Method for analyzing and evaluating rice quality by using metabolome

Abstract

The invention provides a method for analyzing and evaluating rice quality by using metabolome, which comprises the following steps of: A1, providing grains of at least two known rice varieties, extracting metabolites from grain samples, and screening key metabolites C related to the rice grain quality as metabolome for evaluating the rice quality; a2, providing grains of a to-be-evaluated rice sample, and extracting a metabolite D from the grain sample; a3, comparing the metabolite D of the rice to-be-evaluated sample grains in the A2 with the key metabolite C in the A1, and retaining the same metabolite E in the key metabolite C; and A4: respectively obtaining metabolite abundance E1 of the metabolite E in the known rice variety grains and metabolite abundance E2 of the metabolite E in the rice to-be-evaluated sample grains, and calculating correlation between E1 and E2, thereby performing quality evaluation on the rice to-be-evaluated sample grains.

Description

Technical Field

[0001] This invention belongs to the field of botany, and more specifically, this invention relates to a method for evaluating rice quality using metabolomics analysis. Background Technology

[0002] Rice is one of the world's most important food crops, providing a vital staple food source for most people worldwide, especially in Asia. Domesticated from wild rice over 10,000 years ago, rice boasts numerous varieties and a wide distribution, cultivated not only extensively throughout Asia but also in smaller quantities in Europe, the Americas, and Australia. Rice contains a variety of nutrients, such as carbohydrates, proteins, vitamins, and minerals—all essential for human life. Its genetic diversity has played a crucial role in both domestication and modern breeding. Its adaptability to diverse ecological environments and agronomic conditions makes it a key resource for variety improvement in response to increasing food demand and environmental changes. Research on rice germplasm conservation and variety improvement can enhance rice's adaptability, yield, and quality, advance crop breeding technology, and provide an important foundation for future food security and environmental protection.

[0003] The metabolome, as a dynamic whole of endogenous metabolites in an organism, reflects the metabolic state of the organism at different stages or under different physiological conditions. Through metabolomics analysis, we can gain a deeper understanding of the metabolic differences among rice varieties and their relationship with quality, yield, and stress resistance. Metabolomics helps to discover key metabolic pathways and products in quality formation, providing a theoretical basis for rice quality improvement. Simultaneously, metabolomics can assess the stress response and biosafety of rice, providing scientific guidance for cultivation management and breeding practices. By combining transcriptomics with metabolomics, we can further reveal the gene regulatory mechanisms affecting rice phenotypes, providing new ideas for genetic improvement. In conclusion, metabolomics research is of great value in improving rice quality.

[0004] In recent years, with the development of metabolomics detection technologies, such as the combination of high-resolution mass spectrometry and ultra-high-performance liquid chromatography, rice metabolites can be detected with unprecedented sensitivity, resolution, accuracy, and throughput. Researchers have been able to analyze the metabolic changes in rice at different growth stages and under different environmental conditions more deeply. These studies have deepened our understanding of rice metabolic processes. Furthermore, rice metabolomics research has also explored aspects such as abiotic stress responses and disease resistance, providing theoretical support for breeding more resilient rice varieties.

[0005] However, the lack of methods in this field to assess rice quality through metabolomics makes it difficult to accurately evaluate rice quality, thus limiting its development and application. Summary of the Invention

[0006] The purpose of this invention is to provide a method for evaluating rice quality using metabolomics analysis.

[0007] In a first aspect of the present invention, a method for evaluating rice quality using metabolomics analysis is provided, comprising the following steps:

[0008] A1: Provide grains from at least two known rice varieties, extract metabolites from these grain samples, and screen for key metabolites C that are related to rice grain quality as a metabolome for assessing rice quality.

[0009] A2: Provide rice grains to be evaluated, and extract metabolite D from the grain sample;

[0010] A3: Compare metabolite D of rice grains in A2 with key metabolite C in A1, and retain the same metabolite E.

[0011] A4: Obtain the metabolite abundance E1 in the grains of known rice varieties and the metabolite abundance E2 in the grains of the rice sample to be evaluated, respectively, and calculate the correlation between E1 and E2 to evaluate the quality of the grains of the rice sample to be evaluated.

[0012] In one or more embodiments, in step A1, the known rice varieties are at least two, for example, more than two, more than three, more than five, more than eight, more than ten, more than twenty, more than thirty (e.g., 30, 31, 32, 32, 34), more than thirty-five (e.g., 35, 36, 37, 38, 39), more than forty or more known rice varieties.

[0013] In one or more embodiments, the rice quality includes, but is not limited to, appearance quality and cooking / eating quality.

[0014] In one or more specific embodiments, the appearance quality of the rice includes, but is not limited to: growth period, plant height, panicle length, and thousand-grain weight.

[0015] In one or more specific embodiments, the cooked and edible quality of the rice includes, but is not limited to, the content of amylose.

[0016] In one or more embodiments, in step A1, the known rice varieties are the 36 rice varieties shown in Table 1.

[0017] In one or more embodiments, in step A1, the known rice variety is a japonica rice or an indica rice variety.

[0018] In one or more embodiments, the screening in step A1 includes: annotating the types of metabolites in known rice grains using SMILES, and screening key metabolites C related to rice grain quality using the coefficient of variation (CV).

[0019] In one or more embodiments, metabolites with SMILES annotations and a coefficient of variation greater than 100 are designated as key metabolites C related to rice grain quality.

[0020] In one or more embodiments, the coefficient of variation is the coefficient of variation among the abundance of grain metabolites in all known rice varieties.

[0021] In one or more embodiments, the coefficient of variation (CV) is calculated as follows: coefficient of variation (CV) = standard deviation ÷ mean × 100%, where the standard deviation is the standard deviation of the abundance of metabolites in grains of all known rice varieties, and the mean is the average abundance of metabolites in grains of all known rice varieties.

[0022] In one or more embodiments, the method for annotating the types of metabolites in known rice grains using SMILES includes the following steps:

[0023] B1: Perform chromatography-mass spectrometry analysis on the metabolites obtained in A1 to obtain the mass spectrometry data of A1;

[0024] B2: Annotate the mass spectrometry data of A1 using experimental reference spectra or computer-predicted spectra to obtain SMILES of metabolites in A1.

[0025] In one or more embodiments, the SMILES are derived from one or more of the following databases: Metlin, MassBank, ReSpect, KEGG, Pubchem, and KNApSAck.

[0026] In one or more embodiments, the chromatography-mass spectrometry analysis in step B1 includes: gas chromatography-mass spectrometry analysis and liquid chromatography-mass spectrometry analysis; more preferably, the liquid chromatography-mass spectrometry analysis is performed using UPLC-Q-Orbitrap-HRMS.

[0027] In one or more embodiments, based on the grains of 36 known rice varieties in Table 1, the key metabolites C related to rice grain quality were screened as shown in Table 2.

[0028] In one or more embodiments, step A3 includes: using Tanimoto similarity to compare metabolite Y of rice grains in A2 with key metabolite X in A1.

[0029] In one or more embodiments, the comparison method is as follows: calculate the Tanimoto similarity between the SMILES of the key metabolite C and the SMILES of the rice metabolite D to be evaluated: when the Tanimoto similarity is 1.0, it indicates that metabolite C = metabolite D, and the metabolite is retained; when the Tanimoto similarity is less than 1.0, the metabolite is removed.

[0030] In one or more embodiments, the extraction in step A1 or A2 is as follows: the grains of the rice sample to be evaluated are mixed with an aqueous methanol solution to extract metabolites; preferably, the volume ratio of methanol to water is 7:3.

[0031] In one or more embodiments, the method for evaluating the quality of rice grains in step A4 is as follows: obtain the metabolite abundance E1 of metabolite E in known rice variety grains and the metabolite abundance E2 in rice grains of the rice sample to be evaluated, calculate the correlation between the two metabolite abundances. If the correlation is high, it indicates that the quality of rice grains in the rice sample to be evaluated is similar to the known grain quality; otherwise, it indicates that the quality of rice grains in the rice sample to be evaluated differs significantly from the known grain quality.

[0032] Preferably, the correlation assessment is performed using the cor value, which is calculated using the following formula:

[0033]

[0034] X and Y represent the metabolite abundance matrix E1 in known rice grains and the metabolite abundance matrix E2 in rice grains to be evaluated, respectively. x and y represent the average values ​​of the abundance matrix E1 and the abundance matrix E2, respectively.

[0035] In one or more embodiments, when the known rice variety mentioned in step A1 is a japonica or indica rice variety, the method for evaluating the quality of the rice grains in step A4 is as follows: obtain the metabolite abundance E1 of metabolite E in the grains of the known japonica or indica rice variety and the metabolite abundance E2 in the grains of the rice sample to be evaluated, calculate the correlation between the two metabolite abundances. If the correlation is high, it indicates that the grain quality of the rice sample to be evaluated is similar to that of the known japonica or indica rice variety, indicating that the rice sample to be evaluated belongs to japonica or indica rice; otherwise, it indicates that the grain quality of the rice sample to be evaluated differs greatly from that of the known japonica or indica rice variety, indicating that the rice sample to be evaluated does not belong to japonica or indica rice.

[0036] More preferably, the correlation assessment is performed using the cor value, which is calculated using the following formula:

[0037]

[0038] X and Y represent the metabolite abundance matrix E1 in known japonica or indica rice grains and the metabolite abundance matrix E2 in rice grains to be evaluated, respectively. x and y represent the average values ​​of abundance matrix E1 and abundance matrix E2, respectively.

[0039] In a second aspect of the invention, a method for screening key metabolites related to rice grain quality is provided, the method comprising:

[0040] Provide grains from at least two known rice varieties, extract metabolites from these grain samples, annotate the types of metabolites in the grains of known rice varieties using SMILES, and screen using coefficient of variation (CV) to identify metabolites with SMILES annotation and a coefficient of variation greater than 100 as key metabolites related to rice grain quality.

[0041] In one or more embodiments, the screening is as described in any embodiment of the present invention.

[0042] In a third aspect of the invention, a metabolite related to rice grain quality is provided, which is obtained by screening using the method described in any embodiment of the invention.

[0043] In a fourth aspect of the invention, the use of metabolites obtained by screening using the method described in any embodiment of the invention, or metabolites described in any embodiment of the invention, is provided for:

[0044] (1) Assess rice quality; or

[0045] (2) Distinguish between japonica rice and indica rice; or

[0046] (3) Prepare a detection system for evaluating rice quality; or

[0047] (4) Prepare a detection system for distinguishing between japonica rice and indica rice.

[0048] In a fifth aspect of the invention, a detection system for analyzing and evaluating the quality of rice is provided, the system comprising:

[0049] Detection element: Extraction and detection of metabolites from rice grains to be evaluated;

[0050] Analysis element: Correlation analysis is performed on the metabolites of rice grains in the rice sample to be evaluated and the metabolites of rice samples with known grain quality to predict the quality of rice grains in the rice sample to be evaluated.

[0051] The analysis method is as described in any embodiment of the present invention.

[0052] Other aspects of the invention will be apparent to those skilled in the art from the disclosure herein. Detailed Implementation

[0053] Through in-depth research, the inventors constructed metabolome maps using rice grains from 36 varieties. Based on the coefficient of variation (CV) of metabolite abundance among different varieties, 370 key metabolites related to rice grain quality were screened. These 370 key metabolites were used as evaluation indicators for rice quality. Based on the correlation between the rice grain samples to be evaluated and the 370 key metabolites, the quality of the rice to be evaluated can be assessed.

[0054] This invention provides a method for evaluating rice quality using metabolomics analysis, comprising the following steps:

[0055] A1: Provide grains from at least two known rice varieties, extract metabolites from these grain samples, and screen for key metabolites C that are related to rice grain quality as a metabolome for assessing rice quality.

[0056] A2: Provide rice grains to be evaluated, and extract metabolite D from the grain sample;

[0057] A3: Compare metabolite D of rice grains in A2 with key metabolite C in A1, and retain the same metabolite E.

[0058] A4: Obtain the metabolite abundance E1 in the grains of known rice varieties and the metabolite abundance E2 in the grains of the rice sample to be evaluated, respectively, and calculate the correlation between E1 and E2 to evaluate the quality of the grains of the rice sample to be evaluated.

[0059] In one or more embodiments, in step A1, the known rice varieties are at least two, for example, more than two, more than three, more than five, more than eight, more than ten, more than twenty, more than thirty (e.g., 30, 31, 32, 32, 34), more than thirty-five (e.g., 35, 36, 37, 38, 39), more than forty or more known rice varieties.

[0060] In one or more embodiments, in step A1, the known rice varieties are the 36 rice varieties shown in Table 1.

[0061] In one or more embodiments, in step A1, the known rice variety is a japonica rice or an indica rice variety.

[0062] In this article, the rice quality described includes, but is not limited to, appearance quality and cooking / eating quality. Specifically, the rice appearance quality includes, but is not limited to, growth period, plant height, panicle length, and thousand-grain weight, while the rice cooking / eating quality includes, but is not limited to, amylose content.

[0063] In one or more embodiments, the screening in step A1 includes: annotating the types of metabolites in known rice variety grains using SMILES, and screening key metabolites C related to rice grain quality using the coefficient of variation (CV). Specifically, metabolites with SMILES annotation and a coefficient of variation greater than 100 can be considered as key metabolites C related to rice grain quality. In one or more embodiments, based on the 36 known rice variety grains in Table 1, the key metabolites C related to rice grain quality are shown in Table 2.

[0064] In this document, the coefficient of variation (CV) refers to the coefficient of variation among the abundance of metabolites in grains of all known rice varieties. In one or more specific embodiments, the CV is calculated as follows: CV = Standard Deviation ÷ Mean × 100%, where the standard deviation is the standard deviation of the abundance of metabolites in grains of all known rice varieties, and the mean is the average abundance of metabolites in grains of all known rice varieties. It should be understood that in the metabolomic profile obtained using chromatography-mass spectrometry (GC-MS) analysis, the abundance can be expressed as peak area.

[0065] In this paper, SMILES stands for simplified molecular-input line-entry system, which is a structural feature of a metabolite. One SMILES represents only one specific metabolite structure. In step A3, based on the characteristic that one SMILES represents only one specific metabolite structure, metabolite D of the rice grain sample to be evaluated in A2 can be compared with key metabolite C in A1, and the identical metabolite E can be retained. An exemplary comparison method is as follows: If key metabolite C related to rice grain quality obtained from known rice varieties has the same SMILES as metabolite D of the rice grain sample to be evaluated, then metabolite C and metabolite D are considered to have the same structure and are the same metabolite, and are retained as metabolite E; if metabolite C and metabolite D do not have the same SMILES, then metabolite C and metabolite D have different structures and are not the same metabolite, and are discarded. Specifically, Tanimoto similarity can be used for comparison. The comparison method may be as follows: calculate the Tanimoto similarity between the SMILES of the key metabolite C and the SMILES of the rice metabolite D to be evaluated: when the Tanimoto similarity is 1.0, it indicates that metabolite C = metabolite D, and the metabolite is retained; when the Tanimoto similarity is less than 1.0, the metabolite is removed.

[0066] In this paper, SMILES can be derived from one or more of the following databases: Metlin, MassBank, ReSpect, KEGG, Pubchem, and KNApSAck. Specifically, the method for annotating the types of metabolites in known rice grains using SMILES includes the following steps:

[0067] B1: Perform chromatography-mass spectrometry analysis on the metabolites obtained in A1 to obtain the mass spectrometry data of A1;

[0068] B2: Annotate the mass spectrometry data of A1 using experimental reference spectra or computer-predicted spectra to obtain SMILES of metabolites in A1.

[0069] In this article, the chromatographic and mass spectrometric analyses of metabolites include, but are not limited to, gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). LC-MS can be performed using methods such as orbital ion trap LC-MS / Orbitrap, microporous LC-MS / Q-TOF, and nanoporous LC-MS / Q-TOF. In some specific embodiments, the LC-MS analysis uses UPLC-Q-Orbitrap-HRMS to analyze the metabolites.

[0070] When processing mass spectrometry data, liquid chromatography-mass spectrometry data processing software can be used to perform peak alignment, peak identification, and peak feature extraction. Liquid chromatography-mass spectrometry data processing software can be, but is not limited to, Compound Discoverer 3.2. When processing mass spectrometry data, MS1 / MS2 paired data processing software can be used to obtain MS1 / MS2 paired data from the mass spectrometry data. MS1 / MS2 paired data processing software can be, but is not limited to, Xcalibur software, such as software based on the internal scripts of the Xcalibur Development Kit (XDK).

[0071] When processing mass spectrometry data, metabolite annotation can be performed to identify the types of metabolites, such as by annotating the smils of metabolites to confirm their structures. Methods for metabolite annotation include, but are not limited to, annotation using experimental reference spectra and annotation using computer-predicted spectra. Only one method can be used for metabolite annotation, or two or more methods can be used.

[0072] An exemplary method for annotating experimental reference spectra includes: collecting standard experimental spectra from a public database and performing a similarity analysis between them and the mass spectra obtained in step B2. In some specific implementations, the similarity analysis is performed using a Perl script based on an INCOSS-based spectral similarity comparison scoring algorithm. More specifically, candidate compounds with molecular weights similar to the precursor ions in the unknown spectrum (molecular weight error: 10 ppm) can be selected, and then the INCOSS algorithm is used to score and rank the spectra of the unknown spectrum and the candidate compounds. The candidate compound with a similarity score greater than 0.75 and ranked first is selected as the annotation for the unknown spectrum. Public databases used in this method include, but are not limited to, the Metlin, MassBank, and ReSpect databases.

[0073] An exemplary method for annotating predicted spectra using computers includes: collecting structural information of compounds from public databases to construct a compound structure database; retrieving candidate compounds with molecular weights similar to those in the mass spectra obtained in step B2 from the compound structure database; generating predicted spectra of these compounds using computer software; and performing a similarity analysis between the predicted spectra and the mass spectra obtained in step A2. The compound structure database can utilize OpenBabel software to convert the compound structural information collected from public databases into machine-readable structural information, optionally constructing the compound structure database through operations such as merging and redundancy removal. Public databases used in this method include, but are not limited to, KEGG, Pubchem, and KNApSAck databases. In some specific embodiments, the compound structure database includes machine-readable structural information, such as, but not limited to, molecular formulas, precise molecular weights, simplified molecular-input line-entry systems (SMILES), and IUPAC international chemical identifiers (InCHI). The computer software used to generate predicted spectra of compounds includes, but is not limited to, CFM-ID software. More specifically, for an unknown spectrum, candidate compounds with similar molecular weights (mass error: 5 ppm) to the unknown spectrum can be retrieved from a compound structure database. Then, the theoretical spectra corresponding to the candidate compounds can be obtained through CFM-ID software. The candidate compounds are scored and ranked according to the similarity of the spectra, and the candidate compounds with a similarity score greater than 0.3 are used as structural annotations for the unknown spectrum.

[0074] After processing the mass spectrometry data, the data in the metabolic spectrum can be integrated (e.g., integrating the positive and negative modes and annotations of the metabolic spectrum) to obtain the metabolic spectrum of rice grains. The metabolic spectrum of rice grains includes MS1 / MS2 spectra of all metabolites, where the MS2 spectrum includes instrument, fragmentation mode, collision energy, precursor molecular weight, annotations, and the m / z and relative intensity of the MS2 peak.

[0075] Metabolites from rice grains (e.g., metabolites in step A1 or A2) can be extracted using solvent extraction, for example, by mixing rice grains with an aqueous methanol solution. The volume ratio of methanol to water is 7:3, meaning the aqueous solution contains 70% methanol by volume. Solvent extraction may also include steps such as vortexing, centrifugation, settling, drying, concentration, and filtration.

[0076] In one or more embodiments, the method for evaluating the quality of rice grains in step A4 is as follows: obtain the metabolite abundance E1 of metabolite E in known rice variety grains and the metabolite abundance E2 in rice grains of the rice sample to be evaluated, respectively, and calculate the correlation between the two metabolite abundances. If the correlation between the two is high, it indicates that the quality of rice grains of the rice sample to be evaluated is approximately the known quality; otherwise, it indicates that the quality of rice grains of the rice sample to be evaluated differs significantly from the known quality.

[0077] In one or more embodiments, when the known rice variety mentioned in step A1 is a japonica or indica rice variety, the method for evaluating the quality of the rice grains in step A4 is as follows: obtain the metabolite abundance E1 of metabolite E in the grains of the known japonica or indica rice variety and the metabolite abundance E2 in the grains of the rice sample to be evaluated, respectively, and calculate the correlation between the two metabolite abundances. If the correlation is high, it indicates that the grain quality of the rice sample to be evaluated is similar to that of the known japonica or indica rice variety, indicating that the rice sample to be evaluated belongs to japonica or indica rice; otherwise, it indicates that the grain quality of the rice sample to be evaluated differs greatly from that of the known japonica or indica rice variety, indicating that the rice sample to be evaluated does not belong to japonica or indica rice.

[0078] For example, the correlation assessment is performed using the cor value, which is calculated using the following formula:

[0079]

[0080] X and Y represent the metabolite abundance matrix E1 in known rice grains and the metabolite abundance matrix E2 in rice grains of the sample to be evaluated, respectively. x and y represent the average values ​​of abundance matrices E1 and E2, respectively. When cor > 0, it indicates a positive correlation; when cor < 0, it indicates a negative correlation. Generally, it can be divided into three levels: |cor| < 0.4 indicates a low degree of linear correlation; 0.4 ≤ |cor| < 0.7 indicates a significant correlation; and 0.7 < |cor| < 1 indicates a high degree of linear correlation.

[0081] This invention also provides a method for screening key metabolites related to rice grain quality. The method includes: providing grains from at least two known rice varieties; extracting metabolites from these grain samples; annotating the types of metabolites in the known rice variety grains using SMILES; and screening using the coefficient of variation (CV). Metabolites with SMILES annotation and a CV greater than 100 are identified as key metabolites related to rice grain quality. In one or more embodiments, based on the 36 known rice variety grains in Table 1, the key metabolites C related to rice grain quality are the metabolites shown in Table 2. It should be understood that the structure, molecular formula, simplified molecular-input line-entry system (SMILES), and other information of the metabolites shown in Table 2 can be obtained from public databases such as KEGG, Pubchem, KNApSAck, and ChEBI. In this invention, the set of metabolome maps of the metabolites shown in Table 2 is also referred to as the "standard metabolome map".

[0082] Therefore, the present invention also provides a metabolite related to rice grain quality, which is obtained by screening using the method described in any embodiment of the present invention. In some specific embodiments, the metabolite related to rice grain quality is the metabolite shown in Table 2.

[0083] It should be understood that once metabolites related to rice grain quality are obtained through screening, these metabolites can be used to assess rice quality, to differentiate between japonica and indica rice, to prepare detection systems for assessing rice quality, or to prepare detection systems for differentiating between japonica and indica rice, etc.

[0084] Therefore, the present invention also provides a detection system for analyzing and evaluating rice quality, the system comprising:

[0085] Detection element: Extraction and detection of metabolites from rice grains to be evaluated;

[0086] Analysis element: Correlation analysis is performed on the metabolites of rice grains in the rice sample to be evaluated and the metabolites of rice samples with known grain quality to predict the quality of rice grains in the rice sample to be evaluated.

[0087] The analytical method described herein is as described in any embodiment of the present invention.

[0088] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Experimental methods in the following embodiments, unless otherwise specified, are generally performed under conventional conditions or as recommended by the manufacturer.

[0089] Example 1: Construction of a standard metabolome map of rice grains

[0090] 1. Selection of rice varieties and grain collection

[0091] Grain samples were collected from 36 rice varieties (18 japonica rice varieties and 18 indica rice varieties, as shown in Table 1). For each variety, two biological replicates were collected, and each biological replicate consisted of mature grain materials collected from three different plants.

[0092] Table 1

[0093]

[0094]

[0095] 2. Metabolite detection

[0096] Mature grains from 36 rice varieties were collected in mesh bags and air-dried in a cool, ventilated place. 2g of mature grains were weighed and ground in a tissue homogenizer at 55Hz for 40s. The resulting powder was then stored at -80℃.

[0097] Metabolites were extracted from grains of 36 rice varieties and detected using UPLC-Q-Orbitrap-HRMS non-targeted metabolomics. Mass spectrometry data of all metabolites were obtained.

[0098] Extraction of metabolites from rice grains: Freeze-dried rice grains were ground using a tissue homogenizer. 150 mg of the powder was weighed and mixed with 1.5 ml of 70% methanol aqueous solution. The material was vortexed every 10 minutes for a total of 3 times, and then incubated overnight at 4°C. The supernatant was then transferred to a centrifuge tube and centrifuged at 12,000 g for 15 minutes. The mixture was then dried and concentrated in a vacuum rotary desiccator. 150 μL of 70% methanol aqueous solution B (containing 1 mg / L capsaicin as an internal standard) was added to the dried and concentrated material. Finally, the solution was filtered through a 0.22 μm filter and transferred to a liquid chromatography bottle for further analysis.

[0099] Metabolites in rice grains were detected using untargeted metabolomics with UPLC-Q-Orbitrap-HRMS: Samples were analyzed on a Waters Acquity ultra-high performance liquid chromatography system equipped with an AC-QUITY UPLC BEH C18 column (1.7 μm, 2.1 mm × 100 mm) and Q Exactive mixed Q-orbitrap-HRMS (Thermo Fisher Scientific). The mobile phase used was (A) H₂O (0.04% acetic acid) and (B) acetonitrile (0.04% acetic acid). The chromatographic gradients were: 95:5A / B at 0 min, 5:95A / B at 20.0 min, 5:95A / B at 24.0 min, 95:5A / B at 24.1 min, and 95:5A / B at 30.0 min. The flow rate was 0.25 mL / min, and the injection volume was 5 μL. Metabolites are detected in positive and negative ion modes, with a mass-to-nucleus ratio ranging from 100 to 1200.

[0100] 3. Constructing a standard metabolome map of rice grains

[0101] 1) Use Compound Discoverer 3.2 to perform peak alignment, peak identification, and peak feature extraction on the raw mass spectrometry data of all metabolites; reproducibility < 90%, sample-to-blank ratio < 10%, and peak area not less than 1 × 10⁻⁶. 5 And eliminated the presence of multi-ionic adducts (such as Na+). + K + NH4 + Cl - Isotopes, characterized by fragmentation or dimerization within the source.

[0102] 2) Using the internal scripts in the Xcalibur software based on the Xcalibur Development Kit (XDK), automatically batch-acquire MS1 / MS2 paired spectral data from raw LC-MS / MS files from different rice varieties;

[0103] 3) The metabolite characteristics identified from these different rice varieties were assembled into a metabolic map of rice grain quality, which includes spectral characteristics in ESI positive and negative ion modes, with the following parameters: mass tolerance 5ppm; retention time tolerance 0.2min;

[0104] 4) Metabolite annotation of rice grain metabolic maps based on experimental reference spectra obtained from public databases (Metlin, MassBank, ReSpect) and theoretical spectra predicted by computers based on compound structure information:

[0105] i) Experimental Reference Spectrum Annotation: A local reference spectrum library was constructed by collecting experimental spectra of standard samples from the Metlin, MassBank, and ReSpect databases. A Perl script based on the INCOS-based spectrum similarity comparison and scoring algorithm was written for batch structural annotation of unknown spectra. Candidate compounds with molecular weights similar to the precursor ions of the unknown spectra (molecular weight error: 10 ppm) were selected, and then the INCOS algorithm was used to score and rank the spectra of the unknown spectra and the candidate compounds. The candidate compound with a similarity score greater than 0.75 and ranked first was selected as the annotation for the unknown spectrum.

[0106] ii) Computer-Predicted Theoretical Spectrum Annotation: Structural information of biologically derived compounds was collected and integrated from the KEGG, Pubchem, and KNApSAck databases. OpenBabel software was used to convert the collected raw structural data into machine-readable structural information, including molecular formula, precise molecular weight, simplified molecular-input line-entry system (SMILES), and IUPAC international chemical identifier (InCHI). Compounds with identical structures from the KEGG, Pubchem, and KNApSAck databases were merged and redundancy removed to construct a biologically derived compound structure database (SDBRC). Theoretical spectra of compounds were predicted from the SDBRC database using CFM-ID software as a reference for structural annotation. For an unknown spectrum, candidate compounds with similar molecular weights (mass error: 5 ppm) were retrieved from the SDBRC database. Theoretical spectra of these candidate compounds were then obtained using CFM-ID software, and the candidate compounds were scored and ranked based on spectral similarity. Candidate compounds with a similarity score greater than 0.3 were used as structural annotations for the unknown spectra.

[0107] 5) Constructing a standard metabolome map of rice grains:

[0108] By integrating the metabolic profiles (positive and negative modes) and annotations of rice grains, a metabolic profile of rice grains was obtained, which was named the "Standard Metabolome Profile". This profile includes the MS1 / MS2 spectra of all metabolites detected in rice grains. The MS2 spectra include instrumentation, fragmentation mode, collision energy, precursor molecular weight, annotations, and the m / z and relative intensity of the MS2 peak.

[0109] 4. Screening of key metabolites

[0110] Metabolic maps of rice grains were constructed based on 36 rice varieties. After redundancy removal, the metabolic maps of the 36 rice varieties contained a total of 3520 maps (2002 in positive mode and 1518 in negative mode), annotated to 1377 metabolites. In this standard metabolome map, the abundance (expressed as peak area) of each metabolite varied across different varieties. The coefficient of variation (CV) values ​​of the metabolites were calculated using the following formula:

[0111] Coefficient of variation (CV) = (Standard deviation SD / Mean) × 100%.

[0112] Wherein, the standard deviation (SD) is the standard deviation of the abundance value of a certain metabolite in 36 varieties, and the mean is the average abundance value of a certain metabolite in 36 varieties.

[0113] After calculating the coefficient of variation (CV) values ​​for all metabolites, metabolites with CV values ​​> 100 and annotations were identified as key metabolites related to grain quality in the rice grain metabolism profile (best reflecting the differences in metabolites among different varieties). A total of 370 key metabolites were screened. The 370 key metabolites and their CV values ​​are shown in Table 2.

[0114] Table 2

[0115]

[0116]

[0117]

[0118]

[0119]

[0120]

[0121]

[0122]

[0123]

[0124]

[0125] Example 2: Construction of Metabolic Profiles of Rice Grain Samples to be Evaluated

[0126] This embodiment provides a method for constructing a metabolic profile of a rice grain sample to be evaluated, including the following steps:

[0127] 1) Provide a rice grain sample to be evaluated, and extract metabolites from the grain sample;

[0128] 2) Metabolites were detected using UPLC-Q-Orbitrap-HRMS, and the mass spectrometry data obtained from the detection were processed using Compound Discoverer 3.2 for peak alignment, peak identification, and peak feature extraction.

[0129] 3) Use the internal scripts in the Xcalibur software based on the Xcalibur Development Kit (XDK) to automatically obtain MS1 / MS2 paired data from the original LC-MS / MS files of the rice samples to be evaluated;

[0130] 4) Construct the metabolomic profile of rice grains to be evaluated, and annotate the metabolites of rice grains based on the experimental reference mass spectrometry library and the MS2 spectra with structural annotations obtained from public databases and the theoretical spectra predicted by computer.

[0131] 5) After integration, the metabolic profile of the rice grains to be evaluated is obtained.

[0132] In the process of constructing the metabolic profile of rice grains to be evaluated, the steps are the same as in Example 1, except that the sample used is the rice grain sample to be evaluated.

[0133] Example 3: Analysis and evaluation method for rice grain quality

[0134] This embodiment provides a method for analyzing and evaluating the quality of rice grains. The method is as follows:

[0135] 1. Collect grains from 36 known rice varieties in Table 1, extract metabolites from these grain samples using the method in Example 1, and screen to obtain key metabolites related to rice grain quality, namely the key metabolites shown in Table 2, which are used as metabolomes for evaluating rice quality.

[0136] 2. Provide rice grains to be evaluated, and extract metabolites from the grain sample using the method in Example 1;

[0137] 3. Compare the metabolites of the rice grains to be evaluated in step 2 with the key metabolites in Table 2. The comparison method is as follows:

[0138] Calculate the Tanimoto similarity between the SMILES of the key metabolites in Table 2 and the SMILES of the metabolites annotated in the seed kernels of the sample to be evaluated: when the Tanimoto similarity is 1.0, it indicates that they are the same compound, so the metabolite is retained; otherwise, all metabolites with a Tanimoto similarity less than 1 are removed.

[0139] After comparison, metabolites in rice grains of the samples to be evaluated in A2 that are identical to the key metabolites in Table 2 are retained for correlation analysis in the next step.

[0140] 4. Obtain the abundance of metabolites in grains of known rice varieties and in grains of rice samples to be evaluated, respectively. Calculate the correlation between the two, expressed as the cor value, using the following formula:

[0141]

[0142] X and Y represent the abundance matrices of metabolites in the sample to be evaluated and the known quality rice sample, respectively, and x and y represent the average values ​​of the abundance matrices.

[0143] When cor > 0, it indicates a positive correlation between the two; when cor < 0, it indicates a negative correlation. Generally, it can be divided into three levels: |cor| < 0.4 indicates a low degree of linear correlation; 0.4 ≤ |cor| < 0.7 indicates a significant correlation; and 0.7 < |cor| < 1 indicates a high degree of linear correlation.

[0144] The higher the cor value, the closer the abundance of metabolites between the two varieties, indicating that the quality of the rice grains of the two varieties is closer. The rice variety with the highest cor value is the rice variety that is closest to the grains of the rice sample to be evaluated.

[0145] Example 4: Feasibility Verification of the Evaluation Method of the Invention

[0146] To verify the accuracy of the evaluation method of the present invention, the inventors used Yangdao 6 as the sample to be evaluated and analyzed its correlation with the parent Yangdao 4 using the evaluation method of Example 3.

[0147] Yangdao No. 4 has a moderately compact plant shape, robust stems, a plant height of 110 cm, upright, dark green leaves, large panicles with many grains, slender grains, and medium to high rice quality. Its entire growth period is approximately 140 days, with moderate tillering ability, good late-stage ripening, strong resistance to diseases and pests, and good overall resistance. Yangdao No. 6 itself has short and robust seedlings, good growth, moderate tillering ability, a plant height of 110 cm, robust stems, a total growth period of approximately 156 days, and good rice quality. Yangdao No. 4 is the female parent of Yangdao No. 6, and the two have similar qualities. Therefore, if the correlation (cor value) of the abundance of metabolites between the two is good, it indicates that the method of this invention is feasible.

[0148] The correlation between the abundance of metabolites of Yangdao 6 and Yangdao 4 was found to be cor = 0.92398, demonstrating that the method of this invention has a certain degree of accuracy in assessing rice quality.

[0149] Example 5: Evaluation of Minghui 69 (CX53) using the method of the present invention.

[0150] In this embodiment, Minghui 69 (CX53) was used as the sample to be evaluated, and the evaluation method of Example 3 was used to analyze its correlation with the 36 rice varieties in Table 1.

[0151] The results of the correlation analysis are shown in Table 3.

[0152] Table 3

[0153]

[0154]

[0155]

[0156] The results showed that Minghui 69 (CX53) was most similar to Zhonghan 211 (CX143) (cor = 0.8189124). The quality of Minghui 69 (CX53) can be inferred based on the quality of Zhonghan 211 (CX143). Since Zhonghan 211 (CX143) has moderate drought resistance, a moderate growth period, high yield, susceptibility to rice blast, high susceptibility to bacterial blight, and superior rice quality, it can be inferred that Minghui 69 also has similar quality.

[0157] Example 6: Evaluation of Yangdao 6 (CX86) using the method of the present invention.

[0158] In this embodiment, Yangdao 6 (CX86) was used as the sample to be evaluated, and the evaluation method of Example 3 was used to analyze its correlation with the 36 rice varieties in Table 1.

[0159] The results of the correlation analysis are shown in Table 4.

[0160] Table 4

[0161]

[0162]

[0163] The results are shown in Table 4. Yangdao 6 (CX86) is most similar to Yahuahui 1431 (CX57) (cor = 0.952255). The quality of Yangdao 6 (CX86) can be inferred based on the quality of Yahuahui 1431 (CX57). Since Yahuahui 1431 (CX57) has the characteristics of moderate plant type, strong stem, dark green leaves, large panicles and many grains, high seed setting rate and thousand-grain weight, good yield stability, good late color change, good rice quality, and strong field resistance, it can be inferred that Yangdao 6 (CX86) also has similar qualities.

[0164] Example 7: Phenotypic verification results of the method of the present invention

[0165] To verify the analytical and evaluation results of the method of the present invention used in Examples 5 and 6 for Minghui 69 (CX53) and Yangdao 6 (CX86), the inventors further measured the phenotypes of Yangdao 6, Yahuahui 1431, Minghui 69, and Zhonghan 211, including growth period, plant height, panicle length, thousand-grain weight, and amylose content, to verify from a phenotypic perspective whether the grain quality of the two groups of rice analyzed by the method of the present invention, namely Minghui 69 and Zhonghan 211, and Yangdao 6 and Yahuahui 1431, is similar.

[0166] Thousand-grain weight test: Thousand-grain weight is an appearance quality indicator of rice, representing the weight of one thousand rice seeds, expressed in grams. It is generally measured using air-dried seeds. It reflects the size and plumpness of the seeds, is used to inspect seed quality and crop variety, and is also an important basis for predicting yield in the field. To determine the thousand-grain weight, a sampling method is used, randomly selecting three sets of one thousand seeds, weighing each, and calculating the average.

[0167] Amylose content detection: Amylose content is a quality indicator for cooked rice. Amylose is starch composed of linearly linked α-glucan chains, and its detection can be performed using the amylose iodine colorimetric method. The detection procedure is as follows:

[0168] 1. Remove the husks from the rice grains, grind them into powder, and sieve them through a 100-mesh sieve;

[0169] 2. Accurately weigh 0.1g of sample, pour it into a 100ml volumetric flask, add 1ml of anhydrous ethanol to moisten the sample, shake well, then add 9ml of 1mol / L NaOH solution and shake well.

[0170] 3. Place the volumetric flask in a water bath and heat to disperse. After 10 minutes, remove the volumetric flask and cool it quickly under running water. Add distilled water to the 100ml mark and make up to volume. Tighten the cap and mix by inverting.

[0171] 4. Take 5 ml of NaOH solution (blank, 90 ml of 1 mol / L NaOH solution diluted with distilled water to 1000 ml) and 5 ml of sample solution into 50 ml volumetric flasks, add 30 ml of distilled water, 1 ml of acetic acid solution, and 1 ml of 0.2% iodine solution, start timing immediately, shake well and make up to volume.

[0172] 5. After standing for 20 minutes, use a UV-Vis spectrophotometer, set the wavelength to 620 nm, to measure the absorbance A1 of the blank solution and the absorbance A2 of the sample.

[0173] 6. Calculate the amylose content based on the absorbance value:

[0174] Amylose content (mg / g) = [(A2-A1)×V2×D] / (E×m×V1)

[0175] Where V1 is the sampling volume (ml); V2 is the final volume (ml); D is the dilution factor (if diluted); E is the molar absorptivity; and m is the sample mass (g).

[0176] Table 5

[0177]

[0178] Phenotypic data (Table 5) confirm that Yangdao 6, Yahuahui 1431, Minghui 69, and Zhonghan 211 are phenotypically similar, especially in traits such as growth period, plant height, thousand-grain weight, and amylose content. Since thousand-grain weight is an indicator of rice's appearance quality, and amylose content is an indicator of its cooking and eating quality, this indicates that Yangdao 6, Yahuahui 1431, Minghui 69, and Zhonghan 211 are very similar in quality.

[0179] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims. Furthermore, all documents mentioned in this invention are incorporated herein by reference as if each document were individually incorporated by reference.

Claims

1. A method for evaluating rice quality using metabolomics analysis, comprising the following steps: A1: Provide grains from at least two known rice varieties, extract metabolites from these grain samples, and screen for key metabolites C that are related to rice grain quality as a metabolome for assessing rice quality. A2: Provide rice grains to be evaluated, and extract metabolite D from the grain sample; A3: Compare metabolite D of rice grains in A2 with key metabolite C in A1, and retain the same metabolite E. A4: Obtain the metabolite abundance E1 in the grains of known rice varieties and the metabolite abundance E2 in the grains of the rice sample to be evaluated, respectively, and calculate the correlation between E1 and E2 to evaluate the quality of the grains of the rice sample to be evaluated.

2. The method as described in claim 1, characterized in that, The screening in step A1 includes: using SMILES to annotate the types of metabolites in known rice grains, and using the coefficient of variation to screen for key metabolites C that are related to rice grain quality. Preferably, metabolites with SMILES annotation and a coefficient of variation greater than 100 are identified as key metabolites C related to rice grain quality. More preferably, the coefficient of variation is the coefficient of variation among the abundance of grain metabolites in all known rice varieties; more preferably, the formula for calculating the coefficient of variation is: coefficient of variation = standard deviation ÷ mean × 100%, where the standard deviation is the standard deviation of the abundance of grain metabolites of metabolites in all known rice varieties, and the mean is the mean of the abundance of grain metabolites of metabolites in all known rice varieties.

3. The method as described in claim 2, characterized in that, The method for annotating the types of metabolites in known rice grains using SMILES includes the following steps: B1: Perform chromatography-mass spectrometry analysis on the metabolites obtained in A1 to obtain the mass spectrometry data of A1; B2: Annotate the mass spectrometry data of A1 using experimental reference spectra or computer-predicted spectra to obtain SMILES of metabolites in A1; Preferably, the SMILES are derived from one or more of the following databases: Metlin, MassBank, ReSpect, KEGG, Pubchem, and KNApSAck; Preferably, the chromatography-mass spectrometry analysis in step B1 includes: gas chromatography-mass spectrometry analysis and liquid chromatography-mass spectrometry analysis; more preferably, the liquid chromatography-mass spectrometry analysis is performed using UPLC-Q-Orbitrap-HRMS.

4. The method according to any one of claims 1-3, characterized in that, Step A3 includes: using Tanimoto similarity to compare metabolite Y of rice grains in A2 with key metabolite X in A1; Preferably, the comparison method is as follows: calculate the Tanimoto similarity between the SMILES of the key metabolite C and the SMILES of the rice metabolite D to be evaluated: when the Tanimoto similarity is 1.0, it indicates that metabolite C = metabolite D, and the metabolite is retained; when the Tanimoto similarity is less than 1.0, the metabolite is removed.

5. The method according to any one of claims 1-4, characterized in that, The extraction in step A1 or A2 is as follows: the rice grains to be evaluated are mixed with a methanol-water solution to extract metabolites; preferably, the volume ratio of methanol to water is 7:

3.

6. The method according to any one of claims 1-5, characterized in that, The method for evaluating the quality of rice grains in step A4 is as follows: Obtain the metabolite abundance E1 of metabolite E in the grains of known rice varieties and the metabolite abundance E2 in the grains of the rice sample to be evaluated, respectively, and calculate the correlation between the two metabolite abundances. If the correlation between the two is high, it indicates that the grain quality of the rice sample to be evaluated is approximately the known grain quality; otherwise, it indicates that the grain quality of the rice sample to be evaluated differs significantly from the known grain quality. Preferably, the correlation assessment is performed using the cor value, which is calculated using the following formula: X and Y represent the metabolite abundance matrix E1 in known rice grains and the metabolite abundance matrix E2 in rice grains of the sample to be evaluated, respectively. and These represent the average values ​​of the abundance matrix E1 and the abundance matrix E2, respectively.

7. The method according to any one of claims 1-5, characterized in that, When the known rice variety mentioned in step A1 is a japonica or indica rice variety, the method for evaluating the quality of the rice grains in step A4 is as follows: Obtain the metabolite abundance E1 of metabolite E in the grains of the known japonica or indica rice variety, and the metabolite abundance E2 in the grains of the rice sample to be evaluated, respectively. Calculate the correlation between the two metabolite abundances. If the correlation is high, it indicates that the grain quality of the rice sample to be evaluated is similar to that of the known japonica or indica rice variety, indicating that the rice sample to be evaluated belongs to japonica or indica rice. Conversely, if the correlation is low, it indicates that the grain quality of the rice sample to be evaluated differs significantly from that of the known japonica or indica rice variety, indicating that the rice sample to be evaluated does not belong to japonica or indica rice. More preferably, the correlation assessment is performed using the cor value, which is calculated using the following formula: X and Y represent the metabolite abundance matrix E1 in known japonica or indica rice grains and the metabolite abundance matrix E2 in rice grains of the sample to be evaluated, respectively. and These represent the average values ​​of the abundance matrix E1 and the abundance matrix E2, respectively.

8. A method for screening key metabolites related to rice grain quality, the method comprising: Provide grains from at least two known rice varieties, extract metabolites from these grain samples, annotate the types of metabolites in the grains of known rice varieties using SMILES, and screen using the coefficient of variation, identifying metabolites with SMILES annotation and a coefficient of variation greater than 100 as key metabolites related to rice grain quality. Preferably, the screening is as defined in claim 2 or 3.

9. A metabolite related to rice grain quality, obtained by screening using the method of claim 8.

10. The use of metabolites obtained by screening using the method of claim 8, or the use of metabolites as described in claim 9, for: (1) Assess rice quality; or (2) Distinguish between japonica rice and indica rice; or (3) Prepare a detection system for evaluating rice quality; or (4) Prepare a detection system for distinguishing between japonica rice and indica rice.

11. A detection system for analyzing and evaluating the quality of rice, the system comprising: Detection element: Extraction and detection of metabolites from rice grains to be evaluated; Analysis element: Correlation analysis is performed on the metabolites of rice grains in the rice sample to be evaluated and the metabolites of rice samples with known grain quality to predict the quality of rice grains in the rice sample to be evaluated. The method of analysis is as described in any one of claims 1-7.

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