Microbial marker combination for evaluating quality of rhizoma polygonati 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 quality of polygonatum sibiricum and application, the microbial marker combination comprises one or more of Aspergillus, Cercophora, Escherichia. Shigilla, Mycoplasm, Muribacaceae, Pseudomonas, Colletotrichum and Asaia, a method and an evaluation model for evaluating the quality of polygonatum sibiricum are established by utilizing the microbial marker combination, the sensitivity is 100.0%, the specificity is 72.7%, the AUC is 89.8%, and the evaluation model is used for evaluating the quality of polygonatum sibiricum. The method has high accuracy, specificity and sensitivity, and the evaluation method is economical, practical and easy and convenient to operate and can be widely applied to quality evaluation of rhizoma polygonati.

Description

[0001] This application claims priority to the prior application with the application date of December 5, 2024, the application number of CN2024117774357, and the invention title of "Microbial Marker Combination for Evaluating the Quality of Rhizoma Polygonati and Application", the entire contents of the prior application are embodied in the present application. TECHNICAL FIELD

[0002] The present application belongs to the field of biotechnology, and specifically relates to a microbial marker combination for evaluating the quality of Rhizoma Polygonati and application. BACKGROUND

[0003] Rhizoma Polygonati is a perennial herb belonging to the Liliaceae family. Its dried rhizomes are widely used in traditional Chinese medicine for their spleen-strengthening, lung-moistening, and thirst-quenching properties. The main active ingredient, Rhizoma Polygonati polysaccharide, is a key component of the pharmacological effects of Rhizoma Polygonati and is also the standard for content determination in the Pharmacopoeia, determining the quality of Rhizoma Polygonati.

[0004] Currently, according to the 2020 edition of the Chinese Pharmacopoeia, the quality evaluation of Rhizoma Polygonati is determined by measuring the content of Rhizoma Polygonati polysaccharide using ultraviolet-visible spectrophotometry (UV-Vis). Although UV-Vis is currently the gold standard for determining the active ingredients of Rhizoma Polygonati, this method has certain limitations, including high equipment cost, complex operation, high reagent toxicity, long analysis process, and low sensitivity for certain compounds, which may lead to inaccurate detection of certain components. Therefore, there is an urgent need for a more efficient, fast, and safe alternative technology to evaluate the quality of Rhizoma Polygonati.

[0005] In addition, endophytic bacteria in Rhizoma Polygonati may have important potential effects on its quality. Studies have shown that there are a variety of endophytic bacteria in Rhizoma Polygonati with rich metabolic activity. These endophytic bacteria, through interaction with the host plant, may affect the growth and accumulation of active ingredients of Rhizoma Polygonati in multiple ways. For example, some endophytic bacteria can promote the disease resistance of Rhizoma Polygonati or improve its adaptability to the environment, thereby affecting the overall health and growth rate of the plant; in addition, some endophytic bacteria may indirectly participate in the synthesis and accumulation of active ingredients in Rhizoma Polygonati by synthesizing secondary metabolites, affecting their pharmacological activity and quality stability. Due to the important role of endophytic bacteria in plant growth and development, further research on the relationship between endophytic bacteria and the accumulation of active ingredients in Rhizoma Polygonati has potential guiding significance for the quality control of Rhizoma Polygonati. However, although this field has gradually attracted attention, there is currently a lack of research on systematically evaluating the quality of Rhizoma Polygonati by detecting endophytic bacteria.

[0006] The present application screens specific microorganisms of polygonatum as a microorganism marker combination for evaluating the quality of polygonatum, the microorganism marker combination comprises Aspergillus, Cercophora, Escherichia, Shigella, Mycoplasma, Muribaculaceae, Pseudomonas, Colletotrichum and Asaia, the present application discloses a method for evaluating the quality of polygonatum by using the microorganism marker combination, and establishes an evaluation model, the sensitivity of the microorganism marker combination for evaluating the quality of polygonatum is 100.0%, the specificity is 72.7%, the AUC is 89.8%, has higher accuracy, specificity and sensitivity, and can be widely used for quality evaluation of polygonatum. SUMMARY

[0007] In view of the above technical problems, the present application aims to provide a microorganism marker combination and application for evaluating the quality of polygonatum, specifically comprising the following contents:

[0008] In the first aspect, the present application provides a microorganism marker combination for evaluating the quality of polygonatum, the microorganism marker combination comprises one or more of Aspergillus, Cercophora, Escherichia, Shigella, Mycoplasma, Muribaculaceae, Pseudomonas, Colletotrichum and Asaia.

[0009] Preferably, the microorganism marker combination consists of Aspergillus, Cercophora, Escherichia, Shigella, Mycoplasma, Muribaculaceae, Pseudomonas, Colletotrichum and Asaia.

[0010] In the second aspect, the present application provides the application of the microorganism marker combination in the first aspect in evaluating the quality of polygonatum.

[0011] In the third aspect, the present application provides the application of the microorganism marker combination in the first aspect in preparing reagents, kits and chips for evaluating the quality of polygonatum.

[0012] In the fourth aspect, the present application provides the application of the microorganism marker combination in the first aspect in constructing a model for evaluating the quality of polygonatum.

[0013] In the fifth aspect, the present application provides the application of the reagent for detecting the microorganism marker combination in the first aspect in preparing reagents, kits and chips for evaluating the quality of polygonatum.

[0014] In a sixth aspect, the present application provides a use of the microbial marker combination of the first aspect in constructing a model for evaluating the quality of Rhizoma Polygonati.

[0015] In a seventh aspect, the present application provides a model for evaluating the quality of Rhizoma Polygonati, wherein the model is constructed by the following method:

[0016] (1) Collecting Rhizoma Polygonati samples and dividing them into a general quality group and a high quality group according to the polysaccharide content; wherein the polysaccharide content of the general quality group is between 7.0% and 14.0% according to the Pharmacopoeia standard, and the polysaccharide content of the high quality group is higher than 14.0%;

[0017] (2) After pretreatment of the Rhizoma Polygonati samples, extracting the genomic DNA of the microbial marker combination of claim 1 or 2 from the samples, and detecting the species abundance of the microorganisms;

[0018] (3) Constructing a random forest model for prediction using the species abundance matrix of the samples in different groups, wherein the true label of the general quality group samples is set to 0 and the true label of the high quality group samples is set to 1 in advance.

[0019] The model outputs a probability value belonging to two categories (the sum is 1) for each prediction sample, and we determine the final judgment according to the probability of the sample being predicted as class 0 (general quality group): by default, when the probability of being predicted as class 0 is greater than or equal to 0.5, the sample is determined to be the general quality group (classified as 0), otherwise it is determined to be the high quality group (classified as 1), and the evaluation model is obtained.

[0020] Preferably, the pretreatment comprises: sequentially washing, cutting, soaking in 75% ethanol, soaking in 5% sodium hypochlorite for disinfection, drying, cutting into small pieces, and storing at -80°C.

[0021] Preferably, the step (2) comprises:

[0022] ① Genomic DNA extraction;

[0023] ② PCR amplification of the target fragment: using the genomic DNA extracted in step ① as the template, using specific primers with barcodes, Tks Gflex DNA Polymerase for PCR according to the selection of the sequencing region; performing two rounds of PCR amplification, electrophoresis detection, and magnetic bead purification;

[0024] ③ Library construction and sequencing: taking the purified PCR product for Qubit quantification, adjusting the concentration, using the Illumina NovaSeq 6000 sequencing platform for sequencing, and generating 250 bp double-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 dechimerism, 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) Samples of Polygonatum were collected and divided into a general quality group and a high quality group according to the polysaccharide content; the polysaccharide content of Polygonatum in the general quality group was between 7.0% and 14.0% according to the pharmacopoeia standard; the polysaccharide content of Polygonatum in the high quality group was higher than 14.0%;

[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 target fragments: using the genomic DNA extracted in step (1) as the template, specific primers with barcode, and Tks Gflex DNA Polymerase to perform PCR according to the selection of the sequencing region; two rounds of PCR amplification were performed, and the PCR products were purified by magnetic beads and detected by electrophoresis;

[0035] (3) Library construction and sequencing: the purified PCR products were quantified by Qubit, and the concentration was adjusted. Illumina NovaSeq 6000 sequencing platform was used for sequencing, and 250 bp double-end reads were generated;

[0036] (4) Bioinformatics analysis: after the data were downloaded, the primer sequences were cut off from the raw data sequences using Cutadapt software. DADA2 was used to perform quality filtering, noise reduction, splicing, and chimera removal on the qualified double-end raw data of the previous step to obtain representative sequences and ASV abundance tables. QIIME 2 software package was used to select the representative sequences of each ASV, 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 Unite database;

[0037] (4) Construction of evaluation model: the top 30 bacterial genera and the top 30 fungal genera in the grouping in terms of relative abundance obtained in step (3) were selected to construct a random forest model. Random forest is used to identify important features of microorganisms. According to the influence of the microorganism selection program on the prediction results, the microorganism markers are screened, and the evaluation model is constructed around the default values.

[0038] (5) Model verification: the reliability of the screened microorganism markers was verified by setting the internal test set and the validation set at 7:3.

[0039] Preferably, the genomic DNA extraction in step (1) is extracted according to a conventional method.

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

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

[0042] In a ninth aspect, the present application provides a method for evaluating the quality of Rhizoma Polygonati, comprising the following steps:

[0043] (1) constructing the model of the seventh aspect;

[0044] (2) extracting the genomic DNA of the Rhizoma Polygonati sample to be tested, and detecting the species abundance of the microbial marker combination of the first aspect;

[0045] (3) substituting the species abundance value obtained in step (2) into the model of step (1) for prediction; wherein the model is pre-set with the true label of the quality general group sample as 0 and the quality excellent group as 1, the model will output a probability value (totaling 1) belonging to two categories for each predicted sample, and the final judgment is made according to the probability of the sample being predicted as the 0 class (quality general group): by default, the threshold is 0.5, when the probability of being predicted as the 0 class is greater than or equal to 0.5, the sample is judged as quality general (classified as 0), otherwise it is judged as quality excellent (classified as 1).

[0046] In a tenth aspect, the present application provides a system for evaluating the quality of Rhizoma Polygonati, comprising:

[0047] a pre-input module for at least inputting the data to be evaluated;

[0048] an evaluation module for at least evaluating the data to be evaluated, which is executed by the Rhizoma Polygonati quality evaluation model of the seventh aspect;

[0049] a display module for at least displaying the evaluation results.

[0050] In an eleventh aspect, the present application provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor executes the program to enable the system of the tenth aspect to implement the following steps, comprising:

[0051] collecting and / or inputting the evaluation data of the quality of Rhizoma Polygonati;

[0052] The evaluation data is substituted into the model to calculate the quality of Rhizoma Polygonati.

[0053] The judgment conclusion of the quality grading of Rhizoma Polygonati is output.

[0054] In a twelfth aspect, the present application provides a computer readable storage medium, which has the computer device of the eleventh aspect stored thereon.

[0055] The present application has the following beneficial effects: (1) the present application obtains a microbial marker combination for evaluating the quality of Rhizoma Polygonati for the first time through a large number of screening, and the microbial marker combination comprises one or more of Aspergillus, Cercophora, Escherichia.Shigella, Mycoplasma, Muribaculaceae, Pseudomonas, Colletotrichum and Asaia; (2) secondly, the present application discloses a method for evaluating the quality of Rhizoma Polygonati by using the microbial marker combination, and establishes an evaluation model, and the sensitivity of the microbial marker combination for evaluating the quality of Rhizoma Polygonati is 100.0%, the specificity is 72.7%, and the AUC is 89.8%, and the accuracy, specificity and sensitivity are high; (3) the evaluation method of the present application is economical and practical, and easy to operate, and is suitable for application in the market of traditional Chinese medicinal materials; the evaluation method can greatly improve the defects of high cost, great toxicity, many experimental steps, slow detection speed and complex data analysis caused by the conventional ultraviolet spectrophotometry, and can be widely used for quality evaluation of Rhizoma Polygonati. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1 Number plot of selected variables.

[0057] Figure 2 Species importance plot of selected number.

[0058] Figure 3 ROC curve of model prediction discrimination. DETAILED DESCRIPTION

[0059] The following examples facilitate better understanding of the present application, but do not limit the present application. In the following examples, the experimental methods are conventional methods, unless otherwise specified. In the following examples, the test materials are commercially available, unless otherwise specified.

[0060] In one embodiment, sensitivity, specificity, accuracy and other combinations are used to describe the goodness and reliability of the detection method described in the present application. Several terms used in conjunction with the description of sensitivity, specificity, accuracy include: true positive (TP), true negative (TN), false positive (FP), false negative (FN); wherein if the quality of Rhizoma Polygonati is proved to be good and the given detection experiment also shows that the quality of the sample is good, the test result is considered to be true positive; if the quality of Rhizoma Polygonati is proved to be general and the given detection experiment also shows that the quality of the sample is general, the test result is considered to be true negative; if the result of the detection experiment shows that the quality of the sample which is actually general is good, the detection result is false positive; if the result of the screening experiment shows that the quality of the sample which is actually good is general, the detection result is false negative.

[0061] Sensitivity = TP / (TP + FN) = Number of true positive assessments / Number of all positive assessments;

[0062] Specificity = TN / (TN + FP) = Number of true negative assessments / Number of all negative assessments;

[0063] Accuracy = (TN + TP) / (TN + TP + FN + FP) = Number of correct assessments / Number of all assessments.

[0064] In the present application, the sample set for screening 8 markers and the verification set for evaluating the performance (AUC) of the final model are as follows:

[0065] Data preparation and grouping: all Rhizoma Polygonati samples are determined for the content of Rhizoma Polygonati polysaccharide in the sample stored in a well-ventilated dry place by the ultraviolet spectrophotometry method under the Rhizoma Polygonati content determination item in the current Chinese Pharmacopoeia. According to the determination results of all samples, the quality is defined as a binary variable: the quality general (the content of Rhizoma Polygonati polysaccharide is between 7.0% of the pharmacopoeia standard and 2 times (14.0%) of the pharmacopoeia standard) group and the quality good (the content of Rhizoma Polygonati polysaccharide is higher than 2 times of the pharmacopoeia standard, i.e. more than 14.0%) group.

[0066] Feature (marker) screening: all sample data are used for analysis by the AUCRF package to determine the microbial markers that can best distinguish the two groups. The analysis results show that when the number of markers is 8, the distinguishing performance of the model is best, and the AUC value in the internal cross-validation reaches 0.8333. The purpose of this step is to find the most discriminative feature set from all available data.

[0067] Model construction and independent validation: After the above-mentioned 8 markers were determined, in order to objectively evaluate the real performance of the prediction model constructed, strict training set and validation set division was carried out: 70% of the samples were randomly selected as the training set, and only the characteristics of the 8 markers were used to train the random forest model. Subsequently, the remaining 30% of the samples that did not participate in the model training were used as an independent validation set to test the trained model.

[0068] Validation results: In order to verify the reliability of the screened microbial markers, the internal test set and the validation set were set as 7:3, and the test results on the independent validation set showed that the model based on the 8 markers had excellent performance: the sensitivity was 100.0%, the specificity was 72.7%, and the AUC was as high as 89.8%.

[0069] Example 1 Quality evaluation method of Huangjing

[0070] 1. Sample selection

[0071] 16 high-quality Huangjing samples and 33 general-quality Huangjing samples were selected, and the above-mentioned samples were detected using the marker combination provided by the application, and according to the detection results, the sensitivity, specificity and accuracy of the marker combination provided by the application for Huangjing quality evaluation were judged.

[0072] Fresh, healthy and undamaged Huangjing samples were collected nationwide, and each sample was divided into two pieces from the middle of the rhizome, one was stored in a well-ventilated dry place, and the other was stored in a 4°C refrigerator and the subsequent experiments were completed as soon as possible.

[0073] 2. Determination of the content of Huangjing polysaccharide and grouping

[0074] The content of Huangjing polysaccharide in the sample stored in a well-ventilated dry place was determined by ultraviolet spectrophotometry under the Huangjing content determination item in the current 2020 edition of Chinese Pharmacopoeia. According to the determination results of all samples, the samples were divided into two groups: general-quality group and high-quality group. The specific standard is: the content of Huangjing polysaccharide in the general-quality group is between 7.0% of the pharmacopoeia standard and 2 times (14.0%) of the pharmacopoeia standard; the content of Huangjing polysaccharide in the high-quality group is higher than 2 times of the pharmacopoeia standard, i.e. more than 14.0%.

[0075] 3. Sample surface disinfection

[0076] The Huangjing sample stored at 4°C was washed with a soft brush under running water to remove surface soil and impurities, and a 5 cm long rhizome segment was cut off, washed with sterile water for 5 min under sterile conditions, soaked in 75% ethanol for 2 min, washed with sterile water for 1-2 times, then soaked in 5% sodium hypochlorite for 5 min, washed with sterile water for 7-10 times, and the surface moisture was absorbed with sterile filter paper. Cut into small pieces with sterile scissors and place in a 5 mL centrifuge tube. Store at -80°C for later use.

[0077] 4. Microbial markers of Polygonatum sibiricum

[0078] 4.1 Extraction of genomic DNA

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

[0080] 4.2 PCR amplification of target fragments

[0081] Using the extracted genomic DNA as a template, specific primers with barcodes were used for PCR according to the selection of the sequencing region, and Tks Gflex DNA Polymerase from Takara was used to ensure the amplification efficiency and accuracy. The corresponding regions for bacterial diversity identification: 16S V3-V4 region [primers 343F (5'-TACGGRAGGCAGCAG-3') and 798R (5'-AGGGTATCTAATCCT-3')], and the corresponding regions for fungal ITS diversity identification: ITS [primers ITS1F (5'-CTTGGTCATTTAGAGGAAGTAA-3') and ITS2R (5'-GCTGCGTTCTTCATCGATGC-3')]. One round of PCR amplification was performed (the one-round PCR amplification system and reaction program are shown in Tables 1 and 2), and the one-round PCR product was detected using electrophoresis, then purified using magnetic beads, and the purified product was used as a template for two-round PCR amplification (the two-round PCR amplification system and reaction program are shown in Tables 3 and 4), and again detected using electrophoresis, then purified using magnetic beads, and the purified PCR product was quantified using Qubit.

[0082] The one-round PCR amplification system and reaction program are shown in Tables 1 and 2.

[0083] Table 1. One-round PCR system

[0084] Table 2. One-round PCR reaction program

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

[0086] The two-round PCR amplification system and reaction program are shown in Tables 3 and 4.

[0087] Table 3. Second round PCR system

[0088] Table 4. Second round PCR reaction program

[0089] Note: If there is a slight band in the first round product, the annealing temperature can be appropriately increased to reduce the band

[0090] Magnetic bead purification: Add 20 μL of fully mixed AMPure XP beads (Beads: product = 0.8: 1) in a U-shaped plate, then add the PCR product, mix slowly with a pipette for 10 times, mix well, and place at room temperature for 5 min. Place the magnetic stand for 5 min until the supernatant is transparent, and discard the supernatant. Add 200 μL of freshly prepared 80% ethanol, and place at room temperature for 30 s, then discard the supernatant. Repeat the previous step for a total of two washes. Place at room temperature to dry, dry until no liquid droplets are left (use a 10 μL pipette to check if there are liquid droplets left at the bottom of the tube), and the beads surface has no reflection phenomenon, remove the plate from the magnetic stand. Add 25 μL of H2O elution, mix well, and place at room temperature for 2 min. Place on the magnetic stand for 5 min until the supernatant is transparent, and transfer 20 μL of the supernatant to a new PCR tube.

[0091] Detection: Take 5 μL of the purified second round product for agarose gel electrophoresis detection to detect whether there is a band and whether the band is single.

[0092] 4.3 Library construction and sequencing

[0093] Take 1 μL of the purified second round product for Qubit quantification, adjust the concentration for sequencing. Use Illumina NovaSeq 6000 sequencing platform for sequencing, and generate 250 bp double-end reads.

[0094] 4.4 Bioinformatics analysis

[0095] The original data is in FASTQ format. After the data is downloaded, first use Cutadapt software to cut off the primer sequence of raw data sequence. Then use DADA2 to filter the quality, denoise, splice and dechimeric of the qualified double-end raw data in the previous step according to the default parameters of QIIME 2 (2020.11), and get the representative sequence and ASV abundance table. After selecting the representative sequence of each ASV using the QIIME 2 software package, all 16S representative sequences are aligned and annotated with the Silva (version 138) database, and all ITS representative sequences are aligned and annotated with the Unite database. The species alignment annotation is analyzed using the default parameters of the q2-feature-classifier software.

[0096] Random forest is a machine learning algorithm first proposed by Leo Breiman and Adele Cutler, which can effectively and accurately classify microbial community samples and find out the key components (ASV or species) that can distinguish between groups. The top 30 bacterial genera and fungal genera in relative abundance are used to draw the species importance point diagram using the R package randomForest.

[0097] 5. Evaluation model construction

[0098] The machine learning algorithm is implemented through R (version 4.4.1). The top 30 bacterial genera and the top 30 fungal genera in relative abundance in the group obtained by sequencing analysis are selected to construct a random forest model. Random forest is used to identify important features of microorganisms, and the selected microorganism markers are screened according to the influence of the microorganism selection program on the prediction of the results, and the optimal number of microorganism markers selected to achieve the best prediction accuracy of the model is the number of selected variables Figure 1 , and the important features of the selected optimal number of microorganism markers such as species importance points Figure 2 are shown.

[0099] The random forest optimizes each microorganism marker around its default value. The internal test set and validation set are set at 7:3 to verify the reliability of the selected microorganism markers. ROC curve analysis is used to verify the accuracy of the model.

[0100] 6. Evaluation result determination

[0101] As Figure 1 shown, the top 30 bacterial genera and fungal genera in species abundance are selected as candidate markers to distinguish between general quality and high-quality Rhizoma Polygonati. The AUCRF package is used for analysis, and the results show that when the number of markers is 8, the performance is best, and the AUC value reaches 0.8333. Therefore, these 8 markers are selected for subsequent analysis.

[0102] As shown in Figure 2 As shown in

[0103] Using Aspergillus, Cercophora, Escherichia. Shigella, Mycoplasma, Muribaculaceae, Pseudomonas, Colletotrichum, Asaia, 8 markers for the quality of general and quality of good quality of Huangjing, the results are shown in Figure 3 As shown in

[0104] Example 2 Huangjing quality evaluation system

[0105] The present embodiment provides a system for evaluating the quality of Huangjing, comprising:

[0106] Pre-input module: at least for inputting the abundance data of the microbial marker combination in the Huangjing sample, and transmitting it to the evaluation module;

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

[0108] ​The display module is used for displaying the evaluation result based on the random forest model. The model is constructed based on different grouped sample species abundance matrices, wherein the real labels of the samples in the general quality group are set as 0, and the real labels of the samples in the high quality group are set as 1. The model outputs the probability values (totaling 1) of belonging to two categories for each predicted sample, and finally determines the sample according to the probability of being predicted as the 0 category (general quality group): by default, the threshold is 0.5, and when the probability of being predicted as the 0 category is greater than or equal to 0.5, the sample is determined as the general quality (classified as 0), otherwise, the sample is determined as the high quality (classified as 1). Therefore, when the predicted value is 0, the quality of the rhizoma polygonati is determined as general (the content of rhizoma polygonati polysaccharide is between 7.0% of the pharmacopoeia standard and 2 times of the pharmacopoeia standard, i.e. 14.0%); when the predicted value is 1, the quality of the rhizoma polygonati is determined as high (the content of rhizoma polygonati polysaccharide is higher than 2 times of the pharmacopoeia standard, i.e. more than 14.0%).

[0109] Embodiment 3 Computer device

[0110] The embodiment provides an electronic device which can be in the form of a computer device (for example, can be a server device) including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the method for evaluating the quality of the rhizoma polygonati with multiple flowers in the first embodiment of the application when executing the computer program.

[0111] The processor executes the program to enable the system to implement the following steps, including:

[0112] (1) collecting and / or inputting the evaluation data for evaluating the quality of the rhizoma polygonati, i.e. the abundance data of the microbial marker combination;

[0113] (2) substituting the evaluation data into the model for calculation to obtain the judgment conclusion of the general / high quality of the rhizoma polygonati;

[0114] (3) outputting the judgment conclusion of the general / high quality of the rhizoma polygonati.

[0115] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the foregoing detailed description, such 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 for embodiment.

[0116] Embodiment 4 Computer readable storage medium

[0117] The embodiment provides a computer readable storage medium, which stores the computer device. The program is executed by the processor to implement the steps of the method for evaluating the quality of the rhizoma polygonati in the first embodiment of the application.

[0118] More specifically, the readable storage medium can include, but is not limited to, a portable disc, 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.

[0119] In possible implementation manners, the present application can also be implemented in the form of a program product, which includes program codes for causing terminal equipment to execute the steps of the method for evaluating the quality of Rhizoma Polygonati in the embodiment one of the present application when the program product is run on the terminal equipment.

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

[0121] In summary, the present application provides a microbial marker combination for evaluating the quality of Rhizoma Polygonati, which includes one or more of Cercophora, Escherichia.Shigella, Mycoplasma, Muribaculaceae, Pseudomonas, Colletotrichum and Asaia; the present application discloses a method for evaluating the quality of Rhizoma Polygonati using the microbial marker combination, and establishes an evaluation model, and the microbial marker combination has a sensitivity of 100.0%, a specificity of 72.7%, and an AUC of 89.8% for evaluating the quality of Rhizoma Polygonati, and has high accuracy, specificity and sensitivity. The evaluation method of the present application is economical and practical, and easy to operate, and is suitable for application in traditional Chinese medicine markets; and can greatly improve the defects of high cost, high toxicity, many experimental steps, slow detection speed and complex data analysis caused by conventional high performance liquid chromatography, and can be widely used for quality evaluation of Rhizoma Polygonati.

[0122] The above description is a detailed description of the preferred embodiments of the present application, but the embodiments are not intended to limit the scope of the patent application of the present application, and any equivalent changes or modifications made under the technical spirit of the present application should be included in the patent scope of the present application.

Claims

1. A combination of microbial biomarkers for evaluating the quality of Polygonatum sibiricum, characterized in that, The microbial biomarker combination consists of Aspergillus , Cercophora , Escherichia Shigella , Mycoplasma , Muribaculaceae , Pseudomonas , Colletotrichum and Asaia composition.

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

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

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 quality of Polygonatum sibiricum, or in the construction of models for evaluating the quality of Polygonatum sibiricum.

5. A model for evaluating the quality of Polygonatum sibiricum, characterized in that, The model construction method includes: (1) Samples of Polygonatum were collected and divided into a general quality group and a high quality group according to the polysaccharide content; the polysaccharide content of Polygonatum in the general quality group was between 7.0% and 14.0% according to the pharmacopoeia standard; the polysaccharide content of Polygonatum in the high quality group was higher than 14.0%; (2) After pretreatment of the Polygonatum sample, the genomic DNA of the combination of microbial markers described in claim 1 is extracted from the sample, and the species abundance of the microorganism is detected; (3) Construct a random forest model for prediction using the species abundance matrix of samples in different groups, where the true label of the general quality group is set to 0 and the true label of the high quality group is set to 1, and the evaluation model is 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 quality of Polygonatum sibiricum, 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 judged to be of average quality. When the prediction is 1, it is judged to be of high quality.

8. A system for evaluating the quality of Polygonatum sibiricum, 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, executed by the Polygonatum quality evaluation 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 quality of Polygonatum sibiricum; The evaluation data is substituted into the model for calculation to obtain a judgment on whether Polygonatum is of high quality; Output a conclusion on whether Polygonatum is of high quality.

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

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