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Article

Novel Genomic Regions and Gene Models Controlling Copper and Cadmium Stress Tolerance in Wheat Seedlings

by
Amira M. I. Mourad
1,2,*,
Sara Baghdady
3,
Fatma Al-Zahraa M. Abdel-Aleem
3,
Randa M. Jazeri
3 and
Andreas Börner
1
1
Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstrasse 3, D-06466 Seeland, Germany
2
Department of Agronomy, Faculty of Agriculture, Assiut University, Assiut 71526, Egypt
3
Genetics Department, Faculty of Agriculture, Assuit University, Assuit 71526, Egypt
*
Author to whom correspondence should be addressed.
Agronomy 2024, 14(12), 2876; https://doi.org/10.3390/agronomy14122876
Submission received: 22 October 2024 / Revised: 17 November 2024 / Accepted: 27 November 2024 / Published: 3 December 2024
(This article belongs to the Special Issue Selection and Breeding of Low Heavy Metal Accumulating Cultivars to Minimize Heavy Metal Accumulation)
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Abstract

:
Heavy metal pollution is a global issue that affects plant growth and human health. Copper and cadmium are two significant heavy metals that have become more concentrated in many soils. These metals are taken up by many plants, including wheat, and can cause various diseases in humans. The most effective way to mitigate the harmful effects of heavy metals is to grow tolerant wheat genotypes. In the current study, two different pot experiments were conducted to understand the genetic control of copper and cadmium tolerance in wheat seedlings. Two populations were used in this study, consisting of 92 genotypes for the copper experiment and 73 genotypes for the cadmium experiment. In both experiments, a replicated complete block design with three replications was used. Highly significant differences were found between the tested genotypes for all studied traits in both metals, except for root weight and the ratio between shoot weight and root weight under cadmium contamination. Single-marker analysis was performed for all significant traits, and a total of 265 and 381 markers were found to be significantly associated with seedling traits under copper and cadmium conditions, respectively. Of these markers, only eight were commonly associated with the tolerance to both metals. These markers were located within five different gene models that were functionally annotated to control heavy metal tolerance. Gene enrichment of the five identified genes revealed two key genes that significantly influenced eight biological processes, six molecular functions, and three Kyoto Ecyclopedia of Genes and Genomes (KEGG) pathways involved in heavy metal tolerance. The sources of the eight markers and their associated genes were identified in twelve genotypes, including one Egyptian and one Kazakhstani genotype, which showed superior responses to copper and cadmium, respectively. These genes and the genotypes carrying them are crucial for future breeding programs aimed at enhancing heavy metal tolerance in wheat.

1. Introduction

Heavy metal pollution has become a serious problem worldwide that affects the environment, plants, animals, and human health. The excessive use of fertilizer and pesticides, industrialization, and the progress in petrochemical and chemical industries are some of the factors causing this problem [1,2]. Copper (Cu) and cadmium (Cd) are two important heavy metals that are released into the environment and can persist for long periods in plant soils and waters, leading to long-term ecological and health impacts [3]. Even though a low concentration of Cu is essential for all living organisms [4], a high concentration of Cu negatively affects soil bacteria, disrupting nutrient cycles and reducing soil fertility [5]. Cu usually accumulates in the top part of different soils and is not very mobile in soils due to its specific adsorption onto minerals and organic fractions. Therefore, different events and phenomena cause Cu-contaminated soils such as dust fallout, mining, former wood treatment sites, deposits of metal scraps and organic residues, and crop applications of Cu-based fungicides [6,7,8,9,10,11]. Cu uptake occurs in different types of plants and organisms and accumulates in their tissues, leading to toxic effects up the food chain. The short-term human consumption of plants contaminated with Cu causes gastrointestinal distress, including nausea, vomiting, and diarrhea, while long-term consumption leads to liver and kidney damage, as well as neurological issues [12]. Cd contamination can occur from many sources, such as phosphate fertilizers, industrial emissions like batteries, the burning of plastics and waste materials, cigarette smoking, and the effort to mine and refine other heavy metals such as lead and zinc [13]. Unlike Cu, Cd persists in soils and leaches into the groundwater, causing risks to both terrestrial and aquatic ecosystems. Furthermore, cadmium can be taken up by plants, entering the food chain and causing health problems to humans like kidney damage, bone weakness, cancer, and respiratory issues that result in lung damage [14,15].
Wheat (Triticum aestivum L.) is the third most important cereal crop worldwide. Bread is the main source of energy in the diet of many countries, especially those who live below the poverty level. A high level of the consumption of wheat contaminated with heavy metals threatens human health [16]. The allowed concentration of Cu and Cd in wheat flour was reported to range from 0.5 to 5 mg/kg and from 0.001 to 0.1 mg/kg, respectively [17]. However, the level of Cu and Cd exceeded the allowed level in wheat flour [18,19]. Moreover, a high concentration of Cu affects wheat growth as it interferes with the uptake of other essential minerals such as zinc, iron, and phosphorus, resulting in a reduction in root and shoot growth, leaf wilting, and necrosis, and increasing the production of reactive oxygen species (ROS). This results in proteins, lipids, and cell DNA being damaged [20]. As Cd is a non-essential element like Cu, a low concentration of Cd inhibits wheat growth, interferes with chlorophyll production, reduces photosynthetic efficiency, inhibits important enzymes, and competes with the uptake of essential nutrients [3]. Wheat was classified as an excluder plant based on Baker’s classification [21]. An excluder plant is a plant that prevents toxic ions from entering the system or transfers them to a less sensitive tissue. Both Cu and Cd usually enter wheat plants through roots. They differ slightly in their accumulation inside wheat. Cu usually accumulates in roots and the old leaves so wheat avoids its harmful effects, while Cd accumulates in roots, leaves, and grains [22,23].
The initial step towards the safe exploitation of Cu- and Cd-polluted soils is to develop tolerant cultivars that can produce a high yield with low Cu and Cd contents [5,24,25,26]. This can be achieved by evaluating the different genetic backgrounds of wheat germplasms and by selecting the most tolerant genotypes. Selected genotypes could be used in future breeding programs to produce new cultivars with high tolerance to Cu and Cd. However, traditional breeding programs are usually laborious and time-consuming [27,28]. Combining genetic analyses with breeding programs will accelerate the improvement of Cu and Cd tolerance [29,30,31]. Association mapping (AM) is one effective way to identify the genomic regions that control specific target traits. It has been used widely to detect the genetic control of different biotic and abiotic stresses in wheat [32,33,34]. AM can be applied in different ways, such as the examination of quantitative trait loci (QTLs), single-marker analysis (SMA), and genome-wide association analysis (GWAS). All these ways differ in the required plant materials and number of genotypes required to run the analysis, but they do not differ in their accuracy [35]. Few efforts have been made to identify the genetic control of Cu and Cd tolerance in wheat [36,37,38,39,40,41].
The objectives of the current study are to (1) study the genetic variation in Cu tolerance in a set of 92 highly diverse wheat genotypes at the seedling growth stage, (2) study the genetic variation in Cd tolerance in a set of 72 highly diverse wheat genotypes at the seedling growth stage, (3) identify candidate genes associated with Cu and Cd tolerance at the seedling growth stage, (4) detect common genes and genomic regions that control tolerance to both Cu and Cd heavy metals, and (5) select the most promising wheat genotypes for Cu and Cd tolerance.

2. Material and Methods

2.1. Experimental Design and Plant Materials

In this study, two different seedling experiments were conducted: a copper experiment (Exp I) and cadmium experiment (Exp II). Both experiments were conducted at the same time in the Plant Genetics lab, Genetics Department, Faculty of Agriculture, Assuit University, Egypt. The room temperature was 23–25 °C during the experiment. Both experiments were performed using small pots (diameter size 10 cm).
Exp I was conducted using a total of 92 spring wheat genotypes. These genotypes were new and old cultivars collected from 15 different countries, in addition to 2 genotypes from unknown countries (Table S1). Out of these 92 genotypes, 12 were local Egyptian cultivars and breeding lines. The seeds of Egyptian genotypes were obtained from the Egyptian governorate, while seeds of non-Egyptian genotypes were obtained from the USDA-ARS, United States. Furthermore, non-Egyptian genotypes were reported to be highly adapted to the Egyptian conditions [42]. The tested set was reported to be highly diverse in its response to abiotic stresses, including heavy metals [43]. As such, more information on Cu tolerance could be provided using this set of genotypes. The experimental design was replicated complete block design (RCBD), and there were three replications (Figure S1). Each genotype was represented as three single seedlings/replication. Before planting, soil was contaminated by adding 160 mg/kg soil of copper sulfate (CuSO4) to reach a concentration of 250 mg/kg soil [44]. After contamination and before planting, soil samples were collected from the contaminated and non-contaminated soils and analyzed at the Analytical Chemistry Unit (ACAL), Department of Chemistry, Faculty of Science, Assuit University, Asyut, Egypt.
Exp II was conducted using a set of 73 spring wheat genotypes collected from 14 different countries, including Egypt (10), Afghanistan (7), Algeria (3), Australia (4), Canada (4), Ethiopia (1), Germany (2), Greece (3), Iran (9), Kazakhstan (3), Kenya (2), Morocco (10), Oman (4), and Saudi Arabia (6), in addition to 5 genotypes from unknown countries (Table S2). Most of these genotypes were included in Exp I, except for nine. The source of seeds was the same as described in Exp_I. The experimental design was also an RCBD, with three replications/genotype (Figure S1). In each replication, each genotype was represented as three single seedlings/pot. Before planting, the soil was contaminated using 7.5 mg/kg soil of cadmium chloride (CdCl2). After contamination, soil samples were collected from contaminated and non-contaminated pots and sent for analysis to the Analytical Chemistry Unit (ACAL), Department of Chemistry, Faculty of Science, Assuit University, Asyut, Egypt.

2.2. Phenotyping of Seedling Traits and Statistical Analysis

Fourteen days after sowing, each single seedling was carefully removed from the pot, cleaned to remove the remaining soil from its root, and photographed using a high-resolution camera. The following seedling traits were measured for each seedling: (1) biological weight (BW, gm), (2) shoot weight (SW, gm), (3) root weight (RW, gm), (4) the ratio between shoot weight and root weight (SW/RW), (5) shoot length (SL, cm), (6) root length (RL, cm), (7) the ratio between shoot length/root length (SL/RL), and (8) leaf width (LW, cm). BW, SW, and RW were measured using a digital scale, while SL, RL, and LW were measured using ImageJ software v. 1.45 [45].
For each trait measured under each treatment, analysis of variance (ANOVA) was run using PLABSTAT software v. 3A of 2003-08-16 [46] based on the following model:
Yij = µ + rj + gi + grij (error)
where Yij is the observation of genotype i in replication j, μ is the general mean, and rj and gi refer to the effects of replications and genotypes, respectively. grij is the interaction between genotypes and replications (error). Genotypes were considered to be random effects, while the remaining factors were considered to be fixed effects.
The broad-sense heritability was calculated for each studied trait under each treatment using the PLABSTAT-HERT command. The phenotypic correlation between each pair of studied traits under each condition was calculated and visualized using the SRPLOT platform [47].

2.3. Single-Marker Analysis for Copper and Cadmium Tolerance

The tested genotypes were sequenced using the Genotyping-by-Sequencing (GBS) Kansas State University, Manhattan, KS, USA and 25 K Infinium iSelect array (25 K-set) by SGS Institute Fresenius GmbH TraitGenetics Section (Gatersleben, Germany) [32,42]. A final set of 11,362 and 21,093 SNP markers were produced by each sequencing method, respectively. These markers were reported to cover different parts of the wheat genome and should be used together to detect genomic regions associated with wheat’s tolerance to different abiotic stresses as well as tolerance to different biotic stresses [28]. These markers were used to understand more about the genetic control of seedling growth under Cd- and Cu-contaminated conditions. To obtain this goal, single-marker analysis (SMA) was conducted for each significant trait using PowerMarker Software v.3.25 [48] using the following model:
Y = µ + f (marker) + error
where Y is the trait value, µ is the population mean, and f (marker) is a function of the significant markers. Significant markers were detected based on −log10 (p-value) > 3.00 level. The marker allele effect and phenotypic variation explained by the marker were detected using TASSEL v 5.0 software [49]. Gene models harboring the significant markers were detected using the EnsemblePlants platform [50]. The functional annotation of the identified gene models was performed using the International Wheat Genome Sequencing Consortium (IWGSC) V.1.0.

2.4. Gene Enrichment and Gene Network of the Identified Gene Models

In order to provide greater understanding of the genetic control of Cd and Cu tolerance in wheat seedlings, common gene models that harboring markers significantly associated with the tolerance of both metals were detected. Gene enrichment of these genes based on the Kyoto Ecyclopedia of Genes and Genomes (KEGG), biological process (BP), molecular function (MF), and cellular component (CC) pathways was detected using the ShinyGo 0.76 database [51]. False discovery rate (p-value < 0.01) was applied to detect the highly significant pathways. Enrichment results were visualized using the SRPLOT platform [47].

2.5. Selection of Superior Genotypes Carrying Common Markers Associated with Cu and Cd Stress Tolerance

To select superior genotypes that could be used in future breeding programs to promote Cu and Cd tolerance, the presence of the target alleles of significant markers associated with the tolerance of both metals was detected in all the tested genotypes. The total number of target alleles in each genotype was calculated. The genetic distance between each pair of selected genotypes was calculated using TASSEL v.5 software [49] and visualized as a phylogeny tree using the iTOL v.7 database [52].

3. Results

Soil analysis before and after adding the contamination solution is presented in Table 1. Based on the soil analysis results, the pH was almost the same in the non-contaminated (Ctrl) and contaminated soils, with values of 8.32, 8.22, and 8.42 in Ctrl, Cu, and Cd soils, respectively. The heavy metal content increased in the contaminated soils compared with Ctrl soils, with a value of 233.19 mg/kg for Cu compared with one of 9.842 mg/kg soil in the Ctrl and a value of 0.138 mg/kg for Cd compared with one of 0.055 mg/kg soil in Ctrl soil. Different responses in terms of the cation content in the contaminated soils were found via the treatment. The Mg2+ concentration increased in Cu soils while it decreased in Cd soils. Na+ increased in both Cu soil and Cd soil, while K+ did not change due to Cu and Cd contamination compared with Ctrl soil. Cl- increased from 1.96 mg/kg to 38.76 due to Cd contamination while it decreased to 0.944 due to Cu contamination. The saturation capacity ranged from 21.74% to 21.52% in Ctrl and Cu soils, respectively. The soil conductivity decreased to 869 in Cd soils while it increased to 977 in Cu soils compared with the value of 887 seen in Ctrl soil.

3.1. Genetic Variation in Seedling Traits in the Studied Plant Materials

Highly significant differences (p-value < 0.01) among the 92-genotypes evaluated in Exp I were found in all the studied seedling traits (Table 2). The distribution of each studied seedling trait under Cu is presented in Figure 1 and Figure S2. BW ranged from 0.02 gm to 0.35 gm, with an average value of 0.08 gm. SW ranged from 0.003 gm to 0.20 gm, with an average value of 0.052 gm. RW ranged from 0.008 to 0.24 gm, with an average value of 0.034 gm. The SW/RW ratio ranged from 0.15 to 10.07, with an average value of 2.27. SL and RL ranged from 2.03 to 19.83 cm and from 3.023 to 16.62 cm, with average values of 10.99 and 7.35 cm for SL and RL, respectively. A range of 0.39–3.38 was found for SL/RL ratio under Cu conditions, with an average value of 1.55. LW ranged from 0.046 to 0.36 cm, with an average value of 0.20 cm. The broad-sense heritability exceeded 0.50 for all the studied seedling traits with a value ranging from 0.55 for SW/RW to 0.99 for LW (Table 2).
In Cd-contaminated soil, highly significant differences were found among the 73 genotypes used in Exp II regarding all the seedling studied traits, except for RW and SW/RW (Table 2). The phenotypic variation in these seedling traits under Cd conditions is presented in Figure 1 and Figure S3. BW ranged from 0.04 to 0.56 gm, with an average value of 0.13 gm. SW ranged from 0.02 to 0.28 gm, with an average value of 0.09 gm. SL ranged from 0.43 to 21.79 cm, with an average value of 12.24 cm. It ranged from 3.96 to 9.66 cm, with an average value of 7.65 cm for RL. The ratio between SL and RL ranged from 0.11 to 3.37, with an average value of 1.59. LW ranged from 0.03 to 0.44 cm, with an average value of 0.22.

3.2. Phenotypic Correlation for Seedling Traits Under Copper and Cadmium Conditions

To provide a greater understanding of the relationship between the studied seedling traits prevailing under Cu- and Cd-contaminated soils, the phenotypic correlation between each pair of the significant seedling traits studied was calculated. Under Cu-contamination conditions, significant positive correlations were found between each pair of the studied traits, except RW and SW/RW, SL and SL/RL, and RW and SL/RL, which had significant negative correlations (Figure 2a) Furthermore, no significant correlation was found between RW and SL or LW. Under Cd contamination conditions, significant positive correlations were found between LW and all the studied traits, and between SL and all the studied traits except for SW (Figure 2b). A significant negative correlation was found between BW and SL, between BW and SL/RL, and between SL/RL and both SW and RL. No significant correlation was found between SW and either SL or RL.

3.3. Genetic Control of Seedling Traits Under Heavy Metal Stresses

3.3.1. Single-Marker Analysis of Seedling Traits Under Cu-Contaminated Soils

The genotypes tested in Exp I showed highly significant differences for all the studied traits. Therefore, single-marker analysis (SMA) was conducted for all the seedling traits studied. Detailed SMA results are presented in Table S3, while a summary of these results is presented in Table 3. In summary, a total number of 265 markers were significantly associated with the seedling traits studied under Cu conditions. The highest number of these markers was associated with SL, with 61 markers (Table 3), while the lowest number of significant markers was associated with LW with only 16 markers. These markers were distributed among all 21 wheat chromosomes in addition to the 34 markers located on an unknown chromosome (Un). The lowest numbers of significant markers were found on chromosomes 4D, which carried one marker associated with both BW and RW, and 5D, which carried only one significant marker associated with SL (Figure 3a). On the other hand, the highest number of significant markers was found on chromosome 2B, which carried 33 significant markers associated with all the seedling traits studied except SW/RW and SL/RL. The phenotypic variation explained by these significant markers (R2) ranged from 12.59% for marker “wsnp_Ku_c5359_9531713”, associated with SL, to 28.01% for marker “AX-94531050”, associated with SL/RL (Table 3 and Table S3). The effect of the target allele of these significant markers ranged from 0.02 gm for the “AX-86173157” marker associated with SW to 5.27 cm for the “IACX7905” marker associated with SL. Notably, 28 markers were found to be significantly associated with two different seedling traits (as shown in Table S3). Almost all of these 28 markers were associated with two different weight or length traits such as SW and RW, RW and BW, RW and BW, SL and RL, or SL and SL/RL. However, four markers namely, “CAP7_c4056_108”, “S2A_759454249”, “S4A_738781757”, and “S7A_720585903”, were associated with one weight and one length trait. These were SW and LW, SW/RW and LW, SW and SL, and SW/RW and SL, respectively.

3.3.2. Single-Marker Analysis of Seedling Traits Under Cd-Contaminated Soils

Under cadmium stress (Exp II), the 73 genotypes tested only showed highly significant differences for six seedling traits. Therefore, RW and SW/RW were excluded from SMA. The detailed SMA results are presented in Table S4. In summary, a total of 381 markers were associated with the studied seedling traits, with numbers 194, 75, 25, 63, 63, and 43 associated with BW, SW, SL, RL, SL/RL, and LW, respectively (Table 3). These markers were distributed among the 21 wheat chromosomes. The highest significant markers/chromosomes were found on 3B and 6A chromosomes, with 41 markers each (Figure 3a). On the other hand, the lowest number of significant markers were found on 2D and 6D chromosomes, with only two significant markers each. The phenotypic variation explained by these markers (R2) ranged from 17.27% for “Tdurum_contig75811_1629”, “BS00079237_51”, “Tdurum_contig52015_426”, and “Kukri_c42622_417” markers associated with SW to 56.18% for the marker “IAAV8692”, which is associated with BW (Table 3 and Table S4). The effect of the target alleles of these markers ranged from 0.04 gm for the “T” allele of the “AX-95231332” marker associated with SW to 9.86 cm for the “C” allele of the “RAC875_c30123_913” marker associated with SL. Notably, 82 markers were found to be significantly associated with two different traits such as BW and SW, SL and SL/RL, and SL and RL. Out of these 82 markers, “AX-94684421” and “BobWhite_c20876_475” markers were found to be significantly associated with (RL and LW) and (BW and SL), respectively.

3.4. Genomic Regions Controlling Wheat Seedlings’ Tolerance to Both Copper and Cadmium Stresses

Out of the identified SNP markers identified in Exp I and Exp II, eight markers were commonly associated with Cu and Cd tolerance (Figure 3b). These common markers were distributed among four different chromosomes, with one, three, three, and one markers found for 1B, 2A, 2B, and 7A chromosomes, respectively (Table 4). Five of these markers were associated with RW under Cu stress and with BW under Cd stress. The remaining three markers were arranged as follows: “RAC875_c2110_117” and “AX-158575246” markers were associated with RW under Cu stress and with RL under Cd stress, and the “Kukri_c24408_743” marker was associated with SW under Cu stress and with BW under Cd stress. All these markers had a major effect on the trait they were controlling as they had an R2 value >10%, with a higher value under Cd stress than Cu stress. The target allele associated with increasing the associated trait was the same under Cu and Cd stress for all eight markers.

Gene Models Controlling Cu and Cd Tolerance, Their Functional Annotation, and Gene Enrichment

To provide more comprehensive understanding of the role these common markers play in heavy metal tolerance, gene models harboring these markers were detected. A total of five gene models were found to harbor seven markers (Table 4). Each gene model harbored one significant marker, except for the two genes (TraesCS2A02G527700 and TraesCS2B02G172400) harboring two significant markers. The functional annotation of these five models was performed and is presented in Table 4. These gene models were found to functionally control the production of Mei2-like protein, chalcone synthase, ubiquitin carboxyl-terminal hydrolase, small nuclear RNA-activating complex (SNAPc), 2-oxoglutarate (2OG), and Fe(II)-dependent oxygenase superfamily protein.
The gene enrichment of the five identified gene models was performed to further investigate the genetic control of these gene models with regard to heavy metal tolerance. A cut of the value of 1% false discovery rate (FDR) was applied to detect the highly significant pathways. Eight, six, and three significant pathways were identified based on BP, MF, and KEGG (Figure 4 and Table S5). Furthermore, gene networks of the identified pathways and their genetic controls were investigated (Figure 5). The eight BP pathways were found to work together in one network and were controlled by one gene model, TraesCS2A02G538800 (Figure 5a). Moreover, the six MF pathways were found to work in two different networks (Figure 5b). Network 1 contains four pathways and is controlled by the TraesCS2A02G538800 gene, and network 2 contains two pathways and is controlled by the TraesCS2A02G527700 gene. The three KEGG pathways were organized in one network (Figure 5c) and controlled by one gene model, namely, TraesCS2A02G527700.

3.5. Selection of Superior Genotypes for Cu and Cd Tolerance

Out of the evaluated genotypes in both Exp I and Exp II, twelve were found to carry the target allele of the common eight markers significantly associated with both Cu and Cd tolerance (Table 5). These genotypes belong to six different countries, with five Egyptian genotypes, two Iranian genotypes, and two Moroccan genotypes, as well as one genotype from Germany, Kazakhstan, and Kenya. The number of significant markers associated with both Cu and Cd tolerance ranged from one marker in seven genotypes to six markers in one genotype (Sakha 93) (Figure 6a). The response of these twelve genotypes to both Cu and Cd was investigated and ranked for each genotype in comparison with the others in the evaluated population based on each studied seedling trait (Table 5). Notably, “Sakha 93” which carried six significant markers, showed the best response to Cd based on BW, SW, and RW, while it showed a low response to Cd based on SL, RL, and LW. Unfortunately, this genotype was not included in the population evaluated for Cu in Exp I. Furthermore, “Grekum 105” showed the best response to Cu based on BW, SW, and RW. It had an intermediate response to Cd based on the same seedling characteristics and carried only one significant marker.
The genetic distance between each pair of the selected genotypes was calculated to determine the possibility of using these genotypes as superior parents in future breeding programs. Based on the neighborhood genetic distance, the 12 genotypes were distributed among five different subpopulations (Figure 6b). The highest genetic distance was found between the Egyptian genotype “Sids 12”, which carries three significant markers, and the Iranian genotype “1668”, which carries two significant markers (Table S6). However, the genetic distance between the superior genotype under Cu “Grekum 105” and the superior one under Cd “Sakha 93” was also high, with a value of 0.3959 representing the genetic variation between both genotypes.

4. Discussion

Cu has many beneficial effects on wheat growth when present in a balanced amount. For example, it is essential for enzyme activation, photosynthesis, improving grain yield and quality, and enhancing drought tolerance and disease resistance [53,54]. However, due to the continuous use of Cu-based fungicides and industrial advancements, Cu concentrations have exceeded the critical limit in some soils worldwide. In contrast, Cd is a harmful element with no positive effect on wheat growth, and even low concentrations can harm wheat plants [55]. Therefore, breeding wheat genotypes that can tolerate high concentrations of Cu and Cd will accelerate the advancement of wheat breeding programs, particularly during the early stages of the wheat life cycle.
As determined via the soil analysis, the concentration of Cu increased from 9.842 mg/kg to 233.19 mg/Kg (Table 1). It was reported that the maximum permissible level of Cu in wheat soils is 100 mg/kg [56]. The Cd concentration increased from 0.055 to 0.14 mg/kg soil in Exp II. The critical limit of Cd for wheat is 0.54 mg/kg [57]. Therefore, we can conclude that the concentration of Cu in the contaminated soils exceeded the allowed critical limit, thus providing a good understanding of the response of wheat genotypes exposed to high concentrations of this metal. While the concentration of Cd was intermediate. The pH degrees and the concentration of cations and anions slightly changed in the contaminated soils in Exp I and Exp II compared with the Ctrl soil, except for Na+ and Cl−. Therefore, it can be stated that the traits studied are mainly affected by Cu and Cd.

4.1. Genetic Variation and Correlation of Seedling Traits Under Cu and Cd

The presence of highly significant differences among the studied traits under Cu stress and most of the studied traits under Cd suggests that the selection of superior genotypes under Cu and Cd stresses is possible using the evaluated materials (Table 2). High degrees of heritability (H2 > 0.50) were found for all the studied traits under Cu and Cd stress, except for SW/RW under Cd stress, suggesting that these traits could be used as selection indices for highly tolerant genotypes. The average of all traits studied in Exp II is higher than that in Exp I (Figure 1, Figures S2 and S3). As most of the plant materials evaluated in Exp I were also presented in Exp II, we can conclude that the effect of Cu at 200 mg/kg was more harmful than Cd, standing at 0.14 mg/kg soil. Previous studies reported the severe reduction in root growth (RG), shoot growth (SG), chlorophyll content (CC), nutrient uptake (NU), and biomass (B), as well as high ROS production (ROS) in wheat plants growing under Cu (>100 mg/kg soil) [58,59], while mild and moderate inhibition of RG, SG, CC, NU, and ROS was observed in wheat plants growing under 0.2 mg/kg Cd [60]. Significant and highly significant correlations were found among most of the studied seedling traits under Cu and Cd stresses, suggesting that these traits could be a valuable parameter for further studies of Cu and Cd tolerance in wheat seedlings. Previous studies reported the presence of significant positive correlations between RL and SL, as well as between root and shoot biomass, under Cu and Cd stress, which confirms our findings [61]. The studied seedling traits were also highly correlated under different abiotic stresses such as alkaline–saline conditions and drought, which confirms their efficiency as selection parameters for many abiotic stresses including heavy metals [62,63].

4.2. Marker-Trait Associated with Cu and Cd Tolerance

Both wheat populations used in Exp I and Exp II are highly diverse and were collected from 15 and 14 different countries, respectively (Tables S1 and S2). Moreover, they were reported to be highly diverse genotypes that were successfully used to detect genomic regions associated with different biotic and abiotic stresses [28,42,64]. In the current study, highly significant differences were observed among the tested genotypes for almost all studied traits, as were high degrees of broad-sense heritability. Thus, identifying marker–trait association will be highly informative. The number of genotypes in each experiment is suitable for SMA [62]. Furthermore, the two types of marker data (GBS-SNPs and 25 K-SNP) have been widely used to detect markers associated with different traits [32].
In the current study, SMA detected 265 and 381 markers significantly associated with Cu and Cd, respectively (Table 3, Tables S3 and S4). These markers were distributed across the 21 wheat chromosomes (Figure 3 and Figure S4). Furthermore, few markers were found to be common to different traits under each condition. The wide distribution of these markers across the wheat genome, as well as the low number of common markers, suggests the complicity of the genetic system in controlling tolerance to Cu and Cd. The fact that such a complex genetic system controls seedling traits was previously reported, which confirms our results [62,65,66]. Out of the identified markers associated with Cu tolerance in the current study, five markers (S7B_373077367, S7B_373077367, S5A_510445229, S7B_358496280, and Excalibur_rep_c108293_345) were reported to be significantly associated with kernel traits under lead and tin stresses [67]. In addition, five more markers (S7B_358496280, Excalibur_rep_c108293_345, AX-94527379, BobWhite_c23392_496, BS00022279_51) that were associated with Cd tolerance in the current study were reported to be significantly associated with kernel traits under tin stress in the same study [67]. The presence of these ten markers previously reported to be associated with heavy metal tolerance confirms our current findings.

4.3. Putative Genomic Regions and Gene Models That Are Commonly Associated with Cu and Cd Tolerance

Out of the large number of markers significantly associated with Cu and Cd, only eight markers were found to be common to both metals (Figure 3b). Moreover, these markers were associated with different seedling traits under Cu and Cd stresses (Table 4). This low number of common markers again suggests the complicity of the wheat genome and the presence of different genomic systems that control the tolerance to different heavy metal stresses. These markers were only distributed among four chromosomes, with three markers on 2A, three markers on 2B, one marker on 1B, and one marker on 7A. A genomic region on the 2B chromosome has previously been identified as being significantly associated with the accumulation of cadmium, iron, and zinc in grains, highlighting the critical role of the 2B chromosome in enhancing heavy metal tolerance in wheat [40,67]. Five gene models were found to contain the eight common markers. The functional annotation of these gene models was directly associated with heavy metal tolerance. For example, chalcone synthase, produced by TraesCS2A02G527700, was reported to be stimulated in several plants exposed to Cu and Cd stresses [68,69]. Furthermore, 2-oxoglutarate (2OG) and the Fe(II)-dependent oxygenase superfamily protein controlled by the TraesCS2B02G568800 gene were reported to play important roles in metal ion binding [70].
To provide a greater understanding of the role played by these identified five gene models in heavy metal tolerance, gene enrichment was investigated. Out of these five genes, one gene (TraesCS2A02G538800) was found to play an important role in controlling eight BP and four MF pathways that lead to heavy metal tolerance (FDR p-value < 0.01). The eight BP pathways work together in one network that contains pathways important to the cellular protein catabolic process. The role played by these BP pathways in improving heavy metals is not very clear; however, they were found to be important for metal ion transport and the cellular transition of metal ions as a response to heavy metal stress [71]. Based on the MF pathways, TraesCS2A02G538800 controls the activity of important pathways, such as ubiquitin-like protein peptidase activity. One of the ubiquitin-like proteins is E3-ubiquitin ligase, which is reported to be a critical protein in Cd tolerance in Solanum lycopersicum L. [72].
Furthermore, one more gene model was found to control two MF pathways and three KEGG pathways (TraesCS2A02G527700) (Figure 4). The two MF pathways led to the production of acyltransferase activity. Acyltransferases are enzymes that play a crucial role in plant metabolism by catalyzing the transfer of acyl groups to various substrates. In wheat, these enzymes are involved in lipid biosynthesis, membrane formation, and the regulation of various metabolic pathways. Their activity can significantly influence plant growth, development, and stress responses, including tolerance to environmental stresses such as heavy metal toxicity [73]. The three KEGG pathways controlled by the TraesCS2A02G527700 gene model were found in one network. This network performs some important functions, such as flavonoid biosynthesis (Figure 5c). Flavonoids form complexes with heavy metals, leading the development of an effective method of plant defense against heavy metals [74]. Therefore, we can conclude that the five gene models identified, especially the two genes identified based on gene enrichment, seem to be highly important in improving heavy metal tolerance in wheat.

4.4. Selection of Superior Genotypes for Cu and Cd Tolerance

Selecting superior genotypes for use in future breeding programs aimed at heavy metal tolerance is challenging. In the recent few years, genomic selection (GS) has been reported to be a promising way of selecting superior genotypes based on their genomic sequences, one that will accelerate the future of breeding programs, especially in improving complex traits [75]. The first step in GS is to detect the significant markers associated with the target trait. In our study, eight markers were determined to be associated with both Cu and Cd tolerance. These markers were found in eight genotypes of both populations used in Exp I and Exp II (Table 5 and Figure 6). Furthermore, we checked the response of these 12 selected genotypes to both Cu and Cd. The Egyptian genotype “Sakha 93”, which carried the highest number of significant markers, was the best genotype for BW, SW, and RW under Cd stress. Previous studies reported the presence of superior genotypes for heavy metal tolerance in Egyptian wheat germplasm [43]. Under Cu stress, “Grekum 105” from Kazakhstan was the best genotype in terms of BW and SW. This genotype only carried the target allele of one significant marker. Furthermore, a high genetic distance was found between these two genotypes (Sakha 93 and Grekum 105). Previous studies reported that the best genotypes for use in breeding programs are those that have a high genetic distance [76]. Therefore, these two genotypes could be used to improve both Cd and Cu tolerance in spring wheat.

5. Conclusions

Heavy metal pollution is a common global issue that affects both wheat growth and human health. Our current study shows that wheat seedling tolerance to Cu and Cd is a complex trait, as it is controlled by a wide genetic system distributed across the 21 wheat chromosomes. Identifying common genomic regions controlling the tolerance to both heavy metals will aid in improving wheat’s tolerance to these critical contaminations. In this study, eight markers located within five different gene models were identified. The functional annotation of these five genes was associated with metal tolerance in wheat, confirming their relevance. Furthermore, gene enrichment reduced the number of identified genes to two key genes that control the production of important biological processes (BP), molecular functions (MF), and KEGG pathways contributing to heavy metal tolerance. We identified two genotypes that showed the best responses to Cu and Cd, carrying six and one target allele, respectively, among the eight common markers. Crossing these two genotypes in future breeding programs will accelerate the development of wheat varieties with improved heavy metal tolerance due to their exceptional responses, the presence of key common markers, and their substantial genetic divergence.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy14122876/s1, Figure S1. Diagram represents the experimental design (Randomized Complete Block Design (RCBD)) used in this study; Figure S2. Boxplots represent the distribution of the different studied seedling traits under copper stress condition; Figure S3. Boxplots represent the distribution of the different studied seedling traits under cadmium stress condition; Figure S4. The distribution of significant markers associated with the different studied seedling traits under copper (a) and cadmium (b) contamination conditions across the 21-wheat chromosomes; Table S1: List of 92-genotypes evaluated to copper tolerance in Exp_I, their country of origin, and PI_code; Table S2: List of 73-genotypes evaluated to cadmium tolerance in Exp_I, their country of origin, and PI_code; Table S3: Single amrker analysis of seedling traits under copper contaminated soils; Table S4: Single marker analysis of the studied seedling traits under cadmium contaminated soils; Table S5: Gene enrichemnt anaylsis of the identified eight gene models commonly associated with the copper and cadmium tolerance; Table S6: The genetic distance among each pair of the selected superior genotypes for both Cu and Cd tolerance.

Author Contributions

A.M.I.M. designed the experiment, helped in the phenotypic scoring, performed the genetic and phenotyping analysis, discussed the results, and drafted the manuscript. S.B., F.A.-Z.M.A.-A. and R.M.J. helped with phenotypic evaluation. A.B. helped in drafting the manuscript. The authors agreed to be accountable for the content of the work. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by Alexander von Humboldt foundation. The publication of this article was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—801 HE 9114/1-1.

Data Availability Statement

The phenotypic and sequence data are available from the corresponding author on request.

Acknowledgments

The authors would like to thank Prof. Dr. Ahmed Sallam, Genetics Department, Faculty of Agriculture, Assiut University, for providing a space in his lab to conduct the required experiments.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The distribution of the studied seedling traits under copper (Cu) and cadmium (Cd) conditions: (a) biological weight (BW); (b) shoot weight (SW); (c) root weight (SW); (d) shoot weight to root weight (SW/RW); (e) shoot length (SL); (f) root length (RL); (g) shoot length to root length (SL/RL); (h) leaf width (LW).
Figure 1. The distribution of the studied seedling traits under copper (Cu) and cadmium (Cd) conditions: (a) biological weight (BW); (b) shoot weight (SW); (c) root weight (SW); (d) shoot weight to root weight (SW/RW); (e) shoot length (SL); (f) root length (RL); (g) shoot length to root length (SL/RL); (h) leaf width (LW).
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Figure 2. Correlation between the seedling traits studied in the evaluated genotypes under copper-contaminated soil (a) and cadmium-contaminated soil (b) conditions.
Figure 2. Correlation between the seedling traits studied in the evaluated genotypes under copper-contaminated soil (a) and cadmium-contaminated soil (b) conditions.
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Figure 3. Marker traits associated with seedling traits under copper and cadmium stress conditions, as identified by single-marker analysis (SMA): (a) the distribution of the identified significant markers associated with seedling traits across wheat chromosomes; (b) the number of total significant markers associated with Cu, Cd, and both Cu and Cd stresses.
Figure 3. Marker traits associated with seedling traits under copper and cadmium stress conditions, as identified by single-marker analysis (SMA): (a) the distribution of the identified significant markers associated with seedling traits across wheat chromosomes; (b) the number of total significant markers associated with Cu, Cd, and both Cu and Cd stresses.
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Figure 4. The gene enrichment of the common gene models harboring markers significantly associated with copper and cadmium tolerance in wheat seedlings.
Figure 4. The gene enrichment of the common gene models harboring markers significantly associated with copper and cadmium tolerance in wheat seedlings.
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Figure 5. A gene network of the identified enrichment pathways based on biological process (BP, (a)), molecular function (MF, (b)), and Kyoto encyclopedia of genes and genomes (KEGG, (c)), and the gene models controlling them.
Figure 5. A gene network of the identified enrichment pathways based on biological process (BP, (a)), molecular function (MF, (b)), and Kyoto encyclopedia of genes and genomes (KEGG, (c)), and the gene models controlling them.
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Figure 6. (a) The number of target alleles associated with the tolerance to both copper and cadmium in the 12 selected genotypes, carrying the eight markers commonly associated with Cu and Cd tolerance. (b) The phylogeny tree represents the genetic distance between each pair of the 12 selected genotypes.
Figure 6. (a) The number of target alleles associated with the tolerance to both copper and cadmium in the 12 selected genotypes, carrying the eight markers commonly associated with Cu and Cd tolerance. (b) The phylogeny tree represents the genetic distance between each pair of the 12 selected genotypes.
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Table 1. Soil analysis of the non-contaminated soils (Ctrl), Cu-contaminated soils (Cu), and Cd-contaminated soils (Cd), demonstrating pH, heavy metal concentration, cation and anion content, saturation capacity, and soil conductivity.
Table 1. Soil analysis of the non-contaminated soils (Ctrl), Cu-contaminated soils (Cu), and Cd-contaminated soils (Cd), demonstrating pH, heavy metal concentration, cation and anion content, saturation capacity, and soil conductivity.
TreatmentCtrlCuCd
pH8.328.228.42
Heavy metals concentration (mg/kg soil)
Cd0.055 --0.138
Cu9.842233.19--
Cations (mg/kg soil)
Mg2+2.4363.340.68
K+0.240.250.28
Na+6.2922.2211.44
Anions (mg/kg soil)
Cl−1.690.94438.76
Saturation capacity (%)20.7421.5220.98
Soil conductivity887977869
Table 2. The analysis of variance represents the source of variance (S.O.V.), degree of freedom (d.f.), and mean square (M.S.) of the studied seedling traits (biological weight (BW), shoot weight (SW), root weight (RW), SW/RW ratio, shoot length (SL), root length (RL), SL/RL ratio, and leaf width (LW)) under cadmium- and copper-contaminated soil conditions.
Table 2. The analysis of variance represents the source of variance (S.O.V.), degree of freedom (d.f.), and mean square (M.S.) of the studied seedling traits (biological weight (BW), shoot weight (SW), root weight (RW), SW/RW ratio, shoot length (SL), root length (RL), SL/RL ratio, and leaf width (LW)) under cadmium- and copper-contaminated soil conditions.
Cu
S.O.V.BWSWRWSW/RWSLRLSL/RLLW
d.fM.S.d.fM.S.d.fM.S.d.fM.S.d.fM.S.d.fM.S.d.fM.S.d.fM.S.
Rep (R)20.02 **20.01 **20.00262.50 **231.25 *216.68 **20.0520.01 **
Gen (G)910.01 **890.00 **900.002 **898.23 **9142.37 **9116.01 **910.84 **900.28 **
G × R1820.001710.001800.001713.691827.291821.851820.081800.00
Total275--262--272--262--275--275--275--272--
Heritability (H2)0.690.640.680.550.830.880.910.99
Cd
S.O.V.BWSWRWSW/RWSLRLSL/RLLW
d.fM.S.d.fM.S.d.fM.S.d.fM.S.d.fM.S.d.fM.S.d.fM.S.d.fM.S.
Rep (R)20.02 **20.01 *20.002375.50 **274.52 **225.49 **21.73 **20.00
Gen (G)721.16 **720.01 **720.257218.627287.89 **7212.50 **721.47 **710.23 **
G × R1440.001390.001440.0413916.011437.461431.291430.211410.00
Total218--213------213--217--217--217--214--
Heritability (H2)0.870.780.930.140.920.900.850.96
* p < 0.05, ** p < 0.01.
Table 3. Summary of SNP markers significantly associated with the seedling traits of biological weight (BW), shoot weight (SW), root weight (RW), SW/RW ratio, shoot length (SL), root length (RL), SL/RL ratio, and leaf width (LW) in copper- and cadmium-contaminated soils, as identified by single-marker analysis (SMA).
Table 3. Summary of SNP markers significantly associated with the seedling traits of biological weight (BW), shoot weight (SW), root weight (RW), SW/RW ratio, shoot length (SL), root length (RL), SL/RL ratio, and leaf width (LW) in copper- and cadmium-contaminated soils, as identified by single-marker analysis (SMA).
Contaminated SoilSeedling TraitNo. Significant MarkersNo. Chromosomes−log10R2Allele Effect
CuBW43113.02–4.7412.68–20.850.03–0.07
SW52143.02–4.5412.99–21.550.02–0.05
RW35113.02–4.7912.72–20.860.03–0.06
SW/RW15103.02–4.6913.55–21.391.08–2.60
SL61173.01–5.0412.59–21.922.63–5.27
RL28 93.01–5.9612.87–26.101.73–3.18
SL/RL43143.01–6.4212.82–28.010.36–0.75
LW1653.00–3.4612.75–16.770.05–0.08
Total265213.00–6.4212.59–28.010.02–5.27
CdBW194213.01–11.1917.48–56.180.06–0.45
SW75183.02–6.8417.27–38.190.04–0.19
SL25123.00–4.7317.85–28.974.78–9.86
RL63133.01–4.3517.58–25.931.61–5.63
SL/RL63123.01–4.6418.02–31.450.64–1.24
LW43113.02–4.1218.01–24.950.07–0.16
Total381213.00–11.1917.27–56.180.04–9.86
Table 4. A list of markers significantly associated with copper and cadmium tolerance in wheat seedlings.
Table 4. A list of markers significantly associated with copper and cadmium tolerance in wheat seedlings.
MarkerChromPositionTraitLog10 (p-Value)R2Target AlleleAllele EffectGene ModelFunctional Annotation
CuCdCuCdCuCdCuCdCuCd
Kukri_rep_c106834_1391B4349225RWBW3.223.6113.9120.85TT0.030.13TraesCS1B02G008000Mei2-like protein
AX-1115599272A747088588RWBW3.184.2613.5825.03AA0.040.17TraesCS2A02G527700Chalcone synthase
IACX32452A747089736RWBW3.114.2613.5625.03TT0.040.14
BobWhite_c5178_1882A751511240RWBW3.834.1216.5823.84CC0.060.21TraesCS2A02G538800Ubiquitin carboxyl-terminal hydrolase
RAC875_c2110_1172B146736196RWRL3.043.9713.0823.66TT0.045.62TraesCS2B02G172400Small nuclear RNA-activating complex (SNAPc)
AX-1585752462B146736220RWRL3.094.0413.1423.71TT0.045.63
BS00023202_512B759491069RWBW3.874.1216.5623.48TT0.060.21TraesCS2B02G5688002-oxoglutarate (2OG) and Fe(II)-dependent oxygenase superfamily protein
Kukri_c24408_7437A670765573SWBW3.103.0613.8418.10TT0.050.12NANA
Table 5. A list of spring wheat genotypes that carry significant markers associated with Cu and Cd tolerance, their country of origin, their PI ID, and their rank among the tested populations under each heavy metal for biological weight (BW), shoot weight (SW), root weight (RW), shoot length (SL), root length (RL), and leaf width (LW).
Table 5. A list of spring wheat genotypes that carry significant markers associated with Cu and Cd tolerance, their country of origin, their PI ID, and their rank among the tested populations under each heavy metal for biological weight (BW), shoot weight (SW), root weight (RW), shoot length (SL), root length (RL), and leaf width (LW).
GenotypeCountryPI CodeBWSWRWSLRLLW
CuCdCuCdCuCdCuCdCuCdCuCd
Sakha_93Egypt-NA1NA1NA1NA70NA55NA60
Gimmiza_9Egypt-715386581034454133127741
Sids_12Egypt-4254583352858554NA67
1668IranPI222677854685455052692451707
15IranPI3819638342802749195392128017
Misr_1 Egypt-*2*2*3*60*39NA4
Gimmiza_10Egypt-*71*68*73*73*69NANA
Janetzkis SommerweizenGermanyPI1916009221*186546901587459043
Grekum 105KazakhstanPI438962131130235612937363010
Rhodesian SabaneroKenyaPI2302025482052443744549587156
1372MoroccoPI525434326195731021194310101
1130MoroccoPI52528224443556141666515038747
* Is a missing value.
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Mourad, A.M.I.; Baghdady, S.; Abdel-Aleem, F.A.-Z.M.; Jazeri, R.M.; Börner, A. Novel Genomic Regions and Gene Models Controlling Copper and Cadmium Stress Tolerance in Wheat Seedlings. Agronomy 2024, 14, 2876. https://doi.org/10.3390/agronomy14122876

AMA Style

Mourad AMI, Baghdady S, Abdel-Aleem FA-ZM, Jazeri RM, Börner A. Novel Genomic Regions and Gene Models Controlling Copper and Cadmium Stress Tolerance in Wheat Seedlings. Agronomy. 2024; 14(12):2876. https://doi.org/10.3390/agronomy14122876

Chicago/Turabian Style

Mourad, Amira M. I., Sara Baghdady, Fatma Al-Zahraa M. Abdel-Aleem, Randa M. Jazeri, and Andreas Börner. 2024. "Novel Genomic Regions and Gene Models Controlling Copper and Cadmium Stress Tolerance in Wheat Seedlings" Agronomy 14, no. 12: 2876. https://doi.org/10.3390/agronomy14122876

APA Style

Mourad, A. M. I., Baghdady, S., Abdel-Aleem, F. A. -Z. M., Jazeri, R. M., & Börner, A. (2024). Novel Genomic Regions and Gene Models Controlling Copper and Cadmium Stress Tolerance in Wheat Seedlings. Agronomy, 14(12), 2876. https://doi.org/10.3390/agronomy14122876

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