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Identification of miRNAs and evaluation of candidate genes expression profile associated with drought stress in barley
Plant Gene 20 (2019) 100205
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Plant Gene journal homepage: www.elsevier.com/locate/plantgene
Identification of miRNAs and evaluation of candidate genes expression profile associated with drought stress in barley
T
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Sajjad Zarea, Farhad Nazarian-Firouzabadia, , Ahmad Ismailia, Hassan Pakniyatb a b
Department of Agronomy and Plant Breeding, Faculty of Agriculture, Lorestan University, Khorramabad, Iran Department of Crop Production and Plant Breeding, College of Agriculture, Shiraz University, Shiraz, Iran
A R T I C LE I N FO
A B S T R A C T
Keywords: Bioinformatics Drought stress EST Gene expression Hordeum
To identify miRNAs and assess the expression of genes involved in drought stress tolerance, leaf and root ESTs were analyzed in barley. To this end, the EGassembler bioinformatics service and IDEG6 were used for the preprocessing and identify differentially expressed genes among EST libraries, respectively. Furthermore, root contigs were analyzed by C-mii software for miRNAs identification. The expression profile of two drought tolerance candidate genes was studied, using Real time-PCR in a 2 (Nimruz and Spontaneum) × 2 (control and 50% FC) factorial experiment in a completely randomized design with 3 replications. Results of this study showed that N-butyl-N-methylpiperidinium (Pip1;4) and a non-specific lipid transport protein (nsLTP) were found to express differently under drought stress. The expression level of barley HvPip1;4 (HvPiP1;4) and non-specific lipid transport (HvnsLTP) genes increased by 95.98 and 54.53 fold after 72 h of drought stress, respectively. There was a significant difference (P < .05) between two genotypes with respect to the expression level of candidate genes. Nimruz cultivar had a higher expression level for both candidate genes. Comparison analysis of miRNAs showed that miR2102 had a differential expression between non-stressed and drought stress libraries in barley root tissue. Furthermore, HvPiP1;4 and HvnsLTP were miR6201and miR5052 target genes.
1. Introduction Barley)Hordeum vulgare L. (is currently the fourth most important cereal grain worldwide (FAO, 2017) and ranked second after wheat in Iran. In comparison to other major cereal crops, barley is relatively tolerant to dry and cool environments and is a typical crop of poor farmers in most developing countries (Steduto et al., 2012). However, barley productivity is severely impaired by various stresses specially drought stress (Rizza et al., 1994). Growth and development of crop plants are severely affected by abiotic stresses (Seki et al., 2003) among which water-deficit stress has a crucial devastating impact on yield and yield components (Farooq et al., 2012; Farooq et al., 2009). Plants combat drought stress by reprogramming gene expression in complex overlapping mechanisms (Ahuja et al., 2010; Golldack et al., 2011) suggesting that drought tolerance is a quantitative trait which is controlled by a number of quantitative trait loci (QTLs). These QTLs are located on several chromosomes in barley, with major drought QTLs clustering in chromosomes 2H and 5H (Teulat et al., 1998; Tondelli et al., 2006) accounted for 12–22% increase in grain yield under drought conditions (Zhang et al., 2012). Few moderate- to large-scale gene expression studies have identified a number of genes involved in
⁎
drought response (Bray, 2004; Kawaguchi et al., 2004; Kreps et al., 2002; Ozturk et al., 2002; Seki et al., 2002; Talamè et al., 2006). For instance, expressed sequence tags (EST) are employed for transcriptome analysis in an organism at various stages of development or under different experimental conditions. EST analysis is a powerful tool to discover and characterize stress responsive genes and developmental cues involved in several plant species (Gruber et al., 2012; Lata et al., 2010; Shamloo-Dashtpagerdi et al., 2013; Zhuang and Zhu, 2014). Although > 500,000 barley EST sequences are now publicly available, a large portion is still classified as unknown function (Tommasini et al., 2008). Elucidating the function of genes and the extent to which such genes are expressed at particular condition are some of the greatest challenges in biology. MicroRNAs (miRNAs) are among the central molecules for naturally occurring regulation of gene expression. Many miRNAs play a regulatory role in activating biotic and abiotic stress defence or response gene networks (Zhu et al., 2011). Therefore, the identification of miRNAs associated with drought stress is an essential precursor to understand the regulating function of genes involved in drought stress. To this end, large scale bioinformatics analysis has enabled miRNA discovery in plants such as wheat, maize, barley, Arabidopsis thaliana and Medicago sativa (Schreiber et al., 2011).
Corresponding author. E-mail address: [email protected] (F. Nazarian-Firouzabadi).
https://doi.org/10.1016/j.plgene.2019.100205 Received 11 March 2019; Received in revised form 30 July 2019; Accepted 19 September 2019 Available online 17 October 2019 2352-4073/ © 2019 Elsevier B.V. All rights reserved.
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2.4. Plant material and drought stress treatments
Families of miRNAs may be conserved between species but unique miRNAs have also been discovered in each species analyzed (Schreiber et al., 2011). In this study, two EST libraries derived from barley leaves and roots in non-stress and drought stress conditions were mined in order to gain an insight into drought responsive genes and miRNAs. Furthermore, the expression profile of selected stress responsive candidate genes was assessed.
The expression profile of drought responsive candidate genes was studied, using real time-PCR in a 2 × 2 factorial experiment, based on a completely randomized design (CRD) with three replications in pots. A cultivated barley genotype (Nimruz) as a drought stress tolerant and a wild barley diploid genotype (Spontaneum), as a sensitive genotype were treated in two different irrigation regimes (control and 50% FC). Briefly, the barley seeds were planted in pots (20 cm in height and 15 cm in diameter) containing 2.5 Kg sterile soil. Soil moisture was kept constant at 100% FC for controls and 50% of FC for drought treatment plants. Water stress was imposed at the five-leaf stage for two weeks by stopping the irrigation for the water-stressed plants. The sampling was done at three time points (Zero, 24 and 72 h after imposing drought stress). For determination of field capacity (FC), the soil was placed in an oven for 48 h at 150 °C. The soil was weighed (A), and later saturated with water. Pots were covered with aluminum foils to prevent water evaporation, followed with placing them at normal temperature for 24 h (B). The FC was calculated from the following Eq. (1).
2. Materials and methods 2.1. EST data collection Two EST barley libraries; (1) a leaf library (Lib.13887) containing 2016 sequences and (2) a barley root library (Lib.9798) consisting of 1469 sequences from a non-stress condition were downloaded from NCBI database and compared with a barley root library (Lib.9796) with 3863 sequences from drought stress condition. The root library was obtained from roots of three weeks old drought stressed barley plants cv Optic.
FC = 100 ×
B−A B
(1)
Leaf samples were frozen in liquid nitrogen and kept in a − 80 °C freezer.
2.2. EST sequence processing, categorizing and assembly The EST sequences were processed by using EGassembler, an online bioinformatics service (http://egassembler.hgc.jp) (Masoudi-Nejad et al., 2006). The output includes contigs, singletons and assembled sequence of ESTs. The ESTs of every library were clustered together and assembled. Contigs with at least 10 EST sequences were considered as high expressed genes (Zhou et al., 2003). To identify the sequences repeatedly transcribed in each library, ESTs sequences were clustered and assembled by using EGassembler with overlap percent identity cut off 80% (Shamloo-Dashtpagerdi et al., 2013). To identify differentially expressed genes between libraries computationally, all EST sequences of the two libraries were clustered and assembled together by using EGassembler. The contigs were then subjected to Audic and Claverie test (α = 0.05) by IDEG6 software (http://telethon.bio.unipd.it/bioinfo/IDEG6) to address whether the number of ESTs of each library contributing to each contig were significantly different. A unique miRNA was considered differentially expressed when P < .001 was achieved based on a general chi-squared test. The total number of ESTs in each library and the number of library specific ESTs in each contig were the required inputs to do Audic and Claverie tests. All the unigenes (contigs and singletons) of each library were subjected to similarity searches against the barley proteins (http://web.expasy.org/docs/swiss-prot_guideline.html) by BLASTX (Evalue = 10−5) using CLC Genomics Workbench software (CLC-bio, Aarhus, Denmark) to obtain protein codes needed to assign functional categories.
2.5. RNA isolation and real-time PCR analysis Total RNA was extracted from frozen leaf and root samples by using Trizol reagent (Life Technology, Invitrogen, USA). RNA samples were treated with RNase free DNase (Promega, USA) to remove possible genomic DNA contaminations. cDNAs were synthesized using iScriptcDNA Synthesis Kit (Bio-Rad, USA). Gene-specific real time RTPCR primers were designed by Oligo v.5.1 software (Guo et al., 2009), and synthesized (MWG Co., Germany). Real time RT-PCR was conducted in triplicates, using three biological and three technical cDNA replicates (iQSYBR Green Supermix kit, BIO-RAD, USA) on an iCycler iQ thermocycler Real Time PCR Detection system (BIO-RAD, USA) according to the manufacturer's instructions (Internal control). 2.6. Stem-loop reverse transcription Stem-loop reverse transcription (SL RT) primers were designed manually for miR2102 and miR414 following the criteria as described earlier (Varkonyi-Gasic et al., 2007), miRNA SL RT was carried out using 50 ng of leaf small RNA fractions. 0.5 μl of 10 mM dNTP mix was added to RNA, incubated for 5 min at 65 °C and then kept on ice for 2 min. To the above mix, 2 μl of 5× First Strand Buffer, 1 μl of 0.1 M DTT and 0.5 μl of Superscript III reverse transcriptase (100 units) were added. A master mix of the above recipe was prepared for ten tubes and distributed equally. Thereafter, 1 μl of miRNA-specific SL RT primer was added. The reaction was performed as 16 °C, 30 min for one cycle; 30 °C, 30 s; 42 °C, 30 s; 50 °C, 1 s for 60 cycles followed by incubation at 85 °C for 5 min and finally hold at 4 °C. The miR2102 (GTGCTATGGA GTGCACCCTGGCAGGTATTCGCACTGGATACGACCAGGGG) and miR414 (GTGCTATCCACTGCAGGGTCCGACCTATTCGCACTGGATACG ACCAGGGC) primers were used for stem loop reverse transcriptase analyses.
2.3. Identification miRNAs associated with drought stress To identify miRNAs associated with drought stress in barley, the obtained contigs from root data were analyzed by C-mii software (Numnark et al., 2012). Sequences were considered as potential miRNAs that had a MFEI (Minimum free energy index) equal to or > 0.6. Furthermore, the potential miRNA sequences were screened with ≤4 nucleotide mismatches in sequence with known plant mature miRNAs; and less than six mismatches with the opposite miRNA* sequence in the other arm (Zhang et al., 2006a; Zhang et al., 2006b). PsRNA Target (http://plantgrn.noble.org/psRNAtarget) and PMTED (http://pmted.agrinome.org/by_gene.jsp) online software were used to predict miRNA targets.
2.7. Measurement of physiological traits 2.7.1. Relative water content Relative water content (RWC) was measured using leaves after imposing drought treatments (Zero, 24 and 72 h). Briefly, leaves were sealed within plastic bags and quickly transferred to the laboratory and fresh weights were recorded within two hours after excision. Turgid 2
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transfer proteins are small, basic proteins present in abundance in higher plants (Kader, 1996) which are involved in key processes of plant cytology (Liu et al., 2015), response to biotic and abiotic stresses (Kiełbowicz-Matuk et al., 2008), cuticle cell wall formation (Jacq et al., 2017) growth and development (Edstam et al., 2014) sexual reproduction (Chae et al., 2010), resistance to plant pathogens (Sarowar et al., 2009), seed development and germination (Pagnussat et al., 2012). Cameron et al. (2006) reported a 6-fold increase in transcript abundance of tree tobacco nsLTP genes when tobacco leaves were subjected to drying treatments, suggesting that nsLTPs are involved in cuticle deposition.
weights were recorded after soaking leaves in distilled water in test tubes for 16 to 18 h at room temperature and under the low light conditions. After soaking, leaves were quickly and carefully blotted dry with tissue paper and turgid weight recorded. Dry weights were recorded after oven drying the leaf samples for 72 h at 70 °C and RWC was calculated according to below formula (Schonfeld et al., 1988).
RWC(%) =
(Fresh weight−Dry weight) × 100 Turgid weight−Dry weight
(2)
2.7.2. Electrolyte leakage (cell membrane stability) For electrolyte leakage (EL), leaf discs (6 mm in diameter) were cut off and washed to remove solutes from both leaf surfaces and cell damaged areas. Chopped leaves were then transferred to the tubes containing 15 ml of distilled water at room temperature. After 24 h, EC was measured (EC0). The solutions were autoclaved for 15 min and the EC was measured (EC1) again and represented as the percentage of total ions released (Eq. (3)) (Bajji et al., 2002).
CMS =
EC 0 × 100 EC1
3.2. Root EST sequences analysis To explore the differentially expressed genes between barley root normal and stressed libraries, all ESTs of the two libraries were assembled and the contigs generated were then analyzed. Assembly of ESTs yielded 692 contigs and 2743 singletons. The number of ESTs varied from two (354 contigs) to 39 (one contig), the average length of contigs was 698 bp long. Using Audic and Claverie test, 137 contigs were significantly (p < .05) and differentially represented between normal and stress transcriptomes. The 22 contigs were up-regulated while the 115 contigs were down-regulated. The contigs were subjected to BLASTN search and it was found that 120 contigs were functionally annotated (E-value ≤10−5). The highest up-regulation was noticed for a gene from the aquaporin family HvPiP1;4 (contig 320) and the highest reduction in expression was assigned to a gene with unknown function (contig 162). Aquaporins belong to a major intrinsic protein (MIP) family (Chaumont et al., 2000), highly hydrophobic with six membranespanning domains and molecular masses of 26 to 34 kD (Chaumont et al., 2000). Generally speaking, aquaporins are divided into four subfamilies: plasma membrane intrinsic proteins (PIPs), tonoplast intrinsic proteins (TIPs), NOD26-like intrinsic proteins and small basic intrinsic proteins. The PIPs are further divided into two subclasses. PiP1s have little or lack water channel activity, whereas the PIP2s exhibit a high water permeability (Chaumont et al., 2000). It has been reported that following salt and abscisic acid stresses, TdPIP1;1 and TdPIP2;1 genes are regulated in a salt-tolerant wheat (Ayadi et al., 2011). According to the research studies, the integrated functions of aquaporins under various physiological conditions remain elusive. For instance, overexpression of PIP1;4 or PIP2;5 proteins in Arabidopsis thaliana and Nicotiana tabacum transgenic plants led to a rapid water loss under dehydration stress, whereas the same transgenic plants showed an enhanced water flow and germination speed under cold stress (Jang et al., 2007).
(3)
In which CMS: Cell Membrane Stability, EC0: EC before autoclave, EC1: EC after autoclave. 2.8. Statistical analysis Data were analyzed by using the general linear model (GLM) procedure of the statistical analysis system, SAS. Least significance difference (LSD) test (p ≤ .05) was used to compare the means. 3. Results and discussion 3.1. Leaf EST sequences analysis It is estimated that > 500,000 barley ESTs are publicly available, but many ESTs are still classified as unknown function (Tommasini et al., 2008). In this study, a total of 1939 high-quality ESTs belonging to leaf drought stress libraries were retained for clustering to generate non-redundant sets of transcripts, which provide information on the tissues in which they are expressed. The high-quality ESTs had an average length of 579 bp. Assembly of ESTs yielded 1578 unigenes (193 contigs and 1394 singletons) encompassing 28.11% of ESTs. The number of ESTs varied from two (188 contigs) to 25 (one contig). At this level of EST sampling, each tissue shared a very low percentage of transcripts (13–26%). In line with thesis findings, Worch et al. (2011) identified 613 drought-responsive ESTs in barley seedlings. Furthermore, in a similar study, clustering and assembly of wheat ESTs under drought stress resulted in 2376 unique sequences, 75% of which were represented only once (Ergen and Budak, 2009). The unigenes were annotated using BLASTX homology search against the barley protein database. Out of 193 unigenes, 84 unigenes (43.52%) showed a significant similarity (E value cut-off 10−5) with genes of known or putative function. Eighty-three unigenes (43.01%) were matched to genes with unknown function. The remaining 26 unigenes (13.47%) displayed no significant match with the public protein database implying that they may be specific genes to barley or genes whose biological functions have not yet been reported. Thus, they can be taken into account as a source for gene discovery. According to EST sequences, number of contigs and results of BLASTX hit for genes with high expression (at least five EST sequences) in drought condition, 21 genes were detected in barley leaf libraries (Table 1). Contig 9 with 25 EST sequences showed the highest expression followed by contig173 with 22 EST sequences, encoding a nonspecific lipid transport protein (nsLTP). Interestingly, an increase in a nsLTP expression has been reported in three barley genotypes following drought stress treatments (Guo et al., 2009). Plant non-specific lipid-
3.3. Expression patterns of two barley candidate genes under drought stress Differential expression of candidate gens is interesting because the genes with significant differential expression between the two genotypes can be explored as potential candidate genes conferring drought tolerance in barley, by using transgenic overexpression. To validate the results of ESTs comparative analysis, two differentially expressed candidate genes were analyzed by qRT-PCR. Real-time PCR analysis confirmed the differential expression of these genes under drought stress conditions. According to the EST library results, PIP1;4 (contig 320 from root library) and non-specific lipid transport (contig 173 from leaf library) were selected for further evaluation. Expression pattern of HvsnLTP and HvPiP1;4 genes were investigated in two barley genotypes under normal and drought stress conditions. Results of Realtime RT-PCR analysis showed that each genotype had a different expression pattern for both genes (Fig. 1). As it can be seen from Fig. 1, both genes had a higher expression level in Nimruz cultivar in comparison to that of Spontaneum genotype. The transcription level of HvsnLTP was increased by 54-folds following drought stress in Nimruz 3
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Table 1 Leaf EST sequences number and results of BLASTX hit for gene with high expression (at least 5 EST sequences) in drought stress condition. Query Contig Contig Contig Contig Contig Contig Contig Contig Contig Contig Contig Contig Contig Contig Contig Contig Contig Contig Contig Contig Contig
9 173 98 2 118 40 27 26 1 167 101 186 128 143 176 17 123 82 50 56 37
#EST
Lowest E-value
Entry
Protein name
Gene ontology
25 22 16 14 11 10 9 7 7 7 6 6 6 6 5 5 5 5 5 5 5
1.12E-64 1.74E-48 2.18E-97 2.35E-54 3.05E-61 1.28E-86 2.22E-97 2.09E-20 4.41E-19 4.77E-19 1.21E-35 2.79E-25 2.04E-79 2.33E-26 1.96E-13 1.01E-34 5.42E-06 2.25E-74 1.01E-34 7.50E-38 2.13E-31
F2EBL3 F2E1T8 M0Y7A8 Q42848 Q40047 F2CT91 M0YHQ0 F2EC03 I3QM88 M0XTV9 F2E5Q1 M0VPD4 F2CT51 Q7DM44 F2DXU8 F2EJL5 Q40051 F2DF63 F2CSG7 M0 V695 M0WR69
Predicted protein non-specific lipid-transfer protein Uncharacterized protein Non-specific lipid-transfer protein Transmembrane protein Ribulose bisphosphate carboxylase small chain Uncharacterized protein Hexosyltransferase Metallothionein Uncharacterized protein Profilin Uncharacterized protein Ribulose bisphosphate carboxylase small chain Lichenase Predicted protein Predicted protein (Uncharacterized protein) Glycine rich protein (Fragment) Predicted protein (Uncharacterized protein) Non-specific lipid-transfer protein Non-specific lipid-transfer protein Uncharacterized protein
Hordeum vulgare var. Distichum Lipid transport Phosphatidylcholine biosynthetic process Lipid transport – Carbon fixation – – – Fatty acid beta-oxidation – – Carbon fixation Carbohydrate metabolic process – – – Polyamine metabolic process Lipid transport Lipid transport –
3.4. Three miRNAs are induced by drought stress
after 72 h, whereas only a slight or no significant elevations were observed in Spontaneum genotype. The expression results tempting to draw conclusion that the expression of HvPiP1;4 gene is genotype dependent and influenced by drought stress (P < .05). The transcription level of HvPiP1;4 increased by 96 folds under drought stress in Nimruz after 72 h. A number of researchers have previously observed the upregulation of aquaporins in response to water stress in other plant species (Alexandersson et al., 2005; Aroca et al., 2006). Guo et al. (2009) reported a similar differential induction of drought-responsive genes in drought-tolerant barley genotypes in response to drought stress during the reproductive stage. Hu et al. (2012) reported that overexpression of a wheat aquaporin gene, TaAQP8, enhances salt stress tolerance in transgenic tobacco. Similarly, Gao et al. (2010) by monitoring overexpression of a putative aquaporin gene from wheat showed that TaNIP-expressing transgenic Arabidopsis lines had a significant higher salt tolerant capacity (Gao et al., 2010).
To identify potential miRNAs associated with drought stress, root data were analyzed by C-mii software (Numnark et al., 2012). Three miRNAs (ath-miR414, osa-miR414, osa-miR2102-5p) with relatively a higher expression level were identified (Table 2) exhibiting a differential expression pattern between root non-stressed and drought stressed libraries (p-value≤.05). Searching between known miRNAs in barley by using the psRNA target tool revealed that HvPiP1;4 gene is one of the target genes of miR6201 (5′-UGACCCUGAGCACUCAUACCG-3′; (Lv et al., 2012)). In addition, the non-specific lipid transport gene is also a target for miR5052 (5′-ACCGGCUGGACGGUAGGCAUA-3′ (Schreiber et al., 2011)). Evaluation between detected genes (HvPiP1;4 and HvnsLTP) and detected miRNAs (miR414 and miR2102) showed no relations. it turned out that histone H2A gene seem to be a target for miR2102 and searching between miRNAs in barley by using the psRNA target tool showed that HvPiP1;4 gene is one of the target genes of miR6201 (
110
a
Ge ne re lativ e e xpression
100 90
HvnsLTP
80
HvPIP1;4
70 a 60 50
b
b
40 30
10
c
c
20 f g
ef fg
d e
e
c
c d e
d e ef
d
c e
de
0 0
24
72
0
Spontaneum
24
72
Nimruz
0
24
72
0
Spontaneum
Control
24
72
Nimruz Drought stress
Fig. 1. Real-time RT-PCR analysis of HvsnLTP and HvPiP1;4 genes at different time points (0, 24 and 72 h) following water stress (means comparison was done by using least significance difference (LSD) test (P ≤ .05) separately for genes). 4
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Table 2 Name, sequence and contig number of identified miRNAs from root libraries. Mature miRNA
Input sequence name
Precursor miRNA MFEI(kcal/mol)
Predicted miRNA sequence 5′ → 3′
ath-miR414 osa-miR2102-5p osa-miR414
Contig 377 Contig 219 Contig 377
−0.774 −0.648 −0.774
UUUUCAUCAUCAUCAUCGUCA CGGCUCGCCGCCGCCGCCAA UUUUCAUCAUCAUCAUCGUCA
Re lativ e ge ne e xpression
3.5
miR414
3.0
a
miR2102 c de
2.0
fg
g b
1.5 1.0
b
a
2.5
b
b b
a
a
b a a
cd
de
ef
a
a
b
h c
0.5 0.0 0
24
72
0
Spontaneum
24
72
Nimruz
0
24
72
0
Spontaneum
Control
24
72
Nimruz Drought stress
Fig. 2. Expression analysis of miR414 and miR2102 genes at different time points (0, 24 and 72 h) following water stress (means comparison performed by using least significance difference (LSD) test (P ≤ .05) separately for genes.
EL from plant leaves was found to increase as drought stress intensified. Drought stress led to 16 and 23% increase in EL in Nimruz and Spontaneum genotypes, respectively (Fig. 4). In line with these findings, EL in barley (Velasco-Arroyo et al., 2018), rice (Chen et al., 2018), Arabidopsis (Hu et al., 2012) was also significantly increased following drought stress. There was a significant (P < .05) difference between two genotypes with respect to EL, suggesting that Nimruz cultivar was more tolerant to drought stress than Spontaneum genotype. According to correlation between physiological and molecular parameter (Table 3), it was found that drought stress led to RWC reduction and increasing EC as well as the expression level of studied genes, suggesting a negative correlation between RWC and other parameters (Table 3). miR414 involve in sucrose metabolism that it is extremely responsive to internal and external signals and consequently alters development and stress adjustment (Wan et al., 2011). These signal paths may also be used to preserve water present in the leaves. Since these miRNAs contribute to plant growth and development (Guleria and Yadav, 2011), it can also help improve water absorption.
5′-UGACCCUGAGCACUCAUACCG-3′; (Lv et al., 2012)). In addition, the non-specific lipid transport gene is also a target for miR5052 (5′-ACC GGCUGGACGGUAGGCAUA-3′ (Schreiber et al., 2011)). Both miR414 and miR2102 genes showed an upregulated expression under drought stress in two genotypes but there were significant differences between two genotypes (Fig. 2). Interestingly, a higher expression level of miR414 was recorded in young leaves in comparison with old leaves, suggesting miR414 may play an important role in regulation of plant growth and development (Guleria and Yadav, 2011). Furthermore, miR414 target gens such as high mobility group proteins, SNF2 transcriptional regulator, C2H2 zinc finger proteins, pentatricopeptide repeat-containing proteins, F-Box family proteins, DNA store keepers and RNA recognition motifs play major roles in posttranscriptional modifications (Yin and Shen, 2010). Although, miR2102 is significantly up-regulated in response to arsenate and arsenite stress in rice (Sharma et al., 2015), Upon drought treatment, expression of miR2102 was significantly increased in wheat (Akdogan et al., 2016). 3.5. Physiological traits
4. Conclusion To see the differences between tolerant and sensitive barley genotypes, some physiological traits were studied. Results of RWC and EC parameters confirmed that the appropriate genotypes have been chosen for this study. Furthermore, by monitoring the physiological traits, it was confirmed that stress treatment had a significant effect on barley plants. To assess the effect of water stress on various physiological parameters of barley genotypes, the RWC and EL were measured. According to analysis of variances, RWC was significantly (P < .01) affected depending on cultivar and drought stress treatments. Although, Nimruz cultivar maintained a higher RWC at control condition, drought stress led to 10 and 13% RWC reduction in Nimruz and Spontaneum genotypes, respectively (Fig. 3). As the sampling time increased, the RWC decreased and the lowest RWC was obtained after 72 h. According to Bandurska et al. (2017) drought stress results in a gradual decrease of water content in barley genotypes.
In conclusion, water scarcity is among main agronomy challenges worldwide, and has a profound effect on yield losses in crop plants. Barley is one of the most important crop plants in arid and semiarid regions. Annual rainfall distribution shows that 74% of the total area in Iran (122.5 million hectares) benefits < 200 mm rainfall, and its distribution is variable and non-uniform; while merely 10% of rainfalls occur during hot and dry seasons. Recent genetic studies of wild and landrace (i.e. primitive domesticate) barley collections have showed that barley is a polycentric origin plant and different regional wild populations contributing putative adaptive variations (Nevo and Chen, 2010). Iran is one of the most likely barley domestication sites and it seems that the progenitor of cultivated barley, is a selfing annual grass predominantly originated from Mediterranean Irano-Turanian region (Abedini et al., 2017). In this study HvPiP1;4 and HvsnLTP genes were identified based on expression profiles in response to drought stress in 5
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120
RWC (%)
100
bc
ab
bc
cd
a
de
e
f
fg
80
b
g h
60 40 20
0 0
24
72
0
Control
24
72
0
Drought stress
24
72
0
Control
Spontaneum
24
72
Drought stress Nimruz
Fig. 3. Mean comparisions of RWC content of barly genotypes at different time points (0, 24 and 72 h) following water stress using least significance difference (LSD) test (P ≤ .05).
2.5
Ele ctrolyte le akage (%)
a
2.0
b c
1.5
de
b
de
de
de
ef
f
cd ef
1.0 0.5
0.0 0
24
72
Control
0
24
72
Drought stress
0
24 Control
Spontaneum
72
0
24
72
Drought stress Nimruz
Fig. 4. Mean comparisions of electrolyte leakage of barly genotypes at different time points (0, 24 and 72 h) following water stress by using least significance difference (LSD) test (P ≤ .05).
are the target genes for miR6201 and miR5052 genes, respectively. Furthermore, to assess the effect of water stress on various physiological parameters of barley genotypes, the RWC and EL were measured. It was founded that drought stress led to reduction in RWC and increase in EL in both Nimruz and Spontaneum genotypes but these changes were limited in Nimruz cultivar. Although, the biological functions of most barley nsLTPs has not yet been elucidated, analyses of this study, especially identification of differentially expressed genes can establish a foundation for further understanding the functional significance of miRNAs in barley water stress resistance, which may contribute to barley molecular breeding in the future.
Table 3 Correlation coefficient between physiological and the expression level of miRNAs and their target genes.
EC HvnsLTP HvPiP1;4 miR414 miR2102
RWC
EC
HvnsLTP
HvPiP1;4
miR414
−0.41* −0.15ns −0.46** −0.21ns −0.44**
0.11ns 0.26ns 0.51** 0.62**
0.90** 0.53** 0.31ns
0.48** 0.41*
0.77**
ns, *and ** non-significant, significant at 5 and 1% probability level, respectively.
Appendix A. Supplementary data the barely. It was found that Nimruz genotype as tolerant cultivar, had a higher expression level for candidate genes. The expression of HvPiP1;4 and HvsnLTP genes was increased by 95.98 and 54.53 fold after 72 h, respectively. The miR414 and miR2102 genes with a relatively higher expression level were identified. Both miR414 and miR2102 genes showed an upregulated expression under drought stress in two genotypes. Despite the fact HvPiP1;4 and HvnsLTP genes and identified miRNAs were upregulated following drought stress, none of the genes seem to be targets for miRNAs. HvPiP1;4 and HvnsLTP genes
Supplementary data to this article can be found online at https:// doi.org/10.1016/j.plgene.2019.100205. References FAO, 2017. Food and Agriculture Organization of the United Nations. Abedini, R., Ghanegolmohammadi, F., Pishkamrad, R., Pourabed, E., Jafarnezhad, A., Shobbar, Z.-S., Shahbazi, M., 2017. Plant dehydrins: shedding light on structure and
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Contents lists available at ScienceDirect
Plant Gene journal homepage: www.elsevier.com/locate/plantgene
Identification of miRNAs and evaluation of candidate genes expression profile associated with drought stress in barley
T
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Sajjad Zarea, Farhad Nazarian-Firouzabadia, , Ahmad Ismailia, Hassan Pakniyatb a b
Department of Agronomy and Plant Breeding, Faculty of Agriculture, Lorestan University, Khorramabad, Iran Department of Crop Production and Plant Breeding, College of Agriculture, Shiraz University, Shiraz, Iran
A R T I C LE I N FO
A B S T R A C T
Keywords: Bioinformatics Drought stress EST Gene expression Hordeum
To identify miRNAs and assess the expression of genes involved in drought stress tolerance, leaf and root ESTs were analyzed in barley. To this end, the EGassembler bioinformatics service and IDEG6 were used for the preprocessing and identify differentially expressed genes among EST libraries, respectively. Furthermore, root contigs were analyzed by C-mii software for miRNAs identification. The expression profile of two drought tolerance candidate genes was studied, using Real time-PCR in a 2 (Nimruz and Spontaneum) × 2 (control and 50% FC) factorial experiment in a completely randomized design with 3 replications. Results of this study showed that N-butyl-N-methylpiperidinium (Pip1;4) and a non-specific lipid transport protein (nsLTP) were found to express differently under drought stress. The expression level of barley HvPip1;4 (HvPiP1;4) and non-specific lipid transport (HvnsLTP) genes increased by 95.98 and 54.53 fold after 72 h of drought stress, respectively. There was a significant difference (P < .05) between two genotypes with respect to the expression level of candidate genes. Nimruz cultivar had a higher expression level for both candidate genes. Comparison analysis of miRNAs showed that miR2102 had a differential expression between non-stressed and drought stress libraries in barley root tissue. Furthermore, HvPiP1;4 and HvnsLTP were miR6201and miR5052 target genes.
1. Introduction Barley)Hordeum vulgare L. (is currently the fourth most important cereal grain worldwide (FAO, 2017) and ranked second after wheat in Iran. In comparison to other major cereal crops, barley is relatively tolerant to dry and cool environments and is a typical crop of poor farmers in most developing countries (Steduto et al., 2012). However, barley productivity is severely impaired by various stresses specially drought stress (Rizza et al., 1994). Growth and development of crop plants are severely affected by abiotic stresses (Seki et al., 2003) among which water-deficit stress has a crucial devastating impact on yield and yield components (Farooq et al., 2012; Farooq et al., 2009). Plants combat drought stress by reprogramming gene expression in complex overlapping mechanisms (Ahuja et al., 2010; Golldack et al., 2011) suggesting that drought tolerance is a quantitative trait which is controlled by a number of quantitative trait loci (QTLs). These QTLs are located on several chromosomes in barley, with major drought QTLs clustering in chromosomes 2H and 5H (Teulat et al., 1998; Tondelli et al., 2006) accounted for 12–22% increase in grain yield under drought conditions (Zhang et al., 2012). Few moderate- to large-scale gene expression studies have identified a number of genes involved in
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drought response (Bray, 2004; Kawaguchi et al., 2004; Kreps et al., 2002; Ozturk et al., 2002; Seki et al., 2002; Talamè et al., 2006). For instance, expressed sequence tags (EST) are employed for transcriptome analysis in an organism at various stages of development or under different experimental conditions. EST analysis is a powerful tool to discover and characterize stress responsive genes and developmental cues involved in several plant species (Gruber et al., 2012; Lata et al., 2010; Shamloo-Dashtpagerdi et al., 2013; Zhuang and Zhu, 2014). Although > 500,000 barley EST sequences are now publicly available, a large portion is still classified as unknown function (Tommasini et al., 2008). Elucidating the function of genes and the extent to which such genes are expressed at particular condition are some of the greatest challenges in biology. MicroRNAs (miRNAs) are among the central molecules for naturally occurring regulation of gene expression. Many miRNAs play a regulatory role in activating biotic and abiotic stress defence or response gene networks (Zhu et al., 2011). Therefore, the identification of miRNAs associated with drought stress is an essential precursor to understand the regulating function of genes involved in drought stress. To this end, large scale bioinformatics analysis has enabled miRNA discovery in plants such as wheat, maize, barley, Arabidopsis thaliana and Medicago sativa (Schreiber et al., 2011).
Corresponding author. E-mail address: [email protected] (F. Nazarian-Firouzabadi).
https://doi.org/10.1016/j.plgene.2019.100205 Received 11 March 2019; Received in revised form 30 July 2019; Accepted 19 September 2019 Available online 17 October 2019 2352-4073/ © 2019 Elsevier B.V. All rights reserved.
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2.4. Plant material and drought stress treatments
Families of miRNAs may be conserved between species but unique miRNAs have also been discovered in each species analyzed (Schreiber et al., 2011). In this study, two EST libraries derived from barley leaves and roots in non-stress and drought stress conditions were mined in order to gain an insight into drought responsive genes and miRNAs. Furthermore, the expression profile of selected stress responsive candidate genes was assessed.
The expression profile of drought responsive candidate genes was studied, using real time-PCR in a 2 × 2 factorial experiment, based on a completely randomized design (CRD) with three replications in pots. A cultivated barley genotype (Nimruz) as a drought stress tolerant and a wild barley diploid genotype (Spontaneum), as a sensitive genotype were treated in two different irrigation regimes (control and 50% FC). Briefly, the barley seeds were planted in pots (20 cm in height and 15 cm in diameter) containing 2.5 Kg sterile soil. Soil moisture was kept constant at 100% FC for controls and 50% of FC for drought treatment plants. Water stress was imposed at the five-leaf stage for two weeks by stopping the irrigation for the water-stressed plants. The sampling was done at three time points (Zero, 24 and 72 h after imposing drought stress). For determination of field capacity (FC), the soil was placed in an oven for 48 h at 150 °C. The soil was weighed (A), and later saturated with water. Pots were covered with aluminum foils to prevent water evaporation, followed with placing them at normal temperature for 24 h (B). The FC was calculated from the following Eq. (1).
2. Materials and methods 2.1. EST data collection Two EST barley libraries; (1) a leaf library (Lib.13887) containing 2016 sequences and (2) a barley root library (Lib.9798) consisting of 1469 sequences from a non-stress condition were downloaded from NCBI database and compared with a barley root library (Lib.9796) with 3863 sequences from drought stress condition. The root library was obtained from roots of three weeks old drought stressed barley plants cv Optic.
FC = 100 ×
B−A B
(1)
Leaf samples were frozen in liquid nitrogen and kept in a − 80 °C freezer.
2.2. EST sequence processing, categorizing and assembly The EST sequences were processed by using EGassembler, an online bioinformatics service (http://egassembler.hgc.jp) (Masoudi-Nejad et al., 2006). The output includes contigs, singletons and assembled sequence of ESTs. The ESTs of every library were clustered together and assembled. Contigs with at least 10 EST sequences were considered as high expressed genes (Zhou et al., 2003). To identify the sequences repeatedly transcribed in each library, ESTs sequences were clustered and assembled by using EGassembler with overlap percent identity cut off 80% (Shamloo-Dashtpagerdi et al., 2013). To identify differentially expressed genes between libraries computationally, all EST sequences of the two libraries were clustered and assembled together by using EGassembler. The contigs were then subjected to Audic and Claverie test (α = 0.05) by IDEG6 software (http://telethon.bio.unipd.it/bioinfo/IDEG6) to address whether the number of ESTs of each library contributing to each contig were significantly different. A unique miRNA was considered differentially expressed when P < .001 was achieved based on a general chi-squared test. The total number of ESTs in each library and the number of library specific ESTs in each contig were the required inputs to do Audic and Claverie tests. All the unigenes (contigs and singletons) of each library were subjected to similarity searches against the barley proteins (http://web.expasy.org/docs/swiss-prot_guideline.html) by BLASTX (Evalue = 10−5) using CLC Genomics Workbench software (CLC-bio, Aarhus, Denmark) to obtain protein codes needed to assign functional categories.
2.5. RNA isolation and real-time PCR analysis Total RNA was extracted from frozen leaf and root samples by using Trizol reagent (Life Technology, Invitrogen, USA). RNA samples were treated with RNase free DNase (Promega, USA) to remove possible genomic DNA contaminations. cDNAs were synthesized using iScriptcDNA Synthesis Kit (Bio-Rad, USA). Gene-specific real time RTPCR primers were designed by Oligo v.5.1 software (Guo et al., 2009), and synthesized (MWG Co., Germany). Real time RT-PCR was conducted in triplicates, using three biological and three technical cDNA replicates (iQSYBR Green Supermix kit, BIO-RAD, USA) on an iCycler iQ thermocycler Real Time PCR Detection system (BIO-RAD, USA) according to the manufacturer's instructions (Internal control). 2.6. Stem-loop reverse transcription Stem-loop reverse transcription (SL RT) primers were designed manually for miR2102 and miR414 following the criteria as described earlier (Varkonyi-Gasic et al., 2007), miRNA SL RT was carried out using 50 ng of leaf small RNA fractions. 0.5 μl of 10 mM dNTP mix was added to RNA, incubated for 5 min at 65 °C and then kept on ice for 2 min. To the above mix, 2 μl of 5× First Strand Buffer, 1 μl of 0.1 M DTT and 0.5 μl of Superscript III reverse transcriptase (100 units) were added. A master mix of the above recipe was prepared for ten tubes and distributed equally. Thereafter, 1 μl of miRNA-specific SL RT primer was added. The reaction was performed as 16 °C, 30 min for one cycle; 30 °C, 30 s; 42 °C, 30 s; 50 °C, 1 s for 60 cycles followed by incubation at 85 °C for 5 min and finally hold at 4 °C. The miR2102 (GTGCTATGGA GTGCACCCTGGCAGGTATTCGCACTGGATACGACCAGGGG) and miR414 (GTGCTATCCACTGCAGGGTCCGACCTATTCGCACTGGATACG ACCAGGGC) primers were used for stem loop reverse transcriptase analyses.
2.3. Identification miRNAs associated with drought stress To identify miRNAs associated with drought stress in barley, the obtained contigs from root data were analyzed by C-mii software (Numnark et al., 2012). Sequences were considered as potential miRNAs that had a MFEI (Minimum free energy index) equal to or > 0.6. Furthermore, the potential miRNA sequences were screened with ≤4 nucleotide mismatches in sequence with known plant mature miRNAs; and less than six mismatches with the opposite miRNA* sequence in the other arm (Zhang et al., 2006a; Zhang et al., 2006b). PsRNA Target (http://plantgrn.noble.org/psRNAtarget) and PMTED (http://pmted.agrinome.org/by_gene.jsp) online software were used to predict miRNA targets.
2.7. Measurement of physiological traits 2.7.1. Relative water content Relative water content (RWC) was measured using leaves after imposing drought treatments (Zero, 24 and 72 h). Briefly, leaves were sealed within plastic bags and quickly transferred to the laboratory and fresh weights were recorded within two hours after excision. Turgid 2
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transfer proteins are small, basic proteins present in abundance in higher plants (Kader, 1996) which are involved in key processes of plant cytology (Liu et al., 2015), response to biotic and abiotic stresses (Kiełbowicz-Matuk et al., 2008), cuticle cell wall formation (Jacq et al., 2017) growth and development (Edstam et al., 2014) sexual reproduction (Chae et al., 2010), resistance to plant pathogens (Sarowar et al., 2009), seed development and germination (Pagnussat et al., 2012). Cameron et al. (2006) reported a 6-fold increase in transcript abundance of tree tobacco nsLTP genes when tobacco leaves were subjected to drying treatments, suggesting that nsLTPs are involved in cuticle deposition.
weights were recorded after soaking leaves in distilled water in test tubes for 16 to 18 h at room temperature and under the low light conditions. After soaking, leaves were quickly and carefully blotted dry with tissue paper and turgid weight recorded. Dry weights were recorded after oven drying the leaf samples for 72 h at 70 °C and RWC was calculated according to below formula (Schonfeld et al., 1988).
RWC(%) =
(Fresh weight−Dry weight) × 100 Turgid weight−Dry weight
(2)
2.7.2. Electrolyte leakage (cell membrane stability) For electrolyte leakage (EL), leaf discs (6 mm in diameter) were cut off and washed to remove solutes from both leaf surfaces and cell damaged areas. Chopped leaves were then transferred to the tubes containing 15 ml of distilled water at room temperature. After 24 h, EC was measured (EC0). The solutions were autoclaved for 15 min and the EC was measured (EC1) again and represented as the percentage of total ions released (Eq. (3)) (Bajji et al., 2002).
CMS =
EC 0 × 100 EC1
3.2. Root EST sequences analysis To explore the differentially expressed genes between barley root normal and stressed libraries, all ESTs of the two libraries were assembled and the contigs generated were then analyzed. Assembly of ESTs yielded 692 contigs and 2743 singletons. The number of ESTs varied from two (354 contigs) to 39 (one contig), the average length of contigs was 698 bp long. Using Audic and Claverie test, 137 contigs were significantly (p < .05) and differentially represented between normal and stress transcriptomes. The 22 contigs were up-regulated while the 115 contigs were down-regulated. The contigs were subjected to BLASTN search and it was found that 120 contigs were functionally annotated (E-value ≤10−5). The highest up-regulation was noticed for a gene from the aquaporin family HvPiP1;4 (contig 320) and the highest reduction in expression was assigned to a gene with unknown function (contig 162). Aquaporins belong to a major intrinsic protein (MIP) family (Chaumont et al., 2000), highly hydrophobic with six membranespanning domains and molecular masses of 26 to 34 kD (Chaumont et al., 2000). Generally speaking, aquaporins are divided into four subfamilies: plasma membrane intrinsic proteins (PIPs), tonoplast intrinsic proteins (TIPs), NOD26-like intrinsic proteins and small basic intrinsic proteins. The PIPs are further divided into two subclasses. PiP1s have little or lack water channel activity, whereas the PIP2s exhibit a high water permeability (Chaumont et al., 2000). It has been reported that following salt and abscisic acid stresses, TdPIP1;1 and TdPIP2;1 genes are regulated in a salt-tolerant wheat (Ayadi et al., 2011). According to the research studies, the integrated functions of aquaporins under various physiological conditions remain elusive. For instance, overexpression of PIP1;4 or PIP2;5 proteins in Arabidopsis thaliana and Nicotiana tabacum transgenic plants led to a rapid water loss under dehydration stress, whereas the same transgenic plants showed an enhanced water flow and germination speed under cold stress (Jang et al., 2007).
(3)
In which CMS: Cell Membrane Stability, EC0: EC before autoclave, EC1: EC after autoclave. 2.8. Statistical analysis Data were analyzed by using the general linear model (GLM) procedure of the statistical analysis system, SAS. Least significance difference (LSD) test (p ≤ .05) was used to compare the means. 3. Results and discussion 3.1. Leaf EST sequences analysis It is estimated that > 500,000 barley ESTs are publicly available, but many ESTs are still classified as unknown function (Tommasini et al., 2008). In this study, a total of 1939 high-quality ESTs belonging to leaf drought stress libraries were retained for clustering to generate non-redundant sets of transcripts, which provide information on the tissues in which they are expressed. The high-quality ESTs had an average length of 579 bp. Assembly of ESTs yielded 1578 unigenes (193 contigs and 1394 singletons) encompassing 28.11% of ESTs. The number of ESTs varied from two (188 contigs) to 25 (one contig). At this level of EST sampling, each tissue shared a very low percentage of transcripts (13–26%). In line with thesis findings, Worch et al. (2011) identified 613 drought-responsive ESTs in barley seedlings. Furthermore, in a similar study, clustering and assembly of wheat ESTs under drought stress resulted in 2376 unique sequences, 75% of which were represented only once (Ergen and Budak, 2009). The unigenes were annotated using BLASTX homology search against the barley protein database. Out of 193 unigenes, 84 unigenes (43.52%) showed a significant similarity (E value cut-off 10−5) with genes of known or putative function. Eighty-three unigenes (43.01%) were matched to genes with unknown function. The remaining 26 unigenes (13.47%) displayed no significant match with the public protein database implying that they may be specific genes to barley or genes whose biological functions have not yet been reported. Thus, they can be taken into account as a source for gene discovery. According to EST sequences, number of contigs and results of BLASTX hit for genes with high expression (at least five EST sequences) in drought condition, 21 genes were detected in barley leaf libraries (Table 1). Contig 9 with 25 EST sequences showed the highest expression followed by contig173 with 22 EST sequences, encoding a nonspecific lipid transport protein (nsLTP). Interestingly, an increase in a nsLTP expression has been reported in three barley genotypes following drought stress treatments (Guo et al., 2009). Plant non-specific lipid-
3.3. Expression patterns of two barley candidate genes under drought stress Differential expression of candidate gens is interesting because the genes with significant differential expression between the two genotypes can be explored as potential candidate genes conferring drought tolerance in barley, by using transgenic overexpression. To validate the results of ESTs comparative analysis, two differentially expressed candidate genes were analyzed by qRT-PCR. Real-time PCR analysis confirmed the differential expression of these genes under drought stress conditions. According to the EST library results, PIP1;4 (contig 320 from root library) and non-specific lipid transport (contig 173 from leaf library) were selected for further evaluation. Expression pattern of HvsnLTP and HvPiP1;4 genes were investigated in two barley genotypes under normal and drought stress conditions. Results of Realtime RT-PCR analysis showed that each genotype had a different expression pattern for both genes (Fig. 1). As it can be seen from Fig. 1, both genes had a higher expression level in Nimruz cultivar in comparison to that of Spontaneum genotype. The transcription level of HvsnLTP was increased by 54-folds following drought stress in Nimruz 3
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Table 1 Leaf EST sequences number and results of BLASTX hit for gene with high expression (at least 5 EST sequences) in drought stress condition. Query Contig Contig Contig Contig Contig Contig Contig Contig Contig Contig Contig Contig Contig Contig Contig Contig Contig Contig Contig Contig Contig
9 173 98 2 118 40 27 26 1 167 101 186 128 143 176 17 123 82 50 56 37
#EST
Lowest E-value
Entry
Protein name
Gene ontology
25 22 16 14 11 10 9 7 7 7 6 6 6 6 5 5 5 5 5 5 5
1.12E-64 1.74E-48 2.18E-97 2.35E-54 3.05E-61 1.28E-86 2.22E-97 2.09E-20 4.41E-19 4.77E-19 1.21E-35 2.79E-25 2.04E-79 2.33E-26 1.96E-13 1.01E-34 5.42E-06 2.25E-74 1.01E-34 7.50E-38 2.13E-31
F2EBL3 F2E1T8 M0Y7A8 Q42848 Q40047 F2CT91 M0YHQ0 F2EC03 I3QM88 M0XTV9 F2E5Q1 M0VPD4 F2CT51 Q7DM44 F2DXU8 F2EJL5 Q40051 F2DF63 F2CSG7 M0 V695 M0WR69
Predicted protein non-specific lipid-transfer protein Uncharacterized protein Non-specific lipid-transfer protein Transmembrane protein Ribulose bisphosphate carboxylase small chain Uncharacterized protein Hexosyltransferase Metallothionein Uncharacterized protein Profilin Uncharacterized protein Ribulose bisphosphate carboxylase small chain Lichenase Predicted protein Predicted protein (Uncharacterized protein) Glycine rich protein (Fragment) Predicted protein (Uncharacterized protein) Non-specific lipid-transfer protein Non-specific lipid-transfer protein Uncharacterized protein
Hordeum vulgare var. Distichum Lipid transport Phosphatidylcholine biosynthetic process Lipid transport – Carbon fixation – – – Fatty acid beta-oxidation – – Carbon fixation Carbohydrate metabolic process – – – Polyamine metabolic process Lipid transport Lipid transport –
3.4. Three miRNAs are induced by drought stress
after 72 h, whereas only a slight or no significant elevations were observed in Spontaneum genotype. The expression results tempting to draw conclusion that the expression of HvPiP1;4 gene is genotype dependent and influenced by drought stress (P < .05). The transcription level of HvPiP1;4 increased by 96 folds under drought stress in Nimruz after 72 h. A number of researchers have previously observed the upregulation of aquaporins in response to water stress in other plant species (Alexandersson et al., 2005; Aroca et al., 2006). Guo et al. (2009) reported a similar differential induction of drought-responsive genes in drought-tolerant barley genotypes in response to drought stress during the reproductive stage. Hu et al. (2012) reported that overexpression of a wheat aquaporin gene, TaAQP8, enhances salt stress tolerance in transgenic tobacco. Similarly, Gao et al. (2010) by monitoring overexpression of a putative aquaporin gene from wheat showed that TaNIP-expressing transgenic Arabidopsis lines had a significant higher salt tolerant capacity (Gao et al., 2010).
To identify potential miRNAs associated with drought stress, root data were analyzed by C-mii software (Numnark et al., 2012). Three miRNAs (ath-miR414, osa-miR414, osa-miR2102-5p) with relatively a higher expression level were identified (Table 2) exhibiting a differential expression pattern between root non-stressed and drought stressed libraries (p-value≤.05). Searching between known miRNAs in barley by using the psRNA target tool revealed that HvPiP1;4 gene is one of the target genes of miR6201 (5′-UGACCCUGAGCACUCAUACCG-3′; (Lv et al., 2012)). In addition, the non-specific lipid transport gene is also a target for miR5052 (5′-ACCGGCUGGACGGUAGGCAUA-3′ (Schreiber et al., 2011)). Evaluation between detected genes (HvPiP1;4 and HvnsLTP) and detected miRNAs (miR414 and miR2102) showed no relations. it turned out that histone H2A gene seem to be a target for miR2102 and searching between miRNAs in barley by using the psRNA target tool showed that HvPiP1;4 gene is one of the target genes of miR6201 (
110
a
Ge ne re lativ e e xpression
100 90
HvnsLTP
80
HvPIP1;4
70 a 60 50
b
b
40 30
10
c
c
20 f g
ef fg
d e
e
c
c d e
d e ef
d
c e
de
0 0
24
72
0
Spontaneum
24
72
Nimruz
0
24
72
0
Spontaneum
Control
24
72
Nimruz Drought stress
Fig. 1. Real-time RT-PCR analysis of HvsnLTP and HvPiP1;4 genes at different time points (0, 24 and 72 h) following water stress (means comparison was done by using least significance difference (LSD) test (P ≤ .05) separately for genes). 4
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Table 2 Name, sequence and contig number of identified miRNAs from root libraries. Mature miRNA
Input sequence name
Precursor miRNA MFEI(kcal/mol)
Predicted miRNA sequence 5′ → 3′
ath-miR414 osa-miR2102-5p osa-miR414
Contig 377 Contig 219 Contig 377
−0.774 −0.648 −0.774
UUUUCAUCAUCAUCAUCGUCA CGGCUCGCCGCCGCCGCCAA UUUUCAUCAUCAUCAUCGUCA
Re lativ e ge ne e xpression
3.5
miR414
3.0
a
miR2102 c de
2.0
fg
g b
1.5 1.0
b
a
2.5
b
b b
a
a
b a a
cd
de
ef
a
a
b
h c
0.5 0.0 0
24
72
0
Spontaneum
24
72
Nimruz
0
24
72
0
Spontaneum
Control
24
72
Nimruz Drought stress
Fig. 2. Expression analysis of miR414 and miR2102 genes at different time points (0, 24 and 72 h) following water stress (means comparison performed by using least significance difference (LSD) test (P ≤ .05) separately for genes.
EL from plant leaves was found to increase as drought stress intensified. Drought stress led to 16 and 23% increase in EL in Nimruz and Spontaneum genotypes, respectively (Fig. 4). In line with these findings, EL in barley (Velasco-Arroyo et al., 2018), rice (Chen et al., 2018), Arabidopsis (Hu et al., 2012) was also significantly increased following drought stress. There was a significant (P < .05) difference between two genotypes with respect to EL, suggesting that Nimruz cultivar was more tolerant to drought stress than Spontaneum genotype. According to correlation between physiological and molecular parameter (Table 3), it was found that drought stress led to RWC reduction and increasing EC as well as the expression level of studied genes, suggesting a negative correlation between RWC and other parameters (Table 3). miR414 involve in sucrose metabolism that it is extremely responsive to internal and external signals and consequently alters development and stress adjustment (Wan et al., 2011). These signal paths may also be used to preserve water present in the leaves. Since these miRNAs contribute to plant growth and development (Guleria and Yadav, 2011), it can also help improve water absorption.
5′-UGACCCUGAGCACUCAUACCG-3′; (Lv et al., 2012)). In addition, the non-specific lipid transport gene is also a target for miR5052 (5′-ACC GGCUGGACGGUAGGCAUA-3′ (Schreiber et al., 2011)). Both miR414 and miR2102 genes showed an upregulated expression under drought stress in two genotypes but there were significant differences between two genotypes (Fig. 2). Interestingly, a higher expression level of miR414 was recorded in young leaves in comparison with old leaves, suggesting miR414 may play an important role in regulation of plant growth and development (Guleria and Yadav, 2011). Furthermore, miR414 target gens such as high mobility group proteins, SNF2 transcriptional regulator, C2H2 zinc finger proteins, pentatricopeptide repeat-containing proteins, F-Box family proteins, DNA store keepers and RNA recognition motifs play major roles in posttranscriptional modifications (Yin and Shen, 2010). Although, miR2102 is significantly up-regulated in response to arsenate and arsenite stress in rice (Sharma et al., 2015), Upon drought treatment, expression of miR2102 was significantly increased in wheat (Akdogan et al., 2016). 3.5. Physiological traits
4. Conclusion To see the differences between tolerant and sensitive barley genotypes, some physiological traits were studied. Results of RWC and EC parameters confirmed that the appropriate genotypes have been chosen for this study. Furthermore, by monitoring the physiological traits, it was confirmed that stress treatment had a significant effect on barley plants. To assess the effect of water stress on various physiological parameters of barley genotypes, the RWC and EL were measured. According to analysis of variances, RWC was significantly (P < .01) affected depending on cultivar and drought stress treatments. Although, Nimruz cultivar maintained a higher RWC at control condition, drought stress led to 10 and 13% RWC reduction in Nimruz and Spontaneum genotypes, respectively (Fig. 3). As the sampling time increased, the RWC decreased and the lowest RWC was obtained after 72 h. According to Bandurska et al. (2017) drought stress results in a gradual decrease of water content in barley genotypes.
In conclusion, water scarcity is among main agronomy challenges worldwide, and has a profound effect on yield losses in crop plants. Barley is one of the most important crop plants in arid and semiarid regions. Annual rainfall distribution shows that 74% of the total area in Iran (122.5 million hectares) benefits < 200 mm rainfall, and its distribution is variable and non-uniform; while merely 10% of rainfalls occur during hot and dry seasons. Recent genetic studies of wild and landrace (i.e. primitive domesticate) barley collections have showed that barley is a polycentric origin plant and different regional wild populations contributing putative adaptive variations (Nevo and Chen, 2010). Iran is one of the most likely barley domestication sites and it seems that the progenitor of cultivated barley, is a selfing annual grass predominantly originated from Mediterranean Irano-Turanian region (Abedini et al., 2017). In this study HvPiP1;4 and HvsnLTP genes were identified based on expression profiles in response to drought stress in 5
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120
RWC (%)
100
bc
ab
bc
cd
a
de
e
f
fg
80
b
g h
60 40 20
0 0
24
72
0
Control
24
72
0
Drought stress
24
72
0
Control
Spontaneum
24
72
Drought stress Nimruz
Fig. 3. Mean comparisions of RWC content of barly genotypes at different time points (0, 24 and 72 h) following water stress using least significance difference (LSD) test (P ≤ .05).
2.5
Ele ctrolyte le akage (%)
a
2.0
b c
1.5
de
b
de
de
de
ef
f
cd ef
1.0 0.5
0.0 0
24
72
Control
0
24
72
Drought stress
0
24 Control
Spontaneum
72
0
24
72
Drought stress Nimruz
Fig. 4. Mean comparisions of electrolyte leakage of barly genotypes at different time points (0, 24 and 72 h) following water stress by using least significance difference (LSD) test (P ≤ .05).
are the target genes for miR6201 and miR5052 genes, respectively. Furthermore, to assess the effect of water stress on various physiological parameters of barley genotypes, the RWC and EL were measured. It was founded that drought stress led to reduction in RWC and increase in EL in both Nimruz and Spontaneum genotypes but these changes were limited in Nimruz cultivar. Although, the biological functions of most barley nsLTPs has not yet been elucidated, analyses of this study, especially identification of differentially expressed genes can establish a foundation for further understanding the functional significance of miRNAs in barley water stress resistance, which may contribute to barley molecular breeding in the future.
Table 3 Correlation coefficient between physiological and the expression level of miRNAs and their target genes.
EC HvnsLTP HvPiP1;4 miR414 miR2102
RWC
EC
HvnsLTP
HvPiP1;4
miR414
−0.41* −0.15ns −0.46** −0.21ns −0.44**
0.11ns 0.26ns 0.51** 0.62**
0.90** 0.53** 0.31ns
0.48** 0.41*
0.77**
ns, *and ** non-significant, significant at 5 and 1% probability level, respectively.
Appendix A. Supplementary data the barely. It was found that Nimruz genotype as tolerant cultivar, had a higher expression level for candidate genes. The expression of HvPiP1;4 and HvsnLTP genes was increased by 95.98 and 54.53 fold after 72 h, respectively. The miR414 and miR2102 genes with a relatively higher expression level were identified. Both miR414 and miR2102 genes showed an upregulated expression under drought stress in two genotypes. Despite the fact HvPiP1;4 and HvnsLTP genes and identified miRNAs were upregulated following drought stress, none of the genes seem to be targets for miRNAs. HvPiP1;4 and HvnsLTP genes
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