Microbial marker combination for evaluating geosity of polygonatum kingianum and application of microbial marker combination

Abstract

The invention belongs to the technical field of biology, and particularly relates to a microbial marker combination for evaluating the genuine nature of polygonatum sibiricum and application, the microbial marker combination is composed of Escherichia. Shigilla, Acremonium, Pseudomonas, Serratia, Mortierella, Wallemia, Engydonium, Sporidesmium, Ceratobasidium, Clonostachys and Ralstachys, an evaluation method for the genuine nature of polygonatum sibiricum is established, the sensitivity is 91.7%, the specificity is 100.0%, the AUC is 93.8%, and the method has the advantages of being high in sensitivity, strong in specificity and high in accuracy and can be used for evaluating the genuine nature of polygonatum sibiricum. The method can be used for judging whether the production area of the polygonatum sibiricum is a genuine Suhua production area or not, and can be widely applied to genuine evaluation of the polygonatum sibiricum.

Description

[0001] This application claims priority to the earlier application filed on December 5, 2024, with application number CN2024117774342 and invention title "A combination of microbial markers for evaluating the authenticity of Polygonatum odoratum and its application", the entire contents of which are set forth in this application. Technical Field

[0002] This invention belongs to the field of biotechnology, specifically relating to a microbial biomarker composition for evaluating the authenticity of Polygonatum odoratum and its application. Background Technology

[0003] Authentic medicinal herbs refer to Chinese medicinal materials that grow in specific geographical environments, possess superior quality, significant therapeutic effects, a long history, and distinct regional characteristics. The quality and efficacy of medicinal herbs are closely related to their growing environment, particularly the influence of climate, soil, and geographical location on the synthesis and accumulation of their active ingredients. Although the same medicinal herb is widely distributed within China, herbs from different producing areas often exhibit significant differences in composition, quality, and efficacy. For example, herbs such as angelica, astragalus, and licorice show substantial differences in efficacy depending on their origin. Therefore, identifying authentic medicinal herbs is a crucial basis for assessing their quality and efficacy.

[0004] Polygonatum is the dried rhizome of *Polygonatum kingianum* Coll. et Hemsl., *Polygonatum sibiricum* Red., or *Polygonatum cyrtonema* Hua, belonging to the Liliaceae family. It is widely distributed in Anhui, Gansu, Sichuan, and Yunnan provinces of China. Polygonatum has been widely used in traditional Chinese medicine since ancient times, possessing anti-aging, blood sugar-lowering, blood lipid-lowering, memory-enhancing, anti-tumor, immune-regulating, antiviral, and anti-inflammatory effects. Studies have shown that Polygonatum produced in the Jiuhua Mountain area of ​​Anhui Province is of superior quality and has significant therapeutic effects, with comprehensive evaluation indicators superior to Polygonatum from other producing areas. It is characterized by "superior quality and excellent efficacy," and is therefore hailed as a genuine medicinal material from this region.

[0005] Currently, commonly used methods for evaluating the authenticity of medicinal materials mainly include morphological identification, chromatographic analysis, and mass spectrometry. While morphological identification is simple and easy to perform, it is greatly influenced by the observer's subjective factors and is difficult to quantitatively evaluate the quality of medicinal materials. Modern analytical techniques such as chromatography and mass spectrometry can provide more accurate information, but they usually have drawbacks such as high cost, cumbersome operation, and slow detection speed. Therefore, there is an urgent need for an efficient, accurate, and low-cost method to quickly assess the authenticity of Polygonatum sibiricum.

[0006] In recent years, microbiome research has provided new insights into the quality control of traditional Chinese medicinal materials. The microbiome refers to the entirety of the microbial community and its genome inside and outside the host plant, including bacterial, archaea, fungal, and viral populations, as well as their metabolic functions and ecological roles within and outside the host plant. Endophytic flora specifically refers to the microbial community that is implanted within the plant and lives in symbiosis with the host plant. As a medicinal plant, the endophytic flora of Polygonatum sibiricum plays a crucial role in its growth, metabolism, and accumulation of active ingredients. Significant differences exist in the types, abundance, and diversity of endophytic flora in Polygonatum sibiricum from different origins, and these differences are closely related to the variations in its medicinal efficacy and chemical composition. However, there is currently no method in this field to evaluate the authenticity of Polygonatum sibiricum using endophytic flora.

[0007] To address the aforementioned technical problems, this invention systematically analyzed the endophytic communities of Polygonatum rhizomes from one authentic producing area (Jiuhua Mountain, Anhui) and three non-authentic producing areas (Wenshan, Yunnan; Suining, Sichuan; and Dingxi, Gansu). Endophytic bacteria with varying abundances were identified as biomarkers, and specific endophytic bacteria were screened as a microbial biomarker combination for evaluating the authenticity of Jiuhua Polygonatum. This microbial biomarker combination includes: Escherichia Shigella, Acremonium, Pseudomonas, Serratia, Mortierella, Wallemia, Engyodontium, Sporidesmium, Ceratobasidium, Clonostachys, and Ralstonia, providing an effective evaluation of authenticity. Meanwhile, this invention, combined with a random forest model, yields a method for evaluating whether Polygonatum sibiricum is from Jiuhua Mountain, Anhui Province. Compared with traditional methods, it can more quickly and accurately determine whether Polygonatum sibiricum originates from the authentic producing area of ​​Jiuhua Mountain, Anhui Province. It has high sensitivity and strong specificity, and can greatly improve the shortcomings of conventional morphological identification methods, such as high subjectivity and low accuracy, and methods such as chemical fingerprinting, which are costly, complex to operate, slow to detect, and have high chemical reagent toxicity. It meets the practical demand for speed and efficiency in the field of medicinal material circulation and has practical significance and application value in the production and quality evaluation of medicinal materials. Summary of the Invention

[0008] To address the aforementioned technical problems, the present invention aims to provide a combination of microbial biomarkers for evaluating the geographical origin of Polygonatum odoratum and its application, specifically including the following:

[0009] In a first aspect, the present invention provides a combination of microbial markers for evaluating the authenticity of Polygonatum odoratum, the combination of microbial markers comprising Escherichia shigella, Acremonium, Pseudomonas, Serratia, Mortierella, Wallemia, Engyodontium, Sporidesmium, Ceratobasidium, Clonostachys, and Ralstonia.

[0010] Secondly, the present invention provides the application of the combination of microbial markers described in the first aspect above in evaluating the geographical origin of Polygonatum odoratum.

[0011] Thirdly, the present invention provides the application of the combination of microbial markers described in the first aspect above in the preparation of reagents, kits, and chips for evaluating the authenticity of Polygonatum odoratum.

[0012] Fourthly, the present invention provides an application of the combination of microbial biomarkers described in the first aspect above in constructing a model for evaluating the authenticity of Polygonatum odoratum.

[0013] Fifthly, the present invention provides the application of reagents for detecting the combination of microbial markers described in the first aspect in the preparation of reagents, kits, and chips for evaluating the authenticity of Polygonatum odoratum.

[0014] In a sixth aspect, the present invention provides the application of detecting the combination of microbial biomarkers described in the first aspect in constructing a model for evaluating the authenticity of Polygonatum odoratum.

[0015] Seventhly, the present invention provides a model for evaluating the authenticity of Polygonatum odoratum, the model construction method comprising:

[0016] (1) Sample processing: Collect samples of Polygonatum odoratum from authentic Jiuhua and non-authentic Jiuhua, and perform preprocessing;

[0017] (2) Extract genomic DNA from the microbial biomarker combination described in the first aspect above in the sample, and detect the species abundance of the microorganism;

[0018] (3) A random forest model was constructed using the species abundance matrix of samples from different groups for prediction, where the true label of the authentic Jiuhua group was set to 0 and the non-authentic group to 1.

[0019] The model outputs a probability value (summing up to 1) for each predicted sample belonging to two categories. The final determination is based on the probability that the sample is predicted to be in category 0 (authentic group): the default threshold is 0.5. When the probability of being predicted to be in category 0 is greater than or equal to 0.5, the sample is determined to be authentic Jiuhua Huangjing (classified as 0), otherwise it is determined to be non-authentic Huangjing (classified as 1).

[0020] Preferably, the pretreatment includes: washing, cutting, soaking in 75% ethanol, disinfecting by soaking in 5% sodium hypochlorite, absorbing moisture, cutting into small pieces, and storing at -80°C.

[0021] Preferably, step (2) includes:

[0022] ① Genomic DNA extraction;

[0023] ② PCR amplification of the target fragment: Using the genomic DNA extracted in step ① as a template, PCR was performed using specific primers with barcodes and Tks Gflex DNA Polymerase, according to the selected sequencing region; two rounds of PCR amplification were performed, followed by electrophoresis detection and magnetic bead purification.

[0024] ③ Library construction and sequencing: The purified PCR product was quantified using Qubit, the concentration was adjusted, and sequencing was performed using the Illumina NovaSeq 6000 sequencing platform to generate 250 bp paired-end reads;

[0025] ④ Bioinformatics Analysis: After the data was processed, the Cutadapt software was first used to cut out the primer sequences from the raw data sequences. Then, DADA2 was used to perform quality control analysis on the qualified paired-end raw data from the previous step, including quality filtering, noise reduction, splicing, and dechimeric removal, to obtain representative sequences and an ASV abundance table. The QIIME 2 software package was used to select representative sequences for each ASV, and all 16S representative sequences were compared and annotated with the Silva (version 138) database, and all ITS representative sequences were compared and annotated with the Unie database.

[0026] Preferably, the genomic DNA extraction in step ① is performed using conventional methods.

[0027] Preferably, the quality filtering described in step ④ is performed according to the default parameters of QIIME 2 (2020.11), and the comparison annotation is analyzed using the default parameters of the q2-feature-classifier software.

[0028] Preferably, the sequencing region described in step ② includes a region corresponding to bacterial diversity identification and a region corresponding to fungal ITS diversity identification; the region corresponding to bacterial diversity identification is the 16S V3-V4 region [primer 343F (5'-TACGGRAGGCAGCAG-3') and 798R (5'-AGGGTATCTAATCCT-3')], and the region corresponding to fungal ITS diversity identification is the ITS [primer ITS1F (5'-CTTGGTCATTTAGAGGAAGTAA-3') and ITS2R (5'-GCTGCGTTCTTCATCGATGC-3')].

[0029] Eighthly, the present invention provides a method for screening combinations of microbial biomarkers as described in the first aspect above, the method comprising:

[0030] (1) Collection of Polygonatum samples: The samples were divided into the native group and the non-native group according to the collection area;

[0031] (2) Processing of Polygonatum samples: Take the sample obtained in step (1), wash it, cut it, soak it in 75% ethanol and 5% sodium hypochlorite for disinfection, absorb the water, cut it into small pieces, and store it at -80℃.

[0032] (3) Detection of biomarkers in Polygonatum sibiricum samples:

[0033] ① Genomic DNA extraction;

[0034] ② PCR amplification of the target fragment: Using the genomic DNA extracted in step ① as a template, PCR was performed using specific primers with barcodes and Tks Gflex DNA Polymerase, according to the selected sequencing region; two rounds of PCR amplification were performed, followed by electrophoresis detection and magnetic bead purification.

[0035] ③ Library construction and sequencing: The purified PCR product was quantified using Qubit, the concentration was adjusted, and sequencing was performed using the Illumina NovaSeq 6000 sequencing platform to generate 250 bp paired-end reads;

[0036] ④ Bioinformatics Analysis: After the data was processed, the Cutadapt software was first used to cut out the primer sequences from the raw data sequences. Then, DADA2 was used to perform quality control analysis on the qualified paired-end raw data from the previous step, including quality filtering, noise reduction, splicing, and chimera removal, to obtain representative sequences and an ASV abundance table. The QIIME 2 software package was used to select representative sequences for each ASV, and all 16S representative sequences were compared and annotated with the Silva (version 138) database, and all ITS representative sequences were compared and annotated with the Unie database.

[0037] (4) Constructing an evaluation model: Select the top 30 bacterial genera and top 30 fungal genera with the highest relative abundance in the group obtained from the sequencing analysis in step (3) to construct a random forest model. The random forest is used to identify the important characteristics of microorganisms. Microbial biomarkers are screened according to the influence of the microbial selection procedure on the result prediction. The model is optimized around its default value to construct an evaluation model.

[0038] (5) Model validation: The reliability of the screened microbial biomarkers was validated by setting up an internal test set and validation set in a 7:3 ratio.

[0039] Preferably, the genomic DNA extraction in step ① is performed using conventional methods.

[0040] Preferably, the quality filtering described in step ④ is performed according to the default parameters of QIIME 2 (2020.11), and the comparison annotation is analyzed using the default parameters of the q2-feature-classifier software.

[0041] Preferably, the sequencing region described in step ② includes a region corresponding to bacterial diversity identification and a region corresponding to fungal ITS diversity identification; the region corresponding to bacterial diversity identification is the 16S V3-V4 region [primer 343F (5'-TACGGRAGGCAGCAG-3') and 798R (5'-AGGGTATCTAATCCT-3')], and the region corresponding to fungal ITS diversity identification is the ITS [primer ITS1F (5'-CTTGGTCATTTAGAGGAAGTAA-3') and ITS2R (5'-GCTGCGTTCTTCATCGATGC-3')].

[0042] Ninthly, the present invention provides a method for evaluating the authenticity of Polygonatum odoratum, the method comprising the following steps:

[0043] (1) Construct the model described in the sixth aspect above;

[0044] (2) Extract genomic DNA from the Polygonatum sibiricum sample to be tested and detect the species abundance of the combination of microbial markers described in the first aspect above;

[0045] (3) Substitute the species abundance values ​​obtained in step (2) into the model described in step (1) to make predictions;

[0046] The model pre-sets the true label of the authentic group sample to be 0 and the non-authentic group to be 1. The species abundance value is substituted into the model described in step (1). The model will output a probability value belonging to the two categories for each predicted sample (the sum is 1). The final judgment is made based on the probability that the sample is predicted to be of category 0 (authentic group): the default threshold is 0.5. When the probability of being predicted to be of category 0 is greater than or equal to 0.5, the sample is judged to be authentic Jiuhua Huangjing (classified as 0), otherwise it is judged to be non-authentic Huangjing (classified as 1).

[0047] In a tenth aspect, the present invention provides a system for evaluating the authenticity of Polygonatum odoratum, comprising:

[0048] Pre-input module: Used at least for inputting the data to be evaluated;

[0049] Evaluation module: This module is used at least to evaluate the data to be evaluated, and is executed by the Solomon's seal authenticity evaluation model described in the sixth aspect above.

[0050] Display module: Used at least to display evaluation results.

[0051] Eleventhly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the program, it causes the system described in the ninth aspect to perform the following steps, including:

[0052] Collect and / or input evaluation data on the authenticity of Polygonatum odoratum;

[0053] The evaluation data is substituted into the model for calculation to obtain a judgment on whether the Polygonatum is authentic.

[0054] Output the conclusion on whether Huangjing (Polygonatum odoratum) is authentic.

[0055] In a twelfth aspect, the present invention provides a computer-readable storage medium having the computer device described in the tenth aspect stored thereon.

[0056] The beneficial effects of this invention are: (1) After extensive screening, this invention has for the first time obtained a combination of microbial markers for evaluating whether a Polygonatum sibiricum is the authentic Jiuhua Polygonatum sibiricum. The combination of microbial markers includes one or more of Escherichia shigella, Acremonium, Pseudomonas, Serratia, Mortierella, Wallemia, Engyodontium, Sporidesmium, Ceratobasidium, Clonostachys, and Ralstonia; (2) Secondly, this invention utilizes the above-mentioned... The combination of microbial markers established an evaluation model for whether Polygonatum is the authentic Jiuhua Polygonatum and provided a method for its establishment. The model can determine whether the place of origin of Polygonatum is the authentic producing area of ​​Jiuhua Mountain, Anhui. It is economical, easy to operate, and suitable for application in the Chinese medicinal materials market. (3) When evaluating the authenticity of Polygonatum, the combination of microbial markers has a sensitivity of 91.7%, a specificity of 100.0%, and an AUC of 93.8%. It has high sensitivity and strong specificity, which can greatly improve the shortcomings of conventional morphological identification methods such as strong subjectivity, low accuracy, high cost, complex operation, slow detection speed and high toxicity of chemical reagents. It can be widely used for the evaluation of the authenticity of Polygonatum. Attached Figure Description

[0057] Figure 1 A graph showing the number of selected variables.

[0058] Figure 2 A point map showing the importance of the selected number of species.

[0059] Figure 3 ROC curve of model prediction discrimination. Detailed Implementation

[0060] The following embodiments are provided to better understand the present invention, but are not intended to limit the invention. Unless otherwise specified, the experimental methods used in the following embodiments are conventional methods. Unless otherwise specified, the experimental materials used in the following embodiments are commercially available.

[0061] In one specific embodiment, sensitivity, specificity, accuracy, and other combinations are used to describe the goodness and reliability of the detection method described in this invention. Several terms used in conjunction with the descriptions of sensitivity, specificity, and accuracy include: true positive (TP), true negative (TN), false positive (FP), and false negative (FN); wherein, if it is proven that the Polygonatum is authentic Jiuhua Polygonatum, and the given detection experiment also indicates that the sample is authentic Jiuhua Polygonatum, the test result is considered a true positive; if it is proven that the Polygonatum is not authentic Jiuhua Polygonatum, and the given detection experiment also indicates that the sample is not authentic Jiuhua Polygonatum, the test result is considered a true negative; if the detection result indicates that a sample that is actually not authentic Jiuhua Polygonatum is authentic Jiuhua Polygonatum, the test result is a false positive; if the detection result indicates that a sample that is actually authentic Jiuhua Polygonatum is not authentic Jiuhua Polygonatum, the test result is a false negative.

[0062] Sensitivity = TP / (TP + FN) = Number of true positive assessments / Number of all positive assessments;

[0063] Specificity = TN / (TN+FP) = number of true negative assessments / total number of negative assessments;

[0064] Accuracy = (TN+TP) / (TN+TP+FN+FP) = Number of correct assessments / Total number of assessments.

[0065] In this invention, the sample set used to screen 11 biomarkers and the validation set used to evaluate the final model performance (AUC) are processed as follows:

[0066] Data preparation and grouping: All Polygonatum samples from the four producing areas were defined as a binary variable based on their authenticity: Authentic Jiuhua Polygonatum group (producing area: Jiuhua Mountain, Anhui) and Non-Authentic Polygonatum group (producing area: non-Jiuhua Mountain, Anhui).

[0067] Feature (biomarker) screening: All sample data were used and analyzed using the AUCRF package to determine the microbial biomarkers that best distinguished the two groups. The analysis showed that the model achieved the best discriminative performance when the number of biomarkers was 11, with an internal AUC value of 0.8541 in cross-validation. The purpose of this step was to identify the most discriminative set of features from all available data.

[0068] Model Construction and Independent Validation: After identifying the 11 markers, a rigorous training and validation set was implemented to objectively evaluate the true performance of the constructed prediction model. 70% of the samples were randomly selected as the training set, and the random forest model was trained using only the features of these 11 markers. Subsequently, the remaining 30% of samples, which were not involved in model training, were used as an independent validation set to test the trained model.

[0069] Validation results: The reliability of the screened microbial biomarkers was verified by setting the internal test set and validation set in a 7:3 ratio. The test results on the independent validation set showed that the model based on these 11 biomarkers had excellent performance: sensitivity of 91.7%, specificity of 100.0%, and AUC of 93.8%.

[0070] Example 1: Evaluation Method for the Geographical Origin of Polygonatum

[0071] Fifteen samples of authentic Jiuhua Polygonatum and 37 samples of non-Jiuhua Polygonatum from different producing areas were selected. The biomarker combination provided by this invention was used to test the above samples. Based on the test results, the sensitivity, specificity and accuracy of the biomarker combination provided by this invention for evaluating the authenticity of Jiuhua Polygonatum were determined.

[0072] 1. Collection of Polygonatum sibiricum samples

[0073] Fresh and healthy 5-year-old Polygonatum samples were collected from Jiuhuashan, Anhui Province, the traditional producing area of ​​Polygonatum, and other non-traditional producing areas (Wenshan, Yunnan; Suining, Sichuan; and Dingxi, Gansu) in September and October. The samples were divided into traditional and non-traditional groups according to the collection area. Specific collection information is shown in Table 1. The surface soil and impurities were washed with running water, and complete rhizomes of about 5 cm in length were cut.

[0074] Table 1. Information on Polygonatum sibiricum sample collection

[0075] 2. Sample surface disinfection

[0076] Take the above samples, rinse with sterile water for 5 min, soak in 75% ethanol for 1 min, rinse with sterile water 1-2 times, then disinfect by soaking in 5% sodium hypochlorite for 3 min, rinse with sterile water 7-10 times, and blot dry with sterile filter paper. Cut into small pieces with sterile scissors and place in 5 mL centrifuge tubes for storage at -80℃ for later use.

[0077] 3. Detection of biomarkers in Polygonatum sibiricum

[0078] 3.1 Genomic DNA Extraction

[0079] Genomic DNA was extracted from the samples using a plant genomic DNA extraction kit [Tiangen Biotech (Beijing) Co., Ltd.], following the kit's instructions. The concentration and purity of the DNA were assessed using a NanoDrop 2000 (Thermo Fisher Scientific, USA) and agarose gel electrophoresis. The extracted DNA was stored at -20°C.

[0080] 3.2 PCR amplification of the target fragment

[0081] Using extracted genomic DNA as a template, PCR was performed using barcode-specific primers and Takara's Tks Gflex DNA Polymerase, based on the selected sequencing regions, to ensure amplification efficiency and accuracy. Bacterial diversity identification corresponded to the 16S V3-V4 region [primers 343F (5'-TACGGRAGGCAGCAG-3') and 798R (5'-AGGGTATCTAATCCT-3')], while fungal ITS diversity identification corresponded to the ITS region [primers ITS1F (5'-CTTGGTCATTTAGAGGAAGTAA-3') and ITS2R (5'-GCTGCGTTCTTCATCGATGC-3')]. A first round of PCR amplification was performed (the first round of PCR amplification system and reaction procedure are shown in Tables 2 and 3). The first round of PCR products were detected by electrophoresis, purified by magnetic beads, and used as templates for the second round of PCR amplification (the second round of PCR amplification system and reaction procedure are shown in Tables 4 and 5). Electrophoresis was performed again, and the products were purified by magnetic beads. The purified PCR products were then quantified using Qubit.

[0082] The PCR amplification system and reaction procedure for one round are shown in Tables 2 and 3.

[0083] Table 2. One-round PCR system

[0084] Table 3. One-round PCR reaction procedure

[0085] Note: If the DNA concentration is too high or contains many impurities, the annealing temperature can be appropriately increased to avoid unwanted banding.

[0086] The two-round PCR amplification system and reaction procedure are shown in Tables 4 and 5.

[0087] Table 4. Second-round PCR system

[0088] Table 5. Two-round PCR reaction procedure

[0089] Note: If the first batch of products has slight impurities, the annealing temperature can be appropriately increased to reduce impurities.

[0090] Magnetic bead purification: Add 20 μL of thoroughly mixed AMPure XP beads (Beads:Product = 0.8:1) to a U-shaped plate, then add the PCR product. Gently pipette the mixture 10 times to mix, and incubate at room temperature for 5 min. Place on a magnetic rack for 5 min until the supernatant is clear, then discard the supernatant. Add 200 μL of freshly prepared 80% ethanol, incubate at room temperature for 30 s, then discard the supernatant. Repeat the previous step, washing twice in total. Air dry at room temperature until no droplets remain (check the bottom of the tube with a 10 μL pipette for any droplets) and the bead surface shows no reflectivity. Remove the plate from the magnetic rack. Add 25 μL of H2O to elute, pipette 10 times to mix thoroughly, and incubate at room temperature for 2 min. Place on a magnetic rack for 5 min until the supernatant is clear, then transfer 20 μL of the supernatant to a new PCR tube.

[0091] Detection: Take 5 μL of the purified second-round product and perform agarose gel electrophoresis to detect whether there are bands and whether the bands are single.

[0092] 3.3 Library construction and sequencing

[0093] 1 μL of the purified second-round product was quantified using Qubit sequencing, and the concentration was adjusted for sequencing. Sequencing was performed using the Illumina NovaSeq 6000 sequencing platform, generating 250 bp paired-end reads.

[0094] 3.4 Bioinformatics Analysis

[0095] The raw data was in FASTQ format. After the data was processed, the Cutadapt software was first used to cut out the primer sequences from the raw data sequences. Then, DADA2 was used to perform quality control analysis on the qualified paired-end raw data from the previous step according to the default parameters of QIIME 2 (2020.11), including quality filtering, noise reduction, splicing, and dechimeric analysis, to obtain representative sequences and an ASV abundance table. Representative sequences for each ASV were selected using the QIIME 2 software package, and all 16S representative sequences were aligned and annotated with the Silva (version 138) database, and all ITS representative sequences were aligned and annotated with the Unie database. Species alignment and annotation were analyzed using the q2-feature-classifier software with default parameters.

[0096] Random forest is a machine learning algorithm, first proposed by Leo Breiman and Adele Cutler. This algorithm can effectively and accurately classify microbial community samples and identify key components (ASVs or species) that distinguish between groups. Taking the top 30 bacterial and fungal genera by relative abundance, a species importance dot plot is generated using the R package randomForest, as shown below. Figure 2 As shown.

[0097] 4. Evaluation Model Construction

[0098] The machine learning algorithm was implemented using R (version 4.4.1). A random forest model was constructed by selecting the top 30 bacterial genera and the top 30 fungal genera by relative abundance in the groupings obtained from sequencing analysis. The random forest was used to identify the importance characteristics of microorganisms. Microbial biomarkers were screened based on the impact of the microbial selection procedure on the prediction results to achieve the best predictive accuracy of the model. The optimal number of selected microbial biomarkers is shown in the number plot of selected variables, and the importance characteristics of the optimal number of screened microbial biomarkers are shown in the species importance dot plot.

[0099] Random forests were used to optimize each microbial biomarker around its default values. The reliability of the selected microbial biomarkers was validated by setting the internal test and validation sets in a 7:3 ratio. ROC curve analysis was used to verify the model's accuracy.

[0100] 5. Evaluation Result Determination

[0101] like Figure 1 As shown, the top 30 genera of bacteria and fungi in terms of species abundance were selected as candidate biomarkers to distinguish *Polygonatum jiuhuaense* from other origins. Analysis using the AUCRF package revealed that the best distinguishing performance was achieved when 11 biomarkers were used, with an AUC value of 0.8541. Therefore, these 11 biomarkers were selected for further analysis.

[0102] like Figure 2As shown, after marker selection, 11 markers—Escherichia shigella, Acremonium, Pseudomonas, Seratia, Mortierella, Wallemia, Engyodontium, Sporidesmium, Ceratobasidium, Clonostachys, and Ralstonia—have the best classification performance. Ranked by importance, they are: Escherichia shigella, Acremonium, Pseudomonas, Seratia, Mortierella, Wallemia, Engyodontium, Sporidesmium, Ceratobasidium, Clonostachys, and Ralstonia.

[0103] Eleven markers, including *Escherichia shigella*, *Acremonium*, *Pseudomonas*, *Serratia*, *Mortierella*, *Wallemia*, *Engyodontium*, *Sporidesmium*, *Ceratobasidium*, *Clonostachys*, and *Ralstonia*, were used to distinguish *Polygonatum jiuhuaense* from non-*Polygonatum jiuhuaense*. The results are as follows: Figure 3 As shown, the sensitivity was 91.7%, the specificity was 100.0%, and the AUC was 93.8%.

[0104] Example 2: Evaluation System for the Geographical Origin of Polygonatum

[0105] This embodiment provides a system for evaluating the authenticity of Polygonatum odoratum, including:

[0106] Pre-input module: used at least to input the abundance data of the microbial biomarker combinations in the Polygonatum sibiricum sample and transmit it to the evaluation module;

[0107] Evaluation module: at least used to evaluate the abundance data of the microbial biomarker combination in the Polygonatum sample according to the method described in Example 1, and obtain the results; wherein, the abundance data of the microbial biomarker combination can be collected through the pre-input module, or the abundance data of the microbial biomarker combination can be obtained from other sources.

[0108] The display module is used to display at least the evaluation results based on the random forest model. This model is constructed using a species abundance matrix of samples in different groups, where the true label for the authentic group (Jiuhua Huangjing) is pre-defined as 0, and for the non-authentic group as 1. The model outputs the probability values ​​(summing up to 1) of each predicted sample belonging to either of the two categories, and makes the final determination based on the probability of the sample being predicted as class 0: a default threshold of 0.5 is used; when the probability of being predicted as class 0 is greater than or equal to 0.5, the sample is determined to be in the authentic group (classified as 0), otherwise it is determined to be in the non-authentic group (classified as 1). Therefore, when the predicted value is 0, the Huangjing authenticity determination result is Jiuhua Huangjing; when the predicted value is 1, the Huangjing authenticity determination result is non-Jiuhua Huangjing.

[0109] Example 3: Computer Equipment

[0110] This embodiment provides an electronic device, which can be represented in the form of a computing device (e.g., a server device), including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it can implement the method for evaluating the authenticity of Polygonatum odoratum in Embodiment 1 of this invention.

[0111] When the processor executes the program, it causes the system to perform the following steps, including:

[0112] (1) Collect and / or input evaluation data for the evaluation of the authenticity of Polygonatum odoratum, namely, the abundance data of microbial biomarker combinations;

[0113] (2) Substitute the evaluation data into the model for calculation to obtain the judgment on whether Huangjing is authentic;

[0114] (3) Output the judgment on whether Huangjing is authentic.

[0115] It should be noted that although several units / modules or sub-units / modules of the electronic device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0116] Example 4: Computer-readable storage medium

[0117] This embodiment provides a computer-readable storage medium on which the aforementioned computer device is stored. When the program is executed by a processor, it implements the steps of the method for evaluating the authenticity of Polygonatum odoratum in Embodiment 1 of this invention.

[0118] The readable storage medium may be more specifically adopted, including but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0119] In a possible implementation, the present invention can also be implemented as a program product, which includes program code. When the program product is run on a terminal device, the program code is used to cause the terminal device to perform the steps of implementing the method for evaluating the authenticity of Polygonatum odoratum in Embodiment 1 of the present invention.

[0120] The program code for executing the present invention can be written in any combination of one or more programming languages. The program code can be executed entirely on the user device, partially on the user device, as a standalone software package, partially on the user device and partially on a remote device, or entirely on a remote device.

[0121] In summary, this invention provides a combination of microbial markers for evaluating whether a Polygonatum sibiricum is authentic Jiuhua Polygonatum sibiricum. The combination of microbial markers includes one or more of Escherichia shigella, Acremonium, Pseudomonas, Serratia, Mortierella, Wallemia, Engyodontium, Sporidesmium, Ceratobasidium, Clonostachys, and Ralstonia. This invention utilizes the aforementioned combination of microbial markers to establish... An evaluation model for determining whether Polygonatum sibiricum is from Jiuhua Mountain, Anhui Province, is provided, along with a method for establishing it. The model can determine whether Polygonatum sibiricum originates from the authentic producing area of ​​Jiuhua Mountain, Anhui Province. It is economical, easy to operate, and suitable for use in the Chinese medicinal materials market. When evaluating the authenticity of Polygonatum sibiricum from Jiuhua Mountain using the microbial biomarker combination, the sensitivity is 91.7%, the specificity is 100.0%, and the AUC is 93.8%. It exhibits high sensitivity and strong specificity, significantly improving upon the shortcomings of conventional phenotypic identification methods, such as high subjectivity and low accuracy, and chemical fingerprinting methods, which are costly, complex, slow, and involve highly toxic chemical reagents. This model can be widely used to evaluate the authenticity of Polygonatum sibiricum.

[0122] The above description is a detailed description of the preferred embodiments of the present invention. However, the embodiments are not intended to limit the scope of the patent application of the present invention. All equivalent changes or modifications made under the technical spirit of the present invention should fall within the patent scope covered by the present invention.

Claims

1. A combination of microbial biomarkers for evaluating the authenticity of Polygonatum odoratum, characterized in that, The microbial biomarker combination consists of Escherichia Shigella , Acremonium , Pseudomonas , Serratia , Mortierella , Wallemia , Engyodontium , Sporidesmium , Ceratobasidium , Clonostachys and Ralstonia composition.

2. The application of the combination of microbial markers as described in claim 1 in evaluating the authenticity of Polygonatum odoratum.

3. The application of the microbial biomarker combination as described in claim 1 in the preparation of reagents, kits, and chips for evaluating the authenticity of Polygonatum odoratum, or in the construction of models for evaluating the authenticity of Polygonatum odoratum.

4. The application of the reagent for detecting the combination of microbial markers as described in claim 1 in the preparation of reagents, kits, and chips for evaluating the authenticity of Polygonatum odoratum, or in the construction of models for evaluating the authenticity of Polygonatum odoratum.

5. A model for evaluating the authenticity of Polygonatum odoratum, characterized in that, The model construction method includes: (1) Sample processing: Collect samples of Polygonatum odoratum from authentic Jiuhua and non-authentic Jiuhua, and perform preprocessing; (2) Extract genomic DNA from the microbial biomarker combination described in claim 1 from the sample and detect the species abundance of the microorganism; (3) A random forest model was constructed using the species abundance matrix of samples from different groups for prediction. The true label of the authentic Jiuhua group was set to 0 and that of the non-authentic group was set to 1. The evaluation model was obtained.

6. The model as described in claim 5, characterized in that, The pretreatment includes: washing, cutting, soaking in 75% ethanol, disinfecting by soaking in 5% sodium hypochlorite, absorbing moisture, cutting into small pieces, and storing at -80℃.

7. A method for evaluating the authenticity of Polygonatum odoratum, characterized in that, The method includes the following steps: (1) Construct the model as described in claim 5 or 6; (2) Extract genomic DNA from the Polygonatum sibiricum sample to be tested and detect the species abundance of the microbial biomarker combination described in claim 1; (3) Substitute the species abundance value obtained in step (2) into the model described in step (1) to make a prediction; when the prediction is 0, the sample is determined to be genuine Jiuhua Huangjing, and when the prediction is 1, it is determined to be non-genuine Huangjing.

8. A system for evaluating the authenticity of Polygonatum odoratum, characterized in that, include: Pre-input module: Used at least for inputting the data to be evaluated; Evaluation module: at least used to evaluate the data to be evaluated, performed by the model as described in claim 5 or 6; Display module: Used at least to display evaluation results.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it causes the system of claim 8 to perform the following steps: Collect and / or input evaluation data on the authenticity of Polygonatum odoratum; The evaluation data is substituted into the model for calculation to obtain a judgment on whether the Polygonatum is authentic. Output the conclusion on whether the Polygonatum is genuine Jiuhua Polygonatum.

10. A computer-readable storage medium having stored thereon the computer device as claimed in claim 9.

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