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 comprises one or more of Escherichia. Shigiella, Acremonium, Pseudomonas, Serratii a, Mortierella, Wallemia, Engydonium, Sporidesmium, Ceratobasidium, Clonostachys and Ralstachys, an evaluation method for the genuine nature of the polygonatum sibiricum is established, the sensitivity is 91.7%, the specificity is 100.0%, the AUC is 93.8%, and the application of the microbial marker combination to the evaluation of the genuine nature of the polygonatum sibiricum is established. The method has the characteristics of high sensitivity, strong specificity and high accuracy, can judge whether the production area of the rhizoma polygonati is a genuine production area of the rhizoma polygonati, and can be widely applied to genuine evaluation of the rhizoma polygonati.

Description

Technical Field

[0001] The invention belongs to the field of biotechnology, and particularly relates to a microbial marker composition for evaluating the authenticity of polygonatum sibiricum and an application thereof. Background Art

[0002] Authentic Chinese medicinal materials refer to Chinese medicinal materials that grow in a specific geographical environment, have excellent quality, significant efficacy, a long history, and significant regional characteristics. The quality and efficacy of Chinese medicinal materials are closely related to the ecological environment in which they grow, especially factors such as climate, soil, and geographical location, which have an important influence on the synthesis and accumulation of their active ingredients. Although the same Chinese medicinal material is widely distributed in China, medicinal materials from different origins often have significant differences in composition, quality, and efficacy. For example, Chinese angelica, astragalus, licorice and other medicinal materials have very different efficacy depending on their origin. Therefore, the identification of authentic medicinal materials is the key basis for evaluating their quality and efficacy.

[0003] Polygonatum sibiricum is the dried rhizome of Polygonatum kingianum Coll.et Hemsl., Polygonatum sibi ricum Red. or Polygonatum cyrtonema Hua, which are plants of the Liliaceae family. They are widely distributed in Anhui, Gansu, Sichuan, Yunnan and other places in China. Polygonatum sibiricum has been widely used in the field of traditional Chinese medicine since ancient times. It has the effects of anti-aging, lowering blood sugar, lowering blood lipids, improving and improving memory, anti-tumor, regulating immunity, anti-virus, anti-inflammatory and so on. Studies have shown that the Polygonatum sibiricum produced in the Jiuhua Mountain area of ​​Anhui Province is of high quality and significant efficacy. The comprehensive evaluation index is better than that of Polygonatum sibiricum produced in other places. It has the characteristics of "high quality and good effect", so it is known as a genuine medicinal material.

[0004] At present, the commonly used methods for evaluating the authenticity of medicinal materials mainly include morphological identification of medicinal materials, chromatography analysis, mass spectrometry analysis, etc. Although the morphological identification method is simple and easy, it is greatly affected by the subjective factors of the observer, and it 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 disadvantages 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 evaluate the authenticity of Polygonatum sibiricum.

[0005] In recent years, the study of microbiome has provided new ideas for the quality control of Chinese herbal medicines. Microbiome refers to the whole of the microbial community and its genome inside and outside the host plant, including microbial populations such as bacteria, archaea, fungi, viruses, and their metabolic functions and ecological effects inside and outside the host plant. Endophytic flora refers specifically to the microbial community that is implanted in the plant and coexists with the host plant. As a medicinal plant, the endophytic flora of Polygonatum sibiricum plays an important role in its growth, metabolism and accumulation of effective ingredients. There are significant differences in the species, abundance and diversity of the endophytic flora of Polygonatum sibiricum from different origins, and these differences are closely related to the differences in the efficacy and chemical composition of Polygonatum sibiricum. However, there is no method in the field to evaluate the authenticity of Polygonatum sibiricum using endophytes.

[0006] In view of the above technical problems, the present invention systematically analyzes the endophytic community of Polygonatum sibiricum, identifies endophytes with different abundances as biomarkers, and screens specific endophytes of Polygonatum sibiricum as a microbial marker combination for evaluating the authenticity of Polygonatum sibiricum. The microbial marker combination includes Escherichia.Shigella, Acremonium, Pseudomonas, Serratia, Mortierel la, Wallemia, Engyodontium, Sporidesmium, Ceratobasidium, Clonostachys and Ralstonia, which can provide an effective authenticity evaluation. At the same time, the present invention combines the random forest model to obtain a method for evaluating the authenticity of Polygonatum sibiricum. Compared with traditional methods, it can more quickly and accurately determine whether the origin of Polygonatum sibiricum is the authentic production area of ​​Polygonatum sibiricum, Anhui Jiuhua Mountain, with high sensitivity and strong specificity. It can greatly improve the defects of conventional trait identification methods such as strong subjectivity and low accuracy, high cost, complex operation, slow detection speed and high toxicity of chemical reagents, etc., and meet the actual demands for fast and efficient circulation of medicinal materials, and has practical significance and application value in the production and quality evaluation of medicinal materials. Summary of the invention

[0007] In view of the above technical problems, the purpose of the present invention is to provide a combination of microbial markers and applications for evaluating the authenticity of Polygonatum sibiricum, which specifically include the following contents:

[0008] In a first aspect, the present invention provides a microbial marker combination for evaluating the authenticity of Polygonatum sibiricum, wherein the microbial marker combination includes one or more of Escherichia.Shigella, Acremonium, Pseudomonas, Serratia, Mortierella, Wallemia, Engyodontium, Sporidesmium, Ceratobasidium, Clonostachys and Ralstonia.

[0009] Preferably, the microbial marker combination consists of Escherichia.Shigella, Acremonium, Pseudomonas, Serratia, Mortierella, Wallemia, Engyodontium, Sporidesmium, Ceratobasidium, Clonostachys and Ralstonia.

[0010] In a second aspect, the present invention provides the use of the microbial marker combination described in the first aspect in evaluating the authenticity of Polygonatum sibiricum.

[0011] In a third aspect, the present invention provides an application of the microbial marker combination described in the first aspect in the preparation of reagents, kits, and chips for evaluating the authenticity of Polygonatum sibiricum.

[0012] In a fourth aspect, the present invention provides an application of the microbial marker combination described in the first aspect in constructing a model for evaluating the authenticity of Polygonatum sibiricum.

[0013] In a fifth aspect, the present invention provides the use of a reagent for detecting the combination of microbial markers described in the first aspect in the preparation of a reagent, a kit, or a chip for evaluating the authenticity of Polygonatum sibiricum.

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

[0015] In a sixth aspect, the present invention provides a model for evaluating the authenticity of Polygonatum sibiricum, and the model construction method comprises:

[0016] (1) Sample processing: Collect authentic and non-authentic Polygonatum odoratum samples and perform pre-processing;

[0017] (2) extracting genomic DNA of the microbial marker combination described in the first aspect from the sample, and detecting the species abundance of the microorganisms;

[0018] (3) The random forest model was constructed based on the species abundance matrix of samples in different groups for progressive prediction, where the prediction value of the authentic group was 0 and the prediction value of the non-authentic group was 1, and the evaluation model was obtained.

[0019] Preferably, the pretreatment comprises: washing, cutting, soaking in 75% ethanol, soaking in 5% sodium hypochlorite for disinfection, drying, cutting into small pieces, and storing the polygonatum sibiricum sample at -80°C in sequence.

[0020] Preferably, the step (2) comprises:

[0021] ①Genomic DNA extraction;

[0022] ② PCR amplification of target fragment: using the genomic DNA extracted in step ① as a template, according to the selection of the sequencing region, using specific primers with barcodes and Tks Gflex DNA Polymerase for PCR; performing two rounds of PCR amplification, electrophoresis detection, and magnetic bead purification;

[0023] ③Library construction and sequencing: The purified PCR product was quantified by Qubit, the concentration was adjusted, and the sequencing was performed using the Illumina NovaSeq 6000 sequencing platform to generate 250 bp double-end reads;

[0024] ④ Bioinformatics analysis: After the data was downloaded, the Cutadapt software was first used to cut the primer sequence from the rawdata sequence. DADA2 was used to perform quality control analysis such as quality filtering, noise reduction, splicing, and chimera removal on the qualified double-ended rawdata in the previous step to obtain representative sequences and ASV abundance tables. The QIIME 2 software package was used to select the representative sequences of 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 Unite database.

[0025] Preferably, the genomic DNA extraction described in step ① is performed according to conventional methods.

[0026] Preferably, the quality filtering described in step ④ is performed according to the default parameters of QIIME 2 (2020.11), and the alignment annotation is analyzed using the default parameters of q2-feature-classifier software.

[0027] 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: 16S V3-V4 region [primers 343F (5'-TACGGRAGGCAG CAG-3') and 798R (5'-AGGGTATCTAATCCT-3')], and the region corresponding to fungal ITS diversity identification: ITS [primers ITS1F (5'-CTTGGTCATTTAGAGGAAGTAA-3') and ITS2R (5'-GCTGCGTTCTTCATCGATGC-3')].

[0028] In a seventh aspect, the present invention provides a method for screening the microbial marker combination described in the first aspect, the method comprising:

[0029] (1) Collecting Polygonatum sibiricum samples: The samples were divided into authentic group and non-authentic group according to the collection area;

[0030] (2) Polygonatum sibiricum sample processing: 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°C;

[0031] (3) Biomarker detection of Polygonatum sibiricum samples:

[0032] ①Genomic DNA extraction;

[0033] ② PCR amplification of target fragment: using the genomic DNA extracted in step ① as a template, according to the selection of the sequencing region, using specific primers with barcodes and Tks Gflex DNA Polymerase for PCR; performing two rounds of PCR amplification, electrophoresis detection, and magnetic bead purification;

[0034] ③Library construction and sequencing: The purified PCR product was quantified by Qubit, the concentration was adjusted, and the sequencing was performed using the Illumina NovaSeq 6000 sequencing platform to generate 250 bp double-end reads;

[0035] ④ Bioinformatics analysis: After the data was downloaded, the rawdata sequence was first cut off by using the Cutadapt software, and the qualified double-ended rawdata in the previous step was subjected to quality control analysis such as quality filtering, noise reduction, splicing, and chimera removal using DADA2 to obtain representative sequences and ASV abundance tables; the representative sequences of each ASV were selected using the QIIME 2 software package, 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 Unite database;

[0036] (4) Constructing an evaluation model: Selecting the top 30 bacterial genera and the top 30 fungal genera ranked in relative abundance in the group obtained by sequencing analysis in step (3) to construct a random forest model. The random forest is used to identify the important features of microorganisms. Microbial markers are screened based on the impact of the microbial selection program on the result prediction, and the default values ​​are optimized to construct an evaluation model.

[0037] (5) Model validation: The internal test set and validation set were set up in a 7:3 ratio to verify the reliability of the screened microbial markers.

[0038] Preferably, the genomic DNA extraction described in step ① is performed according to conventional methods.

[0039] Preferably, the quality filtering described in step ④ is performed according to the default parameters of QIIME 2 (2020.11), and the alignment annotation is analyzed using the default parameters of q2-feature-classifier software.

[0040] 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: 16S V3-V4 region [primers 343F (5'-TACGGRAGGCAG CAG-3') and 798R (5'-AGGGTATCTAATCCT-3')], and the region corresponding to fungal ITS diversity identification: ITS [primers ITS1F (5'-CTTGGTCATTTAGAGGAAGTAA-3') and ITS2R (5'-GCTGCGTTCTTCATCGATGC-3')].

[0041] In an eighth aspect, the present invention provides a method for evaluating the authenticity of Polygonatum sibiricum, the method comprising the following steps:

[0042] (1) constructing the model described in the sixth aspect;

[0043] (2) extracting genomic DNA from the Polygonatum cyrtonema sample to be tested, and detecting the species abundance of the microbial marker combination described in the first aspect above;

[0044] (3) Substituting the species abundance value obtained in step (2) into the model described in step (1) for prediction; wherein, when the prediction value is 0, it is authentic Polygonatum sibiricum; and when the prediction value is 1, it is non-authentic Polygonatum sibiricum.

[0045] In a ninth aspect, the present invention provides a system for evaluating the authenticity of Polygonatum sibiricum, comprising:

[0046] Pre-input module: at least used to input the data to be evaluated;

[0047] Evaluation module: at least used to evaluate the data to be evaluated, which is executed by the polygonatum authenticity evaluation model described in the sixth aspect;

[0048] Display module: at least used to display the evaluation results.

[0049] In a tenth aspect, the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the system according to the ninth aspect implements the following steps, including:

[0050] Collect and / or input data on the authenticity of Polygonatum sibiricum;

[0051] Substitute the evaluation data into the model for calculation to get the conclusion of whether the polygonatum is authentic;

[0052] Output the judgment conclusion of whether the Polygonatum sibiricum is authentic.

[0053] In an eleventh aspect, the present invention provides a computer-readable storage medium on which the computer device described in the tenth aspect is stored.

[0054] The beneficial effects of the present invention are as follows: (1) After a large number of screenings, the present invention obtains for the first time a combination of microbial markers for evaluating the authenticity of Polygonatum sibiricum, the combination of microbial markers comprising Escherichia.Shigella, Acremonium, Pse One or more of Udomonas, Serratia, Mortierella, Wallemia, Engyodontium, Sporidesmium, Ceratobasidium, Clonostachys and Ralstonia; (2) Secondly, the present invention uses the microbial marker combination to establish an evaluation model for the authenticity of Polygonatum sibiricum, and provides an establishment method. The model can determine whether the origin of Polygonatum sibiricum is the authentic production area of ​​Polygonatum sibiricum, Jiuhua Mountain, Anhui Province. It is economical, easy to operate, and suitable for application in the Chinese medicinal materials market; (3) When the microbial marker combination is used to evaluate the authenticity of Polygonatum sibiricum, the sensitivity is 91.7%, the specificity is 100.0%, and the AUC is 93.8%. It has high sensitivity and specificity, can greatly improve the defects of conventional trait identification methods such as strong subjectivity and low accuracy, chemical fingerprint method with high cost, complex operation, slow detection speed and high toxicity of chemical reagents, and can be widely used for the authenticity evaluation of Polygonatum sibiricum. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 Quantitative plots of selected variables;

[0056] Figure 2 dot plots of species importance for a selected number of species;

[0057] Figure 3 ROC curve of model prediction discrimination. DETAILED DESCRIPTION

[0058] The following examples are provided to facilitate a better understanding of the present invention, but are not intended to limit the present invention. The experimental methods in the following examples are conventional methods unless otherwise specified. The experimental materials used in the following examples are commercially available unless otherwise specified.

[0059] In a specific embodiment, sensitivity, specificity, accuracy and other combinations are used to describe the goodness and reliability of the detection method described in the present invention. Several terms used together with the description of sensitivity, specificity and accuracy include: true positive (TP), true negative (TN), false positive (FP), false negative (FN); wherein, if it is proved that Polygonatum sibiricum is authentic Jiuhua Polygonatum sibiricum, and a given detection experiment also shows that the sample is authentic Jiuhua Polygonatum sibiricum, the test result is considered to be true positive; if it is proved that Polygonatum sibiricum is not authentic Jiuhua Polygonatum sibiricum, and a given detection experiment also shows that the sample is not authentic Jiuhua Polygonatum sibiricum, the test result is considered to be true negative; if the test result shows that a sample that is not actually authentic Jiuhua Polygonatum sibiricum is authentic Jiuhua Polygonatum sibiricum, the test result is false positive; if the test result shows that a sample that is actually authentic Jiuhua Polygonatum sibiricum is not authentic Jiuhua Polygonatum sibiricum, the test result is false negative.

[0060] Sensitivity = TP / (TP+FN) = number of true positive assessments / number of all positive assessments;

[0061] Specificity = TN / (TN+FP) = number of true negative assessments / number of all negative assessments;

[0062] Accuracy = (TN + TP) / (TN + TP + FN + FP) = number of correct evaluations / number of all evaluations.

[0063] Example 1 Method for evaluating the authenticity of Polygonatum sibiricum

[0064] Fifteen samples of Jiuhua polygonatum and 37 non-Jiuhua polygonatum samples from other different origins were selected, and the marker combination provided by the present invention was used to detect the above samples. Based on the test results, the sensitivity, specificity and accuracy of the marker combination provided by the present invention for evaluating the authenticity of polygonatum were determined.

[0065] 1. Collection of Polygonatum sibiricum Samples

[0066] Fresh and healthy samples of Polygonatum cyrtonema were collected from Jiuhua Mountain in Anhui Province and other producing areas, and the samples were divided into authentic and non-authentic groups according to the collection area. The surface soil and impurities were washed off with running water, and the complete rhizome of about 5 cm in length was cut.

[0067] 2. Sample surface disinfection

[0068] Take the above samples, rinse with sterile water for 5 minutes, soak in 75% ethanol for 1 minute, rinse with sterile water 1-2 times, soak in 5% sodium hypochlorite for 3 minutes, rinse with sterile water 7-10 times, and dry the surface water with sterile filter paper. Cut into small pieces with sterile scissors and store in 5mL centrifuge tubes at -80℃ for later use.

[0069] 3. Detection of Polygonatum sibiricum biomarkers

[0070] 3.1 Genomic DNA extraction

[0071] The genomic DNA of the samples was extracted using a plant genomic DNA extraction kit (Tiangen Biochemical Technology (Beijing) Co., Ltd.) according to the kit instructions. The concentration and purity of the DNA were detected using NanoDrop 2000 (Thermo Fisher Scientific, USA) and agarose gel electrophoresis, and the extracted DNA was stored at -20°C.

[0072] 3.2 PCR amplification of target fragment

[0073] The extracted genomic DNA was used as a template, and according to the selection of the sequencing region, PCR was performed using specific primers with barcodes and Takara's Tks Gflex DNA Polymerase to ensure amplification efficiency and accuracy. The corresponding region for bacterial diversity identification was the 16S V3-V4 region [primers 343F (5'-TACGGRAGGCAGCAG-3') and 798R (5'-AGGGTATCTAATCCT-3')], and the corresponding region for fungal ITS diversity identification was ITS [primers ITS1F (5'-CTTGGTCATTTAGAGGAAGTAA-3') and ITS2R (5'-GCTGCGTTCTTCATCGATGC-3')]. A round of PCR amplification was performed (the system and reaction procedure of the first round of PCR amplification were shown in Tables 1 and 2), the products of the first round of PCR were detected by electrophoresis, purified by magnetic beads after detection, used as templates for the second round of PCR after purification, and a second round of PCR amplification was performed (the system and reaction procedure of the second round of PCR amplification were shown in Tables 3 and 4), and detected by electrophoresis again, purified by magnetic beads after detection, and the PCR products were quantified by Qubit after purification.

[0074] The amplification system and reaction procedure of the first round of PCR are shown in Tables 1 and 2, and the amplification system and reaction procedure of the second round of PCR are shown in Tables 3 and 4.

[0075] Table 1 One-round PCR system

[0076]

[0077] Table 2 One round PCR reaction program

[0078]

[0079] Note: If the DNA concentration is high or contains a lot of impurities, the annealing temperature can be appropriately increased to avoid impurity bands.

[0080] Table 3 Second round PCR system

[0081]

[0082] Table 4 Second round PCR reaction program

[0083]

[0084] Note: If there is slight impurity in the product of one round, the annealing temperature can be appropriately increased to reduce the impurity.

[0085] Magnetic bead purification: Add 20 μL of well-mixed AMPure XP beads (Beads: product = 0.8:1) to a U-shaped plate, then add the PCR product, pipette slowly 10 times, mix well, and place at room temperature for 5 minutes. Place on the magnetic stand for 5 minutes until the supernatant is transparent, and discard the supernatant. Add 200 μL of freshly prepared 80% ethanol, room temperature for 30 seconds, and discard the supernatant. Repeat the previous step and wash twice in total. Place at room temperature to dry, dry until there are no droplets remaining (use a 10 μL pipette to check whether there are droplets remaining at the bottom of the tube), and there is no reflection on the surface of the beads, and remove the plate from the magnetic stand. Add 25 μL H2O to elute, pipette 10 times, mix well, and room temperature for 2 minutes. Place on the magnetic stand for 5 minutes until the supernatant is transparent, and transfer 20 μL of the supernatant to a new PCR tube.

[0086] 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.

[0087] 3.3 Library construction and sequencing

[0088] Take 1 μL of the purified second-round product for Qubit quantification, adjust the concentration for sequencing, and use the Illumina NovaSeq6000 sequencing platform for sequencing to generate 250 bp double-end reads.

[0089] 3.4 Bioinformatics analysis

[0090] The raw data is in FASTQ format. After the data is downloaded, first use Cutadapt software to cut the primer sequence from the raw data sequence. Then use DADA2 to perform quality control analysis such as quality filtering, noise reduction, splicing, and chimera removal on the qualified double-end rawdata in the previous step according to the default parameters of QIIME 2 (2020.11) to obtain representative sequences and ASV abundance tables. After using the QIIME 2 software package to select the representative sequences of each ASV, all 16S representative sequences are compared and annotated with the Silva (version138) database, and all ITS representative sequences are compared and annotated with the Unite database. The species comparison annotation is analyzed using the default parameters of the q2-feature-classifier software.

[0091] Random forest is a machine learning algorithm first proposed by Leo Breiman and Adele Cutler. The algorithm can effectively and accurately classify microbial community samples and find key components (ASVs or species) that can distinguish differences between groups. Take the top 30 bacterial and fungal genera in relative abundance and use the R package randomForest to draw a species importance dot map, such as Figure 2 shown.

[0092] 4. Evaluation model construction

[0093] The machine learning algorithm was implemented in R (version 4.4.1). The top 30 bacterial genera and top 30 fungal genera in relative abundance in the group obtained by sequencing analysis were selected to construct a random forest model. Random forests were used to identify the important features of microorganisms, and microbial markers were screened according to the impact of the microbial selection procedure on the prediction of the results to achieve the best prediction accuracy of the model. The optimal number of selected microbial markers is shown in the quantitative plot of the selected variables, and the importance features of the optimal number of screened microbial markers are shown in the species importance dot plot.

[0094] Random forests were used to optimize each microbial marker around its default value. The internal test set and validation set were set according to a 7:3 ratio to verify the reliability of the screened microbial markers. ROC curve analysis was used to verify the accuracy of the model.

[0095] 5. Evaluation results

[0096] The species abundance matrix of samples in different groups was modeled, analyzed and predicted to obtain an evaluation model. When the prediction value is 0, the authenticity of Polygonatum sibiricum is determined to be Jiuhua Polygonatum; when the prediction value is 1, the authenticity of Polygonatum sibiricum is determined to be non-Jiuhua Polygonatum.

[0097] like Figure 1As shown in the figure, the top 30 bacterial and fungal genera in species abundance were selected as candidate markers to distinguish Jiuhua Polygonatum from Polygonatum from other origins. The AUCRF package was used for analysis, and the results showed that when the number of markers was 11, the discrimination performance was the best, and the AUC value reached 0.8541. Therefore, these 11 markers were selected for subsequent analysis.

[0098] like Figure 2 As shown, after marker selection, 11 markers including Escherichia.Shigella, Acremonium, Pseudomonas, Serratia, Mortierella, Wallemia, Engyodontium, Sporidesmium, Ceratobasidium, Clonostachys, and Ralstonia had the best classification performance, and the order of importance was Escherichia.Shigella, Ac remonium, Pseudomonas, Serratia, Mortierella, Wallemia, Engyodontium, Sporidesmium, Ceratobasidium, Clonostachys, and Ralstonia.

[0099] A total of 11 markers, including Escherichia.Shigella, Acremonium, Pseudomonas, Serratia, Mortierella, Wallemia, Engyodontium, Sporidesmium, Ceratobasidium, Clonostachys and Ralstonia, were used to distinguish Jiuhua Polygonatum from non-Jiuhua Polygonatum. 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%.

[0100] Example 2 Polygonatum authentic evaluation system

[0101] This embodiment provides a system for evaluating the authenticity of Polygonatum sibiricum, including:

[0102] Pre-input module: used at least to input the combined abundance data of the microbial markers in the polygonatum sample and transmit it to the evaluation module;

[0103] Evaluation module: at least used to evaluate the abundance data of the microbial marker combination in the Polygonatum sibiricum sample according to the method described in Example 1 to obtain the result; wherein, the abundance data of the microbial marker combination can be collected through the pre-input module, and the abundance data of the microbial marker combination can also be obtained from other sources.

[0104] The display module is at least used to display the evaluation result. When the prediction value is 0, the result of the authenticity judgment of the polygonatum is Jiuhua polygonatum; when the prediction value is 1, the result of the authenticity judgment of the polygonatum is not Jiuhua polygonatum.

[0105] Embodiment 3 Computer equipment

[0106] The present embodiment provides an electronic device, which can be expressed in the form of a computing device (for example, a server device), including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the method for evaluating the authenticity of Polygonatum odoratum in the first embodiment of the present invention can be implemented when the processor executes the computer program.

[0107] When the processor executes the program, the system implements the following steps, including:

[0108] (1) Collecting and / or inputting evaluation data for the authenticity evaluation of Polygonatum sibiricum, i.e., the abundance data of microbial marker combinations;

[0109] (2) Substituting the evaluation data into the model for calculation, and obtaining the conclusion of whether the polygonatum sibiricum is authentic;

[0110] (3) Output the judgment conclusion on whether the Polygonatum sibiricum is authentic.

[0111] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present 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 into multiple units / modules to be embodied.

[0112] Embodiment 4 Computer readable storage medium

[0113] This embodiment provides a computer-readable storage medium on which the computer device is stored. When the program is executed by a processor, the steps of the method for evaluating the authenticity of Polygonatum cyrtonema in the first embodiment of the present invention are implemented.

[0114] The readable storage medium may include but is not limited to: a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device or any suitable combination of the above.

[0115] In a possible implementation, the present invention can also be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps of the method for evaluating the authenticity of Polygonatum sibiricum in Example 1 of the present invention.

[0116] The program code for executing the present invention may be written in any combination of one or more programming languages, and may be executed entirely on a user device, partially on a user device, as an independent software package, partially on a user device and partially on a remote device, or entirely on a remote device.

[0117] In summary, the present invention provides a combination of microbial markers for evaluating the authenticity of Polygonatum sibiricum, wherein the combination of microbial markers comprises Escherichia.Shigella, Acremonium, Pseudomonas, Serratia, Mortierella, Walle mia, Engyodontium, Sporidesmium, Ceratobasidium, Clonostachys and Ralstonia; the present invention establishes an evaluation model for the authenticity of polygonatum by using the microbial marker combination, and provides an establishment method, the model can determine whether the origin of polygonatum is the authentic production area of ​​polygonatum, Jiuhua Mountain, Anhui Province, the model is economical, easy to operate, and suitable for application in the Chinese medicinal materials market; when the microbial marker combination is used to evaluate the authenticity of polygonatum, the sensitivity is 91.7%, the specificity is 100.0%, and the AUC is 93.8%, which has high sensitivity and specificity, can greatly improve the defects of conventional trait identification methods such as strong subjectivity and low accuracy, and chemical fingerprint methods such as high cost, complex operation, slow detection speed and high toxicity of chemical reagents, and can be widely used in the evaluation of the authenticity of polygonatum.

[0118] The above description is a detailed description of the preferred feasible embodiments of the present invention, but the embodiments are not intended to limit the scope of the patent application of the present invention. All equivalent changes or modified changes completed under the technical spirit suggested by the present invention should fall within the patent scope covered by the present invention.

Claims

1. A combination of microbial markers for evaluating the authenticity of Polygonatum sibiricum, characterized in that: The microbial marker combination includes one or more of Escherichia.Shigella, Acremonium, Pseudomonas, Serratia, Mortierella, Wallemia, Engyodontium, Sporidesmium, Ceratobasidium, Clonostachys and Ralstonia.

2. The microbial marker combination according to claim 1, characterized in that: The microbial marker combination consists of Escherichia.Shigella, Acremonium, Pseudomonas, Serratia, Mortierella, Wallemia, Engyodont ium, Sporidesmium, Ceratobasidium, Clonostachys and Ralstonia.

3. Use of the microbial marker combination as described in claim 1 or 2 in evaluating the authenticity of Polygonatum sibiricum.

4. Use of the microbial marker combination as described in claim 1 or 2 or a reagent for detecting the microbial marker combination as described in claim 1 or 2 in the preparation of a reagent, a kit, or a chip for evaluating the authenticity of Polygonatum sibiricum, or in the construction of a model for evaluating the authenticity of Polygonatum sibiricum.

5. A model for evaluating the authenticity of Polygonatum sibiricum, characterized in that: The model building method comprises: (1) Sample processing: Collect authentic and non-authentic Polygonatum odoratum samples and perform pre-processing; (2) extracting genomic DNA of the microbial marker combination described in claim 1 or 2 from the sample, and detecting the species abundance of the microorganisms; (3) The random forest model was constructed based on the species abundance matrix of samples in different groups for progressive prediction, where the prediction value of the authentic group was 0 and the prediction value of the non-authentic group was 1, and the evaluation model was obtained.

6. The model according to claim 5, characterized in that The pretreatment comprises: washing, cutting, soaking in 75% ethanol, soaking in 5% sodium hypochlorite for disinfection, drying, cutting into small pieces, and storing at -80°C on the polygonatum sibiricum sample in sequence.

7. A method for evaluating the authenticity of Polygonatum sibiricum, characterized in that: The method comprises the following steps: (1) constructing the model described in claim 5 or 6; (2) extracting genomic DNA from the Polygonatum cyrtonema sample to be tested, and detecting the species abundance of the microbial marker combination described in claim 1 or 2; (3) Substituting the species abundance value obtained in step (2) into the model described in step (1) for prediction; wherein, when the prediction value is 0, it is authentic Polygonatum sibiricum; and when the prediction value is 1, it is non-authentic Polygonatum sibiricum.

8. A system for evaluating the authenticity of Polygonatum sibiricum, characterized in that: include: Pre-input module: at least used to input the data to be evaluated; Evaluation module: at least used to evaluate the data to be evaluated, which is executed by the polygonatum authenticity evaluation model described in claim 5 or 6; Display module: at least used to display the 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, the system of claim 8 implements the following steps, including: Collect and / or input data on the authenticity of Polygonatum sibiricum; Substitute the evaluation data into the model for calculation to get the conclusion of whether the polygonatum is authentic; Output the judgment conclusion of whether the Polygonatum sibiricum is authentic.

10. A computer-readable storage medium having stored thereon the computer device according to claim 9.

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