Transcriptomic dynamics provide an insight into the mechanism for silicon-mediated alleviation of salt stress in cucumber plants

Ecotoxicology and Environmental Safety 174 (2019) 245–254

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Ecotoxicology and Environmental Safety journal homepage: www.elsevier.com/locate/ecoenv

Transcriptomic dynamics provide an insight into the mechanism for siliconmediated alleviation of salt stress in cucumber plants

T

Yongxing Zhua,c,1, Junliang Yina,c,1, Yufei Lianga, Jiaqi Liua, Jianhua Jiaa, Heqiang Huod, ⁎ ⁎ Zefeng Wub, Ruolin Yangb, , Haijun Gonga, a

College of Horticulture, Northwest A&F University, Yangling 712100, Shaanxi, China College of Life Sciences, Northwest A&F University, Yangling 712100, Shaanxi, China c College of Horticulture and Gardening, College of Agronomy, Yangtze University, Jingzhou 434025, Hubei, China d Mid-Florida Research and Education Center, University of Florida, Institute of Food and Agricultural Sciences, 2725 South Binion Road, Apopka, FL 32703, USA b

A R T I C LE I N FO

A B S T R A C T

Keywords: Cucumber Salt stress Silicon Transcriptome Gene expression

Salinity decreases the yield and quality of crops. Silicon (Si) has been widely reported to have beneficial effects on plant growth and development under salt stress. However, the mechanism is still poorly understood. In an attempt to identify genes or gene networks that may be orchestrated to improve salt tolerance of cucumber plants, we sequenced the transcriptomes of both control and salt-stressed cucumber leaves in the presence or absence of added Si. Seedlings of cucumber ‘JinYou 1’ were subjected to salt stress (75 mM NaCl) without or with addition of 0.3 mM Si. Plant growth, photosynthetic gas exchange and transcriptomic dynamics were investigated. The results showed that Si addition improved the growth and photosynthetic performance of cucumber seedlings under salt stress. The comparative transcriptome analysis revealed that Si played an important role in shaping the transcriptome of cucumber: the expressions of 1469 genes were altered in response to Si treatment in the control conditions, and these genes were mainly involved in ion transport, hormone and signal transduction, biosynthetic and metabolic processes, and stress and defense responses. Under salt stress alone, 1482 genes with putative functions associated with metabolic processes and responses to environmental stimuli have changed their expression levels. Si treatment shifted the transcriptome of salt-stressed cucumber back to that of the control, as evidenced that among the 708 and 774 genes that were up- or down-regulated under salt stress, a large majority of them (609 and 595, respectively) were reverted to the normal expression levels. These results suggest that Si may act as an elicitor to precondition cucumber plants and induce salt tolerance. The study may help us understand the mechanism for silicon-mediated salt tolerance and provide a theoretical basis for silicon application in crop production in saline soils.

1. Introduction Salinity is one of the most serious abiotic stresses that restrains crop productivity. High level of salinity harms plants in several ways, such as induction of osmotic stress, ion toxicity and oxidative stress, and alteration of nutritional balance (Munns and Tester, 2008; Zhu, 2016; Yang and Guo, 2018). Substantial efforts such as breeding salt-tolerant varieties and adopting improved cultivation practices have been made to alleviate the adverse effects of salt stress on plants. Besides, application of exogenous substances is an alternative solution to improve salt tolerance of crops. In the past decades, increasing studies have demonstrated the beneficial effects of silicon (Si) on salt tolerance in

different plant species (Zhu and Gong, 2014; Coskun et al., 2016), suggesting the potential application of Si fertilizer in crop production in saline soils. Si is the second most abundant element in the soil (Epstein, 1999). Previous studies have shown that Si application can improve plant growth under a variety of abiotic and biotic stresses, such as plant diseases, salinity, drought and heavy metal toxicity in some important crops (Van Bockhaven et al., 2015a, 2015b; Debona et al., 2017; Coskun et al., 2019; Etesami and Jeong, 2018; Frew et al., 2018). The beneficial role of Si in the alleviation of salt stress has received much attention (Zhu and Gong, 2014; Coskun et al., 2016). The proposed mechanisms for Si-mediated salt tolerance in plants include the

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Corresponding authors. E-mail addresses: [email protected] (R. Yang), [email protected] (H. Gong). 1 These authors have contributed equally to this work. https://doi.org/10.1016/j.ecoenv.2019.02.075 Received 8 January 2019; Received in revised form 2 February 2019; Accepted 25 February 2019 0147-6513/ © 2019 Elsevier Inc. All rights reserved.

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following aspects: decreasing tissue Na+ and/or Cl− accumulations (Gong et al., 2006; Shi et al., 2013; Flam-Shepherd et al., 2018), improving root water uptake (Liu et al., 2015; Wang et al., 2015b; Zhu et al., 2015), regulating the levels of plant hormones and polyamines (Lee et al., 2010; Kim et al., 2014; Yin et al., 2015), and increasing antioxidant defense abilities (Zhu et al., 2004; Sienkiewicz-Cholewa et al., 2018). Very recently, Coskun et al. (2019) proposed the “apoplastic obstruction hypothesis” as a common mechanism for the role of Si in stress alleviation. However, up to date, direct evidence is still lacking. Therefore, although the mechanism for Si-mediated salt tolerance in plants has been extensively studied, it still remains largely unknown and needs more investigations. High-throughput techniques, such as proteomics, microarray analysis and RNA-Seq approaches have been widely used to identify differentially expressed genes or proteins among different tissues, cultivars, and treatments (Yang et al., 2018; Yin et al., 2018). Using these techniques, several studies have been conducted to investigate the regulatory role of Si on gene expressions under biotic stresses. For example, using microarray technique, Fauteux et al. (2006) found that Si addition did not markedly affect the gene expression (except 2 genes) in Arabidopsis plants in the control conditions, but it contributed to a more efficient defense response under powdery mildew stress. Chain et al. (2009) also analyzed the effect of Si on transcriptomic change in wheat under control and powdery mildew stress, and observed that Si had little effect on the gene expressions under control conditions, with only 47 genes showing differential response. By contrast, under powdery mildew stress, Si supply nearly reversed the gene expressions regulated by the pathogen stress alone. Brunings et al. (2009) analyzed the gene expression pattern in rice using microarray technique and found that Si addition changed the expressions of 221 genes in the control plants, and it reduced 60% of the differentially expressed genes (DEGs) in plants infected with Magnaporthe oryzae with compared to the control. Using proteomic technology, Chen et al. (2015) found that Si addition altered the abundance of 26 proteins in the roots of tomato inoculated with Ralstonia solanacearum, with a good amount of them being associated with energy/metabolism and defense response. Van Bockhaven et al. (2015b) studied the transcriptome of rice plants infected with the fungus Cochliobolus miyabeanus. They found that the pathogen treatment inhibited the plant photosynthetic processes and nitrate reduction, resulting in senescence and cell death; while Si treatment impaired these metabolic processes and increased the photorespiration rates of the infected plants. Recently, Rasoolizadeh et al. (2018) conducted a comparative transcriptomic analysis in soybean and Phytophthora sojae, and observed that in response to pathogen stress, the defense-related genes including receptors in Si-free plants and effectors in the pathogen were both up-regulated; while in the presence of added Si, the plant transcriptome was largely unaffected under pathogen stress and the expression of effector genes in the pathogen was reduced. Holz et al. (2015) identified 1136 DEGs induced by supplemental sodium silicate in in-vitro-generated cucumber clone. However, under abiotic stresses, transcriptomic profiling of DEGs caused by Si application in plants is very limited. Nwugo and Huerta (2011) used a proteomic approach to investigate the effect of Si on cadmium tolerance in rice, and identified 50 proteins that were regulated by Si. These proteins were involved in physiological process such as redox homeostasis, photosynthesis and regulation/protein synthesis. They suggested that Si might be actively involved in the modulation of biochemical processes and make plants sturdier and more tolerant to stresses. Muneer and Jeong (2015) analyzed the Si-mediated proteomic changes in tomato roots under salt stress, and found that Si regulated the abundance of proteins associated with stress response, plant hormones, transcription regulation and secondary metabolism. They also observed that the oxidative stress parameters and Si concentration correlated with the proteome data, and suggested an active involvement of Si in salt tolerance in tomato plants. All these studies indicate that omics may be a useful tool to provide an insight into the

mechanism for Si-mediated stress tolerance in plants. However, up to date, information on the underpinnings of Si-mediated salt tolerance is still lacking at the whole transcriptomic level. In higher plants, Si accumulation varies among species. Gramineae and Cyperacea plants usually have high Si accumulation; Cucurbitales, Urticales, and Commelinaceae plants have intermediate Si accumulation; and most of the other plants have low capabilities of Si accumulation (Mitani and Ma, 2005). Cucumber (Cucumis sativus L.) is considered as an intermediate Si accumulator (Mitani and Ma, 2005), and it is sensitive to salt stress (Zhu et al., 2016). Si-mediated salt tolerance in cucumber has been investigated in several studies. Zhu et al. (2004) found that Si addition could protect cucumber tissues through increasing antioxidant enzyme activities and thus reducing membrane oxidative damage under salt stress. Zhu et al. (2015, 2016) observed that Si addition improved root water uptake and photosynthetic assimilate transport in cucumber under salt stress. These studies imply that Si may be involved in the regulation of physiological and biochemical processes in cucumber under salt stress. However, the mechanism for Si-mediated salt tolerance still remains to be investigated. In this study, we profiled the transcriptome derived from the leaves of cucumber grown in normal or salt stress conditions in the presence or absence of added Si. Our analysis revealed that Si changed the transcriptome of cucumber in the control conditions, and it induced the transcriptomic profile of salt-stressed plants back to that of the control. Our results suggest that Si may act as an elicitor to precondition cucumber plants and induce salt tolerance. This study may provide a theoretical basis for silicon application in crop production in saline soils. 2. Materials and methods 2.1. Plant materials and treatments Cucumber (C. sativus L. ‘JinYou 1’) seeds were rinsed thoroughly in distilled water and germinated on moist gauze in an incubator at 28 °C for 2 days. The germinated seeds were sown in quartz sands in a greenhouse with natural light, and the temperatures were adjusted to 28 °C/18 °C (day/night). The relative humidity in the greenhouse was 50–70%. The seedlings were transferred to 15-L plastic containers filled with aerated 1/4 strength of modified Hoagland nutrient solution at two-leaf stage. Three days later, the strength of Hoagland solution was increased to 1/2. The components of the nutrient solution were as described previously (Zhu et al., 2016). Seven days after transplanting, Si (0.3 mM) and salt (75 mM) treatment were started by adding sodium silicate (Na2SiO3·9H2O) and sodium chloride (NaCl) to the nutrient solution. There were four treatments: control (CT), Si treatment (Si), salt stress (NaCl), and salt stress plus Si (NaSi). The pH of nutrient solution was adjusted to 6.0 using 0.2 M H2SO4 or 1 M KOH every day, and the solutions were renewed every 3 days. 2.2. Leaf tissue preparation for RNA-Seq Three days after salt and Si treatment, the recently fully expanded leaves were collected with every 3 of them being mixed as one biological replicate for each treatment. The collected samples were immediately frozen in liquid nitrogen and then stored at −80 °C until analysis. Every treatment had three biological replicates. However, during the storage in the freezer, the sample of one replicate of Si treatment was contaminated. Therefore, for RNA-Seq analysis, every treatment had three replicates except Si treatment alone, which had two replicates. 2.3. Plant growth and photosynthesis After 10 days of salt and Si treatment, the net CO2 assimilation rate 246

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Table 1 Effects of silicon on plant growth and gas exchange parameters of cucumber seedlings under salt stress. Treatment

Plant dry weight (mg)

Pn (μmol CO2 m−2 s−1)

gs (mol H2O m−2 s−1)

Tr (mmol H2O m−2 s−1)

CT Si Na NaSi

1150.7 1147.8 762.1 955.4

15.4 16.5 5.3 12.4

0.58 0.56 0.14 0.26

3.11 3.12 1.43 2.34

± ± ± ±

121.2a 91.6a 101.7c 97.7b

± ± ± ±

2.7a 3.2a 1.6c 2.8b

± ± ± ±

0.20a 0.25a 0.11c 0.11b

± ± ± ±

0.91a 0.81a 0.71c 0.82b

The gas exchange parameters were measured after 10 days of salt stress (n = 8). The plant dry weight was measured after 15 days of salt stress (n = 9). Means ± SD followed by different letters indicates a significant difference at P < 0.05. CT, control; Si, silicon; Na, NaCl treatment; NaSi, NaCl plus silicon treatment; Pn, net photosynthetic rate; gs, stomatal conductance; Tr, transpirational rate.

(Pn), stomatal conductance (gs), and transpiration rate (Tr) were measured on the new fully expanded leaves using a portable photosynthesis system (LI-6400, LI-COR., Lincoln, NE, USA). The gas exchange parameters were measured at a photo flux density of 800 μmol m−2 s−1. Each treatment included eight replicates. After 15 days of salt stress treatment, the plants were harvested and the dry weights were recorded after being dried at 80 °C for 48 h.

annotated all genes in the reference cucumber genome using Blast2GO (Götz et al., 2008). GO terms and KEGG pathways of the DEGs were manually extracted and submitted to an online tool argriGO to obtain the GO functional clues of these DEGs (http://bioinfo.cau.edu.cn/ agriGO/index.php) (Du et al., 2010). In this analysis, the hypergeometric test was used to detect significantly enriched GO terms (FDR < 0.05, P < 0.05).

2.4. RNA-Seq library preparation and sequencing

2.8. Real-time PCR confirmation

RNA-Seq library construction and sequencing were conducted by Gene Denovo Co. (Guangzhou, China). The poly(A)-containing mRNA molecules were purified from 10 mg of total RNA using poly-T oligoattached magnetic beads. The extracted mRNAs were fragmented into 200-bp-long pieces using RNA Fragmentation Buffer (Kapa, Biosystems). The cleaved Illumina Sequencing mRNA fragments were reverse-transcribed into first-strand cDNA using random primers, followed by second-strand cDNA synthesis using DNA polymerase I and RNase H. Then, the cDNA fragments went through an end repair process, the addition of a single “A” base, and ligation of the adapter sequences. Finally, the cDNA products were purified and enriched to construct libraries for non-strand-specific RNA-Seq. Sequencing of the libraries was conducted on the Illumina HiSeq. 2500 platform to produce 125 bp pair-end reads. The sequencing reads data were submitted to the National Center for Biotechnology Information (NCBI) Sequence Read Archive with accession number of GSE116265.

Real-time PCR analysis was performed on a CFX 96 Real-Time PCR system (Bio-Rad) using SYBR Green Master Mix (Vazyme Biotech Co. Ltd, Nanjing, China). The cDNA was synthesized from 500 ng of total RNA, including a special step for genomic DNA digestion using HiScript™ Reverse Transcriptase (Vazyme Biotech Co. Ltd., Nanjing, China) according to the manufacturer's instructions. A pair of primers for each of the selected genes (Table S1) was designed with Primer 5.0 software. The correlation between the FPKM values (obtained from RNA-Seq) and their corresponding real-time PCR crossing threshold (Ct) values were analyzed. 3. Results 3.1. Plant growth and photosynthesis In normal conditions, Si application had no obvious effect on the photosynthetic gas exchange or dry weight of cucumber plants (Table 1). Salt stress significantly decreased the dry weight, net photosynthetic rate, transpirational rate and stomatal conductance of cucumber plants; while Si addition alleviated the deleterious effect of salt stress on these parameters (Table 1).

2.5. Reads preprocessing, mapping and estimation of gene expression level Raw reads were filtered by read quality, after which adapter sequences were trimmed if detectable. Clean reads were aligned to the reference genome of cucumber (v2, http://cucurbitgenomics.org/ organism/2) with TopHat2, with 2 mismatches allowed (Trapnell et al., 2012). Uniquely-mapped reads were used to estimate the expression levels of genes. We calculated the read-count-based expression levels for cucumber genes. Specifically, we first counted the number of reads mapped to the gene body region for each gene using the intersectBed command implemented in the BEDTools package (https://code. google.com/p/bedtools/). The expression levels of the cucumber annotated genes across all samples were then converted to the FPKM (fragments per kilobase of exon per million fragments mapped) values and normalized using edgeR package (Robinson et al., 2010).

3.2. Summary of RNA-Seq data The transcriptomes of cucumber plants under different treatments were generated through the high throughput RNA sequencing. Totally, ~232 million 125-nt pair-end raw reads were produced. After quality control, ~227 million clean reads (98.07%) were produced, averaged ~21 million reads per sample. Using the TopHat2 software with default settings (Trapnell et al., 2012), a total of 197,241,926 reads (86.78%) were successfully mapped to the cucumber genome, most of which were uniquely-mapped reads (86.08%). Collectively, 20,090 genes showed expression signal in at least one of these samples, with the number of expressed genes ranging from 17,768 in CT3 to 18,454 in Si2 across these samples (Table 2).

2.6. Identification of differentially expressed genes (DEGs) EdgeR was also used to identify the differentially expressed genes (DEGs) between each pairwise comparisons of interest. The false discovery rate (FDR) was used to determine the threshold of the P value in multiple tests. A threshold of FDR≤ 0.05 and an absolute value of log2 (Ratio)≥ 1 were used to define DEGs.

3.3. Gene expression profiles To understand the global relationship between samples, we first performed a hierarchical clustering on the gene expression matrix, using the Pearson correlation coefficient as a proxy of similarity between transcriptomes. As shown in Fig. S1, the 11 samples were separated into two large clusters: one cluster containing samples treated with Si alone, and the remaining samples converged to a big cluster.

2.7. GO enrichment test of DEGs To infer the potential biological functions of DEGs, we first 247

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Table 2 Summary of sequence assembly after Illumina sequencing. Sample

CT1 CT2 CT3 Si1 Si2 Na1 Na2 Na3 NaSi1 NaSi2 NaSi3 all

Expressed genes (% of annotated genesa)

RNA-Seq reads Raw

Clean (% of raw)

Unique mapped (% of clean)

20,985,792 21,465,372 21,021,160 19,931,918 21,830,038 21,205,828 18,395,800 22,440,906 20,152,862 20,843,152 23,312,780 231,585,608

20,594,070 (98.13%) 21,068,968 (98.15%) 20,639,324 (98.18%) 19,541,094 (98.04%) 21,455,134 (98.28%) 20,771,886 (97.95%) 18,049,380 (98.12%) 21,965,096 (97.88%) 19,746,138 (97.98%) 20,439,036 (98.06%) 22,842,732 (97.98%) 227,112,858 (98.07%)

18,246,885 (88.60%) 18,612,930 (88.34%) 18,190,522 (88.14%) 17,003,745 (87.02%) 18,097,967 (84.35%) 17,460,806 (84.06%) 15,109,955 (83.71%) 18,497,722 (84.21%) 16,557,123 (83.85%) 16,885,095 (82.61%) 20,830,724 (91.19%) 195,493,474 (86.08%)

18,207 17,868 17,768 18,130 18,454 18,003 18,087 17,984 17,881 18,183 18,173 20,090

(78.32%) (76.86%) (76.43%) (77.99%) (79.38%) (77.44%) (77.80%) (77.36%) (76.91%) (78.21%) (78.17%) (86.42%)

a

The annotated gene number of 23,248 (Li et al. 2011) was used in the calculation. CT, control; Si, silicon; Na, NaCl treatment; NaSi, NaCl plus silicon treatment. Every treatment had three biological replicates, except silicon treatment alone (Si), which had two replicates.

stimulus), metabolism (GO:0006595, polyamine metabolic process; GO:0006796, phosphate metabolic process, GO:0006800, oxygen and reactive oxygen species metabolic process), signaling (GO:0007165, signal transduction; GO:0009692, ethylene metabolic process; GO:0009696, salicylic acid metabolic process; GO:0009694, jasmonic acid metabolic process), and ion homeostasis (ion transport (GO:0006811) were significantly enriched (Fig. 2). Transcription factors (TFs) play pivotal roles in orchestrating gene transcription network in the responses of plants to stimulus/stress. In this study, we found that 63 TFs, including 12 ERF, 9 WRKY, 4 MYB, 3 bZIP, 3 HSP, 3 TCP and 2 NAC were differentially expressed in the leaves, with most of them (52/63) being up-regulated by Si addition (Table S2). Twenty-four genes related to plant hormone signal transduction pathway (ko04075) showed significant expression changes after Si treatment (Table S3). Besides, 38 phenylpropanoid biosynthesis-related genes (ko00940, Table S4) have also been identified in this study, and the expressions of all these genes were up-regulated by added Si (Table S4).

Interestingly, although the data derived from CT and Si samples were not clustered together, the data from NaSi and Na samples that were grown under salt stress, formed a compact “monophyletic” branch. The good biological repeatability of these transcriptomes derived from the same condition indicates a stable and distinct response of cucumber plants to these stimuli (Fig. S1). To systematically explore the transcriptomic dynamics, we conducted pair-wised comparisons between different treatments of interest (Fig. 1) and identified differentially-expressed genes (DEGs). Totally, 3201 genes represented changed expression levels at least once in these comparisons. Below we further describe the putative functions of these DEGs according to their associated annotations. 3.4. Effect of Si on transcriptome in non-stress conditions In comparison to the transcriptomes of the samples that were grown in normal condition (CT), 1237 and 232 genes were identified as upregulated and down-regulated, respectively, in the samples that were treated with Si in the leaves (Fig. 1). To infer the potential functions of these DEGs, we performed GO enrichment test. GO terms associated with plant stress response (GO:0045087, innate immune response; GO:0006950, response to stress; GO:0009607, response to biotic

3.5. Transcriptomic response to salt stress NaCl treatment alone caused hundreds of genes up-/down-regulated (708/774) in the leaves (Fig. 2). Functional enrichment analysis indicated that the DEGs were enriched in GO terms with a good amount of them being associated with responses to various stresses, such as response to stress (GO:0006950) and response to abiotic stimulus (GO:0009628), response to oxidative stress (GO:0006979), antioxidant activity (GO:0016209), regulation of response to stress (GO:0080134), and regulation of defense response (GO:0031347) (data not shown). Furthermore, it was observed that 120 TFs in leaves showed significant expression changes in response to the salt stress, comprising of 18 ERF, 11 bHLH, 11 WRKY, 9 MYB, 7 bZIP, 6 GAGA, 5 HSP, 5 TCP, 4 NAC, and 44 other kinds of TFs (Table S5). 3.6. Effect of Si on the transcriptome under salt stress To reveal the effect of exogenous Si on the transcriptome under salt stress, NaSi and Na samples were compared with each other and the differentially-expressed genes were identified. Remarkably, only 29 DEGs were found, with 19 and 10 DEGs being up- and down-regulated, respectively (Fig. 1). The functions of these DEGs are versatile, including transcription factors: AP2 (Csa6G091830), bHLH (Csa1G009660), MYB (Csa3G199590) and NAC (Csa3G062590); cationic amino acid transporter (Csa1G056920); MAPKKK (Csa1G532310), serine/threonine-protein kinase (Csa2G354110); oxidative stress protein (Csa2G000790, Csa3G435530); and auxin-induced

Fig. 1. The number of differentially expressed genes between each treatment comparison. CT, control; Si, silicon; Na, NaCl treatment; NaSi, NaCl plus silicon treatment; up, up-regulated; down, down-regulated. 248

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Fig. 2. GO enrichment of differentially expressed genes for the comparison of silicon treatment versus control conditions (Si vs CT). The differentially expressed genes were submitted to an online tool argriGO to obtain the GO functional clues (http://bioinfo.cau.edu. cn/agriGO/index.php) (Du et al., 2010), and hypergeometric test was used to detect significantly enriched GO terms (FDR < 0.05, P < 0.05). BP, biological process; MF, molecular function.

Table 3 Genes that were differentially expressed between Na and NaSi samples. Gene Transcription factor Csa1G009660 Csa3G062590 Csa3G199590 Csa6G091830 Signal transduction Csa2G190740 Csa2G382470 Csa6G104650 Csa6G516960 Kinase/phosphatase Csa1G532310 Csa2G354110 Photosynthetic-associated Csa1G033310 Csa1G062380 Csa3G144230 Defense or stress-related Csa2G000790 Csa3G435530 Housekeeping genes Csa3G132580 Csa3G133180 Csa3G154410 Csa3G337350 Csa4G051530 Csa5G239640 Csa5G585970 Csa6G432270 Csa6G500440 Csa6G520420 Transporter Csa1G056920 Csa2G377370 Unknown function Csa4G111600 Csa4G000820

Annotation

Na-NaSi comparison

Transcription factor bHLH47-like isoform 1 NAC domain-containing protein 69-like Transcription factor MYB44-like AP2 domain transcription factor RAP2

D D U U

Proline/serine rich protein Conserved hypothetical protein Auxin-induced protein 5NG4-like F-box protein SKIP27-like

U U U U

Mitogen-activated protein kinase kinase kinase A-like CBL-interacting serine/threonine-protein kinase 1-like

U U

Chlorophyll a-b binding protein P4, chloroplastic-like 5′-adenylylsulfate reductase 1, chloroplastic-like ATP sulfurylase 1, chloroplastic-like

D D D

Oxidative stress 3 Oxidative stress 3

U U

Tetratricopeptide repeat-like superfamily protein Probable pectate lyase 15-like Phylloplanin-like Kirola-like Endoglucanase 6-like RNA-dependent RNA polymerase 1-like Peptide chain release factor 1-like Lateral root primordium protein-related isoform 3, partial Nitric oxide synthase-interacting protein GDSL esterase/lipase At1g29670-like

D U U U U D U U U D

Cationic amino acid transporter 5-like Cation transport regulator-like protein 2-like

D D

Unknown Unknown

U U

D, downregulated (added silicon decreased the gene expression under salt stress); U, upregulated.

expression levels for the up-regulated genes was decreased 6.37% in comparison with the expression data in NaSi samples. Consistently, the averaged amplitude of change for the down-regulated genes was decreased 10.03% in Si-added plants under stress condition (Fig. 3A). The DEGs induced by NaCl treatment tended to regulate their expression toward the gene expression pattern of the control (CT) when exogenous

protein (Csa6G104650) (Table 3). To better unravel any subtle effect of exogenous Si on reshaping the salt stressed transcriptomes, we focused on the DEGs that showed significant expression divergence between CT and Na samples, and compared the expression levels of these genes among CT, Na and NaSi samples. Interestingly, the averaged magnitude of the increase in the

249

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Fig. 4. RNA-seq sequencing data confirmation by qRT-PCR. Correlation between the ΔCt values from qRT-PCR (y axis) and log2 tranformed FPKM values from RNA-seq (x axis) is shown. Five genes (Table S1) were randomly selected for qRT-PCR analysis on all replicates of the four treatments. ΔCt values were calculated by subtracting the Ct values of reference gene from those of the selected genes.

axis). Thus, along the direction of each line, we can observe the expression dynamics of these salt stress-invoked DEGs when exogenous Si was added. Notably, among the 774 down-regulated genes under salt stress in comparison with the normal condition, a large majority (595) have reverted to the expression level in normal conditions. In line with this pattern, 609 of the 708 up-regulated gene detected between CT and Na showed no significant changes in the expression level between Si and NaSi. Collectively, these data suggest a potential role of Si in alleviating salt stress. 3.7. Validation of gene expression by qRT-PCR To confirm the reliability of RNA-Seq data produced from this project, five genes were randomly selected for real-time PCR analysis. The details of these genes and their specific primers are shown in Table S1. The results showed that the expression levels of these genes in different treatments obtained by real-time PCR were strongly correlated with those obtained in the RNA-seq analysis (Fig. 4), confirming the reliability of the RNA-Seq data.

Fig. 3. Effect of silicon on the expression dynamics of genes responded to salt stress. (A) The distribution of expression levels of all genes and those that were either down-regulated or up-regulated genes between CT and NaCl samples in CT, Na and NaSi, respectively. (B) Effect of silicon on the expression level of genes invoked by NaCl stress. The head end (with cycle) of each line represents the expression levels (logarithmic scale) of a particular DEG in CT (x-axis) and Na (y-axis) samples, while the tail end (without cycle) represents the expression levels of this gene in Si (x-axis) and NaSi (y-axis) samples. Thus, genes can be classified into six groups according to their expression pattern across the four treatment conditions, which are reflected from the slope of the lines. For example, a purple line that associates with one Na up-regulated gene illustrates the particular combinatorial expression pattern of this gene: 1) the expression fold change for the comparison of Na versus CT is greater than that for NaSi versus Si; 2) the expression level in Na is higher than that in NaSi; 3) the expression level in Si is higher than that in CT. Therefore, genes with this type of expression dynamics provide evidence of Si-mediated alleviation to salt stress in plants. Pie charts at the upper-left and lower-right corners show the number of genes with the six different expression dynamics that belong to either the up- or the down-regulated genes in the comparison of Na versus CT, respectively. CT, control; Na, NaCl treatment; NaSi, NaCl plus silicon treatment.

4. Discussion Si has been widely reported to improve salt tolerance in various plants (Zhu and Gong, 2014; Coskun et al., 2016). Si-mediated salt tolerance in cucumber has been reported previously (Zhu et al., 2004; Zhu et al., 2015, 2016). Similar phenomenon was also observed in this study, as the inhibitions in growth and photosynthesis were partly reversed by Si addition under salt stress (Table 1). Although the beneficial role of Si on salt tolerance of plants has been well documented, the mechanisms still remain not very clear. Very recently, Coskun et al. (2019) proposed a working model - “apoplastic obstruction hypothesis”, and attempted to clarify the action mechanism of Si under various stresses. This hypothesis can explain some phenomena very well. For instance, Si deposition in the apoplast can delay or prevent the attack of insects and pathogens. In saline conditions, Yeo et al. (1999) and Gong et al. (2006) found that Si deposition in rice roots blocks the apoplastic pathway of Na+ transport and therefore decreases Na+ accumulation in the shoot. Recently, Flam-Shepherd et al. (2018) also observed that Si can decrease Na+ transport through the apoplastic pathway in some rice cultivars, and they also observed that Si addition does not affect unidirectional Na+ transport or Na+ cycling in the root. However,

Si was added (Fig. 3A). To further illustrate the potential alleviative effect of Si on salt stress cucumber plants, the DEGs identified between Na and CT samples were extracted and the combinatorial expression pattern for these genes in Na and NaSi samples were compared with that in CT and Si samples (Fig. 3B). The head end (with cycle) of each line in the Fig. 3B represented the averaged expression levels of a particular DEG in CT (xaxis) and Na (y-axis) samples, while the tail end (without cycle) displayed the expression levels of the same gene in Si (x-axis) and NaSi (y250

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there are some cases that the “apoplastic obstruction hypothesis” may not perfectly elucidate the action mechanism of Si. For example, Bosnic et al. (2018) found that Si addition increased the salt tolerance and shoot Na+ accumulation in maize - a Si accumulator (Mitani et al., 2009). We previously observed that Si addition did not affect or only slightly decreased the shoot Na+ concentrations in cucumber, which is dependent on the cultivars; and the transpirational rate was not decreased by added Si, either (Zhu et al., 2015). Therefore, the mechanism for Si-mediated salt tolerance may be more complex than that current studies proposed, and further investigations should be pursued. The released cucumber reference genome (v.2) contains 23,248 annotated coding genes (Li et al., 2011). Zhang et al. (2014) reported that nearly seventeen thousand genes (65% of reference genes in Li et al., 2011) were detected in cucumber root. Zhao et al. (2015) and Jiang et al. (2015) detected about twenty-three and twenty thousand genes in cucumber multicellular trichome and fruits, respectively. In this study, 86.42% (20,090) of known genes (Li et al., 2011) were detected. On average, about eighteen thousand of reference genes were detected to be expressed in each sample. These results verified the reliability of the present RNA-Sequencing experiment.

in rice. Transcription factors are essential regulators that play important roles in plant growth and development, and in response to environmental stresses (Zhou et al., 2018). In this study, 63 transcription factors (including ERF, WRKY, NAC, MYB, bZIP, HSP, TCP, etc.) were differentially expressed between the control and Si treatment (Table S2). Most of these transcription factors (52/63) were up-regulated in gene expressions by Si treatment (Table S2). In the study by Holz et al. (2015), 10 transcription factors were found to be differentially expressed with Si addition and 8 of them belonged to HSP family. In this study, only 3 of 63 transcription factors were identified as HSP transcription factors (Csa3G822450, Csa6G517310), which may be due to the difference in cucumber cultivars and cultivation conditions. Previous studies have demonstrated that overexpression of different transcription factors can regulate stress tolerance in different plants (Zhao et al., 2018; Chen et al., 2018). For example, in Arabidopsis and tobacco plants, overexpressions of NAC and WRKY genes have been shown to improve salt tolerance (Yokotani et al., 2009; Li et al., 2015). Overexpression of ethylene-responsive transcription factors genes like JcERF, TaERF3, LchERF enhanced tolerance to drought or salt stresses and resulted in the up-regulation of many defense- and stress-related genes in transgenic plant (Tang et al., 2007; Rong et al., 2014; Wu et al., 2014). Similar to other TFs, MYB transcription factors play a key role in plant development and abiotic stress tolerance such as drought, salt, and cold in plants (Gao et al., 2017). The overexpression of MYB in transgenic tobacco and Arabidopsis showed enhanced tolerance to salt stress (Dai et al., 2007; Li et al., 2016). Therefore, in this study, Simediated expression up-regulations of transcription factors may contribute to an enhancement of stress tolerance when plants are subjected to environmental stress. In this study, GO terms for the metabolic processes of jasmonic acid (GO:0009694), salicylic acid (GO:0009696), ethylene (GO:0009692) and ROS (GO:0006800) were significantly enriched (Fig. 2). Moreover, the expressions of 24 plant hormone signal transduction pathway-related genes were regulated by Si, with 10 and 14 of them being downand up-regulated, respectively (Table S3). This suggests that Si may be involved in the regulation of plant signal transduction, which regulates the physiological and biochemical processes in plants. Up to date, studies on the possible involvement of Si in signaling are very limited. Ye et al. (2013) reported that Si can prime jasmonate-mediated antiherbivore defense responses in rice plants. Vivancos et al. (2015) ruled out the involvement of salicylic acid signaling in Si-mediated resistance against powdery mildew in Arabidopsis, which seems to be contradictory to the results of this study (Fig. 3). Given the complexity of signaling network, more work is still needed to clarify the possible interaction between Si and these plant hormones mediated signaling processes under various stress conditions. Phenylpropanoid metabolism has been implicated in disease resistance, plant growth regulation, and nutrient deficiency responses (Wei et al., 2017; Zhou et al., 2018). In this study, all the DEGs (38) involved in phenylpropanoid biosynthesis pathway were up-regulated by Si addition under non-stress condition (Table S4). This suggests that Si may be involved in growth regulation and environmental adaptation (especially disease resistance) process. The regulatory role of Si on the expression of plant hormone signal transduction pathway-related genes, phenylpropanoid biosynthesis genes as well as transcription factor gene may imply an eliciting effect.

4.1. Effect of Si in control conditions The effect of Si on the transcriptome has been investigated in several studies with different results. Fauteux et al. (2006) reported that Si application only altered the relative abundance of 2 transcripts in nearly 40,000 transcripts in Arabidopsis plants. In wheat and soybean, transcriptomic analysis also showed little effect of Si in non-stress conditions, with the expressions of only 47 and 50 genes being altered (Chain et al., 2009; Rasoolizadeh et al., 2018). However, Holz et al. (2015) performed a comprehensive transcriptome analysis on the effect of Si in cucumber line B10 clones cultured in vitro and found 572 and 564 genes were up- or down-regulated, respectively; and many of these differentially expressed genes were involved in primary metabolism (photosynthesis, transport, and biosynthesis). Different results were reported in rice. Watanabe et al. (2004) observed that Si addition did not induce huge differences in gene expression under favorable condition since only 20 genes was differentially expressed upon Si treatment. In contrast, Van Bockhaven et al. (2015b) found that Si treatment alone induced differential expressions of 1822 genes in rice leaves and most of the genes were down-regulated. Similarly, Brunings et al. (2009) reported that Si addition significantly alter the expression levels of 221 rice genes. In this study, comparison of gene expressions between the control and Si-treated plants revealed that 1469 genes were differentially expressed in the leaves (Fig. 1). The reason for these inconsistent results is unclear. Coskun et al. (2019) argued that Si has little direct role in plants under control conditions, and the reported effects are actually its alleviative role on unintended stresses during plant growth. In this study, we can not realize any possible background stress that may have occurred during the plant treatment. On the other hand, there is a possibility that the effect of Si is associated with plant species/ genotypes. In future, investigations on Si effect are needed in different plant species and cultivars in a tightly controlled environmental conditions. In the present study, GO terms that are significantly enriched for the comparison of Si versus control (Si/CT), such as response to stress (GO:0006950), innate immune response (GO:0045087), response to biotic stimulus (GO:0009607) and response to stress (GO:0006950) (Fig. 3), supported the ability of Si to protect plants against various abiotic and biotic stresses (Debona et al., 2017). We also noticed a high percentage of genes from the functional group, such as polyamine metabolic process (GO:0006796), carboxylic acid transport (GO:0046942) and ammonia-lyase activity (GO:0016841) (Fig. 2). This implies the participation of Si in regulating carbon and nitrogen metabolism in plants, as Detmann et al. (2012) reported that Si modulates the carbon/nitrogen balance and stimulates amino acid remobilization

4.2. Impact of NaCl on transcriptome of cucumber Salt stress seriously influences many physiological and biochemical processes in plants and results in the alteration of plant metabolism (Yang and Guo, 2018). Here, we present a transcriptomic analysis of salt stress responses in cucumber, which will help us to understand cellular responses to NaCl treatment, and contribute to the identification of candidate genes for enhancement of salt tolerance in cucumber. 251

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In tomato, Ghareeb et al. (2011) proposed that Si induced bacterial wilt resistance through priming the defense ability of plants, since the expression of defense marker genes was mainly altered after challenging the plants with pathogen. However, our data demonstrated that Si alone altered the expressions of a large number of genes related to defense responses in cucumber under control conditions. This suggests that, in the present experimental conditions, Si might act as an elicitor that preconditioned plants, and therefore conferred stress tolerance of cucumber.

In this study, many enriched GO terms of DEGs between the control and salt stress were associated with stress responses, such as response to stress (GO:0006950) and response to abiotic stimulus (GO:0009628), response to oxidative stress (GO:0006979), antioxidant activity (GO:0016209), regulation of response to stress (GO:0080134), and regulation of defense response (GO:0031347). The changes are mostly in agreement with other analyses of DEGs under salt stress (Dang et al., 2013; Sun et al., 2010; Wang et al., 2015a; Yuan et al., 2016). Transcription factors play pivotal roles in modulating plant stress responses (Wu et al., 2014; Chen et al., 2018). Similar to Si treatment, a good amount of transcription factor unigenes (120) were differentially expressed in the leaves after salt treatment (Table S5). These differentially expressed TFs belonged to WRKY, MYB, ERF, bHLH, bZIP, GAGA, HSP, TCP, NAC, etc. (Table S5), suggesting the involvement of a complicated transcriptional regulation network in the response of cucumber to salt stress. In this study, we observed a large number of salt-response genes in cucumber, which may provide a useful source for studying the mechanism of stress tolerance in cucumber.

5. Conclusions In summary, we report here the first dataset that comprehensively shows the transcriptional changes in cucumber with Si addition under both normal and stress conditions. In normal condition, the expressions of 1469 genes were altered in response to Si treatment, and these genes were mainly involved in ion transport, hormone and signal transduction, biosynthetic and metabolic processes, and stress and defense responses. Under salt stress, Si treatment shifted the transcriptome of stressed cucumber back to that of the control, with the majority of upand down-regulated genes were reverted to the normal expression levels. The results suggest that Si may act as an elicitor that preconditions cucumber plants and induce stress tolerance.

4.3. Effect of Si under salt stress Using omics technologies, some scholars have investigated the regulatory role of Si on gene expressions under biotic stresses (Fauteux et al., 2006; Brunings et al., 2009; Chen et al., 2015; Van Bockhaven et al., 2015a). Under abiotic stress, relevant information is very limited. Nwugo and Huerta (2011) and Muneer and Jeong (2015) respectively investigated the effect of Si on proteome changes under cadmium toxicity in rice and salt stress in tomato, suggesting the involvement of Si in the modulation of biochemical processes under abiotic stresses. However, the effect of Si on transcriptome is still lacking under abiotic stress. In this study, the expressions of 29 genes were significantly altered by added Si in the leaves under salt stress (Table 3). These genes include 4 transcription factor genes, 2 kinase/phosphatase genes, 3 photosynthetic-associated genes, and 2 defense or stress-related genes. The genes of two transcription factors, MYB44-like (Csa3G199590) and AP2 domain transcription factor RAP2 (Csa6G091830) were significantly up-regulated by Si treatment under salt stress (Table 3). Previous studies have shown that overexpression of MYB44 enhanced drought and salt tolerance in Arabidopsis and soybean (Jung et al., 2007; Seo et al., 2012). In Arabidopsis, AP2/ERF transcription factor RAP2.6 has been found to respond to different stresses, such as high salt, osmotic stress and cold conditions, and may play a role in ABA-mediated signaling pathway (Zhu et al., 2010). Therefore, Si-mediated expression up-regulation of these two transcription factor genes may have contributed to the enhanced salt tolerance in cucumber. In this work, an auxin-induced protein 5NG4-like gene (Csa6G104650), which is involved in the transport of molecules that function at the downstream of auxin response (Busov et al., 2004; Diray-Arce et al., 2015), was also up-regulated by Si addition under salt stress (Table 3). This suggests that Si might regulate the auxin signaling pathway. In order to illustrate the effect of exogenous Si on salt stress transcriptome, the mean expression levels of all DEGs, up-regulated DEGs and down-regulated DEGs (CT vs NaCl) were calculated, respectively (Fig. 3A). The results showed that the mean expression of DEGs induced by NaCl treatment tended to recover to the expression level of the control (CT) in the presence of added Si. To further detect the dynamic changes in transcriptome, all DEGs in Na vs CT comparison were extracted and their expression levels in all the four treatments were presented (Fig. 3B). The figure also clearly showed that, in the presence of added Si, the expression levels of most of the DEGs induced by salt stress were recovered to the control levels (Fig. 3B). Our results are consistent with those reported by Farooq et al. (2016), who reported that the transcriptional response of rice to Cd toxicity was mostly reversed by Si supply.

Acknowledgments This work was supported by the National Key Research and Development Program of China (2018YFD1000800), National Natural Science Foundation of China (31772290, 31471866, 31701911) and Talent Introduction Startup Fund of Northwest A&F University (Z111021404). Conflicts of interest The authors declare that they have no conflict of interest. Appendix A. Supporting information Supplementary data associated with this article can be found in the online version at doi:10.1016/j.ecoenv.2019.02.075. References Bosnic, P., Bosnic, D., Jasnic, J., Nikolic, M., 2018. Silicon mediates sodium transport and partitioning in maize under moderate salt stress. Environ. Exp. Bot. 155, 681–687. Brunings, A., Datnoff, L.E., Ma, J.F., Mitani, N., Nagamura, Y., Rathinasabapathi, B., Kirst, M., 2009. Differential gene expression of rice in response to silicon and rice blast fungus Magnaporthe oryzae. Annu. Appl. Biol. 155, 161–170. Busov, V.B., Johannes, E., Whetten, R.W., Sederoff, R.R., Spiker, S.L., Lanz-Garcia, C., Goldfarb, B., 2004. An auxin-inducible gene from loblolly pine (Pinus taeda L.) is differentially expressed in mature and juvenile-phase shoots and encodes a putative transmembrane protein. Planta 218, 916–927. Chain, F., Côté-Beaulieu, C., Belzile, F., Menzies, J.G., Bélanger, R.R., 2009. A comprehensive transcriptomic analysis of the effect of silicon on wheat plants under control and pathogen stress conditions. Mol. Plant-Microbe Interact. 22, 1323–1330. Chen, D., Chai, S., McIntyre, C.L., Xue, G., 2018. Overexpression of a predominantly rootexpressed NAC transcription factor in wheat roots enhances root length, biomass and drought tolerance. Plant Cell Rep. 37, 225–237. Chen, Y., Liu, M., Wang, L., Lin, W., Fan, X., Cai, K., 2015. Proteomic characterization of silicon-mediated resistance against Ralstonia solanacearum in tomato. Plant Soil 387, 425–440. Coskun, D., Britto, D.T., Huynh, W.Q., Kronzucker, H.J., 2016. The role of silicon in higher plants under salinity and drought stress. Front. Plant Sci. 7, 1072. Coskun, D., Deshmukh, R., Sonah, H., Menzies, J.G., Reynolds, O., Ma, J.F., Kronzucker, H.J., Bélanger, R.R., 2019. The controversies of silicon's role in plant biology. New Phytol. 221, 67–85. Dai, X., Xu, Y., Ma, Q., Xu, W., Wang, T., Xue, Y., Chong, K., 2007. Overexpression of an R1R2R3 MYB gene, OsMYB3R-2, increases tolerance to freezing, drought, and salt stress in transgenic Arabidopsis. Plant Physiol. 143, 1739–1751. Dang, Z.H., Zheng, L.L., Wang, J., Gao, Z., Wu, S.B., Qi, Z., Wang, Y.C., 2013. Transcriptomic profiling of the salt-stress response in the wild recretohalophyte Reaumuria trigyna. BMC Genom. 14, 29.

252

Ecotoxicology and Environmental Safety 174 (2019) 245–254

Y. Zhu, et al.

Seo, J.S., Sohn, H.B., Noh, K., Jung, C., An, J.H., Donovan, C.M., Somers, D.A., Kim, D.I., Jeong, S., Kim, C., Kim, H.M., Lee, S., Choi, Y.D., Moon, T.W., Kim, C.H., Cheong, J., 2012. Expression of the Arabidopsis AtMYB44 gene confers drought/salt-stress tolerance in transgenic soybean. Mol. Breed. 29, 601–608. Shi, Y., Wang, Y., Flowers, T.J., Gong, H., 2013. Silicon decreases chloride transport in rice (Oryza sativa L.) in saline conditions. J. Plant Physiol. 170, 847–853. Sienkiewicz-Cholewa, U., Sumisławska, J., Sacała, E., Dziągwa-Becker, M., Kieloch, R., 2018. Influence of silicon on spring wheat seedlings under salt stress. Acta Physiol. Plant. 40, 54. Sun, W., Xu, X., Zhu, H., Liu, A., Liu, L., Li, J., Hua, X., 2010. Comparative transcriptomic profiling of a salt-tolerant wild tomato species and a salt-sensitive tomato cultivar. Plant Cell Physiol. 51, 997–1006. Tang, M., Sun, J., Liu, Y., Chen, F., Shen, S., 2007. Isolation and functional characterization of the JcERF gene, a putative AP2/EREBP domain-containing transcription factor, in the woody oil plant Jatropha curcas. Plant Mol. Biol. 63, 419–428. Trapnell, C., Roberts, A., Goff, L., Pertea, G., Kim, D., Kelley, D.R., Pimentel, H., Salzberg, S.L., Rinn, J.L., Pachter, L., 2012. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578. Van Bockhaven, J., Spíchal, L., Novák, O., Strnad, M., Asano, T., Kikuchi, S., Höfte, M., De Vleesschauwer, D., 2015a. Silicon induces resistance to the brown spot fungus Cochliobolus miyabeanus by preventing the pathogen from hijacking the rice ethylene pathway. New Phytol. 206, 761–773. Van Bockhaven, J., Steppe, K., Bauweraerts, I., Kikuchi, S., Asano, T., Höfte, M., De Vleesschauwer, D., 2015b. Primary metabolism plays a central role in molding silicon-inducible brown spot resistance in rice. Mol. Plant Pathol. 16, 811–824. Vivancos, J., Labbé, C., Menzies, J.G., Bélanger, R.R., 2015. Silicon-mediated resistance of Arabidopsis against powdery mildew involves mechanisms other than the salicylic acid (SA)-dependent defence pathway. Mol. Plant Pathol. 16, 572–582. Wang, J., Li, B., Meng, Y., Ma, X., Lai, Y., Si, E., Yang, K., Ren, P., Shang, X., Wang, H., 2015b. Transcriptomic profiling of the salt-stress response in the halophyte Halogeton glomeratus. BMC Genom. 16, 169. Wang, S., Liu, P., Chen, D., Yin, L., Li, H., Deng, X., 2015a. Silicon enhanced salt tolerance by improving the root water uptake and decreasing the ion toxicity in cucumber. Front. Plant Sci. 6, 759. Watanabe, S., Shimoid, E., Ohkamab, N., Hayashid, H., Yoneyamad, T., Yazakie, Y., Fujiie, F., Shinboe, K., Yamamotoe, K., Sakatae, K., Sasakie, T., Kishimotoe, N., Kikuchi, S., Fujiwara, T., 2004. Identification of several rice genes regulated by Si nutrition. Soil Sci. Plant Nutr. 50, 1273–1276. Wei, Q., Zhang, F., Sun, F., Luo, Q., Wang, R., Hu, R., Chen, M., Chang, J., Yang, G., He, G., 2017. A wheat MYB transcriptional repressor TaMyb1D regulates phenylpropanoid metabolism and enhances tolerance to drought and oxidative stresses in transgenic tobacco plants. Plant Sci. 265, 112–123. Wu, D., Ji, J., Wang, G., Guan, C., Jin, C., 2014. LchERF, a novel ethylene-responsive transcription factor from Lycium chinense, confers salt tolerance in transgenic tobacco. Plant Cell Rep. 33, 2033–2045. Yang, Y., Guo, Y., 2018. Elucidating the molecular mechanisms mediating plant salt-stress responses. New Phytol. 217, 523–539. Yang, Z., Zheng, H., Wei, X., Song, J., Wang, B., Sui, N., 2018. Transcriptome analysis of sweet sorghum inbred lines differing in salt tolerance provides novel insights into salt exclusion by roots. Plant Soil 430, 423–439. Ye, M., Song, Y., Long, J., Wang, R., Baerson, S.R., Pan, Z., Zhu-Salzman, K., Xie, J., Cai, K., Luo, S., Zeng, R., 2013. Priming of jasmonate-mediated anthherbivore defense responses in rice by silicon. Proc. Natl. Acad. Sci. USA 110, E3631–E3639. Yeo, A.R., Flowers, S.A., Rao, G., Welfare, K., Senanayake, N., Flowers, T.J., 1999. Silicon reduces sodium uptake in rice (Oryza satival L.) in saline conditions and this is accounted for by a reduction in the transpirational bypass flow. Plant Cell Environ. 22, 559–565. Yin, J., Liu, M., Ma, D., Wu, J., Li, S., Zhu, Y., Han, B., 2018. Identification of circular RNAs and their targets during tomato fruit ripening. Postharvest Biol. Technol. 136, 90–98. Yin, L., Shiwen, W., Tanaka, K., Fujihara, S., Itai, A., Den, X., Zhang, S., 2015. Siliconmediated changes in polyamines participate in silicon-induced salt tolerance in Sorghum bicolor L. Plant Cell Environ. 39, 245–258. Yokotani, N., Ichikawa, T., Kondou, Y., Matsui, M., Hirochika, H., Iwabuchi, M., Oda, K., 2009. Tolerance to various environmental stresses conferred by the salt-responsive rice gene ONAC063 in transgenic Arabidopsis. Planta 229, 1065–1075. Yuan, F., Lyu, M.A., Leng, B., Zhu, X., Wang, B., 2016. The transcriptome of NaCl-treated Limonium bicolor leaves reveals the genes controlling salt secretion of salt gland. Plant Mol. Biol. 91, 241–256. Zhang, C., Wang, Y., Fu, J., Dong, L., Gao, S., Du, D., 2014. Transcriptomic analysis of cut tree peony with glucose supply using the RNA-Seq technique. Plant Cell Rep. 33, 111–129. Zhao, J., Pan, J., Guan, Y., Nie, J., Yang, J., Qu, M., He, H., Cai, R., 2015. Transcriptome analysis in Cucumis sativus identifies genes involved in multicellular trichome development. Genomics 105, 296–303. Zhao, Q., Zhong, M., He, L., Wang, B., Liu, Q., Pan, Y., Jiang, B., Zhang, L., 2018. Overexpression of a chrysanthemum transcription factor gene DgNAC1 improves drought tolerance in chrysanthemum. Plant Cell Tiss. Org. 135, 119–132. Zhou, Y., Ma, J., Xie, J., Deng, L., Yao, S., Zeng, K., 2018. Transcriptomic and biochemical analysis of highlighted induction of phenylpropanoid pathway metabolism of citrus fruit in response to salicylic acid, Pichia membranaefaciens and oligochitosan. Postharvest Biol. Technol. 142, 81–92. Zhu, J.-K., 2016. Abiotic stress signaling and responses in plants. Cell 167, 313–324. Zhu, Q., Zhang, J., Gao, X., Tong, J., Xiao, L., Li, W., Zhang, H., 2010. The Arabidopsis AP2/ERF transcription factor RAP2.6 participates in ABA, salt and osmotic stress responses. Gene 457, 1–12.

Debona, D., Rodrigues, F.A., Datnoff, L.E., 2017. Silicon's role in abiotic and biotic plant stresses. Annu. Rev. Phytopathol. 55, 85–107. Detmann, K.C., Araújo, W.L., Martins, S.C.V., Sanglard, L.M.V.P., Reis, J.V., Detmann, E., Rodrigues, F.Á., Nunes-Nesi, A., Fernie, A.R., DaMatta, F.M., 2012. Silicon nutrition increases grain yield, which, in turn, exerts a feed-forward stimulation of photosynthetic rates via enhanced mesophyll conductance and alters primary metabolism in rice. New Phytol. 196, 752–762. Diray-Arce, J., Clement, M., Gul, B., Khan, M.A., Nielsen, B.L., 2015. Transcriptome assembly, profiling and differential gene expression analysis of the halophyte Suaeda fruticosa provides insights into salt tolerance. BMC Genom. 16, 353. Du, Z., Zhou, X., Ling, Y., Zhang, Z., Su, Z., 2010. agriGO: a GO analysis toolkit for the agricultural community. Nucleic Acids Res. 38, W64–W70. Epstein, E., 1999. Silicon. Ann. Rev. Plant Physiol. Plant Mol. Biol. 50, 641–664. Etesami, H., Jeong, B.R., 2018. Silicon (Si): review and future prospects on the action mechanisms in alleviating biotic and abiotic stresses in plants. Ecotoxicol. Environ. Saf. 147, 881–896. Farooq, M.A., Detterbeck, A., Clemens, S., Dietz, K., 2016. Silicon-induced reversibility of cadmium toxicity in rice. J. Exp. Bot. 67, 3573–3585. Fauteux, F., Chain, F., Belzile, F., Menzies, J.G., Belanger, R.R., 2006. The protective role of silicon in the Arabidopsis-powdery mildew pathosystem. Proc. Natl. Acad. Sci. USA 103, 17554–17559. Flam-Shepherd, R., Huynh, W.Q., Coskun, D., Hamam, A.H., Britoo, D.T., Kronzucker, H.J., 2018. Membrane fluxes, bypass flows, and sodium stress in rice: the influence of silicon. J. Exp. Bot. 69, 1679–1692. Frew, A., Weston, L.A., Reynolds, O.L., Gurr, G.M., 2018. The role of silicon in plant biology: a paradigm shift in research approach. Ann. Bot. 121, 1265–1273. Gao, F., Zhou, J., Deng, R., Zhao, H., Li, C., Chen, H., Suzuki, T., Park, S., Wu, Q., 2017. Overexpression of a tartary buckwheat R2R3-MYB transcription factor gene, FtMYB9, enhances tolerance to drought and salt stresses in transgenic Arabidopsis. J. Plant Physiol. 214, 81–90. Ghareeb, H., Bozsó, Z., Ott, P.G., Repenning, C., Stahl, F., Wydra, K., 2011. Transcriptome of silicon-induced resistance against Ralstonia solanacearum in the silicon non-accumulator tomato implicates priming effect. Physiol. Mol. Plant Pathol. 75, 83–89. Gong, H.J., Randall, D.P., Flowers, T.J., 2006. Silicon deposition in the root reduces sodium uptake in rice (Oryza sativa L.) seedlings by reducing bypass flow. Plant Cell Environ. 29, 1970–1979. Götz, S., García-Gómez, J.M., Terol, J., Williams, T.D., Nagaraj, S.H., Nueda, M.J., Robles, M., Talón, M., Dopazo, J., Conesa, A., 2008. High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res. 36, 3420–3435. Holz, S., Kube, M., Bartoszewski, G., Huettel, B., Büttner, C., 2015. Initial studies on cucumber transcriptome analysis under silicon treatment. Silicon. https://doi.org/ 10.1007/s12633-015-9335-2. Jiang, L., Yan, S., Yang, W., Li, Y., Xia, M., Chen, Z., Wang, Q., Yan, L., Song, X., Liu, R., Zhang, X., 2015. Transcriptomic analysis reveals the roles of microtubule-related genes and transcription factors in fruit length regulation in cucumber (Cucumis sativus L.). Sci. Rep. 5, 8031. Jung, C., Seo, J.S., Han, S.W., Koo, Y.J., Kim, C.H., Song, S.I., Nahm, B.H., Choi, Y.D., Cheong, J.J., 2007. Overexpression of AtMYB44 enhances stomatal closure to confer abiotic stress tolerance in transgenic Arabidopsis. Plant Physiol. 146, 623–635. Kim, Y.H., Khan, A.L., Waqas, M., Shim, J.K., Kim, D.H., Lee, K.Y., Lee, I.J., 2014. Silicon application to rice root zone influenced the phytohormonal and antioxidant responses under salinity stress. J. Plant Growth Regul. 33, 137–149. Lee, S.K., Sohn, E.Y., Hamayun, M., Yoon, J.Y., Lee, I.J., 2010. Effect of silicon on growth and salinity stress of soybean plant grown under hydroponic system. Agrofor. Syst. 80, 333–340. Li, C., Ng, C.K.Y., Fan, L., 2015. MYB transcription factors, active players in abiotic stress signaling. Environ. Exp. Bot. 114, 80–91. Li, T., Sun, J., Bi, Y., Peng, Z., 2016. Overexpression of an MYB-Related gene FvMYB1 from Fraxinus velutina increases tolerance to salt stress in transgenic tobacco. J. Plant Growth Regul. 35, 632–645. Li, Z., Zhang, Z., Yan, P., Huang, S., Fei, Z., Lin, K., 2011. RNA-Seq improves annotation of protein-coding genes in the cucumber genome. BMC Genom. 12, 540. Liu, P., Yin, L., Wang, S., Zhang, M., Deng, X., Zhang, S., Tanaka, K., 2015. Enhanced root hydraulic conductance by aquaporin regulation accounts for silicon alleviated saltinduced osmotic stress in Sorghum bicolor L. Environ. Exp. Bot. 111, 42–51. Mitani, N., Chiba, Y., Yamaji, N., Ma, J.F., 2009. Identification and characterization of maize and barley Lsi-2-like silicon efflux transporters reveals a distinct silicon uptake system from that in rice. Plant Cell 21, 2133–2142. Mitani, N., Ma, J.F., 2005. Uptake system of silicon in different plant species. J. Exp. Bot. 56, 1255–1261. Muneer, S., Jeong, B.R., 2015. Proteomic analysis of salt-stress responsive proteins in roots of tomato (Lycopersicon esculentum L.) plants towards silicon efficiency. Plant Growth Regul. 77, 133–146. Munns, R., Tester, M., 2008. Mechanisms of salinity tolerance. Annu. Rev. Plant Biol. 59, 651–681. Nwugo, C.C., Huerta, A.J., 2011. The effect of silicon on the leaf proteome of rice (Oryza sativa L.) plants under cadmium-stress. J. Proteome Res. 10, 518–528. Rasoolizadeh, A., Labbé, C., Sonah, H., Deshmukh, R.K., Belzile, F., Menzies, J.G., Bélanger, R.R., 2018. Silicon protects soybean plants against Phytophthora sojae by interfering with effector-receptor expression. BMC Plant Biol. 18, 97. Robinson, M.D., McCarthy, D.J., Smyth, G.K., 2010. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140. Rong, W., Qi, L., Wang, A., Ye, X., Du, L., Liang, H., Xin, Z., Zhang, Z., 2014. The ERF transcription factor TaERF3 promotes tolerance to salt and drought stresses in wheat. Plant Biotechnol. J. 12, 468–479.

253

Ecotoxicology and Environmental Safety 174 (2019) 245–254

Y. Zhu, et al.

tolerance by increasing root water uptake in Cucumis sativus L. Plant Cell Rep. 34, 1629–1646. Zhu, Z., Wei, G., Li, J., Qian, Q., Yu, J., 2004. Silicon alleviates salt stress and increases antioxidant enzymes activity in leaves of salt-stressed cucumber (Cucumis sativus L.). Plant Sci. 167, 527–533.

Zhu, Y., Gong, H., 2014. Beneficial effects of silicon on salt and drought tolerance in plants. Agron. Sustain. Dev. 34, 455–472. Zhu, Y., Guo, J., Feng, R., Jia, J., Han, W., Gong, H., 2016. The regulatory role of silicon on carbohydrate metabolism in Cucumis sativus L. under salt stress. Plant Soil 406, 231–249. Zhu, Y., Xu, X., Hu, Y., Han, W., Yin, J., Li, H., Gong, H., 2015. Silicon improves salt

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