Construction and testing of a medicinal plant quality prediction model based on mixed effects model and rhizospheric microorganisms

Abstract

The present invention belongs to the field of environmental microorganisms, and specifically relates to a method for constructing and testing a medicinal plant quality prediction model based on a mixed-effect model and rhizosphere microorganisms, comprising the following steps: (1) determining sampling points in a medicinal plant planting area; (2) collecting, processing, and quantifying quality indicators of plant samples; (3) collecting rhizosphere soil samples, extracting DNA, and conducting high-throughput sequencing; (4) constructing a training set and a test set; (5) screening associated microorganisms, model construction, and coefficient extraction based on the training set; and (6) model verification based on the test set. The present invention, through high-throughput sequencing, sample set splitting, and a mixed-effect model, establishes a medicinal plant quality prediction model using rhizosphere microorganisms for the first time, which can effectively predict the content of the main active substances in medicinal plants, laying a foundation for scientifically and efficiently predicting and improving the quality of medicinal plants during the medicinal plant planting process, and has great significance for the quality control and production of high-quality traditional Chinese medicines.

Description

Technical Field

[0001] The present invention belongs to the technical field of environmental microorganisms, and in particular relates to a construction and testing method of a medicinal plant quality prediction model based on a mixed effect model and rhizosphere microorganisms. Background Art

[0002] Medicinal plants, such as Panax notoginseng, provide numerous ecological niches for the growth and reproduction of bacteria and other microorganisms. The rhizosphere—the soil in close contact with plant roots—is the most diverse region on Earth and a hotspot for plant-microbe interactions. Advances in plant microbiome research are leading to the understanding that plants and their rhizosphere microbiomes should be viewed as a coevolved functional entity, with the microbiome having a significant impact on plant adaptability and metabolism.

[0003] The primary active ingredients of medicinal plants are secondary metabolites, such as saponins, flavonoids, and alkaloids. Unlike primary metabolites such as sugars, proteins, and lipids, secondary metabolites are not essential for plant growth and development, but rather mediators of plant interactions with both the biotic and abiotic environments. Microorganisms are the most important biotic factors in a plant's environment, and numerous studies have revealed the strong influence of microorganisms in niches such as the rhizosphere on the accumulation of plant secondary metabolites. For example, Pseudomonas aeruginosa in the rhizosphere of Arabidopsis thaliana can enhance the synthesis of the defensive secondary metabolite glucosinolates in roots; and microorganisms in the rhizosphere of plants such as Panax notoginseng also drive the accumulation of triterpenoid saponins in their host roots. This close connection between rhizosphere microorganisms and the active ingredients of medicinal plants suggests that the former may serve as effective predictors of medicinal plant quality.

[0004] Unlike agricultural crops, the production of medicinal plants focuses not only on yield (such as the size of the medicinal parts) but also on quality traits, as measured by the content of active ingredients. However, these quality traits are invisible to the naked eye, and their evaluation requires destructive sampling of plant specimens, which incurs significant additional costs for precious Chinese medicinal materials such as Panax notoginseng and ginseng. The development of predictive models for medicinal plant quality based on external factors is a key priority in the perception, management, and intervention of Chinese medicine quality. The close connection between rhizosphere microorganisms and the accumulation of active ingredients in medicinal plants lays the foundation for using the former as a predictor of the latter. Furthermore, the rapid development of high-throughput sequencing has significantly reduced the cost of obtaining information on rhizosphere soil microbial communities, facilitating the construction of microbial-based predictive models for medicinal plant quality.

[0005] Medicinal plant cultivation is often dispersed and multi-regional. Traditional linear models cannot exclude the influence of planting site effects when considering the relationship between microbial abundance and medicinal plant quality indicators. Therefore, using mixed-effects models, incorporating planting site as a random effect, can effectively detect the true effect size of microbial groups on medicinal plant quality indicators and improve the model's predictive power.

[0006] In summary, the present invention comprehensively uses high-throughput sequencing and mixed-effect models to provide an implementation strategy for the construction and verification of a medicinal plant quality prediction model based on rhizospheric microorganisms, which is helpful for quality control in the cultivation process of medicinal plants and is of great significance to the production of high-quality traditional Chinese medicine. Summary of the Invention

[0007] In order to overcome the problems existing in the prior art, the purpose of the present invention is to provide a method for constructing and testing a medicinal plant quality prediction model based on a mixed effect model and rhizospheric microorganisms.

[0008] The technical solution provided by the present invention is:

[0009] A method for constructing and testing a medicinal plant quality prediction model based on a mixed effects model and rhizospheric microorganisms comprises the following steps:

[0010] (1) Determination of sampling points in medicinal plant plantations: With the medicinal material authentic production area as the center, sampling points are selected within a certain distance around the authentic production area. The number of locations is recorded as N.

[0011] (2) Plant sample collection, processing and quality index testing: At each sampling point, three medicinal plant samples were collected as biological replicates. For each plant material, the medicinal part was taken, freeze-dried and ground into fine powder. The extraction was performed according to the extraction method specified in the pharmacopoeia. The content of the main active ingredient was determined by HPLC and other methods as a quality indicator.

[0012] (3) Rhizosphere soil sample collection, DNA extraction and high-throughput sequencing: When collecting medicinal plant samples, rhizosphere soil samples of the corresponding plant materials were collected; total soil DNA was extracted using a DNA extraction kit, bacterial marker genes were amplified using PCR, and sequences were determined using a high-throughput sequencing platform. The sequences were analyzed using a bioinformatics process to generate a bacterial community data table, in which bacterial groups were represented by exact sequence variants (ASVs);

[0013] (4) Construction of training set and test set: Divide all the samples into training set and test set, with the training set size of 2N and the test set size of N;

[0014] (5) Screening of associated microorganisms, model construction, and coefficient extraction based on the training set: Using the training set, first screen microbial groups based on the relative abundance of rhizospheric microorganisms and their correlation with quality indicators; incorporate the screened microbial groups into the mixed effect model, fit the model, and extract the effect coefficients;

[0015] (6) Model testing based on the test set: Based on the microbial groups and their effect coefficients selected in (5), the relative abundance of the corresponding microorganisms in the test set is used to predict the quality score, and an association analysis is performed with the quality indicators measured in the test set to test and evaluate the model quality.

[0016] Preferably, in step (1), the sampling is carried out within a range of not less than 300 km from the authentic production area, the maximum distance between sampling points is not less than 600 km, and the number of locations is not less than 20.

[0017] Preferably, in step (2), the three plant samples come from three different plots at the same sampling point, with the distance between the plots being no less than 30 m, and each plant sample consists of no less than 10 healthy plants.

[0018] Preferably, in step (3), the sampling method for the rhizosphere soil sample is: shake off the large pieces of soil carried by the root system, then place the root system in sterile ultrapure water for 20 minutes, and centrifuge at 13,000 rpm to collect the precipitate as the rhizosphere soil sample.

[0019] Preferably, in step (3), the primers for PCR amplification of the bacterial marker gene are 16S V4 region primers, the forward primer sequence is 515F: 5'-GTGCCAGCMGCCGCGG-3', and the reverse primer sequence is 806R: 5'-GGACTACHVGGGTWTCTAAT-3'.

[0020] Preferably, in step (3), the DADA2 process in QIIME2 is used to process the downloaded sequences to obtain accurate sequence variants (ASVs) of the microorganisms.

[0021] Preferably, in step (4), the training set includes two samples randomly selected from each sampling location, and the test set includes the remaining one sample from each sampling location.

[0022] Preferably, in step (5), the relationship between the relative abundance of microbial ASVs in the training set and the quality index is calculated using Spearman correlation, and the screening threshold of microbial ASVs included in the hybrid model is P <0.05.

[0023] Preferably, in step (5), the mixed effect model is:

[0024]

[0025] in, It is a quality indicator of medicinal plants; is the relative abundance of ASVs in the training set samples, representing the fixed effect, is the coefficient of the fixed effect; captures the grouping of locations, representing random effects, is the coefficient of the random effect; represents the residual, It is the effect coefficient of the extracted microbial ASVs.

[0026] Preferably, in step (6), the effect coefficient ( β ) is multiplied by the relative abundance of microbial ASVs in the test set, and the sum is added to obtain the predicted quality score.

[0027] As a preference, in step (6), the calculated quality score is subjected to Spearman correlation analysis with the measurement quality index of the test set samples, and the correlation value is compared with the P Value testing and evaluation of model quality.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] This method uses large-scale sampling across authentic production areas to obtain samples of medicinal plants and rhizosphere microorganisms. Chemical analysis is used to determine medicinal plant quality indicators, and high-throughput sequencing is used to obtain rhizosphere microbial community data. A training set and a test set are then used to select microbial groups associated with medicinal plant quality indicators. Microbial effect coefficients are estimated using a mixed-effects model. The test set is then used to test the model's predictive ability. This method provides a new model construction and testing method for predicting medicinal plant quality, which can aid in the perception, management, and improvement of medicinal plant quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 shows the taxonomic distribution of rhizospheric bacterial ASVs used for model construction;

[0031] Figure 2 shows the distribution of effect coefficients of bacterial ASVs estimated by the mixed-effects model;

[0032] Figure 3 shows the correlation between the quality score predicted by the model and the total saponin content of the test set samples. DETAILED DESCRIPTION

[0033] The present invention will be further described in detail below with reference to specific examples.

[0034] Example 1: Construction and verification of a Panax notoginseng quality prediction model based on a mixed effects model and rhizosphere microorganisms

[0035] 1. Determination of sampling points for the medicinal plant Panax notoginseng

[0036] The medicinal plant Panax notoginseng is native to Wenshan Prefecture, Yunnan Province. In this case, a sampling area with a radius of 300 km was defined, centered on Wenshan Prefecture. Within this area, 26 sampling points were identified, with the maximum distance between sampling points being 669 km. The abbreviations and longitude and latitude coordinates of the sampling points are shown in Table 1.

[0037] Table 1 Abbreviations and longitude and latitude coordinates of sampling points of medicinal plant Panax notoginseng

[0038]

[0039] 2. Acquisition of Panax notoginseng Plant Samples and Determination of Rhizosphere Microbial Community

[0040] (1) Acquisition of Panax notoginseng plant samples. At each sampling point, three plots were demarcated 30 m apart. Ten healthy three-year-old Panax notoginseng plants were taken from each plot as a sample, and a total of 26 × 3 = 78 Panax notoginseng samples were obtained.

[0041] (2) Rhizosphere soil collection. Place each Panax notoginseng root sample in a sterile ziplock bag, gently shake off large pieces of soil, and then place the root system in sterile ultrapure water. Ultrasonicate at 70 Hz for 20 minutes to wash away the rhizosphere soil. Place the rhizosphere soil collection solution in a 50 ml sterile centrifuge tube and centrifuge at room temperature at 13,000 rpm for 20 minutes. Discard the supernatant and the precipitate is the rhizosphere soil sample.

[0042] (3) Marker gene amplification. Total DNA from soil samples was extracted using a soil DNA extraction kit. Primers 515F (5'-GTGCCAGCMGCCGCGG-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3') were used to amplify the V4 region of the bacterial 16S rRNA gene. The PCR system was as follows: 5×FastPfu Buffer 4 μl, 2.5 mM dNTPs 2 μl, 5 μM forward primer 0.8 μl, 5 μM reverse primer 0.8 μl, FastPfu DNA polymerase 0.4 μl, template DNA 10 ng, and ultrapure water was added to 20 μl. PCR reaction parameters were as follows: 95°C for 5 min, 27× (95°C for 30 s, 55°C for 30 s, 72°C for 45 s), and 72°C for 10 min. The PCR products were detected by 1% agarose gel electrophoresis to detect the size and concentration of the amplified bands and then purified using a nucleic acid purification kit.

[0043] (4) Amplicon high-throughput sequencing and bioinformatics processing. The PCR-purified products were used to construct sequencing libraries according to the Illumina Miseq platform instructions, and paired-end sequencing was performed using a Miseq sequencer. The Fastq data were removed using QIIME2 for adapter removal, quality control, and double-end merging. The DADA2 plug-in was used to denoise and remove singletons. The obtained ASV representative sequences were taxonomically annotated against the SILVA132 database using the feature-classifier plug-in. A table of rhizospheric bacterial communities was finally obtained, and a total of 14,447 ASVs were identified.

[0044] 3. Determination of Panax notoginseng root quality indicators

[0045] After removing the rhizosphere soil, Panax notoginseng root samples were cut into small pieces approximately 5 mm in length, freeze-dried, and ground into powder. 1 g of the uniformly mixed dry powder was added to 10 ml of methanol, weighed, and extracted by ultrasonication at 100 Hz for 30 min. The weight loss was then compensated with methanol. The extract was filtered through a 0.22 μm filter membrane, and 10 μl of the filtrate was injected into an HPLC system. The contents of the major saponins R1, Rb1, Rd, Re, and Rg1 in the Panax notoginseng roots were determined at 203 nm. The mobile phase consisted of acetonitrile (A) and water (B), and the elution conditions were as follows: 19% A for 0–12 min; 19–36% A for 12–60 min. The flow rate was 1.0 ml / min. -1 , column temperature 25 ° C. A standard curve was drawn based on the standard products, and the contents of the five major saponins in Panax notoginseng roots were calculated. The sum of the five major saponins represented the total saponin content. The results are shown in Table 2.

[0046] Table 2 Total saponin content in various Panax notoginseng root samples

[0047]

[0048] 4. Construction of training and test sets

[0049] For each of the 26 locations, 2 samples are randomly selected as the training set, and the remaining 1 sample is used as the test set. The training set contains 26×2=52 samples, and the test set contains 26 samples.

[0050] 5. Screening of associated microorganisms, model building, and coefficient extraction based on the training set

[0051] (1) Screening of associated microorganisms based on the training set. First, the average relative abundance of microbial ASVs in the training set was calculated, and only ASVs with a relative abundance ≥ 0.01% (1898) were retained. Then, the Spearman correlation between these high-abundance ASVs and the quality index of Panax notoginseng (total saponin content in the root system) was calculated, and only those with significant correlation with the quality index were retained.P <0.05) were used for model construction. A total of 195 bacterial ASVs were screened and their taxonomic distribution at the phylum level is as follows Figure 1 shown.

[0052] (2) Calculate the effect coefficient using the mixed-effect model. The quality index of the training set samples was used as the dependent variable, the relative abundance of the ASVs screened in step (1) was used as the fixed effect, and the sampling location was used as the random effect. The lme4 package in R was used to fit the linear mixed-effect model. The effect coefficient of ASVs was extracted. β . β The distribution of values ​​is as follows Figure 2 shown.

[0053] 6. Model verification based on the test set

[0054] Based on the bacterial ASVs obtained in step 5, their relative abundance in the test set was extracted, and the quality score of Panax notoginseng was calculated according to the following formula:

[0055]

[0056] in, β j It is The effect coefficient of each ASV, It is The relative abundance of ASVs in the test set. The value range is 1-n (n=195). The calculated quality score.

[0057] The quality score was subjected to Spearman correlation analysis with the quality index of the test set Panax notoginseng samples to obtain the correlation coefficient rho = 0.63, P = 0.0008, indicating that the model constructed based on the training set can well predict the total saponin content of Panax notoginseng samples in the test set, verifying the effectiveness of this method. Figure 3 shown.

[0058] In summary, the present invention constructed a medicinal plant quality prediction model using rhizosphere microorganisms in the training set through large-scale sampling, rhizosphere microbiome determination, sample set splitting and mixed effect modeling, and verified the effectiveness of the model using the test set.

[0059] The foregoing descriptions of specific exemplary embodiments of the present invention are for purposes of illustration and description. These descriptions are not intended to limit the invention to the precise forms disclosed, and it is apparent that many variations and modifications are possible in light of the foregoing teachings. The exemplary embodiments have been selected and described for the purpose of explaining the specific principles of the invention and their practical application, thereby enabling those skilled in the art to realize and utilize a variety of exemplary embodiments of the invention and various options and modifications. The scope of the invention is intended to be defined by the claims and their equivalents.

Claims

1. A method for constructing and testing a medicinal plant quality prediction model based on a mixed effects model and rhizospheric microorganisms, characterized in that: The following steps are involved: (1) Determination of sampling points in medicinal plant plantations: With the medicinal material authentic production area as the center, sampling points are selected within a certain distance around the authentic production area. The number of locations is recorded as N. (2) Plant sample collection, processing and quality index testing: At each sampling point, three medicinal plant samples were collected as biological replicates. For each plant material, the medicinal part was taken, freeze-dried and ground into fine powder. The extraction was performed according to the extraction method specified in the pharmacopoeia, and the content of the main active ingredient was determined by HPLC as a quality indicator. (3) Rhizosphere soil sample collection, DNA extraction and high-throughput sequencing: When collecting medicinal plant samples, rhizosphere soil samples of the corresponding plant materials were collected; total soil DNA was extracted using a DNA extraction kit, bacterial marker genes were amplified using PCR, and the sequences were determined using a high-throughput sequencing platform. The bioinformatics process was used to analyze the sequences and generate a bacterial community data table, in which bacterial groups were represented by accurate sequence variants (ASVs); (4) Construction of training set and test set: Divide all the samples into training set and test set, with the training set size of 2N and the test set size of N; (5) Screening of associated microorganisms, model construction, and coefficient extraction based on the training set: Using the training set, first screen microbial groups based on the relative abundance of rhizospheric microorganisms and their correlation with quality indicators; incorporate the screened microbial groups into the mixed effect model, fit the model, and extract the effect coefficients; (6) Model testing based on the test set: Based on the microbial groups and their effect coefficients screened in (5), the relative abundance of the corresponding microorganisms in the test set is used to predict the quality score, and an association analysis is performed with the quality indicators measured in the test set to test and evaluate the model quality; In step (1), sampling is carried out within a range of not less than 300 km from the authentic production area, with the maximum distance between sampling points not less than 600 km, and the number of locations is not less than 20; In step (2), the three plant samples came from three different plots at the same sampling point, with a distance of no less than 30 m between plots, and each plant sample consisted of no less than 10 healthy plants; In step (3), the sampling method for rhizosphere soil samples is as follows: shake off the large pieces of soil carried by the roots, then place the roots in sterile ultrapure water for 20 minutes, and centrifuge at 13,000 rpm to collect the precipitate as the rhizosphere soil sample; In step (5), the mixed effect model is: ； in, It is a quality indicator of medicinal plants; is the relative abundance of microbial ASVs in the training set samples, representing the fixed effect, is the effect coefficient of the extracted microbial ASVs; captures the grouping of locations, representing random effects, is the coefficient of the random effect; represents the residual; In step (6), the effect coefficient obtained in step (5) is β The predicted quality score is obtained by multiplying it with the relative abundance of microbial ASVs in the test set and summing them up.

2. The construction and testing method according to claim 1, characterized in that: In step (4), the training set contains two randomly selected samples from each sampling location, and the test set contains the remaining sample from each sampling location.

3. The construction and testing method according to claim 1, characterized in that: In step (5), Spearman correlation was used to calculate the relationship between the relative abundance of microbial ASVs in the training set and the quality index. The screening threshold of microbial ASVs included in the hybrid model was P < 0.

05.

4. The construction and testing method according to claim 1, characterized in that: In step (6), the calculated quality score is subjected to Spearman correlation analysis with the measurement quality index of the test set samples. P Value testing and evaluation of model quality.

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