Background

Like all other crops, maize plants grown under natural conditions are exposed to various abiotic stresses throughout their life cycle [1,2,3]. Water deficit stress is considered as one of the most important environmental factors that adversely affect maize production [2, 4, 5]. A lack of water decreases the seedling survival rate and increases the post-pollination embryo abortion rate, ultimately leading to decreased yield [1, 6, 7]. In China, more than 60% of the agricultural land devoted to corn production has encountered long-term or seasonal drought conditions, which may reduce yields by as much as 30% [8]. To ensure high survival rates and production under drought conditions, maize plants rely on several strategies, including drought avoidance, escape and tolerance [9,10,11]. Consequently, several biological processes are affected through changing global gene expression patterns [25, 26]. Over-expression of ZmGOLS2 significantly increases galactinol and raffinose contents and results in enhanced drought tolerance in Arabidopsis thaliana plants. Interestingly, expression of ZmGOLS2 is regulated by ZmDREB2A TF, which reportedly affects maize drought tolerance [18, 27]. Trehalose influences several biological processes in rice seedlings [28, 29]. Furthermore, over-expression of a rice trehalose-6-phosphate phosphatase gene under the control of a flower-specific promoter leads to the accumulation of sucrose in the ear. Field trials revealed that the transgenic maize grain yield was significantly higher than that of non-transgenic controls under mild and severe drought stress conditions [4]. These findings suggest that the up- or down-regulated expression of genes encoding TFs or metabolic factors can increase maize drought tolerance during the seedling and reproductive stages. Additionally, identifying DEGs responsive to drought stress using RNA sequencing (RNA-seq) technology may provide useful information for elucidating the mechanisms mediating drought stress responses [30].

RNA sequencing is a classical technique that has been used to identify drought-responsive pathways or genes that are active during the seedling stage under various abiotic stress conditions. Min et al. [Evaluation of relative water content

To evaluate the effects of drought treatments, we measured the seedling RWC. Briefly, paper bags were first baked at 65 °C for 3 days until reaching a constant weight. Fresh leaves were weighed (WF) and then soaked in distilled water for 24 h. The leaves were weighed again to obtain the saturated weight (WFT), after which they were fixed at 105 °C for 30 min. The leaves were then placed in the dried paper bags and incubated at 80 °C for 3 days. Three independent samples were used to determine the constant dry weight (WD). The RWC was calculated based on the following formula: RWC = (WF − WD) / (WFT − WD) × 100%. The Student’s t-test was used to detect significant differences (P < 0.01) between the data for the drought-treated and control samples. Data from two biological replicates (four plants per replicate) were analyzed, and are presented in the figures as the mean of two replicates ± standard deviation (SD).

Measurement of leaf length

The length of the second seedling leaf for all samples was measured after the 3-day drought, 6-day drought, and 1-day water recovery treatments. A ruler was used to measure the length from the leaf tip to the sheath for 20~ 25 plants at each time-point. The leaf length data underwent a one-way analysis of variance using Microsoft Excel software. The Student’s t-test was used to detect significant differences (P < 0.01) between the data for the drought-treated and control samples. Data from two biological replicates (four plants per replicate) were analyzed, and are presented in the figures as the mean of two replicates ± SD.

Gas exchange rate and chlorophyll fluorescence measurement

The gas exchange rate and chlorophyll fluorescence following different drought treatments were measured using the LI-6400 portable photosynthesis system (LI-COR Company, Lincoln, NE, USA) according to the manufacturer instructions with some modifications. First, seedlings in pots were kept in one large dark box for 40 min to determine the minimum (Fo) and maximum (Fm) fluorescences. The Fo was recorded under the lowest modulated light conditions, while the Fm and variable chlorophyll fluorescence (Fv) were assessed after an exposure to saturating white light (6000 μmol m− 2 s− 1) for 0.8 s. Steady-state fluorescence (Fs) was measured by exposing plants to white light (500 μmol m− 2 s− 1) until the leaf photosynthetic activity reached a steady-state. A second maximum fluorescence (Fm′) was recorded following another exposure to saturating white light (6000 μmol m− 2 s− 1) for 0.8 s. The highest quantum efficiency of photosystem II (PSII) was calculated using the following formula: Fv/Fm = (Fm − Fo)/Fm , while the actual quantum yield of PSII electron transport was determined as follows: ΦPSII = (Fm′ − Fs)/Fm′. The measurements involved the third leaf of each plant. Two biological replicates were analyzed, with three plants per replicate.

Measurement of chlorophyll contents

The drought-induced changes to chlorophyll contents were assessed using a SPAD-502 (Soil and Plant Analyzer Development) portable chlorophyll meter (Konica Minolta Inc., Tokyo, Japan). The third fully expanded leaf (from the top) of each seedling was analyzed after the 3-day drought, 6-day drought, and 1-day water recovery treatments. Each leaf was analyzed three times at different sites. The chlorophyll content of each leaf was based on the average of three readings. The measurement was completed using two biological replicates, with four plants per replicate. The average of all readings was used for the following data analysis. Data are presented in figures as the mean of two replicates ± SD.

Total RNA extraction, qRT-PCR, and RNA sequencing

Total RNA was extracted from B73 seedling shoots (i.e., aerial parts) using Trizol reagent (Invitrogen). For the qRT-PCR analysis, the extracted total RNA was treated with RQ1 RNase-free DNase (Promega), after which first-strand cDNA was amplified using M-MLV Reverse Transcriptase. The qRT-PCR was completed using the ABI 7500 Real-Time PCR System (Applied Biosystems, USA) and SYBR Premix (Thermo Scientific, USA). A more thorough description of the qRT-PCR procedure is provided in one of our previous publications [11], and the primers used to amplify the nine genes were designed with the Premier 5 (v5.0) program (see Additional file 1). Two independent experiments were completed, each with three technical replicates. The results of a representative experiment are provided, with data presented as the mean ± SD (n = 3). The extracted total RNA was also used to prepare RNA-seq libraries according to the Illumina Standard mRNA-seq Library Preparation kit (Illumina). The RNA-seq was completed using the Illumina HiSeq 2000 system as previously described [11]. The RNA-seq experiment (including the library construction) was completed with two biological replicates.

Identification of differentially expressed genes

The 125-bp paired-end reads generated by the Illumina HiSeq 2000 system were aligned with the B73 reference genome (v2) using TopHat (v2.0.6) [33], with default settings for all parameters. The unique mapped reads were used in the following analyses. The default parameters of the Cuffdiff (v2.2.1) program were used to analyze gene expression levels in terms of fragments per kilobase per million mapped reads (FPKM) and to identify DEGs [34]. The genes with an absolute log2 fold change value (treated/control) ≥ 1 (adjusted P ≤ 0.05 [32]) were considered as DEGs. The RNA-seq data were deposited in the NCBI database (Accession number is: SRP101911; https://www.ncbi.nlm.nih.gov).

Gene ontology enrichment, MapMan annotation and gene clustering

We used the default settings of the agriGO online tool (http://bioinfo.cau.edu.cn/agriGO/) to analyze the functional enrichment of all DEGs. Significant GO terms (q ≤ 0.05) were selected. Different metabolic pathways associated with the DEGs were identified with the MapMan program [35]. The up- and down-regulated genes are indicated in red and blue, respectively. The MapMan program is a user-driven tool that displays genomic data sets on diagrams of metabolic pathways and other biological processes. For the cluster classification, the DEGs were grouped into 10 clusters with the K-means algorithm of the MultiExperiment Viewer program (v4.9.0) based on the log2 fold change values (treated/control).

Prediction of photosynthesis-related genes using BLASTP

The protein sequences encoded by the DEGs associated with the light-harvesting complex (LHC), PSII, and photosystem I (PSI) were used as queries in a BLASTP search of the Nr database to obtain a full annotation. An E-value < 0.01 was selected as the cutoff.

Measurements of the contents of GA, ABA and SA

Control and drought treatment seedlings of 3d, 6d and re-watered were used to measure the contents of GA, ABA and SA, respectively. Three replicates were prepared at each time points. We measured their contents according to the instructions of standard hormonal kit (ELISA): MM-012601 for GA, MM-013801 for ABA, and MM3372201 for SA (products of Jiangsu **gMei Bio.Company).

Results

Physiological responses to drought stress and water recovery

To investigate the physiological responses of maize seedlings to water deficit and recovery, the phenotypic traits, including RWC and leaf length, were evaluated at the following three time-points: 3 and 6 days after initiating the drought treatment and after a 1-day water recovery period (Fig. 1a-c). The RWCs of drought-treated leaves decreased to 62.7% and 49.8% after 3 and 6 days, respectively (Fig. 1d). Meanwhile, the RWCs of the drought-treated and control seedlings were similar following the water recovery period. Additionally, the drought-treated leaves were significantly shorter than the control leaves after the 3-day drought, 6-day drought, and 1-day water recovery treatments (Fig. 1e). Then we calculated elongation rate of leaf between control and drought treatment seedlings (see Additional file 2: Figure S1). In the drought treatment stage (3d~6d), the elongation rate of the drought treatment seedlings were lower than control seedlings, which best matched the short leaf and lower photosynthetic rate in the drought seedlings. But in the re-watered stage (6d~re-watered), the rate of drought treatment seedlings were higher than control samples. This might be explained by the high water absorption of the re-watered seedlings. The other analyzed phenotypic traits were also significantly affected by water deficit stress. For example, at 3-day and 6-day drought treatment, the leaves were wilted and obviously rolled. In contrast, the leaves of seedlings that were normally watered (i.e., controls) no changes after 3 and 6 days. After re-watering for 24 h, the leaves of all drought-treated plants recovered and were more similar to the controls compared to drought stressed plants (Fig. 1a–c). However, the re-watered drought-treated seedlings remained smaller than the controls and some leaf tips were gray or yellow (Fig. 1e). These results indicated that seedling growth was inhibited by drought conditions.

Fig. 1
figure 1

Physiological responses of seedling leaves affected by drought treatments and the water recovery period. Phenotypic responses of B73 seedlings to drought stress (DS) and water recovery treatments at different time points (a: 3-day drought; b: 6-day drought; c: 1-day water recovery). The pots on the right and left correspond to the drought-treated and well-watered control plants, respectively. The relative water content and leaf length were measured using seedling leaves after a 3-day (3d) or 6-day drought treatment (6d) and a 1-day water recovery period (re-watered, d and e). The values in d and e are presented as the mean ± standard deviation of three biological replicates, with each replicate consisting of three plants. The asterisks indicate significant differences (P < 0.001) according to the Student’s t-test. The leaf length (d), relative water content (e), photosynthetic rate (f), Fv/fm (g), SPAD (h), and ΦPSII (i) values were recorded for drought-treated (grey) and control seedlings (white) at three time points. All measurements were completed with the third seedling leaf

Full size image

Photosynthetic systems are susceptible to damage during responses to water deficit stress [1) and the exposure to drought stress decreased overall plant size.

Carbohydrate metabolism is important for the survival of drought-treated seedlings

Carbohydrate metabolism is one of the most important plant processes for absorbing the energy generated during photosynthesis, and its substrates have been reported to be involved in drought stress responses in addition to acting as energy sources. Changes to the expression of genes associated with carbohydrate metabolism alter the carbohydrate contents of different tissues. Additionally, drought stress also induces the accumulation of different sugars, including glucose [13, 20]. The ectopic expression of genes related to carbohydrate metabolism improves drought tolerance in maize [4] and rice [29]. Our data revealed that the expression of GRMZM2G139300, which encodes a cell wall enzyme that hydrolyzes sucrose into glucose and fructose, was up-regulated after the 6-day drought treatment (i.e., FPKM: drought/control was 83.50/18.36), suggesting that the sucrose biosynthetic and metabolic pathways were induced by drought stress. According to GO analyses, most carbohydrate-related processes were enriched under drought conditions (Fig. 3), including five categories related to carbohydrate metabolism. We also observed that the expression levels of genes associated with oligosaccharide metabolism or disaccharide biosynthesis and metabolism were mainly up-regulated after the 6-day drought treatment (Fig. 3).

It has been reported that over-expression of NLP7, encoding RWP-RK transcription factor, in transgenic tobacco plants resulted in enhanced carbon and nitrogen assimilation as well as an elevated photosynthetic rate [56, 57], implying that the activities of the carbohydrate and nitrogen metabolic pathways are coordinated. In our data, we also observed that several processes related to carbon and nitrogen metabolism and biosynthesis were over-represented during GO analyses. In addition, some nitrogen metabolism-related candidate genes belonging to the RWP-RK TF family were differentially expressed. We concluded that carbohydrate and nitrogen metabolic activities were repressed under drought conditions, which resulted in carbon and nitrogen deficiencies. The insufficient carbon and nitrogen levels considerably affected chloroplast development, which led to lower SPAD values. In other words, modulating the expression of genes influencing carbohydrate or nitrogen metabolic pathways may be a viable option for enhancing drought tolerance in maize seedlings.

Conclusions

We herein describe the results of our comprehensive investigation regarding physiological responses and gene expression patterns in plants treated with drought stress and a water recovery period. Phenotypic measurements suggested that water deficit stress decreased the photosynthetic efficiency and inhibited cell division, resulting in the production of relatively small seedling leaves. More than 6000 DEGs were detected through RNA-seq analysis, with many different TF families identified as sensitive to drought stress. Among the DEGs, the expression levels of more than 30 genes related to photosynthetic systems were down-regulated under drought conditions, which was consistent with the corresponding phenotypic variations in chlorophyll fluorescence, SPAD values, and photosynthetic efficiency. The results of GO analyses revealed that many drought-responsive pathways, including those related to carbohydrate and nitrogen metabolism, were induced by drought conditions. The amount of GA was decreased during drought treatments, specifically at 6d and showed no significance difference at the re-watered stage. However, ABA showed opposite pattern in comparison with that of GA. So, GA and ABA might participate in drought response to regulate plant growth. Most of genes related to cell wall development also exhibited down-regulation, which best explain the phenotype of relative small leaves. Taken together, our findings might serve as a useful resource for future investigations of the specific functions of these drought-responsive genes.