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Article

Verification of Seed-Priming-Induced Stress Memory by Genome-Wide Transcriptomic Analysis in Wheat (Triticum aestivum L.)

1
Institute of Agronomy, Georgikon Campus, Hungarian University of Agriculture and Life Sciences, 8360 Keszthely, Hungary
2
Festetics Doctoral School, Institute of Agronomy, Georgikon Campus, Hungarian University of Agriculture and Life Sciences, 8360 Keszthely, Hungary
3
Department of Agricultural Biochemistry, Faculty of Agriculture, Cairo University, Giza 12613, Egypt
4
Heavy Metals Department, Central Laboratory for The Analysis of Pesticides and Heavy Metals in Food (QCAP), Dokki, Cairo 12311, Egypt
*
Authors to whom correspondence should be addressed.
Agronomy 2025, 15(6), 1365; https://doi.org/10.3390/agronomy15061365
Submission received: 14 May 2025 / Revised: 28 May 2025 / Accepted: 30 May 2025 / Published: 2 June 2025

Abstract

:
In line with the latest challenges, agriculture has many options to protect against stress conditions. Seed-priming treatment was applied to winter wheat genotype AG Hurrem with Dr. Green seed-priming fertilizer, which is a commonly used seed fertilizer containing macro- and microelements. Genome-wide transcriptomic analysis was performed to examine the effects of treatments. In seed-primed plants, defense response pathways such as purine and thiamine metabolism, glutathione pathway, and phenylpropanoid biosynthesis were activated. At the same time, photosynthesis and some cellular respiration processes were downregulated and suppressed. Furthermore, in samples of plants previously exposed to priming and subsequently to drought stress, biochemical pathways activated during seed priming showed positive modulation, thus confirming the long-term traces of the priming effects of previous treatments and their repeated inducibility in the genome, i.e., the presumed existence of stress memory. The in silico analyses were also supported by laboratory antioxidant enzyme activity measurements. The priming technique and the preventive approach that can be offered with it may be a promising option for developing sustainable agricultural production in the future.

1. Introduction

Drought stress greatly limits the fulfillment of plants’ genetic potential. Some plant development phases are particularly sensitive to the harmful effects of drought stress, which are difficult to reduce later. The farmer can achieve much more if he does not wait for the stress effects to occur but rather prepares the plants in advance for the upcoming stress by taking advantage of their properties. Today, numerous studies have proven that the plant has a double-layered immune system [1], and the immune system’s resistance can be stimulated by priming treatments [2].
Among the priming treatments, the so-called seed-priming can activate the existing defense responses in the plant organism as early as possible, at the beginning of the germination process, and thus strengthen the plant immune system. According to Thakur et al. (2022), it improves essential factors for farmers, such as reducing emergence time, eliminating the need for reseeding, enhancing the uniformity of the plant population, and making cultivation practices more efficient [3].
Several publications have been published on this topic in recent years, of which Sher et al. (2019) discussed the advantages of seed-priming in their summary work [4]. Zammali et al. (2022) studied Lobularia maritima plants using seed-priming [5]. Germination rates were improved by seed-priming with 10 mM KNO3 or 50 mM thiourea. In the case of other seed-priming treatments, such as 10 µM salicylic acid or 1 mM proline, the same research group experienced negative effects. The effectiveness of seed-priming treatments depends mainly on the species of plant to be treated and the active ingredient used in the seed primer.
In their review article, Farooq et al. (2019) listed in detail the advantages and disadvantages of the most common seed-priming techniques while writing about new, progressive seed-priming techniques, such as pretreatment with nanoparticles [6]. Alhammad et al. (2023) studied corn plants after seed-priming with ZnO nanoparticles [7]. They found that the germination vigor and germination energy of plants primed with nanoparticles significantly increased, and germination time and Na+ uptake were reduced compared to unprimed plants. In addition, treatments increased the long-term seed embedment rate, grain weight, K+ content, and α-amylase activity of corn plants.
Hussain et al. (2022) outlined the beneficial effects of contemporary approaches, including hydropriming, osmopriming, chemical priming, biopriming, hormone priming, and nutritive priming [8]. Early priming treatment may also benefit extreme weather situations caused by climate change, such as drought, as it can trigger plant biochemical processes that may participate in later stress responses. Szalai et al. (2016) treated corn seeds with salicylic acid [9]. They found that it had a beneficial effect on polyamine metabolism, which, as a defense response, may be indirectly related to the stress tolerance of plants [9]. Alam et al. (2022) found that seed pollination had a beneficial effect on cantaloupe under water deficit stress, positively influencing antioxidant enzyme activity, which is one of the most effective general defense responses [10].
According to Louis et al. (2023), seed-priming causes mild, stimulative stress to the seeds, and this pretreatment induces specific changes at the physiological and molecular levels [11]. Seed-priming enhances the efficacy of DNA repair mechanisms, activating specific signaling proteins and transcription factors, which results in rapid and effective stress tolerance. This learned stress tolerance can be sustained for a prolonged duration, at later developmental stages, or even in future generations. Comprehending the activation of stress-responsive genes, their molecular processes, and the alterations induced by seed-priming will enhance plant resilience. In their review article discussing the beneficial physiological effects of seed-priming and the stress memory of primed plants, Marthandan et al. (2020) collected the knowledge that supports that the treatments result in increased germination rates, more vigorous plant stands, production of cellular protective compounds, and enhanced antioxidant enzyme responses that may protect treated plants from damage caused by drought-stress [12]. Additional review studies have also highlighted the advantageous effects of seed-priming in mitigating drought stress [13,14,15,16].
Ding et al. (2012) primed Arabidopsis plants and then subjected them to drought stress at a later phenophase [17]. The primed plants exhibited elevated transcript levels of drought-responsive genes in response to successive drought conditions. The time for which a priming stimulus is retained in memory may be influenced by various factors, such as the length of exposure to the stimulus, its strength, or the organism’s internal state. Ding et al. (2018) define ‘transcriptional memory’ as a transcriptional response following a recurring stress that differs from the transcriptional response to a primary stress [18]. Pastor et al. (2013) found that genes regulating defenses show faster and stronger transcriptional activation under stress conditions if they have already undergone a prior priming [19]. It is believed that cofactors accumulated during priming remain inactive in plant cells and are activated as primary responses during re-stress. Lukić et al. (2020) were the first to demonstrate that the antioxidant enzyme system plays a role in the long-term maintenance of plant stress memory after priming [20]. Their findings indicate that drought-stress defense amplifies the activity of enzymes known as antioxidants, which is essential for mitigating damage caused by oxidation and preserving resistance (stress memory) to future drought stress.
Tabassum et al. (2018) investigated the relationship between priming and stress memory in wheat under drought stress [21]. They found that the physiological parameters of primed plants were better adapted than those of untreated counterparts, and these properties were maintained over several generations. Priming had a positive effect on the development of water balance parameters and osmolyte accumulation. Similarly, Hussain et al. (2018) found that seed-primed wheat plants maintained their vigor and beneficial physiological parameters throughout the growing season under drought stress, compared to untreated control plants [22]. The authors attributed the main reason for this to the fact that increased antioxidant enzyme activity following the priming reaction reduced the damaging effects of secondary oxidative stress.
Previous studies have highlighted the beneficial effects of seed-priming treatments, particularly under stressful conditions [23,24,25,26]. Still, we have not yet found any publication investigating the biochemical processes stimulated by seed-priming and their changes at a deeper level, at the gene level. Therefore, we experimented with wheat plants under drought conditions using a plant seed-priming substance containing macro- and microelements and then performed a genome-wide analysis of the tested plants. Our goal was to determine the transcriptomic changes in the nutrient-containing seed-priming preparation we used, which was induced in wheat plants exposed to drought stress. To date, no comprehensive study has not been conducted in seed-primed and then drought-stressed winter wheat to date, and thus, we cannot find any references in the literature focusing on either the short- and long-term effects of seed-priming or gene expression changes in the antioxidant enzyme system. Therefore, we applied seed-priming treatment to winter wheat plants grown in pots in a greenhouse and then subjected them to drought stress to examine the formation of stress memory. Leaf samples were taken from them in four replicates per treatment for antioxidant enzyme activity measurements and for comprehensive transcriptomic studies.
We paid attention to the results of the literature, which indicate that seed treatment techniques can benefit the activity of plants’ antioxidant enzyme systems. This is one of the best ways to verify the priming effect. To this end, we examined the individual gene expression changes of some antioxidant enzymes using bioinformatics tools. In addition to in silico analyses, the studies were supplemented with traditional enzyme activity measurements.

2. Materials and Methods

2.1. Plant Material, Seed-Priming, and Drought-Stress Treatments

For the experiments, wheat seeds of the AG Hurem (AgroMag, Szeged, Jósika Street, Hungary) variety were used after sterilizing the seeds with 0.1% NaClO for 2 min, and then they were cleaned with deionized water to remove all chloride residues. This is an early wheat variety with excellent baking parameters. Capable of exceptionally high yields. Characterized by above-average productivity, adaptability, and resistance to lodging. Resistant to most diseases affecting wheat. Excellent utilization of water and nutrients in the soil, thus performing well in both intensive and extensive growing conditions.
Half of the seeds were primed with a simple, commonly used macro- and micronutrient seed fertilizer, the active ingredients of which were as follows:
P2O5: 250 g/kg, K2O: 170 g/kg, B: 2.5 g/kg, Cu: 1.75 g/kg, Fe: 35 g/kg, Mn: 30 g/kg, Mo: 0.25 g/kg, Zn: 32.5 g/kg (Dr. Green seed-priming, Chrzanów, Poland). The seeds were soaked in a solution of 10 g/250 mL of seed-priming agent for 24 h at 25 °C in the dark with continuous moderate agitation. The treated seeds were subsequently flushed four times with distilled water and drained to restore their natural moisture content. The seeds of the plants were subsequently sowed in pots measuring 20 cm in diameter and 1 cm in depth. On 11 November 2023, control and pretreatment wheat seeds were sowed in uniformly sized pots, using a plastic net and gravel cover at the base to enhance drainage and irrigation. Each pot contained a uniform 1:1 blend of Ramann-type brown forest soil characteristic of the region and peat. Ten wheat seeds were planted in each pot.
The moisture ratio and ability to hold water of the soil–peat mixture were measured gravimetrically to regulate irrigation in both the untreated and treated pots [27]. The control and seed-primed plants were then irrigated with equal amounts of water throughout the growing season. Drought stress was induced by applying water volumes reduced by 50%. Sixteen of the potted plants were kept as controls, sixteen were treated with seed-priming, and then eight pots of both the control and primed plants were subjected to drought stress starting from the 30th day after planting.
To assess the enzymatic activity in the plants, 4 × 100 g of healthy, fresh, matured leaves were harvested from both the untreated and the treated specimens. For bioinformatics analyses, 30–50 milligrams of leaf tissue were collected from each resilient, healthy wheat plant in four replicates per treatment. The specimens for each study consist of plant specimens gathered from the pots, which may be regarded as copies of one another. The experiment was terminated on 8 March 2024. The studied plant stands were control (untreated), drought-stressed, seed-primed, and seed-primed and drought-stressed.

2.2. In Silico Genome-Wide Analyses

Samples were collected from the various treatments on the eighth day, anticipating the impact of drought treatments that commenced on the thirtieth day post-sowing. All samples were obtained from mature, healthy, young leaves preserved in 1 mL RNALater (Invitrogen by Thermo Fisher Scientific Inc., Waltham, MA, USA) solution at −20 °C from the time of collection until sequencing. The gathered samples underwent preliminary assessment for the NGS (Next Generation Sequencing) method, with only leaf tissue samples possessing a total RNA RIN value of ≥7 being eligible for subsequent procedures.
The messenger RNA (mRNA) was isolated from high-quality samples using paramagnetic NEXTFLEX® Poly(A) Beads 2.0. Following fragmentation, strand-specific next-generation sequencing library preparation was conducted with the NEXTFLEX® Rapid Directional RNA-Seq 2.0 kit. The combined libraries were processed employing the Illumina NovaSeq 6000 next-generation sequencing platform with a deep sequencing method (2 × 150 bp paired-end reads; estimated paired-end read count of 50 million/specimen) [28]. The authors quality-checked the raw reads, removed low-quality regions, and filtered the reads using FastQC version 0.12.0 (https://timkahlke.github.io/LongRead_tutorials/QC_F.html (accessed on 4 April 2025)) and Trimmomatic version 0.39 (http://www.usadellab.org/cms/index.php?page=trimmomatic (accessed on 5 April 2025)) software [29]. The quality-assured reads were subsequently assembled into a de novo transcriptome. The operation was executed with the accessible Trinity software, version 2.15.2 (http://TrinityRNASeq.sourceforge.net (accessed on 6 April 2025) [30]. The Trinity sequence assembled short nucleotide sequences into longer contigs, and its special feature is that it does not require a reference genome.
The contigs of the de novo transcriptome, comprising longer, unidentified sequences, were found with the CloudBlast sequence alignment method incorporated in the OmicsBox BioBam software, version 3.4 [31]. GO (gene Ontology) terms were associated (i.e., mapped) to the blasted sequences using the Blast2GO (A feature from OmicsBox BioBam software, version 3.4) (accessed on 6 April 2025) [32]. Functional annotation was then performed using the EggNOG mapper (A feature from OmicsBox BioBam software, version 3.4), which is suitable for the functional identification of new, previously unknown sequences [33].
To perform differential expression analysis, the individual expression levels of the de novo transcriptome contigs must be assessed. Without a reference genome, the software generates a map of the transcripts. Subsequent to mapping, the software estimates the reads by taking into account the gene coordinates [34]. The software generates a summary of the results in a count table output file. Pairwise differential analysis of expression was conducted utilizing the generated count table and the NOIseq program (https://bioconductor.org/packages/release/bioc/html/NOISeq.html) (accessed 6 April 2025)) between the contigs of the control and treated samples to determine which genes showed statistically significant differences in expression (probability > 0.9) as a result of each treatment [35,36].
By comparing de novo blasted, mapped, and annotated transcriptome data, under- or overrepresented, i.e., significantly lower or higher than average, down- and upregulated sequences were selected from the treated stocks [37]. We categorized them according to their molecular functions and employed this information to enhance our comprehension of the effects of our treatments on biological functioning [38]. To identify biochemical pathways that responded positively or negatively to the treatments, we performed a combined pathway analysis using the Plant Reactome [39] and KEGG [40] databases.

2.3. Individual Gene Analyses

An excellent indicator of priming effects that stimulate plant defense mechanisms is activating members of the antioxidant enzyme system. Therefore, we identified some individual antioxidant enzyme genes (catalase, peroxidase, glutathione reductase in chloroplast and in the cytosol) in the de novo transcriptome and estimated their transcription levels using the NOISeq (A feature from OmicsBox BioBam software, version 3.4) [35]. The software settings used raw counts; hence, the results represented the raw expression values of the given sequences. This setting is closest to the laboratory enzyme activity measurement results because it considers the expression of all genes that have at least a minimum number of reads in a given biological sample.

2.4. Antioxidant Enzyme Activity Measurements

Leaf samples were collected simultaneously with the sequencing samples from the control and all treated plants in four replicates. A modified protocol from Venisse et al. (2001) was used for sample extraction [41]. The buffer comprised 1% polyvinylpyrrolidone, 1 mM polyethylene glycol, 1 mM phenylmethylsulfonyl fluoride, 8 w/v% polyvinylpyrrolidone, and 0.01 v/v% Triton X-100. The resulting solutions underwent centrifugation at 11,500 rpm for 10 min at 4 °C. The resulting liquid was utilized to ascertain the enzyme production values [41].

2.4.1. Estimation of the Peroxidase (POX) Activity

One unit of peroxidase catalyzes the formation of 1.0 milligram of purpurogallin from pyrogallol in 20 s at pH 6.0 and 20 °C, corresponding to approximately 18 µM units per minute at 25 °C. The composition consisted of 50 μL of leaf extract combined with 2 mL of the reaction mixture, which included 100 mM phosphate buffer (pH 6), 5% Pyrogallol, and 0.5% H2O2. Absorbance: 420 nm, synthesis of perpurogalline. Absorbance was measured every 20 s for 2 min at 420 nm [42]. Reaction Mixture: 50 μL leaf extract combined with two milliliters of reaction mixture consisting of a 100 mM phosphate buffer solution (pH 6), 5% Pyrogallol, and 0.5% hydrogen peroxide.

2.4.2. Estimation of the Catalase (CAT) Activity

The catalase facilitates the breakdown of hydrogen peroxide into water and oxygen. The enzyme assessment relies on the quantification of residual hydrogen peroxide through titration with potassium permanganate solution [43,44].

2.4.3. Estimation of the Glutathione Reductase (GR) Activity

The glutathione reductase activity was determined by measuring the rate of NADPH oxidation at 340 nm, as described [41]. The reaction mixture was 100 µL of the leaf extract + 2 mL of an assay mixture (0.1 M Tris buffer, 2 mM EDTA, 50 µM NADPH, and 0.5 mM GSSG). The GR activity was calculated using the molar extinction coefficient 6.2 mM−1 cm−1 of the NADPH and expressed as U mg−1 protein.

2.5. Statistical Analysis

In each case, four replicates of the experiments were included in the study, based on which the mean ± SD values were calculated. The WASP software version 1.0 facilitated statistical analysis, and the data discrepancies were mathematically assessed by one-way ANOVA. The LSD test was employed as a statistical tool at significance levels of 1% and 5% [45].

3. Results

3.1. Genome-Wide Transcriptomic Analyses

A de novo transcriptome was produced utilizing 313,854,191 short reads following the pre-screening of the samples. The short reads were obtained from expanded biological materials analyzed from four replicates for each treatment. The de novo transcriptome reconstruction yielded 345,406 transcripts, from which 179,883 identifiable genes were ascertained. The average length of the genes was 815 bp. The super transcriptome is available in the Fasta File as supplementary.
The whole super transcriptome, subjected to Transcriptome Shotgun Assembly (TSA), was blasted, mapped, and annotated, and RNA-seq read quantification was performed to determine the individual expression levels of the de novo transcriptome contigs (Table S1). TSA is an archive of computationally assembled transcript sequences from primary data such as SRAs (see the Data Availability Statement) and Next Generation Sequencing Technologies. The overlapping sequence reads from the complete transcriptome were assembled in silico into transcripts by computational methods. This was necessary because differential expression analysis requires estimating individual expression levels of the contigs. The number of multiple aligned reads in the untreated control set was 12,028,388, in the untreated drought-stressed set 12,492,909, in the seed-priming treatment 12,174,152, and in the drought-stressed and seed-primed data 11,864,667.
Pairwise differential expression analyses were performed to obtain pairwise gene-level expression differences between biological samples. The analyses were performed in three combinations: between the data sets of control and drought-stressed plants, between the data sets of control and seed-priming plants, and between the data sets of drought-stressed and seed-priming drought-stressed plants.
The comparative analysis of the control and drought-stressed stands yielded 5837 differentially expressed sequences (likelihood > 0.9), of which 2938 were upregulated, and 2900 were downregulated (Table S2). The comparison of the control and seed-priming treatments resulted in 15,750 differentially expressed sequences (likelihood > 0.9), of which 8655 were upregulated and 7095 were downregulated (Table S3). From the analysis between drought-stressed and seed-priming drought-stressed datasets, 3666 out of 7828 differentially expressed sequences were upregulated, and 4162 were downregulated (Table S4). Gene enrichment analyses further narrowed the datasets and identified 822 over- and 481 underrepresented genes between control and drought-stressed treatments, 3982 over- and 2737 underrepresented genes between control and seed-priming treatments, and 1957 over- and 1754 underrepresented genes in the drought-stressed and seed-priming drought-stressed datasets. We identified individual genes, determined their GO members, and performed their functional annotation in all three comparisons.
By extracting the information obtained after identification from all three comparisons, it can be concluded that the majority of the contigs of the super transcript are sequences from wheat (Figure 1A). In all cases, biochemical processes involved in the defense response were activated most as a result of the treatments (Figure 1B). As a result of the treatments, most of the changes were depicted in membrane-bound processes, among which transmembrane transport played a prominent role (Figure 1C), which is also supported by the increase in transferase enzyme activity (Figure 1D).
In all three comparisons, we identified under- and overrepresented genes by performing their blast, mapping, and functional annotation. Based on the data obtained from the combined pathway analyses, it was determined that 187 upregulated, overexpressed sequences could be linked to 223 biochemical pathways according to the KEGG database, while the Plant Reactome analysis inserted 378 upregulated, overexpressed sequences into 345 different pathways in the control vs. drought-stressed datasets.
According to the studies’ results, gene expression differences between control and drought-stressed plants occurred mainly in purine and thiamine metabolism (49 and 48 genes), starch and sucrose metabolism (14 genes), and biosynthesis of various plant secondary metabolites (7 genes) (Figures S5–S8). The genes were upregulated and overexpressed in all cases.
Based on the results, we determined that 872 upregulated, overexpressed genes (between control vs. seed-priming) activated by the stimulatory effect of seed-priming were involved in 245 different biochemical pathways based on the analysis in the KEGG database. A total of 3035 upregulated, overexpressed genes were associated with 930 biochemical pathways in the Plant Reactome database. Among these pathways, the most positively modulated were purine metabolism (190 genes), thiamine metabolism (188 genes), starch and sucrose metabolism (50 genes), glutathione metabolism (41 genes), phenylpropanoid biosynthesis (38 genes), and biosynthesis of various plant secondary metabolites (18 genes). The gene expression of each pathway, activated by drought stress, was upregulated by seed-priming, and on top of all that, the activation of glutathione (Figure 2) and phenylpropanoid metabolism appeared as a novelty. All of the listed genes were upregulated and overexpressed in seed-priming-treated leaf samples.
Among the genes activated by seed-priming treatments, 18 are involved in the function of glutathione transferase (EC: 2.5.1.18), 2 are involved in the function of glutathione peroxidase (EC: 1.11.1.9), and 1 is involved in the function of the ornithine decarboxylase enzyme (EC: 4.1.1.17), which play a prominent role in cellular detoxification processes (Figure 2). During the same treatments, out of 38 genes activated in the phenylpropanoid biochemical pathway, 14 played a role in stimulating 4-coumarate-CoA ligase (EC: 6.2.1.12), 14 peroxidases (EC: 1.11.1.7), 7 phenylalanine ammonia lyases (EC: 4.3.124), 2 shikimate-O-hydroxycinnamoyl transferases (EC: 2.3.1.133) and 1 gene cinnamyl alcohol dehydrogenase (EC: 1.1.1.195) (Figure S9).
The KEGG database linked 383 genes activated in seed-primed and drought-stressed plants to 187 biochemical pathways, while the Plant Reactome 1534 sequences indicated their role in 606 pathways in the same dataset. According to the analyses, the upregulation in thiamine and purine (70 and 69 genes) metabolism can also be observed here. Also prominent is the increase in the acceleration of starch and sucrose metabolism (24 genes), phenylpropanoid biosynthesis (23 genes), and glutathione metabolism (21), but all of these are more moderately pronounced than in the stress-free, seed-primed plant samples. All genes in the analysis are upregulated and overexpressed.
Comparing drought-stressed plants with drought-stressed plants after seed-priming, ten of the genes activated in the phenylpropanoid biosynthesis process (Figure 3) are involved in the function of the enzyme 4-coumarate-CoA ligase (EC: 6.2.1.12), nine are involved in the function of phenylalanine ammonia lyase (EC: 4.3.1.2.4), three are involved in the function of peroxidase (EC: 1.11.1.7) and one is involved in the function of shikimate-O-hydroxycinnamoyltransferase (EC: 2.3.1.133).
During the same treatments, among the activated genes involved in glutathione metabolism, nineteen catalyzed the function of glutathione transferase (EC: 2.5.1.28), one catalyzed the function of glutathione disulfide reductase (EC: 1.8.1.7), and one gene induced the function of ribonucleoside diphosphate reductase (EC: 1.17.4.1) (Figure S10). The effects of each treatment also resulted in downregulated genes.
Combined pathway analysis of drought stress revealed that the KEGG database assigned 118 downregulated genes to 125 biochemical pathways, while the same dataset yielded 256 genes and 259 pathways in the Plant Reactome analysis. Drought stress downregulated five genes in the ubiquinone and other terpenoid-quinone biosynthesis pathway, three genes in the nucleotide sugar metabolism pathway, and three genes in the cofactor biosynthesis pathway.
Subjecting the seed-primed plants dataset to the same analyses, a surprising amount of activity was observed. The KEGG analysis associated 870 downregulated genes with 377 biochemical pathways, while the Plant Reactome associated 1636 sequences with 887 pathways. Of these, 77 genes were down-expressed in the carbon fixation by the Calvin cycle pathway, 54 genes in the glycolysis/gluconeogenesis pathway, 40 genes in the glyoxylate and dicarboxylate pathway, 39 genes in pyruvate metabolism, 36 genes in amino sugar and nucleotide sugar metabolism, 30 genes in the pentose phosphate pathway and 23 in other carbon fixation pathways.
According to KEGG analyses, comparing the seed-priming dataset with the seed-priming and drought-stressed datasets resulted in 401 downregulated genes in 215 affected pathways, while comparing the same dataset with the Plant Reactome database resulted in 1334 down-expressed sequences in 644 affected pathways. The most affected pathways here were ubiquinone and other terpenoid quinone biosynthesis (14 genes) and isoquinone alkaloid biosynthesis (13 genes).
From the bioinformatic analyses, the gene expression changes obtained from the comparison of control and drought-stress treatments occur exclusively under the influence of drought stress. From the comparison of control and primed treatments, we can conclude to what extent the priming treatment alone modulated the biochemical pathways. However, the comparison of drought-stress and primed+drought-stress treatments suggests that the priming treatment induced a kind of memory effect. To compare the treatment effects, we plotted the up- and down-regulated genes in a Venn diagram (Figure 4A,B).
Based on these, it can be concluded that 129 up- and 69 downregulated genes were induced by both drought stress and seed priming, while 8526 up- and 7026 downregulated genes can be detected solely due to the priming treatment. The latter will be the ones that may play a role in activating the plant immune system and in the short and long-term induction of defense responses.

3.2. Individual Gene Analyses

Based on the literature data, we investigated the activity of some antioxidant enzymes using bioinformatics methods, which can also be measured by traditional methods in our laboratory. This required individual sequence analyses, for which we selected previously identified sequences of catalase, peroxidase, and glutathione reductase enzymes from the wheat genome from the NCBI public database, then we searched for sequences that could correspond to these known sequences in the de novo transcriptome from our experiment, and compared their transcriptional values in the individual treatments (Figure 5A–D).

3.3. Enzyme Activity Measurements

To verify the accuracy of the in silico results, we supported the change in the activity of the antioxidant enzymes in question with laboratory measurements (Figure 6A–C). In summary, both drought-stress and seed preparation treatments, alone and in combination, had significant, measurable effects on plant physiological processes. As expected, the stress effect suppressed some assimilative sub-processes, but the impact on the activation of defense mechanisms was more pronounced.
The preliminary seed-priming treatments caused a substantial, significant change in the activation of defense mechanisms, several of which were in common with those observed in drought-stress treatments (purine and thiamine metabolism). As a novelty, the metabolism of phenylpropanoid and glutathione was also activated, which seems to confirm the plant’s perception of seed preparation treatments as eustressors.
In the samples of plants pretreated with seed preparation and then exposed to drought stress, the activity experienced in the defense mechanisms was preserved compared to the control plants. This seems to support the fact that stress perception by the dual immune system can be proven in plants, as well as the existence of stress memory experienced after the preliminary priming treatments. These results are supported by the individual in silico and laboratory enzyme activity measurements, which validate the bioinformatic analyses.

4. Discussion

In our experiments, a seed-priming conditioner containing macro- and microelements was tested on wheat plants under greenhouse conditions. We examined the effect of the pretreatment on both control and drought-stress combined treatments. Drought stress markedly elevated the quantity of elevated, overproduced genes in purine and thiamine metabolism, starch and sucrose metabolism, and the manufacture of diverse secondary metabolites that are found in plants.
The role of purine [46,47] and thiamine in overcoming stressors is supported by numerous studies [14,48,49]. Some studies have also shown that activated starch and sucrose metabolism helped plants cope with the disadvantages of abiotic stressors [50,51,52]. Literature data support the idea that producing secondary metabolites is plants’ primary and prominent stress response [53,54,55]. We also found that drought stress has a detrimental effect on the function of genes involved in the ubiquinone and other terpenoid-quinone biosynthesis pathways, the amino and nucleotide sugars metabolic pathway, and the biosynthetic pathway of the cofactor.
Ubiquinone primarily plays a role in electron transport during photosynthesis and cellular respiration. Still, together with its terpenoid quinone derivatives, it acts as an antioxidant and also appears in signal transduction processes [56]. Nucleotide sugars serve as sugar donors for the action of glycosyltransferase enzymes in various biochemical processes, which can modify the functions of other metabolites [57]. In addition, nucleotide sugars are essential building blocks of the sugar-phosphate backbone of nucleic acids, without which cell division processes would also be inhibited [58]. In the absence of cofactor production, the catalytic activity of enzymes is inhibited [59]. If these cellular processes are inhibited, assimilation and, with it, growth and development processes may be impaired.
Stress-induced gene expression changes may support the basic idea that plant life processes shift from assimilation to defensive responses under challenging conditions. The biochemical pathways analyzed above that were activated in drought stress also showed activity following seed-priming treatments. Still, the number of upregulated overexpressed genes greatly exceeded those observed in drought stress. In addition, two crucial new pathways were modulated: glutathione metabolism and phenylpropanoid biosynthesis.
Glutathione (GSH) is a tripeptide composed of the following amino acids: glycine, cysteine, and glutamic acid. The prerequisite for the activation of glutathione metabolism and transcription is the appearance of hydrogen peroxide (i.e., reactive oxygen species—secondary oxidative stress) in the body. In addition to preventing oxidative stress, GSH plays an important role in cellular metabolism. As an antioxidant, it protects cells from reactive oxidative intermediates (ROI), such as free radicals and hydrogen peroxide. Reduced glutathione (GSH) has a significant impact on cellular detoxification. The predominance of the oxidized form (GSSG) in the body represents a considerable stress blockade. Enzymes located in the plant cytoplasm, which catalyze the union of glutathione molecules with various cytotoxic compounds (conjugate formation), are also part of the plant’s defense system. Activation of these enzymes was observed following seed-priming treatments (Figure 2). The plant can then secrete the glutathione conjugates into the vacuole or apoplast. There are numerous references in the literature regarding the role of glutathione as a cellular detoxifier during stress [60,61,62].
The activation of the phenylpropanoid biosynthesis pathway was also noteworthy after seed-priming, which has been confirmed in several publications as a stress defense mechanism [46,63]. The phenylpropanoid pathway is a plant’s metabolic route that produces various secondary metabolites, including lignin, flavonoids, coumarins, and lignans. It begins with the amino acid phenylalanine and leads to diverse aromatic compounds, playing crucial roles in plant growth, development, and defense against stress and pathogens [64].
In addition, it is noteworthy that several genes were underexpressed by seed-priming treatments, including the carbon fixation by the Calvin cycle pathway, the glycolysis/gluconeogenesis pathway, the glyoxylate and dicarboxylate pathway, the pyruvate metabolism, nucleotide sugar metabolism, the pentose phosphate pathway, and other carbon fixation pathways.
Our transcriptomic analysis also reveals that certain sub-processes of photosynthesis, cellular respiration, and energy production are downregulated and suppressed as a result of seed pretreatment. The biochemical pathway of nucleotide sugar metabolism is already downmodulated under drought stress. In addition, the underactivity of the above-listed pathways also confirms the assumption that the plant’s immunostimulant priming acts as a kind of eustressor, activating cellular immune responses but also causing depression in primary assimilative and energy-producing processes [65,66]. The plant focuses most of its energy on triggering defensive responses, producing protective compounds, and survival rather than on the production of assimilates.
The third comparison of our experiment aimed to prove (or disprove) the long-term persistence of the seed-priming effect, i.e., the existence of stress memory in plants and its inducibility. To this end, we subjected our plants with previously seed-priming-activated immune systems to drought stress. We compared the evolution of their gene expression levels with those of “only” drought-stressed plants.
In this case, the most positively modulated biochemical pathways were the same as those observed in plants primed with seed-priming alone, but their gene expression levels were lower. This confirms the assumption that the immune system of plants primed at early developmental stages can be stimulated with bioactive compounds. This more prepared, resilient, and conditioned state can be maintained in the long term, providing real protection against potential later stress effects [11,14]. In seed-primed and drought-stressed plants, the downregulation in ubiquinone and other terpenoid quinone biosynthesis and isoquinoline alkaloid biosynthesis was observed but less pronounced than in plant samples that were seed-primed only.
To validate the genome-wide analysis, we performed laboratory studies to measure the activity of certain antioxidant enzymes (catalase, peroxidase, glutathione reductase), which showed a similar trend as the in silico analyses of the expression of individual genes. It was found that the antioxidant defense system was activated by seed-priming [67], and this activity, although to a lower extent, was significantly maintained and could be induced in later stages of plant development, even after stress. This is also proof that plant immune responses can be activated and kept in the long term.

5. Conclusions

One of the most pressing problems of our time is the protection against extreme environmental conditions caused by climate change. The basic requirements of sustainable agriculture include reducing environmental pollution and an ecological approach. These requirements can be met by a novel approach in which, by exploiting the potential inherent in the immune system of plants, we induce an immune response using bioactive compounds before the onset of stress effects. The preliminary treatment is based on the activation of priming reactions, which mainly affect the protective responses against stress.
In our experiment, we examined the effects of a commonly used seed-priming preparation containing macro- and microelements and found that the priming preparation, as a weak stressor, induces eustress in the plant, which has an immunostimulating effect. Based on the protective responses of healthy plants, the primed plants exposed to the stress effect later responded better to the stress, thus suffering less damage. With this experiment, we supported the hypothesis that plants not only have an immune system but that this immune system can be activated and can also store previous information and later recall it, i.e., primed plants have an activatable stress memory. This preventive approach may facilitate adaptation to environmental stressors in the future and maybe a cost-effective and regenerative farming tool for agriculture.

Supplementary Materials

The supplementary data are presented in the supplementary file Fasta and Excel book, worksheets 1–10 as: https://www.mdpi.com/article/10.3390/agronomy15061365/s1. Table S1: Count table of the de novo transcript; Table S2: Differentially expressed genes between control and drought-stressed data; Table S3: Differentially expressed genes between control and seed-priming data; Table S4: Differentially expressed genes between drought-stressed and seed-priming drought-stressed data; Figure S5: The most active biochemical pathways in the drought stressed plants (Purine metabolism); Figure S6: The most active biochemical pathways in the drought stressed plants (Thiamine metabolism); Figure S7: The most active biochemical pathways in the drought stressed plants (Starch and sucrose metabolism); Figure S8: The most active biochemical pathways in the drought stressed plants (Biosynthesis of various plant secondary metabolites); Figure S9: Seed priming activated enzymes on the phenylpropanoid biochemical pathway.

Author Contributions

Conceptualization, K.D. and Z.T.; data curation, K.D., M.A., R.R. and D.A.-H.; formal analysis, K.D. and M.A.; funding acquisition, Z.T.; investigation, K.D., M.A. and Z.T.; methodology, K.D. and M.A.; project administration, K.D. and Z.T.; resources, Z.T.; software, K.D. and Z.T.; supervision, K.D. and Z.T.; validation, K.D., M.A., R.R. and D.A.-H.; visualization, K.D. and R.R.; writing—original draft, K.D.; writing—review and editing, K.D., M.A., R.R., D.A.-H. and Z.T. All authors have read and agreed to the published version of the manuscript.

Funding

The Hungarian University of Agriculture and Life Sciences Research Excellence Programme and Flagship Research Groups Programme supported this work.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Data are contained within this article.
NGS library preparation and sequencing:
After sequencing, the following data were obtained per sample:
-
Number of reads for control sequences: 56,067,452
-
Number of reads for drought-stressed sequences: 61,000,344
-
Number of reads for seed-primed sequences: 62,857,128
-
Number of reads for seed-primed and drought-stressed sequences: 61,610,194
-
Length of the raw reads: 151 bp
The raw reads (SRA’s) were deposited in the National Center for Biotechnology Information (NCBI) database under the following accession numbers: Raw reads were deposited in the National Center for Biotechnology Information (NCBI) database under the following accessions:
(1)
Repository name: Wheat treated by ZnO nanoparticles or by seed-priming conditioner; Data identification number: PRJNA1142041; Direct URL to data: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1142041 (accessed on 30 July 2024).
(2)
Repository name: Wheat_1_R1 and Wheat_1_R2; Data identification number: SRR30042189; Direct URL to data: https://www.ncbi.nlm.nih.gov/sra/?term=SRR30042189 (accessed on 30 July 2024).
(3)
Repository name: Wheat_5_R1 and Wheat_5_R2; Data identification number: SRR30042187; Direct URL to data: https://www.ncbi.nlm.nih.gov/sra/?term=SRR30042187 (accessed on 30 July 2024).
(4)
Repository name: Wheat_17_R1 and Wheat_17_R2; Data identification number: SRR30042184; Direct URL to data: https://www.ncbi.nlm.nih.gov/sra/?term=SRR30042184 (accessed on 30 July 2024).
(5)
Repository name: Wheat_21_R1 and Wheat_21_R2; Data identification number: SRR30042188; Direct URL to data: https://www.ncbi.nlm.nih.gov/sra/?term=SRR30042188 (accessed on 30 July 2024).

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. (A) Source of transcripts; (B) biological processes implicated in the treatment effects; (C) cellular organelles engaged in the treatment outcomes; (D) enzymatic categories triggered due to the treatments. The biochemical processes and trends involved are the same for each treatment; hence, only a few figures are shown as examples.
Figure 1. (A) Source of transcripts; (B) biological processes implicated in the treatment effects; (C) cellular organelles engaged in the treatment outcomes; (D) enzymatic categories triggered due to the treatments. The biochemical processes and trends involved are the same for each treatment; hence, only a few figures are shown as examples.
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Figure 2. Enzymes in glutathione metabolism activated by seed-priming treatments. The figure was generated using the combined pathway analysis submenu of the OmicsBox software https://www.biobam.com/omicsbox version 3.4/ (accessed on 6 April 2025) and is presented in its original form. The image illustrates the sub-processes activated by the identified genes. The abbreviations indicate all genes and enzymes (EC code classification name) involved in the functioning of the given biochemical pathway, of which those marked in color underwent significant changes as a result of the treatment.
Figure 2. Enzymes in glutathione metabolism activated by seed-priming treatments. The figure was generated using the combined pathway analysis submenu of the OmicsBox software https://www.biobam.com/omicsbox version 3.4/ (accessed on 6 April 2025) and is presented in its original form. The image illustrates the sub-processes activated by the identified genes. The abbreviations indicate all genes and enzymes (EC code classification name) involved in the functioning of the given biochemical pathway, of which those marked in color underwent significant changes as a result of the treatment.
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Figure 3. Enzymes activated by drought-stress and seed-priming with drought-stress combined treatments in phenylpropanoid biosynthesis. The figure was produced utilizing the Combined Pathway Analysis option of the OmicsBox software version 3.4 available at https://www.biobam.com/omicsbox/ (accessed on 6 April 2025) and is displayed in its unaltered state. The figure depicts the sub-processes initiated by the identified genes. The designations denote all genes and enzymes (EC code categorization name) implicated in the operation of the specified biochemical pathway, with those highlighted in color exhibiting substantial alterations due to the treatment.
Figure 3. Enzymes activated by drought-stress and seed-priming with drought-stress combined treatments in phenylpropanoid biosynthesis. The figure was produced utilizing the Combined Pathway Analysis option of the OmicsBox software version 3.4 available at https://www.biobam.com/omicsbox/ (accessed on 6 April 2025) and is displayed in its unaltered state. The figure depicts the sub-processes initiated by the identified genes. The designations denote all genes and enzymes (EC code categorization name) implicated in the operation of the specified biochemical pathway, with those highlighted in color exhibiting substantial alterations due to the treatment.
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Figure 4. Number of genes upregulated (A) and downregulated (B) by individual and combined treatments.
Figure 4. Number of genes upregulated (A) and downregulated (B) by individual and combined treatments.
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Figure 5. Gene expression of antioxidant enzymes in different treatments based on in silico analyses. Catalase (A), Peroxidase (B), Chloroplast Glutathione Reductase (C), and Cytosolic Glutathione Reductase (D).
Figure 5. Gene expression of antioxidant enzymes in different treatments based on in silico analyses. Catalase (A), Peroxidase (B), Chloroplast Glutathione Reductase (C), and Cytosolic Glutathione Reductase (D).
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Figure 6. Average activities of antioxidant enzymes in different treatments based on laboratory analyses. Catalase (A), Peroxidase (B), and Glutathione Reductase (C).
Figure 6. Average activities of antioxidant enzymes in different treatments based on laboratory analyses. Catalase (A), Peroxidase (B), and Glutathione Reductase (C).
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Decsi, K.; Ahmed, M.; Abdul-Hamid, D.; Rizk, R.; Tóth, Z. Verification of Seed-Priming-Induced Stress Memory by Genome-Wide Transcriptomic Analysis in Wheat (Triticum aestivum L.). Agronomy 2025, 15, 1365. https://doi.org/10.3390/agronomy15061365

AMA Style

Decsi K, Ahmed M, Abdul-Hamid D, Rizk R, Tóth Z. Verification of Seed-Priming-Induced Stress Memory by Genome-Wide Transcriptomic Analysis in Wheat (Triticum aestivum L.). Agronomy. 2025; 15(6):1365. https://doi.org/10.3390/agronomy15061365

Chicago/Turabian Style

Decsi, Kincső, Mostafa Ahmed, Donia Abdul-Hamid, Roquia Rizk, and Zoltán Tóth. 2025. "Verification of Seed-Priming-Induced Stress Memory by Genome-Wide Transcriptomic Analysis in Wheat (Triticum aestivum L.)" Agronomy 15, no. 6: 1365. https://doi.org/10.3390/agronomy15061365

APA Style

Decsi, K., Ahmed, M., Abdul-Hamid, D., Rizk, R., & Tóth, Z. (2025). Verification of Seed-Priming-Induced Stress Memory by Genome-Wide Transcriptomic Analysis in Wheat (Triticum aestivum L.). Agronomy, 15(6), 1365. https://doi.org/10.3390/agronomy15061365

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