Open Access

De novo transcriptome analysis of gene responses to pest feeding in leaves of Panax ginseng C. A. Meyer

  • Authors:
    • Guangsheng Xi
    • Yanling Wang
    • Le Yin
    • Yunjia Wang
    • Shengxue Zhou
  • View Affiliations

  • Published online on: May 22, 2019     https://doi.org/10.3892/mmr.2019.10275
  • Pages: 433-444
  • Copyright: © Xi et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

The aim of the present study was to investigate the transcriptomic differences between Panax ginseng [Renshen (RS)] plants bitten by pests (n=3, test group; samples defined as RS11‑13) or not (n=3, control group; samples defined as RS1‑3) using de novo RNA sequencing on an Illumina HiSeq™ 2000 platform. A total of 51,097,386 (99.6%), 49,310,564 (99.5%), 59,192,372 (99.6%), 60,338,540 (99.5%), 56,976,410 (99.6%) and 54,226,588 (99.6%) clean reads were obtained for RS11, RS12, RS13, RS1, RS2 and RS3, respectively. De novo assembly generated 370,267 unigenes, 927 of which were differentially expressed genes (DEGs), including 782 significantly upregulated and 145 significantly downregulated genes. Function enrichment analysis revealed that these DEGs were located in 28 significantly enriched Kyoto Encyclopedia of Genes and Genomes pathways, including phenylpropanoid biosynthesis (for example, TRINITY_DN30766_c0_g2_i1, encoding peroxidase 20) and mitogen‑activated protein kinase (MAPK) signaling (TRINITY_DN85589_c0_g1_i1, encoding WRKY transcription factor 75). Weighted gene co‑expression network analysis identified modules including TRINITY_DN85589_c0_g1_i1, TRINITY_DN58279_c0_g1_i1 [encoding aspartyl protease (AP)] and TRINITY_DN74866_c0_g2_i1 [encoding 12‑oxophytodienoate reductase (OPR)] that may be the most significantly associated with pest responses. In this module, TRINITY_DN85589_c0_g1_i1 may co‑express with TRINITY_DN58279_c0_g1_i1 or TRINITY_DN74866_c0_g2_i1. WRYK and AP have been suggested to promote the activity of antioxidant peroxidase. Collectively, the findings from the present study suggested that a MAPK‑WRKY‑OPR/AP‑peroxidase signaling pathway may be a potentially important mechanism underlying defense responses against pests in ginseng plants.

Introduction

Panax ginseng C. A. Meyer is a popular medicinal plant species grown in northeast China. Previous studies have reported that ginseng exhibits a wide range of pharmacological effects (1), including antifatigue (2), antitumor (3), antioxidant (4), antidiabetic (5), anti-obesity (6) and immunomodulatory (3) effects. Thus, there is notable demand for ginseng products on the market; however, in the wild, ginseng plants are susceptible to attack from a range of native and invasive pests (7), including Locusta migratoria L., Loxostege sticticalis and Xestia c-nigrum, which lead to substantial losses in production and quality. Thus, it is necessary to understand the molecular mechanisms underlying plant-pest interaction, particularly resistance and defense against pest feeding, to optimize the environmental conditions and develop resistant ginseng varieties.

Previous studies have investigated the activity of molecular response mechanisms to pest herbivory in various plants, including plant hormone signal transduction [involving jasmonic acid (JA), ethylene, abscisic acid (AA) and salicylic acid] and transcriptional activation of defense-associated genes [superoxide dismutase (SOD), peroxidase, ascorbate peroxidase (APX), polyphenol oxidase, phenylalanine ammonia lyase, catalase (CAT) and glutathione-S-transferase (GST)] (8,9); however, there is limited information regarding the defense responses of ginseng against pests. In the present study, RNA sequencing (RNA-Seq) was conducted to analyze transcriptomic responses to pest attacks in ginseng plants.

Materials and methods

Plant materials

The 4-year-old Panax ginseng C. A. Meyer was cultivated in the experimental fields of Jilin Agricultural Science and Technology College (44°02‘33.34’ N, 126°06‘22.64’ W). Jilin City is located in a temperate continental monsoon climate, with an annual mean temperature of 5.6°C (high, 22.1°C) and a mean annual rainfall of 679 mm. A total of 6 ginseng plants were included in the study; 3 (RS11, RS12 and RS13; test group) were exposed to feeding by pests (mainly Locusta migratoria L.; Fig. 1), whereas 3 (RS1, RS2 and RS3; control group) were not. Leaves were harvested following exposure to pests for 3–4 days, and three replicates were conducted for each plant to pool the samples. Following cleaning, the leaves were immediately frozen in liquid nitrogen, and stored at −80°C until further use.

RNA isolation and sequencing

Total RNA was extracted from the samples using TRIzol® reagent (Thermo Fisher Scientific, Inc.). The integrity of the total RNA was determined via 1% agarose gel electrophoresis and its concentration was quantified using an Agilent 2100 Bioanalyzer (Agilent Technologies, Inc.). Total RNA (~1 µg) with RNA Integrity Number ≥8 was used for library construction using a NEBNext® Ultra™ RNA Library Prep kit for Illumina® (New England BioLabs, Inc.) following the manufacturer's protocols: The NEBNext Poly(A) mRNA Magnetic Isolation Module was used for isolation of poly(A) mRNA. mRNA fragmentation and priming was conducted using NEBNext First Strand Synthesis Reaction Buffer and NEBNext Random Primers. Fragmented RNA was reverse transcribed into first-strand cDNA using the ProtoScript II Reverse Transcriptase, and second-strand cDNA was synthesized using the Second Strand Synthesis Enzyme Mix (all New England BioLabs, Inc.). Double-stranded cDNA was purified via AxyPrep Mag PCR Clean-up (Axygen; Corning, Inc.) and then treated with the End Prep Enzyme Mix (New England BioLabs, Inc.) to repair the ends, and attach a dA-tail to one end and adaptors to the two ends. Size selection of adaptor-ligated DNA was also performed using AxyPrep Mag PCR Clean-up, and fragments of ~360 bp were recovered. Then, 11 cycles of PCR amplification were performed using P5 (5′-AGATCGGAAGAGCGTCGTGTAGGGAAAGA-3′) and P7 (5′-GATCGGAAGAGCACACGTCTGAACTCCAGTCACAAGACGGAATCTCGTATGCCGTCTTCTGCTTG-3′) primers with Phusion® Hot Start Flex 2X Master Mix (New England Biolabs, Inc.) under the following thermocycling conditions: 98°C for 10 sec, 60°C for 30 sec, and 72°C for 15 sec, and 72°C for 10 min, to enrich the purified cDNA. The PCR products were cleaned up using AxyPrep Mag PCR Clean-up, validated using an Agilent 2100 Bioanalyzer and quantified using a Qubit 2.0 Fluorometer (Invitrogen; Thermo Fisher Scientific, Inc.). The cDNA library was sequenced by Genewiz, Inc. using an Illumina HiSeq 2000 sequencer (Illumina, Inc.) in 2×150 bp paired-end (PE) mode.

RNA-Seq data analysis

Raw Illumina data were demultiplexed using BCL2FASTQ software (version 2.20; Illumina, Inc.). Raw read quality was determined using FastQC (version 0.10.1; http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The reads were pre-processed using Cutadapt (version 1.9.1; http://cutadapt.readthedocs.io/en/stable/) (10) to remove residual adaptor sequences, and reads with low-quality bases (<20 nt in length), N content >10% and length <75 bp following trimming. High-quality clean data in fastq format were assembled de novo to generate the unigene sequence file using the Trinity program (version 2.2.0) with the default parameters (11). Unigenes were annotated via Basic Local Alignment Search Tool against public databases, including non-redundant protein database (nr; http://blast.ncbi.nlm.nih.gov/Blast.cgi), Clusters of Orthologous Groups (COG; http://www.ncbi.nlm.nih.gov/COG/) and Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.kegg.jp).

Bowtie2 version 2.1.0 (12) with the default parameters was used to map the clean reads to the unigenes. RNA-Seq by Expectation Maximization version 1.2.6 (13) was used to estimate the expression levels (fragments per kilobase per million mapped reads) of genes and isoforms from the PE clean data. The differentially expressed genes (DEGs) between the test and control groups were identified using the Bioconductor package DESeq2 (version 1.6.3; http://bioconductor.org/packages/release/bioc/html/DESeq2.html) (14), a model based on a negative binomial distribution. The P-value was adjusted by Benjamini and Hochberg's method (15) to control for false discovery rate (FDR). FDR <0.05 and |log2 fold change (FC)| >1 (FC >2) were set as the threshold value. The underlying functions of DEGs were predicted via KEGG pathway enrichment analyses with a hypergeometric test. An adjusted P-value (Q-value) <0.05 was considered to be statistically significant. In addition, weighted gene co-expression network analysis (16) was performed to identify significant modules of highly associated genes related to pest responses from the DEGs and all unigenes. Highly connected genes may be regarded as hub genes. The top co-expression pairs (weight >0.6) were used to construct the co-expression network using Cytoscape software (version 2.8; www.cytoscape.org/) (17).

Results

Illumina sequence analysis

To determine the global transcriptome profile of ginseng in response to pests, three RNA libraries were constructed and deep RNA-Seq was performed on the leaves of ginseng plants from the test and control groups. A total of 51,323,700, 49,534,966, 59,458,254, 60,617,462, 57,222,860 and 54,469,800 raw reads were generated for the RS11, RS12, RS13, RS1, RS2 and RS3 samples, respectively (Table I). Following quality control to remove the low-quality reads and adaptor sequences, 51,097,386 (99.6%), 49,310,564 (99.5%), 59,192,372 (99.6%), 60,338,540(99.5%), 56,976,410 (99.6%) and 54,226,588 (99.6%) clean reads were retained for further analysis (Table I). Additionally, >98% of the reads exhibited an average quality score of >20 (Q20) and the GC content was consistently ~43% for all samples, suggesting that the sequencing was highly accurate.

Table I.

Quality control results.

Table I.

Quality control results.

A, Raw reads

SamplesLengthReadsBasesQ20, %Q30, %GC, %N, ppm
RS1115051,323,7007,698,555,00097.8694.9843.60413.32
RS1215049,534,9667,430,244,90097.8394.9343.38410.77
RS1315059,458,2548,918,738,10097.9595.1443.77406.53
RS115060,617,4629,092,619,30097.8094.8543.56406.93
RS215057,222,8608,583,429,00097.8294.8944.07406.57
RS315054,469,8008,170,470,00097.8594.9743.88410.32

B, Clean reads

SamplesLengthReadsBasesQ20, %Q30, %GC, %N, ppm

RS11148.8751,097,3867,606,807,84398.1395.3243.658.09
RS12148.8549,310,5647,339,855,63698.1195.2743.438.01
RS13148.8059,192,3728,807,982,12798.2295.4743.818.09
RS1148.8860,338,5408,983,240,21298.0795.2043.628.03
RS2148.8956,976,4108,482,933,39798.0995.2244.128.16
RS3148.8754,226,5888,072,497,76898.1295.3143.938.08

[i] Q20 and Q30, the percentage of bases with Phred values >20 and >30, respectively; GC content, the GC ratio of the total base number.

De novo assembly of the clean reads produced 11,548,589 contigs of 678,729,555 nucleotides (nt); the average length of these contigs was 58.77 nt, with an N50 of 48 nt. Further assembly of these contigs generated 370,267 unigenes with a mean length and N50 of 626.17 and 839 nt, respectively. A total of 230,086 unigenes (62.14%) were 200–500 nt in length; 83,195 unigenes (22.47%) were 500–1,000 nt; and 56,985 unigenes (15.39%) were >1,000 nt.

Functional annotation results revealed that 200,394 unigenes (54.1%) were annotated to at least one public database. In total, 191,132 unigenes were annotated to the Nr database, among which 62,196 unigenes were identified in Daucus carota subsp. sativus [including TRINITY_DN85589_c0_g1_i1, which may encode WRKY transcription factor 75 (gi|1040876417|ref|XP_017248451.1|); TRINITY_DN74866_c0_g2_i1, which may encode 12-oxophytodienoate reductase (OPR)2-like isoform X1 (gi|1040859474|ref|XP_017240506.1|); and TRINITY_DN30766_c0_g2_i1, which may encode peroxidase 20 (gi|1040813078|ref|XP_017228400.1|); Fig. 2]. Of the 97,892 unigenes that were assigned to the COG database, 11,938 unigenes belonged to the cluster ‘Post-translational modification, protein turnover, chaperones’ [including TRINITY_DN58279_c0_g1_i1, aspartyl protease (AP; KOG1339)], followed by ‘general function prediction only’ [11,648 unigenes, including TRINITY_DN74866_c0_g2_i1, NADH:flavin oxidoreductase/ODR (KOG0134); Fig. 3]. A total of 53,451 unigenes were mapped to 128 KEGG pathways, among which ‘signal transduction’ was the most enriched, featuring 15,940 unigenes, including TRINITY_DN85589_c0_g1_i1 (encoding WRKY transcription factor 33; Fig. 4). WRKY transcription factor 33 was enriched in the ‘MAPK signaling pathway-plant’ (ko04016) and in ‘plant-pathogen interactions’ (ko04626) (data not shown). ‘Carbohydrate metabolism’ was the second most enriched pathway, featuring 8,759 unigenes, including those involves in phenylpropanoid biosynthesis, such as TRINITY_DN30766_c0_g2_i1.

DEG analysis

Comparative transcriptome profiling yielded 927 DEGs, including 782 significantly upregulated and 145 significantly downregulated genes (Table II). The heat map indicated that these DEGs (Fig. 5) could clearly distinguish between the test and control groups. These DEGs were significantly enriched into 28 KEGG pathways (Fig. 6; Table III), including ‘phenylpropanoid biosynthesis’ (TRINITY_DN30766_c0_g2_i1) and ‘MAPK signaling pathway-plant’ (TRINITY_DN85589_c0_g1_i1).

Table II.

Top 20 upregulated and downregulated differentially expressed genes.

Table II.

Top 20 upregulated and downregulated differentially expressed genes.

Gene IDlog2FCFDR
TRINITY_DN178238_c0_g1_i15.80 1.25×10−103
TRINITY_DN16862_c0_g2_i15.12 1.73×10−62
TRINITY_DN91119_c0_g4_i24.86 4.09×10−55
TRINITY_DN16862_c0_g1_i14.69 1.36×10−50
TRINITY_DN71994_c0_g2_i14.66 1.30×10−49
TRINITY_DN101501_c0_g1_i24.61 4.50×10−51
TRINITY_DN86652_c0_g1_i14.60 3.58×10−51
TRINITY_DN109207_c1_g3_i14.44 2.91×10−46
TRINITY_DN87037_c0_g2_i24.22 3.23×10−41
TRINITY_DN106559_c0_g1_i14.13 5.08×10−38
TRINITY_DN109207_c1_g7_i14.04 1.77×10−35
TRINITY_DN101501_c0_g1_i13.98 1.90×10−34
TRINITY_DN109207_c1_g4_i13.68 5.59×10−28
TRINITY_DN38537_c0_g1_i13.54 8.21×10−38
TRINITY_DN109207_c1_g4_i23.51 4.92×10−25
TRINITY_DN105289_c0_g1_i23.48 1.19×10−24
TRINITY_DN58279_c0_g1_i11.59 1.06×10−06
TRINITY_DN30766_c0_g2_i11.49 6.01×10−04
TRINITY_DN85589_c0_g1_i11.35 5.32×10−03
TRINITY_DN111795_c3_g1_i71.045 3.00×10−02
TRINITY_DN118817_c1_g2_i6−1.66 8.48×10−05
TRINITY_DN119005_c2_g8_i2−1.68 6.85×10−05
TRINITY_DN117632_c1_g3_i4−1.68 1.35×10−05
TRINITY_DN94357_c3_g1_i6−1.69 5.64×10−05
TRINITY_DN119337_c3_g11_i6−1.71 3.85×10−05
TRINITY_DN108111_c1_g12_i4−1.76 2.38×10−05
TRINITY_DN113154_c3_g3_i11−1.85 6.97×10−06
TRINITY_DN117824_c1_g15_i8−1.87 4.71×10−06
TRINITY_DN100396_c0_g3_i2−1.90 3.17×10−06
TRINITY_DN111108_c0_g1_i9−1.91 2.95×10−06
TRINITY_DN116269_c4_g2_i9−1.95 1.49×10−06
TRINITY_DN119178_c2_g5_i2−1.95 6.83×10−07
TRINITY_DN114880_c1_g1_i1−1.99 7.82×10−07
TRINITY_DN113891_c0_g1_i2−2.12 6.78×10−08
TRINITY_DN118920_c1_g3_i21−2.20 1.98×10−08
TRINITY_DN114499_c0_g1_i5−2.22 8.01×10−09
TRINITY_DN116506_c3_g5_i18−2.30 2.10×10−09
TRINITY_DN117970_c1_g2_i1−2.40 1.79×10−10
TRINITY_DN117566_c0_g1_i1−2.82 3.79×10−15
TRINITY_DN113064_c0_g3_i4−3.55 8.26×10−26

[i] FC, fold change; FDR, false discovery rate.

Table III.

KEGG pathways for the differentially expressed genes.

Table III.

KEGG pathways for the differentially expressed genes.

Pathway IDPathwayGene listQ-value
ko00940 Phenylpropanoid TRINITY_DN38537_c0_g1_i1, TRINITY_DN119027_c3_g1_i5, 1.40×10−21
biosynthesis TRINITY_DN90698_c1_g1_i1, TRINITY_DN93128_c0_g1_i1,
TRINITY_DN104257_c1_g1_i1, TRINITY_DN104257_c1_g1_i5,
TRINITY_DN104257_c1_g1_i2, TRINITY_DN117744_c5_g27_i4,
TRINITY_DN119027_c3_g1_i1, TRINITY_DN111296_c1_g1_i1,
TRINITY_DN119027_c3_g1_i2, TRINITY_DN119027_c3_g1_i7,
TRINITY_DN56484_c1_g1_i1, TRINITY_DN110990_c3_g4_i4,
TRINITY_DN2548_c0_g1_i1, TRINITY_DN110990_c3_g4_i3,
TRINITY_DN30766_c0_g2_i1, TRINITY_DN102469_c0_g1_i1,
TRINITY_DN77906_c0_g1_i1, TRINITY_DN111296_c1_g4_i3,
TRINITY_DN102469_c0_g2_i1, TRINITY_DN119565_c6_g21_i1,
TRINITY_DN114431_c3_g1_i1, TRINITY_DN116123_c1_g9_i3
ko01040Biosynthesis of TRINITY_DN115911_c2_g17_i2, TRINITY_DN70767_c1_g1_i1, 9.31×10−16
unsaturated fatty acids TRINITY_DN115911_c2_g17_i5, TRINITY_DN38619_c0_g1_i1,
TRINITY_DN62336_c0_g1_i1, TRINITY_DN108852_c3_g3_i1,
TRINITY_DN35750_c0_g1_i1, TRINITY_DN77948_c0_g1_i1,
TRINITY_DN115911_c2_g17_i7, TRINITY_DN105028_c0_g5_i1,
TRINITY_DN105028_c0_g7_i1, TRINITY_DN75773_c0_g1_i1,
TRINITY_DN117863_c6_g4_i1, TRINITY_DN115911_c2_g22_i2,
TRINITY_DN115911_c2_g34_i1, TRINITY_DN2487_c0_g1_i1,
TRINITY_DN105028_c0_g1_i1
ko00073Cutin, suberine and wax TRINITY_DN106559_c0_g1_i1, TRINITY_DN105289_c0_g1_i2, 1.20×10−12
biosynthesis TRINITY_DN110555_c1_g3_i1, TRINITY_DN103944_c0_g2_i1,
TRINITY_DN108493_c1_g1_i3, TRINITY_DN112895_c0_g1_i2,
TRINITY_DN112895_c0_g1_i1, TRINITY_DN110677_c1_g1_i2,
TRINITY_DN110677_c1_g1_i1
ko01212Fatty acid metabolism TRINITY_DN112064_c0_g1_i3, TRINITY_DN115911_c2_g17_i2, 1.33×10−10
TRINITY_DN70767_c1_g1_i1, TRINITY_DN115911_c2_g17_i5,
TRINITY_DN38619_c0_g1_i1, TRINITY_DN62336_c0_g1_i1,
TRINITY_DN108852_c3_g3_i1, TRINITY_DN35750_c0_g1_i1,
TRINITY_DN77948_c0_g1_i1, TRINITY_DN115911_c2_g17_i7,
TRINITY_DN105028_c0_g5_i1, TRINITY_DN88167_c0_g1_i2,
TRINITY_DN105028_c0_g7_i1, TRINITY_DN75773_c0_g1_i1,
TRINITY_DN117863_c6_g4_i1, TRINITY_DN115911_c2_g22_i2,
TRINITY_DN115911_c2_g34_i1, TRINITY_DN2487_c0_g1_i1,
TRINITY_DN105028_c0_g1_i1
ko00360Phenylalanine TRINITY_DN119027_c3_g1_i5, TRINITY_DN117744_c5_g27_i4, 3.63×10−08
metabolism TRINITY_DN119027_c3_g1_i1, TRINITY_DN111296_c1_g1_i1,
TRINITY_DN119027_c3_g1_i2, TRINITY_DN119027_c3_g1_i7,
TRINITY_DN59880_c0_g2_i1, TRINITY_DN77906_c0_g1_i1,
TRINITY_DN111296_c1_g4_i3, TRINITY_DN119565_c6_g21_i1,
TRINITY_DN114431_c3_g1_i1
ko00941Flavonoid biosynthesis TRINITY_DN117744_c5_g27_i4, TRINITY_DN103733_c0_g4_i1, 5.32×10−06
TRINITY_DN56484_c1_g1_i1, TRINITY_DN25782_c0_g1_i1,
TRINITY_DN119565_c6_g21_i1
ko00040Pentose and glucuronate TRINITY_DN102935_c1_g1_i1, TRINITY_DN105582_c5_g2_i3, 1.54×10−04
interconversions TRINITY_DN107139_c0_g1_i2, TRINITY_DN61993_c1_g1_i1,
TRINITY_DN65703_c1_g1_i1, TRINITY_DN108358_c0_g2_i5,
TRINITY_DN108358_c0_g2_i1, TRINITY_DN96538_c0_g1_i2,
TRINITY_DN113543_c1_g1_i1
ko00591Linoleic acid metabolism TRINITY_DN164530_c0_g1_i1, TRINITY_DN38928_c0_g1_i1, 1.83×10−04
TRINITY_DN118754_c5_g7_i4, TRINITY_DN118754_c5_g7_i1
ko04745 Phototransduction-fly TRINITY_DN116200_c1_g13_i2, TRINITY_DN103329_c0_g1_i3, 3.82×10−04
TRINITY_DN116190_c4_g2_i4, TRINITY_DN113735_c2_g1_i1,
TRINITY_DN118958_c1_g1_i1, TRINITY_DN118958_c1_g3_i1
ko00945Stilbenoid, diarylheptanoid TRINITY_DN117744_c5_g27_i4, TRINITY_DN56484_c1_g1_i1, 1.26×10−03
and gingerol biosynthesis TRINITY_DN119565_c6_g21_i1
ko05412ARVC TRINITY_DN116200_c1_g13_i2, TRINITY_DN116190_c4_g2_i4, 1.82×10−03
TRINITY_DN113735_c2_g1_i1, TRINITY_DN118958_c1_g1_i1,
TRINITY_DN118958_c1_g3_i1
ko05414Dilated cardiomyopathy TRINITY_DN116200_c1_g13_i2, TRINITY_DN116190_c4_g2_i4, 1.82×10−03
TRINITY_DN113735_c2_g1_i1, TRINITY_DN118958_c1_g1_i1,
TRINITY_DN118958_c1_g3_i1
ko04210Apoptosis TRINITY_DN113765_c2_g1_i4, TRINITY_DN80010_c0_g2_i1, 8.98×10−03
TRINITY_DN109158_c4_g3_i4, TRINITY_DN116200_c1_g13_i2,
TRINITY_DN116190_c4_g2_i4, TRINITY_DN113735_c2_g1_i1,
TRINITY_DN118958_c1_g1_i1, TRINITY_DN111795_c3_g1_i7,
TRINITY_DN107791_c3_g1_i5, TRINITY_DN118958_c1_g3_i1
ko00592α-linolenic acid TRINITY_DN164530_c0_g1_i1, TRINITY_DN38928_c0_g1_i1, 9.96×10−03
metabolism TRINITY_DN118754_c5_g7_i4, TRINITY_DN118754_c5_g7_i1,
TRINITY_DN61851_c1_g2_i1
ko00908Zeatin biosynthesis TRINITY_DN116264_c1_g3_i5, TRINITY_DN95422_c1_g1_i2 1.32×10−02
ko05130Pathogenic TRINITY_DN113765_c2_g1_i4, TRINITY_DN80010_c0_g2_i1, 1.52×10−02
Escherichia coli infection TRINITY_DN109158_c4_g3_i4, TRINITY_DN116200_c1_g13_i2,
TRINITY_DN116190_c4_g2_i4, TRINITY_DN113735_c2_g1_i1,
TRINITY_DN118958_c1_g1_i1, TRINITY_DN118958_c1_g3_i1
ko05410HCM TRINITY_DN116200_c1_g13_i2,TRINITY_DN116190_c4_g2_i4, 1.52×10−02
TRINITY_DN113735_c2_g1_i1,TRINITY_DN118958_c1_g1_i1,
TRINITY_DN118958_c1_g3_i1,
ko02024Quorum sensing TRINITY_DN112064_c0_g1_i3, TRINITY_DN102935_c1_g1_i1, 1.52×10−02
TRINITY_DN61993_c1_g1_i1, TRINITY_DN108358_c0_g2_i5,
TRINITY_DN108358_c0_g2_i1, TRINITY_DN97205_c0_g2_i1,
TRINITY_DN96538_c0_g1_i2
ko05416Viral myocarditis TRINITY_DN116200_c1_g13_i2, TRINITY_DN116190_c4_g2_i4, 1.78×10−02
TRINITY_DN113735_c2_g1_i1, TRINITY_DN118958_c1_g1_i1,
TRINITY_DN107791_c3_g1_i5, TRINITY_DN118958_c1_g3_i1
ko05133Pertussis TRINITY_DN112623_c3_g1_i4, TRINITY_DN106201_c1_g2_i7, 2.30×10−02
TRINITY_DN103329_c0_g1_i3, TRINITY_DN75555_c1_g1_i1,
TRINITY_DN31358_c0_g2_i1, TRINITY_DN109488_c1_g1_i11,
TRINITY_DN111795_c3_g1_i7, TRINITY_DN109488_c1_g1_i6,
TRINITY_DN88066_c0_g1_i1
ko00904Diterpenoid biosynthesis TRINITY_DN105174_c1_g1_i1, TRINITY_DN108094_c1_g2_i1 2.47×10−02
ko04611Platelet activation TRINITY_DN116200_c1_g13_i2, TRINITY_DN116190_c4_g2_i4, 2.47×10−02
TRINITY_DN113735_c2_g1_i1, TRINITY_DN118958_c1_g1_i1,
TRINITY_DN111795_c3_g1_i7, TRINITY_DN118958_c1_g3_i1
ko05140Leishmaniasis TRINITY_DN112623_c3_g1_i4, TRINITY_DN106201_c1_g2_i7, 2.77×10−02
TRINITY_DN75555_c1_g1_i1, TRINITY_DN31358_c0_g2_i1,
TRINITY_DN109488_c1_g1_i11, TRINITY_DN111795_c3_g1_i7,
TRINITY_DN109488_c1_g1_i6, TRINITY_DN88066_c0_g1_i1
ko00052Galactose metabolism TRINITY_DN114499_c0_g1_i5, TRINITY_DN112361_c0_g1_i1, 3.33×10−02
TRINITY_DN105582_c5_g2_i3, TRINITY_DN114499_c0_g1_i7,
TRINITY_DN114401_c0_g1_i7, TRINITY_DN116821_c1_g3_i6
ko04614Renin-angiotensin system TRINITY_DN113178_c1_g1_i7, TRINITY_DN118282_c3_g5_i8 3.33×10−02
ko04016MAPK signaling TRINITY_DN101657_c1_g1_i1, TRINITY_DN115200_c1_g1_i3, 3.33×10−02
pathway-plant TRINITY_DN103013_c0_g1_i1, TRINITY_DN103329_c0_g1_i3,
TRINITY_DN113050_c3_g1_i3, TRINITY_DN85589_c0_g1_i1,
TRINITY_DN87820_c0_g1_i2
ko04670Leukocyte TRINITY_DN116200_c1_g13_i2, TRINITY_DN116190_c4_g2_i4, 3.71×10−02
transendothelial TRINITY_DN113735_c2_g1_i1, TRINITY_DN118958_c1_g1_i1,
migration TRINITY_DN118958_c1_g3_i1
ko01524Platinum drug resistance TRINITY_DN104989_c0_g2_i1, TRINITY_DN80125_c0_g2_i1, 4.25×10−02
TRINITY_DN111271_c1_g1_i3, TRINITY_DN111271_c1_g1_i3,
TRINITY_DN111795_c3_g1_i7, TRINITY_DN107791_c3_g1_i5

[i] ARVC, arrhythmogenic right ventricular cardiomyopathy; HCM, hypertrophic cardiomyopathy; MAPK, mitogen-activated protein kinase; Q-value, adjusted P-value.

To determine the important genes involved in pest responses, a co-expression network was constructed. DEGs with similar patterns of expression were grouped into four modules via hierarchical average linkage clustering (Fig. 7A). The turquoise module may be the most strongly associated with pest responses, as the majority of the top 50 genes (including TRINITY_DN30766_c0_g2_i1 and TRINITY_DN85589_c0_g1_i1) in the co-expression network (weight >0.74; Fig. 7B) were located in the turquoise module. TRINITY_DN85589_c0_g1_i1 co-expressed with TRINITY_DN58279_c0_g1_i1 or TRINITY_DN74866_c0_g2_i1 (data not shown) in co-expression networks for DEGs or all unigenes.

Discussion

In the present study, the genetic response to pest bites in the leaves of Jilin ginseng plants was sequenced. Analysis of gene sequencing and expression revealed that activation of the MAPK pathway via the upregulation of WRKY transcription factors, and the co-expression of AP or ODR, may be an important mechanism in response to pest stress in ginseng plants.

Increasing evidence has indicated that WRKY transcription factors are expressed in response to various types of stress, including salt (18), drought (19), heat (20), abscisic acid (19), salicylic acid (21), pathogens (2123) and herbivores (24,25). Overexpression of WRKY is reported to increase the transcription of antioxidant enzyme genes, including APX, CAT, GST and SOD to reduce reactive oxygen species content, positively regulating plant responses to stress (26,27) and suppressing leaf senescence (28). Furthermore, a series of other stress-associated genes, including cold-regulated 15a (COR15A), COR15B, COR413, COR6.6 (29), OsFRDL4 (30), DgNCED3A, DgNCED3B, DgP5CS, DgCSD1 and DgCSD2 (31) were also reported to be upregulated in WRKY transgenic plants compared with in wild-type plants. Consistent with these studies, it was demonstrated in the present study that WRKY75 and WRKY33 may be significantly upregulated in the leaves of ginseng plants in response to herbivore bites.

The roles of WRKY75 or 33 remain unclear; however, the present study predicted that WRKY may be regulated by upstream MAPKs and interact downstream with ODR or AP. In a previous study, chromatin immunoprecipitation assays revealed that WRKY33 is a substrate of MAPK3/MAPK6, two pathogen-responsive MAPKs, involved in the induction of phytoalexin camalexin production in Arabidopsis thaliana (32). Additionally, mutations of the MAPK3/MAPK6 phosphorylation sites in WRKY33 reduces its ability to promote camalexin induction (32). Similarly, the levels of WRKY53 transcription were reported to be positively regulated by MAPK3/MAPK6 in rice (33). Adachi et al (34) revealed that WRKY transcription factors functioned as substrates of the MAPK kinase 2/salicylic acid-induced protein kinase/wound-induced protein kinase signaling cascade. In the present study, MAPK10 (TRINITY_DN111795_c3_g1_i7) was demonstrated to be significantly upregulated, indicating that a MAPK10/WRKY33 signaling cascade may be a mechanism underlying pest responses in ginseng plants.

OPRs belong to a family of flavin-dependent oxidoreductases. OPRs are reported to convert 12-oxophytodienoate into 12-oxophytoenoic acid and participate in the biosynthesis of JA from linolenic acid via the Vick-Zimmerman pathway (3537). JA is considered to be a signaling molecule involved in stress responses to wounds and herbivore infestation; Xin et al (38) reported that OPR3 was highly expressed in the leaves of Camellia sinensis (L.) exposed to Ectropis obliqua Prout, accompanied by increased JA levels. AP was also demonstrated to regulate fungal and osmotic stress responses in plants (3942). Overexpression of AP may lead to increased AA levels and promote the activities of various antioxidants, inducing protective autophagy and conferring resistance to stress (40,43). Consistent with these studies, AP was also found to be upregulated in the present study. Thus, it is hypothesized that OPRs and AP may be important downstream targets of WRKY33 in ginseng plants during pest responses.

In addition to the MAPK pathway, the phenylpropanoid biosynthesis pathway was also identified to be significantly enriched with DEGs, including peroxidase 20. Peroxidase is an important antioxidant for pest resistance (44). WRKY (45) and AP (40) promote the transcription of peroxidase; the MAPK and phenylpropanoid biosynthesis pathways may be associated regulatory mechanisms underlying pest resistance.

There are certain limitations to the present study. The sample size was small, which may explain the lack of statistical significance observed for the expression of OPRs. Furthermore, additional experiments (i.e., silencing, PCR, determination of enzyme activity and hormone level detection) (8) are required to validate the importance of the hypothesized MAPK10-WRKY33-OPR/AP-peroxidase 20 pathway or pathways, and their dependence on JA or AA in the pest resistance of ginseng plants.

To the best of our knowledge, the present study reports the first investigation of the transcriptional responses of Panax ginseng C. A. Meyer to pest feeding. The findings suggested that MAPK10-WRKY33-OPR/AP-peroxidase 20 signaling may be a important mechanism underlying defense responses against pests. Further experiments should be conducted to support these conclusions and increase our understanding of plant resistance to pest feeding.

Acknowledgements

Not applicable.

Funding

The present study was supported by the National Natural Science Foundation of China subsidization project for the study of insect resistance of ginsenosides and its effect on the evolution of environmental suitability of ginseng (grant no. 31470420), the 13th Five-Year Science Project of the Jilin Provincial Department of Education: Screening of active components of traditional Chinese medicine targets for stroke-type cerebral ischemia (project no. JJKH20180743KJ), Jilin Province Chinese Medicine Key Subjects of Jilin Agricultural Science and Technology College, and the Provincial Key Laboratory of Technological Innovation in Production and Utilization of Authentic herbs for Jilin Province Production.

Availability of data and materials

The datasets used and/or analyzed during the present study are available from the corresponding author upon reasonable request.

Authors' contributions

GSX and SXZ designed the study. GSX, YLW, LY collected the samples. GSX and YJW contributed to the statistical analyses. GSX, YLW, LY and SXZ interpreted the data. GSX drafted the manuscript. SXZ revised the manuscript. All authors read and approved the final version of the manuscript.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Glossary

Abbreviations

Abbreviations:

JA

jasmonic acid

AA

abscisic acid

SOD

superoxide dismutase

APX

ascorbate peroxidase

CAT

catalase

GST

glutathione-S-transferase

nr

non-redundant

COG

Clusters of Orthologous Groups

KEGG

Kyoto Encyclopedia of Genes and Genomes

DEGs

differentially expressed genes

FDR

false discovery rate

OPRs

12-oxophytodienoate reductases

AP

aspartyl protease

References

1 

Nam KY, Kim YS, Shon MY and Park JD: Recent advances in studies on chemical constituents and biological activities of Korean black ginseng (Panax ginseng C. A. Meyer). Korean J Pharmacognosy. 46:173–188. 2015.

2 

Bao L, Cai X, Wang J, Zhang Y, Sun B and Li Y: Anti-fatigue effects of small molecule oligopeptides isolated from Panax ginseng. A. Meyer in mice. Nutrients. 8:E8072016. View Article : Google Scholar : PubMed/NCBI

3 

Jiao L, Zhang X, Li B, Liu Z, Wang M and Liu S: Anti-tumor and immunomodulatory activities of oligosaccharides isolated from Panax ginseng C.A. Meyer. Int J Biol Macromol. 65:229–233. 2014. View Article : Google Scholar : PubMed/NCBI

4 

Jiao L, Li B, Wang M, Liu Z, Zhang X and Liu S: Antioxidant activities of the oligosaccharides from the roots, flowers and leaves of Panax ginseng C.A. Meyer. Carbohydr Polym. 106:293–298. 2014. View Article : Google Scholar : PubMed/NCBI

5 

Shishtar E, Jovanovski E, Jenkins A and Vuksan V: Effects of Korean white ginseng (Panax Ginseng C.A. Meyer) on vascular and glycemic health in type 2 diabetes: Results of a randomized, double blind, placebo-controlled, multiple-crossover, acute dose escalation trial. Clin Nutr Res. 3:89–97. 2014. View Article : Google Scholar : PubMed/NCBI

6 

Lee SG, Lee YJ, Jang MH, Kwon TR and Nam JO: Panax ginseng leaf extracts exert anti-obesity effects in high-fat diet-induced obese rats. Nutrients. 9:9992017. View Article : Google Scholar :

7 

Kim HJ, Cheong SS, Kim DW, Suk PJ, Ryu J, Bea YS and Yoo SJ: Investigation into disease and pest incidence of Panax ginseng in Jeonbuk Province. Korean J Med Crop Sci. 16:33–38. 2008.

8 

Li J, Zhu L, Hull JJ, Liang S, Daniell H, Jin S and Zhang X: Transcriptome analysis reveals a comprehensive insect resistance response mechanism in cotton to infestation by the phloem feeding insect Bemisia tabaci (whitefly). Plant Biotechnol J. 14:1956–1975. 2016. View Article : Google Scholar : PubMed/NCBI

9 

Hill MG, Wurms KV, Davy MW, Gould E, Allan A, Mauchline NA, Luo Z, Ah Chee A, Stannard K, Storey RD and Rikkerink EH: Transcriptome analysis of Kiwifruit (Actinidia chinensis) bark in response to armoured scale insect (Hemiberlesia lataniae) feeding. PLoS One. 10:e01416642015. View Article : Google Scholar : PubMed/NCBI

10 

Martin M: CUTADAPT removes adapter sequences from high-throughput sequencing reads. Embnet J. 17:2011. View Article : Google Scholar

11 

Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R, Zeng Q, et al: Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 29:644–652. 2011. View Article : Google Scholar : PubMed/NCBI

12 

Langmead B and Salzberg SL: Fast gapped-read alignment with Bowtie 2. Nat Methods. 9:357–359. 2012. View Article : Google Scholar : PubMed/NCBI

13 

Li B and Dewey CN: RSEM: Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 12:3232011. View Article : Google Scholar : PubMed/NCBI

14 

Love MI, Huber W and Anders S: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15:5502014. View Article : Google Scholar : PubMed/NCBI

15 

Benjamini Y and Hochberg Y: Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Stat Soc Series. 57:289–300. 1995.

16 

Langfelder P and Horvath S: WGCNA: An R package for weighted gene co-expression network analysis. BMC Bioinformatics. 9:5592008. View Article : Google Scholar : PubMed/NCBI

17 

Kohl M, Wiese S and Warscheid B: Cytoscape: Software for visualization and analysis of biological networks. Methods Mol Biol. 696:291–303. 2011. View Article : Google Scholar : PubMed/NCBI

18 

Qin Y, Tian Y, Han L and Yang X: Constitutive expression of a salinity-induced wheat WRKY transcription factor enhances salinity and ionic stress tolerance in transgenic Arabidopsis thaliana. Biochem Biophys Res Commun. 441:476–481. 2013. View Article : Google Scholar : PubMed/NCBI

19 

Ren X, Chen Z, Liu Y, Zhang H, Zhang M, Liu Q, Hong X, Zhu JK and Gong Z: ABO3, a WRKY transcription factor, mediates plant responses to abscisic acid and drought tolerance in Arabidopsis. Plant J. 63:417–429. 2010. View Article : Google Scholar : PubMed/NCBI

20 

He GH, Xu JY, Wang YX, Liu JM, Li PS, Chen M, Ma YZ and Xu ZS: Drought-responsive WRKY transcription factor genesTaWRKY1andTaWRKY33from wheat confer drought and/or heat resistance in Arabidopsis. BMC Plant Biol. 16:1162016. View Article : Google Scholar : PubMed/NCBI

21 

van Verk MC, Pappaioannou D, Neeleman L, Bol JF and Linthorst HJ: A novel WRKY transcription factor is required for induction of PR-1a gene expression by salicylic acid and bacterial elicitors. Plant Physiol. 146:1983–1995. 2008. View Article : Google Scholar : PubMed/NCBI

22 

Park CJ, Shin YC, Lee BJ, Kim KJ, Kim JK and Paek KH: A hot pepper gene encoding WRKY transcription factor is induced during hypersensitive response to Tobacco mosaic virus and Xanthomonas campestris. Planta. 223:168–179. 2006. View Article : Google Scholar : PubMed/NCBI

23 

Dang F, Wang Y, She J, Lei Y, Liu Z, Eulgem T, Lai Y, Lin J, Yu L and Lei D: Overexpression of CaWRKY27, a subgroup IIe WRKY transcription factor of Capsicum annuum, positively regulates tobacco resistance to Ralstonia solanacearum infection. Physiol Plant. 150:397–411. 2014. View Article : Google Scholar : PubMed/NCBI

24 

Hu L, Ye M, Li R and Lou Y: OsWRKY53, a versatile switch in regulating herbivore-induced defense responses in rice. Plant Signal Behav. 11:e11693572016. View Article : Google Scholar : PubMed/NCBI

25 

Huangfu J, Li J, Ran L, Meng Y, Peng K, Zhang T and Lou Y: The transcription factor OsWRKY45 negatively modulates the resistance of rice to the brown planthopper Nilaparvata lugens. Int J Mol Sci. 17:6972016. View Article : Google Scholar :

26 

Shi WN, Liu DD, Hao LL, Wu CA, Guo X and Li H: GhWRKY39, a member of the WRKY transcription factor family in cotton, has a positive role in disease resistance and salt stress tolerance. Plant Cell Tissue Organ Culture. 118:17–32. 2014. View Article : Google Scholar

27 

Zhu D, Hou L, Xiao P, Guo Y, Deyholos MK and Liu X: VvWRKY30, a grape WRKY transcription factor, plays a positive regulatory role under salinity stress. Plant Sci. 280:2018.doi.org/10.1016/j.plantsci.2018.03.018. PubMed/NCBI

28 

Yang L, Ye C, Zhao Y, Cheng X, Wang Y, Jiang YQ and Yang B: An oilseed rape WRKY-type transcription factor regulates ROS accumulation and leaf senescence in Nicotiana benthamiana and Arabidopsis through modulating transcription of RbohD and RbohF. Planta. 247:1323–1338. 2018. View Article : Google Scholar : PubMed/NCBI

29 

Zhang L CJ, Sun X, Zhao T, Li M, Wang Q, Li S and Xin H: Overexpression of VaWRKY14 increases drought tolerance in Arabidopsis by modulating the expression of stress-related genes. Plant Cell Rep. 37:1159–1172. 2018. View Article : Google Scholar : PubMed/NCBI

30 

Li GZ, Wang ZQ, Yokosho K, Ding B, Fan W, Gong QQ, Li GX, Wu YR, Yang JL, Ma JF and Zheng SJ: Transcription factor WRKY22 promotes aluminum tolerance via activation of OsFRDL4 expression and enhancement of citrate secretion in rice (Oryza sativa). New Phytol. 219:149–162. 2018. View Article : Google Scholar : PubMed/NCBI

31 

Liang QY, Wu YH, Wang K, Bai ZY, Liu QL, Pan YZ, Zhang L and Jiang BB: Chrysanthemum WRKY gene DgWRKY5 enhances tolerance to salt stress in transgenic chrysanthemum. Sci Rep. 7:47992017. View Article : Google Scholar : PubMed/NCBI

32 

Mao G, Meng X, Liu Y, Zheng Z, Chen Z and Zhang S: Phosphorylation of a WRKY transcription factor by two pathogen-responsive MAPKs drives phytoalexin biosynthesis in Arabidopsis. Plant Cell. 23:1639–1653. 2011. View Article : Google Scholar : PubMed/NCBI

33 

Hu L, Ye M, Li R, Zhang T, Zhou G, Wang Q, Lu J and Lou Y: The rice transcription factor WRKY53 suppresses herbivore-induced defenses by acting as a negative feedback modulator of mitogen-activated protein Kinase activity. Plant Physiol. 169:2907–2921. 2015.PubMed/NCBI

34 

Adachi H, Ishihama N, Nakano T, Yoshioka M and Yoshioka H: Nicotiana benthamiana MAPK-WRKY pathway confers resistance to a necrotrophic pathogen Botrytis cinerea. Plant Signal Behav. 11:e11830852016. View Article : Google Scholar : PubMed/NCBI

35 

Müssig C, Biesgen C, Lisso J, Uwer U, Weiler EW and Altmann T: A novel stress-inducible 12-oxophytodienoate reductase from Arabidopsis thaliana provides a potential link between brassinosteroid-action and jasmonic-acid synthesis. J Plant Physiol. 157:143–152. 2000. View Article : Google Scholar

36 

Schaller F, Biesgen C, Müssig C, Altmann T and Weiler EW: 12-Oxophytodienoate reductase 3 (OPR3) is the isoenzyme involved in jasmonate biosynthesis. Planta. 210:979–984. 2000. View Article : Google Scholar : PubMed/NCBI

37 

Tani T, Sobajima H, Okada K, Chujo T, Arimura SI, Tsutsumi N, Nishimura M, Seto H, Nojiri H and Yamane H: Identification of the OsOPR7 gene encoding 12-oxophytodienoate reductase involved in the biosynthesis of jasmonic acid in rice. Planta. 227:517–526. 2008. View Article : Google Scholar : PubMed/NCBI

38 

Xin Z, Zhang J, Ge L, Lei S, Han J, Zhang X, Li X and Sun X: A putative 12-oxophytodienoate reductase gene CsOPR3 from Camellia sinensis, is involved in wound and herbivore infestation responses. Gene. 615:18–24. 2017. View Article : Google Scholar : PubMed/NCBI

39 

Contour-Ansel D, Torres-Franklin ML, Zuily-Fodil Y and de Carvalho MH: An aspartic acid protease from common bean is expressed ‘on call’ during water stress and early recovery. J Plant Physiol. 167:1606–1612. 2010. View Article : Google Scholar : PubMed/NCBI

40 

Guo R, Zhao J and Wang X, Guo C, Li Z, Wang Y and Wang X: Constitutive expression of a grape aspartic protease gene in transgenic Arabidopsis confers osmotic stress tolerance. Plant Cell Tissue Organ Culture. 121:275–287. 2015. View Article : Google Scholar

41 

Yao X, Xiong W, Ye T and Wu Y: Overexpression of the aspartic protease ASPG1 gene confers drought avoidance in Arabidopsis. J Exp Bot. 63:2579–2593. 2012. View Article : Google Scholar : PubMed/NCBI

42 

Guo R, Tu M and Wang X, Zhao J, Wan R, Li Z, Wang Y and Wang X: Ectopic expression of a grape aspartic protease gene, AP13, in Arabidopsis thaliana improves resistance to powdery mildew but increases susceptibility to Botrytis cinerea. Plant Sci. 248:17–27. 2016. View Article : Google Scholar : PubMed/NCBI

43 

Li Y, Kabbage M, Liu W and Dickman MB: Aspartyl protease mediated cleavage of BAG6 is necessary for autophagy and fungal resistance in plants. Plant Cell. 28:233–247. 2016.PubMed/NCBI

44 

Moran PJ and Cipollini DF Jr: Effect of wind-induced mechanical stress on soluble peroxidase activity and resistance to pests in cucumber. J Phytopathol. 147:313–316. 2008. View Article : Google Scholar

45 

Yang G, Zhang W, Liu Z, Yi-Maer AY, Zhai M and Xu Z: Both JrWRKY2 and JrWRKY7 of Juglans regia mediate responses to abiotic stresses and abscisic acid through formation of homodimers and interaction. Plant Biol (Stuttg). 19:268–278. 2017. View Article : Google Scholar : PubMed/NCBI

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July-2019
Volume 20 Issue 1

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Spandidos Publications style
Xi G, Wang Y, Yin L, Wang Y and Zhou S: De novo transcriptome analysis of gene responses to pest feeding in leaves of Panax ginseng C. A. Meyer. Mol Med Rep 20: 433-444, 2019
APA
Xi, G., Wang, Y., Yin, L., Wang, Y., & Zhou, S. (2019). De novo transcriptome analysis of gene responses to pest feeding in leaves of Panax ginseng C. A. Meyer. Molecular Medicine Reports, 20, 433-444. https://doi.org/10.3892/mmr.2019.10275
MLA
Xi, G., Wang, Y., Yin, L., Wang, Y., Zhou, S."De novo transcriptome analysis of gene responses to pest feeding in leaves of Panax ginseng C. A. Meyer". Molecular Medicine Reports 20.1 (2019): 433-444.
Chicago
Xi, G., Wang, Y., Yin, L., Wang, Y., Zhou, S."De novo transcriptome analysis of gene responses to pest feeding in leaves of Panax ginseng C. A. Meyer". Molecular Medicine Reports 20, no. 1 (2019): 433-444. https://doi.org/10.3892/mmr.2019.10275