Abstract Rice is a staple food for over half of the global population, necessitates efficient and cost-effective production methods to ensure food security. However, direct seeding of rice often encounters challenges due to adverse environmental conditions, resulting in increased seeding costs. In this study, we analyzed the germination and physiological data of sixty-six rice varieties under cold and submergence conditions. Our results demonstrate that selecting rice varieties with superior germination capacity in these adverse conditions can improve germination rates by 39.43%. Transcriptomic and metabolomic analyses of two contrasting varieties revealed potential regulatory mechanisms involving hormonal pathways and the glycerophospholipid metabolism pathway. Furthermore, we found that the exogenous application of specific metabolites provides a cost-effective seed enhancement strategy for varieties with poor germination capacity. These findings suggest that combining suitable variety selection with seed enhancement treatments can significantly reduce seeding costs in rice production. This research offers valuable insights for developing resilient rice varieties and cost-effective seeding strategies, potentially contributing to improved rice cultivation practices and enhanced global food security. Subject terms: Agriculture, Metabolomics __________________________________________________________________ Rice varieties with enhanced cold and submergence tolerance show 39.43% higher germination and -omics approaches reveal potential key pathways involved. Exogenous metabolites boost germination in lower performers, potentially reducing seeding costs in direct-seeded rice. Introduction Rice is one of the most important food crops amongst the human population, with nearly 50% of the population depending on it for food^[44]1. Cultivating enough food to feed the rapidly growing world’s population is an important food security aspect^[45]2. Food crops that utilize less water, land, and labor, as well as the adoption of advanced cultivation practices to resist more environmental stress, are thus needed. Seed germination is a vital stage in crop production. Notably, research on enhancing germination has been growing steadily over the years. In this study, we conducted a literature review on improving the germination rate of rice by searching the Web of Science using the keyword “enhance rice germination rate.” In the “MeSH Headings” section of the website, we identified relevant keywords, including “gene expression regulation in plants,” “genetically modified plants,” and “signal transduction.” Of note, there are various methods of enhancing the germination rate of rice seeds, which are primarily categorized into two approaches. The first approach is crop breeding^[46]3, which entails studying the transcription and metabolic changes that occur during seed germination as a result of genetic variation^[47]4. Breeding strategies utilize crop genetic diversity to improve existing varieties or develop different ones^[48]4,[49]5. Transgenic breeding and genome editing technologies have significantly accelerated the breeding process. The other approach involves seed enhancement techniques. Exogenous application of hormones^[50]6 and seed coating^[51]7 are the most commonly utilized techniques for promoting seed germination. The market offers a wide range of seed varieties, each with varying germination rates. Moreover, practical production requires numerous agricultural practices. A disharmony between the selected variety and the implemented agricultural practices can thus result in negative effects, such as a decline in seed germination rates^[52]8. Direct seeding is one of the most widely used agricultural practices for rice cultivation and is being increasingly employed by Asian farmers, especially the Chinese^[53]9. The advantage of direct seeding includes reducing production costs and labor^[54]9. Water seeding is a better method of direct seeding, primarily attributable to its superior weed control and reduced vulnerability to animal pests^[55]1. However, the submergence condition may induce oxygen limitation, thereby subjecting the rice seedlings to stress^[56]1. In recent years, climate change has resulted in significant temperature fluctuations^[57]10. Water seeding exposes the seeds to prolonged cold conditions because of the temperature fluctuations, causing combined stresses of submergence and cold to rice seeds, thus limiting seed germination. Enhancing crop yield in suboptimal environments involves employing strategies such as enhancing the resilience of crops to survive flooding conditions^[58]11. Rice varieties have varying responses to cold stress, which is further exacerbated by persistent and pronounced climate fluctuations. Therefore, it is imperative to carefully select rice varieties with a strong resistance to cold stress, which is ideally suited for water seeding. In this study, we screened 66 rice varieties to identify those with superior performance based on their germination, morphological, and physiological-biochemical indexes under both single and combined submergence and cold conditions. We further analyzed the transcriptional and metabolic mechanisms of the resistant and sensitive rice varieties under single and combined submergence and cold stresses to explore the potential metabolic and physiological pathways for seed enhancement. Subsequently, leveraging the insights from the transcriptional and metabolic analyses, we evaluated the regulatory effects of candidate substances on seed germination and seedling growth. Additionally, we compared the cost-effectiveness of exogenously applying these metabolites to improve seed germination rate with the cost of selecting high-quality varieties. This comparison underscores the feasibility of exogenous application as a cost-saving strategy. Our findings demonstrate the significant impact of these potential strategies in reducing rice seeding costs within rice production systems. The findings form a basis for governments of rice-growing countries to formulate agricultural policies that can better support their respective domestic agricultural development. Results Rice varieties responses to adverse environments The large-scale adoption of direct-seeded rice (DSR) technology necessitates identifying and utilizing rice varieties tolerant to various stresses, including cold and submergence (Fig. [59]1). Therefore, we employed a standardized evaluation method to thoroughly assess the characteristics of 66 rice varieties (Supplementary Table [60]1; Supplementary Note [61]1), with the aim of providing an objective appraisal of their performance under cold and submergence. Our evaluation process began with the documentation of the germination rate of the sprouted seeds, succeeded by the measurement of their morphological and physiological-biochemical indicators under stress conditions (Supplementary Notes [62]2 and [63]3). To account for the cumulative effects of germination, morphology, and physiological-biochemical performance across the 66 varieties, we also calculated the D value (Supplementary Data [64]2; Supplementary Note [65]4). This computation facilitated a comprehensive evaluation of each variety’s response to stress conditions. The D value distribution revealed a range of stress tolerance among the 66 varieties, with some exhibiting superior performance compared to others. For instance, “Jingliangyou 1468” (abbreviated as RR for resistance rice) consistently displayed higher D values across all stress conditions, while “Basimadi 385” (abbreviated as SR for sensitivity rice) exhibited lower D values, particularly under cold and submergence stress (Supplementary Data [66]2 and [67]3). As a result, these two varieties were selected for further in-depth transcriptome and metabolome analyses. Fig. 1. Rice cultivation area under direct-seeded rice in various countries. [68]Fig. 1 [69]Open in a new tab Percentage of rice cultivation area under direct-seeded rice (DSR) in different countries. Different colors represent different area ranges. Source data from Kumar et al.^[70]9 and Rao et al.^[71]28. Transcriptomic response of seedlings to experimental environment and validation of key response genes by qRT-PCR We analyzed the impact of cold and submergence treatments on the rice transcriptome using RNA sequencing (RNA-Seq). We examined divergent transcriptional profiles of two distinct rice varieties exposed to diverse environmental conditions using PCA analysis (Fig. [72]2a) because of their significance in influencing rice performance in stressful environmental conditions. Venn analysis of these two distinct varieties further uncovered significant distinctions between them (Fig. [73]2b). Among the identified differentially expressed genes (DEGs), 37 DEGs were consistently expressed in both varieties (Fig. [74]2b), highlighting the critical role of these 37 DEGs in mediating rice’s response to stress. Herein, they are referred to as the common 37 DEGs (Fig. [75]2b). Among the 37 commonly expressed DEGs, transcription factor analysis showed predominant association with the NAC and WRKY families (Fig. [76]3a). These families often regulate plant hormone expression to enhance stress resistance^[77]12,[78]13. An analysis of the expression profiles of the NAC and WRKY transcription factor families in the two distinct varieties revealed significantly higher expression levels in the RR compared to the SR (Fig. [79]3b, c). To validate the expression profiles of NAC and WRKY family genes obtained from transcriptome sequencing analyses, we performed qRT-PCR analysis to evaluate 10 genes in RR that exhibited significant expression changes in response to submergence treatment. All of these genes were considered to be associated with submergence. The expression patterns of these ten genes in qRT-PCR analysis were the same as those in RNA-Seq analysis (Supplementary Fig. [80]1). Fig. 2. Transcriptome analysis of stress responses in rice varieties. [81]Fig. 2 [82]Open in a new tab a PCA results elucidating the variations in transcriptomes among the samples in response to the stressors and diverse conditions. b Venn diagrams showing the number of differentially expressed genes (DEGs) for RR and SR under diverse stress conditions. Fig. 3. The identified transcription factors for the common set of 37 DEGs, including the expression patterns of NAC and WRKY family genes in RR and SR in varying environmental conditions. [83]Fig. 3 [84]Open in a new tab a A chord diagram illustrating the transcription factors identified within these 37 DEGs. The diagram utilizes PlantTFDB with Hmmscan E-value ≤ 0.00001. The expression profiles of NAC (b) and WRKY (c) transcription factors. In these profiles, red denotes upregulated while green denotes downregulated genes, with values calculated using the FPKM of each gene to derive the z-score. The Volcano plots visualize genes that were upregulated, downregulated, or exhibited no significant changes (Fig. [85]4). In this study, the Volcano plot following Deseq2 analysis, revealed the gene expression pattern at different cutoff points with a fold change (|log[2]FC|) of > 1 and an adjusted p-value < 0.05 (Fig. [86]4). We observed that the gene expression under the combined stress of low temperature and submergence was similar to that under a submergence environment alone (Fig. [87]4). To further investigate the pathways involved in these DEGs, we conducted a Gene Ontology (GO) analysis. The common 37 DEGs were enriched in “signal transduction” and the “hormone-mediated signaling pathway” (Fig. [88]5). Similarly, we conducted GO pathway analysis on the DEGs induced by SR (529 DEGs) and RR (188 DEGs), as depicted in Supplementary Fig. 2. The DEGs induced by SR (529 DEGs) and RR (188 DEGs) exhibited a predominant association with the “stimulus” and “stress” pathways (Supplementary Fig. [89]2). This finding suggested that the hormone-mediated signaling pathway is a crucial avenue for investigating relevant transcriptional responses. We utilized the fold change (log[2]FC) of DEGs in hormone synthesis expression to infer that different hormones may play significant roles under different stresses (Fig. [90]6). For example, auxin might play a predominant role under various stresses (Fig. [91]6). Furthermore, there was a resemblance in the transcriptional expression patterns between the combined stress of submergence and cold and the stress of cold alone, which was consistent with the results of transcription analysis and hormone synthesis expression (Figs. [92]4 and [93]6). Fig. 4. Volcano plots of differentially expressed genes (DEGs) under various treatment conditions in RR and SR. [94]Fig. 4 [95]Open in a new tab Volcano plots displaying the DEGs identified in RR (a–c) and SR (d–f) under distinct treatment conditions. Differential expression was analyzed using DESeq2. The resulting p-values were subsequently adjusted to control the False Discovery Rate (FDR). DEGs meeting the criteria of |log[2]FC| of > 1 and p-adjust <0.05 were considered significantly different. Fig. 5. The top 20 biological process pathways significantly enriched in the GO annotation of the common 37 DEGs. [96]Fig. 5 [97]Open in a new tab The common 37 DEGs refer to the 37 DEGs that were consistently expressed in both rice varieties analyzed, as identified through Venn analysis of the DEGs from the two varieties (refer to Fig. [98]2b). Fig. 6. Expression analysis of genes involved in phytohormone biosynthesis pathways under different treatments. [99]Fig. 6 [100]Open in a new tab Boxplots showing the expression profiles of transcripts involved in ethylene (a), auxin (b), and jasmonic acid (c) biosynthetic pathways for RR under different treatments. The y-axis is log[2]FC of DEGs in the synthesis of ethylene (a), auxin (b), and jasmonic acid (c). The heatmap is clustering the z-score of each gene for ethylene (a), auxin (b), and jasmonic acid (c) biosynthetic pathways for RR and SR under different treatments. In the profiles, red denotes the upregulated while green denotes the downregulated genes, with the values calculated using the FPKM of each gene to calculate the z-score. Metabolic response of seedlings to environmental adaptations A comprehensive analysis of the metabolic profiles of RR and SR under diverse environmental conditions was first done to elucidate their compositions, to gain a complete view of rice seedling in response to different stress. PCA analysis conducted to discern relationships among individual samples (Fig. [101]7a, b) revealed that both cultivars demonstrated substantial differentiation both in the positive and negative ion modes. This finding indicated that the metabolic profiles potentially played a pivotal role in elucidating the varying performance of the two cultivars under distinct environmental conditions (Fig. [102]7). In total, 918 biochemical compounds were identified (Supplementary Data [103]4). A Venn analysis was then done to detect the differentially expressed metabolites (DEMs) that were consistently expressed in both cultivars across the various stress conditions (Fig. [104]7c). RR had 91 DEMs consistently expressed across all three conditions and were denoted as RR-common-induced DEMs (Fig. [105]7c). In contrast, SR had 58 DEMs consistently expressed in all the three conditions and was designated as SR-common-induced DEMs (Fig. [106]7c). RR-common-induced DEMs and SR-common-induced DEMs commonly expressed compounds likely play a key role in stress resistance, and their differential expression levels between RR and SR warrant further investigation. A Venn analysis of RR-common-induced DEMs and SR-common-induced DEMs revealed the presence of 10 compounds shared by both, referred to as the common 10 DEMs (Fig. [107]7c). The remaining 81 unique compounds in RR and 48 unique compounds in SR were thus categorized as unique RR-common-induced DEMs and unique SR-common-induced DEMs, respectively (Fig. [108]7c). A pathway enrichment analysis of the common 10 DEMs (at a significance threshold of p < 0.05) highlighted their predominant concentration within the Glycerophospholipid metabolism pathway (Fig. [109]8). Building upon prior research highlighting the significance of this metabolic pathway in plant stress resilience, we aim to experimentally validate its role through exogenous application of pathway-specific compounds^[110]6. Independent enrichment pathway analysis of the unique RR-common-induced DEMs and unique SR-common-induced DEMs uncovered their predominant association with pathways related to amino acid and lipid metabolism (Fig. [111]8). Supporting prior transcriptome data, established literature reveals a significant association between these two metabolic pathways and hormone expression levels^[112]12. Fig. 7. Metabolomic analysis of stress responses in rice varieties. [113]Fig. 7 [114]Open in a new tab PCA results showing the differences in metabolites among the samples in response to the stresses in the positive ion mode (a) and negative ion mode (b). c Venn diagrams showing the number of differentially expressed metabolites (DEMs) for RR and SR under various stress conditions. Fig. 8. An overview of the enriched metabolic pathways with a significance level of p < 0.05 is provided. [115]Fig. 8 [116]Open in a new tab A Venn analysis of RR-common-induced DEMs and SR-common-induced DEMs revealed 10 compounds shared by both, hereafter referred to as the “common 10 DEMs” (refer to Fig. [117]7c). The remaining 81 unique compounds in RR and 48 unique compounds in SR were categorized as unique RR-common-induced DEMs and unique SR-common-induced DEMs, respectively (refer to Fig. [118]7c). Seed enhancement experiment The common 10 DEMs were predominantly enriched in the Glycerophospholipid metabolism pathway (Fig. [119]8). Previous studies on cucumber exposed to early chilling stress have also reported metabolite enrichment in the Glycerophospholipid metabolism pathway^[120]6. Several metabolites enriched in the Glycerophospholipid metabolism pathway exhibiting the capacity to enhance the resistance of cucumber to cold stress were thus selected for validation experiments^[121]6. Six metabolites (Eth, Cho, PEth, PCho, PC, PE) were selected and exogenously applied to both RR and SR at T1S1, T1S2, T2S1, and T2S2 at concentrations ranging between 0 and 100 μmol/L to confirm their involvement in enhancing rice’s resilience against submergence and cold stress. Following a 24-h soaking period at room temperature in metabolite solutions Subsequently, seeds were placed in T1S1, T1S2, T2S1, or T2S2 environments for 5 days of growth. Then, all seedlings, regardless of treatment, underwent a 5-day recovery period under normal temperature and non-submerged conditions. The exogenous application of metabolites had a more pronounced effect on SR compared to RR (Fig. [122]9). Among the six compounds examined, Eth, Cho, PEth, and PCho demonstrated a significantly greater promotion of the germination rate in SR under stress conditions, ranging from 8.61% to 20.97% with an average increase of 14.13% compared to T1S1 (Fig. [123]9). Fig. 9. Germination and growth parameters in rice varieties under stress. Fig. 9 [124]Open in a new tab An assessment of the germination rate, germination index, bud dry weight, root dry weight, and total dry weight for RR (a) and SR (b). The data represent the mean values obtained from three independent replications, with error bars indicating the standard error of the mean. The control group (CK) was subjected to a concentration of 0 μmol/L. The dry weights of the bud, root, and total are all expressed in mg. Discussion Global warming has led to extreme weather, including cold stress, which significantly limits agricultural production^[125]10. This study examined the impact of cold stress on seed germination of 66 rice varieties under water seeding. The study aimed to demonstrate practical solutions for improving agricultural production. Transcriptional and metabolic analyses of two excellent varieties confirmed the results of the metabolic analysis and revealed the significant positive impacts of employing good agricultural practices and varieties with excellent qualities in agriculture. Omics techniques, specifically transcriptomics and metabolomics, were employed to analyze the responses of two rice varieties, RR and SR, to different stress conditions. The transcriptomic data provided insights into rice’s response to submergence and cold stress. The response to concurrent flooding and low-temperature stress mirrored the response to flooding in isolation. However, these results align with prior research^[126]14,[127]15, suggesting that the amalgamation of stresses can alleviate the damage induced by a single stressor. Specifically, flooding attenuated the harm inflicted by cold stress. The upregulated genes also exhibited similarities at a genetic level, with the genes Os03g0745000 and Os02g0758000 being significantly upregulated in both RR-T2S1 and RR-T2S2 (Fig. [128]4). Os03g0745000 encodes heat shock transcription factors (HSFs)^[129]16. HSFs participate in regulating the expression of heat shock proteins (HSPs), which are critical in the protection against stress damage and many other important biological processes. This phenomenon also explains why its expression increased: to enhance the rice’s resistance to stress. Os02g0758000 has been previously reported to be involved in the Cd stress response^[130]12, and our data suggest it may also respond to submergence and combined submergence and cold conditions. However, further genetic and biochemical experiments are needed to validate the specific functions and roles of these genes under the studied stress conditions. Thus, our findings should be considered preliminary and require further validation through more targeted genetic approaches, including gene knockout to conclusively determine the precise roles of these genes in submergence and combined submergence and cold conditions. Hormones significantly influence the regulation of plant responses to stress^[131]14,[132]15. Our findings suggest that ethylene may play a role in RR’s response to submergence stress (T1S2), consistent with previous observations^[133]17. The top 20 biological process pathways (Fig. [134]5), which were significantly enriched in the GO annotation of the common 37 DEGs, were predominantly related to signaling, indicating a potential role of hormones in modulating plant responses to stress. Genes from the NAC and WRKY families were observed to be differentially expressed in response to the combination of cold and submergence stress. This study provides preliminary data on the potential involvement of these genes in the response to combined submergence and cold stress, building upon prior studies that examined their roles in cold stress independently^[135]12,[136]13. qRT-PCR validation of the transcriptome data supported the differential expression of these two families (Supplementary Fig. [137]1). Nonetheless, further molecular biological experiments are needed to validate these results. Subsequent metabolic pathway enrichment analysis highlighted the significance of the glycerophospholipid metabolism pathway (Fig. [138]8). To validate this finding, we exogenously added the core compounds of this pathway and observed its impact on stress tolerance. Indeed, the glycerophospholipid metabolism pathway indeed played a crucial role in enabling plants to withstand cold, flooding, and complex stresses. Interestingly, this pathway also contributed to cucumber’s cold stress response by enhancing the accumulation of PCho and PEth^[139]6. Among the six validated metabolites, Eth, Cho, PEth, and PCho exhibited stress-protective properties. Considering the practical application in agriculture, we evaluated the cost of applying these metabolites versus the cost savings associated with improved seed germination induced by their application. Remarkably, our analysis revealed that the cost of applying the metabolites ($7.59) was lower than the estimated cost savings ($29.93). This finding further underscores the potential of metabolite application as a viable approach to translate metabolomics findings into practical agricultural applications. For a detailed cost analysis, please refer to Supplementary Notes [140]7. Despite the promising results, we acknowledge limitations. Our data stem from controlled indoor experiments utilizing superior varieties. High germination rates may not necessarily translate into high yields under field conditions. Moreover, compared to transplanted rice, direct-seeded rice has a shorter growth period and may exhibit poorer resistance to biotic and abiotic stresses at later growth stages^[141]18. Therefore, we plan to validate these findings through field experiments to assess the performance of these varieties in agriculture settings. Another concern is the high seed usage for direct-seeded rice, which not only increases costs but also results in dense seedlings with poor ventilation, potentially leading to severe diseases in later growth stages^[142]19. However, precision rice hill-drop drilling (PRHDD) offers a promising solution. This technique promotes a uniform crop stand, effectively mitigating the problems of high disease and pest incidence associated with the irregular seed distribution inherent to manual broadcasting. Notably, PRHDD achieves these benefits while concurrently reducing seed usage and boosting yield^[143]19. Additionally, although direct-seeded rice cultivation is gaining popularity in China, transplanted rice remains the dominant method. Previous meta-analyses suggest that transplanted rice ultimately yields higher than direct-seeded rice^[144]18. However, considering the diverse direct seeding methods (e.g., dry, wet, water) and the varying frequency of extreme weather events (e.g., sudden low temperatures) across different regions, further research is necessary to compare the final yields of these two cultivation approaches. Furthermore, the current study focused on rice varieties commonly grown in Southern China. Additional research might be required to confirm the applicability of these findings to other rice-growing regions and varieties. Currently, maize, rice, wheat, and soybeans jointly contribute to approximately 66% of the world’s agricultural caloric supply^[145]7. This phenomenon has led to increased pressures on agricultural production owing to the dual challenges of climate change and a rapidly expanding global population^[146]7. Addressing this pressing challenge requires exploring effective methods to enhance agricultural productivity. Rice, a critical food source, faces increasing threats from cold-induced damage in directly seeded varieties due to more frequent occurrences of extreme weather events. This article highlights the increasing threat of cold-induced damage to directly seeded rice varieties in real-world agricultural contexts, which is further propelled by the rising occurrences of extreme weather events worldwide. Moreover, it delineates essential measures to tackle these environmental challenges through augmenting the screening of existing rice varieties and utilizing multi-omics analysis to enhance germination in adverse conditions, with specific attention to result validation, especially those based on the metabolome. Additionally, by evaluating the economic implications of varietal differences and the potential cost savings achievable through the application of metabolites. These analyses underscore the urgent requirement to implement measures to mitigate the prevalence of extreme global climates. Methods Plant materials and growth conditions Sixty-six rice varieties commonly grown in Southern China were employed as the experimental materials (Supplementary Table [147]1). The seeds of all the varieties were obtained from the College of Agriculture, South China Agricultural University (SCAU) in Guangzhou, China. The experiment was set up at the College of Agriculture, SCAU, China, between March 2021 and August 2022. Rice seeds were maintained at a stable temperature and light conditions with a 12 h light/12 h dark photoperiod and 60% relative humidity. The seeds were winnowed to remove seeds without embryos prior to exposure to the experimental conditions. The seeds were then thoroughly washed 4–5 times to eliminate any remaining husks, immersed in a 2% NaClO solution for 15 min, and then carefully rinsed with water to mitigate any seed-related issues that could affect the experimental results. The germination test was conducted within a 14 cm × 8.3 cm × 7 cm (length × width × height) germination box. The variety selection experiment based on the germination indexes was conducted over a period of 10 days using 2 layers of filter paper as cushioning material for the seed, which was evenly covered with a 2 cm-thick layer of quartz sand (8–16 mesh) and exclusively with deionized water. A completely randomized experimental design comprising three distinct stress treatments: normal temperature with submergence (T1S2), cold treatment without submergence (T2S1), and cold treatment with submergence (T2S2) was employed. The control treatment (T1S1) involved maintaining normal temperature conditions without submergence. The normal temperature treatment was consistently maintained at 25 °C, while the cold treatment was set at 15 °C. Additionally, a submergence depth of 5 cm was consistently maintained for seedlings in all treatments. Seedlings in all treatments were exposed to normal temperature conditions without submergence for an additional five days after being subjected to 5 days of stress treatment. Each treatment was replicated four times. Two distinct rice cultivars, namely “Jingliangyou 1468” (herein referred to as RR) and “Basimadi 385” (herein referred to as SR), were chosen for comprehensive transcriptome and metabolome investigations. RNA extraction, sequencing, analysis, and qRT-PCR assays RNA isolation from every sample was done using the Plant RNA Purification Reagent (Invitrogen), followed by RNA-Seq library preparations using the Illumina® Stranded mRNA Prep (Illumina, San Diego, CA), and sequencing on an Illumina NovaSeq 6000 platform (Illumina, San Diego, CA). The paired-end raw sequences were first subjected to quality control using fastp 0.19.5 set at default parameters^[148]20. The clean reads were then individually aligned to the reference genome in the orientation mode using the HISAT2 2.1.0 software^[149]21. The mapped reads from each sample were subsequently assembled using StringTie 2.1.2 in a reference-guided approach^[150]22. To identify differentially expressed genes (DEGs) between two distinct samples, we quantified the expression level of each transcript using the fragments per kilobase of transcript per million mapped reads (FPKM) method. Gene abundances were quantified using RSEM 1.3.3^[151]23. Differential expression analysis was primarily performed using DESeq2 1.24.0^[152]24. Genes with a fold change (|log[2]FC|) of >1 and p-adjust <0.05 were considered to be significantly differentially expressed genes (DEGs). Functional enrichment analysis was further conducted to identify the significantly enriched DEGs in Gene Ontology (GO) terms and metabolic pathways. Benjamini–Hochberg-corrected p-values ≤ 0.05 of the DEGs were employed for comparison against the whole-transcriptome background to further refine the selection of DEGs. GO functional enrichment analysis was performed utilizing Goatools 0.6.5^[153]25. Total RNA was isolated from RR leaves under T1S1 and T1S2 conditions using TRIzol reagent (Invitrogen). To eliminate genomic DNA contamination, DNase I (Takara) treatment was subsequently performed. cDNA synthesis was achieved through reverse transcription using M-MLV reverse transcriptase (Promega). Quantitative real-time PCR (qRT-PCR) was conducted on an ABI StepOnePlus system (Applied Biosystems) with SYBR Green reagent (Takara) to quantify the expression of specific genes. Relative gene expression was quantified using the 2^−ΔΔCt method^[154]26. Metabolomics profiling Leaves of at least three independent seedlings for each sample were harvested and stored at −80 °C awaiting LC–MS/MS analysis. The leaf samples were ground into fine powder by placing 50 mg of solid leaf sample in a 2 mL centrifuge tube containing a 6 mm grinding bead, and subsequent grinding using a tissue grinder. Isolation of the leaf extracts was done using a 400 µL solution of methanol: water (4:1, v/v) containing 0.02 mg/mL internal standard (L-2-chlorophenylalanine). The extraction mixture was ground in a frozen tissue grinder for 6 min (−10 °C, 50 Hz), followed by low-temperature ultrasonic extraction for 30 min (5 °C, 40 kHz). The extraction mixture was then let to stand at −20 °C for 30 min, followed by centrifugation for 15 min (4 °C, 13,000 × g). The supernatant was transferred to an injection vial with an insert tube for subsequent LC–MS/MS analysis on a Thermo UHPLC-Q Exactive HF-X system equipped with an ACQUITY HSS T3 column (100 mm × 2.1 mm i.d., 1.8 μm; Waters, USA) at Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). The analysis was conducted by reconstituting the samples in a 100 µL loading solution of acetonitrile: water (1:1, v/v) and briefly sonicating them in a water bath set at 5 °C. A pooled quality control sample (QC) was created by combining equal volumes of all individual samples as part of system conditioning and quality control. The QC samples were subjected to the same disposal and testing procedures as the analytical samples. The QC samples represented the entire sample set and were injected at regular intervals (every n^th plant sample) to assess analysis stability. The samples underwent mass spectrometry analysis after separation. Solvent A consisted of 0.1% formic acid in water and acetonitrile (95:5, v/v), while solvent B comprised 0.1% formic acid in acetonitrile, isopropanol, and water (47.5:47.5:5, v/v/v). The solvent gradient was programmed as follows: 0–0.1 min: 0% B to 5% B, 0.1–2 min: 5% B to 25% B, 2–9 min: 25% B to 100% B, 9–13 min: 100% B, 13–13.1 min: 100% B to 0% B, 13.1–16 min: 0% B. Samples were injected at a volume of 2 µL with a flow rate of 0.4 mL/min. The column temperature was maintained at 40 °C, and all samples were stored at 4 °C during analysis. Mass spectrometric data were acquired using a Thermo UHPLC-Q Exactive Mass Spectrometer equipped with an electrospray ionization (ESI) source operating in either positive or negative ion mode. The optimal conditions were as follows^[155]13: heater temperature at 400°C, capillary temperature at 320 °C, sheath gas flow rate at 40 arb, aux gas flow rate at 10 arb, ion-spray voltage floating (ISVF) at −2800 V in negative mode, and 3500 V in positive mode, with normalized collision energy at 20–40–60 V rolling for MS/MS. MS/MS resolution was set at 17500, and the full MS resolution was 70000. Data acquisition was conducted in Data Dependent Acquisition (DDA) mode across a mass range of 70–1050 m/z. The data were analyzed through the free online majorbio cloud platform ([156]cloud.majorbio.com). The procedures for LC–MS analysis are detailed in the Supplementary Note [157]5. Seed enhancement experiment Metabolite selection and seed priming were conducted following the procedures detailed by Liu et al.^[158]6 and Li et al.^[159]27, respectively. The exogenous solutions comprised of the following compounds: Phosphatidylethanolamin (PE), phosphatidylcholine (PC), phosphorylcholine chloride (PCho), O-phosphorylethanolamine (PEth), choline (Cho), and ethanolamin (Eth). The exogenous solutions were prepared at concentrations of 0, 25, 50, 75, and 100 μmol/L. Seeds were soaked in metabolite solutions for 24 h at room temperature before sowing. The seeds were then subjected to T1S1, T1S2, T2S1, and T2S2 treatments, while the other treatments remained similar to those of previous experiments in this study. The metabolite application method is detailed in Supplementary Note [160]6. Statistics and reproducibility Detailed methodologies for statistical analyses employed in this study are described in the corresponding experimental sections and Supplementary information. The three primary analytical techniques—RNA sequencing, metabolomic profiling, and qPCR—were each performed using three independent biological replicates. The qRT-PCR primers utilized in this analysis were synthesized by China Sangon Biotech (Shanghai) Co., Ltd. (Supplementary Data [161]5). For statistical analysis, “Microsoft Excel 2021” software was utilized to input, organize, and analyze the experimental data to derive the membership function values. Graphical representations, including transcriptome and metabolome plots, were generated using Origin Pro 2023. A world map was created using Python 3.11.3 and Pyecharts version 2.0.3, with data sourced from Kumar et al.^[162]9 and Rao et al.^[163]28. A GO heatmap was generated through an online data analysis and visualization platform, accessed at [164]https://www.bioinformatics.com.cn on 20 Feb 2023. All experiments were conducted in a completely randomized design, comprising three replicates per treatment. The experimental setup and conditions were carefully controlled and monitored to minimize variability and ensure consistent results across different replicates. Reporting summary Further information on research design is available in the [165]Nature Portfolio Reporting Summary linked to this article. Supplementary information [166]Supplementary information^ (576.3KB, pdf) [167]42003_2024_6766_MOESM2_ESM.pdf^ (30.8KB, pdf) Description of Additional Supplementary File [168]Supplementary Data 1^ (56.6KB, xlsx) [169]Supplementary Data 2–5^ (413.7KB, xlsx) [170]Reporting Summary^ (71.7KB, pdf) Acknowledgements