WO2021190456A1 - 一种基于代谢组全局分析的植物性别鉴定方法 - Google Patents

一种基于代谢组全局分析的植物性别鉴定方法 Download PDF

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WO2021190456A1
WO2021190456A1 PCT/CN2021/082126 CN2021082126W WO2021190456A1 WO 2021190456 A1 WO2021190456 A1 WO 2021190456A1 CN 2021082126 W CN2021082126 W CN 2021082126W WO 2021190456 A1 WO2021190456 A1 WO 2021190456A1
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ginkgo
metabolome
targeted
metabolites
sex
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French (fr)
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闫建斌
杜然
廖庆刚
黄三文
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中国农业科学院农业基因组研究所
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/62—Detectors specially adapted therefor
    • G01N30/72—Mass spectrometers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/86—Signal analysis
    • G01N30/8675—Evaluation, i.e. decoding of the signal into analytical information
    • G01N30/8682—Group type analysis, e.g. of components having structural properties in common

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  • This application relates to the field of identification of plant sex, in particular to a method for identification of gender of Ginkgo biloba based on metabolome global analysis.
  • Ginkgo is one of the relict tree species in my country and has the reputation of living fossil. It is not only the most important tree species for greening in northern my country, but also an important source of extraction of cardio-cerebral vascular specific drugs such as ginkgolides and ginkgo flavonoids, as well as the production source of important medicinal and food products such as ginkgo. Ginkgo is a dioecious gymnosperm, and there are certain differences between male and female trees in tree shape, bioactive substance production, etc. For the purpose of producing green street trees, it is necessary to choose to cultivate male plants as much as possible; and for ginkgo production The nursery needs female plants.
  • Ginkgo is a dioecious gymnosperm, and its floral organs have significant differences between male and female. However, it takes about 15 years or more for ginkgo to bloom after germination. It is impossible to identify male and female by this method in the seedling stage.
  • one of the objectives of this application provides a method for plant sex identification based on global metabolome analysis.
  • This method uses global analysis of Ginkgo metabolome differences so that the male and female plants of Ginkgo can be identified at an early stage, and its The operation is simple and the accuracy is high.
  • One aspect of this application provides a method for plant sex identification based on metabolome global analysis, which includes the following steps:
  • the non-targeted metabolome detects the metabolites in the ginkgo leaves to be tested, and the non-targeted metabolome data is obtained;
  • the step 1) includes: extracting the metabolites of the ginkgo tree seedling leaves and internal reference leaves to be tested, respectively, and then performing non-targeted metabolome detection.
  • the internal reference leaf is the leaf of a ginkgo tree whose sex has been determined and belongs to the same species as the ginkgo to be tested.
  • the method for extracting the metabolites is: using an organic solvent to extract the metabolites from the leaves.
  • the organic solvent includes methanol and ethanol.
  • the method for extracting metabolites includes the following steps:
  • Ginkgo leaves are frozen, ground and crushed, mixed with 80% methanol, and then sonicated;
  • the step 1) includes metabolome identification and data processing
  • the process of metabolome identification and data processing includes: using ultra-high resolution mass spectrometry to detect metabolites, using mass spectrometry software to extract mass spectrometry information, based on commercial databases, local self-built databases and standards, etc., to perform metabolites Annotation; Annotated substances with secondary structure, screening qualitative and quantitative ion pair and retention time and other information.
  • the step 1) mass spectrometry conditions are: use BEH C18 chromatographic column (100 ⁇ 2.1mm, 1.7 ⁇ m) for separation; mobile phase A is methanol, mobile phase B is 5mmol/L Ammonium acetate, the flow rate is 0.3ml/min, the injection volume is 1 ⁇ l, the column temperature is 45°C; the HESI source operates in negative ion mode, the collision gas pressure is 1.5mTorr, the spray voltage is 2.8kV, the capillary temperature is 320°C, and the heater temperature is 300°C.
  • the step 1) the mobile phase is gradually increased from 50:50 to 95:5 (A:B, v/v).
  • the quantitative calibration of the non-targeted metabolome data also includes the use of MRM monitoring technology based on internal standard correction to perform the detection of the non-targeted metabolome detected in step 1).
  • Metabolites are calibrated for targeted content based on information such as ion pair, retention time, and relative intensity.
  • the step 2) mass spectrometry conditions are: a BEH C18 chromatographic column (100 ⁇ 2.1 mm, 1.7 ⁇ m) is used for separation.
  • the mobile phase A is methanol
  • the mobile phase B is 5 mmol/L ammonium acetate
  • the flow rate is 0.3 ml/min.
  • the injection volume is 1 ⁇ l
  • the column temperature is 45°C.
  • the HESI source operates in the negative ion mode, the collision gas pressure is 1.5mTorr, the spray voltage is 2.8kV, the capillary temperature is 320°C, and the heater temperature is 300°C.
  • the step 2) the mobile phase is gradually increased from 50:50 to 95:5 (A:B, v/v).
  • the specific method in step 3) is: comparing the data processing in step 2) with the metabolic data processing of Ginkgo biloba of known gender to determine the gender of the ginkgo to be tested.
  • the specific method in step 3) is: taking the ginkgo leaf metabolome test data of known male and female sex as a reference, in the ginkgo sample to be tested, the metabolome of the ginkgo biloba is statistically processed Later, after hierarchical clustering analysis (using Ward clustering algorithm), the samples that are classified under the same branch as the known male ginkgo samples are judged to be males; those that are classified under the same branch as the known female ginkgo samples It is judged as female.
  • the method for plant sex identification based on metabolome global analysis is characterized in that it comprises the following steps:
  • S2 Use ultra-high resolution mass spectrometry to detect metabolites, use mass spectrometry software to extract mass spectrometry information, and establish qualitative and quantitative ion pair information for the detected substances;
  • step S3 Use MRM monitoring technology based on internal standard correction to calibrate the target content of the non-targeted metabolites detected in step S2 based on ion pair information, retention time, and relative intensity;
  • step S4 Compare the data processing in step S3 with the non-targeted metabolism data processing of Ginkgo biloba with a known gender to determine the gender of the ginkgo to be tested.
  • the mobile phase was gradually increased from 50:50 to 95:5 (A:B, /v).
  • the injection volume is 1 ⁇ l, and the column temperature is 45°C.
  • the HESI source operates in negative ion mode, the collision gas pressure is 1.5mTorr, the spray voltage is 2.8kV, the capillary temperature is 320°C, and the heater temperature is 300°C.
  • Measure the concentration of qualitative substances to obtain semi-quantitative concentration data of corresponding metabolites.
  • the detection results are processed by TraceFinder software of Thermoelectric Company's mass spectrometer, combined with Compound Discoverer software to annotate the secondary structure metabolites, and extract the qualitative precursor ions and quantitative product ions of the corresponding substances, and establish the MRM form of supervision mass spectrometry.
  • the supervisory mass spectrometry MRM table established based on the non-targeted metabolome data, using Thermo Company UHPLC-TSQ Quantis tandem mass spectrometry equipped with HESI ion source for detection, using BEH C18 column (100 ⁇ 2.1 mm, 1.7 ⁇ m) for separation.
  • the mobile phase A is methanol
  • the mobile phase B is 5 mmol/L ammonium acetate
  • the flow rate is 0.3 ml/min.
  • the mobile phase was gradually increased from 50:50 to 95:5 (A:B, v/v).
  • the injection volume is 1 ⁇ l
  • the column temperature is 45°C.
  • the HESI source operates in the negative ion mode, the collision gas pressure is 1.5mTorr, the spray voltage is 2.8kV, the capillary temperature is 320°C, and the heater temperature is 300°C. Measure the concentration of qualitative substances to obtain semi-quantitative concentration data of corresponding metabolites.
  • step 2) Based on the data measured in step 2), use the TraceFinder software of Thermal Power Company's mass spectrometer for data processing, and use the MetaboAnalyst package in R language to perform hierarchical clustering analysis (using Ward clustering algorithm) to obtain groupings.
  • Ginkgo leaf metabolome test data with known male and female sex is used as a reference. If the known Ginkgo biloba samples are divided under the same branch as the male, it is judged as male; and the known Ginkgo biloba samples divided under the same branch as the female are judged as female.
  • this application provides the application of non-targeted metabolomics analysis or plant sex identification methods based on metabolome global analysis in any one or more of the following 1)-4):
  • the application of non-targeted metabonomics analysis, or the plant sex identification method based on global metabolome analysis in the identification of the male and female sex of Ginkgo biloba can determine the male and female sex of Ginkgo biloba in the seedling stage of Ginkgo biloba, and thus according to the male and female sex of Ginkgo biloba seedlings.
  • Different genders are used for different purposes. For example, for the purpose of producing green street trees, male ginkgo seedlings are selected; for the purpose of ginkgo production, female ginkgo seedlings are selected.
  • the application of non-targeted metabolomics analysis or plant sex identification method based on metabolome global analysis in screening the characteristic metabolites of Ginkgo biloba.
  • the characteristic metabolites of Ginkgo biloba of different genders are screened as the marker metabolites for identifying male and female Ginkgo biloba plants, so that the marker metabolites can be used to quickly identify the gender of Ginkgo biloba.
  • non-targeted metabolomics analysis or the application of plant sex identification methods based on metabolome global analysis in identifying the genetic relationship of Ginkgo biloba Based on the non-targeted metabolome data of Ginkgo biloba leaves, systematic clustering analysis was used to obtain global difference information, and a genetic tree based on metabolome differences was used to identify the genetic relationship of Ginkgo biloba.
  • non-targeted metabolomics analysis or the application of plant sex identification methods based on metabolome global analysis in the breeding of Ginkgo biloba seedlings.
  • the target ginkgo is selected for breeding according to the characteristic metabolites of ginkgo; or the target ginkgo is selected for breeding according to the genetic relationship, and the breeding of ginkgo seedlings can be realized scientifically and effectively, and the target ginkgo seedlings can be obtained.
  • This application is based on the non-targeted metabolome data of Ginkgo biloba leaves and uses systematic cluster analysis to obtain global difference information. By comparing the difference with known gender control samples, gender can be determined at the seedling stage of Ginkgo biloba, with stable and accurate results. The advantages of good repeatability have important application value for guiding the selection, breeding and refined development of ginkgo seedlings.
  • Figure 1 shows the results of OPLS-DA analysis of leaf metabolites of 5 male trees and 5 female trees provided in Example 1 of the application.
  • Fig. 2 shows the kinship tree established based on the metabolome difference of the ginkgo to be tested provided in Example 2 of the application.
  • FIG. 3 shows the OPLS-DA analysis result diagram of the grouped samples of Ginkgo biloba to be tested based on the kinship tree established in Example 2 of the application.
  • Figure 4 shows the kinship tree established based on the metabolome difference of the ginkgo to be tested provided by the comparative example of this application.
  • Figure 5 shows the OPLS-DA analysis result of the grouped samples of the ginkgo to be tested based on the kinship tree provided by the comparative example of this application, where the scores plot refers to the score distribution map.
  • test materials used in the following examples are all purchased from conventional biochemical reagent stores.
  • Ginkgo trees and leaves were randomly selected from major ginkgo planting bases in Pizhou City, Jiangsu province.
  • the liquid phase conditions are: BEH C18 chromatographic column (100 ⁇ 2.1mm, 1.7 ⁇ m) for separation.
  • the mobile phase A is methanol
  • the mobile phase B is 5 mmol/L ammonium acetate
  • the flow rate is 0.3 ml/min.
  • the mobile phase was gradually increased from 50:50 to 95:5 (A:B, v/v).
  • the injection volume is 1 ⁇ L
  • the column temperature is 45°C.
  • the ESI source operates in negative ion mode, with a high-purity nitrogen shell gas flow rate of 35arb, auxiliary gas 15arb, and high-purity argon collision gas pressure 1.5mTorr.
  • the setting parameters are as follows: spray voltage 2.8kV, capillary temperature 320°C, heater temperature 300°C.
  • the detection results were processed and analyzed by TraceFinder software provided by Thermoelectric Company's mass spectrometer, established qualitatively detected and annotated ion pair table of all non-targeted metabolites, and further adopted Thermo Fisher UHPLC-TSQ Quantis equipped with BEH C18 chromatographic column The sample is detected by a triple quadrupole mass spectrometer.
  • the liquid phase conditions are: BEH C18 chromatographic column (100 ⁇ 2.1mm, 1.7 ⁇ m) for separation.
  • the mobile phase A is methanol
  • the mobile phase B is 5 mmol/L ammonium acetate
  • the flow rate is 0.3 ml/min.
  • the mobile phase was gradually increased from 50:50 to 95:5 (A:B, v/v).
  • the injection volume is 1 ⁇ L
  • the column temperature is 45°C.
  • the ESI source operates in negative ion mode, with a high-purity nitrogen shell gas flow rate of 35arb, auxiliary gas 15arb, and high-purity argon collision gas pressure 1.5mTorr.
  • the setting parameters are as follows: spray voltage 2.8kV, capillary temperature 320°C, heater temperature 300°C.
  • the detection results are processed and analyzed by TraceFinder software supporting the thermoelectric company's mass spectrometer to obtain the accurate content data of the corresponding metabolites.
  • OPLS-DA Orthogonal partial least squares discriminant analysis
  • the liquid phase conditions are: BEH C18 chromatographic column (100 ⁇ 2.1mm, 1.7 ⁇ m) for separation.
  • the mobile phase A is methanol
  • the mobile phase B is 5 mmol/L ammonium acetate
  • the flow rate is 0.3 ml/min.
  • the mobile phase was gradually increased from 50:50 to 95:5 (A:B, v/v).
  • the injection volume is 1 ⁇ L
  • the column temperature is 45°C.
  • the ESI source operates in negative ion mode, with a high-purity nitrogen shell gas flow rate of 35arb, auxiliary gas 15arb, and high-purity argon collision gas pressure 1.5mTorr.
  • the setting parameters are as follows: spray voltage 2.8kV, capillary temperature 320°C, heater temperature 300°C.
  • the detection results were processed and analyzed by TraceFinder software provided by Thermoelectric Company's mass spectrometer, established qualitatively detected and annotated ion pair table of all non-targeted metabolites, and further adopted Thermo Fisher UHPLC-TSQ Quantis equipped with BEH C18 chromatographic column The sample is detected by a triple quadrupole mass spectrometer.
  • the liquid phase conditions are: BEH C18 chromatographic column (100 ⁇ 2.1mm, 1.7 ⁇ m) for separation.
  • the mobile phase A is methanol
  • the mobile phase B is 5 mmol/L ammonium acetate
  • the flow rate is 0.3 ml/min.
  • the mobile phase was gradually increased from 50:50 to 95:5 (A:B, v/v).
  • the injection volume is 1 ⁇ L
  • the column temperature is 45°C.
  • the ESI source operates in negative ion mode, with a high-purity nitrogen shell gas flow rate of 35arb, auxiliary gas 15arb, and high-purity argon collision gas pressure 1.5mTorr.
  • the setting parameters are as follows: spray voltage 2.8kV, capillary temperature 320°C, heater temperature 300°C.
  • the detection results are processed and analyzed by TraceFinder software supporting the thermoelectric company's mass spectrometer to obtain the accurate content data of the corresponding metabolites.
  • the results obtained are hierarchical clustering (Hierarchical Clustering), grouped based on Heatmap analysis data, and the experimental results are shown in Figure 2.
  • the kinship tree of 30 samples has two large branches at the root, where the left branch contains 14 samples and the right branch contains 16 samples; based on the kin tree branches, the samples are divided into two groups Among them, the 14 trees in the group of 5 known as males are all males; the 16 trees in the group of 5 known as females are all females.
  • This application can directly identify male and female Ginkgo biloba in the early stage of seedlings, solves the problem that Ginkgo biloba seedlings cannot be accurately identified for a long time, and facilitates the process of optimizing the allocation of Ginkgo male and female plant resources and their rational utilization.
  • the early sex identification of the seedlings of Ginkgo biloba makes the sex identification of Ginkgo no longer restricted by time and space conditions, and has important use and economic value for guiding the use of ginkgo for urban greening and economic forest cultivation.
  • the liquid phase conditions are: BEH C18 chromatographic column (100 ⁇ 2.1mm, 1.7 ⁇ m) for separation.
  • the mobile phase A is methanol
  • the mobile phase B is 5 mmol/L ammonium acetate
  • the flow rate is 0.3 ml/min.
  • the mobile phase was gradually increased from 50:50 to 95:5 (A:B, v/v).
  • the injection volume is 1 ⁇ L
  • the column temperature is 45°C.
  • the ESI source operates in negative ion mode, with a high-purity nitrogen shell gas flow rate of 35arb, auxiliary gas 15arb, and high-purity argon collision gas pressure 1.5mTorr.
  • the setting parameters are as follows: spray voltage 2.8kV, capillary temperature 320°C, heater temperature 300°C.
  • the detection results are processed and analyzed by TraceFinder software supporting the thermoelectric company's mass spectrometer, based on commercial databases, local databases, etc., for annotation and identification of unknowns, and the peak area corresponding to the annotated substance is extracted as the quantitative result.
  • the results obtained are hierarchical clustering (Hierarchical Clustering), grouped based on Heatmap analysis data, and the experimental results are shown in Figure 4.
  • the 30 sample kinship tree has two large branches at the root, where the left branch contains 18 samples and the right branch contains 12 samples; based on the kin tree branches, the samples are divided into two groups , Among them, samples 1-5, known as males, are scattered on both sides of the Y axis, and samples No. 6-10, known as females, are also scattered on both sides of the Y week.
  • OPLS-DA was used to analyze the metabolome difference of the grouped samples, and the experimental results are shown in Figure 5.
  • the results in Figure 5 show that the distribution of the groups based on the kinship tree branches on the OPLS-DA graph is significantly separated, and they are distributed on both sides of the Y axis.
  • the OPLS-DA evaluation index Q2 0.199, indicating that the grouping model is not valid.
  • 10 samples of known sex are similar to the results of hierarchical clustering.
  • Female and male samples are distributed on both sides of the Y axis, which proves that although 30 samples can be divided into two groups based on non-targeted metabolome data, they are grouped There is no regularity, and the identification of male and female samples cannot be achieved.

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Abstract

一种基于代谢组全局分析的植物性别鉴定方法，包含如下步骤：1)非靶向代谢组检测待测银杏叶片中的代谢物，获得非靶向代谢组数据；2)对步骤1)中非靶向代谢组数据进行定量校准；3)基于步骤2)中代谢组数据的差异鉴定待测银杏的性别。该基于银杏叶片的非靶向代谢组数据，利用系统聚类分析得到全局性差异信息，通过与已知性别对照样品的差异比较，在银杏幼苗期即可判定出性别，具有结果稳定准确、重复性好的优点，对于指导银杏种苗分选、繁育和精细化开发等均具有重要应用价值。

Description

一种基于代谢组全局分析的植物性别鉴定方法 技术领域
本申请涉及植物性别的鉴定领域，特别涉及一种基于代谢组全局分析的银杏性别鉴定方法。
背景技术
银杏是我国孓遗树种之一，有活化石的美誉。其不仅是我国北方最重要的绿化用树种，还是银杏内酯、银杏黄酮等心脑血管特效药物的重要提取来源，也是白果等重要药食同源产品的生产来源。银杏作为雌雄异株的裸子植物，其雌雄树在树形、生物活性物质产量等上面均存在一定的差异，以生产绿化行道树为目的，需尽可能选择培育雄性植株；而以白果生产为目的的苗圃则需要雌性植株。银杏作为雌雄异株的裸子植物，其花器有着显著的雌雄差异，但是银杏自发芽后需要约15年甚至更长时间才能开花，无法在树苗期通过该方法进行雌雄鉴定。
国内外学者曾在银杏的形态学、生理生化指标、同工酶谱、化学药剂处理和染色体核型等方面进行了研究。通过形态特征鉴定植物性别，比较简单直观，只需对植物的形态特征观察对比即可，操作较为简单。观察树形、叶片分裂等方式虽然简便易行，但行质指标判断主观因素影响较大，缺少数字化和图谱的明确标准，极易发生谬误。对 器官的判断虽然兼具准确以及简便，但是需要植物度过幼年期后才可观察到，因此，早期鉴定存在一定难度。有研究表明银杏雌雄株在生理生化指标上存在差异，但此类差异会受到气候、生长环境、树龄等多方面的影响。生理生化指标大多力求从不同侧面探寻对已知性别的成年植株之间进行测定，表现出一定差异性，但能否用于苗木早期性别鉴定有待于进一步研究。目前，染色体形态特征是进行银杏雌雄性别鉴定的重要方法之一，也是最直接的遗传证据。但根据核型观察，对银杏性别决定机制属XY型还是ZW型并无定论，依靠核型观察确定植物性别没有实际意义。精确雌雄鉴定方法的缺失，导致以生产行道树或者生产白果的银杏树农在种植过程中无法在树苗期区分雌雄，带来了较高的时间和经济损失。
发明内容
鉴于此，本申请的目的之一提供了一种基于代谢组全局分析的植物性别鉴定方法，该方法通过银杏代谢组的全局性差异分析，使得在早期即可鉴定出银杏的雌雄株，且其操作简单，准确率高。
为了实现本申请的上述目的，本申请采用如下技术方案：
本申请一方面提供一种基于代谢组全局分析的植物性别鉴定方法，其包含如下步骤：
1)非靶向代谢组检测待测银杏叶片中的代谢物，获得非靶向代谢组数据；
2)对步骤1)中非靶向代谢组数据进行定量校准；
3)基于步骤2)中代谢组数据的差异鉴定待测银杏的性别。
在本申请的一个具体实施方式中，所述步骤1)包括：分别提取待测银杏树幼苗叶片和内参叶片的代谢物后进行代谢组非靶向检测。
在本申请的一个具体实施方式中，所述的内参叶片为与待测银杏属于同一品种的已经确定性别的银杏树的叶子。例如，已经通过开花和是否结白果确定性别的银杏树的叶子。
在本申请的一个具体实施方式中，所述代谢物的提取方法为：采用有机溶剂抽提叶片中的代谢物。
示例性地，所述有机溶剂包括甲醇和乙醇。
在本申请的一个具体实施方式中，所述有机溶剂为80％浓度甲醇(V/V，甲醇：水＝80：20)。
在本申请的一个具体实施方式中，所述代谢物的提取方法包括如下步骤：
(1)银杏叶片经冷冻、研磨粉碎，加入80％浓度甲醇混合均匀后超声；
(2)离心后得上清液，干燥；
(3)加入80％浓度甲醇复溶，获得银杏叶片中的代谢物。
在本申请的一个具体实施方式中，所述步骤1)中包括代谢组鉴定和数据处理；
所述代谢组鉴定和数据处理的过程包括：采用超高分辨质谱进行代谢物的检测，采用质谱分析软件抽提质谱信息，基于商业化数据库、本地自建数据库和标准品等，对代谢物进行注释；注释出的具有二级结构的物质，筛选定性和定量离子对与保留时间等信息。
在本申请的一个具体实施方式中，所述步骤1)质谱条件为：采用BEH C18色谱柱(100×2.1mm，1.7μm)进行分离；流动相A为甲醇，流动相B为5mmol/L的乙酸铵，流速为0.3ml/min，进样量1μl，柱温45℃；HESI源在负离子模式下运行，碰撞气压力1.5mTorr，喷雾电压2.8kV，毛细管温度320℃，加热器温度300℃。
在本申请的一个具体实施方式中，所述步骤1)流动相从50:50逐步提升至95:5(A:B，v/v)。
在本申请的一个具体实施方式中，所述步骤2)中，所述对非靶向代谢组数据进行定量校准还包括采用基于内标校正的MRM监控技术对步骤1)检测出的非靶向代谢物进行基于离子对、保留时间和相对强度等信息的靶向含量校准。
在本申请的一个具体实施方式中，所述步骤2)质谱条件为：采用BEH C18色谱柱(100×2.1mm,1.7μm)进行分离。流动相A为甲醇，流动相B为5mmol/L的乙酸铵，流速为0.3ml/min。进样量1μl，柱温45℃。HESI源在负离子模式下运行，碰撞气压力1.5mTorr，喷雾电压2.8kV，毛细管温度320℃，加热器温度300℃。
在本申请的一个具体实施方式中，所述步骤2)流动相从50:50逐步提升至95:5(A:B，v/v)。
在本申请的一个具体实施方式中，所述步骤3)中的具体方法为：将步骤2)中的数据处理与已知性别银杏的代谢数据处理进行对比判定待测银杏的性别。
在本申请的一个具体实施方式中，所述步骤3)中的具体方法为： 以已知雌雄性别的银杏叶代谢组检测数据作为参考，待检测的银杏样品中，其代谢组在统计学处理后，经系统聚类(Hierarchical Clustering)分析(采用Ward聚类算法)，与雄性已知银杏样品划分在同一个分支下的则判定为雄性；与雌性已知银杏样品划分在同一个分支下的则判定为雌性。
在本申请的一个具体实施方式中，所述基于代谢组全局分析的植物性别鉴定方法，其特征在于，包含如下步骤：
S1：采用有机溶解分别提取待测银杏树幼苗叶片和内参叶片的代谢物后进行代谢组非靶向检测，获得非靶向代谢组数据；
S2：采用超高分辨质谱进行代谢物的检测，采用质谱分析软件抽提质谱信息，并建立检测到物质的定性和定量离子对等信息；
S3：采用基于内标校正的MRM监控技术对步骤S2检测出的非靶向代谢物进行基于离子对信息、保留时间和相对强度等信息的靶向含量校准；
S4：将步骤S3中的数据处理与已知性别银杏的非靶向代谢数据处理进行对比判定待测银杏的性别。
在本申请的一个具体实施方式中，具体包括如下步骤：
1)选择待测银杏幼苗的叶片，加入80％浓度甲醇(V/V，甲醇：水＝80：20)震荡，放入功率为200w的超声清洗机中超声30min，离心取上清备用；采用装有BEH C18色谱柱的赛默飞UHPLC-QExactive TM组合型四极杆Orbitrap质谱仪对样品进行检测。液相条件为：BEH C18色谱柱(100×2.1mm，1.7μm)进行分离。流动相A为甲醇，流动 相B为5mmol/L的乙酸铵，流速为0.3ml/min。流动相从50：50逐步提升至95：5(A:B，/v)。进样量为1μl，柱温45℃。HESI源在负离子模式下运行，碰撞气压力1.5mTorr，喷雾电压2.8kV，毛细管温度320℃，加热器温度300℃。对定性的物质进行浓度测定，获得对应代谢物的半定量浓度数据。检测结果采用热电公司质谱配套的TraceFinder软件进行数据处理，并结合Compound Discoverer软件对获得二级结构的代谢物进行注释，并抽提对应物质的定性母离子和定量子离子，建立监督质谱MRM表格。
2)基于UHPLC-TSQ高灵敏质谱，基于非靶向代谢组数据建立的监督质谱MRM表格，采用Thermo公司配备HESI离子源的UHPLC-TSQ Quantis串联质谱进行检测，采用BEH C18色谱柱(100×2.1mm,1.7μm)进行分离。流动相A为甲醇，流动相B为5mmol/L的乙酸铵，流速为0.3ml/min。流动相从50:50逐步提升至95:5(A:B,v/v)。进样量1μl，柱温45℃。HESI源在负离子模式下运行，碰撞气压力1.5mTorr，喷雾电压2.8kV，毛细管温度320℃，加热器温度300℃。对定性的物质进行浓度测定，获得对应代谢物的半定量浓度数据。
3)基于步骤2)中测定的数据，采用热电公司质谱配套的TraceFinder软件进行数据处理，采用R语言中MetaboAnalyst包进行系统聚类(Hierarchical Clustering)分析(采用Ward聚类算法)获得分组，以已知雌雄性别的银杏叶代谢组检测数据作为参考，与雄性已知银杏样品划分在同一个分支下的则判定为雄性；与雌性已知银杏样 品划分在同一个分支下的则判定为雌性。
本申请另一方面提供了非靶向代谢组学分析，或基于代谢组全局分析的植物性别鉴定方法在如下1)-4)中任意一种或多种的应用：
1)鉴定银杏的雌雄性别；
2)筛选银杏的特征代谢物；
3)鉴定银杏的亲缘关系；
4)银杏种苗繁育。
示例性地，非靶向代谢组学分析，或基于代谢组全局分析的植物性别鉴定方法在鉴定银杏雌雄性别中的应用，可以在银杏幼苗期判断出银杏的雌雄性别，从而根据银杏幼苗的雌雄性别选择不同的使用目的，例如若出于生产绿化行道树为目的，则选择雄性银杏幼苗；若出于白果生产为目的，则选择雌性银杏幼苗。
示例性地，非靶向代谢组学分析，或基于代谢组全局分析的植物性别鉴定方法在筛选银杏的特征代谢物中的应用。基于银杏叶片的非靶向代谢组数据，筛选出不同性别银杏的特征代谢物，作为鉴定银杏雌雄株的标志性代谢物，从而可以利用标志性代谢物快速鉴定银杏性别。
示例性地，非靶向代谢组学分析，或基于代谢组全局分析的植物性别鉴定方法在鉴定银杏的亲缘关系中的应用。基于银杏叶片的非靶向代谢组数据，利用系统聚类分析得到全局性差异信息，基于代谢组差异建立的亲缘性树，鉴定银杏的亲缘关系。
示例性地，非靶向代谢组学分析，或基于代谢组全局分析的植物 性别鉴定方法在银杏种苗繁育中的应用。例如，出于不同的使用目的，根据银杏的特征代谢物选择目标银杏进行育种；或根据亲缘关系选择目标银杏进行育种，科学有效地实现银杏种苗繁育，获得目标银杏种苗。
示例性的，本申请至少具有以下优势之一：
本申请基于银杏叶片的非靶向代谢组数据，利用系统聚类分析得到全局性差异信息，通过与已知性别对照样品的差异比较，在银杏幼苗期即可判定出性别，具有结果稳定准确、重复性好的优点，对于指导银杏种苗分选、繁育和精细化开发等均具有重要应用价值。
附图说明
图1所示为本申请实施例1提供的5株雄树和5株雌树叶片代谢物OPLS-DA分析结果图。
图2所示为本申请实施例2提供的待测银杏基于代谢组差异建立的亲缘性树。
图3所示为本申请实施例2提供的待测银杏基于亲缘树建立的分组样的OPLS-DA分析结果图。
图4所示为本申请对比例提供的待测银杏基于代谢组差异建立的亲缘性树。
图5所示为本申请对比例提供的待测银杏基于亲缘树建立的分组样的OPLS-DA分析结果图，其中scores plot指的是得分分布图。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述。显然，所描述的实施例仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
下述实施例中的实验方法，如无特殊说明，均为常规方法。
下述实施例中所用的试验材料，如无特殊说明，均为自常规生化试剂商店购买得到的。
以下实施例中的定量试验，均设置三次重复实验，结果取平均值。
银杏树及树叶随机选自江苏省邳州市各大银杏种植基地。
实施例1
在银杏开花期，分别选取开花雄树和开花雌树各5棵，标号，采集开花枝条上的叶片各5-10片。每棵树上采集的样品采用液氮速冻固定，之后在低温环境下充分混合，研磨粉碎。
每个样品称取100mg充分粉碎的叶片，加入1mL的80％浓度甲醇(V/V，甲醇：水＝80：20，0.1％甲酸)，旋涡震荡后置于功率不小于200w的超声清洗机中，超声30分钟。
取出超声后的样品，14000g离心10分钟，取上清低温旋干，加入50微升的80％甲醇复溶。
取复溶的样品，采用装有BEH C18色谱柱的赛默飞UHPLC-Q Exactive TM组合型四极杆Orbitrap质谱仪对样品进行检测。
液相条件为：BEH C18色谱柱(100×2.1mm，1.7μm)进行分离。流动相A为甲醇，流动相B为5mmol/L的乙酸铵，流速为0.3ml/min。流动相从50:50逐步提升至95:5(A:B,v/v)。进样量1μL，柱温45℃。ESI源在负离子模式下运行，高纯氮气壳气流量35arb，辅助气15arb，高纯氩碰撞气压力1.5mTorr。设置参数如下：喷雾电压2.8kV，毛细管温度320℃，加热器温度300℃。
检测结果采用热电公司质谱配套的TraceFinder软件进行数据处理并分析，建立定性检测并注释出的全部非靶向代谢物的离子对表，进一步采用装有BEH C18色谱柱的赛默飞UHPLC-TSQ Quantis三重四极杆质谱仪对样品进行检测。
液相条件为：BEH C18色谱柱(100×2.1mm，1.7μm)进行分离。流动相A为甲醇，流动相B为5mmol/L的乙酸铵，流速为0.3ml/min。流动相从50:50逐步提升至95:5(A:B,v/v)。进样量1μL，柱温45℃。ESI源在负离子模式下运行，高纯氮气壳气流量35arb，辅助气15arb，高纯氩碰撞气压力1.5mTorr。设置参数如下：喷雾电压2.8kV，毛细管温度320℃，加热器温度300℃。
检测结果采用热电公司质谱配套的TraceFinder软件进行数据处理并分析，获得对应代谢物的精确含量数据。
采用OPLS-DA(正交偏最小二乘法，Orthogonal partial least squares discriminant analysis，简称OPLS-DA)对测得得样品进行代谢组分析，结果如图1所示。结果显示，雄性叶片样品和雌性叶片样品在分布上出现了显著分离，说明雌性和雄性在代谢物上存在显著的 性别差异。
实施例2
选取待测性别的银杏树20棵，标号，分别采集银杏树的叶片各5-10片；同时采集与待测性别同品种的、已知道性别的银杏树雌雄各5棵，分别采集叶片各5-10片。每棵树上采集的样品采用液氮速冻固定，之后在低温环境下充分混合，研磨粉碎。
每个样品称取100mg充分粉碎的叶片，加入1mL的80％浓度甲醇(V/V，甲醇：水＝80：20，0.1％甲酸)，旋涡震荡后置于功率不小于200w的超声清洗机中，超声30分钟。
取出超声后的样品，14000g离心10分钟，取上清低温旋干，加入100微升的80％甲醇复溶。
取复溶的样品，采用装有BEH C18色谱柱的赛默飞UHPLC-Q Exactive TM组合型四极杆Orbitrap质谱仪对样品进行检测。
液相条件为：BEH C18色谱柱(100×2.1mm，1.7μm)进行分离。流动相A为甲醇，流动相B为5mmol/L的乙酸铵，流速为0.3ml/min。流动相从50:50逐步提升至95:5(A:B,v/v)。进样量1μL，柱温45℃。ESI源在负离子模式下运行，高纯氮气壳气流量35arb，辅助气15arb，高纯氩碰撞气压力1.5mTorr。设置参数如下：喷雾电压2.8kV，毛细管温度320℃，加热器温度300℃。
检测结果采用热电公司质谱配套的TraceFinder软件进行数据处理并分析，建立定性检测并注释出的全部非靶向代谢物的离子对表，进一步采用装有BEH C18色谱柱的赛默飞UHPLC-TSQ Quantis三重 四极杆质谱仪对样品进行检测。
液相条件为：BEH C18色谱柱(100×2.1mm，1.7μm)进行分离。流动相A为甲醇，流动相B为5mmol/L的乙酸铵，流速为0.3ml/min。流动相从50:50逐步提升至95:5(A:B,v/v)。进样量1μL，柱温45℃。ESI源在负离子模式下运行，高纯氮气壳气流量35arb，辅助气15arb，高纯氩碰撞气压力1.5mTorr。设置参数如下：喷雾电压2.8kV，毛细管温度320℃，加热器温度300℃。
检测结果采用热电公司质谱配套的TraceFinder软件进行数据处理并分析，获得对应代谢物的精确含量数据。
将其所得的结果进行系统聚类(Hierarchical Clustering)，基于Heatmap分析数据进行分组，实验结果如图2所示。从图2中可以看出，30个样品亲缘树在根部分为两个大分支，其中左侧分支包含14个样品，右侧分支包含16个样品；基于亲缘树分支，将样品分为两组，其中，已知为雄性的5棵所在的分组含有的14棵树均为雄性；已知为雌性的5棵所在的分组含有的16棵树均为雌性。
采用OPLS-DA对分组样品进行代谢组差异分析，实验结果如图3所示。图3结果显示，基于亲缘树分支建立的分组，其在OPLS-DA图上的分布出现了显著地分离，分布在Y轴的两侧，且已知雌雄的样品其所在分组与系统聚类结果相同，OPLS-DA评估指标Q2＝0.844，证明两组样品的分组是可信的，即对30棵树基于代谢组进行的雌雄的分组是可靠的。
本申请可以在种苗早期直接鉴定出银杏的雌雄株，解决了长期以 来银杏幼苗无法进行准确鉴定的问题，有利于对银杏雌雄株资源进行优化配置及其合理利用的进程。对银杏的实生苗进行早期性别鉴定，使银杏的雌雄鉴定不再受时空条件限制，对引导利用银杏进行城市绿化及经济林的培植具有重要的使用价值和经济价值。
对比例
选取待测性别的银杏树20棵，标号，分别采集银杏树的叶片各5-10片；同时已知道性别的银杏树雌雄各5棵，分别采集叶片各5-10片。每棵树上采集的样品采用液氮速冻固定，之后在低温环境下充分混合，研磨粉碎。
每个样品称取100mg充分粉碎的叶片，加入1mL的80％浓度甲醇(V/V，甲醇：水＝80：20，0.1％甲酸)，旋涡震荡后置于功率不小于200w的超声清洗机中，超声30分钟。
取出超声后的样品，14000g离心10分钟，取上清低温旋干，加入100微升的80％甲醇复溶。
取复溶的样品，采用装有BEH C18色谱柱的赛默飞UHPLC-Q Exactive TM组合型四极杆Orbitrap质谱仪对样品进行检测。
液相条件为：BEH C18色谱柱(100×2.1mm，1.7μm)进行分离。流动相A为甲醇，流动相B为5mmol/L的乙酸铵，流速为0.3ml/min。流动相从50:50逐步提升至95:5(A:B,v/v)。进样量1μL，柱温45℃。ESI源在负离子模式下运行，高纯氮气壳气流量35arb，辅助气15arb，高纯氩碰撞气压力1.5mTorr。设置参数如下：喷雾电压2.8kV，毛细管温度320℃，加热器温度300℃。
检测结果采用热电公司质谱配套的TraceFinder软件进行数据处理并分析，基于商业化数据库、本地数据库等进行未知物的注释鉴定，并提取注释出的物质所对应的峰面积作为定量结果。
将其所得的结果进行系统聚类(Hierarchical Clustering)，基于Heatmap分析数据进行分组，实验结果如图4所示。从图4中可以看出，30个样品亲缘树在根部分为两个大分支，其中左侧分支包含18个样品，右侧分支包含12个样品；基于亲缘树分支，将样品分为两组，其中，已知为雄性的1-5号样品分散在Y轴两侧，已知为雌性的6-10号样品也分散在Y周两侧。
采用OPLS-DA对分组样品进行代谢组差异分析，实验结果如图5所示。图5结果显示，基于亲缘树分支建立的分组，其在OPLS-DA图上的分布出现了显著地分离，分布在Y轴的两侧，OPLS-DA评估指标Q2＝0.199，说明分组模型不成立。同时，已知性别的10个样品与系统聚类结果类似，雌性和雄性样品在Y轴两侧均有分布，证明虽然基于非靶向代谢组数据能够将30个样品分为两组，但是分组并无规律性，无法实现雌雄样品的鉴定。
以上所述仅为本申请的较佳实施例而已，并不用以限制本申请，凡在本申请的精神和原则之内，所作的任何修改、等同替换等，均应包含在本申请的保护范围之内。

Claims (15)

  1. 一种基于代谢组全局分析的植物性别鉴定方法，其特征在于，包含如下步骤：
    1)非靶向代谢组检测待测银杏叶片中的代谢物，获得非靶向代谢组数据；
    2)对步骤1)中非靶向代谢组数据进行定量校准；
    3)基于步骤2)中代谢组数据的差异鉴定待测银杏的性别。
  2. 如权利要求1所述的方法，其特征在于，所述步骤1)包括：分别提取待测银杏树幼苗叶片和内参叶片的代谢物后进行代谢组非靶向检测。
  3. 如权利要求2所述的方法，其特征在于，所述的内参叶片为与待测银杏属于同一品种的已经确定性别的银杏树的叶子。
  4. 如权利要求2或3所述的方法，其特征在于，所述代谢物的提取方法为：采用有机溶剂抽提叶片中的代谢物。
  5. 如权利要求4所述的方法，其特征在于，所述有机溶剂包括甲醇和乙醇。
  6. 如权利要求5所述的方法，其特征在于，所述有机溶剂为80％浓度甲醇(V/V，甲醇：水＝80：20)。
  7. 如权利要求1-6任一项所述的方法，其特征在于，所述步骤1)中包括代谢组鉴定和数据处理；
    所述代谢组鉴定和数据处理的过程包括：采用超高分辨质谱进行代谢物的检测，采用质谱分析软件抽提质谱信息，并建立检测到物质的定性和定量离子对。
  8. 如权利要求7所述的方法，其特征在于，所述质谱条件为：采用BEH C18色谱柱(100×2.1mm，1.7μm)进行分离；流动相A为甲醇，流动相B为5mmol/L的乙酸铵，流速为0.3ml/min，进样量1μl，柱温45℃；HESI源在负离子模式下运行，碰撞气压力1.5mTorr，喷雾电压2.8kV，毛细管温度320℃，加热器温度300℃。
  9. 如权利要求8所述的方法，其特征在于，所述流动相从50:50逐步提升至95:5(A:B，v/v)。
  10. 如权利要求1-9任一项所述的方法，其特征在于，所述步骤2)中，采用基于内标校正的MRM监控技术对步骤1)检测出的非靶向代谢物进行基于离子对信息的靶向含量校准。
  11. 如权利要求10所述的方法，其特征在于，所述质谱条件为：采用BEH C18色谱柱(100×2.1mm，1.7μm)进行分离；流动相A为甲醇，流动相B为5mmol/L的乙酸铵，流速为0.3ml/min，进样量1μl，柱温45℃；HESI源在负离子模式下运行，碰撞气压力1.5mTorr，喷雾电压2.8kV，毛细管温度320℃，加热器温度300℃。
  12. 如权利要求11所述的方法，其特征在于，所述流动相从50:50逐步提升至95:5(A:B，v/v)。
  13. 如权利要求1-11任一项所述的方法，其特征在于，所述步骤3)中的具体方法为：将步骤2)中的数据处理与已知性别银杏的非 靶向代谢数据处理进行对比判定待测银杏的性别。
  14. 如权利要求13所述的方法，其特征在于，所述步骤3)中的具体方法为：以已知雌雄性别的银杏叶代谢组检测数据作为参考，待检测的银杏样品中，其代谢组在统计学处理后，经系统聚类(Hierarchical Clustering)分析(采用Ward聚类算法)，与雄性已知银杏样品划分在同一个分支下的则判定为雄性；与雌性已知银杏样品划分在同一个分支下的则判定为雌性。
  15. 非靶向代谢组学分析，或权利要求1-14任一项所述的方法，在如下1)-4)中任意一种或多种的应用：
    1)鉴定银杏的雌雄性别；
    2)筛选银杏的特征代谢物；
    3)鉴定银杏的亲缘关系；
    4)银杏种苗繁育。
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