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Article

Seed Priming with Poly-Gamma-Glutamic Acid (γ-PGA) Improves Rice Germination Performance under Drought Conditions

by
Conrado Dueñas, Jr.
1,
Cinzia Calvio
1,
Inez Hortense Slamet-Loedin
2,
Untung Susanto
3 and
Anca Macovei
1,*
1
Department of Biology and Biotechnology ‘L. Spallanzani’, University of Pavia, 27100 Pavia, Italy
2
Trait and Genome Engineering Cluster, Rice Breeding Innovations, International Rice Research Institute, DAPO Box 7777, Metro Manila, Manila 1277, Philippines
3
Research Center for Food Crops of the National Research and Innovation Agency, KST Soekarno, Cibinong Science Center, KM 49 Jakarta—Bogor, Cibinong, Boger 16911, West Java, Indonesia
*
Author to whom correspondence should be addressed.
Agriculture 2024, 14(6), 926; https://doi.org/10.3390/agriculture14060926
Submission received: 22 April 2024 / Revised: 6 June 2024 / Accepted: 10 June 2024 / Published: 12 June 2024

Abstract

:
Drought poses a significant threat to global food security, particularly impacting rice cultivation during the germination stage. In this study, a soil-based system that utilizes soil moisture content was used to simulate optimal and stress conditions to assess the effect of the specific seed priming protocols on germination. Eleven rice varieties, representative of indica and japonica subspecies, grown in different ecosystems and having diverse nutrient contents, were treated with water or solutions of either poly-gamma-glutamic acid (γ-PGA) or denatured γ-PGA. Collected data regarding germinability and stress indices revealed different drought sensitivity between japonica and indica subspecies and genotype-specific responses to priming. Particularly, γ-PGA improved germination of highly susceptible indica varieties whereas water soaking was more effective for the moderately sensitive japonica varieties. Integrative analyses evidenced differences between biofortified and non-biofortified rice under γ-PGA treatment, suggesting a possible correlation between γ-PGA efficacy and Zn/Fe seed content. These findings underline that priming strategies should be tailored based on genotype and therefore this factor should be always taken under consideration for future works. The current study provides relevant information for optimizing seed priming techniques to sustain the development of drought-resilient crops as a sustainable strategy to address agricultural resilience and safeguard food security amidst environmental challenges.

1. Introduction

Poly-gamma-glutamic acid (γ-PGA) is a natural biopolymer that can be primarily synthesized by Bacillus species [1]. It is formed of repetitive glutamic acid monomers linked by ɣ-glutamyl bonds which confer resistance to proteases [2]. Noteworthy attributes of γ-PGA include non-toxicity, biodegradability, high biocompatibility, and water solubility, which underpin its extensive use across medical, food, cosmetic, and agricultural sectors [3]. When used as a soil conditioner, γ-PGA enhances soil aggregation, mitigates compaction, and consequently improves aeration and water infiltration [4]. Moreover, the application of γ-PGA in soil fosters the colonization of growth-promoting bacteria in plant roots, thereby stabilizing soil pH and facilitating the bioremediation of heavy metals [5]. This biopolymer also enhances root nutrient uptake, particularly nitrogen (N), phosphorus (P), and potassium (K) [6]. A study in which spores from a Bacillus subtilis strain that produces γ-PGA were used as soil fertilizer reported enhanced soil fertility and improved biocontrol against pathogenic fungi [7]. The hygroscopic and water-retaining properties inherent in the chemical structure of γ-PGA [4,8] can also contribute to developing drought-resilient crop solutions.
Drought stress stands as a big obstacle in the pursuit of global food security, exacerbated by the prevailing climate change crisis [9]. Environmentally, drought is characterized by prolonged periods of limited rainfall with elevated evaporation rates, resulting in diminished soil moisture content [10]. This hampers plant growth by reducing photosynthetic capacity, altering leaf biochemistry, and impairing reproductive functions, alongside inhibiting microbial activity in the soil [11,12]. Consequently, these factors compromise yield in terms of quantity and quality, further diminishing the economic value [13]. Additionally, the presence of drought stress increases the susceptibility to pests and diseases [14], exacerbating the detrimental impact on crop productivity, particularly in high-value crops like rice. All these consequences call for effective strategies to mitigate the adverse effects of drought stress on agricultural systems to safeguard global food security.
Rice (Oryza sativa L.) is a high-value agricultural product since it ranks as the third most cultivated crop worldwide [15]. But despite being cultivated in diverse growing conditions, rice is notably susceptible to drought, particularly during germination and reproductive stages, due to its semi-aquatic nature. While extensive research regarding drought effects during the reproductive phase has been carried out due to its direct impact on yield [16,17,18,19], studies focusing on its impact on germination remain limited. Germination is a pivotal process for plants, marking the transition of seeds from a quiescent to a metabolic active state, which culminates in radicle emergence and subsequent seedling growth [20,21]. At a cellular level, drought stress during germination induces a reduction in turgor pressure and cell wall plasticity [22]. Cell wall plasticity is crucial during germination as it facilitates processes such as water uptake, respiration, and nutrient mobilization to support seedling growth [23]. Furthermore, drought stress disrupts endogenous hormonal balance and triggers excessive accumulation of reactive oxygen species (ROS), surpassing optimal thresholds, thereby impairing membrane transport, and reducing ATP production [24,25]. To alleviate the adverse impacts of drought on the agronomic performance of high-value crops like rice, it is imperative to use multiple adaptive measures, including the cultivation of drought-resilient varieties, precision irrigation techniques, and seed treatments.
Drought stress during germination can be effectively managed through different seed treatments denominated as seed priming. These are recognized as sustainable and user-friendly pre-sowing techniques dedicated to enhancing seed germination even under suboptimal or stress conditions [26,27]. The process involves a controlled imbibition that allows seeds to progress to the pre-germinative phase [28,29]. In rice cultivation, various priming methods have been employed to alleviate the impact of drought; these include hydropriming [30], chemopriming with hormones [31], or the use of other plant growth regulators [32]. A recent study by Dueñas et al. [33] demonstrated that seed treatments with γ-PGA, denatured PGA, and iron pulsing were able to enhance rice germination performance when exposed to polyethylene glycol (PEG)-induced osmotic stress.
Despite recent advances, research investigating the effects of seed treatments on rice germination performance under varying soil conditions is still limited due to the complexity of the soil characteristics and varying moisture contents. Many studies focusing on rice germination utilize PEG as a technique to induce water stress in experimental settings [33,34,35]. Although PEG screening serves as a valuable method for simulating water scarcity, its ecological relevance is limited due to the complex interactions of plant-soil systems, soil structure, and microbial activity [36]. A controlled system where rice germination can be studied under soil drought conditions is still lacking.
Based on the above premises, the aim of this study includes the development of a soil-based drought system using varying moisture contents to simulate drought during germination. Subsequently, different seed priming protocols are proposed to improve germination during drought and test the soil system. For seed priming, water (WS), γ-PGA (PGA), and denatured PGA (dPGA) solutions were used. To address the genotype specificity of most seed priming protocols [29,33], we used 11 rice varieties with different grain mineral contents, belonging to japonica and indica subgroups, representative of lowland and upland ecosystems. Biofortified rice lines with enhanced zinc (Zn) and iron (Fe) grain content, developed through conventional breeding [37,38], transgenic approaches [39], and genome editing (GEd) [40], were also investigated due to putative alterations in the germination behavior [41].

2. Materials and Methods

2.1. Seed Materials

A total of 11 rice genotypes were used in this study. Five japonica subspecies (Apollo, Carnaroli, Cerere, Lomello, and Unico), recognized as economically important local Italian varieties, were provided by the Sardo Piemontese Sementi (SA.PI.SE, https://sapise.it/). The Lomello variety (japonica subspecies) was obtained from the plant germplasm collection of the University of Pavia Botanical Garden (https://terraeambiente.dip.unipv.it/it/dipartimento/risorse/banca-del-germoplasma-vegetale). Indica subspecies, representative of the irrigated lowland system, include the IR64 mega-variety along with several biofortified lines developed using conventional breeding, transgenic, or GEd approaches. The high Zn lines, NSIC Rc460 (abbreviated as Rc460) and Inpari IR NutriZinc (abbreviated as Inpari), were developed and provided by IRRI (International Rice Research Institute) and IAARD (Indonesian Agency for Agricultural Research and Development), respectively. The high Fe/Zn transgenic lines, IRS493-112 (abbreviated as IRS493) and IRS495-301 (abbreviated as IRS495), were derived from the confined field trial study conducted by Trijatmiko et al. [39], while CRISPR-Cas9 was used to develop the IRS1421-021 (abbreviated as IRS1421) line characterized by elevated Zn concentration in the grains [40]. The engineered lines were produced in the IR64 background through Agrobacterium-mediated transformation as previously reported and characterized [39,40].

2.2. Seed Priming Treatments

The extraction of γ-PGA and the subsequent generation of dPGA were conducted consistently with the methodology previously reported [33]. Figure 1 depicts a schematic illustration of the experimental design. For seed priming, a total of 30 seeds were placed into an open-mouth flat tube containing 3 mL of a 1 g L−1 solution of either γ-PGA or dPGA. The seeds were allowed to undergo a 16 h soaking period at ambient temperature before sowing. To differentiate the impact of PGA and dPGA from that of water solution, a water soaking (WS) treatment, consisting in immerging the seeds in 3 mL of H2O, was incorporated into the experimental design. This was performed also because WS has been traditionally employed as a pre-sowing treatment in rice cultivation [42]. The developed seed priming protocols are specifically intended to facilitate direct planting without the need for dry-back. Control (CTRL) sets of unprimed seeds were included. Following the treatments, seeds were directly planted in the soil, and germination was monitored for seven days.

2.3. Soil Drought Stress System

To examine the influence of seed treatments on the germination performance of rice under simulated soil conditions, a soil drought stress system was developed based on existing literature for other crops [43,44]. The soil system was designed to simulate three different moisture levels corresponding to saturated soil (SS) for optimal growth conditions, field capacity (FC), and drought stress (DS). The soil moisture levels simulate different degrees of stress considering the documented water potential values in an open field setting for clay loam soil. Specifically, the moisture levels were 0 kPa for SS, −33 kPa for FC, and −73.6 kPa for DS [45,46].
The rice seeds were sown in 8 cm by 10 cm trays containing 20 g of oven-dried sieved clay loam soil (Compo Sana®, Viridea, Pavia, Italy). Water weights of 11.46 g or 7.96 g were added to the soil in each tray for FC and DS, respectively. These weights were determined using the soil volumetric moisture content % (VMC) and bulk density [46,47,48]. FC consists of 36% VMC, while DS comprises 25% VMC. In instances where the volumetric mass of a tray exceeded the predetermined lower threshold (9.87 g of FC or 6.69 g DS) per daily weight measurement, an equivalent amount of water was added to restore the weights to their original levels. A complete randomized design (CRD) was used with three replications for each soil condition. The system was established in a growth chamber with a controlled environment at a temperature of 25.20 ± 2.40 °C, relative humidity of 52 ± 4.08%, and light conditions at a photon flux density of 150 μmol m−2 s−1 applied with a 16/8 h photoperiod.

2.4. Germination Parameters

To evaluate the germination performance, several parameters were tested. Germinated seeds were counted at 24 h intervals for seven days. Seeds were considered germinated when shoots emerged from the soil, indicating that they had reached the S2 stage [49]. Parameters regarding germinability, seedlings trait, and drought stress tolerance indices were computed [49,50,51]. Parameters to assess germinability included germination percentage (G%), germination index (GI), mean germination time (MGT), and synchronization index (Z). Root length (Root), shoot length (Shoot), and seedling vigor index I (VII) were employed as metrics for evaluating seedling growth. Stress tolerance indices chosen to assess drought responses included the germination drought stress tolerance index at 7 days (GDTIMax), shoot length drought stress tolerance index (SLDTI), and root length drought stress tolerance index (RLDTI). The values obtained from the calculation of all germination parameters are provided in an Excel sheet as a supplementary dataset.
Table 1 displays the formulas employed for the computation of each parameter with a comprehensive description of the resulting values.
The ImageJ application (https://imagej.nih.gov/ij/) was utilized to measure the shoot and root length.

2.5. Statistical Analyses

Statistical analyses were carried out using the Statistical Tool for Agricultural Research 2.0.1 (STAR 2.0.1) software. Significant differences among varieties and treatments were determined through a two-way ANOVA (analysis of variance) method. The significance of the results was evaluated at a 0.05% level using the Duncan test. Means with the same designated letters are indicated as not significantly different. PCA (principal component analysis), hierarchical clustering analysis, and visualization of data were conducted using the Originpro 2023b software (OriginLab Corporation, Northampton, MA, USA) following the user guide specifications.

3. Results

3.1. The Indica and Japonica Varieties Are Characterized by Different Levels of Responsiveness to Priming and Drought at the Germination Stage

To evaluate the effect of seed priming treatments on the germination potential under soil drought conditions, 11 selected rice varieties were investigated. Germination percentage (G%) was monitored daily for seven days (Figure 2). All raw data obtained in this work are provided in the supplementary dataset file. Given the high number of analyzed samples (treatments x genotypes x time), data are represented as color-coded heatmaps, where dark green represents low to null germination while dark brown represents high to maximum germination.
Under no-drought (SS, saturated soil) control conditions, the average initiation of germination was four days after sowing (DAS) across all rice lines and treatments (Figure 2a). Among the investigated varieties, the high Zn biofortified Rc460 variety showed the lowest G% (ranging between 1 and 8% at 7DAS) for both CTRL and priming treatments. All the remaining 10 varieties presented different levels of germination improvement after priming. Namely, PGA treatments resulted in a significant improvement of G% in Carnaroli (at 4 and 5 DAS), Inpari, and IR64 (at 4 and 6 DAS). The dPGA treatment enhanced G% only in Apollo (at days 3–7) from the japonica varieties, while it had a better impact on indica varieties Inpari (days 4–6), IR64 (4DAS), IRS495 (days 4–6), and IRS1421 (days 4–7). The WS treatment had a positive impact on Apollo (4 DAS), Inpari (5–6 DAS), IR64 (4–5 DAS), IRS493 (4–5 DAS), and IRS1421 (4–7 DAS), while a negative effect was observed for Cerere and Unico. The maximum G% (97.67 ± 4.04%) under SS was registered for dPGA-treated IR64 at 6 and 7 DAS.
The FC conditions (−33 kPa, 36% VMC) appear to be already damaging especially for the indica varieties, which show low germination even after priming treatments (Figure 2b). However, the Inpari high Zn variety presented a significant G% enhancement after PGA (45.67 ± 7.51%) and dPGA (31 ± 7.21%) priming compared to the unprimed control (1.73 ± 1%) at 7 DAS. Similarly, the GEd line IRS1421, still characterized by high Zn, displayed higher G% after all priming treatments; specifically, a 4-fold increase was observed for PGA and WS and 6-fold for dPGA treatments. Among the japonica varieties, Apollo and Cerere were the most responsive to priming, mainly with PGA. When considering the stress response under FC, Carnaroli presented higher G% also under non-primed conditions (60 ± 3% at 7 DAS). Still, in Carnaroli, all the treatments appear to have significantly improved G% by about 1.3-fold.
The DS treatment (−73.6 kPa, 25% VMC) refers to a high-stress condition specifically from the point of view of rice germination (Figure 2c). As they are highly susceptible to water scarcity, the majority of indica varieties failed to germinate. The only exception was represented by Inpari, where priming with dPGA was able to rescue germination up to 8 ± 1.73% at 7 DAS while in CTRL it was null. Among the japonica varieties, germination in the absence of priming ranged from 1 ± 1.73% in Cerere to 28 ± 1.72% in Carnaroli. This allowed us to differentiate between drought-sensitive (Cerere, Unico, Lomello) and drought-tolerant (Carnaroli, Apollo) varieties. Additionally, among the tolerant varieties, Carnaroli was highly responsive to all priming treatments, with PGA (61 ± 3.46%) being the most effective, leading to a 2-fold G% increase compared to CTRL. Among the sensitive varieties, the G% in Cerere was highly improved by WS, reaching 41 ± 10.58% at 7 DAS. Lomello and Unico were also responsive to priming but to a lesser extent compared to Cerere.
To better characterize the germination behavior, other parameters representative of germination speed (MGT, GI), synchronization (Z), and seedling vigor (VII) were evaluated (Figure 3). The data used to generate this figure are given in the supplementary dataset file. Considering the MGT, the indica varieties seem to have germinated faster than the japonica ones (Figure 3a). Carnaroli and Unico varieties exhibited a one-day decrease in germination time under SS conditions with the application of PGA treatment, while in Apollo and Unico this was observed also for dPGA treatment. Under stress conditions, only Cerere and Unico presented improvements in MGT following PGA treatment. For the indica varieties, a longer time was generally required for germination under drought conditions. However, for the IRS1421 line, faster germination was observed after the dPGA treatment. The GI was calculated because is indicative of both germination and speed (Figure 3b). Improved GI values were registered for the biofortified Inpari and IRS1421 accessions following treatment with PGA and dPGA under FC conditions. For the japonica varieties, all priming treatments resulted in enhanced GI for Carnaroli and Lomello for both FC and DS. PGA and WS improved the GI values in Cerere, whereas dPGA and WS led to an increase in GI in Unico under DS conditions. Regarding the syncronicity index (Figure 3c), improved values were registered for the WS-treated Carnaroli and dPGA-treated Cerere under SS conditions. The FC soil presented significantly improved Z values for Apollo, Lomello, and Inpari after PGA and dPGA priming. Concerning the DS conditions, only Lomello presented better Z values after all priming treatments compared to CTRL.
Seedling vigor (VII) is determined as the product of measuring the length of roots and shoots at maximum day of germination in relation to G% (Figure 3d). Most evident differences were observed under SS conditions, where most priming treatments resulted in improved seedling growth for all varieties, except Rc460. In the case of FC soil conditions, improved VII values were registered for Carnaroli (PGA, WS), Cerere (all priming), Unico (WS), Inpari (PGA, dPGA), IRS493 (PGA), and IRS1421 (dPGA, WS). Differently, under DS conditions, only the japonica varieties showed improved VII; namely, Apollo, Cerere, and Lomello for WS, and Carnaroli for PGA and WS.
Overall, the gathered data evidence different levels of sensitivity and tolerance between japonica and indica varieties. Among the indica varieties, the biofortified Inpari NutriZinc was most tolerant to stress and responsive to PGA and dPGA priming. For the japonica varieties, Cerere, Unico, and Lomello are characterized as drought-sensitive at the germination stage, while Carnaroli and Apollo can be considered drought-tolerant based on the monitored germination parameters. The priming treatments mitigated the effects of drought stress in a genotype-specific manner.

3.2. Drought Stress Indices Calculated at Germination Indicate Distinct Behaviors of Indica and Japonica Varieties in Response to Priming

To examine the influence of priming treatments on the tolerance index at germination, several parameters, namely drought tolerance index at maximum day (GMaxDTI), shoot length drought stress tolerant index (SLDTI), and root length drought stress tolerant index (RLDTI), were calculated (Figure 4). The values represent percentage (%) of fold-changes to SS, where high values indicate high stress tolerance. The numeric data used to generate this figure are available in the supplementary dataset. Based on the GMaxDTI, the treatment that led to higher tolerance in the japonica varieties was WS, while PGA improved drought tolerance in the indica varieties under FC conditions (Figure 4a). Conversely, the DS conditions were highly damaging to indica varieties, while all priming treatments improved stress tolerance in japonica varieties, with WS appearing as the most effective treatment (Figure 4b). The data recovered from the SLDTI calculations are in agreement with the GMaxDTI data, where WS improves shoot growth in japonica varieties while PGA enhances it among the indica varieties (Figure 4c,d). Regarding the RLDTI parameter, the data evidence that root growth is highly inhibited by drought. In a few cases (Inpari, IR64, IRS493), PGA seems to be able to slightly improve root growth (Figure 4e,f).
Overall, the results suggest that all priming treatments can improve drought stress tolerance during rice germination; while in the japonica varieties, the WS treatment appears to be most effective, for the indica highly susceptive varieties, PGA priming had the most positive effects.

3.3. Integrative Analyses Support the Distinct Behavior in Response to Priming and Soil Drought Stress Based on Genetic Variability

To evaluate clustering patterns across the seed treatments, a principal component analysis (PCA) was conducted using all the germination data collected from the 11 rice varieties sown in soil with SS, FC, or DS moisture content (Figure 5). Under SS conditions, no distinct grouping was evidenced among the rice lines in the PCA plot (Figure 5a). Differently, the observed clusters within the FC group exhibited differentiation between the indica and japonica subspecies, as seen in Figure 5b. Though FC was established to have 100% water availability [48], the water potential in clay loam soil is −33 kPa. The overlap observed for the indica rice group (green circle) provides additional evidence of the greater sensitivity to drought compared to the japonica group (blue circle). Still, in FC soil, Lomello, Cerere, and Unico cluster closer together and are a bit more separated from Apollo and Carnaroli. This is explained by the germination data, where the first group (Lomello, Cerere, Unico) exhibited higher vulnerability to drought and moderate response to priming while the second group (Apollo, Carnaroli) showed a more enhanced tolerance to stress after priming. This distinction becomes more pronounced under DS conditions (Figure 5c). Here, the non-responsive cluster includes all the indica highly sensitive species (dark red circle), while the Lomello, Unico, Cerere, and Apollo cluster forms an intermediate group (moderate sensitivity, pink circle). Carnaroli appears as the most distant point, clustering separately from the others (yellow circle), as it is the sole variety that exhibited a consistent response to all priming treatments, leading to enhanced drought stress tolerance.
While the PCA grouping was carried out keeping in mind the soil moisture conditions, an additional hierarchical cluster analysis was conducted considering the influence of the priming treatments (Figure 6). To generate the dendrograms, germination parameters calculated for each variety were used and a 70% similarity threshold was applied. The clusters generated for the non-primed (CTRL) samples (Figure 6a) represent a combination of indica and japonica varieties organized in four groups; for instance, in group 1 there are present the japonica Apollo variety together with the indica biofortified lines IRS1421 and IRS493, while group 3 presents two japonica varieties (Carnaroli, Cerere) and the conventionally bred high Zn Inpari variety. In the WS treatment, the majority of clusters (except for groups 3 and 4) consist of single rice lines (Figure 6b). Group 3 consists of three indica (IR64, Inpari, IRS495) and one japonica (Lomello) varieties, whereas in group 4 only two japonica varieties (Cerere, Unico) are found. Interestingly, the clustering in the PGA (Figure 6c) and dPGA (Figure 6d) treatments are mostly based on rice nutrient content. For instance, the PGA groups 1 (IR64, Lomello, Apollo) to 3 (Cerere, Unico) are formed of non-biofortified rice lines, including both indica and japonica. Differently, groups 4 (Inpari) and 5 (Rc460, IRS493, IRS495, IRS1421) include all the indica biofortified rice lines, independent of the method used for their production. A similar pattern is observed for the dPGA group 2, representative of non-biofortified varieties, and group 4, consisting of biofortified lines.
Overall, the integrative analyses provide interesting clues related to specific patterns when looking separately at the response to stress or response to priming. For instance, when the soil moisture content is taken as the main criterion of sample distribution, the indica varieties are grouped as being highly susceptible to drought while the japonica varieties are grouped separately, displaying different levels of sensitivity/tolerance, with Carnaroli being most tolerant, specifically after the priming treatments. Differently, when seed priming is used as the criterion for sample distribution, only the PGA treatments allow a clear separation among the samples, mainly distinguishing between biofortified and non-biofortified lines. This indicates that the PGA treatment has different effects on the rice lines according to their Zn/Fe mineral content.

4. Discussion

The present study aimed to assess the efficacy of seed priming in enhancing rice germination under drought conditions. Given that priming protocols are known to be not only species- but also genotype-specific [29,33], a collection of 11 rice varieties, representative of indica and japonica groups, typically grown under lowland or upland conditions, respectively, were used in this study. Moreover, because rice germination can be affected also by the grain mineral content [41], high Zn or Fe biofortified lines were incorporated in this study.
To address this goal, the first step of the study was the development of a soil drought system to be used for rice germination screening. For the successful initiation and completion of germination, rice seeds need an adequate water content. Drought stress during germination results in decreased germination rates, reduced seedling vigor, and delayed seedling emergence [52]. Additionally, it triggers the overproduction of ROS, ultimately leading to seedling mortality [53]. So far, germination screening has been mainly based on artificial systems employing PEG to simulate water stress; however, these often fail to capture the intricate complexity of drought stress experienced in field conditions [34,35]. The developed soil system is based on the interconnection between soil type, soil moisture, and water potential. Specific calculations regarding soil bulk density and upper and lower limits for specific water content were carried out along with daily weighting to maintain the same drought intensity for the entire duration of the study. A similar approach was performed by Hou et al. [54], who specifically calculated the percentage of soil water content, water holding capacity, and relative water content, as a system to test rice seed germination under direct drought cropping. The reliability of the developed system derives from the consistent results obtained for some of the tested varieties when used in parallel with the PEG-simulation system. For instance, Carnaroli consistently demonstrated water stress tolerance both in the soil system and in PEG treatments [33]. Nonetheless, other varieties (e.g., Lomello, Unico) exhibited different germination behaviors in the two systems, underlining once more the need for multiple and diverse screening systems for this type of stress. These differences can be attributed to the complexity of the soil environment compared to the use of PEG, which is also used for inducing osmotic stress [55,56].
Subsequently, to mitigate the adverse impacts of drought stress on rice germination, novel and sustainable seed priming methods were developed in this study. The use of naturally produced polymers like γ-PGA with highly viscous, biodegradable, and biocompatible characteristics represents a cost-effective strategy that can contribute to promoting a circular economy [57]. Additionally, dPGA serves as a nutri-priming agent since it represents a source of glutamic acid, required by plants to produce GABA (gamma-aminobutyric acid) molecules known to play important roles in drought stress tolerance and nitrogen mobilization in germinating seeds [58]. These protocols were tested in the Apollo, Carnaroli, Cerere, Lomello, and Unico varieties, belonging to the japonica group, as well as in IR64, Inpari, Rc460, IRS493, IRS495, and IRS1421 of the indica subspecies. While Inpari and Rc460 are Zn biofortified varieties obtained through conventional breeding, IRS493, IRS495, and IRS1421 are genetically modified biofortified lines [39,40]. The calculated germination parameters, in terms of percentage speed, synchronicity, seedling development, but also stress indices, aided in distinguishing between varieties with high susceptibility or tolerance to drought. For the indica subspecies, even a −33 kPa soil water potential (FC treatment) was perceived as high stress, despite the 100% water availability. This high sensitivity may stem from differences in growing conditions, as indica varieties are typically cultivated in irrigated lowlands. However, following priming with PGA and dPGA, the Inpari biofortified variety germinated under drought conditions. This response may be explained by the fact that the breeding program for Inpari included the use of the Ciherang sub-1 variety, known for its connection to drought tolerance via the SUB1A regulator [59]. Additionally, the IRS1421 line, developed through genome editing, exhibited an enhanced germination index and seedling vigor following treatments with dPGA, possibly related to the removal of the regulatory control on cytokinin when the OsNAS2 gene promoter was edited [40]. Among the japonica varieties, Cerere, Unico, and Lomello can be classified as drought-sensitive, while Carnaroli and Apollo are more tolerant. When considering the developed priming protocols, germination was improved in response to all the treatments, including WS. These distinct germination behaviors were supported also by the integrative PCA and hierarchical clustering analyses. Furthermore, this analysis also revealed that PGA had a better effect on the biofortified lines, indicating that this type of priming can be used to improve germination under low moisture conditions in these lines with improved nutrient content.
The observed improvement in the germination performance under drought conditions following priming is in line with other studies performed on rice; however, these studies used different priming methods, mainly based on the use of elicitors [60,61] or microorganisms [62]. In the first case, rice seed priming with hormonal (methyl jasmonate, salicylic acid) or chemical elicitors (paclobutrazol, triacontanol) resulted in enhanced antioxidant defense and resistance to drought [32,60]. In a more recent work, a cocktail containing Fe/Zn micronutrients and methyl jasmonate was also reported to be highly efficient in mitigating the deleterious impact of drought [61]. Additionally, when the effects of potassium nitrate, salicylic acid, and silicon dioxide priming were evaluated, faster emergence, seedling growth, and drought resistance were observed in dry ecosystems [63]. It should be noted that the majority of these studies were conducted using only one or two rice varieties.

5. Conclusions

To conclude, this work provides a soil system that can be used to screen rice susceptibility/tolerance to drought at the germination stage. Additionally, sustainable priming methods based on γ-PGA and dPGA, applied to test the efficacy of the soil system, proved to enhance germination performance under optimal and limited soil water contents. The data gathered in this study indicate that all priming treatments improved rice drought tolerance in a genotype-specific manner. Namely, for the japonica varieties, the WS treatment was most effective, while for indica varieties, the γ-PGA priming presented the most positive effects. These priming treatments can be tailored to specific genotypes, suggesting the need for future investigations encompassing even broader germplasm collections to include other rice subspecies (e.g., tropical japonica, AUS) and more diverse ecosystem conditions. The soil-based screening system hereby developed can be envisioned as an alternative and adaptable platform that focuses on the germination stage to aid breeders and seed technologists in their efforts to mitigate the impact of drought on crop production and to ensuring food security worldwide.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agriculture14060926/s1, Supplementary dataset.

Author Contributions

Conceptualization A.M.; methodology, C.D.J. and A.M.; validation, A.M. and C.D.J., formal analysis, C.D.J. and A.M.; Investigation, C.D.J.; resources, C.C., I.H.S.-L. and U.S.; data curation, C.D.J., writing-original draft preparation, A.M. and C.D.J.; writing-review and editing, A.M., C.C., I.H.S.-L. and U.S.; visualization, A.M., C.D.J., C.C., I.H.S.-L. and U.S.; supervision A.M. and C.C.; project administration, A.M.; funding acquisition, A.M. and I.H.S.-L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Rice Research provided through the International Rice Research Institute (IRRI), Philippines, FRG (Fondi Ricerca Giovani) received through the University of Pavia from the Italian Ministry of Education and Research (MUR), and the PhD scholarship received through the Dipartimenti di Eccellenza program (2018–2022)—Department of Biology and Biotechnology ‘L. Spllanzani’, University of Pavia, provided by MUR.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Data is contained within the article.

Acknowledgments

The authors would like to acknowledge Carlo Minoia and his team from SA.PI.SE (Sardo Piemontese Sementi) Coop. Agricola (https://sapise.it/), as well as Graziano Rossi and Andrea Mondoni from the University of Pavia, Botanical Garden Plant Germplasm Bank (https://terraeambiente.dip.unipv.it/it/dipartimento/risorse/banca-del-germoplasma-vegetale), for kindly providing the seed materials used in this study.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Figure 1. Overview of the experimental design used in this study. CTRL, unprimed control; WS, water soaking; PGA, poly-gamma-glutamic acid; dPGA, denatured poly-gamma-glutamic acid; DAS, days after sowing; SS, saturated soil; FC, field capacity; DS, drought stress. Blue arrow indicates soaking in water for 16 h. Yellow arrow indicates soaking in γ-PGA for 16 h. Green arrow indicates soaking in dPGA for 16 h. Brown arrow indicates the duration of growth for 7 days in the soil.
Figure 1. Overview of the experimental design used in this study. CTRL, unprimed control; WS, water soaking; PGA, poly-gamma-glutamic acid; dPGA, denatured poly-gamma-glutamic acid; DAS, days after sowing; SS, saturated soil; FC, field capacity; DS, drought stress. Blue arrow indicates soaking in water for 16 h. Yellow arrow indicates soaking in γ-PGA for 16 h. Green arrow indicates soaking in dPGA for 16 h. Brown arrow indicates the duration of growth for 7 days in the soil.
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Figure 2. Heatmap representation of germination percentage (G%) collected from 11 rice varieties subjected to priming and sown in soil under (a) saturated soil (SS), (b) field capacity (FC), and (c) drought stress (DS) conditions, monitored from 1 to 7 days after sowing (DAS). Different letters indicate statistically significant (p ≤ 0.05) differences among samples determined using Duncan’s test. CTRL, unprimed control; PGA, priming with a poly-gamma-glutamic acid solution; dPGA, priming with a denatured poly-gamma-glutamic acid solution; WS, water soaking.
Figure 2. Heatmap representation of germination percentage (G%) collected from 11 rice varieties subjected to priming and sown in soil under (a) saturated soil (SS), (b) field capacity (FC), and (c) drought stress (DS) conditions, monitored from 1 to 7 days after sowing (DAS). Different letters indicate statistically significant (p ≤ 0.05) differences among samples determined using Duncan’s test. CTRL, unprimed control; PGA, priming with a poly-gamma-glutamic acid solution; dPGA, priming with a denatured poly-gamma-glutamic acid solution; WS, water soaking.
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Figure 3. Heatmap representation for (a) mean germination time (MGT), (b) germination index (GI), (c) synchronicity index (Z), and (d) seedling vigor index (VII) in response to priming and soil drought. Different letters indicate statistically significant (p ≤ 0.05) differences among samples determined using Duncan’s test. SS, saturated soil; FC, field capacity; DS, drought stress; CTRL, unprimed control; PGA, priming with a poly-gamma-glutamic acid solution; dPGA, priming with a denatured poly-gamma-glutamic acid solution; WS, water soaking.
Figure 3. Heatmap representation for (a) mean germination time (MGT), (b) germination index (GI), (c) synchronicity index (Z), and (d) seedling vigor index (VII) in response to priming and soil drought. Different letters indicate statistically significant (p ≤ 0.05) differences among samples determined using Duncan’s test. SS, saturated soil; FC, field capacity; DS, drought stress; CTRL, unprimed control; PGA, priming with a poly-gamma-glutamic acid solution; dPGA, priming with a denatured poly-gamma-glutamic acid solution; WS, water soaking.
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Figure 4. Plot representations for drought stress index calculations at the germination stage considering 11 rice varieties treated with seed priming. (a) Germination drought tolerant index at maximum day (GmaxDTI) under field capacity (FC). (b) GmaxDTI under drought stress (DS). (c) Shoot length drought stress tolerant index (SLDTI) under FC conditions. (d) SLDTI under DS conditions. (e) Root length drought stress tolerant index (RLDTI) under FC conditions. (f) RLDTI under DS conditions. Data are represented as percentage of fold-changes to control (SS, saturated soil), where high values indicate high stress tolerance. CTRL, unprimed control; PGA, priming with a poly-gamma-glutamic acid solution; dPGA, priming with a denatured poly-gamma-glutamic acid solution; WS, water soaking.
Figure 4. Plot representations for drought stress index calculations at the germination stage considering 11 rice varieties treated with seed priming. (a) Germination drought tolerant index at maximum day (GmaxDTI) under field capacity (FC). (b) GmaxDTI under drought stress (DS). (c) Shoot length drought stress tolerant index (SLDTI) under FC conditions. (d) SLDTI under DS conditions. (e) Root length drought stress tolerant index (RLDTI) under FC conditions. (f) RLDTI under DS conditions. Data are represented as percentage of fold-changes to control (SS, saturated soil), where high values indicate high stress tolerance. CTRL, unprimed control; PGA, priming with a poly-gamma-glutamic acid solution; dPGA, priming with a denatured poly-gamma-glutamic acid solution; WS, water soaking.
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Figure 5. PCA loading plots generated based on germination parameters collected from 11 rice varieties treated with seed priming and sown under (a) saturated soil (SS), (b), field capacity (FC), and (c) drought stress (DS) conditions.
Figure 5. PCA loading plots generated based on germination parameters collected from 11 rice varieties treated with seed priming and sown under (a) saturated soil (SS), (b), field capacity (FC), and (c) drought stress (DS) conditions.
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Figure 6. Hierarchical cluster analysis produced based on the germination data retrieved from 11 rice varieties treated/untreated with priming and drought stress. (a) Unprimed control (CTRL). (b) Water soaking (WS). (c) Priming with a poly-gamma-glutamic acid solution (PGA). (d) Priming with a denatured poly-gamma-glutamic acid solution (dPGA). The blue dotted line represents the 70% similarity threshold.
Figure 6. Hierarchical cluster analysis produced based on the germination data retrieved from 11 rice varieties treated/untreated with priming and drought stress. (a) Unprimed control (CTRL). (b) Water soaking (WS). (c) Priming with a poly-gamma-glutamic acid solution (PGA). (d) Priming with a denatured poly-gamma-glutamic acid solution (dPGA). The blue dotted line represents the 70% similarity threshold.
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Table 1. Germination parameters used to evaluate the germination performance of rice varieties after seed priming treatment under three soil moisture conditions.
Table 1. Germination parameters used to evaluate the germination performance of rice varieties after seed priming treatment under three soil moisture conditions.
ParameterUnit Formula of Calculation Description of Formula
Germination Indices
G%%G% = (no of seeds germinated/to number sown) ∗ 100
GI-GI = (7 × n1) + (6 × n2) + · · · + (1 × n10)n1, n2… n7 = No. of germinated seeds on the first and subsequent days until the last day; 7, 6 … and 1 are weights given to the number of germinated seeds
MGT day MGT = ∑ f ·x/∑ ff = seed germinated on day x
Z -Z = ∑cni,2/∑ ni ∗ ∑ (ni − 1/2)where Cni,2 = ni (ni − 1)/2 while ni is the number of germinated seeds per day
Seedling Trait
Rootmmmeasured using ImageJ program
Shoot mmmeasured using ImageJ program
VII-VII = (Root + Shoot) ∗ G%
Drought stress Tolerance Indices
GMaxDTI%(G% at maximum day under stress conditions/G% at maximum day under non stressed condition) ∗ 100
SLDTI%(StLn under stress conditions/StLn under non-stressed condition) ∗ 100
RLDTI%(RtLn under stress conditions/RtLn under non-stressed condition) ∗ 100
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Dueñas, C., Jr.; Calvio, C.; Slamet-Loedin, I.H.; Susanto, U.; Macovei, A. Seed Priming with Poly-Gamma-Glutamic Acid (γ-PGA) Improves Rice Germination Performance under Drought Conditions. Agriculture 2024, 14, 926. https://doi.org/10.3390/agriculture14060926

AMA Style

Dueñas C Jr., Calvio C, Slamet-Loedin IH, Susanto U, Macovei A. Seed Priming with Poly-Gamma-Glutamic Acid (γ-PGA) Improves Rice Germination Performance under Drought Conditions. Agriculture. 2024; 14(6):926. https://doi.org/10.3390/agriculture14060926

Chicago/Turabian Style

Dueñas, Conrado, Jr., Cinzia Calvio, Inez Hortense Slamet-Loedin, Untung Susanto, and Anca Macovei. 2024. "Seed Priming with Poly-Gamma-Glutamic Acid (γ-PGA) Improves Rice Germination Performance under Drought Conditions" Agriculture 14, no. 6: 926. https://doi.org/10.3390/agriculture14060926

APA Style

Dueñas, C., Jr., Calvio, C., Slamet-Loedin, I. H., Susanto, U., & Macovei, A. (2024). Seed Priming with Poly-Gamma-Glutamic Acid (γ-PGA) Improves Rice Germination Performance under Drought Conditions. Agriculture, 14(6), 926. https://doi.org/10.3390/agriculture14060926

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