (RNA-seq) involves: S2.1.1 Library construction and sequencing: Total RNA was extracted from exosomal tissues using the TRIzol method. RNA integrity was confirmed by Agilent 2100 assay (RIN≥8.0). Small RNA libraries were constructed using the Illumina TruSeq kit and sequenced using NovaSeq 6000 (paired-end 150bp, ≥20M reads / sample). S2.1.2 Data Analysis: After the raw data were subjected to FastQC quality control and Trimmomatic purification, known miRNAs were identified by comparing them with the miRBase database (v22), new miRNAs were predicted using miRDeep2, and a list of differentially expressed genes (including miRNAs and mRNAs) was generated.

[0025] Specifically, the metabolomics analysis process in step two (UPLC-QTOF-MS): S2.2.1 Chromatographic separation: Metabolites were separated using an ACQUITY UPLC BEH C18 column with a gradient elution of 0.1% to 0.0105% formic acid-acetonitrile (flow rate 0.3 mL / min). S2.2.2 Mass spectrometry detection: Switch to electrospray ionization (ESI) positive and negative ion modes, scan the range of 50-1500 m / z, and obtain the characteristic peaks of metabolites; S2.2.3 Data preprocessing: Peak extraction, alignment and normalization were performed using Progenesis QI software, and the data were compared with the HMDB and KNApSAcK databases to generate a metabolite abundance matrix; S2.2.4 Output: Standardized transcriptomic and metabolomic data (such as differentially expressed gene list and metabolite abundance matrix).

[0026] The advantages are: through bottom-up exosome isolation and multi-omics analysis, the beneficial effect of comprehensively revealing the anti-inflammatory and regenerative mechanism of medicinal plant exosomes is achieved. In particular, the bottom-up isolation process from plant tissue to exosomes ensures that subsequent analysis is based on real exosome components, thereby avoiding interference from impurities and providing a reliable foundation for subsequent mechanism research. Combined with transcriptomic and metabolomic data, the active ingredients and regulatory information of exosomes are comprehensively captured at the gene expression and metabolite levels to construct a "data panorama" and provide multi-dimensional clues and evidence for in-depth analysis of the anti-inflammatory and regenerative mechanism.

[0027] Specifically, the bioinformatics analysis and mechanism prediction process in step three: S3.1 Data Integration and Differential Analysis: Using the "PlantExoAnalyzer" tool, input the standardized data from step two, and use the DESeq2 algorithm to screen differentially expressed genes (|log2FC|>1, FDR corrected p<0.05) to identify differentially expressed miRNAs and mRNAs; S3.2 Conserved miRNA screening: Align differential miRNA sequences with the miRBase database, analyze cross-species conservation (e.g., homology of miR159 in plants and mammals), and generate a candidate list of conserved miRNAs. S3.3 Pathway enrichment and network construction: ClusterProfiler was used to enrich differentially expressed genes using the KEGG / GO pathway (p < 0.01). A miRNA-target gene-pathway interaction network was constructed using Cytoscape to identify core regulatory modules (such as the miR159 / NF-κB axis). S3.4 Algorithm-assisted prediction: Automated analysis (such as DESeq2 differential analysis and clusterProfiler pathway enrichment) is achieved through R language code, improving analysis efficiency and identifying key pathways and molecules related to anti-inflammatory regeneration; S3.5, Output: Mechanism hypothesis (e.g., specific miRNAs exert anti-inflammatory effects by regulating the NF-κB pathway).

[0028] Specifically, in step three, when using the "PlantExoAnalyzer" tool for bioinformatics analysis, the differential expression analysis module will filter out genes with low expression (less than 10 reads) to avoid low-quality data interfering with the results. When constructing the "miRNA-target gene-pathway" interaction network, the Cytoscape software is used to set the node size to be proportional to the connectivity and the edge color to be distinguished according to the regulatory relationship (red for activation and blue for inhibition) in order to more intuitively present the characteristics of the regulatory network.

[0029] The advantages are: through bioinformatics analysis and algorithm-assisted prediction, it achieves the beneficial effect of efficiently mining key targets and regulatory pathways. Using tools such as "PlantExoAnalyzer" and algorithms such as DESeq2 and ClusterProfiler, multi-omics data are integrated and analyzed to quickly screen differentially expressed genes and conserved miRNAs, thereby identifying key regulatory modules and pathways. By constructing a "miRNA-target gene-pathway" interaction network, the regulatory relationship is presented intuitively, locking in the core targets and pathways related to anti-inflammatory regeneration, greatly improving the efficiency of experimental research, and thus reducing the blind spots in research.

[0030] Specifically, the in vitro inflammation model validation process in step four: S4.1.1 Cell treatment: RAW264.7 macrophages were induced to have inflammation by LPS (1 μg / mL) for 24-26 h, and then treated with exosomes at a concentration of 10-100 μg / mL for 48-49 h. S4.1.2 Detection indicators: The concentrations of TNF-α and IL-6 inflammatory factors were measured by ELISA, and the expression of TLR4 and NF-κB p65 inflammation-related genes was detected by qPCR.

[0031] Specifically, step four involves validating the animal regeneration model: S4.2.1 Model Construction: An 8mm diameter full-thickness skin defect was created on the back of C57BL / 6 mice, and hyaluronic acid gel containing exosomes was applied locally (once daily for 14 days). S4.2.2 Using wound healing rate: ImageJ analyzes and evaluates the wound area recorded in digital photographs. Evaluation indicators include wound healing rate, collagen deposition, and Ki-67 positive cell count. S4.2.3, Histopathology: H&E staining was used to observe epidermal regeneration, and Masson staining was used to assess collagen deposition; S4.2.4 Immunohistochemistry: Detection of Ki-67 (cell proliferation marker) and α-SMA (fibroblast activation marker). S4.2.5 Output: Experimental data to validate mechanism hypotheses (e.g., exosomes reduce the release of inflammatory factors by inhibiting the NF-κB pathway and accelerate wound healing by promoting Ki-67 expression).

[0032] The advantages are: through in vitro and in vivo verification, the beneficial effect of confirming the complete mechanism from molecular mechanism to physiological effect is achieved. In vitro experiments verify the regulatory role of exosomes on inflammatory factors and pathway gene expression at the cellular level to clarify the molecular mechanism. In vivo experiments use animal models to verify the promoting effect of exosomes on physiological effects such as wound healing rate and tissue regeneration, linking molecular mechanism with physiological function to form a complete chain of evidence. Strict standardized technical details and experimental procedures ensure the reproducibility of experiments and the reliability of results, thus providing a scientific basis for the development and application of medicinal plant exosomes.

[0033] The method of the present invention is applied to actual scenario embodiments 1-3 as follows: Example 1 (Ginseng root and stem exosomes inhibit macrophage inflammation via the miR159-NF-κB axis) Step 1: Select fresh ginseng rhizomes, grind them in liquid nitrogen, and extract exosomes by differential centrifugation (differential centrifugation gradient: 300×g→10,000×g→100,000×g). Purify the samples using a sucrose gradient (30%–40% density layer). Observe the cup-shaped structure of the samples (particle size: 30–150 nm) using TEM. Detect the TET8 positivity and Calnexin negativity of the samples using Western blot. Finally, determine the protein concentration (0.5–1.2 mg / mL) using the BCA method.

[0034] Step 2: RNA-seq sequencing of the transcriptome revealed high expression of miR159 and differential gene enrichment in the NF-κB pathway. UPLC-QTOF-MS of the metabolome detected anti-inflammatory metabolites (such as ginsenoside Rg3).

[0035] Step 3: Bioinformatics prediction of miR159 targeting the NF-κB gene (p65) and construction of an interaction network showing miR159 as the core regulatory node; Step 4: 50 μg / mL ginseng exosomes were added to LPS-induced RAW264.7 cells. ELISA showed a 40%–50% reduction in TNF-α / IL-6, and qPCR confirmed downregulation of NF-κB p65 expression. When exosome gel was applied to a mouse skin defect model, the wound healing rate increased by 30%. Immunohistochemistry showed a 2-fold increase in Ki-67+ cells and upregulation of α-SMA expression.

[0036] Conclusion: Ginseng exosomes reduce inflammation by inhibiting the NF-κB pathway through miR159, while simultaneously promoting fibroblast activation and accelerating regeneration.

[0037] Example 2 (Aloe vera leaf exosomes promote anti-inflammation by activating the PPARγ pathway through metabolites) Step 1: Exosomes were extracted from aloe vera leaves by differential centrifugation (300×g→10,000×g→100,000×g), purified by sucrose gradient, and particle size distribution was determined by NTA method (peak value 80nm). Western blot confirmed TET8 positivity and Calnexin negativity.

[0038] Step 2: Differentially expressed genes in the transcriptome were enriched in the PPARγ pathway, and high abundance of fatty acids (such as linolenic acid) and flavonoids were detected in the metabolome. Step 3: Bioinformatics prediction identifies PPARγ as the key target and its metabolite linolenic acid as a potential activator. Step 4: Exosome treatment of LPS-induced macrophages showed a 60% reduction in IL-6 by ELISA. The PPARγ agonist (GW1929) antagonism experiment confirmed pathway dependence. After applying exosomes to mouse wounds, Masson staining showed a 50% increase in collagen deposition, and H&E staining showed accelerated epidermal regeneration.

[0039] Conclusion: Aloe exosomes inhibit inflammation by activating the PPARγ pathway through metabolites and promote collagen deposition and tissue repair.

[0040] Example 3 (Danshen exosomes promote angiogenesis by regulating the GRF pathway via miR396) Step 1: Extract exosomes from the rhizomes of Salvia miltiorrhiza by differential centrifugation, purify with a sucrose gradient (30%–40% layer), confirm the cup-shaped structure using TEM; verify TET8 positivity using Western blot to rule out Calnexin contamination; Step 2: miR396 was highly expressed in the transcriptome, differentially expressed genes were enriched in the growth regulatory factor (GRF) pathway, and pro-angiogenic components such as tanshinone IIA were detected in the metabolome. Step 3: Bioinformatics prediction of miR396 targeting the GRF5 gene, and network construction showing that it is the core regulatory module; Step 4: Exosome treatment of HUVEC cells showed that the length of blood vessels increased by 1.8 times in tube formation experiments, and qPCR confirmed that GRF5 expression was downregulated. In a mouse wound model, the CD31+ microvessel density of the exosome treatment group increased by 2.3 times, and the wound healing rate increased by 45%.

[0041] Conclusion: Danshen exosomes promote angiogenesis and accelerate wound healing by regulating the GRF pathway through miR396.

[0042] The common verification logic of Examples 1-3 is reflected in the following: clarifying mechanistic clues through multi-omics data (key miRNAs / genes in the transcriptome and characteristic metabolites in the metabolome), and combining in vitro and in vivo verification—focusing on molecular mechanisms in vitro (such as inflammatory factors and pathway gene expression), and verifying physiological effects in vivo (healing rate and tissue regeneration indicators). At the same time, strictly following standardized technical details (differential centrifugation parameters, TEM / NTA / WB characterization and NovaSeq / UPLC-QTOF sequencing platform) ensures the reproducibility of the experiment. Through the systematic verification of Examples 1-3, the scientificity and practicality of the method of this invention in the whole chain of "separation-analysis-verification" are fully demonstrated.

[0043] Examples 4-5 below systematically elucidate the molecular mechanisms of medicinal plant exosomes in anti-inflammation and tissue regeneration through standardized exosome preparation, multi-omics in-depth analysis, and rigorous functional validation. The specific details are as follows: Example 4 (Analysis of the anti-inflammatory mechanism of ginseng exosomes) Step 1: Exosome preparation: Extracted from ginseng rhizome, yield 1.2 mg / g fresh weight, NTA showed a peak particle size of 110 nm, purity >90%; Step 2, Multi-omics analysis: RNA-seq identified differential miR159 (log2FC=2.5, p=0.003); metabolomics detected ginsenoside Rg1 (m / z 823.4 [M+Na]+); Step 3, Target Prediction: miR159 targets TLR4 3'UTR (TargetScan prediction, binding site score ≥90); molecular docking (AutoDock Vina) shows binding energy of -22.5 kcal / mol.

[0044] Functional validation: After exosome intervention, TNF-α secretion in RAW264.7 cells decreased by 58% (p<0.001); TLR4 mRNA expression was downregulated by 40% (qPCR validation).

[0045] Example 2 (Study on the regeneration function of Centella asiatica exosomes) Step 1: Exosome characterization: The main peak of particle size distribution was 95 nm, and the marker protein TET8 was positive; metabolomics identification showed that asiaticoside (m / z 487.3 [M+H]+) was present. Step 2, Pathway Enrichment: KEGG analysis showed activation of the PI3K / Akt pathway (p=0.005), and network analysis suggested that miR396a regulates the PDK1 gene.

[0046] Animal experiments: The wound healing time in the exosome gel treatment group was shortened by 30% compared with the control group (p<0.05); immunohistochemistry showed that the number of Ki-67 positive cells increased by 3 times.

[0047] Summary: Examples 4-5 continue the systematic research logic of "multi-omics analysis - target prediction - functional verification". Precise exosome preparation and characterization (such as NTA particle size analysis and marker protein detection) ensure sample reliability. Example 4 focuses on ginseng exosomes, combining bioinformatics prediction and molecular docking technology to clarify the anti-inflammatory molecular mechanism of miR159 targeting TLR4. In vitro and in vivo experimental data confirm its significant inhibitory effect on inflammatory factor secretion and key gene expression. Example 5 targets Centella asiatica exosomes, using KEGG pathway enrichment and network analysis to identify the PI3K / Akt signaling pathway. Animal models were used to verify its regenerative function of accelerating wound healing and promoting cell proliferation. Both studies reinforced the credibility of the conclusions through quantitative indicators (such as the proportion of shortened healing time and the fold change in gene expression) and statistical analysis. This not only further verifies the universality of the method of this invention in elucidating the mechanism of medicinal plant exosomes, but also provides a theoretical basis and technical demonstration for drug development in the fields of anti-inflammation and tissue repair.

[0048] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for identifying an anti-inflammatory regenerative mechanism of a medicinal plant exosome, characterized in that, Comprising the following steps: Step one, isolate high-purity exosomes from medicinal plant tissues, and transfer the qualified exosome samples to the next step; Step two, collect transcriptome and metabolome data of the qualified exosome samples; Step three, input the standardized data of transcriptome and metabolome into bioinformatics analysis process, integrate multi-omics data through algorithm tools, identify key regulatory modules and pathways, and propose mechanism hypothesis; Step four, design in vitro and in vivo functional verification experiments based on the regulatory mechanism predicted in step three.

2. The method of identifying an anti-inflammatory regenerative mechanism of a medicinal plant exosome according to claim 1, characterized in that: Sample pretreatment in step one: S1.1.1, sample processing: select fresh medicinal plant tissues, grind into powder after quick freezing with liquid nitrogen, suspend in pre-cooled PBS buffer, and stand for 25-30 minutes to release the cell contents; S1.1.2, differential centrifugation purification: first centrifuge the suspension at 300xg for 10-15 minutes at 3-5℃, then transfer the supernatant to a new centrifuge tube and centrifuge at 10,000xg for 30-45 minutes at 3-5℃, then transfer the supernatant to an ultracentrifuge tube, use Beckman OptimaXPN-80 centrifuge at 100,000xg for 65-70 minutes at 3-5℃, and finally collect the exosome precipitate; S1.1.3, collect the sample: resuspend the collected exosome precipitate in 20%-50% sucrose gradient solution, centrifuge at 100,000xg for 2-2.5 hours, and collect high-purity exosomes in the 30%-40% density layer.

3. The method of claim 2, wherein the method is for identifying an anti-inflammatory regenerative mechanism of a medicinal plant exosome. Exosome sample characterization in step one: S1.2.1, transmission electron microscopy characterization: treat the exosome sample with 1.95%-2% phosphotungstic acid negative staining, observe the morphology of the exosomes by transmission electron microscopy, and typical exosomes should present a cup-shaped structure with a diameter of 30-150nm; S1.2.2, use Malvern NanoSight NS300 to measure the particle size distribution of the exosomes; S1.2.3, perform Western blot analysis on the exosome sample to detect the exosome marker proteins TET8 and Calnexin; S1.2.4, finally use the BCA method to measure the protein content of the exosomes, and the standard curve range is 0.1-2 mg / mL.

4. The method of claim 3, wherein the method is for identifying an anti-inflammatory regenerative mechanism of a medicinal plant exosome. In step one, when performing transmission electron microscopy characterization, the negative staining time of the exosome sample should be controlled within 3-5 minutes, and the excess staining solution should be absorbed with filter paper after staining, and then observed under the electron microscope after natural air drying; when measuring the particle size distribution by nanoparticle tracking analysis, the sample dilution factor should be adjusted according to the exosome concentration.

5. The method of identifying an anti-inflammatory regenerative mechanism of a medicinal plant exosome according to claim 1, characterized in that: Transcriptome sequencing process in step two: S2.1.1, library construction and sequencing: extract total RNA from exosomes using TRIzol method, confirm RNA integrity by Agilent 2100 detection, construct small RNA library, and perform NovaSeq 6000 sequencing (double-end 150bp, ≥20M reads / sample); S2.1.2, data analysis: raw data were quality controlled by FastQC and trimmed by Trimmomatic, known miRNAs were identified by aligning miRBase database, new miRNAs were predicted by miRDeep2, and the list of differentially expressed genes was generated.

6. The method of identifying an anti-inflammatory regenerative mechanism of a medicinal plant exosome according to claim 4, characterized in that: The process of metabolomics analysis in step two is as follows: S2.2.1, chromatographic separation: ACQUITY UPLC BEH C18 column was used for separation of metabolites by gradient elution with 0.1%-0.0105% formic acid water-acetonitrile; S2.2.2, mass spectrometry detection: switch the positive and negative ion modes of electrospray ionization, scan range is 50-1500m / z, and the characteristic peaks of metabolites are obtained; S2.2.3, data preprocessing: peak extraction, alignment and normalization are performed by Progenesis QI software, and the HMDB and KNApSAcK databases are compared to generate a metabolite abundance matrix; S2.2.4, output: normalized transcriptome and metabolome data.

7. The method of identifying an anti-inflammatory regenerative mechanism of a medicinal plant exosome according to claim 1, characterized in that: The process of bioinformatics analysis and mechanism prediction in step three is as follows: S3.1, data integration and differential analysis: using "PlantExoAnalyzer" tool, input the normalized data of step two, screen differentially expressed genes by DESeq2 algorithm, identify differential miRNAs and mRNAs; S3.2, screening of conserved miRNAs: align the differential miRNA sequences with the miRBase database, analyze the cross-species conservation, and generate a list of candidate conserved miRNAs; S3.3, pathway enrichment and network construction: use ClusterProfiler to perform KEGG / GO pathway enrichment on differential genes, construct "miRNA-target gene-pathway" interaction network by Cytoscape, and identify core regulatory modules; S3.4, algorithm-assisted prediction: realize automatic analysis through R language code, improve analysis efficiency, and lock key pathways and molecules related to anti-inflammatory regeneration; S3.5, output: mechanism hypothesis.

8. The method of identifying an anti-inflammatory regenerative mechanism of a medicinal plant exosome according to claim 7, characterized in that: In the process of bioinformatics analysis using "PlantExoAnalyzer" tool in step three, low-expression genes will be filtered in the differential expression analysis module, and in the construction of "miRNA-target gene-pathway" interaction network, the node size is set to be proportional to the connectivity, and the color of the edge is distinguished according to the regulatory relationship by Cytoscape software.

9. The method of identifying an anti-inflammatory regenerative mechanism of a medicinal plant exosome according to claim 1, characterized in that: The process of in vitro inflammation model verification in step four is as follows: S4.1.1, cell treatment: after RAW264.7 macrophages are induced by LPS for 24-26h, 10-100μg / mL exosomes are added for 48-49h; S4.1.2, detection index: ELISA method is used to detect the concentrations of TNF-α and IL-6 inflammatory factors, and qPCR is used to detect the expressions of TLR4 and NF-κB p65 inflammation-related genes.

10. The method of identifying an anti-inflammatory regenerative mechanism of a medicinal plant exosome according to claim 1, characterized in that: Animal regeneration model verification in step four: S4.2.1, model construction: make a full-thickness skin defect with a diameter of 8mm on the back of C57BL / 6 mice, and apply hyaluronic acid gel containing exosomes locally; S4.2.

2. Wound healing rate: ImageJ analysis of digital photographs of wound area; S4.2.

3. Histopathology: H&E staining to observe epidermal regeneration, Masson staining to assess collagen deposition; S4.2.

4. Immunohistochemistry: Detection of Ki-67 and a-SMA; S4.2.

5. Output: Experimental data validate mechanism hypothesis.

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