Gene SbERF71 capable of improving waterlogging tolerance of sorghum, protein, application and breeding method

Abstract

The invention provides a gene SbERF71 capable of improving waterlogging tolerance of sorghum, protein, application and a breeding method, and belongs to the technical field of crop breeding. On the basis of a sorghum plant with waterlogging tolerance, it is determined that SbERF71 is a key gene affecting the waterlogging tolerance of sorghum through RNA extraction and library building, RNA-Seg data analysis, weighted gene co-expression network analysis (WGCNA) and other methods. The SbERF71 gene is related to gene expression of various stress reaction pathways (such as energy metabolism and aero-tissue formation) in sorghum, is a central regulatory factor of sorghum waterlogging, and is beneficial to reaction of sorghum to flooding stress, expression height up-regulation of sorghum in a flooding stress environment and enhancement of flooding resistance of sorghum, so that sorghum can adapt to waterlogging stress. The invention reveals that sorghum can adapt to molecular and physiological mechanisms of waterlogging stress, provides a new direction for breeding of sorghum waterlogging-resistant varieties, and makes a contribution to food safety in waterlogging agricultural areas.

Description

Technical Field

[0001] The present invention relates to the technical field of crop breeding, and in particular to a gene SbERF71 capable of improving waterlogging tolerance of sorghum, a protein, an application thereof and a breeding method. Background Art

[0002] To cope with waterlogging stress, plants have evolved various morphological and physiological mechanisms to adapt to flooded environments. A key adaptation is the formation of adventitious roots (ARs). Under waterlogging conditions, the taproot is often unable to effectively absorb water and nutrients, leading to the emergence of ARs as a compensatory mechanism. For example, barley, a waterlogging-tolerant species, exhibits a significant increase in AR formation under stress. Aerenchyma formation is another important adaptation. Rice is more waterlogged-tolerant than many other dryland crops, such as barley and wheat, benefiting from the constitutive aerenchyma in its roots. As aeration conditions deteriorate, the proportion of aerenchyma increases, enhancing tolerance. In barley, although the root system has relatively little aerenchyma under normal conditions, waterlogging causes a significant increase. Metabolic changes are also essential, as waterlogging limits aerobic respiration and triggers anaerobic pathways, such as ethanol and lactic acid fermentation, to provide energy. Key enzymes in these pathways, such as pyruvate decarboxylase (PDC) and alcohol dehydrogenase (ADH), play crucial roles in waterlogging tolerance. In addition, enzymes involved in carbohydrate metabolism, such as sucrose synthase and trehalose 6-phosphate phosphatase, were also associated with plant responses to waterlogging.

[0003] Transcriptional regulation also plays a key role in how plants respond to waterlogging stress. Transcription factors (TFs) from families such as MYB, ERF, WRKY, NAC, and bZIP have been shown to regulate genes involved in hypoxia signaling, reactive oxygen species (ROS) scavenging, and root morphological adaptation. For example, ERF family members, such as SUBMERGENCE1A in rice and ZmEREB180 in maize, regulate anaerobic metabolism, gibberellin degradation, and adventitious root formation. The WRKY family, including WRKY22 and WRKY33, also influences waterlogging tolerance by interacting with other factors to remove ROS. Furthermore, genes associated with metabolic pathways have been extensively studied for their roles in waterlogging tolerance. For example, alcohol desaturase (ADH) in barley is a key player in the response to waterlogging, while phenylalanine metabolism genes in Cynodon dactylon and biosynthetic metabolism genes in peanut are associated with flood tolerance. In peanut, waterlogging inhibits the expression of starch and sucrose metabolism genes, reducing sucrose import and conversion capacity, ultimately affecting yield.

[0004] Despite significant progress in understanding the effects of waterlogging stress on crop morphology, photosynthesis, and yield, root-specific physiological responses and transcriptional regulatory mechanisms in sorghum remain poorly understood. This knowledge gap limits the development of waterlogging-tolerant sorghum varieties through targeted breeding strategies.

[0005] Therefore, revealing the molecular and physiological mechanisms by which sorghum can adapt to waterlogging stress and improving its waterlogging tolerance are crucial for breeding waterlogging-tolerant sorghum varieties. Summary of the Invention

[0006] The purpose of the present invention is to provide a gene SbERF71, protein, application and breeding method that can improve the waterlogging tolerance of sorghum. The SbERF71 gene is related to the gene expression of multiple stress response pathways (such as energy metabolism and aerenchyma formation) in sorghum. It is a central regulatory factor of sorghum waterlogging, which helps sorghum respond to waterlogging stress. Its expression is highly upregulated in a waterlogging stress environment, enhancing sorghum's waterlogging resistance and enabling sorghum to adapt to waterlogging stress.

[0007] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0008] The present invention provides a gene SbERF71 capable of improving waterlogging tolerance of sorghum. The nucleotide sequence of the gene SbERF71 is shown in SEQ ID NO.1.

[0009] The present invention also provides a protein encoded by the gene SbERF71 that can improve the waterlogging tolerance of sorghum. The amino acid sequence of the protein encoded by the gene SbERF71 that can improve the waterlogging tolerance of sorghum is shown in SEQ ID NO.2.

[0010] The present invention also provides an application of a gene SbERF71 capable of improving waterlogging tolerance of sorghum in improving waterlogging tolerance of sorghum.

[0011] The present invention also provides an application of a gene SbERF71 capable of improving waterlogging tolerance of sorghum in breeding waterlogging-tolerant sorghum plants.

[0012] The present invention also provides an application of a protein encoded by the gene SbERF71 capable of improving waterlogging tolerance of sorghum in improving waterlogging tolerance of sorghum.

[0013] The present invention also provides a method for breeding waterlogging-tolerant sorghum plants, the breeding method comprising:

[0014] A waterlogging-tolerant gene SbERF71 overexpression vector was constructed, and the gene SbERF71 overexpression vector was transferred into plants to construct waterlogging-tolerant plants.

[0015] Preferably, the waterlogging-tolerant plant includes one of sorghum, corn, and millet.

[0016] The beneficial effects of the present invention compared with the prior art are:

[0017] Based on waterlogging-tolerant sorghum plants, this study identified SbERF71 as a key gene influencing sorghum waterlogging tolerance through methods such as RNA extraction and library construction, RNA-Segment data analysis, and weighted gene co-expression network analysis (WGCNA). The SbERF71 gene is associated with the expression of genes involved in multiple stress response pathways in sorghum, such as energy metabolism and aerenchyma formation. It is a central regulator of sorghum waterlogging, contributing to sorghum's response to waterlogging stress. Its expression is highly upregulated in waterlogging-stressed environments, enhancing sorghum's waterlogging resistance and enabling sorghum to adapt to waterlogging.

[0018] The present invention reveals the molecular and physiological mechanisms by which sorghum can adapt to waterlogging stress, provides a new direction for the breeding of waterlogging-resistant sorghum varieties, and contributes to food security in waterlogged agricultural areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 This is an effect diagram of the root anaerobic respiratory enzyme activity between "S208" and "S015" in Example 1 of the present invention, wherein A is pyruvate decarboxylase (PDC) activity; B is lactate dehydrogenase (LDH) activity; C is alcohol dehydrogenase (ADH) activity; the results are SD + mean, ns indicates no significant difference, and ns indicates significant differences at p < 0.05 and p < 0.01, respectively;

[0021] Figure 2 Figure 1 shows the transcriptome analysis results of the roots of “S208” and “S015” under control and flooding conditions in Example 1 of the present invention, where A is the principal component analysis (PCA) of transcript changes to separate samples under control and flooding conditions; B is the DEGS in the roots of “S208” and “S015” under control and waterlogging conditions, including up-regulated and down-regulated DEGs; C is a Venn diagram describing the number of all DEGs expressed in the root tissues of “S208” and “S015” genotypes under stress and control conditions;

[0022] Figure 3Figures 1 and 2 are the analysis results of "S208" and "S015" in Example 1 of the present invention. A is the GO enrichment bar chart and KEGG enrichment scatter plot of flooding response DEGs in the GO enrichment analysis of "S015"-W and "S015"-CK; B is the GO enrichment analysis of "S208"-W and "S208"-CK; C is the KEGG pathway analysis of "S015"-W and 'S015-CK; D is the KEGG pathway analysis of "S208"-W and "S208"-CK;

[0023] Figure 4 This is a heat map of flooding-responsive DEGs in "S208" and "S015" in Example 1 of the present invention; A is the energy metabolism pathway related to DEGs; B is the DEGs related to cell wall organization or biogenesis; C is the separation target;

[0024] Figure 5 Figure 1 is a weighted gene co-expression network analysis (WGCNA) diagram of the waterlogging response gene module in sorghum in Example 1 of the present invention, wherein A is a cluster dendrogram with similarity based on topological overlap and the assigned module colors; B is the correlation between modules and features; C is the gene interaction network of hub genes in the brown module; D is the gene interaction network of hub genes in the yellow module;

[0025] Figure 6 This is a graph showing the DEGS results in RNA-seq results verified by qRT-PCR in Example 1 of the present invention, wherein AH are the expression levels of 8 genes determined by RNA-seq and qRT-PCR, respectively, displayed on the left and right Y axes, wherein A is the ADH1 subtype X1 gene, B is the SORBI 3002G415600 gene, C is the SORBI 3003G197200 gene; D is the SORBI gene 3004G004500, E is the abscisic acid 8′-hydroxylase 1 gene, F is the SORBI 3009G71800 gene, and G is the SORBI 3009G169000 gene, H is XTH22 gene, the gene column and red line represent the results of RNA-seq and qRT-PCR analysis, respectively, and the error bars represent the mean ± SE value (n = 3); I is the correlation between the log2 (fold change) of the eight DEGs analyzed by RNA-seq (X axis) and qRT-PCR (Y axis). DETAILED DESCRIPTION

[0026] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as limiting the present invention, but rather as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0027] It should be understood that the terms described herein are intended only to describe particular embodiments and are not intended to limit the present invention. In addition, for numerical ranges herein, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Each smaller range between any intermediate value within a stated value or stated range and any other stated value or intermediate value within the stated range is also encompassed by the present invention. The upper and lower limits of these smaller ranges may be independently included or excluded within the scope.

[0028] Unless otherwise indicated, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art. Although only preferred methods and materials are described herein, any methods and materials similar or equivalent to those described herein may also be used in the practice or testing of the present invention. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods and / or materials associated with the documents. In the event of any conflict with any incorporated document, the contents of this specification shall prevail.

[0029] It will be apparent to those skilled in the art that various modifications and variations may be made to the specific embodiments described herein without departing from the scope or spirit of the invention. Other embodiments will be apparent to those skilled in the art from the description of the invention. The description and examples are intended to be exemplary only.

[0030] The words “include,” “including,” “have,” “contain,” etc. used in this document are open-ended terms, meaning including but not limited to.

[0031] Example 1

[0032] Example 1 of the present invention provides a method for analyzing the waterlogging tolerance factor of sorghum, and the specific steps are as follows:

[0033] (1) Selection of plant materials:

[0034] From June to October 2024, 100 sorghum varieties were evaluated for waterlogging tolerance at the five-leaf stage. Flooding stress was applied to these varieties, and various phenotypic traits were measured, including plant height, number of adventitious roots, and biomass. These data were then compared with the control group to assess the waterlogging tolerance of each variety. Based on these evaluations, two numbered sorghum varieties, S208 (waterlogging tolerant) and S015 (waterlogging sensitive), were selected and subjected to waterlogging stress. The anaerobic respiratory enzyme activities of different sorghum varieties under waterlogging treatment were tested, and the results are shown as follows: Figure 1 shown.

[0035] Figure 1The results showed that under flooding conditions, there were significant differences in the activities of pyruvate decarboxylase (PDC), lactate dehydrogenase (LDH) and alcohol dehydrogenase (ADH) between the two genotypes, "S208" and "S015". In both genotypes, the activities of anaerobic respiratory enzymes increased under flooding stress, but the increase was more obvious in "S208". In "S208", compared with the control group, the PDC activity increased by 1.98 times, 3.33 times, 3.27 times and 2.80 times at 3, 6, 9 and 12 days of waterlogging, respectively. In contrast, the increases in "S015" at the same time points were 1.54 times, 3.31 times, 2.22 times and 1.55 times, respectively. The LDH activity in the roots of "S208" increased by 10.48-fold, 16.10-fold, 44.72-fold, and 66.26-fold on days 3, 6, 9, and 12, respectively, while the corresponding increases in "S015" were 5.53-fold, 13.93-fold, 32.10-fold, and 44.06-fold. Similarly, the ADH activity of "S208" increased by 1.81-fold, 2.93-fold, 4.68-fold, and 5.30-fold on days 3, 6, 9, and 12, respectively, while that of "S015" increased by 1.80-fold, 2.23-fold, 3.38-fold, and 3.98-fold. These results indicate that the anaerobic respiratory enzyme activities of "S208" were consistently higher than those of "S015," contributing to its excellent tolerance to waterlogging stress.

[0036] (2) RNA extraction, library preparation, RNA sequencing, and sequence assembly

[0037] "S208" and "S015" were divided into two groups, half grown under standard control conditions and the other half subjected to a 24-hour flooding stress regimen. Roots of the four treatment groups S015-CK (control), S015-W (waterlogged), S208-CK (control) and S208-W (waterlogged) were collected separately. Each sample included 3 biological replicates, for a total of 12 libraries. Total RNA was extracted from root tissue using the RNAprep PurePlant P1us kit (Tiangen, China). The RNA extraction procedure followed the described CTAB extraction method.

[0038] First, root tissue was collected, frozen in liquid nitrogen, and ground into a powder in a pre-chilled mortar with continuous addition of liquid nitrogen. The powder was transferred to a pre-warmed RNase-free tube, and CTAB extraction buffer (2% CTAB, 100 mM Tris-HCl pH 8.0, 20 mM EDTA pH 8.0, 1.4 M NaCl, 2% PVP-40, and freshly added 0.2% beta-mercaptoethanol) was added at a ratio of 1 mL per 0.1 g of tissue. The mixture was thoroughly mixed and incubated at 65°C for 10-15 minutes, gently inverting every 2-3 minutes. An equal volume of chloroform:isoamyl alcohol (24:1) was added, and the tube was inverted. The mixture was then centrifuged at 12,000 × g for 15 minutes at 4°C, and the upper aqueous phase was transferred to a fresh tube. To precipitate RNA, 0.6-1 volume of isopropanol was added, and the tube was incubated at -20°C for at least 30 minutes or overnight. After centrifugation at 12,000 × g for 15 minutes at 4°C, discard the supernatant, wash the RNA pellet with 70% ethanol, and then centrifuge again at 7,500 × g for 5 minutes at 4°C. Air-dry the pellet for 5-10 minutes, then dissolve in 30-50 μL of Tangen elution buffer and incubate at 55-60°C for 10-15 minutes.

[0039] In order to remove genomic DNA contamination, DNase treatment was performed during RNA extraction, and RNA integrity was assessed using a Nanodrop 2000 spectrophotometer (ThermoFisher Scientific, Wilmington, Delaware, USA), a Qubit 2.0 fluorometer (Life Technologies, Carlsbad, California, USA), and an Agilent 2100 bioanalyzer (Agilent Technologies, Santa Clara, California, USA). Subsequently, rRNA was removed using a RiboZero™ magnetic kit (Epicentre, Madison, W, USA) to enrich for mRNA. Random primers were then used to reverse transcribe mRNA into cDNA. The cDNA fragments were purified using a QiaQuick PCR purification kit (Qiagen Venlo, the Netherlands), followed by end repair, addition of A bases, and connection to lumina sequencing adapters. The appropriate size range of the connection product was selected by gel electrophoresis, followed by PCR amplification. RNA sequencing (RNA-seq) was performed using a lumina NovaSeq 6000 platform (Gene Denovo Biotechnology Co., Guangzhou, China).

[0040] After quality control, the libraries were pooled and sequenced to generate 150-bp paired-end reads. Sequencing was performed by synthesizing and detecting fluorescently labeled dNTPs, which were incorporated into the complementary strand. The sequence information was then detected by a sequencer and processed using computer software. Library construction and RNA sequencing analysis were performed at Metware Co., Ltd. in Wuhan, China.

[0041] (3) RNA-Seg data analysis

[0042] Raw reads obtained from sequencing were processed to remove adapter sequences and reads consisting entirely of adenine (A) bases. Furthermore, low-quality reads (more than 50% of bases with a quality score of Q ≤ 20) were omitted to ensure the retention of high-quality clean reads. The remaining clean reads were aligned to the Sorghum bicolor reference genome (Sorghum_bicolor_NCBIv3) using Tophat2 software. Gene expression levels were quantified using fragments per kilobase exon per million fragments mapped (FPKM) values ​​calculated using StringTie software. Differentially expressed genes (DEGs) between the flooding treatment and control groups were identified using DESeq2 for differential expression analysis. DEGs were further filtered based on log2FoldChange >= 1 and false discovery rate (FDR) < 0.05. Functional enrichment analysis of DEGs was performed by mapping them to Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. For GO enrichment, DEGs were mapped to the GO database (http: / / www.geneontology.org / ) using BLAST, and gene counts for each GO term were calculated. For pathway enrichment, DEGs were aligned to the KEGG web server (http: / / www.kegg.jp / ) using BLAST to identify significantly enriched KEGG pathways.

[0043] For enrichment analysis, hypergeometric tests were used for GO and KEGG pathways. In RNA-seq analysis, sequences were aligned using HISAT2 and gene expression was quantified using StringTie. The results are as follows:

[0044] 1) Transcriptome analysis of sorghum roots under waterlogging stress To investigate the molecular mechanisms of sorghum response to waterlogging stress

[0045] After 24 hours of waterlogging stress, roots were collected from four treatment groups: "S015"-CK (control), "S015"-W (waterlogged), "S208"-CK (control), and "S208"-W (waterlogged). Each sample included three biological replicates, for a total of 12 libraries. The sorghum plant transcriptome was sequenced using the high-throughput Lumina sequencing platform, obtaining at least 6.51 Gb of clean bases for each sample. Low-quality reads were filtered out based on base quality values, and all samples had Q20 scores greater than 98.03% and Q30 scores greater than 94.00%. The GC content was greater than 50.58%, indicating high-quality sequencing data suitable for subsequent analysis.

[0046] Principal component analysis (PCA) was performed on the transcriptome data, such as Figure 2 Figure 3 (A). Three replicates of two waterlogging-tolerant genotypes (S015 and S208) clustered closely together under both the control and flooding treatments, indicating good reproducibility and high-quality transcriptome data. This confirms that the sequencing data are reliable and meet the requirements for further bioinformatics analysis.

[0047] DEGS between different treatments were identified using the exon model fragments per kilobase per million mapped fragments (FPKM) method. Genes with a log fold change > 1 and a p-value < 0.05 were considered differentially expressed. Figure 2 As shown in Figure B, a total of 1828 DEGS were identified between "S015"-W and "S015"-CK, of which 972 genes were downregulated and 856 genes were upregulated. In the comparison between "S208"-W and "S208"-CK, 4252 DEGS were identified, of which 1575 genes were upregulated and 2577 genes were downregulated.

[0048] In addition, under 24 h waterlogging stress, 503 DEGS were unique in “S015”, while 2827 DEGS were unique in “S208” under the same conditions. A total of 1325 DEGS were shared between the two genotypes ( Figure 2 Middle C). These results indicate that ‘S015’ and ‘S208’ have different transcriptional responses to waterlogging stress, with specific genes being differentially regulated in each genotype.

[0049] 2) Gene Ontology (GO) enrichment analysis of DEGs

[0050] To explore the functions of DEGs in sorghum under waterlogging stress, G0 enrichment analysis was performed. The differentially expressed genes between the two sorghum varieties after waterlogging stress were divided into three categories: cellular components, molecular functions, and biological processes.

[0051] After flooding stress, significant enrichment of genes related to oxygenase activity, including monooxygenase and dioxygenase activities, was observed in both sorghum varieties. In addition, GO terms such as "response to hypoxia" (GO: 0071456) and "response to oxygen levels" (GO: 0036294) were significantly enriched in "S015"-W and "S015"-CK. In contrast, molecular functions such as UDP-glucosyltransferase activity (GO: 0035251), phenylpropanoid metabolism (GO: 0009698) and flavonoid metabolism (GO: 0009812) were significantly enriched in "S208"-W and "S208"-CK ( Figure 3 (middle AB).

[0052] KEGG pathway enrichment analysis was performed. The results showed that differentially expressed genes between the two sorghum varieties under waterlogging stress and control conditions were primarily enriched in energy metabolism and hormone pathways. These pathways included plant hormone signal transduction (KO04075), secondary metabolite biosynthesis (KO01110), phenylpropanoid biosynthesis (KO00940), pyruvate and glucose metabolism (KO00620), carbon metabolism (KO01200), glycolysis (KO00010), starch and sucrose metabolism (KO00500), zeatin biosynthesis (KO00908), galactose metabolism (KO00052), and carotenoid biosynthesis (KO00906).

[0053] However, certain pathways were more significantly enriched in specific varieties. For example, pathways related to unsaturated fatty acid biosynthesis (ko01040), the pentose phosphate pathway (ko00030), and amino acid biosynthesis (ko01230) were enriched in "S015"-W and "S015"-CK, while pathways related to glycerophospholipid metabolism (ko00564), oleoresin lactone biosynthesis (ko00905), and plant-pathogen interaction (ko04626) were enriched in "S208"-W and "S208"-CK. These findings indicate that waterlogging-tolerant sorghum strains exhibit distinct enrichment patterns in KEGG pathways under waterlogging stress.

[0054] Next, the present invention focused on the expression of DEGs in significantly enriched pathways, such as starch and sucrose metabolism, plant secondary metabolite biosynthesis, phenylpropanoid biosynthesis, glycolysis, and plant hormone signaling. The analysis showed that under water stress, the expression of differentially expressed genes in these pathways in "S015" was generally lower than that in "S208." For example, in the biosynthesis of secondary metabolites, "S015" had 225 differentially expressed genes under flooding stress, while "S208" had 500 genes involved in the same process.

[0055] In starch and sucrose metabolism, under flooding stress, the number of upregulated genes in "S015" was 6 and the number of downregulated genes was 14, while the number of upregulated genes in "S208" was 16 and 39, respectively. In the plant hormone signal transduction pathway, "S015" showed upregulation of 30 genes and downregulation of 39 genes, while "S208" showed upregulation of 55 genes and downregulation of 102 genes ( Figure 3 C and D).

[0056] 3) Analysis of differentially expressed genes related to energy metabolism, hormone regulation, and cell wall modification under waterlogging stress

[0057] Energy deprivation caused by hypoxia is one of the main factors affecting the survival of waterlogged plants. In the present invention, KEGG enrichment analysis showed that metabolic pathways such as starch and sucrose metabolism (SSM), pyruvate metabolism (PYM), and glycolysis (GLY) were significantly enriched in the differentially expressed genes of the two sorghum varieties. Further analysis of key genes in these metabolic pathways revealed that genes encoding enzymes such as sucrose synthase (SUS) 1 (SORBI 3010G072300), trehalose-6-phosphatase (T6P) (SORBI 3002G184600), ADH1 isoform X1 (SORBI 3001G097600), ADH2 (SORBI 3005G103300), pyruvate kinase (PK) (SORBI3005G034400), and PK were significantly expressed after waterlogging stress. The expression of these genes in (SORBI3001G326900) indicated that “S208” exhibited stronger anaerobic respiratory enzyme activity compared to “S015”, which was consistent with the higher expression levels of anaerobic respiratory enzyme activity in S208’ ( Figure 4 Middle A).

[0058] The formation of aerenchyma enhances ventilation between roots and stems, increases oxygen availability, and helps maintain normal root function. This process is an important mechanism for plants to adapt to waterlogging stress. We identified several DEGS associated with aerenchyma formation, including xyloglucan endotransglycosidase (XTH22, XTH24, XTH30), xyloglucan glycosyltransferase (XGT9), and pectinesterase (PE, PEI8, PE53 subtype X2). These genes were significantly upregulated in response to waterlogging, and their expression was more pronounced in “S208”-W than in “S015”-W. For example, the expression of XTH24 in “S208”-W was 3.4 times higher than that in “S015”-W. In addition, after waterlogging treatment, the air mass formation of “S208” was significantly greater than that of “S015” (, Figure 4 Middle B)

[0059] Ethylene is a key hormone in plant responses to waterlogging. Among the DEGs related to ethylene signaling, one ethylene receptor gene (ETR2) and four ethylene-responsive transcription factors (ERF4, ERF24, ERF71, and ERF1 09) were significantly upregulated after waterlogging. In addition to ethylene, DEGs related to other hormones were also identified, such as cytokinin (CTK), gibberellin (GA), and abscisic acid (ABA). Notably, genes such as abscisic acid receptor (PYL3L), abscisic acid 8′-hydroxylase 1 (SORBI3004G268700), and the key rate-limiting enzyme of ABA biosynthesis (NCED1) were significantly upregulated after waterlogging stress. In addition, red erythrocyte responsive protein 2 and several growth hormone responsive proteins, including IAA9X2, IAA23, and SAUR71, were identified and upregulated after waterlogging, and the expression level of “S208”-W was significantly higher than that of “S015”-W ( Figure 4 Middle C).

[0060] Transcription factors (TFs) play a key role in plant responses to environmental stress. To cope with adversity, plants activate TFSs through signal transduction pathways, enhancing resistance by inducing downstream expression of stress-related genes. In the present invention, 49 of the 4655 DEGs were annotated as transcription factors. The most abundantly annotated TF families were AP2 / ERF, AUX, WRKY, bHLH, MYB, NAC, and C2H2. The largest number of DEGs were annotated as ERF transcription factors, with 18 such genes identified. Among them, ERF109 (SORBI3002G225700), ERF3-like (SORBI 3006G259000), SbERF71 (SORBI 3003G148600), ERF8 (SORBL 3009G184400), and ERF11 (SORBI 3007G077100) were highly upregulated after waterlogging. In addition, 8 WRKY genes were identified, among which WRKY71 (SORBI3004G065900) showed a more obvious upregulation in "S208" after flooding treatment. In the MTB family, a total of 8 genes were identified, including MYB4 (SORBL3001G219200) and MYB44 (SORBI3010G201388). Both genes were significantly upregulated after flooding and showed higher expression levels in "S208" than in "S015". In addition, 3 AUX-related genes, 4 NAC-related genes and 7 bHLH-related genes were identified ( Figure 4 Middle D).

[0061] (4) Weighted gene co-expression network analysis (WGCNA)

[0062] WGCNA is a method developed based on established protocols for analyzing gene expression patterns across multiple samples. The fundamental concept of WGCNA is scale-free network distribution, where the correlation coefficients of expression matrices are weighted to assign highly coevolving genes to the same gene cluster across the network. This process then enables the assignment of entire genes to multiple modules.

[0063] To identify the key regulatory genes involved in the response to waterlogging stress in two sorghum varieties, we performed weighted gene co-expression network analysis (WGCNA) on 4655 DEGs. This analysis divided the genes into 8 different modules ( Figure 5 Among them, the "brown" and "yellow" modules showed significant correlations with various physiological and biological traits of sorghum seedlings under waterlogging stress (r>|0.8|).

[0064] The “brown” module was significantly associated with stem diameter (r=0.94, p=5.5e-06), average adventitious root length (r=0.94, p=5.5e-06), number of adventitious roots (r=0.87, p=0.000233), aerenchyma area ratio (r=0.94, p=5.5e-06), lactate dehydrogenase activity (r=0.85, p=0.00046), and alcohol dehydrogenase activity (r=0.87, p=0.00023) ( Figure 5 Similarly, the yellow module was associated with the average adventitious root length (r = 0.86, p = 0.00033), aerenchyma area ratio (r = 0.89, d8b9b420-d216-424, f8b184ed-a271-4457-a5ac-61c014d15308, d8b9b420-d216-424b-bd44-60c3bbb4b36), pyruvate decarboxylase activity (r = 0.97, p = 1.8e-07), lactate dehydrogenase activity (r = 0.94, p = 5.5e-06), and alcohol dehydrogenase activity (r = 0.95, p = 2.3e-06) ( Figure 5 Middle D)

[0065] Gene network visualization of the "brown" and "yellow" modules revealed several candidate hub genes. In the "yellow" module, the hub genes included SORBI 3003G148600 (SbERF71), SORBI3006G245500 (AIL5), SORBI3006G018800 (ETR2), SORBI3001G481400 (ERF1), SORBL3006G127900 (bHLH129), and SORBL3004G207200 (bHLH80). In the "brown" module, the identified hub genes were SORBL3003G037500 (WRKY72), SORBL3009G100500 (WRKY26), SORBI3010G246400 (XTH22), and SORBI_3010G246600 (XTH24). These hub genes may play key roles in regulating the expression of other genes within their respective modules, thereby contributing to plant responses to waterlogging stress, with SORBI 3003G148600 (SbERF71) playing a major role.

[0066] The nucleotide sequence of the SbERF71 gene is (SEQ ID NO.1):

[0067]

[0068] The amino acid sequence of the SbERF71 protein is (SEQ ID NO. 2):

[0069]

[0070]

[0071] (5) Quantitative real-time PCR verification

[0072] The authenticity of the DEGs screened by RNA sequencing was verified by qRT-PCR, and 8 DEGs were randomly selected for further verification by qRT-PCR. Primers were designed using Primer Premier 5.0 software. The Gene IDs of the 8 DEGs used in the study, as well as all primers, are shown in Table 1. GAPDH was used as an internal reference gene. The qPCR conditions were as follows: initial denaturation at 95°C for 30 seconds, followed by 95°C for 5 seconds and 60°C for 30 seconds for a total of 40 cycles. Relative quantification method (2 -ΔΔCt ) were used for expression analysis. All qRT-PCR experiments were performed with three biological replicates and three technical replicates. The average of these technical replicates was then used as the representative data point for the corresponding biological replicate in our subsequent statistics.

[0073] To verify the accuracy of transcriptome sequencing, eight genes related to flooding stress were randomly selected for qRT-PCR analysis. A significant positive correlation was observed between qRT-PCR and RNA sequencing data (R = 0.78) (R2 = 0.78). Figure 6 ). This result proves the accuracy and reliability of the transcriptome sequencing results.

[0074] Plant hormones, particularly ethylene, are key regulators of plant responses to waterlogging. We identified several DEGs associated with ethylene signaling, auxin signaling, cytokinin signaling, and abscisic acid (ABA) signaling. The upregulation of key genes, including the ABA receptor gene (PYL3L) and the ABA biosynthesis gene (NCED1), suggests that ABA plays an important role in waterlogging responses. Furthermore, the high expression of IAA-responsive genes in "S208" suggests that auxin signaling may promote adventitious root growth and development, further contributing to waterlogging tolerance.

[0075] Finally, transcription factors play an important role in plant adaptation to environmental stress. Our RNA-seq data revealed that several TE families, including AP2 / ERF, WRKY, bHLH, and MYBd, are involved in waterlogging stress responses. Weighted gene co-expression network analysis (WGCNA) identified SbERF71 and WRKY72 as key genes.

[0076] The present invention discovered a unique transcription factor regulatory pattern in sorghum. Although some transcription factors (such as ERF and WRKY) are involved in waterlogging stress responses in multiple species, the specific genes and their expression patterns vary. For example, in sorghum, SbERF71 and WRKY72 were identified as key regulatory factors by WGCNA, and their regulatory mechanisms may be different from those in rice. In rice, SUBMERGENCE1A plays a major role in regulating anaerobic metabolism and adventitious root formation, while in sorghum, the role of SbERF71 may be more related to coordinating the expression of genes involved in multiple stress response pathways (such as energy metabolism and aerenchyma formation). These TFs can regulate the expression of genes involved in adventitious root formation, aerenchyma development and anaerobic respiration, thereby better understanding the molecular mechanisms of sorghum waterlogging tolerance.

[0077] Table 1 Primer sequences

[0078]

[0079] (6) Statistical analysis

[0080] Student’s t-test was performed using SPSS software (v.19.0) to determine statistically significant differences between treatments, where * and ** indicate significant differences at p < 0.05 and p < 0.01, respectively.

[0081] Waterlogging stress severely affects plant growth by inducing root hypoxia, disrupting energy metabolism, and triggering morphological and physiological adaptations. Physiological analysis showed that "S208" exhibited significantly increased activities of anaerobic respiratory enzymes such as pyruvate decarboxylase (PDC), lactate dehydrogenase (LDH), and alcohol dehydrogenase (ADH), which help produce energy under hypoxia. Transcriptomic analysis further identified key genes related to carbohydrate metabolism, such as sucrose synthase (SUS1) and trehalose-6-phosphatase (T6P), which were highly expressed in "S208". These findings indicate that efficient carbohydrate decomposition and energy homeostasis enhance the waterlogging tolerance of "S208". Key adaptive processes, including aerenchyma formation and adventitious root growth, are associated with the upregulation of genes involved in cell wall modification, such as XTH24 and XGT9. Hormone signaling pathways, especially those mediated by ethylene, auxin, and abscisic acid (ABA), also contribute to stress adaptation. Weighted gene co-expression network analysis (WGCNA) identified transcription factors such as SbERF71, WRKY72, and XTH22 as central regulators of waterlogging.

[0082] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A gene that improves sorghum's waterlogging tolerance SbERF71 , characterized in that, The gene SbERF71 The nucleotide sequence is shown in SEQ ID NO.

1.

2. A gene that improves sorghum's waterlogging tolerance SbERF71 The encoded protein is characterized in that The gene capable of improving sorghum waterlogging tolerance SbERF71 The amino acid sequence of the encoded protein is shown in SEQ ID NO.

2.

3. A gene capable of improving waterlogging tolerance in sorghum as claimed in claim 1 SbERF71 Application in improving waterlogging tolerance of sorghum.

4. A gene capable of improving waterlogging tolerance in sorghum as claimed in claim 2 SbERF71 Application of the encoded protein in improving waterlogging tolerance in sorghum.

5. A gene capable of improving waterlogging tolerance in sorghum as claimed in claim 1 SbERF71 Application in breeding of waterlogging-tolerant sorghum plants.

6. A method for breeding waterlogging-tolerant plants, characterized in that: The breeding method is: Building genes for waterlogging tolerance SbERF71 Overexpression vector and gene SbERF71 The overexpression vector was transferred into plants to construct waterlogging-tolerant plants.

7. The method for breeding waterlogging-tolerant plants according to claim 6, wherein: The waterlogging-tolerant plant includes one of sorghum, corn, and millet.

Images