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High level of UV-B irradiation followed by dark treatment accumulates secondary metabolites in Clematis terniflora DC. To investigate the response mechanism under high level of UV-B irradiation followed by dark treatment, transcriptomic and proteomic analyses were performed in leaves of Clematis terniflora DC. The experimental design for the transcriptomic and proteomic analyses in leaves of C. terniflora under stresses was organized into a picture. For transcriptomics, mRNA-sequencing technology was used. Genes identified in leaves of C. terniflora at starting point, high level of UV-B irradiation, and high level of UV-B irradiation followed by dark treatment were listed; genes with different expression levels at starting point, high level of UV-B irradiation, and high level of UV-B irradiation followed by dark treatment were also presented in this DiB article. For proteomics, a gel-free/label-free proteomic technique was used. Proteins with different abundances in leaves at starting point, high level of UV-B irradiation, and high level of UV-B irradiation followed by dark treatment were presented in this DiB article. In order to monitor the expression levels of genes under the stress, quantitative reverse transcription polymerase chain reaction was performed. The primer sequences of genes selected for quantitative reverse transcription polymerase chain reaction was presented in this DiB article.
Value of the data
The malondialdehyde content in leaves of C. terniflora under different doses of UV-B irradiation was measured (Fig. 1). The experimental design for the proteomic and transcriptomic analyses in leaves of C. terniflora under stresses was organized into a picture and presented in the DiB article (Fig. 2). The primer sequences of genes selected for quantitative reverse transcription polymerase chain reaction were presented in this DiB article (Table 1). Genes identified in leaves of C. terniflora at starting point (Table 2), high level of UV-B irradiation (Table 3), and high level of UV-B irradiation followed by dark treatment (Table 4) were presented. Genes with different expression levels at starting point, high level of UV-B irradiation, and high level of UV-B irradiation followed by dark treatment (Tables 5 and 6) were presented in this DiB article. Proteins with different abundances in leaves at starting point, high level of UV-B irradiation, high level of UV-B irradiation followed by dark treatment (Tables 7 and 8) were also presented in this DiB article. Furthermore, the original proteomic data exported from proteome discoverer have been attached (starting point, Supplemental Tables 1–3; UV-B, Supplemental Tables 4–6; UV-B+D, Supplemental Tables 7–9).
Seeds of Clematis terniflora DC. were incubated in water and the germinated seeds were sown into seedbeds. The seedlings were transplanted into potted containers and placed in greenhouse, which was controlled at 28–30 °C, 70–80% relative humidity, and 160 µmol m−2 s−1 of white light irradiance. After 6 weeks, plants were treated with UV-B irradiation in a cabinet, which was controlled at 25–30 °C, 80% relative humidity. The intensity of UV-B irradiation on the surface was at 104.4 kJ m−2 d−1, which was measured by a UV Light Meter (Beijing Normal University, Beijing, China). After UV-B irradiation, plants were treated with dark for 48 h. Leaves in the area from the basal 10–60 cm in each plant were collected for transcriptomic, proteomic, phytochemical, and enzymatic analyses. Five leaves were collected for one replication and 3 independent biological replicates were performed. Independent biological replicates mean that plants were treated with different date.
Proteins were extracted, reduced, alkylated, digested, and analyzed by nanoLC-MS/MS. Peptide ions were analyzed on a nanospray LTQ Orbitrap MS (Thermo Fisher Scientific, San Jose, CA, USA) operated in data-dependent acquisition mode with the installed Xcalibur software (version 2.1; Thermo Fisher Scientific). The acquired MS spectra were used for protein identification. Proteins were identified using the Mascot search engine (version 2.5.1; Matrix Science, London, UK) with UniProtKB/Swiss-Prot database (Viridiplantae; Release 2015_11, 37,036 sequences, http://www.uniprot.org/uniprot/). DTA files were generated from acquired raw data files and then converted to Mascot generic files using Proteome Discoverer software (version 184.108.40.2068; Thermo Fisher Scientific). Trypsin was specified as the proteolytic enzyme and one missed cleavage was allowed. Peptide mass tolerance was set at 10 ppm, fragment mass tolerance was set at 0.8 Da, and peptide charges were set at +2, +3, and +4. An automatic decoy database search was performed as part of the search. Mascot data were filtered with the Mascot percolator to improve the accuracy and sensitivity of peptide identification . False discovery rates for peptide identification of all searches were less than 1.0%. Peptides with a percolator ion score of more than 13 and a p-value less than 0.05 were used for protein identification.
The Mascot search data were exported in msf format for SIEVE analysis (version 2.1.377; Thermo Fisher Scientific). The relative abundances of peptides and proteins were compared between samples. For the analysis, the chromatographic peaks detected by MS were aligned and the peptide peaks were detected as a frame. Chromatographic peak areas of each sample within a single frame were compared and the ratios between samples in each frame were determined. The frames detected in the MS/MS scan were matched to the imported Mascot search data. The ratio of peptides between samples was determined from the relative variance-weighted average of the ratios in frames, which matched the peptides in the MS/MS spectrum. The ratios of peptides were further integrated to determine the ratio of the corresponding protein. In the differential analysis of protein abundance, total ion current was used for normalization. The minimum requirement for the identification of a protein was a minimum of 2 matched peptides and a p-value less than 0.05.
RNAs were extracted, sequenced, and analyzed. The mRNA-sequencing libraries were sequenced using a paired end read protocol with 100 base pairs of data collected per run on the Hiseq. 2000 sequencing platform (Illumina). De novo assembly of the transcripts was carried out using the short read assembly program ‘Trinity’ , . Unigene sequences were aligned by BLASTX to UniProtKB/Swiss-Prot database using an E-value cut-off of 10−5. Coding sequence regions were determined for the highest-ranked proteins using BLAST. Unigenes that could not be aligned to database were scanned by ESTScan  to determine the nucleotide (5′−3′) and amino acid sequences of the coding regions. The value of reads per kilobase million (RPKM) was used to represent the expression level of genes. For gene identification analysis, the alternatively spliced genes were filed according to the criterion which is more than 100 by the RPKM value of transcripts . For gene expression analysis, all the genes with RPKM value were used. A significant change of gene expression between samples is identified as 10-fold change of RPKM value.
Protein and gene functions were categorized using MapMan bin codes (http://mapman.gabipd.org/) . The predication of identified proteins and genes derived from C. terniflora was performed by transferring annotations to the Arabidopsis genome and consideration of orthologous genes.
The authors thank Ms. C. Gao of Zhejiang University for sample preparation and thank Ms. X. Wang and Mr. X. Yin of the National Institute of Crop Science for useful discussion. This work was supported by the National Science Foundation of China (Grant no. 81274033).