CN112326826A - Method for screening key metabolites responding to high-temperature stress of poplar - Google Patents
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Abstract
The invention discloses a method for screening key metabolites responding to high-temperature stress of poplar, which detects the metabolic change rule of poplar leaves under the high-temperature stress through a GC/TOF-MS technology and screens small molecule metabolites and metabolic pathways of poplar responding to the high temperature, thereby explaining the regulation and control mechanism of poplar responding to the high-temperature stress on the metabonomics level. 67 differential metabolites are screened, and the differential genes under high temperature stress are mainly enriched in valine, leucine and isoleucine biosynthesis; galactose metabolism; glycine, serine and threonine metabolism pathways. The invention also provides an important reference basis for the study of the high temperature stress of the trees and provides a valuable reference for the study of the high temperature resistance of woody ornamental plants.
Description
Technical Field
The invention belongs to the technical field of metabonomics, relates to a metabonomic analysis method for poplar high temperature stress, and particularly relates to a method for screening key metabolites responding to poplar high temperature stress.
Background
Climate change increases the frequency of the occurrence of extreme high temperatures worldwide. High temperature stress is one of the major environmental stresses that limit plant growth, metabolism, and yield. Plants, as sessile organisms, have evolved a complex variety of systems that respond to high temperatures, e.g., plants utilize antioxidant enzymes to scavenge excess Reactive Oxygen Species (ROS) produced by oxidative stress, mitigate osmotic stress injury by accumulating some osmolyte species (proline, betaine, soluble sugars, etc.). Studies on tree response to high temperature stress have focused mainly on the population, individuals, organs, and cells level, while studies on the molecular level have been less. The change condition of the metabolites under high temperature stress can be directly determined by utilizing metabonomics research. Studies on the high temperature stress metabolome have been conducted in plants such as arabidopsis, tomato, maize and wheat, and some metabolites have been identified that are obtained in response to heat stress. For example: some soluble sugars (sucrose, glucose, betanin, raffinose) and some organic acids (citric acid, fumaric acid, malic acid, ferulic acid). In addition, the Chinese white poplar is widely distributed in China, has tall, big and straight trunks, green leaves, beautiful tree postures, and handsome and straight leaves, is commonly used for urban and rural street trees and shade trees, and is an important greening and ornamental tree species. In particular, the Chinese white poplar has strong adaptability, can still normally grow under the urban heat island effect in summer, and shows that the Chinese white poplar has strong high temperature resistance. Therefore, understanding the high temperature resistance mechanism of poplar has practical significance and application value.
During the study we designed temperature treatment for poplar: control and high temperature groups. Differential metabolites in high temperature and control leaves were determined by GC-MS. The invention aims to screen small molecule metabolites of poplar responding to high temperature, to find the metabolic change rule of poplar under high temperature stress, to explain the regulation and control mechanism of poplar responding to high temperature stress on the metabonomics level, to provide important reference basis for the research of tree high temperature stress and to provide valuable reference for the research of woody ornamental plant high temperature resistance.
Disclosure of Invention
The invention aims to provide a method for screening key metabolites responding to high-temperature stress of poplar, which can be used for detecting the metabolic related change rule of the poplar responding to the high temperature and providing a powerful analysis method for detecting the change condition of a plant high-temperature stress metabolite passage.
The purpose of the invention can be realized by the following technical scheme:
a method for screening key metabolites responding to high-temperature stress of poplar trees comprises the following steps:
(1) sample preparation: processing poplar under different temperature conditions to obtain high temperature group and control group poplar leaf samples, and performing metabolite preparation on the high temperature group and control group poplar leaf samples to respectively obtain high temperature group and control group metabolites;
(2) and (3) detecting metabolites of the high-temperature group and the control group based on GC/TOF-MS: performing derivatization treatment on the metabolites of the high-temperature group and the control group obtained in the step (1), and performing GC/TOF-MS detection analysis to obtain detection data of the metabolites of the high-temperature group and the control group;
(3) and (3) data analysis: and sequentially carrying out pretreatment analysis, principal component and correlation analysis, orthogonal partial least square method discriminant analysis and interclass difference analysis on the obtained high-temperature group and control group metabolite detection data to obtain a difference metabolite between the high-temperature group and the control group, namely a key metabolite responding to the high-temperature stress of the poplar, and carrying out metabolic pathway analysis.
As a preferred technical solution, the temperature conditions for treating the poplar of the high temperature group and the poplar of the control group in the step (1) are respectively as follows: control group (control, CTRL group): treating at 25 deg.C for 6 hr; high temperature group (heat, group H): firstly, heating up in a gradient way, when the temperature of 25 ℃ is raised to 35 ℃ within half an hour, staying for 2 hours, then heating up from 35 ℃ to 45 ℃, and maintaining the temperature at 45 ℃ for processing for 5 hours; taking the 3 rd to 4 th leaf under the apical bud, sampling time points: 5h at 45 ℃.
As a preferred technical scheme, the preparation process of the metabolite in the step (1) comprises the following steps: blade taking out deviceAdding a tissue sample into an EP tube, and adding an extraction solvent and a ribitol solution, wherein the extraction solvent is methanol and H according to a volume ratio2A solvent with the ratio of O to 1 being 3: 2 mg/ml; the extract was treated with a grinder for 4 minutes, followed by an ice-water bath for 5 minutes, repeated 2 times, and refrigerated and centrifuged for 15 minutes (4 ℃,13000 rpm); the supernatant was transferred to a GC/MS glass flask and the extract was dried in a vacuum concentrator to obtain the metabolite.
As a preferred technical scheme, the process of performing derivatization treatment on the obtained metabolites of the high-temperature group and the control group in the step (2) is as follows: adding a methoxylamine reagent into the dried metabolite, slowly mixing uniformly, and incubating for 30 minutes at 80 ℃; subsequently, BSTFA was added rapidly and incubated at 70 ℃ for 1.5 h.
As a preferred technical scheme, in the step (2), an Agilent 7890 gas chromatography-time of flight combined instrument with a DB-5MS capillary column (5% diphenyl, 95% dimethyl polysiloxane) is adopted for GC/TOF-MS detection and analysis, and the specific analysis conditions of GC/TOF-MS are as follows: sample introduction amount: 1 μ L, no-shunt mode; carrier gas: helium gas; blowing flow rate of a front sample inlet: 3 mL. min-1(ii) a Column flow rate: 1 mL. min-1(ii) a Column temperature: holding at 50 deg.C for 1 min, increasing to 310 deg.C at a rate of 10 deg.C per minute, and holding for 8 min; temperature of a front sample inlet: 280 ℃; transmission line temperature: 270 ℃; ion source temperature: 220 ℃; ionization voltage: -70 eV; the scanning mode is as follows: 50-500 m/z; scanning rate: 20 spectra/sec; solvent retardation: 460s, the acquisition mode is full scan mode.
As a preferred technical scheme, the pretreatment analysis of the metabolite detection data of the high-temperature group and the control group obtained in the step (3) is to filter the data to remove noise data to obtain data which has no noise interference and can be used for statistical analysis.
Most preferably, an Agilent ChemStation workstation is adopted to collect a total ion flow chart (TIC) for visual inspection, whether an instrument meets the requirement of metabonomics analysis or not when analyzing a sample is judged through overlapping of a plurality of sample maps, and the stability and repeatability are investigated. Meanwhile, in order to better analyze downstream data, the data are filtered, and the aim of removing noise data to obtain data which are free of noise interference and can be used for statistical analysis is achieved. The method comprises the following steps: and (3) quartile range or interquartile distance (interquartile range), and peak area data with a single vacancy value of 50% or less or with a vacancy value of 50% or less in all groups. And simulating missing values in the original data, wherein the numerical simulation method is a one-half method of the minimum value to fill. And carrying out standardized processing on the data after filling, wherein the method is an internal standard normalization method.
The principal component and correlation analysis in step (3) is: and performing principal component analysis on the data after all samples are preprocessed, and preliminarily knowing the overall difference among the samples in each group and the variation degree in each group.
The process of the orthogonal partial least square discriminant analysis in the step (3) is as follows: performing orthogonal correction partial least squares discriminant analysis (OPLS-DA) on the multidimensional statistical model by using SIMCA software (V14.1, MKS Data analysis Solutions, Umea, Sweden), and maximally highlighting the difference between the interior of the model and the predicted principal component (predicted component); the method comprises the following steps that (1) OPLS-DA firstly carries out a data scale conversion mode of LOG conversion and UV formatting treatment, and then modeling analysis is carried out on the first main component and the second main component; the quality of the model was checked by 7-fold cross-validation and the R obtained after cross-validation2Y (representing the interpretability of the variable Y) and Q2(representing the predictability of the model) the effectiveness of the model is judged; then, the arrangement sequence of the classification variables y is changed for a plurality of times randomly (n is 200) through an arrangement experiment to obtain corresponding different random Q2Further checking the effectiveness of the value on the model; r2Y (representing the interpretability of the Y variable), Q2(representing the predictability of the model) closer to 1 indicates that the OPLS-DA model accounts for the differences between the two sets of samples better.
In the process, a multidimensional statistical model is required to be established, and the specific method comprises the following steps: collecting a total ion flow chart (TIC) by adopting an Agilent ChemStation workstation, carrying out deconvolution analysis after taking an original data signal, carrying out qualitative metabolite and integral, finally carrying out later-stage arrangement in Excel software, and organizing a result into a two-bit data matrix comprising an observed quantity (sample), a metabolite and a metabolic pathway; the edited data matrix was imported into SIMCA (version 14.1) for Principal Component Analysis (PCA), orthogonal partial least squares analysis (OPLS) and differential metabolites were mined according to the VIP values of OPLS-DA.
The process of the analysis of the difference between groups in the step (3) is as follows: differentially expressed metabolites were obtained by analytically comparing the quantitative data for metabolites between the two groups of samples, using VIP >1 and P-VALUE <0.05 as thresholds.
The step (3) of establishing the multidimensional statistical model specifically comprises the following steps: collecting a total ion flow chart (TIC) by adopting an Agilent ChemStation workstation, carrying out deconvolution analysis after taking an original data signal, carrying out qualitative metabolite and integral, finally carrying out later-stage arrangement in Excel software, and organizing a result into a two-bit data matrix comprising an observed quantity (sample), a metabolite and a metabolic pathway; the edited data matrix was imported into SIMCA (version 14.1) for Principal Component Analysis (PCA), orthogonal partial least squares analysis (OPLS) and differential metabolites were mined according to the VIP values of OPLS-DA.
The invention discloses a method for screening key metabolites responding to high-temperature stress of poplar, which detects the metabolic change rule of poplar leaves under the high-temperature stress through a GC/TOF-MS technology and screens small molecule metabolites and metabolic pathways of poplar responding to the high temperature, thereby explaining the regulation and control mechanism of poplar responding to the high-temperature stress on the metabonomics level. 67 differential metabolites are screened, and the differential genes under high temperature stress are mainly enriched in valine, leucine and isoleucine biosynthesis; galactose metabolism; glycine, serine and threonine metabolism pathways. The invention also provides an important reference basis for the study of the high temperature stress of the trees and provides a valuable reference for the study of the high temperature resistance of woody ornamental plants.
The invention has the beneficial effects that:
the method can screen the micromolecular metabolite of the poplar responding to high temperature, lays a good foundation for breeding the poplar heat-resistant molecules, and has potential application value.
Drawings
FIG. 1 shows experimental materials and design in example 1 of the present invention; wherein (a) a poplar growing population; (b) a sampling position (position indicated by an arrow); (c) experimental design schematic, HT: heat treatment; CTRL: a control group; the upper line represents the time of heat treatment. The lower dotted line represents the time of the room temperature control. The arrows indicate the sampling time points (qPCR:25 ℃ for 2h,35 ℃ for 2h,45 ℃ for 2h,5h,12 h; metabolome: 45 ℃ for 5h,25 ℃ for 10 h).
FIG. 2 is a graph of Principal Component Analysis (PCA) scores of leaf samples according to example 4 of the present invention.
FIG. 3 is a graph of discriminant analysis by the orthometric partial least squares method in example 4 of the present invention, wherein (a): an OPLS-DA score map; (b) the method comprises the following steps A displacement check chart; (c) the method comprises the following steps OPLS-DA load map.
FIG. 4 is a diagram of the metabolite enrichment metabolic pathway of poplar leaves at high temperature in example 5 of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. From the following description and these examples, one skilled in the art can ascertain the essential characteristics of this invention, and without departing from the spirit and scope thereof, can make various changes and modifications of the invention to adapt it to various usages and conditions.
The invention adopts the excellent 84K poplar as a material, screens the micromolecular metabolites of the poplar responding to high temperature through the analysis of the biological information of metabonomics, finds out the metabolic change rule of the poplar under the high temperature stress and explains the regulation and control mechanism of the poplar responding to the high temperature stress on the metabonomics level. The screening of the small molecular metabolites of the poplar responding to high temperature lays a good foundation for breeding the heat-resistant molecules of the poplar, and has potential application value.
Example 1 treatment of poplar at temperature conditions to obtain control and high temperature poplar leaf samples
Control group (control, CTRL group): treatment at 25 ℃ for 6 hours, high temperature group (heat, group H): the temperature is increased in a gradient way, when the temperature is increased to 35 ℃ within half an hour, the temperature is increased to 45 ℃ from 35 ℃ after the temperature is kept for 2 hours, and the temperature is maintained at 45 ℃ for treatment for 5 hours. Taking the 3 rd to 4 th leaf under the apical bud, sampling time points: 45 ℃ for 5h (as shown in FIG. 1).
Example 2 preparation of metabolites from leaf samples of high temperature and control groups
0.06g of leaf tissue sample was added to a 2ml EP tube, and then added to 0.48ml of an extraction solvent (methanol: H)2O (v: v) ═ 3: 1) and 24 μ L of a solution of ribitol (2 mg/ml); the extract was treated with a grinder for 4 minutes, ice-water bath for 5 minutes (2 repetitions), refrigerated and centrifuged for 15 minutes (4 ℃,13000 rpm); 0.35ml of the supernatant was transferred to a 2ml GC/MS glass flask. Drying the extract in a vacuum concentrator; adding 80 μ L of methoxylamine reagent (commercially available product) into the dried metabolite, slowly mixing, and incubating at 80 deg.C for 30 min; subsequently, 100. mu.L of BSTFA was added rapidly and incubated at 70 ℃ for 1.5 h.
Example 3 detection of metabolites in hyperthermia and control groups based on GC/TOF-MS
GC/TOF-MS analysis was performed using an Agilent 7890 gas chromatograph-time-of-flight spectrometer equipped with a DB-5MS capillary column (5% diphenyl, 95% dimethylpolysiloxane). The specific detection and analysis conditions of GC/TOF-MS are as follows: sample introduction amount: 1 μ L, no-shunt mode; carrier gas: helium gas; blowing flow rate of a front sample inlet: 3 mL. min-1(ii) a Column flow rate: 1 mL. min-1(ii) a Column temperature: holding at 50 deg.C for 1 min, increasing to 310 deg.C at a rate of 10 deg.C per minute, and holding for 8 min; temperature of a front sample inlet: 280 ℃; transmission line temperature: 270 ℃; ion source temperature: 220 ℃; ionization voltage: -70 eV; the scanning mode is as follows: 50-500 m/z; scanning rate: 20 spectra/sec; solvent retardation: 460s, the acquisition mode is full scan mode.
Example 4 analysis of leaf differential metabolome assay data in the high temperature and control groups
(1) Leaf metabolism data preprocessing analysis
An Agilent ChemStation workstation is adopted to collect a total ion flow chart (TIC) for visual inspection, whether the instrument meets the analysis requirement of metabonomics or not when analyzing a sample is judged through overlapping of a plurality of sample maps, and the stability and repeatability are inspected. Meanwhile, in order to better analyze downstream data, the data are filtered, and the aim of removing noise data to obtain data which are free of noise interference and can be used for statistical analysis is achieved. The method comprises the following steps: and (3) quartile range or interquartile distance (interquartile range), and peak area data with a single vacancy value of 50% or less or with a vacancy value of 50% or less in all groups. And simulating missing values in the original data, wherein the numerical simulation method is a one-half method of the minimum value to fill. And carrying out standardized processing on the data after filling, wherein the method is an internal standard normalization method.
(2) Principal component and correlation analysis
And performing principal component analysis on the preprocessed data of all samples (including quality control samples) so as to preliminarily know the overall difference among the samples in each group and the variation degree in each group. In this example 4, the two groups of samples were distinguished significantly (as shown in fig. 2).
(3) Discriminant analysis by orthogonal partial least squares
The method is characterized in that SIMCA software (V14.1, MKS Data analysis Solutions, Umea, Sweden) is used for performing the judgment analysis (OPLS-DA) of the orthogonal correction partial least squares method, a multidimensional statistical model needs to be established in the process, and the specific method comprises the following steps: collecting a total ion flow chart (TIC) by adopting an Agilent ChemStation workstation, carrying out deconvolution analysis after taking an original data signal, carrying out qualitative metabolite and integral, finally carrying out later-stage arrangement in Excel software, and organizing a result into a two-bit data matrix comprising an observed quantity (sample), a metabolite and a metabolic pathway; the edited data matrix was imported into SIMCA (version 14.1) for Principal Component Analysis (PCA), orthogonal partial least squares analysis (OPLS) and differential metabolites were mined according to the VIP values of OPLS-DA. The differences inside the model associated with the Predictive components are highlighted to the maximum. The OPLS-DA firstly performs a data scale conversion mode of LOG conversion and UV formatting treatment, and then performs modeling analysis on the first and second main components. The quality of the model was checked by 7-fold cross-validation and obtained after cross-validation, R2Y (representing the interpretability of the variable Y) and Q2(representing the predictability of the model) the effectiveness of the model was judged. After that, the permutation sequence of the classification variables y is changed several times randomly (n is 200) through the permutation experiment to obtain correspondingly different random Q2And further checking the validity of the model. R2Y (representing the interpretability of the Y variable), Q2(representing model predictability) the closer to 1 means that the OPLS-DA model explains two betterDifferences between group samples. Displacement check intercept R2＝0.99，Q2The robustness of the model can be well embodied by 0.0318. The left and right end substances of the loading map are potential differential markers (as shown in fig. 3).
(4) Analysis of differences between groups
Differentially expressed metabolites were obtained by analytically comparing the quantitative data for metabolites between the two groups of samples, using VIP >1 and P-VALUE <0.05 as thresholds. Mapping all metabolites through a KEGG, PubChem and other common metabolite databases, selecting corresponding species databases for searching, and finally performing metabolic pathway enrichment and topological analysis to obtain a pathway analysis diagram.
Example 5 analysis of leaf differential metabolites and metabolic pathways in the high temperature and control groups
(1) Differential metabolite of poplar leaves under high temperature stress compared with control group
Differential metabolites in hyperthermia and control leaves were determined by GC-MS and screened for a total of 67 differential metabolites, with unknown analytes removed, listing the differential metabolites up-regulated TOP-15 (Table 3-3) and down-regulated TOP-14, respectively. Under high-temperature stress, leucine (leucine), N-Methyl-DL-alanine (N-Methyl-DL-alanine), indanone (indanone), 6-Aminopenicillanic Acid (6-aminopenicilanic Acid), proline (proline), Isoleucine (Isoleucine) are significantly increased, succinylhomoserine (O-succinylhomoserine), androstenedione (androstandiol), citrulline (citrulline), Pyrrole-2-Carboxylic Acid (pyrole-2-Carboxylic Acid), diaminobutyric Acid (diaminobutyric Acid), uracil (uracil) and the like are significantly decreased. Compared with a normal-temperature control group, the change of the amino acids is most obvious under the condition of high temperature, the change is increased by more than 20 times, proline is increased by 6LOG-fold, the levels of isoleucine, valine (valine) and serine (serine) are increased by more than 3 times, and the levels of citrulline and succinylhomoserine are obviously reduced by more than 3 times. Among the organic acids, pyruvic acid and fumaric acid were significantly changed at high temperature compared to the room temperature control group, and in addition, some of the saccharides were also significantly changed in our experiment compared to the room temperature control group, such as melibiose and raffinose were significantly increased, and in addition, inositol level was 0.27133, which was lower than 0.35396 in room temperature (see table 1).
Table 1 is a list of the high temperature and control differential metabolites of poplar leaves in example 5
(2) Metabolic pathway of poor foreign body of poplar leaf compared with control group under high temperature stress
Under high temperature stress, the differential genes are mainly enriched in the following pathways: biosynthesis of valine, leucine and isoleucine; galactose metabolism; glycine, serine, threonine metabolic pathways (as shown in figure 4).
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.
Claims (9)
1. A method for screening key metabolites responding to high-temperature stress of poplar trees is characterized by comprising the following steps:
(1) sample preparation: processing poplar under different temperature conditions to obtain high temperature group and control group poplar leaf samples, and performing metabolite preparation on the high temperature group and control group poplar leaf samples to respectively obtain high temperature group and control group metabolites;
(2) and (3) detecting metabolites of the high-temperature group and the control group based on GC/TOF-MS: performing derivatization treatment on the metabolites of the high-temperature group and the control group obtained in the step (1), and performing GC/TOF-MS detection analysis to obtain detection data of the metabolites of the high-temperature group and the control group;
(3) and (3) data analysis: and sequentially carrying out pretreatment analysis, principal component and correlation analysis, orthogonal partial least square method discriminant analysis and interclass difference analysis on the obtained high-temperature group and control group metabolite detection data to obtain a difference metabolite between the high-temperature group and the control group, namely a key metabolite responding to the high-temperature stress of the poplar, and carrying out metabolic pathway analysis.
2. The method according to claim 1, wherein the temperature conditions for treating the poplar of the high temperature group and the control group in the step (1) are respectively as follows:
control group: treating at 25 deg.C for 6 hr;
high-temperature group: firstly, heating up in a gradient way, when the temperature of 25 ℃ is raised to 35 ℃ within half an hour, staying for 2 hours, then heating up from 35 ℃ to 45 ℃, and maintaining the temperature at 45 ℃ for processing for 5 hours; taking the 3 rd to 4 th leaf under the apical bud, sampling time points: 5h at 45 ℃.
3. The method according to claim 1, wherein the metabolite preparation process in step (1) is: adding a leaf tissue sample into an EP tube, and then adding an extraction solvent and a ribitol solution, wherein the extraction solvent is methanol and H according to a volume ratio2A solvent with the ratio of O to 1 being 3: 2 mg/ml; treating the extract with a grinder for 4 minutes, then carrying out ice-water bath for 5 minutes, repeating the treatment for 2 times, and carrying out refrigerated centrifugation for 15 minutes; the supernatant was transferred to a GC/MS glass flask and the extract was dried in a vacuum concentrator to obtain the metabolite.
4. The method according to claim 1, wherein the derivatizing process of the metabolites of the high temperature group and the control group obtained in the step (2) is: adding a methoxylamine reagent into the dried metabolite, slowly mixing uniformly, and incubating for 30 minutes at 80 ℃; subsequently, BSTFA was added rapidly and incubated at 70 ℃ for 1.5 h.
5. The method according to claim 1, wherein Agilent 7890 gas chromatography-time of flight combined instrument equipped with DB-5MS capillary column is adopted in step (2) for GC/TOF-MS detection and analysis, and the specific analysis conditions of GC/TOF-MS are as follows: sample introduction amount: 1 μ L ofA shunting mode; carrier gas: helium gas; blowing flow rate of a front sample inlet: 3 mL. min-1(ii) a Column flow rate: 1 mL. min-1(ii) a Column temperature: holding at 50 deg.C for 1 min, increasing to 310 deg.C at a rate of 10 deg.C per minute, and holding for 8 min; temperature of a front sample inlet: 280 ℃; transmission line temperature: 270 ℃; ion source temperature: 220 ℃; ionization voltage: -70 eV; the scanning mode is as follows: 50-500 m/z; scanning rate: 20 spectra/sec; solvent retardation: 460s, the acquisition mode is full scan mode.
6. The method of claim 1, wherein the pre-processing analysis of the metabolite detection data obtained from the high temperature group and the control group in step (3) is to filter the data to remove noise and obtain data that is free of noise interference and can be used for statistical analysis.
7. The method of claim 1, wherein the principal component and correlation analysis in step (3) is: and performing principal component analysis on the data after all samples are preprocessed, and preliminarily knowing the overall difference among the samples in each group and the variation degree in each group.
8. The method of claim 1, wherein the process of the orthogonal partial least squares discriminant analysis in step (3) is as follows: performing orthogonal correction partial least square method discriminant analysis on the model by using SIMCA software, and maximally highlighting the difference between the interior of the model and the predicted principal component; the method comprises the following steps that (1) OPLS-DA firstly carries out a data scale conversion mode of LOG conversion and UV formatting treatment, and then modeling analysis is carried out on the first main component and the second main component; the quality of the model was checked by 7-fold cross-validation and the R obtained after cross-validation2Y and Q2Judging the effectiveness of the model; then, the arrangement sequence of the classification variable y is changed for multiple times randomly through an arrangement experiment to obtain corresponding different random Q2Further checking the effectiveness of the value on the model; r2Y，Q2Closer to 1 indicates that the OPLS-DA model accounts for the difference between the two sets of samples better.
9. The method of claim 1, wherein the inter-group difference analysis in step (3) comprises: differentially expressed metabolites were obtained by analytically comparing the quantitative data for metabolites between the two groups of samples, using VIP >1 and P-VALUE <0.05 as thresholds.
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