Rape cold-resistant gene and application thereof

Abstract

The invention discloses a rape cold-resistant gene and application thereof, and belongs to the technical field of gene engineering. Four rape specific lone genes (BnaOGs) and 2859 brassica specific genes (BSGs) are identified through comparative genomics and multi-database artificial verification by combining UniProt-KB, Nrdb and the like, and the characteristics of short sequence, high GC content, no transcription factor and the like are found. Aiming at a BSG gene BnaA03G0334200ZS, a CDS sequence of the BSG gene BnaA03G0334200ZS is cloned, a pEGOEP35S-H overexpression vector containing a 35S strong promoter and a hygromycin selection marker is constructed, and a positive plant is obtained through agrobacterium-mediated transformation of brassica napus Westar. Functional verification shows that overexpression of the gene remarkably enhances the cold resistance of the rape, plant damage is slight at low temperature, the relative conductivity and MDA content are reduced, and the activities of SOD, POD and CAT are improved. The invention provides a new target and technical support for cultivation of cold-resistant rape varieties, and has a wide application prospect.

Description

Technical Field

[0001] This invention belongs to the field of genetic engineering technology, and in particular relates to the cold-resistant gene in rapeseed and its application. Background Technology

[0002] Orphan genes (OGs) are species-specific genes lacking homologous sequences from closely related species. They evolve through replication, differentiation, or de novo origination and play a crucial role in adaptive evolution. Current research on orphan genes has made progress in plants such as Chinese cabbage and coriander, revealing characteristics such as short sequences, high GC content, few transcription factors, and limited functional annotation. Their expression patterns often exhibit tissue specificity or stress response. However, in the important oilseed crop, rapeseed (Brassica napus), the systematic identification, classification, and association mechanisms between orphan genes and environmental adaptations such as cold stress remain unclear. Existing technologies have failed to comprehensively elucidate the structural characteristics, evolutionary patterns, and functions of rapeseed orphan genes, particularly regarding the expression regulation patterns and cold tolerance functions of rapeseed-specific orphan genes (BnaOGs) and Brassica genus-specific genes (BSGs) under cold stress. This gap limits a deeper understanding of the adaptive evolutionary mechanisms of rapeseed and the development and utilization of related gene resources.

[0003] Rapeseed (Brassica napus) is an important oilseed crop globally, contributing approximately 13-16% of plant oil production. However, its growth and yield are easily limited by low-temperature stress. Current technologies for improving rapeseed cold tolerance largely rely on the discovery and utilization of evolutionarily conserved genes. However, conserved genes often have limitations such as pleiotropic functions or narrow adaptability, making it difficult to meet the adaptability requirements of rapeseed to complex low-temperature environments. Orphans, as species-specific genetic resources, have the potential to drive lineage-specific adaptations, but their application in improving rapeseed cold tolerance has not yet been systematically developed. Summary of the Invention

[0004] This invention provides an application of a protein in improving the cold resistance of rapeseed, the amino acid sequence of which is shown in SEQ ID NO.4.

[0005] The present invention also provides an application of nucleic acid in improving the cold resistance of rapeseed, wherein the nucleic acid encodes the aforementioned protein.

[0006] Preferably, the nucleotide sequence of the nucleic acid is as shown in SEQ ID NO.3.

[0007] The present invention also provides a method for improving the cold resistance of rapeseed by increasing the expression and / or activity of the above-mentioned protein in rapeseed.

[0008] Preferably, the method for enhancing protein expression and / or activity is to use a highly active promoter to drive the nucleic acid molecule encoding the protein.

[0009] The present invention also provides an application of an expression cassette in improving the cold resistance of rapeseed, the expression cassette comprising a 35S promoter and a nucleic acid molecule encoding the protein sequence shown in SEQ ID NO.4.

[0010] The present invention also provides an application of a recombinant expression vector in improving the cold resistance of rapeseed, wherein the recombinant expression vector contains a nucleic acid molecule encoding the protein sequence shown in SEQ ID NO.4.

[0011] Preferably, the recombinant expression vector is pEGOEP35S-H as the starting vector.

[0012] Compared with the prior art, the present invention has the following beneficial effects: This invention targets the BSG gene BnaA03G0334200ZS, clones its CDS sequence, constructs the pEGOEP35S-H overexpression vector containing a strong 35S promoter and a hygromycin selection marker, and transforms it into Brassica napus Westar using Agrobacterium-mediated transformation to obtain positive plants. Functional validation shows that this gene overexpression significantly enhances the cold tolerance of rapeseed: plant damage at low temperatures is mild, relative conductivity and MDA content are reduced, and SOD, POD, and CAT activities are increased. This invention provides a new target and technical support for the breeding of cold-tolerant rapeseed varieties, with broad application prospects. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the identification process for BnaOGs, BSGs, CSGs, and ECGs in the rapeseed genome in Example 1. This process integrates PlantGDB unique transcripts (PUTs), the non-redundant protein database (Nrdb), the expressed sequence tag (EST) database, and the Nr / Nt database for comparative analysis. Yellow boxes indicate candidate genes (BnaOGs, BSGs, CSGs, and ECGs) that passed the screening classification, while blue boxes represent reference databases used to exclude sequences from corresponding lineages.

[0014] Figure 2 This chart shows the chromosome distribution patterns of BSGs and CSGs in rapeseed in Example 1. The chart displays the distribution of the 19 chromosomes and scaffolds (horizontal axis), with the left vertical axis showing the number of genes (BSGs: green dots, CSGs: orange squares) and the right vertical axis showing the relative percentage (BSGs: yellow triangles, CSGs: purple diamonds).

[0015] Figure 3The structure and evolutionary differentiation of different gene groups in Example 1. (A) Comparison of CDS and protein length (left vertical axis CDS length: purple triangle; right vertical axis protein length: green dot); (B) GC content and AT / GC ratio (left vertical axis GC content: purple triangle; right vertical axis AT / GC ratio: green dot); (C) Transcription factor prediction (left vertical axis TF number: green dot; right vertical axis TF percentage: purple box); (D) Domain prediction (left vertical axis number of genes containing domains: green solid circle; right vertical axis percentage: purple triangle); (E) Coding potential (left vertical axis number of genes: green dot for coding genes / pink triangle for non-coding genes; right vertical axis percentage: blue box for coding genes / purple triangle for non-coding genes); (F) Subcellular localization (x-axis compartment markers: purple triangle for BnaOGs, green dot for BSGs, yellow cross for CSGs, black box for ECGs).

[0016] Figure 4 This is a comprehensive analysis of gene function annotation across multiple databases in Example 1. (A) Annotation percentage of each type of gene in multiple databases (color distinguishes different databases); (BD) Venn diagrams show the annotation status of BSGs (B), CSGs (C), and ECGs (D) in the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), SwissProt, and NR databases, respectively.

[0017] Figure 5 This shows the tissue and stress expression patterns of BnaOGs from rapeseed in Example 1. (A) Tissue-specific expression profile. Columns and rows represent different tissues and BnaOGs, respectively, and FPKM values ​​indicate expression levels. (B) Stress response expression profile. Columns and rows represent different stress treatments and BnaOGs, respectively, and FPKM values ​​reflect expression changes. In the heatmap, red indicates high expression, and white indicates low expression. Genes and tissues are clustered according to the similarity of expression patterns.

[0018] Figure 6 This is a heatmap of the expression patterns of core BSGs in rapeseed in Example 1. Columns and rows represent different BSGs and tissue types, respectively, and FPKM values ​​are indicated by a red-to-white gradient to show expression intensity. Hierarchical clustering analysis was performed based on expression spectrum similarity.

[0019] Figure 7 This is a heatmap of the expression patterns of core BSGs in rapeseed under stress in Example 1. Columns and rows represent different BSGs and stress types, respectively, and FPKM values ​​are represented by a color gradient from white (lowest) to red (highest). Co-expressed genes and function-related stresses are clustered according to the correlation of expression profiles.

[0020] Figure 8This is a tissue-specific expression profile of BnaOGs and core BSGs from rapeseed in Example 1. (A–D) Expression patterns of BnaOGs and (E–I) core BSGs in different rapeseed tissues. The vertical axis represents the relative expression levels normalized to the internal reference genes BnaPPR and BnaGDI1. Data are presented as mean ± standard error (three biological replicates). Different lowercase letters in the bar chart indicate statistically significant differences (one-way ANOVA, p < 0.05).

[0021] Figure 9 This shows the expression patterns of rapeseed BnaOGs and core BSGs in response to low-temperature stress in Example 1. The dynamic expression changes of (A–D) BnaOGs and (E–I) core BSGs during low-temperature stress are illustrated. The vertical axis represents the relative expression levels normalized to the internal reference genes BnaPPR and BnaGDI1. The horizontal axis represents the duration of low-temperature stress; CK represents the untreated control group (0 h), and C1–C5 correspond to stress treatments of 2, 4, 8, 12, and 24 hours, respectively. Data are presented as mean ± standard error (three biological replicates). Lowercase letters in the bars indicate statistically significant differences (one-way ANOVA, p < 0.05).

[0022] Figure 10 This document describes the construction of the overexpression vector for the orphan gene BnaA03G0334200ZS and the detection of transgenic plants in Example 2. (A) Map of the BnaA03G0334200ZS overexpression vector. (B) Detection of T0 generation transgenic plants. The detection primer sequences were HYGF (SEQ ID NO.1): CCGGATGCCTCCGCTCGAAGTA and HYGR (SEQ ID NO.2): GCCGTGCACAGGGTGTCACGTT, with a target fragment length of 486 bp. M represents the DNA Marker, 1-8 represent different overexpression lines, P represents the positive plasmid, and C represents the negative control.

[0023] Figure 11 The expression abundance of BnaA03G0334200ZS in the overexpressing plants in Example 2 is shown on the vertical axis. The vertical axis represents the relative expression levels normalized to the internal reference genes BnaPPR and BnaGDI1. Data are presented as mean ± standard error (three biological replicates). Different lowercase letters in the bar chart indicate statistically significant differences (one-way ANOVA, p < 0.05).

[0024] Figure 12In Example 2, overexpression of BnaA03G0334200ZS significantly enhanced the cold tolerance of Brassica napus. (A) Phenotypic comparison of WT and OE-2 lines after low-temperature treatment. (BC) Determination of relative conductivity and MDA in WT and OE lines. (DF) Determination of enzyme activities of SOD, POD, and CAT in WT and OE lines. Data are expressed as mean ± standard error (three biological replicates). , and The values ​​indicate significant differences compared to WT at the p<0.05, p<0.01, and p<0.001 levels, respectively. Detailed Implementation

[0025] Example 1 1. Materials and Methods 1.1. Database Retrieval The genome sequence of Brassica napus ZS11v0 was obtained from the Brassica napus multi-omics database (BnIR, https: / / yanglab.hzau.edu.cn / BnIR / ). All genome data were downloaded from Phytozome (https: / / phytozome-next.jgi.doe.gov / ), Brassica database (BRAD, http: / / brassicadb.cn / # / ), and BnIR.

[0026] 1.2. Homology Screening and Classification The E-value threshold for homology screening was set to 1e−3. Genes were divided into four categories: BnaOGs (genes specific to Brassica napus), BSGs (genes with at least one homologous gene in other Brassica species, excluding Brassica napus), CSGs (genes with at least one homologous gene in non-Brassica species of the Brassicaceae family), and ECGs (genes with at least one homologous sequence in species outside the Brassicaceae family).

[0027] 1.3. Bioinformatics Analysis Chromosomal distribution data for different gene categories were obtained from BnIR. The length of coding sequences (CDS) and proteins, the GC content of CDS, and the AT / GC ratio were analyzed. Transcription factor (TF) prediction was performed using BnIR, and protein conserved domain prediction was conducted using the PfamScan online tool on the Majorbio cloud platform (https: / / cloud.majorbio.com / page / tools / ). Coding potential was analyzed using Coding Potential Calculator 2 (CPC2, http: / / cpc2.cbi.pku.edu.cn), and subcellular localization prediction was performed using the MultiLoc tool on the Majorbio cloud platform.

[0028] 1.4. Functional annotation and expression spectrum analysis Functional annotation of different gene categories was performed in BnIR. Transcriptome analysis of BnaOGs and BSGs was performed using RNA-seq data published in BnIR, following bioinformatics workflows from previous studies. Expression pattern visualization was performed using TBtools-II software (v2.225).

[0029] 1.5. Sample Collection and qRT-PCR Analysis Seeds of the rapeseed variety ZS11 were purchased from Wuhan Zhongyou Seed Industry Technology Co., Ltd. (Hubei, China). Four weeks after planting, roots, stems, leaves, petioles, and shoot tips were collected, with three biological replicates per group (10 plants per replicate). Four-week-old rapeseed plants were subjected to 4°C cold stress treatment. Leaves were collected at different time points (CK: 0 h control; C1: 2 h; C2: 4 h; C3: 8 h; C4: 12 h; C5: 24 h; C6: 48 h), with three biological replicates per time point (3 plants per replicate). All samples were rapidly flash-frozen in liquid nitrogen and stored at −80°C for later use. RNA extraction, cDNA synthesis, and qRT-PCR were performed according to the literature. The BnaPPR and BnaGDI1 genes were used as internal controls, and a 2... −ΔΔCt The relative expression level was calculated using a method with three technical replicates per group. The primer sequences used are shown in Table S1.

[0030] 1.6. Statistical Analysis and Data Visualization qRT-PCR data were analyzed using IBM SPSS Statistics (v26.0) with one-way ANOVA and Duncan's multiple comparison test. Results are expressed as mean ± standard error (SE). Graphs were plotted using GraphPad Prism (v8.0.2) and TBtools-II (v2.225).

[0031] 2. Results 2.1 Identification and Classification of Rapeseed-Specific Genes (OGs) To screen for BnaOGs, BSGs, CSGs, and ECGs in rapeseed, we integrated transcriptome and plant genome sequence data, employing a rigorous manual review process. Based on BLASTP, TBLASTN, and BLASTN (E value <1e−3), 107,233 rapeseed genes in the ZS11v0 genome were screened and classified. Comparative genomics analysis with other genomes and unique transcripts (PUTs) generated by PlantGDB was performed, excluding sequences from corresponding lineages. Figure 1Initially, candidate BnaOGs, BSGs, CSGs, and ECGs were obtained. To improve identification accuracy and reduce false positives, these candidate genes were further manually validated using the UniProt Knowledge Base (UniProt-KB), the Non-Redundant Protein Database (Nrdb), the Expressed Sequence Tag (EST) database, and the Nr / Nt database. The gene set was ultimately divided into four categories: 4 BnaOGs, 2,859 BSGs, 9,650 CSGs, and 94,720 ECGs.

[0032] Due to the extremely small number of BnaOGs, subsequent analyses focused solely on chromosome distribution and proportion for BSGs and CSGs. Figure 2 Chromosomal distribution analysis showed that BSGs and CSGs were relatively evenly distributed across the 19 chromosomes, with average proportions of 4.85% and 4.98%, respectively. Comparative analysis revealed differences in gene distribution: chromosome C03 had the highest enrichment of BSGs (9.13%) and CSGs (8.63%), while chromosome A10 had the lowest proportions (1.36% and 2.11%, respectively), suggesting that longer chromosomes may be more prone to carrying more of these OGs. Furthermore, 225 BSGs (7.87%) and 519 CSGs (5.38%) were located in unassembled scaffold regions, suggesting that these genes may be located in complex genomic regions containing repetitive elements or structural variations, leading to difficulties in chromosome-level assembly.

[0033] 2.2 Structural and evolutionary differentiation characteristics of different gene groups Through multi-dimensional computational analysis including sequence length distribution, GC content variation, transcription factor prediction, domain composition, coding potential, and subcellular localization, we systematically resolved the structural and evolutionary characteristics of different gene groups. The results showed that the average CDS and protein length of BnaOGs, BSGs, and CSGs were significantly shorter than those of ECGs. Figure 3 A). BnaOGs exhibited the highest GC content (56.19%) and the lowest AT / GC ratio (0.84), showing a strong GC bias. Figure 3 B); while the GC / AT distribution of BSGs (46.80%), CSGs (45.45%), and ECGs (46.67%) was relatively balanced, with AT / GC ratios all greater than 1. Notably, CSGs showed the most significant AT bias (AT / GC = 1.23), followed by BSGs (1.19) and ECGs (1.16). Transcription factor analysis revealed significant class differences: ECGs contained the most TFs (5,453) and the highest percentage (5.76%), while CSGs contained only 2 (0.02%), and BSGs and BnaOGs were completely absent. Figure 3C) indicates that TF coding ability is highly evolutionarily conserved in ECGs. Protein domain predictions show a gradient distribution: ECGs have the highest number of domain genes (70, 901) and the highest percentage (74.85%), followed by CSGs (376, 3.90%) and BSGs (1, 0.03%), with BnaOGs being completely absent. Figure 3 D), suggesting significant differences in functional differentiation and evolutionary trajectory. Coding potential analysis showed that BnaOGs (75.00%) and BSGs (74.01%) were rich in non-coding genes, while CSGs (53.80%) and ECGs (91.15%) were predominantly coding genes. Figure 3 E). Subcellular localization prediction reveals the compartmentalization characteristics of various genes ( Figure 3 F): BnaOGs are highly specialized (75% located in the cytoplasm, 25% in the endoplasmic reticulum); BSGs and CSGs have similar distributions (48.79-55.88% in the cytoplasm, 15.77-16.55% in the extracellular space); ECGs, however, exhibit diverse localizations (46.53% in the cytoplasm, 10.27% in the nucleus, 11.28% in chloroplasts, etc.). The gradual acquisition of organelle targeting signals from BnaOGs to ECGs may reflect an evolutionary process of subcellular functional specialization. These findings highlight the significant structural and evolutionary differentiation of BnaOGs, BSGs, and CSGs relative to ECGs.

[0034] 2.3 Functional annotation of different gene groups Through systematic comparisons of four major gene databases—GO, KEGG, SwissProt, and NR—we evaluated the functional annotation coverage of different gene groups (BnaOGs, BSGs, CSGs, and ECGs). Functional annotation analysis showed that the annotation level of each gene group increased progressively. Figure 4 A): BnaOGs were not annotated in any database; only 29 BSGs (1.01%) received minimal annotation. Figure 4 B); 2,264 CSGs (23.46%) had a moderate level of annotation ( Figure 4 C); while ECGs had the highest number of annotated genes (76,371) and the highest percentage (80.63%). Figure 4 D). The high annotation coverage of ECGs suggests that they represent known functional elements, while the near-zero annotation of BSGs and BnaOGs may reflect novel functions or atypical biological roles not included in existing databases. These results may reflect fundamental differences in evolutionary origin and functional importance among different gene groups.

[0035] 2.4 Dynamic Expression Patterns of BnaOGs in Different Tissues and Stress Adaptation To elucidate the potential adaptive functions of BnaOGs, we systematically analyzed the expression dynamics of four BnaOGs in key tissues and stress responses. Based on transcriptome data published in the Rapeseed Information Resource (BnIR), we found significant differences among the four BnaOGs: BnaC09G0092200ZS (BnaOG4) showed high transcriptional abundance (average FPKM > 5 in all tissues), BnaC02G0325900ZS (BnaOG2) showed moderate expression (average FPKM > 1), while the expression levels of BnaC01G0509900ZS (BnaOG1) and BnaC03G0392700ZS (BnaOG3) were close to the detection limit. Figure 5 A). Under various stress conditions, the expression patterns of BnaOGs further differentiated: BnaOG3 showed no transcriptional activity, BnaOG4 maintained constitutively high expression (mean FPKM>5), BnaOG2 maintained basal expression (mean FPKM<1), while BnaOG1 exhibited significant stress-induced characteristics (mean FPKM>1), especially under salt, drought, heat, freezing, and cold stress, it was strongly upregulated. Figure 5 (B) These results indicate functional differentiation of BnaOGs: constitutive high expression of BnaOG4 suggests its involvement in housekeeping functions of basal metabolism, BnaOG1 may function as a multi-stress response factor, and transcriptional silencing of BnaOG3 may indicate pseudogenotyping. In summary, expression differentiation mediates the functional diversification of BnaOGs in rapeseed developmental program coordination and stress adaptation.

[0036] 2.5 Tissue-specific and stress-response expression characteristics of BSGs Based on phylogenetic identification of BSGs, we hypothesize that they may possess tissue-specific expression and stress response regulatory characteristics. Transcriptome analysis of 2,637 BSGs (excluding scaffold region genes) showed that 1,984 genes (75.24%) were detectably expressed (mean FPKM > 0), of which 334 BSGs showed moderate expression (mean FPKM ≥ 2), including 78 highly expressed genes (mean FPKM ≥ 10). Figure 6 Expression analysis under multiple stress conditions revealed that 2,338 BSGs were not expressed or expressed at low levels (mean FPKM < 2), and 299 genes showed constitutive expression (mean FPKM ≥ 2), including 64 highly expressed genes (mean FPKM ≥ 10). Figure 7Further analysis identified 2,170 expressed BSGs (mean FPKM>0), including 294 tissue-specific genes, 186 stress-responsive genes, and 1,690 constitutively expressed genes. These results indicate that BSGs exhibit tissue-specific or stress-induced expression patterns in rapeseed, and the transcriptional silencing of a large number of BSGs may suggest their potential functions in undetected tissues, developmental stages, or under stress conditions.

[0037] 2.6 Tissue-specific expression of BnaOGs and core BSGs The expression patterns of four BnaOGs and five BSGs in the five major tissues during the seedling stage showed significant tissue-specific differences. Figure 8 BnaOG1 and BnaOG3 exhibited highly similar expression profiles, with dominant expression primarily in root and stem tissues and significantly lower expression levels in other organs. Transcriptional analysis showed that BnaOG2, BnaOG4, and BnaA03G0334200ZS shared highly consistent expression patterns, with their transcripts accumulating mainly in stem and root tissues compared to other organs. BnaC06G0110800ZS and BnaA03G0429000ZS showed dual enhanced expression in both leaf and stem tissues. Conversely, BnaA05G0385700ZS was specifically upregulated in stems and petioles, while BnaC06G0163900ZS exhibited a typical root-stem-petiole enrichment pattern—maintaining stable high expression in roots, stems, and petioles, but significantly lower expression levels in leaves and shoot tips. These results reveal the specific expression patterns of BnaOGs and BSGs in different tissues, suggesting that subsequent functional differentiation may shape the complex tissue-specific regulatory pathways in rapeseed.

[0038] 2.7 Low-temperature response expression of BnaOGs and core BSGs qRT-PCR analysis revealed the differential expression regulatory patterns of four BnaOGs and five BSGs under low temperature stress. Figure 9BnaOG1 and BnaC06G0110800ZS were consistently and significantly downregulated throughout the stress treatment, suggesting they may play a role in stress avoidance or growth regulation. Conversely, BnaOG2, BnaOG4, BnaA03G0334200ZS, and BnaA05G0385700ZS formed a coordinated late-stage response module, exhibiting peak expression in the later stages of stress, indicating their involvement in adaptation-related functions. BnaA03G0429000ZS and BnaC06G0163900ZS showed sustained activation in both early and late stages of stress, while BnaOG3 exhibited a unique mid-stage response pattern—specifically induced expression in the later stages of stress treatment. These OGs collectively constitute a precise regulatory framework for rapeseed environmental responses: different expression modules respond to specific stress signals while maintaining synergistic network functions. The OGs identified in this study provide new target genes for understanding the stress adaptation mechanisms and resistance genetic improvement of this important crop.

[0039] Example 2 To investigate the function of the BnaA03G0334200ZS gene, we first constructed its plant overexpression vector. The CDS sequence of Brassica napus Westar was obtained from a rapeseed multi-omics database. Primers were designed, and the CDS of BnaA03G0334200ZS was cloned from Westar seedling cDNA. This CDS was then recombined into the plant binary expression vector pEGOEP35S-H, which contains a strong 35S promoter and a hygromycin selection marker. The recombinant plasmid pEGOEP35S-H::BnaA03G0334200ZS was successfully constructed. Figure 10 A). The verified recombinant plasmid was transformed into Brassica napus Westar using Agrobacterium-mediated transformation, and transgenic positive plants overexpressing BnaA03G0334200ZS (BnaA03G0334200ZSOE-1, 2, 3, 7, 8) were successfully obtained. Hygromycin detection results are shown in […]. Figure 10 B.

[0040] >BnaA03G0334200ZS-CDS sequence (SEQ ID NO.3): ATGGTCTGCTCCGGCGTTCGGGGAATGGTTCTCAGGCGTTGGATTTCCATCTCTTCCCGGTCTTTGTCTTGCTTCCTGCGGTCCTGTCAAATGGTGTTCCTCCTCCGGGTTTTGCGCATCACCCGGTGTCTTGGTTCGAGTATTGGTCTTCAAGTGGTGTTTGGCTCTGTTTCCTTCC GACGGAGTCTTCTTCTCTCTGCTTCAGTAAAGAACTTACTCTAGTTCTTAGGGTAAGAATACGTGTGGAGTCTGGTCTTGCTTCGCGAGTCGTCGGAGCCGTCAGTTCCTCGGTTGTGGCGTATTTTGGTGTTCATCCATTCCAGTGGTGTAGCGGTCTTATGGCTGCTTCGCCAGGATGA.

[0041] The amino acid sequence (SEQ ID NO.4) encoded by BnaA03G0334200ZS is as follows: MVCSGVRGMVLRRWISISSPGLCLASPAVLSNGVPPPGFAHHPVSWFEYWSSSGVWLCFLPTESSSLCFSKELTLVLRVRIRVESGLASRVVGAVSSSVVAYFGVHPFQWCSGLMAASPG.

[0042] The gene structure of the expression cassette is as follows: CaMV 35S promoter-BnaA03G0334200ZS::GFP-NOSterminator, where the gene and GFP are fused together, and the stop codon TGA needs to be removed during construction.

[0043] The sequence (SEQ ID NO.5) of the CaMV 35S promoter is as follows: TGAGACTTTTCAACAAAGGGTAATATCCGGAAACCTCCTCGGATTCCATTGCCCAGCTATCTGTCACTTTATTGTGAAGATAGTGGAAAAGGAAGGTGGCTCCTACAAATGCCATCATTGCGATAAAGGAAAGGCCATCGTTGAAGATGCCTCTGCCGACAGTGGTCCCAAAG ATGGACCCCCACCCACGAGGAGCATCGTGGAAAAAGAAGACGTTCCAACCACGTCTTCAAAGCAAGTGGATTGATGTGATATCTCCACTGACGTAAGGGATGACGCAACAATCCCACTATCCTTCGCAAGACCCTTCCTCTATAAGGAAGTTTCATTTCATTTGGAGAGAACA.

[0044] The terminator is named NOS terminator, and its sequence (SEQ ID NO.6) is as follows: GATCGTTCAAACATTTGGCAATAAAGTTTCTTAAGATTGAATCCTGTTGCCGGTCTTGCGATGATTATCATATAATTTCTGTTGAATTACGTTAAGCATGTAATAATTAACATGTAATGCATGACGT TATTTATGAGGTGGGTTTTTATGATTAGAGTCCCGCAATTATACATTTAATACGCGATAGAAAACAAAATATAGCGCGCAAACTAGGATAAATTATCGCGCGGTGTCATCTATGTTACTAGATC.

[0045] To quantify the expression level of the target gene in positive plants, the transcript abundance of BnaA03G0334200ZS in wild-type (WT) and three independent T1 generation overexpression lines (OE-1, OE-2, OE-3) was detected using qRT-PCR. Figure 11 The results showed that, compared with WT, the mRNA level of BnaA03G0334200ZS was significantly upregulated in all three OE lines. Subsequent experiments will be conducted primarily in the independent line OE-2, which had the highest expression level.

[0046] Based on the successful acquisition of overexpression plants, WT and T2 generation OE-2 lines were subjected to low-temperature treatment (day / night temperature of 15±2°C / 4±2°C) for 15 days at the four-leaf stage, and their cold tolerance was evaluated. The results showed that WT exhibited cold injury symptoms such as stunted growth and leaf wilting after low-temperature stress, while OE-2 grew well, with leaves remaining relatively open and green, and only some older leaves showing slight damage symptoms, indicating stronger overall cold tolerance. Figure 12 A). This result intuitively demonstrates that overexpression of BnaA03G0334200ZS can alleviate the morphological damage caused by low temperature to rapeseed plants and enhance their cold tolerance. To elucidate the mechanism of improved cold tolerance at the physiological level, we measured the relative conductivity, MDA content, and activities of three key antioxidant enzymes in the leaves of WT and OE-2 plants after low-temperature treatment. The relative conductivity and MDA content of the OE-2 line were significantly lower than those of WT ( Figure 12 BC), while the activities of SOD, POD and CAT were significantly higher than those of WT ( Figure 12 (DF). These overexpressing plants exhibited enhanced cold tolerance and a synergistically activated antioxidant defense system under low-temperature stress. This strongly suggests that the orphan gene BnaA03G0334200ZS effectively alleviates low-temperature-induced oxidative damage and ultimately improves the cold tolerance of Brassica napus by positively regulating antioxidant enzyme activity and enhancing reactive oxygen species scavenging capacity.

[0047] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. Use of a protein in increasing cold tolerance of Brassica napus, characterized in that, The amino acid sequence of the protein is shown as SEQ ID NO.

4.

2. Use of a nucleic acid for increasing cold tolerance in Brassica napus, characterized in that, The nucleic acid encodes the protein as claimed in claim 1.

3. Use according to claim 2, characterized in that, The nucleotide sequence of the nucleic acid is shown as SEQ ID NO.

3.

4. A method for increasing cold tolerance in Brassica napus, characterized in that, Increasing the expression and / or activity of the protein as claimed in claim 1 in Brassica napus.

5. The method of claim 4, wherein, The method for increasing the expression and / or activity of the protein is using a high-activity promoter to drive the nucleic acid molecule encoding the protein.

6. Use of an expression cassette for increasing cold tolerance in Brassica napus, characterized in that, The expression cassette comprises a 35S promoter and a nucleic acid molecule encoding the protein with the sequence shown as SEQ ID NO.

4.

7. Use of a recombinant expression vector for increasing cold tolerance in Brassica napus, characterized in that, The recombinant expression vector comprises a nucleic acid molecule encoding the protein with the sequence shown as SEQ ID NO.

4.

8. Use according to claim 7, characterized in that, The recombinant expression vector is pEGOEP35S-H as the starting vector.

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