Combined transcriptional and metabolic analysis of the differences in salt tolerance responses of tillers in different rice varieties

Combined transcriptional and metabolic analysis of the differences in salt tolerance responses of tillers in different rice varieties | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Combined transcriptional and metabolic analysis of the differences in salt tolerance responses of tillers in different rice varieties Jin-ji Tu, Jun-hua Zhang, Yixi Dai, xiao Wang, WenKang Huang, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8270222/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 15 You are reading this latest preprint version Abstract Background Soil salinization is a significant factor contributing to the reduction of arable land. To enhance rice productivity in saline-alkali soils, understanding the salt tolerance mechanisms of rice varieties is essential. This study focused on investigating the salt tolerance mechanisms in the tillers of two rice varieties: the salt-tolerant CMG and the salt-sensitive 9311, using morphophysiological, transcriptomic, and metabolomic methods. Results The activities of antioxidant enzymes SOD, POD, and APX in the tiller nodes of CMG were significantly higher than those in 9311. In contrast, the levels of MDA (malondialdehyde) and hydrogen peroxide in CMG tiller nodes were relatively lower, suggesting a more effective response to salt stress. Both varieties responded to saline-alkali stress through similar metabolic pathways, including amino acid metabolism (such as alanine, aspartic acid, glutamic acid metabolism, and arginine biosynthesis), amino acid acyl-tRNA biosynthesis, oxidative phosphorylation, and phenylpropanoid biosynthesis. However, CMG exhibited unique metabolic pathways such as glycerophospholipid metabolism, which is associated with membrane lipid remodeling, and the biosynthesis of the stratum corneum, suppositories, and waxes, which play a key role in reducing water loss and preventing sodium ion entry. Additionally, CMG showed a greater ability to regulate plant hormone signaling pathways, particularly those involving abscisic acid (ABA) and jasmonic acid (JA), to coordinate the expression and metabolic activities of defense genes. Conclusion The tiller nodes of CMG primarily focus on strengthening their own defenses against stress and reducing Na + toxicity after salt stress. In contrast, the tiller nodes of 9311 enhance photosynthetic efficiency by transferring stress responses to the leaves. This study provides valuable insights into the molecular mechanisms and metabolic pathway dynamics of salt tolerance in rice, offering a new perspective for further research on the salt tolerance mechanisms of rice under saline-alkali stress. Rice Salt stress Tillering Transcriptome metabolom Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1 Background Salt stress is one of the major abiotic stresses that negatively impacts the growth and yield of crops, thereby restricting agricultural development [ 1 , 2 ]. Due to rising groundwater levels, high salinity, and improper irrigation practices, the area of salinized land worldwide is expanding at a rate of 0.3 to 1.5 million hectares per year [ 3 ]. Soil with an electrical conductivity (EC) of 4 dS/m is typically considered saline-alkali land (equivalent to 40 mM NaCl), creating an osmotic pressure of approximately 0.2 MPa. This condition adversely affects the yields of most crops [ 4 ]. Plants exposed to salt stress develop complex adaptive strategies to mitigate these negative effects [ 5 ]. These adaptations include modifications in morphology, physiology, and metabolite profiles through changes in transcript levels [ 6 , 7 ]. For instance, sesame enhances its salt tolerance by promoting the biosynthesis of abscisic acid (ABA), which is achieved through the up-regulation of genes related to 9-cis-epoxy-carotenoid dioxygenase [ 8 ]. Similarly, kale increases its antioxidant capacity by up-regulating the expression of genes encoding superoxide dismutase (SOD), catalase (CAT), ascorbic acid peroxidase (APX), and ascorbic acid oxidase (AAO), thereby improving its resistance to drought stress [ 9 ]. Rice (Oryza sativa L.), a staple food for over 50% of the world's population, is a moderately salt-sensitive crop [ 10 , 11 ]. Salt stress can impair the morphophysiological traits of rice, ultimately reducing both grain yield and quality [ 12 , 13 ]. The tillering stage is crucial in determining the structure of rice plants, the number of panicles, and the final yield. Factors such as tiller number and tiller angle are key determinants of rice quality and yield [ 14 ]. As members of the Gramineae family, rice and other crops generate new tillers on the main stem, each of which has its own independent root system, aiding the plant’s survival under varying environmental conditions [ 15 ]. The yield of rice is influenced mainly by aboveground structural traits, such as plant height, tiller number, tiller angle, leaf angle, and panicle size [ 16 ]. Tillers grow independently through adventitious roots, which are important for determining the number of panicles [ 17 ]. Therefore, tiller germination and growth are crucial agronomic traits that influence rice yield [ 18 ]. Salt stress can reduce tiller numbers, with primary and secondary tillers being more susceptible than the main stem, which often leads to yield reduction [ 19 , 20 ]. Salt stress induces osmotic stress, ionic stress from excessive Na⁺ accumulation, and oxidative damage caused by elevated reactive oxygen species (ROS) levels [ 21 , 22 ]. Plants perceive salt stress through changes in osmotic pressure and sodium ion concentration, which trigger response pathways related to ion homeostasis, osmotic regulation, and redox control. Ion homeostasis involves the removal of sodium ions or their compartmentalization into vacuoles to maintain Na/K balance [ 23 ]. Permeation pressure regulation relies on the synthesis of compatible solutes such as proline, glycine betaine, and carbohydrates, which help retain water and stabilize cell structure [ 24 ]. REDOX regulation activates the antioxidant system to detoxify ROS and reduce oxidative damage. Plant hormones like ABA, ethylene (ET), and brassinosteroids (BR) support the coordination of these pathways [ 25 ]. Superoxide dismutase (SOD) isoenzymes play a critical role in mitigating salt-induced oxidative stress in rice roots [ 26 ]. Plants possess both enzymatic and non-enzymatic antioxidant defense systems to protect cells from the damaging effects of ROS. Key antioxidant enzymes include CAT, guaiacol peroxidase (POX), and APX, while non-enzymatic antioxidants include ascorbic acid (AsA), glutathione (GSH), phenolic compounds, and α-tocopherols. Furthermore, salt stress can disrupt plant metabolism and gene expression, leading to the accumulation or depletion of specific metabolites and causing imbalances in cellular protein levels [ 28 ]. The advent of sequencing technologies has facilitated the use of transcriptome sequencing to explore the metabolic pathways involved in plant responses to salt stress and to identify candidate genes for salt tolerance. Transcriptome sequencing has been used to investigate the salt tolerance mechanisms in rice [ 11 ]. In addition, metabolomics has become a powerful tool to study the complex metabolites in plants under biotic and abiotic stresses, offering valuable insights into metabolic pathways and related data [ 29 ]. This study selected two different rice varieties, 9311 and CMG, and compared their morphophysiological differences and transcriptomic profiles under salt stress. The goal was to identify key genes involved in the root system's response to salt stress in these two varieties. 2 Methods 2.1 Test Materials Rice varieties (Oryza sativa L.) CMG (HD-961, salt-tolerant, local variety) and 9311 (conventional indica rice) were used as experimental materials. Provided by the Germplasm Resource Bank of the College of Coastal Agriculture, Guangdong Ocean University 2.2. Test Method This experiment was conducted at the College of Coastal Agriculture, Guangdong Ocean University (21.2°N, 110.32°E). When the rice seedlings grow to three leaves and one heart, transplant them into plastic POTS. The size of the plastic POTS is 19 cm×15 cm×18 cm in diameter×bottom diameter×height. The transplanting depth is approximately 2 cm. Each pot has 4 holes, with one plant in each hole, and the spacing between them is about 4 cm. Each pot contains 3 kilograms of laterite soil. The physical and chemical properties of the soil are as follows: The soil organic carbon is 32.4 g·kg − 1; Available phosphorus: 4.0 mg·kg − 1; Available potassium: 48.4 mg·kg − 1; Alkali-hydrolyzed nitrogen, 37.1 mg·kg − 1; The soil pH value is 7.23. During the growth period of rice, keep the water levels of each treatment in the growth container consistent. A total of two treatments were set up: (1) control (distilled water + 0% NaCl), and (2) S (distilled water + 0.3% NaCl), with approximately 0.3% NaCl added in total. The salt content of the water layer was detected by handheld SKD1688-TR-6 EC meter (Shunkeda Technology Co., Ltd., Beijing, China) to ensure that the salt content remained relatively stable. 2.3. Determination Items and Methods The processed materials were collected respectively and their physiological response index values were measured. Among them, the activity of superoxide dismutase (SOD) was determined by the NBT method [ 30 , 31 ]; The activity of ascorbic acid peroxidase (APX) was measured by the sulfenicylic acid method [ 32 ]; Catalase (CAT) activity was determined by spectrophotometry (Shanghai Yuanxi UV-5100B, China) [ 33 ]; The content of soluble protein was measured by Coomassie brilliant blue staining [ 34 ]; The content of soluble sugar was measured by the anthrone method [ 35 ]; The content of malondialdehyde (MDA) was determined by the thiobarbituric acid method [ 36 ]. 2.4. Total RNA Extraction and Transcriptome and Metabolome Analysis 9311 and CMG (HD96-1) were selected as the experimental rice varieties. On the 7th day of NaCl stress, RNA was extracted from the junction of rice roots and stems, and three biological replicates were set for each treatment. Transcriptome sequencing was carried out as previously described [ 37 ]. Subsequently, the library was sequenced on an Illumina sequencer in PE150 mode. Filter the original sequencing data to generate clean data for high-quality analysis. The clean data were compared with the reference soybean genome (Williams 82.a4.v1) using HISAT2 v2.0.5. For gene expression analysis, HTSeq (version 0.9.1) was used to statistically compare the read count value of each gene with the original level of gene expression. Differentially expressed genes were analyzed using DESeq. The criteria for selecting differentially expressed genes were as follows: log2FoldChange > 1, and significant P value < 0.05. topGO was used to analyze the enrichment of gene ontology (GO). KEGG Pathway Enrichment (3.4.4) software was used to analyze the data, which was then added to the NCBI database with the registration number PRJNA1238547. 2.5. qRT-PCR Analysis To verify the reliability of RNA sequencing data, real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression of 10 randomly selected differentially expressed genes in the transcriptome and the internal reference gene UBO5. According to the manufacturer's instructions, total RNA was reverse transcribed into single-stranded cDNA using TransScript® All-in-One First-Strand cDNA Synthesis SuperMix for qPCR (one-step gDNA removal). According to the manufacturer's instructions, quantitative real-time PCR was performed using the ABI Quant Studio 6 Flex thermal cycling apparatus (USA) and the SYBR Green PCR kit (Trans, China). Up18S rRNA was used as the internal control. The relative expression uses the 2 − ΔΔCT method. All reactions were conducted in triplicate, and three biological replicates were set for each gene (Supplementary Table :S1). 2.6. Statistical Analysis Data were collated using Excel2010. One-way ANOVA, Duncan's multiple comparisons and Pearson's bivariate correlation analyses were conducted using SPSS24.0, and plots were plotted using Origin21. 3 Results and Analysis 3.1. Effects of Salt Stress on Rice Tillering The effects of salt stress on the base width of tillering nodes and the number of tillers in rice are shown in Fig. 1 . Compared with the control, the stem base width and tillering number of 9311 and CMG decreased under salt stress. On the 7th day, the stem base widths of 9311 and CMG decreased by 11.12% and 6.91% respectively compared with CK. These results indicate that salt stress significantly inhibited the stem base development and tillering ability of 9311 and CMG, and there were obvious differences in salt tolerance among different varieties. 3.2. Effects of Salt Stress on MDA Content in Rice Tillers The effect of salt stress on the MDA content in rice tillers is shown in Fig. 2 . From 7 to 35 days, the MDA content in the control groups of 9311 and CMG gradually decreased, while that in the S treatment group gradually increased. It is worth noting that at 14 days, the MDA content in the S treatment groups of 9311 and CMG began to be higher than that in the control group. In the S treatment, the MDA content on the 35th day of 9311 increased significantly by 103.45% compared with that on the 7th day, and the CMG was 74.32%. 3.3. Effects of Salt Stress on Hydrogen Peroxide Content in Rice tillers Under salt stress, the increase of hydrogen peroxide triggers oxidative damage to tillers, and the H₂O₂ clearance capabilities of different rice varieties vary (Figs. 3 A-B). From 7 to 35 days, the hydrogen peroxide content in the S treatment groups of 9311 and CMG first increased and then decreased, reaching the highest at 14 days. The hydrogen peroxide content in the S treatment group was higher than that in the control group. In the S treatment, the hydrogen peroxide content on the 14th day of 9311 increased significantly by 22.95% compared with that on the 7th day, and the CMG was 14.04%. 3.4. The Effect of Salt Stress on the Activity of Antioxidant Enzymes in Rice tillers Salt stress significantly altered the activity of the antioxidant enzyme system in rice tillers, as shown in Fig. 4 . Compared with the control group, the activities of SOD, POD and APX in the S treatment group of 9311 and CMG increased significantly from 7 to 28 days. The SOD activity in the S treatment group of 9311 first increased and then decreased from 7 to 35 days, reaching the peak on the 28th day, with a significant increase of 36.98% compared to the 7th day. The SOD activity in the S treatment group of CMG reached the peak on the 21st day, with a significant increase of 7.98% compared to the 7th day (Figs. 4 A, E). Peroxidase (POD) and ascorbic acid peroxidase (APX) show similar trends. Under salt stress, the growth rate of peroxidase (APX) in CMG is much greater than that in 9311. The CAT activity in the CMG salt treatment group continued to increase from 28 to 35 days, while there was no significant difference from 7 to 21 days. At 28 days, the CAT activity in the S treatment group began to be higher than that in the control group (Fig. 4 G). 3.5. Transcriptomic Analysis 3.5.1. Transcriptome Sequencing Results and Identification of Differentially Expressed Genes The tiller segments of 9311 and CMG treated with control (distilled water + 0% NaCl) and S (distilled water + 0.3% NaCl) were subjected to transcriptome sequencing to analyze the characteristics of gene expression changes. The results show that a total of 37,421,954 to 554,333,410 high-quality valid readings were obtained for each sample. The Q20 and Q30 values of the sequencing data reached 98.39–98.58% and 95.28–95.71% respectively (Appendix 1,S2), indicating that the quality of the transcriptome data is excellent and suitable for subsequent in-depth analysis. Differential gene expression analysis identified 1,132 differentially expressed genes in 9,311 tillers (DEGs, including 695 up-regulated and 437 down-regulated, Fig. 5 B) and 888 DEGs in CMG tillers (406 up-regulated and 482 down-regulated, Fig. 5 C), respectively. Among them, 96 and 47 genes were upregulated and downregulated respectively in the 9311 and CMG tillers. Venn diagram analysis indicated that 51 DEGs were upregulated in the 9311 tiller joint but significantly downregulated in the CMG tiller joint, while 34 DEGs were upregulated in the CMG tiller joint but significantly downregulated in the 9311 tiller joint (Fig. 5 A). 3.5.2. GO and KEGG Analyses Were Performed on the Differentially Expressed Genes (DEGs) of the 9311 and CMG Tillers The biological functions of differentially expressed genes under salt stress conditions were evaluated by GO enrichment analysis. The top 30 GO entries with the lowest error detection rate (FDR) were selected for display (Figs. 6 A and B), which visually reflect the biological processes, cellular components, and molecular functions. In the 9311 tillers, DEGs were significantly enriched in items such as seque-specific DNA-binding transcription factor activity, chitinase activity, chlorophyll binding, glucan endonection-1,3-β-D-glucosidase activity, extracellular region, chloroplast thymoid membrane, defense response, defense response against fungi, and photosynthesis (Fig. 6 A; Appendix 1,S3). The differentially expressed genes (DEGs) in CMG tillers are primarily concentrated in functional categories such as sequence-specific DNA-binding transcription factor activity, sequence-specific DNA binding, DNA binding within transcriptional regulatory regions, extracellular bodies, extracellular space, responses to abscisic acid, drought stress, fungal defense, and salicylic acid (Fig. 6 B; Appendix 1,S4). Further, KEGG enrichment analysis was conducted to explore the effects of salt stress on DEGs at the roots of Huang Huazhan and Changmao Valley (Figs. 6 C and D). The results showed that the DEGs of the 9311 tiller were co-enriched in multiple pathways, including "amino acid and nucleotide sugar metabolism", "phenylacetone biosynthesis", "taurine and low taurine metabolism", "zeaxin biosynthesis", "MAPK signaling pathway - plant", "photosynthesis - antenna protein", and "photosynthesis", etc. (Fig. 6 C; Appendix 1,S5). The DEGs co-enrichment of CMG tiller segments includes "extracellular polysaccharide biosynthesis", "MAPK signaling pathway - plant", "biosynthesis of benzoxazine compounds", "biosynthesis of lignin, lignin and wax", "glycerophospholipid metabolism", "biosynthesis of phenylacetone" and "plant-pathogen interaction", etc. (Fig. 6 D; Appendix S6. 3.5.3. Identification and Analysis of DEG Related to Antioxidant Enzymes A total of 25 DEGs related to POD and CAT responded to salt stress at the 9311 and CMG tillers (Appendix 1,S7). Among them, 13 were differentially expressed at the 9311 tiller. The POD differentially expressed genes were Os10g0109600, Os07g0677100 and Os04g0423800, all of which were significantly upregulated. The CAT differentially expressed genes were Os11g0155500 and Os06g0539400. Both have been significantly downgraded. CMG contains 12 related differentially expressed genes, among which Os06g0539400 and Os02g0115700 are the only differentially expressed genes of POD and CAT respectively, and both show significant upregulation. 3.5.4. Verify the Differential Genes Through qRT-PCR Analysis To analyze the expression levels of genes linked to two varieties under saline-alkali stress conditions, the expression pattern of DEG was verified by qRT-PCR. It was found that the antioxidant enzymes responding to salt stress were mainly POD and CAT, among which the expressions of 7 were significantly different (Fig. 7 , Appendix 1,S1). Five DEGs were highly expressed under salt stress, namely Os07g0677100, Os10g0109600, Os04g0423800, Os11g0155500 and Os06g0539400. Among them, only Os06g0539400 was downregulated under salt stress. Os01g0294700 and Os02g0115700 in CMG were significantly upregulated under salt stress, while Os10g0109600 had the highest upregulation of 12 times when treated with a salt concentration of 0.3%. 3.6. Metabolomics Analysis 3.6.1. Analysis of Differential Metabolites This study conducted a comparative analysis of differentially abundant metabolites in the 9CK-VS-9S and MCK-VS-MS treatment groups. PCA analysis and the overall heat map of metabolites show that the dispersion among samples is relatively small (Figs. 8 A, B), and there are significant differences among different treatments (Figs. 8 C, F), confirming the stability and reliability of the instrumental analysis and test results. The results indicated that 4414 and 4041 differential accumulation metabolites (DAMs) were identified respectively in the comparison of 9CK-VS-9S and MCK-VS-MS (Appendix 1,S8). Among all the samples, a total of 8,975 DAMs were identified. For the 9CK-VS-9S comparison group, 2339 compounds were up-regulated and 2075 compounds were down-regulated. For the MCK-VS-MS comparison group, 2421 compounds were upregulated and 1620 compounds were downregulated. Among them, 1022 compounds were up-regulated in 9311 and CMG, 589 compounds were down-regulated in 9311 and CMG, 358 compounds were simultaneously up-regulated in 9311 and down-regulated in CMG, and 412 compounds were down-regulated in 9311 and up-regulated in CMG. 3.6.2. KEGG Analysis and Z-Score Analysis of Differential Metabolites As shown in Figs. 9 A- 9 B, the relative contents of Inositol cyclic phosphate, 3-Methyloctadecane, Gluconic acid and Gluconic acid were relatively high in the 9CK-VS-9S comparison group. 3-(2-Methylthio)ethylmalate, 4-aminobenzoate, Shiromodiol diacetate, Inositol cyclic phosphate and alpha-D-Glucose in the MCK-VS-MS comparison group The content of 1,6-bisphosphate is relatively high, and salt stress significantly affects the metabolic content in the tillers of 9311 and CMG. In the rich pathways of KEGG, 9311 and DAMs of CMG tillers have a total of 86 identical pathways (Fig. 9 E). The main metabolic pathways in the 9311 tiller include: acyl-trNA biosynthesis of amino acids, arginine biosynthesis, metabolic processes of alanine, aspartic acid and glutamic acid, oxidative phosphorylation, biosynthesis of phenylpropyl compounds and linoleic acid metabolism, etc. (Fig. 9 C). The main metabolic pathways in CMG tillers include: the metabolic processes of alanine, aspartic acid and glutamic acid, oxidative phosphorylation, amino acid acyl-trNA biosynthesis, plant hormone signal transduction, linoleic acid metabolism and arginine biosynthesis, etc. (Fig. 9 D). 3.7. Combined Analysis of Transcriptome and Metabolome By analyzing the co-expression networks of differentially expressed genes (DEGs) and differentially accumulated metabolites (DAMs) in 9311 and CMG tillers, the relationship between gene expression and metabolic regulation was explored. Selecting all the differential metabolites and differential mRNAs to establish the O2PLS model can more accurately identify key regulatory phenomena. As shown in Fig. 10 A, among the metabolomics of 9CK-VS-9S, the more important ones are: Compounds such as 2-O-Glutaroyl-1-O-pa, Dihydroconiferyl alc, VAPIPROST, Vanillin and N-Jasmonoylisoleucin Genes such as Os01g0256500, Os01g0642200, OsMT3a and OsIPS1 are of relatively strong importance. Similarly, VAPIPROST and Withaperuvin C are relatively important metabolites in MCK-VS-MS, and the gene Os01g0256500 is prominent in the transcriptome. This study employed a nine-quadrant graph to demonstrate the correlation between DEGs and DAMs (Figs. 10 B, D). Findings revealed that there was no notable discrepancy in the 5th quadrant. In the 3rd and 7th quadrants, it was manifested that the differential expression patterns of mRNA and metabolites were compatible, presenting a direct correlation between mRNA and metabolites. mRNA could positively regulate the changes in metabolites, whereas the 1st and 9th quadrants displayed the opposite regulatory pattern. The KEGG co-enrichment pathways of 9CK-VS-9S differentially expressed genes (DEGs) and differentially expressed metabolites (DAMs) include: biosynthesis of phenylpropanoids, arginine biosynthesis, acyl-trNA biosynthesis of amino acids, and metabolism of amino sugars and nucleotide sugars. The KEGG co-enrichment pathways of MCK-VS-MS include: The biosynthesis of phenylalanine compounds, glycerophospholipid metabolism, biosynthesis of the stratum corneum, emboli and waxy substances, as well as the metabolic processes of alanine, aspartic acid and glutamic acid (Fig. 10 E, G). By analyzing the correlation between differentially expressed genes (DEGs) and differentially expressed metabolites (DAMs), it was found that the differentially expressed metabolites N-Carbamoylputrescine and Gibberellin in the 9311 tiller were positively correlated with the differentially expressed gene Os01g0256500 (Fig. 10 F). 4. Discussion Salt stress leads to a huge waste of land resources and also causes significant economic losses worldwide [ 38 ]. Therefore, enhancing the salt tolerance of rice is of great significance for addressing food security issues. This study conducted an in-depth investigation into the salt tolerance mechanisms of CMG(HD96-1)and 9311 rice varieties under salt stress by analyzing transcriptome, metabolome, and morphophysiological characteristics, taking into account key related genes and important metabolites of salt stress. 4.1. Effects of Salt Stress on Photosynthetic Metabolism in Rice Previous studies have shown that salt stress first reduces chlorophyll content, lowers photosynthetic gas exchange parameters, and causes photoinhibition; these changes then reduce photosynthetic efficiency and inhibit root nutrient absorption, ultimately suppressing plant growth [ 39 , 40 ]. The results of this study indicated that salt stress significantly reduced the tillering number and stem base width of 9311 and CMG. The stem base width of 9311 was higher than that of CMG from 7 to 35 days, and the tillering number of CMG was higher than that of 9133 from 7 to 21 days. Under salt stress, the stem base width of 9311 reached its peak in the second week, while that of CMG was in the fourth week. It is worth noting that the tillering number of CMG was significantly higher than 9311 in the third week, and the tillering inhibition effect on CMG under salt stress was greater than 9133. These situations may be related to the differences in salt tolerance and salt tolerance strategies between the two. 4.2. Effects of Salt Stress on MDA Content in Rice Salt stress first induces ionic stress and osmotic stress in plants; these stresses then cause metabolic imbalance and toxic accumulation of ROS, thereby bringing about oxidative damage to the plants [ 41 ]. Excessively high levels of ROS can lead to membrane lipid peroxidation, and MDA is a key marker of oxidative lipid damage [ 42 ]. In this study, the MDA levels in the tiller nodes of 9311 and CMG increased under salt stress conditions, indicating severe oxidative damage in rice plants and further confirming the excessive accumulation of ROS under such conditions. An increase in malondialdehyde (MDA) content demonstrates that salt stress induces oxidative stress in rice cells, which in turn causes severe damage to the cell membrane system. The MDA content of CMG is lower than 9311, indicating that the degree of oxidative damage to CMG is lower than 9311. 4.3. Effects of Salt Stress on the Activity of Antioxidant Enzymes and Hydrogen Peroxide Content in Rice Regarding stress-induced physiological responses, various biotic and abiotic stresses (including salt stress) can induce ROS accumulation and oxidative stress; the main ROS in plants are hydroxyl radicals, hydrogen peroxide (H₂O₂), superoxide anions and singlet oxygen [ 43 ]. These ROS are mainly produced in exosomes, chloroplasts, mitochondria and peroxisomes [ 44 ]. At low concentrations, ROS acts as the basic signaling molecule for regulating growth and stress responses [ 44 , 45 ]. Salt induces gene transcription encoding respiratory burst oxidase homologous D (RBOHD) and RBOHF, thereby catalyzing H2O2 production [ 44 ]. The results of this study indicate that salt stress significantly increases the hydrogen peroxide content of 9311 and CMG. The hydrogen peroxide content of both reaches its peak at 14 days, while the hydrogen peroxide content of CMG is slightly lower than that of 9311. Regarding the maintenance of ROS balance under stress, plants activate enzymatic and non-enzymatic antioxidant systems to scavenge ROS in cells, thereby preserving ROS homeostasis [ 44 , 46 ]. In S-type cytoplasmic male sterile maize lines, the sterilizing gene ZmORF355 (OPEN READING FRAME 355) exhibits moderate expression in mitochondria; this expression promotes salt tolerance by inducing ROS accumulation and activating diverse antioxidant enzymes [ 47 ]. Plants have evolved sophisticated mechanisms to resist oxidative stress triggered by salt stress, among which antioxidant enzymes such as SOD, CAT and POD are involved. These enzymes are of great importance for ROS scavenging. When exposed to salt stress, a rise in ROS levels can bring about a significant increase in the activities of enzymes including SOD, POD and CAT [ 48 ]. This is consistent with this study. The physiological research results show that the activities of SOD, APX and POD in 9311 and CMG significantly increase under salt stress. The SOD enzyme activity of 9311 under salt stress reaches its peak at 28 days, while that of CMG is 21 days. Similarly, under salt stress, the APX activities of 9311 and CMG reached their peaks at 21 and 14 days respectively, and the peak POD activities of both were reached at 28 days. CMG is equipped with a more rapid antioxidant enzyme response system, and this trait contributes to the enhancement of salt tolerance. Salt-tolerant receptor-like cytoplasmic kinase 1 (STRK1) exerts a phosphorylating effect on CATALASE C (Cat C) to activate it, further maintaining H₂O₂ homeostasis in rice. Plants overexpressing OsSTRK1 have higher catalase activity, lower hydrogen peroxide content, higher accumulation, and higher salt tolerance compared to the untransformed control plants [ 49 ]. Transcriptomic results indicated that there were more differentially expressed antioxidant enzyme genes in the CMG tillers, among which the POD regulatory gene Os01g0294700 and the CAT regulatory gene Os02g0115700 were significantly upregulated. This corresponds to the fact that in this study, CMG has a lower hydrogen peroxide content and a more rapid antioxidant enzyme response mechanism than 9311. 4.4. Genes and Metabolites related to salt tolerance in Rice under Salt Stress This study used transcriptomic analysis to uncover the molecular mechanisms underlying the differential salt stress responses at tiller nodes in CMG and 9311. Both varieties showed significant enrichment of DEGs related to transcription factor activity, especially sequence-specific DNA binding, indicating a universal genomic response to salt stress. The enrichment of "defense response against fungi" in both varieties highlights the crosstalk between abiotic and biotic stress signals. The GO analysis of 9311 also revealed the enrichment of terms such as "chlorophyll binding," "photosynthesis," and "photosynthetic antenna protein," suggesting that 9311 may focus on protecting its photosynthetic machinery from ROS-induced damage. In contrast, CMG demonstrated a more complex salt stress response, with the enrichment of terms like "response to abscisic acid" and "response to drought stress," indicating a more precise and coordinated stress response. In the KEGG analysis, CMG exhibited unique pathways such as "biosynthesis of keratin, imine, and wax," which are critical for forming physical barriers that reduce water loss and prevent sodium ion entry. These pathways underscore the role of CMG in building a robust physical barrier to combat salt stress. Additionally, the enrichment of "benzoxazine biosynthesis" and "plant-pathogen interaction" pathways in CMG enhances its ability to produce specific antibacterial metabolites, further contributing to its resilience under stress. In contrast, 9311's response was primarily focused on amino acid metabolism and energy production, suggesting a more passive approach to managing stress. The identification of the "phenylpropanoid biosynthesis" pathway in both varieties suggests its importance in salt tolerance, particularly in synthesizing lignin for cell wall reinforcement and flavonoids for antioxidant defense. The identification of thousands of DAMS in the two comparison groups in the metabolomics results indicates that salt stress profoundly disturbs the metabolic homeostasis of rice tillers. It is worth noting that although both CMG and 9311 made large-scale metabolic responses to stress, there were significant differences in the specific patterns of metabolite accumulation between the two. For instance, 358 compounds were upregulated in 9311 but downregulated in CMG, and 412 compounds were downregulated in 9311 but upregulated in CMG. This strongly suggests that the two varieties adopted different metabolic adaptation strategies. More metabolites in CMG are upregulated, which may indicate that it has stronger metabolic activity or synthetic capacity to cope with stress. The analysis of specific accumulated metabolites provides clues for variety characteristics. The high content of substances such as cyclic phosphate and Gluconic acid in 9311 May be related to its attempt to maintain osmotic balance and eliminate reactive oxygen species. The relatively high content of 4-aminobenzoate, Shiromodiol diacetate and the key intermediate of energy metabolism alpha-D-Glucose 1,6-bisphosphate in CMG This implies that it may have more advantages in energy supply, REDOX balance and the synthesis of secondary metabolites, which provides a metabolic basis for its better salt tolerance performance. KEGG pathway enrichment analysis revealed the similarities and differences in response strategies between the two varieties. Regarding the pathway enrichment characteristics of the two, both are enriched in amino acid metabolism (alanine, aspartate, glutamate metabolism, arginine biosynthesis), aminoacyl-tRNA biosynthesis, oxidative phosphorylation, and phenylpropanoid biosynthesis. This finding demonstrates that core processes like energy production, amino acid homeostasis maintenance, and defensive secondary metabolite synthesis are conserved mechanisms for rice's salt stress response. However, species-specific pathways characterize its uniqueness: the specific enrichment pathways of 9311 are relatively few, and its response is more concentrated on basic amino acids and energy metabolism. In contrast, CMG is uniquely enriched in "plant hormone signal transduction". This discovery is of crucial importance, indicating that CMG can more effectively utilize hormone signals such as abolic acid (ABA) and jasmonic acid (JA) to coordinate the expression and metabolic activities of downstream defense genes, thereby making more precise and efficient adaptive adjustments. This might be a key regulatory aspect for its superior salt tolerance compared to 9311. To deeply analyze the regulatory basis of metabolic changes, we have constructed an association network between DEGs and DAMs. The O2PLS model successfully screened out genes and metabolites that play an important role in the interaction network. In 9311, phenylpropane and hormone-related metabolites such as Vanillin (vanillin), N-Jasmonoylisoleucin (jasmonic acid-isoleucin, JA-Ile, the active form of jasmonic acid), as well as genes related to stress response (such as the metallothionein gene OsMT3a) were identified as key nodes. It is worth noting that the gene Os01g025650 was identified as an important gene in both varieties' models, suggesting that it may be a core salt stress regulator, and its function is worthy of further study. In CMG, the significance of metabolites such as VAPIPROST and Withaperuvin C is prominent. Ultimately, through KEGG co-enrichment analysis, the core biological pathways that are co-regulated by transcription and metabolism were identified. The "biosynthesis of phenylpropyl compounds" pathway was co-enriched in both varieties, fully demonstrating the core position of this pathway in the response to salt stress. Lignin produced by pathway metabolism can directly act on the cell walls of rice tillers and independent branches, enhancing mechanical strength by increasing the degree of lignification of the cell walls and reducing the penetration of salt ions. At the same time, supplementing the antioxidant effect of flavonoids not only alleviates ionic toxicity but also protects the occurrence and development of adventitious roots at the tillering nodes, providing physiological support for the normal growth of independent branches. The differences between the two varieties are reflected in other co-enrichment pathways: The co-enrichment pathways of 9311 include "amino sugar and nucleotide sugar metabolism", which is related to cell wall remodeling. It mainly enhances the cell structure stability of the main stem and early tillers by regulating cell wall remodeling; CMG, on the other hand, is uniquely co-enriched in "glycerophospholipid metabolism" (related to membrane lipid remodeling) and "biosynthesis of the stratum corneum, emboli and waxy substances". The latter is particularly important because the formation of keratin, cork and wax in the plant epidermis plays an irreplaceable role in reducing water loss and preventing sodium ions from entering the tillering nodes of rice. This discovery confirms from the gene-metabolite association level that CMG builds a stronger physical barrier by activating the expression of related genes and the synthesis of metabolites, which is another important mechanism for its superior salt tolerance to 9311. The common gradient of 0.3% simulated moderate salt stress can not only effectively induce the salt tolerance response of rice, but also avoid rapid plant death caused by excessive salt concentration, which is convenient for observing the salt tolerance differences of rice of different genotypes. This study provides a strong reference basis for the practice of increasing rice production in saline-alkali land [ 50 ]. 5. Conclusion This study systematically depicted the metabolic landscape of rice tillers under salt stress and the basis of their transcriptional regulation. Compared with 9311, CMG exhibits higher antioxidant enzyme activity, lower MDA and hydrogen peroxide content, and weaker oxidative damage under salt stress. From the perspective of metabolic processes, CMG demonstrates superior salt tolerance, which is attributed to its multi-level collaborative response mechanism: more efficient signal perception and transduction, and early coordination of defense responses by strengthening plant hormone signaling pathways (such as ABA). More proactive physical barrier construction, by synergistically regulating gene expression and metabolic flow, vigorously synthesizes hydrophobic barrier substances such as keratin and wax, effectively preventing sodium ion infiltration and water loss; A stronger metabolic foundation maintains efficient energy metabolism and REDOX balance. The response of 9311, on the other hand, is relatively passive, focusing more on basic osmotic regulation and mobilizing photosynthesis to resist stress. These findings provide valuable target genes and metabolite markers for the genetic improvement of crop salt tolerance. In this study, we clarified the intrinsic molecular mechanisms by which salt-tolerant wild rice HD96-1 and 9311 adapt to salt stress. This research provides valuable empirical data for further studies on improving salt tolerance in rice or breeding new salt-tolerant varieties. Abbreviations ROS reactive oxygen species MDA malondialdehyde DEGs differentially expressed genes POD peroxidase SOD superoxide dismutase qRT-PCR quantitative real-time polymerase chain reaction GO gene ontology ABA abscisic acid JA jasmonic acid ASA ascorbic acid GSH glutathione APX ascorbate peroxidase KEGG Kyoto Encyclopedia of Genes and Genomes DAMs differential metabolites Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Clinical trial number not applicable Funding This research was funded by Guangdong Provincial Department of Agriculture and Rural Affairs (2024KJ31); Guangdong Provincial Education Department Key Field Special Project for Colleges and Universities, No.2021ZdZX4027; Innovation Team Project of Universities in Guangdong Province, No.2021KCXTD011; Binhai Agricultural Engineering Technology Research Center (230420020). Author Contribution J.T. responsible for manuscript writing, investigation, data collation and formal analysis. Y.X. and Y.L. contributed to methodology and conceptualization. D.Z. and N.F. helps conceptualize, obtain funding, manage and supervise projects. J.Z., Y.D., X.W. and W.H. assist in writing censorship and editing. X.Z., M.Z., W.M., R.D. and Z.S. assist in investigation. All the authors participated in the preparation, writing and revision of the manuscript and adopted the submitted manuscript. Acknowledgements Not applicable. 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1","display":"","copyAsset":false,"role":"figure","size":1051102,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of salt stress on stem base width and tiller number of rice. (A): Wide stem base; (B): Tiller number. 0 represents the control group, and S represents the salt stress treatment group. The experimental data were expressed as the mean and standard deviation (SE) of four biological replicates. Significance analysis was conducted using the Duncan model. Different lowercase letters indicated statistically significant differences (P\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8270222/v1/41d16700528a1d4c99abef10.png"},{"id":98330120,"identity":"ded14754-95d4-4143-b285-a0ab8b64fdc8","added_by":"auto","created_at":"2025-12-16 15:15:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":690868,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of salt stress on MDA content in rice tillers. (A): MDA content in 9311 tiller nodes; (B): MDA content in CMG tiller nodes. 0 represents the control group, and S represents the salt stress treatment group. The experimental data were expressed using the mean and standard deviation (SE) of four biological replicates. Significance analysis was conducted using the Duncan model, and different lowercase letters indicated statistically significant differences (P\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8270222/v1/a8d52e494b53d03dc58af1ea.png"},{"id":98437811,"identity":"e756281b-7dea-40a9-9b70-3fc4090df439","added_by":"auto","created_at":"2025-12-17 16:58:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":839149,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of salt stress on hydrogen peroxide content in rice tillers. (A): Hydrogen peroxide content in 9311 tiller nodes; (B): The hydrogen peroxide content of CMG tiller nodes was expressed as the mean and standard deviation (SE) of four biological replicates in the experimental data. Significance analysis was conducted using the Duncan model, and different lowercase letters indicated statistically significant differences (P\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8270222/v1/a2c7eb6cdef9fe351af70625.png"},{"id":98330126,"identity":"b269aedb-7808-4ee5-938b-7d605e373b71","added_by":"auto","created_at":"2025-12-16 15:15:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":9177069,"visible":true,"origin":"","legend":"\u003cp\u003eThe effect of salt stress on the activity of antioxidant enzymes in rice tillers. (A): SOD enzyme activity (9311); (B): POD enzyme activity (9311); C: CAT enzyme activity (9311); (D): APX enzyme activity (9311); (E): SOD enzyme activity (CMG) F): POD enzyme activity (CMG); (G): CAT enzyme activity (CMG); (H): APX enzyme activity (CMG), the experimental data were expressed as the mean and standard deviation (SE) of four biological replicates. Significance analysis was conducted using the Duncan model, and different lowercase letters indicated statistically significant differences (P\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8270222/v1/9646027281cd10f95e02dd9e.png"},{"id":98330123,"identity":"a0a84389-974b-4190-a489-b7df1db169d0","added_by":"auto","created_at":"2025-12-16 15:15:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3993573,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Sequencing Data and differentially expressed genes (DEGs). (A): Venn diagrams of DEGS in the tiller nodes of 9311 and CMG. (B): Volcano map of differentially expressed genes at the 9311 tiller joint; (C): Volcano map of differentially expressed genes in CMG tillers.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8270222/v1/3d591fb234b0101d84d451a8.png"},{"id":98330129,"identity":"7df531a4-fe0f-4423-bb24-11dd2c667db0","added_by":"auto","created_at":"2025-12-16 15:15:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":4337801,"visible":true,"origin":"","legend":"\u003cp\u003eGO enrichment analysis and KEGG enrichment analysis of differentially expressed genes (DEGs) in 9311 and CMG tillers. (A): The GO pathway is enriched in the 9311 tiller nodes; (B): The GO pathway is enriched in the tillers of CMG. The KEGG pathway is enriched in the tillers of (C): 9311. (D): The KEGG pathway is enriched in the tillers of CMG. The GO enrichment bar chart, with the vertical axis representing the enriched GO terms and the horizontal axis indicating the number of differentially expressed genes in that term. Different colors are used to distinguish biological processes, cellular components and molecular functions. KEGG enrichment plot, with the horizontal axis representing enrichment values and the vertical axis representing enrichment pathways. The size of the bubbles indicates the number of differentially expressed genes. The color of the bubbles indicates the size of the FDR value.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8270222/v1/56e11f1de4fe30d21b42c01d.png"},{"id":98437314,"identity":"88efbf34-4b65-4dec-b9b7-b795d7aca024","added_by":"auto","created_at":"2025-12-17 16:57:12","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":228989,"visible":true,"origin":"","legend":"\u003cp\u003eqRT-PCR detection results of 7 genes of CMG and 9311 tillers under normal group and S treatment group (0.3% NaCl) treatment, and the expression patterns of the selected genes were analyzed. The data are the average values of three biological experiments ± standard errors. The standard errors are displayed in the error bar above the column. * indicates significant differences among different treatments (P\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-8270222/v1/f73135336a66d97118c51483.png"},{"id":98436339,"identity":"9bb57fba-fcd0-4373-a0f1-5b63f58a8fff","added_by":"auto","created_at":"2025-12-17 16:55:26","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":12066187,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolome analysis of 9311 and CMG tillers. (A): PCA analysis of the 9311 tiller joint; (B): PCA analysis of CMG tiller nodes; (C): Hierarchical clustering analysis of heat maps of CMG metabolites; (D): 9311 Differential Metabolite Statistics Table (E): Statistical Table of Differential Metabolites of CMG (F): 9311 Metabolite heat Map Hierarchical Clustering analysis G: Venn diagram of differential metabolites. 9CK-VS-9S represents the water-only control group of rice variety 9311 compared with salt stress treatment, and MCK-VS-MS represents the water-only control group of rice variety CMG compared with salt stress treatment.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-8270222/v1/f5bd0c1f2bf434fc4bcee61f.png"},{"id":98330138,"identity":"ccca7212-9db5-4ea9-86d6-755263788caa","added_by":"auto","created_at":"2025-12-16 15:15:28","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":15624117,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG analysis and Z-score analysis of differential metabolites in the tillers of 9311 and CMG. (A): Z-score analysis of differential metabolites at the 9311 tiller node; (B): Z-score analysis of differential metabolites of CMG tillers; The enrichment of DAMs of (C): 9CK-VS-9S in KEGG; (D): The enrichment of DAMs by MCK-VS-MS in KEGG; Venn diagrams of the KEGG pathway at CMG and 9311 tillers.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-8270222/v1/ddeeedf3d15a437cf1db3172.png"},{"id":98438290,"identity":"84381666-f969-4686-a456-74f301a3972e","added_by":"auto","created_at":"2025-12-17 16:58:57","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":19310581,"visible":true,"origin":"","legend":"\u003cp\u003eCombined transcriptome and metabolome analysis of 9311 and CMG tillers. O2PLS analysis of 9CK-VS-9S differentially expressed genes (DEGs) and differentially expressed metabolites (DAMs). The horizontal axis represents the size of the first principal component value, blue indicates metabolites, and orange indicates mRNA. Sorting was conducted using the first principal component, and by default, the top 20 important features from each group were chosen for bar chart display. The longer the column, the stronger its importance. Nine-quadrant graph analysis of B: 9CK-VS-9S differentially expressed genes (DEGs) and differentially expressed metabolites (DAMs). The X-axis represents the multiple of metabolite differences taken as log2 pairs, and the Y-axis represents the multiple of mRNA differences taken as log2 pairs. Red dots (Opposite changes) indicate that the trends of metabolites and mRNA changes are opposite, blue dots (Homodirectional changes) indicate that the trends of metabolites and mRNA changes are consistent, and green dots (Metabolic changes) indicate only metabolic differences. Purple dots (Transcriptomic changes) indicate only mRNA differences, while black dots (None) indicate non-differences in both metabolism and mRNA. O2PLS analysis of differentially expressed genes (DEGs) and differentially expressed metabolites (DAMs) of MCK-VS-MS; D: Nine-quadrant graph analysis of differentially expressed genes (DEGs) and differentially expressed metabolites (DAMs) of MCK-VS-MS; KEGG co-enrichment analysis of E: 9CK-VS-9S differentially expressed genes (DEGs) and differentially expressed metabolites (DAMs). The X-axis represents the enrichment factor value, and the Y-axis represents the enrichment pathway. The color indicates the magnitude of the P. color value. The redder the color, the stronger the enrichment degree. The size of the dots represents the number of differentially expressed mRNAs or differentially expressed metabolites, with the left circle representing the metabolome and the right triangle representing the transcriptome. If a pathway corresponds to both triangles and circles, it indicates that the pathway is enriched in both omics. Correlation chord chart of F: 9CK-VS-9S differentially expressed genes (DEGs) and differentially expressed metabolites (DAMs). The red nodes in the upper half represent differentially expressed mRNAs, while the blue nodes in the lower half represent differentially expressed metabolites. The lines connecting the nodes indicate the correlation between the two, with the red lines representing positive correlations and the green lines representing negative correlations. KEGG co-enrichment analysis of G: 9CK-VS-9S differentially expressed genes (DEGs) and differentially expressed metabolites (DAMs). H: Correlation chord chart of differentially expressed genes (DEGs) and differentially expressed metabolites (DAMs) of MCK-VS-MS.\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-8270222/v1/e4e4ed2c1199c8b7f445247e.png"},{"id":98622461,"identity":"5f9b80bf-b41b-4e60-870c-74873178de56","added_by":"auto","created_at":"2025-12-19 16:55:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":54404582,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8270222/v1/9958b9f0-dcee-47ae-bc63-c36ce65fa9eb.pdf"},{"id":98330124,"identity":"ea326189-582b-42df-b464-5b72b80d2ec6","added_by":"auto","created_at":"2025-12-16 15:15:28","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":46113,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8270222/v1/aae250b25ef2d4628919a1fc.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Combined transcriptional and metabolic analysis of the differences in salt tolerance responses of tillers in different rice varieties","fulltext":[{"header":"1 Background","content":"\u003cp\u003eSalt stress is one of the major abiotic stresses that negatively impacts the growth and yield of crops, thereby restricting agricultural development [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Due to rising groundwater levels, high salinity, and improper irrigation practices, the area of salinized land worldwide is expanding at a rate of 0.3 to 1.5\u0026nbsp;million hectares per year [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Soil with an electrical conductivity (EC) of 4 dS/m is typically considered saline-alkali land (equivalent to 40 mM NaCl), creating an osmotic pressure of approximately 0.2 MPa. This condition adversely affects the yields of most crops [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Plants exposed to salt stress develop complex adaptive strategies to mitigate these negative effects [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These adaptations include modifications in morphology, physiology, and metabolite profiles through changes in transcript levels [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. For instance, sesame enhances its salt tolerance by promoting the biosynthesis of abscisic acid (ABA), which is achieved through the up-regulation of genes related to 9-cis-epoxy-carotenoid dioxygenase [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Similarly, kale increases its antioxidant capacity by up-regulating the expression of genes encoding superoxide dismutase (SOD), catalase (CAT), ascorbic acid peroxidase (APX), and ascorbic acid oxidase (AAO), thereby improving its resistance to drought stress [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRice (Oryza sativa L.), a staple food for over 50% of the world's population, is a moderately salt-sensitive crop [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Salt stress can impair the morphophysiological traits of rice, ultimately reducing both grain yield and quality [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The tillering stage is crucial in determining the structure of rice plants, the number of panicles, and the final yield. Factors such as tiller number and tiller angle are key determinants of rice quality and yield [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. As members of the Gramineae family, rice and other crops generate new tillers on the main stem, each of which has its own independent root system, aiding the plant\u0026rsquo;s survival under varying environmental conditions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The yield of rice is influenced mainly by aboveground structural traits, such as plant height, tiller number, tiller angle, leaf angle, and panicle size [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Tillers grow independently through adventitious roots, which are important for determining the number of panicles [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Therefore, tiller germination and growth are crucial agronomic traits that influence rice yield [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Salt stress can reduce tiller numbers, with primary and secondary tillers being more susceptible than the main stem, which often leads to yield reduction [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSalt stress induces osmotic stress, ionic stress from excessive Na⁺ accumulation, and oxidative damage caused by elevated reactive oxygen species (ROS) levels [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Plants perceive salt stress through changes in osmotic pressure and sodium ion concentration, which trigger response pathways related to ion homeostasis, osmotic regulation, and redox control. Ion homeostasis involves the removal of sodium ions or their compartmentalization into vacuoles to maintain Na/K balance [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Permeation pressure regulation relies on the synthesis of compatible solutes such as proline, glycine betaine, and carbohydrates, which help retain water and stabilize cell structure [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. REDOX regulation activates the antioxidant system to detoxify ROS and reduce oxidative damage. Plant hormones like ABA, ethylene (ET), and brassinosteroids (BR) support the coordination of these pathways [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Superoxide dismutase (SOD) isoenzymes play a critical role in mitigating salt-induced oxidative stress in rice roots [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Plants possess both enzymatic and non-enzymatic antioxidant defense systems to protect cells from the damaging effects of ROS. Key antioxidant enzymes include CAT, guaiacol peroxidase (POX), and APX, while non-enzymatic antioxidants include ascorbic acid (AsA), glutathione (GSH), phenolic compounds, and α-tocopherols. Furthermore, salt stress can disrupt plant metabolism and gene expression, leading to the accumulation or depletion of specific metabolites and causing imbalances in cellular protein levels [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe advent of sequencing technologies has facilitated the use of transcriptome sequencing to explore the metabolic pathways involved in plant responses to salt stress and to identify candidate genes for salt tolerance. Transcriptome sequencing has been used to investigate the salt tolerance mechanisms in rice [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In addition, metabolomics has become a powerful tool to study the complex metabolites in plants under biotic and abiotic stresses, offering valuable insights into metabolic pathways and related data [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This study selected two different rice varieties, 9311 and CMG, and compared their morphophysiological differences and transcriptomic profiles under salt stress. The goal was to identify key genes involved in the root system's response to salt stress in these two varieties.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Test Materials\u003c/h2\u003e \u003cp\u003eRice varieties (Oryza sativa L.) CMG (HD-961, salt-tolerant, local variety) and 9311 (conventional indica rice) were used as experimental materials. Provided by the Germplasm Resource Bank of the College of Coastal Agriculture, Guangdong Ocean University\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Test Method\u003c/h2\u003e \u003cp\u003eThis experiment was conducted at the College of Coastal Agriculture, Guangdong Ocean University (21.2\u0026deg;N, 110.32\u0026deg;E). When the rice seedlings grow to three leaves and one heart, transplant them into plastic POTS. The size of the plastic POTS is 19 cm\u0026times;15 cm\u0026times;18 cm in diameter\u0026times;bottom diameter\u0026times;height. The transplanting depth is approximately 2 cm. Each pot has 4 holes, with one plant in each hole, and the spacing between them is about 4 cm. Each pot contains 3 kilograms of laterite soil. The physical and chemical properties of the soil are as follows: The soil organic carbon is 32.4 g\u0026middot;kg\u0026thinsp;\u0026minus;\u0026thinsp;1; Available phosphorus: 4.0 mg\u0026middot;kg\u0026thinsp;\u0026minus;\u0026thinsp;1; Available potassium: 48.4 mg\u0026middot;kg\u0026thinsp;\u0026minus;\u0026thinsp;1; Alkali-hydrolyzed nitrogen, 37.1 mg\u0026middot;kg\u0026thinsp;\u0026minus;\u0026thinsp;1; The soil pH value is 7.23. During the growth period of rice, keep the water levels of each treatment in the growth container consistent. A total of two treatments were set up: (1) control (distilled water\u0026thinsp;+\u0026thinsp;0% NaCl), and (2) S (distilled water\u0026thinsp;+\u0026thinsp;0.3% NaCl), with approximately 0.3% NaCl added in total. The salt content of the water layer was detected by handheld SKD1688-TR-6 EC meter (Shunkeda Technology Co., Ltd., Beijing, China) to ensure that the salt content remained relatively stable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Determination Items and Methods\u003c/h2\u003e \u003cp\u003eThe processed materials were collected respectively and their physiological response index values were measured. Among them, the activity of superoxide dismutase (SOD) was determined by the NBT method [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]; The activity of ascorbic acid peroxidase (APX) was measured by the sulfenicylic acid method [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]; Catalase (CAT) activity was determined by spectrophotometry (Shanghai Yuanxi UV-5100B, China) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]; The content of soluble protein was measured by Coomassie brilliant blue staining [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]; The content of soluble sugar was measured by the anthrone method [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]; The content of malondialdehyde (MDA) was determined by the thiobarbituric acid method [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Total RNA Extraction and Transcriptome and Metabolome Analysis\u003c/h2\u003e \u003cp\u003e9311 and CMG (HD96-1) were selected as the experimental rice varieties. On the 7th day of NaCl stress, RNA was extracted from the junction of rice roots and stems, and three biological replicates were set for each treatment. Transcriptome sequencing was carried out as previously described [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Subsequently, the library was sequenced on an Illumina sequencer in PE150 mode. Filter the original sequencing data to generate clean data for high-quality analysis. The clean data were compared with the reference soybean genome (Williams 82.a4.v1) using HISAT2 v2.0.5.\u003c/p\u003e \u003cp\u003eFor gene expression analysis, HTSeq (version 0.9.1) was used to statistically compare the read count value of each gene with the original level of gene expression. Differentially expressed genes were analyzed using DESeq.\u0026nbsp;The criteria for selecting differentially expressed genes were as follows: log2FoldChange\u0026thinsp;\u0026gt;\u0026thinsp;1, and significant P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. topGO was used to analyze the enrichment of gene ontology (GO). KEGG Pathway Enrichment (3.4.4) software was used to analyze the data, which was then added to the NCBI database with the registration number PRJNA1238547.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. qRT-PCR Analysis\u003c/h2\u003e \u003cp\u003eTo verify the reliability of RNA sequencing data, real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression of 10 randomly selected differentially expressed genes in the transcriptome and the internal reference gene UBO5. According to the manufacturer's instructions, total RNA was reverse transcribed into single-stranded cDNA using TransScript\u0026reg; All-in-One First-Strand cDNA Synthesis SuperMix for qPCR (one-step gDNA removal). According to the manufacturer's instructions, quantitative real-time PCR was performed using the ABI Quant Studio 6 Flex thermal cycling apparatus (USA) and the SYBR Green PCR kit (Trans, China). Up18S rRNA was used as the internal control. The relative expression uses the 2\u0026thinsp;\u0026minus;\u0026thinsp;ΔΔCT method. All reactions were conducted in triplicate, and three biological replicates were set for each gene (Supplementary Table :S1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Statistical Analysis\u003c/h2\u003e \u003cp\u003eData were collated using Excel2010. One-way ANOVA, Duncan's multiple comparisons and Pearson's bivariate correlation analyses were conducted using SPSS24.0, and plots were plotted using Origin21.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results and Analysis","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Effects of Salt Stress on Rice Tillering\u003c/h2\u003e \u003cp\u003eThe effects of salt stress on the base width of tillering nodes and the number of tillers in rice are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Compared with the control, the stem base width and tillering number of 9311 and CMG decreased under salt stress. On the 7th day, the stem base widths of 9311 and CMG decreased by 11.12% and 6.91% respectively compared with CK. These results indicate that salt stress significantly inhibited the stem base development and tillering ability of 9311 and CMG, and there were obvious differences in salt tolerance among different varieties.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Effects of Salt Stress on MDA Content in Rice Tillers\u003c/h2\u003e \u003cp\u003eThe effect of salt stress on the MDA content in rice tillers is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. From 7 to 35 days, the MDA content in the control groups of 9311 and CMG gradually decreased, while that in the S treatment group gradually increased. It is worth noting that at 14 days, the MDA content in the S treatment groups of 9311 and CMG began to be higher than that in the control group. In the S treatment, the MDA content on the 35th day of 9311 increased significantly by 103.45% compared with that on the 7th day, and the CMG was 74.32%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Effects of Salt Stress on Hydrogen Peroxide Content in Rice tillers\u003c/h2\u003e \u003cp\u003eUnder salt stress, the increase of hydrogen peroxide triggers oxidative damage to tillers, and the H₂O₂ clearance capabilities of different rice varieties vary (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-B). From 7 to 35 days, the hydrogen peroxide content in the S treatment groups of 9311 and CMG first increased and then decreased, reaching the highest at 14 days. The hydrogen peroxide content in the S treatment group was higher than that in the control group. In the S treatment, the hydrogen peroxide content on the 14th day of 9311 increased significantly by 22.95% compared with that on the 7th day, and the CMG was 14.04%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4. The Effect of Salt Stress on the Activity of Antioxidant Enzymes in Rice tillers\u003c/h2\u003e \u003cp\u003eSalt stress significantly altered the activity of the antioxidant enzyme system in rice tillers, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Compared with the control group, the activities of SOD, POD and APX in the S treatment group of 9311 and CMG increased significantly from 7 to 28 days. The SOD activity in the S treatment group of 9311 first increased and then decreased from 7 to 35 days, reaching the peak on the 28th day, with a significant increase of 36.98% compared to the 7th day. The SOD activity in the S treatment group of CMG reached the peak on the 21st day, with a significant increase of 7.98% compared to the 7th day (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, E). Peroxidase (POD) and ascorbic acid peroxidase (APX) show similar trends. Under salt stress, the growth rate of peroxidase (APX) in CMG is much greater than that in 9311. The CAT activity in the CMG salt treatment group continued to increase from 28 to 35 days, while there was no significant difference from 7 to 21 days. At 28 days, the CAT activity in the S treatment group began to be higher than that in the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Transcriptomic Analysis\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.5.1. Transcriptome Sequencing Results and Identification of Differentially Expressed Genes\u003c/h2\u003e \u003cp\u003eThe tiller segments of 9311 and CMG treated with control (distilled water\u0026thinsp;+\u0026thinsp;0% NaCl) and S (distilled water\u0026thinsp;+\u0026thinsp;0.3% NaCl) were subjected to transcriptome sequencing to analyze the characteristics of gene expression changes. The results show that a total of 37,421,954 to 554,333,410 high-quality valid readings were obtained for each sample. The Q20 and Q30 values of the sequencing data reached 98.39\u0026ndash;98.58% and 95.28\u0026ndash;95.71% respectively (Appendix 1,S2), indicating that the quality of the transcriptome data is excellent and suitable for subsequent in-depth analysis. Differential gene expression analysis identified 1,132 differentially expressed genes in 9,311 tillers (DEGs, including 695 up-regulated and 437 down-regulated, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) and 888 DEGs in CMG tillers (406 up-regulated and 482 down-regulated, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), respectively. Among them, 96 and 47 genes were upregulated and downregulated respectively in the 9311 and CMG tillers. Venn diagram analysis indicated that 51 DEGs were upregulated in the 9311 tiller joint but significantly downregulated in the CMG tiller joint, while 34 DEGs were upregulated in the CMG tiller joint but significantly downregulated in the 9311 tiller joint (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.5.2. GO and KEGG Analyses Were Performed on the Differentially Expressed Genes (DEGs) of the 9311 and CMG Tillers\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe biological functions of differentially expressed genes under salt stress conditions were evaluated by GO enrichment analysis. The top 30 GO entries with the lowest error detection rate (FDR) were selected for display (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and B), which visually reflect the biological processes, cellular components, and molecular functions. In the 9311 tillers, DEGs were significantly enriched in items such as seque-specific DNA-binding transcription factor activity, chitinase activity, chlorophyll binding, glucan endonection-1,3-β-D-glucosidase activity, extracellular region, chloroplast thymoid membrane, defense response, defense response against fungi, and photosynthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA; Appendix 1,S3). The differentially expressed genes (DEGs) in CMG tillers are primarily concentrated in functional categories such as sequence-specific DNA-binding transcription factor activity, sequence-specific DNA binding, DNA binding within transcriptional regulatory regions, extracellular bodies, extracellular space, responses to abscisic acid, drought stress, fungal defense, and salicylic acid (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB; Appendix 1,S4). Further, KEGG enrichment analysis was conducted to explore the effects of salt stress on DEGs at the roots of Huang Huazhan and Changmao Valley (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC and D). The results showed that the DEGs of the 9311 tiller were co-enriched in multiple pathways, including \"amino acid and nucleotide sugar metabolism\", \"phenylacetone biosynthesis\", \"taurine and low taurine metabolism\", \"zeaxin biosynthesis\", \"MAPK signaling pathway - plant\", \"photosynthesis - antenna protein\", and \"photosynthesis\", etc. (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC; Appendix 1,S5). The DEGs co-enrichment of CMG tiller segments includes \"extracellular polysaccharide biosynthesis\", \"MAPK signaling pathway - plant\", \"biosynthesis of benzoxazine compounds\", \"biosynthesis of lignin, lignin and wax\", \"glycerophospholipid metabolism\", \"biosynthesis of phenylacetone\" and \"plant-pathogen interaction\", etc. (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD; Appendix S6.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.5.3. Identification and Analysis of DEG Related to Antioxidant Enzymes\u003c/h2\u003e \u003cp\u003eA total of 25 DEGs related to POD and CAT responded to salt stress at the 9311 and CMG tillers (Appendix 1,S7). Among them, 13 were differentially expressed at the 9311 tiller. The POD differentially expressed genes were Os10g0109600, Os07g0677100 and Os04g0423800, all of which were significantly upregulated. The CAT differentially expressed genes were Os11g0155500 and Os06g0539400. Both have been significantly downgraded. CMG contains 12 related differentially expressed genes, among which Os06g0539400 and Os02g0115700 are the only differentially expressed genes of POD and CAT respectively, and both show significant upregulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.5.4. Verify the Differential Genes Through qRT-PCR Analysis\u003c/h2\u003e \u003cp\u003eTo analyze the expression levels of genes linked to two varieties under saline-alkali stress conditions, the expression pattern of DEG was verified by qRT-PCR. It was found that the antioxidant enzymes responding to salt stress were mainly POD and CAT, among which the expressions of 7 were significantly different (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Appendix 1,S1). Five DEGs were highly expressed under salt stress, namely Os07g0677100, Os10g0109600, Os04g0423800, Os11g0155500 and Os06g0539400. Among them, only Os06g0539400 was downregulated under salt stress. Os01g0294700 and Os02g0115700 in CMG were significantly upregulated under salt stress, while Os10g0109600 had the highest upregulation of 12 times when treated with a salt concentration of 0.3%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Metabolomics Analysis\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.6.1. Analysis of Differential Metabolites\u003c/h2\u003e \u003cp\u003eThis study conducted a comparative analysis of differentially abundant metabolites in the 9CK-VS-9S and MCK-VS-MS treatment groups. PCA analysis and the overall heat map of metabolites show that the dispersion among samples is relatively small (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, B), and there are significant differences among different treatments (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC, F), confirming the stability and reliability of the instrumental analysis and test results. The results indicated that 4414 and 4041 differential accumulation metabolites (DAMs) were identified respectively in the comparison of 9CK-VS-9S and MCK-VS-MS (Appendix 1,S8). Among all the samples, a total of 8,975 DAMs were identified. For the 9CK-VS-9S comparison group, 2339 compounds were up-regulated and 2075 compounds were down-regulated. For the MCK-VS-MS comparison group, 2421 compounds were upregulated and 1620 compounds were downregulated. Among them, 1022 compounds were up-regulated in 9311 and CMG, 589 compounds were down-regulated in 9311 and CMG, 358 compounds were simultaneously up-regulated in 9311 and down-regulated in CMG, and 412 compounds were down-regulated in 9311 and up-regulated in CMG.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.6.2. KEGG Analysis and Z-Score Analysis of Differential Metabolites\u003c/h2\u003e \u003cp\u003eAs shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA-\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB, the relative contents of Inositol cyclic phosphate, 3-Methyloctadecane, Gluconic acid and Gluconic acid were relatively high in the 9CK-VS-9S comparison group. 3-(2-Methylthio)ethylmalate, 4-aminobenzoate, Shiromodiol diacetate, Inositol cyclic phosphate and alpha-D-Glucose in the MCK-VS-MS comparison group The content of 1,6-bisphosphate is relatively high, and salt stress significantly affects the metabolic content in the tillers of 9311 and CMG. In the rich pathways of KEGG, 9311 and DAMs of CMG tillers have a total of 86 identical pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE). The main metabolic pathways in the 9311 tiller include: acyl-trNA biosynthesis of amino acids, arginine biosynthesis, metabolic processes of alanine, aspartic acid and glutamic acid, oxidative phosphorylation, biosynthesis of phenylpropyl compounds and linoleic acid metabolism, etc. (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). The main metabolic pathways in CMG tillers include: the metabolic processes of alanine, aspartic acid and glutamic acid, oxidative phosphorylation, amino acid acyl-trNA biosynthesis, plant hormone signal transduction, linoleic acid metabolism and arginine biosynthesis, etc. (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Combined Analysis of Transcriptome and Metabolome\u003c/h2\u003e \u003cp\u003eBy analyzing the co-expression networks of differentially expressed genes (DEGs) and differentially accumulated metabolites (DAMs) in 9311 and CMG tillers, the relationship between gene expression and metabolic regulation was explored. Selecting all the differential metabolites and differential mRNAs to establish the O2PLS model can more accurately identify key regulatory phenomena. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA, among the metabolomics of 9CK-VS-9S, the more important ones are: Compounds such as 2-O-Glutaroyl-1-O-pa, Dihydroconiferyl alc, VAPIPROST, Vanillin and N-Jasmonoylisoleucin Genes such as Os01g0256500, Os01g0642200, OsMT3a and OsIPS1 are of relatively strong importance. Similarly, VAPIPROST and Withaperuvin C are relatively important metabolites in MCK-VS-MS, and the gene Os01g0256500 is prominent in the transcriptome. This study employed a nine-quadrant graph to demonstrate the correlation between DEGs and DAMs (Figs.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB, D). Findings revealed that there was no notable discrepancy in the 5th quadrant. In the 3rd and 7th quadrants, it was manifested that the differential expression patterns of mRNA and metabolites were compatible, presenting a direct correlation between mRNA and metabolites. mRNA could positively regulate the changes in metabolites, whereas the 1st and 9th quadrants displayed the opposite regulatory pattern. The KEGG co-enrichment pathways of 9CK-VS-9S differentially expressed genes (DEGs) and differentially expressed metabolites (DAMs) include: biosynthesis of phenylpropanoids, arginine biosynthesis, acyl-trNA biosynthesis of amino acids, and metabolism of amino sugars and nucleotide sugars. The KEGG co-enrichment pathways of MCK-VS-MS include: The biosynthesis of phenylalanine compounds, glycerophospholipid metabolism, biosynthesis of the stratum corneum, emboli and waxy substances, as well as the metabolic processes of alanine, aspartic acid and glutamic acid (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eE, G). By analyzing the correlation between differentially expressed genes (DEGs) and differentially expressed metabolites (DAMs), it was found that the differentially expressed metabolites N-Carbamoylputrescine and Gibberellin in the 9311 tiller were positively correlated with the differentially expressed gene Os01g0256500 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eSalt stress leads to a huge waste of land resources and also causes significant economic losses worldwide [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Therefore, enhancing the salt tolerance of rice is of great significance for addressing food security issues. This study conducted an in-depth investigation into the salt tolerance mechanisms of CMG(HD96-1)and 9311 rice varieties under salt stress by analyzing transcriptome, metabolome, and morphophysiological characteristics, taking into account key related genes and important metabolites of salt stress.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Effects of Salt Stress on Photosynthetic Metabolism in Rice\u003c/h2\u003e \u003cp\u003ePrevious studies have shown that salt stress first reduces chlorophyll content, lowers photosynthetic gas exchange parameters, and causes photoinhibition; these changes then reduce photosynthetic efficiency and inhibit root nutrient absorption, ultimately suppressing plant growth [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The results of this study indicated that salt stress significantly reduced the tillering number and stem base width of 9311 and CMG. The stem base width of 9311 was higher than that of CMG from 7 to 35 days, and the tillering number of CMG was higher than that of 9133 from 7 to 21 days. Under salt stress, the stem base width of 9311 reached its peak in the second week, while that of CMG was in the fourth week. It is worth noting that the tillering number of CMG was significantly higher than 9311 in the third week, and the tillering inhibition effect on CMG under salt stress was greater than 9133. These situations may be related to the differences in salt tolerance and salt tolerance strategies between the two.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Effects of Salt Stress on MDA Content in Rice\u003c/h2\u003e \u003cp\u003eSalt stress first induces ionic stress and osmotic stress in plants; these stresses then cause metabolic imbalance and toxic accumulation of ROS, thereby bringing about oxidative damage to the plants [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Excessively high levels of ROS can lead to membrane lipid peroxidation, and MDA is a key marker of oxidative lipid damage [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In this study, the MDA levels in the tiller nodes of 9311 and CMG increased under salt stress conditions, indicating severe oxidative damage in rice plants and further confirming the excessive accumulation of ROS under such conditions. An increase in malondialdehyde (MDA) content demonstrates that salt stress induces oxidative stress in rice cells, which in turn causes severe damage to the cell membrane system. The MDA content of CMG is lower than 9311, indicating that the degree of oxidative damage to CMG is lower than 9311.\u003c/p\u003e \u003cp\u003e \u003cb\u003e4.3. Effects of Salt Stress on the Activity of Antioxidant Enzymes and Hydrogen Peroxide Content in Rice\u003c/b\u003e \u003c/p\u003e \u003cp\u003eRegarding stress-induced physiological responses, various biotic and abiotic stresses (including salt stress) can induce ROS accumulation and oxidative stress; the main ROS in plants are hydroxyl radicals, hydrogen peroxide (H₂O₂), superoxide anions and singlet oxygen [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. These ROS are mainly produced in exosomes, chloroplasts, mitochondria and peroxisomes [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. At low concentrations, ROS acts as the basic signaling molecule for regulating growth and stress responses [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Salt induces gene transcription encoding respiratory burst oxidase homologous D (RBOHD) and RBOHF, thereby catalyzing H2O2 production [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The results of this study indicate that salt stress significantly increases the hydrogen peroxide content of 9311 and CMG. The hydrogen peroxide content of both reaches its peak at 14 days, while the hydrogen peroxide content of CMG is slightly lower than that of 9311. Regarding the maintenance of ROS balance under stress, plants activate enzymatic and non-enzymatic antioxidant systems to scavenge ROS in cells, thereby preserving ROS homeostasis [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In S-type cytoplasmic male sterile maize lines, the sterilizing gene ZmORF355 (OPEN READING FRAME 355) exhibits moderate expression in mitochondria; this expression promotes salt tolerance by inducing ROS accumulation and activating diverse antioxidant enzymes [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Plants have evolved sophisticated mechanisms to resist oxidative stress triggered by salt stress, among which antioxidant enzymes such as SOD, CAT and POD are involved. These enzymes are of great importance for ROS scavenging. When exposed to salt stress, a rise in ROS levels can bring about a significant increase in the activities of enzymes including SOD, POD and CAT [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. This is consistent with this study. The physiological research results show that the activities of SOD, APX and POD in 9311 and CMG significantly increase under salt stress. The SOD enzyme activity of 9311 under salt stress reaches its peak at 28 days, while that of CMG is 21 days. Similarly, under salt stress, the APX activities of 9311 and CMG reached their peaks at 21 and 14 days respectively, and the peak POD activities of both were reached at 28 days. CMG is equipped with a more rapid antioxidant enzyme response system, and this trait contributes to the enhancement of salt tolerance. Salt-tolerant receptor-like cytoplasmic kinase 1 (STRK1) exerts a phosphorylating effect on CATALASE C (Cat C) to activate it, further maintaining H₂O₂ homeostasis in rice. Plants overexpressing OsSTRK1 have higher catalase activity, lower hydrogen peroxide content, higher accumulation, and higher salt tolerance compared to the untransformed control plants [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Transcriptomic results indicated that there were more differentially expressed antioxidant enzyme genes in the CMG tillers, among which the POD regulatory gene Os01g0294700 and the CAT regulatory gene Os02g0115700 were significantly upregulated. This corresponds to the fact that in this study, CMG has a lower hydrogen peroxide content and a more rapid antioxidant enzyme response mechanism than 9311.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Genes and Metabolites related to salt tolerance in Rice under Salt Stress\u003c/h2\u003e \u003cp\u003eThis study used transcriptomic analysis to uncover the molecular mechanisms underlying the differential salt stress responses at tiller nodes in CMG and 9311. Both varieties showed significant enrichment of DEGs related to transcription factor activity, especially sequence-specific DNA binding, indicating a universal genomic response to salt stress. The enrichment of \"defense response against fungi\" in both varieties highlights the crosstalk between abiotic and biotic stress signals. The GO analysis of 9311 also revealed the enrichment of terms such as \"chlorophyll binding,\" \"photosynthesis,\" and \"photosynthetic antenna protein,\" suggesting that 9311 may focus on protecting its photosynthetic machinery from ROS-induced damage. In contrast, CMG demonstrated a more complex salt stress response, with the enrichment of terms like \"response to abscisic acid\" and \"response to drought stress,\" indicating a more precise and coordinated stress response.\u003c/p\u003e \u003cp\u003eIn the KEGG analysis, CMG exhibited unique pathways such as \"biosynthesis of keratin, imine, and wax,\" which are critical for forming physical barriers that reduce water loss and prevent sodium ion entry. These pathways underscore the role of CMG in building a robust physical barrier to combat salt stress. Additionally, the enrichment of \"benzoxazine biosynthesis\" and \"plant-pathogen interaction\" pathways in CMG enhances its ability to produce specific antibacterial metabolites, further contributing to its resilience under stress. In contrast, 9311's response was primarily focused on amino acid metabolism and energy production, suggesting a more passive approach to managing stress. The identification of the \"phenylpropanoid biosynthesis\" pathway in both varieties suggests its importance in salt tolerance, particularly in synthesizing lignin for cell wall reinforcement and flavonoids for antioxidant defense.\u003c/p\u003e \u003cp\u003eThe identification of thousands of DAMS in the two comparison groups in the metabolomics results indicates that salt stress profoundly disturbs the metabolic homeostasis of rice tillers. It is worth noting that although both CMG and 9311 made large-scale metabolic responses to stress, there were significant differences in the specific patterns of metabolite accumulation between the two. For instance, 358 compounds were upregulated in 9311 but downregulated in CMG, and 412 compounds were downregulated in 9311 but upregulated in CMG. This strongly suggests that the two varieties adopted different metabolic adaptation strategies. More metabolites in CMG are upregulated, which may indicate that it has stronger metabolic activity or synthetic capacity to cope with stress. The analysis of specific accumulated metabolites provides clues for variety characteristics. The high content of substances such as cyclic phosphate and Gluconic acid in 9311 May be related to its attempt to maintain osmotic balance and eliminate reactive oxygen species. The relatively high content of 4-aminobenzoate, Shiromodiol diacetate and the key intermediate of energy metabolism alpha-D-Glucose 1,6-bisphosphate in CMG This implies that it may have more advantages in energy supply, REDOX balance and the synthesis of secondary metabolites, which provides a metabolic basis for its better salt tolerance performance.\u003c/p\u003e \u003cp\u003eKEGG pathway enrichment analysis revealed the similarities and differences in response strategies between the two varieties. Regarding the pathway enrichment characteristics of the two, both are enriched in amino acid metabolism (alanine, aspartate, glutamate metabolism, arginine biosynthesis), aminoacyl-tRNA biosynthesis, oxidative phosphorylation, and phenylpropanoid biosynthesis. This finding demonstrates that core processes like energy production, amino acid homeostasis maintenance, and defensive secondary metabolite synthesis are conserved mechanisms for rice's salt stress response. However, species-specific pathways characterize its uniqueness: the specific enrichment pathways of 9311 are relatively few, and its response is more concentrated on basic amino acids and energy metabolism. In contrast, CMG is uniquely enriched in \"plant hormone signal transduction\". This discovery is of crucial importance, indicating that CMG can more effectively utilize hormone signals such as abolic acid (ABA) and jasmonic acid (JA) to coordinate the expression and metabolic activities of downstream defense genes, thereby making more precise and efficient adaptive adjustments. This might be a key regulatory aspect for its superior salt tolerance compared to 9311. To deeply analyze the regulatory basis of metabolic changes, we have constructed an association network between DEGs and DAMs. The O2PLS model successfully screened out genes and metabolites that play an important role in the interaction network. In 9311, phenylpropane and hormone-related metabolites such as Vanillin (vanillin), N-Jasmonoylisoleucin (jasmonic acid-isoleucin, JA-Ile, the active form of jasmonic acid), as well as genes related to stress response (such as the metallothionein gene OsMT3a) were identified as key nodes. It is worth noting that the gene Os01g025650 was identified as an important gene in both varieties' models, suggesting that it may be a core salt stress regulator, and its function is worthy of further study. In CMG, the significance of metabolites such as VAPIPROST and Withaperuvin C is prominent.\u003c/p\u003e \u003cp\u003eUltimately, through KEGG co-enrichment analysis, the core biological pathways that are co-regulated by transcription and metabolism were identified. The \"biosynthesis of phenylpropyl compounds\" pathway was co-enriched in both varieties, fully demonstrating the core position of this pathway in the response to salt stress. Lignin produced by pathway metabolism can directly act on the cell walls of rice tillers and independent branches, enhancing mechanical strength by increasing the degree of lignification of the cell walls and reducing the penetration of salt ions. At the same time, supplementing the antioxidant effect of flavonoids not only alleviates ionic toxicity but also protects the occurrence and development of adventitious roots at the tillering nodes, providing physiological support for the normal growth of independent branches. The differences between the two varieties are reflected in other co-enrichment pathways: The co-enrichment pathways of 9311 include \"amino sugar and nucleotide sugar metabolism\", which is related to cell wall remodeling. It mainly enhances the cell structure stability of the main stem and early tillers by regulating cell wall remodeling; CMG, on the other hand, is uniquely co-enriched in \"glycerophospholipid metabolism\" (related to membrane lipid remodeling) and \"biosynthesis of the stratum corneum, emboli and waxy substances\". The latter is particularly important because the formation of keratin, cork and wax in the plant epidermis plays an irreplaceable role in reducing water loss and preventing sodium ions from entering the tillering nodes of rice. This discovery confirms from the gene-metabolite association level that CMG builds a stronger physical barrier by activating the expression of related genes and the synthesis of metabolites, which is another important mechanism for its superior salt tolerance to 9311. The common gradient of 0.3% simulated moderate salt stress can not only effectively induce the salt tolerance response of rice, but also avoid rapid plant death caused by excessive salt concentration, which is convenient for observing the salt tolerance differences of rice of different genotypes. This study provides a strong reference basis for the practice of increasing rice production in saline-alkali land [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study systematically depicted the metabolic landscape of rice tillers under salt stress and the basis of their transcriptional regulation. Compared with 9311, CMG exhibits higher antioxidant enzyme activity, lower MDA and hydrogen peroxide content, and weaker oxidative damage under salt stress. From the perspective of metabolic processes, CMG demonstrates superior salt tolerance, which is attributed to its multi-level collaborative response mechanism: more efficient signal perception and transduction, and early coordination of defense responses by strengthening plant hormone signaling pathways (such as ABA). More proactive physical barrier construction, by synergistically regulating gene expression and metabolic flow, vigorously synthesizes hydrophobic barrier substances such as keratin and wax, effectively preventing sodium ion infiltration and water loss; A stronger metabolic foundation maintains efficient energy metabolism and REDOX balance. The response of 9311, on the other hand, is relatively passive, focusing more on basic osmotic regulation and mobilizing photosynthesis to resist stress. These findings provide valuable target genes and metabolite markers for the genetic improvement of crop salt tolerance. In this study, we clarified the intrinsic molecular mechanisms by which salt-tolerant wild rice HD96-1 and 9311 adapt to salt stress. This research provides valuable empirical data for further studies on improving salt tolerance in rice or breeding new salt-tolerant varieties.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereactive oxygen species\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMDA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emalondialdehyde\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edifferentially expressed genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePOD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eperoxidase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSOD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esuperoxide dismutase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eqRT-PCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003equantitative real-time polymerase chain reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egene ontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eABA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eabscisic acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eJA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ejasmonic acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eascorbic acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGSH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eglutathione\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPX\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eascorbate peroxidase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDAMs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edifferential metabolites\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eClinical trial number\u003c/h2\u003e \u003cp\u003enot applicable\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was funded by Guangdong Provincial Department of Agriculture and Rural Affairs (2024KJ31); Guangdong Provincial Education Department Key Field Special Project for Colleges and Universities, No.2021ZdZX4027; Innovation Team Project of Universities in Guangdong Province, No.2021KCXTD011; Binhai Agricultural Engineering Technology Research Center (230420020).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.T. responsible for manuscript writing, investigation, data collation and formal analysis. Y.X. and Y.L. contributed to methodology and conceptualization. D.Z. and N.F. helps conceptualize, obtain funding, manage and supervise projects. J.Z., Y.D., X.W. and W.H. assist in writing censorship and editing. X.Z., M.Z., W.M., R.D. and Z.S. assist in investigation. All the authors participated in the preparation, writing and revision of the manuscript and adopted the submitted manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThis RNA-seq raw data can be found on the NCBI repository, accession number:PRJNA1300947. All datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLiu X, Hu Q, Yan J, Sun K, Liang Y, Jia M, Meng X, Fang S, Wang Y, Jing Y, Liu G, Wu D, Chu C, Smith SM, Chu J, Wang Y, Li J, Wang B. Exogenous melatonin promotes the salt tolerance by removing active oxygen and maintaining ion balance in wheat (Triticum aestivum L.). Front. Plant Sci. 2021;12:787062.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Wei L, Wang Z, Wang T. Physiological and molecular features of Puccinellia tenuiflora tolerating salt and alkaline-salt stress. J Integr Plant Biol. 2013;55:262\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao C, Zhang H, Song C, Zhu JK, Shabala S. Mechanisms of plant responses and adaptation to soil salinity. 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J Integr Plant Biol. 2023;65:656\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuihui Z, Xin L, Zisong X, Yue W, Zhiyuan T, Meijun A, Yuehui Z, Wenxu Z, Nan X, Guangyu S. Toxic effects of heavy metals Pb and Cd on mulberry (Morus alba L.) seedling leaves: Photosynthetic function and reactive oxygen species (ROS) metabolism responses. Ecotoxicol Environ Saf. 2020;195:110469.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou YB, Liu C, Tang DY, Yan L, Wang D, Yang YZ, Gui JS, Zhao XY, Li LG, Tang XD, Yu F, Li JL, Liu LL, Zhu YH, Lin JZ, Liu XM. The receptor-like cytoplasmic kinase STRK1 phosphorylates and activates CatC, thereby regulating H2O2 homeostasis and improving salt tolerance in rice. Plant Cell. 2018;30:1100\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMunns R, Tester M. Mechanisms of salinity tolerance. Annu Rev Plant Biol. 2008;59:651\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Rice, Salt stress, Tillering, Transcriptome, metabolom","lastPublishedDoi":"10.21203/rs.3.rs-8270222/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8270222/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSoil salinization is a significant factor contributing to the reduction of arable land. To enhance rice productivity in saline-alkali soils, understanding the salt tolerance mechanisms of rice varieties is essential. This study focused on investigating the salt tolerance mechanisms in the tillers of two rice varieties: the salt-tolerant CMG and the salt-sensitive 9311, using morphophysiological, transcriptomic, and metabolomic methods.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe activities of antioxidant enzymes SOD, POD, and APX in the tiller nodes of CMG were significantly higher than those in 9311. In contrast, the levels of MDA (malondialdehyde) and hydrogen peroxide in CMG tiller nodes were relatively lower, suggesting a more effective response to salt stress. Both varieties responded to saline-alkali stress through similar metabolic pathways, including amino acid metabolism (such as alanine, aspartic acid, glutamic acid metabolism, and arginine biosynthesis), amino acid acyl-tRNA biosynthesis, oxidative phosphorylation, and phenylpropanoid biosynthesis. However, CMG exhibited unique metabolic pathways such as glycerophospholipid metabolism, which is associated with membrane lipid remodeling, and the biosynthesis of the stratum corneum, suppositories, and waxes, which play a key role in reducing water loss and preventing sodium ion entry. Additionally, CMG showed a greater ability to regulate plant hormone signaling pathways, particularly those involving abscisic acid (ABA) and jasmonic acid (JA), to coordinate the expression and metabolic activities of defense genes.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe tiller nodes of CMG primarily focus on strengthening their own defenses against stress and reducing Na\u0026thinsp;+\u0026thinsp;toxicity after salt stress. In contrast, the tiller nodes of 9311 enhance photosynthetic efficiency by transferring stress responses to the leaves. This study provides valuable insights into the molecular mechanisms and metabolic pathway dynamics of salt tolerance in rice, offering a new perspective for further research on the salt tolerance mechanisms of rice under saline-alkali stress.\u003c/p\u003e","manuscriptTitle":"Combined transcriptional and metabolic analysis of the differences in salt tolerance responses of tillers in different rice varieties","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-16 15:15:23","doi":"10.21203/rs.3.rs-8270222/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-07T06:39:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-02T13:07:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-28T08:03:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"237995665666683252608080072891827952191","date":"2025-12-23T20:48:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-23T19:26:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"65910325899361750166024938117756638130","date":"2025-12-22T17:29:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"269670447733363552925212561918152547490","date":"2025-12-22T14:37:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260457865113773903121650139331643379995","date":"2025-12-22T11:13:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"85294793548174333738329558595831427580","date":"2025-12-22T10:10:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"198289219756267081525582717838530670373","date":"2025-12-13T14:18:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-11T05:04:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-09T04:33:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-08T12:07:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-08T12:03:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2025-12-03T12:20:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"74ab4304-8d1b-434d-af12-f58687bb0f84","owner":[],"postedDate":"December 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-01-07T06:53:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-16 15:15:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8270222","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8270222","identity":"rs-8270222","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}