A method for predicting sensitivity to browning in lettuce

Abstract

The application discloses a method for predicting the browning sensitivity of lettuce, and relates to the technical field of molecular genetics.The method for predicting the browning sensitivity of lettuce comprises the following steps: S1, determining the LsPPO4 gene expression amount of a to-be-detected lettuce sample on the second day after fresh cutting; S2, inputting into a linear regression model for predicting the browning degree after four days to obtain a Y value of the predicted browning degree; and S3, determining the browning sensitivity of the lettuce according to the obtained Y value.The method can be used for rapid identification of the browning sensitivity of lettuce and creation of new browning-resistant varieties, realizes rapid and accurate discrimination of the browning phenotype of lettuce, provides technical support for the processing industry and browning-resistant variety breeding, and has good application prospect and high application value.

Description

Technical Field

[0001] This invention relates to the field of molecular genetics, specifically to a method for predicting the browning susceptibility of lettuce. Background Technology

[0002] Lettuce (Lactuca sativa L. var. angustana) is an important vegetable widely cultivated in my country. Its fleshy stem is rich in protein, vitamins, polysaccharides, and phenolic compounds, and its development as a ready-to-eat or ready-to-use vegetable product after fresh-cut processing is rapid. However, mechanical damage during fresh-cutting disrupts cell compartmentalization, causing phenolic substrates in the vacuoles to come into contact with polyphenol oxidase (PPO) in the cytoplasm. Under aerobic conditions, PPO is oxidized to produce quinones, which further polymerize to form brown pigments, resulting in severe enzymatic browning. This deteriorates the product's appearance and quality, causing economic losses.

[0003] Currently, methods for controlling browning mostly employ physical treatments (such as low temperature and modified atmosphere packaging) or chemical preservatives (such as ascorbic acid). However, these technologies suffer from problems such as short-lasting effects, the potential for off-flavors, or chemical residues, and cannot fundamentally address the browning sensitivity of the variety itself. In fact, high-quality raw materials are the primary factor determining product quality; therefore, screening for superior browning-resistant varieties is of great significance for solving the browning problem in lettuce processing at its source.

[0004] Currently, rapid and accurate methods for identifying browning resistance in lettuce varieties are scarce. In production practice, reliance on manual visual judgment or sensory scoring is crucial, which is not only inefficient and lacks repeatability but is also susceptible to individual differences, growth environment, and post-harvest treatment conditions. This makes it difficult to consistently reflect the inherent characteristics of the variety and fails to provide a reliable screening basis for browning resistance breeding. Numerous studies have shown that the significant differences in browning levels among different fruit and vegetable varieties stem from differences in the composition and expression of related genes such as polyphenol oxidase, i.e., determined by genotype. Therefore, establishing a predictive model of browning phenotype at the gene expression level can overcome the interference of environmental and human factors, yielding more objective and reproducible identification results. Summary of the Invention

[0005] The purpose of this invention is to provide a method for predicting the browning susceptibility of lettuce, which can be used for rapid identification of lettuce browning susceptibility and creation of new browning-resistant varieties. It enables rapid and accurate identification of lettuce browning phenotypes, provides technical support for the processing industry and the breeding of browning-resistant varieties, and has good application prospects and high application value.

[0006] The technical solution of this invention to solve the above-mentioned technical problems is as follows: A method for predicting the browning sensitivity of lettuce is provided, comprising the following steps: S1. Determine the LsPPO4 gene expression level of the fresh-cut lettuce sample on the second day after cutting. S2. Substitute the values ​​into the linear regression model for predicting the degree of browning after 4 days to obtain the Y value for the predicted degree of browning. S3. Determine the browning sensitivity of lettuce based on the obtained Y value.

[0007] Furthermore, in step S1, the LsPPO4 gene is a biomarker for predicting the browning susceptibility of lettuce, and the nucleotide sequence of the LsPPO4 gene is shown in SEQ ID NO.1.

[0008] Furthermore, in step S1, the expression level of the LsPPO4 gene is determined using quantitative real-time PCR.

[0009] Furthermore, the nucleotide sequence of the quantitative upstream primer LsPPO4-F for the LsPPO4 gene is shown in SEQ ID NO.2, and the nucleotide sequence of the quantitative downstream primer LsPPO4-R is shown in SEQ ID NO.3.

[0010] Furthermore, in step S2, the linear regression model for predicting the degree of browning after 4 days is as follows: Y = -3.2735X + 30.341, R 2 =0.913; Where X represents the expression level of the LsPPO4 gene in lettuce on day 2, and Y represents the predicted expression level of the LsPPO4 gene in lettuce on day 4.

[0011] Furthermore, in step S3, the larger the Y value, the stronger the browning sensitivity of the lettuce.

[0012] Furthermore, in step S3, when the ΔBI value is greater than 10, it is considered a material prone to browning; when the ΔBI value is less than 5, it is considered a material resistant to browning; and the rest are intermediate materials.

[0013] The present invention has the following beneficial effects: 1. This invention provides a method for predicting the browning susceptibility of lettuce. By detecting the expression level of the LsPPO4 gene and the browning index in the fleshy stems of multiple lettuce materials on day 2, a significant correlation was found between the gene expression level and the degree of browning, and a specific quantitative regression equation (R²) was obtained. 2 =0.913). Using this equation, the ΔBI value can be predicted based on the expression level of the LsPPO4 gene in the sample to be tested. Combined with a preset threshold, a method for rapidly predicting the browning susceptibility of lettuce varieties by detecting the expression level of the key gene LsPPO4 has been successfully established.

[0014] 2. The method for predicting the browning susceptibility of lettuce provided by this invention has high accuracy (R). 2It can reach 0.913), and is simple and convenient to operate. It can be used for batch sampling and can accurately determine the browning sensitivity of materials in the same batch, providing a scientific basis for variety selection and quality control in fresh-cut lettuce processing.

[0015] 3. The present invention provides a method for predicting the browning susceptibility of lettuce, which can be used to guide the breeding of browning-resistant materials. The browning susceptibility of lettuce can be determined early in the post-harvest treatment period by detecting the expression level of the LsPPO4 gene in the fleshy stem on day 2. This serves as a reference parameter for the breeding of browning-resistant varieties (materials), significantly improving breeding efficiency. Attached Figure Description

[0016] Figure 1 Scatter plot of regression model predicted values ​​and measured values ​​for predicting the ΔBI value of lettuce on day 4 based on LsPPO4 expression level on day 2; Figure 2 Scatter plot of regression model predicted values ​​and measured values ​​for predicting the Δa* value of lettuce on day 4 based on LsPPO4 expression level on day 2; Figure 3 Scatter plot of regression model predictions and measured values ​​for predicting the ΔE value of lettuce on day 4 based on LsPPO4 expression level on day 2. Detailed Implementation

[0017] The present invention will be further illustrated below with reference to specific embodiments, but the embodiments do not limit the present invention in any way. Unless otherwise specified, the reagents, methods, and equipment used in the present invention are conventional reagents, methods, and equipment in this technical field.

[0018] The regression equation established in this embodiment is a stepwise regression method, which is a conventional method unless otherwise specified. The raw materials can be obtained from publicly available commercial sources unless otherwise specified. The color difference detector used is a CR-400 colorimeter (KONICA, Japan). The test raw materials (fresh sliced ​​lettuce, material numbers QY1, BK1, BK2, BK3, BY1, K2, K3) are shown in Table 1. The verification materials A1 (resistant to browning) and A2 (prone to browning) were harvested from the Modern Agricultural Science and Technology Innovation Demonstration Base of Sichuan Academy of Agricultural Sciences. The selected materials were of uniform maturity and free from defects, diseases, and substandard fruits.

[0019] Table 1. Test Material Information Table Example 1:

[0020] Extraction and detection of RNA from lettuce fleshy stems: Seven lettuce samples with different browning phenotypes were prepared (numbered QY1, BK1, BK2, BK3, BY1, K2, and K3). All materials were harvested uniformly in the same season, and fleshy stems of uniform size, free from pests, diseases, and mechanical damage were selected and stored at 4℃ for later use. Sampling method: The middle section of the stem of each lettuce was sliced ​​and stored at 4℃. Samples were taken on the second day after fresh cutting, and RNA was extracted after mixing the samples.

[0021] RNA extraction was performed using a plant total RNA extraction kit (such as Tiangen DP441). All mortars, EP tubes, pipette tips, and ddH2O used were sterilized by enzyme inactivation.

[0022] Example 2:

[0023] Reverse transcription and qRT-PCR: RNA extracted from fleshy stems was reverse transcribed into cDNA (using the Takara RR047A kit, following the manufacturer's instructions). Using lettuce 18S rRNA (accession number: HM047292.1) as an internal control, the expression level of the LsPPO4 gene on day 2 after fresh cutting was detected by real-time quantitative PCR. Specific primers for real-time quantitative PCR were designed based on the LsPPO4 gene sequence. The upstream primer for LsPPO4 quantification was LsPPO4-F (SEQ ID NO.2): 5'-GGAACTGGGATGCACCTGAT-3'; the downstream primer for LsPPO4 quantification was LsPPO4-R (SEQ ID NO.3): 5'-GGAGGTTTGTTTCGATCGGC-3'. The 18SR gene was used as an internal control gene, with its upstream primer 18SR-F: 5'-GTGCGCGATCATGACAAGAC-3'; and its downstream primer 18SR-R: 5'-CTTTCAACCCGATTCACCGC-3'.

[0024] The qRT-PCR reaction system consisted of 10 μL containing 5 μL of TB Green Premix Ex Taq II (Takara, Dalian, China), 0.5 μL each of forward and reverse primers, 1 μL of cDNA template, and 3 μL of ddH2O. The reaction was performed on a LightCycler® 96 real-time quantitative PCR system (Roche, Mannheim, Germany). The amplification program was: 95℃ pre-denaturation for 30 sec; 95℃ denaturation for 5 sec, 60℃ annealing for 30 sec, for 40 cycles; finally, melting curve analysis was performed (temperature increased to 95℃). This invention directly uses the ΔCq value (i.e., LsPPO4). CT -Internal Reference CT As an indicator of gene expression level, a smaller ΔCq value indicates a higher LsPPO4 gene expression level. Each sample was tested in triplicate.

[0025] Example 3:

[0026] Lettuce browning degree test: 3.1 Pre-treatment of lettuce raw materials Three lettuce stems were randomly selected from each variety. After removing excess leaves, the lettuce was rinsed with clean water to remove surface dirt, and then soaked in 200 ppm NaClO for two minutes. The stems were then peeled using a clean knife, and the peeled fleshy stems were sliced ​​into round pieces (3-4 mm thick) using a vegetable slicer (TW-802 vegetable slicer). The slices were soaked in clean water, and excess water was removed using a small manual vegetable dehydrator. The slices were then packaged into PE boxes, 20 boxes per variety, with 20 slices per box. After packaging, the lettuce was stored at (4±0.5)℃, and samples were taken every two days, with three boxes of each variety randomly selected for testing each time.

[0027] 3.2 Detection of browning degree in lettuce (color difference measurement) Color difference was measured at 0 h (initial) and 4 days after fresh cutting. Three boxes of each material were randomly selected each time, and the L*, a*, and b* values ​​of the center part of the cut surface of the lettuce slices were measured using a colorimeter (CR400, KONICA, Japan). Twenty points were measured for each sample, and the average value was taken.

[0028] Calculate the change in browning index (ΔBI) on day 4: ; ; Further calculation of the change in browning index (ΔBI): ; Wherein, BI4 is the browning index of the sample on day 4, and BI0 is the initial browning index measured immediately after fresh cutting; the larger the ΔBI value, the more severe the browning.

[0029] Simultaneously calculate the color difference change (Δa∗) and tolerance value (ΔE): ; ; in, This is the a* value for the sample on day 4. This is the initial a* value measured immediately after fresh cutting.

[0030] The measured values ​​of ΔBI, Δa*, and ΔE for the seven lettuce materials are shown in Table 2. In this experiment, BY1, BK1, BK3, and BK2 were materials prone to browning, while K2 and K3 were materials resistant to browning.

[0031] Table 2. Measured values ​​of browning index of 7 lettuce materials on the 4th day after fresh cutting. Example 4:

[0032] The relationship between LsPPO4 gene expression level and the degree of browning in lettuce: The ΔCq values ​​of the LsPPO4 gene were measured on day 2 for seven lettuce materials (three biological replicates per material). Table 3 shows the ΔCq values ​​for each sample. Linear regression equations were established with the ΔCq value (denoted as X) as the independent variable and the changes in browning index (ΔBI), total color difference (ΔE), and red-green color difference (Δa*) measured on day 4 as dependent variables. The equations are shown in Table 3. Figures 1-3 As shown.

[0033] The regression equations for ΔBI and ΔCq are: Y = -3.2735X + 30.341, R0 2 = 0.913; The regression equations for Δa* and ΔCq are: Y = -1.0052X + 14.482, R0 2 = 0.7255; The regression equation for ΔE and ΔCq is: Y = -0.5079X + 13.77, R0 2 = 0.3465.

[0034] Since the ΔBI model has the highest coefficient of determination and can more comprehensively reflect the color changes caused by browning, ΔBI was chosen as the main phenotypic indicator for predicting the browning sensitivity of lettuce. The larger the predicted ΔBI value, the more severe the browning. According to this equation, the ΔCq value of the sample to be tested is substituted into the equation to calculate the predicted ΔBI value. If the predicted value is greater than the set threshold of 10, it is judged as easily browning lettuce; less than 5, it is judged as browning resistant lettuce; and lettuce greater than 5 and less than 10 is judged as intermediate material.

[0035] Table 3. Expression levels of LsPPO4 in 7 fresh-cut lettuce materials on day 2. Example 5:

[0036] Establishment of a method for predicting the browning susceptibility of lettuce: Based on the above correlation analysis, the ΔBI value was selected as the characterization index of browning degree, and the expression level of LsPPO4 on day 2 (X) was used as the independent variable to establish a linear regression equation: Y = aX + b; Where Y is the predicted ΔBI value. Based on the measured data of 7 samples (3 replicates per sample), the regression equation is: Y = -3.2735X + 30.341, R 2 =0.913; This model can predict the degree of browning in fresh-cut lettuce on day 4 with good accuracy. The larger the Y value, the stronger the browning sensitivity of the lettuce. According to preliminary analysis, when the ΔBI value is greater than 10, it is a material prone to browning, and when the ΔBI value is less than 5, it can be regarded as a browning-resistant material.

[0037] Those skilled in the art can adjust the threshold upwards or downwards according to actual breeding or processing needs in order to screen for materials with stronger resistance to browning.

[0038] This method eliminates the need to wait until the fourth day to actually measure the degree of browning. It only requires detecting LsPPO4 expression levels via qRT-PCR on the second day after fresh cutting, and then substituting this information into the equation to quickly determine the browning sensitivity of lettuce varieties. This significantly shortens the detection cycle and offers high accuracy (R0). 2 =0.913), which allows for batch sampling and provides a scientific basis for screening fresh-cut lettuce processing varieties and breeding for browning resistance.

[0039] Example 6:

[0040] Reliability validation of the model for predicting browning susceptibility in lettuce: 6.1 Validation Materials and Methods Two independent lettuce varieties not involved in the modeling were selected, each exhibiting a distinct browning phenotype: a browning-resistant variety (A1) and a browning-prone variety (A2). Twenty fleshy stems from each material were collected and fresh-cut according to the method described in Example 2. RNA was extracted from samples on day 2, and the ΔCq value of the LsPPO4 gene was measured (three biological replicates per material, average value taken). Simultaneously, each material was stored under the same conditions until day 4, and the change in browning index (ΔBI) was measured, with 20 replicates per material.

[0041] The measured ΔCq values ​​of each material were substituted into the equation Y=-3.2735X+30.341 established in Example 3 to calculate the predicted ΔBI value. The predicted value was compared with the measured ΔBI value on day 4, and the browning phenotype was determined according to the threshold (predicted ΔBI<5 indicates browning resistance, >10 indicates browning susceptibility) to verify the accuracy of the model.

[0042] 6.2 Verification Results The measured ΔCq value, predicted ΔBI value, measured ΔBI value, and phenotypic determination results of the two verification materials are shown in Table 3. Table 4 shows that, based on the browning phenotypic discrimination threshold set in this invention (predicted ΔBI value < 5.0 indicates resistance to browning, > 10 indicates susceptibility to browning), A1's predicted ΔBI = 2.007 < 5.0, indicating resistance to browning, consistent with the actual phenotype; A2's predicted ΔBI = 11.467 > 5.0, indicating susceptibility to browning, also consistent with the actual phenotype.

[0043] Table 4 Measured ΔCq value, predicted ΔBI value, measured ΔBI value and phenotypic determination results In summary, the regression model established in this invention can accurately distinguish between browning-resistant and browning-prone lettuce varieties, and the deviation between the predicted and measured values ​​is small. It has high quantitative prediction accuracy and reliable classification ability, and can be used for rapid identification in production and breeding.

[0044] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for predicting the browning susceptibility of lettuce, characterized in that, Includes the following steps: S1. Determine the LsPPO4 gene expression level of the fresh-cut lettuce sample on the second day after cutting. S2. Substitute the values ​​into the linear regression model for predicting the degree of browning after 4 days to obtain the Y value for the predicted degree of browning. S3. Determine the browning sensitivity of lettuce based on the obtained Y value.

2. The method for predicting the browning sensitivity of lettuce according to claim 1, characterized in that, In step S1, the LsPPO4 gene is a biomarker for predicting the browning susceptibility of lettuce, and the nucleotide sequence of the LsPPO4 gene is shown in SEQ ID NO.

1.

3. The method for predicting the browning sensitivity of lettuce according to claim 1, characterized in that, In step S1, the expression level of the LsPPO4 gene was determined using real-time PCR.

4. The method for predicting the browning sensitivity of lettuce according to claim 2, characterized in that, The nucleotide sequence of the quantitative upstream primer LsPPO4-F for the LsPPO4 gene is shown in SEQ ID NO.2, and the nucleotide sequence of the quantitative downstream primer LsPPO4-R is shown in SEQ ID NO.

3.

5. The method for predicting the browning sensitivity of lettuce according to claim 1, characterized in that, In step S2, the linear regression model for predicting the degree of browning after 4 days is as follows: Y=-3.2735X +30.341,R 2 =0.913; Where X represents the expression level of the LsPPO4 gene in lettuce on day 2, and Y represents the predicted expression level of the LsPPO4 gene in lettuce on day 4.

6. The method for predicting the browning sensitivity of lettuce according to claim 1, characterized in that, In step S3, the larger the Y value, the stronger the browning sensitivity of the lettuce.

7. The method for predicting the browning sensitivity of lettuce according to claim 1, characterized in that, In step S3, when the ΔBI value is greater than 10, it is considered a material prone to browning; when the ΔBI value is less than 5, it is considered a material resistant to browning; and the rest are intermediate materials.

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