Method for mining China-hemp gibberellin regulation related genes based on proteomics

Abstract

The invention discloses a method for mining China-hemp gibberellin regulation related genes based on proteomics, and belongs to the technical field of China-hemp differential gene identification. According to the method, on the basis of an unlabeled quantitative proteomics technology (Label free), proteome detection is carried out on China-hemp leaf materials subjected to gibberellin spraying treatment and untreated China-hemp leaf materials through LC-MS / MS, differential proteins under different treatment conditions are obtained by comparing proteomics research methods, and the differential proteins are used for detecting the content of the gibberellin in the China-hemp leaf materials and detecting the content of the gibberellin in the China-hemp leaf materials. By analyzing KEGG metabolic pathways and enriched differential proteins under different treatments, the hemp metabolic pathways and differential genes responding to gibberellin regulation and control are identified, a basis is provided for gibberellin molecular signal pathway regulation and control, and a gene reference is provided for hemp molecular breeding.

Description

Technical Field

[0001] The present invention belongs to the technical field of hemp differential gene identification, and in particular relates to a method for mining hemp gibberellin regulation-related genes based on proteomics. Background Art

[0002] Hemp (Cannabis sativa L.), also known as industrial hemp, is a plant of the genus Cannabis in the family Cannabaceae. It refers to a hemp variety that has been artificially selected and approved as a crop variety. The tetrahydrocannabinol content of the top leaves and flower spikes of the plant group during the flowering period is less than 0.3%, and it no longer has the value of extracting drugs or directly using it as drugs. Hemp is an economic crop with extremely high economic benefits, and all parts of its plants have extremely high economic value. Hemp is mainly promoted and planted on a large scale in Yunnan and Heilongjiang provinces in my country. Its hemp fiber is the main use of hemp. Hemp fiber products have a soft texture and have the advantages of antibacterial and breathable, moisture absorption and perspiration, anti-ultraviolet radiation resistance, etc. Hemp fiber is about to become a raw material in many fields such as military and civilian clothing and household goods. The planting and processing of hemp brings extremely high economic benefits to growers.

[0003] Label-free quantitative proteomics technology is an important mass spectrometry quantitative method in recent years. The samples used for comparison do not need to be labeled with expensive stable isotope labels. The extracted proteins are directly enzymatically hydrolyzed and then analyzed by mass spectrometry to generate data. After the large-scale spectra are normalized by software, the intensity of the corresponding mass spectrometry peaks, peak area and other factors are compared to determine the relative expression changes of proteins in several groups of samples. Proteomics technology has been widely used in plants, including plant population genetic proteomics, plant environmental signal response and adaptation mechanism proteomics, plant tissue and organ proteomics, plant subcellular proteomics, etc.

[0004] Hormones play an important regulatory role in the life cycle of plants. Studying the signal transduction and action mechanism of plant hormones is one of the important components of proteomics. Gibberellic acid, as one of the essential hormones for plant growth and development, plays an important role in promoting plant seed germination, stem elongation, fruit quality, etc. In agronomic production, exogenous gibberellins have been widely used as plant growth regulators on a variety of plants to increase yields and improve economic benefits.

[0005] Through proteomics technology, the differential genes of hemp under gibberellin treatment were analyzed, which provided a preliminary research basis for in-depth research on the signal transduction and action mechanism of gibberellin in hemp, and provided guidance for further screening of key genes for molecular plant breeding and experimental research. Summary of the invention

[0006] The purpose of the present invention is to use exogenous gibberellins at appropriate concentrations as plant growth regulators, focus on studying their effects on differential gene changes in hemp during the rapid growth period, clarify differential gene changes, differential metabolic pathway changes, functional annotations of differential proteins after gibberellins treatment, provide data references for subsequent mining of gibberellins regulating hemp signal transduction and action mechanisms, provide gene references for subsequent further gibberellins regulating hemp growth and development and molecular breeding, and provide a method for mining hemp gibberellins-regulated related genes based on proteomics.

[0007] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0008] A method for mining hemp gibberellin regulation-related genes based on proteomics, the method comprising:

[0009] Step 1: Spray the leaves of hemp with 50 mg / mL gibberellin during the rapid growth period, and the control group was sprayed with distilled water and planted in outdoor pots;

[0010] Step 2: 7 days after the spraying treatment, hemp leaves from the control group and the group sprayed with exogenous gibberellin were selected. Three groups of leaves were selected from each of the control group and the experimental group. The leaf samples were treated with liquid nitrogen after collection and stored at -80°C. The next step was proteomic analysis. Liquid nitrogen treatment prevented tissue RNA degradation.

[0011] Step 3: Sample processing and protein extraction:

[0012] (1) Take out 500 mg of sample in the frozen state, transfer it to an MP shaking tube, add 1 ml of BPP lysis buffer; shake it three times using a high-throughput tissue grinder, each time for 40 seconds; centrifuge at 4°C 12000g for 20 min to take the supernatant, add an equal volume of Tris-saturated phenol (pH>7.8), and shake at 4°C for 10 min; centrifuge at 4°C 12000g for 20 min to take the phenol phase, add an equal volume of BPP lysis buffer, and shake at 4°C for 10 min; centrifuge at 4°C 12000g for 20 min to take the phenol phase, add 5 times the volume of pre-cooled ammonium acetate methanol solution, and precipitate the protein at -20°C overnight; centrifuge at 4°C 12000g for 20 min the next day, discard the supernatant, add 90% pre-cooled acetone to the precipitate, mix well, centrifuge and discard the supernatant, repeat twice; dissolve the precipitate with protein lysis buffer containing protease inhibitor cocktail; sonicate on ice for 2 min; centrifuge at 4°C 12000g for 20 min, and take the protein supernatant;

[0013] (2) Take 100 μg of protein sample; add TEAB (triethylammonium bicarbonate buffer) to a final concentration of 100 mM; add TCEP (tri(2-carboxyethyl)phosphine) to a final concentration of 10 mM, and react at 37°C for 60 min; add IAM (iodoacetamide) to a final concentration of 40 mM, and react at room temperature in the dark for 40 min; add pre-cooled acetone to each tube (acetone: sample v / v = 6:1), precipitate at -20°C for 4 h; centrifuge at 10000 g for 20 min, and take the precipitate; fully dissolve the sample with 100 μL 100 mM TEAB; add trypsin according to the ratio of enzyme: protein (m / m) = 1:50, and enzymatically hydrolyze at 37°C overnight;

[0014] (3) After trypsin digestion, the peptides were vacuum pumped dry; the peptides were re-dissolved with 0.1% trifluoroacetic acid (TFA); HLB ( Peptide desalting was performed using HLB 96-Well Plate 30 μm, solid phase extraction column peptide desalting treatment) and the peptides were dried using a vacuum concentrator; peptide quantification was performed using the Thermo Fisher Scientific (Cat. No.: 23275) peptide quantification kit.

[0015] Step 4: LC-MS / MS analysis

[0016] The peptide samples were separated by EASY-nLC 1200 liquid phase system, and the chromatographic column was C18 column;

[0017] The samples separated by nano-HPLC were analyzed by mass spectrometry using a timsTOF Pro2 mass spectrometer with an electrospray voltage of 1.5 kV. The peptide parent ions and their secondary fragments were detected and analyzed by high-resolution TOF. The secondary mass spectrometry scan range was 100-1700 m / z. The data acquisition on the timsTOF Pro2 was performed in parallel accumulation serial fragmentation (PASEF) acquisition mode. During the MS / MS data acquisition process, each TIMS cycle included 1 MS and 10 PASEF MS / MS scans (the charge number of the parent ion was 0-5). The dynamic exclusion time of the MS / MS scan was 24 s.

[0018] Step 5: Import the original file of mass spectrometry into MaxQuant version 2.0.3.1 software system for library search analysis; the database used in this study is Uniprot; parameter settings: fixed modification is Carbamidomethyl; variable modification is Oxidation; protein cleavage is trypsin; the maximum allowable error range of parent ion mass number is ±10ppm; the false discovery rate (FDR) of peptide identification is set to FDR≤0.01; the identified protein contains at least one specific peptide; Step 5 realizes the qualitative and quantitative analysis of protein;

[0019] Step 6: Gene mining through various databases, protein function annotation information and expression difference analysis;

[0020] Step 7: Data analysis and plotting were done based on BLAST2GO, R, and Python software;

[0021] Step 8: PCA analysis (Principal Component Analysis), i.e., principal component analysis, generates PCA analysis graphs of the gibberellin treatment group and the untreated group based on the R language ggplot2 software package ( Figure 1 );

[0022] Step 9: Compare all proteins identified in this experiment with five major databases (Uniprot, Pfam, EggNOG, GO and KEGG databases) to obtain comprehensive functional information of proteins and make statistics on the annotation status of each database;

[0023] Step 10: Fold Change (FC) refers to the fold difference of the expression of the same protein in two samples, which is mainly calculated by the quantitative value of protein abundance. In the Labelfree project, the default FC for up-regulated proteins is generally 2, and the FC for down-regulated proteins is generally <0.5. P-value usually determines whether a protein is different between two groups. It is not based on the single indicator of FC difference, but is combined with other statistical parameters (such as p-value) to judge. P-value refers to the use of statistical methods to calculate the difference significance of a protein between two groups. The p-value in proteomics is generally set to <0.05 to indicate a difference, and the difference statistical method is based on t-test.

[0024] Step 11: The differential protein volcano map is drawn based on R language ggplot2, with the horizontal axis being Log2FC and the vertical axis being -Log 10 (P-value), based on which 757 differentially expressed proteins were obtained, 615 up-regulated proteins, and 142 down-regulated proteins ( Figure 2 );

[0025] Step 12: Based on the clusterProfiler package of R language, KEGG PATHWAY enrichment analysis was performed on the proteins in the protein set, and Fisher's exact test was used for enrichment analysis; the p-value was corrected using the BH method; the corrected p-value (Corrected P-Value) was set to 0.05 as the threshold, and the KEGG pathway that met this condition was defined as the KEGG pathway that was significantly enriched in the protein set. The bubble chart was drawn using the ggplot2 package of R language, and the KEGG database was used to study the metabolic pathways and functions enriched by differentially expressed proteins ( Figure 3 );

[0026] Step 13: Use the differential protein binding metabolic pathways obtained by KEGG enrichment analysis to further analyze and speculate the important genes related to gibberellin regulation of hemp.

[0027] Furthermore, in step three (1), the BPP lysate comprises 200 mM Tris-Base, 200 mM NaCl, 50 mM sodium ascorbate, 1% deoxycholate, 1% NP-40, 1% Triton X-100, 1% Tween-20, 5 mM EDTA, 30% sucrose, 50 mM sodium tetraborate, and 1% PVPP.

[0028] Furthermore, in step three (1), the protein lysis solution is 8M urea + 1% SDS (sodium dodecyl sulfate).

[0029] Furthermore, in step 3 (1), 1 μl of protease inhibitor was added to every 100 μl of protein lysis buffer.

[0030] Furthermore, in step 4, mobile phase A: 2% acetonitrile 0.1% formic acid, mobile phase B: 80% acetonitrile 0.1% formic acid; separation gradient: 0-45min, mobile phase B linearly increased from 3% to 28%; 45-50min, mobile phase B linearly increased from 28% to 44%; 50-55min, mobile phase B linearly increased from 44% to 90%; 55-60min, mobile phase B linearly maintained at 90%.

[0031] The beneficial effects of the present invention compared with the prior art are as follows: the present invention is based on label-free quantitative proteomics technology, and performs proteomic detection on hemp leaf materials treated with and without gibberellin spraying by LC-MS / MS, and obtains differential proteins under different treatment conditions by comparative proteomics research methods, and identifies metabolic pathways and differential genes of hemp that respond to gibberellin regulation by analyzing KEGG metabolic pathways and enriched differential proteins under different treatments, thereby providing a basis for the regulation of gibberellin molecular signaling pathways and providing a genetic reference for hemp molecular breeding. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is the principal component analysis diagram of the gibberellin treatment group (CL) and the control group (CK);

[0033] Figure 2 The volcano map of differentially expressed proteins between the gibberellin treatment group (CL) and the control group (CK);

[0034] Figure 3 This is the KEGG enrichment analysis diagram of differential proteins. DETAILED DESCRIPTION

[0035] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments, but is not limited thereto. Any modification or equivalent replacement of the technical solution of the present invention that does not depart from the spirit and scope of the technical solution of the present invention should be included in the protection scope of the present invention.

[0036] Embodiment 1:

[0037] A method for proteomics mining of hemp gibberellin regulation-related genes, the method specifically comprising:

[0038] Step 1: Spray the leaves of hemp with 50 mg / mL gibberellin during the rapid growth period, and the control group was sprayed with distilled water and planted in outdoor pots;

[0039] Step 2: 7 days after the spraying treatment, the control group and hemp leaves sprayed with exogenous gibberellin were selected. Three groups of leaves were selected for each treatment. The leaf samples were collected and treated with liquid nitrogen. After treatment, they were stored at -80°C for proteomic analysis.

[0040] Step 3: Sample processing and protein extraction:

[0041] (1) Take out 500 mg of sample from the frozen state, transfer it to an MP shaking tube, and add 1 ml BPP lysate; use a high-throughput tissue grinder to shake 3 times, 40 seconds each time; centrifuge at 4℃12000g for 20min, take the supernatant, add an equal volume of Tris-saturated phenol (pH>7.8), and shake at 4℃ for 10min; centrifuge at 4℃12000g for 20min, take the phenol phase, add an equal volume of BPP lysate, and shake at 4℃ for 10min; centrifuge at 4℃12000g for 20min, take the phenol phase, add 5 times the volume of pre-cooled ammonium acetate methanol solution, and precipitate the protein at -20℃ overnight; centrifuge at 4℃12000g for 20min the next day, discard the supernatant, add 90% pre-cooled acetone to the precipitate, mix well, centrifuge and discard the supernatant, repeat 2 times; dissolve the precipitate with protein lysis buffer, the lysis buffer is 8M urea + 1% SDS (sodium dodecyl sulfate), containing protease inhibitor cocktail, add 1μl protease inhibitor for every 100μl lysis buffer); sonicate on ice for 2min; centrifuge at 4℃12000g for 20min, take the protein supernatant;

[0042] (2) Take 100 μg of protein sample; add TEAB (triethylammonium bicarbonate buffer) to a final concentration of 100 mM; add TCEP (tri(2-carboxyethyl)phosphine) to a final concentration of 10 mM, and react at 37°C for 60 min; add IAM (iodoacetamide) to a final concentration of 40 mM, and react at room temperature in the dark for 40 min; add pre-cooled acetone to each tube (acetone: sample v / v = 6:1), precipitate at -20°C for 4 h; centrifuge at 10000 g for 20 min, and take the precipitate; fully dissolve the sample with 100 μL 100 mM TEAB; add trypsin according to the ratio of enzyme: protein (m / m) = 1:50, and enzymatically hydrolyze at 37°C overnight;

[0043] (3) After trypsin digestion, the peptides were vacuum pumped dry; the peptides were re-dissolved with 0.1% trifluoroacetic acid (TFA); HLB ( HLB 96-Well Plate 30μm, solid phase extraction column peptide desalting treatment) for peptide desalting, vacuum concentrator to dry;

[0044] Step 4: LC-MS / MS analysis

[0045] (1) The peptide samples were separated by EASY-nLC 1200 liquid phase system, and the chromatographic column was C18 column. Mobile phase A: 2% acetonitrile 0.1% formic acid, mobile phase B: 80% acetonitrile 0.1% formic acid. Separation gradient: 0-45min, mobile phase B linearly increased from 3% to 28%; 45-50min, mobile phase B linearly increased from 28% to 44%; 50-55min, mobile phase B linearly increased from 44% to 90%; 55-60min, mobile phase B linearly maintained at 90%.

[0046] (2) The samples separated by nanoliter HPLC were analyzed by mass spectrometry using a timsTOF Pro2 mass spectrometer with an electrospray voltage of 1.5 kV. The peptide parent ions and their secondary fragments were detected and analyzed by high-resolution TOF. The secondary mass spectrometry scan range was 100-1700 m / z. The data acquisition on the timsTOF Pro2 was performed in parallel accumulation serial fragmentation (PASEF) acquisition mode. During the MS / MS data acquisition process, each TIMS cycle included 1 MS and 10 PASEF MS / MS scans (the charge number of the parent ion was 0-5). The dynamic exclusion time of the MS / MS scan was 24 s.

[0047] Step 5: Import the original file of mass spectrometry into MaxQuant version 2.0.3.1 software system for library search analysis. The database used in this study is Uniprot. Parameter settings: fixed modification is Carbamidomethyl; variable modification is Oxidation; protein cleavage is trypsin; the maximum allowable error range of parent ion mass number is ±10ppm; the false discovery rate (FDR) of peptide identification is set to FDR≤0.01. The identified protein contains at least one specific peptide.

[0048] Step 6: Gene mining is performed through various databases, protein function annotation information and expression difference analysis.

[0049] Step 7: Data analysis and plotting were completed based on BLAST2GO, R, Python and other software.

[0050] Step 8: PCA analysis (Principal Component Analysis), i.e., principal component analysis, generates PCA analysis graphs of the gibberellin treatment group and the untreated group based on the R language ggplot2 software package ( Figure 1 ).

[0051] Step 9: Compare all proteins identified in this experiment with the five major databases (Uniprot, Pfam, EggNOG, GO and KEGG databases) to obtain comprehensive functional information of the proteins and perform statistics on the annotation status of each database.

[0052] Step 10: Fold Change (FC) refers to the multiple of the expression difference of the same protein in two samples, which is mainly calculated by the quantitative value of protein abundance. In the Labelfree project, the default is FC>2 for up-regulated proteins and FC<0.5 for down-regulated proteins. P-value usually determines whether a protein is different between two groups, not just based on the single indicator of FC, but combined with other statistical parameters (such as p-value). P-value refers to the use of statistical methods to calculate the significance of the difference between a protein in two groups. The p-value in proteomics is generally set to <0.05 to indicate a difference, and the statistical method of difference is based on t-test.

[0053] Step 11: The differential protein volcano map is drawn based on R language ggplot2, with the horizontal axis being Log2FC and the vertical axis being -Log 10 (P-value), based on which 757 differentially expressed proteins were obtained, 615 up-regulated proteins, and 142 down-regulated proteins ( Figure 2 ).

[0054] Step 12: Based on the R language clusterProfiler package, KEGG PATHWAY enrichment analysis was performed on the proteins in the protein set. The calculation principle was the same as the GO functional enrichment analysis, and the enrichment analysis was performed using Fisher's exact test. The p-value was corrected using the BH method. The corrected p-value (Corrected P-Value) was set at 0.05 as the threshold. The KEGG pathway that met this condition was defined as the KEGG pathway that was significantly enriched in the protein set. The bubble chart was drawn using the R language ggplot2 package, and the KEGG database was used to study the metabolic pathways and functions enriched by differentially expressed proteins ( Figure 3 ).

[0055] After KEGG enrichment analysis of the differentially expressed proteins, 12 significantly differentially expressed metabolic pathways were identified (p<0.05). The detailed pathway names, IDs, p values, and differentially expressed genes are shown in Table 1.

[0056] Table 1 KEGG enriched pathway details

[0057]

[0058]

[0059] Step 13: Use the differential protein binding metabolic pathways obtained by KEGG enrichment analysis to further analyze and speculate the important genes related to gibberellin regulation of hemp.

Claims

1. A method for mining hemp gibberellin regulation-related genes based on proteomics, characterized in that: The method is: Step 1: Spray the leaves of hemp with 50 mg / mL gibberellin during the rapid growth period, and the control group was sprayed with distilled water and planted in outdoor pots; Step 2: 7 days after the spraying treatment, hemp leaves from the control group and the group sprayed with exogenous gibberellin were selected. Three groups of leaves were selected from each of the control group and the experimental group. The leaf samples were treated with liquid nitrogen after collection and stored at -80°C after treatment. Step 3: Sample processing and protein extraction: (1) Take out 500 mg of sample in the frozen state, transfer it to an MP shaking tube, add 1 ml of BPP lysis buffer; shake it three times using a high-throughput tissue grinder, each time for 40 seconds; centrifuge at 4°C 12000g for 20 min to take the supernatant, add an equal volume of Tris-saturated phenol (pH>7.8), and shake at 4°C for 10 min; centrifuge at 4°C 12000g for 20 min to take the phenol phase, add an equal volume of BPP lysis buffer, and shake at 4°C for 10 min; centrifuge at 4°C 12000g for 20 min to take the phenol phase, add 5 times the volume of pre-cooled ammonium acetate methanol solution, and precipitate the protein at -20°C overnight; centrifuge at 4°C 12000g for 20 min the next day, discard the supernatant, add 90% pre-cooled acetone to the precipitate, mix well, centrifuge and discard the supernatant, repeat twice; dissolve the precipitate with protein lysis buffer containing protease inhibitor cocktail; sonicate on ice for 2 min; centrifuge at 4°C 12000g for 20 min, and take the protein supernatant; (2) Take 100 μg of protein sample; add TEAB (triethylammonium bicarbonate buffer) to a final concentration of 100 mM; add TCEP (tri(2-carboxyethyl)phosphine) to a final concentration of 10 mM, and react at 37°C for 60 min; add IAM (iodoacetamide) to a final concentration of 40 mM, and react at room temperature in the dark for 40 min; add pre-cooled acetone to each tube (acetone: sample v / v = 6:1), precipitate at -20°C for 4 h; centrifuge at 10000 g for 20 min, and take the precipitate; fully dissolve the sample with 100 μL 100 mM TEAB; Trypsin was added at an enzyme:protein ratio of 1:50 and enzymatic digestion was carried out at 37°C overnight; (3) After trypsin digestion, the peptides were vacuum pumped dry; the peptides were re-dissolved with 0.1% trifluoroacetic acid (TFA); HLB ( HLB 96-Well Plate 30μm, solid phase extraction column peptide desalting treatment) for peptide desalting, vacuum concentrator to dry; Step 4: LC-MS / MS analysis The peptide samples were separated by EASY-nLC 1200 liquid phase system, and the chromatographic column was C18 column; The samples separated by nano-HPLC were analyzed by mass spectrometry using a timsTOF Pro2 mass spectrometer with an electrospray voltage of 1.5 kV. The peptide parent ions and their secondary fragments were detected and analyzed by high-resolution TOF. The secondary mass spectrometry scan range was 100-1700 m / z. The data acquisition on the timsTOF Pro2 was performed in parallel accumulation serial fragmentation (PASEF) acquisition mode. During the MS / MS data acquisition process, each TIMS cycle included 1 MS and 10 PASEF MS / MS scans (the charge number of the parent ion was 0-5). The dynamic exclusion time of the MS / MS scan was 24 s. Step 5: Import the original file from the mass spectrometer into the MaxQuant version 2.0.3.1 software system was used for database search analysis; the database used in this study was Uniprot; parameter settings: fixed modification was Carbamidomethyl; variable modification was Oxidation; protein cleavage was trypsin; the maximum allowable error range of the parent ion mass number was ±10ppm; the false discovery rate (FDR) of peptide identification was set to FDR≤0.01; the identified protein contained at least one specific peptide; Step 6: Gene mining through various databases, protein function annotation information and expression difference analysis; Step 7: Data analysis and plotting were done based on BLAST2GO, R, and Python software; Step 8: PCA analysis (Principal Component Analysis), i.e., principal component analysis, generates PCA analysis graphs of the gibberellin-treated group and the untreated group based on the R language ggplot2 software package; Step 9: Compare all proteins identified in this experiment with five major databases (Uniprot, Pfam, EggNOG, GO and KEGG databases) to obtain comprehensive functional information of proteins and make statistics on the annotation status of each database; Step 10: Fold Change (FC) refers to the fold difference of the expression of the same protein in two samples, which is mainly calculated by the quantitative value of protein abundance. In the Labelfree project, the default FC for up-regulated proteins is generally 2, and the FC for down-regulated proteins is generally <0.

5. P-value usually determines whether a protein is different between two groups. It is not based on the single indicator of FC difference, but is combined with other statistical parameters (such as p-value) to judge. P-value refers to the use of statistical methods to calculate the difference significance of a protein between two groups. The p-value in proteomics is generally set to <0.05 to indicate a difference, and the difference statistical method is based on t-test. Step 11: The differential protein volcano map is drawn based on R language ggplot2, with the horizontal axis being Log2FC and the vertical axis being -Log 10 (P-value), based on which 757 differentially expressed proteins were obtained, including 615 up-regulated proteins and 142 down-regulated proteins; Step 12: Based on the clusterProfiler package of R language, KEGG PATHWAY enrichment analysis was performed on the proteins in the protein set, and Fisher's exact test was used for enrichment analysis; the p-value was corrected using the BH method; the corrected p-value (Corrected P-Value) was set at 0.05 as the threshold, and the KEGG pathway that met this condition was defined as the KEGG pathway that was significantly enriched in the protein set. The bubble chart was drawn using the ggplot2 package of R language, and the KEGG database was used to study the metabolic pathways and functions enriched by differentially expressed proteins; Step 13: Use the differential protein binding metabolic pathways obtained by KEGG enrichment analysis to further analyze and speculate the important genes related to gibberellin regulation of hemp.

2. The method for mining hemp gibberellin regulation-related genes based on proteomics according to claim 1, characterized in that: In step 3 (1), the BPP lysis buffer contains 200 mM Tris-Base, 200 mM NaCl, 50 mM sodium ascorbate, 1% deoxycholate, 1% NP-40, 1% Triton X-100, 1% Tween-20, 5 mM EDTA, 30% sucrose, 50 mM sodium tetraborate, and 1% PVPP.

3. The method for mining hemp gibberellin regulation-related genes based on proteomics according to claim 1, characterized in that: In step 3 (1), the protein lysis solution is 8M urea + 1% SDS (sodium dodecyl sulfate).

4. The method for mining hemp gibberellin regulation-related genes based on proteomics according to claim 1, characterized in that: In step 3 (1), add 1 μl of protease inhibitor for every 100 μl of protein lysate.

5. The method for mining hemp gibberellin regulation-related genes based on proteomics according to claim 1, characterized in that: In step 4, mobile phase A: 2% acetonitrile 0.1% formic acid, mobile phase B: 80% acetonitrile 0.1% formic acid; separation gradient: 0-45min, mobile phase B linearly increased from 3% to 28%; 45-50min, mobile phase B linearly increased from 28% to 44%; 50-55min, mobile phase B linearly increased from 44% to 90%; 55-60min, mobile phase B linearly maintained at 90%.

Images