2.1 Plant material
Qing 0160 and Qing 0305 were collected from Xining City, Qinghai Province, China(N.36.434419,E.101.450759).The samples were identified by Liling Jiang and deposited at the National duplicate Genbank for crops, Xining, Qinghai province, China. The specimen accession number were ZDM1651 and ZDM 8091.
2.2 Sample preparation and RNA isolation
Two highland barley genotypes were used for this study, including one pyroxsulam-sensitive genotype and one insensitive genotype. All highland barley seedlings were grown in a potting medium under a 11 h light (25°C)/13 h dark (20°C) day/night cycle with 75% humidity. The pyroxsulam herbicide was applied during the two-leaf stage, with the typical field application concentration of 12.5 g/666.7 m2. Leaf samples were harvested at 0, 1 and 6 days after treatment with pyroxsulam, then stored at − 80°C for metabolite profiling and RNA-sequencing. Total RNA was isolated using an RNA extraction kit from (Sangon, Shanghai, China).
2.3 Library preparation for transcriptome sequencing
Total RNA was used as input material for the RNA sample preparations. Briefly, mRNA was purified from total RNA using poly-T oligo-attached magnetic beads. Fragmentation was carried out using divalent cations under elevated temperature in First Strand Synthesis Reaction Buffer (5X). First-strand cDNA was synthesized using a random hexamer primer and M-MuLV Reverse Transcriptase, followed by RNaseH RNA digestion. Second-strand cDNA synthesis was subsequently performed using DNA Polymerase I and dNTPs. The remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. After adenylation of the 3’ ends of the DNA fragments, adaptors with hairpin loop structures were ligated to prepare for hybridization. In order to select cDNA fragments that were 370–420 bp in length, the library fragments were purified with the AMPure XP system (Beckman Coulter, Beverly, USA). Then, PCR was performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers and an index (X) Primer. Finally, PCR products were purified using the AMPure XP system and library quality was assessed on the Agilent Bioanalyzer 2100 system.
2.4 Sequence data mapping and transcriptome analysis
Raw FASTQ formatted reads were first processed through in-house Perl scripts to generate clean reads, with adaptor sequences trimmed and reads containing Ns or low-quality bases removed. Scripts were then used to calculate Q20, Q30 and GC content. All the downstream analyses were based on clean data with only high-quality reads retained.
Reference genome and gene model annotation files were downloaded from a public database. The reference genome index was built using Hisat2 v2.0.5 and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5.
2.5 Differential expression analysis
Differential expression analysis of the two conditions (three biological replicates per condition) was performed using the DESeq2 R package (1.20.0). The resulting P-values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate. Genes with an adjusted P-value < 0.05 found by DESeq2 were considered differentially expressed.
2.6 GO and KEGG enrichment analysis of differentially expressed genes
Gene Ontology (GO) enrichment analysis of differentially expressed genes (DEGs) was implemented via the clusterProfiler R package. GO terms with corrected P-values less than 0.05 were considered significantly enriched. KEGG was employed to determine high-level function of DEGs (http://www.genome.jp/kegg/). We used the clusterProfiler R package to test the statistical enrichment of DEGs in KEGG pathways.
2.7 Metabolite extraction
Each sample consisted of 100 mg of tissue, which was ground with liquid nitrogen, followed by resuspension in prechilled 80% methanol and 0.1% formic acid by vortexing. The samples were incubated on ice for 5 min and then centrifuged at 15,000 rpm and 4°C for 5 min. Some of the supernatant was diluted to a final concentration containing 53% methanol with LC-MS grade water. The samples were subsequently transferred to a fresh Eppendorf tube and were then centrifuged at 15,000 g and 4°C for 10 min. Finally, the supernatant was injected into an LC-MS/MS system for analysis . Liquid sample (100 µL) and prechilled methanol (400 µL) were mixed by vortexing [2–3]. Samples and prechilled 80% methanol were mixed by vortexing, and then sonicated for 6 min [4–5].
2.8 UHPLC-MS/MS analysis
UHPLC-MS/MS analyses were performed using a Vanquish UHPLC system (Thermo Fisher, Germany) coupled with an Orbitrap Q ExactiveTM HF mass spectrometer (Thermo Fisher, Germany) at the Novogene Co., Ltd. (Beijing, China). Samples were injected onto a Hypersil Gold column (100×2.1 mm, 1.9 µm) using a 17 min linear gradient at a flow rate of 0.2 mL/min. The eluents for the positive polarity mode were eluent A (0.1% FA in water) and eluent B (methanol). The eluents for the negative polarity mode were eluent A (5 mM ammonium acetate, pH 9.0) and eluent B (methanol). The solvent gradient was set as follows: 2% B, 1.5 min; 2-100% B, 12.0 min; 100% B, 14.0 min; 100-2% B, 14.1 min; and 2% B, 17 min. Q ExactiveTM HF mass spectrometer was operated in positive/negative polarity mode with spray voltage of 3.2 kV, capillary temperature of 320°C, sheath gas flow rate of 40 arb and aux gas flow rate of 10 arb.
2.9 Data processing and metabolite identification
The raw data files generated by UHPLC-MS/MS were processed using Compound Discoverer 3.1 software (CD3.1, Thermo Fisher) to perform peak alignment, peak picking and quantitation for each metabolite. The main parameters were set as follows: retention time tolerance, 0.2 minutes; actual mass tolerance, 5 ppm; signal intensity tolerance, 30%; signal/noise ratio, 3; and minimum intensity, 100,000. Next, peak intensities were normalized to the total spectral intensity. The normalized data were used to predict the molecular formula based on additive ions, molecular ion peaks and fragment ions. Peaks were then matched with the mzCloud (https://www.mzcloud.org/), mzVault and MassList databases to obtain accurate qualitative and relative quantitative results. Statistical analyses were performed using the statistical software R (R version R-3.4.3), Python (Python 2.7.6 version) and CentOS (CentOS release 6.6). When data were not normally distributed, normal transformations were attempted using the area normalization method.
3.0 Data analysis
Metabolites were annotated using the KEGG database (https://www.genome.jp/kegg/pathway.html), the human metabolome database (HMDB, https://hmdb.ca/metabolites) and LIPID Maps database (http://www.lipidmaps.org/). Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed with MetaX. We applied univariate analysis (t-test) to calculate the statistical significance (P-value).