Skip to main content
Journal of Ginseng Research logoLink to Journal of Ginseng Research
. 2013 Apr;37(2):227–247. doi: 10.5142/jgr.2013.37.227

Insilico profiling of microRNAs in Korean ginseng (Panax ginseng Meyer)

Ramya Mathiyalagan 1, Sathiyamoorthy Subramaniyam 1, Sathishkumar Natarajan 1, Yeon Ju Kim 1, Myung Suk Sun 1, Se Young Kim 1, Yu-Jin Kim 1, Deok Chun Yang 1,*
PMCID: PMC3659641  PMID: 23717176

Abstract

MicroRNAs (miRNAs) are a class of recently discovered non-coding small RNA molecules, on average approximately 21 nucleotides in length, which underlie numerous important biological roles in gene regulation in various organisms. The miRNA database (release 18) has 18,226 miRNAs, which have been deposited from different species. Although miRNAs have been identified and validated in many plant species, no studies have been reported on discovering miRNAs in Panax ginseng Meyer, which is a traditionally known medicinal plant in oriental medicine, also known as Korean ginseng. It has triterpene ginseng saponins called ginsenosides, which are responsible for its various pharmacological activities. Predicting conserved miRNAs by homology-based analysis with available expressed sequence tag (EST) sequences can be powerful, if the species lacks whole genome sequence information. In this study by using the EST based computational approach, 69 conserved miRNAs belonging to 44 miRNA families were identified in Korean ginseng. The digital gene expression patterns of predicted conserved miRNAs were analyzed by deep sequencing using small RNA sequences of flower buds, leaves, and lateral roots. We have found that many of the identified miRNAs showed tissue specific expressions. Using the insilico method, 346 potential targets were identified for the predicted 69 conserved miRNAs by searching the ginseng EST database, and the predicted targets were mainly involved in secondary metabolic processes, responses to biotic and abiotic stress, and transcription regulator activities, as well as a variety of other metabolic processes.

Keywords: Panax ginseng, MicroRNA, Expressed sequence tag, Deep sequencing

INTRODUCTION

MicroRNAs (miRNAs) are a class of small, nonprotein-coding RNAs with lengths of approximately 21 nucleotides (nt) that act as post-transcriptional regulators in eukaryotes [1]. Like other genes, mature miRNAs also have their own miRNA genes, which are transcribed from their own miRNA genes. In plants, miRNA genes are initially transcribed into primary miRNAs (primiRNAs) by pol II [2]. Pri-miRNAs are processed into miRNA precursors (pre-miRNAs) by DICER-LIKE1, which are able to fold into a perfect or near-perfect secondary hairpin structure, and processed into a miRNA duplex (miRNA:miRNA*). It further leads to the release of mature miRNA by the unwinding of the duplexes [1]. Mature miRNAs are assembled into the RNA-induced silencing complex (RISC) to direct the RISC to their complementary target sites in the messenger RNA (mRNA). The activity of miRNA on a target mRNA is dependent on the degree of base pairing, and in the case of perfect or near-perfect base pairing, it leads to target mRNA degradation in plants [3]. Therefore, the perfect or near-perfect base matching of miRNA to the targets makes the computational prediction of miRNAs easier in plants compared to animals, and they have been successfully applied in many plants [4-6]. miRNA genes are an important class of fine-tuning regulators, playing an important role in a wide range of developmental, biological, and metabolic processes in plants, including metabolism, stress response, vegetative phase change, organogenesis, and signal transduction [7,8].

To date, different approaches have been employed to identify miRNAs in various species, including: 1) direct cloning after isolation of small RNAs with a computational strategy 2) expressed sequence tags (ESTs) analysis, and 3) high throughput sequencing of small RNA [9,10]. Among these three methods, we employed EST analysis and high throughput sequencing of small RNAs to discover Panax ginseng Meyer miRNAs. Comparisons of miRNA of different plant species show that miRNAs have been highly conserved throughout evolution. Its conserved nature helps to identify the miRNA from different plant species by comparative EST based homolog searches, which have been successfully applied in many species, including potato [11], citrus [12], switch grass [13], lettuce [14], and tobacco [15], and it is applicable for those species in which whole genome sequence information is not available [16]. Even though the miRNAs are conserved, some of the miRNAs often express at low levels, or are expressed only in specific tissue or under specific conditions. A new generation of sequencing technologies like high-throughput pyrosequencing technology allows for the identification of lowly expressed or tissue specific expressed miRNA, which was reported in several species such as grapevine [9], tomato [17], and grapevine flower and berry [18].

Based on the annotation criteria, to date 18,226 miRNAs have been deposited in the miRNA registry database (miRBase; release 18.0, http://microrna.sanger.ac.uk) from various species. Although miRNAs have been identified and validated in many plant species, they are largely unknown in P. ginseng (Korean ginseng), which is a traditionally known medicinal plant in oriental medicine where the roots of the plant are mainly used for medicinal purposes. The genus Panax is derived from panacea, which means a cure-all and longevity. It is a slow growing perennial herb of the Araliaceae family, and because of its mysterious power in oriental medicine, people have been using ginseng roots and its extracts to increase physical strength and vigor, and revitalize the body and mind [19]. Ginseng has been used in Korea, as well as other countries such as China and Japan. It contains triterpene ginseng saponins called ginsenosides, which are responsible for its various pharmacological activities, including immune system modulation, anti-stress activities, anti-hyperglycemic activities, anti-inflammatory, anti-oxidant, and anti-cancer effects. It also has polysaccharides, flavonoides, peptides, polyacetylic alcohols, and fatty acids [20,21]. In recent years, the increasing evidence of miRNA identification and characterization in other important food crops such as rice, maize, arabidopsis, potato, tomato, citrus, grape fruit, and medicinal tuber crops [22], and also the prediction of terpenoid pathway genes targeting miRNAs in various plant species [11,23,24], as well as evidence of root development related miRNA [25], all induces insight into the analysis of miRNA in P. ginseng. Here, we first report the profiling of miRNA and their targets in P. ginseng (Korean ginseng).

MATERIALS AND METHODS

Plant materials and small RNA sequencing

The flower buds, leaves, and roots were collected from 6-year field grown P. ginseng plants in south Korea. Immediately after collection, the samples were stored in liquid nitrogen for further analysis. A small RNA library of three samples was constructed using a TruSeq small RNA sample preparation kit, the concentration of RNA was analyzed using a bioanlyzer to determine the RNA integrity number, and a 28s rRNA:18s rRNA ration and ribogreen were used to analyze the RNA concentration. The good qualities of RNA were taken for sequencing using Illumina’s Genome Analyzer IIx (GAIIx). The sequence reads were initially trimmed by removing the adapter sequences and low quality sequences with a phred score below 20. Finally, the small RNA sequence was taken in FASTQ format for further bioinformatics analysis.

Transcriptome sequences and microRNA registry database sequences

All known miRNAs of mature plants from different plant species were used as reference miRNA for predicting the conserved miRNA in P. ginseng. Known plant miRNAs (reference miRNAs) from the miRBase database (release 17) [26] were derived from different plant species, including Arabidopsis thaliana, Oryza sativa, Glycine max, Brassica napus, Medicago truncatula, Sorghum bicolor, Zea mays, and Saccharum officinarum, as well as all of the other plant species. The complete transcriptome sequences for P. ginseng were collected from the ginseng EST database (http://www.bioherbs.khu.ac.kr/ggrb) to predict the miRNA for P. ginseng [27].

Expressed sequence tag based conserved microRNA prediction

The small RNA raw sequence reads from Illumina’s GAIIx were converted from the FASTQ to the FASTA format, and then the redundancy sequences were removed using the FASTX tool kit (http://hannonlab.cshl.edu/fastx_toolkit), and the remaining unique sequences were selected for further analysis. Sequences 18 to 27 nt bases long were used to do BLASTN against the Rfam database to remove other small RNAs such as transfer RNA (tRNA), ribosomal RNA (rRNA), small interfering RNA (siRNA), small nuclear RNA (snRNA), and small nucleolar RNAs (snoRNA). The remaining sequences were used for the EST based miRNA prediction with the stranded protocol using the mirCheck tool, as well as a custom-made perl script [28]. Firstly, the sequences were matched using the PatScan algorithm with the miRBase database (release 17) to predict the conserved miRNAs in ginseng. Sequences with ≤3 mismatches were considered to be conserved miRNAs in ginseng. Those conserved miRNAs were taken for further analysis, as described in the workflow (Fig. 1).

Fig. 1. Flow chart of microRNA (miRNA) identification in Panax ginseng. Both expressed sequence tag (EST) analysis and high throughput sequencing methodology were used for the identification of conserved miRNA in P. ginseng.

Fig. 1.

MicroRNA validation and digital expression analysis

To validate the predicted miRNAs, further evaluation was conducted using the RNA secondary structure prediction with RNAFold (http://rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgi), a web based tool. The predicted structures were evaluated with miRCheck with known criteria for plant miRNA prediction. Further confirmation was carried out through the minimal free-folding energy (MFE) and MFE index (MFEI) [29,30]. Those sequences which passed the previous steps were matched with individual samples using the patScan algorithm, having 3 mismatches to get the digital gene expression. Finally, the exact matched read counts were calculated using a custom made perl script.

Target prediction and functional analysis

Predicted miRNA sequences were subjected to target prediction using the web based server psRNATarget (http://plantgrn.noble.org/psRNATarget). This tool has an option to predict user submitted RNAs vs. user submitted transcripts, and we used that option to predict all of the targets. All of the unique transcripts were taken for the functional annotation using the blast2go functional annotation tool. Transcripts were prepared through the de novo assembly and blasted against the non-redundant database, and then subjected to gene ontology analysis.

RESULTS AND DISCUSSION

Generally, miRNAs can be predicted by analysis of EST and sequencing of small RNAs. Here, we used both of EST analysis and high through put sequencing methodology for the identification of conserved miRNA in P. ginseng (Fig. 1).

Computational identification of conserved microRNA by expressed sequence tag analysis

The identification of conserved miRNAs by EST analysis is greatly facilitated by the conserved properties of miRNA families among various plant species [29]. To identify the complete set of conserved miRNAs by computational predictions, the availability of the complete genome sequences is a pre-requisite. If complete genomic sequences are lacking, fragmented data like EST and high-throughput genomic sequences have been used [31]. Using the homology based strategy, lots of conserved miRNAs have been identified in various plant species, including potato (Solanum tuberosum) [11], switch grass (Panicum virgatum) [13], lettuce (Lactuca sativa) [14], and rapeseed (B. napus) [32]. We employed the computational based approaches to predict miRNAs in P. ginseng with available ginseng EST sources from our lab.

miRNA sequences of various plant species were predicted and deposited in the miRBase database [33]. We used miRBase mature miRNA sequences as a reference sequence to predict miRNAs in ginseng using similarity searches. Mature plant miRNAs from different plant species were downloaded from miRBase, redundant sequences were then removed, and non-redundant unique sequences were blasted with P. ginseng EST sequences. Using homology searches, 69 miRNAs belonging to 44 conserved miRNA families were identified after repeated and protein coding sequences were removed (Table 1).

Table 1.

Identified homology based conserved miRNA in Panax ginseng

miRNA family Sequence Length of mature miRNA Reference Precursor EST Length of precursor Location (3’|5’) GC% MFE (kcal/mol) MFEI

miR156a TTTACGGAAGATTGAGAGGAC 21 bna contig45577 60 3’ 46.67 30 0.64
miR159a AUAGCAGUGAAGGCAGCUCCU 21 osa contig47841 94 3’ 44.68 31.91 0.71
miR164 UGGAGAAUCAAGGCCCUUGAG 21 osa contig50068 248 3’ 41.53 33.06 0.8
miR169h GAACUGAAGAUGACUUGACGG 21 mtr contig14564 133 5’ 29.32 20.53 0.7
miR172f GUAAUCAUGAUCAUGCUGCU 20 sbi contig47560 287 3’ 42.51 33.59 0.79
miR319b UUGGAGUGAAGGAAACUCCA 20 mtr contig41016 72 5’ 36.11 34.03 0.94
miR319e UUGGAGUGAAGGAAACUCCAU 21 vvi contig41016 72 5’ 36.11 34.03 0.94
miR319f UAGCAGUGAAGGCAGCUCCU 20 ptc contig47841 94 3’ 44.68 31.91 0.71
miR319g UAGGACUGGAGGCAGCUUCU 20 ptc contig54185 55 3’ 54.55 49.45 0.91
miR319h UUAGGACUGGAGGCAGCUUCU 21 vvi contig54185 55 3’ 54.55 49.45 0.91
miR396b UUCCACAUCUAUCUUUAUCU 20 vvi contig23595 366 5’ 33.61 25.05 0.75
miR396c UUCCUCGCCUUUCUUGCUCUU 21 ptc contig58969 263 5’ 55.51 39.35 0.71
miR397 UCAUUGAGCACAAUGUUGUUG 21 zma contig39102 88 5’ 39.77 31.82 0.8
miR408a AUGCACUGCCUCUUCCCUGGC 21 ath contig30298 102 3’ 51.96 45.5 0.85
miR414d UCAUCAUCAUCAUCAUCAUCA 21 ath contig45947 60 3’ 45 25.7 0.95
miR414e UCAUCAUCAUCAUCAUCAUCA 21 ath contig45947 60 3’ 45 42.83 0.95
miR414f UCAUCAUCAUCAUCAUCAUCA 21 osa contig46717 328 5’ 47.87 32.99 0.69
miR414h UCAUCAUCAUCAUCAUCGAAU 21 osa contig27681 229 5’ 41.92 32.97 0.79
miR414i UCAUGGGCAUCAUCAUGGUCA 21 ath contig54204 85 3’ 44.71 35.65 0.8
miR414j UCGAAUUCAUCAUCAUCAUCA 21 ath contig27681 241 5’ 41.49 34.44 0.83
miR414l UCUUCGUCAUCUUCAUCUUCC 21 osa contig55217 118 5’ 47.46 38.39 0.81
miR417 GAACAAAAUGAAUUUGUUCGA 21 ath contig47470 60 5’ 28.33 37 1.31
miR419e UUAUUGAUGAUGAGGAUGAUG 21 ath contig58582 192 3’ 25.52 25.78 1.01
miR446 CAUCAAUAUGAAUAUGUCAGAUGC 24 osa contig48939 106 5’ 36.79 31.13 0.85
miR482 CCUUUCCUAUUCCUCCCAUACC 22 vvi contig29669 101 3’ 46.53 41.7 0.88
miR482a CCUAUUCCUCCCAUACC 17 ptc contig29669 101 3’ 46.53 41.7 0.88
miR482c CCUUUCCUAUUCCUCCCAUA 20 ptc contig29669 97 3’ 48.45 40.41 0.83
miR530a GGCAUCUGCACCUGAACUUU 20 ptc contig48663 353 5’ 42.78 30.31 0.71
miR783 AAGCUUUUUUCUGUCAUGUUC 21 ath contig18217 346 5’ 45.38 32.95 0.73
miR815 GAGGGGAAAGAGGUGAUUGGG 21 osa contig59498 187 3’ 52.94 37.33 0.71
miR816a GUGACAUACUCUACUUCAGC 20 osa contig48862 90 5’ 36.67 25.6 0.7
miR834b UUGUAGUAGUGGCGGUGGCAA 21 ath contig32379 67 3’ 52.24 37.61 0.72
miR846 UUGAAUUUUAGCGGUUGAAUU 21 ath contig33367 129 3’ 34.11 27.05 0.79
miR847a UCAAACUUCUUCUUCUUGAUC 21 ath contig50301 186 5’ 43.01 30 0.7
miR847b UCAAUCUUCUUCUUCUUCUUG 21 ath contig46552 202 5’ 42.57 29.06 0.68
miR847c UCUCUUCUCUUCUUCUUUAUA 21 ath contig00227 166 3’ 39.76 29.4 0.74
miR854b GAGGAGGAGGAGGAGGAGGAG 21 ath contig45937 119 5’ 30.25 24.12 0.8
miR854d GAUGAGGAGGAGGAGGAGGAU 21 ath contig33518 280 5’ 40.71 29.05 0.71
miR854e GAAGAGGAGAGAGAUGAGGAG 21 ath contig26917 169 3’ 48.52 32.78 0.68
miR854c GAUGAGGAUGAGGAUGAGGAU 21 ath contig34488 261 5’ 42.15 29.04 0.69
miR1132h UAUUAUGGGACGGAGGUAG 19 tae contig63186 273 3’ 30.4 28.54 0.94
miR1134 CAACAAGAAGAAGAAGUAGAAGAU 24 tae contig12044 144 5’ 25.69 31.66 0.85
miR1436c UUAUCCUGGGACGGAGGGAGU 21 osa contig61393 264 3’ 34.09 34.33 1.01
miR1436d UUAUUAUGGGACGGAGGUAGU 21 osa contig63186 275 3’ 30.9 80.51 0.94
miR1439a UAUAGGAAUGGAGGGAGUAUU 21 osa contig16765 277 3’ 29.96 25.13 0.84
miR1439b UUUAGGAACGGAGGGAGUACU 21 osa contig56537 267 3’ 32.21 28.13 0.87
miR1439c UUUAGGAAUGGAGGGAGUAAU 21 osa contig56367 281 3’ 28.83 27.94 0.97
miR1439d UUUGGGAAUGGAGGGAGUAAU 21 osa contig10661 236 3’ 25 22.2 0.89
miR1439e UUUGGGGAUGGAGAGAGUAUU 21 osa contig27854 283 3’ 29.68 23.92 0.81
miR1439h UUUAGGAACGGAGGGAGUACU 21 osa contig56537 267 3’ 32.2 75.1 0.87
miR1448 CUUUCCUAUUCCUCCCAUAC 20 ptc contig29669 99 3’ 47.47 40.9 0.87
miR1534a UAUUUUGUGGAUAUAGUAAU 20 gma contig49029 70 3’ 31.43 26.86 0.85
miR1886c UGAGAUGAGAUCUGGGUUUGG 21 ath contig11222 98 3’ 42.86 38.47 0.9
miR20975p AGGGAAGGGAAGGGAAGGGAAG 22 osa contig15753 69 5’ 42.03 43.62 1.04
miR21015p AUAUUUUUACAAGUAAAAUUGU 22 osa contig17137 123 5’ 38.21 48.62 1.27
miR2108b UUAAUGUUUUGUCUAAGUGAG 21 gma contig50952 65 3’ 32.31 46.15 1.43
miR2109c UGCGAGUUUCUGGGGCUCUG 20 gma contig56006 346 5’ 51.45 40.64 0.79
miR2112b CUUUAUAUAUGCAUUUGUGCU 21 ath contig55266 270 3’ 32.59 23.8 0.73
miR2606a UACAAUUUCUAAGUUGCUUUG 21 mtr contig46524 141 5’ 37.59 26.88 0.72
miR2607 AUGUGAUUAUGUAAUGAUAGU 21 mtr contig38869 116 5’ 25.86 26.81 1.04
miR2626 AACGUCGUGGUUAAGGGUGUC 21 mtr contig62419 56 5’ 39.29 50.89 1.3
miR2628a CAUAACUGAAUGAUUAGUAA 20 mtr contig23672 71 5’ 28.17 27.75 0.98
miR2628b GAUGCAAGGAUGAUGAGUCA 20 mtr contig11869 189 5’ 42.33 31.64 0.75
miR2642 AUGAUUUUCACCAAAUCUUGC 21 mtr contig07593 77 5’ 40.26 30.26 0.75
miR2643b UUUGGGAUCAGAUAUAAGACA 21 mtr contig22496 363 5’ 36.08 99.8 0.76
miR2658 AUGUGACCUUUUUUAUGUGC 20 mtr contig28456 74 3’ 32.43 31.89 0.98
miR2665 UGCUUUCAUGCCAAGAUUUGA 21 mtr contig49532 60 5’ 33.33 27 0.81
miR2673b CCGCCUCUUCUUCCUCUUCCGC 22 mtr contig52872 189 5’ 55.03 41.33 0.75
miR2937 AAAAGAGCUUUUGAGGGAGUU 21 ath contig45835 79 3’ 43.04 41.9 0.97

miRNA, microRNA, EST, expressed sequence tag, GC, guanine-cytosine content, MFE, minimal free-folding energy, MFEI, minimal free-folding energy index.

The majority of predicted miRNAs included miR414, miR1132, miR1439, miR319, miR482, miR847, miR854, miR1436, and miR2628. The mature miRNA sequences were grouped into same member families based on mature miRNA sequence similarity searches using miRBase. In our predictions, the miR414 family was predicted to have the largest abundance of miRNA members (7 members) (Fig. 2), which was also reported in rice (O. sativa) [34], Stevia (Stevia rebaudiana) [35], and opium poppy (Papaver somniferum) [36], while the highest abundance of the same family was reported in switch grass (11 members) [13].

Fig. 2. Abundance or frequency of microRNA (miRNA) families in Panax ginseng. miR414, miR1439 and miR319 families has highest abundance of miRNAs.

Fig. 2.

The second largest representative miRNA family was miR1439, where 6 members were identified in our predictions. Only 3 miRNA members of the miR1439 family were predicted in potato [11], whereas 6 members were predicted in P. ginseng. Previously, miR1439 was listed as a new rice miRNA [37] and salt induced miRNA in rice [34], and later it was identified in tobacco [15] and potato. Therefore, the prediction of some plant miRNAs in certain plant species may be responsible for special functions, and be conserved in particular species. Another family, miR319, was predicted with 5 members, while miR482, miR847, and miR854 contained 3 members in each family. Additionally, 2 members were contained in each miR1436, miR2628, and miR396 families. The rest of the families were represented with only one member. miR319, reported for various plant development functions like the regulation of leaf senescence, leaf morphogenesis, and leaf complexity [38], and stress regulation of miR319 was reported in sugarcane [39].

Two miRNA847s were reported in A. thaliana [40] and A. lyrata [41]. Interestingly, in our study, 3 members of miRNA847 were predicted in P. ginseng. Another miRNA, miR1436, was identified in this study, which was reported in barley [42], switch grass [13], and rice [34], while 7 members were identified for the same family in potato [11].

We further analyzed the characteristics of conserved miRNAs to distinguish from other small RNAs (Table 1). The length of mature miRNAs varies from 17 to 24 nt, where the majority of miRNAs are confined to 21 nt, followed by 20 and 19 nt (Fig. 3A). The typical lengths of plant mature miRNA sequences are 21 nt, which are in the highest abundance in ginseng miRNAs, similar to other plant species [13,32]. It was reported that the length of pre-miRNAs in plants ranges from 60 to >400 nt [43,44]. The length of precursor miRNAs in P. ginseng varies significantly from 55 to 366 nt; however, the majority of pre-miRNAs are 60 to 139 nt in length (Fig. 3B), which is similar to reports of other plant species [11,13].

Fig. 3. (A) Length distribution of predicted microRNAs (miRNAs) in Panax ginseng, the majority of miRNAs are confined to 21 nucleotides. (B) Length of precursor miRNAs (pre-miRNAs) in P. ginseng, the length varies significantly from 55 to 366 nucleotides.

Fig. 3.

Having lower MFE is important for the sequences to form stable secondary loop structures for high thermodynamic stability [30]. In this study, the MFE value of identified P. ginseng miRNAs ranged from -20.53 to -99.8 kcal/mol, with an average of -35.78 kcal/mol. This MFE value of pre-miRNAs in the present study is consistent with previous reports [32]. MFEI was a valuable criteria used to distinguish potential miRNAs from other types of RNAs. If the MFEI value of the pre-miRNA was higher than 0.85, that sequence was considered to be a potential miRNA [44]. The average MFEI of the predicted P. ginseng miRNAs was 0.851 (Table 1).

Sequence analysis of small RNAs from deep sequencing

We used the high throughput Illumina sequencing technology to sequence small RNAs in P. ginseng in order to validate the expression patterns of the EST based predicted conserved P. ginseng miRNAs. In high throughput sequencing technology, total of 56,430,729 raw sequences were obtained from the 6-year-old flower buds, leaves, and lateral roots of P. ginseng. After removing the low quality sequences, the remaining sequences with length ranging from 17 to 27 nt were obtained. The sequences were further processed to remove other RNAs and redundant sequences. Finally, a total of 5,353,559 non-redundant sequence reads were used for miRNA analysis (Table 2).

Table 2.

Distribution of small RNA reads in sequenced Panax ginseng tissues

Description Leaves Flower buds Lateral roots Total

Raw sequences 24258021 10211169 21961539 56430729
Adaptor/quality/length (17-27 nt) trimmed 22803528 7021215 16777031 46601774
Matching t/rRNAs 919530 400059 470384 1789973
Redundant sequence 13625423 2789895 4714124 21129442
Non-redundant sequence 3849681 693590 810288 5353559
Total non-redundant sequence 5353559

nt, nucleotides, t/rRNA, transfer RNA/ribosomal RNA.

Digital gene expressions of conserved microRNAs in Panax ginseng by deep sequencing

Non-redundant small RNA sequences were used to analyze the digital gene expression pattern of already predicted conserved miRNAs in P. ginseng. The small RNA sequences with 100% miRNA sequence similarity with homology based predicted miRNA sequences were used for digital gene expression studies in three tissues. Among the predicted miRNA families by small RNA analysis, miR414 and miR1439 contained the largest number of miRNA with four members, followed by the miR854 family with 3 members. Other families such as miR1436 and miR482 were represented with 2 members in each family. The remaining families had only one member of miRNA.

The expression level of each of the miRNA families also varied. The miRNA family miR482a showed a very high level of expression (number of reads) with the largest number of reads in each organ, such as 740 reads in the flower buds, 13,510 reads in the leaves, and 178 reads in the lateral roots. Followed by, miRNAs such as miR1132h, miR816a, and miR1436d showing the second largest abundant expression of miRNA reads in all three libraries. The miRNAs miR2626, miR1132f, miR1436b and c, miR1439, miR854c and d, and miR414d, e, and f were predicted with >100 miRNA reads in total for all 3 libraries, whereas miRNAs such as miR1534a, miR2658, miR482c, miR414h, and miR156b were predicted with lower expressions.

Tissue specific expression patterns were also observed, as miR1534a was expressed in lateral roots, but it was not expressed in flower buds and leaves, whereas miR414h and miR2097 were detected in flower buds and leaves tissues, but not in lateral roots. The miRNA families miR1448, miR156b, and miR2673b showed expressions in leaves and lateral roots, but not in the flower buds. miRNAs such as miR2658 and miR482c have shown expressions only in leaves, and not in the other tissues (Table 3).

Table 3.

Digital gene expressions of conserved microRNAs (miRNAs) in Panax ginseng by deep sequencing

miRNA family Mature miRNA sequence Sequence length Flower buds Leaves Lateral roots

miR1132h UAUUAUGGGACGGAGGUAG 19 251 1235 90
miR1436c UUAUCCUGGGACGGAGGGAGU 21 14 97 3
miR1436d UUAUUAUGGGACGGAGGUAGU 21 184 933 61
miR1439a UAUAGGAAUGGAGGGAGUAUU 21 2 12 1
miR1439b UUUAGGAACGGAGGGAGUACU 21 23 150 12
miR1439h UUUAGGAACGGAGGGAGUACU 21 23 150 12
miR1439c UUUAGGAAUGGAGGGAGUAAU 21 3 28 6
miR1448 CUUUCCUAUUCCUCCCAUAC 20 0 15 1
miR1534a UAUUUUGUGGAUAUAGUAAU 20 0 0 2
miR169h GAACUGAAGAUGACUUGACGG 21 4 16 1
miR2097-5p AGGGAAGGGAAGGGAAGGGAAG 22 1 16 0
miR2626 AACGUCGUGGUUAAGGGUGUC 21 49 554 7
miR2658 AUGUGACCUUUUUUAUGUGC 20 0 4 0
miR2673b CCGCCUCUUCUUCCUCUUCCGC 22 0 27 7
miR414d UCAUCAUCAUCAUCAUCAUCA 21 5 130 2
miR414e UCAUCAUCAUCAUCAUCAUCA 21 5 130 2
miR414f UCAUCAUCAUCAUCAUCAUCA 21 5 130 2
miR414h UCAUCAUCAUCAUCAUCGAAU 21 1 6 0
miR482a CCUAUUCCUCCCAUACC 17 740 13510 178
miR482c CCUUUCCUAUUCCUCCCAUA 20 0 6 0
miR816a GUGACAUACUCUACUUCAGC 20 44 1244 7
miR854b GAGGAGGAGGAGGAGGAGGAG 21 13 63 17
miR854c GAUGAGGAGGAGGAGGAGGAG 21 16 123 20
miR854d GAUGAGGAGGAGGAGGAGGAU 21 11 86 14

Tissue specific expressions of miRNAs were reported in various plant species [9,24]. Even though the root was considered to be the main functional part in P. ginseng, leaves and flower buds were also reported for various ginsenosides. This kind of tissue specific expressions of miRNAs represents an interesting topic for further in-depth analysis.

The size distribution patterns of the identified small RNAs in P. ginseng were observed such that the majority of the small RNAs were 21 nt in size, followed by 20 nt, 22 nt, and 19 nt, as in the reports of other plant species, such as grapevine [9] and tomato [45].

Target prediction

Predicting potential targets of miRNA based on a computational approach were aided by the perfect and near perfect complementary characteristics of miRNA with their target mRNA [46]. In order to understand the putative functions of predicted miRNAs, 346 potential targets were identified for the predicted 69 conserved miRNAs by searching the ginseng EST database. Most of the miRNA targets were predicted (Appendix 1), whereas for some miRNAs such as miR482a, miR816, and miR1132, targets were unable to be predicted, which may be due to the limited number of EST sequences available in the databases. Most of the miRNAs were identified with more than one target, especially the miR414 families identified with 68 targets, the miR854 families with 44 targets, and the miR1439 families with 29 targets, which is consistent with the notion that one miRNA may have many targets [47]. Gene Ontology based functional classification of targets was analyzed for understanding the miRNA-gene regulatory network based on biological process and molecular function. In this study, predicted target functions were classified into biological process, molecular function, and cellular component. The main biological process of miRNA targets which involved in transport, protein modification process, regulation of transcription, response to various biotic and abiotic stimulus, secondary metabolic process, and regulation of gene expression which has important role in ginseng (Fig. 4A). The molecular function of predicted miRNA targets is involve in transporter, kinase activity, transcription factor, and protein binding (Fig. 4B) and plasma membrane is an main cellular component of miRNA targets (Fig. 4C).

Fig. 4. MicroRNA (miRNA) targets grouped with Gene Ontology function. The main biological process of miRNA targets which involved in transport (A). The molecular function of predicted miRNA targets are involve in transporter (B) and plasma membrane is an main cellular component of miRNA targets (C).

Fig. 4.

The predicted putative target genes not only involved in the transcription factors, but also various physiological processes targeting miRNAs were predicted (Appendix 1). Transcription factors were targeted by the miR1439e, miR2109c, miR414h, miR414i, miR419e, miR5309, miR847a, and miR854 families. In our study, the miR414 family was identified with the largest number of targets, and this miR414 family was reported to be involved in lateral root development in potato [11]. In addition, miR397 and miR1533 were shown to be involved in lateral root development in potato, which the miR397 family was also predicted in P. ginseng, whereas miR1533 was initially identified and later removed from P. ginseng miRNAs due to the lower MFEI value.

In the present study, miR156a was predicted which was reported for leaf development, vegetative phase change, flowering, and fruit development by targeting the squamosa promoter binding (SPB) protein like family of transcription factors in other plant species [48]. It was also reported that higher levels of expression of miR156/157 could prolong root growth and development in the tuberous medicinal plant [22], but SPB targeting miR156 was unable to be predicted because of the limitless P. ginseng EST sequences. miR319, reportedly playing an important function in leaf morphogenesis [49], was identified in P. ginseng.

Ginsenosides, very important triterpenoid secondary metabolites in the medicinal plant P. ginseng, were reported for their various pharmacological properties. Genes such as 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGR), farnesyl diphosphate synthase (FPS), geranyl-diphosphate synthase, squalene synthase, and squalene epoxidase (SE) were reported as putative ginsenoside pathway genes [27], and hydroxylation by cytochrome P450 and glycosylation mediated by UDP-glycosyltransferases lead to synthesis of various ginsenosides. Overexpression of P. ginseng squalene synthase was shown to increase the ginsenoside production [50]. These putative ginsenoside pathway genes were predicted as the miRNA targets, especially SE targeting miR854b and miR854c. To support this, previous reports have shown that SE was the target of miRNAs, especially miR1533 [11]. Previous reports on the target identification showed that HMGR and FPS were targeted by different miRNAs [11,23]. Accordingly, our results also showed that miR854e was identified to target FPS, while miRNA targeting HMGR was also predicted, but due to the lower MEFI value, it was removed in our analysis. Various cytochrome and glucosyltransferase targeting miRNAs were predicted in this study, as in the reports of other plant species [11,22]. Ginsenoside Ro is the only oleanane-type pentacyclic triterpene, which is a minor component in P. ginseng, and has different pharmacological effects. Beta-amyrin synthase converts 2, 3-oxidosqualene to beta-amyrin, which leads to the production of oleanane type ginsenosides (Ro). miRNAs such as miR1439b and miR1439h were predicted to target beta amyrin sythase in P. ginseng, which was also reported in potato [11]. Various reports have shown a high similarity between predicted miRNA and their targets to previously reported miRNA and their targets. Alternatively, our miRNAs and target predictions showed less similarity with previously reported known miRNAs and their targets. The lower availability of P. ginseng transcriptomes in GenBank, and the lower number of phylogenetic relations, or the lower similarity with other known crops, could be one of the possible reasons for less conservation in nature of P. ginseng miRNAs compared to other known miRNAs.

Some of the conserved miRNAs are expressed lower or below detection level in the case of the number of reads of small RNA sequences analyzed in flower buds, leaves, and lateral root tissues, and it may be present in other tissues that have not yet been analyzed. Most of the miRNA predictions in other plant species mainly used the young stage in their samples, whereas in contrast, we used fully matured tissues to sequence small RNA. These may be possible causes for the less expressed miRNAs in P. ginseng analyzed tissues. Numerous ginseng specific novel miRNAs may show a high level of expression in other tissues or organs, or different developmental stages are yet to be investigated and further experiments would provide more species specific miRNAs.

To sum up, we discovered 69 miRNAs in Korean ginseng, and tissue specific expression patterns of the identified miRNAs were analyzed using digital gene expressions of deep sequenced small RNAs of the flower buds, leaves, and lateral roots. Therefore, these results provide a basis for the regulatory roles of miRNA in ginseng. To get better insight into the miRNAs in ginseng, further studies on sRNA sequencing from specific tissues will be carried out.

Appendix 11.

Target prediction of identified microRNAs (miRNAs) in Panax ginseng

miRNA family Target protein Target ID

miR1134 1-O-acylglucose:anthocyanin-O-acyltransferase- like protein Contig60089
miR1134 NAC domain-containing Contig56197
miR1134 Oligopeptide transporter OPT family Contig23794
miR1134 PDI-like protein Contig45218
miR1134 RESA-like protein with Contig45096
miR1134 Ribosome biogenesis protein Contig45306
miR1134 RNA recognition motif-containing protein Contig61121
miR1436c Cytochrome C oxidase polypeptide Contig48926
miR1436d Protein kinase Contig09014
miR1439a Chromatin remodeling complex subunit Contig45689
miR1439a Enzyme of the cupin superfamily Contig42038
miR1439a Proton-dependent oligopeptide transport family protein Contig30763
miR1439a Synaptic glycoprotein SC2 Contig35006
miR1439b Beta-amyrin synthase Contig54769
miR1439b Chromatin remodeling complex subunit Contig45689
miR1439b Disease resistance protein Contig11515
miR1439b Enzyme of the cupin superfamily Contig42038
miR1439b Nitroreductase family protein Contig53658
miR1439c Chromatin remodeling complex subunit Contig45689
miR1439c Disease resistance protein Contig11515
miR1439c Enzyme of the cupin superfamily Contig42038
miR1439c Transcription factor jumonji domain-containing protein Contig45164
miR1439d Armadillo beta-catenin repeat family protein Contig45166
miR1439d Disease resistance protein Contig11515
miR1439d Enzyme of the cupin superfamily Contig42038
miR1439e Enzyme of the cupin superfamily Contig42038
miR1439e Nucleic acid binding Contig46706
miR1439e Protein phosphatase Contig38335
miR1439e Serine endopeptidase Contig33916
miR1439e Squalene monooxygenase Contig47397
miR1439e TATA-associated factor II 58 Contig51124
miR1439e WRKY transcription Contig59342
miR1439h Amino acid Contig46145
miR1439h Beta-amyrin synthase Contig54769
miR1439h Chromatin remodeling complex subunit Contig45689
miR1439h Disease resistance protein Contig11515
miR1439h Enzyme of the cupin superfamily Contig42038
miR1439h Nitroreductase family protein Contig53658
miR1448 CC-NBS-LRR resistance protein Contig55112
miR1448 Cinnamyl alcohol dehydrogenase-like protein Contig48716
miR1448 Ftsh11 ( protease 11) ATP-dependent peptidase ATPase metallopeptidase Contig23505
miR1448 mRNA binding protein precursor Contig49813
miR1448 Pentatricopeptide repeat-containing Contig49516
miR1534a Acyl- oxidase Contig09994
miR1534a Calcium-dependent protein Contig33156
miR1534a Chromosome region maintenance protein 1 Contig48609
miR1534a Cytochrome c oxidase polypeptide vc Contig48926
miR1534a Cytochrome p450 monooxygenase CYP72A59 Contig11848
miR1534a Elongation factor 1-alpha Contig15951
miR1534a Fat domain-containing protein Contig49029
miR1534a Glucan synthase component Contig49469
miR1534a Glutathione peroxidase Contig51675
miR1534a Lipase class 3 family protein Contig42396
miR1534a Lon protease Contig13984
miR1534a Multidrug resistance protein Contig52708
miR1534a Phosphomethyl pyrimidine kinase thiamin-phosphate pyrophosphorylase Contig52615
miR1534a Pre-mRNA splicing factor rna Contig01168
miR1534a Pyrophosphate-energized vacuolar membrane proton Contig03662
miR1534a R2R3-myb transcription factor myb11 Contig10498
miR156a Dead deah box helicase family protein Contig46517
miR156a Methionine synthase Contig23436
miR156a PPR protein Contig17870
miR156a Hypothetical Protein Contig45577
miR156a Soluble starch synthase iv-2 Contig47036
miR156a Vitamin-b12 independent methionine 5-methyltetrahydropteroyltriglutamate-homocysteine Contig31422
miR159a Delta-1-pyrroline-5-carboxylate dehydrogenase Contig26802
miR159a Shaker-like potassium channel Contig56945
miR164 F-box family protein Contig35247
miR169h 26s protease regulatory subunit Contig24305
miR169h Heterogeneous nuclear ribonucleoprotein A2 Contig33032
miR169h Ketose-bisphosphate aldolase class-ii family protein Contig15415
miR172f Calcium-binding allergen OLE Contig36501
miR172f Lipase class 3 family protein Contig52276
miR172f Type ii peroxiredoxin Contig57487
miR1886c Cation chloride cotransporter Contig50840
miR1886c CCAAT-binding transcription factor family protein Contig48908
miR1886c Ketose-bisphosphate aldolase class-ii family protein Contig11222
miR1886c Phospholipase D Contig53739
miR1886c Receptor protein kinase clavata1 Contig30888
miR2097-5p 2OG-FE oxygenase family protein Contig20782
miR2097-5p ABA response element binding factor Contig36126
miR2097-5p Cellulose synthase Contig11555
miR2097-5p DNA binding Contig52665
miR2097-5p Gamma-adaptin 1 Contig58073
miR2097-5p Heat shock Contig15069
miR2097-5p MCA1 (mid1-complementing activity 1) Contig45919
miR2097-5p Nucleolar protein Contig15060
miR2097-5p Trehalose-6-phosphate synthase Contig27435
miR2101-5p ELP1 (edm2-like protein1) Contig32343
miR2101-5p Inositol-tetrakisphosphate 1 Contig46141
miR2101-5p Meprin and traf homology domain-containing protein math domain-containing protein Contig45263
miR2101-5p Protein binding Contig48832
miR2101-5p S-adenosylmethionine-dependent methyltransferase Contig52110
miR2101-5p SPL1-related2 protein Contig17762
miR2108b Serine threonine protein kinase Contig48746
miR2108b With no lysine kinase Contig45290
miR2109c Transcription factor, putative Contig43484
miR2112b C2 domain-containing protein Contig44978
miR2112b Cytochrome Contig46603
miR2112b Transcriptional repressor Contig23975
miR2606a ARF1-binding protein Contig31431
miR2606a ATP binding Contig51306
miR2606a Heat shock protein 70 -interacting Contig35223
miR2607 Cytosolic phosphoglucomutase Contig21451
miR2607 Potassium transporter Contig10118
miR2626 Obtusifoliol 14-alpha demethylase Contig46095
miR2626 Zinc finger (C3HC4-type ring finger) family protein Contig50055
miR2628a Bromodomain protein Contig30826
miR2628b mRNA splicing Contig45688
miR2642 Cinnamoyl- reductase Contig49468
miR2642 Cytochrome c6 Contig62852
miR2642 Exocyst complex subunit SEC15-like family protein Contig51982
miR2642 Pectinacetylesterase family protein Contig16753
miR2642 Photosystem i PSAH protein Contig57701
miR2642 Plasma membrane h+-ATPase Contig19168
miR2642 Serine-threonine protein plant- Contig51145
miR2643b TPR repeat-containing protein Contig48906
miR2658 Homeodomain leucine zipper protein Contig28456
miR2658 Metalloendopeptidase Contig51850
miR2658 Phosphoinositide binding Contig62161
miR2665 3-phosphoserine phosphatase Contig49532
miR2665 Ap2 ERF domain-containing transcription factor Contig56891
miR2665 Diacylglycerol acyltransferase Contig48735
miR2665 Multidrug resistance protein ABC transporter family Contig17769
miR2673b 2-cys peroxiredoxin Contig49523
miR2673b 6b-interacting protein 1 Contig37386
miR2673b CBS domain-containing protein Contig13027
miR2673b Della protein Contig45937
miR2673b E3 ubiquitin ligase Contig47870
miR2673b Glycine-rich protein 2b Contig38426
miR2673b Glycine-rich RNA-binding protein Contig60922
miR2673b H Aca ribonucleoprotein complex subunit 1-like protein 1 Contig54326
miR2673b Inositol phosphate kinase Contig51804
miR2673b Kinesin light Contig45627
miR2673b Phospholipid cytidylyltransferase Contig32791
miR2673b Protein kinase Contig46112
miR2673b Ribosomal protein l17-like protein Contig53111
miR2673b Hypothetical protein Contig49580
miR2673b Tata-binding protein-associated factor 2n-like Contig33856
miR2673b WRKY transcription Contig62618
miR2673b Zinc finger Contig37605
miR2937 Dme DNA n-glycosylase DNA-(apurinic or apyrimidinic site) lyase Contig45773
miR2937 Dynamin-related protein expressed Contig47241
miR2937 Heat shock protein binding protein Contig23728
miR2937 Phospho ribosylformylglycinamidine synthase Contig56549
miR2937 Serine-threonine protein plant- Contig48306
miR319b Transcription factor WRKY4 Contig48636
miR319b L1 specific homeobox gene atml1 ovule-specific homeobox protein a20 Contig55022
miR319b Receptor protein kinase clavata1 Contig30156
miR319e Transcription factor WRKY4 Contig48636
miR319e L1 specific homeobox gene atml1 ovule-specific homeobox protein a20 Contig55022
miR319e Receptor protein kinase clavata1 Contig30156
miR319f Delta-1-pyrroline-5-carboxylate dehydrogenase Contig26802
miR319f Proteasome subunit alpha type 3 Contig25577
miR319g Five finger-containing phosphoinositide Contig51379
miR319g Phototropic-responsive NPH3 family protein Contig36246
miR319g Ubiquitin-protein PUB49 Contig48309
miR319h Phototropic-responsive NPH3 family protein Contig36246
miR319h Stromal membrane-associated Contig19616
miR396b Acyl- oxidase Contig09994
miR396b Beta-glucosidase-like protein Contig07198
miR396b Heat shock protein Contig53532
miR396c ABC transporter family protein Contig48065
miR396c AP2 ERF domain-containing transcription factor Contig24365
miR396c ELF3 homologue Contig10723
miR396c Mitochondrial substrate carrier Contig46804
miR396c Splicing factor Contig58523
miR396c Type-B response regulator Contig33927
miR397 Actin Contig20734
miR397 Cell division protein Contig47714
miR397 Cytosolic malate dehydrogenase Contig39102
miR397 Multidrug resistance-associated protein Contig23354
miR397 Synaptic glycoprotein SC2 Contig35004
miR408a Chemocyanin precursor Contig57069
miR414d 60s ribosomal protein l6 Contig51433
miR414d ADP-glucose pyrophosphorylase family protein Contig52871
miR414d Ascorbate peroxidase Contig36806
miR414d CBL-interacting serine threonine-protein Contig46717
miR414d Conserved hypothetical protein Contig46471
miR414d Cytochrome p450 Contig61265
miR414d Late embryogenesis abundant protein LEA14 Contig49375
miR414d NLI interacting factor family protein Contig12161
miR414d Pre-mRNA-splicing factor CWC-22 Contig30561
miR414d Protein phosphatase Contig39881
miR414d Ring finger containing Contig48197
miR414d RNA helicase Contig12914
miR414e 60s ribosomal protein l6 Contig51433
miR414e ADP-glucose pyrophosphorylase family protein Contig52871
miR414e Ascorbate peroxidase Contig36806
miR414e CBL-interacting serine threonine-protein Contig46717
miR414e Conserved hypothetical protein Contig46471
miR414e Cytochrome p450 Contig61265
miR414e Late embryogenesis abundant protein LEA14 Contig49375
miR414e NLI interacting factor family protein Contig12161
miR414e Pre-mRNA-splicing factor CWC-22 Contig30561
miR414e Protein phosphatase Contig39881
miR414e Ring finger containing Contig48197
miR414e RNA helicase Contig12914
miR414f 60s ribosomal protein l6 Contig51433
miR414f ADP-glucose pyrophosphorylase family protein Contig52871
miR414f Ascorbate peroxidase Contig36806
miR414f CBL-interacting serine threonine-protein Contig46717
miR414f Conserved hypothetical protein Contig46471
miR414f Cytochrome p450 Contig61265
miR414f Late embryogenesis abundant protein LEA14 Contig49375
miR414f NLI interacting factor family protein Contig12161
miR414f Pre-mRNA-splicing factor CWC-22 Contig30561
miR414f Protein phosphatase Contig39881
miR414f Ring finger containing Contig48197
miR414f RNA helicase Contig12914
miR414h 60s ribosomal protein l6 Contig51433
miR414h Ascorbate peroxidase Contig36806
miR414h CBL-interacting serine threonine-protein Contig46717
miR414h Conserved hypothetical protein Contig46471
miR414h Cytochrome p450 Contig61265
miR414h Heavy-metal-associated domain-containing protein Contig27681
miR414h Late embryogenesis abundant protein LEA14 Contig49311
miR414h PIN1 Contig43002
miR414h Pre-mRNA-splicing factor Cwc-22 Contig30561
miR414h Ring finger containing Contig48197
miR414h Zinc finger Contig47019
miR414i Luminal binding protein Contig16316
miR414i Pentatricopeptide repeat-containing protein Contig48382
miR414i Zip transporter Contig21400
miR414j Cytochrome p450 reductase Contig45338
miR414j Heat shock factor Contig50711
miR414j Heavy-metal-associated domain-containing protein Contig27681
miR414j Kinase family protein Contig24024
miR414j Lectin protein kinase family protein Contig21894
miR414j MYB transcription factor Contig16861
miR414j Phosphatidylinositol-4-phosphate 5-kinase family protein Contig50328
miR414j Small RAS-like GTP-binding protein Contig07528
miR414j Tryptophanyl-tRNA synthetase Contig34813
miR414j Vacuolar morphogenesis protein Contig08171
miR414l Copalyl diphosphate synthase Contig52365
miR414l DNA binding Contig36892
miR414l Heat shock factor protein HSF30 Contig23567
miR414l Insulinase containing expressed Contig30114
miR414l Leucine-rich repeat-containing Contig54865
miR414l Pescadillo-like protein Contig30996
miR414l RNA polymerase ii transcription elongation factor SPT5 Contig31454
miR414l Ubiquitin-protein ligase 1 Contig17692
miR417 Binding protein Contig37222
miR417 Nucleic acid binding Contig33218
miR419e Argonaute family member Contig09488
miR419e Auxin response factor 4 Contig30161
miR419e Beta-glactosidase 8 Contig50325
miR419e Bromodomain protein Contig45057
miR419e Bzip transcription factor Contig52353
miR419e Dna binding protein Contig49741
miR419e Heavy-metal-associated domain-containing protein Contig61082
miR419e Nucleosome assembly Contig33375
miR419e Polyphenol oxidase Contig27778
miR419e SIT4 phosphatase-associated family protein Contig27966
miR446 Beta-galactosidase like protein Contig45051
miR482 Alpha-glucosidase Contig47628
miR530a COP1-interacting protein 7 Contig30564
miR530a ISP4-like protein Contig45556
miR530a Zinc finger protein Contig27700
miR783 Pentatricopeptide repeat-containing Contig55799
miR783 Short-chain dehydrogenase reductase family protein Contig52837
miR783 Vacuolar protein sorting-associated Contig50413
miR815 Adenylate kinase Contig34836
miR815 Chromatin remodeling complex subunit Contig04324
miR815 Cullin-like 1 protein Contig30323
miR815 Dead-box protein Contig31488
miR815 Fat domain-containing protein Contig29906
miR815 Glutamyl-tRNA amidotransferase subunit A Contig34203
miR815 Lysosomal alpha-glucosidase Contig49082
miR815 RPH1 (resistance to phytophthora 1) Contig38523
miR834b Histone H3 Contig50215
miR834b Receptor-like serine threonine protein kinase ARK3 Contig45114
miR834b Set domain protein Contig46763
miR846 Glucan endo beta-glucosidase Contig42642
miR846 Kip-related cyclin-dependent kinase inhibitor 7 Contig47511
miR846 Multidrug resistance protein Contig49696
miR846 RNA-binding protein CP31 Contig49296
miR847a Bile acid: sodium symporter family protein Contig58467
miR847a Heat shock protein Contig01966
miR847a Viral A-type inclusion protein Contig47741
miR847a Zinc finger Contig51073
miR847b Amino acid binding Contig46296
miR847b Amino acid permease Contig18864
miR847b Binding protein Contig30978
miR847b Chlorophyll a, b-binding protein Contig50308
miR847b Geranylgeranyl pyrophosphate synthase-related protein Contig49005
miR847b Inositol -trisphosphate 5 6 kinase Contig46827
miR847b Serine threonine protein kinase Contig34614
miR847b TPR domain containing protein Contig45092
miR847b Vitamin-B12 independent methionine 5-methyltetrahydropteroyltriglutamate-homocysteine Contig23525
miR847c 2-dehydro-3-deoxyphosphoheptonate aldolase 3-deoxy-d-arabino-heptulosonate 7-phosphate synthetase Contig28077
miR847c Amidohydrolase domain-containing protein Contig56377
miR847c Cytosolic phosphoglycerate kinase 1 Contig13961
miR847c DNA repair protein RAD4 family Contig36417
miR847c Eukaryotic translation initiation factor 4g Contig17702
miR847c PSI type iii chlorophyll a b-binding protein Contig52383
miR847c Small nuclear ribonucleoprotein E Contig56509
miR847c Vacuolar ATP synthase subunit Contig47586
miR847c Vq motif-containing protein Contig35306
miR847c Zinc finger Contig48641
miR854b CRR3 (chlororespiratory reduction 3) Contig53843
miR854b Ethylene responsive element binding factor Contig49258
miR854b MYB-related transcription factor LBM2-like Contig59010
miR854b Squalene epoxidase Contig52768
miR854b Starch branching enzyme ii Contig30265
miR854b Transcription initiation factor iib Contig34296
miR854b UDP-n-acetylglucosamine: dolichol phosphate n-acetylglucosamine-1-p transferase Contig32764
miR854c Transcription factor WRKY4 Contig56799
miR854c CRR3 (chlororespiratory reduction 3) Contig53843
miR854c DNA binding Contig35557
miR854c Ethylene responsive element binding factor Contig49258
miR854c F-box family protein Contig47190
miR854c Gamma response i protein Contig21322
miR854c Hypothetical protein Contig56684
miR854c Phototropic-responsive NPH3 family protein Contig46596
miR854c Plastid division protein Contig45829
miR854c Polynucleotide phosphorylase Contig32431
miR854c Protein binding protein Contig34488
miR854c RNA helicase Contig12919
miR854c SAS10 U3 ribonucleoprotein family protein Contig27731
miR854c Squalene epoxidase Contig52768
miR854c Starch branching enzyme ii Contig30265
miR854c Transcription initiation factor iib Contig34296
miR854c Type i phosphodiesterase nucleotide pyrophosphatase family protein Contig46494
miR854c U4 u6 small nuclear ribonucleoprotein PRP3 Contig26841
miR854c Ubiquitin-conjugating enzyme e2 I Contig14762
miR854d Transcription factor WRKY4 Contig56799
miR854d Aminoacyl-tRNA synthetase family Contig15841
miR854d C-4 sterol methyl oxidase Contig48724
miR854d Chalcone isomerase Contig52143
miR854d Galactosyltransferase family protein Contig04535
miR854d Heat shock protein 90 Contig06778
miR854d PDV2 (plastid division2) Contig53900
miR854d Serine threonine protein kinase Contig21634
miR854e Cell division protein Contig11097
miR854e Farnesyl diphosphate synthase Contig50044
miR854e Glutathione reductase Contig12638
miR854e Inositol-tetrakisphosphate 1 Contig33622
miR854e Multicatalytic endopeptidase proteasome beta subunit Contig48772
miR854e Pbf68 protein Contig53517
miR854e Phospholipase D Contig51301
miR854e Pre-mRNA splicing factor PRP38 Contig48032
miR854e TCP family transcription Contig47975
miR854e Type i phosphodiesterase nucleotide pyrophosphatase family protein Contig46494

Acknowledgments

The research funding was supported by the Korea Institute of Planning & Evaluation for Technology in Food, Agriculture, Forestry & Fisheries (KIPET no. 309019-3) and by a grant from the Next-Generation BioGreen 21 Program (SSAC, grant#: PJ00952903), Rural Development Administration, Republic of Korea. The ginseng sample used in this study was provided by Kyung Hee University, South Korea.

References

  • 1.Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004;116:281–297. doi: 10.1016/s0092-8674(04)00045-5. [DOI] [PubMed] [Google Scholar]
  • 2.Zhou X, Ruan J, Wang G, Zhang W. Characterization and identification of microRNA core promoters in four model species. PLoS Comput Biol. 2007;3:e37. doi: 10.1371/journal.pcbi.0030037. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Jones-Rhoades MW, Bartel DP, Bartel B. MicroRNAS and their regulatory roles in plants. Annu Rev Plant Biol. 2006;57:19–53. doi: 10.1146/annurev.arplant.57.032905.105218. [DOI] [PubMed] [Google Scholar]
  • 4.Lu Y, Yang X. Computational identification of novel microRNAs and their targets in Vigna unguiculata. Comp Funct Genomics. 2010;pii:128297. doi: 10.1155/2010/128297. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Sunkar R, Zhu JK. Novel and stress-regulated microRNAs and other small RNAs from Arabidopsis. Plant Cell. 2004;16:2001–2019. doi: 10.1105/tpc.104.022830. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Zhang B, Pan X, Anderson TA. Identification of 188 conserved maize microRNAs and their targets. FEBS Lett. 2006;580:3753–3762. doi: 10.1016/j.febslet.2006.05.063. [DOI] [PubMed] [Google Scholar]
  • 7.Ambros V. The functions of animal microRNAs. Nature. 2004;431:350–355. doi: 10.1038/nature02871. [DOI] [PubMed] [Google Scholar]
  • 8.Jagadeeswaran G, Zheng Y, Li YF, Shukla LI, Matts J, Hoyt P, Macmil SL, Wiley GB, Roe BA, Zhang W, et al. Cloning and characterization of small RNAs from Medicago truncatula reveals four novel legume-specific microRNA families. New Phytol. 2009;184:85–98. doi: 10.1111/j.1469-8137.2009.02915.x. [DOI] [PubMed] [Google Scholar]
  • 9.Pantaleo V, Szittya G, Moxon S, Miozzi L, Moulton V, Dalmay T, Burgyan J. Identification of grapevine microRNAs and their targets using high-throughput sequencing and degradome analysis. Plant J. 2010;62:960–976. doi: 10.1111/j.0960-7412.2010.04208.x. [DOI] [PubMed] [Google Scholar]
  • 10.Song C, Wang C, Zhang C, Korir NK, Yu H, Ma Z, Fang J. Deep sequencing discovery of novel and conserved microRNAs in trifoliate orange (Citrus trifoliata). BMC Genomics. 2010;11:431. doi: 10.1186/1471-2164-11-431. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Xie F, Frazier TP, Zhang B. Identification, characterization and expression analysis of microRNAs and their targets in the potato (Solanum tuberosum). Gene. 2011;473:8–22. doi: 10.1016/j.gene.2010.09.007. [DOI] [PubMed] [Google Scholar]
  • 12.Song C, Fang J, Li X, Liu H, Thomas Chao C. Identification and characterization of 27 conserved microRNAs in citrus. Planta. 2009;230:671–685. doi: 10.1007/s00425-009-0971-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Xie F, Frazier TP, Zhang B. Identification and characterization of microRNAs and their targets in the bioenergy plant switchgrass (Panicum virgatum). Planta. 2010;232:417–434. doi: 10.1007/s00425-010-1182-1. [DOI] [PubMed] [Google Scholar]
  • 14.Han Y, Zhu B, Luan F, Zhu H, Shao Y, Chen A, Lu C, Luo Y. Conserved miRNAs and their targets identified in lettuce (Lactuca) by EST analysis. Gene. 2010;463:1–7. doi: 10.1016/j.gene.2010.04.012. [DOI] [PubMed] [Google Scholar]
  • 15.Frazier TP, Xie F, Freistaedter A, Burklew CE, Zhang B. Identification and characterization of microRNAs and their target genes in tobacco (Nicotiana tabacum). Planta. 2010;232:1289–1303. doi: 10.1007/s00425-010-1255-1. [DOI] [PubMed] [Google Scholar]
  • 16.Zhao CZ, Xia H, Frazier TP, Yao YY, Bi YP, Li AQ, Li MJ, Li CS, Zhang BH, Wang XJ. Deep sequencing identifies novel and conserved microRNAs in peanuts (Arachis hypogaea L.). BMC Plant Biol. 2010;10:3. doi: 10.1186/1471-2229-10-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Mohorianu I, Schwach F, Jing R, Lopez-Gomollon S, Moxon S, Szittya G, Sorefan K, Moulton V, Dalmay T. Profiling of short RNAs during fleshy fruit development reveals stage-specific sRNAome expression patterns. Plant J. 2011;67:232–246. doi: 10.1111/j.1365-313X.2011.04586.x. [DOI] [PubMed] [Google Scholar]
  • 18.Wang C, Wang X, Kibet NK, Song C, Zhang C, Li X, Han J, Fang J. Deep sequencing of grapevine flower and berry short RNA library for discovery of novel microRNAs and validation of precise sequences of grapevine microRNAs deposited in miRBase. Physiol Plant. 2011;143:64–81. doi: 10.1111/j.1399-3054.2011.01481.x. [DOI] [PubMed] [Google Scholar]
  • 19.Vogler BK, Pittler MH, Ernst E. The efficacy of ginseng. A systematic review of randomised clinical trials. Eur J Clin Pharmacol. 1999;55:567–575. doi: 10.1007/s002280050674. [DOI] [PubMed] [Google Scholar]
  • 20.Choi KT. Botanical characteristics, pharmacological effects and medicinal components of Korean Panax ginseng C A Meyer. Acta Pharmacol Sin. 2008;29:1109–1118. doi: 10.1111/j.1745-7254.2008.00869.x. [DOI] [PubMed] [Google Scholar]
  • 21.Kim SK, Park JH. Trends in ginseng research in 2010. J Ginseng Res. 2011;35:389–398. doi: 10.5142/jgr.2011.35.4.389. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Yang Y, Chen X, Chen J, Xu H, Li J, Zhang Z. Differential miRNA expression in Rehmannia glutinosa plants subjected to continuous cropping. BMC Plant Biol. 2011;11:53. doi: 10.1186/1471-2229-11-53. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Pani A, Mahapatra RK, Behera N, Naik PK. Computational identification of sweet wormwood (Artemisia annua) microRNA and their mRNA targets. Genomics Proteomics Bioinformatics. 2011;9:200–210. doi: 10.1016/S1672-0229(11)60023-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Xu Q, Liu Y, Zhu A, Wu X, Ye J, Yu K, Guo W, Deng X. Discovery and comparative profiling of microRNAs in a sweet orange red-flesh mutant and its wild type. BMC Genomics. 2010;11:246. doi: 10.1186/1471-2164-11-246. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Meng Y, Ma X, Chen D, Wu P, Chen M. MicroRNA-mediated signaling involved in plant root development. Biochem Biophys Res Commun. 2010;393:345–349. doi: 10.1016/j.bbrc.2010.01.129. [DOI] [PubMed] [Google Scholar]
  • 26.Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ. miRBase: tools for microRNA genomics. Nucleic Acids Res. 2008;36(Database issue):D154–D158. doi: 10.1093/nar/gkm952. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Sathiyamoorthy S, In JG, Lee BS, Kwon WS, Yang DU, Kim JH, Yang DC. Insilico analysis for expressed sequence tags from embryogenic callus and flower buds of Panax ginseng C. A. Meyer. J Ginseng Res. 2011;35:21–30. [Google Scholar]
  • 28.Jones-Rhoades MW. Prediction of plant miRNA genes. Methods Mol Biol. 2010;592:19–30. doi: 10.1007/978-1-60327-005-2_2. [DOI] [PubMed] [Google Scholar]
  • 29.Zhang B, Pan X, Cannon CH, Cobb GP, Anderson TA. Conservation and divergence of plant microRNA genes. Plant J. 2006;46:243–259. doi: 10.1111/j.1365-313X.2006.02697.x. [DOI] [PubMed] [Google Scholar]
  • 30.Zhang B, Pan X, Stellwag EJ. Identification of soybean microRNAs and their targets. Planta. 2008;229:161–182. doi: 10.1007/s00425-008-0818-x. [DOI] [PubMed] [Google Scholar]
  • 31.Sunkar R, Jagadeeswaran G. Insilico identification of conserved microRNAs in large number of diverse plant species. BMC Plant Biol. 2008;8:37. doi: 10.1186/1471-2229-8-37. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Dhandapani V, Ramchiary N, Paul P, Kim J, Choi SH, Lee J, Hur Y, Lim YP. Identification of potential microRNAs and their targets in Brassica rapa L. Mol Cells. 2011;32:21–37. doi: 10.1007/s10059-011-2313-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Kozomara A, Griffiths-Jones S. miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res. 2011;39(Database issue):D152–D157. doi: 10.1093/nar/gkq1027. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Sunkar R, Zhou X, Zheng Y, Zhang W, Zhu JK. Identification of novel and candidate miRNAs in rice by high throughput sequencing. BMC Plant Biol. 2008;8:25. doi: 10.1186/1471-2229-8-25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Guleria P, Yadav SK. Identification of miR414 and expression analysis of conserved miRNAs from Stevia rebaudiana. Genomics Proteomics Bioinformatics. 2011;9:211–217. doi: 10.1016/S1672-0229(11)60024-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Unver T, Parmaksiz I, Dundar E. Identification of conserved micro-RNAs and their target transcripts in opium poppy (Papaver somniferum L.). Plant Cell Rep. 2010;29:757–769. doi: 10.1007/s00299-010-0862-4. [DOI] [PubMed] [Google Scholar]
  • 37.Luo YC, Zhou H, Li Y, Chen JY, Yang JH, Chen YQ, Qu LH. Rice embryogenic calli express a unique set of microRNAs, suggesting regulatory roles of microRNAs in plant post-embryogenic development. FEBS Lett. 2006;580:5111–5116. doi: 10.1016/j.febslet.2006.08.046. [DOI] [PubMed] [Google Scholar]
  • 38.Schommer C, Bresso EG, Spinelli S, Palatnik J. Role of microRNA miR319 in plant development. In: Sunkar R, ed. MicroRNAs in plant development and stress responses. Springer; Berlin: 2012. pp. 29–47. [Google Scholar]
  • 39.Thiebaut F, Rojas CA, Almeida KL, Grativol C, Domiciano GC, Lamb CR, Engler Jde A, Hemerly AS, Ferreira PC. Regulation of miR319 during cold stress in sugarcane. Plant Cell Environ. 2012;35:502–512. doi: 10.1111/j.1365-3040.2011.02430.x. [DOI] [PubMed] [Google Scholar]
  • 40.Rajagopalan R, Vaucheret H, Trejo J, Bartel DP. A diverse and evolutionarily fluid set of microRNAs in Arabidopsis thaliana. Genes Dev. 2006;20:3407–3425. doi: 10.1101/gad.1476406. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Fahlgren N, Jogdeo S, Kasschau KD, Sullivan CM, Chapman EJ, Laubinger S, Smith LM, Dasenko M, Givan SA, Weigel D, et al. MicroRNA gene evolution in Arabidopsis lyrata and Arabidopsis thaliana. Plant Cell. 2010;22:1074–1089. doi: 10.1105/tpc.110.073999. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Kantar M, Unver T, Budak H. Regulation of barley miRNAs upon dehydration stress correlated with target gene expression. Funct Integr Genomics. 2010;10:493–507. doi: 10.1007/s10142-010-0181-4. [DOI] [PubMed] [Google Scholar]
  • 43.Smalheiser NR, Torvik VI. Mammalian microRNAs derived from genomic repeats. Trends Genet. 2005;21:322–326. doi: 10.1016/j.tig.2005.04.008. [DOI] [PubMed] [Google Scholar]
  • 44.Zhang BH, Pan XP, Cox SB, Cobb GP, Anderson TA. Evidence that miRNAs are different from other RNAs. Cell Mol Life Sci. 2006;63:246–254. doi: 10.1007/s00018-005-5467-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Moxon S, Jing R, Szittya G, Schwach F, Rusholme Pilcher RL, Moulton V, Dalmay T. Deep sequencing of tomato short RNAs identifies microRNAs targeting genes involved in fruit ripening. Genome Res. 2008;18:1602–1609. doi: 10.1101/gr.080127.108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Jones-Rhoades MW, Bartel DP. Computational identification of plant microRNAs and their targets, including a stress-induced miRNA. Mol Cell. 2004;14:787–799. doi: 10.1016/j.molcel.2004.05.027. [DOI] [PubMed] [Google Scholar]
  • 47.Lewis BP, Burge CB, Bartel DP. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell. 2005;120:15–20. doi: 10.1016/j.cell.2004.12.035. [DOI] [PubMed] [Google Scholar]
  • 48.Wu G, Poethig RS. Temporal regulation of shoot development in Arabidopsis thaliana by miR156 and its target SPL3. Development. 2006;133:3539–3547. doi: 10.1242/dev.02521. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Palatnik JF, Allen E, Wu X, Schommer C, Schwab R, Carrington JC, Weigel D. Control of leaf morphogenesis by microRNAs. Nature. 2003;425:257–263. doi: 10.1038/nature01958. [DOI] [PubMed] [Google Scholar]
  • 50.Shim JS, Lee OR, Kim YJ, Lee JH, Kim JH, Jung DY, In JG, Lee BS, Yang DC. Overexpression of PgSQS1 increases ginsenoside production and negatively affects ginseng growth rate in Panax ginseng. J Ginseng Res. 2010;34:98–103. [Google Scholar]

Articles from Journal of Ginseng Research are provided here courtesy of Elsevier

RESOURCES