Multi-omics-based Changbai Mountain Chinese bee honey characteristic component analysis method

Abstract

The invention relates to the technical field of honey identification, in particular to a multi-omics-based Changbai Mountain Chinese bee honey characteristic component analysis method. According to the method, mass spectrometric detection is carried out on the Changbai Mountain apis cerana honey and the reference substance honey, qualitative and quantitative analysis is carried out on metabolites and proteins of the honey, metabonomics and proteomics information is obtained, and metabonomics and proteomics difference information of the Changbai Mountain apis cerana honey and the reference substance honey is obtained; the method comprises the following steps: carrying out functional annotation on the Chinese bee honey, finding out KEGG pathways annotated by two groups of schools by utilizing KEGG database pathway analysis, carrying out expression correlation analysis and O2PLS analysis on the KEGG pathways annotated by the two groups of schools, and screening characteristic components of the Chinese bee honey in the Changbai Mountain; potential characteristic substances of the Changbai Mountain apis cerana honey are obtained through screening, and a scientific basis is further provided for origin traceability and quality identification of the apis cerana honey.

Description

Technical Field

[0001] The present invention relates to the technical field of honey identification, and in particular to a method for analyzing characteristic components of honey from Changbai Mountain Chinese honey based on multi-omics. Background Art

[0002] The Chinese honey bee (Apis cerana cerana) is a subspecies of the Oriental honey bee (Apis cerana). The Changbai Mountain Chinese honey bee (Apis cerana cerana in Changbai Mountain) is the only ecotype of the Oriental honey bee in the Changbai Mountain ecosystem. Its population has been declining annually and is now endangered-to-maintained. Honey is not only beloved by consumers for its unique flavor, but also possesses a wide range of pharmacological and biological functions as a natural dietary antioxidant. Apis cerana honey has been a unique honey variety and folk medicine in my country for thousands of years. Changbai Mountain Chinese honey, brewed from wild flower nectar collected by Chinese honey bees, boasts benefits such as nourishing the spleen and kidneys, moistening the lungs and intestines, soothing the five internal organs, harmonizing various medicinal properties, and detoxifying. It is a high-quality, specialty honey with limited production. Since the Bohai Kingdom period during the Tang Dynasty, its superior quality and unique flavor have been widely used by officials and the public as tribute, gifts, and commodities. The government established a special Dasheng Ula agency in Jilin to collect and supply tribute honey to the imperial family. Honey in China is primarily produced by Chinese bees (Apis cerana var. melanogaster) and Italian bees (Apis mellifera var. Italiana). Due to limited production and local consumer preferences, the price of Chinese bee honey is 10 times higher than that of Western bees (Apis mellifera var. Italiana). Bee colony breeding and honey harvesting from wild beehives remain a crucial source of livelihood for residents in economically underdeveloped mountainous areas. Driven by profit, the counterfeiting and sale of Chinese bee honey is becoming increasingly serious.

[0003] The diversity of botanical and geographical sources contributes to differences in honey's nutritional composition. Specific chemicals in honey can serve as reliable evidence for its traceability and identification. However, a unique identification technique for Changbai Mountain honey is currently lacking, making it impossible to distinguish it from other types of honey based on its functional components. Summary of the Invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention aims to provide a method for analyzing the characteristic components of honey from Changbai Mountain Chinese honey based on multi-omics, to analyze the characteristic components of honey from Changbai Mountain, and to provide a scientific basis for tracing the origin and quality identification of honey from Changbai Mountain.

[0005] In order to solve the above problems, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a method for analyzing characteristic components of honey from Apis cerana cerana var. cerana in Changbai Mountain based on multi-omics, comprising:

[0007] Mass spectrometry was performed on honey from the Changbai Mountain Chinese honey and reference honey, and metabolites and proteins of the honey were qualitatively and quantitatively analyzed to obtain metabolomics and proteomics information.

[0008] The metabolomics and proteomics difference data of Changbai Mountain Apis cerana honey and reference honey were analyzed to obtain the metabolomics and proteomics difference information of Changbai Mountain Apis cerana honey and reference honey;

[0009] Based on the metabolomics and proteomics difference information, functional annotation of the metabolomics and proteomics difference information was performed, and pathway analysis was performed using the KEGG database;

[0010] Based on the functional annotation and pathway analysis results of the metabolomics and proteomics differential information, the KEGG pathways annotated by both omics were found. Expression correlation analysis and O2PLS analysis were performed on the KEGG pathways annotated by both omics to screen the characteristic components of Changbai Mountain Chinese honey.

[0011] As an implementable method, the mass spectrometry detection of the Changbai Mountain Apis cerana honey and the reference honey and the qualitative and quantitative analysis of the metabolites of the honey include:

[0012] Ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS) was used to detect the metabolites of honey. The chromatographic column was an Agilent SB-C18. The mobile phase consisted of solvent A (0.1% formic acid in ultrapure water) and solvent B (0.1% formic acid in acetonitrile). The gradient program was as follows: initial conditions were 95% A, 5% B. A linear gradient was applied to 5% A, 95% B within 9 minutes and held for 1 minute, followed by an adjustment to 95% A, 5.0% B within 1 minute and held for 3 minutes. The flow rate was set at 0.35 mL / min, the column temperature was 40°C, and the injection volume was 2 μL. The mass spectrometry conditions included: source temperature 500°C; ion spray voltage 5500 V / -4500 V; ion source gas I, gas II, and curtain gas were set at 50, 60, and 25 psi, respectively; collision-induced dissociation was set to high; triple quadrupole scanning was acquired in multiple reaction monitoring (MRM) mode with collision gas nitrogen set to medium, and the declustering voltage and collision energy of individual MRM ion pairs were optimized. A specific set of MRM ion pairs was monitored in each time period based on the metabolites eluting in each time period.

[0013] Based on the mass spectrometry data of metabolites obtained by mass spectrometry detection, the metabolites were qualitatively and quantitatively analyzed by mass spectrometry according to the metabolic database to obtain the metabolomics information of honey.

[0014] As an implementable method, the metabolomics difference data analysis of the Changbai Mountain Apis cerana honey and the reference honey includes:

[0015] The OPLS-DA model was used to analyze the metabolomics data and obtain the importance projection (VIP) of the metabolomics data; and the fold change of the metabolites in the control group was calculated;

[0016] Metabolites with importance projection VIP>1 and fold change ≥2 and fold change ≤0.5 were selected to determine the metabolomic difference information between Changbai Mountain Chinese honey and the reference honey.

[0017] As an implementable method, the mass spectrometry detection of the Changbai Mountain Apis cerana honey and the reference honey is performed to perform qualitative and quantitative analysis of the honey protein, including:

[0018] Mass spectrometry analysis of honey using LC-MS / MS, including:

[0019] Nano-liquid chromatography: Samples were separated using a Vanquish Neo UHPLC nano-liquid chromatography system; mobile phase A was 0.1% formic acid in water, and mobile phase B was 0.1% formic acid in acetonitrile; the injection mode was a capture-analysis dual-column method, with the capture column being the PepMap Neo Trap Cartridge and the analytical column being the Easy-Spray TM PepMap TM Neo UHPLC column, analytical column temperature 55°C, sample load 200 ng, flow rate 2.5 μl / min, effective gradient 6.9 min, total time 8 min;

[0020] Orbitrap Astral mass spectrometer detection: DIA analysis was performed using a Vanquish Neo system for chromatographic separation, and the separated samples were analyzed by DIA mass spectrometry using an Orbitrap Astral high-resolution mass spectrometer. Detection mode: positive ion and parent ion scan range 380-980 m / z, primary mass spectrometry resolution 240,000 at 200 m / z, Normalized AGC Target 500%, Maximum IT 5 ms. MS2 adopted DIA data acquisition mode, with 299 scan windows, Isolation Window 2 Th, HCD Collision Energy 25%, Normalized AGC Target 500%, and Maximum IT 3 ms.

[0021] The DIA-NN software was used to perform qualitative and quantitative analysis of proteins on the mass spectrometry data of honey to obtain proteomic information.

[0022] As an implementable method, the proteomic difference data analysis of the Changbai Mountain Apis cerana honey and the reference honey is performed to obtain the proteomic difference information of the Changbai Mountain Apis cerana honey and the reference honey, including:

[0023] The screening criteria for significantly different proteins include:

[0024] For replicates, biological replicates ≥ 2: when the difference is grouped into two groups of samples, FC ≥ 1.5 or FC ≤ 0.6667, and P-value ≤ 0.05 are defined as proteins with significant differences; when the difference is grouped into more than two groups of samples, P-value ≤ 0.05 is met; for non-replicate items: when the difference is grouped into two groups of samples, FC ≥ 1.5 or FC ≤ 0.6667 are defined as proteins with significant differences.

[0025] As an implementation method, the functional annotation of the metabolomics and proteomics difference information is performed based on the metabolomics and proteomics difference information, and pathway analysis is performed using the KEGG database, including:

[0026] Based on the metabolomics difference information, the KEGG database was used to perform functional annotation of metabolites, and KEGG pathway classification was performed based on the KEGG annotation information of differential metabolism; multiple significantly enriched KEGG pathways were selected, and all differential metabolites in the KEGG pathways were clustered and analyzed to analyze the changes in the content of substances in important metabolic pathways in different honey groups; KEGG pathway enrichment analysis was performed based on the differential metabolites, and the top 20 pathways with the highest hypergeometric test p-value were screened for analysis to determine the differential metabolites in the pathways; the differential abundance scores of the top 20 pathways were calculated;

[0027] Based on the proteomic difference information, GO, KEGG, and KOG databases were used for protein functional annotation and enrichment analysis. The domain structure of differential proteins was used for functional annotation and enrichment analysis using the InterPro database. The subcellular localization prediction of differential proteins was analyzed using WoLF PSORT software, and the signal peptide localization of differential proteins was analyzed using SignalP software.

[0028] As an implementable method, based on the functional annotation and pathway analysis results of the metabolomics and proteomics differential information, the KEGG pathways annotated by both omics were found. Expression correlation analysis and O2PLS analysis were performed on the KEGG pathways annotated by both omics to screen the characteristic components of Changbai Mountain Chinese honey, including:

[0029] In each differential group, the correlations with the absolute value of the Pearson correlation coefficient greater than 0.8 and the p-value less than 0.05 were screened. The fold differences between the corresponding proteins and metabolites in the correlations were displayed in a nine-quadrant plot, and the quadrants 1-9 were separated from left to right and from top to bottom by black dotted lines.

[0030] All differential metabolites and differential proteins were selected to establish an O2PLS model for O2PLS analysis, to determine the overall correlation and independence between the two data groups, to draw a loading diagram, and to screen out important variables that have a significant impact on the other group.

[0031] In a second aspect, the present invention provides the characteristic components of Changbai Mountain Apis cerana honey obtained by analyzing the characteristic components of Changbai Mountain Apis cerana honey based on multi-omics analysis method, wherein the metabolomics difference information includes: Cyclo (Pro-Leu), D-Ribulose 5-phosphate, cis-4-Hydroxy-D-proline, Hydroxyproline, cis-3-Hydroxy-L-proline, LysoPC 17:2 (2n isomer), Lys-Ala, Pyridoxine phosphate, Lys-Asn, 12 (13) -EpOME, 3-Methylsuberic acid and (9Z, 11Z) -13-hydroxyhexadeca-9, 11-dienoic acid;

[0032] The proteomic difference information includes: APICC_01685, APICC_04696, APICC_00562, APICC_08137, APICC_07862, APICC_09184, APICC_08085, APICC_04206, APICC_05792, APICC_01096, APICC_03450, APICC_06442 and APICC_10171.

[0033] As an embodiment, the characteristic components include: A0A2A3EDE3 (APICC_03450) and Sasp001236 (7-Hydroxy-tryptophan), A0A2A3EDR1 (APICC_06442) and pme0124 (L-Glycyl-L-proline), Safp001796 (4-Hydroxy-L-tryptophan), A0A2A3EPZ0 (APICC_04206) and Lmbn001288 ((S)-2-Acetolactate), A0A2A3E4S5 (APICC_01096) and Lcfn121066 (Glu-Glu-Phe), A0A2A3EHG0 (APICC_10171) and MWS201471 (Lys-Tyr), A0A2A3EIU8 (APICC_07862) and PD0280915 (Leu Ser Ile), A0A2A3EMA9 (APICC_09184) and PD0280915 (Leu Ser Ile), A0A2A3EPJ1 (APICC_08085) and pme0008 (L-Citrulline), A0A2A3E771 (APICC_04696) and Lmqn000432 (sn-Glycero-3-phospho-1-inositol), and A0A2A3EHG0 (APICC_10171) and PDP228396 ((5e,10r)-10-methyl-7,8,9,10-tetrahydro-3h-oxecine-2,4-dione), A0A2A3E771 (APICC_04696) and pma6455 (D-Ribulose 5-phosphate).

[0034] As an embodiment, the characteristic components include: A0A2A3EHG0 (Hyaluronidase), A0A2A3E5C0 (Acetylcholinesterase), A0A2A3EKU2 (Glucosylceramidase), A0A2A3E8C9 (alpha-glucosidase), A0A2A3ED74 (Chitinase protein Idgf4), A0A2A3ERC7 (Glutathione peroxidase), A0A2A3ESV4 (Arylsulfatase J), A0A2A3E889 (Hexamerin), A0A2A3EMG3 (Transcription elongation factor spt6) and A0A2A3EM77 (Histone H2B).

[0035] In a third aspect, the present invention provides the use of the metabolomics difference information or proteomics difference information or the characteristic components of Changbai Mountain Apis cerana honey in identifying Changbai Mountain Apis cerana honey.

[0036] The beneficial effects of the present invention are as follows: the present invention uses Changbai Mountain Chinese honey as the research object, and uses Aba honey (from Aba Chinese honey), Qiongzhong honey (from Hainan Chinese honey), Tibet honey (from Tibet Chinese honey), and Shennong hundred-flower honey (from Huazhong Chinese honey) as control group samples, and uses UPLC-MS / MS metabolomics technology and Ultra Fast proteomics technology to analyze the metabolite and protein differences of honey from different regions, screens and obtains potential characteristic substances of Changbai Mountain Chinese honey, and further provides a scientific basis for its origin tracing and quality identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is the nine-quadrant diagram for interpreting the image in Example 1.

[0038] Figure 2 This is the metabolite composition diagram of Changbai Mountain honey in Example 2.

[0039] Figure 3 This is a graph showing the CV values of Changbai Mountain honey and each control group in Example 2.

[0040] Figure 4 This is the PCA analysis diagram of Changbai Mountain honey and each control group in Example 2.

[0041] Figure 5 This is a Venn diagram of the differential metabolites between Changbai Mountain honey and each control group in Example 2.

[0042] Figure 6This is a comparison chart of the differential metabolites between Changbai Mountain honey and Tibetan honey in Example 2.

[0043] Figure 7 These are the differential metabolites between Changbai Mountain honey and the control group in Example 2.

[0044] Figure 8 This is a comparison chart of the differential metabolites between Changbai Mountain Honey and Qiongzhong Honey in Example 2.

[0045] Figure 9 This is a comparison chart of the differential metabolites between Changbai Mountain Honey and Shennong Honey in Example 2.

[0046] Figure 10 This is a comparison chart of the differential metabolites between Changbai Mountain honey and Aba honey in Example 2.

[0047] Figure 11 This is a statistical visualization diagram of the protein identification results in Example 2.

[0048] Figure 12 This is an overview of the functional annotation results of the identified proteins in Example 2.

[0049] Figure 13 This is the peptide length distribution result diagram in Example 2.

[0050] Figure 14 This is the protein credible distribution result diagram in Example 2.

[0051] Figure 15 This is a distribution diagram of the number of missed cleavage sites of the peptide segment in Example 2.

[0052] Figure 16 This is the principal component analysis diagram of the Changbai Mountain honey and control protein samples in Example 2.

[0053] Figure 17 This is a correlation analysis chart of Changbai Mountain honey and the control protein sample in Example 2.

[0054] Figure 18 This is a graph showing the difference in protein quantity between Changbai Mountain honey and the control group in Example 2.

[0055] Figure 19 This is a Venn diagram of the differential proteins between Changbai Mountain honey and the control group in Example 2.

[0056] Figure 20 This is a graph showing the difference in protein between Changbai Mountain honey and the control group in Example 2.

[0057] Figure 21 This is a bar chart showing the functional enrichment of GO terms for differentially expressed proteins (DEPs) between Changbai Mountain honey and Tibetan honey in Example 2.

[0058] Figure 22 This is a bar chart showing the functional enrichment of GO terms for differentially expressed proteins (DEPs) between Changbai Mountain honey and Qiongzhong honey in Example 2.

[0059] Figure 23 This is a bar chart showing the functional enrichment of GO terms for differentially expressed proteins (DEPs) between Changbai Mountain honey and Shennong honey in Example 2.

[0060] Figure 24 This is a bar chart showing the functional enrichment of GO terms for differentially expressed proteins (DEPs) between Changbai Mountain honey and Aba honey in Example 2.

[0061] Figure 25 This is the domain function annotation and enrichment analysis results of the differentially expressed proteins DEP between Changbai Mountain honey and Tibetan honey in Example 2.

[0062] Figure 26 This is the domain function annotation and enrichment analysis results of the differentially expressed protein DEP between Changbai Mountain honey and Qiongzhong honey in Example 2.

[0063] Figure 27 This is the domain function annotation and enrichment analysis results of the differentially expressed protein DEP between Changbai Mountain honey and Shennong honey in Example 2.

[0064] Figure 28 This is the domain function annotation and enrichment analysis results of the differentially expressed protein DEP between Changbai Mountain honey and Aba honey in Example 2.

[0065] Figure 29 This is the subcellular localization result of DEP, the differential protein between Changbai Mountain honey and Tibetan honey in Example 2.

[0066] Figure 30 This is the subcellular localization result of DEP, the differential protein between Changbai Mountain honey and Qiongzhong honey in Example 2.

[0067] Figure 31 This is the subcellular localization result of DEP, the differential protein between Changbai Mountain honey and Shennong honey in Example 2.

[0068] Figure 32 This is the subcellular localization result of DEP, the differential protein between Changbai Mountain honey and Aba honey in Example 2.

[0069] Figure 33 This is a bubble diagram of the KEGG pathway enrichment analysis of differentially expressed proteins (DEPs) between Changbai Mountain honey and Tibetan honey in Example 2.

[0070] Figure 34This is a bubble diagram of the KEGG pathway enrichment analysis of differentially expressed proteins (DEPs) between Changbai Mountain honey and Qiongzhong honey in Example 2.

[0071] Figure 35 This is a bubble diagram of the KEGG pathway enrichment analysis of differentially expressed proteins (DEPs) between Changbai Mountain honey and Shennong honey in Example 2.

[0072] Figure 36 This is a bubble diagram of the KEGG pathway enrichment analysis of differentially expressed proteins (DEPs) between Changbai Mountain honey and Aba honey in Example 2.

[0073] Figure 37 This is a relationship diagram between differentially expressed proteins and metabolites in Example 2.

[0074] Figure 38 This is a graph of the top 10 differentially expressed proteins that have a significant impact on metabolomics in Example 2. DETAILED DESCRIPTION

[0075] The present invention will be further described in detail below with reference to specific embodiments.

[0076] It should be noted that these embodiments are only used to illustrate the present invention, rather than to limit the present invention. Simple improvements to the method based on the concept of the present invention fall within the scope of protection claimed by the present invention.

[0077] Example 1

[0078] See also Figure 1 , is a multi-omics-based method for analyzing the characteristic components of honey from the Changbai Mountain Chinese honeybee, including:

[0079] S100. Perform mass spectrometry on Changbai Mountain Apis cerana honey and reference honey, conduct qualitative and quantitative analysis on the metabolites and proteins of the honey, and obtain metabolomics and proteomics information.

[0080] The reference honeys include: Aba honey (from Aba bee), Qiongzhong honey (from Hainan bee), Tibet honey (from Tibet bee), and Shennong honey (from Huazhong bee).

[0081] Changbai Mountain Chinese honey is collected from a bee farm in Malugou Town, Changbai County, Jilin Province (located within the Changbai Mountain Chinese Honey Bee Nature Reserve, E128°04′08″, N41°31′56″). Honey samples were provided by local beekeepers, and the bees used are purebred Changbai Mountain Chinese honey bees. Honey samples were collected in mid-to-late October, filtered briefly, bottled, and stored at 4°C until ready for use. All honey samples in the control group were products with China's national geographical indications: Tibet honey was collected from Sotong Village, Guxiang, Bomi County, Tibet (E95°17′19″, N30°01′14″), and the bee species used for brewing was Apis cerana tibetana; Qiongzhong honey was collected from Hongmao Town, Qiongzhong Li and Miao Autonomous County, Hainan Province (E109°41′32″, N19°02′03″), and the bee species used for brewing was Apis cerana hainanensis; Shennong flower honey was collected from Songluo Village, Songluo Township, Shennongjia Forest District, Hubei Province (E110°36′29″, N31°39′40″), and the bee species used for brewing was Apis cerana huazhong; Aba honey was collected from Balba Village, Jiaomuzu Township, Ma'erkang City, Aba Prefecture, Sichuan Province (E102°01′33″, N31°58′09″), and the bee species used for brewing was Apis cerana hainanensis. All honey samples are naturally mature, sourced from purebred Apis mellifera ecotypes, raised in traditional drum-rearing systems. Three batches of honey samples were collected from each sampling site over three consecutive years, from 2022 to 2024. Three samples from each batch were tested, each replicated three times.

[0082] Metabolomics analysis:

[0083] Sample Preparation and Extraction: Thaw the sample from a -80°C freezer. Vortex for 1 minute. If vortexing is not possible, manually stir with a weighing spoon for 30 seconds. Vortex for 15 minutes. Add the internal standard extract (600 μL of extractant per 50 mg of sample) pre-chilled at -20°C in 70% methanol in water as appropriate and vortex for 15 minutes. The internal standard extract was prepared by dissolving 1 mg of the standard in 1 mL of 70% methanol in water to prepare a 1000 μg / mL stock solution. This 1000 μg / mL stock solution was further diluted with 70% methanol to prepare a 250 μg / mL internal standard solution. Centrifuge (12,000 rpm, 4°C) for 3 minutes. Filter the supernatant through a microporous membrane (0.22 μm) and store in a vial for LC-MS / MS analysis.

[0084] Ultra-high performance liquid chromatography and tandem mass spectrometry were used to detect the metabolites of honey;

[0085] The chromatographic column was Agilent SB-C18 (1.8 μm, 2.1 mm × 100 mm);

[0086] The mobile phase consisted of solvent A (ultrapure water containing 0.1% formic acid) and solvent B (acetonitrile containing 0.1% formic acid);

[0087] The gradient program was as follows: initial conditions were 95% A, 5% B. A linear gradient was applied to 5% A, 95% B within 9 min and held for 1 min, followed by an adjustment to 95% A, 5.0% B within 1 min and held for 3 min. The flow rate was set at 0.35 mL / min, the column temperature was 40 °C, and the injection volume was 2 μL.

[0088] Mass spectrometry conditions included: source temperature 500°C; ion spray voltage (IS) 5500 V (positive ion mode) / -4500 V (negative ion mode); ion source gas I (GSI), gas II (GSII), and curtain gas (CUR) set to 50, 60, and 25 psi, respectively; collision-induced dissociation (CAD) set to high; triple quadrupole scanning in multiple reaction monitoring (MRM) mode with collision gas nitrogen set to medium. Declustering potential (DP) and collision energy (CE) were optimized for individual MRM transitions. A specific set of MRM transitions was monitored in each time period based on the metabolites eluting during that time period.

[0089] Based on the mass spectrometry data of metabolites obtained by mass spectrometry detection, the metabolites were qualitatively and quantitatively analyzed by mass spectrometry according to the metabolic database to obtain the metabolomics information of honey.

[0090] Based on the self-built database MWDB (metware database), the substances were qualitatively identified according to the secondary spectrum information. During the analysis, the isotope signals, repeated signals containing K+ ions, Na+ ions, NH4+ ions, and repeated signals of fragment ions of other substances with larger molecular weight were removed.

[0091] Metabolite quantification is accomplished using the multiple reaction monitoring (MRM) analysis of a triple quadrupole mass spectrometer. In the MRM mode, the quadrupole first screens the precursor ions (parent ions) of the target substance, eliminating ions corresponding to other molecular weight substances to preliminarily eliminate interference; the precursor ions are induced by the collision chamber and then break into many fragment ions. The fragment ions are then filtered through the triple quadrupole to select the required characteristic fragment ion, eliminating interference from non-target ions, making quantification more accurate and reproducible. After obtaining the metabolite mass spectrometry analysis data of different samples, the peak area of all substance chromatographic peaks is integrated, and the mass spectrometry peaks of the same metabolite in different samples are integrated and corrected.

[0092] Mass spectrometry data were processed using Analyst 1.6.3 software. The total ion current (TIC) of the mixed quality control (QC) sample (a plot of the sum of the intensities of all ions in the mass spectrum at each time point versus time) and the multi-peak plot (XIC) of the MRM metabolite detection multi-substance extraction ion current spectrum) are shown. The horizontal axis represents the retention time (RT) of the metabolite detection, and the vertical axis represents the ion current intensity (in cps, counts per second) of the ion detection.

[0093] Based on the local metabolic database, the metabolites of the sample were qualitatively and quantitatively analyzed by mass spectrometry. The monitoring mode MRM metabolite detection multi-peak diagram shows the substances that can be detected in the sample, and each chromatographic peak of different colors represents a detected metabolite. The characteristic ions of each substance are screened out by the triple quadrupole, and the signal intensity (CPS) of the characteristic ions is obtained in the detector. The mass spectrometry file of the sample is opened with MultiQuant software to perform chromatographic peak integration and correction. The peak area of each chromatographic peak represents the relative content of the corresponding substance. Finally, all chromatographic peak area integration data are exported and saved.

[0094] Mass spectrometry was performed on Changbai Mountain Chinese honey and reference honey to conduct qualitative and quantitative analysis of honey protein:

[0095] First, remove the sample from -80°C and thaw on ice. Then, add PMSF to the sample at a final concentration of 1 mM and mix thoroughly. Centrifuge at 4500 g for 10 min at 4°C, and collect the supernatant. Finally, measure protein concentration using a BCA kit (Shanghai Biotech Co., Ltd.). If the sample concentration is too low, transfer the supernatant to a 10Kd ultrafiltration tube (Beckman Coulter Inc., Brea, CA) and repeat ultrafiltration and concentration. Collect the protein solution in the ultrafiltration tube and measure protein concentration using a BCA kit (Shanghai Biotech Co., Ltd.).

[0096] According to the protein concentration, 100 μg of protein solution was taken and the volume was made up to 200 μl with 8 M urea. Then, DTT (final concentration 5 mM) was added for reduction at 37°C for 45 min. It was alkylated with iodoacetamide (final concentration 11 mM) for 15 min in a dark room at room temperature. Then, 800 μl of 25 mM ammonium bicarbonate solution and 2 μl of trypsin (Promega, V5280) were added and digested at 37°C overnight. The enzymatic peptides were adjusted to pH 2-3 with 20% TFA and desalted with C18 (Millipore, Billerica, MA) column material. Finally, the column was purified by Pierce TM The peptide concentration was determined using a quantitative peptide detection reagent and standard kit (Thermo Fisher).

[0097] LC-MS / MS detection

[0098] Nanoliquid Chromatography: Samples were separated using a Vanquish Neo UHPLC nanoliquid chromatography system. Mobile phase A consisted of 0.1% formic acid in water, and mobile phase B consisted of 0.1% formic acid in acetonitrile (100% acetonitrile). The injection mode was a capture-analyze dual-column method, with the trap column being a PepMap Neo Trap Cartridge (300 μm, 5 mm, 5 μm) and the analytical column being an Easy-Spray TM PepMap TM Neo UHPLC column (150 μm x 15 cm, 2 μm). The temperature of the analytical column was controlled at 55°C by an integrated column oven, the sample load was 200 ng, the flow rate was 2.5 μl / min, the effective gradient was 6.9 minutes, and the total run time was 8 minutes.

[0099] Orbitrap Astral mass spectrometer detection: DIA analysis was performed using a nanoliter-speed Vanquish Neo system (Thermo Fisher Scientific) for chromatographic separation. Samples separated by nanoliter-scale HPLC were analyzed by DIA (data-independent) mass spectrometry using an Orbitrap Astral high-resolution mass spectrometer (ThermoScientific). Detection mode: Positive ionization, parent ion scan range: 380-980 m / z, primary mass spectrometer resolution: 240,000 at 200 m / z, Normalized AGC Target: 500%, Maximum Time Intensity (IT): 5 ms. MS2 data acquisition was performed in DIA mode with 299 scan windows, an Isolation Window of 2 Th, an HCD Collision Energy of 25%, a Normalized AGC Target of 500%, and a Maximum Time Intensity (IT) of 3 ms.

[0100] The DIA-NN software was used to perform qualitative and quantitative analysis of proteins on the mass spectrometry data of honey to obtain proteomic information.

[0101] Database retrieval is a complex computational process that requires specialized mass spectrometry data analysis software for data analysis. The DIA mass spectrometry data for this project were searched using DIA-NN (v1.8.1) using the Libraryfree method. The search parameters were as follows: the database was the uniprotkb_proteome_UP000242457_Apis_cerana_cerana_2024_12_23.fasta database (9931 sequences), with the deep learning-based parameter selected to predict a spectral library; the MBR (Match Between Runs) option was selected to generate a spectral library from the DIA data, which was then used to reanalyze the DIA data to obtain protein qualitative and quantitative results; and the FDR (False Discovery Rate) for both precursor ion and protein level identification was filtered to 1%. The filtered data was then used for subsequent bioinformatics analysis.

[0102] S200. Performing differential data analysis on the metabolomics and proteomics of the Changbai Mountain Apis cerana honey and the reference honey to obtain differential information on the metabolomics and proteomics of the Changbai Mountain Apis cerana honey and the reference honey.

[0103] The metabolomics difference data of Changbai Mountain Apis cerana honey and reference honey were analyzed to obtain the metabolomics difference information between Changbai Mountain Apis cerana honey and reference honey, including:

[0104] The CV value, or coefficient of variation, is the ratio of the standard deviation of the raw data to the mean of the raw data and reflects the degree of data dispersion. The empirical cumulative distribution function (ECDF) can be used to analyze the frequency of substances with CV values below the reference value. A higher proportion of substances with low CV values in QC samples indicates more stable experimental data: Over 85% of QC samples have CV values below 0.5, indicating relatively stable experimental data; over 75% of QC samples have CV values below 0.3, indicating very stable experimental data.

[0105] PCA analysis

[0106] Principal component analysis (PCA) was performed on samples (including quality control samples) to provide a preliminary understanding of the overall metabolite differences between groups and the degree of variability within groups. PCA results showed a trend of metabolome separation between groups, indicating whether there were differences in the metabolome within the sample groups.

[0107] Metabolomics data has the characteristics of "high dimension and massive", so it is necessary to combine univariate statistical analysis and multivariate statistical analysis methods, and analyze from multiple angles according to the characteristics of the data, and finally accurately mine differential metabolites. Univariate statistical analysis methods include hypothesis testing (Hypothesis testing) and fold change (FC) analysis. Multivariate statistical analysis methods include principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), etc. The variable importance projection (VIP) obtained based on the OPLS-DA model (biological replicates ≥ 3) can preliminarily screen out metabolites that differ between different groups. At the same time, the P-value / FDR (biological replicates ≥ 2) or FC value of the univariate analysis can be combined to further screen out differential metabolites. The screening criteria for differential metabolites in this project are:

[0108] The OPLS-DA model was used to analyze the metabolomics data and obtain the importance projection (VIP) of the metabolomics data; and the fold change of the metabolites in the control group was calculated;

[0109] Metabolites with importance projection VIP>1 and fold change ≥2 and fold change ≤0.5 were selected to determine the metabolomic difference information between Changbai Mountain Chinese honey and the reference honey.

[0110] The VIP value indicates the influence of the inter-group difference of the corresponding metabolite on the classification and discrimination of each group of samples in the model. Metabolites with a VIP greater than 1 are generally considered to have significant differences. Metabolites are considered to have significant differences when the difference between the control and experimental groups is more than 2 times or less than 0.5.

[0111] During the analysis, the data was processed using Z-score standardization (unit variance scaling), which normalizes the data based on the mean and standard deviation of the original data. The processed data conforms to the standard normal distribution. The calculation method is: the original data is centered and divided by the standard deviation of the variable. The formula is as follows: (where μ is the mean and σ is the standard deviation).

[0112] After the differential metabolites are determined, the differential metabolites are analyzed and displayed in the form of differential metabolite bar charts, differential metabolite radar charts, differential metabolite VIP value charts, differential metabolite volcano charts, differential metabolite scatter plots, differential metabolite hierarchical clustering trees, differential metabolite clustering heat maps, differential metabolite correlations, differential metabolite Z-value charts, differential metabolite violin charts, K-Means, and differential metabolite Venn diagrams.

[0113] Differential metabolite bar chart: After qualitative and quantitative analysis of the detected metabolites, combined with the grouping of specific samples, the fold changes of the metabolite quantitative information in each group are compared.

[0114] Radar chart of differential metabolites: The differences in the quantitative results of metabolites in different groups were calculated. Among the differential metabolites identified based on the screening criteria, the top 10 metabolites with the largest absolute log2FC values were selected to draw radar charts.

[0115] VIP value plot of differential metabolites: For the differential metabolites identified based on the screening criteria in each group comparison, the top 20 metabolites with the largest VIP values in the OPLS-DA model are selected for display.

[0116] Volcano plot of differential metabolites: mainly used to show the relative content differences of metabolites in two groups of samples and the statistical significance of the differences.

[0117] Differential metabolite scatter plot: Differential metabolite scatter plot is mainly used to show the relative content differences of different categories of substances in two groups of samples.

[0118] Hierarchical clustering tree of differential metabolites: Hierarchical clustering analysis is performed on samples from different comparison groups to form a cluster tree that shows the similarity between samples. Samples that are clustered in the same cluster have a higher similarity between them.

[0119] Cluster heat map of differential metabolites: In order to facilitate the observation of the changing pattern of the relative content of metabolites, the original relative content of the differential metabolites identified by the application of the screening criteria was processed by UV (Unit Variance Scaling) row by row.

[0120] Correlations between differential metabolites: Different metabolites have synergistic or mutually exclusive relationships. Correlation analysis can help measure the metabolic proximities between significantly differential metabolites, which helps further understand the mutual regulatory relationships between metabolites during changes in biological states. Pearson correlation analysis was used to analyze the correlations of differential metabolites identified according to the screening criteria.

[0121] Differential Metabolite Z-score Plot: Z-score standardization (i.e., unit variance scaling (UV) standardization) normalizes the relative levels of differential metabolites in different samples by calculating Z-scores. This plot provides a very intuitive view of the distribution of each differential metabolite across different groups.

[0122] K-Means: In order to study the changing trends of the relative contents of metabolites in different groups, the relative contents of all differential metabolites identified according to the screening criteria in all comparison groups were subjected to UV (unit variance scaling) processing, followed by K-means cluster analysis.

[0123] Venn diagram of differential metabolites: The relationship between different groups of differential metabolites is displayed in the form of a Venn diagram.

[0124] The proteomic difference data of Changbai Mountain Apis cerana honey and reference honey were analyzed, and the proteomic difference information of Changbai Mountain Apis cerana honey and reference honey was obtained, including:

[0125] The screening criteria for significantly different proteins include:

[0126] For repeated items, biological replicates ≥ 2: when the difference is grouped into two groups of samples, FC ≥ 1.5 or FC ≤ 0.6667, and P-value ≤ 0.05 are defined as proteins with significant differences; when the difference is grouped into more than two groups of samples, P-value ≤ 0.05 is met; for non-repeated items, biological replicates = 1: when the difference is grouped into two groups of samples, FC ≥ 1.5 or FC ≤ 0.6667 are defined as proteins with significant differences.

[0127] Bioinformatics analysis of differentially expressed proteins

[0128] In order to thoroughly understand the functional characteristics of different proteins, comprehensive functional annotations were performed on the identified proteins and the differentially expressed proteins in each comparison group, including Gene Ontology (GO), KOG functional classification, KEGG pathway, protein domain, subcellular localization, and signal peptide (SignalP).

[0129] After differentially expressed proteins were identified, volcano plots, cluster heatmaps, Venn diagrams, total cluster heatmaps, and K-means analysis of differentially expressed proteins and total proteins were performed. Volcano plots allow for a quick overview of the differences in expression levels between the two sample groups, as well as the statistical significance of these differences. The volcano plots were generated by taking the logarithm of the fold difference (to the base 2) and the absolute value of the logarithm of the P-value (to the base 10) for each differentially expressed protein.

[0130] To facilitate the observation of differential protein expression patterns across samples, we performed z-score normalization on the differentially expressed proteins and plotted a cluster heat map. Clustering by row allows us to directly identify proteins with similar expression patterns; clustering by column allows us to directly determine the reproducibility between samples.

[0131] Venn diagrams can be used to identify the unique and shared significantly differentially expressed proteins and their distribution across different differentially expressed groups. For groups of five or fewer, the relationships between differentially expressed proteins can be presented using a Venn diagram; for groups of more than five, a petal diagram can be used.

[0132] The significantly differentially expressed proteins in each differential group were combined, and then the overall cluster heat map was drawn as above.

[0133] In order to study the expression level change trend of differential proteins in different samples, the quantitative values of all differential proteins were standardized using the scale() function of R language, and then Kmeans cluster analysis was performed.

[0134] In order to simultaneously study the expression level trends of all identified proteins in the entire project in different samples, the quantitative values of all proteins were standardized using the scale() function of the R language, and then Kmeans cluster analysis was performed.

[0135] S300 , performing functional annotation on the metabolomics and proteomics difference information according to the metabolomics and proteomics difference information, and performing pathway analysis using the KEGG database.

[0136] KEGG analysis is an important step in the joint analysis of metabolomics and proteomics. It is based on the KEGG annotation (enrichment) analysis results of differential metabolites and differentially expressed proteins to identify KEGG pathways annotated to both omics.

[0137] The role of the Kyoto Encyclopedia of Genes and Genomes (KEGG) database: KEGG (Kyoto Encyclopedia of Genes and Genomes) is a comprehensive bioinformatics database that integrates diverse bioinformatics resources, including genomes, compounds, reactions, and metabolic pathways. In this joint analysis, it provides a platform for functional annotation and pathway analysis of metabolome and proteome data. By mapping differentially expressed metabolites and proteins to KEGG pathways, we can understand the biochemical reactions and metabolic pathways in which they participate, thereby revealing changes in related biological processes.

[0138] Based on the metabolomics and proteomics difference information, functional annotation of the metabolomics and proteomics difference information was performed, and pathway analysis was performed using the KEGG database, including:

[0139] Based on the metabolomics difference information, the KEGG database was used to perform functional annotation of metabolites, and KEGG pathway classification was performed based on the KEGG annotation information of differential metabolism; multiple significantly enriched KEGG pathways were selected, and all differential metabolites in the KEGG pathways were clustered and analyzed to analyze the changes in the content of substances in important metabolic pathways in different honey groups; KEGG pathway enrichment analysis was performed based on the differential metabolites, and the top 20 pathways with the highest hypergeometric test p-value were screened for analysis to determine the differential metabolites in the pathways; and the differential abundance scores of the top 20 pathways were calculated.

[0140] The p-value is calculated by the following formula:

[0141]

[0142] Where N represents the metabolite data with KEGG annotations in all metabolites, n represents the number of differential metabolites in N, M represents the number of metabolites in a KEGG pathway in N, and m represents the number of differential metabolites in a KEGG pathway in M. The closer the P-value is to 0, the more significant the enrichment.

[0143] The differential abundance score was calculated by the following formula:

[0144]

[0145] Based on the proteomic difference information, the GO, KEGG, and KOG databases were used for protein functional annotation and enrichment analysis. The structural domains of differential proteins were used for functional annotation and enrichment analysis using the InterPro database. The subcellular localization prediction of differential proteins was analyzed using WoLF PSORT software, and the signal peptide localization of differential proteins was analyzed using SignalP software. The differentially expressed protein interaction network was analyzed using the StringDB protein interaction database for protein interaction analysis.

[0146] GO consists of three main components: biological process, molecular function, and cellular component. The GO database provides a standardized way to describe gene functions, which can better understand how these functions work in organisms.

[0147] KEGG is the leading public database for pathways (https: / / www.genome.jp / kegg / ), an information network connecting known molecular interactions, such as metabolic pathways, complexes, and biochemical reactions. KEGG pathways primarily encompass metabolism, genetic information processing, environmental information processing, cellular processes, human diseases, and drug development. Pathway analysis can identify the most important biochemical metabolic pathways and signal transduction pathways in which proteins participate. Typically, without specifying a single species, identified proteins are compared to the KEGG large species database (animals, plants, fungi, bacteria, etc.) to obtain KEGG annotation results. The number of differentially expressed proteins included in each KEGG pathway is then counted and a bar graph is plotted.

[0148] S400: Based on the functional annotation and pathway analysis results of metabolomics and proteomics differential information, identify KEGG pathways annotated to both omics and perform expression correlation analysis and O2PLS analysis on the KEGG pathways annotated to both omics to screen the characteristic components of Changbai Mountain Chinese honey, including:

[0149] In each differential group, the correlations with the absolute value of the Pearson correlation coefficient greater than 0.8 and the p-value less than 0.05 were screened, and the differences between the corresponding proteins and metabolites were displayed in a nine-quadrant diagram. The quadrants were divided into 1-9 quadrants from left to right and from top to bottom with black dotted lines. The interpretation of the quadrants can be found in Figure 1 .

[0150] All differential metabolites and differential proteins were selected to establish an O2PLS model for O2PLS analysis. The overall correlation and independence between the two data groups were determined. The model parameters were set to crossval_o2m / crossval_o2m_adjR2 cross validation to obtain the optimal O2PLS. The loading diagram was drawn to screen out important variables that had a significant impact on the other group.

[0151] Example 2

[0152] Based on the UPLC-MS / MS detection platform and public database, a total of 890 metabolites were detected in Changbai Mountain honey. Figure 2As shown in the figure, the composition of metabolite categories from high to low is: Amino acids and derivatives (35.96%), Lipids (22.58%), Organic acids (18.54%), Others (11.80%) and Nucleotides and derivatives (11.12%). In this study, the proportion of substances with CV values of less than 0.3 in the quality control (QC) samples was greater than 75%, which confirmed the stability of the analytical system and the reliability of the obtained data (such as Figure 3 The PCA results showed that all honey samples were divided into 5 distinct groups, indicating that there were significant differences between Changbai Mountain honey and Tibetan honey, Shennongjia honey, Aba honey and Qiongzhong honey (e.g. Figure 4 ).

[0153] The relationship between the differential metabolites in each group was displayed in the form of a Venn diagram. The results showed that there were 51 unique differential metabolites between Changbai Mountain honey and Tibetan honey; 71 unique differential metabolites between Changbai Mountain honey and Qiongzhong honey; 28 unique differential metabolites between Changbai Mountain honey and Shennongjia honey; 33 unique differential metabolites between Changbai Mountain honey and Aba honey; and 112 common differential metabolites among the four comparison groups. (e.g. Figure 5 ).

[0154] Based on the screening criteria, a total of 393 metabolites were identified between Changbai Mountain honey and Tibetan honey. The results of the Z value plot of the differential metabolites showed (e.g. Figure 6), 3-nitro-L-tyrosine, Barbiturate, 1,7-Dimethylxanthine, 4-Guanidinobutanoate, (4-acetylphenyl)-L-alanine, (2-carboxyethyl)-L-phenylalanine, Gly-Gly-Phe, Hydroxythreonine in Changbai Mountain honey xyloside, Maltitol, N-Acetyl-L-phenylalanine, 2-{4-amino-1H-imidazo[4,5-d]pyridazin-1-yl}-5-(hydroxymethyl)oxolane-3,4-diol, Adenosine, 3-Methylxanthine, Methyl 2-furoate, 9-Arabinosyladenine, 2-Decenedioic acid, N-Lactoylphenylalanine, (9Z,11Z)-13-hydroxyhexadeca-9,11-dienoic The relative contents of metabolites such as acid, Pyridoxine, AMP, Mevalonic Acid Glucoside, (6Z,9Z,12Z)-Octadecatrienoic acid, Cyclo(Pro-Leu), Cyclo(D-Leu-L-Pro), 2-Oxopentanoic acid, D-Ribulose 5-phosphate, Perseitol-1-O-xyloside, D-Sorbitol, Allitol, gamma-resorcylate, Aminomalonate, 9-Alpha-Ribofuranosyladenine, and Hydroxy-o-tolyl-acetic acid were significantly higher in Changbai Mountain honey than in Tibetan honey. Combined with the results of the Venn diagram, it was found that among the 51 unique differential metabolites (CBS vs XZ), Cyclo(Pro-Leu) and D-Ribulose 5-phosphate were substances with significantly higher relative contents in Changbai Mountain honey than in Tibetan honey (such as Figure 7 ).

[0155] There are 373 metabolites that are different between Changbai Mountain honey and Qiongzhong honey. The metabolites in Changbai Mountain honey are 9,12-Octadecadien-6-Ynoic Acid, Lys-Asn, Sphinganine (dihydrosphingosine), 9-OxoODE, LysoPC 17:2 (2n isomer), Perseitol-1-O-xyloside, 4-Guanidinobutanoate, Rabdosia acidA, Barbiturate, N-Benzoyl-(2R,3S)-3-phenylisoserine, Dehydroascorbate, Gorlic acid, cis-4-Hydroxy-D-proline, Hydroxyproline, cis-3-Hydroxy-L-proline, (9Z,11Z)-13-hydroxyhexadeca-9,11-dienoic acid, 10,16-Dihydroxyhexadecanoic acid acid, Lys-Thr, 12(13)-EpOME, Ser-Val-Leu, Maltitol, 9,16-Dihydroxypalmitic acid, D-Sorbitol, Benzocyclobutyl-1-carboxylic acid, AMP, N-Lactoylphenylalanine, 3-Methylxanthine, MevalonicAcid Glucoside, LysoPC 18:3(2n isomer), Nicotinate, LysoPC 16:2(2n isomer), 3-Bromo-tyrosine, Phosphatidylcholine lyso 18:3, Pyridoxine phosphate, Lys-Ala, LysoPE 18:2, LysoPC 15:0(2n isomer), 3,4,5-trihydroxy-1-{[(2E)-3-(4-hydroxyphenyl)prop-2-enoyl]oxy}cyclohexane-1-carboxylic The relative content of metabolites such as acid was significantly higher than that of Qiongzhong honey (such as Figure 8). Combined with the results of the Venn diagram, it was found that among the 71 unique differential metabolites (CBS vs QZ), cis-4-Hydroxy-D-proline, Hydroxyproline, cis-3-Hydroxy-L-proline, LysoPC 17:2(2n isomer), Lys-Ala, Pyridoxine phosphate, Lys-Asn and 12(13)-EpOME were substances with significantly higher relative contents in Changbai Mountain honey than in Qiongzhong honey (such as Figure 7 ).

[0156] There are 324 different metabolites between Changbai Mountain honey and Shennong honey. 3-nitro-L-tyrosine, Barbiturate, Hydroxythreonine xyloside, Adenosine, Dehydroascorbate, 9-Alpha-Ribofuranosyladenine, 9-Arabinosyladenine, 3-Methylsubericacid, Kestose,

[0157] The relative contents of metabolites such as {4-amino-1H-imidazo[4,5-d]pyridazin-1-yl}-5-(hydroxymethyl)oxolane-3,4-diol, Allitol, 4-Guanidinobutanoate, (9Z,11Z)-13-hydroxyhexadeca-9,11-dienoicacid, Perseitol-1-O-xyloside, 2-Oxopentanoic acid, (6Z,9Z,12Z)-Octadecatrienoicacid, Methyl 2-furoate, and D-Sorbitol were significantly higher than those in Shennong Baihua honey (such as Figure 9 ). Combined with the results of the Venn diagram, it was found that among the 28 unique differential metabolites, 3-Methylsubericacid was a substance with a significantly higher relative content in Changbai Mountain honey than in Shennong honey (such as Figure 7 ).

[0158] There are 383 different metabolites between Changbai Mountain honey and Aba honey. The results showed that the Changbai Mountain honey contained Benzocyclobutyl-1-carboxylic acid, LysoPC 15:0, Sphinganine, 9-OxoODE, Sepiaterin, Glycerol 9,11,13-octadecatrienoyl ester, Rabdosia acid A, Hibiscus acid, (2-carboxyethyl)-L-phenylalanine, 2-Hydroxyisobutyric acid, 2-Hydroxybutanoic acid, LysoPC 18:2 (lysophosphatidylcholine 18:2), Gly-Gly-Phe (Glycine-Glycine-Phenylalanine), (4-acetylphenyl)-L-alanine ((4-acetylphenyl)-L-alanine), Lys-Thr (L-lysine-L-threonine), Ser-Val-Leu (Seryl-Valyl-Leucine), HexosylLPE16:0,

[0159] The relative contents of metabolites such as (9Z,11Z)-13-hydroxyhexadeca-9,11-dienoic acid, 1-α-linolenoyl-glycerol, cis-4-hydroxy-D-proline, succinate, LysoPC 15:0(2n isomer), γ-resorcylate, N-lactoylphenylalanine, 2-oxopentanoic acid and mevalonic acid glucoside were significantly higher than those in Aba honey (such as Figure 10 Among the 33 unique differential metabolites, no substance was found whose relative content in Changbai Mountain honey was higher than that in Aba honey.

[0160] In all four groups, the relative contents of (9Z,11Z)-13-hydroxyhexadeca-9,11-dienoic acid in Changbai Mountain honey were significantly higher than those in its control group. Based on the above results, Cyclo(Pro-Leu), D-Ribulose 5-phosphate, cis-4-Hydroxy-D-proline, Hydroxyproline, cis-3-Hydroxy-L-proline, LysoPC 17:2(2n isomer), Lys-Ala, Pyridoxine phosphate, Lys-Asn, 12(13)-EpOME, 3-Methylsubericacid and (9Z,11Z)-13-hydroxyhexadeca-9,11-dienoic acid were considered as potential characteristic metabolites in Changbai Mountain honey.

[0161] The number of protein sequences in the database used was 9932, the total number of peptides identified was 1425, the total number of proteins identified was 202, and the total number of proteins quantified was 202 (e.g. Figure 11 Afterwards, the functional annotation results of all identified proteins were statistically analyzed. The results showed that the number of proteins annotated to GO, KOG, KEGG, Subcellular, SignalP, and IPR pathways were 165, 161, 189, 202, 63, and 194, respectively (e.g. Figure 12 ).

[0162] The peptide length distribution results showed that most peptides were distributed between 7 and 20 amino acids, which was consistent with the general rules based on enzymatic hydrolysis and mass spectrometry fragmentation. The distribution of the identified peptide lengths met the quality control requirements (such as Figure 13 The peptide number distribution results show that the protein group contains a large number of peptides, indicating that these proteins are credible (e.g. Figure 14 The distribution of the number of missed cleavage sites in peptide segments reflects the thoroughness of enzyme cleavage. The number of missed cleavage sites in peptide segments is 0, accounting for 84.28%, indicating that the enzyme cleavage is relatively thorough and is beneficial for identification (e.g. Figure 15 ).

[0163] The results of principal component analysis of protein samples showed that there were significant differences between Changbai Mountain honey and Tibetan honey, Shennongjia honey, Aba honey and Qiongzhong honey (such as Figure 16 The results showed that the correlation coefficients between groups of different samples were all greater than 0.9, indicating that the experimental reproducibility was high (e.g. Figure 17 Based on the above qualitative and quantitative quality assessment of proteins, it is shown that the protein data obtained in this experiment are reliable.

[0164] Based on the significant difference protein screening criteria, 99 differential proteins were found between Changbai Mountain honey and Tibetan honey, of which 17 were upregulated and 82 were downregulated; 69 differential proteins were found between Changbai Mountain honey and Aba honey, of which 19 were upregulated and 50 were downregulated; 58 differential proteins were found between Changbai Mountain honey and Qiongzhong honey, of which 17 were upregulated and 41 were downregulated; 78 differential proteins were found between Changbai Mountain honey and Aba honey, of which 15 were upregulated and 63 were downregulated (e.g. Figure 18 ).

[0165] The results of the Venn diagram showed that there were 15 common differential proteins among the four comparison groups, and 23 unique differential proteins between Changbai Mountain honey and Tibetan honey, including APICC_01685 (mannose-6-phosphate isomerase), APICC_04696 (Solute carrier family 2, facilitated glucose transporter member), APICC_00562 (Alpha-galactosidase), APICC_08137

[0166] (S-adenosylmethionine synthase), APICC_07862 (Glutamate receptor U1), APICC_09184 (Glucosylceramidase) and APICC_08085 (Chymotrypsin inhibitor) are proteins with significantly higher relative contents in Changbai Mountain honey than in Tibetan honey. There are 8 unique differentially expressed proteins between Changbai Mountain honey and Qiongzhong honey, among which APICC_04206 (Peroxidasin) is a protein with significantly higher relative content in Changbai Mountain honey than in Qiongzhong honey. There are 10 unique differentially expressed proteins between Changbai Mountain honey and Shennongjia honey, among which APICC_05792 (MD-2-related lipid-recognition domain-containing protein) and APICC_01096 (Serine Proteinasestubble) is a protein with a significantly higher relative content in Changbai Mountain honey than in Shennong honey; there are 12 unique metabolites between Changbai Mountain honey and Aba honey, among which APICC_03450 (Hymenoptaecin) and APICC_06442 (Vacuolarprotonpump subunit B) are proteins with a significantly higher relative content in Changbai Mountain honey than in Aba honey (such as Figure 19 、 Figure 20 ).

[0167] In all four groups, the relative content of APICC_10171 (Hyaluronidase) in Changbai Mountain honey was significantly higher than that in the control group. Based on the above results, APICC_01685, APICC_04696, APICC_00562, APICC_08137, APICC_07862, APICC_09184, APICC_08085, APICC_04206, APICC_05792, APICC_01096, APICC_03450, APICC_06442 and APICC_10171 are potential characteristic proteins in Changbai Mountain honey. Figure 20 ).

[0168] Functional annotation and enrichment analysis of differentially expressed proteins

[0169] The GO annotation and enrichment results of DEPs in each group showed that in the Biological Process category, they were mainly annotated in translation, carbohydrate metabolic process, glycolytic process (translation, carbohydrate metabolic process, glycolytic process); in the Cellular Component category, they were mainly annotated in ribosome, lysosome, extracellular region, nucleus (ribosome, lysosome, extracellular region, cell nucleus); in the Molecular Function category, they were mainly annotated in metal ion binding, GTPbinding (metal ion binding, GTP binding). Statistics were made for the top 3 KOG functional classifications, and DEPs were mainly concentrated in General function prediction only, Posttranslational modification, protein turnover, chaperones, Carbohydrate transport and metabolism (general function prediction, posttranslational modification, protein turnover, chaperones, carbohydrate transport and metabolism) (such as Figure 21-24 ).

[0170] The results of DEP domain function annotation and enrichment analysis showed that the differentially expressed proteins in the four groups were all annotated to Hemocyanin, C-terminal domain superfamily, Hemocyanin, N-terminal domain superfamily, Hemocyanin / hexamerin, Di-copper centre-containing domain superfamily, Hemocyanin, N-terminal, Hemocyanin, C-terminal, Hemocyanin / hexamerin middledomain, Immunoglobulin E-set (Hemocyanin C-terminal domain superfamily, Hemocyanin N-terminal domain superfamily, Hemocyanin / hexamer, Di-copper centre-containing domain superfamily, Hemocyanin N-terminal, Hemocyanin C-terminal, Hemocyanin / hexamer middle domain, Immunoglobulin E-set) Figures 25-28 The subcellular localization results of DEP showed that the subcellular structures with the largest number of differentially expressed proteins annotated in each differential group were Cytoplasm and Extracell (cytoplasm and extracellular) ( Figure 29-32 ).

[0171] Biological processes are complex and holistic. Integrating multi-omics data for analysis can make up for data problems caused by factors such as missing data and noise when analyzing single-omics data. Multi-omics can verify each other and reduce false positives caused by single-omics analysis. Based on the KEGG annotation and enrichment analysis results of the differential metabolites and differential proteins in this experiment, the KEGG pathways annotated to both the metabolome and the proteome were found. The results showed that the differential proteins in each group were annotated to the beta-Alanine metabolism pathway ( Figures 33-36 ).

[0172] The results of the preliminary screening of potential characteristic proteins showed that A0A2A3EDE3 (APICC_03450) and Sasp001236 (7-Hydroxy-tryptophan), A0A2A3EDR1 (APICC_06442) and pme0124 (L-Glycyl-L-proline), Safp001796 (4-Hydroxy-L-tryptophan), A0A2A3EPZ 0 (APICC_04206) and Lmbn001288 ((S)-2-Acetolactate), A0A2A3E4S5 (APICC_01096) and Lcfn121066 (Glu-Glu-Phe), A0A2A3EHG0 (APICC_10171) and MWS201471 (Lys-Tyr), A0A2A3EIU8 (APICC_07862) and PD0280915 (Leu Ser Ile), A0A2A3EMA9 (APICC_09184) and PD0280915 (Leu Ser Ile), A0A2A3EPJ1 (APICC_08085) and pme0008 (L-Citrulline), A0A2A3E771 (APICC_04696) and Lmqn000432 (sn-Glycero-3-phospho-1-inositol) are proteins and metabolites with inconsistent regulatory trends. A0A2A3EHG0 (APICC_10171) and PDP228396 ((5e,10r)-10-methyl-7,8,9,10-tetrahydro-3h-oxecine-2,4-dione), A0A2A3E771 (APICC_04696) and pma6455 (D-Ribulose 5-phosphate) are proteins and metabolites with positive correlation ( Figure 37 ).

[0173] O2PLS results showed that the top 10 differentially expressed proteins with the greatest impact on metabolomics were: A0A2A3EHG0 (Hyaluronidase), A0A2A3E5C0 (Acetylcholinesterase), A0A2A3EKU2 (Glucosylceramidase), A0A2A3E8C9 (alpha-glucosidase), A0A2A3ED74 (Chitinase protein Idgf4), A0A2A3ERC7 (Glutathione peroxidase), A0A2A3ESV4 (ArylsulfataseJ), A0A2A3E889 (Hexamerin), A0A2A3EMG3 (Transcription elongation factor spt6), and A0A2A3EM77 (Histone H2B) Figure 38 ).

[0174] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described with reference to the preferred embodiments of the present invention, it should be understood by those skilled in the art that various changes can be made in form and details without departing from the spirit and scope of the present invention as defined in the appended claims.

Claims

1. A method for analyzing characteristic components of honey from Changbai Mountain Apis cerana based on multi-omics, characterized in that: include: Mass spectrometry was performed on honey from the Changbai Mountain Chinese honey and reference honey, and metabolites and proteins of the honey were qualitatively and quantitatively analyzed to obtain metabolomics and proteomics information. The metabolomics and proteomics difference data of Changbai Mountain Apis cerana honey and reference honey were analyzed to obtain the metabolomics and proteomics difference information of Changbai Mountain Apis cerana honey and reference honey; Based on the metabolomics and proteomics difference information, functional annotation of the metabolomics and proteomics difference information was performed, and pathway analysis was performed using the KEGG database; Based on the functional annotation and pathway analysis results of the metabolomics and proteomics differential information, the KEGG pathways annotated by both omics were found. Expression correlation analysis and O2PLS analysis were performed on the KEGG pathways annotated by both omics to screen the characteristic components of Changbai Mountain Chinese honey.

2. The method for analyzing characteristic components of honey from Apis cerana cerana var. cerana var. Changbai Mountain based on multi-omics according to claim 1, characterized in that: The mass spectrometry detection of the Changbai Mountain Apis cerana honey and the reference honey and the qualitative and quantitative analysis of the metabolites of the honey include: Ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS) was used to detect the metabolites of honey. The chromatographic column was an Agilent SB-C18. The mobile phase consisted of solvent A (0.1% formic acid in ultrapure water) and solvent B (0.1% formic acid in acetonitrile). The gradient program was as follows: initial conditions were 95% A, 5% B. A linear gradient was applied to 5% A, 95% B within 9 minutes and held for 1 minute, followed by an adjustment to 95% A, 5.0% B within 1 minute and held for 3 minutes. The flow rate was set at 0.35 mL / min, the column temperature was 40°C, and the injection volume was 2 μL. The mass spectrometry conditions included: source temperature 500°C; ion spray voltage 5500 V / -4500 V; ion source gas I, gas II, and curtain gas were set at 50, 60, and 25 psi, respectively; collision-induced dissociation was set to high; triple quadrupole scanning was acquired in multiple reaction monitoring (MRM) mode with collision gas nitrogen set to medium, and the declustering voltage and collision energy of individual MRM ion pairs were optimized. A specific set of MRM ion pairs was monitored in each time period based on the metabolites eluting in each time period. Based on the mass spectrometry data of metabolites obtained by mass spectrometry detection, the metabolites were qualitatively and quantitatively analyzed by mass spectrometry according to the metabolic database to obtain the metabolomics information of honey.

3. The method for analyzing characteristic components of honey from Apis cerana cerana var. cerana var. Changbai Mountain based on multi-omics according to claim 2, characterized in that: The metabolomics difference data analysis of Changbai Mountain Apis cerana honey and reference honey includes: The OPLS-DA model was used to analyze the metabolomics data and obtain the importance projection (VIP) of the metabolomics data; and the fold change of the metabolites in the control group was calculated; Metabolites with importance projection VIP>1 and fold change ≥2 and fold change ≤0.5 were selected to determine the metabolomic difference information between Changbai Mountain Chinese honey and the reference honey.

4. The method for analyzing characteristic components of honey from Apis cerana cerana var. cerana var. Changbai Mountain based on multi-omics according to claim 3, characterized in that: The method of performing mass spectrometry detection on the Changbai Mountain Chinese honey and the reference honey and performing qualitative and quantitative analysis on the protein of the honey includes: Mass spectrometry analysis of honey using LC-MS / MS, including: Nano-liquid chromatography: Samples were separated using a Vanquish Neo UHPLC nano-liquid chromatography system; mobile phase A was 0.1% formic acid in water, and mobile phase B was 0.1% formic acid in acetonitrile; the injection mode was a capture-analysis dual-column method, with the capture column being the PepMap Neo Trap Cartridge and the analytical column being the Easy-Spray TM PepMap TM Neo UHPLC column, analytical column temperature 55°C, sample load 200 ng, flow rate 2.5 μl / min, effective gradient 6.9 min, total time 8 min; Orbitrap Astral mass spectrometer detection: DIA analysis was performed using a Vanquish Neo system for chromatographic separation, and the separated samples were analyzed by DIA mass spectrometry using an Orbitrap Astral high-resolution mass spectrometer. Detection mode: positive ion and parent ion scan range 380-980 m / z, primary mass spectrometry resolution 240,000 at 200 m / z, Normalized AGC Target 500%, Maximum IT 5 ms. MS2 adopted DIA data acquisition mode, with 299 scan windows, Isolation Window 2 Th, HCD Collision Energy 25%, Normalized AGC Target 500%, and Maximum IT 3 ms. The DIA-NN software was used to perform qualitative and quantitative analysis of proteins on the mass spectrometry data of honey to obtain proteomic information.

5. The method for analyzing characteristic components of honey from Apis cerana cerana var. cerana var. Changbai Mountain based on multi-omics according to claim 4, characterized in that: The proteomic difference data analysis of the Changbai Mountain Apis cerana honey and the reference honey is performed to obtain the proteomic difference information of the Apis cerana honey and the reference honey, including: The screening criteria for significantly different proteins include: For replicates, biological replicates ≥ 2: when the difference is grouped into two groups of samples, FC ≥ 1.5 or FC ≤ 0.6667, and P-value ≤ 0.05 are defined as proteins with significant differences; when the difference is grouped into more than two groups of samples, P-value ≤ 0.05 is met; for non-replicate items: when the difference is grouped into two groups of samples, FC ≥ 1.5 or FC ≤ 0.6667 are defined as proteins with significant differences.

6. The method for analyzing characteristic components of honey from Apis cerana cerana var. cerana var. Changbai Mountain based on multi-omics according to claim 5, characterized in that: According to the metabolomics and proteomics difference information, functional annotation of the metabolomics and proteomics difference information is performed, and pathway analysis is performed using the KEGG database, including: Based on the metabolomics difference information, the KEGG database was used to perform functional annotation of metabolites, and KEGG pathway classification was performed based on the KEGG annotation information of differential metabolism; multiple significantly enriched KEGG pathways were selected, and all differential metabolites in the KEGG pathways were clustered and analyzed to analyze the changes in the content of substances in important metabolic pathways in different honey groups; KEGG pathway enrichment analysis was performed based on the differential metabolites, and the top 20 pathways with the highest hypergeometric test p-value were screened for analysis to determine the differential metabolites in the pathways; the differential abundance scores of the top 20 pathways were calculated; Based on the proteomic difference information, GO, KEGG, and KOG databases were used for protein functional annotation and enrichment analysis. The domain structure of differential proteins was used for functional annotation and enrichment analysis using the InterPro database. The subcellular localization prediction of differential proteins was analyzed using WoLF PSORT software, and the signal peptide localization of differential proteins was analyzed using SignalP software.

7. The method for analyzing characteristic components of honey from Apis cerana cerana var. cerana var. Changbai Mountain based on multi-omics according to claim 6, characterized in that: Based on the functional annotation and pathway analysis results of metabolomics and proteomics differential information, we found the KEGG pathways annotated by both omics. We then performed expression correlation analysis and O2PLS analysis on the KEGG pathways annotated by both omics to screen the characteristic components of Changbai Mountain Chinese honey, including: In each differential group, the correlations with the absolute value of the Pearson correlation coefficient greater than 0.8 and the p-value less than 0.05 were screened. The fold differences between the corresponding proteins and metabolites in the correlations were displayed in a nine-quadrant plot, and the quadrants 1-9 were separated from left to right and from top to bottom by black dotted lines. All differential metabolites and differential proteins were selected to establish an O2PLS model for O2PLS analysis, to determine the overall correlation and independence between the two data groups, to draw a loading diagram, and to screen out important variables that have a significant impact on the other group.

8. The characteristic components of Changbai Mountain Apis cerana honey obtained by analyzing the characteristic components of Changbai Mountain Apis cerana honey based on multi-omics analysis method according to any one of claims 1 to 7, characterized in that: The metabolomic difference information included: Cyclo(Pro-Leu), D-Ribulose 5-phosphate, cis-4-Hydroxy-D-proline, Hydroxyproline, cis-3-Hydroxy-L-proline, LysoPC 17:2(2n isomer), Lys-Ala, Pyridoxine phosphate, Lys-Asn, 12(13)-EpOME, 3-Methylsuberic acid and (9Z,11Z)-13-hydroxyhexadeca-9,11-dienoic acid; The proteomic difference information includes: APICC_01685, APICC_04696, APICC_00562, APICC_08137, APICC_07862, APICC_09184, APICC_08085, APICC_04206, APICC_05792, APICC_01096, APICC_03450, APICC_06442 and APICC_10171.

9. The characteristic component of Changbai Mountain Apis cerana honey according to claim 8, characterized in that: The characteristic components include: A0A2A3EDE3 and Sasp001236, A0A2A3EDR1 and pme0124, Safp001796, A0A2A3EPZ0 and Lmbn001288, A0A2A3E4S5 and Lcfn121066, A0A2A3EHG0 and MWS201471, A0A2A3EIU8 and PD0280915, A0A2A3EMA9 and PD0280915, A0A2A3EPJ1 and p me0008, A0A2A3E771 and Lmqn000432 and A0A2A3EHG0 and PDP228396, A0A2A3E771 and pma6455, or A0A2A3EHG0, A0A2A3E5C0, A0A2A3EKU2, A0A2A3E8C9, A0A2A3ED74, A0A2A3ERC7, A0A2A3ESV4, A0A2A3E889, A0A2A3EMG3, and A0A2A3EM77.

10. Use of the metabolomics difference information or proteomics difference information according to claim 8 or the characteristic components of the Changbai Mountain Apis cerana honey according to claim 9 in identifying the Changbai Mountain Apis cerana honey.

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