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%, and the detection result is accurate. AUC is 89.8%, 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

Technical Field

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

[0002] Polygonatum sibiricum is a perennial herb belonging to the genus Polygonatum of the Liliaceae family. Its dried rhizome is widely used in clinical Chinese medicine, and has the effects of nourishing the spleen and stomach, moistening the lungs and relieving cough, promoting body fluid and quenching thirst, etc. Its main active ingredient, Polygonatum sibiricum polysaccharide, is the key component of the pharmacological effects of Polygonatum sibiricum, and is also the content determination standard stipulated in the pharmacopoeia, which determines the quality of Polygonatum sibiricum.

[0003] Currently, according to the Chinese Pharmacopoeia, the quality evaluation of Polygonatum sibiricum is carried out by measuring the content of Polygonatum sibiricum polysaccharides using ultraviolet spectrophotometry (UV-Vis). Although UV-Vis is the gold standard for determining the effective ingredients of Polygonatum sibiricum, this method has certain limitations, including high equipment cost, complex operation, high reagent toxicity, and time-consuming analysis process. In addition, the low sensitivity for certain compounds may lead to the inability to accurately detect certain components. Therefore, there is an urgent need for a more efficient, fast and safe alternative technology to evaluate the quality of Polygonatum sibiricum.

[0004] In addition, endophytes in Polygonatum may have important potential effects on its quality. Studies have shown that endophytes in Polygonatum are diverse and metabolically active. These endophytes may affect the growth and accumulation of active ingredients of Polygonatum in many aspects through interactions with host plants. For example, some endophytes can promote the disease resistance of Polygonatum or improve its adaptability to the environment, thereby affecting the overall health and growth rate of the plant; in addition, some endophytes may indirectly participate in the synthesis and accumulation of active ingredients in Polygonatum by synthesizing secondary metabolites, affecting its pharmacological activity and quality stability. Due to the important role of endophytes in plant growth and development, further research on their relationship with the accumulation of active ingredients in Polygonatum has potential guiding significance for the quality control of Polygonatum. However, although this field has gradually received attention, there is currently a lack of research on systematically evaluating the quality of Polygonatum by detecting endophytes.

[0005] The invention screens specific polygonatum microorganisms as a microbial marker combination for evaluating the quality of polygonatum, wherein the microbial marker combination comprises Aspergillus, Cercophora, Escherichia.Shigella, Mycoplasma, Muribacula cae, Pseudomonas, Colletotrichum and Asaia. The invention discloses a method for evaluating the quality of polygonatum by using the microbial marker combination, and establishes an evaluation model. 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 polygonatum, has high accuracy, specificity and sensitivity, and can be widely used for the quality evaluation of polygonatum. Summary of the invention

[0006] In view of the above technical problems, the purpose of the present invention is to provide a microbial marker combination and application for evaluating the quality of Polygonatum sibiricum, which specifically includes the following contents:

[0007] In a first aspect, the present invention provides a microbial marker combination for evaluating the quality of Polygonatum sibiricum, wherein the microbial marker combination comprises one or more of Aspergillus, Cercophora, Escherichia.Shigella, Mycoplasma, Muribaculaceae, Pseudomonas, Colletotrichum and Asaia.

[0008] Preferably, the microbial marker combination consists of Aspergillus, Cercophora, Escherichia.Shigella, My coplasma, Muribaculaceae, Pseudomonas, Colletotrichum and Asaia.

[0009] In a second aspect, the present invention provides the use of the microbial marker combination described in the first aspect in evaluating the quality of Polygonatum sibiricum.

[0010] 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 quality of Polygonatum sibiricum.

[0011] 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 quality of Polygonatum sibiricum.

[0012] 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, and a chip for evaluating the quality of Polygonatum sibiricum.

[0013] In a sixth aspect, the present invention provides an application of detecting the combination of microbial markers described in the first aspect in constructing a model for evaluating the quality of Polygonatum sibiricum.

[0014] In a sixth aspect, the present invention provides a model for evaluating the quality of Polygonatum sibiricum, and the model construction method comprises:

[0015] (1) Collecting polygonatum samples, and dividing them into a general quality group and a high quality group according to the polysaccharide content; the polysaccharide content of the general quality group was between 7.0% and 14.0% according to the pharmacopoeia standard; the polysaccharide content of the high quality group was higher than 14.0%;

[0016] (2) After the polygonatum sample is pretreated, the genomic DNA of the microbial marker combination according to claim 1 or 2 is extracted from the sample to detect the species abundance of the microorganisms;

[0017] (3) The random forest model was constructed based on the species abundance matrix of samples in different groups to make predictions, where the prediction value of the general quality group was 0 and the prediction value of the high quality group was 1, thus obtaining the evaluation model.

[0018] 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.

[0019] Preferably, the step (2) comprises:

[0020] ①Genomic DNA extraction;

[0021] 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;

[0022] 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;

[0023] 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.

[0024] Preferably, the genomic DNA extraction described in step ① is performed according to conventional methods.

[0025] 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.

[0026] 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')].

[0027] In a seventh aspect, the present invention provides a method for screening the microbial marker combination described in the first aspect, the method comprising:

[0028] (1) Collecting polygonatum samples, and dividing them into a general quality group and a high quality group according to the polysaccharide content; the polysaccharide content of the general quality group was between 7.0% and 14.0% according to the pharmacopoeia standard; the polysaccharide content of the high quality group was higher than 14.0%;

[0029] (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;

[0030] (3) Biomarker detection of Polygonatum sibiricum samples:

[0031] ①Genomic DNA extraction;

[0032] 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;

[0033] 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;

[0034] 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;

[0035] (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.

[0036] (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.

[0037] Preferably, the genomic DNA extraction described in step ① is performed according to conventional methods.

[0038] 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.

[0039] 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')].

[0040] In an eighth aspect, the present invention provides a method for evaluating the quality of Polygonatum sibiricum, the method comprising the following steps:

[0041] (1) constructing the model described in the sixth aspect above;

[0042] (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;

[0043] (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, the quality is average; and when the prediction value is 1, the quality is excellent.

[0044] In a ninth aspect, the present invention provides a system for evaluating the quality of Polygonatum sibiricum, comprising:

[0045] Pre-input module: at least used to input the data to be evaluated;

[0046] Evaluation module: at least used to evaluate the data to be evaluated, which is executed by the polygonatum quality evaluation model described in the sixth aspect;

[0047] Display module: at least used to display the evaluation results.

[0048] 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:

[0049] Collect and / or input evaluation data of Polygonatum sibiricum quality;

[0050] Substitute the evaluation data into the model for calculation and obtain the judgment conclusion of the quality of Polygonatum sibiricum;

[0051] Output the judgment conclusion of the quality grading of Polygonatum sibiricum.

[0052] 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.

[0053] 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 microbial marker combination for evaluating the quality of polygonatum, wherein the microbial marker combination comprises one or more of Aspergillus, Cercophora, Escherichia.Shi gella, Mycoplasma, Muribaculaceae, Pseudomonas, Colletotrichum and Asaia; (2) secondly, the present invention discloses a method for evaluating the quality of polygonatum using the microbial marker combination, and establishes an evaluation model, wherein the microbial marker combination for evaluating the quality of polygonatum has a sensitivity of 100.0%, a specificity of 72.7%, and an AUC of 89.8%, and has high accuracy, specificity and sensitivity; (3) the evaluation method of the present invention is economical, convenient to operate, and suitable for application in the Chinese medicinal materials market; it can greatly improve the defects of conventional ultraviolet spectrophotometry such as high cost, high toxicity, many experimental steps, slow detection speed and complex data analysis, and can be widely used for the quality evaluation of polygonatum. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 Quantitative plots of selected variables;

[0055] Figure 2 dot plots of species importance for a selected number of species;

[0056] Figure 3 ROC curve of model prediction discrimination. DETAILED DESCRIPTION

[0057] 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.

[0058] 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 the quality of Polygonatum sibiricum is excellent and the given detection experiment also shows that the quality of the sample is excellent, then the test result is considered to be true positive; if it is proved that the quality of Polygonatum sibiricum is average and the given detection experiment also shows that the quality of the sample is average, then the test result is considered to be true negative; if the result of the detection experiment shows that the quality of the sample that is actually average is excellent, then the test result is a false positive; if the result of the screening experiment shows that the quality of the sample that is actually high quality is average, then the test result is a false negative.

[0059] Sensitivity = TP / (TP+FN) = number of true positive assessments / number of all positive assessments;

[0060] Specificity = TN / (TN+FP) = number of true negative assessments / number of all negative assessments;

[0061] Accuracy = (TN + TP) / (TN + TP + FN + FP) = number of correct evaluations / number of all evaluations.

[0062] Embodiment 1 Polygonatum odoratum quality evaluation method

[0063] 16 high-quality Polygonatum sibiricum samples and 33 average-quality Polygonatum sibiricum samples were selected, and the samples were tested using the marker combination provided by the present invention. Based on the test results, the sensitivity, specificity and accuracy of the marker combination provided by the present invention for evaluating the quality of Polygonatum sibiricum were determined.

[0064] Fresh, healthy and undamaged Polygonatum samples were collected nationwide. The rhizome of each sample was divided into two pieces in the middle. One piece was stored in a ventilated and dry place, and the other was stored in a refrigerator at 4°C and subsequent experiments were completed as soon as possible.

[0065] 2. Determination and grouping of Polygonatum sibiricum polysaccharides

[0066] The content of Polygonatum sibiricum polysaccharide in the samples stored in a ventilated and dry place was determined by ultraviolet spectrophotometry under the current "Chinese Pharmacopoeia" under the item of Polygonatum sibiricum content determination. 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 Polygonatum sibiricum polysaccharide in the general quality group is between the pharmacopoeia standard of 7.0% and 2 times the pharmacopoeia standard (14.0%); the content of Polygonatum sibiricum polysaccharide in the high quality group is higher than 2 times the pharmacopoeia standard, that is, more than 14.0%.

[0067] 3. Sample surface disinfection

[0068] Take a sample of Polygonatum sibiricum stored at 4℃, clean the surface soil and impurities with a soft brush under running water, cut off a 5cm long rhizome segment, rinse it with sterile water for 5 minutes under sterile conditions, soak it in 75% ethanol for 2 minutes, rinse it with sterile water 1-2 times, soak it in 5% sodium hypochlorite for 5 minutes, rinse it with sterile water 7-10 times, and dry the surface moisture with sterile filter paper. Cut it into small pieces with sterile scissors and place it in a 5mL centrifuge tube, and store it at -80℃ for later use.

[0069] 4. Detection of Polygonatum sibiricum Microbial Markers

[0070] 4.1 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] 4.2 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; 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]

[0085] Note: If there is slight impurity in the product of one round, the annealing temperature can be appropriately increased to reduce the impurity.

[0086] 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.

[0087] 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.

[0088] 4.3 Library construction and sequencing

[0089] 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.

[0090] 4.4 Bioinformatics analysis

[0091] 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.

[0092] 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 (AS V or species) that can distinguish differences between groups. The top 30 bacterial and fungal genera in relative abundance were taken and the R package randomForest was used to draw a species importance dot map.

[0093] 5. Evaluation model construction

[0094] The machine learning algorithm was implemented in R (version 4.4.1). The top 30 bacterial genera and top 30 fungal genera with the highest relative abundance in the group obtained by sequencing analysis were selected to construct the random forest model. The random forest was used to identify the important features of microorganisms, and the microbial marker screening was performed 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, such as the number of selected variables, was selected. Figure 1 As shown in Figure 2, the optimal number of microbial markers for screening is shown in Figure 2. Figure 2 shown.

[0095] 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.

[0096] 6. Evaluation results

[0097] like Figure 1 As shown in the figure, the top 30 bacterial and fungal genera in species abundance were selected as candidate markers to distinguish between ordinary quality Polygonatum sibiricum and high quality Polygonatum sibiricum. The AUCRF package was used for analysis, and the results showed that when the number of markers was 8, the discrimination performance was the best, and the AUC value reached 0.8333. Therefore, these 8 markers were selected for subsequent analysis.

[0098] like Figure 2 As shown, after marker selection, eight markers, namely Aspergillus, Cercophora, Escherichia.Shigella, Mycoplasma, Muribaculaceae, Pseudomonas, Colletotrichum and Asaia, had the best classification performance, and the order of importance was Aspergillus, Cercophora, Escherichia.Shigella, Mycoplasma, Muribaculaceae, Pseudomonas, Colletotrichum and Asaia.

[0099] Eight markers, including Aspergillus, Cercophora, Escherichia.Shigella, Mycoplasma, Muribaculaceae, Pse udomonas, Colletotrichum, and Asaia, were used to distinguish between ordinary quality Polygonatum and high quality Polygonatum. The results are as follows Figure 3 As shown, when the sensitivity is 100.0%, the specificity is 72.7% and the AUC is 89.8%. The above experimental results show that the microbial marker combination of the present invention has high accuracy, specificity and sensitivity for evaluating the quality of Polygonatum sibiricum.

[0100] Embodiment 2 Polygonatum odoratum quality evaluation system

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

[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 results. When the predicted value is 0, the quality judgment result of Polygonatum sibiricum is average (the content of Polygonatum sibiricum polysaccharide is between the pharmacopoeia standard of 7.0% and twice the pharmacopoeia standard, i.e. 14.0%); when the predicted value is 1, the quality judgment result of Polygonatum sibiricum is high quality (the content of Polygonatum sibiricum polysaccharide is higher than twice the pharmacopoeia standard, i.e. more than 14.0%).

[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 quality 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 quality evaluation of Polygonatum cyrtonema, i.e., microbial marker combination abundance data;

[0109] (2) Substituting the evaluation data into the model for calculation, the conclusion of whether the quality of Polygonatum sibiricum is average or high quality is obtained;

[0110] (3) Output the judgment conclusion of whether the quality of Polygonatum sibiricum is general or high quality.

[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 quality of polygonatum sibiricum 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 manner, 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 quality of Polygonatum odoratum 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 microbial marker combination for evaluating the quality of polygonatum, the microbial marker combination includes one or more of Cercophora, Escherichia.Shigella, Mycoplasma, Muribaculaceae, Pseudomonas, Colletotrichum and Asaia; the present invention discloses a method for evaluating the quality of polygonatum using the microbial marker combination, and establishes an evaluation model, the microbial marker combination is used to evaluate the quality of polygonatum with a sensitivity of 100.0%, a specificity of 72.7%, and an AUC of 89.8%, with high accuracy, specificity and sensitivity. The evaluation method of the present invention is economical, easy to operate, and suitable for application in the Chinese medicinal materials market; it can greatly improve the defects of high cost, high toxicity, many experimental steps, slow detection speed and complex data analysis brought by conventional high performance liquid chromatography, and can be widely used in the quality evaluation 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 quality of Polygonatum sibiricum, characterized in that: The microbial marker combination includes one or more of Aspergillus, Cercophora, Escherichia.Shigella, Mycoplasma, Muribaculaceae, Pseudomonas, Colletotrichum and Asaia.

2. The microbial marker combination according to claim 1, characterized in that: The microbial marker combination consists of Aspergillus, Cercophora, Escherichia.Shigella, Mycoplasma, Muribaculaceae, Pseudomonas, Colletotrichum and Asaia.

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

4. Use of the microbial marker combination as claimed in claim 1 or 2 or a reagent for detecting the microbial marker combination as claimed in claim 1 or 2 in preparing a reagent, a kit, or a chip for evaluating the quality of polygonatum, or in constructing a model for evaluating the quality of polygonatum.

5. A model for evaluating the quality of Polygonatum sibiricum, characterized in that: The model building method comprises: (1) Collecting polygonatum samples, and dividing them into a general quality group and a high quality group according to the polysaccharide content; the polysaccharide content of the general quality group was between 7.0% and 14.0% according to the pharmacopoeia standard; the polysaccharide content of the high quality group was higher than 14.0%; (2) After the polygonatum sample is pretreated, the genomic DNA of the microbial marker combination according to claim 1 or 2 is extracted from the sample to detect the species abundance of the microorganisms; (3) The random forest model was constructed based on the species abundance matrix of samples in different groups to make predictions, where the prediction value of the general quality group was 0 and the prediction value of the high quality group was 1, thus obtaining the evaluation model.

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 quality 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, the quality is average; and when the prediction value is 1, the quality is excellent.

8. A system for evaluating the quality 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, and executed by the polygonatum quality 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 evaluation data of Polygonatum sibiricum quality; Substitute the evaluation data into the model for calculation and obtain the judgment conclusion of the quality of Polygonatum sibiricum; Output the judgment conclusion of the quality grading of Polygonatum sibiricum.

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

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