Application of marker metabolite in identification of different harvest periods of porphyra haitanensis

Abstract

The invention discloses application of a marker metabolite in identification of different harvest periods of porphyra haitanensis, and belongs to the field of food analysis and detection. The marker metabolites comprise lysophosphatidylethanolamine 15: 1 *, 2 '-deoxyguanosine, L-gamma-glutamine-L-leucine, lysophosphatidylethanolamine 22: 5, lysophosphatidylethanolamine 17: 1 *, 3, 4-dihydroxyphenylacetic acid *, 12S-hydroxy-5Z, 8Z, 10E, 14Z-eicosatetraenoic acid, lysophosphatidylcholine 18: 0 (2n isomerization) and 1-(2, 4-dihydroxyphenyl)-1-(2, 4-dihydroxyphenyl)-1-(2, 4-dihydroxyphenyl)-1-(2, 4-dihydroxyphenyl)-1-(2, 4-dihydroxyphenyl)-1-(2, 4-dihydroxyphenyl)-1-(2, 4- )-3-(((2-(dimethylamino) ethyoxyl) (hydroxy) phosphoryl) oxy) propan-2-yl palmitate *, and the like. The screening method of the marker metabolite reveals the difference of the porphyra haitanensis in different harvesting periods, the technology is advanced, the experimental result is reliable, and an excellent method is provided for quality identification and quality evaluation of the porphyra haitanensis.

Description

Technical Field

[0001] This invention relates to the field of food analysis and testing, specifically to the application of a marker metabolite in identifying different harvesting periods of *Porphyra yezoensis*. Background Technology

[0002] Pyropia haitanensis is one of my country's most important large seaweed economic crops. Taxonomically, it belongs to the phylum Rhodophyta, class Rhodophyta, order Rhodophyales, family Rhodophyceae, and genus Pyropia. According to the "China Fisheries Statistical Yearbook 2024," the aquaculture production of laver reached 209,939 tons in 2023, a decrease of 3.55% compared to 2022. Pyropia haitanensis accounted for 75% of the total laver production. Pyropia haitanensis has a delicious flavor, is rich in nutrients, high in protein and low in fat, making it an excellent source of protein. It is also rich in various nutrients beneficial to the human body, such as polysaccharides, vitamins, and minerals, and has broad market prospects.

[0003] The harvesting of laver (Porphyra yezoensis) is characterized by "segmentation," meaning that harvested laver can regrow. The first harvest of laver is generally called "first-harvest laver." In Fujian province, the first harvest of laver typically occurs between October and November. First-harvest laver has a long, thin, and narrow thallus. Subsequent harvests are successively called second-harvest, third-harvest, and fourth-harvest, with the thallus gradually thickening and widening with each harvest. The nutritional components, flavor, and sensory qualities of laver differ between harvest periods. The quality and market price of harvested laver products vary significantly depending on the harvest period. Some merchants in the market use third- or fourth-harvest laver instead of first- or second-harvest laver to earn higher profits. While conventional methods such as observing length, width, color, and smell can be helpful, they inevitably involve subjectivity and reliance on experience. Therefore, a rapid and effective analytical method is urgently needed to determine the harvest period of unknown laver. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide an application of marker metabolites in identifying different harvesting periods of Porphyra yezoensis.

[0005] According to one aspect of the present invention, an application of marker metabolites in identifying different harvesting periods of *Porphyra yezoensis* is proposed, said marker metabolites including lysophosphatidylethanolamine 15:1*, 2'-deoxyguanosine, L-γ-glutamyl-L-leucine, lysophosphatidylethanolamine 22:5, lysophosphatidylethanolamine 17:1*, 3,4-dihydroxyphenylacetic acid*, 12S-hydroxy-5Z,8Z,10E,14Z-eicosatetraenoic acid, and lysophosphatidylcholine 1 8:0 (2n isomer), 1-(2,3-dihydroxypropoxy)-3-(((2-(dimethylamino)ethoxy)(hydroxy)phosphoryl)oxy)propyl-2-palmitate*, 4,8-dihydroxyquinoline-2-carboxylic acid, L-malic acid, γ-L-glutamine 3,4-dihydroxybenzene, hydroxydihydrobovolactone*, N-benzylmethyleneisomethylamine, 5-furanose ribosylnicotinamide, astragalic acid and lysophosphatidylethanolamine 17:1 (2n isomer)*.

[0006] According to a second aspect of the present invention, a method for identifying *Purple violet* harvested at different times based on metabolomics is proposed, comprising the following steps:

[0007] S1. Samples of Porphyra yezoensis from different harvest periods were prepared, and the samples were mixed with methanol to obtain test solutions.

[0008] S2. The test solution is analyzed using LC-MS / MS technology to obtain test data and identify its chemical composition;

[0009] S3. Perform metabolomics analysis on the detection data, screen the marker metabolites, and obtain the metabolomics differences of Porphyra yezoensis at different harvesting periods.

[0010] Furthermore, the chromatographic conditions for the LC-MS / MS detection are as follows:

[0011] Column: Agilent SB-C18, 1.8μm, 2.1mm × 100mm;

[0012] Mobile phase: Phase A is ultrapure water with 0.1% formic acid added; Phase B is acetonitrile with 0.1% formic acid added.

[0013] Elution gradient: The proportion of phase B is 5% at 0.00 min, increases linearly to 95% within 9.00 min and is maintained at 95% for 1 min, and decreases to 5% between 10.00 and 11.10 min, and is equilibrated at 5% until 14 min.

[0014] Flow rate: 0.35 mL / min; column temperature: 40 °C; injection volume: 2 μL.

[0015] Furthermore, the mass spectrometry conditions for the LC-MS / MS detection are as follows: electrospray ion source temperature 500℃, positive / negative ion mode voltages 5500V / -4500V respectively; ion source gas I, II and curtain gas pressures set to 50, 60 and 25psi respectively, collision-induced ionization parameter set to high; detection is performed in MRM mode with QQQ scanning, collision gas nitrogen set to medium, and after optimization of declustering voltage and collision energy, specific MRM ion pairs are monitored according to elution period.

[0016] Furthermore, the chemical component identification is a qualitative analysis, which is performed by detecting metabolites through LC-MS / MS data matching database.

[0017] Furthermore, the metabolomics analysis is a quantitative analysis, including preprocessing the detection data described in S2 using Analyst 1.6.3 software to obtain retention time and peak area data files of the substances.

[0018] Furthermore, the metabolomics analysis also includes multivariate statistical data analysis, which includes PCA analysis, hierarchical clustering analysis, and random forest algorithm.

[0019] Furthermore, the multivariate statistical data analysis includes the following steps: importing the preprocessed data into R software, performing PCA analysis first, then performing UV standardization on the metabolite content data, drawing a cluster heatmap and performing hierarchical cluster analysis, and finally using the random forest algorithm to screen marker metabolites.

[0020] Furthermore, the metabolomic differences are obtained by annotating marker metabolites using the KEGG database, classifying the annotation results according to KEGG pathway types, and conducting KEGG pathway enrichment analysis.

[0021] According to a third aspect of the invention, an application of the method in identifying different harvesting periods of *Porphyra yezoensis* is proposed.

[0022] The beneficial effects of this invention are:

[0023] This invention utilizes metabolomics to identify LC-MS / MS data of *Porphyra yezoensis* from different harvesting periods. Compounds in *Porphyra yezoensis* samples from six production areas and four harvesting periods were identified. After preprocessing the raw LC-MS / MS data, a random forest classification model was applied to obtain marker metabolites that distinguish *Porphyra yezoensis* from different harvesting periods. This method clearly reveals the differences between *Porphyra yezoensis* from different harvesting periods, demonstrating that metabolomics can be used to differentiate *Porphyra yezoensis* from different harvesting periods. Furthermore, the advanced technology and reliable experimental results provide an excellent method for the quality identification and evaluation of *Porphyra yezoensis*. Attached Figure Description

[0024] Figure 1 This is a multi-peak chromatogram of MRM metabolite detection in *Porphyra yezoensis* at different harvesting periods, as shown in a specific embodiment of the present invention.

[0025] Figure 2 This is a circular diagram showing the composition of metabolites of *Porphyra yezoensis* at different harvesting periods in a specific embodiment of the present invention.

[0026] Figure 3 This is a PCA 3D score chart of Porphyra yezoensis at different harvesting periods in a specific embodiment of the present invention;

[0027] Figure 4 Heatmap images of *Porphyra yezoensis* at different harvesting periods are shown in a specific embodiment of the present invention.

[0028] Figure 5 These are the top 30 potential biomarker metabolites selected by the random forest classification model in a specific embodiment of the present invention;

[0029] Figure 6 This is a three-fold cross-validation diagram in a specific embodiment of the present invention;

[0030] Figure 7 This is a KEGG classification diagram of 18 marker metabolites of *Porphyra yezoensis* at different harvesting periods in a specific embodiment of the present invention;

[0031] Figure 8 This is a KEGG enrichment map of 18 marker metabolites of *Porphyra yezoensis* at different harvesting periods in a specific embodiment of the present invention. Detailed Implementation

[0032] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0033] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0034] The first objective of this invention is to propose the application of a marker metabolite in identifying different harvesting periods of Porphyra yezoensis, wherein the marker metabolite can effectively distinguish the harvesting period of Porphyra yezoensis.

[0035] The second objective of this invention is to propose a method for identifying *Porphyra yezoensis* at different harvesting periods based on metabolomics, providing a stable and accurate tool for the quality identification and evaluation of *Porphyra yezoensis*.

[0036] The third objective of this invention is to propose an application of the method described therein in identifying different harvesting periods of *Porphyra yezoensis*.

[0037] Example 1

[0038] 1. Instruments and reagents

[0039] 1.1 Instrument: Waters Acquity UPLC-Xevo G2QTOF ultra-high performance liquid chromatography-mass spectrometry system

[0040] 1.2 Reagents: Porphyra yezoensis was collected from Ningbo City, Zhejiang Province; Xiapu City, Pingtan City, Putian City, and Zhangzhou City, Fujian Province; and Shantou City, Guangdong Province. Methanol and acetonitrile were chromatographic grade, and formic acid was analytical grade.

[0041] 2. Preparation of samples of Porphyra yezoensis from different harvesting periods

[0042] Different harvest periods of *Porphyra yezoensis* were used as experimental materials. After digestion, the samples were removed from a -80℃ freezer, thawed on ice, and vortexed for 30 seconds to mix. 3 mL of the sample was then freeze-dried under vacuum. 500 μL of 70% methanol internal standard extraction solution was added, and the mixture was vortexed for 15 min; then centrifuged at 12000 rpm for 3 min at 4℃. The supernatant was collected, filtered through a 0.22 μm microporous membrane, and stored in a sample vial for LC-MS / MS detection.

[0043] Table 1 Information on Laver (Porphyra) in Each Group

[0044]

[0045]

[0046]

[0047] 3. LC-MS / MS Detection of Components in Porphyra yezoensis Samples from Different Harvest Periods

[0048] Chromatographic conditions:

[0049] 1) Column: Agilent SB-C18 1.8μm, 2.1mm*100mm;

[0050] 2) Mobile phase: Phase A is ultrapure water (with 0.1% formic acid added), and Phase B is acetonitrile (with 0.1% formic acid added);

[0051] 3) Elution gradient: The proportion of phase B is 5% at 0.00 min, and increases linearly to 95% within 9.00 min, and is maintained at 95% for 1 min. From 10.00 to 11.10 min, the proportion of phase B decreases to 5% and is balanced at 5% until 14 min.

[0052] 4) Flow rate 0.35 mL / min; column temperature 40℃; injection volume 2 μL.

[0053] Mass spectrometry conditions: Electrospray ionization (ESI) 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) were set to 50, 60, and 25 psi, respectively, and collision-induced ionization parameters were set to high. QQQ scans were performed using MRM mode with the collision gas (nitrogen) set to medium. DP and CE for each MRM ion pair were optimized through further declustering potential (DP) and collision energy (CE). A specific set of MRM ion pairs was monitored at each epoch based on the metabolites eluted within each epoch.

[0054] Based on the above conditions, samples of *Porphyra yezoensis* from different harvesting periods were tested, and MRM charts of *Porphyra yezoensis* from different harvesting periods were obtained. Figure 1 ).

[0055] 4. Identification of chemical components in samples of *Porphyra yezoensis* harvested at different times

[0056] The LC-MS / MS (liquid chromatography-mass spectrometry) detection data obtained above were analyzed, structurally characterized, and identified, and a total of 855 metabolites were detected.

[0057] 5. Identification of marker metabolites in *Porphyra yezoensis* at different harvesting periods

[0058] The raw data from LC-MS / MS detection in step 3 were then preprocessed using Analyst 1.6.3 software, including peak identification, peak alignment, peak matching, and normalization, to obtain retention time and peak area data files. The preprocessed data were then imported into R software for multivariate statistical analysis. First, unsupervised PCA analysis was performed to observe the differentiation of *Porphyra yezoensis* at different harvesting periods. Figure 3 , Figure 4 Principal component analysis (PCA) uses dimensionality reduction to visually represent the differences in secondary metabolites among samples. By performing PCA on samples (including quality control samples), we can gain a preliminary understanding of the overall metabolite differences between groups and the magnitude of variability within each group. PCA score plots provide a very intuitive view of the similarity between samples.

[0059] Metabolite content data were processed using unit variance scaling (UV), and heatmaps were generated using the ComplexHeatmap package in R software. Hierarchical Cluster Analysis (HCA) was then performed to analyze the accumulation patterns of metabolites across different samples. First, the data underwent UV processing, followed by cluster heatmap analysis for all samples, and finally, an R script was used to generate the cluster heatmaps. Figure 5 The horizontal axis represents the sample name, the vertical axis represents the metabolite information, the "Group" indicates the grouping, and different colors represent the different values ​​obtained after standardization of relative content (red represents high content, green represents low content).

[0060] Metabolomics data is characterized by its high dimensionality and massive volume, thus requiring a combination of univariate and multivariate statistical analysis methods. Analysis should be conducted from multiple perspectives based on the data characteristics to accurately identify differentially expressed metabolites. Random forest is a (parallel) ensemble algorithm composed of decision trees, belonging to the Bagging type. By combining multiple weak classifiers, the final result is determined through voting or averaging, resulting in a model with high accuracy and generalization performance, as well as good stability. It is widely used in various business scenarios. The excellent performance of random forest is mainly attributed to the "random" and "forest" elements: one provides resistance to overfitting, and the other enhances accuracy. The random forest package in RStudio was used to screen for marker metabolites that could distinguish different harvesting periods. Figure 7 The reliability of the data was confirmed through three-fold cross-validation. Figure 8 Ultimately, 18 marker metabolites that could distinguish laver from laver harvested at different times were obtained.

[0061] The results of the screened marker metabolites are as follows:

[0062] Table 2. Marker metabolites of *Porphyra yezoensis* at different harvesting periods.

[0063]

[0064]

[0065] The KEGG (Kyoto Encyclopedia of Genes and Genomes) database helps researchers study genes, expression information, and metabolite content as a holistic network. As a leading public database on pathways, KEGG provides integrated pathway queries, including the metabolism of carbohydrates, nucleotides, amino acids, and the biodegradation of organic matter. It not only provides all possible metabolic pathways but also offers comprehensive annotations of the enzymes catalyzing each step of the reaction, including amino acid sequences, links to PDB libraries, and more. It is a powerful tool for conducting in vivo metabolic analysis and metabolic network studies.

[0066] Metabolites interact within organisms, forming different pathways. Metabolites are annotated using the KEGG database. The annotation results for significantly differentially expressed metabolites are categorized according to pathway type in KEGG. Based on the differentially expressed metabolite results, KEGG pathway enrichment analysis is performed. The Rich Factor is the ratio of the number of differentially expressed metabolites in a corresponding pathway to the total number of metabolites annotated in that pathway; a higher Rich Factor indicates a greater degree of enrichment.

[0067] The KEGG pathway enrichment results for marker metabolites are as follows:

[0068] Table 3. KEGG pathway markers of metabolite enrichment

[0069]

[0070] The specific embodiments of this application have been described above, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. The application of a marker metabolite in identifying different harvesting periods of *Porphyra yezoensis*, characterized in that, The marker metabolites include lysophosphatidylethanolamine 15:1*, 2'-deoxyguanosine, L-γ-glutamine-L-leucine, lysophosphatidylethanolamine 22:5, lysophosphatidylethanolamine 17:1*, 3,4-dihydroxyphenylacetic acid*, 12S-hydroxy-5Z,8Z,10E,14Z-eicosatetraenoic acid, lysophosphatidylcholine 18:0 (2n isomer), 1-(2,3-dihydroxypropoxy)-3-(((2-(dimethylamino)ethoxy)(hydroxy)phosphoryl)oxy)propyl-2-palmitate*, 4,8-dihydroxyquinoline-2-carboxylic acid, L-malic acid, γ-L-glutamine 3,4-dihydroxybenzene, hydroxydihydrobovolactone*, N-benzylmethyleneisomethylamine, 5-furanose ribosylnicotinamide, astaxanthin, and lysophosphatidylethanolamine 17:1 (2n isomer)*.

2. A method for identifying *Purple violet* harvested at different times based on metabolomics, characterized in that, Includes the following steps: S1. Samples of Porphyra yezoensis from different harvest periods were prepared, and the samples were mixed with methanol to obtain test solutions. S2. The test solution is analyzed using LC-MS / MS technology to obtain test data and identify its chemical composition; S3. Perform metabolomics analysis on the detection data, screen for the marker metabolites as described in claim 1, and obtain the metabolomics differences of Porphyra yezoensis at different harvesting periods.

3. The method according to claim 2, characterized in that, The chromatographic conditions for LC-MS / MS detection are as follows: Column: Agilent SB-C18, 1.8 μm, 2.1 mm × 100 mm; Mobile phase: Phase A is ultrapure water with 0.1% formic acid added; Phase B is acetonitrile with 0.1% formic acid added. Elution gradient: The proportion of phase B is 5% at 0.00 min, increases linearly to 95% within 9.00 min and is maintained at 95% for 1 min, and decreases to 5% between 10.00 and 11.10 min, and is equilibrated at 5% until 14 min. Flow rate: 0.35 mL / min; column temperature: 40 °C; injection volume: 2 μL.

4. The method according to claim 2, characterized in that, The mass spectrometry conditions for LC-MS / MS detection were as follows: electrospray ion source temperature 500℃, positive / negative ion mode voltages 5500V / -4500V respectively; ion source gas I, II and curtain gas pressures set to 50, 60 and 25psi respectively, collision-induced ionization parameter set to high; detection was performed in MRM mode with QQQ scanning, collision gas nitrogen set to medium, and after optimization of declustering voltage and collision energy, specific MRM ion pairs were monitored according to elution period.

5. The method according to claim 2, characterized in that, The chemical composition identification is a qualitative analysis, and the method is to detect metabolites by matching LC-MS / MS data with a database.

6. The method according to claim 2, characterized in that, The metabolomics analysis is a quantitative analysis, which includes preprocessing the detection data described in S2 using Analyst 1.6.3 software to obtain data files of the retention time and peak area of ​​the substances.

7. The method according to claim 6, characterized in that, The metabolomics analysis also includes multivariate statistical data analysis, which includes PCA analysis, hierarchical clustering analysis, and random forest algorithm.

8. The method according to claim 7, characterized in that, The multivariate statistical data analysis includes the following steps: importing the preprocessed data into R software, performing PCA analysis first, then performing UV standardization on the metabolite content data, drawing a cluster heatmap and performing hierarchical cluster analysis, and finally using the random forest algorithm to screen marker metabolites.

9. The method according to claim 2, characterized in that, The metabolomic differences were obtained by annotating marker metabolites using the KEGG database, classifying the annotation results according to KEGG pathway types, and conducting KEGG pathway enrichment analysis.

10. The application of the method according to any one of claims 2-9 in identifying different harvesting periods of Lavera arborescens.

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