Analysis of the Panax ginseng stem/leaf transcriptome and gene expression during the leaf expansion period

  • Authors:
    • Shichao Liu
    • Meichen Liu
    • Siming Wang
    • Yanling Lin
    • Hui Zhang
    • Qun Wang
    • Yu Zhao
  • View Affiliations

  • Published online on: August 29, 2017     https://doi.org/10.3892/mmr.2017.7377
  • Pages: 6396-6404
Metrics: HTML 0 views | PDF 0 views     Cited By (CrossRef): 0 citations

Abstract

Ginseng (Panax ginseng C.A Meyer) is a widely used herbal remedy, however, the majority of studies have focused on the roots, with less known about the aerial regions of the plant. As the stems and leaves are the primary aerial tissues, the present study characterized their transcriptional profiles using Illumina next‑generation sequencing technology. The gene expression profiles and the functional genes of ginseng stems (GS) and leaves (GL) were analyzed during the leaf‑expansion period. cDNA libraries of the GS and GL of 5‑year‑old ginseng plants were separately constructed. In the GS library, 38,000,000 sequencing reads were produced. These reads were assembled into 99,809 unique sequences with a mean size of 572 bp, and 57,371 sequences were identified based on similarity searches against known proteins. The assembled sequences were annotated using Gene Ontology terms, Clusters of Orthologous Groups classifications and Kyoto Encyclopedia of Genes and Genomes pathways. For GL, >118,000,000 sequencing reads were produced, which were assembled into 73,163 unique sequences, from which 50,523 sequences were identified. Additionally, several genes involved in the regulation of growth‑related, stress‑related, pathogenesis‑related, and chlorophyll metabolism‑associated proteins were found and expressed at high levels, with low expression levels of ginsenoside biosynthesis enzymes also found. The results of the present study provide a valuable useful sequence resource for ginseng in general, and specifically for further investigations of the functional genomics and molecular genetics of GS and GL during early growth.

Introduction

For thousands of years, ginseng has been used as a nutrient and medicine, and as a component in cosmetics and beverages, to improve quality of life and maintain health in humans (13). In Asia in particular, it is considered to be an invaluable herb (4,5). Ginseng can be divided into two types, wild and cultivated, according to their different growing conditions (6). The annual growth cycle of ginseng begins with seeding, followed by leaf expansion, flowering, green fruiting, red fruiting and wilitng (7).

The leaf expansion stage is a critical period in the early growth and differentiation of the plant. During the leaf expansion period, plant nutrients are transported from storage in the roots to the aerial regions, as required for growth. During this period, the plant organs exhibit significant morphological and physiological changes (8). These changes include the rapid growth of the stems and leaves, during which the plants increase in height more rapidly than they grow in width, and the rates of ginsenoside Rb1, Rc and Rd synthesis during this stage are higher, compared with those in other stages of growth (7,9). In addition, plant pests and other diseases, which severely inhibit ginseng growth, frequently take hold during this period (10,11). The molecular mechanisms associated with these features in ginseng remain to be fully elucidated (12,13). Therefore, the present study aimed to perform transcriptome analysis on ginseng leaves (GL) and stems (GS) during this important growth stage.

During the last decade, next-generation sequencing technology has improved the efficiency and speed of gene discovery (14). The development of a novel, high-throughput DNA sequencing method has provided a technique enabling the mapping and quantifying of transcriptomes, and this technology has led to a change in genomics and genetics, which has provided cheaper and faster sequencing information (1517).

In the present study, using the HiSeq™ 2000 Sequencing System platform and the paired-end sequencing method, two de novo transcriptome databases were constructed from cDNA libraries generated from Panax ginseng stems (GS) and leaves (GL). The most frequent transcripts, growth-related, stress-related, pathogenesis-related and redox-associated proteins, and ginsenoside biosynthesis enzymes, expressed at low levels, of interest were identified. These datasets provide useful information on the transcript profile, which can assist in the understanding of tissue-specific biological functions and of the transcriptional regulatory expression mechanism.

Materials and methods

Plant materials and preparation

The plant materials used in the present study were originally collected from the Fusong ginseng planting base (Jilin, China). The stems and leaves were collected from 5-year-old ginseng plants during the leaf expansion period. All samples were washed with distilled water, cut into small sections with a thickness of <1 cm, and then immediately stored in liquid nitrogen for further processing.

RNA isolation and construction of cDNA libraries

Total RNA was isolated from the GS and GL samples separately using a modified TRIzol method according to the manufacturer's protocol. To evaluate the RNA integrity, 10 mg of RNA was fractionated on a 1% agarose gel, stained with ethidium bromide, and visualized using UV light. The presence of intact 28S and 18S rRNA bands was used as the criterion for RNA integrity (18). The quality of the RNA samples was confirmed using an Agilent 2100 bioanalyzer (Agilent Technologies, Inc., Santa Clara, CA, USA), with a minimum RNA integrated number value of eight (19). The samples for the transcriptome analysis were prepared using the Illumina kit (Beijing Genomics Institute, Shenzhen, China) according to the manufacturer's protocol. The mRNA was purified from 10 µg of total RNA using oligo (dT) magnetic beads (Beijing Genomics Institute). Following purification, the mRNA was fragmented into small sections using divalent cations at 94°C. Using these short fragments as templates, reverse transcriptase and random primers were used to synthesize first-strand cDNA. Second-strand cDNA was synthesized using DNA polymerase I (Takara Biotechnology Co., Ltd., Dalian, China) and RNase H (Takara Biotechnology Co, Ltd.), respectively. The cDNA fragments underwent an end-repair process and were ligated to adapters. These products were purified and enriched using polymerase chain reaction (PCR) to produce the final cDNA library. The PCR amplification was performed in a 50 µl reaction mixture containing 10 µl reverse transcription product and 40 µl PCR reaction solution (0.5 µl of each primer, 0.25 µl TaKaRa Ex Taq® HS, 10 µl 5× PCR buffer and 28.75 µl ddH2O). Cycling conditions were as follows: An initial predenaturation step at 94°C for 2 min; followed by 30 consecutive cycles of denaturation at 94°C for 30 sec, annealing at 58°C for 30 sec and extension at 72°C for 1 min.

Gene sequencing, de novo assembly and functional annotation

The cDNA library was sequenced using the Illumina sequencing platform (HiSeq 2000). The average size of library inserts was 200 bp. Image deconvolution and quality value calculations were performed using Illumina GA pipeline 1.3. The raw reads were cleaned by removing adaptor sequences, empty reads and low-quality sequences (20). Trinity version 2.0 software (http://www.trinitysoftware.nl/) was used for de novo assembly.

Different sequences were used for the Basic Local Alignment Search Tool (BLAST) search and annotation against the plant protein database of NR (ftp://ftp.ncbi.nih.gov/blast/db/FASTA) with a significance threshold of E-value ≤10−5 and Swiss-Prot (http://www.uniprot.org/). Functional annotations using Gene Ontology (GO) terms were performed using Blast2go version 3.0 software (https://www.blast2go.com/) with an E-value cut-off of 10−5. After getting GO annotation for every Unigene, all Unigenes were classified based on GO function by Web Gene Ontology Annotation Plot (WEGO) online software (http://wego.genomics.org.cn/cgi-bin/wego/index.pl) to understand the distribution of gene function of the species. Annotation with Clusters of Orthologous Groups (COG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways was performed using BLASTx against the COG (http://www.ncbi.nlm.nih.gov/COG/) and KEGG (http://www.genome.jp/kegg/kegg1.html) databases (2125).

To obtain distinct gene sequences, the unigenes were clustered using TIGR Gene Indices clustering (TGICL) software version 2.1 (https://sourceforge.net/projects/tgicl). For gene expression analysis, the number of expressed reads were counted and normalized using RPKM values based on the following formula: RPKM=109 C/(N × L), where C is the number of mappable reads uniquely aligned to a unigene, N is the total number of mappable reads uniquely aligned to all unigenes, and L is the sum of the unigene in base pairs (26).

Reverse transcription-quantitative PRC (RT-qPCR)

To confirm the relative expression of the target genes in the GS and GL transcriptome data, RT-qPCR was performed using the Mx3000p Real-Time PCR detection system with a One Step SYBR PrimeScript PLUS RT-PCR kit (Takara Biotechnology Co., Ltd.) (27). PrimerPremier 5.0 (Premier Biosoft International, Palo Alto, CA, USA) was applied to determine the primer sequences (28). The PCR amplification was performed in a 25 µl mixture containing 2 µl of the reverse transcription product and 23 µl of the PCR reaction solution (0.5 µl of each primer, 12.5 µl of SYBR® Primix Ex TaqTM (2*), 0.5 µl of ROX Reference Dye II (50*) and 9 µl of ddH2O). The reaction was performed using the following reaction cycles: initial denaturation at 95°C for 30 sec; 40 consecutive cycles of denaturation at 95°C for 5 sec, annealing at 54°C for 15 sec, and extension at 72°C for 30 sec. Tyrosine hydroxylase (TH) and WNK lysine deficient protein kinase 1 genes were used as internal standards. The primer sequences used for RT-qPCR are listed in Tables I and II. The thermal cycle conditions for PCR were as follows: 42°C for 5 min, 95°C for 10 sec, and then 40 cycles of 95°C for 5 sec followed by 60°C for 30 sec. The relative expression levels were calculated using the 2−ΔΔCq method (29).

Table I.

Primers sequences used for reverse transcription-quantitative polymerase chain reaction analysis selected from the Panax ginseng stem database.

Table I.

Primers sequences used for reverse transcription-quantitative polymerase chain reaction analysis selected from the Panax ginseng stem database.

No.GeneRPKMSequence (5′-3′)
1GBR5-like protein32,147.61F: ATTAGTTCAGAGGTCGCAGC
R: ATCCGCTCCTCCCATCAAC
2Specific abundant protein 311,366.95F: GTTGCTCTGGTGGTGCTTCT
R: TGTAAACACTTGCCCTGCCG
3Protease inhibitor/seed storage/LTP family protein   4,117.47F: GCGTTGCCTATGTGCTGTTA
R: CTTGTAACCAACTGGGCGAT
4Major latex-like protein   3,731.75F: GAAAAGTTGGCTCCGTCGTC
R: TCACCAAGTTGTCTTCACCC
5Cyclophilin   2,136.74F: GGCAGGATTGTGATGGAGC
R: TTGAGGGATGACTCGGTGG
6Plasma membrane intrinsic protein 2–1     400.39F: GCCAGGAAAGTGTCGCTAAT
R: TCTCAGCCCCTAATCCAGTG

[i] F, forward; R, reverse.

Table II.

Primer sequences used for reverse transcription-quantitative polymerase chain reaction selected from the Panax ginseng leaf database.

Table II.

Primer sequences used for reverse transcription-quantitative polymerase chain reaction selected from the Panax ginseng leaf database.

No.GeneRPKMSequence (5′-3′)
1Chlorophyll a/b binding protein of99,115.65F: CCCTCTCCTCCCCATCATTC
LHCII type I precursor R: CGGGCTTTTTTCCTGTTTTC
2Chloroplast light-harvesting chlorophyll23,716.69F: CTGACCCCGAGACATTTGCT
a/b-binding protein R: ACTTGACACCATTGCGAGCC
3Chloroplast ferredoxin I14,780.64F: ATGGGTCAGGCTCTGTTCG
R: TCTTTCTCCCCTTCTGGTGT
4Specific abundant protein 337,15.27F: TCTGACTCTGGCAACCGATG
R: CAGGAAGAACCTTGACAGCG
5Catalase-1 precursor3,442.73F: CAGGCAGGAGACAGATACCG
R: CGTTGAGGCGAGACGCTATT
6Cytokine binding protein CBP572,291.8387F: ATCAAACCCCAAACCGACAG
R: GCTGGGCAATCACTTGGTTC

[i] F, forward; R, reverse.

Results

Transcriptome sequencing and assembly

Total RNA was extracted from GS and GL during the leaf-expansion period, followed by reverse transcription into cDNA. Using the Illumina sequencing platform, >38,000,000 and 118,000,000 sequencing reads, respectively, were generated with an average length of 90 bp. The data sets were deposited in the NCBI Array-Express repository with the accession number E-MTAB-937. Following a stringent quality check and data cleaning, 39,000,000 high-quality reads were obtained in the GS and GL libraries. Based on the high-quality reads, a total of 168,300 and 120,241 contigs were assembled, with an average length of 305 and 267 bp, respectively, in GS and GL. The size distribution of these contigs is shown in Table III. The reads were then mapped back to contigs. Using paired-end reads, it was possible to detect contigs from the same transcript, and the distances between these contigs. Following clustering of these unigenes using TGICL software, the contigs generated 98,808 and 73,162 unigenes in GS and GL, respectively, with mean lengths of 572 and 413 bp, respectively. In addition, 57,371 and 50,523 sequences were obtained, respectively, using an E-value cut-off of 10−5. As shown in Fig. 1A and B, the longer gene length resulted in higher quantities of contigs and unigenes in GS, compared with in GL.

Table III.

Overview of the sequencing and assembly in the GS and GL libraries.

Table III.

Overview of the sequencing and assembly in the GS and GL libraries.

FeatureStatistic in GSStatistic in GL
Total base pairs (bp)3,451,590,5403,479,796,000
Total number of reads38,351,00638,664,400
Average read length (bp)9090
Total number of contigs168,300120,241
Mean length of contigs (bp)305267
Total number of unigenes98,80873,162
Mean length of unigenes (bp)572413

[i] GS, ginseng stem; GL, ginseng leaf.

Functional annotation by searching against public databases

The GO terms were used to classify the functions of the predicted GS and GL unigenes. Based on the sequence homology, the annotated unigenes were analyzed with Blast2GO for GO classification. From the GS library, 135,355 sequences were categorized into 44 functional groups using WEGO tool, whereas 126,504 sequences were categorized from the GL library (Fig. 2) (30). The three major categories of biological process, cellular component, and molecular function, were assigned to 50,621/46,912, 55,437/52,700, and 29,297/26,892 GO terms in the GS/GL libraries, respectively. There were high percentages of genes from the categories ‘cell’ (19,229 GS/17,983 GL), ‘cell part’ (17,365/16,421), ‘organelle’ (13,584/12,982), ‘catalytic activity’ (13,343/12,240), ‘binding’ (13,155/11,907), ‘metabolic process’ (11,795/10,892), and ‘cellular process’ (11,497/10,427) in GS and GL. However, the categories ‘cell killing’ (2), ‘locomotion’ (2), ‘nitrogen utilization’ (2), and ‘rhythmic process’ (2) contained the fewest GS genes, and the categories ‘cell killing’ (2), ‘translation regulator activity’ (2), and ‘nitrogen utilization’ (1) contained the fewest GL genes.

COG is an orthologous gene classification database, where each COG protein is assumed to come from protein ancestors. Overall, 30,105 and 22,726 sequences were clustered into 25 COG classifications in GS and GL, respectively.

Among the GS categories, the cluster for ‘General function prediction only’ (4,901; 16.28%) associated with basic physiological and metabolic functions represented the largest group, followed by ‘Transcription’ (2,697; 8.96%), ‘Replication, recombination and repair’ (2,619; 8.70%), ‘Post-translational modification, protein turnover, chaperones’ (2,345; 7.79%), and ‘Signal transduction mechanisms’ (2,050, 6.81%), whereas only a few unigenes were assigned to ‘Extra cellular structures’ (13, 0.04%) and ‘Nuclear structure’ (18; 0.06%), as shown in Fig. 3.

Among the GL categories, the cluster for ‘General function prediction only’ (3,442), ‘Post-translational modification, protein turnover, chaperones’ (1,933), and ‘Transcription’ (1,836) were the three largest groups, representing 15.15, 8.51, and 8.08%, respectively. The groups with the fewest unigenes were ‘Extracellular structures’ (3; 0.01%) and ‘Nuclear structure’ (10; 0.04%), which were the same as for GS (Fig. 3).

Ginseng transcriptome pathway analysis was performed using KEGG mapping. In total, 22,697 and 20,093 sequences were identified with pathway annotations in GS and GL, respectively, and these were functionally assigned to 121 KEGG pathways.

In the GS and GL aerial tissues, the ‘metabolic pathways’ had the highest contribution (22.06 and 24.57%, respectively), followed by ‘Biosynthesis of secondary metabolites’ (10.84 and 12.11%) and ‘Plant-pathogen interaction’ (7.93 and 6.71%), as show in Fig. 4. These annotations of gene or protein names and descriptions, GO terms, putative conserved domains, and potential metabolic pathways provide valuable resources for investigating the specific processes, functions and pathways involved in GS and GL development.

Transcriptional data analysis and summary

The genes with the highest levels of expression in GS encoded a GBR5-like protein, followed by At5g54075, allergen, chlorophyll a/b binding protein of light-harvesting complex II (LHCII) type I precursor, specific abundant protein 3, a hypothetical protein, GBR5, peroxidase, phloem protein 2–2, and pathogenesis-related protein 10 (Table IV). The phytochrome-associated genes were expressed at a high level in GL data, and the gene with the highest expression was chlorophyll a/b binding protein, followed by the LHCII protein Lhcb1, GBR5-like protein, a hypothetical protein, chloroplast light-harvesting chlorophyll a/b-binding protein, At5g54075, chloroplast ferredoxin I, cytochrome P450 like-TBP, photosystem II protein I, and RNA polymerase α subunit (Table V). A number of functional genes were obtained from both transcriptome databases, including growth-associated proteins (GBR5-like protein, GBR5, cyclophilin, cytokinin-repressed protein, and auxin response factor 3), stress-related proteins (specific abundant and drought-induced proteins), pathogenesis-related proteins (pathogenesis-related protein 10, Avr9/Cf-9 rapidly elicited protein, Erwinia-induced protein, and fungal elicitor-induced protein), and redox-related proteins (peroxidase and catalase-1 precursor), as shown in Table VI.

Table IV.

Top 10 most frequent transcripts in the Panax ginseng stem transcriptome library.

Table IV.

Top 10 most frequent transcripts in the Panax ginseng stem transcriptome library.

No.GeneSpeciesAccession no.RPKM
  1GBR5-like proteinPanax ginsenggb|ABD73293.132,147.61
  2At5g54075Arabidopsis thalianagb|AAT46037.116,817.52
  3AllergenKirolasp|P85524.112,958.45
  4Chlorophyll a/b binding protein of LHCII type I precursorPanax ginsenggb|AAB87573.111,366.95
  5Specific abundant protein 3Panax ginsenggb|AAX40471.110,665.18
  6Hypothetical proteinVitis vinifera ref|XP_002280773.18,902.68
  7GBR5Panax ginsenggb|AAP55852.18,511.19
  8PeroxidasePopulus trichocarpgb|ACN97180.17,838.47
  9Phloem protein 2–2Apium graveolens var. Dulcegb|AAM62133.17,704.61
10 Pathogenesis-related protein 10Panax ginsenggb|ACY36943.15,465.76

Table V.

The top 10 most frequent transcripts in Panax ginseng leaf transcriptome library.

Table V.

The top 10 most frequent transcripts in Panax ginseng leaf transcriptome library.

No.GeneSpeciesAccession no.RPKM
  1Chlorophyll a/b binding protein of LHCII type I precursorPanax ginsenggb|AAB87573.199,115.65
  2Light-harvesting complex II protein Lhcb1Populus trichocarpa ref|XP_002316737.130,255.99
  3GBR5-like proteinPanax ginsenggb|ABD73293.129,122.78
  4Hypothetical protein VITISV_001840Vitis viniferaemb|CAN65763.125,185.48
  5Chloroplast light-harvesting chlorophyll a/b-binding proteinArtemisia annuagb|ABQ32304.123,716.69
  6At5g54075Arabidopsis thalianagb|AAT46037.116,026.90
  7Chloroplast ferredoxin ICamellia sinensisgb|AEI83424.114,780.64
  8Cytochrome P450 like_TBPNicotiana tabacumdbj|BAA10929.114,505.00
  9Photosystem II protein IDavidia involucratagb|ADM92705.113,675.17
10RNA polymerase α subunitPanax ginseng ref|YP_086997.110,301.30

Table VI.

Expressed transcripts of associated protein in GS and GL library.

Table VI.

Expressed transcripts of associated protein in GS and GL library.

GSGL


CategoryAssociated protein geneRPKMAssociated protein geneRPKM
Growth-associatedGBR5-like protein32,147.61GBR5-like protein29,122.78
proteinsGBR585,11.19GBR57,096.61
Cyclophilin21,36.74Cytokinin binding protein2,291.83
Cytokinin-repressed protein21,2.24Auxin response factor 364.14
Auxin response factor 351.93Cyclophilin64.06
Stress-relatedSpecific abundant protein10,665.18Specific abundant protein3,715.27
genesMetallothionein-1 like protein3,629.83Drought-induced protein3,011.41
Drought-induced protein1,710.70Metallothionein-1 like protein class I930.61
Cold-inducible protein984.20Cold-inducible protein623.07
Dehydrin 3854.14Dehydrin 2388.63
Dehydrin 2843.81Dehydration-induced protein48.83
Dehydration-induced protein127Dehydrin 118.33
Disease-relatedPathogensis-related protein 105,465.76Pathogensis-related protein 10823.96
proteinsAvr9/Cf-9 rapidly elicited protein589.12Defensin-like protein 173.29
Defensin-like protein 1586.17Erwinia induced protein 168.52
Erwinia induced protein 2362.79Erwinia induced protein 246.68
Fungal elicitor-induced protein243.06Disease resistance-responsive23.21
Disease resistance-responsive161.67Avr9/Cf-9 rapidly elicited protein20.19
Erwinia induced protein 1156.82Fungal elicitor-induced protein12.04
Redox-related proteinsPeroxidase7,838.47Catalase-1 precursor3,442.13
Catalase-1 precursor3,804.35Peroxidase389.15
glutaredoxin283.11glutaredoxin125.08
Ginsenoside skeletonHMGR80.05HMGR539.98
biosynthesis keyGPS21.76GPS136.23
enzymesSS34.06SS77.70
SE31.32SE96.68
β-AS5.92β-AS8.9034
Cytochrome P450371.32Cytochrome P450209.75
GT76.36GT48.96

[i] GS, ginseng stem; GL, ginseng leaf; HMGR, HMG-CoA reductase; GPS, geranylgeranyl pyrophosphate synthase; SS, squalene synthase; SE, squalene epoxidase; β-AS, β-amyrin synthase; GT, glucosyltransferase.

RT-qPCR analysis

RT-qPCR analysis was used to determine the expression levels of target genes using the 2−ΔΔCq method, which is a convenient way of analyzing the relative changes in gene expression levels. A total of six genes were randomly selected in each library. Statistical analysis of the RT-qPCR results showed that the RPKM value was consistent with the 2−ΔΔCq value. The expression profiles of the six genes were consistent with the gene expression results (Fig. 5), supporting the reliability of the RNA-sequencing data.

Discussion

Early developmental processes in plants often occur as a consequence of the initiation and changes in protein expression, and active protein folding during this critical period (31). Investigating plant proteins may assist in identifying important signals in developmental pathways and to understand the physiological functions of specific genes during plant growth. The expression of genes in the aerial regions of ginseng, particular during the leaf-expansion period, remain to be fully elucidated.

In the present study, GS and GL tissues were selected for transcriptome analysis. Illumina-generated sequencing was applied to the characterization and assembly of the transcriptome, which successfully generated and assembled a draft sequence. Prior to the present study, ginseng was represented by only 561 sequences in the NCBI protein database and 12,071 sequences in the NCBI EST database (http://www.ncbi.nlm.nih.gov/protein?term=panax%20ginseng%20) In the present study, >57,372 and 50,523 sequences were produced in GS and GL, respectively. These data provide a substantial contribution to existing sequence resources on GS and GL, and are beneficial to ginseng genetic investigations.

To evaluate the completeness of the transcriptome libraries produced in the present study, and the effectiveness of the annotation process, a search was performed of the annotated sequences for genes classified into GO and COG classifications, and in KEGG pathways. Sequences were assigned to 44 GO and 25 COG classifications, and 121 KEGG pathways. These annotations provide a valuable resource for investigating specific processes, functions, and pathways in ginseng investigations.

The top 10 most frequent transcripts in GS were predominantly genes involved in resisting environmental pressure, and in promoting and organizing rapid growth. The GBR-like protein is the most frequent transcript promoting plant growth. Its high expression has been closely linked to rapid elongation of the GS (32). Chlorophyll a/b binding protein is associated with photosynthetic functions, and is beneficial in storing energy and growth elongation in the GS (33). Specific abundant protein 3 is a stress-related gene, which is involved in altering cell wall characteristics to tolerate water deficit stress under abiotic stress conditions (34,35). Peroxidase assists in increasing plant defenses against pathogens and acts as a catalyst to facilitate a variety of biological processes. As a naturally occurring by-product of oxygen metabolism in the body, peroxidase breaks down hydrogen peroxide into water and oxygen to reduce cytotoxicity (36,37). Phloem protein 2 is a phloem lectin conserved in plants, which is considered to assist in the establishment of phloem-based defenses induced by insect attacks and other stresses, including wounding and oxidative conditions (38). The findings of the present study suggested that in GS, several genes are expressed to promote rapid growth of tissue and reduce the effect of environmental pressure. In GL, the most abundant gene was chlorophyll a/b binding protein, which is involved in photosynthesis. Photosynthesis is a vital source of energy for almost all living organisms and, during the leaf-expansion period, this provides a direct energy source for the growth of plants through the photosynthetic pathway (Table V) (39).

The present study also found several biological process-related proteins in the GS and GL libraries, including growth-associated, stress-related, pathogenesis-related, and redox-related proteins (Table VI). The same growth-associated genes were expressed in GS and GL, however stress-related, pathogenesis-related and redox-related genes were expressed at significantly higher levels in GS, compared with GL. The RPKM value of pathogenesis-related protein 10 was 5,465.76 in GS, but 823.96 in GL, and peroxidase was 7,838.47 in GS, but only 389.15 in GL. These results suggested that GS has a higher defense tolerance and anti-stress ability, compared with GL.

The leaf-expansion period is the initial growth stage in which there is a high incidence of disease, which is a limiting factor in ginseng growth and reproduction (11). It is reported that pathogenesis-related proteins, defined as host-plant proteins, which are induced specifically in disease or associated pathological situations, are associated with the development of systemic acquired resistance against further infection by fungi, bacteria and viruses (40). In the transcription data of the present study, these types of protein, including pathogenesis-related protein 10, Avr9/Cf-9 rapidly elicited protein, defensin-like protein 1, fungal elicitor-induced protein, and Erwinia-induced protein, were found in GS and GL (Table VI). Pathogenesis-related protein 10 is structurally related to ribonucleases and may be active against viruses (39). However, whether they are all required for resistance or are involved in defense gene activation remains to be elucidated (41). Avr9/Cf-9 is induced in response to microbial organisms and is involved in signaling and/or other aspects of the defense response (42). Fungal elicitor-induced protein is induced by fungal infections. Defensin-like protein 1, also known as cysteine-rich antifungal protein 1, possesses antifungal activity (43). Erwinia-induced protein induces hypersensitive responses, including necrosis (44). These data suggested that ginseng, during the leaf-expansion period, has already been infected by viral, fungal and bacterial pathogens, and that appropriate preventative measures be taken prior to this stage to improve the yield and quality of ginseng.

The present study also identified ginsenoside biosynthesis enzymes, including 3-hydroxy-3-methylglutaryl coenzyme A reductase, geranylgeranyl pyrophosphate synthase, squalene synthase, squalene epoxidase, oxidosqualene cyclase, β-amyrin synthase, cytochrome P450 and glucosyltransferase, which are involved in the mevalonate pathway (45). However, their RPKM values were low. Additionally, the transcript encoding dammarendiol synthase, a rate-limiting enzyme in the ginsenoside biosynthesis pathway, was not present in our transcriptome dataset, indicating that ginsenosides were not actively biosynthesized in either the GS or the GL during the leaf-expansion period.

In conclusion, the present study performed de novo transcriptome sequencing of GS and GL during the leaf-expansion period using the Illumina platform. The data showed that >38,000,000 and 118,000,000 sequencing reads were produced in GS and GL, respectively, and these reads were assembled into 99,809 and 50,523 unique sequences, respectively. By performing BLAST analysis of the unigenes against public databases (Nr, Swiss-Prot, KEGG, and COG), functional annotations and classifications were obtained. The substantial number of transcriptomic sequences and their functional annotations provide useful resources for molecular investigations of GS and GL. In addition, several candidate genes were identified, which may be involved in growth and environmental stress responses.

Acknowledgements

The present study was supported by grants from National Natural Foundation of China (grant nos. 81373937, 81503212 and 20140520042JH), the Scientific and Technological Development Program of Jilin, China (grant no. 20140622003JC) and the Strategic Adjustment of the Economic Structure of Jilin Province to Guide the Capital Projects (grant no. 2014N155).

References

1 

Yamada N, Araki H and Yoshimura H: Identification of antidepressant-like ingredients in ginseng root (Panax ginseng CA Meyer) using a menopausal depressive-like state in female mice: Participation of 5-HT2A receptors. Psychopharmacol (Berl). 216:589–599. 2011. View Article : Google Scholar

2 

Chen F, Chen Y, Kang X, Zhou Z, Zhang Z and Liu D: Anti-apoptotic function and mechanism of ginseng saponins in Rattus pancreatic β-cells. Biol Pharm Bull. 35:1568–1573. 2012. View Article : Google Scholar : PubMed/NCBI

3 

Park JD, Rhee DK and Lee YH: Biological activities and chemistry of saponins from Panax ginseng CA Meyer. Phytochemistry Rev. 4:159–175. 2005. View Article : Google Scholar

4 

Chang YS, Seo EK and Gyllenhaal C: Panax ginseng: A role in cancer therapy? Integr Cancer Ther. 2:13–33. 2003. View Article : Google Scholar : PubMed/NCBI

5 

Xiang YZ, Shang HC, Gao XM and Zhang BL: A comparison of the ancient use of ginseng in traditional Chinese medicine with modern pharmacological experiments and clinical trials. Phytother Res. 22:851–858. 2008. View Article : Google Scholar : PubMed/NCBI

6 

Li S, Li J, Yang XL, Cheng Z and Zhang WJ: Genetic diversity and differentiation of cultivated ginseng (Panax ginseng CA Meyer) populations in North-east China revealed by inter-simple sequence repeat (ISSR) markers. Gen Resour Crop Evolution. 58:815–824. 2011. View Article : Google Scholar

7 

Wu D, Austin RS and Zhou S: The root transcriptome for North American ginseng assembled and profiled across seasonal development. BMC Genomics. 14:5642013. View Article : Google Scholar : PubMed/NCBI

8 

Xing YZ and Ma FR: The research of trends of each growing periods in ginseng. J Northeast Normal Univ. 1:57–62. 1981.

9 

Dale JE: The control of leaf expansion. Ann Rev Plant Physiol Plant Mol Biol. 39:267–295. 1988. View Article : Google Scholar

10 

Punja ZK, Wan A and Rahman M: Growth, population dynamics and diversity of Fusarium equiseti in ginseng fields. Eur J Plant Pathol. 121:173–184. 2008. View Article : Google Scholar

11 

Jeger MJ: Analysis of disease progress as a basis for evaluating disease management practices. Annu Rev Phytopathol. 42:61–82. 2004. View Article : Google Scholar : PubMed/NCBI

12 

Choi HI, Waminal NE, Park HM, Kim NH, Choi BS, Park M, Choi D, Lim YP, Kwon SJ, Park BS, et al: Major repeat components covering one-third of the ginseng (Panax ginseng C.A. Meyer) genome and evidence for allotetraploidy. Plant J. 77:906–916. 2014. View Article : Google Scholar : PubMed/NCBI

13 

Jung CH, Seog HM, Choi IW, Choi HD and Choi HY: Effects of wild ginseng (Panax ginseng C.A. Meyer) leaves on lipid peroxidation levels and antioxidant enzyme activities in streptozotocin diabetic rats. J Ethnopharmacol. 98:245–250. 2005. View Article : Google Scholar : PubMed/NCBI

14 

Van Dijk EL, Auger H, Jaszczyszyn Y and Thermes C: Ten years of next-generation sequencing technology. Trends Genet. 30:418–426. 2014. View Article : Google Scholar : PubMed/NCBI

15 

Gupta P, Goel R, Pathak S, Srivastava A, Singh SP, Sangwan RS, Asif MH and Trivedi PK: De novo assembly, functional annotation and comparative analysis of Withania somnifera leaf and root transcriptomes to identify putative genes involved in the with anolides biosynthesis. PLoS One. 8:e627142013. View Article : Google Scholar : PubMed/NCBI

16 

Chan AI, McGregor LM and Liu DR: Novel selection methods for DNA-encoded chemical libraries. Curr Opin Chem Biol. 26:55–61. 2015. View Article : Google Scholar : PubMed/NCBI

17 

Zhang N, Zhang L, Tao Y, Guo L, Sun J, Li X, Zhao N, Peng J, Li X, Zeng L, et al: Construction of a high density SNP linkage map of kelp (Saccharina japonica) by sequencing Taq I site associated DNA and mapping of a sex determining locus. BMC Genomics. 16:1892015. View Article : Google Scholar : PubMed/NCBI

18 

Schroeder A, Mueller O, Stocker S, Salowsky R, Leiber M, Gassmann M, Lightfoot S, Menzel W, Granzow M and Ragg T: The RIN: An RNA integrity number for assigning integrity values to RNA measurements. BMC Mol Biol. 7:32006. View Article : Google Scholar : PubMed/NCBI

19 

Wang X, Li S, Li J, Li C and Zhang Y: De novo transcriptome sequencing in Pueraria lobata to identify putative genes involved in isoflavones biosynthesis. Plant Cell Rep. 34:733–743. 2015. View Article : Google Scholar : PubMed/NCBI

20 

Tsanakas GF, Manioudaki ME, Economou AS and Kalaitzis P: De novo transcriptome analysis of petal senescence in Gardenia jasminoides Ellis. BMC Genomics. 4:5542014. View Article : Google Scholar

21 

Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K and Madden TL: BLAST+: Architecture and applications. BMC Bioinformatics. 10:4212009. View Article : Google Scholar : PubMed/NCBI

22 

Gene Ontology Consortium: The gene ontology (GO) project in 2006. Nucleic Acids Res. 34(Database issue): D322–D326. 2006.PubMed/NCBI

23 

Conesa A, Götz S, García-Gómez JM, Terol J, Talón M and Robles M: Blast2GO: A universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 21:3674–3676. 2005. View Article : Google Scholar : PubMed/NCBI

24 

Tatusov RL, Fedorova ND, Jackson JD, Jacobs AR, Kiryutin B, Koonin EV, Krylov DM, Mazumder R, Mekhedov SL, Nikolskaya AN, et al: The COG database: An updated version includes eukaryotes. BMC Bioinformatics. 4:412003. View Article : Google Scholar : PubMed/NCBI

25 

Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, Kawashima S, Katayama T, Araki M and Hirakawa M: From genomics to chemical genomics: New developments in KEGG. Nucleic Acids Res. 34(Database issue): D354–D357. 2006. View Article : Google Scholar : PubMed/NCBI

26 

Mortazavi A, Williams BA, McCue K, Schaeffer L and Wold B: Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 5:621–628. 2008. View Article : Google Scholar : PubMed/NCBI

27 

Bustin SA, Beaulieu JF, Huggett J, Jaggi R, Kibenge FS, Olsvik PA, Penning LC and Toegel S: MIQE précis: Practical implementation of minimum standard guidelines for fluorescence-based quantitative real-time PCR experiments. BMC Mol Biol. 11:742010. View Article : Google Scholar : PubMed/NCBI

28 

Raso A, Mascelli S, Nozza P, Ugolotti E, Vanni I, Capra V and Biassoni R: Troubleshooting fine-tuning procedures for qPCR system design. J Clin Lab Anal. 25:389–394. 2011. View Article : Google Scholar : PubMed/NCBI

29 

Livak KJ and Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 25:402–408. 2001. View Article : Google Scholar : PubMed/NCBI

30 

Ye J, Fang L, Zheng H, Zhang Y, Chen J, Zhang Z and Wang J, Li S, Li R, Bolund L and Wang J: WEGO: A web tool for plotting GO annotations. Nucleic Acids Res. 34(Web Server Issue): W293–W297. 2006. View Article : Google Scholar : PubMed/NCBI

31 

Takáč T, Pechan T and Šamaj J: Differential proteomics of plant development. J Proteomics. 74:577–588. 2011. View Article : Google Scholar : PubMed/NCBI

32 

Khan Z, Kim SG, Jeon YH, Khan HU, Son SH and Kim YH: A plant growth promoting rhizobacterium, Paenibacillus polymyxa strain GBR-1, suppresses root-knot nematode. Bioresource Technol. 99:3016–3023. 2008. View Article : Google Scholar

33 

Nagao R, Yokono M, Teshigahara A, Akimoto S and Tomo T: Light-harvesting ability of the fucoxanthin chlorophyll a/c-binding protein associated with photosystem II from the Diatom Chaetoceros gracilis as revealed by picosecond time-resolved fluorescence spectroscopy. J PhysChem B. 118:5093–5100. 2014.

34 

Ha YI, Lim JM, Ko SM, Liu JR and Choi DW: A ginseng-specific abundant protein (GSAP) located on the cell wall is involved in abiotic stress tolerance. Gene. 386:115–122. 2007. View Article : Google Scholar : PubMed/NCBI

35 

Jiang T, Zhou B, Luo M, Abbas HK, Kemerait R, Lee RD, Scully BT and Guo B: Expression analysis of stress-related genes in kernels of different maize (Zea mays L.) inbred lines with different resistance to aflatoxin contamination. Toxins (Basel). 3:538–550. 2011. View Article : Google Scholar : PubMed/NCBI

36 

Karthikeyan M, Jayakumar V, Radhika K, Bhaskaran R, Velazhahan R and Alice D: Induction of resistance in host against the infection of leaf blight pathogen (Alternaria palandui) in onion (Allium cepa var. aggregatum). Indian J Biochem Biophys. 42:3712005.PubMed/NCBI

37 

Salleh FM, Evans K, Goodall B, Machin H, Mowla SB, Mur LA, Runions J, Theodoulou FL, Foyer CH and Rogers HJ: A novel function for a redox-related LEA protein (SAG21/AtLEA5) in root development and biotic stress responses. Plant Cell Environ. 35:418–429. 2012. View Article : Google Scholar : PubMed/NCBI

38 

Zhang C, Shi H, Chen L, Wang X, Lü B, Zhang S, Liang Y, Liu R, Qian J, Sun W, et al: Harpin-induced expression and transgenic overexpression of the phloem protein gene AtPP2-A1 in Arabidopsis repress phloem feeding of the green peach aphid Myzuspersicae. BMC Plant Biol. 11:112011. View Article : Google Scholar : PubMed/NCBI

39 

Barber J: Photosynthetic energy conversion: Natural and artificial. Chem Soc Rev. 38:185–196. 2009. View Article : Google Scholar : PubMed/NCBI

40 

Van Loon LC and Van Strien EA: The families of pathogenesis-related proteins, their activities and comparative analysis of PR-1 type proteins. Physiol Mol Plant Pathol. 55:85–97. 1999. View Article : Google Scholar

41 

Varet A, Parker J, Tornero P, Nass N, Nürnberger T, Dangl JL, Scheel D and Lee J: NHL25 and NHL3, two NDR1/HIN1-like genes in Arabidopsis thaliana with potential role(s) in plant defense. Mol Plant Microbe Interact. 15:608–616. 2002. View Article : Google Scholar : PubMed/NCBI

42 

Durrant WE, Rowland O, Piedras P, Hammond-Kosack KE and Jones JD: cDNA-AFLP reveals a striking overlap in race-specific resistance and wound response gene expression profiles. Plant Cell. 12:963–977. 2000. View Article : Google Scholar : PubMed/NCBI

43 

Cole AM, Ganz T, Liese AM, Burdick MD, Liu L and Strieter RM: Cutting edge: IFN-inducible ELR−CXC chemokines display defensin-like antimicrobial activity. J Immunol. 167:623–627. 2001. View Article : Google Scholar : PubMed/NCBI

44 

Kelman A and Sequeira L: Resistance in plants to bacteria. R Soc Lond Proc. 181:247–266. 1972; View Article : Google Scholar

45 

Chen S, Luo H, Li Y, Sun Y, Wu Q, Niu Y, Song J, Lv A, Zhu Y, Sun C, et al: 454 EST analysis detects genes putatively involved in ginsenoside biosynthesis in Panax ginseng. Plant Cell Rep. 30:1593–1601. 2011. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

November 2017
Volume 16 Issue 5

Print ISSN: 1791-2997
Online ISSN:1791-3004

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
APA
Liu, S., Liu, M., Wang, S., Lin, Y., Zhang, H., Wang, Q., & Zhao, Y. (2017). Analysis of the Panax ginseng stem/leaf transcriptome and gene expression during the leaf expansion period. Molecular Medicine Reports, 16, 6396-6404. https://doi.org/10.3892/mmr.2017.7377
MLA
Liu, S., Liu, M., Wang, S., Lin, Y., Zhang, H., Wang, Q., Zhao, Y."Analysis of the Panax ginseng stem/leaf transcriptome and gene expression during the leaf expansion period". Molecular Medicine Reports 16.5 (2017): 6396-6404.
Chicago
Liu, S., Liu, M., Wang, S., Lin, Y., Zhang, H., Wang, Q., Zhao, Y."Analysis of the Panax ginseng stem/leaf transcriptome and gene expression during the leaf expansion period". Molecular Medicine Reports 16, no. 5 (2017): 6396-6404. https://doi.org/10.3892/mmr.2017.7377