SciELO - Scientific Electronic Library Online

vol.43 issue3Draft genome sequence of Wickerhamomyces anomalus LBCM1105, isolated from cachaça fermentation author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand




Related links


Genetics and Molecular Biology

Print version ISSN 1415-4757On-line version ISSN 1678-4685

Genet. Mol. Biol. vol.43 no.3 Ribeirão Preto  2020  Epub June 26, 2020 

Genomics and Bioinformatics

De novo sequencing of Bletilla striata (Orchidaceae) transcriptome and identification of genes involved in polysaccharide biosynthesis

Junfeng Niu1 

Guangming Zhao1 

Zeyuan Mi1 

Lijun Chen1 

Shuai Liu1 

Shiqiang Wang1 

Donghao Wang1 

Zhezhi Wang1

1Shaanxi Normal University, College of Life Sciences, Key Laboratory of the Ministry of Education for Medicinal Resources and Natural Pharmaceutical Chemistry, National Engineering Laboratory for Resource Development of Endangered Crude Drugs in Northwest of China, Xi'an, Shaanxi, China


Bletilla striata polysaccharide (BSP) is the main component of Bletilla striata, which has important pharmacological and pharmacological effects; however, due to the lack of genetic data, the metabolic pathways of BSP remain unclear. For this study, 11 representative resources of B. striata were analyzed, and the BSP contents of the different samples were significantly different; however, the monosaccharide composition of BSP was glucose and mannose. The representative samples were selected to observe their life history in situ, which were then divided and cultured in a greenhouse. Finally, samples from various organs of different plants were combined for transcriptome sequencing using the Illumina system. Our results summarized the BSP metabolic pathway, and we found that there were eight enzyme genes involved in biosynthesis, but these genes showed tissue specificity. Following qRT-PCR validation and comparative analysis, manA showed the highest expression; however, there were significant differences between the two germplasm resources in which the BSP content was significantly different, while UGP2, GPI, PMM, and GMPP had significant differences between the two samples. In summary, this study lays the foundation for further research into BSP metabolism and other physiological processes at the molecular level.

Keywords: Bletilla striata; transcriptome; germplasm resources; polysaccharide biosynthesis; qRT-PCR


Bletilla striata, typically known as an important species of the Bletilla genus, is a significant medicinal compound for the treatment of many diseases, as recorded by Chinese pharmacopoeia (Chen et al., 2018; Zhang et al., 2019a). It is an ornamental flower but belongs to the Orchidaceae family (He et al., 2017). Wild B. striata sources are primarily distributed across Yunnan, Shaanxi, Gansu, Hubei, and Zhejiang Provinces of China at elevations from 110-3,200 m (He et al., 2017), as well as the Korean Peninsula and Japan (Chung et al., 2013). At present, various constituents have been extracted and isolated from dried B. striata pseudobulbs, including polysaccharides, benzyl, phenanthrax, astragalus, terpenes, ethers, and anthocyanins (Guo et al., 2016; Li et al., 2018).

However, polysaccharides are one of the main components, which plays a variety of biological functions (Wang and Meng, 2015; Zhang et al., 2019b). Research has revealed that BSP not only facilitates coagulation by inducing the expression of vascular endothelial growth factor (VEGF), but also prevent gastric ulcers by forming a protective film that coats the stomach wall, and is a common immune modulator. (Luo et al., 2018, 2019; Zhang et al., 2019a). The structure and function of BSP is closely related to its biological activity, which is often modified or cross-linked with other substances for use as a biomaterial in wound healing and drug delivery applications (Wang et al., 2018). Although B. striata has numerous functions, data on its genetic makeup remains scar3ce, and the polysaccharide metabolism pathway is not clear. Therefore, it is necessary to conduct in-depth genetic research to discover its genes and their functionalitiess.

With the development and implementation of advanced DNA sequencing technologies, the biological metabolisms of active components may be discerned in medicinal plants (Fu et al., 2019; Liang et al., 2019; Niazian, 2019). There are few studies on the biosynthesis of polysaccharides (this research has focused primarily on fungal species) due to their unique structures and large molecular weights (Ribeiro et al., 2012; Zhang et al., 2019b). The biosynthesis pathways of Ganoderma lucidum polysaccharides provide a preliminary study of the various synthesis pathways of medicinal plants (Gao et al., 2015).

The pharmacopoeia of the People's Republic of China has entered the genus Polygonatum cyrtonema and Polygonatum sibiricum, both of which employ polysaccharides as their key components, and their polysaccharide metabolic pathways have been elucidated (Wang et al., 2017; Wang et al., 2019a). It has been verified, via hot water extraction and ion exchange chromatography purification, that BSP is composed of glucose and mannose; however, the genetic data related to its polysaccharide biosynthesis is still unknown (Kong et al., 2015).

For this study, comparative transcriptomes were analyzed using the roots, stems, leaves, flowers and seeds of B. striata. After determining the candidate genes involved in BSP biosynthesis, we selected the highest and lowest BSP germplasm contents, respectively, and verified the quality of the database and related genes involved in the BSP metabolic pathway by quantitative real-time PCR (qRT-PCR). The results of this study provided a theoretical basis for the study of B. striata.

Materials and Methods

Ethics statement

All experimental materials for this study were collected in China, but did not cause the species to be threatened or endangered. This research was conducted at the National Engineering Laboratory for Resource Development of Endangered Chinese Crude Drugs in Northwest China, Xi'an, China.

Plant materials

The transcriptome-sequenced plants were collected from Zhen'an County, Shaanxi Province, in China on July 20, 2013. Once the plants were transplanted with soil at the greenhouse of National Engineering Laboratory for the Development of Endangered Medicinal Materials Resources in Northwest China, we separated apart the pseudobulbs of B. striata one by one for individual propagation, and finally the sample was divided into 11 plants. We chose to extract samples from the flowering to fruiting stage. At this time, the upper portion of the peduncle has a flower bud, whereas the lower part has a capsule that is formed by fertilization. Six samples were randomly selected for sampling. The roots, stems, leaves, flowers and fruits of different organs from all of the specific plants were collected equally and mixed together. After the samples (each about 2.0 g) were collected, they were flash-frozen with liquid nitrogen and stored at −80 °C.

Eleven regionally representative populations of B. striata were collected from 2014 to 2015, all of which were identified by Prof. Yaping Xiao (College of Life Science, Shaanxi Normal University). The origins of the rhizome samples collected from different germplasms are specified in Table S1, and all materials were grown in the laboratory germplasm field.

Isolation, purification and detection of polysaccharides

The method of extracting the polysaccharides from B. striata rhizomes drew from the relevant articles previously published by our research group (Niu et al., 2019). Once the fresh plant material was dried at 60 °C, it was ground to a powder and passed through a 60 mesh screen. The removal of lipids from the dry powder sample (100g) was achieved mixing it with petroleum ether, followed by twice extracting with distilled water (80 °C) for three hours (1:30, w: v). Once the extract was concentrated on a rotary evaporator, 95% alcohol (about a four-fold volume) was added and left overnight at 4 °C, after which the polysaccharide was precipitated and filtered. Following the removal of proteins using a repeated freeze-thaw method, the crude BSP polysaccharides were obtained via dialysis and lyophilization.

BSP monosaccharide composition

GC-MS was employed to determine the composition of the BSP monosaccharides. The BSP sample (2.0 mg) was initially added to 2 M trifluoroacetic acid (TFA), and following hydrolyzation at 100 °C for 90 min, it was converted to alditol acetates. Subsequently, the acetylated structure was evaluated using an Agilent GC-MS QQQ7000 with a HP-5 fused silica capillary flame ionization detector.

The temperature gradient of the column was increased from a starting temperature of 160 °C at a rate of 4 °C min−1 to 250 °C, which was held for five min. The temperature of the injector and detector of the instrument was set to 250 °C, N2 was selected as the carrier gas and the flow rate was 1 mL min−1. The H2 flow rate and air flow rate were 30 mL min−1, and 400 mL min−1, respectively. The following standard monosaccharides were used as references: rhamnose, fucose, arabinose, xylose, mannose, galactose, and glucose.

Establishment of library and sequencing (mRNA-Seq)

Transcriptome sequencing plants were collected from Zhen'an County, in Shaanxi Province. Mixed samples of collected roots, stems, leaves, flowers, and fruits were separately sequenced. The total RNA for each sample was extracted following the instructions of the total RNA Isolation Kit (TIANGEN, Beijing, China). High quality RNA was evaluated for library building using the HiSeq 2500 platform.

De novo assembly and functional annotation

The Unigene library for this species was obtained by filtering rRNA and low-quality reads (two-terminal sequences) by data evaluation. Trinity software was used for the mixed assembly of sample data (Grabherr et al., 2011). The most important transcripts were selected as unigenes (Mortazavi et al., 2008), and Blastx (Altschul et al., 1997) comparison was carried out with four common protein databases: NR (non-redundant protein database) (Pruitt et al., 2005), Swiss-Prot Database (Apweiler et al., 2004), KEGG Database (Kyoto Encyclopedia of Genes and Genomes protein Database) (Kanehisa et al., 2004), and COGs (Clusters of Orthologous Groups) (Tatusov et al., 2000). The functional annotation of the relevant unigene coding proteins was performed by comparing the data from proteins with similar results in the database.

Analysis of differential gene expression

Firstly, we compared the sequencing results with the published Unigenes databas, using Bowtie software (version 1.1.2, JHU, Baltimore, MD, USA) (Langmead et al., 2009). Secondly, RSEM (Li and Dewey, 2011) was performed to reflect the expression levels of different genes, and the abundance of corresponding transcripts was determined by calculating the values of RPKM (Reads Per Kilobase of transcript per Million mapped reads) (Trapnell et al., 2010). Finally, DESeq software (Anders and Huber, 2010) was used to further compare the gene expression patterns from different germplasm resources, SXZA and AHXC, and five different organs: roots, stems, leaves, flowers and fruits.

Identification of genes related to BSP biosynthetic pathway

Through the annotation of unigenes in the KEGG database, the genes involved in the polysaccharide metabolism pathway were summarized as candidate genes. Candidate genes for further reference included KEGG metabolic pathways of starch and sucrose metabolism, as well as amino sugar and nucleotide sugar metabolism on the metabolic pathways involved in the gene's comments. As a result, further screening participated in the reaction of related enzymes, where all of the enzymes and Arabidopsis thaliana annotated information in the database, combined with Swiss-Prot, and a further annotated database relating to enzyme glycosyl transferase (GTs, Glycosyltransferases).

Real-time PCR

We selected GAPDH as the reference gene, and a total of eight candidate genes were screened, where the related primers being designed using Premier 3.0, with all of the above presented in Table S2. The total RNA was extracted from two germplasm groups, SXZA and AHXC, as well as five different organs, roots, stems, leaves, flowers, and fruits. Reverse transcription to cDNA was performed according to the instructions of Takara's Prime-ScriptTM RT Master Mix (TaKaRa, Kyoto, Japan).

Real-time PCR experiments were performed on a Light Cycler 96 Instrument (Roche, Germany) according to the 2×Sybr Green qPCR Mix instructions (Aidlab, Beijing, China). The relative expression was calculated using the 2−ΔΔCt method (Schmittgen and Livak, 2008). All qRT-PCR experiments involved in this study underwent three biological and three technical replications.

Statistical analysis

All experimental results in this study were expressed as a mean standard deviation (SD), using one-way analysis of variance (ANOVA) of DPS, and Duncans multivariate test was performed. Values were considered statistically significant, when p < 0.05.


BSP composition and B. striata germplasm (SXZA) content

Water-soluble polysaccharides were extracted from the dried rhizomes of the B. striata germplasm (SXZA), which were collected from Zhen'an County, in Shaanxi Province, China. As shown in Figure 1, the content of BSP was 22.85 ± 0.04%, which was comprised of glucose and mannose, and the mole ratio was 0.745:0.255 by gas chromatography. We observed the life history of the whole plant from 2012 to 2013 on the original land (E109°9’11″, N33°25’33″). The plants were segmented into multiple plants by pseudobulbs, and the flowering to fruiting stage was further selected. Transcriptome sequencing was performed by mixing the different organs of different plants.

Figure 1 BSP composition of B. striata germplasm SXZA. (A) Monosaccharide standards, arrows 1-7 represent rhamnose, fucose, arabinose, xylose, mannose, glucose, and galactose, respectively. (B) Arrows 5 and 6 represent mannose and glucose. 

Polysaccharide yields from different germplasm resources

A total of 11 germplasm resources of B. striata were collected from Shaanxi, Anhui, Hubei, Sichuan, and Henan Provinces from 2013 to 2015, and the polysaccharide content were analyzed between the different samples. As shown in Table 1, the highest polysaccharide content was 36.48 ± 0.27% in Shangzhou, of Shaanxi Province, whereas the lowest sample was 20.56 ± 0.04% in Xuancheng, of Anhui Province.

Table 1 The polysaccharide content of B. striata in different germplasm resources. 

No. Region polysaccharide yield
1 Shangzhou, Shaanxi province (SXSZ) 36.48 ± 0.27 aA
2 Liuba, Shaanxi province (SXLB) 27.2 ± 0.35 deD
3 Lvyang, Shaanxi province (SXLY) 31.450.25 bB
4 Ningqiang, Shaanxi province (SXNQ) 26.580.16 deD
5 Qichun, Hubei province (HBQC) 29.79 ± 0.04 cC
6 Baokang, Hubei province (HBBK) 27.18 ± 0.38 dD
7 Mianxian, Shaanxi province (SXMX) 26.5 ± 0.03 eD
8 Zhenan, Shaanxi province (SXZA) 22.85 ± 0.04 gF
9 Lushi, Henan province (HNLS) 25.56 ± 0.03 fE
10 Xuancheng, Anhui province (AHXC) 20.56 ± 0.04 hG
11 Bashan, Anhui province (AHBS) 22.77 ± 0.05 gF

BSP content is significantly different between germplasms at p < 0.05 (different lowercase letters) and p < 0.01 (different capital letters).

Transcriptome sequencing, de novo assembly, and assessment of assembly program

Using the Illumina sequencing system, we obtained 11.4 Gb of clean data from five mixed samples. The clean data and Q30 values of each sample were greater than 2.04 Gb and 93.26% (Table 2). Following the removal of the adapter sequence, ambiguous reads, and low-quality reads in order, the remaining data was compiled using Trinity software, where after 4,784,551 contigs were obtained and the N50 was 48 bp. With clustering contigs based on pairing information and the similarity of overlapping sequences, we obtained 115,004 transcripts and the N50 was 1,927bp. The most important transcripts were selected as unigenes, where not only were 58,397 unigenes obtained, but their N50 attained 1,385 bp. The lengths greater than 1,000 bp and 500 bp accounted for 23.97% and 42.54%, respectively (Table 3).

Table 2 Output statistics of B. striata transcriptome sequencing. 

Samples Tissues Raw Reads Raw Dates (G) GC percentage N percentage Q30 percentage
B. striata Leaves 7,740,447 2.07 46.52% 0.00% 93.26%
Stems 7,909,965 2.04 46.20% 0.00% 93.35%
Roots 8,289,337 2.15 46.32% 0.00% 93.44%
Flowers 9,896,931 2.54 47.19% 0.00% 93.31%
Seeds 10,280,142 2.60 46.12% 0.00% 93.49%

The roots, stems, leaves, flowers and seeds of the sequencing samples were collected from the same germplasm SXZA.

Table 3 Output statistics from assembly of B. striata transcriptome sequencing. 

Length range (bp) Number of contigs Transcript abundance Number of unigenes
0-300 4,738,699 (99.04%) 23,017 (20.02%) 20,023 (34.30%)
300-500 17,220 (0.36%) 17,967 (15.62%) 13,520 (23.16%)
500-1,000 13,849 (0.29%) 21,495 (18.69%) 10,842 (18.57%)
1,000-2,000 10,069 (0.21%) 30,052 (26.13%) 8,896 (15.24%)
> 2,000 4,714 (0.10%) 22,473 (19.54%) 5,098 (8.73%)
Total number 4,784,551 115,004 58,376
Total length 242,503,081 138,028,967 45,367,096
N50 length 48 1,927 1,385
Mean length 50.68 1200.21 777.11

Functional annotations

All unigenes generated by the Illumina platform were compared with NR, Swiss-Prot, GO, Subsequently, amino acid sequences were predicted and, unigenes were compared with the Pfam database to obtain annotation information. In these public databases, a total of 33,344 unigenes (57.10%) were consistent with the homologous sequences. Among them, 13,232 (22.66%) were 300 to 1,000 bp long while 13,224 (22.64%) were longer than 1,000 bp (Table 4).

Table 4 Summary statistics of functional annotations for B. striata unigenes via public databases. 

Database Annotated number 300<=length< 1,000 Number longer than 1,000 bp
COG 11,282 (19.32%) 3,664 (6.27%) 5,720 (9.805)
GO 21,036 (36.02%) 7,730 (13.24%) 9,406 (16.11%)
KEGG 7,736 (13.25%) 2,910 (4.98%) 3,314 (5.67%)
Swissprot 21,050 (36.05%) 7,857 (13.45%) 9,855 (16.88%)
NR 33,239 (56.92%) 13,189 (22.59%) 13,221 (22.64%)
All_Annotated 33,344 (57.10%) 13,232 (22.66%) 13,224 (22.64%)

When compared with the NR database, approximately 56.92% (33,239) of the unigenes were successfully annotated. Among these unigenes, 17.80% had the best matches to the Piriformospora indica sequences.

Unigenes that completed the NR database comparison were further compared with the GO database, and 21,036 (36.02%) unigenes were annotated and assigned to 56 functional groups based on the GO terms annotations (Figure 2A). Among these unigenes related to “cell components”, 12,799 (60.84%) were classified as “cell” (GO: 0005623), followed by “organelles” (GO: 0043226; 11,166 or 53.08%). However, among the unigenes related to “molecular function”, 47.69% were classified as catalytic activity (GO: 0003824; 10,032) and 46.73% were classified as binding activity (GO: 0005488; 9,830). The GO terms of biological processes were divided into “cellular process” (GO: 0009987; 58.58% or 12,322) and “metabolic process” (GO: 0008152; 63% or 13,254).

Figure 2 GO and COG functional annotations of B. striata transcriptome. (A) GO classification results for annotated unigenes in B. striata. The B. striata unigenes were annotated using BLAST searches against the public databases GO, and the results were categorized and viewed using WEGO. Percentage of genes (y-axis) indicates the proportion of B. striata unigenes that have relevant GO annotations in the three major GO domains “cellular components”, “molecular function”, and “biological process”. (B) The B. striata unigenes were compared with the public database COG using BLAST software to obtain annotation information, the abscissa represents the contents of each classification, and the ordinate represents the number of genes. 

By comparing the transcriptome data of B. striata with the public COG database, the function and classification of unigenes were predicted. In general, 11,282 unigenes (19.33%) were divided into 24 categories (Figure 2B). Among these, the largest functional group was “General function prediction only” (2,898, 25.29% of the COG annotations), followed by “Replication, recombination and repair” (1,280, 11.35%) and “Translation, ribosomal structure and biogenesis” (1,206, 10.69%).

Finally, through comparisons with four public protein databases, we obtained 58,063 CDSs, which accounted for 99.46% of all identified unigenes. The length distribution of CDSs revealed that 21,443 were from 200 to 1,000 bp long, and 25.82% were longer than 500 bp. In addition, 5,847 (10.07%) were from 1,000 to 2,000 bp long, 1,353 (2.33%) were 2,000 to 3,000 bp long, and 557 (0.96%) were longer than 3,000 bp (Figure S1).

Identification of transcription factors

When compared with the plant transcription factor database (Jin et al., 2014), we obtained 13,764 unigenes that had a good matching coefficients (identity >80%). These unigenes belonged to 59 families of plant transcription factors (Table 5), of which the most abundant unigenes were bHLH, NAC, MYB-related, MYB, WRKY, FAR1, B3, ERF, C2H2, and bZIP families.

Table 5 Putative transcription factors in B. striata unigenes. 

TF family NU NTP Percentage (%) TF family NU NTP Percentage (%)
bHLH 1350 11428 0.12 Nin-like 204 1002 0.20
MYB 809 8746 0.09 HB-other 215 987 0.22
ERF 589 8688 0.07 NF-YA 98 943 0.10
NAC 889 8133 0.11 ARR-B 33 914 0.04
C2H2 589 7336 0.08 WOX 117 903 0.13
MYB_related 844 6410 0.13 CO-like 59 854 0.07
bZIP 526 6258 0.08 DBB 35 764 0.05
WRKY 654 5936 0.11 GRF 34 752 0.05
B3 598 4051 0.15 YABBY 176 725 0.24
C3H 480 4019 0.12 E2F/DP 168 692 0.24
G2-like 452 3935 0.11 GeBP 120 683 0.18
GRAS 393 3915 0.10 BES1 61 651 0.09
HD-ZIP 373 3436 0.11 CPP 77 594 0.13
M-type 254 2978 0.09 EIL 33 531 0.06
MIKC 217 2864 0.08 CAMTA 80 518 0.15
LBD 143 2779 0.05 SRS 34 506 0.07
Trihelix 257 2599 0.10 BBR-BPC 40 492 0.08
FAR1 633 2542 0.25 LSD 22 402 0.05
Dof 109 2312 0.05 RAV 36 289 0.12
GATA 186 2229 0.08 Whirly 12 233 0.05
ARF 261 1914 0.14 VOZ 32 227 0.14
HSF 170 1833 0.09 NF-X1 46 176 0.26
TALE 276 1797 0.15 HB-PHD 33 160 0.21
AP2 89 1776 0.05 S1Fa-like 161 158 1.02
TCP 218 1704 0.13 LFY 5 100 0.05
SBP 88 1675 0.05 HRT-like 13 95 0.14
NF-YB 172 1334 0.13 STAT 56 84 0.67
ZF-HD 44 1066 0.04 SAP 2 63 0.03
NF-YC 97 1018 0.10 NZZ/SPL 2 45 0.04

NU, number of unigenes; NTP, number of TF genes in PlantTFDB database; percentage = NU//NTP.

KEGG pathway analysis

To further elucidate the biological metabolic processes of B. striata, we obtained 7,736 unigenes which were involved 112 metabolic pathways based on a comparison with the KEGG database (Table S3).

Among these unigenes, 529 were related to “ribosome pathways”, 364 played the “Oxidative phosphorylation pathways” function, 276 were involved in “Protein processing in endoplasmic reticulum”, 254 for “Protein processing in endoplasmic reticulum Spliceosome”, and 253 for “Glycolysis/Gluconeogenesis” (Table 6). Through analysis, a total of 1,364 unigenes were mapped to related metabolic pathways of carbohydrates, which mainly involved metabolic pathways such as glycolysis/gluconeogenesis, starch and sucrose metabolism, pyruvate metabolism, and amino sugar and nucleotide sugar metabolism (Table 7).

Table 6 The 20 most-represented KEGG pathways (KO, KEGG Orthology). 

Metabolism pathway Number of unigenes (KO) entry
Ribosome 529 (6.17%) ko03010
Oxidative phosphorylation 364 (4.25%) ko00190
Protein processing in endoplasmic reticulum 276 (3.22%) ko04141
Spliceosome 254 (2.96%) ko03040
Glycolysis / Gluconeogenesis 253 (2.95%) ko00010
Purine metabolism 222 (2.59%) ko00230
RNA transport 222 (2.59%) ko03013
Starch and sucrose metabolism 207 (2.42%) ko00500
Plant hormone signal transduction 179 (2.09%) ko04075
Carbon fixation in photosynthetic organisms 172 (2.01%) ko00710
Ubiquitin mediated proteolysis 163 (1.90%) ko04120
Pyrimidine metabolism 158 (1.84%) ko00240
Pyruvate metabolism 158 (1.84%) ko00620
Amino sugar and nucleotide sugar metabolism 153 (1.79%) ko00520
Phagosome 149 (1.73%) ko04145
Arginine and proline metabolism 147 (1.72%) ko00330
Citrate cycle (TCA cycle) 144 (1.68%) ko00020
Plant-pathogen interaction 144 (1.68%) ko04626
Peroxisome 142 (1.61%) ko04146
Endocytosis 138 (1.56%) ko04144

Table 7 The number of unigenes involved in polysaccharide biosynthesis in B. striata database. 

Metabolism pathway Number of unigenes (KO) entry
Glycolysis / Gluconeogenesis 253 ko00010
Starch and sucrose metabolism 207 ko00500
Pyruvate metabolism 158 ko00620
Amino sugar and nucleotide sugar metabolism 153 ko00520
Citrate cycle (TCA cycle) 144 ko00020
Pentose phosphate pathway 105 ko00030
Fructose and mannose metabolism 97 ko00051
Pentose and glucuronate interconversions 81 ko00040
Galactose metabolism 64 ko00052
N-Glycan biosynthesis 58 ko00510
Glycosylphosphatidylinositol(GPI)-anchor biosynthesis 18 ko00563
Other glycan degradation 15 ko00511
Glycosaminoglycan degradation 11 ko00531

Candidate genes involved in BSP biosynthesis

To better understand the metabolic pathways of BSP, referring to the annotation information on the KO 00500 and KO 00520 metabolic pathways in the KEGG database, we determined the key enzyme genes involved in these pathways (Table 8).

Table 8 Unigenes related to the biosynthesis of polysaccharide in B. striata. 

Enzyme code Enzyme name Abbreviation Number hexokinase HK 8 fructokinase scrK 12 glucose-6-phosphate isomerase GPI 6 phosphoglucomutase pgm; 13 uridine-diphosphate glucose pyrophosphorylase UGP2 1 mannose-6-phosphate isomerase manA 3 phosphomannomutase PMM 4 mannose-1-phosphate guanylyltransferase GMPP 6

The precursors of all plant polysaccharides were nucleoside-bisphosphate-sugar (NDP-sugar). Under the action of different glycosyltransferases (GTs), NDP-sugars were catalyzed to increased sugar chains to form new polysaccharides (Gutmann et al., 2017; Yuan et al., 2018a).

The results indicated that the polysaccharides of B. striata were derived from glucose and mannose; therefore, the metabolic processes of UDP-glucose (UDP-Glc) and GDP-mannose (GDP-Man) were the key metabolic pathway of BSP. UDP-Glc and GDP-Man were derived from glucose-6-phosphate (Glc-6P) and fructose-6-phosphate (Fru-6P), respectively. Under the action of NDP-sugar interconversion enzymes (NDP-sugar conversion enzymes), they carried out the next reaction.

Referring to previous research progress on polysaccharide metabolism (Liepman et al., 2010; Wang et al., 2019a; Zhang et al., 2019b), the plant polysaccharide pathway consisted of three steps. Firstly, sucrose was converted to Glc-6P and fructose under the action of β-fructofuranosidase (encoded by sacA) and phosphotransferase system (encoded by scrA). Fructokinase (encoded by scrK) and hexokinase (encoded by HK) were also involved in the biosynthesis of fructose to Fru-6P. Under the action of related enzymes, Glc-6P and Fru-6P were formed into UDP-Glc and GDP-Man respectively (Zhang et al., 2017; Geiger et al., 2019), which were glucose phosphotransase (encoded by pgm), uridine diphosphate glucose pyrophosphorylase (UGP2), glucose-6-phosphoisomerase (GPI), mannose-6-phosphoisomerase (manA), mannose phosphate enzyme (PMM), and mannose-1-phosphotransferase (GMPP).

Secondly, under the action of a series of enzymes, UDP-Glc formed NDP-sugars. The main pathways were UDP-Glc, which formed NDP-sugars through a series of enzymes. Thirdly, NDP-sugars were added to various sugar residue chains to form polysaccharide complex units under the action of glycosyltransferases (Figure 3).

Figure 3 Proposed pathways for polysaccharide biosynthesis in B. striata. Activated monosaccharide units, marked in blue with yellow background, key enzymes, marked in red with green background; and bolded text with pink background indicates key intermediates. From left to right, the colors represent leaves, stems, roots, flowers, and seeds organization, respectively. The full line arrows represent the identified enzymatic reactions, and the dashed line arrows represent multiple enzymatic reactions by multiple steps. 

In the first step reaction, glucose and fructose were converted under the action of a series of kinases, and the related enzymes included hexokinases (HKs) and fructokinases (scrKs). By comparison, we concluded that 537-bp Unigene 33226 was a HK homologue with the highest amino acid homology (93%) to HK in Dendrobium catenatum (Sequence ID: XM_020834402.2) (Figures S2, S3). In addition, the 1,488-bp Unigene 44091 was consistent in homology with the scrK from D. catenatum (Sequence ID: XM_020847040.2) (Figures S2, S4).

The results of the comparative transcriptome in Table 8 were found in the stem of the main accumulation site of BSP, and directed from Glc-6P through the intermediate product Fru-6P, Man-6P, and Man-1P. Eventually, GDP-Man, and related enzymes GPI, manA, PMM, and GMPP exhibited extremely strong activity, which means that the biotransformation process, from Glc-6P to GDP-Man may have been its primary metabolic pathway.

Unigenes involved in BSP metabolism were expressed by the RPKM value in transcriptome database. Under general conditions, the unigene with the maximum RPKM value was selected as the gene expression quantity (Table 9).

Table 9 Unigenes expression levels in BSP biosynthesis calculated by the method of RPKM. 

Gene Enzyme name Roots Stems Leaves Flowers
GAPDH glyceraldehyde-3-phosphate dehydrogenase 46.33 52.52 161.32 25.62
GMPP mannose-1-phosphate guanylyltransferase 12.49 21.32 18.87 10.44
PMM phosphomannomutase 45.4 105.53 65.89 70.58
manA mannose-6-phosphate isomerase 118.54 446.52 57.38 41.65
HK hexokinase 9.4 8.36 6.3 10.42
scrk fructokinase 10.81 11.52 4.44 24.06
GPI glucose-6-phosphate isomerase 82.38 106.15 57.53 66.08
pgm phosphoglucomutase 67.89 144.18 63.33 248.17
UGP2 uridine-diphosphate glucose pyrophosphorylase 59.08 123.13 56.87 208.29

Depending on the abundance of the transcribed RNA-seq assay, we selected eight candidate unigenes; GMPP, PMM, manA, HK, scrK, GPI, pgm and UGP2. In the database, the least consistent with their homologs was scrK (Unigene 44,091; 98% identity), followed by PMM (Unigene 36,348; 99%), manA (Unigene 45,816; 99%), and GPI (Unigene 32,627; 99%). The remaining identity of the unigenes was 100%, which were GMPP (Unigene 39,307), HK (Unigene 33,226), pgm (Unigene 46,080) and UGP2 (Unigene 31559). All these details were aligned as shown in Table S4, which confirmed that the selection of unigenes related to BSP metabolism was quite conservative.

It can be seen from Table 8 that the expression levels of related enzyme genes in the roots, stems, leaves, and flowers of B. striata were different, among which GMPP, manA and GPI had the highest expression levels in stems. Both pgm and UGP2 were the most expressed in flowers, while PMM, HK, and scrK were most expressed in seeds.

Analysis of BSP biosynthetic pathway

In combination with the database annotation of the polysaccharide metabolic pathway and the experimental results of the monosaccharide composition of B. striata, we obtained the potential biosynthesis pathways for BSP formation from sucrose.

The BSP synthesis pathway primarily consisted of three steps. Firstly, under the action of related enzymes such as UGP2, pgm, GPI, manA, PMM, and GMPP, Glc-6-p and Fru-6-p formed UDP-Glc and GDP-Man, respectively. Secondly, the NDP-sugars were added to sugar residues to form complex polysaccharide units. Therefore, in the BSP metabolic pathway, UDP-Glc and GDP-Man were formed mainly by the key enzyme genes in step one, and finally complex units were formed through the NDP-sugars by NSEs in step two. Thirdly, under the action of related GTs, the NDP-sugars were added to the polysaccharide. The logarithms of the RPKM values of different tissues in the metabolic pathway were expressed as different colors (from left to right, the colors represent leaves, stems, roots, flowers, and seeds). Here, a normalized data processing method was used and the drawing was completed on a heat map generated by the R software (Figure 3).

Quantitative real-time PCR analysis

Quantitative real-time PCR can reflect the expression abundance of related genes in the BSP metabolic pathway, and we selected GAPDH as the experimental reference. We screened the expression patterns of GMPP, PMM, manA, HK, scrK, GPI, pgm, and UGP2 genes in the BSP metabolic pathways for different organs of B. striata (SXZA). According to the expression values of related genes in various tissues, HK, pgm, and UGP2 had the largest content in flowers, while GMPP, manA, and GPI had the highest content in stems. These genes may play an important role in the biosynthesis of BSP (Figure 4).

Figure 4 Expression patterns of some novel transcripts related to polysaccharide biosynthesis in B. striata. (a-h) expression patterns of key enzyme genes GMPP, PMM, manA, HK, scrK, GPI, pgm, and UGP2 in the BSP pathway. 

We further compared the expression patterns of different transcripts with the RPKM values of related genes in the transcriptome sequencing data group. GMPP, scrK, GPI, and UGP2 had the same expression patterns in the two groups of data, while the expression of the remaining four genes was inconsistent. Futher, according to the polysaccharide yield analysis of different germplasm resources, we selected the fresh plant pseudobulb (SXSZ-82 and AHXC-218) germplasm resources for experiments to verify the expression levels of related genes in different tissues and parts. Compared with the reference gene, it was concluded that there were no significant differences in expression level quantities between HK, scrK, and pgm. It was observed that manA had the highest expression between the two species; however, there were significant difference between them, while UGP2, GPI, PMM, and GMPP had considerable differences between the two samples (Figure 5).

Figure 5 The expression of candidate unigenes involved in BSP biosynthesis between different germplasm resources. AHCX and SXSZ represent samples collected from Xuancheng No. 218 in Anhui Province and Shangzhou No. 82 in Shaanxi Province, respectively. 


To date, this is the first time that De novo sequencing of the different organs of B. striata using the Illumina HiSeq 2500 platform explored the metabolic pathways of polysaccharides. After completing the assembly, we obtained 11.4 Gb of clean data and 58,397 unigenes, which made it possible to study the BSP pathway at the molecular level. Compared to the published databases of other medicinal orchid plants, B. striata had more unigenes than did Dactylorhiza hatagirea (37,371) and Gastrodia elata (56,884), but fewer than Dendrobium huoshanense (478,361), Cymbidium goeringii (85,868), and Dendrobium officinale (145,791) (Yuan et al., 2018b; Dhiman et al., 2019; Wang et al., 2019b).

The average unigene length was 777.11 bp, which was longer than D. hatagirea (551.10 bp) and D. huoshanense (764.73 bp), but less than C. goeringii (1,194 bp) and G. elata (844.34 bp). In our experimental results, 23.97% of the sequence length was greater than 1,000bp, which indicated that the use of the Illumina technology platform had a higher assembly quality and stability in the research of medicinal plants.

There have been published studies on the overall transcriptome of the B. striata plant over its entire developmental stage. The transcriptome sequencing of samples from the protocorm stage to root, stem, leaf, and pollenated capsule stages were performed, and 127,261 overlapping unigenes were identified with mean unigene lengths of 612 bp. The authors successfully screened five markers using EST-SSR technology for their identification with their relatives (Xu et al., 2018).

With the advent of the big data era and the development of omics technology, increasing numbers of studies on polysaccharide metabolic pathways have been conducted. Through the transcriptome analysis of medicinal plants Codonopsis pilosula using high-throughput sequencing technologies, the biosynthetic pathways of codonopsis polysaccharides were summarized and verified, which initially elaborated the polysaccharide metabolic pathways of medicinal plants (Gao et al., 2015).

Similarly, polysaccharide is the main medicinal component of the Polygonatum genus. The polysaccharide metabolism pathways of P. cyrtonema and P. sibiricum were thoroughly investigated, which were consistent and comprised of three steps. However, due to the differences in monosaccharide composition and content, the expression of some key enzyme genes in the polysaccharide metabolic pathway may be different (Wang et al., 2017; Wang et al., 2019a). The genome sequencing of D. candidum was completed, which provided ample support for the study of polysaccharide metabolism pathways in other orchids (Zhang et al., 2016a; Zhang et al., 2016b).

Through related bioinformatics software analysis, the candidate unigenes of the BSP metabolic pathway were identified. According to previous experimental results, the BSP composition was glucose and mannose; however, the molar ratio was different (Figure S5). Therefore, what was of more interest was the conversion between glucose and mannose, and the further formation of polysaccharide coincidence units. We further analyzed the expression levels of related genes in the polysaccharide pathway in different B. striata tissues and concluded that the expression levels of manA and UGP2 were the highest (Figure 4).

As is well recognized, UGP2 is the key enzyme gene for glucose-1-phosphate to form UDP-Glc, while manA is the key enzyme for fructose-6-phosphate to form mannose-6-phosphate in plants. The expression level of manA in plants was 3.36 times that of UGP2, while the molar ratio of mannose to glucose in the same material of B. striata was 2.91:1. The expression levels of the two key genes mentioned above were approximately consistent with the composition ratio of the two monosaccharides, and it is predicted that there may be some relationship between them, which requires verification through further experimentation.

The main component polysaccharide of B. striata accumulates in the pseudobulb (Liao et al., 2019). We can observe from Figure 5 that manA has the highest expression in the stems of SXSZ-82 and AHXC-218 sample; however, the expression of the former is four times that of the latter. According to the experimental results in Table 1, the polysaccharide yield of the SXSZ-82 sample was 36.48%, which was much larger than 22.77% in the AHXC sample. While manA is a key gene inducing Glc-1P to the Man-6P pathway, we can understand that there should be a correlation between high-activity samples and highly expressed genes. The further focus would be the gene expression of key enzymatic polysaccharide pathways, and the study of their correlations between enzyme activity and the BSP content.

GMPP also showed strong biological activity in the B. striata polysaccharide metabolic pathway, as a key enzyme gene in the pathway, GMPP plays an important role in the accumulation of GDP-Man in plants. Similarly, in A. rabidopsis thaliana, the establishment of mutants and a yeast hybrid experiment also confirmed that GMPP was the rate-limiting enzyme synthesized by GDP-Man (Sawake et al., 2015). The same conclusion was also drawn in the study of the polysaccharide metabolic pathway of P. sibiricum (Wang et al., 2017). In the process of studying the polysaccharide metabolism pathway, a complex network structure is formed due to the variety of monosaccharide components. It is possible to further implement such work by selecting species that are similar in type to BSP, have a small variety of structures and a well-defined overall structure.

Although the transcriptomes of different organs of B. striata were sequenced in this study, its bioinformatics database was established, and the enzyme genes in relevant BSP metabolic pathways were summarized. However, the research on B. striata is primarily focused on pharmacology, and there are relatively few studies on its molecular biology. The complexity of the BSP structure makes it difficult to study its monosaccharide composition. Therefore, future research should focus on verifying the functional prediction of related genes through modern molecular biology and proteomics techniques.


As a common Chinese medicinal plant, B. striata has a variety of pharmacological effects. In this study, a bioinformatics database was established by performing transcriptome analysis on different organs of B. striata. Our experimental results provided useful information for studying the BSP metabolic pathway of this species, and the functions of several genes were confirmed by qRT-PCR. These research results not only contribute to laying the foundation for further elaborating the biosynthesis of related polysaccharides, but also provide candidate genes for metabolite synthesis, which can be employed for the evaluation of germplasm resources in B. striata.


This study was financially supported by the Key R&D Program of Shaanxi Province (2019SF-307) and the Key R&D Program of Shanxi Province (201603D3111003), the Fundamental Research Funds for the Central Universities (GK201906008 and GK201806006), the National Natural Science Foundation of China (31670299) and the National Key Technologies R & D Program for Modernization of Traditional Chinese Medicine (2017YFC1701300 and 2017YFC1700706).


Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W and Lipman DJ (1997) Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res 25:3389-3402. [ Links ]

Anders S and Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11:R106. [ Links ]

Apweiler R, Bairoch A, Wu CH, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M et al. (2004) UniProt: the Universal Protein knowledgebase. Nucleic Acids Res 32:D115-D119. [ Links ]

Chen Z, Cheng L, He Y and Wei X (2018) Extraction, characterization, utilization as wound dressing and drug delivery of Bletilla striata polysaccharide: A review. Int J Biol Macromol 120:2076-2085. [ Links ]

Chung MY, López-Pujol J and Chung MG (2013) Low genetic diversity in marginal populations of Bletilla striata (Orchidaceae) in southern Korea: Insights into population history and implications for conservation. Biochem Syst Ecol 46:88-96. [ Links ]

Dhiman N, Sharma NK, Thapa P, Sharma I, Kumar Swarnkar M, Chawla A, Shankar R and Bhattacharya A (2019) De novo transcriptome provides insights into the growth behaviour and resveratrol and trans-stilbenes biosynthesis in Dactylorhiza hatagirea - An endangered alpine terrestrial orchid of western Himalaya. Sci Rep 9:13133. [ Links ]

Fu SH, Lei M, Zhang YQ, Deng ZM, Shi J and Hao DD (2019) De novo transcriptome analysis of Tibetan medicinal plant Dysphania schraderiana. Genet Mol Biol 42:480-487. [ Links ]

Gao JP, Wang D, Cao LY and Sun HF (2015) Transcriptome sequencing of Codonopsis pilosula and identification of candidate genes involved in polysaccharide biosynthesis. PLoS One 10:e0117342. [ Links ]

Geiger J, Doelker R, Salö S, Roitsch T and Dalgaard LT (2019) Physiological phenotyping of mammalian cell lines by enzymatic activity fingerprinting of key carbohydrate metabolic enzymes: A pilot and feasibility study. BMC Res Notes 12:682. [ Links ]

Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R, Zeng Q et al. (2011) Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol 29:644-652. [ Links ]

Guo JJ, Dai BL, Chen NP, Jin LX, Jiang FS, Ding ZS and Qian CD (2016) The anti-Staphylococcus aureus activity of the phenanthrene fraction from fibrous roots of Bletilla striata. BMC Complement Altern Med 16:491. [ Links ]

Gutmann A, Lepak A, Diricks M, Desmet T and Nidetzhy B (2017) Glycosyltransferase cascades for natural product glycosylation: Use of plant instead of bacterial sucrose synthases improves the UDP-glucose recycling from sucrose and UDP. Biotechnol J 12:1600557. [ Links ]

He X, Wang X, Fang J, Zhao Z, Huang L, Guo H and Zheng X (2017) Bletilla striata: Medicinal uses, phytochemistry and pharmacological activities. J Ethnopharmacol 195:20-38. [ Links ]

Jin JP, Zhang H, Kong L, Gao G and Luo JC (2014) PlantTFDB 3.0: A portal for the functional and evolutionary study of plant transcription factors. Nucleic Acids Res 42:D1182-D1187. [ Links ]

Kanehisa M, Goto S, Kawashima S, Okuno Y and Hattori M (2004) The KEGG resource for deciphering the genome. Nucleic Acids Res 32:D277-D280. [ Links ]

Kong LS, Yu L, Feng T, Yin XJ, Liu TJ and Dong L (2015) Physicochemical characterization of the polysaccharide from Bletilla striata: Effect of drying method. Carbohyd Polym 125:1-8. [ Links ]

Langmead B, Trapnell C, Pop M and Salzberg SL (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10:R25. [ Links ]

Li B and Dewey CN (2011) RSEM: Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12:323. [ Links ]

Li JY, Kuang MT, Yang L, Kong QH, Hou B, Liu ZH, Chi XQ, Yuan MY, Hu JM and Zhou J (2018) Stilbenes with anti-inflammatory and cytotoxic activity from the rhizomes of Bletilla ochracea Schltr. Fitoterapia 127:74-80. [ Links ]

Liang TT, Zou LQ, Sun SJ, Kuang XJ, Wei JH, Wang LZ, Li Y and Sun C (2019) Hybrid sequencing of the Gynostemma pentaphyllum transcriptome provides new insights into gypenoside biosynthesis. BMC Genomics 20:632. [ Links ]

Liao ZC, Zeng R, Hu LL, Maffucci KG and Qu Y (2019) Polysaccharides from tubers of Bletilla striata: Physicochemical characterization, formulation of buccoadhesive wafers and preliminary study on treating oral ulcer. Int J Biol Macromol 122:1035-1045. [ Links ]

Liepman AH, Wightman R, Geshi N, Turner SR and Scheller HV (2010) Arabidopsis - a powerful model system for plant cell wall research. Plant J 61:1107-1121. [ Links ]

Luo L, Zhou Z, Xue J, Wang Y, Zhang J, Cai X, Liu YQ and Yang F (2018) Bletilla striata polysaccharide has a protective effect on intestinal epithelial barrier disruption in TAA-induced cirrhotic rats. Exp Ther Med 16:1715-1722. [ Links ]

Luo L, Liu YQ, Cai X, Wang Y, Xue J, Zhang J and Yang F (2019) Bletilla striata polysaccharides ameliorates lipopolysaccharide-induced injury in intestinal epithelial cells. Saudi J Gastroenterol 25:302-308. [ Links ]

Mortazavi A, Williams BA, McCue K, Schaeffer L and Wold B (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5:621-628. [ Links ]

Niazian M (2019) Application of genetics and biotechnology for improving medicinal plants. Planta 249:953-973. [ Links ]

Niu JF, Wang SP, Wang BL, Chen LJ, Zhao GM, Liu S, Wang SQ and Wang ZZ (2019) Structure and anti-tumor activitity of a polysaccharide from Bletilla ochracea Schltr. Int J Biol Macromol 154:1548-1555. [ Links ]

Pruitt KD, Tatusova T and Maglott DR (2005) NCBI Reference Sequence (RefSeq): A curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res 33:D501-D504. [ Links ]

Ribeiro DA, Cota J, Alvarez TM, Brüchli F, Bragato J, Pereira BM, Pauletti BA, Jackson G, Pimenta MT, Murakami MT et al. (2012) The Penicillium echinulatum secretome on sugar cane bagasse. PLoS One 7:e50571. [ Links ]

Sawake S, Tajima N, Mortimer JC, Lao J, Ishikawa T, Yu X, Yamanashi Y, Yoshimi Y, Kawai-Yamada M, Dupree P et al. (2015) KONJAC1 and 2 are key factors for GDP-mannose generation and affect L-ascorbic acid and glucomannan biosynthesis in Arabidopsis. Plant Cell 27:3397-3409. [ Links ]

Schmittgen TD and Livak KJ (2008) Analyzing real-time PCR data by the comparative C(T) method. Nat Protoc 3:1101-1108. [ Links ]

Tatusov RL, Galperin MY, Natale DA and Koonin EV (2000) The COG database: A tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res 28:33-36. [ Links ]

Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, Van Baren MJ, Salzberg SL, Wold BJ and Pachter L (2010) Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 28:511-515. [ Links ]

Wang W and Meng H (2015) Cytotoxic, anti-inflammatory and hemostatic spirostane-steroidal saponins from the ethanol extract of the roots of Bletilla striata. Fitoterapia 101:12-18. [ Links ]

Wang SQ, Wang B, Hua WP, Niu JF, Dang KK, Qiang Y and Wang ZZ (2017) De Novo assembly and analysis of Polygonatum sibiricum transcriptome and identification of genes involved in polysaccharide biosynthesis. Int J Mol Sci 18:1950. [ Links ]

Wang LC, Wu YJ, Li JS, Qiao HZ and Di LQ (2018) Rheological and mucoadhesive properties of polysaccharide from Bletilla striata with potential use in pharmaceutics as bio-adhesive excipient. Int J Biol Macromol 120:529-536. [ Links ]

Wang CK, Peng DY, Zhu JH, Zhao DR, Shi YY, Zhang SX, Ma KL, Wu JW and Huang LQ (2019a) Transcriptome analysis of Polygonatum cyrtonema Hua: Identification of genes involved in polysaccharide biosynthesis. Plant Methods 15:65. [ Links ]

Wang Y, Shahid MQ, Ghouri F, Ercisli S and Baloch FS (2019b) Development of EST-based SSR and SNP markers in Gastrodia elata (herbal medicine) by sequencing, de novo assembly and annotation of the transcriptome. 3 Biotech 9:292. [ Links ]

Xu DL, Chen HB, Aci M, Pan YC, Shangguan YN, Ma J, Li L, Qian G and Wang QY (2018) De novo assembly, characterization and development of EST-SSRs from Bletilla striata transcriptomes profiled throughout the whole growing period. PLoS One 13:e0205954. [ Links ]

Yuan S, Yin S, Liu M and Kong JQ (2018a) Isolation and characterization of a multifunctional flavonoid glycosyltransferase from Ornithogalum caudatum with glycosidase activity. Sci Rep 8:5886. [ Links ]

Yuan YD, Yu MY, Jia ZH, Song XE, Liang YQ and Zhang JC (2018b) Analysis of Dendrobium huoshanense transcriptome unveils putative genes associated with active ingredients synthesis. BMC Genom 19:978. [ Links ]

Zhang GQ, Xu Q, Bian C, Tsai WC, Yeh CM, Liu KW, Yoshida K, Zhang LS, Chang SB, Chen F et al. (2016a) The Dendrobium catenatum Lindl. genome sequence provides insights into polysaccharide synthase, floral development and adaptive evolution. Sci Rep 6:19029. [ Links ]

Zhang JX, He CM, Wu KL, Teixeira da Silva JA, Zeng SJ, Zhang XH, Yu ZM, Xia HQ and Duan J (2016b) Transcriptome analysis of Dendrobium officinale and its application to the identification of genes associated with polysaccharide synthesis. Front Plant Sci 7:5. [ Links ]

Zhang J, Yu L, Lin M, Yan Q and Yang ST (2017) n-Butanol production from sucrose and sugarcane juice by engineered Clostridium tyrobutyricum overexpressing sucrose catabolism genes and adhE2. Bioresource Technol 233:51-57. [ Links ]

Zhang C, Gao F, Gan S, He YN, Chen ZJ, Liu XW, Fu CM, Qu Y and Zhang JM (2019a) Chemical characterization and gastroprotective effect of an isolated polysaccharide fraction from Bletilla striata against ethanol-induced acute gastric ulcer. Food Chem Toxicol 131:110539. [ Links ]

Zhang N, Tang ZF, Zhang J, Li X, Yang ZQ, Yang C, Zhang ZF and Huang ZX (2019b) Comparative transcriptome analysis reveals the genetic basis underlying the biosynthesis of polysaccharides in Hericium erinaceus. Bot Stud 60:15. [ Links ]

Associate Editor: Rogério Margis

Received: January 02, 2020; Accepted: April 16, 2020

Send correspondence to Zhezhi Wang. Shaanxi Normal University, 3504 Gewu building, No. 620, West Chang'an Avenue, Chang'an District, Xi'an, Shaanxi Province, 710119, China. E-mail:

Conflict of Interest

All authors of this article declare that there are no conflicts of interest in this study.

Author Contributions

JFN and ZZW conceived and designed the experiments. SL, SQW, DHW and LJC performed the experiments. JFN and GMZ analyzed the data. JFN, SQW and ZYM contributed reagents/materials/analytical tools. JFN drafted the manuscript, DHW and ZZW revised it. All authors have discussed and commented on the manuscript.

Creative Commons License License information: This is an open-access article distributed under the terms of the Creative Commons Attribution License (type CC-BY), which permits unrestricted use, distribution and reproduction in any medium, provided the original article is properly cited.