Skip to Content
MDPIMDPI

Advanced

LifeLife
PDF
  • Article
  • Open Access

19 February 2026

20 Pages

Deciphering the Boron Toxicity Tolerance Mechanisms of Triticum dicoccoides via RNA-Sequencing

,
,
,
,
,
,
,
and
1
Department of Soil Science and Plant Nutrition, Faculty of Agriculture, Selcuk University, Konya 42130, Türkiye
2
Department of Plant Biology, Faculty of AgriSciences, Mendel University in Brno, 61300 Brno, Czech Republic
3
Department of Genetics and Plant Breeding, Hajee Mohammad Danesh Science and Technology University, Dinajpur 5200, Bangladesh
4
Department of Field Crops, Faculty of Agriculture, Selcuk University, Konya 42130, Türkiye
Life2026, 16(2), 353;https://doi.org/10.3390/life16020353
This article belongs to the Section Plant Science
Version Notes
Order Reprints

Abstract

Boron (B) toxicity is one of the major abiotic stresses limiting wheat productivity in arid and semi-arid regions of the world. Thus, it is important to understand the molecular basis of tolerance in boron-tolerant wheat genetic resources for effective breeding. Wild emmer wheat is a valuable genetic resource for tolerance to multiple abiotic stresses; however, the molecular mechanisms behind boron toxicity tolerance in this species has not been sufficiently characterized. Here, we present the first RNA sequencing-based transcriptomic analysis of B toxicity response in a boron-tolerant Triticum dicoccoides genotype, PI362036. Shoot tissues exposed to high boron (10 mM B) for 7 days showed extensive transcriptional reprogramming with 2783 differentially expressed genes. Functional enrichment analyses showed that B toxicity significantly altered the genes associated with biosynthesis of secondary metabolites, metabolic pathways, ribosomal activity, carbon metabolism, RNA transport, photosynthesis–antenna proteins, and citrate cycle pathway. Several transcription factor families, including TIG, MYB, MYB-related families, NAC, C2H2-GATA, ARF, and AP2-EREBP families, showed significant differential regulation, emphasizing their regulatory roles in B stress adaptation. Collectively, this study provides the first comprehensive transcriptomic framework of boron toxicity tolerance in T. dicoccoides under short-term high boron exposure, identifying candidate genes and pathways that may be exploited for improving boron tolerance in cultivated wheat through targeted breeding strategies. Given that boron toxicity in agricultural systems primarily arises from excessive boron accumulation in soils and irrigation water, the identified pathways offer insight into early adaptive responses of shoot tissues to elevated boron availability.

1. Introduction

Globally, wheat is one of the foremost cereal crops, supporting the dietary requirements of around 35% of the world’s population [1]. While approximately one-fifth of wheat production is used for livestock feed, more than two-thirds of it is allocated to human consumption. Although there has been an increase in food production, food insecurity remains a global challenge, with malnutrition and hunger affecting more than 700 million people in the world [2]. This food insecurity is further exacerbated by climate change, as arable areas in key wheat production zones are significantly reduced. Among different climate-driven constraints, abiotic stresses pose a major threat to wheat production, particularly in arid and semi-arid regions around the world.
Boron (B) toxicity is a major but underexplored abiotic stress affecting wheat yield in agricultural regions containing excessive boron in irrigation water and soil [3,4]. In countries such as South Australia, Spain, Chile, Iraq, Turkey, and the United States, soils or irrigation water contain high boron concentrations, leading to B toxicity in agricultural systems [5,6,7,8,9,10]. In Turkey, B toxicity impacts 26.6% of agricultural land in the Central Anatolian region, undermining approximately 800,000 hectares [11]. B toxicity damages the plants at physiological, biochemical, and molecular levels, hindering some of the crucial plant functions such as photosynthesis, water transport, and cellular metabolism, including genetic processes, hormonal and nutrient imbalances, membrane damage, and reactive oxygen species production [12,13]. Chlorosis at leaf tips, localized necrosis, and stunted root growth are some of the most common symptoms of B toxicity in plants [14,15,16]. Although an excess of B impairs cellular metabolism and inhibits plant growth, it is an essential micronutrient required for normal plant development and plays an important role in cell wall structure, membrane integrity, reproductive development, and carbohydrate transport. Due to its involvement in basic metabolic processes, boron is routinely included in standard plant nutrition media such as Hoagland’s nutrient solution in the form of boric acid to facilitate normal growth.
The concentration range between boron deficiency and toxicity is extremely narrow. In most crops, below 0.1–0.5 mg B L−1 in soil solution develops deficiency symptoms, whereas concentrations exceeding 1–2 mg B L−1 develop toxicity symptoms [17,18]. These thresholds may vary according to species, genotypes, and experimental conditions. In hydroponic systems, boron is beneficial at 3–50 µM but becomes toxic at higher concentrations, often above 1–5 mM, depending on the sensitivity of crops [19,20]. Therefore, while boron is indispensable, a slight increase beyond its requirement can result in toxicity, particularly in arid and semi-arid regions.
Under normal conditions, plants take in B by utilizing passive diffusion, while in a boron-toxic environment, boric acid transport pathways and borate transporters regulate the uptake of B. Irrigation water that is rich in high B and inadequate soil leaching instigates the buildup of B in the upper layers of soil [21]. Though B deficiency in soil can be managed by the addition of fertilizers, it is difficult to confront B toxicity, as it is not possible to wash large agricultural lands [12,22]. Thus, it is crucial to recognize B-tolerant genotypes, understand the molecular mechanism behind their boron tolerance, and utilize them in breeding programs to manage B toxicity [16,22].
Wheat wild species serve as a potential genetic reservoir for elevating tolerance to different abiotic stresses. Wild emmer wheat (Triticum dicoccoides), progenitor of durum wheat and A and B genome donor of bread wheat, is a promising wheat genetic resource [23,24,25]. Though the molecular basis of resilience of wild emmer to various stresses such as drought, salinity, Fusarium head blight, powdery mildew, stripe rust, stem rust, and leaf rust [26,27,28,29] has been widely reported, the molecular mechanism behind its B toxicity tolerance has not been well investigated. In this study, we aimed to characterize genome-wide transcriptional changes in the shoot tissues of a boron-tolerant T. dicoccoides genotype, PI362036, under B-toxic conditions via RNA sequencing followed by functional annotation, pathway enrichment, and transcription factor analysis. This represents the first RNA seq-based transcriptomic investigation of B toxicity response in T. dicoccoides. By identifying key differentially expressed genes and regulatory pathways, this study seeks to provide candidate genes and molecular insights associated with boron stress tolerance that can be used in breeding programs directed toward developing B toxicity resilience in wheat.

2. Materials and Methods

2.1. Plant Material, Boron Toxicity Treatment, and Measurement of Growth Parameters

In our TUBITAK 1001 project (Grant No. 119O455), more than 150 genotypes, including wild and neglected wheat species, were screened under high boron (10 mM B) in different ploidy groups, and the well-known B-tolerant Turkish bread wheat cultivar Bolal 2973 [30,31] was used as a reference genotype to estimate their level of tolerance to boron toxicity. Among the screened genotypes, T. dicoccoides, PI362036, which was received from the USDA National Small Grains Collection, was identified as one of the most tolerant tetraploid genotypes based on the results obtained from physiological analysis (physiological data of PI362036 and Bolal 2973 is presented in Section 3). During the randomized design screening experiment at Selcuk University, Türkiye, seeds were surface-sterilized and kept in the dark for three days, after which the uniform seedlings were transferred to hydroponic pots containing half-strength Hoagland’s solution (pH 6.0) adapted to 16,000 lux light intensity, 22 ± 1 °C temperature, and 16/8 light/dark photoperiod. After 4 days of growth in half-strength Hoagland’s solution, plants were supplied with Hoagland’s solution containing 3.1 μM boron for the control treatment and 10 mM boron for the B toxicity treatment in the form of boric acid (H3BO3) [15,31,32]. Each treatment consisted of three biological replicates. Subsequently, followed by seven days of treatment, plants were harvested at the tillering stage (Feekes scale 4–5), and growth parameters, including root–shoot lengths, fresh and dry weights, were measured [31]. At the same time, shoot samples were collected from both the treatments in triplicate for molecular analysis, immediately frozen in liquid nitrogen, and stored at −80 °C until RNA extraction.

2.2. Isolation of RNA, cDNA Library Synthesis, and Transcriptome Sequencing of Harvested T. dicoccoides Leaf Tissues

In molecular analysis, total RNA was isolated from triplicate leaf samples following the manual extraction method using 1000 µL of QIAzol Lysis Reagent (QIAGEN, Hilden, Germany) [10]. The extracted RNA pellet of leaf samples was resuspended in 100 µL of nuclease-free, double-distilled water, and RNA integrity, quality, and concentration were assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA) and NanoDrop spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA), respectively. Equal amount of RNA samples with Integrity Numbers (RIN) exceeding 7 and A260/A280 ratios ranging from 2.0 to 2.1 from three biological replicates per treatment were pooled, and cDNA libraries were constructed. These libraries were sequenced using the DNBseq platform (MGI Tech Co., Ltd, Shenzhen, China.), generating around 96 million high-quality reads per sample. DNBseq technology, built around DNA nanoballs (DNBs), enables highly accurate and efficient sequencing. Raw reads were quality checked, and low-quality reads were removed. BGI Tech Solutions (Hong Kong) Co., Limited, used internal software SOAPnuke (v1.5.2) to filter reads. Only clean reads with Phred quality scores above 30 from both treatments were retained for downstream analysis.

2.3. Differential Gene Expression Analysis

High-quality reads were mapped to the Triticum aestivum reference genome (version IWGSC_CS_RefSeq_v2.1) using the HISAT2 software [v2.0.4; Johns Hopkins University, Baltimore, MD, USA], enabling efficient spliced transcript alignment. In parallel, Bowtie2 [v2.2.5] was utilized to align the reads to the associated reference gene set. Gene expression levels were quantified as fragments per kilobase of transcript per million mapped reads (FPKM), transcript abundance was normalized using RSEM (v1.2.12) and Cufflinks (v2.2.1), respectively. Differentially expressed genes (DEGs) between control and boron toxicity treatments were detected through PossionDis with criteria FDR ≤ 0.001, and fold change ≥ 2.

2.4. Functional Characterization, Gene Ontology, and KEGG Pathway Enrichment Analysis: Identification of Transcription Factor-Encoding DEGs

Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to pinpoint genes that are notably differentially expressed in molecular functions, biological processes, cellular components, and specific pathways. GO analysis filtered DEGs based on their roles and identified GO terms with a threshold Q value of <0.05 that were significantly overrepresented among the DEGs relative to the whole genome background. Likewise, KEGG analysis identified significantly enriched metabolic, signaling, or biochemical pathways related to DEGs, with a Q value of ≤0.05 considered statistically significant.

2.5. RT-qPCR Validation of RNA-Seq Data

RT-qPCR is widely used to validate the results of RNA sequencing by confirming the changes in expression of selected genes under stress in plants. To substantiate the RNA-Seq results obtained from leaf tissues, eight genes showing notable differential expressions between high B treatment and the control were randomly selected for validation. These included TdsαDOX-like (TraesCS3B02G549000; α-DOX), TdsNAS (TraesCS6A02G093000; NAS), TdsSFR1 (TraesCS3B02G001500; SFR1), TdsGELP-like (TraesCS3B02G219300; GELP), TdsFTSH9 (TraesCS3B02G252900; FTSH9), TdsDCAF13-like (TraesCS3B02G191500; DCAF13), TdsILK1-like (TraesCS3B02G306500; ILK1), and TdsUXS1-like (TraesCS3B02G388100; UXS1) genes. Their expression levels were assessed using RT-qPCR (Supplementary Table S1). Unique primers for each of the directed genes were designed using NCBI Primer-BLAST. The wheat glyceraldehyde-3-phosphate dehydrogenase gene (TaGAP) was used as an internal reference to normalize the expression of genes. Total RNA isolated from independent biological replicates was reverse transcribed into complementary DNA (cDNA) using the Transcriptor First Strand cDNA Synthesis Kit (Roche, Mannheim, Germany).
Further, 100 ng of 1:10 dilutions of synthesized cDNA were mixed with 2 μL each of (10 μM) forward and reverse primers, 8 μL of Maxima SYBR Green qRT-PCR Master Mix (Thermo Scientific), and 6 μL of RNase-free water to restore the final volume to 20 μL. With a pre-denaturation step at 95 °C for 6 min, followed by 35–45 cycles of denaturation at 95 °C for 10 s, annealing at 45–60 °C for 30 s, and extension at 72 °C for 30 s, qPCR was conducted in triplicate for both biological and technical replicates [10]. Post-amplification, melting curves were generated by incrementally raising the temperature from 50 °C at 4 °C/s to 95 °C at 0.1 °C/s. These curves facilitated the calculation of the standard error of the mean for target and reference gene expression across the two treatments and confirmed primer specificity. Relative expression levels were determined as suggested by Livak and Schmittgen [33].

2.6. Statistical Analysis

Growth and physiological data were collected for three independent biological replicates and were analyzed using analysis of variance (ANOVA) implemented in Graphpad Prism (version 8.0.2.263). Mean comparisons were performed at a significance level of p ≤ 0.05. For transcriptome analysis, statistical significance of differential gene expression was determined using adjusted p-values and false discovery rate (FDR) correction, as described above. GO and KEGG enrichment analyses were considered significant at Q ≤ 0.05. RT-qPCR results are presented as mean ± standard error of the mean (SEM). Log10-transformed expression values were analyzed using Welch’s unpaired t-test, and expression with a p-value < 0.05 was considered significant.

3. Results

3.1. Effect of B Toxicity Stress on Physiological Growth of T. dicoccoides, PI362036, and T. aestivum, Bolal 2973

B toxicity tolerance of wheat genotypes is widely determined using root and shoot growth metrics [30,31,34,35,36]. Hence, the B-toxicity tolerance of the T. dicoccoides genotype PI362036 and the well-known B-tolerant Bolal 2973 were compared based on root–shoot growth parameters. With reduced leaf chlorosis (Supplementary Figure S1), T. dicoccoides PI362036 showed higher resilience to B toxicity across all growth traits, except root length, relative to Bolal 2973 (Table 1; Supplementary Table S2). In particular, under high B treatment, Bolal 2973 showed a 28% decrease in shoot length (SL) and a 33% reduction in root length (RL) in comparison to the control, while PI362036 showed a 13% increase in SL and a 36% reduction in RL. Similarly, a 52% and 23% reduction in root and shoot fresh weight was experienced in Bolal 2973, whereas PI362036 reflected a modest 35% reduction and even a 5% increase in SFW. While a 36% reduction was observed in RDW in Bolal 2973, PI362036 demonstrated a 9% increase in RDW under B toxicity relative to control. While there was 1% decrease in shoot dry weight (SDW) in Bolal 2973 under B toxicity, PI362036 showed a substantial 51% increase in SDW in high B relative to control.
Table 1. Growth trait percentages for Bolal 2973 (T. aestivum) and PI362036 (T. dicoccoides) under 10 mM B as compared with control conditions. A negative sign (−) indicates a percentage decrease (individual data for triplicates are presented in Supplementary Table S2).

3.2. Transcriptome Sequencing and Genome Read Alignment

Leaves of T. dicoccoides genotype cultivated under boron (B)-toxic conditions were compared with those grown under control conditions via RNA sequencing employing the DNBseq platform. A total of 192.18 million clean reads, with an average of approximately 96 million reads per library, were obtained from cDNA libraries generated from control and B-toxicity treatments (Supplementary Table S3). The T. aestivum reference genome (version IWGSC_CS_RefSeq_v2.1) was employed to align 12.29 million and 13.53 million high-quality reads from the control and B-toxic libraries. With mapping efficiencies of 3.60% and 4.45%, the control and B-toxic libraries identified 32,452 and 41,221 genes, respectively, along with 36,519 and 47,693 transcripts (Supplementary Table S3). RNA sequencing also revealed 2663 novel genes and 9869 transcripts that can be significantly associated with B-toxicity responses (Supplementary Table S3) [37].

3.3. Differential Gene Expression in T. dicoccoides Leaves Exposed to Boron Toxicity Conditions

Leaves of T. dicoccoides showed significant regulatory changes in 2783 genes under B toxicity, with 2649 and 134 genes reflecting increased and reduced expression, respectively (Figure 1).
Figure 1. The scatter plot illustrates differentially expressed genes in the leaves of Triticum dicoccoides (Tds) genotype PI362036 under high-B treatment (10 mM TB) relative to control treatment (3.1 µM B), with up- and down-regulation indicated by red and blue dots. The X-axis (A) represents the mean expression level in leaves under the control treatment (coded by 5CL) transformed to a log2 scale, while the Y-axis (M) depicts the log2-transformed fold change in leaves under high boron treatment (coded by 5TL).

3.4. Functional Classification of Differentially Regulated Genes via Gene Ontology (GO) Enrichment

To identify the pathways most significantly affected by B toxicity stress, a Gene Ontology (GO) Enrichment Analysis was performed, encompassing molecular function, biological process, and cellular component categories, on genes exhibiting differential regulation (Figure 2; Supplementary Figure S2). The differentially expressed genes (DEGs) were grouped into 21 biological process categories, three cellular component categories, and 15 molecular function categories. The most enriched GO terms in the biological process domain included cellular and metabolic processes, with 1410 and 1152 DEGs, respectively (Figure 2). While the cellular anatomical entity and intracellular, with 1665 and 1227 DEGs, respectively, were the predominant GO terms in the cellular component category, the binding and catalytic activity were the leading terms in molecular function, with 1479 and 1216 DEGs, respectively (Figure 2). The functional roles of DEGs of T. dicoccoides under B toxicity can be comprehended from the obtained results. With the genes linked to cytoplasm (GO:0005737), intracellular (GO:0005622), and ribosomes (GO:0005840) being most enriched under B toxicity in cellular component category, a significant enrichment was identified in the structural molecule activity (GO:0005198), ribosome structural constituent (GO:0003735), RNA binding (GO:0003723), and unfolded protein binding (GO:0051082) in the molecular function category. Additionally, genes related to the peptide biosynthetic process (GO:0043043), translation (GO:0006412), the peptide metabolic process (GO:0006518), the amide biosynthetic process (GO:0043604), and the cellular amide metabolic process (GO:0043603) were prominently enriched in the biological process.
Figure 2. GO classification of DEGs in T. dicoccoides leaves under B-toxicity (10 mM B) versus control (3.1 μM B). X-axis: number of DEGs; Y-axis: GO categories.

3.5. Boron Toxicity-Responsive KEGG Pathways in T. dicoccoides

Specific biological processes regulated by gene interactions are identified by pathway-based analysis. Thus, pathway enrichment analysis of DEGs was evaluated via the KEGG database. Five major categories, including cellular processes, organismal systems, environmental information processing, metabolism, and genetic information processing, were observed on pathway annotations for 3829 DEGs that belong to 127 unique KEGG pathways (Table 2; Supplementary Figure S3). Biosynthesis of secondary metabolites, metabolic pathways, ribosomal activity, carbon metabolism, and RNA transport were the pathways with the highest DEGs. Folding, sorting, degradation, translation, and energy metabolism, along with global and overview maps, were the pathways with the most DEGs at Level 2 (Table 2). Maximum enrichment in KEGG analysis was observed in photosynthesis–antenna proteins, followed by the citrate cycle pathway (Figure 3).
Table 2. Highly enriched KEGG pathways in response to boron toxicity in T. dicoccoides PI362036. “Pathway ID” refers to the specific identifier assigned to each pathway, while “Number of genes” indicates the count of differentially expressed genes (DEGs) associated with each respective pathway.
Figure 3. Functional enrichment analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways was conducted for differentially expressed genes (DEGs) in Triticum dicoccoides leaves grown under B-toxic conditions (10 mM B) relative to a control treatment (3.1 μM B). The X-axis represents the enrichment factor, while the Y-axis lists the pathway names. A higher enrichment factor indicates greater significance of enrichment. The size of each data point corresponds to the number of DEGs associated with the respective pathway.

3.6. Boron Toxicity-Responsive Transcription Factors in T. dicoccoides Leaves

The expression of gene sequences is modulated by the interaction of their specific cis-regulatory elements with transcription factors (TFs) (Figure 4). Across 24 distinct families, 115 TFs showed differential expression of genes in T. dicoccoides leaves subjected to B toxicity stress relative to the control treatment. The TIG (a transcription factor immunoglobulin-like DNA-binding domain), MYB, and MYB-related families were the most abundant, with 14, 12, and 11 transcripts, respectively. Additionally, 8–10 transcripts were reported in NAC, C2H2-GATA, ARF, and AP2-EREBP families (Figure 4).
Figure 4. Transcription factor families consisting of differentially expressed genes (DEGs) in the leaves of Triticum dicoccoides subjected to high boron treatment (10 mM B) relative to control conditions (3.1 μM B). While the Y-axis represents different transcription factor families with differentially expressed genes, the X-axis presents the number of genes differentially expressed in each of the transcription factor families.

3.7. RT-qPCR-Based Expression Analysis of Selected Genes

The relative expression of genes α-DOX, NAS, SFR1, GELP, FTSH9, DCAF13, ILK1, and UXS1 in RT-qPCR under B toxicity, as relative to control, was comparable to the outcomes of RNA sequencing analysis (Figure 5). The analysis of Log10-transformed expression values showed that TdsDCAF13-like exhibited a statistically significant upregulation under 10 mM boron stress (p < 0.05). However, other genes, including TdsαDOX-like, TdsNAS, TdsGELP-like, TdsILK1-like, and TdsUXS1-like, showed consistent but non-significant upregulation while TdsSFR1 showed consistent but non-significant downregulation (Table S4). In alignment with 14.84- and 14.7-fold upregulation in transcriptome sequencing, TdsαDOX-like and TdsNAS genes showed a one-fold increase in expression in RT-qPCR. More than 13-fold changes in TdsGELP-like, TdsDCAF13-like, TdsILK1-like, and TdsUXS1-like genes in RNA sequencing, and two- to four-fold increases in RT-qPCR, reflected their strong responsiveness to high B. With a significant 13.8-fold change in transcriptome data, the TdsFTSH9 gene displayed a minimal increase in RT-qPCR. Opposed to other genes, a downregulation was seen in the TdsSFR1 gene in RT-qPCR, aligning with RNA sequencing (Supplementary Tables S1 and S4; Figure 5).
Figure 5. The levels of expression of the (a) TdsαDOX-like, (b) TdsNAS, (c) TdsSFR1, (d) TdsGELP-like, (e) TdsFTSH9, (f) TdsDCAF13-like, (g) TdsILK1-like, and (h) TdsUXS1-like genes in control and B-toxicity treated wheat leaves, quantified by RT-qPCR, normalized to the levels of the reference gene TaGAP, and expressed on a log2 scale relative to the levels of the same gene in control treatment (level in control treatment given a value of 1). These values represent the average expression of three independent replicates, and the error bars represent the standard error of the mean between biological replicates. While TdsαDOX-like, TdsNAS, TdsGELP-like, TdsFTSH9, TdsDCAF13-like, TdsILK1-like, and TdsUXS1-like genes showed an upregulation, TdsSFR1 showed downregulation under boron toxicity as compared to the control treatment.

4. Discussion

4.1. Greater B Toxicity Tolerance in T. dicoccoides Genotype, PI362036, than T. aestivum Genotype, Bolal-2973

Changes in growth characteristics under B toxicity reflect B tolerance, with T. dicoccoides showing less chlorosis and reduced growth inhibition than Bolal 2973, indicating greater resilience [10,16,30,31,32,34,35,36]. Corroborating with previous findings, a few genotypes of wild or underutilized wheat species, such as Aegilops sp., T. dicoccum, T. dicoccoides, and T. zhukovskyi, possess higher B tolerance than conventional bread wheat cultivars [10,16,31,32,38]. As B interacts with cell wall structures and metabolic pathways, B toxicity impedes cell division or elongation, hindering plant growth [30,39]. The lesser disruption of cellular processes in the T. dicoccoides genotype may have resulted in its increased shoot length under B stress. Root–shoot dry weight is an important attribute to evaluate genotypic tolerance to various abiotic stresses [31,36,40]. Previously, various B toxicity-tolerant genotypes were shown to have greater SDW relative to less tolerant ones under high B [10,16,31,41]. Increased root dry weight despite reduced root length and fresh weight may result from decreased root water content and B-induced cell wall modifications, such as pectin and polysaccharide accumulation, that facilitate B sequestration [42,43]. In PI362036, roots were more sensitive to B toxicity than shoots, likely due to impaired meristematic activity and reduced water uptake limiting B translocation to shoots, while the maintenance of shoot biomass may reflect enhanced carbon reallocation and sustained photosynthetic capacity typical of B-tolerant genotypes [16,32,44,45,46].

4.2. Gene Ontology Enrichment of T. dicoccoides Genes Under Boron Toxicity Stress

B toxicity led to noteworthy enrichment of DEGs associated with cellular anatomical structures and intracellular components, suggesting that comprehensive cellular reorganization can be an important strategy in B-tolerant genotypes [16,32]. The enrichment of genes related to cytoplasmic elements, ribosomes, structural molecule activity, and RNA binding highlights coordinated regulation of protein synthesis, cellular maintenance, and post-transcriptional control under B stress [47]. The protein misfolding developed by B-induced oxidative damage is stabilized by the expression of genes associated with unfolded protein binding [48], while enrichment of catalytic activity reflects stimulation of detoxification, antioxidant defense, and boron-chelating pathways [49]. Enrichment of genes involved in peptide biosynthesis, metabolism, and translation can be involved in facilitating cellular stability and boron compartmentalization [50,51,52]. The presence of both up- and downregulated genes in key cellular GO categories underscores the importance of cellular restructuring in B toxicity.

4.3. KEGG Pathways Involved in Boron Toxicity Response in T. dicoccoides

Differential expression of genes linked to metabolic pathways under B toxicity may involve a network of indirect protein–protein interactions governing the pathways associated with photosynthesis, oxidative phosphorylation, and linoleic acid metabolism [53]. Significant gene participation in secondary metabolite biosynthesis suggests the synthesis of secondary metabolites such as antioxidants under high B, alleviating the effect of reactive oxygen species [10,54]. Significant enrichment in ribosomes, RNA transport, and protein processing in the endoplasmic reticulum-related pathways highlights enhanced translational activity and post-transcriptional regulation required for cellular recovery and enhances B toxicity tolerance [10,32,50,51]. Significant differential regulation in the carbon metabolism pathway directs towards a shift in carbon processing in T. dicoccoides leaves to adapt to B toxicity stress [18,55].

4.4. Transcription Factors Activated in T. dicoccoides Leaves Under Boron Toxicity Stress

The activated TF families, including TIG, MYB, MYB-related families, NAC, C2H2-GATA, ARF, and AP2-EREBP TFs (Figure 4), might have increased the genotype’s resilience by differential regulation of stress-responsive genes. The calmodulin-binding transcription activators (CAMTA) family possesses a conserved functional domain that is TIG (a transcription factor immunoglobulin-like DNA-binding domain) and CAMTAs are sensitive to abiotic stresses such as cold, drought, and salinity [56]. The significantly upregulated TIG TF genes in our study are found in the nucleus, participating in DNA binding and sequence-specific DNA-binding. In response to Ca2+ signals triggered by stress, these TFs bind with specific DNA motifs in target gene promoters and modulate gene expression [57]. The upregulation of CAMTAs (TIG-domain) under boron toxicity stress indicates that Ca2+-mediated signaling via CAMTAs may be involved in boron stress perception [58].
Different biological and physiological processes, including signal transduction, hormone biosynthesis, growth regulation, cell differentiation, and the regulation of primary and secondary metabolic pathways, are known to involve MYB transcription factors. The upregulation of MYB and MYB-related transcription factor (TF) genes in this study corroborates with previous studies emphasizing their role in assisting B stress adaptation in boron-tolerant wheat genotypes [32,59]. The damaging consequences of boron toxicity are reduced by the regulation of metabolic pathways, such as flavonoid biosynthesis, by these transcription factors [60]. Various functional pathways, notably including plant hormone signal transduction and the spliceosome, involve MYB-regulated DEGs, highlighting their role in boron toxicity adaptation. The upregulation of these TFs under high B may have facilitated metabolic reprogramming and increased detoxification via flavonoid biosynthesis and ROS scavenging.
NAC is one of the important TFs that has been identified as B toxicity-responsive in different plant species. In our study, all the identified NAC TF genes were significantly upregulated under B toxicity, similar to barley [48] and T. dicoccum [32] leaves, showing increased expression of NAC-domain TF genes, and contrary to O. sativa [61], T. urartu [16], and T. zhukovskyi [10], where high B supply downregulated the expression of the NAC domain. The significantly upregulated NAC TF genes in the T. dicoccoides genotype in our study are found either in the nucleus or as an integral component of the membrane, participating in DNA binding and involved in the regulation of transcription. These TFs regulate abiotic stress adaptation via modulation of genes involved in protein homeostasis, senescence, and membrane stability.
A cross-talk between boron stress responses and phytohormone signaling pathways leading to stress acclimation might be possible via the induction of AP2/EREBP and ARF transcription factors [62]. The transcriptional regulation linked to redox balance, light-responsive processes, and nutrient homeostasis can be associated with the induction of C2H2-GATA transcription factors. The differential regulation of different TFs demonstrated that their coordinated action is associated with B toxicity tolerance in T. dicoccoides.

4.5. RT-qPCR-Based Assessment of Expression Levels of Selected Genes

RT-qPCR was conducted to validate the gene expression of eight genes with significant expression in transcriptome sequencing. While RNA sequencing showed pronounced fold changes in the expression of selected genes, qRT-PCR showed modest regulation. Similar RNA-seq/qRT-PCR variations have been observed in previous studies, typically arising from platform-specific biases, differences in normalization methods, and dynamic range and sensitivity of qPCR vs. RNA-seq [63,64,65,66,67].
The TdsαDOX-like gene is involved in biological functions such as fatty acid biosynthesis, the oxidative stress response, oxylipin production, and the cellular detoxification of reactive oxygen species, and molecular functions such as heme binding, oxidoreductase activity, and peroxidase activity. Increased expression of α-DOX genes in wheat has been previously reported under different biotic stresses, such as powdery mildew and Fusarium graminearum [68,69], and abiotic stresses such as low temperature (4 °C) [70]. Moreover, its downregulation in T. urartu leaves B toxicity stress, suggesting its role in developing high B tolerance, warranting further detailed characterization [16].
TdsNAS, encoding nicotianamine synthase that regulates iron homeostasis and transport, was upregulated under high B, which is similar to its induction under iron deficiency, and given the role of wheat TaNAS-D in enhancing salt tolerance, it may likewise contribute to B tolerance [71,72,73,74,75].
The TdsSFR1 gene, showing downregulation under B toxicity in our study, showed significantly increased expression in cold stress and even in B stress in previous studies [32,70]. The significant differences in the expression of this gene in various stresses can be credited to the homologs of this gene, emphasizing the need for their functional characterization.
TdsGELP-like gene regulates hydrolase activity at the molecular level and lipid metabolism at the biological level. Similar to our results, studies on different plants showed an increase in the expression of this gene under Obuda pepper virus infection [76], drought stress, and salt stress [77]. However, in T. urartu, the results were contrary to our outcomes, where this gene showed a downregulation under B stress [16].
TdsFTSH9 regulates ATP hydrolysis, hydrolase function, ATP binding, nucleotide binding, metalloendopeptidase, and ATP-dependent peptidase activity at the molecular level and response to heat stress and proteolysis at the biological level. Similar to our results, studies on different plants showed an increase in the expression of this gene under stripe rust infection [68], salinity stress [78], and temperature extremes, including heat and cold stress [79]. However, in T. urartu, the results were contrary to our outcomes, where this gene showed a downregulation under B stress [16].
The TdsDCAF13-like gene, associated with rRNA processing and protein ubiquitination, was upregulated, which is consistent with stress-induced expression of DCAF genes in Arabidopsis, tomato, and B-tolerant T. dicoccum and supported by evidence that DCAF mutations increase susceptibility to multiple abiotic stresses [32,80,81].
Although poorly characterized in wheat, TdsILK1-like has been implicated in ozone- and drought-responsive signaling in Arabidopsis [82]. Finally, the TdsUXS1-like gene, which catalyzes UDP-xylose biosynthesis for cell wall polysaccharide formation, was upregulated under B toxicity, in line with its induction during fungal infections and B stress in wheat, underscoring a potential role in structural or metabolic stress adaptation [68].
Based on different outcomes, T. dicoccoides genotype PI362036 seems to involve different strategies for high B tolerance, including cell wall-based sequestration, intracellular compartmentalization, and metabolic and translation reprogramming. The transcriptomic profile of PI362036 closely aligned with its physiological tolerance traits, including reduced leaf chlorosis, increased shoot dry weight, and maintenance of shoot growth under high B. The enrichment of photosynthesis–antenna protein genes and chloroplast-associated DEGs likely contributed to improved photosynthetic efficiency and sustained biomass accumulation. Concurrently, significant induction of carbon metabolism and ribosomal pathways suggests enhanced carbon assimilation and protein synthesis supporting shoot growth under B stress. The high induction of ribosomal genes, RNA-binding proteins, RNA transport pathways, and transcription factor families such as MYB, NAC, and TIG/CAMTA further reflects metabolic and translational reprogramming required to maintain cellular homeostasis.
Several enriched pathways and DEGs pointed toward effective cellular detoxification and compartmentalization strategies. Upregulation of genes involved in secondary cell wall modification, carbohydrate metabolism, and UDP-xylose biosynthesis (UXS1-like) suggests enhanced boron binding to pectic polysaccharides, enabling cell wall-based sequestration [83]. Additionally, enrichment of KEGG pathways associated with endocytosis, ER protein processing, and vesicle trafficking supports intracellular compartmentalization of excess B [84]. Genes involved in oxidative stress mitigation and protein quality control, including TdsαDOX-like and FTSH9, along with ER-associated protein processing pathways, likely protect photosynthetic and metabolic machinery from B-induced oxidative damage [85,86]. The induction of secondary metabolite biosynthesis, oxidative phosphorylation, and carbon metabolism pathways further suggests increased production of antioxidant-related compounds, such as phenolics and flavonoids, which are well known for their ROS-scavenging roles [87]. The coordinated upregulation of MYB and NAC transcription factors, which are key regulators of antioxidant enzymes and secondary metabolite biosynthesis, reinforces the activation of robust ROS defense mechanisms under B toxicity [88]. Collectively, these responses explain the reduced chlorosis and sustained shoot biomass observed in PI362036.
The high and reproducible upregulation of different genes in both RNA-sequencing and qPCR validation makes them a potential candidate for functional validation. For example, the TdsGELP-like gene involved in lipid remodeling and membrane stability can maintain cellular integrity under boron stress conditions. The identified boron-responsive candidate genes and key pathways provided a molecular framework for future research in this direction. Targeted validation of these genes using CRISPR/Cas9-mediated gene editing or transgenic overexpression in wheat or model systems such as Arabidopsis thaliana will confirm roles in conferring boron toxicity tolerance and can facilitate their deployment in wheat improvement programs.

5. Conclusions

Despite extensive research on boron toxicity in wheat, little is known regarding the molecular mechanisms conferring tolerance, and it is largely limited to physiological traits and known boron transporters, with most of the studies focused on root responses of cultivated durum and bread wheat. Thus, shoot-dependent genome-wide transcriptomic analyses of wheat wild species remain scarce. This study represents the first attempt at RNA sequencing-based transcriptomic analysis of shoot tissues of the boron-tolerant T. dicoccoides genotype, PI362036. A total of 2783 significantly differentially expressed genes (DEGs) were identified, with 2649 upregulated and 134 downregulated, indicating extensive transcriptional reprogramming in response to B stress. The enrichment of different gene ontology terms, such as cellular and metabolic processes, cellular anatomical entity, and intracellular, and binding and catalytic activity, highlighted a comprehensive cellular reorganization and activation of biochemical responses in the tolerant genotype. The extensive transcriptional changes in this genotype under B toxicity involved metabolic pathways, photosynthesis-related processes, ribosomal activity, RNA transport, and protein processing, along with the coordinated regulation of multiple transcription factor families. The results not only provided candidates for functional validation through reverse genetics and CRISPR/Cas-based genome editing but also for the development of molecular markers for the introgression of boron toxicity tolerance from wild emmer into elite wheat cultivars.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/life16020353/s1; Figure S1: Comparison of growth responses in Triticum dicoccoides genotype PI362036 and Triticum aestivum genotype Bolal 2973 following 7 days of hydroponic treatment. Figure S2: Classification of upregulated and downregulated differentially expressed genes based on Gene ontology (GO) analysis in Triticum dicoccoides leaves exposed to boron toxicity (10 mM B) relative to control conditions (3.1 μM B). Figure S3: KEGG pathway classification of differentially expressed genes in Triticum dicoccoides leaves under boron-toxic conditions (10 mM B) relative to control treatment (3.1 μM B). X-axis: number of genes; Y-axis: KEGG functional categories. Supplementary Tables: Table S1: Details of DEGs of T. dicoccoides leaves analyzed through RT-qPCR and their primer sequences. Table S2: Data on six growth traits, including individual measurements and mean values (± standard error of the mean, n = 3) for Triticum dicoccoides genotype PI362036 and Triticum aestivum genotype Bolal 2973 grown under high boron (10 mM B) and control (3.1 μM B) treatments obtained from the screening of more than 150 wheat accessions. Table S3: Comparison of reference genome alignment quality and RNA sequencing data for Triticum dicoccoides (Td) genotype PI362036 leaves grown under high boron (10 mM B) and control (3.1 μM B) conditions. Table S4: Log10-transformed expression values of DEGs of T. dicoccoides leaves using Welch’s unpaired t-test, where p < 0.05 is significant.

Author Contributions

Study conceptualized and designed by A.P. and M.K.K.; experiments done by B.S. under the supervision of A.P.; analysis performed by M.K.K., T.V. and M.A.; manuscript written by A.P. Investigation by M.H. (Mehmet Hamurcu) and M.H. (Mustafa Harmankaya); funding acquisition by A.P.; manuscript reviewed and edited by M.K.K., A.T. and S.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the TUBITAK 1001 project with grant no. 119O455 and the Selcuk University BAP project with grant no. 23401004.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The sequence data related to this project has been submitted to NCBI SRA database under the bioproject accession number PRJNA1335954 with SRA accessions, SRR35657273 and SRR35657272.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Grote, U.; Fasse, A.; Nguyen, T.T.; Erenstein, O. Food Security and the Dynamics of Wheat and Maize Value Chains in Africa and Asia. Front. Sustain. Food Syst. 2021, 4, 617009. [Google Scholar] [CrossRef] [Scilit]
  2. UNICEF. The State of Food Security and Nutrition in the World 2024; UNICEF: New York, NY, USA, 2024. [Google Scholar]
  3. Ortiz, R.; Sayre, K.D.; Govaerts, B.; Gupta, R.; Subbarao, G.V.; Ban, T.; Hodson, D.; Dixon, J.M.; Ortiz-Monasterio, J.I.; Reynolds, M. Climate change: Can wheat beat the heat? Agric. Ecosyst. Environ. 2008, 126, 46–58. [Google Scholar] [CrossRef] [Scilit]
  4. Cairns, J.E.; Prasanna, B.M. Developing and deploying climate-resilient maize varieties in the developing world. Curr. Opin. Plant Biol. 2018, 45, 226–230. [Google Scholar] [CrossRef] [Scilit]
  5. Cartwright, B.; Zarcinas, B.; Mayfield, A. Toxic concentrations of boron in a red-brown earth at Gladstone, South Australia. Soil Res. 1984, 22, 261–272. [Google Scholar] [CrossRef] [Scilit]
  6. Nable, R.O.; Bañuelos, G.S.; Paull, J.G. Boron toxicity. Plant Soil 1997, 193, 181–198. [Google Scholar] [CrossRef] [Scilit]
  7. Kalayci, M.; Alkan, A.; Cakmak, I.; Bayramoğlu, O.; Yilmaz, A.; Aydin, M.; Ozbek, V.; Ekiz, H.; Ozberisoy, F. Studies on differential response of wheat cultivars to boron toxicity. Euphytica 1998, 100, 123–129. [Google Scholar] [CrossRef] [Scilit]
  8. Jefferies, S.P.; Pallotta, M.A.; Paull, J.G.; Karakousis, A.; Kretschmer, J.M.; Manning, S.; Islam, A.K.M.R.; Langridge, P.; Chalmers, K.J. Mapping and validation of chromosome regions conferring boron toxicity tolerance in wheat (Triticum aestivum). Theor. Appl. Genet. 2000, 101, 767–777. [Google Scholar] [CrossRef] [Scilit]
  9. El-Shazoly, R.M.; Metwally, A.A.; Hamada, A.M. Salicylic acid or thiamin increases tolerance to boron toxicity stress in wheat. J. Plant Nutr. 2019, 42, 702–722. [Google Scholar] [CrossRef] [Scilit]
  10. Pandey, A.; Khan, M.K.; Hamurcu, M.; Brestic, M.; Topal, A.; Gezgin, S. Insight into the Root Transcriptome of a Boron-Tolerant Triticum zhukovskyi Genotype Grown under Boron Toxicity. Agronomy 2022, 12, 2421. [Google Scholar] [CrossRef] [Scilit]
  11. Gezgin, S.; Dursun, N.; Hamurcu, M.; Harmankaya, M.; Onder, M.; Sade, B.; Topal, A.; Soylu, S.; Akgun, N.; Yorgancilar, M.; et al. Boron Content of Cultivated Soils in Central-Southern Anatolia and its Relationship with Soil Properties and Irrigation Water Quality. In Boron in Plant and Animal Nutrition; Springer: Boston, MA, USA, 2002; pp. 391–400. [Google Scholar]
  12. Reid, R.J.; Hayes, J.E.; Post, A.; Stangoulis, J.C.R.; Graham, R.D. A critical analysis of the causes of boron toxicity in plants. Plant Cell Environ. 2004, 27, 1405–1414. [Google Scholar] [CrossRef] [Scilit]
  13. Landi, M.; Degl’Innocenti, E.; Pardossi, A.; Guidi, L. Antioxidant and photosynthetic responses in plants under boron toxicity: A review. Am. J. Agric Biol. Sci. 2012, 7, 255–270. [Google Scholar] [CrossRef] [Scilit]
  14. Sakamoto, T.; Inui, Y.T.; Uraguchi, S.; Yoshizumi, T.; Matsunaga, S.; Mastui, M.; Umeda, M.; Fukui, K.; Fujiwara, T. Condensin II alleviates DNA damage and is essential for tolerance of boron overload stress in Arabidopsis. Plant Cell 2011, 23, 3533–3546. [Google Scholar] [CrossRef] [Scilit]
  15. Çatav, Ş.S.; Genç, T.O.; Kesik Oktay, M.; Küçükakyüz, K. Effect of boron toxicity on oxidative stress and genotoxicity in wheat (Triticum aestivum L.). Bull. Environ. Contam. Toxicol. 2018, 100, 502–508. [Google Scholar] [CrossRef] [Scilit]
  16. Uyar, G.S.; Pandey, A.; Hamurcu, M.; Vyhnanek, T.; Harmankaya, M.; Topal, A.; Gezgin, S.; Khan, M.K. Transcriptome Analysis Reveals Key Genes Involved in the Response of Triticum urartu to Boron Toxicity Stress. Agronomy 2025, 15, 191. [Google Scholar] [CrossRef] [Scilit]
  17. Brdar-Jokanović, M. Boron toxicity and deficiency in agricultural plants. Int. J. Mol. Sci. 2020, 21, 1424. [Google Scholar] [CrossRef] [Scilit]
  18. Princi, M.P.; Lupini, A.; Araniti, F.; Longo, C.; Mauceri, A.; Sunseri, F.; Abenavoli, M.R. Boron Toxicity and Tolerance in Plants: Recent Advances and Future Perspectives. Plant Met. Interact. 2016, 115–147. [Google Scholar] [CrossRef] [Scilit]
  19. Shah, A.; Wu, X.; Ullah, A.; Fahad, S.; Muhammad, R.; Yan, L.; Jiang, C. Deficiency and toxicity of boron: Alterations in growth, oxidative damage and uptake by citrange orange plants. Ecotoxicol. Environ. Saf. 2017, 145, 575–582. [Google Scholar] [CrossRef] [Scilit]
  20. Pereira de Souza Junior, J.; de Mello Prado, R.; Machado dos Santos Sarah, M.; Felisberto, G. Silicon mitigates boron deficiency and toxicity in cotton cultivated in nutrient solution. J. Plant Nutr. Soil Sci. 2019, 182, 805–814. [Google Scholar] [CrossRef] [Scilit]
  21. Camacho-Cristóbal, J.J.; Rexach, J.; González-Fontes, A. Boron in plants: Deficiency and toxicity. J. Integr. Plant Biol. 2008, 50, 1247–1255. [Google Scholar] [CrossRef] [Scilit]
  22. Hua, T.; Zhang, R.; Sun, H.; Liu, C. Alleviation of boron toxicity in plants: Mechanisms and approaches. Crit. Rev. Environ. Sci. Technol. 2021, 51, 2975–3015. [Google Scholar] [CrossRef] [Scilit]
  23. Ozkan, H.; Brandolini, A.; Pozzi, C.; Effgen, S.; Wunder, J.; Salamini, F. A reconsideration of the domestication geography of tetraploid wheats. Theor. Appl. Genet. 2005, 110, 1052–1060. [Google Scholar] [CrossRef] [Scilit]
  24. Matsuoka, Y. Evolution of Polyploid Triticum Wheats under Cultivation: The Role of Domestication, Natural Hybridization and Allopolyploid Speciation in their Diversification. Plant Cell Physiol. 2011, 52, 750–764. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  25. Pandey, A.; Khan, M.K.; Ozkan, H.; Arifuzzaman, M.; Hasanuzzaman, M.; Gupta, O.P.; Bairwa, R.K. Insight into the wheat genetic resources with emphasis on Aegilops and Triticum species. In Wheat Wild Relatives; Elsevier: London, UK, 2025; pp. 1–37. [Google Scholar]
  26. Huang, L.; Raats, D.; Sela, H.; Klymiuk, V.; Lidzbarsky, G.; Feng, L.; Krugman, T.; Fahima, T. Evolution and adaptation of wild emmer wheat populations to biotic and abiotic stresses. Annu. Rev. Phytopathol. 2016, 54, 279–301. [Google Scholar] [CrossRef] [Scilit]
  27. Peng, J.H.; Sun, D.F.; Peng, Y.L.; Nevo, E. Gene Discovery in Triticum dicoccoides, the Direct Progenitor of Cultivated Wheats. Cereal Res. Commun. 2013, 41, 1–22. [Google Scholar] [CrossRef] [Scilit]
  28. Turgay, E.B. Screening the promising wild emmer wheat genotypes containing the Yr15 gene in Türkiye and Syria through molecular markers. Genet. Resour. Crop Evol. 2025, 72, 1169–1182. [Google Scholar] [CrossRef] [Scilit]
  29. Singh, P.; Dhaliwal, S.K.; Kaur, S.; Kaur, M.; Mavi, G.; Chhuneja, P.; Kaur, S. Harnessing potential grain traits from pre-breeding backcross introgression lines of wild emmer wheat. Cereal Res. Commun. 2025, 53, 507–515. [Google Scholar] [CrossRef] [Scilit]
  30. Pallotta, M.; Schnurbusch, T.; Hayes, J.; Hay, A.; Baumann, U.; Paull, J.; Langridge, P.; Sutton, T. Molecular basis of adaptation to high soil boron in wheat landraces and elite cultivars. Nature 2014, 514, 88–91. [Google Scholar] [CrossRef] [Scilit]
  31. Khan, M.K.; Pandey, A.; Hamurcu, M.; Avsaroglu, Z.Z.; Ozbek, M.; Omay, A.H.; Elbasan, F.; Omay, M.R.; Gokmen, F.; Topal, A. Variability in physiological traits reveals boron toxicity tolerance in Aegilops species. Front. Plant Sci. 2021, 12, 736614. [Google Scholar] [CrossRef] [Scilit]
  32. Khan, M.K.; Pandey, A.; Hamurcu, M.; Rajpal, V.R.; Vyhnanek, T.; Topal, A.; Raina, S.N.; Gezgin, S. Insight into the Boron Toxicity Stress-Responsive Genes in Boron-Tolerant Triticum dicoccum Shoots Using RNA Sequencing. Agronomy 2023, 13, 631. [Google Scholar] [CrossRef] [Scilit]
  33. Livak, K.J.; Schmittgen, T.D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 2001, 25, 402–408. [Google Scholar] [CrossRef] [Scilit]
  34. Baccari, N.; Krouma, A. Insight into the Physiological Traits Underlying the Genotypic Differences in Response of Durum Wheat (Triticum durum Desf.) to Boron Availability. Russ. J. Plant Physiol. 2024, 71, 36. [Google Scholar] [CrossRef] [Scilit]
  35. Schnurbusch, T.; Langridge, P.; Sutton, T. The Bo1-specific PCR marker AWW5L7 is predictive of boron tolerance status in a range of exotic durum and bread wheats. Genome 2008, 51, 963–971. [Google Scholar] [CrossRef] [Scilit]
  36. Metwally, A.; El-Shazoly, R.; Hamada, A.M. Effect of boron on growth criteria of some wheat cultivars. J. Biol. Earth Sci. 2012, 2, B1–B9. [Google Scholar]
  37. Safavi-Rizi, V.; Herde, M.; Stöhr, C. RNA-Seq reveals novel genes and pathways associated with hypoxia duration and tolerance in tomato root. Sci. Rep. 2020, 10, 1692. [Google Scholar] [CrossRef] [Scilit]
  38. Emon, R.; Gustafson, K.; Bebeli, P.; Jahiruddin, M.; Haque, M.; Ross, K.; Gusafson, J. Screening Aegilops-Triticum species for Boron tolerance. Afr. J. Agr. Res. 2012, 7, 1931–1936. [Google Scholar]
  39. Schnurbusch, T.; Collins, N.C.; Eastwood, R.F.; Sutton, T.; Jefferies, S.P.; Langridge, P. Fine mapping and targeted SNP survey using rice-wheat gene colinearity in the region of the Bo1 boron toxicity tolerance locus of bread wheat. Theor. Appl. Genet. 2007, 115, 451–461. [Google Scholar] [CrossRef] [Scilit]
  40. Nejad, S.G.; Savaghebi, G.; Farahbakhsh, M.; Amiri, R.M.; Rezaei, H. Tolerance of some wheat varieties to boron toxicity. Cereal Res. Commun. 2015, 43, 384–393. [Google Scholar] [CrossRef] [Scilit]
  41. Torun, A.A.; Yazici, A.; Erdem, H. ÇAKMAKİ: Genotypic variation in tolerance to boron toxicity in 70 durum wheat genotypes. Turk. J. Agric. For. 2006, 30, 49–58. [Google Scholar]
  42. Li, X.; Kamran, M.; Saleem, M.H.; Al-Ghamdi, A.A.; Al-Hemaid, F.M.; Elshikh, M.S.; Zhao, S.; Riaz, M. Potential application of melatonin in reducing boron toxicity in rice seedlings through improved growth, cell wall composition, proline, and defense mechanisms. Chemosphere 2023, 335, 139068. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  43. Riaz, M.; Kamran, M.; Rizwan, M.; Ali, S.; Wang, X. Foliar application of silica sol alleviates boron toxicity in rice (Oryza sativa) seedlings. J. Hazard. Mater. 2022, 423, 127175. [Google Scholar] [CrossRef] [Scilit]
  44. Choi, E.Y.; Kolesik, P.; McNeill, A.; Collins, H.; Zhang, Q.; Huynh, B.L.; Graham, R.; Stangoulis, J. The mechanism of boron tolerance for maintenance of root growth in barley (Hordeum vulgare L.). Plant Cell Environ. 2007, 30, 984–993. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  45. Oikonomou, A.; Ladikou, E.-V.; Chatziperou, G.; Margaritopoulou, T.; Landi, M.; Sotiropoulos, T.; Araniti, F.; Papadakis, I.E. Boron excess imbalances root/shoot allometry, photosynthetic and chlorophyll fluorescence parameters and sugar metabolism in apple plants. Agronomy 2019, 9, 731. [Google Scholar] [CrossRef] [Scilit]
  46. Wilder, S.L.; Scott, S.; Waller, S.; Powell, A.; Benoit, M.; Guthrie, J.M.; Schueller, M.J.; Awale, P.; McSteen, P.; Matthes, M.S. Carbon-11 radiotracing reveals physiological and metabolic responses of maize grown under different regimes of boron treatment. Plants 2022, 11, 241. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  47. Yan, Y.; Gan, J.; Tao, Y.; Okita, T.W.; Tian, L. RNA-binding proteins: The key modulator in stress granule formation and abiotic stress response. Front. Plant Sci. 2022, 13, 882596. [Google Scholar] [CrossRef] [Scilit]
  48. Öz, M.T.; Yilmaz, R.; Eyidoğan, F.; de Graaff, L.; Yücel, M.; Öktem, H.A. Microarray analysis of late response to boron toxicity in barley (Hordeum vulgare L.) leaves. Turk. J. Agric. For. 2009, 33, 191–202. [Google Scholar] [CrossRef] [Scilit]
  49. Seth, K.; Aery, N.C. Boron induced changes in biochemical constituents, enzymatic activities, and growth performance of wheat. Acta Physiol. Plant. 2017, 39, 244. [Google Scholar] [CrossRef] [Scilit]
  50. Uluisik, I.; Kaya, A.; Fomenko, D.E.; Karakaya, H.C.; Carlson, B.A.; Gladyshev, V.N.; Koc, A. Boron stress activates the general amino acid control mechanism and inhibits protein synthesis. PLoS ONE 2011, 6, e27772. [Google Scholar] [CrossRef] [Scilit]
  51. Wang, G.; DiTusa, S.F.; Oh, D.H.; Herrmann, A.D.; Mendoza-Cozatl, D.G.; O’Neill, M.A.; Smith, A.P.; Dassanayake, M. Cross species multi-omics reveals cell wall sequestration and elevated global transcript abundance as mechanisms of boron tolerance in plants. New Phytol. 2021, 230, 1985–2000. [Google Scholar] [CrossRef] [Scilit]
  52. Chunduri, V.; Kaur, A.; Kaur, S.; Kumar, A.; Sharma, S.; Sharma, N.; Singh, P.; Kapoor, P.; Kaur, S.; Kumari, A. Gene expression and proteomics studies suggest an involvement of multiple pathways under day and day–night combined heat stresses during grain filling in wheat. Front. Plant Sci. 2021, 12, 660446. [Google Scholar] [CrossRef] [Scilit]
  53. Padmanabhan, P.; Babaoğlu, M.; Terry, N. A comparative transcriptomic analysis of the extremely boron tolerant plant Puccinellia distans with the moderately boron tolerant Gypsophila arrostil. Plant Cell Rep. 2012, 31, 1407–1413. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  54. Kumar, S.; Pandey, A.K. Chemistry and biological activities of flavonoids: An overview. Sci. World J. 2013, 2013, 162750. [Google Scholar] [CrossRef] [Scilit]
  55. Landi, M.; Margaritopoulou, T.; Papadakis, I.E.; Araniti, F. Boron toxicity in higher plants: An update. Planta 2019, 250, 1011–1032. [Google Scholar] [CrossRef] [Scilit]
  56. Akbudak, M.A.; Çetin, D.; Filiz, E.; Srivastava, V. Genome-wide exploration and analysis of plant stress-responsive CAMTA transcription factor genes in Brachypodium distachyon and their expression patterns under environmental challenges. S. Afr. J. Bot. 2024, 166, 208–217. [Google Scholar] [CrossRef] [Scilit]
  57. Zhou, Q.; Zhao, M.; Xing, F.; Mao, G.; Wang, Y.; Dai, Y.; Niu, M.; Yuan, H. Identification and expression analysis of CAMTA genes in tea plant reveal their complex regulatory role in stress responses. Front. Plant Sci. 2022, 13, 910768. [Google Scholar] [CrossRef] [Scilit]
  58. Abdel-Hameed, A.A.; Liao, W.; Prasad, K.V.; Reddy, A.S. CAMTAs, a family of calmodulin-binding transcription factors, are versatile regulators of biotic and abiotic stress responses in plants. Crit. Rev. Plant Sci. 2024, 43, 171–210. [Google Scholar] [CrossRef] [Scilit]
  59. Kayıhan, C.; Öz, M.T.; Eyidoğan, F.; Yücel, M.; Öktem, H.A. Physiological, Biochemical, and Transcriptomic Responses to Boron Toxicity in Leaf and Root Tissues of Contrasting Wheat Cultivars. Plant Mol. Biol. Report. 2017, 35, 97–109. [Google Scholar] [CrossRef] [Scilit]
  60. Aksoy, E.; Kayıhan, C. Transcription factors and target genes involved in plant responses to high boron adaptation. In Boron in Plants and Agriculture; Elsevier: London, UK, 2022; pp. 207–231. [Google Scholar]
  61. Ochiai, K.; Shimizu, A.; Okumoto, Y.; Fujiwara, T.; Matoh, T. Suppression of a NAC-like transcription factor gene improves boron-toxicity tolerance in rice. Plant Physiol. 2011, 156, 1457–1463. [Google Scholar] [CrossRef] [Scilit]
  62. Thilakarathne, A.S.; Liu, F.; Zou, Z. Plant Signaling Hormones and Transcription Factors: Key Regulators of Plant Responses to Growth, Development, and Stress. Plants 2025, 14, 1070. [Google Scholar] [CrossRef] [Scilit]
  63. Azameti, M.K.; Ranjan, A.; Singh, P.; Gaikwad, K.; Singh, A.K.; Dalal, M.; Arora, A.; Rai, V.; Padaria, J.C. Transcriptome profiling reveals the genes and pathways involved in thermo-tolerance in wheat (Triticum aestivum L.) genotype Raj 3765. Sci. Rep. 2022, 12, 14831. [Google Scholar] [CrossRef] [Scilit]
  64. Dudziak, K.; Sozoniuk, M.; Szczerba, H.; Kuzdraliński, A.; Kowalczyk, K.; Börner, A.; Nowak, M. Identification of stable reference genes for qPCR studies in common wheat (Triticum aestivum L.) seedlings under short-term drought stress. Plant Methods 2020, 16, 58. [Google Scholar] [CrossRef] [Scilit]
  65. Alyahya, N.; Taybi, T. Comparative transcriptomic profiling reveals differentially expressed genes and important related metabolic pathways in shoots and roots of a Saudi wheat cultivar (Najran) under salinity stress. Front. Plant Sci. 2023, 14, 1225541. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  66. Rachinger, N.; Fischer, S.; Böhme, I.; Linck-Paulus, L.; Kuphal, S.; Kappelmann-Fenzl, M.; Bosserhoff, A.K. Loss of gene information: Discrepancies between RNA sequencing, cDNA microarray, and qRT-PCR. Int. J. Mol. Sci. 2021, 22, 9349. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  67. Consortium, S.M.-I. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat. Biotechnol. 2014, 32, 903–914. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  68. Zhang, H.; Yang, Y.; Wang, C.; Liu, M.; Li, H.; Fu, Y.; Wang, Y.; Nie, Y.; Liu, X.; Ji, W. Large-scale transcriptome comparison reveals distinct gene activations in wheat responding to stripe rust and powdery mildew. BMC Genom. 2014, 15, 898. [Google Scholar] [CrossRef] [Scilit]
  69. Nussbaumer, T.; Warth, B.; Sharma, S.; Ametz, C.; Bueschl, C.; Parich, A.; Pfeifer, M.; Siegwart, G.; Steiner, B.; Lemmens, M.; et al. Joint Transcriptomic and Metabolomic Analyses Reveal Changes in the Primary Metabolism and Imbalances in the Subgenome Orchestration in the Bread Wheat Molecular Response to Fusarium graminearum. G3 Genes Genomes Genet. 2015, 5, 2579–2592. [Google Scholar] [CrossRef] [Scilit]
  70. Li, Q.; Zheng, Q.; Shen, W.; Cram, D.; Fowler, D.B.; Wei, Y.; Zou, J. Understanding the biochemical basis of temperature-induced lipid pathway adjustments in plants. Plant Cell 2015, 27, 86–103. [Google Scholar] [CrossRef] [Scilit]
  71. Higuchi, K.; Watanabe, S.; Takahashi, M.; Kawasaki, S.; Nakanishi, H.; Nishizawa, N.K.; Mori, S. Nicotianamine synthase gene expression differs in barley and rice under Fe-deficient conditions. Plant J. 2001, 25, 159–167. [Google Scholar] [CrossRef] [Scilit]
  72. Lee, S.; Jeon, U.S.; Lee, S.J.; Kim, Y.-K.; Persson, D.P.; Husted, S.; Schjørring, J.K.; Kakei, Y.; Masuda, H.; Nishizawa, N.K. Iron fortification of rice seeds through activation of the nicotianamine synthase gene. Proc. Natl. Acad. Sci. USA 2009, 106, 22014–22019. [Google Scholar] [CrossRef] [Scilit]
  73. Nozoye, T.; Kim, S.; Kakei, Y.; Takahashi, M.; Nakanishi, H.; Nishizawa, N.K. Enhanced levels of nicotianamine promote iron accumulation and tolerance to calcareous soil in soybean. Biosci. Biotechnol. Biochem. 2014, 78, 1677–1684. [Google Scholar] [CrossRef] [Scilit]
  74. Nozoye, T.; Otani, M.; Senoura, T.; Nakanishi, H.; Nishizawa, N.K. Overexpression of barley nicotianamine synthase 1 confers tolerance in the sweet potato to iron deficiency in calcareous soil. Plant Soil 2017, 418, 75–88. [Google Scholar] [CrossRef] [Scilit]
  75. Kaur, G.; Shukla, V.; Meena, V.; Kumar, A.; Tyagi, D.; Singh, J.; Kandoth, P.K.; Mantri, S.; Rouached, H.; Pandey, A.K. Physiological and molecular responses to combinatorial iron and phosphate deficiencies in hexaploid wheat seedlings. Genomics 2021, 113, 3935–3950. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  76. Kalapos, B.; Juhász, C.; Balogh, E.; Kocsy, G.; Tóbiás, I.; Gullner, G. Transcriptome profiling of pepper leaves by RNA-Seq during an incompatible and a compatible pepper-tobamovirus interaction. Sci. Rep. 2021, 11, 20680. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  77. Su, H.G.; Zhang, X.H.; Wang, T.T.; Wei, W.L.; Wang, Y.X.; Chen, J.; Zhou, Y.B.; Chen, M.; Ma, Y.Z.; Xu, Z.S.; et al. Genome-Wide Identification, Evolution, and Expression of GDSL-Type Esterase/Lipase Gene Family in Soybean. Front. Plant Sci. 2020, 11, 726. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  78. He, Q.; Lin, Y.; Tan, H.; Zhou, Y.; Wen, Y.; Gan, J.; Li, R.; Zhang, Q. Transcriptomic profiles of Dunaliella salina in response to hypersaline stress. BMC Genom. 2020, 21, 115. [Google Scholar] [CrossRef] [Scilit]
  79. Luo, S.; Kim, C. Current Understanding of Temperature Stress-Responsive Chloroplast FtsH Metalloproteases. Int. J. Mol. Sci. 2021, 22, 12106. [Google Scholar] [CrossRef] [Scilit]
  80. Seo, K.I.; Lee, J.H.; Nezames, C.D.; Zhong, S.; Song, E.; Byun, M.O.; Deng, X.W. ABD1 is an Arabidopsis DCAF substrate receptor for CUL4-DDB1-based E3 ligases that acts as a negative regulator of abscisic acid signaling. Plant Cell 2014, 26, 695–711. [Google Scholar] [CrossRef] [Scilit]
  81. Weber, M.; Beyene, B.; Nagler, N.; Herfert, J.; Schempp, S.; Klecker, M.; Clemens, S. A mutation in the essential and widely conserved DAMAGED DNA BINDING1-Cullin4 ASSOCIATED FACTOR gene OZS3 causes hypersensitivity to zinc excess, cold and UV stress in Arabidopsis thaliana. Plant J. 2020, 103, 995–1009. [Google Scholar] [CrossRef] [Scilit]
  82. Xu, E.; Vaahtera, L.; Horak, H.; Hincha, D.K.; Heyer, A.G.; Brosche, M. Quantitative trait loci mapping and transcriptome analysis reveal candidate genes regulating the response to ozone in Arabidopsis thaliana. Plant Cell Environ. 2015, 38, 1418–1433. [Google Scholar] [CrossRef] [Scilit]
  83. Pagliuso, D.; Pedro de Jesus Pereira, J.; Ulrich, J.C.; Barbosa Cotrim, M.E.; Buckeridge, M.S.; Grandis, A. Carbon allocation of Spirodela polyrhiza under boron toxicity. Front. Plant Sci. 2023, 14, 1208888. [Google Scholar] [CrossRef] [Scilit]
  84. Aniento, F.; Sánchez de Medina Hernández, V.; Dagdas, Y.; Rojas-Pierce, M.; Russinova, E. Molecular mechanisms of endomembrane trafficking in plants. Plant Cell 2021, 34, 146–173. [Google Scholar] [CrossRef] [Scilit]
  85. Wang, F.; Qi, Y.; Yu, F. The biological functions of FtsH in plant organelle protein homeostasis. J. Exp. Bot. 2025, 76, 4220–4231. [Google Scholar] [CrossRef] [Scilit]
  86. Dogra, V.; Kim, C. Chloroplast protein homeostasis is coupled with retrograde signaling. Plant Signal. Behav. 2019, 14, 1656037. [Google Scholar] [CrossRef] [Scilit]
  87. Sharma, A.; Shahzad, B.; Rehman, A.; Bhardwaj, R.; Landi, M.; Zheng, B. Response of Phenylpropanoid Pathway and the Role of Polyphenols in Plants under Abiotic Stress. Molecules 2019, 24, 2452. [Google Scholar] [CrossRef] [Scilit]
  88. Rao, M.J.; Zheng, B. The Role of Polyphenols in Abiotic Stress Tolerance and Their Antioxidant Properties to Scavenge Reactive Oxygen Species and Free Radicals. Antioxidants 2025, 14, 74. [Google Scholar] [CrossRef] [Scilit]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Article Metrics

Citations

Article Access Statistics

Journal Statistics
Multiple requests from the same IP address are counted as one view.
Diagnostic Value of Mean Platelet Volume and Hematological Inflammatory Ratios in Brucellosis: A Case–Control Study
Previous
Coronary Artery Spasm in Patients with Type 2 Diabetes Mellitus
Next