Method for detecting polygonatum kingianum rhizome metabolites under different forest medicine compound systems based on UPLC-MS / MS (Ultra Performance Liquid Chromatography-Mass Spectrometry / Mass Spectrometry)

Abstract

The invention belongs to the technical field of chemical analysis, and particularly relates to a method for detecting polygonatum kingianum rhizome metabolites under different forest medicine compound systems based on UPLC-MS / MS. The detection method provided by the invention can accurately detect the contents of the polygonatum kingianum rhizome metabolites under different forest medicine compound systems, and lays a foundation for researching the relationship and difference between the polygonatum kingianum rhizome metabolites under different forest medicine compound systems.

Description

Technical Field

[0001] This invention belongs to the field of chemical analysis technology, specifically relating to a method for detecting metabolites of Polygonatum yunnanensis rhizomes under different forest-medicine complex systems based on UPLC-MS / MS. Background Technology

[0002] Yunnan Polygonatum is one of the important source plants of the medicinal herb Polygonatum. Its dried rhizome is sweet and neutral in nature, and enters the spleen, lung, and kidney meridians, making it a typical example of food and medicine sharing the same origin. Medicinally, it has significant effects such as tonifying qi and nourishing yin, strengthening the spleen, moistening the lungs, and benefiting the kidneys. It can effectively treat various symptoms such as spleen and stomach qi deficiency, fatigue, stomach yin deficiency, dry mouth and poor appetite, lung deficiency and dry cough, consumptive cough with hemoptysis, deficiency of essence and blood, soreness and weakness of the waist and knees, premature graying of hair, and internal heat and thirst. From ancient medical classics to modern clinical practice, the medicinal value of Yunnan Polygonatum has been continuously explored and confirmed. For example, the *Mingyi Bielu* (Records of Famous Physicians) from the Northern and Southern Dynasties period recorded its properties and effects. Subsequent ancient herbal texts have also emphasized its medicinal effects, especially its role in nourishing yin and moistening dryness, strengthening the spleen and replenishing qi, and benefiting the kidneys and strengthening the body. Modern medical research has shown that Polygonatum yunnanense contains numerous active ingredients such as polysaccharides, steroidal saponins, flavonoids, amino acids and trace elements, volatile components, lignin and alkaloids. These components endow Polygonatum yunnanense with various pharmacological effects, including anti-tumor, immune regulation, anti-Alzheimer's disease, myocardial protection, anti-fatty liver, kidney protection, bone protection, blood sugar regulation and anti-aging. It plays a positive role in the treatment of tuberculosis, diabetes, respiratory diseases and cardiovascular diseases.

[0003] The forest-medicine complex system, as an ecosystem integrating multiple forestry elements, provides a diverse and unique environment for the growth of *Polygonatum yunnanensis*. Researchers have long been studying the metabolites of *Polygonatum yunnanensis* rhizomes, achieving certain results in chemical composition analysis. Using various advanced analytical techniques, such as high-performance liquid chromatography (HPLC), mass spectrometry (MS), and nuclear magnetic resonance (NMR), a variety of chemical components have been identified in the rhizomes of *Polygonatum yunnanensis*, including polysaccharides, steroidal saponins, flavonoids, amino acids, trace elements, volatile components, lignin, and alkaloids. However, research on the metabolites of *Polygonatum yunnanensis* rhizomes under different forest-medicine complex systems is significantly insufficient. Most studies focus only on the growth status and composition analysis of *Polygonatum yunnanensis* under single cultivation models, lacking comparative studies of *Polygonatum yunnanensis* in different forest-medicine complex systems. Summary of the Invention

[0004] The purpose of this invention is to provide a method for detecting metabolites of Polygonatum yunnanense rhizomes under different forest-medicine compound systems based on UPLC-MS / MS. The detection method provided by this invention can accurately detect the content of metabolites of Polygonatum yunnanense rhizomes under different forest-medicine compound systems, laying the foundation for studying the relationship and differences between metabolites of Polygonatum yunnanense rhizomes under different forest-medicine compound systems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: This invention provides a method for detecting metabolites of Polygonatum yunnanense rhizomes in different forest-medicine complex systems based on UPLC-MS / MS, comprising the following steps: The sample to be tested and the methanol-water internal standard extract were mixed to obtain the test solution; The content of metabolites in the analyte was obtained by ultra-high performance liquid chromatography-tandem mass spectrometry. The separation conditions for ultra-high performance liquid chromatography (UHPLC) included: a C18 column; mobile phase A being formic acid aqueous solution and mobile phase B being formic acid acetonitrile solution; gradient elution; flow rate of 0.35 mL / min; column temperature of 40℃; and injection volume of 2 μL. The gradient elution procedure is as follows: 0.00 ~ 9 min: The volume percentage of mobile phase B increases linearly from 5% to 95%; 9.00 ~ 10.00 min: The volume percentage of the mobile phase B is 95%; 10.00~11.10 min: The volume percentage of mobile phase B decreased linearly from 95% to 5%; 11.10~14.0 min: The volume percentage of mobile phase B is 5%; Mass spectrometry conditions included: electrospray ionization source, ion source temperature 500℃; QQQ scan in MRM mode.

[0006] Preferably, the methanol-water internal standard extract is prepared by dissolving the standard in an aqueous methanol solution.

[0007] Preferably, the volume concentration of the methanol-water solution is 70%; and the concentration of the methanol-water internal standard extract is 250 μg / mL.

[0008] Preferably, the injection volume of the ultra-high performance liquid chromatography is 2 μL; the flow rates of the mobile phases A and B are 0.35 mL / min.

[0009] Preferably, the chromatographic column is an Agilent SB-C18 column; the column temperature is 40°C.

[0010] Preferably, the volume concentration of formic acid in the formic acid aqueous solution is 0.1%; the volume concentration of formic acid in the formic acid acetonitrile solution is 0.1%.

[0011] Preferably, the ion spray voltage is 5500 V in positive ion mode and -4500 V in negative ion mode; the gas pressures of ion source gas I, gas II, and curtain gas are set to 50, 60, and 25 psi, respectively; the collision gas is set to medium; and the collision-induced ionization parameter is set to high.

[0012] Preferably, the forest-medicinal herb compound system includes one or more of the following: the Yunnan Polygonatum-Economic Walnut Forest-Medicinal Herb Compound System, the Yunnan Polygonatum-Coniferous Pine Forest-Medicinal Herb Compound System, and the Yunnan Polygonatum-Broadleaf Melon Forest-Medicinal Herb Compound System.

[0013] Preferably, the mass-to-volume ratio of the sample to be tested and the methanol-water internal standard extract is 30 mg: 1500 μL.

[0014] Preferably, the metabolites include flavonoids, amino acids and their derivatives, alkaloids, lipids, terpenoids, phenolic acids, lignans and coumarins, nucleotides and their derivatives, organic acids, steroids, quinones, tannins and other components.

[0015] The detection method provided by this invention can accurately detect the content of metabolites in the rhizomes of Polygonatum yunnanense under different forest-medicine composite systems, laying the foundation for studying the relationship and differences among the metabolites in the rhizomes of Polygonatum yunnanense under different forest-medicine composite systems. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 CV distribution plot (A) and PCA score plot (B) for different treatments; Figure 2 Clustering plots (A) and volcano plots (B) for different treatments; where a, UPyF vs UAn; b, UPyF vs UJR; c, UAn vs UJR; Figure 3 A scatter plot of differentially metabolized substances; Figure 4 Violin plots of differential metabolites under UPyF and UAn treatment conditions; Figure 5 Violin plots of differential metabolites under UPyF and UJR treatments; Figure 6 Violin plots of differential metabolites under UAn and UJR treatments; Figure 7 A bar chart showing the fold difference under UPyF and UAn treatment conditions; Figure 8 Bar chart showing the fold difference under UPyF and UJR treatment conditions; Figure 9 A bar chart showing the fold difference under UAn and UJR treatment conditions; Figure 10 K-Means diagram of differential metabolites; Figure 11 This is the metabolic pathway for the biosynthesis of isoflavones (MetMap180); Figure 12 Linoleic acid metabolic pathway (ko00591); Figure 13 Box plots of soil factors for Polygonatum yunnanensis under different forest-medicine composite systems; Figure 14 For soil microbial sparseness curves and species numbers; Figure 15 The variation of soil microbial diversity indices under different forest-medicinal herb composite systems is shown; where A is the Shannon index and B is the Chao index. Figure 16 For the analysis of β-diversity of soil microorganisms; Figure 17 The dominant phyla and genera of soil microorganisms under different forest-medicine composite systems are represented, where A represents the phylum level of soil microorganisms and B represents the genus level of soil microorganisms. Figure 18 Principal coordinate analysis (PCoA) of soil microorganisms, where A represents the genus level; B represents the KEGG level; and C represents the NOG level. Figure 19 Analysis of differential microbial communities in soil under the economic walnut forest-Polygonatum yunnanense conditions; Figure 20 Analysis of differential microbial communities in soil under coniferous forest conditions of Pinus yunnanensis-Polygonatum yunnanensis; Figure 21 Analysis of differential microbial community in soil under broadleaf forest conditions of *Melon sieboldii*-*Polygonatum yunnanense*. Figure 22 The complexity of soil microbial networks is categorized as follows: A) Economic walnut forest-Polygonatum yunnanense; B) Coniferous forest-Pinus yunnanense; C) Broadleaf forest-Melon sylvestris yunnanense; a) Pure stands; b) Forest-medicinal herb complex system. Figure 23 The effects of different treatments on the stability of microbial networks; where A is the proportion of nodes remaining after randomly removing some nodes; and B is the proportion of nodes remaining after deleting 50% of the nodes. Figure 24PCoA analysis for genes encoding carbon / nitrogen / sulfur cycle functions (A), correlation analysis between differentially expressed genes and differentially expressed microbial genera (B); a, economic walnut forest - Polygonatum yunnanense; b, coniferous forest - Pinus yunnanense; c, broadleaf forest - Melon sylvestris var. yunnanense; Figure 25 Structural equation modeling (SEM) analysis of different forest-medicine composite systems is used. The width and number on the arrows represent path strength and effectiveness coefficient, respectively, and blue or red arrows represent positive or negative causal relationships, respectively. A, Economic walnut forest - Polygonatum yunnanense; B, Coniferous forest - Pine yunnanense; C, Broadleaf forest - Melon sieboldii - Polygonatum yunnanense; a, Path; b, Standardization effect; p<0.05, p<0.01, p<0.001; Figure 26 A circular diagram showing the composition of metabolite categories. Detailed Implementation

[0018] This invention provides a method for detecting metabolites of Polygonatum yunnanense rhizomes in different forest-medicine complex systems based on UPLC-MS / MS, comprising the following steps: The sample to be tested and the methanol-water internal standard extract were mixed to obtain the test solution; The content of metabolites in the analyte was obtained by ultra-high performance liquid chromatography-tandem mass spectrometry. The separation conditions for ultra-high performance liquid chromatography (UHPLC) include: a C18 column; mobile phase A being formic acid aqueous solution and mobile phase B being formic acid acetonitrile solution; gradient elution; and the gradient elution program is as follows: 0.00 ~ 9 min: The volume percentage of mobile phase B increases linearly from 5% to 95%; 9.00 ~ 10.00 min: The volume percentage of the mobile phase B is 95%; 10.00~11.10 min: The volume percentage of mobile phase B decreased linearly from 95% to 5%; 11.10~14.0 min: The volume percentage of the mobile phase B is 5%; Mass spectrometry conditions included: electrospray ionization source, ion source temperature 500℃; QQQ scan in MRM mode.

[0019] As one embodiment of the present invention, the forest-medicine composite system includes one or more of the following: the Yunnan Polygonatum-Economic Walnut Forest Forest-Medicine Composite System, the Yunnan Polygonatum-Coniferous Pine Forest-Medicine Composite System, and the Yunnan Polygonatum-Broadleaf Melon Forest-Medicine Composite System.

[0020] As one embodiment of the present invention, the preferred method for preparing the methanol-water internal standard extract is to dissolve the standard in an aqueous methanol solution; the volume concentration of the aqueous methanol solution is 70%; and the concentration of the methanol-water internal standard extract is 250 μg / mL.

[0021] In one embodiment of the present invention, the chromatographic column is preferably an Agilent SB-C18 column; the volume concentration of formic acid in the formic acid aqueous solution is preferably 0.1%; the volume concentration of formic acid in the formic acid acetonitrile solution is preferably 0.1%; the column temperature is 40℃; the flow rates of mobile phases A and B are 0.35 mL / min; and the injection volume is 2 μL.

[0022] As one embodiment of the present invention, the mass spectrometry conditions preferably include: ion spray voltage: 5500 V in positive ion mode; -4500 V in negative ion mode; gas pressures of ion source gas I, gas II and curtain gas are set to 50, 60 and 25 psi, respectively; collision gas is set to medium; and collision-induced ionization parameter is set to high.

[0023] To further illustrate the present invention, the following detailed description of the invention's solutions, in conjunction with the accompanying drawings and embodiments, is provided, but should not be construed as limiting the scope of protection of the present invention.

[0024] Reagents and instruments involved in the examples: Table 1 Information on Standards and Reagents

[0025] Table 2 Instrument Information

[0026] Example 1 1.1 Extraction of dried samples (1) Biological samples were placed in a freeze dryer (Scientz-100F) and freeze-dried under vacuum for 63 h; (2) Grind into powder using a grinder (MM 400, Retsch) (30 Hz, 1.5 min); (3) Weigh 30mg of sample powder and add 1500μL of 70% methanol-water internal standard extraction solution pre-cooled at -20℃ (if less than 30mg, add 1500μL of extraction solution for every 30mg sample). The internal standard extraction solution is prepared by dissolving 1 mg of standard (2-chlorophenylalanine, purity: 98%, manufacturer: Bailingwei, batch number: LBCOR15, CAS: 14091-11-3, internal standard concentration: 1PPM (mg / L)) in 1 mL of 70% methanol-water to prepare a 1000 μg / mL standard stock solution. The 1000 μg / mL stock solution is further diluted with 70% methanol to prepare a 250 μg / mL internal standard solution. (4) Vortex once every 30 minutes, each lasting 30 seconds, for a total of 6 vortices; (5) After centrifugation (12000 rpm, 3 min), aspirate the supernatant, filter the sample with a microporous membrane (0.22 μm pore size), and store it in a sample vial for UPLC-MS / MS analysis.

[0027] 1.2 Chromatographic and Mass Spectrometric Acquisition Conditions The data acquisition instrument system mainly includes ultra-high performance liquid chromatography (ExionLC). TM AD) and tandem mass spectrometry; Ultra-high performance liquid chromatography (UHPLC) conditions included: Column: Agilent SB-C18 (1.8 µm, 2.1 mm) The column was 100 mm thick. Mobile phase A was 0.1% formic acid aqueous solution, and mobile phase B was 0.1% formic acid acetonitrile solution. The gradient elution program was as follows: 0.00 ~ 9 min: the volume percentage of mobile phase B increased linearly from 5% to 95%; 9.00 ~ 10.00 min: the volume percentage of mobile phase B was 95%; 10.00 ~ 11.10 min: the volume percentage of mobile phase B decreased linearly from 95% to 5%; 11.10 ~ 14.0 min: the volume percentage of mobile phase B was 5%; the flow rate was 0.35 mL / min; the column temperature was 40℃; and the injection volume was 2 μL.

[0028] Mass spectrometry conditions included: electrospray ionization (ESI) temperature of 500 °C; ion spray voltage (IS): 5500 V (positive ion mode) / -4500 V (negative ion mode); ion source gas I (GSI), gas II (GSII), and curtain gas (CUR) were set to 50, 60, and 25 psi, respectively; collision-induced ionization parameter was set to high; QQQ scan used MRM mode with collision gas (nitrogen) set to medium.

[0029] 1.3 Sample Under the conditions of the Yunnan Polygonatum-economic walnut forest-medicinal plantation complex system, Yunnan Polygonatum (UJR) was studied. Polygonatum yunnanense (UPyF) under the combined system of Polygonatum yunnanense and pine forest in Yunnan coniferous forest. Under the conditions of the Yunnan Polygonatum-broadleaf forest-winter melon forest-medicinal plant composite system, Yunnan Polygonatum (UAn) was used. Table 3 Sample Number Information

[0030] 1.4 Data Analysis 1.4.1 Figure 26 A circular diagram was drawn to classify all detected metabolites according to their composition, specifically: flavonoids (15.11%), amino acids and their derivatives (13.64%), alkaloids (12.84%), lipids (9.82%), terpenoids (9.02%), phenolic acids (8.36%), lignans and coumarins (5.24%), nucleotides and their derivatives (3.4%), organic acids (4.39%), steroids (1.65%), quinones (0.85%), tannins (0.14%), and other components (15.53%). Flavonoids and amino acids and their derivatives were the most numerous.

[0031] 1.4.2 Differential Soil Metabolites under Different Treatments The coefficient of variation (CV) reflects the dispersion of the data. A higher percentage (over 85%) of substances in the QC sample have CV values ​​less than 0.5, indicating relatively stable experimental data. Conversely, a higher percentage (over 75%) of substances in the QC sample have CV values ​​less than 0.3, indicating very stable experimental data. The CV distribution diagram of this invention is shown below. Figure 1 In section A, experimental data showed that the proportion of substances with a CV value less than 0.5 in the QC sample was higher than 95%, and the proportion of substances with a CV value less than 0.3 in the QC sample was higher than 84%. This indicates that the experimental data of Rhizoma Polygonatum yunnanensis in Yunnan were very stable under different forest-medicine compound system conditions.

[0032] 1.4.3 Principal Component Analysis Principal component analysis (PCA) was performed on the metabolite detection results of different samples (including quality control samples). The PCA score plots of the mass spectrometry data of each group of samples and quality control samples are shown below. Figure 1 The results in section B showed that different tree species (walnut forest, Yunnan pine and winter melon) led to significant differences in the differential metabolites of the rhizomes of Polygonatum yunnanense in the forest-medicine complex system.

[0033] 1.4.4 Cluster Analysis The measured metabolite content data were processed using unit variance scaling (UV), and heatmaps were generated using the ComplexHeatmap package in R software. Hierarchical cluster analysis was performed on the accumulation patterns of metabolites among different samples. The results are shown in [Figure number missing]. Figures 2-3 Cluster analysis revealed that UPyF significantly upregulated the most metabolites compared to UAn and UJR. Further analysis showed that UPyF had 1063 differentially regulated metabolites compared to UAn, with 896 upregulated and 167 downregulated. Among these, amino acids and their derivatives, phenolic acids, nucleotides and their derivatives, flavonoids, quinones, lignans and coumarins, alkaloids, organic acids, lipids, terpenes, and tannins were significantly upregulated, while steroid metabolites were significantly downregulated. Compared to UJR, UPyF had 935 differentially regulated metabolites, with 767 upregulated and 168 downregulated. Among these, amino acids and their derivatives, phenolic acids, nucleotides and their derivatives, flavonoids, quinones, lignans and coumarins, alkaloids, terpenes, lipids, organic acids, and tannins were significantly upregulated, while steroid metabolites were significantly downregulated. Compared with UJR, UAn showed 737 differentially regulated metabolites, with 444 upregulated and 293 downregulated. Among them, quinones, terpenes, and tannins were significantly upregulated, while amino acids and their derivatives, phenolic acids, nucleotides and their derivatives, flavonoids, lignans and coumarins, alkaloids, organic acids, steroids, and lipids were significantly downregulated. The statistics are shown in Table 4.

[0034] Table 4. Differential metabolites under different treatments

[0035] 1.4.5 Determination of specific differentially metabolites under different treatments For the differentially metabolites identified based on screening criteria in each group comparison, the top 20 differentially metabolites with the highest VIP values ​​in the OPLS-DA model were selected. For example... Figure 4 As shown in Table 5, compared with UAn, 19 differentially metabolites were significantly upregulated in UPyF, belonging to flavonoids (7), phenolic acids (4), lignans and coumarins (1), organic acids (1), amino acids and their derivatives (1), alkaloids (1), and other categories (4). Meanwhile, one differentially metabolite was significantly downregulated in UPyF (amino acids and their derivatives) (VIP>2, p<0.001).

[0036] Table 5. Classification of Differential Metabolites

[0037] like Figure 5As shown in Table 6, compared with UJR, 17 differentially metabolites were significantly upregulated in UPyF, belonging to organic acids (1), flavonoids (6), lignans and coumarins (3), phenolic acids (1), amino acids and their derivatives (1), nucleotides and their derivatives (1), and other categories (4). Meanwhile, 3 differentially metabolites were significantly downregulated in UPyF: terpenes (2) and amino acids and their derivatives (1) (VIP>1, p<0.001).

[0038] Table 6. Classification of Differential Metabolites

[0039] like Figure 6 As shown in Table 7, compared with UJR, five differentially metabolites in UAn were significantly upregulated, belonging to phenolic acids (2), amino acids and their derivatives (1), lignans and coumarins (1), and nucleotides and their derivatives (1). Meanwhile, 15 differentially metabolites in UAn were significantly downregulated, belonging to terpenes (8), other metabolites (2), tannins (1), amino acids and their derivatives (2), lignans and coumarins (1), and alkaloids (1) (VIP>1, p<0.001).

[0040] Table 7 Classification of Differential Metabolites

[0041] 1.4.6 Detailed Changes in Metabolites of Rhizome of Polygonatum yunnanense To further illustrate the detailed changes in metabolites in the rhizomes of *Polygonatum yunnanensis* under the three forest-medicine complex systems, this study selected the top 20 metabolites with the largest fold differences in each group for analysis, such as... Figure 7 As shown, compared with UAn, UPyF contained a significant increase in 19 differentially metabolites, namely other metabolites ((2Z)-2-[(3,4-dimethoxyphenyl)methylidene]-5,6-dimethoxy-3H-inden-1-one), flavonoids (3'-Methoxydaidzin-4'-O-glucoside / iso-5,7-dihydroxy-6,8-dimethyl-3-(4'-hydroxy-3'-methoxybenzyl)chroman-4-one). / 5,7-dihydroxy-6,8-dimethyl-3-(2'-hydroxy-4'-methoxybenzyl)-chroman-4-one / 5,7-Dihydroxy-6,8-dimethyl-3-(3'-hydroxy-4'-methoxybenzyl)-chroma4-one ), phenolic acids ((2R,3R,4S,5S,6R)-2-[3-(2,4-dihydroxyphenyl)propoxy]-6-(hydroxymethyl)oxane-3,4,5-trio), lignans and coumarins (Verrucosin / saikolignanoside D / Horsfieldin) (e.g., Cagayanone A, Acutissimalignan B, dihydrosesamin). However, only one differentially metabolized substance was significantly increased in UAn, specifically the steroid (Ruscogenin-1-O-carboxyglucosyl(1,2)rhamnoside). Figure 8 As shown, compared with UJR, UPyF contains a significant increase in 19 differentially metabolized substances, namely flavonoids (5,7-dihydroxy-3-(4'-hydroxybenzyl)-chroman-4-on / Desmethyl-ophiopogonone B / Disporopsin / 5,7-dihydroxy-3-(2',4'-dihydroxybenzyl)-chroman-4-one / Disporopsin / ), lignans and coumarins (Olivil-4'-O-glucoside / Cagayanone A / Alangilignoside D / ), alkaloids (3-(4-hydroxyphenyl)-N-(4-hydroxystyryl)acrylamide / N-coumaroyl tyramine 4'-O-beta-D-glucoside / Tyrosine-triglucoside / ), amino acids and their derivatives (Glu-Ala-Leu / ), etc. In the UJR, only one differentially metabolite was significantly increased, specifically steroids (Polygodoside G). For example... Figure 9As shown, compared with UJR, UAN contains a significant increase in 9 differentially metabolites, namely phenolic acids (Phloretate), flavonoids (Quercetin 3-O-beta-D-glucosyl-(1->2)-beta-D-glucoside), alkaloids (3-(4-hydroxyphenyl)-N-(4-hydroxystyryl)acrylamide / Peimisine / Veratrobasine), amino acids and their derivatives (Leu-Glu-Val / Ile-Asp-Leu), others (4'-Hydroxyacetophenone), steroids (Ruscogenin-1-O-carboxyglucosyl(1,2)rhamnoside), etc. Eleven differentially metabolites were significantly increased in UJR, specifically lignin and coumarin (Scopolin / Dehydrodiconiferyl alcohol-gamma'-O-glucoside), other metabolites ((2R,3S,4S)-4-(hydroxymethyl)oxolane-2,3,4-triol), and terpenoids (Cannabifolin C Flavonoids (Quercetin-3-O-robinobioside / Gallocatechin 3-O-gallate / Petunidin-3-O-(6''-Op-coumaroyl)glucoside-5-O-rhamnoside / Cremastranone), amino acids and their derivatives (N-Methyl-α-aminoisobutyric acid / Arginylhistidine), nucleotides and their derivatives (2'-Deoxyinosine 5'-phosphate).

[0042] 1.4.7 Trends in the relative content of metabolites in different groups To investigate the relative content trends of metabolites in different groups, all differentially identified metabolites in the comparison groups were subjected to unit variance scaling (UV) processing, followed by K-means cluster analysis. Figure 10 As shown, there were ten different trends in the three treatment groups. Among them, the UPyF treatment showed significantly higher levels of differentially metabolites than the UJR and UAn treatments in 5 groups, the UAn treatment showed significantly higher levels of differentially metabolites than the UJR and UPyF treatments in 3 groups, and the UJR treatment showed significantly higher levels of differentially metabolites than the UAn and UPyF treatments in 2 groups.

[0043] 1.4.8 Changes in the enrichment of metabolic pathways of Polygonatum yunnanensis under different forest-medicine complex systems Compared with UJR and UAn treatments, UPyF treatment showed the highest number of upregulated and differentially expressed metabolites. Therefore, KEGG pathway enrichment analysis was performed on UPyF treatment. The results indicated that these differentially expressed metabolites were mainly enriched in the biosynthesis of homoisoflavones, caffeic acid derivatives, linoleic acid metabolism, p-coumarol glycosides, and cinnamic acid derivatives (p<0.05). Furthermore, significantly differentially expressed metabolites in UPyF were screened out, such as… Figures 11-12 As shown, significantly different metabolites were found to be mainly enriched in the biosynthesis of isoflavones (MetMap180) and the linoleic acid metabolic pathway (ko00591) in lipid metabolism. Specifically, in the isoflavone biosynthesis pathway, 15 differentially expressed metabolites were significantly increased in UPyF. Furthermore, in the linoleic acid metabolic pathway, the levels of differentially expressed metabolites (Arachidonate, 12,13-DHOME, (7S,8S)-DiHODE, 9,10-Dihydroxy-12,13-epoxyoctadecanoate, 9,12,13-TriHOME) were significantly increased in UPyF treatment, while the level of 9(S)-HPODE was significantly decreased. Metagenomic analysis revealed that linoleic acid 8R-lipoxygenase (EC: 1.13.11.60) had the highest expression level in UPyF treatment.

[0044] Example 2 This invention also analyzes the relationships between soil factors, microbial α-diversity, soil microbial community composition, soil microbial function, microbial network complexity, microbial network stability, and rhizome metabolites of *Polygonatum yunnanense* under the conditions of *Polygonatum yunnanense*-economic walnut forest, *Polygonatum yunnanense*-coniferous pine forest, and *Polygonatum yunnanense*-broadleaf melon forest-dry winter melon integrated system. The specific research is as follows: 2.1 Soil Factor Analysis of Polygonatum yunnanensis under Different Forest-Medicine Composite System Conditions like Figure 13As shown, compared with the soil of a monoculture economic walnut forest (UJRCK), the soil of *Polygonatum yunnanense* (UJR) under the *Polygonatum yunnanense*-economic walnut forest-medicinal herb intercropping system showed a significant increase in total potassium content, while the contents of organic matter, total nitrogen, total phosphorus, hydrolyzable nitrogen, available potassium, available phosphorus, ammonium nitrogen, and nitrate nitrogen significantly decreased. Compared with the soil of a monoculture coniferous *Pinus yunnanensis* (UPyCK), the soil of *Polygonatum yunnanense* (UPyF) under the *Polygonatum yunnanense*-coniferous *Pinus yunnanensis*-medicinal herb intercropping system showed a significant increase in total phosphorus, total potassium, available potassium, available phosphorus, and nitrate nitrogen, while the contents of organic matter, total nitrogen, hydrolyzable nitrogen, and ammonium nitrogen significantly decreased. Compared with the soil of a monoculture broadleaf *Momordica charantia* (UAnCK), the soil of *Polygonatum yunnanense* (UAn) under the *Polygonatum yunnanense*-broadleaf *Momordica charantia*-medicinal herb intercropping system showed a significant increase in organic matter, total nitrogen, total phosphorus, hydrolyzable nitrogen, and available phosphorus, while the contents of total potassium, available potassium, ammonium nitrogen, and nitrate nitrogen significantly decreased.

[0045] 2.2 Soil microbial sparseness curve and species abundance analysis like Figure 14 As shown, the sparse curves of soil microorganisms under different forest-medicinal herb intercropping systems were analyzed, and the sparse curves of most soil microorganisms tended to be flat. The Good's coverage index reached 99.3, and the coverage indices of all samples were high and close to saturation, indicating that the proportion of undetected species in the soil microbial community study was at a low level, and the number and types of detected species could cover the vast majority of microbial species. Furthermore, compared with UJRCK and UAnCK, the number of species of soil microorganisms in UJR and UAn soils was significantly increased. However, there was no significant difference in the number of species of soil microorganisms between UPyCK and UPy. Meanwhile, under the three pure forest models, the number of microbial species was highest in the monoculture Yunnan pine soil, followed by the monoculture walnut forest, and lowest in the *Polygonatum yunnanense* soil. Under the three forest-medicinal herb intercropping systems, the *Polygonatum yunnanense*-*Polygonatum yunnanense* forest-medicinal herb intercropping system had the highest number of soil microbial species, while the *Polygonatum yunnanense*-walnut forest-medicinal herb intercropping system had the lowest number of soil microbial species.

[0046] 2.3 Analysis of soil microbial diversity 2.3.1 Changes in α-diversity of soil microorganisms Changes in the chao and shannon indices of soil microorganisms under different planting patterns are as follows: Figure 15As shown in the figure. Compared with UJRCK, the Shannon index of UJR soil showed no significant difference, while the Chao index increased significantly. Compared with UPyCK, the Shannon index of UPyF soil increased significantly, while the Chao index showed no significant difference. Compared with UAnCK, the Shannon index of UAn soil decreased significantly, while the Chao index increased significantly. Under the three monoculture models, the variation pattern of soil microbial Shannon index was UJRCK>UAnCK>UPyCK, and the variation pattern of soil microbial Chao index was UPyCK>UJRCK>UAnCK. Under the three forest-medicinal herb compound systems, the variation pattern of soil microbial Shannon index was UJR>UAn>UPyF, and the variation pattern of soil microbial Chao index was UAn>UPyF>UJR.

[0047] As shown in Table 8, the effects of different treatments and different forest-medicine composite systems on soil microbial α-diversity were analyzed using one-way ANOVA in the main effects. The three treatments (walnut forest, Yunnan pine, and winter melon), the two systems (pure forest and forest-medicine composite), and the interaction effects between treatments and systems all significantly affected soil microbial α-diversity. Among them, the Yunnan pine treatments (UPyCK and UPyF) had the greatest impact on α-diversity (0.9285), followed by winter melon (UAn and UAn) (0.9017), while the walnut forest treatments (UJRCK and UJR) had the smallest impact on α-diversity (0.8812).

[0048] Table 8. Effects of treatments and systems on soil microbial α-diversity.

[0049] 2.3.2 Changes in β-diversity of soil microorganisms The Bray-Curtis distance algorithm was used to determine the changes in β-diversity of soil microbial communities under different planting patterns, such as... Figure 16 As shown. Compared with UJRCK, there was no significant difference in β-diversity in UJR soil. Compared with UPyCK and UAnCK, β-diversity in both UPyF and UAn soils was significantly increased. Under the three monoculture models, soil microbial β-diversity was significantly increased in UPyCK soil, while the difference was not significant in UJRCK and UAnCK soils. Under the three forest-medicinal herb complex systems, the variation pattern of soil microbial β-diversity was UPyF>UAn>UJR.

[0050] 2.4 Community structure characteristics of soil microorganisms 2.4.1 Clustering results of soil microorganisms at different taxonomic levels Under different forest-medicine compound treatments, the differences in soil microorganisms of *Polygonatum yunnanensis* in the forest-medicine compound system were analyzed at the kingdom, phylum, class, order, family, genus, and species levels. Table 9 shows that there were no significant differences among the different treatments at the kingdom level. Compared with UJRCK, soil microorganisms in UJR showed a significant increase at the phylum, genus, and species levels, a decrease at the class and order levels, but no difference at the family level. Compared with UPyCK, soil microorganisms in UPy showed a significant increase at the phylum, order, genus, and species levels, no significant difference at the class level, but a significant decrease at the family level. Compared with UANCK, soil microorganisms in UAN showed a significant increase at all levels of phylum, class, order, family, genus, and species. Meanwhile, under the three monoculture models, UPyCK showed a significant increase at all levels of phylum, class, order, family, genus, and species. Furthermore, under the forest-medicine compound system conditions, UAN showed a significant increase at all levels of phylum, class, order, family, genus, and species, followed by UPy, and the lowest level was found in UJR soil.

[0051] Table 9. Statistical table of microbial species at various levels in soil samples

[0052] 2.4.2 Analysis of soil microbial community composition at the phylum level Soil microbial communities were screened and analyzed under different forest-medicinal herb compounding conditions, with the microbial phylum level as follows: Figure 17 As shown, the top ten dominant phyla in terms of relative abundance are: Pseudomonadota (51.16%), Actinomycetota (22.46%), Acidobacteriota (14.58%), Chloroflexota (4.24%), Candidatus Rokubacteria (1.35%), Verrucomicrobiota (0.98%), Gemmatimonadota (0.97%), Nitrospirota (0.86%), Mucoromycota (0.55%), and Nitrososphaerota (0.54%), accounting for approximately 97.69% of all microbial communities.

[0053] Under the pure stand model, the Yunnan pine soil showed the greatest increase in phylum-level microorganisms, namely: Pseudomonadota, Actinomycetota, Acidobacteriota, Chloroflexota, Candidatus Rokubacteria, Verrucomicrobiota, Gemmatimonadota, and Mucoromycota. The walnut forest soil was followed by Nitrospirota and Nitrososphaerota. In contrast, the content of the top ten phyla was significantly reduced in the *Agropyron cristatum* soil. Under the forest-medicinal herb integrated system conditions, compared with UJRCK, the contents of Pseudomonadota, Actinomycetota, Acidobacteriota, Chloroflexota, Verrucomicrobiota, Gemmatimonadota, Mucoromycota, and Nitrososphaerota were significantly increased in UJR soil. Compared with UPyCK, the contents of Pseudomonadota, Actinomycetota, Chloroflexota, Candidatus_Rokubacteria, Gemmatimonadota, Nitrospirota, Mucoromycota, and Nitrososphaerota were significantly increased in UPyF soil. Compared with UAnCK, the contents of Pseudomonadota, Actinomycetota, Chloroflexota, Acidobacteriota, Candidatus Rokubacteria, Verrucomicrobiota, Mucoromycota, and Nitrososphaerota were significantly increased in UAn soil.

[0054] In general, the cultivation of winter melon significantly increased the content of Pseudomonadota, Actinomycetota, Acidobacteriota, Chloroflexota, Verrucomicrobiota, Mucoromycota, and Nitrososphaerota in the soil of Polygonatum yunnanense. Meanwhile, the cultivation of Pinus yunnanense significantly increased the content of Candidatus Rokubacteria and Gemmatimonadota in the soil of Polygonatum yunnanense. Furthermore, the cultivation of walnut forests significantly increased the content of Nitrospirota in the soil of Polygonatum yunnanense.

[0055] 2.4.3 Analysis of soil microbial genus-level community composition At the genus level, the top ten dominant bacterial groups in terms of relative abundance were: Bradyrhizobium (24.89%), Trebonia (13.06%), Streptomyces (3.36%), Rhodoplanes (2.37%), Pseudolabrys (1.91%), Actinomadura (1.69%), Mycobacterium (1.59%), Rhodanobacter (1.55%), Burkholderia (1.48%), and Pseudomonas (1.47%), accounting for approximately 53.37% of all microbial groups.

[0056] Under the monoculture model, the soil of *Pinus yunnanensis* showed the greatest increase in the number of genus-level microorganisms, while the content of the top ten genera was significantly reduced in the soil of *Gnaphalium affine*, a trend consistent with the phylum-level changes. Under the forest-medicinal herb complex system, compared with UJRCK, the content of *Bradyrhizobium*, *Trebonia*, *Streptomyces*, *Rhodoplanes*, *Actinomadura*, *Mycobacterium*, *Rhodanobacter*, *Burkholderia*, and *Pseudomonas* was significantly increased in UJR soil. Compared to UPyCK, the UPyF soil showed a significant increase in the levels of Trebonia (13.06%), Streptomyces, Pseudolabrys, Actinomadura, Mycobacterium, Rhodanobacter, and Pseudomonas. Compared to UAnCK, the top ten microbial communities were significantly elevated in UAn soil.

[0057] In summary, planting *Gnaphalium affine* significantly increased the abundance of horizontal microorganisms in the soil of *Polygonatum yunnanense*, including: *Bradyrhizobium*, *Trebonia*, *Streptomyces*, *Rhodoplanes*, *Actinomadura*, *Mycobacterium*, *Rhodanobacter*, and *Pseudomonas*. Planting *Pinus yunnanense* only significantly increased the abundance of *Burkholderia* in the soil of *Polygonatum yunnanense*. However, compared to *Gnaphalium affine* and *Pinus yunnanense*, planting walnut forests did not increase the abundance of horizontal microorganisms in the soil of *Polygonatum yunnanense*.

[0058] 2.5 Analysis of differences in soil microbial community composition To determine the changes in soil microbial community structure under different planting patterns, we used PCoA to analyze the similarities and differences in soil microbial community structure (genea, COG function, and KEGG function levels), such as... Figure 18 As shown.

[0059] Based on genus-level analysis, the PC1 and PC2 axes explained 69.38% and 15.55% of the total, respectively. At the KEGG function level, the PC1 and PC2 axes explained 96.62% and 2.02% of the total, respectively. At the NOG function level, the PC1 and PC2 axes explained 68.52% and 9.86% of the total, respectively. Furthermore, compared with monoculture forests, the soil microbial community composition of different forest-medicinal herb composite systems differed significantly. In addition, the soil microbial community structure also differed significantly among forest-medicinal herb composite systems. Furthermore, as shown in Figure 10, both treatments and systems significantly altered the soil microbial community and function.

[0060] Table 10 shows the relative contributions of treatments and approaches to microbial β-diversity, COG function, and KEGG function, revealed by multivariate ANOVA (Adonis) and similarity analysis (Anosim).

[0061] 3.6 Analysis of Differential Microbial Communities in Soil like Figure 19As shown, differential microbial communities in the treated soil were determined based on Lefse multilevel species discrimination and linear discriminant analysis (LDA > 3). The UJRCK soil contained 29 differentially expressed bacterial groups, namely Eukaryota, Viruses, Pseudomonadota (Betaproteobacteria, Gammaproteobacteria, Burkholderiales, Hyphomicrobiales, Xanthobacteraceae, Pseudolabrys, Gammaproteobacteria_bacterium, Pseudolabrys_taiwanensis, Pseudomonadota_bacterium, Betaproteobacteria_bacterium_RIFCSPLOWO2_12_FULL_65_14), Nitrospirota (from phylum to species), Candidatus_Rokubacteria (phylum, species), Ascomycota, Bacteroidota (Flavobacteriia), Gemmatimonadota, Myxococcota, and Deltaproteobacteria (class, species).There are 34 differential flora in UJR soil, namely Bacteria, Archaea, Actinomycetota (Actinomycetes, Streptosporangiales, Pseudonocardiales, Kitasatosporales, Mycobacteriales, Micromonosporales, Treboniaceae,, Streptomycetaceae, Thermomonosporaceae, Pseudonocardiaceae, Trebonia, Trebonia_kvetii), Acidobacteriota (Terriglobia, Acidobacteriota_bacterium, Candidatus_Angelobacter_sp_Gp1_AA117, Candidatus_Angelobacter), Chloroflexota (Chloroflexota_bacterium), Nitrososphaerota, Pseudomonadota (Alphaproteobacteria, Xanthomonadales, Rhodanobacteraceae, Bradyrhizobium, Alphaproteobacteria_bacterium, Rhodanobacter_sp_C06).

[0062] Such as Figure 20As shown, there are 14 differential bacterial communities in UPyCK soil, namely Acidobacteriota (Terriglobia, Acidobacteriaceae, Candidatus_Sulfotelmatobacter, Acidobacteriota_bacterium), Pseudomonadota (Alphaproteobacteria, Hyphomicrobiales, Nitrobacteraceae, Bradyrhizobium, Alphaproteobacteria_bacterium), Deltaproteobacteria, Deltaproteobacteria_bacterium. There are 23 differential bacterial communities in UPy soil, namely Bacteria, Archaea, Actinomycetota (Actinomycetes, Streptosporangiales, Kitasatosporales, Mycobacteriales, Pseudonocardiales, Treboniaceae, Streptomycetaceae, Trebonia, Trebonia_kvetii), Chloroflexota (Chloroflexota_bacterium), Gemmatimonadota (Gemmatimonadota_bacterium), Pseudomonadot (Gammaproteobacteria, Betaproteobacteria, Gammaproteobacteria_bacterium, Betaproteobacteria_bacterium), Nitrososphaerota, Candidatus_Bathyarchaeota (Candidatus_Bathyarchaeota_archaeon).

[0063] As Figure 21As shown, the UAnCK soil contained 49 differentially expressed bacterial groups, namely Bacteria, Eukaryota, Pseudomonadota (Betaproteobacteria, Alphaproteobacteria, Hyphomicrobiales, Burkholderiales, Xanthobacteraceae, Burkholderiaceae, Nitrobacteraceae, Alcaligenaceae, Comamonadaceae, Rhizobiaceae, Bordetella, Betaproteobacteria_bacterium, Betaproteobacteria_bacterium_RIFCSPLOWO2_ 12_FULL_65_14, Pseudolabrys_taiwanensis, Bradyrhizobium_icense, Burkholderia_pseudomallei, Bordetella_pertussis), Nitrospirota (from phylum to species), Acidobacteriota (phylum, genus, species), Candidatus_Rokubacteria (phylum, species), Gemmatimonadota (phylum, species), Myxococcota, Mucoromycota (from phylum to order, Rhizopodaceae), Deltaproteobacteria (class, species). The UAn soil contains 38 differentially expressed bacterial groups, namely Archea, Actinomycetota (phylum, class, genus, species: Streptosporangiales, Kitasatosporales, Pseudonocardiales, Mycobacteriales, Micromonosporales, Terriglobales, Catenulisporales, Treboniaceae, Thermomonosporaceae, Acidobacteriaceae), Chloroflexota (phylum, species), Gammaproteobacteria (Xanthomonadales, Rhodanobacteraceae), Beijerinckiaceae, and Nitrososphaerota (Candidatus_Nitrosotalea, Nitrososphaerota_archaeon).

[0064] 2.7 Analysis of the network complexity and stability of soil microorganisms 2.7.1 Network Complexity Analysis of Soil Microorganisms Network complexity analysis was performed on the top 100 genera in terms of total abundance, revealing significant differences in microbial network complexity across different treatments. For example... Figure 22 As shown in Table 11, compared with CK, the network edges, graph density, mean degree, and average clustering coefficient of *Polygonatum yunnanense* soil microorganisms under different planting patterns all increased significantly, while the graph diameter and average path length decreased significantly. Meanwhile, in the pure forest system, the network nodes, edges, graph density, mean degree, and average clustering coefficient of UPyCK soil microorganisms all increased significantly, while the graph diameter and average path length decreased significantly. Therefore, the network complexity from high to low is: UPyCK > UANCK > UJRCK. Under the forest-medicine composite system, the changes in microbial network complexity are consistent with those in the pure forest system, from high to low: UPyF > UAN > UJR. This indicates that planting walnut, *Pinus yunnanense*, and *Melia azedarach* all increased the network complexity of *Polygonatum yunnanense* soil microorganisms, but the microbial network complexity was highest in the *Pinus yunnanense* system.

[0065] Table 11. Seven topological coefficients of soil microbial subnetworks under different treatments.

[0066] 2.7.2 Network stability analysis of soil microorganisms Consistent with the complexity of the microbial network, the top 100 genera in terms of total abundance were selected for co-network stability analysis. This revealed significant differences in the stability of the soil microbial network under *Polygonatum yunnanensis* in different forest-medicinal herb complex systems. For example... Figure 23 As shown, compared with the control (CK), the network stability of soil microorganisms in *Polygonatum yunnanense* significantly increased under different planting patterns. In the monoculture system, the stability of the microbial network, from highest to lowest, was: UPyCK > UAnCK > UJRCK. Meanwhile, under the forest-medicine complex system, the stability of the microbial network, from highest to lowest, was: UPyF > UAn > UJR. This indicates that planting walnut, *Pinus yunnanense*, and *Melia azedarach* all increased the network stability of soil microorganisms in *Polygonatum yunnanense*, but the stability of the microbial network was highest in the *Pinus yunnanense* system.

[0067] 2.8 Expression of functional genes in soil CNS cycling To determine the specific expression levels and changes of genes involved in CNS cycling in *Polygonatum yunnanensis* soil under forest cover, a total of 547,010 genes were detected in soil samples. 32,307, 4,226, and 8,044 non-redundant genes were identified as participating in soil carbon, nitrogen, and sulfur cycling, respectively. PCoA analysis showed significant differences in functional genes related to carbon, nitrogen, and sulfur cycling between monoculture soils and *Polygonatum yunnanensis* soils in forest-medicinal herb intercropping systems (R0.05).2 =0.895, p=0.001). For example... Figure 24 As shown in Figure A, the functional genes of the *Polygonatum yunnanense* soil in the forest-medicine composite system are significantly different compared with those of pure forest soil. Specifically, compared with UJRCK soil, among the top 10 functional genes in relative abundance in UJR soil, 8 are functional genes involved in carbon cycling, namely FdhA (ID: K00148), ACADS.bcd (ID: K00248), Crt (ID: K01715), ACSS1_2.acs (ID: K01895), sdhA.frdA (ID: K00239), ACAT.atoB (ID: K00626), fdoG.fdhF.fdwA (ID: K00123), and ENO1_2_3.eno (ID: K01689); 1 is a functional gene involved in nitrogen cycling, namely ncd2.npd (ID: K00459); and 1 is a functional gene involved in sulfur cycling, namely moeZR.moeBR (ID: K21147). Meanwhile, compared with UPyFCK soil, among the top 10 functional genes in relative abundance in UPyF soil, 5 are functional genes involved in carbon cycling, namely pmoC-amoC (ID: K10946), GPI.pgi (ID: K01810), ACAT.atoB (ID: K00626), fadN (ID: K07516), fdoG.fdhF.fdwA (ID: K00123), 3 are functional genes involved in nitrogen cycling, namely glnA.GLUL (ID: K01915), nasC.nasA (ID: K00372), narG.narZ.nxrA (ID: K00370), and 2 are functional genes involved in sulfur cycling, namely sqr (ID: K17218) and fccA (ID: K17230). Furthermore, compared to UAnCK soil, among the top 10 functional genes with relative abundance in UAn soil, 6 are functional genes involved in carbon cycling, namely PGD.gnd.gntZ (ID: K00033), hdrD (ID: K08264), ACSS1_2.acs (ID: K01895), sdhA.frdA (ID: K00239), gfa (ID: K03396), and pgi-pmi (ID: K15916); 1 is a functional gene involved in nitrogen cycling, namely GLU.gltS (ID: K00284); and 3 are functional genes involved in sulfur cycling, namely dmdB (ID: K20034), mnmA.trmU (ID: K00566), and ssuB (ID: K15555).

[0068] Correlation analysis was performed between the top ten most abundant microbial genera and differentially functional genes. Figure 24 In the study (B), significant differences were found in the functional genes of soil microorganisms related to carbon, nitrogen, and sulfur cycling in different forest-medicinal herb complex systems. In UJR soils, functional genes involved in carbon cycling (Crt, fdoG, fdhF, fdwA, ACAT, atoB, ACSS1_2.acs) were significantly negatively correlated with Pseudomonas, while they were significantly positively correlated with other genera. In UPyF soil, functional genes involved in carbon cycling (GPI.pgi, pmoC-amoC), nitrogen cycling (glnA.GLUL), and sulfur cycling (fccA) were significantly positively correlated with Mycobacterium, Actinomadura, Rhodoplanes, Trebonia, Pseudomonas, and Streptomyces. Conversely, functional genes involved in carbon cycling (ACAT.atoB, fdoG.fdhF.fdwA), nitrogen cycling (nasC.nasA, narG.narZ.nxrA), and sulfur cycling (sqr) were significantly negatively correlated with Bradyrhizobium and Rhodoplanes. In UAN soil, functional genes involved in carbon cycling (gfa, pgi-pmi) showed no correlation with the top ten most abundant genera, while other functional genes involved in carbon, nitrogen, and sulfur cycling were significantly positively correlated with the most abundant genera.

[0069] 2.9 Potential influencing factors of microbial community and metabolites under different forest-medicine composite systems This study analyzed the relationships between soil factors, microbial α-diversity, soil microbial community composition, soil microbial function, microbial network complexity, microbial network stability, and rhizome metabolites of *Polygonatum yunnanense* under three forest-medicinal plantation systems: *Polygonatum yunnanense*-economic walnut forest, *Polygonatum yunnanense*-coniferous *Pinus yunnanensis* forest, and *Polygonatum yunnanense*-broadleaf *Melia azedarach* forest. Specifically, under the *Polygonatum yunnanense*-economic walnut forest forest-medicinal plantation system, microbial community composition directly and significantly positively influences *Polygonatum yunnanense* metabolites. Furthermore, soil factors can indirectly affect *Polygonatum yunnanense* metabolites through microbial community composition. Soil microbial community composition and microbial function directly and negatively affect microbial network stability. Soil microbial α-diversity can further influence microbial network stability through microbial function. Microbial community composition can also directly and negatively influence microbial network complexity. Standardized effects showed that microbial community composition (-0.947) and soil factors (-0.98) were the strongest predictors of *Polygonatum yunnanense* metabolites. Figure 25 As shown.

[0070] Under the conditions of the *Polygonatum yunnanensis*-coniferous forest *Pinus yunnanensis* forest-medicinal plant complex system, microbial function, microbial network complexity, and stability directly and positively influence the metabolites of *Polygonatum yunnanensis* rhizomes. Simultaneously, microbial function can also indirectly affect the metabolites of *Polygonatum yunnanensis* rhizomes through microbial network complexity and stability. Furthermore, microbial α-diversity can significantly and negatively influence microbial community composition, network complexity and stability, and microbial function, respectively. Soil factors also significantly negatively / positively influence microbial α-diversity and microbial function. Standardized effects show that microbial function (0.99) and microbial α-diversity (-0.802) are the strongest predictors of *Polygonatum yunnanensis* rhizome metabolites.

[0071] Under the conditions of the *Polygonatum yunnanense*-broadleaf forest-winter melon forest-medicinal herb composite system, soil factors, microbial community composition, and microbial network stability directly and significantly negatively / positively affect the metabolites of *Polygonatum yunnanense*. Simultaneously, microbial α-diversity can also indirectly affect the metabolites of *Polygonatum yunnanense* rhizomes through microbial community composition and microbial network stability. Furthermore, soil factors can also significantly negatively affect the metabolites of *Polygonatum yunnanense* through microbial network stability. Soil factors can further affect microbial function through microbial α-diversity. Standardized effects showed that the soil factor (0.798) was the strongest predictor of *Polygonatum yunnanense* rhizome metabolites.

[0072] Although the above embodiments have provided a detailed description of the present invention, they are only some embodiments of the present invention, and not all embodiments. Other embodiments can be obtained based on these embodiments without creative effort, and these embodiments all fall within the protection scope of the present invention.

Claims

1. A method for detecting rhizome metabolites of Polygonatum yunnanense under different forest-medicine complex systems based on UPLC-MS / MS, characterized in that, Includes the following steps: The sample to be tested and the methanol-water internal standard extract were mixed to obtain the test solution; The content of metabolites in the analyte was obtained by ultra-high performance liquid chromatography-tandem mass spectrometry. The separation conditions for ultra-high performance liquid chromatography include: a C18 column; mobile phase A being formic acid aqueous solution and mobile phase B being formic acid acetonitrile solution; and gradient elution. The gradient elution procedure is as follows: 0.00 ~ 9 min: The volume percentage of mobile phase B increases linearly from 5% to 95%; 9.00 ~ 10.00 min: The volume percentage of the mobile phase B is 95%; 10.00~11.10 min: The volume percentage of mobile phase B decreased linearly from 95% to 5%; 11.10~14.0 min: The volume percentage of the mobile phase B is 5%; Mass spectrometry conditions included: electrospray ionization source, ion source temperature 500℃; QQQ scan in MRM mode.

2. The method as described in claim 1, characterized in that, The methanol-water internal standard extract is prepared by dissolving the standard in an aqueous methanol solution.

3. The method as described in claim 2, characterized in that, The volume concentration of the methanol-water solution is 70%; the concentration of the methanol-water internal standard extract is 250 μg / mL.

4. The method as described in claim 1, characterized in that, The injection volume of the ultra-high performance liquid chromatography is 2 μL; the flow rates of mobile phase A and mobile phase B are 0.35 mL / min.

5. The method as described in claim 1, characterized in that, The chromatographic column was an Agilent SB-C18 column; the column temperature was 40℃.

6. The method as described in claim 1, characterized in that, The volume concentration of formic acid in the formic acid aqueous solution is 0.1%; the volume concentration of formic acid in the formic acid acetonitrile solution is 0.1%.

7. The method as described in claim 1, characterized in that, The mass spectrometry conditions include: ion spray voltage: 5500 V in positive ion mode; -4500 V in negative ion mode; gas pressures of ion source gas I, gas II, and curtain gas are set to 50, 60, and 25 psi, respectively; collision gas is set to medium; and collision-induced ionization parameter is set to high.

8. The method as described in claim 1, characterized in that, The forest-medicinal herb complex system includes one or more of the following: the Yunnan Polygonatum-Economic Walnut Forest Forest-Medicinal Herb Complex System, the Yunnan Polygonatum-Coniferous Pine Forest-Medicinal Herb Complex System, and the Yunnan Polygonatum-Broadleaf Melon Forest-Medicinal Herb Complex System.

9. The method as described in claim 1, characterized in that, The mass-to-volume ratio of the sample to be tested and the methanol-water internal standard extract was 30 mg: 1500 μL.

10. The method as described in claim 1, characterized in that, The metabolites include flavonoids, amino acids and their derivatives, alkaloids, lipids, terpenoids, phenolic acids, lignans and coumarins, nucleotides and their derivatives, organic acids, steroids, quinones, tannins and other components.

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