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Drought and salinity stress response in wheat: physiological and TaNAC gene expression analysis in contrasting Egyptian wheat genotypes
J Plant Biotechnol 2020;47:1-14
Published online March 31, 2020
© 2020 The Korean Society for Plant Biotechnology.

D. Abd El-Moneim · Mesfer M. Alqahtani · Mohamed A. Abdein · Mousa O. Germoush

Department of Plant Production (Genetic branch), Faculty of Agricultural and Environmental sciences, Arish University, Egypt
Department of Biological Sciences, Faculty of Science and Humanities, Shaqra University, P. O. Box 1040, Ad-Dawadimi 11911, Saudi Arabia
Biology Department, Faculty of Arts and Science, Northern Border University, Rafha, Saudi Arabia
Biology Department, College of Science, Jouf University, Sakaka, Al jouf, Saudi Arabia
Correspondence to: e-mail: dabdelmoniem@aru.edu.eg
Received December 16, 2019; Revised January 23, 2020; Accepted January 27, 2020.
cc This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Drought and salinity are significant stressors for crop plants, including wheat. The relationship between physiological mechanisms and gene expression is important for stress tolerance. NAC transcription factors (TFs) play vital roles in abiotic stress. In this study, we assessed the expression of four TaNAC genes with some physiological traits of nine Egyptian wheat genotypes under different concentrations of PEG and NaCl. All the physiological traits that we assessed declined under both stress conditions in all genotypes. In addition, all the genes that we measured were induced under both stress conditions in young leaves. Shandaweel 1, Bani Seuf 7, Sakha 95, and Misr 2 genotypes showed higher gene expression and were linked with a better genotypic performance in physiological traits under both stress conditions. In addition, we found an association between the expression of NAC genes and physiological traits. Overall, NAC genes may act as beneficial markers for selecting for genotypic tolerance to these stress conditions in wheat.
Keywords : Wheat, Gene expression, NAC transcription factor, RWC, Drought, Salinity
Introduction

Wheat (Triticum aestivum L.) have a considerable role between crop species grown as staple food sources. Nakashima et al. 2011 stated that abiotic stresses, such as salinity and drought, influence the productivity and growth of plants, including crops. Producing higher-yielding genotypes with good technological quality that are resistant or tolerant to a broad range of biotic and abiotic stresses is a very important target. Tolerance to stress condition defined as an ability of plants to grow in low water potential and in this way, high relative water content (RWC) is one of tolerance mechanisms to stress conditions (Sinclair and Ludlow, 1985). In this regard, (Schonfeld et al. 1988) and (Ghogdi et al. 2012) revealed that RWC decreased by increasing of the drought and salinity levels and it was higher in tolerant wheat cultivars than sensitive ones. Many important leaf traits such as emergence, elongation, expansion, leaf enlargement and stomatal opening are important characteristics that influence plant water relations. Leaf area plasticity is an important strategy to regulate the water use in crops. (Franco et al. 1997) and (Ghaderi et al. 2015) found a reduction in the total leaf area under salinity and drought stress respectively. The lower leaf area ratio of tolerant cultivar is a beneficial morphological adaptation as the reduction in the photoassimilate per unit of plant material (LAR) reduces the water use (Richards 1992). In the same context, (Abbas et al. 2015) found higher leaf area ratio for salt sensitive cultivar than salt tolerant cultivar under drought and salinity stress. On the contrary, the sensitive cultivar to drought showed more decline in leaf emergence and leaf area (Ghaderi et al. 2015). Furthermore, leaf size along with leaf longevity were shrunk as a result of salinity stress (El‐Hendawy et al. 2009). In addition, the width and length of wheat leaves reduced approximately by 20~30% when were exposed to 120 mM NaCl (Hu et al. 2000). Transcription factors (TFs) are the main regulators of different genes involved in the reduction and/or protection from the stress cellular damage (Agarwal and Jha 2010). TFs play important roles either activating or preventing target gene expression in plants (Puranik et al. 2012; Nakashima et al. 2012). NAC superfamily is one of the largest plant-specific TF families. Primarily, NAC domain originated from the ATAF1, ATAF2, and CUC2 genes in Arabidopsis and NAM (No Apical Meristem) gene in petunia, (Souer et al. 1996; Aida et al. 1997; 1999). (Yamaguchi-Shinozaki et al. 1992) described the first NAC gene was in Arabidopsis (Responsive To Dehydration 26). 117 and 151 genes in Arabidopsis and rice have been identified as NAC family members, respectively (Nuruzzaman et al. 2010). Moreover, NAC TFs Clearly showed a very important role in plant's responses to biotic stress (Kim et al. 2007) and abiotic stresses (Ozhuner et al. 2013). In Arabidopsis AtNAC2 has many roles in responding to salinity stress (He et al. 2005). Moreover, under low oxygen conditions At-NAC102 is involved in seed germination regulation (Christianson et al. 2009). In rice, overexpression of OsNAC5, OsNAC9, and OsNAC10 increase drought tolerance (Jeong et al. 2010 and 2013; Redillas et al. 2012). Meanwhile, OsNAC6 and OsNAC045 overexpression in rice enhances tolerances to drought, salt stresses (Nakashima et al. 2007; Zheng et al. 2009). Furthermore, the expression of OsNAC78 was specifically induced in a drought tolerant line (Moumeni et al. 2011). Similar observation was noticed from groundnut plants transformed with MuNAC4 gene of horse gram (Pandurangaiah et al. 2014). In cotton, expression patterns of six NAC members were induced by abiotic stresses (Meng et al. 2009). Expression of ZmSNAC1 in maize was regulated by many abiotic stresses (Lu et al. 2012). In soybean, Overexpression of GmNAC20 and GmNAC11 ameliorate salt tolerance in transgenic Arabidopsis (Hao et al. 2011). However, (Quach et al. 2014) reported that the overexpression of soybean gene GmNAC004 acts during moderate drought. In wheat, (Xia et al. 2010a and 2010b) revealed that TaNAC8 involved in wheat defense responses to both abiotic and biotic stresses. Also, (Mao et al. 2012, Huang et al. 2015) and (Xue et al. 2006) reported that TaNAC2, TaNAC29 and TaNAC69 upregulated by various abiotic stresses. On other hand, (Mao et al. 2012 and 2014). showed that overexpression of TaNAC2 and TaNAC67 in transgenic arabidopsis enhanced tolerances to drought, salt and freezing stresses Investigating the molecular responses to drought and salinity stress and their association to physiological indicators may provide important information for evaluating different wheat genotypes under abiotic stresses. In this regard, The target of this study was to investigate the relationships between the expression levels of TaNAC genes and some physiological traits to drought and salinity tolerance in some Egyptian wheat genotypes.

Materials and Methods

Plant material

Nine Egyptian wheat cultivars (Triticum astivum), (Giza 168, Misr 2, Shandaweel 1, Misr 1, Sids 12, Misr 3, Sakha 95, Bani Seuf 7 and Sohag 4) were chosen because of their different tolerance for drought and salinity stress.

Growth conditions and stress treatments

Seeds of each genotype disinfected by immersion in Ca (ClO)2 solution containing 5% of chlorine, for 5 min. Seeds were then washed 3 times with sterilized distilled water. Seeds of each genotype were placed on the moist Whatman germination papers in petri dishes to provide appropriate moisture for seed germination. After 3 days germinated seeds were transferred in eight 50×20 mm plastic pot / genotype (10 seeds / pot) in three replications for every stress. All the pots containing sand, soil and peat (1:1:1). All the genotypes were grown under greenhouse conditions. The temperature was 25 ± 2°C, the relative humidity was 50% and a photoperiod of 14 h. Seedlings were watered daily with tap water for 3 weeks. Subsequently, drought and salinity stress treatments were imposed in the fourth week. Eight pots of each wheat genotype in three replications were treated with four treatments for every stress separately at the same time. Drought and salinity treatments were imposed by dissolving (0,5,15,25 % PEG 6000) or (0,50,150 and 250 mm NaCl) in distilled water. Solutions of PEG 6000 were prepared according to weight by volume (Bayoumi et al. 2008). After exposure to treatments for one week, leaves were directly frozen in liquid nitrogen and kept at -80°C for further analysis.

Physiological traits measurements

RWC was estimated in control and stressed seedlings. Fully expanded leaves were excised and fresh weight (FW) was directly recorded; thereafter leaves were soaked for four hours in distilled water at room temperature under a constant light, and the turgid weight (TW) was recorded. After drying for 24 hours at 80°C total dry weight (DW) was recorded. RWC was calculated according to the equation of (Tambussi et al. 2005): RWC (%) = [(FW-DW)/ (TW-DW)] x 100. Leaf Area (LA), Leaf length (LL) and Max Leaf wide (MLW) were recorded for the different studied genotypes / treatments with a portable leaf area meter LI-3000C.

Real time PCR analysis

Total RNA was isolated from plant material using Plant RNA reagent (Invitrogen, USA) according to the manufacturer's instructions. cDNA was synthesized using an oligo (dT20) primer from total RNA samples that were pre-treated with RNase-free DNase I (Xue and Loveridge, 2004) and purified through Qiagen RNeasy column (Qiagen, Australia). Transcript levels were quantified by real-time PCR with an ABI Prism 7900 sequence detection system (Applied Biosystems) using SYBR Green PCR Master Mix (Applied Biosystems) according to the manufacturer's instructions. TaActin gene used as internal reference genes for calculating relative transcript levels of genes of the interest in each comparative analysis. Sequences and names of primer pairs used are listed in the (Table 1). PCR was performed in three technical replicates. The reaction mixture was contained 2 µl of cDNA sample, 0.6 µl of each forward and reverse primer, 10 µl of SYBR Green and 6.8 µl of PCR grade water. PCR conditions include an initial cycle at 50°C for 2 min, one cycle at 95°C for 10 min, followed by 45 cycles at 95°C for 15 s and at 60°C for 1 min. The quantification of the relative transcript levels was performed using the comparative CT method (Livak and Schmittgen 2001).

Primer sequences used for real-time gene expression analysis in this study

F R Reference
TaNAC2a GGTAGTGCGGTGCTTCCAAT TGAATGTTGTTGCTCGTCCC Tang et al. 2012
TaNAC7 ATCGCCAAGCCACCCACAGG GGAGGGGCCATTGGAGAAGC Tang et al. 2012
TaNAC69-1 TGCCTCCCGAAAACCCA TTGTTCACGTAGCCGTTGTTGT Xue G. et al. 2011
TaNAC69-3 AACAATGGCTACGTGAACATCGA AAACTGCCGCTGGACCTCTT Xue G. et al. 2011
TaActin CTTGTATGCCAGCGGTCGAACA CTCATAATCAAGGGCCACGTA Wang et al. 2013

Statistical analysis

Drought and salinity experiments were carried out in a random complete block design. All data were represented as ± (SD) of three replicates. One, two-way analysis of variance (ANOVA) was used to test the differences between the means of different variables. If there is significant tukey test was used to detect source of difference. For all statistical tests P-values < 0.05 was considered to be statistically significant. Data and statistical analysis were performed using Minitab version 18. The degree of correlation between studied genotypes (two genotypes as a model for tolerant group and another two genotypes as model for sensitive group for every stress) and all studied traits, transcript abundance for studied genes was calculated using Pearson correlation coefficient

Results

Physiological analyses

The stress, genotype and interaction between them had highly significant effects at P-values < 0.05 statistical level (Tables 2 and 3). As shown, in (Figs. 1 and 2) all the genotypes contrasting in their response to both stresses. Fig. 1 panels (a) to (d) revealed that Sakha 95 (95.15), Bani Seuf 7 (61.54) and Shandaweel 1 (32.40) were the highest RWC means under 5,15 and 25% of PEG respectively. Meanwhile, Giza 168 (73.75 & 25.57) and Misr 3 (15) had the lowest means of RWC under 5,15 and 25% of PEG. In addition, leaf area of Sakha 95, Bani Seuf 7 and Misr 2 genotypes were recorded the highest means (34.72, 22.05 and 5.27) under 5,15 and 25% of PEG respectively. Meanwhile, Giza 168 had lowest means (12.06 & 2.64) under 5 and 25% of PEG respectively and Sids 12 had lowest means under 15% PEG (10.64) for leaf area. Moreover, the highest means of leaf length under 5,15 and 25% of PEG were (63.87, 56 and 47.13) for Bani Seuf 7, Misr 3 and Bani Seuf 7 respectively. Notably, the lowest means under 5,15 and 25% of PEG were (23.10, 30.70 and 14.10) for Giza 168. For Max leaf wide, Bani Seuf 7 had the highest means under 5 and 15% PEG (1.15 and 1) respectively, while Shandaweel 1 had the highest means under 25% of PEG. Meanwhile, the lowest means under 5,15 and 25% of PEG were (0.60, 0.45 and 0.40) for Misr 3, Sakha 95 and Giza 168, respectively. On the other hand, (Fig. 2) panels (a) to (d) revealed that the mean analyses for most of studied genotypes and traits were decreased significantly under different concentrations of NaCl except Leaf area for (Bani Seuf 7, Misr 1 and Sids12), Leaf length for (Bani Seuf 7) and Max leaf wide for (Giza168, Misr 2, Shandaweel 1and Sids 12). The highest means under 50,150 and 250 mM NaCl for RWC were 97, 97 and 89.8 for Sohag 4, Bani Seuf 7 and Shandaweel 1 respectively. Meanwhile, Bani Seuf 7, Misr1 and Giza 168 had the lowest means 87.1, 62.1 and 50.9 under 50,150 and 250 mM NaCl respectively. Leaf area for genotypes Misr 2, Misr 2 and Sakha 95 were recorded the highest means 34.7, 26.3 and 27.7 under 50,150 and 250 mM NaCl respectively. Meanwhile, Sids 12 under 50 &150 mM NaCl (9.7,13.7) and Bani Seuf 7 under 250 mM NaCl (12.1) were the lowest means. In addition, the highest leaf length means under 50,150 and 250 mM NaCl were (51.1, 44.1 and 40.1) for Sohag 4, Misr2 and Misr 1 respectively, while the lowest means were (20.3 & 19.6) under 50 and 150 mM NaCl for Sids 12 and (21.4) under 250 mM NaCl for Giza 168. For Max leaf wide, Misr 2, Misr 2 and Bani Seuf 7 genotypes had the highest means (1.5, 1.2 and 1.3) under 50, 150 and 250 mM NaCl. Meanwhile, the lowest means under 50,150 and 250mM NaCl were (0.7, 0.6 and 0.6) for Sids 12, Misr1 and Misr 3, respectively.

Analysis of Variance of Physiological traits versus genotypes drought stress

df Leaf area Leaf length Leaf wide RWC

F-Value P-Value F-Value P-Value F-Value P-Value F-Value P-Value
Genotype 8 28.89 0.000 43.12 0.000 23.13 0.000 7.42 0.000
Drought 3 294.58 0.000 69.88 0.000 170.87 0.000 996.12 0.000
Genotype* Drought 24 14.66 0.000 15.68 0.000 17.42 0.000 5.80 0.000

Error 72
Total 107

P-Value at (P < 0.05) according to Tukey's test



Analysis of Variance of Physiological traits versus genotypes, salinity stress

Source df Leaf area Leaf length Leaf wide RWC

F-Value P-Value F-Value P-Value F-Value P-Value F-Value P-Value
Genotype 8 27.64 0.000 21.25 0.000 1.21 0.304 4.05 0.001
Salinity 3 27.17 0.000 23.75 0.000 1.57 0.204 61.31 0.000
Genotype* Salinity 24 3.45 0.000 5.64 0.000 1.77 0.034 6.64 0.000

Error 72
Total 107

P-Value at (P < 0.05) according to Tukey's test



Fig. 1. Comparison of the physiological traits of the studied genotypes under different concentrations of PEG. (A) Leaf Area; (B) Leaf Length; (C) Max Leaf Width; (D) RWC. Bars represent standard deviation. Columns with different lower case letters indicate significant difference at P < 0.05 (Tukey test). All bars represent the mean of three replications

Fig. 2. Comparison of the physiological traits of the studied genotypes under different concentrations of NaCl. (A) Leaf Area; (B) Leaf Length; (C) Max Leaf Width; (D) RWC. Bars represent standard deviation. Columns with different lower case letters indicate significant difference at P < 0.05 (Tukey test). All bars represent the mean of three replications

Gene expression analyses

With a view to study the relationships between gene expression levels and physiological traits for the studied genotypes, four abiotic stress responsive genes, TaNAC2a, TaNAC7, TaNAC69-1 and TaNAC69-3 were selected to determinate their expression levels under drought and/or salinity conditions. Stress, genotype and interaction had highly significant effects at P-values < 0.05 statistical level (Tables 4 and 5). As it is evident, in (Fig. 3) panels (a) to (b) we observed that Bani Seuf 7 and Shandaweel 1 recorded the highest expression pattern and it increased by 3, 9 and 18 fold comparing with the untreated control under 5,15 and 25% PEG, respectively, while Sids 12 had the lowest expression under 15 & 25% of PEG and Sohag 4 under 5% PEG. Undoubtedly, in (Fig. 4) panels (a) to (b) Misr 2 genotype showed the highest expression pattern and it increased by 3, 3.5 and 9 fold compared with the untreated control under 50,150 and 250 mM NaCl, respectively. While, the expression of Sids 12 was the lowest under 50 & 250 mM NaCl and Misr 1 under 50 mM NaCl. Briefly, the relative expression of Shandaweel 1 and Misr2 was sharply increased and recorded the highest expression under 25% PEG and 250 mM NaCl respectively. Moreover, Sids 12 recorded the lowest expression under 25% PEG and 250 mM NaCl. As shown in (Figs. 3 and 4) no significant expression of any studied genes in control seedlings. The highest expression levels between all studied genes was under 25 % PEG and 250 mM NaCl except for TaNAC2a and TaNAC7 under 150 mM NaCl. Under PEG stress the highest expression means were 10, 3, 16 and 18 times more than the control for TaNAC2a, TaNAC7, TaNAC69-1 and TaNAC69-3 respectively. Meanwhile, under NaCl the highest expression means were 3,3,9 and 8 times more than the control for TaNAC2a, TaNAC7, TaNAC69-1 and TaNAC69-3 respectively. In addition, under drought stress conditions all studied genes were expressed higher than under salinity stress for all the studied concentrations except TaNAC7 was opposite. Notably, under both stresses the expression levels of TaNAC69-1 and TaNAC69-3 were very seems each other and had the highest fold change comparing with the control especially under the highest concentrations followed by the expression levels of TaNAC2a, TaNAC7 under PEG stress and TaNAC7, TaNAC2a under NaCl stress. From (Fig. 5) we concluded that there are four different expression patterns of the studied genes /genotypes. First pattern includes all the genotypes that its level of mRNA was increased and the expression of all the studied genes upregulated under 5,15 and 25% of PEG or 50,150 and 250 mM of NaCl. In drought experiment expression of Shandaweel 1 for all the studied genes and Sakha 95 for all the studied genes except TaNAC2a belongs to this pattern of expression. In addition, in salinity experiment, expression of Misr 2 and Sakha 95 for TaNAC69-1 and TaNAC69-3 genes belongs to this pattern of expression. Second pattern includes all the genotypes that its level of mRNA was decreased after treated with 5% PEG or 50 mM NaCl and then increased after treated with 15% PEG or 150 mM NaCl and finally its expression decreased after treated with 25% PEG or 250 mM of NaCl. For example, in drought experiment expression, of Sohag 4 for all the studied genes belongs to this pattern of expression. Moreover, in salinity experiment, expression of Sohag 4 and Misr 3 for all the studied genes belongs to this pattern of expression. Third pattern includes all the genotypes that its level of mRNA was increased after treated with 5% PEG or 50 mM NaCl and then decreased after treated with 15% PEG or 150 mM NaCl and finally its expression increased after treated with 25% PEG or 250 mM of NaCl. In this regard, in drought experiment the expression of Misr 2 and Sakha 95 for just TaNAC2a gene are belongs for this pattern. In the same time, in salinity experiment, expression of Misr 2, Shandaweel 1 and Bani Seuf 7 for just TaNAC2a gene are belongs for this pattern. Finally, fourth pattern includes all the genotypes that its level of mRNA was decreased and the expression of all the studied genes downregulated under 5,15 and 25% of PEG or 50,150 and 250 mM of NaCl. In this context, under drought conditions, expression of Misr 1 for all studied genes except TaNAC69-3 and expression of Misr 3 for TaNAC69-1 and TaNAC2a are examples for this pattern. On other hand, in salinity experiment, expression of Giza 168 and Misr 1 for all the studied genes except TaNAC2a and TaNAC69-3 respectively, are examples for this pattern.

Analysis of Variance of gene expression versus genotypes, drought stress

Source df TaNAC2a TaNAC7 TaNAC69-1 TaNAC69-3

F-Value P-Value F-Value P-Value F-Value P-Value F-Value P-Value
Genotype 8 122.12 0.000 14.78 0.000 39.53 0.000 13.93 0.000
Drought 3 123.38 0.000 196.3 0.000 313.94 0.000 53.84 0.000
Genotype* Drought 24 33.31 0.000 21.42 0.000 48.97 0.000 15.87 0.000

Error 72
Total 107

P-Value at (P < 0.05) according to Tukey's test



Analysis of Variance of gene expression versus genotypes, salinity stress

Source df TaNAC2a TaNAC7 TaNAC69-1 TaNAC69-3

F-Value P-Value F-Value P-Value F-Value P-Value F-Value P-Value
Genotype 8 25.13 0.000 21.78 0.000 56.96 0.000 46.86 0.000
Salinity 3 221.81 0.000 190.73 0.000 77.07 0.000 95.35 0.000
Genotype* Salinity 24 25.42 0.000 17.40 0.000 23.38 0.000 15.05 0.000

Error 72
Total 107

P-Value at (P < 0.05) according to Tukey's test



Fig. 3. Gene expression of (A) TaNAC2a, (B) TaNAC7, (C) TaNAC69-1, and (D) TaNAC69-3. Data represent the mean fold change in relative expression versus the control sample, normalized to the reference gene, TaActin. Genotype expression under different concentrations of PEG was measured in three replications. Bars represent standard deviation. Columns with different lower case letters indicate significant difference at P < 0.05 (Tukey test)

Fig. 4. Gene expression of (A) TaNAC2a, (B) TaNAC7, (C) TaNAC69-1, and (D) TaNAC69-3. Data represent the mean fold change in relative expression compared with the control samples, normalized to the reference gene, TaActin. Genotype expression under different concentrations of NaCl was measured in three replications. Bars represent standard deviation. Columns with different lower case letters indicate significant difference at P < 0.05 (Tukey test)

Fig. 5. Different expression patterns for all studied genes and genotypes. The first, second, third, and fourth patterns are colored with red, orange, blue, and green, respectively

Correlation analysis

In this study, an existent correlation was elucidated between studied genes expression and various abiotic stress responsive, physiological parameters for tolerant and sensitive genotypes. In (Fig. 6) (a) we observed that Shandaweel 1 represented high positive correlation with all studied genes under different concentrations of PEG and the highest correlation was with TaNAC2a (0.873). Meanwhile, (Fig. 6) (b) showed that Sids 12 had positive correlation with all studied genes and the highest correlation was with TaNAC69-1 (0.502). On other hand, (Fig. 7) (a) revealed that Sakha 95 recorded high positive correlation with all studied genes under different concentrations of NaCl and the highest correlation was with TaNAC69-1 (0.913). Moreover, Giza168 showed positive correlation with all studied genes and the highest correlation was with TaNAC69-1 (0.959) (Fig. 7) (b). On other hand, in (Table 6) Bani Seuf 7 showed significant negative correlation among expression levels of all studied genes and physiological traits under drought stress. The highest correlation was between TaNAC7and RWC (-0.958). Moreover, the four studied genes had a positive correlation between each other, and the higher correlation was between TaNAC69-1 and TaNAC69-3 (0.942). Also, the correlation between all studied traits was positive. The highest correlation was between leaf area and leaf wide (0.922). On other hand, in (Table 7) Misr 2 showed significant negative correlation among the expression of all studied genes and physiological traits under salinity stress. The highest correlation was between TaNAC69-1 and RWC (-0.945). Moreover, positive correlation was found between all studied genes and the highest correlation was between TaNAC69-1 and TaNAC69-3 (0.916). Also, the correlation between all studied traits were positive. The highest correlation was between leaf area and RWC (0.800).

Fig. 6. Correlation coefficient analysis between the relative expression of studied genes and drought stress. (A) shandaweel 1 (tolerant genotype); (B) Sids 12 (sensitive genotype)

Fig. 7. Correlation coefficient analysis between the relative expression of studied genes and salinity stress. (A) Shakha 95 (tolerant genotype); (B) Giza 168 (sensitive genotype)

Correlation coefficients analysis between relative expression of studied genes and physiological traits of Misr2 genotype under salinity stress condition

TaNAC2a TaNAC7 TaNAC69-1 TaNAC69-3 Leaf Area Leaf Length Leaf wide
TaNAC7 0.803
0.002
TaNAC69-1 0.119 0.042
0.713 0.897
TaNAC69-3 0.262 0.130 0.916
0.410 0.687 0.000
Leaf Area -0.004 -0.011 -0.836 -0.775
0.989 0.972 0.001 0.003
Leaf Length -0.672 -0.336 -0.647 -0.628 0.411
0.017 0.286 0.023 0.029 0.185
Leaf wide -0.256 -0.193 -0.612 -0.504 0.779 0.236
0.423 0.548 0.035 0.095 0.003 0.461
RWC -0.041 -0.257 -0.945 -0.887 0.800 0.511 0.572
0.898 0.420 0.000 0.000 0.002 0.09 0.052

Every cell represents values of (Pearson correlation & P-Value at (P < 0.05)


Discussion

Plants undergo variety of changes from physiological adaptation to gene expression after exposure to abiotic stress, (Shinozaki et al. 2007). To react with these abiotic stresses, plants have developed many strategies enabling them to integrate activities at the whole-plant level. These strategies may involve avoidance and/or development of tolerance mechanisms. According to (Dencic et al. 2000), wheat is paid special attention due to its morphological traits during drought and salinity stress, including leaf (shape, expansion, area, size, senescence and waxiness). In this study, four leaf physiological parameters (RWC, LA, LL and MLW) were estimated for stressed and unstressed seedlings. A noticeable significant decline in all studied physiological traits under both stresses for most of the studied genotypes as shown in (Figs. 1 and 2). Accordingly, we classified the studied genotypes into three groups based on their stress tolerance ability. Under drought stress (Bani Seuf 7, Shandaweel 1 and Sakha 95) were tolerant genotypes, (Misr 2, Sohag 4 and Misr 1) were moderate genotypes, while (Giza 168, Misr 3 and Sids 12) were sensitive genotypes. In this regard, (Mickky et al. 2017) studied some morphological traits on wheat seedlings after exposing wheat seedlings to PEG treatments and Sids 13 seemed to be the most tolerant variety followed by Masr 1, Masr 2, Gimmaza 9, Gimmaza 11, Sids 12, Sakha 93, Sakha 94, and Giza 186 and finally came Shandawel 1 with the maximum sensitivity. On the other hand, our results showed that under salinity stress, the tolerant genotypes included (Misr 2, Bani Seuf 7 and Sohag 4), the moderate genotypes included (Shandaweel 1and Sakha 95), and the sensitive genotypes included (Sids 12, Giza 168, Misr 1 and Misr 3). In this context, (Hamam et al. 2014) found that Giza 168, Sids 12 were more moderated to salinity at early growth stages. Interestingly, we found some genotypes that could be able to tolerate both drought and salinity at the same time such as (Bani Seuf 7) and other genotypes can be sensitive to drought and salinity as (Sids 12, Giza 168, Misr 3). Moreover, we observed that the tolerant genotypes under both stresses had the highest means for all studied traits suggested that these genotypes have avoided osmotic stress resulted from both stresses. Furthermore, the sensitive genotypes recorded the lowest means for all studied traits, which may indicate that those genotypes showed a lower capacity to accord with stress conditions. In the same context, (Eftekhari et al. 2017; Kamoshita et al. 2000; Schonfeld et al. 1988) revealed that RWC for tolerant cultivar retain major amount of water than the non-tolerant and RWC reduced significantly under drought stress conditions. Under drought conditions, the decreasing in RWC fundamentally related with the capacity of more tolerant genotypes to better absorb soil, water and to prevent water loss through stomata (Keyvan et al. 2010). In the same direction, the decrease in RWC under salinity conditions in wheat genotypes was reported by (Ghogdi et al. 2012; Farooq and Azam 2006; Ouhaddach et al. 2018; Sairam et al. 2002). Similarly, our results are in agreement with previous data by (Rizza et al.2004; Rucker 1995 et al.; Dalirie et al. 2010) and (Franco et al. 1997; Ouhaddach et al. 2018) that showed low increase in leaf area under drought and salinity stress respectively. Overall, these results suggest that RWC is a relevant tool for screening drought tolerance. These findings were in accordance with the previous results by (Teulat et al. 2003). Moreover, (Chaves et al. 2009) stated that plants adapted to drought and salinity by inhibition of leaf growth, as a consequence, leaf area reduced that allows plants to cut water losses by lowering transpiration and delaying the onset of more severe stress. On other hand, (Anjum et al. 2016) reported that any abiotic stress decrease leaf size. (Passioura et al. 1996; Shao et al. 2008) confirmed that leaf extension, leaf size and longevity can be limited under water stress respectively. Meanwhile, (Hu and Schmidhalter 2000 and 2001; Neumann 1993) concluded that under salt stress, leaf length, leaf width and leaf extensibility were decreased. On the contrary, (Lonbani and Arzani 2011) stated that wheat flag leaf length and area increased while the flag leaf width did not change under drought stress. Notably, the highest reduction percentage under both stresses was under 25% PEG or 250 mM NaCl. These findings were in accordance with (Nayer et al. 2012) for leaf growth and RWC under salinity stress. In addition, the reduction percentage for all studied traits under drought stress was higher than under salinity stress except in leaf length parameter which gave opposite results. This proposed that ater flow and biosynthetic activity in the growing tissues might be more inhibited by drought more than by salinity. Consequently, using the above mentioned physiological parameters were very promising for screening drought and salt tolerant wheat genotypes. In wheat, many NAC genes have been isolated, and various NACs displayed various expression patterns or played different roles in response to environmental stimuli (Xia et al. 2010a and b; Baloglu et al. 2012). In this research, we focused fundamentally on evaluating leaves expression because (He et al. 2005; Mitsuda et al. 2007) confirmed that this tissue specifically expressed TFs that played critical roles in plant development and growth. We found that TaNAC genes were induced simultaneously by two stress treatments in young leaves, which was consistent with the results of NACs in Arabidopsis, rice and soybean (Fujita et al. 2004; Hu et al. 2006; Nakashima et al. 2007; Hu et al. 2008; Tran et al. 2009). In accordance with these results (Tang et al. 2012) revealed that TaNAC2a, TaNAC7 and TaNAC4a had a higher expression level in leaves than in stems and roots under dehydration and salinity stresses. Moreover, senescent leaves had higher expression of TaNAC6, TaNAC13 and TaNAC29 than in young leaves under NaCl, PEG6000, treatments, proposed that their functions might be correlated with leaf senescence (Huang et al. 2015). Our results showed that the relative expression for studied genes was higher under drought stress than salinity stress in all the studied concentrations except TaNAC7 showing an opposite expression tendency under both stresses, so this requires further scrutiny and investigations (Figs. 3 and 4). This indicated that drought-tolerant wheat genotypes counter the damage caused by drought stress more effectively. Consequently, According to gene expression results, we divided all studied genotypes into three groups. The first group includes tolerant genotypes (Bani Seuf 7, Shandaweel 1, Sakha 95 and Misr2) and (Misr 2, Sakha95 and Bani Seuf 7) under drought and salinity stress, respectively. The Second group includes moderate genotypes (Sohag4 and Misr1) and (Bani Seuf 7, Sohag4, Misr3, Shandaweel 1and Sids 12) under drought and salinity stresses, respectively. The third group includes sensitive genotypes (Sids12, Giza168 and Misr3) and (Giza168 and Misr 1) for drought and salinity stress, respectively. Moreover, our results showed that, TaNAC69-1 and TaNAC69-3 genes had the highest expression fold change vs. controls. These results were congruence in line with (Xue et al. 2006 and 2011) under drought and salinity stresses. Also, the expression of TaNAC69-1 and TaNAC69-3 were they look very similar in both stresses. This similarity comes from the amino acid sequence alignment of the three TaNAC69 genes share amino acids with identity >95%. (Xue et al. 2006). Because of sharp expression increase of Shandaweel 1 and Bani Seuf 7 in all Studied genes except TaNAC7 and the expression of Misr 2 for TaNAC69-1 and TaNAC69-3 under NaCl stress we confirmed the role of these genes in the protective mechanisms activated by plants in response to drought and salinity stresses. Simultaneously, our results considered Giza 168 as sensitive genotype, but interestingly the expression of TaNAC69-1 and TaNAC69-3 were upregulated, which suggesting that the transcription levels of these genes do not correlated with adaptation response of the plant, but it reflected the failure of sensitive cultivars to cope the stress conditions. These results were in agreement with (Yousfi et al. 2016). From the results shown in (Fig. 5) we concluded high variability in the expression of NAC gene family in the studied genotypes, this confirming the high complication of the mechanisms that regulate the expression of these genes. Each gene might have a different function in the molecular plant response to salinity and drought stresses. This characteristic has also been stated by (Tang et al. 2012) whereas the mRNA levels of (TaNAC7 & TaNAC13) and (TaNAC4a & TaNAC6) showed similar expressions under various stresses, while others showed different expression levels under various treatments, such as TaNAC2a under salt and dehydration treatments, TaNTL5 and TaNAC4a under cold and drought treatments. Differently, (Zhou et al. 2008) showed that under high salinity there is a significant increase of mRNA levels of many dehydration-inducible NAC genes. Overall, from (Fig. 5) we suggested that all the genotypes in first, second and third patterns can be considered as tolerant and moderate tolerant to drought and salinity stresses. But the genotypes in fourth pattern considered to be sensitive genotypes reflecting the weakness of metabolic functioning of the plant. These findings in accordance with (Yousfi et al. 2016). In the present investigation, there were significant positive correlations at (p<0.05) between the expression levels of tolerant and sensitive genotypes under drought and salinity stresses (Figs. 6 and 7). The results confirm that the tolerant genotypes highly correlated with the high expression of studied genes under both stresses. In the same time, the sensitive genotypes represent low correlation with the gene expression. Contrary, (Niazi et al. 2014) revealed that no significant correlation between GSTF1 expression profiles at 50, 100, and 200 mM NaCl concentrations and 100 mM ABA treatment in the cultivars. Interestingly, in this paper we have analyzed the relationship between the TaNAC expression profiles and the physiological data (Tables 6 and 7). However, little is known about gene expression and its morphophysiological consequences during abiotic stress such as drought and salinity. Our results showed significant correlations between all the studied physiological traits and the expression levels. The highest correlation was between TaNAC7 and RWC (-0.958) and between leaf area and wide (0.922) for Bani Suef 7 genotype under drought stress. Moreover, for Misr 2 genotype under salinity the highest correlation was between TaNAC69-1 and RWC (-0.945) and between leaf area and RWC (0.800). In addition, the high gene expression similarity between TaNAC69-1 and TaNAC69-3 confirmed by significant positive correlation between each other.

Conclusions

This study examines the potential role of four TaNAC genes as markers of the genotypic performance in nine Egyptian bread wheat genotypes under drought and salinity stress by qPCR. Physiological traits including, RWC, Leaf Area, Leaf length and Max Leaf wide were analyzed. Overall, we observed significantly drastic decrease in all the studied physiological traits during both stresses for all the genotypes. In addition, high differential expression patterns in all studied genes were noted in studied genotypes under both stresses. Importantly, according to our results we concluded that genotypes (Bani Seuf 7 & Shandawee l) and (Giza 168 & Sids 12) were tolerant and sensitive genotypes under PEG stress respectively. Moreover, (Misr 2 & Bani Seuf 7) and (Giza 168 & Misr 1) were tolerant and sensitive genotypes under NaCl stress respectively. Also, the relative gene expression showed significant correlation with studied physiological parameters. Collectively, our results support that TaNAC genes involved in diverse roles during stress regulation and wheat development and can be transferred in high-yield wheat genotypes to enhance drought and salinity tolerance. So, it can be said that selected genes of NAC TF described in this paper have a role in gene expression regulation in leaves under drought and salinity stress and can be transferred in high-yield wheat genotypes to enhance drought and salinity tolerance. Such observations make them good indicators of different behavior within the genotypes identified as ‘resistant’ and ‘sensitive’. Moreover, same conclusion for the studied physiological traits of the different genotypes.

Acknowledgment

The authors are very grateful to Dr. Abdallah Musa and Dr. Luke Esau, Bioscience Core Laboratory at King Abdullah University of Science and Technology (KAUST) for their technical support.

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