Method for efficiently screening alkali-resistant genes of rice

Abstract

The invention discloses a method for efficiently screening a rice alkali-resistant gene and relates to the technical field of gene engineering. The method comprises the following steps: carrying out seedling culture and alkali stress treatment on an alkali-resistant rice variety and an alkali-sensitive rice variety, and finding out an obvious phenotype difference of the two rice varieties; the method comprises the following steps: sampling rice tissues at different time points after alkaline stress, and carrying out sequencing and conjoint analysis on growth indexes, physiological and biochemical indexes, transcriptomics and metabonomics on the obtained rice tissue samples; screening differential expression genes and differential accumulation metabolites from omics sequencing results for data analysis; and then jointly annotating the obtained differential expression genes and differential accumulation metabolites into KEGG collateral dredging, correspondingly annotating and analyzing the differential expression genes and differential accumulation metabolites in each pathway, and screening out genes and major regulatory pathways which respond to alkali stress in combination with expression change conditions. The invention provides a method for efficiently screening rice alkali stress response genes.

Description

Technical Field

[0001] This invention relates to the field of genetic engineering technology, and specifically to a method for efficiently screening alkali-tolerant genes in rice. Background Technology

[0002] my country has a vast area of ​​saline-alkali land, which is widely distributed. According to statistics, the existing area of ​​saline-alkali soil is approximately 100 million hectares. 2 Due to improper fertilization and irrigation, the area of ​​secondary salinization in China is expanding rapidly. It is estimated that by 2040, more than 43% of arable land will be affected by salinization, severely impacting crop yields and hindering agricultural development. Soil salinization is a major stress type affecting plant growth and crop yield. Compared with neutral salt stress (NaCl), alkaline salt stress (Na2CO3 or NaHCO3) is characterized by a higher pH value, causing more severe alkaline damage to plant growth. The Songnen Plain in Northeast my country is a typical area of ​​concentrated soda saline-alkali soil, accounting for about 8% of the total saline-alkali land area in the country. This alkaline stress is a key limiting factor that seriously restricts agricultural production and is a significant threat to my country's food security and ecological security. Therefore, protecting and rationally utilizing saline-alkali soil resources is of great importance.

[0003] Rice, as one of my country's most important food crops, is an effective way to efficiently utilize saline-alkali soil in saline-alkali areas. However, its growth is subject to various adverse effects from saline-alkali stress. More seriously, alkali stress leads to excessive accumulation of reactive oxygen species (ROS) in the roots, exacerbating root damage and seedling mortality. Inhibiting excessive ROS accumulation in roots and inducing the expression of abiotic stress genes to alleviate root damage is one of the main pathways of ABA-induced resistance, suggesting that the ABA signaling pathway activates gene transcription networks of multiple metabolic pathways. How ABA regulates the expression of key genes in its synthesis, metabolism, and transduction pathways to improve alkali stress resistance still requires further investigation. Therefore, in-depth exploration of the physiological and molecular mechanisms of rice's response to alkali stress and the discovery of important alkali-tolerant gene resources will provide a scientific basis for improving rice alkali tolerance and cultivating alkali-tolerant rice germplasm resources. This will also be of great significance for genetically improving alkali-tolerant rice varieties, increasing grain yields in saline-alkali areas, and protecting the ecological environment.

[0004] Transcriptomics is a discipline that studies gene transcription and its regulatory mechanisms in cells, aiming to reveal the molecular mechanisms underlying specific biological processes and diseases. Analyzing genome expression under stress at the overall transcriptional level enhances our understanding of the complex regulatory networks related to stress adaptation and tolerance, and is crucial for constructing stress-related genome transcriptional regulatory networks. Metabolomics is an ideal platform for studying plant metabolism, becoming a primary technique for revealing plant genetic diversity, plant development, tissue and organ differentiation, adaptation mechanisms of plants to abiotic and biotic stresses, and the potential mechanisms of interaction between plants and symbiotic microorganisms. Combined transcriptomics and metabolomics analysis provides a comprehensive approach to gene and metabolite analysis. Transcriptomics research aims to reveal differentially expressed genes under different conditions, while metabolites, the final products of gene transcription, act as a bridge between genes and phenotypes. Through integrated data analysis, key candidate genes and regulatory pathways for rice alkali tolerance have been identified, providing important theoretical basis for breeding alkali-tolerant rice varieties, elucidating rice alkali tolerance mechanisms, and improving the alkali tolerance regulatory network. Summary of the Invention

[0005] To address the problems existing in the aforementioned background art, this invention provides a highly efficient method for screening alkali-tolerant genes in rice. This offers a more practically significant solution for screening alkali-tolerant germplasm resources and discovering functional genes in rice genetic breeding.

[0006] This invention discloses a method for efficiently screening alkali-tolerant genes in rice, comprising the following steps:

[0007] Two rice varieties, one alkali-tolerant and one alkali-sensitive, were cultured and subjected to alkali stress treatment to identify significant phenotypic differences. Rice tissue samples were collected at different time points after alkali stress, and growth indicators, physiological and biochemical indicators, transcriptomics, and metabolomics sequencing and combined analysis were performed on the obtained rice tissue samples. Differentially expressed genes (DEGs) and differentially accumulated metabolites (DAMs) were screened from the omics sequencing results for data analysis.

[0008] The differentially expressed genes and differentially accumulated metabolites were then annotated into the KEGG network. Differentially expressed genes and differentially accumulated metabolites in each pathway were annotated and analyzed accordingly. Based on the expression changes, genes and major regulatory pathways that respond to alkaline stress were screened out.

[0009] Furthermore, the alkali-sensitive and alkali-tolerant varieties are Jiudao 51 (J51) and Dongdao 4 (D4), respectively.

[0010] Furthermore, the results of transcriptomics sequencing need to be validated by gene expression.

[0011] Furthermore, the rice seedling cultivation process: after soaking, disinfecting, soaking, and germinating the rice seeds, they are irrigated with culture solution and cultivated until the 3-leaf stage.

[0012] Furthermore, rice seedlings cultured to the 3-leaf stage were subjected to alkali stress treatment with a 15 mM Na2CO3 alkaline solution. The pH of the alkaline solution was 10.79±0.025, and the soluble salt concentration EC was 2.85±0.006 mS / cm.

[0013] Furthermore, the growth indicators include rice seedling survival rate, fresh weight, and fresh weight loss rate.

[0014] Furthermore, the physiological and biochemical indicators include superoxide anion (O2). − Content of ), hydrogen peroxide (H2O2) content, malondialdehyde (MDA) content, osmotic substances content, antioxidant enzyme activity and ion content.

[0015] Furthermore, the transcriptomics sequencing includes RNA extraction and detection, library construction and sequencing, Stringtie transcript construction and HISAT2 reference genome sequencing alignment, differentially expressed gene GO, and KEGG bioinformatics analysis.

[0016] Furthermore, the metabolomics sequencing includes sample extraction, HPLC-MS analysis, metabolite quantification, and differential expression analysis.

[0017] Furthermore, the joint analysis includes a statistical test of the significance of the metabolite correlation analysis, with a significance level of P-value < 0.05 as the significance threshold.

[0018] Furthermore, the differential gene expression was verified using real-time quantitative PCR (qRT-PCR) technology, with rice actin as an internal reference gene. Primers were designed to verify whether the expression pattern of the differentially expressed genes screened under alkaline stress was accurate.

[0019] This invention utilizes genetic engineering, molecular biology, and various plant physiological and biochemical assays to provide an efficient method for screening rice genes responding to alkali stress through phenotypic observation, physiological and biochemical index detection, and transcriptomics data analysis. This method is also applicable to screening stress-resistance genes in other crops that respond to various abiotic stresses. This invention not only provides guidance for the efficient utilization of soda-saline-alkali land in Northeast China and the discovery and verification of rice alkali stress genes, but also offers a valuable reference scheme for the efficient screening of stress-resistance genes and the elucidation of stress-resistance mechanisms in various crops. Attached Figure Description

[0020] Figure 1 This describes the effects of alkali stress on the growth phenotypes of rice seedlings D4 and J5 in Example 2.

[0021] Figure 2 This describes the effects of alkali stress on plasma membrane damage and ROS accumulation in rice seedlings of varieties D4 and J5 in Example 3.

[0022] Figure 3 This is an example of how alkaline stress affects the proline and soluble sugar content in the leaves of rice seedlings of varieties D4 and J51 in Example 3.

[0023] Figure 4 This describes the effect of alkali stress on the ion content of leaves in D4 and J51 seedlings in Example 3.

[0024] Figure 5 This describes the effect of alkali stress on the antioxidant enzyme activity of leaves in D4 and J51 seedlings in Example 3.

[0025] Figure 6 This is a principal component analysis diagram of the sample in Example 4;

[0026] Figure 7 This is a statistical graph of differentially expressed genes in rice leaves under alkali stress in Example 4;

[0027] Figure 8 This is a bubble chart showing the Top 20 GO enrichment of differentially expressed genes in rice seedling leaves from different comparison groups under the classification of biological processes in Example 4.

[0028] Figure 9 This is a bubble chart showing the Top 20 KEGG enrichment of differentially expressed genes in the leaves of rice seedlings from different comparison groups in Example 4.

[0029] Figure 10 This refers to the qRT-PCR validation of the D4 and J51 transcriptome data in Example 5;

[0030] Figure 11 This is a PCA score chart of the metabolomics data quality control samples in Example 6;

[0031] Figure 12 This is a diagram of the OPLS-DA model among different metabolite groups in Example 6;

[0032] Figure 13 This is a heatmap showing the clustering of metabolites from all samples in Example 6;

[0033] Figure 14 This is a statistical chart of the differences in metabolites among the groups in Example 6;

[0034] Figure 15 This is a Venn diagram showing the differences in metabolites between groups in Example 6;

[0035] Figure 16This is a bubble chart of the Top 20 KEGG enrichment of differential metabolites in the leaves of rice seedlings from different comparison groups in Example 6.

[0036] Figure 17 This is a co-enrichment diagram of differential metabolites and differential genes between D4AS and J51AS under alkaline stress in Example 7;

[0037] Figure 18 This is a diagram showing the analysis of key genes and metabolites in the ABA signal transduction and biosynthesis pathways of rice varieties D4 and J51 under alkali stress in Example 7. Detailed Implementation

[0038] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all 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 scope of protection of the present invention.

[0039] Example 1

[0040] A method for efficiently screening alkali-tolerant genes in rice:

[0041] Step 1: Rice Seedling Cultivation: After soaking, disinfecting, and rinsing both alkali-sensitive and alkali-tolerant rice seeds, they were placed in a 30℃ constant temperature incubator for 48 hours, followed by germination in a 28℃ constant temperature incubator for 24 hours. Rice seeds with the same germination rate were selected and sown in black plastic culture boxes with 96-well culture plates. After culturing for 5-7 days, nutrient solution was added, and the seeds were further cultured in an artificial climate chamber with a photoperiod of 25℃ (light), 20℃ (dark), and 12 hours to the 3-leaf stage.

[0042] Step 2: Alkaline stress treatment: Rice seedlings cultivated to the 3-leaf stage were transferred to a 15 mM Na2CO3 solution (pH 10.79±0.025, EC 2.85±0.006 mS / cm) to simulate alkaline stress conditions. CK was the control without stress (continued to be cultured in nutrient solution). The pH and EC of the solution were measured with a pH meter and a salt content meter, respectively.

[0043] Step 3: Growth index determination: On the 3rd and 5th days of alkali stress treatment, the survival rate of seedlings was investigated; rice seedlings with wilted and wrinkled leaves, and whole plants curled and turned yellow were considered dead. Five seedlings were randomly selected from each treatment to measure their plant height and fresh weight. Fresh weight loss rate = (fresh weight of control rice seedlings - fresh weight of rice seedlings under alkali stress) / fresh weight of control rice seedlings × 100%.

[0044] Step 4: Measurement of physiological and biochemical indicators: Superoxide anion (O2) − The content of O2 was determined using a wavelength of 530 nm. − Calculation of absorbance values ​​from the reaction with hydroxylamine; determination of hydrogen peroxide (H2O2) content using the absorbance values ​​of the reaction products of H2O2 and titanium sulfate at a wavelength of 415 nm; O2 content in leaves. − Reactive oxygen species such as H2O2 were observed to accumulate in leaves after staining with 3,3'-diaminobenzidine (DAB) and nitrotetrazole blue chloride (NBT), respectively. Malondialdehyde (MDA) content was quantitatively determined using the thiobarbituric acid reaction method. Soluble sugar content was determined using the anthrone colorimetric method. Proline content was determined using the ninhydrin colorimetric method. Antioxidant enzyme activity was determined by placing fresh leaf samples (0.1 g) into 2 mL sterile centrifuge tubes, rapidly freezing them in liquid nitrogen, and then adding 1 mL of phosphate buffer (50 mM, pH 7.8). The supernatant was then used to determine the activities of antioxidant enzymes such as SOD, CAT, POD, and APX. Na+ content in rice leaves was also measured. + K + Ca 2+ and Mg 2+ The content was determined using inductively coupled plasma mass spectrometry (ICPS-7500).

[0045] Step 5: Transcriptomics and Metabolomics Sequencing: Four biological replicates were performed for each treatment in the no-stress control group and the alkali-stress treatment group. Total RNA was extracted from rice after 24 hours of culture for library construction and transcriptome sequencing. Metabolome sequencing was performed using HPLC-MS. Analysis data of expressed genes and metabolites were obtained.

[0046] Step Six: Based on the transcriptomics data, DEGs were screened using |Fold Change| ≥ 2 and FDR < 0.05 as the standard threshold. The FPKM values ​​of differentially expressed genes were centered and normalized, and hierarchical cluster analysis was performed. Venn diagrams were used to display differentially expressed genes among different samples. Functional annotation and enrichment analysis of differentially expressed genes were conducted. GO classification and enrichment analysis, KEGG annotation and pathway enrichment analysis were used to annotate the biological processes, molecular functions, and cellular components of differentially expressed genes, analyzing in which metabolic pathways they function. Based on the metabolomics data, DAMs were screened using |Fold Change| > 2 or < 0.5 and T-test P < 0.05 as the standard threshold. DAMs were annotated using KEGG metabolic pathway metabolite function annotation and classification, using the KEGG database. Cluster analysis mainly involved hierarchical cluster analysis of all DAMs for each comparison pair.

[0047] Step 7: Quantitative real-time PCR (qRT-PCR) to verify the accuracy of the transcriptomics sequencing results.

[0048] Step 8: Combining the enrichment results of DEGs and DAMs, a combined transcriptional and metabolic analysis was conducted to reveal the regulatory mechanism of alkali tolerance in rice. DEGs and DAMs were jointly annotated into the KEGG network, and corresponding annotations and analyses were performed on DEGs and DAMs in each pathway. Furthermore, the main regulatory pathways in rice's response to salt-alkali stress were analyzed based on their expression changes.

[0049] The alkali-sensitive and alkali-tolerant varieties are Jiudao 51 (J51) and Dongdao 4 (D4), respectively.

[0050] Example 2

[0051] Rice seedling growth after alkali stress treatment:

[0052] Two types of rice seedlings cultivated to the 3-leaf stage in Example 1 were simultaneously subjected to alkali stress treatment in a 15 mM Na2CO3 alkaline solution, with a control group without alkali stress. After 3 days of alkali stress treatment, both rice varieties showed varying degrees of wilting, wrinkling, whole-plant curling, and yellowing of leaves, exhibiting alkali damage phenotypes. However, the degree of wilting and damage to the leaves of the alkali-tolerant variety D4 seedlings was significantly lower. After 5 days of alkali stress treatment, the difference in alkali tolerance between D4 and J51 seedlings was more significant than on the 3rd day of stress, and the growth of J51 seedlings was more significantly inhibited. Further investigation and measurement of the survival rate and aboveground fresh weight loss rate of two rice varieties on days 3 and 5 of alkali stress treatment revealed that on day 3 of alkali stress, the survival rate of D4 was 19.53% higher than that of J51, and the aboveground fresh weight loss rate was 8.41% lower than that of J51, with highly significant differences (P < 0.01). On day 5 of alkali stress, the survival rate of D4 was 21.88% higher than that of J51, and the aboveground fresh weight loss rate was 17.68% lower than that of J51, with highly significant differences (P < 0.01). This indicates that the differences in alkali tolerance among rice seedlings with different resistance levels become more significant with prolonged stress duration.

[0053] Figure 1 To illustrate the effects of alkali stress on the growth phenotypes of rice seedlings D4 and J5 in this embodiment, seedling growth was observed 3 and 5 days after alkali stress. Seedling survival rate and aboveground fresh weight loss rate were calculated. Scale bar = 5 cm. Values ​​are expressed as mean ± SD, n = 4. ** indicates significant correlation (P < 0.01). CK represents normal conditions, and AS represents alkali stress conditions.

[0054] Example 3

[0055] Measurement of physiological and biochemical indicators of rice seedlings after alkali stress treatment:

[0056] Two types of rice seedlings cultured to the 3-leaf stage in Example 1 were simultaneously subjected to alkali stress treatment in a 15 mM Na2CO3 alkaline solution, with a control group without alkali stress. On the 5th day after the alkali stress treatment, samples were taken from the aboveground parts of the rice seedlings to measure superoxide anion free radicals (O2). − The content of hydrogen peroxide (H2O2), malondialdehyde (MDA), osmotic substances and ions, and antioxidant enzyme activity.

[0057] The staining results showed that rice seedlings under alkaline stress had superoxide anion (O2) levels in their leaves. − The accumulation of oxygen and hydrogen peroxide (H2O2) occurred. Following alkaline stress, O2 was observed in the leaves of both D4 and J51 rice varieties. − The enrichment of H2O2 was observed, with J51 showing a higher enrichment level than D4, indicating that the salt- and alkali-sensitive variety J51 accumulated more ROS under alkali stress. Under alkali stress, both varieties showed increased O2... − Both ROS and H2O2 contents increased significantly, with a greater increase in J51. ROS accumulation was also higher. Under alkaline stress, free radicals act on lipids, causing peroxidation and producing malondialdehyde (MDA). Alkaline stress significantly increased MDA content in both rice varieties, with J51 showing a significantly higher MDA content than D4 (P < 0.05). Figure 2 Soluble sugars and proline are two important osmotic regulators in rice. Under normal conditions, there was no significant difference in the contents of soluble sugars and proline between the D4 and J51 varieties. Alkali stress significantly increased the contents of proline and soluble sugars in the leaves of both varieties, but there was no significant difference between the varieties (P < 0.05). Figure 3 Under normal conditions, rice varieties Na4 and J51... + Mg 2+ K + Ca 2+ Content and K + / Na + ratio and Ca 2+ / Na + There was no significant difference between the two, but alkali stress significantly reduced the potassium content in the leaves of rice seedlings of both varieties. + Ca 2+ Content and K + / Na + ratio and Ca 2+ / Na + Compared to, it increased Na + Content, while Mg 2+ The Na content showed no significant difference before and after stress (P < 0.05). Under alkali stress, the Na content in D4 variety seedlings...+ The content was significantly lower than that of J51 (P < 0.05); K + Ca 2+ Mg 2+ Content and K + / Na + ratio and Ca 2+ / Na + The ratio is higher than J51, Mg 2+ The content was lower than that of J51, but the difference was not statistically significant (P < 0.05). Figure 4 Alkaline stress significantly increased the activities of superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and ascorbate peroxidase (APX) in the leaves of D4 and J51 rice seedlings, with higher SOD, POD, APX, and CAT activities in D4 compared to J51. Furthermore, in the ascorbate-glutathione (AsA-GSH) cycle, alkaline stress significantly increased the activities of glutathione peroxidase (GPX) and glutathione reductase (GR), with D4 exhibiting stronger enzyme activity than J51. Compared to the control, alkaline stress significantly increased the activities of monodehydroascorbate reductase (MDHAR) and dehydroascorbate reductase (DHAR) in the leaves of D4 rice seedlings, while J51 showed a decreasing trend, with higher MDHAR and DHAR activities in D4 compared to J51. Figure 5 ).

[0058] Figure 2 This example illustrates the effects of alkali stress on plasma membrane damage and ROS accumulation in rice seedlings of varieties D4 and J5. Figure 3 This example illustrates the effect of alkali stress on the proline and soluble sugar content in the leaves of rice seedlings of varieties D4 and J51. Figure 4 This example illustrates the effect of alkali stress on the ion content of leaves in D4 and J51 seedlings. Figure 5 This figure shows the effect of alkali stress on the activity of antioxidant enzymes in the leaves of D4 and J51 seedlings in this embodiment. Values ​​are mean ± SD, n = 4. Different letters represent significant differences according to Ducan's test (P < 0.05), CK represents normal conditions, and AS represents alkali stress conditions.

[0059] Example 4

[0060] Transcriptomic analysis of rice seedlings after alkali stress treatment:

[0061] Based on the growth and physiological and biochemical indicators of rice seedlings from two rice varieties (alkali-sensitive and alkali-tolerant) after alkali stress, and in order to further explore the key functional regulatory genes in response to alkali stress and improve the alkali tolerance regulatory network, samples were taken from rice seedlings 24 h after alkali stress treatment in Example 1 for transcriptomics, metabolomics sequencing, and combined analysis.

[0062] Four comparison groups were constructed for two varieties (D4 and J51) with control and alkali treatment groups (CK and AS): D4CK vs. D4AS, J51CK vs. J51AS, D4CK vs. J51CK, and D4AS vs. J51AS. To identify key factors in the transcriptome data, PCA was used to visualize changes in different varieties and under different treatment conditions. PCA results showed that two principal components (PC1 and PC2) clearly distinguished the different varieties and treatments. High correlations were observed between biological replicates within the same group, indicating good reproducibility within the selected sample groups. Figure 6 ).

[0063] In this embodiment, the screening criteria for differentially expressed DEGs were |fold change| > 2 and FDR < 0.05. The results showed that 3210 upregulated DEGs and 6359 downregulated DEGs were identified in D4 under CK (control group) and AS (alkaline stress) conditions, respectively; while 2762 upregulated DEGs and 4261 downregulated DEGs were identified in J51 under CK (control group) and AS (alkaline stress) conditions, respectively. The expression levels of these genes changed under alkaline stress, and the differential expression was greater in D4 compared to J51. Focusing on the differences between the two varieties under alkaline stress (D4AS vs. J51AS), 1226 upregulated DEGs and 2322 downregulated DEGs were identified. These intrinsic differences in gene expression may have contributed to the stronger tolerance of the D4 variety to alkaline stress compared to J51.

[0064] Venn diagrams were used to illustrate the overlap and specificity of differentially expressed genes before and after stress treatment in different varieties. In the comparison of DEGs in the D4-CK vs. J51-CK and D4-CK vs. D4-AS groups, a total of 87 genes were upregulated and 240 genes were downregulated. These DEGs only showed expression changes in D4 plants in response to alkaline stress, but differential expression existed between D4 and J51 under normal conditions. Furthermore, in the comparison of DEGs in the D4AS vs. J51AS, J51CK vs. J51AS, and D4CK vs. D4AS groups, a total of 311 genes were upregulated and 379 genes were downregulated, indicating that the expression levels of these genes changed in both D4 and J51 plants in response to alkaline stress, but the degree of expression difference was greater in D4 seedlings under alkaline stress. In the comparison of DEGs in the D4CK vs. J51CK and D4AS vs. J51AS groups, there was an overlap of 418 upregulated genes and 801 downregulated genes, indicating that these genes jointly respond to normal conditions and alkaline stress conditions. Figure 7 ).

[0065] To further understand the molecular mechanisms of the response of D4 and J51 rice varieties to alkaline stress, enrichment analysis was performed on differentially expressed genes (DEGs) using Metascape (http: / / metascape.org), and GO (Gene Ontology) analysis was used to classify DEGs according to different functional categories. DEGs can be divided into three categories: biological progress, molecular function, and cellular component. Since plant responses to abiotic stress are closely related to biological processes, we further analyzed the Top 20 GO enrichment pathways of differentially expressed genes (DEGs) in rice leaves under alkaline stress, focusing on their biological progress function. Figure 8Under alkaline stress, DEGs in varieties D4 and J51 were mainly enriched in the following pathways: stress response (GO:0006950), defense response (GO:0006952), photosynthesis (GO:0015979), stimulus response (GO:0050896), terpene biosynthesis (GO:0016114), response to temperature stimuli (GO:0009266), terpene metabolism (GO:0006721), isoprene biosynthesis (GO:0008299), transmembrane transport (GO:0055085), abscisic acid biosynthesis (GO:0009688), and carotenoid biosynthesis. Synthesis processes (GO:0043289), tertiary alcohol biosynthesis processes (GO:1902645), isoprene metabolism processes (GO:0006720), amino acid transmembrane transport (GO:0003333), carbon fixation (GO:0015977), sesquiterpene biosynthesis processes (GO:0016106), responses to abiotic stimuli (GO:0009628), cell surface receptor signaling pathways (GO:0007166), photosynthesis, light harvesting in photosystem I (GO:0009768), reduced pentose phosphate cycle (GO:0019253), etc.

[0066] Metabolic pathway analysis of DEGs was conducted using the KEGG database. Compared with CK conditions, 1823 DEGs were annotated by KEGG in the salt-tolerant variety D4 under alkali stress, mainly involving 134 metabolic pathways. Among them, DEGs involved in secondary metabolite biosynthesis (ko01110) were more numerous, accounting for 29.79% of DEGs; while in the salt-sensitive variety J51, 1347 DEGs were annotated by KEGG, mainly involving 133 metabolic pathways. In J51, DEGs involved in metabolic pathways (ko01100) were more numerous, accounting for 51%; while DEGs involved in secondary metabolite biosynthesis (ko01110) accounted for 32.37%. Figure 9In the D4AS vs. J51AS comparison group, the number of DEGs involved in metabolic pathways (ko01100) was relatively large, accounting for 54.8% of the DEGs annotated by KEGG, while the number of DEGs involved in secondary metabolite biosynthesis (ko01110) accounted for 35.25%. In addition, pathways such as plant hormone signal transduction (ko04075) (42 genes, 7.33%), phenylpropane biosynthesis (ko00940) (39 genes, 6.81%), photosynthesis (21 genes, 3.66%), flavonoid biosynthesis (ko00941) (11 genes, 1.92%), photosynthesis (ko00195) (21 genes, 3.66%), and cutin, cork resin and wax biosynthesis (ko00073) (8 genes, 1.40%) were significantly enriched.

[0067] Figure 6 This is a principal component analysis diagram of the sample in this embodiment; Figure 7 This is a statistical graph showing the differentially expressed genes in rice leaves under alkali stress in this embodiment; Figure 8 This is a bubble chart showing the Top 20 GO enrichment of differentially expressed genes in rice seedling leaves from different comparison groups under the classification of biological processes and functions in this embodiment. Figure 9 This is a bubble chart showing the Top 20 KEGG enrichment of differentially expressed genes in the leaves of rice seedlings from different comparison groups in this embodiment.

[0068] Example 5

[0069] DEG expression pattern validation by qRT-PCR:

[0070] To confirm the reliability of the transcriptomics data in the above embodiments, the relative expression levels of 12 genes involved in hormone signal transduction, stratum corneum formation, flavonoid biosynthesis, and hormone biosynthesis and metabolism were detected by qRT-PCR (quantitative Real-Time PCR). Figure 10 The results of qRT-PCR were consistent with those of transcriptome analysis, confirming the reliability of transcriptomics. Representative genes were selected, and the expression levels of differentially expressed genes were further validated using qRT-PCR. Total RNA was extracted from rice and reverse transcribed using M-MLV reverse transcriptase. Rice β-actin was used as an internal reference gene, and cDNA from alkali-treated rice leaves was used as a template for qRT-PCR analysis. Figure 10 This section presents the qRT-PCR validation results of the D4 and J51 transcriptomics data in this embodiment.

[0071] Example 6

[0072] Metabolomics analysis of rice seedlings after alkali stress treatment:

[0073] To determine the effects of alkali stress on rice seedlings, extensively targeted metabolomics analysis was performed on leaf materials of rice seedlings subjected to alkali stress for 24 hours in Example 1 using LC-MS. The results showed no significant differences in metabolites within each sample group, but significant differences between groups. The first principal component explained 25.7% of the total variance, and the second principal component explained 21.4% (Figure 11). Considering the complexity of the data, the OPLS-DA method was used to more accurately analyze the differences between groups. According to the OPLS-DA analysis results, samples from different treatments were clearly separated in each figure, and intra-group clustering was also obvious, indicating significant differences in metabolites between different treatment groups. Figure 12 Hierarchical cluster analysis was used to classify the accumulation and variation models of metabolites in 16 samples from treatments J51 and D4. Figure 13 The changes in metabolites were observed through cluster analysis of samples and metabolites in the heatmap. Significant differences in the relative content of metabolites were observed under different treatments. Figure 14 Therefore, further analysis was conducted on the differentially expressed metabolites (DEMs) of D4 and J51 under different treatments. The VIP value of the OPLS-DA multivariate statistical analysis and the P-value of the univariate t-test were used to screen for significantly different metabolites (DEMs) between different comparison groups. The threshold for difference was: variable weight (VIP) ≥ 1 in the OPLS-DA model and T-test P < 0.05. The number of DEMs differed between different groups ( Figure 15 The intersection and specificity of metabolites in different differential groups are shown by Venn diagrams.

[0074] A total of 65 DAMs were detected in the D4 variety before and after alkali stress (D4CK vs. D4AS group), of which 42 DAMs were upregulated and 23 DAMs were downregulated. In the J51 variety, 79 DAMs were detected and screened before and after alkali stress (J51CK vs. J51AS), of which 50 DAMs were upregulated and 29 DAMs were downregulated. Before alkali stress, 99 DAMs were detected and screened between D4 and J51 varieties (D4CK vs. J51CK), of which 55 DAMs were upregulated and 44 DAMs were downregulated. After alkali treatment, a comparative analysis of D4 and J51 rice varieties (D4AS vs. J51AS) screened 70 DAMs, of which 43 DAMs were upregulated and 27 DAMs were downregulated.

[0075] Metabolic pathway enrichment analysis of DAMs was performed using the KEGG database. Compared with the control (CK) condition, 33 DAMs in the salt-tolerant variety D4 were annotated by KEGG, mainly involving 54 metabolic pathways. Among them, DAMs involved in metabolic pathways (ko01100) accounted for a large number, representing 78.79% of all DAMs, while DAMs involved in secondary metabolite biosynthesis (ko01110) accounted for 54.55% of all DAMs. In contrast, 35 DAMs in the salt-sensitive variety J51 were annotated by KEGG, mainly involving 53 metabolic pathways. Among them, DAMs involved in metabolic pathways (ko01100) accounted for a large number, representing 80% of all DAMs, similar to the results of D4. DAMs involved in amino acid biosynthesis (ko01230) accounted for 28.57%. Figure 16 ).

[0076] Figure 11 This is a PCA score chart of the metabolomics data quality control samples in this embodiment; Figure 12 This is a diagram of the OPLS-DA model among different metabolite groups in this embodiment; Figure 13 This is a heatmap showing the clustering of metabolites from all samples in this embodiment; Figure 14 This is a statistical chart showing the differences in metabolites among the groups in this embodiment; Figure 15 This is a Venn diagram showing the differences in metabolites between groups in this embodiment; Figure 16 This is a bubble chart showing the Top 20 KEGG enrichment of differential metabolites in the leaves of rice seedlings from different comparison groups in this embodiment.

[0077] Example 7

[0078] Combined transcriptomic and metabolomic analysis of rice seedlings after alkali stress treatment:

[0079] Further screening and analysis of KEGG co-enriched pathways in the transcriptome DEGs and metabolome DAMs of the D4AS vs J51AS comparison group in Example 1 yielded 36 co-enriched metabolic pathways. Most of these pathways were related to metabolic pathways and the biosynthesis of plant secondary metabolites, including carbon fixation, carbon metabolism, glyoxylate and dicarboxylate metabolism in photosynthetic organisms. Figure 17 The differentially expressed genes were primarily annotated in plant hormone signaling pathways, indicating that the response of rice seedlings to alkali stress is mainly related to plant hormone signal transduction pathways. This was further analyzed using the GO enrichment directed acyclic plot of the transcriptome, which showed the hierarchical relationship of GO entries. Further analysis of key genes in abscisic acid signal transduction and synthesis pathways was conducted, and the role of abscisic acid in the rice response to alkali stress was elucidated by combining metabolite change trends obtained from the metabolome.

[0080] The content of the differential metabolite ABA in the leaves of rice seedlings D4 and J51 significantly increased after alkali stress. Simultaneously, the expression levels of OsNCED1 and OsNCED3 in the ABA synthesis pathway were significantly upregulated under stress conditions, with higher expression levels in D4 than in J51 under stress. This is consistent with the results of the difference in stress resistance phenotypes between D4 and J51, indicating that ABA may play a positive regulatory role in the rice response to alkali stress. Furthermore, differentially expressed genes in the ABA signaling pathway were analyzed at the transcriptional level. The expression levels of DEGs such as OsPP2C06, OsPP2C34, OsPP2C30, OsPP2C33, OsPP2C49, OsPP2C51, and OsPP2C59 were significantly increased under alkali stress compared to normal conditions, indicating that the ABA signaling pathway actively participates in the rice response to alkali stress. Figure 18 ).

[0081] Figure 17 This is a co-enrichment diagram of differential metabolites and differential genes of D4AS vs. J51AS under alkaline stress in this embodiment; Figure 18 This diagram illustrates the analysis of key genes and metabolites in the ABA signal transduction and biosynthesis pathways of rice varieties D4 and J51 under alkali stress in this embodiment.

Claims

1. A method for efficiently screening alkali-tolerant genes in rice, characterized in that, The method includes the following steps: Two rice varieties, one alkali-tolerant and one alkali-sensitive, were cultured and subjected to alkali stress treatment to identify significant phenotypic differences. Rice tissue samples were collected at different time points after alkali stress, and growth indicators, physiological and biochemical indicators, transcriptomics, metabolomics sequencing, and combined analysis were performed on the obtained rice tissue samples. Differentially expressed genes and differentially accumulated metabolites were screened from the omics sequencing results for data analysis. The differentially expressed genes and differentially accumulated metabolites were then annotated into the KEGG network. Differentially expressed genes and differentially accumulated metabolites in each pathway were annotated and analyzed accordingly. Based on the expression changes, genes and major regulatory pathways that respond to alkaline stress were screened out.

2. The method for efficiently screening alkali-tolerant genes in rice according to claim 1, characterized in that, The results of transcriptomics sequencing need to be verified by gene expression.

3. The method for efficiently screening alkali-tolerant genes in rice according to claim 1, characterized in that, Rice seedling cultivation process: After soaking, disinfecting, soaking, and germinating the rice seeds, they are irrigated with culture solution and cultivated until the 3-leaf stage.

4. The method for efficiently screening alkali-tolerant genes in rice according to claim 1, characterized in that, Rice seedlings cultured to the 3-leaf stage were subjected to alkali stress treatment using a 15 mM Na2CO3 alkaline solution. The pH of the alkaline solution was 10.79±0.025, and the soluble salt concentration EC was 2.85±0.006 mS / cm.

5. The method for efficiently screening alkali-tolerant genes in rice according to claim 1, characterized in that, The growth indicators include rice seedling survival rate, fresh weight, and fresh weight loss rate.

6. The method for efficiently screening alkali-tolerant genes in rice according to claim 1, characterized in that, The physiological and biochemical indicators include superoxide anion content, hydrogen peroxide content, malondialdehyde content, osmotic substance content, antioxidant enzyme activity, and ion content.

7. The method for efficiently screening alkali-tolerant genes in rice according to claim 1, characterized in that, The transcriptomics sequencing includes RNA extraction and detection, library construction and sequencing, Stringtie transcript construction and HISAT2 reference genome sequencing alignment, differentially expressed gene GO, and KEGG bioinformatics analysis.

8. The method for efficiently screening alkali-tolerant genes in rice according to claim 1, characterized in that, The metabolomics sequencing includes sample extraction, HPLC-MS analysis, metabolite quantification, and differential expression analysis.

9. The method for efficiently screening alkali-tolerant genes in rice according to claim 1, characterized in that, The joint analysis includes a statistical test of the significance of the metabolite correlation analysis, with a significance level of P-value < 0.05 as the significance threshold.

10. The method for efficiently screening alkali-tolerant genes in rice according to claim 1, characterized in that, The differential gene expression was verified using real-time quantitative PCR technology, with rice actin as an internal reference gene. Primers were designed to verify whether the expression pattern of the differentially expressed genes screened under alkaline stress was accurate.

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