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miRNA Analysis of Leaves of “Zijuan” Tea ( Camellia sinensis ) Based on High-throughput Sequencing | Song | Genomics and Applied Biology

Research Article

miRNA Analysis of Leaves of “Zijuan” Tea (Camellia sinensis) Based on High-throughput Sequencing  

Weixi Song , Lifei Xia , Yiping Tian , Huibin Jiang , Yunnan Sun , Dehe Liu , Linbo Chen
1 Tea Research Institute, Yunnan Academy of Agricultural Sciences, Yunnan Engineering Research Center of Tea Germplasm Innovation and Matching Cultivation, Menghai, 666201, China
2 Yunnan Provincial Key Laboratory of Tea Science, Menghai, 666201, China
Author    Correspondence author
Genomics and Applied Biology, 2019, Vol. 10, No. 1   doi: 10.5376/gab.2019.10.0001
Received: 24 Sep., 2018    Accepted: 30 Oct., 2018    Published: 25 Jan., 2019
© 2019 BioPublisher Publishing Platform
This article was first published in Genomics and Applied Biology (2018, 06: 2489-2497) in Chinese, and here was authorized to translate and publish the paper in English under the terms of Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Preferred citation for this article:

Song W.X., Xia L.F., Tian Y.P., Jiang H.B., Sun Y.N., Liu D.H., and Chen L.B., 2019, miRNA analysis of leaves of “Zijuan” tea (Camellia sinensis) based on high-throughput sequencing, Genomics and Applied Biology, 10(1): 1-9 (doi: 10.5376/gab.2019.10.0001)

Abstract

“Zijuan” is specific tea resource with abundant secondary metabolites, like catechins, anthocyanins, flavonoids, etc. Its biosynthesis is a network of multiple metabolic pathways connected by associated nodes, which is controlled by a variety of structural and regulatory genes. MicroRNAs (miRNAs), as a non-coding RNAs, play important roles in plant growth, development and secondary metabolism by regulating gene expression. In this study, four independent miRNA libraries of bud, second-leaf, open surface leaf and mature-leaf of “Zijuan” tea were constructed and sequenced by high-throughput sequencing. 126 known miRNAs were identified and divided into 26 families, and 119 novel miRNAs were predicted. Based on the transcriptome data of “Zijuan” tea, 724 and 2,285 target genes were predicted for known and novel miRNAs, respectively. The predicted target genes were mostly transcription factors, which included MYB and bHLH transcription factors for regulating the biosynthesis of secondary metabolites -- anthocyanins and flavonoids. All the above results would provide a theoretical basis for further studies on miRNA regulating the development of tea leaf and the biosynthesis of secondary metabolites in Camellia sinensis.

Keywords
“Zijuan” Camellia sinensis (L.); High-throughput sequencing; miRNA; Target genes

Background

Camellia sinensis (L.) is an important cash crop for leaf use. It contains a large amount of tea polyphenol (TP), a secondary metabolite, which is beneficial to human health. TP is an important component that determines the quality and health efficacy of tea. The content of TP is about 18%~36% of the dry weight of young buds, in which catechin is about 70% of the total tea polyphenols (Xia and Gao, 2009). “Zijuan” is a special tea resource, its young bud leaves are purple. Not only its catechin content is high, but its anthocyanin content is 10 times higher than that of common tea (Cai et al., 2010). Catechin and anthocyanin are both flavonoid compounds. The biosynthesis of catechins and anthocyanins is a network of multiple metabolic pathways connected by associated nodes, which is controlled by a variety of structural and regulatory genes.

 

MicroRNA, also known as miRNAs, is an endogenous non-coding single-stranded small molecule RNA, which exists widely in organisms and is composed of about 21~25 nucleotides (Bartel, 2004). In 1993, miRNA lin-4 was cloned for the first time in Caenorhadits elegans, and it was found that miRNA lin-4 played an important role in postembryonic development (Lee et al., 1993). Later, a large number of miRNA were found in the model plant of Arabidopsis thaliana (Sunkar and Zhuwei, 2004), rice (Sunkar et al., 2005) and crops, such as maize (Mica et al., 2006), cotton (Kwak et al., 2009) and soybean (Xu et al., 2013). It had been shown that miRNA could recognize target gene mRNA by complementary pairing with target mRNA, and degrade target mRNA or inhibit target mRNA translation at the transcriptional level, thereby regulating the abundance and function of target mRNA (Bartel, 2004). In plants, miRNAs could regulate organogenesis and differentiation, metabolism, growth and development, signal transduction of hormones, response to abiotic stress and biological stress and other biological processes (Zhang et al., 2007; Kwak et al., 2009; Xu et al., 2013).

 

In view of the fact that plant miRNAs is an important regulatory factor of plant growth and development, secondary metabolism and stress resistance, the new generation of high-throughput sequencing technique was used to analyze the miRNA of young bud, second leaf, open leaf and mature leaf of “Zijuan”, respectively. miRNAs and its corresponding target genes in tea leaves of “Zijuan” were excavated, which provided theoretical guidance for further study on the regulation of growth and development, secondary metabolism and stress resistance of tea leaves by miRNAs.

 

1 Results and Analysis

1.1 High-throughput sequencing and assembly

In this study, 11,904,437 sequences were obtained from young buds of “Zijuan” by high-throughput sequencing, 10,532,877 sequences were obtained from the second leaves, 12,701,347 sequences were obtained from the open leaves, and 12,042,833 sequences were obtained from the mature leaves. After removing the sequences with inferior quality, contaminated joints, no 3’ connectors or inserts, and Reads containing ployA/T/G/C, 11,710,509, 10,354,353, 12,493,732, 11,832,854 Reads were obtained, respectively (Table 1).

 

Table 1 List of sequencing data quality

 

1.2 sRNA length screening and analysis

The Clean reads screening length ranged 18~30 nt, and sRNA was mainly distributed in the range of 21~24 nt, of which 24 nt was the most (Figure 1). These sRNA distributions were consistent with other reports (Kwak et al., 2009). Then, the screened sRNA was located on the corresponding reference sequence by bowtie software, and the distribution of sRNA in the reference sequence was analyzed. The number of bud’s Clean reads mapped to reference sequence was 5,410,363, the second leaf was 5,135,534, the open leaf was 5,944,894, and the mature leaf was 3,831,890. Mapped sRNA was annotated with NCBI database (http://www.ncbi.nlm.nih.gov/) and Rfam database. These annotated non-coding RNAs included known miRNA, new miRNAs, rRNAs, tRNAs, snRNAs, snoRNAs, TAS gene (Table 2).

 

Figure 1 Abundance of small RNA sequence size

Note: ZJ-1: Bud; ZJ-2: Second-leaf; ZJ-3: Open surface leaf; ZJ-4: Mature-leaf

 

Table 2 Numbers of each small identified RNA classification

 

1.3 Identification of known miRNA and analysis of new miRNA gene

The Reads mapped onto the reference sequence were compared with the miRNA sequences of grape in miRBase, and 126 known miRNAs were identified and divided into 26 families. Among these miRNA families, MIR169_2 had 22 members, MIR395 had 14 members, MIR399 and MIR171_1 had 9 members, MIR166 and MIR156 had 8 members, MIR403 had 6 members, MIR160, MIR167_1 and MIR319 had 5 members, MIR396, MIR172 and MIR164 had 4 members, MIR159, MIR398, MIR535 and MIR394 had 3 members, MIR393, MIR477 and MIR482 had 2 members, MIR168, MIR390, MIR3630, MIR162_1, MIR408 and MIR397 had only 1 member (Figure 2).

 

Figure 2 Members of miRNA family

 

Since tea plants do not have complete genomic sequencing data and do not have small RNA background, only the unique small RNA sequences were mapped to the transcriptome of “Zijuan” tea tree to identify potential new miRNA sequences. The symbolic hairpin structure of miRNA precursor was used in the prediction of new miRNA (Friedlander et al., 2012; Wen et al., 2012). According to BLASTn search and the prediction of hairpin structure, 119 new mature miRNAs were found.

 

1.4 Prediction and functional analysis of miRNA target genes

The target genes of 126 known miRNA families and 119 new miRNAs were identified by psRobot software, and the corresponding relationship between miRNA and target genes was obtained. The known miRNA families and new miRNAs predicted 724 and 2,285 target genes, respectively, among which 207 and 678 were annotated to function. These annotated target genes included transcription factors and functional gene families with different biological functions. In this study, a single miRNA could regulate multiple target genes. For example, the known miR156a had 27 target genes, while the new miRNA (novel_65) predicted 210 target genes. It was also predicted that multiple miRNAs could regulate one target gene, and some of these miRNAs came from the same family and some from different families.

 

To further understand the biological function of target genes, the Gene Ontology (GO) enrichment analysis and KEGG analysis of obtained 885 genes with functional annotation were carried out. The GO annotation of miRNA target genes was mainly about molecular function, including binding, heterocyclic compound binding, organic ring compound binding, ion binding, nucleic acid binding and so on (Figure 3). The pathway-annotation and classification of miRNA target genes indicated that the target gene annotation was mainly concentrated in the purine metabolism, interaction of plant pathogenic bacteria, protein processing of endoplasmic reticulum and plant hormone signal transduction pathways (Figure 4).

 

Figure 3 GO classifications of miRNA target genes

Note: 1: Binding; 2: Ion binding; 3: Copper ion binding; 4: Heterocyclic compound binding; 5: Organic cyclic compound binding; 6: ADP binding; 7: Nucleic acid binding

 

Figure 4 KEGG pathways of miRNA target genes

 

The predicted target genes were involved in different biological processes, including transcription factors, signal transduction, stress response and so on. There were 70 hypothetical transcription factors in all target genes, divided into 22 families (Table 3), mainly involved in signal transduction and regulation of gene expression. The target genes of miR156 had squamosa promoter-bindingprotein-like (SPL) family transcription factors, which was the most transcription factor family obtained in this research. The target gene of miR164 had NAC transcription factor family, and MYB transcription factor was regulated by miR319, miR845, miR156a and new miRNA. Transcription factors, such as bHLH and C3H, were predicted to be regulated by new miRNA in Zijuan tea.

 

Table 3 Transcription factors of miRNA target genes

 

2 Discussion

Anthocyanin is synthesized by a special branch of the flavonoid pathway, which not only determines the color of flowers and fruits, but also protects plants from various biological and non-biological stresses. Anthocyanin is also a safe and non-toxic natural pigment with pharmacological effects. “Zijuan” is a special tea plant with purple young leaves and high anthocyanin content. Previous studies mainly focused on content changes, component differences and transcriptome analysis (Cai et al., 2010; Lv et al., 2012; Chen et al., 2015). The purpose of this study was to identify miRNA target genes related to anthocyanin metabolism and to provide useful information for studying miRNA regulation of anthocyanin metabolism. Therefore, four miRNA libraries of “Zijuan” tea buds, second leaves, open leaves and mature leaves were constructed and sequenced. Each library produced more than 10 million Clean reads. From the perspective of Q30 of sequences, the mismatch rate of bases, GC content and the length distribution of sequences, the number and quality of miRNA sequences were relatively high, and the 24 nt sequence was the most abundant, which was the same as tobacco (Baksa et al., 2015), asparagus (Chen et al., 2016) and rubber (Wang et al., 2016). A total of 126 known miRNAs and 119 new miRNAs were identified, and these obtained miRNAs were highly conserved with the miRNAs of other plants, which suggested that these miRNAs in tea plants played an important role in regulating their growth and development (Bartel, 2004).

 

In order to better understand the miRNA function of tea plant, the functional analysis of its target genes was conduct. Most of the predicted target genes were transcription factors related to plant growth and development, including SPL, NAC, MYB, ARFs, bHLH and WRKY. These detected miRNA families were highly conserved in different plants, miR164 regulated NAC transcription factor, participated in cell division (Kim et al., 2007), secondary cell wall formation (Zhong et al., 2007) and also participated in the growth and aging of organs (Guo et al., 2006). bHLH transcription factor participated in the synthesis of anthocyanins by regulating the expression of structural genes (Yang et al., 2012), and four new miRNAs were found to be associated with bHLH transcription factor in the predicted miRNAs. In Camellia sinensis, miR156 regulated DFR gene by inhibiting the target gene SPL, thus affected the accumulation of catechin. In Arabidopsis thaliana, miR156 regulated SPL transcription factor to control the biosynthesis of sesquiterpenes. MYB transcription factor directly regulated the expression level of key enzymes in anthocyanin biosynthesis (Kobayashi et al., 2002; Takos et al., 2006). In this study, 7 miRNA target genes associated with MYB transcription factors were obtained, including miR156, mi319, mi845, mi156 and new miRNA novel_65 and novel_14. miRNA166 regulated HD-ZIP transcription factor in plants and was involved in organ building and meristem formation in Arabidopsis thaliana (Green et al., 2005). WRKY transcription factor has been proved to be widely involved in plant vegetative growth, organ development and material metabolism (Zhou et al., 2011; Wei et al., 2015). In this study, the target genes HD-ZIP, SPL, NAC, MYB, ARFs, bHLH and WRKY transcription factors of miR166a, miR156a, miR164a, miR845c, miR160c, novel_65 and novel_113 were analyzed and predicted. These predicted conserved transcription factors of target genes suggested that they had the same function in the growth and development of “Zijuan” tea tree, especially in the regulation of secondary metabolism.

 

Anthocyanin metabolism is a branch of flavonoid biosynthesis pathway. Anthocyanin accumulation in Zijuan tea tree is a complex process involving some precursors and a series of gene regulation. Therefore, the association between miRNA and their target needs further identification on the accumulation mechanism of anthocyanins in “Zijuan”. 

 

3 Materials and Methods

3.1 Plant materials

The tea tree “Zijuan” in the test base of Tea Research Institute of Academy of Sciences in Yunnan province was selected as materials. The young buds, second leaves, open leaves and mature leaves were collected on October 15th, 2016. The samples were quickly fixed with liquid nitrogen and then put into -80°C for miRNA analysis.

 

3.2 Extraction of total RNA

Total RNA was extracted by using TRIzol (Invitrogen, Carlsbad, CA, USA) kit. The total RNA of young buds, second leaves, open leaves, and mature leaves were extracted, and analyzed whether RNA was contaminated and degradation degree by 1% agarose gel electrophoresis. The concentration of RNA was quantified by Qubit, the purity of RNA was detected by Nanodrop, and the integrity of RNA was accurately detected by Agilent 2100.

 

3.3 Construction and sequencing of small RNA library

The Small RNA Sample Pre Kit was used to construct the library of young buds, second leaves, open leaves and mature leaves, respectively. Using the 3’-end hydroxyl of small RNA and the special structure of 5’-end complete phosphate groups, total RNA as the starting sample was directly added to the 3’-end and 5’-end of small RNA, and then reverse transcription was conducted to synthesize cDNA. After PCR amplification, the target DNA fragment was separated by polyacrylamide gel electrophoresis, and the cDNA library was obtained after gel cutting and recovery. Then, Qubit 2.0 was used for preliminary quantification of the cDNA library, and the library was diluted to 1 ng/uL, and then Agilent 2100 was used to detect the insert size of the cDNA library. The effective concentration of the library was more than 2 nmol/L. After the library inspection was qualified, the gene sequencing was performed by Illumina HiSeq 2500 (completed by Beijing Novogene).

 

3.4 Analysis of sequencing data

The original image data files obtained by illumina HiSeqTM2500 sequencing were transformed into Raw reads through base calling analysis. The Raw reads obtained by sequencing were processed and the low-quality reads with the joint were processed to obtain Clean reads. sRNA with a length range of 18~30 was screened for analysis. sRNA with a length range of 18~30 was positioned on the reference sequence by bowtie (regarded as mapped sRNA). The mapped sRNA was compared with the sequences in the grape miRBase library to obtain the detailed information of the small RNA on each sample matching. If grape ncRNA annotation information was available, the ncRNA sequences of the species were used to annotate the small RNA. If not, the tRNA, rRNA, snoRNA and snRNA in Rfam were selected to annotate the small RNA. The analysis of new miRNA was conducted by combining the software of miREvo (Wen et al., 2012) and mirdeep2 (Friedlander et al., 2012). Familial analysis of the obtained new miRNA and known miRNA was carried out to explore the presence of miRNA family of tea plants in other species.

 

3.5 Target gene prediction and functional annotation

The known miRNA and new miRNA obtained from the analysis were predicted by using the software psRobot. According to the corresponding relation between the miRNA and its target genes, gene ontology analysis was conducted on the website (http://www.geneontology.org/) and pathway significant enrichment analysis was conducted on the website of Kyoto Encyclopedia of Genes and Genomes database, respectively.

 

Authors contributions

SWX, XLF is the main executor of the experimental design and experimental research of this research, complete the experimental data analysis and the writing of the first draft of the thesis; TYP, JHB, SYN, LDH participated in the experiment design, the experiment result analysis; CLB is the project’s architect and director, guiding experimental design, data analysis, paper writing and revision. All authors read and approved the final manuscript.

 

Acknowledgments

This study was co-funded by the National Natural Science Foundation of China (31560220), the State key Laboratory of Tea Biology and Resource Utilization (SKLTOF20150105) and the Yunnan Province Talent training Program (20-15HB105). 

 

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