Materials and growth conditions
In this study, we first screened 18 Shanlan rice accessions collected by Department of Agriculture and Forestry, Hainan Tropical Ocean University and Hainan Laboratory of Biodiversity and Rice Germplasm Innovation, Hainan University (Table S1). The rice accessions were derived from four different geographic regions in Hainan province, China, i.e., Baoting, Hainan Academy of Agricultural Sciences, Wuzhishan and Changjiang.
Drought treatments
Simulation of DS experiments by polyethylene glycol (PEG) was carried out in a growth chamber according to the approach as previously documented [17]. The growth chamber environment is: 12 h/12 h photoperiod, 67 ~ 70% air humidity, 400 ~ 500 μmol m− 2 s− 1 PPFD and 32/25 °C air temperature. The 18 Shanlan rice seeds were soaked for 24 hours, germinated at a constant temperature of 30 °C for 24 h, and sowed in a petri dish. After 3 weeks of germination, the seedlings were transplanted into a rice culture medium. After 4 weeks of germination, the seedlings were treated with 25% (w/v) PEG6000 for 12 hours, the degree of leaf wilting and biomass were recorded (Table S1), and two Shanlan upland rice lines with contrasting drought-resistant levels were selected. We hence used Shanlan 1 (SL1, relative sensitive to DS) and Shanlan 10 (SL10, relative tolerance to DS) for further experiments. For an example, the SL1 is selected based on the largest decrease in biomass together with highest degree of leaf rolling caused by DS effects.
To confirm the performance of two Shanlan upland rice (SL1 and SL10) in response to DS, we grew the plants in pots (50 cm × 40 cm × 30 cm) containing commercial peat soil (Pindstrup Substrate no. 4) in the growth chamber. Six plants for each rice line were sowed one pot. The plants were irrigated every 2 days. DS treatments were conducted at 40 days after emergence and then we stopped watering for 20 days. The soil water contents in drought field block were decreased by 40% relative to control.
Agronomic measurements
Plant height and tiller number were recorded in two Shanlan upland rice lines following DS treatments. Dry shoot biomass per plant were determined by collecting the above-ground plants at end of drought treatment, and determined the weight by drying oven at 80 °C for 12 h until constant weight.
Photosynthetic measurements
We used a portable photosynthesis measurement system, Licor 6400XT (LICOR Corp., USA) to measure photosynthetic rates (A), stomatal conductance (gs), and transpiration (E). Instinct water use efficiency (WUE) was calculated by A/gs and the stomatal limitation value (Ls) was calculated using the formula: Ls = 1–Ci/Ca [18]. The flow rates were 400 mmol s− 1, CO2, light density and temperature were 400 ppm, 1500 μmolm− 2 s− 1 and 27 °C, respectively. Four biological replicates were conducted.
In addition, we measured photochemical efficiency by using Multi-Function Plant Efficiency Analyser (M-PEA, PP-Systems). The F
o (minimum fluorescence) and F
m (maximal fluorescence) of leaves in the dark for 20 min were measured. After actinic light and saturation pulse values were applied, leaf chlorophyll fluorescence parameters, F
v/F
m (maximum efficiency of PSII photochemistry under dark-adaption), and non-photochemical quenching (NPQ) were calculated [19].
3,3′-Diamino benzidine (DAB) staining
For H2O2 detection, rice leave samples were stained in 1 mg/mL DAB (pH 3.8) overnight at 25 °C, followed by 3 h of washing in 100% ethanol to remove chlorophyll and then 10 min of boiling for destaining.
mRNA extraction and library preparation
Total RNA was extracted using TRIzol reagent according to the manufacturer’s instructions (Invitrogen, Carlsbad, CA). RNA degradation and contamination was monitored on 1% agarose gels, and purity was checked using the Nano-Photometer spectrophotometer (IMPLEN, CA, USA). RNA integrity was assessed using the RNA Nano 6000 Assay Kit of the Agilent Bioanalyzer 2100 system (Agilent Technologies, CA, USA). A total amount of 1.5 μg RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using NEB Next Ultra RNA Library Prep Kit for Illumina (NEB, USA) following manufacturer’s recommendations and index codes were added to attribute sequences to each sample. The clustering of the index-coded samples was performed on a Bot Cluster Generation System using HiSeq 4000 PE Cluster Kit (Illumina, USA) as reported previously [20]. After cluster generation, the library preparations were sequenced on an Illumina Hiseq 4000 platform and 150 bp paired-end reads were generated.
Read mapping and differentially expressed analysis
The quality of RNA-seq data (fastq files) was assessed by FastQC software (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The adaption and reads with low quality from raw RNA-seq reads were trimmed using trim_galore softare (http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/; adaptor of read1: AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC; adaptor of read2: AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT). RNA-seq analysis was performed by the STAR [21] software (version 2.5.3a; http://github.com/alexdobin/ STAR) with the rice reference genome IRGSP-1.0 as well as a gene transfer format (GTF) file (downloaded from Ensembl Plants http://plants.ensembl.org/).
After generating the genome index, the clean RNA-seq reads were aligned by STAR [21] with ‘—quantMode GeneCounts’ option to count number of reads per gene. Quantification of genes and isoforms was performed using cufflinks version 2.2.1. RNA-seq analysis was performed by the STAR [21] software (version 2.5.3a; http://github.com/alexdobin/STAR) with the rice reference genome IRGSP-1.0 as well as a gene transfer format (GTF) file (downloaded from Ensembl Plants http://plants.ensembl.org/). To identify differentially expressed genes (DEGs) and drought responsive gene (DRGs) between SL1 and SL10 under DS relative to control, a fragment per kilobase of transcript per million mapped reads (FPKM) method was applied to calculate the transcript abundance. Notably, DEGs were determined by the R package ‘DESeq2’ [22] with the reads counts reported by STAR [21]. Only genes with the adjusted P-value < 0.05 were considered as DEGs. To reduce transcription noise, each isoform/gene was included for analysis only if its FPKM values was > 0.01, a value was chosen based on gene coverage saturation analysis as described earlier [23].
GO and KEGG analysis
For Gene Ontology (GO) annotation, we used an in-house Perl script UniProtKB GOA file (ftp.ebi.ac.uk/pub/databases/GO/goa). KOBAS (KEGG Orthology Based An-notation System, v2.0) was applied to identify reprogrammed biochemical pathways of each pathway as previously documented [24]. Both GO and KEGG terms with a corrected P value less than 0.05 were considered significantly enriched among the DEGs.
Quantitative transcript measurements
Based on transcriptomes analysis, qPCR was used to confirm the expressed pattern of DRGs in response to DS in two Shanlan upland rice lines. The RNA sample used is same as transcriptome determinations. RNA extraction and reversed cDNA were performed as described previously [25]. The qPCR analysis was performed using SYBR Green PCR Master Mix (Applied Biosystems, Forster City, CA, USA, 4309155) and a real-time PCR system (ABI StepOnePlus, Applied Biosystems, USA). Primers for qPCR were designed using Primer Prime Plus 5 Software Version 3.0 (Applied Biosystems, USA). Primers were listed in Table S2. The qPCR running program consists of a reverse transcription step at 48 °C for 30 min and a Taq polymerase activation step at 95 °C for 30 s, followed by PCR: 45 cycles at 95 °C for 15 s, 61 °C for 20 s, and 72 °C for 30 s, ensued by a melting cycle. Assays were performed with three biological samples from each treatment, and measurements were replicated three times. The actin1 gene was used as an expression control (housekeeping gene). Relative expression of a gene against Actin1 was calculated as 2−ΔΔCT (ΔCT = CT, gene of interest−CT), as described earlier [26]. The expression levels of three known drought-resistant genes OsLEA3–2 (LOC4332688) [27] and RePRP2.2 (LOC4343033) [28] were also tested as control.
Metabolism determinations
Samples for metabolites measurements were collected immediately after finishing DS treatments for both SL1 and SL10 rice lines. Same marked section of leaves as gas exchange measurements were sampled. A targeted metabolic profiling in the leaves of both Shanlan upland rice lines (SL1 and SL10) collected in control and DS was prepared based on the LC-MS/MS (Triple Quad 6500, SCIEX) procedure as earlier described by [25]. Shortly, ~ 2.5 mg leaf samples collected from SL1 and SL10 Shanlan upland rice lines exposed by DS mentioned above were sampled in 2 ml Eppendorf tube containing pre-cooled metal beads, then immediately stored in liquid nitrogen. The samples were firstly extracted with ball mill at 30 Hz for 5 min, and then the extracted powder was dissolved in 1.5 ml methanol/chloroform mixture and incubated subsequently at − 20 °C for 5 h. Thereafter, the mixture was centrifuged at 2000 g and 4 °C for 10 min and eventually filtered with 0.43 μm organic phase medium (GE Healthcare, 6789–0404).
The metabolomic analysis was performed using metabolon software (Durham, NC, USA). The sample components were identified by comparing the retention time and mass spectra with those of the reference metabolites steeply. Regarding the metabolic compounds identification of each sample, it is highly recommended to consider the mass spectra with the entries of the mass spectra libraries NIST02 and the Golm metabolome database (http://csbdb.mpimp-golm.mpg.de/csbdb/gmd/gmd.html).