CN111337614A - Metabonomics analysis method for components of garlic bulbs in different growth stages - Google Patents
Metabonomics analysis method for components of garlic bulbs in different growth stages Download PDFInfo
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Abstract
The invention discloses a metabonomics analysis method for components of garlic bulbs in different growth stages, and relates to the technical field of detection and analysis. The metabonomic analysis method for the components of the garlic bulbs in different growth stages comprises the following steps: carrying out chemical component non-targeted metabonomics analysis on the garlic bulb samples in different growth stages, processing the collected data to screen out differential metabolites, identifying and confirming the obtained differential metabolites, and analyzing the change rules of the differential metabolites in different growth stages. The different metabolites in the garlic bulbs in different growth periods can be found out quickly and accurately, on one hand, the accumulation rule of the metabolites in the bulbs can be known, on the other hand, the garlic bulbs in different growth stages can be selected according to different requirements on consumption, processing and the like, and therefore a basis is provided for reasonable consumption and timely harvesting of garlic.
Description
Technical Field
The invention relates to the technical field of detection and analysis, in particular to a metabonomics analysis method for components of garlic bulbs in different growth stages.
Background
Garlic (Latin name: Allium sativum L.) is one of the important medicinal and edible plants in the world, and has important physiological functions of resisting bacteria, preventing and resisting cancers, preventing and treating cardiovascular diseases, resisting oxidation and the like. The garlic contains not only functional components such as alliin, but also rich nutrient components such as amino acid, polyphenol, vitamin and the like. The garlic bulbs have edible value in the whole growth process, and the nutritional quality components of the garlic bulbs have larger difference in different growth periods, so that the garlic bulbs in different stages can have different application values. However, the existing detection method cannot intuitively reflect the change condition of each metabolite in different growth stages, and cannot provide guidance for garlic application in different growth stages.
In view of this, the present application is presented.
Disclosure of Invention
The invention aims to provide a metabonomics analysis method for components of garlic bulbs in different growth stages, which can analyze the components of the garlic bulbs in different growth stages and accurately find out different metabolites in the garlic bulbs in different growth stages.
The technical problem to be solved by the invention is realized by adopting the following technical scheme.
The invention provides a metabonomics analysis method for components of garlic bulbs in different growth stages, which comprises the following steps: carrying out chemical component non-targeted metabonomics analysis on the garlic bulb samples in different growth stages, processing the collected data to screen out differential metabolites, identifying and confirming the obtained differential metabolites, and analyzing the change rules of the differential metabolites in different growth stages.
The embodiment of the invention provides a metabonomics analysis method for the components of garlic bulbs in different growth stages, which has the beneficial effects that: according to the method, chemical component non-targeted metabonomics analysis is carried out on garlic bulb samples in different growth stages, collected data are processed to screen out different metabolites, then the obtained different metabolites are identified and confirmed to determine the different metabolites, and the change rules of the different metabolites in different growth stages are analyzed. The different metabolites in the garlic bulbs in different growth periods can be found out quickly and accurately, on one hand, the accumulation rule of the metabolites in the bulbs can be known, on the other hand, the garlic bulbs in different growth stages can be selected according to different requirements on consumption, processing and the like, and therefore a basis is provided for reasonable consumption and timely harvesting of garlic.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a statistical chart of the number of up-regulated and down-regulated metabolites of garlic bulbs in different growth periods;
FIG. 2 is a PCA score plot (A) of garlic bulb and QC samples at different growth periods;
FIG. 3 is a load diagram (B) of garlic bulb and QC samples in different growth periods;
FIG. 4 is a hierarchical clustering heat map of different metabolites of garlic bulbs in different growth stages.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. The examples, in which specific conditions are not specified, were conducted under conventional conditions or conditions recommended by the manufacturer. The reagents or instruments used are not indicated by the manufacturer, and are all conventional products available commercially.
The metabonomics analysis method for the components of the garlic bulbs in different growth stages provided by the embodiment of the invention is specifically explained below.
The embodiment of the invention provides a metabonomics analysis method for components of garlic bulbs in different growth stages, which comprises the following steps: carrying out chemical component non-targeted metabonomics analysis on garlic bulb samples at different growth stages, preprocessing collected data to screen out differential metabolites, identifying and confirming the obtained differential metabolites, and analyzing change rules of the differential metabolites at different growth stages.
The inventor creatively screens and analyzes the different metabolites of the garlic bulbs in different growth stages, so that on one hand, the accumulation rule of the metabolites in the bulbs can be known, and on the other hand, the garlic bulbs in different growth stages can be selected according to different requirements of consumption, processing and the like, thereby providing a basis for reasonable consumption and timely harvesting of garlic.
The analysis method provided by the embodiment of the invention specifically comprises the following steps:
s1 sample preparation
The preparation process of the garlic bulb sample comprises the following steps: peeling the collected garlic bulbs in different growth stages, freezing and drying the peeled garlic bulbs, crushing the peeled garlic bulbs to obtain garlic powder, and extracting the garlic powder to obtain garlic extract so as to be more convenient for separation and qualitative analysis of metabolites.
Wherein the collection process of the garlic bulb samples in different growth stages comprises the following steps: sampling is carried out from the beginning of development of garlic bulblets, and sampling is carried out for 1 time every 1 week until sampling is terminated two weeks after garlic is normally harvested.
Preferably, the freeze drying process is drying at-80 to-60 ℃ for more than 48h, and the metabolite can be fully maintained by drying at a lower temperature, so that the metabolite is prevented from being lost and the accuracy of analysis is not influenced. The pulverizing process comprises pulverizing freeze-dried Bulbus Allii bulb to 50-70 mesh for better extracting effective components.
Further, the preparation of the garlic extract comprises: mixing garlic powder with an extracting agent, performing ultrasonic treatment, and performing centrifugal separation to obtain a supernatant; the extractant is obtained by mixing formic acid, methanol and water, wherein the mass fraction of the formic acid is 0.08-0.12%, and the volume ratio of the methanol to the water is 2-4: 1; the volume of 100mg garlic powder corresponding to the volume of the extractant is 7-8 mL. The proportion of the garlic powder and the extracting agent is adjusted by the inventor to more fully extract the metabolites in the garlic, and if the proportion of the garlic powder and the extracting agent is too large or too small, the extraction type and the extraction amount of the metabolites are affected.
Preferably, the ultrasonic treatment process is ultrasonic treatment at 20-30 deg.C for 10-20 min; the centrifugal separation process is to centrifuge for 3-8min at 3-5 ℃ and 8000-; the extraction system is used for better layering, and supernatant is obtained by separation.
In some preferred embodiments, the preparing of the garlic extract further comprises: filtering the supernatant with 0.1-0.3 μm organic filter membrane to remove impurities such as organic solvent. The equivalent amount of supernatant was taken as a quality control sample to improve the accuracy of the overall analysis method.
S2, non-targeted metabolomics analysis
In the process of non-targeted metabonomics analysis, high performance liquid chromatography is adopted for component separation, and mass spectrum is applied for analysis.
Wherein the chromatographic conditions comprise: the temperature of the chromatographic column is 35-45 ℃, the sample injection flow rate is 0.2-0.4 mu L/min, and the sample injection volume is 1.5-2.5 mu L; the mobile phase comprises a first mobile phase and a second mobile phase, wherein the first mobile phase is an aqueous solution formed by formic acid and ammonium formate, the mass fraction of the formic acid is 0.1-0.2%, the concentration of the ammonium formate is 8-12mM, and the second mobile phase is an acetonitrile solution of the formic acid with the mass fraction of 0.1-0.2%; the gradient elution process is to gradually change the ratio of the first mobile phase to the second mobile phase for elution, and the mass fraction of the second mobile phase is gradually reduced from 90% to 80% in 0-8 min; in 8-13min, the mass fraction of the second mobile phase is gradually reduced from 80% to 70%; the mass fraction of the second mobile phase is gradually reduced from 70% to 60% in 13-16 min; at 16-16.1min, the mass fraction of the second mobile phase is gradually increased from 60% to 90%; and finally, balancing the second mobile phase with the mass fraction of 90% in the mobile phase for 3.5-4.5 min.
It should be noted that the inventors further optimized the chromatographic conditions to better separate the metabolites, especially the selection of the mobile phase and the control of the elution gradient, further improving the separation effect of the metabolites. In some preferred embodiments, the chromatographic column is
Amide column, the inventors found that the selection of this middle chromatography column allows a better separation of the metabolites.
Wherein the mass spectrometry conditions comprise: the resolution is 65000-75000FWHM, the spraying voltage is 2.5-3.5KV, the sheath gas pressure is 25-35psi, the auxiliary gas is 8-12arb, the capillary temperature is 300-350 ℃, and the auxiliary gas temperature is 320-380 ℃; more preferably, the mass spectrum scanning mode is full MS-dd/MS2, and the full MS corresponds to a scanning range of 70-1050 m/z; the resolution corresponding to dd/MS2 is 17000-18000, and NCE is set to 25-35 eV. The control of mass spectrum conditions can further improve the accuracy of analysis and lay a foundation for better screening out different metabolites.
S3 screening differential metabolites
The screening of the differential metabolites comprises the steps of carrying out peak alignment, peak extraction, noise reduction and normalization treatment on the collected original data of the garlic bulbs in different growth periods, and passing through Log2The Fold Change and p-values were screened for differential metabolites at each two adjacent growth stages. The different metabolite means that the content of the metabolite in the garlic bulbs in different growth periods is greatly different. The screening condition of the differential metabolite of every two adjacent growth stages is | Log2Fold Change | > 1 and p < 0.05.
Preferably, the number of up-and down-regulated differential metabolites is counted for each growth stage, and the variation of the metabolites is displayed by a histogram to more clearly display the variation of the differential metabolites.
And performing peak alignment, peak extraction, noise reduction and normalization processing by using Compound Discover software, wherein the parameters in the data processing process of the Compound Discover software are set as follows: the deviation of retention time of peak alignment is 1.5-2.5min, the deviation of compound detection mass is 4-6ppm, the maximum unknown element composition is set to C90, H190, Br3, Cl4, K2, N10, Na2, O15, P3 and S5, the minimum unknown element composition is set to C and H, the minimum peak intensity is set to 450000-550000, the threshold of signal-to-noise ratio (S/N) is 2.5-3.5, and the deviation of mass of primary mass spectrum matching is 4.5-5.5 ppm.
Specifically, the database used in the primary mass spectrogram matching is any one selected from ChEBI, Chembank, FooDB, Massbank, plantacyc, Nature Chemistry and Nature Chemistry Biology.
S4, identification and confirmation of differential metabolites
The identification and confirmation of the differential metabolites are to match with a secondary mass spectrogram and confirm the compounds with the standard substances; the database of secondary mass spectrograms is selected from mzCloud or MoNA; preferably, the score for matching the secondary mass spectrum to the mzCloud database is greater than 75 points. The accuracy of detection of the differential metabolites is further improved through matching of the secondary mass spectrograms, and the accuracy of the overall analysis method is improved.
S5 analysis of variation law of differential metabolites
Analyzing the change rule of the differential metabolite in different growth stages comprises: and exporting the data of the differential metabolites for PCA analysis, reflecting the difference condition of the garlic bulbs in different growth periods through a PCA score map, and analyzing the difference and correlation of the content levels of different components through a PCA load map. The PCA analysis is carried out by leading out the data of the different metabolites from the Compound Discover software to Excel for data processing, and then leading the peak area matrixes of the garlic bulbs and the quality control samples in different growth stages into the SIMCA-P14.1 software for processing.
In some preferred embodiments, analyzing the change rules of the different metabolites at different growth stages further comprises importing the data into MeV software for hierarchical clustering heat map analysis, and analyzing the change rules and similarity of each metabolite during the growth process.
It should be added that the specific software defined in the embodiment of the present invention is only a preferred embodiment, and in other embodiments, other software than the software defined in the embodiment of the present invention may be used for analysis.
The features and properties of the present invention are described in further detail below with reference to examples.
Example 1
The embodiment provides a metabonomics analysis method for the components of garlic bulbs in different growth stages, which comprises the following steps:
in this example, the garlic variety Xuan garlic 917 was grown in test plots (N34 ° 16 '45.26 ", E117 ° 17' 27.29") of agricultural academy of sciences of Jiangsu province, Xuzhou city, Jiangsu province, and was sown in 2018 for 10 months and harvested in 2019 for 5 months. Sampling is carried out from the beginning of development of garlic bulblets, and sampling is carried out for 1 time every 1 week until sampling is terminated two weeks after garlic is normally harvested. The sample collection time is 18 days (week-1) in 2019 in 4 months, 25 days (week-2) in 4 months, 2 days (week-3) in 5 months, 9 days (week-4) in 5 months, 16 days (week-5) in 5 months, 23 days (week-6) in 5 months and 30 days (week-7) in 5 months. All sample collection was set to 3 replicates, each replicate collecting at least 20 garlic bulbs per growth stage.
(1) Sample preparation: after peeling the garlic bulbs, approximately 300g of undamaged garlic bulbs were each repeatedly freeze-dried at-70 ℃. After the garlic is freeze-dried, crushing the garlic by adopting a parallel grinding instrument, sieving the garlic by using a 60-mesh sieve, and storing the garlic in a refrigerator at the temperature of-80 ℃ to be tested. Weighing 200mg of freeze-dried garlic powder sample, placing the sample in a 50mL centrifuge tube, adding 15mL of methanol/water (75/25, v/v) solution containing 0.1% formic acid, and vortexing for 1min to mix the sample thoroughly; subjecting the mixture to ultrasonic treatment at 25 deg.C for 15min, and centrifuging at 4 deg.C for 5min at 10000rpm with a low temperature centrifuge; taking 1mL of supernatant, filtering with 0.2 μm organic filter membrane, and placing in 2mL sample injection vial to obtain Bulbus Allii extractive solution. Equal amounts of each sample solution were taken, mixed well, and placed in a liquid phase vial as a Quality Control (QC) sample.
(2) Non-targeted metabolomic analysis: adopts a Thermo fisher U3000 ultra-high performance liquid chromatograph with a chromatographic column
Amide column (2.1 × 150mM, 3.5 μm), column temperature 40 deg.C, sample flow rate of 0.3 μ L/min, sample volume of 2 μ L, mobile phase of 0.15% formic acid and 10mM ammonium formate aqueous solution (first mobile phase A) and 0.15% formic acid acetonitrile solution (second mobile phase B), gradient elution flow of 0-8min, 90-80% B, 8-13min, 80-70% B, 13-16min, 70-60% B, 16-16.1min, 60-90% B, and final equilibrium with 90% B for 3.9min, total 20 min.
Mass spectrum conditions: thermo scientific Q exact Orbitrap mass spectrometer, using an electrospray ionization (ESI) source, scans in positive and negative modes, respectively. The parameters are set as follows: resolution 70000 (FWHM); the spraying voltage is 3.00 KV; sheath gas pressure 30 psi; auxiliary gas 10 arb; the capillary temperature is 320 ℃; the temperature of the auxiliary gas is 350 ℃; a mass spectrum scanning mode full MS-dd/MS2, wherein the scanning range of full scan is 70-1050 m/z; dd/MS 2: resolution 17500, NCE 30 eV.
(3) Screening for differential metabolites: preprocessing the detection data obtained by the step chromatography and the mass spectrum by using Compound Discover 3.1 software, wherein the preprocessing comprises peak alignment, peak extraction, noise reduction, normalization processing and Compound identification; the following parameters are set in the data processing process of CompundDiscover 3.1: the peak alignment retention time deviation is 2 min; the compound detection mass deviation was 5ppm, the maximum unknown element composition was set to C90, H190, Br3, Cl4, K2, N10, Na2, O15, P3, S5, the minimum unknown element composition was set to C, H, the minimum peak intensity was set to 500000, and the signal-to-noise ratio (S/N) threshold was set to 3; ChEBI, Chembank, FooDB, Massbank, plantaCyc, Nature Chemistry Biology databases are selected from SearchChemscope to carry out matching of primary mass spectrograms, and the mass deviation is 5 ppm. Screening differential metabolites through a data statistics function built in the Compound Discover 3.1 software under the following conditions: | Log2Fold Change | is greater than 1, and p is less than 0.05. The number of up-and down-regulated metabolites for each growth stage was counted according to the above conditions and histogram 1 was generated using Origin 9.1.
As can be seen from fig. 1, the levels of metabolites in garlic bulbs varied greatly between week 1 and week 2, and the amount of up-regulated compounds was greater than the amount of down-regulated compounds, followed by a decrease in the amount of differential metabolites. Week 5 is the local garlic harvest time, with relatively more downregulated compounds between week 5 and week 6 and less metabolite changes between week 6 and week 7.
(4) Identification and confirmation of differential metabolites: and (4) further matching the differential metabolite obtained by screening in the step (3) with a secondary mass spectrogram in databases such as mzCloud and MoNA. Wherein, the scores of the secondary mass spectrograms matched by the mzCloud are all larger than 75 points, 23 compounds are finally confirmed by the standard product, and 42 compounds are finally obtained by the total identification, and the detailed information is shown in the table 1.
TABLE 1 information of 42 compounds identified based on UHPLC Q-exact Orbitrap MS technique
Note that the retention time and secondary mass spectrum are confirmed by standard, the secondary mass spectrum is confirmed by an mzCloud database, the secondary mass spectrum ★ is confirmed by a MoNA database, and other unlabeled compounds are confirmed by mass spectrometry.
The 42 differential metabolites identified were 7 sulfur-containing compounds, 26 amino acids and their derivatives, 5 nucleotides and their derivatives, 2 carbohydrate compounds and 2 other compounds. Of these, 23 compounds were confirmed by standard.
(5) Analysis of Change laws of differential metabolites
And (3) exporting the peak area and other data of the differential metabolites obtained in the step (4) to Excel from Compound Discover 3.1, preprocessing, introducing the peak area matrixes of the garlic bulbs and QC samples (which are 3 repeats) in 7 different growth stages into SIMCA-P14.1 software, and carrying out PCA analysis to know the overall differences of the garlic bulbs in different growth stages.
QC samples were tightly packed together, as shown in fig. 2, indicating that the instrument was well-defined during the experiment. R of PCA model2X＝0.951，Q2The model has better fitting ability and stronger prediction ability as shown by 0.890. As can be seen from the PCA score chart (fig. 2), the garlic bulbs in the first 5 weeks can be clearly distinguished, and self-polymerization is one type, which indicates that the chemical differences of the garlic bulbs in different stages before harvesting are more obvious, and the difference of the garlic bulbs in the 1 st week from other periods is most obvious; the difference between the garlic bulbs at week 6 and week 7 was small. As can be seen from the PCA loading diagram (figure 3), most of the amino acids except Arginine and Alanine are higher in the early development stage of the garlic bulb, while the dipeptide compound containing gamma-glutamyl-is higher in the later growth stage of the garlic; the PCA loading graph can also show the correlation of various compounds, for example, Arginine is the amino acid with the highest content in 20 common free amino acids in garlic, and is inversely correlated with the change level of most other free amino acid contents in the growth and development process of garlic bulbs.
(6) MeV hierarchical clustering heatmap analysis
Preprocessing the 42 differential metabolite data matrix obtained in step (5), and introducing the preprocessed data matrix into MeV software for hierarchical clustering heat map analysis (FIG. 4). Each column in fig. 4 represents a sample of the growing period of the garlic bulb, and each pixel represents a metabolite. The color of the pixel points represents the relative content of the metabolites, and the color from light to dark represents the content from low to high. Hierarchical clustering analysis shows that the garlic bulbs in the first 3 weeks are clustered into one class, and the garlic bulbs in the last 4 weeks are clustered into one class, which indicates that obvious differences exist between the 42 compounds in the garlic bulbs by taking the 3 rd week and the 4 th week as boundaries.
FIG. 4 shows that S- (trans-1-propenyl) -L-cysteine and S-Allyl-L-cysteine among 7 different sulfur-containing compounds were relatively high in the early stage of growth of garlic bulb (first 3 weeks), and after 3 weeks, the contents of γ -L-Glutamyl-S-Allyl-L-cysteine, γ -L-Glutamyl-S-methyl-L-cysteine, γ -L-Glutamyl-S- (trans-1-propenyl) -L-cysteine, γ -Glutamyl-S-allylthio-L-cysteine and γ -Glutamyl-S- (1-propenyl) -L-cysteine were increased. Most common free amino acids are high in 3 weeks before growth, including Threonine, Tryptophan, Tyrosine, Valine, Isoleucine, Phenylalanine, Leucine, and Aspartic acid, etc., while most dipeptide compounds are high in 4 weeks after garlic, including gamma-Glutamylphenylalanine, L-gamma-Glutamyl-L-Leucine, gamma-Glutamyl-L-Valine, gamma-glutamyltrisine, and gamma-glutamylmethionine, etc.
Example 2
This example provides a metabonomic analysis method of the garlic bulb components in different growth stages, which is the same as the specific steps of example 1, except for the control of parameters:
(1) sample preparation: after peeling the garlic bulbs, approximately 300g of undamaged garlic bulbs were each repeatedly freeze-dried at-80 ℃. After the garlic is freeze-dried, crushing the garlic by adopting a parallel grinding instrument, sieving the garlic by using a 60-mesh sieve, and storing the garlic in a refrigerator at the temperature of-80 ℃ to be tested. Weighing 200mg of freeze-dried garlic powder sample, putting the sample into a 50mL centrifuge tube, adding 14mL of methanol/water (50/25, v/v) solution containing 0.08% formic acid, and vortexing for 1min to fully mix the sample; subjecting the mixture to ultrasonic treatment at 20 deg.C for 10min, and centrifuging at 8000rpm at 3 deg.C for 3min with a low temperature centrifuge; taking 1mL of supernatant, filtering with 0.1 μm organic filter membrane, and placing in 2mL sample injection vial to obtain Bulbus Allii extractive solution. Equal amounts of each sample solution were taken, mixed well, and placed in a liquid phase vial as a Quality Control (QC) sample.
(2) Non-targeted metabolomic analysis: adopts a Thermo fisher U3000 ultra-high performance liquid chromatograph with a chromatographic column
Amide column (2.1 × 150mM, 3.5 μm), column temperature 35 deg.C, sample flow rate of 0.2 μ L/min, sample volume of 1.5 μ L, mobile phase of 0.1% formic acid and 8mM ammonium formate aqueous solution (first mobile phase A) and 0.1% formic acid acetonitrile solution (second mobile phase B), gradient elution flow of 0-8min, 90-80% B, 8-13min, 80-70% B, 13-16min, 70-60% B, 16-16.1min, 60-90% B, and finally, 90% B balance for 3.5 min.
Mass spectrum conditions: thermo scientific Q exact Orbitrap mass spectrometer, using an electrospray ionization (ESI) source, scans in positive and negative modes, respectively. The parameters are set as follows: resolution 65000 (FWHM); the spraying voltage is 2.5 KV; sheath gas pressure 25 psi; auxiliary gas 8 arb; the temperature of the capillary tube is 300 ℃; the temperature of the auxiliary gas is 320 ℃; a mass spectrum scanning mode full MS-dd/MS2, wherein the scanning range of full scan is 70-1050 m/z; dd/MS 2: resolution 17500, NCE 25 eV.
Screening for differential metabolites: preprocessing the detection data obtained by the step chromatography and the mass spectrum by using Compound Discover 3.1 software, wherein the preprocessing comprises peak alignment, peak extraction, noise reduction, normalization processing and Compound identification; the following parameters are set in the data processing process of CompundDiscover 3.1: the deviation of retention time for peak alignment was 1.5 min; the compound detection mass deviation was 4ppm, the maximum unknown element composition was set to C90, H190, Br3, Cl4, K2, N10, Na2, O15, P3, S5, the minimum unknown element composition was set to C, H, the minimum peak intensity was set to 500000, and the signal-to-noise ratio (S/N) threshold was set to 2.5; ChEBI, Chembank, FooDB, Massbank, plantaCyc, Nature Chemistry Biology databases are selected from Searchcchemspider to carry out matching of primary mass spectrograms, and the mass deviation is 4.5 ppm. Screening differential metabolites through a data statistics function built in the Compound Discover 3.1 software under the following conditions: | Log 2Fold Change | 1, and p < 0.05. The number of up-and down-regulated metabolites for each growth stage was counted according to the above conditions and histograms were generated using Origin 9.1.
Example 3
This example provides a metabonomic analysis method of the garlic bulb components in different growth stages, which is the same as the specific steps of example 1, except for the control of parameters:
(1) sample preparation: after peeling the garlic bulbs, approximately 300g of undamaged garlic bulbs were each repeatedly freeze-dried at-60 ℃. After the garlic is freeze-dried, crushing the garlic by adopting a parallel grinding instrument, sieving the garlic by using a 60-mesh sieve, and storing the garlic in a refrigerator at the temperature of-80 ℃ to be tested. Weighing 200mg of freeze-dried garlic powder sample, placing the sample in a 50mL centrifuge tube, adding 16mL of methanol/water (100/25, v/v) solution containing 0.12% formic acid, and vortexing for 1min to mix the sample thoroughly; subjecting the mixture to ultrasonic treatment at 30 deg.C for 20min, and centrifuging at 12000rpm at 5 deg.C for 8min with a low temperature centrifuge; taking 1mL of supernatant, filtering with 0.3 μm organic filter membrane, and placing in 2mL sample injection vial to obtain Bulbus Allii extractive solution. Equal amounts of each sample solution were taken, mixed well, and placed in a liquid phase vial as a Quality Control (QC) sample.
(2) Non-targeted metabolomic analysis: adopts a Thermo fisher U3000 ultra-high performance liquid chromatograph with a chromatographic column
Amide column (2.1 × 150mM, 3.5 μm), column temperature 45 deg.C, sample flow rate of 0.4 μ L/min, sample volume of 2.5 μ L, mobile phase of 0.2% formic acid and 12mM ammonium formate aqueous solution (first mobile phase A) and 0.2% formic acid acetonitrile solution (second mobile phase B), gradient elution flow of 0-8min, 90-80% B, 8-13min, 80-70% B, 13-16min, 70-60% B, 16-16.1min, 60-90% B, and finally, 90% B balance for 4.5 min.
Mass spectrum conditions: thermo scientific Q exact Orbitrap mass spectrometer, using an electrospray ionization (ESI) source, scans in positive and negative modes, respectively. The parameters are set as follows: resolution 75000 (FWHM); the spraying voltage is 3.5 KV; sheath gas pressure 35 psi; auxiliary gas 12 arb; the capillary temperature is 350 ℃; the temperature of the auxiliary gas is 380 ℃; a mass spectrum scanning mode full MS-dd/MS2, wherein the scanning range of full scan is 70-1050 m/z; dd/MS 2: resolution 18000, NCE 25 eV.
Screening for differential metabolites: preprocessing the detection data obtained by the step chromatography and the mass spectrum by using Compound Discover 3.1 software, wherein the preprocessing comprises peak alignment, peak extraction, noise reduction, normalization processing and Compound identification; the following parameters are set in the data processing process of CompundDiscover 3.1: the deviation of retention time for peak alignment was 2.5 min; the compound detection mass deviation was 6ppm, the maximum unknown element composition was set to C90, H190, Br3, Cl4, K2, N10, Na2, O15, P3, S5, the minimum unknown element composition was set to C, H, the minimum peak intensity was set to 500000, and the signal-to-noise ratio (S/N) threshold was set to 3.5; ChEBI, Chembank, FooDB, Massbank, plantaCyc, Nature Chemistry Biology databases are selected from Searchcchemspider to carry out matching of primary mass spectrograms, and the mass deviation is 5.5 ppm. Screening differential metabolites through a data statistics function built in the Compound Discover 3.1 software under the following conditions: | Log2Fold Change | is greater than 1, and p is less than 0.05. The number of up-and down-regulated metabolites for each growth stage was counted according to the above conditions and histograms were generated using Origin 9.1.
Example 4
This example provides a metabonomics analysis method of the components of garlic bulbs in different growth stages, which is the same as the specific steps of example 1, except that: the chromatographic column is
C18 column.
Example 5
This example provides a metabonomics analysis method of the components of garlic bulbs in different growth stages, which is the same as the specific steps of example 1, except that: the dosage ratio of the freeze-dried garlic powder to the extracting solution is different, specifically, a 200mg sample of the freeze-dried garlic powder is placed into a 50mL centrifuge tube, and 12mL of methanol/water (75/25, v/v) solution containing 0.1% formic acid is added.
Example 6
This example provides a metabonomics analysis method of the components of garlic bulbs in different growth stages, which is the same as the specific steps of example 1, except that: the dosage ratio of the freeze-dried garlic powder to the extracting solution is different, specifically, a 200mg sample of the freeze-dried garlic powder is placed in a 50mL centrifuge tube, and 20mL of methanol/water (75/25, v/v) solution containing 0.1% formic acid is added.
Test example 1
The compound of example 4 was less isolated. In example 5, the difference between groups is small due to the high concentration of some compounds; in example 6, the concentration of a part of the compound was too low to be lower than the detection limit. Both examples 5 and 6 result in a reduction in the number of differential metabolites.
In summary, the metabonomics analysis method for the components of the garlic bulbs in different growth stages provided by the invention comprises the steps of carrying out chemical component non-targeted metabonomics analysis on the garlic bulb samples in different growth stages, preprocessing the acquired data to screen out different metabolites, then identifying and confirming the obtained different metabolites to determine the different metabolites, and analyzing the change rules of the different metabolites in different growth stages. The different metabolites in the garlic bulbs in different growth periods can be found out quickly and accurately, on one hand, the accumulation rule of the metabolites in the bulbs can be known, on the other hand, the garlic bulbs in different growth stages can be selected according to different requirements on consumption, processing and the like, and therefore a basis is provided for reasonable consumption and timely harvesting of garlic.
The embodiments described above are some, but not all embodiments of the invention. The detailed description of the embodiments of the present invention is not intended to limit the scope of the invention as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Claims (10)
1. A metabonomics analysis method for the components of garlic bulbs in different growth stages is characterized by comprising the following steps:
carrying out chemical component non-targeted metabonomics analysis on garlic bulb samples at different growth stages, processing collected data to screen out differential metabolites, identifying and confirming the obtained differential metabolites, and analyzing change rules of the differential metabolites at different growth stages.
2. The metabonomic analysis method of garlic bulb components of different growth stages according to claim 1, wherein the preparation process of the garlic bulb sample comprises: peeling collected garlic bulbs in different growth stages, freezing and drying the peeled garlic bulbs, crushing the peeled garlic bulbs to obtain garlic powder, and extracting the garlic powder to obtain garlic extract;
preferably, the freeze drying process is drying at-80-60 deg.C for more than 48 h;
preferably, the pulverization process comprises pulverizing freeze-dried Bulbus Allii bulb to 50-70 mesh;
preferably, the collecting process of the garlic bulb samples of different growth stages comprises: sampling is carried out from the beginning of development of garlic bulblets, and sampling is carried out for 1 time every 1 week until sampling is terminated two weeks after garlic is normally harvested.
3. The metabonomic analysis method of garlic bulb components in different growth stages according to claim 2, wherein the preparation of the garlic extract comprises: mixing the garlic powder with an extracting agent, carrying out ultrasonic treatment, and then carrying out centrifugal separation to obtain a supernatant;
preferably, the extractant is obtained by mixing formic acid, methanol and water, wherein the mass fraction of the formic acid is 0.08-0.12%, and the volume ratio of the methanol to the water is 2-4: 1;
preferably, the volume of 100mg of the garlic powder corresponding to the extractant is 7-8 mL;
preferably, the ultrasonic treatment process is ultrasonic treatment at 20-30 deg.C for 10-20 min;
preferably, the centrifugation is performed for 3-8min at 3-5 ℃ and 8000-;
preferably, the preparation of the garlic extract further comprises: filtering the supernatant through an organic filter membrane of 0.1-0.3 mu m;
preferably, the preparation of the garlic extract further comprises: an equal amount of the supernatant was taken as a quality control sample.
4. The metabonomic analysis method of garlic bulb components in different growth stages according to claim 1, wherein the non-targeted metabonomic analysis process adopts high performance liquid chromatography for component separation and mass spectrometry for analysis;
preferably, the chromatographic conditions comprise: the temperature of the chromatographic column is 35-45 ℃, the sample injection flow rate is 0.2-0.4 mu L/min, and the sample injection volume is 1.5-2.5 mu L; the mobile phase comprises a first mobile phase and a second mobile phase, wherein the first mobile phase is an aqueous solution formed by formic acid and ammonium formate, the mass fraction of the formic acid is 0.1-0.2%, the concentration of the ammonium formate is 8-12mM, and the second mobile phase is an acetonitrile solution of the formic acid with the mass fraction of 0.1-0.2%;
preferably, the gradient elution process is to gradually change the ratio of the first mobile phase to the second mobile phase for elution, and the mass fraction of the second mobile phase is gradually reduced from 90% to 80% in 0-8 min; the mass fraction of the second mobile phase is gradually reduced from 80% to 70% in 8-13 min; the mass fraction of the second mobile phase is gradually reduced from 70% to 60% in 13-16 min; at 16-16.1min, the mass fraction of the second mobile phase is gradually increased from 60% to 90%; finally, balancing the second mobile phase with the mass fraction of 90% in the mobile phase for 3.5-4.5 min;
more preferably, the chromatographic column is
And (3) Amide columns.
5. The metabonomic analysis method of garlic bulb components in different growth stages according to claim 4, wherein the mass spectrometric conditions comprise: the resolution is 65000-75000FWHM, the spraying voltage is 2.5-3.5KV, the sheath gas pressure is 25-35psi, the auxiliary gas is 8-12arb, the capillary temperature is 300-350 ℃, and the auxiliary gas temperature is 320-380 ℃; more preferably, the mass spectrum scanning mode is full MS-dd/MS2, and the full MS corresponds to a scanning range of 70-1050 m/z; the resolution corresponding to dd/MS2 is 17000-18000, and NCE is set to 25-35 eV.
6. The metabonomic analysis method for the components of garlic bulbs with different growth stages as claimed in claim 1, wherein the screening for the differential metabolites comprises the steps of performing peak alignment, peak extraction, noise reduction and normalization on the collected original data of the garlic bulbs with different growth stages, and passing through Log2The Fold Change and p values screen out the differential metabolites for each two adjacent growth stages;
preferably, the number of up-and down-regulated differential metabolites is counted for each growth stage, and the variation of the metabolites is displayed by a histogram;
preferably, the screening condition for said differential metabolite for each two adjacent growth phases is | Log2Fold Change | > 1 and p < 0.05.
7. The metabonomic analysis method for the components of garlic bulbs in different growth stages according to claim 6, wherein Compound Discover software is adopted to perform peak alignment, peak extraction, noise reduction and normalization treatment;
preferably, the setting of each parameter in the data processing process of the Compound Discover software is as follows: the deviation of retention time of peak alignment is 1.5-2.5min, the deviation of compound detection mass is 4-6ppm, the maximum unknown element composition is set as C90, H190, Br3, Cl4, K2, N10, Na2, O15, P3 and S5, the minimum unknown element composition is set as C and H, the minimum peak intensity is set as 450000-550000, the threshold value of signal-to-noise ratio (S/N) is 2.5-3.5, and the deviation of mass of primary mass spectrogram matching is 4.5-5.5 ppm;
more preferably, the database used in the primary mass spectrogram matching is selected from at least one of ChEBI, Chembank, FooDB, Massbank, plantacyc, Nature Chemistry and Nature Chemistry Biology.
8. The metabonomic analysis method of garlic bulb components of different growth stages according to claim 1, wherein the identification and confirmation of the differential metabolites is a matching of secondary mass spectra and the confirmation of compounds with standard;
preferably, the database of secondary mass spectra is selected from mzCloud or MoNA;
preferably, the score matching the secondary mass spectrum to the mzCloud database is greater than 75 points.
9. The metabonomic analysis method of garlic bulb components in different growth stages according to claim 1, wherein analyzing the change rule of the different metabolites in different growth stages comprises: exporting the data of the differential metabolites for PCA analysis, reflecting the difference situation of the garlic bulbs in different growth periods through a PCA score map, and analyzing the difference and correlation of the content levels of different components through a PCA load map;
preferably, PCA analysis is carried out by leading the data of the differential metabolites from Compound Discover software to Excel for data processing, and leading the peak area matrixes of the garlic bulbs and the quality control samples in different growth stages to SIMCA-P14.1 software for processing.
10. The metabonomic analysis method of garlic bulb components with different growth stages according to claim 9, wherein analyzing the change rules of the different metabolites at different growth stages further comprises introducing the data into MeV software for hierarchical clustering thermograph analysis to analyze the change rules and similarities of each metabolite during the growth process.
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| CN113125588B (en) * | 2021-03-17 | 2022-01-14 | 广东省农业科学院农业质量标准与监测技术研究所 | Application of metabonomics analysis technology to discrimination of space-time classification of duck dung fragrance single tea |
| CN114878702A (en) * | 2021-06-18 | 2022-08-09 | 上海阿趣生物科技有限公司 | Analysis method of plant metabolite and application thereof |
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