A set of cell membrane lipid markers for characterizing cold tolerance of citrus fruits and application in detection

Abstract

The present application relates to the field of plant physiology and biomarker, and particularly relates to a cell membrane lipid marker combination for characterizing cold tolerance of citrus fruits and application in detection. The present application provides a set of cell membrane lipid marker combination for characterizing cold tolerance of citrus fruits, which comprises 39 kinds of membrane lipid components such as PC(O-38:5), ShexCer(35:0;20), PE-Cer(41:6;20), LPE(36:3), PC(32:0), TG(16:0_18:0_18:1), LPC(20:3) and the like. The process for extracting, purifying and detecting the membrane lipid components of citrus fruits is established, and a cell membrane extraction and detection kit convenient for small sample operation is developed, so as to provide a rapid and simple evaluation method for breeding of cold-tolerant varieties of citrus fruits and selection of reasonable storage temperature after harvesting.

Description

Technical Field

[0001] This invention relates to the field of plant physiology and biomarker science, and in particular to a combination of cell membrane lipid markers characterizing the cold tolerance of citrus fruits and their application in detection. Background Technology

[0002] Citrus is one of the most important fruit crops in my country, characterized by its wide planting area, high yield, large consumption, and long industrial chain. It plays a vital role in fresh consumption, processing, cold chain logistics, and export trade. Citrus fruits continue to undergo active physiological metabolism after harvest, making them susceptible to various factors such as temperature, humidity, mechanical damage, and pathogen infection. Among these, storage temperature is a key factor affecting the maintenance of post-harvest quality and shelf life. Appropriate low-temperature storage can reduce fruit respiration intensity, delay water loss and nutrient consumption, and inhibit the reproduction of pathogenic microorganisms, thereby reducing post-harvest losses and extending shelf life. However, when the storage temperature is lower than the threshold that a specific variety or strain can tolerate, chilling injury can easily be induced, manifesting as peel depression, browning, water-soaked patches, tissue rupture, increased off-flavors, and decreased marketability. Therefore, how to quickly and accurately assess the low-temperature tolerance of citrus fruits early after harvest, and accordingly determine suitable storage temperatures and screen for cold-resistant germplasm materials, has become a pressing technical problem to be solved in the fields of citrus post-harvest biology and storage preservation.

[0003] In existing technologies, research on the low-temperature tolerance of citrus or other fruits mainly focuses on optimizing post-harvest processing, adapting to heat treatment, adjusting storage regimes, and measuring physiological and biochemical indicators. For example, Chinese patent document CN113163781B discloses a method and system for producing cold-resistant fruits to achieve low-temperature quarantine. Its core idea is to improve the fruit's tolerance to subsequent low-temperature quarantine processes through artificial ripening, low-temperature regulation or environmental adaptation, and modified gas treatment, thereby reducing damage caused by low-temperature treatment. This type of technical solution demonstrates that the cold tolerance of fruits can be induced and improved to a certain extent through post-harvest treatment, which has practical significance for regulating the low-temperature adaptation of fruits. Another example is Chinese patent document CN108850127A, which discloses a heat pump device and heat treatment method for citrus preservation and sterilization. It improves the post-harvest storage effect of citrus through heat pump heat treatment, achieving the goal of extending the storage period and improving disease resistance. These technologies provide valuable process pathways for reducing the risk of chilling injury from the perspectives of storage preservation and post-harvest regulation.

[0004] However, the aforementioned existing technologies primarily focus on exogenous treatment methods or storage condition control, emphasizing how to improve or adapt to low-temperature environments, rather than addressing the issue of rapidly assessing the low-temperature tolerance of citrus fruits from the perspective of intrinsic biomarkers. In other words, these technologies often require observing fruit phenotypes or statistically analyzing results such as decay rate, chilling injury index, and weight loss rate after a certain period following treatment. This results in drawbacks such as long detection cycles, reliance on storage trials, and difficulty in making rapid judgments in the early post-harvest period. Especially in scenarios such as breeding screening, variety comparison, batch-harvested fruit grading and storage, and cold chain temperature strategy development, relying solely on long-term storage trials or treatment effect verification often fails to meet the practical demands for high throughput, speed, and standardization.

[0005] From the perspective of plant cold resistance mechanism research, the cell membrane is generally considered the forefront of plant and fruit response to low-temperature stress. Low temperature affects the composition, phase transition, membrane fluidity, and membrane integrity of membrane lipids, thereby inducing ion leakage, reactive oxygen species accumulation, membrane protein dysfunction, and downstream metabolic disorders. For fruits, the pericarp tissue is directly exposed to the external environment and is an important site for sensing and responding to low-temperature stress. Changes in the lipid composition of its cell membrane, especially the plasma membrane, often occur earlier than certain macroscopic phenotypes and are closer to the essential basis of chilling injury. Therefore, if a detection system based on changes in the lipid composition of pericarp cell membranes can be established, and characteristic membrane lipid molecules significantly associated with cold tolerance can be identified, it is expected to form a more direct and faster method for assessing low-temperature tolerance than traditional physiological indicators and long-term storage experiments.

[0006] However, the primary technical challenge in realizing this approach lies in how to efficiently and stably extract high-purity plasma membrane components suitable for lipidomics analysis from citrus peel tissue, which is rich in pigments, essential oils, pectin, polyphenols, and other secondary metabolites. While existing plant plasma membrane extraction techniques have been extensively studied and applied in Arabidopsis, tobacco, seed, and seedling tissues, direct transfer to citrus peel materials often suffers from unstable recovery rates, severe impurity interference, insufficient membrane purity, and poor reproducibility. Compared to conventional plant model tissues, citrus peel, especially the oil cell layer, is rich in volatile oils and complex secondary metabolites. These components are prone to emulsification, adhesion, or co-precipitation during homogenization and centrifugation, thus affecting the accuracy of subsequent membrane lipid detection.

[0007] In the field of plant plasma membrane purification, existing technologies include methods for enriching plasma membranes using aqueous two-phase partitioning. For example, Chinese patent document CN103890182A describes the use of a two-phase partitioning method to purify plasma membranes in plant materials during stress studies, and verifies purity by measuring the activity of membrane marker enzymes such as ATPase. This technique demonstrates the versatility of the Dextran / PEG aqueous two-phase system as a method for separating plant plasma membranes. These existing technologies provide a methodological basis for plant plasma membrane separation and also suggest that evaluating plasma membrane purity through marker enzyme activity is a feasible approach.

[0008] However, existing technologies like CN103890182A are mainly applied to model plant seedlings or other easily processed plant materials, and their purpose is mostly for proteomics analysis, abiotic stress response studies, or general membrane component separation. These technologies do not address the specific sample system of citrus peel by optimizing extract components, designing low-temperature operating conditions, or constructing a plasma membrane preservation system. They also fail to solve the technical problems of compatibility between plasma membrane extraction and lipid detection in the context of high sugar, high acid, high pigment, and high essential oil content in the peel. More importantly, while these existing technologies involve plasma membrane purification, they do not further establish a quantitative correlation between plasma membrane lipid molecules and the low-temperature tolerance of citrus fruit, nor do they propose a specific combination of membrane lipid markers that can be used to characterize citrus cold tolerance.

[0009] In summary, the existing technology has at least the following shortcomings: Firstly, most existing technologies on postharvest cold tolerance of citrus focus on heat treatment, modified atmosphere packaging, adaptation treatment, or storage process optimization, such as CN113163781B and CN108850127A. Although these technologies can alleviate chilling injury to some extent, there is a lack of a technical solution that can quickly determine cold tolerance based on the intrinsic biological characteristics of the sample in the early postharvest period.

[0010] Secondly, existing plant plasma membrane extraction and purification technologies, such as the two-phase partitioning purification approach revealed in CN103890182A, are mostly limited to general plant tissue and membrane protein research. They have not yet established a stable, reproducible, and suitable small-sample plasma membrane extraction process for the special and complex matrix of citrus peel samples, nor have they disclosed a set of plasma membrane lipid biomarkers that are significantly related to the low-temperature tolerance of citrus fruits.

[0011] Third, existing technologies generally lack a solution that integrates plasma membrane extraction, purity assessment, lipid detection, biomarker interpretation, and cold resistance grading into a unified technical chain, making it difficult to simultaneously meet the rapid detection needs in scientific research screening, breeding applications, fruit grading, and post-harvest storage management.

[0012] Therefore, there is an urgent need to develop a new technology for evaluating the cold tolerance of citrus fruits. On the one hand, this technology should establish a more adaptable process for extracting, purifying, and evaluating the cell membrane from citrus peel tissue. On the other hand, it should screen characteristic membrane lipid markers highly correlated with low-temperature tolerance from the lipid composition of the cell membrane, and further construct application methods and supporting reagent systems for rapid detection and classification assessment. Only in this way can the accuracy, timeliness, and practicality of judging the low-temperature tolerance of citrus fruits be fundamentally improved, providing more direct and reliable technical support for the breeding of cold-resistant citrus varieties, the determination of post-harvest storage temperatures, and cold chain preservation management. Summary of the Invention

[0013] The technical objective of this invention is to provide a method for developing and applying cold tolerance markers in citrus fruits. By establishing a process for extracting, purifying, and detecting cell membranes suitable for citrus peel samples, the invention screens and identifies combinations of characteristic membrane lipid markers that are significantly associated with low-temperature tolerance, thereby enabling rapid and accurate assessment of the low-temperature tolerance of citrus fruits. This provides technical support for screening cold-tolerant citrus varieties, optimizing postharvest storage temperatures, and providing early warning of chilling injury risks.

[0014] Firstly, in order to achieve the above-mentioned objectives, the present invention adopts the following technical solution: A combination of cell membrane lipid markers characterizing cold tolerance in citrus fruits includes PC (O-38:5), SHexCer (35:0; 2O), PE-Cer (41:6; 2O), LPE (36:3), PC (32:0), TG (16:0-18:0-18:1), LPC (20:3), MGDG (O-10:0-12:0), PE (8:0-32:9), MGDG (O-28:2-16:0), CE (24:6), PC (O-34:2), DGCC (9:0-26:7), TG (O-8:0-12:0-14:0), ST (24:1; O4; T / 22:2), ST (24:1; O4; T / 17:1), ST (24:1; O4 / 16:4), SL (17:2; O / 26:6), PS (O-12:0_10:0), MGDG (O-16:3_3:0), PE-Cer (23:0; 2O / 26:6), PE-Cer (25:2; 2O / 26:6), PE-Cer (13:1; 2O / 18:3), HexCer (18:1; 3O / 16:0; (2OH)), DGTS (8:0_8:0), LDGCC (34:0), DG (O-14:0_3:0), MGDG (O-11:0_18:2), HexCer (34:2; 3O), HexCer (18:1; 2O / 22:0), PG (16:0_18:2), MGDG (O-19:0_16:0), HexCer (18:1; A total of 39 membrane lipid components were included, including 3O / 24:0; (2OH)), PI (16:0-18:3), PG (22:0), CE (18:2), TG (12:0-12:0-12:0), HexCer (18:2; 2O / 16:0;O), and ST (29:1; O; Hex; FA (16:0)).

[0015] Secondly, the present invention also provides a method for detecting the low-temperature tolerance of citrus fruits, comprising the following steps: S1. Collect a peel sample of the citrus fruit to be tested, mix the peel sample with the plasma membrane extract, and then break down the tissue to obtain a homogenate. S2. The homogenate is subjected to low-speed centrifugation, medium-speed centrifugation and ultra-speed centrifugation in sequence to obtain crude microparticle extract; S3. The crude microparticle extract is added to an aqueous two-phase partitioning system containing dextran and polyethylene glycol for plasma membrane enrichment and purification, and the plasma membrane components are separated. S4. Evaluate the purity of the plasma membrane components, wherein the evaluation of plasma membrane purity includes at least the detection of the activity of the plasma membrane marker enzyme 5'-nucleotidase; S5. Lipids were extracted from the purified plasma membrane components, and the lipid composition of the plasma membrane was detected by liquid chromatography-high resolution mass spectrometry. S6. Assess the low-temperature tolerance of the citrus fruit to be tested based on the presence, relative content, absolute content, abundance change factor, or combination of one or more cold-resistant membrane lipid markers detected. The cold-resistance-related membrane lipid markers are the combination of cell membrane lipid markers.

[0016] Preferably, in step S1, the plasma membrane extraction solution includes: sucrose, Tris, BTP-Mes, ascorbic acid, DTT, Na2_22-EDTA, glycerol, BSA, casein, PMSF and PVP. And / or, step S2 includes: First, centrifuge the homogenate at 300–1000×g for 5–20 min to remove large precipitate particles. Then, centrifuge the supernatant at 8000–20000×g for 30–90 min to remove organelle impurities. Finally, centrifuge the obtained supernatant at 80000–150000×g for 30–90 min to obtain crude microsomal extract. And / or, in step S3, the aqueous two-phase distribution system is a Dextran T500 / polyethylene glycol 3350 system, which further includes sucrose, KCl and phosphate buffer components; And / or, in step S4, the plasma membrane purity evaluation further includes a comparison of the 5'-nucleotidase specific activity between the plasma membrane component and the crude microsomal extract. When the 5'-nucleotidase specific activity of the plasma membrane component is 5 to 30 times that of the crude microsomal extract, the plasma membrane component is determined to meet the lipid detection requirements. And / or, in step S5, the liquid chromatography-high resolution mass spectrometry technique is ultra-high performance liquid chromatography-Orbitrap high resolution mass spectrometry technique; And / or, in step S6, the combination of markers used to determine low-temperature tolerance includes at least one or more of the following seven upregulation markers: PC (O-38:5), SHexCer (35:0;2O), PE-Cer (41:6;2O), LPE (36:3), PC (32:0), TG (16:0-18:0-18:1) and LPC (20:3); And / or, the low-temperature tolerance assessment in step S6 is performed using any of the following methods: 1) Judgment based on a single marker threshold; 2) A scoring model based on the weighted summation of multiple markers; 3) Classification models based on principal component analysis, partial least squares discriminant analysis, orthogonal partial least squares discriminant analysis, logistic regression, support vector machine or random forest.

[0017] Preferably, in step S1, the concentrations of each component in the plasma membrane extract are: 0.33 M sucrose, 80 mM Tris, 5 mM BTP-Mes, 10 mM ascorbic acid, 5 mM DTT, 5 mM Na2_22-EDTA, 10% glycerol, 0.4% BSA, 0.4% casein, 1 mM PMSF, and 0.15% (w / v) PVP; And / or, in step S3, the aqueous two-phase partitioning system comprises 6.2%–8.27% (w / w) Dextran T500, 6.2%–8.27% (w / w) polyethylene glycol 3350, 0.33 M sucrose, 3 mM KCl, and 5 mM potassium phosphate buffer, with a pH of 7.5–8.0; and / or, in step S5, the liquid chromatography conditions include: Phase A is a water / acetonitrile mixture containing 10 mmol / L ammonium formate, wherein the volume ratio of water to acetonitrile is 40:60; Phase B is a mixture of acetonitrile and isopropanol containing 10 mmol / L ammonium formate, wherein the volume ratio of acetonitrile to isopropanol is 10:90. And / or, in step S5, the mass spectrometry conditions include: sheath gas flow rate 30 Arb, auxiliary gas flow rate 10 Arb, capillary temperature 320 ℃, first-stage full scan resolution 60000, second-stage resolution 15000, collision energy using 15 / 30 / 45 NCE mode, and spray voltage of 3.8 kV in positive ion mode and / or -3.4 kV in negative ion mode.

[0018] Preferably, the low-temperature tolerance assessment results include at least three levels of classification results: cold-resistant, intermediate, and cold-sensitive.

[0019] Thirdly, the present invention also provides the application of the detection method in screening the cold tolerance of citrus varieties, setting post-harvest storage temperature, early warning of cold damage risk, or breeding of cold-resistant varieties.

[0020] Fourthly, the present invention also provides a kit for detecting the low-temperature tolerance of citrus fruits, the kit comprising: 1) Plasma membrane extraction module, used to extract crude microparticle extract from citrus peel samples; 2) Plasma membrane purification module, including Dextran T500 / polyethylene glycol 3350 two-phase partitioning reagent, for purifying plasma membrane components; 3) Plasma membrane purity evaluation module, including 5'-nucleotidase activity detection reagent; 4) Lipid detection module, used to detect one or more of the 39 membrane lipid biomarkers; 5) Result interpretation module, used to output the evaluation conclusion of the low temperature tolerance of citrus fruit based on the detection results of the membrane lipid markers.

[0021] Preferably, the 5'-nucleotidase activity detection reagent includes an enzyme reaction solution, a stop solution, and an ammonium molybdate colorimetric solution; And / or, the lipid detection module includes an internal standard, a sample reconstitution solution, and a standardized reagent combination for LC-MS / MS analysis; And / or, the result interpretation module includes a preset threshold table, a standard curve, data processing software and / or a cloud analysis program, used to output low temperature tolerance scores or classification results.

[0022] Preferably, the low-temperature tolerance assessment results include at least three levels of classification results: cold-resistant, intermediate, and cold-sensitive.

[0023] Fifthly, the present invention also provides the application of the kit in screening the cold tolerance of citrus varieties, setting postharvest storage temperature, early warning of chilling injury risk, or breeding of cold-resistant varieties.

[0024] This invention uses the lipid composition of citrus peel cell membranes as the evaluation object for low-temperature tolerance. It establishes an integrated technical route from sample pretreatment, plasma membrane extraction, two-phase purification, purity evaluation to lipidomics detection and biomarker interpretation. Compared with traditional methods that rely on long-term low-temperature storage to observe chilling injury phenotypes, this approach reflects the response of citrus fruits to low-temperature stress earlier and more directly. By screening and obtaining characteristic membrane lipid biomarker combinations significantly correlated with cold tolerance, it not only improves the sensitivity, accuracy, and repeatability of low-temperature tolerance discrimination but also overcomes the problems of long detection cycles, large sample requirements, and difficulty in rapid grading and batch screening in existing technologies. Furthermore, the small-sample plasma membrane extraction and detection reagent system established in this invention is suitable for plant materials such as citrus peel, which have high secondary metabolite content and complex matrices. It provides an operable and scalable technical means for identifying the cold tolerance of citrus varieties, determining post-harvest storage temperatures, providing early warning of chilling injury risks, and conducting cold-resistant breeding research, demonstrating significant practical application value. Attached Figure Description

[0025] Figure 1 This is a flowchart of the cell membrane extraction process.

[0026] Figure 2 This is the phosphate standard curve.

[0027] Figure 3 The TIC spectrum of 'Chunxiang' lipidomics provided in this embodiment of the invention; the vertical axis of the figure represents the signal value, and the horizontal axis represents time, in minutes.

[0028] Figure 4 This is a ring diagram illustrating the classification of metabolites by lipidomics detection in an embodiment of the present invention.

[0029] Figure 5 This is a heatmap of the overall hierarchical clustering analysis of metabolites detected by lipidomics in an embodiment of the present invention. The horizontal axis represents different sample groups, the vertical axis represents all metabolites, and the color blocks at different positions represent the relative expression levels of metabolites at the corresponding positions. Red indicates high expression of the substance, and blue indicates low expression of the substance.

[0030] Figure 6 PCA dispersion plots of 'Chunxiang' fruit peel lipids and Ponkan fruit peel lipids provided in this embodiment of the invention; the horizontal axis PC[1] and the vertical axis PC[2] in the figure represent the scores of the first and second ranked principal components, respectively.

[0031] Figure 7 This is a volcano plot of differentially expressed metabolites; the horizontal axis represents the fold change of each substance in the comparison group (logarithm to base 2), the vertical axis represents the P-value of the Student's t-test (negative logarithm to base 10), and the scatter plot size represents the VIP value of the OPLS-DA model, with larger scatter plots indicating larger VIP values. Significantly upregulated metabolites are shown in red, significantly downregulated metabolites in blue, and metabolites with no significant difference are shown in gray.

[0032] Figure 8 This is a heatmap of hierarchical clustering analysis of differential metabolites between the 'Chunxiang' group and the 'Ponkan' group in an embodiment of the present invention; the horizontal axis represents different experimental groups, the vertical axis represents the differential metabolites compared in the group, and the color blocks at different positions represent the relative expression levels of the metabolites at the corresponding positions. Red indicates that the substance is highly expressed in its group, and blue indicates that the substance is lowly expressed in its group. Detailed Implementation

[0033] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of the present invention.

[0034] Experimental reagents and manufacturer's instructions Ultracentrifugation tubes were purchased from BKMAM (product number 344060); cuvettes were purchased from LABSELECT; BCA Protein Concentration Assay Kit (enhanced version) was purchased from Beyotime; sucrose was purchased from SCR / Hushi (product number 10021418); Tris was purchased from Sigma-Aldrich (product number T1503-500G); BTP was purchased from Aladdin (product number B105640-25g); Mes was purchased from BBI (product number A610341-0100); ascorbic acid was purchased from Aladdin (product number A103535-100g); DTT was purchased from Solarbio (product number D8220-5g); Na2-EDTA was purchased from Solarbio (product number E8030-500); glycerol was purchased from Macklin (product number G810577-500ml); BSA was purchased from Biosharp (product number 4240GR100); Casein was purchased from Aladdin (product number C139524-500g); PMSF was purchased from Beyotime (product number ST506); PVP was purchased from Yuanye Bio-Technology (product number S30269-100g); KCl was purchased from SCR / Hushi (product number 10016318); KH2PO4 was purchased from Aladdin (product number P104075-500g); Dextran T500 was purchased from NJDULY (product number I0115-25g); polyethylene glycol 3350 was purchased from Phytotechnology (product number P714-1KG); 2M Tris-HCl (pH = 7.5) was purchased from Sangon (product number B548139-0500); ethylene glycol was purchased from SCR / Hushi (product number 10009818); AMP was purchased from D&B (product number P371001-25g); MgCl2·6H2O was purchased from BBI (product number A610328-0500); glycine was purchased from Macklin (product number G800880-500g); trichloroacetic acid was purchased from Aladdin (product number T104257-500g); concentrated sulfuric acid was purchased from SCR (product number 10021618); ammonium molybdate was purchased from 3Achem (product number A43562-25g).

[0035] Experimental methods Extraction of plasma membrane Take the pulp and the oil cell layer of the peel of fresh citrus fruits, freeze them quickly in liquid nitrogen, grind the samples thoroughly in a grinder for 3 × 20 seconds, and store them in a -80°C refrigerator; Take 1g of powder from each replicate of citrus sample, for a total of 3g, and place it in a 50ml centrifuge tube. Add 9ml of plasma membrane extraction buffer [0.33M sucrose, 80mM Tris, 5mM BTP-Mes, 10mM ascorbic acid, 5mM DTT, 5mM Na2-EDTA, 10% glycerol, 0.4% BSA, 0.4% casein, 1mM PMSF, 0.15% (w / v) PVP, store at 4℃, add DTT, ascorbic acid and PMSF before use], vortex thoroughly to mix evenly, freeze in a -80℃ freezer for 1h, and then thaw in room temperature water. Repeat this freeze-thaw cycle 3 times. The mixture was ultrasonically broken up in an ice-water bath for 2 minutes at a power of 200W and a frequency of 25kHz. The pulse working mode was adopted, with ultrasonication for 2 seconds and a pause for 10 seconds. After ultrasonication, the mixture was centrifuged at 500g for 10 minutes to remove the precipitate. The homogenate was centrifuged at 12000×g for 1 h at 4℃; The supernatant was centrifuged at 120,000 × g for 1 h at 4 °C; The precipitate was resuspended in 1 ml of precipitate resuspension solution [0.25 M sucrose, 50 mM Tris-HCl (pH=7.5)] using a pipette, and the crude plasma membrane extract was stored at 4 °C.

[0036] Purified plasma membrane Prepare two-phase separation systems: System A [8.27% (w / w) Dextran T500, 8.27% (w / w) polyethylene glycol 3350, 0.33M sucrose, 3mM KCl, 5mM KH2PO4, pH=7.8], System B / C [6.2% (w / w) Dextran T500, 6.2% (w / w) polyethylene glycol 3350, 0.33M sucrose, 3mM KCl, 5mM KH2PO4, pH=7.8]; Add approximately 1 mL of suspension to 3 mL of separation system A and equilibrate with 4 mL of separation system B. Seal the container, vortex thoroughly, and centrifuge at 1500 × g for 10 min at 4 °C. Exchange the upper phases of systems A and B, equilibrate with a balance, seal the container, invert thoroughly, and centrifuge at 1500 × g for 5 min at 4 °C. Transfer the upper phase from another 4 mL system C to an empty tube for later use. Transfer the upper phase from B to C, and the upper phase from A to B. Transfer the upper phase from the empty tube to A, equilibrate with a balance, seal the container, invert thoroughly, and centrifuge at 1500 × g for 5 min at 4 °C. Collect the upper phase from C, transfer the upper phase from B to C, and the upper phase from A to B. Equilibrate, seal the container, invert thoroughly, and centrifuge at 1500 × g for 5 min at 4 °C. Collect the upper phases from A and B and mix them with C. Dilute the upper phase with buffer [0.25M sucrose, 50mM Tris-HCl (pH=7.5)], fill the ultracentrifuge tube as full as possible, balance with a balance, and then ultracentrifuge at 120000×g for 30 minutes at 4°C. Discard the supernatant to obtain the cell membrane precipitate. Add 1 ml of preservation solution to an ultracentrifuge tube and pipette thoroughly until the precipitate is completely resuspended in the preservation solution. Transfer all the solution to a 1.5 ml centrifuge tube and store at -80°C.

[0037] Testing plasma membrane purity Take three 1.5ml centrifuge tubes, add 80μl each of preservation solution, plasma membrane, and microsomes, and add 200μl of enzyme reaction solution to each. After incubating in a 37℃ water bath for 40 minutes, immediately add 1 ml of 8% trichloroacetic acid, mix well, and terminate the reaction. To prepare the ammonium molybdate colorimetric solution, mix 3 mol / L sulfuric acid, deionized water, 2.5% (w / v) ammonium molybdate, and 10% vitamin C in a ratio of 1:2:1:1 in that order. Prepare the solution immediately before use. Take 50 μl of each solution into a cuvette, add 200 μl of ammonium molybdate colorimetric solution, and measure the absorbance at 660 nm using an ELISA reader after 30 min. Calculate the absorbance based on the corresponding phosphorus standard curve. Prepare standard phosphorus solution as shown in Table 1. Table 1 Preparation of standard phosphorus solution

[0038] Take 50 μl of each standard phosphorus solution into a cuvette, add 200 μl of ammonium molybdate colorimetric solution, and measure the absorbance at 660 nm using an ELISA reader after 30 min to plot the phosphorus standard curve. The protein concentrations of plasma membrane extract and microsomal extract were determined using the BCA protein concentration assay kit (enhanced version). The activity of 5'-nucleotidase, a marker enzyme of the plasma membrane, is expressed as the number of micromoles of inorganic phosphorus hydrolyzed per gram of membrane protein after incubation at 37°C for 1 h (μmol·h¹·g¹). When the specific activity of the 5'-NT of the plasma membrane is 5-30 times that of microsomal enzymes, it indicates that the plasma membrane has high purity and can be used for experiments such as biochemical analysis.

[0039] Lipid detection and differential screening Lipid detection and differential substance screening were performed by Shanghai Baiqu Biomedical Technology Co., Ltd., which provided the company with plasma membrane extracts from the peels of 'Chunxiang' and Ponkan oranges for detection and analysis. The specific process is as follows: Metabolite extraction: Take 50 μL of the sample after thawing in an ice-water bath, add water to a final volume of 100 μL, add 480 μL of pre-cooled extraction buffer (MTBE:MeOH = 5:1, v / v) at -40 ℃, and vortex for 60 s. Sonicate in an ice-water bath for 10 min, then incubate at -40 ℃ for 1 h. Centrifuge the sample at 3000 rpm for 15 min at 4 ℃, and transfer 350 μL of the supernatant to a 2.0 mL EP tube for vacuum drying. Add 100 μL of pre-cooled reconstitution solution (DCM:MeOH = 1:1, v / v) containing an isotope-labeled internal standard to the dried metabolite. Vortex for 30 s, and sonicate in an ice-water bath for 10 min. Centrifuge the sample at 12000 rpm for 15 min at 4 ℃. Transfer 50 μL of the supernatant to a sample vial for analysis. On-machine testing The target compounds were separated chromatographically using a Vanquish (Thermo Fisher Scientific) ultra-high performance liquid chromatograph with a Phenomenex Kinetex C18 (2.1 mm × 100 mm, 2.6 μm) column. Phase A of the liquid chromatography consisted of 40% water and 60% acetonitrile, containing 10 mmol / L ammonium formate; Phase B consisted of 10% acetonitrile and 90% isopropanol, containing 10 mmol / L ammonium formate. The default injection volume was 2 μL. The Orbitrap Exploris 120 mass spectrometer was able to acquire primary and secondary mass spectrometry data under the control of software (Xcalibur, version 4.4, Thermo).

[0040] The mass spectrometry conditions include: sheath gas flow rate of 30 Arb, auxiliary gas flow rate of 10 Arb, capillary temperature of 320 °C, first-stage full scan resolution of 60,000, second-stage resolution of 15,000, collision energy of 15 / 30 / 45 NCE mode, and spray voltage of 3.8 kV in positive ion mode and / or -3.4 kV in negative ion mode.

[0041] The ion fragmentation information of the marker is as follows: Table 2 Ion fragment information of differentially metabolites

[0042] Data Analysis To reduce the impact of detection system errors on the results and to better highlight the biological significance of the results, we performed a series of preparations and processing on the raw data. These mainly included the following steps: Deviation filtering: Filters individual features to remove noise. Filters deviations based on the interquartile range.

[0043] Missing value filtering: Filters individual features. Only retains peak area data where no more than 50% of the data are missing in a single group, or where no more than 50% of the data are missing in all groups.

[0044] Missing value imputation: This involves simulating missing values ​​in the original data (missing value recoding). Numerical simulation methods impute the minimum value by half.

[0045] Data normalization: Normalization is performed using the total ion current (TIC) of each sample.

[0046] Univariate statistical analysis (UVA) and screening of differentially expressed metabolites The results were analyzed using orthogonal partial least squares-discriminant analysis (OPLS-DA). OPLS-DA analysis allows us to filter out orthogonal variables in the metabolite group that are not correlated with the categorical variable, and analyzes both non-orthogonal and orthogonal variables separately, thus obtaining more reliable information on the correlation between metabolite differences and experimental groups. Combining the results of monovariate and multivariate statistical analyses, differentially expressed metabolites were identified. The chi-square value criteria were based on the following two indicators: a p-value of less than 0.05 in the Student's t-test; and a variable importance in the projection (VIP) greater than 1 for the first principal component of the OPLS-DA model.

[0047] Experimental results After pretreatment, 51,175 peaks and 868 metabolites were retained. Compared with 'Haruka', Ponkan oranges showed 39 differentially regulated metabolites in 4 categories, including 7 upregulated metabolites and 32 downregulated metabolites.

[0048] The metabolites contain a total of 6 lipids: 452 glycerides (GL), 196 glycerophospholipids (GP), 140 sphingolipids (SP), 44 fatty acyl groups (FA), 34 sterol esters (ST), and 2 isopentenol esters (PR). Compared with 'Haruka', the significantly increased differential metabolites in Ponkan oranges were PC (O-38:5), SHexCer (35:0; 2O), PE-Cer (41:6; 2O), LPE (36:3), PC (32:0), TG (16:0_18:0_18:1), and LPC (20:3), as shown in Table 2; the significantly decreased differential metabolites were MGDG (O-10:0_12:0), PE (8:0_32:9), CE (24:6), PC (O-34:2), DGCC (9:0_26:7), TG (O-8:0_12:0_14:0), ST (24:1; O4; T / 22:2), PS (O-12:0_10:0), etc., as shown in Table 3.

[0049] Table 3. Upregulated lipids in UVA analysis

[0050] Table 4. Downregulated lipids in UVA analysis

[0051] The foregoing description of embodiments of the present invention, through which those skilled in the art are able to implement or use the present invention, will be readily apparent to those skilled in the art. Various modifications to these embodiments will be readily apparent to those skilled in the art. The general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novelty disclosed herein.

Claims

1. A combination of cell membrane lipid markers characterizing cold tolerance in citrus fruits, comprising PC (O-38:5), SHexCer (35:0; 2O), PE-Cer (41:6; 2O), LPE (36:3), PC (32:0), TG (16:0-18:0-18:1), LPC (20:3), MGDG (O-10:0-12:0), PE (8:0-32:9), MGDG (O-28:2-16:0), CE (24:6), PC (O-34:2), DGCC (9:0-26:7), TG (O-8:0-12:0-14:0), ST (24:1; O4; T / 22:2), ST (24:1; O4; T / 17:1), ST (24:1; O4 / 16:4), SL (17:2; O / 26:6), PS (O-12:0_10:0), MGDG (O-16:3_3:0), PE-Cer (23:0; 2O / 26:6), PE-Cer (25:2; 2O / 26:6), PE-Cer (13:1; 2O / 18:3), HexCer (18:1; 3O / 16:0; (2OH)), DGTS (8:0_8:0), LDGCC (34:0), DG (O-14:0_3:0), MGDG (O-11:0_18:2), HexCer (34:2; 3O), HexCer (18:1; 2O / 22:0), PG (16:0_18:2), MGDG (O-19:0_16:0), HexCer (18:1; A total of 39 membrane lipid components were included, including 3O / 24:0; (2OH)), PI (16:0_18:3), PG (22:0), CE (18:2), TG (12:0_12:0_12:0), HexCer (18:2; 2O / 16:0;O), ST (29:1;O); Hex; FA (16:0)).

2. A method for detecting the low-temperature tolerance of citrus fruits, characterized in that, Includes the following steps: S1. Collect a peel sample of the citrus fruit to be tested, mix the peel sample with the plasma membrane extract, and then break down the tissue to obtain a homogenate. S2. The homogenate is subjected to low-speed centrifugation, medium-speed centrifugation and ultra-speed centrifugation in sequence to obtain crude microparticle extract; S3. The crude microparticle extract is added to an aqueous two-phase partitioning system containing dextran and polyethylene glycol for plasma membrane enrichment and purification, and the plasma membrane components are separated. S4. Evaluate the purity of the plasma membrane components, wherein the evaluation of plasma membrane purity includes at least the detection of the activity of the plasma membrane marker enzyme 5'-nucleotidase; S5. Lipids were extracted from the purified plasma membrane components, and the lipid composition of the plasma membrane was detected by liquid chromatography-high resolution mass spectrometry. S6. Assess the low-temperature tolerance of the citrus fruit to be tested based on the presence, relative content, absolute content, abundance change factor, or combination of one or more cold-resistant membrane lipid markers detected. The cold-resistance-related membrane lipid markers are the combination of cell membrane lipid markers described in claim 1.

3. The detection method according to claim 2, characterized in that, In step S1, the plasma membrane extraction solution includes: sucrose, Tris, BTP-Mes, ascorbic acid, DTT, Na2_22-EDTA, glycerol, BSA, casein, PMSF and PVP; And / or, step S2 includes: First, centrifuge the homogenate at 300–1000×g for 5–20 min to remove large precipitate particles. Then, centrifuge the supernatant at 8000–20000×g for 30–90 min to remove organelle impurities. Finally, centrifuge the obtained supernatant at 80000–150000×g for 30–90 min to obtain crude microsomal extract. And / or, in step S3, the aqueous two-phase distribution system is a Dextran T500 / polyethylene glycol 3350 system, which further includes sucrose, KCl and phosphate buffer components; And / or, in step S4, the plasma membrane purity evaluation further includes a comparison of the 5'-nucleotidase specific activity between the plasma membrane component and the crude microsomal extract. When the 5'-nucleotidase specific activity of the plasma membrane component is 5 to 30 times that of the crude microsomal extract, the plasma membrane component is determined to meet the lipid detection requirements. And / or, in step S5, the liquid chromatography-high resolution mass spectrometry technique is ultra-high performance liquid chromatography-Orbitrap high resolution mass spectrometry technique; And / or, in step S6, the combination of markers used to determine low-temperature tolerance includes at least one or more of the following seven upregulation markers: PC (O-38:5), SHexCer (35:0;2O), PE-Cer (41:6;2O), LPE (36:3), PC (32:0), TG (16:0-18:0-18:1) and LPC (20:3); And / or, the low-temperature tolerance assessment in step S6 is performed using any of the following methods: 1) Judgment based on a single marker threshold; 2) A scoring model based on the weighted summation of multiple markers; 3) Classification models based on principal component analysis, partial least squares discriminant analysis, orthogonal partial least squares discriminant analysis, logistic regression, support vector machine or random forest.

4. The detection method according to claim 3, characterized in that, In step S1, the concentrations of each component in the plasma membrane extract are: 0.33 M sucrose, 80 mM Tris, 5 mM BTP-Mes, 10 mM ascorbic acid, 5 mM DTT, 5 mM Na2_22-EDTA, 10% glycerol, 0.4% BSA, 0.4% casein, 1 mM PMSF, and 0.15% (w / v) PVP; And / or, in step S3, the aqueous two-phase partitioning system comprises 6.2%–8.27% (w / w) Dextran T500, 6.2%–8.27% (w / w) polyethylene glycol 3350, 0.33 M sucrose, 3 mM KCl, and 5 mM potassium phosphate buffer, with a pH of 7.5–8.0; and / or, in step S5, the liquid chromatography conditions include: Phase A is a water / acetonitrile mixture containing 10 mmol / L ammonium formate, wherein the volume ratio of water to acetonitrile is 40:60; Phase B is a mixture of acetonitrile and isopropanol containing 10 mmol / L ammonium formate, wherein the volume ratio of acetonitrile to isopropanol is 10:

90. And / or, in step S5, the mass spectrometry conditions include: sheath gas flow rate 30 Arb, auxiliary gas flow rate 10 Arb, capillary temperature 320 ℃, first-stage full scan resolution 60000, second-stage resolution 15000, collision energy using 15 / 30 / 45 NCE mode, and spray voltage of 3.8 kV in positive ion mode and / or -3.4 kV in negative ion mode.

5. The detection method according to any one of claims 2-4, characterized in that, The low-temperature tolerance assessment results include at least three levels of classification: cold-resistant, intermediate, and cold-sensitive.

6. The application of the detection method according to any one of claims 2-5 in screening for cold tolerance of citrus varieties, setting postharvest storage temperature, early warning of cold damage risk, or breeding of cold-resistant varieties.

7. A kit for detecting the low-temperature tolerance of citrus fruits, characterized in that, The kit includes: 1) Plasma membrane extraction module, used to extract crude microparticle extract from citrus peel samples; 2) Plasma membrane purification module, including Dextran T500 / polyethylene glycol 3350 two-phase partitioning reagent, for purifying plasma membrane components; 3) Plasma membrane purity evaluation module, including 5'-nucleotidase activity detection reagent; 4) A lipid detection module for detecting one or more of the 39 membrane lipid biomarkers described in claim 1; 5) Result interpretation module, used to output the evaluation conclusion of the low temperature tolerance of citrus fruit based on the detection results of the membrane lipid markers.

8. The reagent kit according to claim 7, characterized in that, The 5'-nucleotidase activity detection reagent includes an enzyme reaction solution, a stop solution, and an ammonium molybdate colorimetric solution. And / or, the lipid detection module includes an internal standard, a sample reconstitution solution, and a standardized reagent combination for LC-MS / MS analysis; And / or, the result interpretation module includes a preset threshold table, a standard curve, data processing software and / or a cloud analysis program, used to output low temperature tolerance scores or classification results.

9. The reagent kit according to claim 8, characterized in that, The low-temperature tolerance assessment results include at least three levels of classification: cold-resistant, intermediate, and cold-sensitive.

10. The application of the kit according to any one of claims 7 to 9 in screening for cold tolerance of citrus varieties, setting postharvest storage temperature, early warning of chilling injury risk, or breeding of cold-resistant varieties.

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