Metabolite accumulation contributes to differences in seed germination of water-saving and drought-resistance rice under dry direct seeding

Abstract

Dry direct seeding of rice has emerged as an effective method for reducing the excessive water demand associated with conventional rice transplantation, presenting significant potential for enhancing sustainability. However, this cultivation method is hindered by high seed usage and often inconsistent and low seedling emergence. Seed priming, a pre-sowing treatment, has been employed to mitigate these issues, but the inconsistent effects of exogenous priming agents remain a concern. Currently, there is limited molecular-level information on the uneven seedling emergence and effective screening methods for priming agents. In this study, we employed a metabolomics approach using advanced chromatography and mass spectrometry technology to identify differential accumulation of metabolites (DAMs) in seeds with varying germination energies. The seed priming technique was also used to validate the identified DAMs. We investigated the proportion of different specific gravity seeds and the corresponding germination energy across 20 varieties and established a relationship between different specific gravity seeds and germination energy. Our results showed that seeds with high and low germination energy differed in several metabolites, including amino acids, organic acids, and others. We further confirmed the critical role of these DAMs in determining seed germination energy under dry direct seeding. This research provides valuable insights into the metabolic mechanisms associated with germination energy and offers a useful approach for screening effective endogenous seed priming agents.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12870-025-07405-w.

Introduction

Rice (Oryza sativa) is a major cereal crop and a staple food for nearly half of the global population. In China, a country facing significant water scarcity, meeting the water demands of conventional transplanted rice cultivation has long been a challenge [1]. This traditional cultivation method is labor-intensive, inefficient, and produces high greenhouse gas emissions, which pose long-term constraints on sustainable agricultural development [2, 3]. These challenges have led to the exploration of dry direct seeding rice cultivation as a promising alternative.

Dry direct seeding of rice involves sowing seeds directly into non-puddled soil, either manually or mechanically. This method is characterized by high efficiency, resource conservation, and reduced environmental impacts [4]. Dry direct seeding of rice is increasingly used in water-scarce areas across Asia, where it significantly improves water use efficiency and rice yield [5]. However, issues such as poor seedling emergence and uneven germination continue to hinder its large-scale application. Thus, the development of effective and practical technologies to enhance uniform germination and seedling emergence in dry direct seeding of rice is crucial.

Currently, seed treatment technologies are primarily categorized into two categories: seed coating and seed priming. Both approaches aim to enhance seed quality and performance through the application of suitable agents at optimal concentrations [6]. Seed coating agents, such as biochar and encapsulated microbial seed coating agents (ESCA), have been shown to significantly improve seed germination [7, 8]. Similarly, seed priming agents like wood vinegar and inorganic salts, have been documented to yield positive effects [9]. However, the inconsistent efficacy of these exogenous agents limits the advancement of seed treatment technologies, as priming effects vary with seed quality. For example, primed seeds treated with exogenous agents often demonstrate storage intolerance, a rapid decline in seed vigor, and reduced germination performance compared to non-primed seeds [10, 11]. Therefore, selecting appropriate treatment agents is a critical challenge that needs to be addressed.

Metabolomics, a branch of high-throughput functional genomics, enables the direct measurement of biochemical activity by monitoring substrates and products involved in cellular metabolism [12]. Despite its energy, metabolomics remains underutilized in seed research. For example, in a study on lentil seed coats, Elessawy et al. [13] demonstrated the significance of flavones in seed coat pattern formation. A comparative study of soybean seeds under varying temperatures, utilizing both metabolomics and proteomics approaches, revealed global changes in the proteome and metabolome, establishing a positive correlation between various metabolites and proteome alterations [14]. In this study, we investigated the germination performance of 20 rice varieties under dry direct seeding and identified seed specific gravity as a key factor affecting seed germination under dry direct seeding, Further confirmed the proportion of seeds with different specific gravity in each of the 20 varieties, identified differentially accumulating metabolites (DAMs) associated with uneven germination by metabolomics, and validated the correlation between different specific gravity seed germination and DAMs through seed priming. The findings of this study provide comprehensive insights into key metabolites associated with uneven germination, offering a solid foundation for further investigation into the underlying mechanisms and the development of effective seed-priming agents.

Materials and methods

This research was carried out at the Shanghai Agrobiological Gene Center (SAGC), Zhuang Hang Experimental Station, located in Fengxian District, Shanghai. Seed materials used in the study were purchased from Shanghai Tiangu Biotechnology Co, Ltd. (Table S1).

Classification of seeds

Classification of seeds was performed using saline solutions with densities of 1.1 g·mL-1 and 1.2 g·mL-1. (ⅰ) The dry weight of each seed sample measured and recorded as W. (ⅱ) seeds were placed in a 1.1 g·mL-1 saline solution and stirred for 2 min. The seeds that floated were removed, rinsed with water for 2 min, dried on absorbent paper, and weighed, with the weight recorded as W1 and the seed grade as T1. (ⅲ) The seeds that sank were transferred to a 1.2 g·mL-1 saline solution and stirred for 2 min. Floating seeds were similarly removed, rinsed, dried, and weighed, recorded as W2 and assigned the seed grade as T2. (ⅳ) The remaining seed were collected, rinsed, dried, and weighed, with the weight recorded as W3 and the seed grade as T3. (ⅴ) All seeds were grouped by grade, placed into labeled self-sealing bags, and stored at −80 ℃. The proportion of each seed grade was calculated using the following formula.

Dry direct seeding method

The experiments were conducted in a growth chamber (GLD-450E-4, Ningbo Ledian Instrument Manufacture Co., Ningbo, China) under a 12-hour photoperiod at 12,000 lx, and a day/night temperature regime of 30/25°C. For dry direct seeding rice treatments, 40 seeds were sown in plastic boxes (11.5 × 11.5 × 5 cm) filled with 400 g of soil at 40%–60% relative soil humidity. All treatments were arranged in a completely randomized design with three replicates, and the experiments were repeated three times to assess germination characteristics, and the formulae were calculated as follows.

Where N is the total number of tested seeds, Dt is the number of days to germination and Gt is the number of germinated seeds on the corresponding days.

Embryo-to-endosperm ratio measurement

A set of graded seeds was dehusked and weighed, and the mass was recorded as W. The embryo was then carefully removed with a blade, and the endosperm was weighed, recorded as W₁. The embryo mass was calculated as W–W₁, and the proportions of each component were subsequently determined.

Starch content measurement

0.1 g frozen seed sample was used for analysis. The sample was mixed with 10 mL of deionized water and incubated in a water bath at constant temperature for 30 min. After cooling and centrifugation, the supernatant was collected. This extraction was repeated three times, and the combined supernatants were adjusted to a defined volume for the determination of soluble sugars, sucrose, and glucose. The remaining residue was dried, resuspended in 2 mL of deionized water, and boiled in a water bath for 20 min. Subsequently, 2 mL of 9.2 mol/L HClO₄ was added with continuous stirring. After cooling, 6 mL of deionized water was added, and the mixture was centrifuged at 2000 r/min for 20 min. The resulting supernatant was collected. The residue was Further extracted with 2 mL of 4.6 mol/L HClO₄ using the same procedure, and the two supernatants were combined. The final volume was adjusted for the determination of soluble starch content. Starch concentration was quantified using the anthrone colorimetric method by measuring absorbance at 620 nm with a spectrophotometer. A standard curve was prepared to calculate starch content in each treatment.

Soluble protein content measurement

0.1 g of frozen seed sample was analyzed with 1 mL of 0.1 M phosphate buffer solution (pH 7.0). The sample was ground into a homogenate in a mortar placed on ice and transferred to a 10 mL centrifuge Tube. The mortar was rinsed with 5 mL of the same phosphate buffer, and the rinsate was combined with the homogenate. The extract was centrifuged at 4 °C and 10,000 r/min for 10 min, and the resulting supernatant was collected as the soluble protein extract. Protein concentration was determined using the Coomassie Brilliant Blue method. Briefly, the G250 dye solution was prepared by dissolving 0.1 g G250 in 50 mL of 90% ethanol, adding 100 mL of 85% phosphoric acid, and diluting the mixture to 1 L with deionized water. Absorbance at 595 nm was measured, and soluble protein content was quantified using a standard curve.

Sample Preparation

Samples were weighed before metabolite extraction, lyophilized, and ground in a 2 mL Eppendorf Tube containing a 5 mm Tungsten bead for 1 min at 65 Hz using a Grinding Mill. Metabolites were extracted with 1 mL of a precooled mixture of methanol, acetonitrile, and water (v/v/v, 2:2:1) and subjected to ultrasonic shaking for 1 h in an ice bath. The mixture was subsequently incubated at −20 °C for 1 h and centrifuged at 14,000 g for 20 min at 4 °C. The supernatant was collected and dried under vacuum. A 100 µL aliquot of the sample was mixed with 400 µL of cold methanol and acetonitrile (v/v, 1:1), vortexed, followed by sonication for 1 h in an ice bath. The mixture was again incubated at −20 °C for 1 h and centrifuged at 14,000 g for 20 min at 4 °C. The supernatant was collected and vacuum-dried for subsequent LC-MS analysis. To ensure data quality for metabolic profiling, quality control (QC) samples were prepared by pooling aliquots of all samples to represent the entire sample set and were used for data normalization. QC samples were prepared and analyzed using the same procedure as the experimental samples in each batch. Dried extracts were reconstituted in 50% acetonitrile, filtered through a 0.22 μm cellulose acetate filter, transferred into 2 mL HPLC vials, and stored at −80 °C until analysis.

Remove the sample from the − 80 °C freezer and grind in liquid nitrogen. Place the powder in an EP tube and add pre-chilled methanol-water (4:1, v/v). Mix thoroughly, incubate at −20 °C overnight, and then centrifuge at 16,000 g for 20 min at 4 °C. Remove the supernatant and evaporate to dryness in a high-speed vacuum centrifuge. For mass spectrometry analysis, dissolve the sample in 100 µL of pre-chilled methanol-water (1:1, v/v) and centrifuge at 20,000 g for 15 min at 4 °C. Amounts of the supernatant should be used for analysis.

UHPLC-MS/MS analysis

Metabolomics profiling was analyzed using a UPLC-ESI-Q-Orbitrap-MS system (UHPLC, Shimadzu Nexera X2 LC-30AD, Shimadzu, Japan) coupled with Q-Exactive Plus (Thermo Scientific, San Jose, USA).

In this study, an ACQUITY ultra-high-performance liquid chromatography system (Waters, Milford, MA, USA) was coupled with a QE high-resolution mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA). Chromatographic separation was performed using an ACQUITY UPLC BEH C18 column (100 mm × 2.1 mm, 1.7 μm) (Waters, USA). The mobile phases consisted of 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B). The gradient elution was as follows: 0–2 min, 5–20% B; 2–4 min, 20–25% B; 4–9 min, 25–60% B; 9–14 min, 60–100% B; 14–16 min, 100% B; 16–16.1 min, 100–5% B; 16.1–18.1 min, 5% B. The column temperature was maintained at 40 °C with a flow rate of 0.35 mL/min, and the injection volume was 5 µL.

The mass spectrometry scan parameters were set as follows: S-lens RF level, 50; mass range, 100 to 1200 m/z; Full MS resolution, 70,000; MS/MS resolution, 17,500; NCE/stepped NCE was set at 10, 20, and 40 eV. The ESI ion source parameters were set as follows: spray voltage, 3800 V; sheath gas flow rate, 40 for ESI + and 5 for ESI−; capillary temperature, 320 °C; probe heater temperature, 350 °C; aux gas flow rate, 10. During the mass spectrometry operation, one QC sample was inserted for every six formal samples. The QC samples were used to evaluate the stability of the mass spectrometry platform throughout the experimental process.

Data analysis

Raw data files were processed by Compound Discoverer™ 2.1 software for initial data processing, including peak detection, peak alignment and peak integration. Briefly, raw files were aligned with adaptive curve setting with 5 ppm mass tolerance and 0.4 min retention time shift. Unknown compounds were detected with a 5 ppm mass tolerance, 3 signal to noise ratio, 30% of relative intensity tolerance for isotope search, and 500,000 Minimum peak intensity, and then grouped with 5 ppm mass and 0.2 min retention time tolerances. A procedural blank sample was used for background substraction and noise removal during the pre-processing step. Peaks with less than a 3-fold increase, compared to blank samples, and those detected in less than 50% of QCs and where the relative standard deviation (%RSD) of the QCs was greater than 30% were removed from the list. Peak areas, across all samples, were subsequently normalized to the total area of the corresponding samples to balance their differences in intensities that may have arisen from instrument instability. Metabolites identified in the processed raw data of mass spectral peaks were searched against both ChemSpider™ chemical structure database (3 ppm mass tolerance) and mzCloud spectral library (precursor and fragment mass tolerance, 10 ppm). Four data sources were selected via the ChemSpider database: Human Metabolome Database (HMDB), Kyoto Encyclopaedia of Genes and Genomes (KEGG), LipidMAPS, Biocyc.

Multivariate statistical analysis

R (version 4.0.3) and associated R packages were used for all multivariate data analyses and modeling. Data were mean-centered using Pareto scaling. Models were developed using principal component analysis (PCA), orthogonal partial least-square discriminant analysis (OPLS-DA) and partial least-square discriminant analysis (PLS-DA). All the models evaluated were tested for overfitting using permutation tests. The descriptive performance of the models was determined using R2X (cumulative) (perfect model: R2X (cum) = 1) and R2Y (cumulative) (perfect model: R2Y (cum) = 1) values while their prediction performance was measured by Q2 (cumulative) (perfect model: Q2 (cum) = 1) and a permutation test (n = 200). The permuted model should not be able to predict classes: R2 and Q2 values at the Y-axis intercept must be lower than those of Q2 and the R2 of the non-permuted model. In OPLS-DA, discriminating metabolites were identified using the variable importance on projection (VIP) score. The VIP score value indicates the contribution of a variable to the discrimination between all the classes of samples. Mathematically, these scores are calculated for each variable as a weighted sum of squares of PLS weights. The mean VIP value is 1, and VIP values over 1 are typically considered significant. Higher VIP scores correlate with stronger discriminatory ability, providing a reliable basis for biomarker selection.

The discriminating metabolites were identified using a statistically significant threshold based on variable influence on projection (VIP) values from the OPLS-DA model and two-tailed Student’s t-test (p value) on the normalized raw data in univariate analysis. The p-value was calculated using one-way analysis of variance (ANOVA) for multiple group comparisons. Metabolites with VIP values greater than 1.0 and p-value below 0.05 were considered statistically significant. Fold change was calculated as the logarithm of the average mass response (area) ratio between two arbitrary classes. Additionally, the identified differential metabolites were used to perform cluster analyses with the R package.

KEGG enrichment analysis

To identify the perturbed biological pathways, metabolite data were analyzed using the KEGG database (http://www.kegg.jp) for pathway analysis. KEGG enrichment analyses were carried out with the Fisher’s exact test, and FDR correction for multiple testing was performed. Pathways with p-values below 0.05 were considered statistically significant.

Seed priming and culture method

Clean, healthy seeds were surface sterilized by soaking in a 1% (v/v) sodium hypochlorite (NaClO) solution for 15 min, followed by three rinses with distilled water. Experimental concentrations of proline (30 mmol·L⁻¹) and phenylalanine (30 mmol·L⁻¹) were selected based on the methodology described by Li et al. [15]. All the experimental concentration of priming agents controlled at 30 mmol·L⁻¹. The sterilized seeds were dried with blotting paper and then primed for 24 h in darkness at 25 °C in either distilled water (hydro-priming), proline solution, phenylalanine solution, serine solution or nicotinic acid solution. The ratio of seed weight to priming solution volume (w/v) was maintained at 1:5. Following the priming period, seeds were rinsed three times with distilled water, blotted dry, and kept at 25 °C, dark until they regained their original weight. The calculation formula of germination characteristics is the same as above.

Results

Seed specific gravity is an important factor influencing seed germination under dry direct seeding

Specific gravity is a key indicator of crop seed maturity and filling, representing the ratio of absolute seed mass to absolute seed volume. In most crops, more mature seeds are fuller, contain greater storage material, and exhibit enhanced germination and seedling morphogenesis [16]. As shown in Fig. 1, we first analyzed the distribution of seeds with different specific gravities in each tested variety. We categorized seed specific gravity into three levels—T3, T2, and T1—in descending order. The results revealed significant differences among varieties. For instance, in V7 (Hanyou 73), T1 seeds accounted for 23.61% of the total seed weight, while T3 seeds made up 41.96%. In contrast, in V14 (Jingliangyou 534), T1 seeds comprised only 4.18%, whereas T3 seeds constituted 92.16%.

Fig. 1.

The proportion of seeds with different specific gravity within 20 varieties

We Further evaluated the germination performance of seeds from 20 varieties with different specific gravities under dry direct seeding conditions. As shown in Table 1, germination performance varied significantly among seeds with different specific gravities. Specifically, as seed specific gravity decreased, germination energy (GE), germination percentage (GP), and germination index (GI) declined significantly, while mean germination time (MGT) remained relatively stable. Based on the decreasing trend of GE, the 20 varieties were classified into three categories:

Table 1.

Seed germination performance of different varieties with different special gravity under dry direct seeding

6Variety	Specific Gravity	GE/%	GP/%	GI	MGT/d
V1	T1	81.33±3.06b	87.33±4.16b	49.39±1.86b	3.30±0.06a
	T2	96.67±2.31a	98.00±2.00a	57.51±0.26a	3.14±0.06a
	T3	96.67±4.16a	98.00±3.46a	58.06±1.51a	3.10±0.03a
V2	T1	68.67±2.31b	74.00±4.00b	40.36±2.57b	3.41±0.23a
	T2	86.67±6.11a	89.33±2.31a	50.78±1.86a	3.25±0.10a
	T3	86.67±6.43a	90.67±3.06a	50.31±1.73a	3.34±0.02a
V3	T1	74.00±2.00b	78.67±3.06b	44.37±2.20c	3.31±0.08a
	T2	84.67±1.15b	88.00±2.00b	50.98±0.69b	3.19±0.13a
	T3	97.33±1.15a	98.00±0.00a	58.48±0.34a	3.07±0.02a
V4	T1	62.00±4.00b	72.67±2.31c	36.50±2.60c	3.72±0.30a
	T2	80.67±7.57a	84.67±7.57b	46.14±4.24b	3.40±0.17ab
	T3	91.33±6.11a	97.33±1.15a	53.51±3.03a	3.38±0.22b
V5	T1	52.00±8.00b	61.33±7.57b	31.96±5.46b	3.61±0.33a
	T2	54.67±6.43b	66.67±6.43b	33.82±2.92b	3.68±0.02a
	T3	78.67±7.02a	88.67±2.31a	44.92±2.34a	3.67±0.16a
V6	T1	79.33±3.06b	84.00±2.00b	44.11±1.16b	3.52±0.17a
	T2	91.33±3.06a	92.67±4.16ab	51.04±2.34a	3.35±0.10a
	T3	90.67±1.15a	94.00±2.00a	49.98±4.23ab	3.48±0.21a
V7	T1	67.50±2.50b	93.33±5.20b	31.99±0.88b	4.17±0.09a
	T2	97.50±2.50a	99.17±1.44a	39.48±0.23a	3.67±0.03b
	T3	98.33±1.44a	100.00±0.00a	41.93±1.00a	3.52±0.08b
V8	T1	46.67±8.08c	71.33±10.07b	30.94±4.57b	4.18±0.07a
	T2	80.00±6.00b	89.33±3.06a	44.93±4.02a	3.68±0.18b
	T3	92.00±0.00a	96.00±2.00a	47.96±3.04a	3.67±0.18b
V9	T1	42.67±7.02c	59.33±8.33b	25.38±3.49c	4.17±0.21a
	T2	66.00±5.29b	82.67±5.77a	37.28±2.91b	4.01±0.09a
	T3	88.67±1.15a	90.00±0.00a	48.68±1.74a	3.41±0.12b
V10	T1	67.33±6.43c	81.33±3.06b	38.07±1.02b	3.90±0.17a
	T2	79.33±5.03b	94.67±1.15a	43.77±3.99ab	3.94±0.29a
	T3	91.33±6.43a	98.67±1.15a	45.96±3.91a	3.88±0.26a
V11	T1	69.33±6.11b	78.67±4.16b	37.72±3.21b	3.82±0.12a
	T2	88.67±5.03a	93.33±5.03a	47.27±4.61a	3.64±0.13a
	T3	95.33±1.15a	97.33±1.15a	50.24±3.39a	3.56±0.24a
V12	T1	75.33±12.06b	81.33±8.08b	41.35±4.88b	3.64±0.11a
	T2	89.33±8.33a	92.67±6.11a	47.34±5.25ab	3.60±0.17a
	T3	92.67±3.06a	97.33±1.15a	50.33±2.30a	3.57±0.16a
V13	T1	34.67±2.31c	40.67±1.15c	19.17±1.97c	3.90±0.31a
	T2	56.67±2.31b	62.00±4.00b	31.96±0.86b	3.62±0.14ab
	T3	86.00±4.00a	86.67±3.06a	48.51±1.56a	3.30±0.08b
V14	T1	50.67±6.43b	50.67±3.06b	27.32±1.37b	3.43±0.16a
	T2	86.00±2.00a	88.00±3.46a	50.22±0.45a	3.23±0.12a
	T3	94.00±3.46a	96.00±2.00a	55.87±2.14a	3.17±0.09a
V15	T1	44.00±2.00b	70.00±3.46b	28.35±2.51b	4.52±0.16a
	T2	88.67±5.77a	93.33±3.06a	49.68±0.64a	3.48±0.10b
	T3	94.00±2.00a	98.67±1.15a	53.61±0.50a	3.40±0.07b
V16	T1	59.33±6.11c	66.67±9.87c	34.69±4.08c	3.57±0.17a
	T2	73.33±8.33b	78.67±6.11b	41.99±3.35b	3.48±0.04a
	T3	92.00±2.00a	93.33±1.15a	51.16±2.62a	3.37±0.18a
V17	T1	47.33±2.31b	63.33±6.11b	28.02±2.26b	4.10±0.15a
	T2	82.00±0.00a	90.00±2.00a	46.28±2.93a	3.60±0.23b
	T3	88.00±2.00a	94.67±5.77a	48.41±2.26a	3.60±0.20b
V18	T1	59.33±6.11c	79.33±10.26b	36.71±3.46b	3.96±0.13a
	T2	73.33±8.33b	95.33±3.06a	45.83±0.95a	3.84±0.16a
	T3	92.00±2.00a	96.00±2.00a	50.93±0.86a	3.49±0.04b
V19	T1	44.67±1.15b	85.33±2.31a	39.46±0.05b	4.01±0.09a
	T2	88.67±5.77a	93.33±3.06a	47.79±1.45a	3.60±0.05b
	T3	94.00±2.00a	91.33±2.31a	47.93±2.08a	3.51±0.06b
V20	T1	47.33±2.31b	79.33±7.57b	37.09±1.69b	3.96±0.19a
	T2	82.00±0.00a	96.00±5.29a	50.55±3.45a	3.54±0.11b
	T3	88.00±2.00a	99.33±1.15a	55.18±0.44a	3.35±0.03b

"abc" indicates the statistically significant difference within the group (p<0.05)

GE Germination energy, GP Germination percentage, GI Germination index, MGT Mean germination time

1. Minimal impact between T2 and T3, significant reduction at T1: In 12 varieties (V1, V2, V4, V6, V7, V11, V12, V14, V15, V17, V19, and V20), GE decreased significantly when seed specific gravity dropped to T1, but no significant difference was observed between T2 and T3. Compared to T2 and T3 seeds, T1 seeds exhibited a significant reduction in average GP (18.76%), GE (31.96%), and GI (26.35%).

2. Significant reduction at T2, no further reduction at T1: In two varieties (V3 and V5), GE decreased significantly when seed specific gravity dropped to T2 compared to T3, but no significant difference was observed between T1 and T2. The average GE, GGP, and GGI of T1 and T2 seeds were significantly lower than those of T3 seeds, by 24.62%, 11.54%, and 22.08%, respectively.

3. Continuous significant reduction across all levels: In six varieties (V8, V9, V10, V13, V16, and V18), GE decreased significantly with each reduction in seed specific gravity. Compared to T3 seeds, T2 seeds exhibited significant reductions in average GP (11.54%), GE (24.62%), and GI (22.08%). Furthermore, compared to T2 seeds, T1 seeds showed additional reductions in average GP (20.70%), GE (27.67%), and GI (24.73%).

The between-subjects effects analysis (Table S2) revealed that variety, specific gravity, and their interaction (variety × specific gravity) significantly affected rice seed germination under dry direct seeding. Among these factors, specific gravity had the strongest effect. In addition, the embryo-to-endosperm ratio, starch content, and soluble protein content were measured in dry seeds of variety V7. As shown in Figure S1, no significant differences were detected in the embryo-to-endosperm ratio or starch content among seeds of different specific gravities. However, T1 seeds exhibited significantly lower soluble protein content than T2 and T3 seeds.

In summary, this study demonstrated that seed specific gravity is the primary factor influencing germination performance under dry direct seeding. However, seed specific gravity is not determined solely by the nutrients stored within the seed, and further investigation is warranted.

Metabolomics analysis of T2 seeds in HY73 and V16

Our findings indicated that seed germination energy under dry direct seeding significantly influences germination rate, germination index, and mean germination time. To identify the key factors affecting germination energy under these conditions, we conducted a metabolomics analysis using T2 seeds from two varieties, V7 (Hanyou 73, HY73) and V16 (Jingliangyou Huanglizhan). These varieties were selected because their T2 seeds exhibited significant differences in germination energy under dry direct seeding, whereas no such differences were observed in T1 and T3 seeds (Fig. 2a).

Fig. 2.

Metabolomics analysis. (a) Comparison of seed germination energy of HY73 and V16; (b) Morphological differences between HY73 and V16 seeds of differential specific gravity; (c) T2 seed principal component analysis of HY73 and V16. PCA score plot showing sample distribution. The blue square represents HY73, and the red triangle represents V16.; (d) Heatmap of metabolomic profile clustering of T2 seeds of HY73 and V16, where columns represent samples and rows correspond to metabolites. Red indicates higher metabolite abundance, while blue represents lower abundance; (e) Volcano plot of differential accumulated metabolites, red indicates up-accumulated metabolites, while blue represents down-accumulated metabolites; (f) Heatmap of 32 metabolites accumulated within T2 seeds of HY73; (g) Heatmap of 32 metabolites accumulated within T2 seeds of V16. “***” indicates the statistically significant difference (p < 0.001) and “ns” indicates no significant difference

Differences in seed specific gravity are often reflected in variations in seed size, particularly in cereal crops, where seed size is a key factor influencing seed quality [17]. Similarly, the specific gravity phenotypes of the two selected varieties exhibited significant differences, with low-specific-gravity seeds being markedly smaller in size compared to high-specific-gravity seeds (Fig. 2b).

To gain a more detailed understanding of metabolite variation across different seed specific gravities and varieties, primary metabolites were identified using UPLC-MS-based extensively targeted metabolomic techniques. Principal component analysis (PCA) was then conducted on all samples to evaluate overall metabolic differences and assess data quality.

As demonstrated in Fig. 2c, the PCA plot illustrates the distribution of the first two principal components (PC1 and PC2). The close clustering of samples in the central region indicates high instrument stability and data reproducibility throughout the experiment. Further analysis revealed a clear separation along PC1 between the HY73 and V16 groups, highlighting significant differences in their metabolic profiles. Additionally, PC2 captured variability within the groups, potentially reflecting individual differences or minor deviations during sample processing. The PCA results confirm the metabolic effects of the experimental treatments and provide a robust dataset for subsequent screening and analysis of differentially accumulated metabolites (DAMs). These findings further validate the stability and reproducibility of the metabolomics analysis while emphasizing the distinct metabolite profiles of the two varieties.

A total of 5,461 metabolites were identified through both qualitative and quantitative analyses. This study aimed to uncover metabolite expression patterns and their potential functional associations across different varieties. Hierarchical clustering analysis, based on standardized metabolomics counts, was performed, and a metabolite-sample bi-directional clustering heat map was generated (Fig. 2d). The expression patterns of these metabolites across all 12 samples are represented using a color gradient from blue (−4.00, indicating low accumulation) to orange (3.00, indicating high accumulation), allowing for a clear visualization of relative metabolite concentration trends. The sample clustering tree revealed that all samples grouped into two main clusters corresponding to HY73 and V16, indicating significant metabolic profile heterogeneity between these groups.

Significant differentially accumulated metabolites within the T2 seeds of HY73 and V16

Among the 5,461 metabolites examined, 243 were explicitly identified. To further investigate significant differential metabolite expression, a combination of univariate and multivariate approaches was applied, incorporating Fold Change (FC), Variable Importance in Projection (VIP), and significance analysis (p-value). The final selection included 134 differentially accumulated metabolites (DAMs), identified based on FC > 1.2 or FC < 0.8 and p-value < 0.05. This set comprised 70 up-accumulated and 64 down-accumulated DAMs, as illustrated in Fig. 2e. Additionally, 64 DAMs were further identified using the criterion of VIP > 1 (Fig. 2f and g), with 32 enriched in the T2 seeds of HY73 and 32 in the T2 seeds of V16. In HY73 T2 seeds, lipid metabolism-related molecules-including γ-linolenic acid ethyl ester, 1-linoleoyl glycerol, and erucamide-were highly abundant. Additionally, amino acids and their derivatives (e.g., D-proline), peptides (e.g., leucyl-proline and Arg-Phe-Arg), and other metabolites such as choline and nicotinic acid were prominently identified (Fig. 2f). Conversely, in V16 T2 seeds, enriched metabolites included amino acids and their derivatives (e.g., L-phenylalanine, D-serine), peptides (e.g., Asn-Glu-Asn-Asn, Ala-Asn-Lys), carbohydrates and their derivatives (e.g., D-glucose-6-phosphate, (R)-malate), as well as fatty acids and their derivatives (e.g., palmitoleic acid, α-eleostearic acid) (Fig. 2g).

In summary, the metabolite profiles of T2 seeds from HY73 and V16, which exhibited significant differences in germination energy under dry direct seeding, also showed substantial variation. Additionally, the classes of enriched metabolites differed markedly between the two varieties. However, the relationship between seed germination energy and differentially accumulated metabolites requires further validation.

Seed priming confirms the indispensable role of differentially accumulated metabolites in seed germination

To explore the relationship between metabolomic differences and variations in seed germination energy under dry direct seeding, four differentially accumulated metabolites (DAMs)-D-proline (Pro), L-phenylalanine (Phe), D-serine (Ser), and nicotinic acid (Nic)-were used for seed priming treatment of T1 seeds from HY73. Their germination performance under dry direct seeding was then assessed.

The results indicated no significant difference in germination percentage (GP) between the control and treated groups of T2 and T3 seeds. However, the GP of treated T1 seeds showed significant changes compared to the control (CK) and hydropriming (HP) groups (Fig. 3a). Specifically, In T1 seeds, Pro-priming did not result in a significant difference compared to CK. In contrast, Phe-priming led to a significant 9.81% decrease in GP, though no significant difference was observed compared to HP. Similarly, Nic- and Ser-priming resulted in comparable reductions in GP, with significant decreases of 13.39% and 10.71%, respectively.

Fig. 3.

The germination performance of HY73 seeds with different specific gravity after seed priming under dry direct seeding (a) Germination percentage; (b) Germination energy; (c) Germination index; (d) Mean germination time. “abc” indicates the statistically significant difference (ANOVA, p < 0.05)

Regarding germination energy (GE), similar to the trends observed in germination percentage, no statistically significant differences were found between the control and treated groups of T2 and T3 seeds (Fig. 3b). However, in T1 seeds, compared to CK, Pro-priming resulted in a notable 19.75% increase in GE, while Phe-priming led to a significant 12.34% decrease. In contrast, Nic- and Ser-priming showed no significant differences in GE.

The germination index (GI) was affected by Pro-, Phe-, Nic-, and Ser-priming across all three seed specific gravity groups (Fig. 3c). In T3 seeds, Pro-, Nic-, and Ser-priming resulted in a slight improvement in GI compared to CK and HP. In T2 seeds, GI significantly increased by 12.08%, 11.63%, and 13.48% for Pro-, Nic-, and Ser-priming, respectively, compared to CK. In T1 seeds, only Pro-priming led to a significant 14.60% increase in GI, whereas Nic-priming caused a significant 8.63% decrease. No significant changes were observed in the GI of Phe- and Ser-priming seeds.

Consistent with the germination index (GI), the effect of priming on the mean germination time (MGT) varied across seed specific gravity groups (Fig. 3d). While Nic- and Ser-priming accelerated germination in T2 and T3 seeds, they had no effect on T1 seeds. Conversely, Pro-priming significantly promoted germination across all seed specific gravity groups, whereas Phe-priming had no effect on MGT in any group.

Discussion

Differences in metabolism influence the germination performance of rice seeds under dry direct seeding

The growing global population has led to a rising demand for food production and water resources. Additionally, the increase in droughts due to global warming underscores the need to enhance crop cultivation methods. Dry direct seeding of rice presents significant potential as an innovative and simplified cultivation method. However, challenges such as high seed requirements, low germination rates, and uneven seedling emergence significantly hinder its development and application [18]. Understanding the molecular mechanisms of rice seed germination at the metabolomic level is crucial not only for advancing our understanding of rice biology but also for improving rice cultivation practices. Previous studies have demonstrated a highly conserved metabolic regulation during rice seed development [19]. In this study, we employed an established non-targeted metabolomics platform to investigate the differences in metabolite accumulation between T2 seeds of two rice varieties with varying germination energy and observed different patterns of metabolite accumulation.

This study found that T2 seeds of HY73 (Hanyou 73) exhibited superior germination performance under dry direct seeding compared to T2 seeds of V16 (Jingliangyou Huanglizhan). Additionally, HY73 seeds showed higher accumulation of γ-linolenic acid ethyl ester, 1-linoleoyl glycerol, erucamide, D-proline, choline, and nicotinic acid, whereas T2 seeds of V16 contained greater levels of L-phenylalanine, D-serine, D-glucose-6-phosphate, (R)-malate, palmitoleic acid, and α-eleostearic acid.

The presence of metabolites associated with lipid metabolism suggests increased lipid synthesis and energy storage during seed maturation. Lipids undergo decomposition through β-oxidation, producing acetyl coenzyme A, which then enters the TCA cycle to generate ATP, the primary energy source for cellular processes. Enhanced lipid metabolism may accelerate energy release, promoting radicle emergence and subsequent seed germination. Additionally, unsaturated fatty acids play a crucial role in synthesizing cell membrane phospholipids, maintaining membrane integrity during germination, and facilitating water and nutrient uptake. The OsMTACP2 gene, which encodes a mitochondria-localized acyl carrier protein, has been shown to indirectly influence reproductive growth following seed germination [20]. Furthermore, the OsLOX1 gene regulates antioxidant enzyme activity, reducing H₂O₂ and MDA accumulation, thereby preserving seed vigor and enhancing drought resistance [21]. Phytohormone signaling is closely related to the regulatory network of lipid metabolism genes. In particular, bZIP family proteins respond to abscisic acid signaling by inhibiting the expression of lipolysis-related genes, thereby delaying germination to enhance stress adaptation [22]. Notably, the lipid metabolites enriched in the T2 seeds of HY73 and V16 differed, with γ-linolenic acid ethyl ester predominantly enriched in HY73 and palmitoleic acid in V16. Both γ-linolenic acid ethyl ester and palmitoleic acid play crucial roles in the phospholipid metabolism pathway of cell membranes. This pathway enhances membrane fluidity and stability, aiding in the restoration of membrane integrity following mechanical stress during seed imbibition or germination [23, 24]. These differences may be attributed to genetic variations in the lipid metabolism pathways of the two varieties during seed maturation.

Possible reasons for the influence of proline and phenylalanine on the germination energy of rice seeds under dry direct seeding

In this study, we found that most of the metabolites differentially accumulated in the T2 seeds of the two varieties belonged to amino acids and their derivatives (Fig. 2f and g). It has been reported that the accumulation of amino acids in rice seeds changes significantly during development [19]. We hypothesized that the differences in amino acid metabolism play a pivotal role in influencing the seed germination energy under dry direct seeding. Consequently, we selected three amino acid substances among the differential metabolites, proline, phenylalanine, and serine, as well as a non-amino acid substance, niacin, for exogenous validation of seed priming. We concluded that proline significantly increased the germination energy of seeds under dry direct seeding, whereas phenylalanine acted in the opposite direction, and the remaining two did not have any significant effect (Fig. 3).

The processes of water uptake, starch metabolism and hypocotyl elongation are all essential for the proper progression of rice seed germination under dry direct seeding (Fig. 4a). Although the role of phenylalanine in stress response is less well-documented, the phenylalanine ammonia-lyase (PAL) pathway has been extensively studied. In this pathway, Oryza sativa phenylalanine ammonia-lyase (OsPAL) catalyzes the conversion of phenylalanine to cinnamate (CA), while abnormal inflorescence meristem 1 (OsAIM1) facilitates the β-oxidative conversion of CA to benzoate (BA), which is subsequently converted to salicylic acid (SA) [25]. In addition, phenylalanine is also a key intermediate in lignin and flavonoid synthesis [26] (Fig. 4b). During the conversion of phenylalanine to salicylic acid (SA), NAD(P)H acts as a hydrogen donor, leading to excessive consumption of H⁺. H⁺ are crucial for the electron transport chain, which may explain the inhibitory effect of phenylalanine on seed germination. Furthermore, phenylalanine accumulation can promote lignin production, which might result in cell wall thickening and reduced water uptake (Fig. 4d). It has been reported that ROS-induced damage to phenylalanine reduces animal cell viability. Reactive oxygen species (ROS) are critical for the seed-to-seedling transition during germination and early seedling development; therefore, such damage may have similar effects on plant cells [27–30].

Fig. 4.

Mechanistic hypothesis of the influence of proline and phenylalanine on the germination energy of rice seeds under dry direct seeding. (a) Illustration of rice germination under dry direct seeding; (b & c) Phenylalanine and proline metabolic pathways, https://www.kegg.jp/; (d) The mechanistic hypothesis of the influence of proline and phenylalanine on the germination energy of rice seeds under dry direct seeding. Normal arrows indicate promotion, while horizontal arrows indicate inhibition

Proline, known to accumulate at high levels in many plants under stress conditions, is considered a potential signal in stress response pathways [31]. The synthesis, accumulation, and metabolism of proline are regulated by both biotic and abiotic stresses and are influenced by intracellular proline concentration [32]. Asparagine, abundant in rice grains, is transported from the roots via the xylem and from the leaves via the phloem, where it is rapidly converted into glutamate and other amino acids necessary for seed development and germination [19]. Asparagine is metabolized into 2-oxoglutarate through the TCA cycle, which subsequently converts into glutamate, a critical intermediate in proline synthesis (https://www.kegg.jp/) (Fig. 4c). The proline biosynthesis pathway is Linked to the TCA cycle, suggesting that the positive effects of proline may be mediated by influencing this cycle, which increased proline during early germination allows more 2-oxoglutarate to enter the cycle, supporting production of substances such as NADH and enhancing energy production through the electron transport chain. The glutamate-to-proline pathway is reversible, increased proline levels may enhance its conversion to glutamate, thereby generating more NADPH, NADH, H+, and ATP, which collectively promote germination. Recent studies have demonstrated that proline can be catalyzed into glutamate by proline dehydrogenase, thereby facilitating the release of energy that is then utilized in germination (Ding and Wang, 2023). Furthermore, we propose that the role of proline in seed germination energy may be attributed to its unique physicochemical properties. As the most water-soluble amino acid, proline stabilizes osmotic potential and preserves seed cell morphology, positively influencing water uptake during germination (Fig. 4d). Overall, proline appeared to positively influence seed germination by regulating osmotic potential, thereby promoting water uptake and enhancing energy production through its metabolic pathway. In contrast, phenylalanine was found to inhibit seed germination, as its metabolic pathway maybe consumes energy and produces compounds that thicken the cell wall, restricting water uptake. However, these hypotheses were not confirmed in this study, emphasizing the need for further investigation.

Notably, seed priming, a pre-sowing treatment method, has garnered significant attention in recent years for its potential to enhance seed germination and early seedling growth. Nevertheless, the screening process for effective priming agents has hindered the advancement of this technology, and underscoring the necessity to standardize effective priming techniques for diverse crop types to ensure sustainable mitigation of abiotic stress. Although proline has been extensively studied and used for seed priming, the findings of this study suggest that it is feasible to screen for seed priming agents to improve seedling emergence uniformity using metabolomics, specifically by focusing on endogenous seed metabolites.

In conclusion, this study investigated the differentially accumulated metabolites (DAMs) in T2 seeds of two varieties with distinct germination energies, offering new insights into the complex mechanisms underlying seed germination. The observed increases in accumulation of amino acids, carbohydrates, fatty acids, and other compounds indicate a correlation between DAMs and enhanced germination energy in T2 seeds of the HY73 variety. The significant improvement in germination energy of seeds primed with DAMs indicated that these metabolites play crucial roles in regulating germination. Furthermore, these results highlight the feasibility of identifying endogenous priming agents through metabolomics profiling and propose a novel strategy for enhancing and standardizing effective priming techniques.

Authos’ contributions

YF. F.: Investigation, Data curation, Writing-original draft, Discussions. GJ. Z.: Writing-review & editing, Discussions. L. M.: Writing-review & editing. JC. L.: Investigation. DP. H.: Investigation. LK. Z.: Investigation. Z. B.: Methodology. QY. B.: Methodology. JS. T.: Methodology. XQ. Y.: Supervision. JG. B.: Investigation, Conceptualization, Funding acquisition, Writing-review & editing. LJ. L.: Supervision, Funding acquisition, Project administration. All authors reviewed the manuscript.

Funding

This research was supported by the Technology System of Rice Industry in Shanghai:

Grant NO. 2023(03) and Dual Carbon Project: Grant NO. 2024(04).

Data availability

Not applicable.

Declarations

Ethics approval and consent to participate

All methods were performed in accordance with the relevant guidelines and.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.