Outline
Comptes Rendus

Molecular biology and genetics/Biologie et génétique moléculaires
RNA-Seq analysis of the wild barley (H. spontaneum) leaf transcriptome under salt stress
Comptes Rendus. Biologies, Volume 338 (2015) no. 5, pp. 285-297.

Abstract

Wild salt-tolerant barley (Hordeum spontaneum) is the ancestor of cultivated barley (Hordeum vulgare or H. vulgare). Although the cultivated barley genome is well studied, little is known about genome structure and function of its wild ancestor. In the present study, RNA-Seq analysis was performed on young leaves of wild barley treated with salt (500 mM NaCl) at four different time intervals. Transcriptome sequencing yielded 103 to 115 million reads for all replicates of each treatment, corresponding to over 10 billion nucleotides per sample. Of the total reads, between 74.8 and 80.3% could be mapped and 77.4 to 81.7% of the transcripts were found in the H. vulgare unigene database (unigene-mapped). The unmapped wild barley reads for all treatments and replicates were assembled de novo and the resulting contigs were used as a new reference genome. This resulted in 94.3 to 95.3% of the unmapped reads mapping to the new reference. The number of differentially expressed transcripts was 9277, 3861 of which were unigene-mapped. The annotated unigene- and de novo-mapped transcripts (5100) were utilized to generate expression clusters across time of salt stress treatment. Two-dimensional hierarchical clustering classified differential expression profiles into nine expression clusters, four of which were selected for further analysis. Differentially expressed transcripts were assigned to the main functional categories. The most important groups were “response to external stimulus” and “electron-carrier activity”. Highly expressed transcripts are involved in several biological processes, including electron transport and exchanger mechanisms, flavonoid biosynthesis, reactive oxygen species (ROS) scavenging, ethylene production, signaling network and protein refolding. The comparisons demonstrated that mRNA-Seq is an efficient method for the analysis of differentially expressed genes and biological processes under salt stress.

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Supplementary materials for this article are supplied as separate files:

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DOI: 10.1016/j.crvi.2015.03.010
Keywords: Electron transport, Flavonoid biosynthesis, Reactive oxygen species

Ahmed Bahieldin 1, 2; Ahmed Atef 1; Jamal S.M. Sabir 1; Nour O. Gadalla 1, 3; Sherif Edris 1, 2; Ahmed M. Alzohairy 4; Nezar A. Radhwan 1; Mohammed N. Baeshen 1; Ahmed M. Ramadan 1, 5; Hala F. Eissa 5, 6; Sabah M. Hassan 1, 2; Nabih A. Baeshen 1; Osama Abuzinadah 1; Magdy A. Al-Kordy 1, 3; Fotouh M. El-Domyati 1, 2; Robert K. Jansen 1, 7

1 Department of Biological Sciences, Faculty of Science, King Abdulaziz University (KAU), P.O. Box 80141, 21589 Jeddah, Saudi Arabia
2 Department of Genetics, Faculty of Agriculture, Ain Shams University, Cairo, Egypt
3 Genetics and Cytology Department, Genetic Engineering and Biotechnology Division, National Research Center, Dokki, Egypt
4 Genetics Department, Faculty of Agriculture, Zagazig University, 44511 Zagazig, Egypt
5 Agricultural Genetic Engineering Research Institute (AGERI), Agriculture Research Center (ARC), Giza, Egypt
6 Faculty of Biotechnology, Misr University for Science and Technology (MUST), 6th October City, Egypt
7 Department of Integrative Biology, University of Texas at Austin, 78712 Austin, USA
@article{CRBIOL_2015__338_5_285_0,
     author = {Ahmed Bahieldin and Ahmed Atef and Jamal S.M. Sabir and Nour O. Gadalla and Sherif Edris and Ahmed M. Alzohairy and Nezar A. Radhwan and Mohammed N. Baeshen and Ahmed M. Ramadan and Hala F. Eissa and Sabah M. Hassan and Nabih A. Baeshen and Osama Abuzinadah and Magdy A. Al-Kordy and Fotouh M. El-Domyati and Robert K. Jansen},
     title = {RNA-Seq analysis of the wild barley {(\protect\emph{H.}~\protect\emph{spontaneum})} leaf transcriptome under salt stress},
     journal = {Comptes Rendus. Biologies},
     pages = {285--297},
     publisher = {Elsevier},
     volume = {338},
     number = {5},
     year = {2015},
     doi = {10.1016/j.crvi.2015.03.010},
     language = {en},
}
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%0 Journal Article
%A Ahmed Bahieldin
%A Ahmed Atef
%A Jamal S.M. Sabir
%A Nour O. Gadalla
%A Sherif Edris
%A Ahmed M. Alzohairy
%A Nezar A. Radhwan
%A Mohammed N. Baeshen
%A Ahmed M. Ramadan
%A Hala F. Eissa
%A Sabah M. Hassan
%A Nabih A. Baeshen
%A Osama Abuzinadah
%A Magdy A. Al-Kordy
%A Fotouh M. El-Domyati
%A Robert K. Jansen
%T RNA-Seq analysis of the wild barley (H. spontaneum) leaf transcriptome under salt stress
%J Comptes Rendus. Biologies
%D 2015
%P 285-297
%V 338
%N 5
%I Elsevier
%R 10.1016/j.crvi.2015.03.010
%G en
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Ahmed Bahieldin; Ahmed Atef; Jamal S.M. Sabir; Nour O. Gadalla; Sherif Edris; Ahmed M. Alzohairy; Nezar A. Radhwan; Mohammed N. Baeshen; Ahmed M. Ramadan; Hala F. Eissa; Sabah M. Hassan; Nabih A. Baeshen; Osama Abuzinadah; Magdy A. Al-Kordy; Fotouh M. El-Domyati; Robert K. Jansen. RNA-Seq analysis of the wild barley (H. spontaneum) leaf transcriptome under salt stress. Comptes Rendus. Biologies, Volume 338 (2015) no. 5, pp. 285-297. doi : 10.1016/j.crvi.2015.03.010. https://comptes-rendus.academie-sciences.fr/biologies/articles/10.1016/j.crvi.2015.03.010/

Version originale du texte intégral

1 Introduction

Barley is an important cereal crop in terms of productivity and global area of cultivation [1]. Cultivated barley, Hordeum vulgare L. ssp. vulgare (H. vulgare), descended from wild barley, H. vulgare L. ssp. spontaneum (H. spontaneum). Barley is a self-pollinating diploid with 7 pairs of chromosomes and a nuclear genome size of 5.1 Gb [2], and it harbors high levels of genetic variation [3,4] that helps it survive in low input and climatically marginal climates. Although H. vulgare is well-studied in terms of genetics, genomics and breeding, little is known about the genetic makeup and genome function of its wild ancestor H. spontaneum. Cultivated barley was earlier reported to contain about 40% of H. spontaneum alleles [5].

Salt tolerance in wild barley has been reviewed by many researchers because it provides a rich source of genes that can be transferred to other crop plants by genetic transformation as well as to cultivated barley by classical breeding methods [6,7]. For plants to survive under salt stress, they must be able to activate cascades of molecular networks involved in stress perception or sensing [8], signal transduction [9], as well as the induction of specific stress-related genes and their encoded metabolites [10–12]. Some of the signaling pathways are specific, but others may cross talk; e.g., MAPK cascades and the cross talk between ABA signaling and biotic signaling [13]. Previous studies on various plant species demonstrated that cross talk involves complex networks of gene regulation [14,15], some of which are mediated by plant hormones such as abscisic acid [9] and ethylene [16], and influenced through specific transcription factors [17]. Cross talk also results in the expression of diverse functional genes for osmoregulation, cell protection and acclimation, such as dehydrins, aquaporins and chaperones [10,12,18].

A draft genome sequence of cultivated barley has been recently described [2] and transcript profiling for the cataloguing of stress-responsive genes has been reported [19,20]. The present study utilizes mRNA-Seq analysis of leaves of the wild barley (H. spontaneum) to examine salt-related genes and biological processes in order to discover novel genes and transcription factors to improve the understanding of the mechanisms underlying the process of salt stress tolerance. The RNA-Seq method has considerable potential to generate high-resolution transcriptome maps sensitive enough to display transcripts with low-levels of expression [21].

2 Materials and methods

2.1 Plant material

Seeds of the self-pollinated wild H. spontaneum were collected from a location in Rafah, North Sinai, Egypt (31.313559, 34.205973) near the Mediterranean Sea (∼38 g/L), where no specific permission is required. Besides, no endangered or protected plant or animal species are grown in this location. Seeds were germinated in the greenhouse in trays filled with potting mix consisting of vermiculite:perlite (1:1) and grown at 14 h of light per day, 80% humidity and 22 °C for two weeks and watered with half-strength Hoagland solution [22]. Seedlings were then salt stressed (500 mM NaCl or 29.22 g/L in half-strength Hoagland solution) at 0, 2, 12 and 24 h time intervals. Leaves of individual plants in three replicates were harvested at each time point except at time point 0 where only two replicates were gathered. All tissues were flash-frozen in liquid nitrogen and stored at −80 °C.

2.2 RNA isolation

Flash-frozen leaf material from individual plants was crushed into a fine powder in a microcentrifuge tube using a sterilized metal rod. Total RNAs were extracted from similar-sized leaf samples collected from emergent leaves using Trizol (Invitrogen, Life Tech, Grand Island, NY, USA) and treated with RNase-free DNase (Promega Corporation, Madison, WI, USA) in the presence of 1 U/μL of RNasin® Plus RNase Inhibitor (Promega) for 2 h at 37 °C. RNAs were quantified and 30 μg (400 ng/μL) was used for RNA-Seq. To test for the presence of DNA contamination in RNA samples, the actin gene was amplified by PCR of the original RNA samples. Purified RNA samples were shipped to Beijing Genomics Institute (BGI), Shenzhen, China in three replicates of each treatment for deep sequencing and generation of datasets (at least 100 million reads per sample).

2.3 Next-generation mRNA sequencing

Filtered reads were aligned with up to two mismatches to the cultivated barley genome as the reference after downloading the H. vulgare unigene transcript sequences from the NCBI database (http://www.ncbi.nlm.nih.gov/unigene). This database consisted of 26,941 transcripts including those annotated as complete and partial CDSs. RSEM v1.1.6, an RNA-Seq quantification tool, was used to estimate the relative abundances and expected read counts for the transcripts. By default, RSEM uses the Bowtie aligner (Bowtie v0.12.1) to map the reads against the transcripts. Transcript quantification of the reference-aligned reads was performed with RSEM, which allowed for the assessment of transcript abundances based on the mapping of RNA-Seq reads to the assembled transcriptome.

Expected read counts were used as input to differential expression analysis by EdgeR (version 3.0.0, R version 2.1.5). Because we had three biological replicates per time point, the median of these values was used as the common dispersion factor for differential expression (DE) analyses. The remaining unmapped sequences were re-aligned against the contigs collectively assembled de novo using the Trinity RNA-Seq Assembly package (r2013-02-25) from total unmapped sequences of all treatments and replicates. Trinity was selected for transcriptome assembly based on recent studies that showed that it performs better than other available methods [23]. DE transcripts were annotated using blast-2-GO software (version 2.3.5, http://www.blast2go.org/). Blastx was performed against the NCBI non-redundant protein database with an E-value cut off of 1 e−5. GO terms were obtained for mapped and unmapped barley transcripts with the default parameters.

To identify clusters with functional enrichment, we determined a significant Pearson correlation through permutation analysis [24]. The resulting clusters were refined by visual inspection and analyzed for GO term enrichment using Blast2GO (http://www.blast2go.org/). We also clustered the RPKM data to provide a representation of absolute abundance of the transcripts.

2.4 Validation of RNA-seq findings by real time PCR

Six transcripts were randomly selected for validating the RNA-Seq data by real time PCR with the actin gene as the reference [25]. Primers were designed using Netprimer software (http://www.premierbiosoft.com/netprimer/index.html) with the following criteria: length ∼20 bases, GC content ∼50%, minimal secondary structures, comparable annealing temperatures (55 °C) of the primer pairs, and PCR products of ∼500 bp. Total RNAs were extracted from individual plants salt-stressed for 2, 12 and 24 h and control plants. Extraction was done in three replicates and RNAs from each treatment were then bulked. Expression levels of transcripts were detected by real time PCR using the Agilent Mx3000P qPCR Systems (Agilent technology, USA). First-strand cDNA was synthesized using 1 μg of total RNA, 0.5 μg of reverse primers of each gene (Table S1) and Superscript II reverse transcriptase (Invitrogen). All cDNA-synthesized samples were diluted (1:10) prior to amplification. The reaction (25 μL) components were 12.5 μL Maxima™ SYBR Green/ROX qPCR master mix, 0.2 μM of each gene forward and reverse primers (Table S1), and PCR-grade water was added up to 22.5 μL. Finally, 2.5 μL of diluted cDNA template were added to the reaction mix. Forty PCR cycles for each gene product included denaturation at 94 °C for 15 s, annealing at 55 °C for 30 s, and extension at 72 °C for 45 s. Amplification for each sample was carried out in triplicate along with a no-template control (NTC, PCR-grade water). Data was collected and amplification plots of ΔRn versus cycle number were generated for analysis. Calculations were made to detect the expression level of each gene under a given treatment relative to its expression under control condition.

3 Results

3.1 Analysis of RNA-Seq datasets

Sequencing of cDNA samples yielded between 103 and 115 million reads corresponding to over 10 billion nucleotides per sample (Table 1). The raw sequencing reads were deposited in the Small Read Archive (SRA) at GenBank (accession number SRP032854). Between 74.8 and 80.3% of the wild barley reads could be mapped to the cultivated barley reference genome; the remaining 20–25% matched no sequences (Table 1). The percentage of transcripts of wild barley found in H. vulgare unigene database (unigene-mapped) ranged from 77.4 to 81.7%. All mapped sequences were in exonic regions of the genome. The unmapped wild barley reads of all treatments and replicates were assembled de novo and the resulting contigs were used as a new reference genome. Then, the unmapped wild barley reads were aligned to this new reference genome and results indicated that between 94.3 and 95.3% of them were de novo-mapped to the new reference. The total number of transcripts generated from alignment with both reference genomes ranged from 54,572 to 59,353. The percentage of transcripts generated from alignment with the wild barley genome contigs ranged from 60.9 to 63.9%.

Table 1

Statistics of RNA-Seq numerical data analysis of Horduem spotanium.

Filea name Treatment time (h) Total number of readsb Number of mapped reads (unigene)c %d of reads Number of unmapped reads (unigene)e Number of reads mapped (de novo)f %g of reads Number of unmappedh reads Number of transcripts (unigene)i % of transcriptsj Number of transcripts (de novo)k Number of transcripts (Total)l
Bs2c_1.fastq/Bs2c_2.fastq 0 106,495,036 85,560,630 80.3 20,934,406 19,814,424 94.7 1,119,982 21,160 78.5 33,962 55,122
Bs3c_1.fastq/Bs3c_2.fastq 0 115,111,594 91,065,533 79.1 24,046,061 22,922,643 95.3 1,123,418 21,698 80.5 33,830 55,528
Bs1_2_1.fastq/Bs1_2_2.fastq 2 104,137,914 79,466,902 76.3 24,671,012 23,417,657 94.9 1,253,355 21,518 80.0 37,498 59,016
Bs2_2_1.fastq/Bs2_2_2.fastq 2 111,668,026 83,885,570 75.1 27,782,456 26,366,413 94.9 1,416,043 21,439 80.0 37,914 59,353
Bs3_2_1.fastq/Bs3_2_2.fastq 2 106,843,457 81,125,022 75.9 25,718,435 24,274,948 94.4 1,443,487 21,687 80.5 37,319 59,006
Bs1_12_1.fastq/Bs1_12_2.fastq 12 110,659,611 83,224,420 75.2 27,435,191 26,120,724 95.2 1,314,467 20,702 76.8 35,890 56,592
Bs2_12_1.fastq/Bs2_12_2.fastq 12 106,831,568 80,825,061 75.7 26,006,507 24,649,525 94.8 1,356,982 21,418 80.0 36,467 57,885
Bs3_12_1.fastq/Bs3_12_2.fastq 12 103,860,256 77,694,090 74.8 26,166,166 24,841,786 94.9 1,324,380 21,747 80.7 36,572 58,319
Bs1_24_1.fastq/Bs1_24_2.fastq 24 112,611,535 87,575,012 77.8 25,036,523 23,769,264 94.9 1,267,259 20,841 77.4 33,731 54,572
Bs2_24_1.fastq/Bs2_24_2.fastq 24 113,972,947 89,370,676 78.4 24,602,271 23,437,546 95.3 1,164,725 21,346 79.2 34,425 55,771
Bs3_24_1.fastq/Bs3_24_2.fastq 24 112,207,066 89,834,579 80.1 22,372,487 21,098,577 94.3 1,273,910 22,017 81.7 34,277 56,294

a Names of RNA-Seq files.

b Total number of reads recovered from wild barley RNA-Seq.

c Number of wild barley reads aligned with Hordeum vulgare reference genome in the unigene database of NCBI (http://www.ncbi.nlm.nih.gov/unigene).

d Percentage of wild barley reads aligned with H. vulgare reference genome.

e Number of reads unaligned with H. vulgare reference genome.

f Number of reads aligned with the new (de novo-assembled) wild barley reference genome generated from contigs generated by de novo-assembly from total unaligned wild barley reads of all treatments and replicates with the reference H. vulgare genome.

g Percentage of reads aligned with the new (de novo-assembled) wild barley reference genome.

h Number of reads unaligned with the new wild barley reference genome.

i Number of wild barely transcripts found in H. vulgare unigene database.

j Percentage of wild barely transcripts found in H. vulgare unigene database.

k Number of wild barley transcripts generated from alignment with the new reference genome.

l Total number of wild barley transcripts generated from alignment with the two reference genomes.

3.2 Clusters of gene expression across time of salt treatment

RNA-Seq data was used to detect the differential expression (DE) of previously annotated barley transcripts, as well as novel transcripts uncovered in this study. To statistically obtain confirmation of the differences in gene expression across treatment time, RPKM-derived read counts were compared using a likelihood ratio test [21]. Statistical analysis was reliable when applied to genes with an RPKM value ≥ 2. To determine DE transcripts, a two-fold (or greater) change in expression and false discovery rate (FDR) of 10−3 or less was required. The resulting number of DE transcripts was 9277 (Fig. 1a), 3861 of which represent DE transcripts from the alignment with H. vulgare reference genome in the unigene database (Fig. 1b). The remaining DE transcripts (5416) resulted from the alignment with the generated wild barley reference genome (Fig. 1c). The number of unigene-mapped DE transcripts with no blast hits was less than 100, while that of de novo-mapped transcripts was over 2300 (Fig. 1b and c, respectively).

Fig. 1

(Color online.) Total number of differentially expressed (DE) transcripts under salt stress: a: total number of DE transcripts (9277 transcripts) subjected to blast2go resulting from reads either aligned or unaligned with Hordeum vulgare reference genome in the unigene database of NCBI (http://www.ncbi.nlm.nih.gov/unigene); b: number of DE transcripts (3861 transcripts) aligned with H. vulgare reference genome; c: number of DE transcripts (5416 transcripts) unaligned with H. vulgare reference genome.

Annotated unigene- and de novo-mapped transcripts (5100) were utilized in generating the expression clusters across time of salt stress treatment. Expression profiles of the DE transcripts were determined by a cluster analysis based on the k-means method using Pearson's correlation distance so that the similarity in relative change for each transcript or among transcripts across time of salt treatment was determined. These data were then subjected to hierarchical clustering using the Pearson correlation as the distance metric. Fig. 2 shows the expression clusters for the DE transcripts mapped on the H. vulgare reference genome (Fig. 2a) and those mapped on the de novo-assembled wild barley reference genome using the unigene unmapped reads (Fig. 2b). Two-dimensional hierarchical clustering classified DE profiles into nine expression clusters according to the similarity of their expression profiles. Visual inspection of these expression groups suggested diverse and complex patterns of regulation. Four out of the nine expression clusters were selected for further comparisons across the time of salt tolerance treatment (Fig. 3). In general, heterogeneity and redundancy were two significant characteristics for selection. Another criterion for selection was the importance of the expression pattern. We did not have a cluster for genes that were upregulated at 2, 12 and 24 h. All upregulated genes at the 2 h time point returned to the 0 h expression level either at 12 or 24 h time points (Fig. 3a–b). We selected both expression patterns as two clusters of upregulation. The third cluster includes transcripts that were downregulated at 2, 12 and 24 h time points (Fig. 3d), and the fourth represents downregulated transcripts at 2 and 12 h time points then returned to the 0 h expression level at 24 h time point (Fig. 3c).

Fig. 2

(Color online.) Hierarchical cluster analysis of gene expression based on log ratio RPKM data for transcripts. Reads were either aligned (a) or unaligned (b) with H. vulgare reference genome in the unigene database of NCBI (http://www.ncbi.nlm.nih.gov/unigene).

Fig. 3

(Color online.) Four selected clusters of gene expression under salt stress (C1 (a), C2 (b), C3 (c) and C4 (d)). See Table 3 for transcripts generated from reads either aligned or unaligned with H. vulgare reference genome in the unigene database of NCBI (http://www.ncbi.nlm.nih.gov/unigene).

3.3 Validation of transcript profiles by real time PCR

Real time PCR of six randomly selected DE transcripts from the mRNA-Seq data resulted in successful amplification of the bands of expected sizes (Fig. S1). Quantification of the band intensities in relation to the actin control supported the direction of change of expression as detected by mRNA-Seq for these transcripts in which expression pattern of four of them fit within cluster 2, while those of the other two fit within cluster 3.

3.4 Analysis of differentially expressed genes

Differentially expressed genes were assigned to functional categories using blast2GO (http://www.blast2go.org/), which provided a valuable resource for detecting specific processes, functions, and pathways during salt stress in wild barley. The results indicated that 3184, 4189 and 4106 transcripts were assigned to the three main categories; “biological process”, “cellular component” and “molecular function”, respectively (Tables 2–4). Most transcripts were assigned to the cluster 3 pattern of gene expression for downregulated transcripts across time of treatment followed by cluster 2 for upregulated transcripts at 2 and 12 h time points (Fig. 3). The number of transcripts recovered by gene ontology analysis for the three main categories was higher than the total number of DE transcripts, which likely indicates that some transcripts were assigned to more than one category. The same criterion was observed for the number of subgroups in a functional group. The numbers of functional groups for the three main categories were 21, 8 and 12 transcripts, respectively (Tables 2–4). The numbers of subgroups within groups of the three categories were 71, 12 and 49 transcripts, respectively. In the three categories, numbers of groups for cluster(s) with more than 1000 transcripts were 2, 3 and 2, respectively. These groups were “cellular process” and “metabolic process” for the “biological process” category, “membrane”, “organelle” and “cell” for the “cellular component” category, and “binding” and “catalytic activity” for the “molecular function” category. The numbers of subgroups for cluster(s) with more than 1000 transcripts were 3, 3 and 0, respectively. These subgroups were “primary metabolic process”, “macromolecule metabolic process” and “cellular metabolic process” for the “biological process category”, and “membrane-bounded organelle”, “organelle part” and “cell part” for the “cellular component” category (Tables 2–4). There were some groups and subgroups of the three main categories in which DE transcripts were either upregulated (clusters 1 and/or 2) or downregulated (clusters 3 and/or 4). The most important components were “cell–cell signaling” (cluster 2) and “cell death” (clusters 3 and 4) subgroups of the “biological process” category and the “electron carrier activity”, “superoxide dismutase activity”, “electron transporter transferring electrons within the cyclic and noncyclic electron transport pathways of photosynthesis activity”, “electron transporter transferring electrons within cytochrome b6/f complex of photosystem II activity” and “structural constituent of cytoskeleton” (clusters 1 and 2) subgroups of the “molecular function” category. There were six subgroups in the “response to stimulus” group that demonstrated both upregulation and downregulation of many different transcripts under salt stress. These subgroups were “cellular response to stimulus”, “response to abiotic stimulus”, “response to stress”, “response to external stimulus”, “response to endogenous stimulus”, and “response to chemical stimulus”.

Table 2

GO functional categorization of DE barley transcripts based on biological process. C1, C2, C3 and C4 represent four clusters of gene expression patterns under salt stress.

Level GO ID GO Term *C1 C2 C3 C4
1 GO:0008150 Biological Process 80 505 2422 177
2 GO:0008283 Cell proliferation 4 5
2 GO:0002376 Immune system process 8 66
3 GO:0002252 Immune effector process 2 2 8
2 GO:0015976 Carbon utilization 2 6
2 GO:0040007 Growth 1 15 46 2
3 GO:0016049 Cell growth 1 15 37 1
3 GO:0048589 Developmental growth 1 10 33
2 GO:0043473 Pigmentation 1 7
3 GO:0043476 Pigment accumulation 1 7
2 GO:0040011 Locomotion 5
3 GO:0042330 Taxis 4
2 GO:0022610 Biological adhesion 4
3 GO:0007155 Cell adhesion 4
2 GO:0065007 Biological regulation 16 102 438 28
3 GO:0065008 Regulation of biological quality 6 23 101 5
3 GO:0065009 Regulation of molecular function 15 53
3 GO:0050789 Regulation of biological process 13 89 394 23
2 GO:0051179 Localization 10 69 429 30
3 GO:0033036 Macromolecule localization 3 12 112
3 GO:0051234 Establishment of localization 10 66 417 30
2 GO:0000003 Reproduction 6 28 170 8
3 GO:0019953 Sexual reproduction 2 17
3 GO:0022414 Reproductive process 6 28 164 4
2 GO:0023052 Signaling 1 35 95 22
3 GO:0007267 Cell-cell signaling 1
2 GO:0071840 Cellular component organization or biogenesis 9 54 430 13
3 GO:0016043 Cellular component organization 9 51 385 13
3 GO:0044085 Cellular component biogenesis 12 242
3 GO:0071554 Cell wall organization or biogenesis 8 55
3 GO:0071841 Cellular component organization or biogenesis at cellular level 6 42 392
2 GO:0009987 Cellular process 60 380 1899 126
3 GO:0006928 Cellular component movement 5 10
3 GO:0016044 Cellular membrane organization 3 89
3 GO:0019725 Cellular homeostasis 3 3 48 3
3 GO:0007017 Microtubule-based process 5 17
3 GO:0007049 Cell cycle 5 19 31 1
3 GO:0007059 Chromosome segregation 2 1 6
3 GO:0007154 Cell communication 2 43 131 23
3 GO:0010118 Stomatal movement 1 2 13
3 GO:0019725 Cellular homeostasis 11 5
3 GO:0030029 Actin filament-based process 3 24
3 GO:0051641 Cellular localization 2 17 137
3 GO:0048869 Cellular developmental process 3 20 102 3
3 GO:0051301 Cell division 1 11 32
2 GO:0016265 Death 3 39 3
3 GO:0008219 Cell death 39 3
2 GO:0032502 Developmental process 7 69 339 14
3 GO:0007568 Aging 1 8 5
3 GO:0021700 Developmental maturation 1 8 19
3 GO:0022611 Dormancy process 1 7
3 GO:0048856 Anatomical structure development 4 55 269 6
2 GO:0008152 Metabolic process 64 408 2022 135
3 GO:0006807 Nitrogen compound metabolic process 25 120 701 19
3 GO:0044238 Primary metabolic process 307 1453 91
3 GO:0009056 Catabolic process 6 81 307 29
3 GO:0009058 Biosynthetic process 13 108 988 38
3 GO:0019637 Organophosphate metabolic process 1 13 140
3 GO:0019748 Secondary metabolic process 1 5 83 4
3 GO:0032259 Methylation 5 112
3 GO:0042440 Pigment metabolic process 3 3 128
3 GO:0043170 Macromolecule metabolic process 38 182 1004 55
3 GO:0044281 Small molecule metabolic process 8 90 560
3 GO:0055114 Oxidation-reduction process 6 69 476
3 GO:0070988 Demethylation 1
3 GO:0071704 Organic substance metabolic process 4 41 254
3 GO:0042445 Hormone metabolic process 1 2 20
3 GO:0044237 Cellular metabolic process 50 299 1617 77
2 GO:0032501 Multicellular organismal process 7 62 317 14
3 GO:0007585 Respiratory gaseous exchange 1 1
3 GO:0008037 Cell recognition 1 3
3 GO:0007275 Multicellular organismal development 6 58 310 14
3 GO:0043480 Pigment accumulation in tissues 1 7
3 GO:0032504 Multicellular organism reproduction 1 1 20
3 GO:0009606 Tropism 3 3
2 GO:0051704 Multi-organism process 8 25 185 2
3 GO:0044419 Interspecies interaction between organisms 3 4
3 GO:0035821 Modification of morphology or physiology of other organism 1 3
3 GO:0051707 Response to other organism 7 22 171
2 GO:0048511 Rhythmic process 3 13
3 GO:0007623 Circadian rhythm 3 13
2 GO:0016032 Viral reproduction 2 3
3 GO:0022415 Viral reproductive process 2 3
2 GO:0050896 Response to stimulus 26 159 52 55
3 GO:0051716 Cellular response to stimulus 6 55 99 22
3 GO:0009628 Response to abiotic stimulus 9 998 388 14
3 GO:0006955 Immune response 2 7 61
3 GO:0006950 Response to stress 19 92 435 26
3 GO:0009605 Response to external stimulus 2 11 54 2
3 GO:0009607 Response to biotic stimulus 7 23 172 12
3 GO:0009719 Response to endogenous stimulus 6 32 111 11
3 GO:0042221 Response to chemical stimulus 11 77 387
3 GO:0051606 Detection of stimulus 4 12
*Time 0 2 h 12 h 24 h
C1 (cluster 1): Control level Up Down Down
C2 (cluster 2): Control level Up Up Down
C3 (cluster 3): Control level Down Down Up
C4 (cluster 4): Control level Down Down Down
Table 3

GO functional categorization of DE barley transcripts based on cellular component. C1, C2, C3 and C4 represent four clusters of gene expression patterns under salt stress.

Level GO ID GO term *C1 C2 C3 C4
1 GO:0005575 Cellular component 79 1302 2636 172
2 GO:0016020 Membrane 30 449 1110 62
3 GO:0044425 Membrane part 6 57 416
2 GO:0043226 Organelle 66 837 2348 106
3 GO:0043227 Membrane-bounded organelle 63 822 2304 104
3 GO:0044422 Organelle part 12 76 1082 9
3 GO:0043228 Non-membrane-bounded organelle 4 43 399 8
3 GO:0031982 Vesicle 25 91 335
3 GO:0019867 Outer membrane 2 9
2 GO:0005623 Cell 72 1189 2523 159
3 GO:0044464 Cell part 72 1184 2523 157
2 GO:0032991 Macromolecular complex 2 49 532 3
3 GO:0043234 Protein complex 2 28 226
3 GO:0032993 Protein-DNA complex 15
2 GO:0005576 Extracellular region 5 35 159 9
3 GO:0048046 Apoplast 4 7 143
3 GO:0044421 Extracellular region part 2
2 GO:0055044 Symplast 5 9 48
2 GO:0031974 Membrane-enclosed lumen 30 179 9
2 GO:0030054 Cell junction 5 9 48
3 GO:0005911 Cell-cell junction 5 9 48
Table 4

GO functional categorization of DE barley transcripts based on molecular function. C1, C2, C3 and C4 represent four clusters of gene expression patterns under salt stress.

Level GO ID GO term *C1 C2 C3 C4
1 GO:0003674 Molecular function 97 1459 2368 182
2 GO:0060089 Molecular transducer activity 1 27 27
2 GO:0000988 Protein binding transcription factor activity 2
3 GO:0000989 Transcription factor binding transcription factor activity 2
2 GO:0045735 Nutrient reservoir activity 1 4
2 GO:0016209 Antioxidant activity 2 33
3 GO:0004784 Superoxide dismutase activity 3 2
3 GO:0004601 Peroxidase activity 2 22
2 GO:0005488 Binding 66 982 1414 134
3 GO:0001871 Pattern binding 4 6 9
3 GO:0003676 Nucleic acid binding 19 265 375 23
3 GO:0003682 Chromatin binding 5
3 GO:0005515 Protein binding 6 141 160 16
3 GO:0008144 Drug binding 9
3 GO:0008289 Lipid binding 1 23 13 4
3 GO:0008430 Selenium binding 5
3 GO:0019825 Oxygen binding 1 4
3 GO:0030246 Carbohydrate binding 4 19 24 7
3 GO:0031406 Carboxylic acid binding 1 3 14
3 GO:0036094 Small molecule binding 32 377 557 67
3 GO:0042277 Peptide binding 1 1 4
3 GO:0042562 Hormone binding 1
3 GO:0043021 Ribonucleoprotein complex binding 1
3 GO:0043167 Ion binding 21 143 541
3 GO:0046906 Tetrapyrrole binding 1 8 112
3 GO:0048037 Cofactor binding 4 38 136
3 GO:0051540 Metal cluster binding 6 48
2 GO:0003824 Catalytic activity 65 949 1519 125
3 GO:0004133 Glycogen debranching enzyme activity 3
3 GO:0008641 Small protein activating enzyme activity 1
3 GO:0008907 Integrase activity 1
3 GO:0016491 Oxidoreductase activity 10 79 418
3 GO:0016740 Transferase activity 31 404 511 57
3 GO:0016787 Hydrolase activity 19 271 450 28
3 GO:0016829 Lyase activity 5 18 99
3 GO:0016853 Isomerase activity 2 12 83
3 GO:0016874 Ligase activity 2 24 62
3 GO:0070283 Radical SAM enzyme activity 3
2 GO:0009055 Electron carrier activity 7 179
3 GO:0045156 Electron transporter, transferring electrons within the cyclic electron transport pathway of photosynthesis activity 6 2
3 GO:0045157 Electron transporter, transferring electrons within the noncyclic electron transport pathway of photosynthesis activity 3 1
3 GO:0045158 Electron transporter, transferring electrons within cytochrome b6/f complex of photosystem II activity 3
2 GO:0030234 Enzyme regulator activity 1 30 17
3 GO:0004857 Enzyme inhibitor activity 8 6
3 GO:0008047 Enzyme activator activity 5 4
3 GO:0019207 Kinase regulator activity 1
3 GO:0060589 Nucleoside-triphosphatase regulator activity 1 5 3
3 GO:0061134 Peptidase regulator activity 7 3
3 GO:0004871 Signal transducer activity 1 27 27
2 GO:0001071 Nucleic acid binding transcription factor activity 4 52 37
3 GO:0003700 Sequence-specific DNA binding transcription factor activity 4 52 37 4
2 GO:0004872 Receptor activity 4 32 32 12
3 GO:0038023 Signaling receptor activity 1 1 8
2 GO:0005198 Structural molecule activity 5 248 2
3 GO:0003735 Structural constituent of ribosome 2 223
3 GO:0005199 Structural constituent of cell wall 1
3 GO:0005200 Structural constituent of cytoskeleton 5
2 GO:0005215 Transporter activity 3 93 229
3 GO:0022857 Transmembrane transporter activity 3 31 173
3 GO:0022892 Substrate-specific transporter activity 3 23 153
3 GO:0042910 Xenobiotic transporter activity 3 3
3 GO:0051184 Cofactor transporter activity 6

Annotated unigene-mapped and de novo-mapped transcripts under subgroups of “electron carrier activity” and “response to abiotic stimulus” that were upregulated after 12 h of salt stress exposure with fold change (FC) of ≥4 (Table S2) fit within the expression pattern cluster 2. These subgroups were selected because they included the most important highly upregulated transcripts related to salt stress. There were thousands of GO hits whose expression was altered ≥4 fold under salt stress (Table S2). The number of GO hits under the selected “electron carrier activity” subgroup was 44, 13 of which were unigene-mapped and 31 were de novo-mapped. These GO hits represented nine genes/gene families, one unigene-mapped and eight de novo-mapped. The number of GO hits under “response to abiotic stimulus” subgroup was 866, 432 unigene-mapped, 99 de novo-mapped and 335 unigene/de novo-mapped. These GO hits represented 88 genes/gene families, 55 unigene-mapped, 15 de novo-mapped and 18 unigene/de novo-mapped.

4 Discussion

Organisms that survive in saline water possess mechanisms to maintain their osmotic balance [26]. It is evident that the number and percentage of transcripts generated from the RNA-Seq data of different replicates grown under salt stress at 2 or 12 h time points are slightly higher than at 0 or 24 h time points (Table 1). This increase may be due to the high level of salt-related gene expression at time points 2 and 12 h, followed by recovery of gene expression after 24 h exposure to salt stress to similar levels as the control plants. The number of DE transcripts (5416) from the alignment with the de novo-assembled wild barley reference genome indicates that many of these transcripts likely represent more than one contig of single genes (Fig. 1c). This conclusion is supported by the fact that the number of annotated DE transcripts mapped on the H. vulgare unigene reference genome is over 3000 out of 3861 (Fig. 1b), whereas transcripts mapped to the de novo-assembled wild barley reference genome number less than 2000 out of 5416 (Fig. 1c).

4.1 Salt stress tolerance via electron transport and exchanger mechanisms

Many cytochrome p450 proteins detected in salt-stressed wild barley are likely involved in electron transfer chains as a mechanism of salt tolerance regulation. p450 usually acts as a terminal oxidase in electron-transfer chains under salt stress with a number of fundamental redox domains, e.g., FAD-containing flavoproteins, ferredoxins [27]. Besides cytochrome p450, we detected a number of salt-regulated transcripts encoding electron transfer flavoproteins and ferredoxins. Flavoproteins function in detoxifying salts in the plant cell [28,29], while ferridoxins were recently reported for their role in abiotic stress signaling [29]. Other detected transcripts encoding vacuolar cation as well as cation proton exchanger in wild barley may act as key factors in the sequestration of sodium (Na+) into vacuoles to avert ion toxicity in the cytosol of plants under salinity stress. Upon influx of N+ into the cell, different ATPases (PM-ATPases, V-ATPases, and V/H-ATPases) are activated [30]. This results in Na+ efflux into the outer rhizosphere by PM Na+/H+ antiporters and/or influx into vacuoles by tonoplast Na+/H+ leading to cellular ion homeostasis, consequently salt tolerance [31]. The cytoplasmic domain of the vacuolar H+-ATPase (V-ATPase), whose transcripts were also salt-regulated in wild barley, were reported to present in a SOS (salt overly sensitive)-containing protein complex [32] with a key role in regulating ion transport under salt stress. Regulation of V-ATPase activity represents an additional key function of SOS2 in the coordination of ion transport changes during salt stress, thus promoting salt tolerance [32]. Upregulation of transcripts encoding plasma membrane (PM)-ATPase in wild barley complements those of other ATPases, as it supported the occurrence of an electrochemical gradient generated under salt stress in the intertidal C4 grass Spartina patens [33]. Although wild barley plants in the present study were stressed by NaCl, an additional transcript encoding calcium-transporting ATPase was upregulated. This enzyme transport protein in the plasma membrane serves to remove calcium (Ca2+) from the cell [34].

4.2 Salt stress tolerance via flavonoid biosynthesis

Flavonoid biosynthesis seems to be one of the biological processes regulating salt tolerance in wild barley. One upregulated transcript, major latex protein gene (MLP) (Table S2), was previously studied in cotton (Gossypium hirsutum) and expression in roots was induced by salt stress [35]. The Gh-MLP promoter contains potential cis-acting elements for response to salt stress and fungal elicitors. Results of RT-PCR showed that expression of Gh-MLP in Arabidopsis is rapidly induced by NaCl, and induction was maintained over 72 h [35]. In our case, expression of this gene dropped back to the control level after 24 h of salt stress exposure. Gh-MLP-transgenic A. thaliana plants showed enhanced salt stress tolerance and transgenic plants allowed seeds to germinate normally after treatment with 75 mM NaCl. Total flavonoid was also enhanced in transgenic Arabidopsis as compared to the control, suggesting that Gh-MLP might also be involved in altering flavonoid content under salt stress [35]. Two wild barley transcripts coding for chalcone synthase (CHS) and two others coding for chalcone isomerase (CHI) were reported to interact under salt stress [36]. CHS, which belongs to a family of polyketide synthase enzymes (PKS), is ubiquitous in higher plants and serves as the initial step for flavonoid biosynthesis [36]. CHI was recently reported to enhance salt tolerance in salt-sensitive yeasts [37]. In the salt-tolerant Millettia pinnata, the level of transcripts involved in flavonoid biosynthesis showed the most remarkable change under salt stress [37]. A transcript for isoflavone reductase-like enzyme was also upregulated in the present study. The enzyme is a key in the isoflavonoid phytoalexin biosynthesis pathway [38] and over expression in transgenic rice (Oryza sativa) reportedly conferred resistance to reactive oxygen species (ROS) stress [39]. Two additional wild barley transcripts encoding transcription factors of the basic helix-loop-helix (bHLH) superfamily are involved in a wide range of growth and developmental signaling pathways, including abscisic acid signaling [40], flavonoid biosynthesis [41] and abiotic stress [42].

4.3 Salt stress tolerance via reactive oxygen species scavenging

It is well-known that cytosolic superoxide dismutase enzymes, either cytSODs (Cu,ZnSOD) or mitSODs (MnSOD), act as antioxidants and protect cellular components during salt stress from being oxidized by reactive oxygen species (ROS) [43–45]. Our results suggest that this mechanism may also be playing a role in salt stress tolerance of wild barley as three transcripts for superoxide dismutase (SOD) activity were highly expressed, while two were less expressed (FC ≤ 4) under salt stress. Other wild barley transcripts encoding electron transporter iron ion binding proteins function in iron-sulfur cluster binding. In several studies, iron ion binding proteins function in detoxification of ROS under salt stress [46–48].

A highly expressed dehydrin (DHN) transcript (FC = 8.44) was also detected. DHNs, or group 2 late embryogenesis abundant (LEA) proteins, have several roles in protecting the plant cell from dehydration stress. One of them exhibits ROS scavenging [49] mediated by the interactions between their amino acid residue and ROS species (e.g., superoxide anion radical O2; singlet oxygen 1O2; hydroxyl radical HO; Hydrogen peroxide H2O2). DHNs also function as antioxidants [50], ion sequestrants [51], and metal ion transporters in plant phloem sap [52]. Under reduced hydration, the K-segments of DHNs adopt α-helical conformation [53]. The amphipathic α-helices can interact with partly dehydrated surfaces of various other proteins protecting them from further loss of the water envelope. DHNs also act as “space-fillers” in which they participate in keeping the original, non-harmful distances among different intracellular complexes that helps maintain the original cell volume, thus preventing cellular collapse [49].

4.4 Salt stress tolerance via ethylene production

Ethylene production is suggested to be a mediator of the stress responses in wild barley. A detected transcript, encoding the ethylene-forming enzyme (e.g., 1-aminocyclopropane-1-carboxylate [ACC] oxidase or ACO), was shown earlier to be indirectly induced under salt stress [54] as it relies on the expression of another set of genes, ethylene receptor genes (such as NTHK1 in tobacco, see Cao et al. [55]). Recent studies have demonstrated that salt tolerance was induced by exogenous 1-aminocyclopropane-1-carboxylate in Arabidopsis [56,57]. More recently, Li et al. [58] indicated that ethylene production and activities of ACO in cucumber (Cucumis sativus) seedlings were increased significantly under salt stress (75 mM). Another detected transcript encoding ethylene-responsive element binding factor (ERF) was considered crucial in earlier studies of cotton under stress [59]. Over expression of sugarcane and soybean ERFs in tobacco conferred tolerance to dehydration [60] and high salinity [61] (200 mM NaCl) stresses.

4.5 Salt stress tolerance via a signaling network

Transcripts encoding two serine threonine-protein kinases (STKs), SpkG and SkpC, were salt-regulated in wild barley. These STKs were previously reported to regulate various cellular functions including stress responses [62,63]. Nonetheless, Liang et al. [64] indicated that growth characteristics of a spkC mutant of the unicellular cyanobacterium Synechocystis were not affected under high salt stress conditions, while, growth of a spkG mutant was completely impaired. The spkG gene plays an essential role in sensing high salt signal directly, rather than mediating signals among other kinases [64,65]. Two transcripts encoding mitogen-activated protein kinases (MAPK) and MAP2K seem to complement the effect of SpkG in wild barley as the MAKP superfamily is part of the serine/threonine kinases. This superfamily is a key player in some of the most essential roles in plant signaling networks, and is tolerant to a variety of stresses including drought and salinity [66].

4.6 Salt stress tolerance via a protein refolding mechanism

Salt stress, like other stresses, results in aggregation of cytoplasmic proteins. The present study is the first to indicate the possible role of a transcript encoding ATP-dependent Clp protease adaptor protein (ClpS) in plants with FC of 9.4. This de novo-mapped transcript was previously recovered in rice (NCBI database, http://www.uniprot.org/uniprot/Q0JNQ7) but information on its response to salt stress was not reported. Expression of ClpS in a metagenomic clone recovered [26] was shown to be involved in increased recognition of aggregated protein for refolding or degradation by the ClpA protease (ClpAP) complex resulting in salt stress tolerance. The Clp family has been shown to act as a molecular chaperone in bacteria [12]. These proteins can reverse protein aggregation resulting from salt stress and also play a role in ATP-dependent degradation of polypeptide chains under salt stress [67].

We can conclude that mRNA-Seq is an efficient high-throughput method for analyzing the wide diversity of genes expressed under salt stress. This technology is a very valuable tool to enhance our understanding of the genetics underlying salt stress tolerance mechanisms in plants. In the present study, a valuable dataset of thousands of DE transcripts was detected, some of which are involved in novel biological processes regulating salt tolerance. Some of the most likely candidates involved in salt stress in wild barley are genes involved in electron transport and exchanger mechanisms, flavonoid biosynthesis, superoxide dismutase (SOD) activity, ethylene production, signaling network, and protein refolding. The results of these comparisons can be utilized to improve salt stress tolerance in cultivated barley as well as other important cereal crops.

Acknowledgements

This project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under Grant no. (14-3-1432/HiCi). The authors, therefore, acknowledge with thanks DSR technical and financial support.


References

[1] D. Schulte; J.C. Timothy; G. Andreas; P. Langridge; T. Matsumoto et al. The international barley sequencing consortium – At the threshold of efficient access to the barley genome, Plant Physiol., Volume 149 (2009), pp. 142-147

[2] K.F. Mayer; R. Waugh; J.W. Brown; A. Schulman; P. Langridge et al. International Barley Genome Sequencing Consortium. A physical, genetic and functional sequence assembly of the barley genome, Nature, Volume 29 (2012), pp. 711-716

[3] D. Gunaskera; M. Santakumari; Z. Glinka; G.A. Berkowitz Wild and cultivated barley genotypes demonstrate varying ability to acclimate to plant water deficits, Plant Sci., Volume 99 (1994), pp. 125-134

[4] L. Petersen; H. Ostergard; H. Giese Genetic diversity among wild and cultivated barley as revealed by RFLP, Theor. Appl. Genet., Volume 89 (1994), pp. 676-681

[5] R. Ellis; B. Foster; L. Handley; D. Gordon; J. Russell et al. Wild barley: a source of genes for crop improvement in the 21st century?, J. Exp. Bot., Volume 51 (2000), pp. 9-17

[6] R. Munns; R.A. James; A. Läuchli Approaches to increasing the salt tolerance of wheat and other cereals, J. Exp. Bot., Volume 57 (2006), pp. 1025-1043

[7] E. Nevo; G. Chen Drought and salt tolerances in wild relatives for wheat and barley improvement, Plant Cell Environ., Volume 33 (2010), pp. 670-685

[8] R. Munns; M. Tester Mechanisms of salinity tolerance, Annu. Rev. Plant Biol., Volume 59 (2008), pp. 651-681

[9] Q. Ma; X. Dai; Y. Xu; J. Guo; Y. Liu et al. Enhanced tolerance to chilling stress in OsMYB3R-2 transgenic rice is mediated by alteration in cell cycle and ectopic expression of stress genes, Plant Physiol., Volume 150 (2009), pp. 244-256

[10] T.J. Close Dehydrins: emergence of a biochemical role of a family of plant dehydration proteins, Physiol. Plant., Volume 97 (1996), pp. 795-803

[11] D.S. Selote; R. Khanna-Chopra Drought-acclimation confers oxidative stress tolerance by inducing coordinated antioxidant defense at cellular and subcellular level in leaves of wheat seedlings, Physiol. Plant., Volume 127 (2006), pp. 494-506

[12] D. Meiri; A. Breiman Arabidopsis ROF1 (FKBP62) modulates thermotolerance by interacting with HSP90.1 and affecting the accumulation of HsfA2-regulated sHSPs, Plant J., Volume 59 (2009), pp. 387-399

[13] G.T. Huang; S.L. Ma; L.P. Bai; L. Zhang; H. Ma et al. Signal transduction during cold, salt, and drought stresses in plants, Mol. Biol. Rep., Volume 39 (2012), pp. 969-987

[14] V. Chinnusamy; K. Schumaker; J.K. Zhu Molecular genetic perspectives on cross-talk and specificity in abiotic stress signalling in plants, J. Exp. Bot., Volume 55 (2004), pp. 225-236

[15] P. Langridge; N. Paltridge; G. Fincher Functional genomics of abiotic stress tolerance in cereals, Brief. Funct. Genomics Proteomics, Volume 4 (2006), pp. 343-354

[16] K. Xu; X. Xu; T. Fukao; P. Canlas; R. Maghirang-Rodriguez et al. Sub1A is an ethylene-response-factor-like gene that confers submergence tolerance to rice, Nature, Volume 442 (2006), pp. 705-708

[17] K. Urano; Y. Kurihara; M. Seki; K. Shinozaki ‘Omics’ analyses of regulatory networks in plant abiotic stress responses, Curr. Opin. Plant Biol., Volume 13 (2010), pp. 132-138

[18] S.D. Tyerman; C.M. Niemietz; H. Bramley Plant aquaporins: multifunctional water and solute channels with expanding roles, Plant Cell Environ., Volume 25 (2002), pp. 173-194

[19] H. Walia; C. Wilson; A. Wahid; P. Condamine; X. Cui et al. Expression analysis of barley (Hordeum vulgare L.) during salinity stress, Funct. Integr. Genomics, Volume 6 (2006), pp. 143-156

[20] M. Ziemann; A. Kamboj; R.M. Hove; S. Loveridge; A. El-Osta et al. Analysis of the barley leaf transcriptome under salinity stress using mRNA-Seq, Acta Physiol. Plant., Volume 35 (2013), pp. 1915-1924

[21] J.C. Marioni; C.E. Mason; S.M. Mane; M. Stephens; Y. Gilad RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays, Genome Res., Volume 18 (2008), pp. 1509-1517

[22] D.R. Hoagland; D.I. Arnon The water-culture method for growing plants without soil, Calif. Agr. Expt. Sta. Circ., Volume 347 (1950), pp. 1-32

[23] J. Zhang; T.A. Ruhlman; J.P. Mower; R.K. Jansen Comparative analyses of two Geraniaceae transcriptomes using next-generation sequencing, BMC Plant Biol., Volume 13 (2013), p. 228

[24] J.A. Brown; G. Sherlock; C.L. Myers; N.M. Burrows; C. Deng et al. Global analysis of gene function in yeast by quantitative phenotypic profiling, Mol. Syst. Biol., Volume 2 (2006), p. 2006.0001

[25] T. Suprunova; T. Krugman; T. Fahima; G. Chen; I. Shams et al. Differential expression of dehydrin genes in wild barley, (Hordeum spontaneum), associated with resistance to water deficit, Plant Cell Environ., Volume 27 (2004), pp. 1297-1308

[26] R.K. Kapardar; R. Ranjan; M. Puri; R. Sharma Sequence analysis of a salt tolerant metagenomic clone, Ind. J. Microbiol., Volume 50 (2010), pp. 212-215

[27] R. Gu; S. Fonseca; L.G. Puskàs; L. Hackler; A. Zvara et al. Transcript identification and profiling during salt stress and recovery of Populus euphratica, Tree Physiol., Volume 24 (2004), pp. 265-276

[28] A. Espinosa-Ruiz; J.M. Belles; R. Serrana; F.A. Culianez-Macla Arabidopsis thaliana AtHAL3: a flavoprotein related to salt and osmotic tolerance and plant growth, Plant J., Volume 20 (1999), pp. 529-539

[29] S.U. Huh; I.J. Lee; B.K. Ham; K.H. Paek Nicotiana tabacum Tsip1-interacting ferredoxin 1 affects biotic and abiotic stress resistance, Mol. Cells, Volume 34 (2012), pp. 43-52

[30] Z.H. Dang; L.I. Zheng; J. Wang; Z. Gao; S.B. Wu et al. Transcriptomic profiling of the salt-stress response in the wild recretohalophyte Reaumuria trigyna, BMC Genomics, Volume 14 (2013), p. 29

[31] M.P. Apse; J.B. Sottosanto; E. Blumwald Vacuolar cation/H+ exchange, ion homeostasis, and leaf development are altered in a T-DNA insertional mutant of AtNHX1, the Arabidopsis vacuolar Na+/H+ antiporter, Plant J., Volume 36 (2003), pp. 229-239

[32] G. Batelli; P.E. Verslues; F. Agius; Q. Qiu; H. Fujii et al. SOS2 promotes salt tolerance in part by interacting with the vacuolar H+-ATPase and upregulating its transport activity, Mol. Cell Biol., Volume 27 (2007), pp. 7781-7790

[33] J. Wu; D.M. Seliskar Salinity adaptation of plasma membrane H+-ATPase in the salt marsh plant Spartina patens: ATP hydrolysis and enzyme kinetics, J. Exp. Bot., Volume 49 (1998), pp. 1005-1013

[34] K.M.K. Huda; M.S.A. Banu; R. Tuteja; N. Tuteja Global calcium transducer P-type Ca2+-ATPases open new avenues for agriculture by regulating stress signaling, J. Exp. Bot., Volume 64 (2013), pp. 3099-3109

[35] J.Y. Chen; X.F. Dai Cloning and characterization of the Gossypium hirsutum major latex protein gene and functional analysis in Arabidopsis thaliana, Planta, Volume 231 (2010), pp. 861-873

[36] C.C. Cain; D.E. Saslowsky; R.A. Walker; B.W. Shirley Expression of chalcone synthase and chalcone isomerase proteins in Arabidopsis seedlings, Plant Mol. Biol., Volume 35 (1997), pp. 377-381

[37] H. Wang; T. Hu; J. Huang; X. Lu; B. Huang et al. The expression of Millettia pinnata chalcone isomerase in Saccharomyces cerevisiae salt-sensitive mutants enhances salt-tolerance, Int. J. Mol. Sci., Volume 14 (2013), pp. 8775-8786

[38] N.L. Paiva; R. Edwards; Y.J. Sun; G. Hrazdina; R.A. Dixon Stress responses in alfalfa (Medicago sativa L.) 11. Molecular cloning and expression of alfalfa isoflavone reductase, a key enzyme of isoflavonoid phytoalexin biosynthesis, Plant Mol. Biol., Volume 17 (1991), pp. 653-667

[39] S.G. Kim; S.T. Kim; Y. Wang; S.K. Kim; C.H. Lee et al. Overexpression of rice isoflavone reductase-like gene (OsIRL) confers tolerance to reactive oxygen species, Physiol. Plant., Volume 138 (2010), pp. 1-9

[40] L.Y. Zhang; M.Y. Bai; J. Wu; J.Y. Zhu; H. Wang et al. Antagonistic HLH/bHLH transcription factors mediate brassinosteroid regulation of cell elongation and plant development in rice and Arabidopsis, Plant Cell, Volume 21 (2009), pp. 3767-3780

[41] A. Baudry; M. Caboche; L. Lepiniec TT8 controls its own expression in a feedback regulation involving TTG1 and homologous MYB and bHLH factors, allowing a strong and cell-specific accumulation of flavonoids in Arabidopsis thaliana, Plant J., Volume 46 (2006), pp. 768-779

[42] F. Li; S. Guo; Y. Zhao; D. Chen; K. Chong et al. Overexpression of a homopeptide repeat-containing bHLH protein gene (OrbHLH001) from Dongxiang wild rice confers freezing and salt tolerance in transgenic Arabidopsis, Plant Cell Rep., Volume 29 (2010), pp. 977-986

[43] S. Miriyala; I. Spasojevic; A. Tovmasyan; D. Salvemini; Z. Vujaskovic et al. Manganese superoxide dismutase, MnSOD and its mimics, Biochim. Biophys. Acta, Volume 1822 (2012), pp. 794-814

[44] P. Diaz-Vivancos; M. Faize; G. Barba-Espin; L. Faize; C. Petri et al. Ectopic expression of cytosolic superoxide dismutase and ascorbate peroxidase leads to salt stress tolerance in transgenic plums, Plant Biotechnol. J., Volume 11 (2013), pp. 976-985

[45] M. Faize; L. Faize; C. Petri; G. Barba-Espin; P. Diaz-Vivancos et al. Cu/Zn superoxide dismutase and ascorbate peroxidase enhance in vitro shoot multiplication in transgenic plum, J. Plant Physiol., Volume 170 (2013), pp. 625-632

[46] J. Krüger; C.M. Thomas; C. Golstein; M.S. Dixon; M. Smoker et al. A tomato cysteine protease required for Cf-2-dependent disease resistance and suppression of autonecrosis, Science, Volume 296 (2002), pp. 744-747

[47] M. Hara; M. Fujinaga; T. Kuboi Radical scavenging activity and oxidative modification of citrus dehydrin, Plant Physiol. Biochem., Volume 42 (2004), pp. 657-662

[48] D. Johnson; D.R. Dean; A.D. Smith; M.K. Johnson Structure, function and formation of biological iron–sulfur clusters, Annu. Rev. Biochem., Volume 74 (2005), pp. 247-281

[49] M. Battaglia; Y. Olvera-Carrillo; A. Garciarrubio; F. Campos; A.A. Covarrubias The enigmatic LEA proteins and other hydrophilins, Plant Physiol., Volume 148 (2008), pp. 6-24

[50] M. Hara; M. Fujinaga; T. Kuboi Metal binding by citrus dehydrin with histidine-rich domains, J. Exp. Bot., Volume 56 (2005), pp. 2695-2703

[51] B.J. Heyen; M.K. Alsheikh; E.A. Smith; C.F. Torvik; D.F. Seals et al. The calcium-binding activity of a vacuole-associated, dehydrin-like protein is regulated by phosphorylation, Plant Physiol., Volume 130 (2002), pp. 675-687

[52] C. Kruger; O. Berkowitz; U.W. Stephan; R. Hell A metal-binding member of the late embryogenesis abundant protein family transports iron in the phloem of Ricinus communis L, J. Biol. Chem., Volume 277 (2002), pp. 25062-25069

[53] M. Hanin; F. Brini; C. Ebel; Y. Toda; S. Takeda et al. Plant dehydrins and stress tolerance: versatile proteins for complex mechanisms, Plant Signal. Behav., Volume 6 (2011), pp. 1503-1509

[54] I.A.M.A. Penninckx; K. Eggermont; F.R.G. Terras; B.P.H.J. Thomma; G.W. De Samblanx et al. Pathogen-induced systemic activation of a plant defensin gene in Arabidopsis follows a salicylic acid-independent pathway, Plant Cell, Volume 8 (1996), pp. 2309-2323

[55] W.H. Cao; J. Liu; Q.Y. Zhou; Y.R. Cao; S.F. Zheng et al. Expression of tobacco ethylene receptor NTHK1 alters plant responses to salt stress, Plant Cell Environ., Volume 29 (2006), pp. 1210-1219

[56] W.H. Cao; J. Liu; X.J. He; R.L. Mu; H.L. Zhou et al. Modulation of ethylene responses affects plant salt-stress responses, Plant Physiol., Volume 143 (2007), pp. 707-719

[57] H. Wang; X.L. Liang; Q. Wan; X.M. Wang; Y.R. Bi Ethylene and nitric oxide are involved in maintaining ion homeostasis in Arabidopsis callus under salt stress, Planta, Volume 230 (2009), pp. 293-307

[58] B. Li; T. Sang; L. He; J. Sun; J. Li et al. Ethylene production in leaves of cucumber seedlings under NaCl stress, J. Am. Soc. Hortic. Sci., Volume 138 (2013), pp. 108-113

[59] L.G. Jin; H. Li; J.Y. Liu Molecular characterization of three ethylene responsive element binding factor genes from cotton, J. Integr. Plant Biol., Volume 52 (2010), pp. 485-495

[60] L.E. Trujillo; M. Sotolongo; C. Menéndez; M.E. Ochogavía; Y. Coll et al. SodERF3, a novel sugarcane ethylene responsive factor (ERF), enhances salt and drought tolerance when overexpressed in tobacco plants, Plant Cell Physiol., Volume 49 (2008), pp. 512-525

[61] G. Zhang; M. Chen; L. Li; Z. Xu; X. Chen et al. Overexpression of the soybean GmERF3 gene, an AP2/ERF type transcription factor for increased tolerances to salt, drought, and diseases in transgenic tobacco, J. Exp. Bot., Volume 60 (2009), pp. 3781-3796

[62] J.M. Neu; S.V. MacMillan; J.R. Nodwell; G.D. Wright StoPK-1, a serine/threonine protein kinase from the glycopeptide antibiotic producer Streptomyces toyocaensis NRRL 15009, affects oxidative stress response, Mol. Microbiol., Volume 44 (2002), pp. 417-430

[63] H. Hussain; P. Branny; E. Allan An eukaryotic-type serine/threonine protein kinase is required for biofilm formation, genetic competence, and acid resistance in Streptococcus mutans, J. Bacteriol., Volume 188 (2006), pp. 1628-1632

[64] C. Liang; X. Zhang; X. Chi; X. Guan; Y. Li et al. Serine/threonine protein kinase SpkG is a candidate for high salt resistance in the unicellular cyanobacterium Synechocystis sp. PCC 6803, PLoS ONE, Volume 6 (2011), p. e18718

[65] X. Zhang; F. Zhao; X. Guan; Y. Yang; C. Liang et al. Genome-wide survey of putative serine/threonine protein kinases in cyanobacteria, BMC Genomics, Volume 8 (2007), p. 395

[66] G. Taj; P. Agarwal; M. Grant; A. Kumar MAPK machinery in plants. Recognition and response to different stresses through multiple signal transduction pathways, Plant Signal. Behav., Volume 5 (2010), pp. 1370-1378

[67] H. Feng; L.M. Gierasch Molecular chaperones: clamps for the Clps?, Curr. Biol., Volume 8 (1998), p. R464-R467


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