A melon germplasm intelligent screening and combination optimization method for waterlogging tolerance breeding

Abstract

This invention discloses an intelligent screening and combination optimization method for melon germplasm for flood-tolerant breeding, belonging to the field of agricultural biology and plant breeding technology. The method involves a three-stage cyclical treatment of melon germplasm resources; collecting and weighting gene expression time-series data, mitochondrial membrane potential dynamic data, and whole-genome methylation sequencing data of each melon germplasm during the three stages of treatment to calculate the Comprehensive Memory Index (CMI); classifying the melon germplasm into memory-generated, memory-acquired, and memory-insensitive types; combining memory-generated germplasm with fast-metabolizing germplasm, and calculating and predicting flood tolerance scores using a combination prediction model to screen for the optimal combination; and applying an inducing agent to the selected optimal combination of germplasm or grafted seedlings for enhanced induction treatment. This method achieves a leap from static flood-tolerant genotype screening to dynamic stress memory ability screening, providing an intelligent and precise technical solution for flood-tolerant melon breeding.

Description

Technical Field

[0001] This invention relates to the field of agricultural biology and plant breeding technology, and in particular to a method for intelligent screening and combination optimization of melon germplasm for waterlogging-resistant breeding. Background Technology

[0002] Melon is an important horticultural crop. In rainy areas, waterlogging is one of the main adverse factors restricting the development of the melon industry. Waterlogging leads to soil hypoxia, which causes root respiration to be hindered, reactive oxygen species to burst, and leaves to decline photosynthesis. In severe cases, it can lead to plant death or a significant reduction in yield.

[0003] Currently, the main methods for breeding melons to withstand waterlogging include: (1) Traditional waterlogging-resistant germplasm screening: Waterlogging-resistant germplasm is screened by observing morphological indicators such as plant height change rate, leaf wilting rate, and adventitious root development ability through seedling flooding treatment. Waterlogging-resistant core germplasm such as M13-06-3 and L45 have been identified. (2) Molecular marker-assisted screening: Five differentially expressed genes related to waterlogging resistance, such as MELO3C003917, MELO3C020588, MELO3C008314, MELO3C025360, and MELO3C021658, were identified by transcriptome sequencing. Early screening was carried out by detecting gene expression levels using qRT-PCR. (3) Hybrid breeding and grafting combination: M13-6-3 is used as the waterlogging-resistant male parent and hybridized with high-quality cultivars, or grafting is carried out using T13 rootstock.

[0004] However, existing technologies have the following shortcomings: First, current screening methods are based on static flood-tolerant genotypes, only detecting the expression levels of specific genes at a single time point, failing to consider the "memory effect" and epigenetic regulatory potential of plants after abiotic stress. The response speed and tolerance threshold improvement of the screened germplasm under repeated flooding conditions are unknown. Second, existing molecular markers target only a few differentially expressed genes, not including multi-dimensional epigenetic indicators such as whole-genome methylation levels and dynamic recovery capacity of mitochondrial function, resulting in limited screening accuracy. Third, existing combinatorial optimization strategies only employ simple hybridization or single rootstock grafting, failing to consider the functional reconstruction of metabolic pathways, making it difficult to achieve the synergistic gain of "rapid response to flooding" and "rapid post-flood repair." Fourth, there is a lack of cultivation enhancement technologies for flood-tolerant traits; conventional varieties cannot obtain similar response characteristics to flood-tolerant materials through pretreatment.

[0005] Therefore, developing an intelligent screening and combinatorial optimization method for waterlogged-tolerant melon germplasm that comprehensively considers adversity memory, epigenetic potential, and metabolic pathway reconstruction is of significant theoretical and practical value. To this end, an intelligent screening and combinatorial optimization method for waterlogged-tolerant melon germplasm is proposed. Summary of the Invention

[0006] The main objective of this invention is to provide an intelligent screening and combination optimization method for melon germplasm for waterlogging-resistant breeding, which can effectively solve the problems in the background technology.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for intelligent screening and combinatorial optimization of melon germplasm for waterlogging-tolerant breeding includes the following steps: S1: Three-stage cyclic treatment of melon germplasm resources, the three-stage cyclic treatment includes: 12 hours of initial flooding start treatment, 72 hours of oxygen supply restoration treatment, and 48 hours of secondary flooding stress treatment. Furthermore, the initial flooding initiation treatment involved placing melon seedlings in a flooded environment with the water level 2-3 cm above the soil surface to induce a hypoxic stress response. The oxygen restoration treatment involved draining the water and then managing the seedlings normally for 72 hours to restore oxygen supply. The secondary flooding stress treatment involved a second flooding treatment for 48 hours to induce moderate waterlogging stress.

[0008] S2: Collect gene expression time-series data, mitochondrial membrane potential dynamic data, and whole-genome methylation sequencing data of each melon germplasm during the three-stage treatment process, perform weighted processing, and calculate the comprehensive memory index (CMI). Furthermore, the gene expression time-series data is a response acceleration index. The calculation method is as follows: The response times of key anaerobic respiration enzyme genes during the initial flood initiation treatment and the second flood stress treatment were determined, respectively. The key anaerobic respiration enzyme genes include pyruvate decarboxylase gene PDC, lactate dehydrogenase gene LDH and alcohol dehydrogenase gene ADH. According to the formula: Calculate the response acceleration index of each gene. ,in This is the time required for gene g expression to reach 50% of its maximum value during the first stress event. The time required for gene g expression to reach 50% of its maximum value under secondary stress; Based on the response acceleration index of each gene Determine the response acceleration index , where G is a set of genes for key enzymes in anaerobic respiration.

[0009] Furthermore, the dynamic data on mitochondrial membrane potential is a mitochondrial function retention index. The calculation method is as follows: The mitochondrial membrane potential of root cells was observed in vivo using two-photon microscopy combined with mitochondrial fluorescent dyes and calculated according to the following formula: ,in This represents the initial mitochondrial membrane potential. This represents the mitochondrial membrane potential at the end of the second flood stress. The time required for the membrane potential to recover to 90% of its initial value. This is the recovery rate constant.

[0010] Furthermore, the whole-genome methylation sequencing data is a methylation stability index. The calculation method is as follows: Whole-genome methylation was performed on samples before, after, and three generations after asexual reproduction, and the methylation was calculated using the following formula: in This represents the methylation level of loci at the end of the recovery period. L represents the methylation level of loci after the end of the second flooding stress and three generations of asexual reproduction, where L is the total number of differentially methylated sites across the genome.

[0011] Furthermore, the Comprehensive Memory Index (CMI) is calculated as follows: ,in For the weighting coefficients, satisfying .

[0012] Preferred weighting coefficients The possible values ​​are: .

[0013] S3: Based on the Comprehensive Memory Index (CMI) and the whole genome methylation sequencing data, melon germplasm is classified into memory genotype, memory-acquiring type, and memory-insensitive type; Furthermore, germplasm classification is based on the following criteria: When M stab If the CMI is ≥0.7 and the CMI is ≥0.6, it is determined to be a memory genotype; When 0.3≤M stab When the CMI is <0.7 and CMI≥0.5, it is determined to be memory acquisition type; Otherwise, it is judged as memory insensitivity type.

[0014] Among them, for different germplasm: Memory-related genetic material has the characteristic of stably transmitting methylation patterns associated with stress memory through mitosis or meiosis, and can be directly used as core breeding material.

[0015] Memory-acquired germplasm has strong epigenetic plasticity, but its methylation pattern is unstable during reproduction, making it suitable for asexual reproduction scenarios such as grafting rootstocks.

[0016] Germplasm with poor memory should be eliminated.

[0017] S4: Combine memory-type germplasm with fast-metabolism germplasm. The combination method is selected from protoplast fusion or chimeric rootstock grafting. The flood tolerance score is calculated and predicted by the combination prediction model to screen the optimal combination. Furthermore, when the combination method is protoplast fusion, using memory-type germplasm as parent A and fast-metabolic germplasm as parent B, symmetrical or asymmetric protoplast fusion is performed via PEG-mediated or electrofusion methods to obtain somatic cell hybrids. The combination prediction model is as follows: ,in The comprehensive memory index is the memory genotype of parent A. The metabolic clearance index is for the rapidly metabolizing parent B. Synergistic effect factor These are the weighting coefficients. For the Sigmoid function, The empirical synergy coefficient, This represents the metabolic profile similarity between parents A and B, with values ​​ranging from 0 to 1. The metabolic clearance index The calculation formula is: ,in This represents the peak superoxide dismutase activity, expressed in U / mg protein. This represents the peak peroxidase activity, expressed in U / mg protein. The value represents the malondialdehyde (MDA) content at the end of the secondary flooding stress, expressed in μmol / g.

[0018] Furthermore, when the combination method is chimeric rootstock-scion grafting, it specifically includes: Using memory-acquiring germplasm as the interplant, a high-quality melon variety as the scion, and a fast-metabolizing germplasm as the rootstock, a three-stage chimera of "scion-interplanter-rootstock" is formed; the flood tolerance gain coefficient of this combination is... Calculate using the following formula: ;in The overall memory index of the anvil. The metabolic clearance index of the anvil. This is the signal transmission efficiency factor, with a value between 0 and 1. The overall score for scion quality is a weighted average of factors such as soluble solids content, single fruit weight, and flesh firmness. To compare the overall score of scion quality, The overall memory index is used as a reference for the rootstock.

[0019] Among them, the characteristics of the rapid metabolic germplasm are: high peak activity of superoxide dismutase (SOD) and peroxidase (POD) within 48 hours after flooding, low accumulation of malondialdehyde (MDA), and the ability to quickly remove reactive oxygen species and regenerate roots.

[0020] S5: Apply an inducing agent to the selected optimal combination of germplasm or grafted seedlings for enhanced induction treatment.

[0021] Furthermore, the enhanced induction treatment specifically includes: when the melon seedlings are at the 2-3 leaf stage, immersing the seedling roots in the inducing agent for 30 minutes, and then transplanting normally 48 hours later; The increase in the comprehensive memory index of melon germplasm after treatment with the inducer. It conforms to the following dose-response model: Where CMI is the overall memory index before processing. To achieve the maximum achievable improvement, it was calibrated through experiments. Where D is the absorption efficiency coefficient and D is the inducer dosage. For the optimal dose, For the characteristic function, when The value is 1 if the condition is met, and 0 otherwise.

[0022] Furthermore, the inducer comprises one or more combinations of 0.5% hydrogen peroxide, 50 μM melatonin, and 10 mM calcium chloride.

[0023] Furthermore, it also includes: intelligent screening objective function optimization, selecting the optimal germplasm subset from the initial germplasm bank according to the following formula: The constraints are satisfied: ,in Let P be the initial germplasm library, and P be the selected subset of germplasm. The weighted methylation potential index of germplasm i. The breeding cost of germplasm i includes germplasm acquisition costs, propagation costs, identification costs, etc. These are the weighting coefficients. This is the maximum number of filters.

[0024] Furthermore, the weighted methylation potential index The calculation method is as follows: ,in The index represents the enrichment of differentially methylated regions in region type k for germplasm i. For regional functional weights.

[0025] Preferably, for promoter regions: β=3; for exon regions: β=2; for intron regions: β=1; for intergenic regions: β=0.5. Beneficial effects

[0026] Compared with the prior art, the present invention has the following beneficial effects: This invention breaks through the traditional framework of "flood-tolerant genotype determinism" and screens germplasm with epigenetic regulatory potential through a three-stage cyclical treatment of "initiation-stress-recovery", thus realizing a leap from static flood-tolerant genotype screening to dynamic stress memory ability screening.

[0027] This invention integrates three dimensions—response acceleration index, mitochondrial function retention index, and methylation stability index—to construct a comprehensive memory index (CMI). Compared with existing technologies that only detect the expression levels of a few genes, this invention offers a more comprehensive evaluation and higher screening accuracy.

[0028] Combinatorial optimization of metabolic pathway reconstruction: This invention proposes two metabolic pathway reconstruction schemes: protoplast fusion and chimeric rootstock grafting. By recombining the functional modules of "memory genotype" and "rapid metabolism type" germplasm, the synergistic gains of "rapid response to flooding" and "rapid repair after flooding" are achieved, overcoming the technical defects of existing hybrid breeding and single rootstock grafting that make it difficult to achieve functional synergy.

[0029] The inducer proposed in this invention can pretreat conventional varieties during the seedling stage, enabling them to acquire response characteristics similar to those of waterlogged-tolerant materials, thus expanding the application range of non-waterlogged-tolerant germplasm.

[0030] This invention establishes a complete quantitative mathematical model, in which all variables can be measured through molecular biology, physiology and phenomics experiments, and is verifiable and operable, providing technical support for the intelligent and precise breeding of waterlogged-resistant melons. Attached Figure Description

[0031] Figure 1 This is a technical roadmap for a method of intelligent screening and combination optimization of melon germplasm for waterlogging-resistant breeding according to the present invention; Figure 2 This is a schematic diagram of the three-stage cyclic processing timeline of the method of the present invention; Figure 3 This is a schematic diagram illustrating the calculation process of the Comprehensive Memory Index (CMI) in the method of this invention. Figure 4 This is a schematic diagram of the decision tree for the germplasm classification criteria of the method of the present invention. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0033] like Figure 1 The technical roadmap shown in this embodiment uses 50 melon germplasm resources to illustrate the implementation process of this scheme.

[0034] Example 1:

[0035] Step 1, Experimental Materials Fifty melon germplasm resources were selected, including: M13-06-3 (known waterlogging tolerant germplasm), L45 (waterlogging tolerant material), T13 rootstock (vegetable type thin-skinned melon), Jiangxi pear melon (mainly cultivated variety), Guangxin No. 1 (heat and moisture tolerant variety), as well as local varieties and wild germplasm.

[0036] Step 2, Induction Treatment like Figure 2 As shown, the melon germplasm resources were subjected to a three-stage cyclic treatment, which included: an initial flooding start-up treatment of 12 hours, an oxygen supply restoration treatment of 72 hours, and a second flooding stress treatment of 48 hours. In this embodiment, the seedlings of each melon germplasm (3-leaf-1-heart stage) were treated as follows: Initial flood control measures: Water level 2-3cm above soil level, treatment for 12 hours; Restoration of oxygen supply: After drainage, maintain normal management for 72 hours; Secondary flooding stress treatment: 48 hours of secondary flooding.

[0037] Step 3, Multi-dimensional Data Collection like Figure 3 As shown, gene expression time-series data, mitochondrial membrane potential dynamic data, and whole-genome methylation sequencing data of various melon germplasms during the three-stage treatment process were collected, weighted, and the Comprehensive Memory Index (CMI) was calculated. Among these, gene expression time-series data served as the response acceleration index. Mitochondrial membrane potential dynamics data is the mitochondrial function retention index (MFI), and whole-genome methylation sequencing data is the methylation stability index. ; Response Acceleration Index The calculation method is as follows: The response times of key anaerobic respiration enzyme genes were measured during the initial flood initiation treatment and the second flood stress treatment. These key anaerobic respiration enzyme genes include pyruvate decarboxylase (PDC), lactate dehydrogenase (LDH), and alcohol dehydrogenase (ADH). The calculation was performed according to the formula: Calculate the response acceleration index of each gene. ,in This is the time required for gene g expression to reach 50% of its maximum value during the first stress event. The time required for gene g expression to reach 50% of its maximum value under secondary stress; based on the response acceleration index of each gene. Determine the response acceleration index , where G is a set of genes for key enzymes in anaerobic respiration.

[0038] The Mitochondrial Function Preservation Index (MFI) is calculated as follows: Mitochondrial membrane potential in root cells is observed in vivo using two-photon microscopy combined with mitochondrial fluorescent dyes, and the result is calculated according to the following formula: ,in This represents the initial mitochondrial membrane potential. This represents the mitochondrial membrane potential at the end of the second flood stress. The time required for the membrane potential to recover to 90% of its initial value. This is the recovery rate constant.

[0039] Whole-genome methylation sequencing data is a methylation stability index The calculation method is as follows: whole-genome methylation sequencing was performed on samples before the second flooding stress, after the second flooding stress, and after three generations of asexual reproduction. The results were calculated according to the following formula: ,in This represents the methylation level of loci at the end of the recovery period. L represents the methylation level of loci after the end of the second flooding stress and three generations of asexual reproduction, where L is the total number of differentially methylated sites across the genome.

[0040] The Comprehensive Memory Index (CMI) is calculated as follows: ,in For the weighting coefficients, satisfying .

[0041] In this embodiment, Gene expression time series data: Root samples were collected at 0h, 3h, 6h, and 12h during the first flooding treatment, 0h, 24h, 48h, and 72h during the recovery period, and 0h, 3h, 6h, 12h, 24h, and 48h during the second flooding treatment. RNA was extracted and the expression levels of PDC, LDH, and ADH genes were detected by qRT-PCR.

[0042] Mitochondrial membrane potential data: The mitochondrial membrane potential of root cells was observed every 12 hours at the end of the second flooding treatment and during the recovery period using two-photon microscopy combined with MitoTrackerRed dye.

[0043] Whole-genome methylation data: Leaf samples were collected at the end of the recovery period and three generations after asexual reproduction for whole-genome methylation sequencing (WGBS).

[0044] Comprehensive memory index calculation: Taking M13-06-3 as an example: the measured S mem =0.78; MFI=0.65; M stab=0.82; Taking w1=0.4, w2=0.3, w3=0.3, we calculate: CMI=0.4×0.78+0.3×0.65+0.3×0.82=0.312+0.195+0.246=0.753.

[0045] Step 5, Germplasm Classification like Figure 4 As shown, germplasm classification is based on the following criteria: When M stab If the CMI is ≥0.7 and the CMI is ≥0.6, it is determined to be a memory genotype; When 0.3≤M stab When the CMI is <0.7 and CMI≥0.5, it is determined to be memory acquisition type; Otherwise, it is judged as memory insensitivity type.

[0046] In this embodiment: M13-06-3: CMI=0.753≥0.6, M stab =0.82≥0.7, indicating a memory-based genetic type.

[0047] L45: CMI=0.58, M stab =0.72, indicating a memory-based genetic type.

[0048] T13 anvil: CMI=0.62, M stab =0.45, which is determined to be memory acquisition type.

[0049] Jiangxi pear melon: CMI=0.35, M stab =0.28, which indicates a memory insensitivity type.

[0050] Example 2: Protoplast Fusion Combination Optimization The combination of memory-type germplasm and fast-metabolic germplasm was selected by protoplast fusion or chimeric rootstock grafting, and the flood tolerance score was calculated and screened by the combination prediction model. When the combination method is protoplast fusion, using memory-type germplasm as parent A and fast-metabolic germplasm as parent B, symmetrical or asymmetric protoplast fusion is performed via PEG-mediated or electrofusion methods to obtain somatic cell hybrids. The combination prediction model is as follows: ,in The comprehensive memory index is the memory genotype of parent A. The metabolic clearance index is for the rapidly metabolizing parent B. Synergistic effect factor These are the weighting coefficients. For the Sigmoid function, This is an empirical synergy coefficient, with a value between 0.3 and 0.5. Metabolic profile similarity between parents A and B, with values ​​ranging from 0 to 1; metabolic clearance index. The calculation formula is: ,in This represents the peak superoxide dismutase activity, expressed in U / mg protein. This represents the peak peroxidase activity, expressed in U / mg protein. The value represents the malondialdehyde (MDA) content at the end of the secondary flooding stress, expressed in μmol / g.

[0051] In this embodiment: Step 21, Parent Selection Parent A (memory genotype): M13-06-3 (CMI=0.753); Parent B (rapidly metabolizing type): Guangxin No. 1 ([SOD]) peak =245U / mg, [POD] peak =189 U / mg, [MDA] T2 =12.3 μmol / g); calculate the metabolic clearance index: MCI B =245×189 / 12.3=46305 / 12.3≈3764.6.

[0052] Step 22, protoplast fusion Leaves from sterile seedlings of M13-06-3 and Guangxin 1 were collected, and protoplasts were prepared by enzymatic hydrolysis. Symmetrical fusion was then performed using the PEG-calcium ion fusion method. Regenerated plants were cultured after fusion, and fusion progeny were screened using molecular markers representing methylation sites.

[0053] Step 23, predict the flood tolerance score with λ1=0.4, λ2=0.4, λ3=0.2, ρ AB =0.65, η=0.4; then γ syn =1 + 0.4 × 0.65 = 1.26; .

[0054] The linear combination value is calculated as follows: 0.4 × 0.753 + 0.4 × 3764.6 + 0.2 × 53.24 × 1.26 = 0.301 + 1505.84 + 13.42 = 1519.56 After normalization using the Sigmoid function, the predicted flood tolerance score P is obtained. fuse ≈0.92 (out of 1.0).

[0055] Step 24, Verify Results The obtained hybrid progeny were subjected to waterlogging stress verification. The results showed that the leaf wilting index of the hybrid progeny under secondary waterlogging stress was reduced by 42% compared with the average value of the two parents, the root activity was increased by 35% compared with M13-06-3, and the soluble solids content of the fruit reached 16.2%, maintaining the excellent quality of Guangxin No. 1.

[0056] Example 3: Optimization of chimeric rootstock-scion grafting combination When the combination method is chimeric rootstock grafting, it specifically includes: using memory-acquiring germplasm as the interstock, using high-quality melon varieties as the scion, and using fast-metabolizing germplasm as the rootstock, forming a three-stage chimera of "scion-interstock-rootstock".

[0057] In this embodiment: Step 31, Material Configuration Scion: Jiangxi pear melon (the main cultivated variety, with excellent quality but poor waterlogging resistance); Intermediate anvil: T13 anvil (memory acquisition type, CMI=0.62); Anchor: Guangxin No. 1 (rapidly metabolized, MCI) B =3764.6); Control rootstock: conventional pumpkin rootstock (CMI=0.35).

[0058] Step 32, grafting operation The three-stage grafting method is adopted: first, the intermediate rootstock (T13 rootstock) is grafted onto the base rootstock (Guangxin No. 1), and after healing, the scion (Jiangxi pear melon) is grafted onto the intermediate rootstock.

[0059] Step 33, Calculation of flood resistance gain coefficient Flood resistance gain coefficient Calculate using the following formula: ; in The overall memory index of the anvil. The metabolic clearance index of the anvil. This is the signal transmission efficiency factor, with a value between 0 and 1. The overall score for scion quality is a weighted average of factors such as soluble solids content, single fruit weight, and flesh firmness. To compare the overall score of scion quality, The overall memory index is used as a reference for the rootstock.

[0060] In this embodiment, ξ = 0.6 is taken, and Q is measured. scion =85, Q 对照接穗 =82, CMI 对照砧木 =0.35.

[0061] G chimeric=1 / 2×(0.62+3764.6) / 0.35×(1+0.6×85 / 82) =1 / 2×10764.9×(1+0.622) =5382.45×1.622≈8730.

[0062] Step 34, Verify Results The three-segment chimera was subjected to flooding stress (48 hours), and the results showed: The leaf wilting index of the chimeric plants was 0.23 (compared to 0.67 for the control pumpkin rootstock grafted seedlings). The recovery rate (new leaf germination rate) 7 days after flooding was 91% (compared to 43% in the control group). Fruit quality: The soluble solids content was 15.8%, which was not significantly different from that of Jiangxi pear melon under normal cultivation conditions (16.0%).

[0063] Example 4: Effect of inducing agent treatment The selected optimal combination of germplasm or grafted seedlings is subjected to intensive induction treatment with an inducing agent. This intensive induction treatment specifically includes: When the melon seedlings are at the 2-3 leaf stage, immerse the seedling roots in an inducing agent for 30 minutes, and transplant them normally 48 hours later. The increase in the overall memory index of melon germplasm after treatment with inducing agents It conforms to the following dose-response model: Where CMI is the overall memory index before processing. To achieve the maximum achievable improvement, it was calibrated through experiments. Where D is the absorption efficiency coefficient and D is the inducer dosage. For the optimal dose, For the characteristic function, when The value is 1 if the condition is met, and 0 otherwise. The inducing agent contains one or more combinations of 0.5% hydrogen peroxide, 50 μM melatonin, and 10 mM calcium chloride.

[0064] In this embodiment: Step 41, Treatment group and control group Treatment group: Jiangxi pear melon seedlings (2-3 leaf stage), roots immersed in an inducing agent ( +50μM melatonin+ Treat for 30 minutes, then transplant after 48 hours.

[0065] Control group: Jiangxi pear melon seedlings, with roots soaked in clean water for 30 minutes, and transplanted 48 hours later.

[0066] Step 42, Induction Effect Two groups of plants were subjected to waterlogging stress (48 hours), and their overall memory index was measured. Control group: CMI=0.35; Processing Group: CMI treated =0.58; The treatment group showed a 65.7% increase in CMI, moving from a "memory insensitivity" level to a "memory acquisition" level. Further analysis revealed that the response time of PDC and LDH genes in the roots of the treatment group under secondary stress was shortened by 42% compared to the control group, and the mitochondrial membrane potential retention rate was increased by 38%.

[0067] Example 5: Optimization of the Objective Function for Intelligent Filtering Select the optimal subset of germplasm from the initial germplasm bank using the following formula: ; The constraints are satisfied: ; in Let P be the initial germplasm library, and P be the selected subset of germplasm. The weighted methylation potential index of germplasm i. The breeding cost of germplasm i includes germplasm acquisition costs, propagation costs, identification costs, etc. These are the weighting coefficients. This represents the upper limit for the number of samples to be screened. Weighted methylation potential index. The calculation method is as follows: ,in The index represents the enrichment of differentially methylated regions in region type k for germplasm i. For regional functional weights.

[0068] In this embodiment: Step 51, Initial Germplasm Bank Fifty melon germplasm resources were selected, and various indicators were calculated.

[0069] Step 52, Parameter Settings Take α1=0.5, α2=0.3, α3=0.2, N max =10, screening criterion CMI≥0.5.

[0070] Step 53, Filter Results Through objective function optimization, the following 10 optimal proton subsets were selected:

[0071] Industrial applicability The intelligent screening and combination optimization method for melon germplasm for flood-tolerant breeding provided by this invention can be widely applied in the field of flood-tolerant melon breeding, specifically including but not limited to: Used for high-throughput screening and evaluation of waterlogging-tolerant germplasm in the melon germplasm resource bank; Used for the breeding of new waterlogging-resistant melon varieties, especially for special varieties for rainy areas in the south and for greenhouse cultivation conditions; Used for the selection and combination optimization of rootstocks in melon grafting cultivation; Used for stress-inducing treatment during the seedling stage of melons to improve the plant's tolerance to waterlogging.

[0072] The procedure is standardized, and the required equipment (qRT-PCR instrument, two-photon microscope, sequencing platform, etc.) are all standard equipment in molecular biology and plant physiology laboratories, which has good scalability and industrial application prospects.

[0073] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent screening and combination optimization of melon germplasm for waterlogging-tolerant breeding, characterized in that, Includes the following steps: S1: Three-stage cyclic treatment of melon germplasm resources, the three-stage cyclic treatment includes: 12 hours of initial flooding start treatment, 72 hours of oxygen supply restoration treatment, and 48 hours of secondary flooding stress treatment. S2: Collect gene expression time-series data, mitochondrial membrane potential dynamic data, and whole-genome methylation sequencing data of each melon germplasm during the three-stage treatment process, perform weighted processing, and calculate the comprehensive memory index (CMI). S3: Based on the Comprehensive Memory Index (CMI) and the whole genome methylation sequencing data, melon germplasm is classified into memory genotype, memory-acquiring type, and memory-insensitive type; S4: Combine memory-type genetics with fast-metabolizing genetics, and use a combination prediction model to calculate the predicted flood tolerance score to select the optimal combination; S5: Apply an inducing agent to the selected optimal combination of germplasm or grafted seedlings for enhanced induction treatment.

2. The method for intelligent screening and combination optimization of melon germplasm for flood-resistant breeding according to claim 1, characterized in that, The gene expression time-series data is a response acceleration index. The calculation method is as follows: The response times of key anaerobic respiration enzyme genes during the initial flood initiation treatment and the second flood stress treatment were determined, respectively. The key anaerobic respiration enzyme genes include pyruvate decarboxylase gene PDC, lactate dehydrogenase gene LDH and alcohol dehydrogenase gene ADH. According to the formula: Calculate the response acceleration index of each gene. ,in This is the time required for gene g expression to reach 50% of its maximum value during the first stress event. The time required for gene g expression to reach 50% of its maximum value under secondary stress; Based on the response acceleration index of each gene Determine the response acceleration index , where G is a set of genes for key enzymes in anaerobic respiration.

3. The method for intelligent screening and combination optimization of melon germplasm for flood-resistant breeding according to claim 1, characterized in that, The dynamic data on mitochondrial membrane potential is the mitochondrial function retention index. The calculation method is as follows: The mitochondrial membrane potential of root cells was observed in vivo using two-photon microscopy combined with mitochondrial fluorescent dyes and calculated according to the following formula: ,in This represents the initial mitochondrial membrane potential. This represents the mitochondrial membrane potential at the end of the second flood stress. The time required for the membrane potential to recover to 90% of its initial value. This is the recovery rate constant.

4. The method for intelligent screening and combination optimization of melon germplasm for flood-resistant breeding according to claim 1, characterized in that, The whole-genome methylation sequencing data is a methylation stability index. The calculation method is as follows: Whole-genome methylation was performed on samples before, after, and three generations after asexual reproduction, and the methylation was calculated using the following formula: ,in This represents the methylation level of loci at the end of the recovery period. L represents the methylation level of loci after the end of the second flooding stress and three generations of asexual reproduction, where L is the total number of differentially methylated sites across the genome.

5. The method for intelligent screening and combination optimization of melon germplasm for flood-resistant breeding according to claim 1, characterized in that, Germplasm classification is based on the following criteria: When M stab ≥ 0.7 and CMI ≥ 0.6, it is determined as memory inheritance type; When 0.3≤M stab When the CMI is <0.7 and CMI≥0.5, it is determined to be memory acquisition type; Otherwise, it is judged as memory insensitivity type.

6. The method for intelligent screening and combination optimization of melon germplasm for flood-resistant breeding according to claim 1, characterized in that, The combination methods in step S4 include protoplast fusion, and when the combination method is protoplast fusion, the combination prediction model is: ; in The comprehensive memory index is the memory genotype of parent A. The metabolic clearance index is for the rapidly metabolizing parent B. Synergistic effect factor These are the weighting coefficients. For the Sigmoid function, The empirical synergy coefficient, Metabolic profile similarity between parents A and B; The metabolic clearance index The calculation formula is: ,in This represents the peak activity of superoxide dismutase. This represents the peak activity of peroxidase. This represents the malondialdehyde (MDA) content at the end of the secondary flooding stress.

7. The method for intelligent screening and combination optimization of melon germplasm for flood-resistant breeding according to claim 1, characterized in that, The combination methods in step S4 include chimeric rootstock-scion grafting, and when the combination method is chimeric rootstock-scion grafting, it specifically includes: Using memory-acquiring germplasm as the interplant, a high-quality melon variety as the scion, and a fast-metabolizing germplasm as the rootstock, a three-stage chimera of "scion-interplanter-rootstock" is formed; the flood tolerance gain coefficient of this combination is... Calculate using the following formula: ,in The overall memory index of the anvil. The metabolic clearance index of the anvil. It is the signal transmission efficiency factor. To assess the overall quality of the scion, To compare the overall score of scion quality, The overall memory index is used as a reference for the rootstock.

8. The method for intelligent screening and combination optimization of melon germplasm for flood-resistant breeding according to claim 1, characterized in that, The enhanced induction treatment specifically includes: when the melon seedlings are at the 2-3 leaf stage, immersing the seedling roots in the inducing agent for 30 minutes, and then transplanting normally 48 hours later; The increase in the comprehensive memory index of melon germplasm after treatment with the inducer. It conforms to the following dose-response model: Where CMI is the overall memory index before processing. To maximize the potential improvement, Where D is the absorption efficiency coefficient and D is the inducer dosage. For the optimal dose, This is an indicator function.

9. A method for intelligent screening and combination optimization of melon germplasm for flood-resistant breeding according to claim 8, characterized in that, The inducer comprises one or more combinations of 0.5% hydrogen peroxide, 50 μM melatonin, and 10 mM calcium chloride.

10. The method for intelligent screening and combination optimization of melon germplasm for flood-resistant breeding according to claim 1, characterized in that, Also includes: The intelligent screening objective function is optimized to select the optimal subset of germplasm from the initial germplasm bank according to the following formula: The constraints are satisfied: ,in Let P be the initial germplasm library, and P be the selected subset of germplasm. The weighted methylation potential index of germplasm i. For the breeding cost of germplasm i, These are the weighting coefficients. This is the maximum number of filters.

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