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Integrated analysis of the transcriptome and metabolome in young and mature leaves of Ginkgo biloba L.
Industrial Crops & Products 143 (2020) 111906
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Integrated analysis of the transcriptome and metabolome in young and mature leaves of Ginkgo biloba L.
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Jing Guoa,1, Yaqiong Wua,1, Guibin Wanga,*, Tongli Wangb, Fuliang Caoa a b
Nanjing Forestry University, Co-Innovation Center for Sustainable Forestry in Southern China, 159 Longpan Road, Nanjing, 210037, China Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia, Vancouver, V6T 1Z4, Canada
A R T I C LE I N FO
A B S T R A C T
Keywords: Ginkgo biloba L. Metabolites Comparative transcriptome Differentially expressed genes Biosynthesis pathway
Ginkgo (Ginkgo biloba L.) is a unique medicinal tree species that mainly depends on the flavonoids and terpenoids found in its leaves. However, the biosynthesis of these metabolites is controlled and regulated by gene actions which remain poorly understood. To improve our understanding of this process, we produced a transcriptome library from young and mature leaves of ginkgo trees using the Illumina HiSeq X Ten sequencing platformin this study. We obtained a total of 57.5 million clean reads, compared with seven databases and revealed 39,404 annotated unigenes. The key differentially expressed genes (DEGs) in young and mature leaves of ginkgo were explored through GO and KEGG metabolic pathway analysis. A total of 1,256 DEGs, including 561 upregulated and 695 downregulated genes were obtained in the ginkgo transcriptome in the mature leaves compared with that of the young leaves. These genes included 23 bHLH, 9 MYB, 5 WRKY, and 4 bZIP genes that act as regulators in flavonoid and terpenoid biosynthesis. One key gene involved in the diterpenoid pathway was downregulated. A gene in the phenylpropanoid biosynthesis pathway was significantly upregulated and significantly negatively correlated with serine in metabolites. Additionally, a metabolomics analysis detected 283 metabolites, and 38 significantly different metabolites were identified. The combined metabolome and transcriptome approach employed in this study constitutes an effective method for assessing the relationships between the expression of critical genes and metabolites related to biosynthetic pathways. Our study provides new ideas for analyzing biosynthesis at the molecular level and will encourage further development of the ginkgorelated pharmaceutical industry.
1. Introduction Plant secondary metabolites are organic compounds which have great avail for plants to deal with abiotic stresses as well as plant development and growth (Loke et al., 2017). Secondary metabolites are primarily produced in leaves, among which terpenoid derivatives, with a (C5H8)n general formula and oxygen with different saturation degrees, constitute a class of important compounds in herbal medicine (Koksal et al., 2011; Vranova et al., 2012). Similar to terpenoids, flavonoids also play important roles in both plants and animals. Due to the functional diversity of these secondary metabolites, widespread attention have received in many kinds of plants globally (Jan et al., 2010). Ginkgo (Ginkgo biloba L.) is a unique medicinal tree species that mainly depends
on the flavonoids and terpenoids in its leaves, these medicinal ingredients also play important roles in dealing with abiotic stresses and plant development in ginkgo. Ginkgo, which is commonly known as a “living fossil”, originated in China and the only remaining species belonging to Ginkgoales (Li et al., 2018; Zhou and Zheng, 2003). Ginkgoales was widely distributed in the Northern Hemisphere during the Jurassic period of the Mesozoic era, began to decline in the late Cretaceous, and became extinct owing to the fourth ice age, miraculously preserved only in China (Crane, 2013; Tralau, 1967). Ginkgo has been cultivated for its medicinal properties and ornamental value for several thousands of years, and it has become a popular medicinal plant in China and many countries and regions of the world in recent years (Zhang et al., 2015). Ginkgo biloba extract
Abbreviations: bHLH, helix-loop-helix; BP, biological process; bZIP, basic region-leucine zipper; CC, cellular component; DAG, directed acyclic graph; DEGs, differentially expressed genes; GBE, Ginkgo biloba extract; Gb_M, ginkgo mature leaves; Gb_Y, ginkgo young leaves; MF, molecular function; MSD, mass selective detection; MYB, v-Myb avian myeloblastosis viral oncogene homolog; NIST, National Institute of Standards and Technology; OPLS-DA, orthogonal projections to latent structures discriminant analysis; PLS-DA, partial least squares DA; QCs, quality controls; TIC, total ion current; VIP, variable influence on projection ⁎ Corresponding author. E-mail address: [email protected] (G. Wang). 1 Jing Guo and Yaqiong Wu contributed equally to this work. https://doi.org/10.1016/j.indcrop.2019.111906 Received 28 January 2019; Received in revised form 21 September 2019; Accepted 23 October 2019 0926-6690/ © 2019 Elsevier B.V. All rights reserved.
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time point. The freshly collected leaves were immediately stored in liquid nitrogen and then stored at -80 ℃ until RNA and metabolite extraction.
(GBE) has become famous for its conspicuous healing efficacy and pharmacological activities and has been used to cure cardiovascular, cerebrovascular and Alzheimer’s diseases (Lin et al., 2002; Rodriguez et al., 2007). GBEs contain quite complex ingredients, among which terpenoids and flavonoids are the major medical compounds. The flavonoids quercetin, catechin, and kaempferol typically originated from phenylpropanoids (Korkina, 2007; Treutter and Treutter, 2006). Ginkgolides and bilobalides, belonging to a class of terpenoids, are ginkgo secondary metabolites synthesized through the isoprenoid biosynthesis pathway (Kim et al., 2006). Many genes work together to regulate the secondary metabolites biosynthesis which usually been detected using genomic approaches. High-throughput sequencing technologies provide insights into variation at the molecular level, and among these technologies, transcriptome sequencing has been widely used as a routine experimental method. RNA-sequencing (RNA-seq) provides solutions for novel gene discovery and transcript identification. The application of these platforms is making outstanding contributions to the discovery and identification of genes that participate in secondary metabolite biosynthesis (Jayakodi et al., 2015; Wu et al., 2018). A large number of functional genes have been annotated via transcriptome sequencing for many plants, such as Camellia nitidissima (Zhou et al., 2017), Dracaena cambodiana (Zhu et al., 2016), Salvia miltiorrhiza (Xu et al., 2015) and Torreya grandis (Suo et al., 2019). Although many studies have considered differential expression in the ginkgo transcriptome, the molecular regulatory pathways and secondary metabolites in young and mature leaves were not considered. Metabolomics, similar to transcriptomics and proteomics, is an important part of systematic biology that focuses on the quantitative analysis of all metabolites in organisms (Wishart et al., 2007). Metabolomics provides insight into ongoing intracellular activities regulated by metabolites, such as cell signaling and energy transfer (Saito and Matsuda, 2010). Over the past few years, combined metabolic and other omics tools have contributed to the identification of functional genes and elucidation of pathways involved in plant metabolism processes (Kusano et al., 2011; Matsuda et al., 2010). For example, the accumulation of anthocyanin in green and purple Asparagus officinalis L. was analyzed through metabolomics and transcriptomics, and analysis of related genes was also performed (Dong et al., 2019). A broad consensus have been reached that the physiological and biochemical features between young and mature leaves were significantly differed (Xue et al., 2014). However, few studies have attempt to investigate the gene expression and metabolic differences between young and mature leaves in plants. In this study, we systematically investigated the gene expression and metabolic differences between young and mature ginkgo leaves. We performed a transcriptomic analysis of two growth stages of ginkgo (young and mature leaves) to identify differentially expressed genes (DEGs) related to secondary metabolism. Additionally, to further understand the difference in the metabolism between young and mature leaves, metabolome analyses were carried out. Our results will enrich plant databases, help direct the selection of ginkgo secondary substances for extraction, and provide more useful information for the application of ginkgo leaves in the practical pharmaceutical industry.
2.2. Metabolome analysis The metabolome analysis mainly consisted of five steps. First, sample preparation was conducted as previously described (Ge et al., 2018). Briefly, 60 mg accurately weighed sample was transferred to a 1.5-mL tube and abstracted with 360 μL cold methanol and 40 μL 2chloro-l-phenylalanine (0.3 mg mL−1), after placed at −80 °C for 2 min and then grounded at 60 Hz for 2 min. The samples were ultrasonicated at 25 °C for 30 min followed by the addition of 200 μL chloroform and vortexed for 1 min. 400 μL water subsequently added and 1 min vortex was conducted again. After 30 min ultrasonication at 25 °C and 10 min centrifugation (12,000 rpm and 4℃). 300 μL supernatant of the samples were transferred to a glass vial for vacuum-dry at 25 °C. After that, 80 μL 15 mg mL−1 methoxylamine hydrochloride in pyridine was added. The obtained mixture was vortexed for 2 min and incubated at 37 °C for 90 min. 80 μL BSTFA (with 1% TMCS) and 20 μL n-hexane was added, following 2 min vortex at 25 °C and 60 min derivatization at 70 °C. After 30 min cooling, the samples for next step GC–MS analysis were prepared. Second, gas chromatography-mass spectrometry (GC–MS) analysis was performed according to a previously described method (Chen et al., 2017). The Agilent 7890B GC system and the Agilent 5977A mass selective detection (MSD) system were used for this analysis (Agilent Technologies Inc., CA, USA). To obtain data that could be evaluated for repeatability, the quality controls (QCs) were injected regularly throughout the analytical run in this analysis step. Third, the detection of differential metabolites were conducted following the previous procedure (Xia et al., 2018), whose fundamental approach was based on the combined statistically significant threshold of variable influence on projection (VIP) values obtained from an orthogonal projections to latent structures discriminant analysis model and P-values obtained from a two-tailed Student’s t-test of the normalized peak areas from different groups. Fourth, ChemStation (version E.02.02.1431, Agilent, USA) software and ChromaTOF software (version 4.34, LECO, St Joseph, MI) were used for data processing. The metabolites were annotated as described by Ning et al. (2019), using the Fiehn or National Institute of Standards and Technology (NIST) database. Finally, for metabolome analysis, the data were log2 transformed in Excel 2016 (Microsoft, USA) (replacing 0 with 0.000001 before transformation), and the resulting data matrix was then imported into SIMCA software (14.0, Umetrics, Umeå, Sweden). We subsequently performed the metabolome analysis following a previously described method (Xiong et al., 2018). 2.3. RNA-seq and differential expression analysis An RNA-seq analysis was performed according to our previously described approach (Wu et al., 2018). Briefly, the total RNA was extracted, purified, and verified for integrity. The standard-compliant samples (RNA integrity number (RIN) ≥7) were prepared and the following analysis were carried out using a TruSeq Stranded mRNA LT Sample Prep Kit (Illumina, San Diego, CA, USA) for the library construction. An Illumina sequencing platform (HiSeq X 10) was used for library sequencing, and 150 bp paired-end reads were generated. The following de novo assembly and functional annotation were performed. In addition, we added two public databases (eggNOG and Pfam) to the analysis. A differential expression analysis was performed following our previously described approach (Wu et al., 2018). Briefly, the fragments per kilobase per million reads (FPKM) method was used for calculating the unigene expression abundance. DEG identification was performed
2. Materials and methods 2.1. Plant materials Three thirty-year-old ginkgo trees showing uniform growthon the campus of Nanjing Forestry University (32°04′47″N, 118°48′56″E), Nanjing, China, were selected for sampling. The selected trees belonged to the same variety and were free of pests and diseases. Mature fully expanded leaves (second healthy leaf from the base of one-year-old branches) and young leaves (second healthy leaf from the top of oneyear-old branches) were collected separately from each tree at the same 2
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Gb_Y1, Gb_Y2 and Gb_Y3 for young leaves) were prepared and analyzed. In total, 59.2 million raw reads were obtained from the six libraries. The raw reads in this study were deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database (accession number: SRP178917). For further analysis, low-quality sequences were filtered out, and 57.5 million clean reads were obtained, among which the average GC content was 45.39% (Table 1). Trinity software was used to assemble the obtained clean reads, resulting in 39,404 unigenes with an N50 length of 2,165 bp and an average length of 1,365 bp (min length: 304 and max length: 15,729). The length of 9,148 (23.22%) of these unigenes exceeded 2 kb. A total of 39,404 unigenes of ginkgo were annotated by BLAST alignment with seven public databases (NR, Swiss-Prot, KEGG, EuKaryotic Orthologous Groups (KOG), eggNOG, Gene Ontology (GO), and Pfam). In summary, 23,820 unigenes with significant matches to the Nr database were obtained, representing 60.45% of the total—the highest percentage (Fig. 2). According to sequence homology analysis, three categories with 47 GO terms were obtained and 13,044 annotated unigenes were included (Figure S2). Three categories were defined as biological process (BP), cellular component (CC), and molecular function (MF). Within the BP category, genes matched 21 GO terms, and the largest subcategory was ‘cellular process’. Within the CC category, genes matched 15 GO terms, and the largest subcategory was ‘cell’. For the MF category, the two most abundant subcategories were ‘binding’ and ‘catalytic activity’, containing 11 GO terms. Through KOG analysis, these unigenes were classified into 25 groups (Figure S3). Among these groups, R, which considered general function prediction only, was the largest group. The KEGG annotation system was used to determine the synthetic pathway of bioactive components in ginkgo through mapping the assembled unigenes (Figure S4). The ‘signal transduction’ (795 unigenes), ‘translation’ (768), and ‘carbohydrate metabolism’ (748 unigenes) pathways had a markedly higher number of unigenes than the other pathways.
using the R package DESeq (http://bioconductor.org/packages/ release/bioc/html/DESeq.html), and specific parameters were set (Wu et al., 2018). Furthermore, the gene expression patterns were obtained through hierarchical clustering analysis (Wang et al., 2010). 2.4. Quantitative real-time PCR (qRT-PCR) The expression of selected candidate genes was determined by qRTPCR. Gene-specific primers were designed for 14 candidate genes based on the obtained sequences (Table S.1). qRT-PCR was performed on the same instruments used by Xu et al. (2018) according to the manufacturer’s instructions. The PCR was performed at 50 °C and 95 °C for 2 min. The thermal cycling conditions were as follows: 45 cycles at 95 °C for 1 s for denaturation and 60 °C for 30 s for annealing and extension. After the reactions, a dissociation curve analysis was conducted to evaluate the specificity of the primers. The glyceraldehyde-3-phosphate dehydrogenase gene in ginkgo (F: GGTGCCAAAAAGGTGGTCAT and R: CAACAACGAACATGGGAGCAT) was used as a reference. The 2−ΔΔCt method was used to calculate the relative changes in the gene expression levels (Livak and Schmittgen, 2001). All the reactions in all experiments were repeated three times. 2.5. Integrated transcriptome and metabolome analysis Based on the gene expression and the metabolite content data, Pearson correlation tests were used to detect associations between discriminant gene expression and discriminant metabolite content, only the detected associations with a P-value≤0.05 were selected. In addition, DEGs and differential metabolites were mapped to the KEGG pathway database to obtain their common pathway information. 3. Results 3.1. Metabolomics profiling
3.3. GO enrichment and KEGG pathway analysis of DEGs The total ion current (TIC) visual examination of all samples revealed a strong instrumental analysis signal, a large peak capacity and good retention time reproducibility (Figure S.1). The compositions of essential metabolites in the young and mature leaves were determined by GC-MS detection, and this analysis identified 283 compounds in the leaves. Based on the component analysis, organic acids were the most abundant metabolites, accounting for 33.21% of the total. Overall, the number of species and quantity of primary metabolites were greater than those of secondary metabolites, indicating that the ginkgo leaves had vigorous primary metabolic activities. For these metabolites, principal component analysis (PCA) accurately grouped all samples into distinct clusters, which reflected the obvious differences between the young and the mature leaves (Fig. 1.A). The scores obtained from partial least squares DA (PLS-DA) and OPLS-DA are shown in Fig. 1.B and C, respectively. The 200-response sorting tests of the OPLS-DA model were performed as shown in Fig. 1D. In general, if the slope of the R2Y and Q2Y lines is closer to the horizontal line, then the model is more likely to be overfit. For response permutation testing (RPT), Q2 was better than zero. Combined multidimensional analysis and unidimensional analysis were used to screen the differential metabolites between the Gb_M and Gb_Y groups, and 38 differential metabolites were obtained (Fig. 1E). Among which, the amounts of 3 hydroxy-3methylglutaric acid and lactitol were 215 and 84 times greater in Gb_Y group than in Gb_M group. And the glutamic acid contents were the most significant differential metabolite among Gb_M and Gb_Y groups (P = 8.5096E-05) which were only two differential metabolites (together with serine) obtained the higher content in Gb_M group.
The FPKM method was applied to calculate the unigene expression. A total of 1,256 significant DEGs (561 upregulated and 695 downregulated genes) between the Gb_M and Gb_Y libraries were detected through the hierarchical clustering of DEGs (Fig. 3A, B). In the heat map, similar color displayed by DEGs represented the high correlation coefficients. Red indicated the highest expression level, and blue represented the lowest expression level. The expression patterns of the young and mature leaf libraries were compared, revealing that the colors of Gb_Y1, Gb_Y2 and Gb_Y3 were similar and that these libraries were classified into the same cluster. We functionally categorized the differentially expressed unigenes using GO terms. A total of 948 unigenes of DEGs were annotated, including 420 upregulated and 528 downregulated unigenes in the two enriched GO term libraries. The subcategories with the highest enrichment degree was ‘cellular process’, followed by ‘metabolic process’, and then the ‘single-organism process’ were the three, as shown in the GO enrichment histogram. ‘Cell’, ‘cell part’ and ‘organelle’ were the three subcategories with the highest enrichment degree in the CC category. In the MF category, the ‘binding’ and ‘catalytic activity’ subcategories represented the two largest groups with the highest enrichment degrees (Fig. 4A). A directed acyclic graph (DAG) was used to display the GO structure results; a node represented a GO term, and an arrow represented a parent node (Fig. 4B). Similar to the functional categories, three GO DAGs were constructed. Enriched GO terms obtained in the DAG of the BP, CC, and MF category were 2, 1, and 3, respectively. KEGG analysis constitutes a crucial tool that enabled us to thoroughly understand specific metabolites synthesis processes, relevant gene functioning and multiple genes interactions at the transcriptome level. Based on the KEGG enrichment analysis, the most significantly
3.2. Assembly and annotation of the ginkgo transcriptome Six RNA libraries (Gb_M1, Gb_M2 and Gb_M3 for mature leaves and 3
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Fig. 1. Metabolomics profiling of young and mature ginkgo leaves. Plots of principal components analysis (PCA) (A), partial least squares discriminant analysis (PLSDA) (B) and orthogonal projections to latent structures DA (OPLS-DA) (C) scores for the analysis of the metabolites in young and mature leaves. D. The 200-response sorting tests of the OPLS-DA model. E. The Z-score is a value converted from the relative metabolite content and used to measure the relative content of other metabolites at the same level.
4
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significantly upregulated in mature ginkgo leaves.
Table 1 Summary of the sequencing quality of six RNA libraries of ginkgo. Sample
raw_reads
raw_bases
clean_reads
Q30
GC
3.5. Analysis of transcripts encoding transcription factors (TFs)
Gb_M1 Gb_M2 Gb_M3 Gb_Y1 Gb_Y2 Gb_Y3
98818352 97836606 98834462 98263300 99533302 98937068
14822752800 14675490900 14825169300 14739495000 14929995300 14840560200
96051404 94972066 96034390 95500032 96751684 95714746
95.43% 95.44% 95.46% 95.59% 95.46% 95.01%
45.35% 45.33% 45.37% 45.37% 45.44% 45.48%
The identification of TFs was performed through comparing the sequences with known TF gene families. A total of 13,849 putative transcripts similar to the TF genes in ginkgo were identified (Supplementary Figure S6). For example, WRKY, v-Myb avian myeloblastosis viral oncogene homolog (MYB), and basic region-leucine zipper (bZIP) among others were included in TF families. Among these TFs, helix-loop-helix (bHLH) and NAC genes were the most abundant. Members of the MYB and bHLH families regulate flavonoid biosynthesis in ginkgo, and 393 and 2,412 unigenes in these families were identified, respectively. Four unigenes encoding bZIP proteins were differentially expressed between young and mature leaves of ginkgo. Among these DEGs, three were upregulated, and one was downregulated. Six MYBrelated genes exhibited differential expression, and three MYB genes were downregulated. Differential expression was detected for 23 bHLH genes, among which eight were upregulated. The expression of five genes encoding WRKY protein exhibited significant differences. For the ERF family, four genes were upregulated, and seven were downregulated.
enriched pathway was ‘environmental information processing–signal transduction’. This pathway was composed mainly of downregulated DEGs, while the ‘genetic information processing–replication and repair’ and ‘metabolism-nucleotide metabolism’ pathways were composed mainly of upregulated DEGs (Figure S5). 3.4. Identification of unigenes related to phenylpropanoid, flavonoid, and terpenoid biosynthesis We obtained 141 and 51 candidate unigenes in the phenylpropanoid biosynthesis (ko00940) pathway and flavonoid biosynthesis (ko00941) pathway from the ginkgo transcriptome, respectively. In the phenylpropanoid biosynthesis pathway, one unigene was significantly upregulated. Two genes were obtained in the flavone and flavonol biosynthesis (ko00944) pathway. We obtained 59 candidate unigenes in the terpenoid backbone (ko00900) biosynthesis pathway from this transcriptome. In the monoterpenoid (ko00902) and diterpenoid biosynthesis (ko00904) pathways, 10 and 31 unigenes were found, respectively. Four unigenes were identified in the sesquiterpenoid and triterpenoid biosynthesis (ko00909) pathway. Among these unigenes, one key gene involved in the diterpenoid pathway was downregulated. In the phenylpropanoid biosynthesis (ko00940) pathway, one gene was
3.6. qRT-PCR validation of differential expression To verify the reliability of the transcriptome data, the transcriptome levels of 14unigenes was determined by qRT-PCR of three biological replicates (Fig. 5). Thirteen of these unigenes displayed expression patterns that were similar to those obtained in the RNA-Seq analysis. Hence, our completed RNA-seq analysis results showed high reproducibility and reliability and would be useful for further studies those focus on key genes that participate in phenylpropanoid, flavonoid and terpenoid accumulation in young and mature leaves of ginkgo. In
Fig. 2. Annotation information obtained from seven different databases. 5
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Fig. 3. Analysis of differentially expressed genes (DEGs) in ginkgo. A. Comparison of the gene transcription profile of the Gb_M and Gb_Y libraries. The red dots represent the significantly upregulated genes, and the green dots represent the significantly downregulated genes. B. Heat map of DEGs based on hierarchical clustering analysis.
2010). Flavonoids are a group of polyphenolic secondary metabolites with a variety of physiological functions (Buer et al., 2010; Saito et al., 2013). Among the 1,256 DEGs, we identified one key gene in the phenylpropanoid biosynthesis (ko00940) pathway that was significantly upregulated in mature leaves. This gene is likely an important regulatory gene upstream of flavonoid biosynthesis in ginkgo. Extensive research has been performed to study the genes related to terpenoid biosynthesis (Yang et al., 2005). Terpenoids are a family of natural compounds for which the basic units are five-carbon (C5) units; for this reason, the scaffold chemical space available for discovering bioactive molecules is limited (Zhou, 2018). The regulation of gene expression in the plant methylerythritol phosphate (MEP) and mevalonate (MVA) pathways mainly occurs at the transcriptional level (Vranova et al., 2013). Moreover, multiple unigenes may be annotated as the same enzyme, mainly because these unigenes belong to different selective splicing transcripts as well as a specific gene family (Yang et al., 2005); a similar situation exists in ginkgo. The analysis of the ginkgo unigenes revealed that one expressed unigene involved in the diterpenoid pathway was downregulated. This unigene might increase the production of terpenoids via the genetic regulation of secondary metabolic pathways.
addition, the RNA-Seq and qRT-PCR results demonstrated that the data were able to evaluate the up-regulation and down-regulation of gene expression. 3.7. Integrated analysis of the transcriptome and metabolome 38 differential metabolites were mainly distributed in 10 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Highly positive correlations were obtained among most of the differential metabolites, and the only exception was serine which negatively correlated with all other differential metabolites. The correlation analysis between DEGs and differential metabolites also showed that serine was negatively correlated with all DEGs (Fig.6A, Table S.2). The combined metabolomics and transcriptome analysis revealed a significant negative correlation between the TRINITY_DN23031_c0_g1_i4_2 gene and serine (correlation coefficient = -0.93) in metabolites (Fig. 6B). In addition, a TF (MYB-related) and the metabolite mannose-6-phosphate were significantly positively correlated. Four genes (TRINITY_DN30689_c0_g1_i1_2, TRINITY_DN28755_c0_g1_i1_1, TRINITY_DN26876_c0_g1_i1_1, and TRINITY_DN2726_c0_g1_i1_1) were significantly correlated with many metabolites (Fig. 6B), and their regulatory relationships need to be further studied.
4.2. Impact of the developmental stage on TFs 4. Discussion Various biological processes, such as developmental regulation, stress responses, and defense elicitation, involve TFs (Feng et al., 2018; Zhao et al., 2018; Shen et al., 2018). The genes in the flavonoid and terpenoid pathways subjected to regulation at the transcriptional level (Winkel-Shirley, 2001). Many studies have revealed that several TFs participated in the flavonoid and terpenoid biosynthesis pathways in plants, and acted as regulator (Allan et al., 2008; Palapol et al., 2009). The MBW complex are consist of R2R3-MYB and bHLH TFs, with the participation of WD40 proteins. This complex regulates flavonoid biosynthesis pathway in plants through controlling transcription of some key genes (Hichri et al., 2011; Jones, 2004; Kumar et al., 2016). Specifically, specific binding to motifs in the promoter region is the main
4.1. Impact of developmental stage on phenylpropanoid, flavonoids and terpenoids The assemblage of all transcription products in cells under certain physiological conditions or at a specific developmental stage is known as the transcriptome, which provides information about gene expression and regulation related to primary as well as secondary metabolite biosynthesis (Li et al., 2012; Liu et al., 2012). Flavonoids are products of phenylpropanoid metabolism, and p-coumaroyl-CoA acts as a precursor in the flavonoid biosynthesis pathway as well as a bridge connecting primary and secondary metabolism (Loke et al., 2017; Vogt, 6
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Fig. 4. Functional GO and KEGG pathway classification of DEGs. A. The enriched GO terms are shown on the x-axis, and the numbers and percentages of the up- and downregulated DEGs are shown on the y-axis. B. Directed acyclic graphs (DAGs) of three main categories are displayed in thumbnail view. The nodes are colored based on the q-value, and a darker color indcates a higher confidance level. The GO terms are presented at the horizontal node position.
spatial regulation of target gene transcription (Hobert, 2008).
way to realize this regulation (Dare et al., 2008). In this study, 23 bHLH and 9 (3 MYB and 6 MYB-related) TF genes were identified through RNA-seq analysis. The subsequent expression analysis revealed that five MYB-related genes were significantly upregulated relative to young leaves and might play key roles in flavonoid biosynthesis in young and mature leaves. In addition, the AP2, ERF, WRKY, bZIP and bHLH genes are all key genes contributing to the terpenoid secondary metabolic pathways (Cheng et al., 2012; Dai et al., 2009; Ma et al., 2009; Memelink and Gantet, 2007). Fifty-two AP2 TFs were obtained in this study and are worth attention because TcAP2 is related to the isoprene metabolic pathway found in Taxus (Dai et al., 2009). CAD1 is a sesquiterpene cyclase that catalyzes the synthesis of sesquiterpene phytoantitoxin and is a key gene in gossypol synthesis. CAD1-A belongs to the CAD1 family, and its promoter contains a TTGAC sequence (also known as the W-box), which can specifically bind to the GaWRKY1 protein. Therefore, three upregulated and two downregulated WRKY genes identified in this study might help regulate terpenoids. In short, TFs are crucial for gene expression regulation through temporary and
4.3. Integrated analysis of the transcriptome and metabolome Metabolites are the intermediate or final products produced in the plant growth process and strongly regulate plant growth and development. The total number of plant metabolites is estimated to be as high as 200,000, which indicates the diversity of natural metabolites in plants (Pichersky and Lewinsohn, 2011; Schwab, 2003). Plant metabolites are not only of great importance to plants themselves, but also important for human energy acquisition and health (De Luca et al., 2012). Plant metabolites can generally be divided into two categories, primary metabolites and secondary metabolites. The high metabolite diversity in plants makes metabolites an ideal target for studying the regulatory mechanism of metabolic biosynthesis. The total number of detectable metabolites in our study was 283, including 38 differential metabolites. These differential metabolites were the substances with the greatest differences between young and mature ginkgo leaves, with 7
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Fig. 5. Quantitative real-time PCR (qRT-PCR) validation of select genes in young and mature ginkgo leaves. A. Expression patterns of 14 unigenes based on the FPKM level. B. qRT-PCR was performed to obtain the relative expression levels. The error bars represent the standard deviations from three replicates.
phenylalanine tyrosine and tryptophan biosynthesis was obtained by mapping DEGs and differential metabolites to the KEGG pathway database at the same time; TRINITY_DN13541_c0_g1_i1_2 was significantly upregulated by 4.733-fold in ko00950, and metabolite tyramine was upregulated 81.73-fold.
which the references can be provided for future studies. The mechanisms underlying plant metabolite changes and the associated genetic mechanisms have been studied in recent years (Meihls et al., 2013; Quadrana et al., 2014; Weng et al., 2012). Combined transcriptome and metabolome information from Arabidopsis provided insight into the association between expression regulation of important metabolites and genes (Saito et al., 2010). In this study, combined metabolomics and transcriptome analysis uncovered a significant negative correlation between the synthesis of a gene (TRINITY_DN23031_c0_g1_i4_2) in the phenylalanine pathway (00940) and serine in metabolites (P < 0.01) (Fig. 6B), and this finding provides a reference for the correlations between secondary metabolites and genes. The effect of this gene is likely to initiate the metabolism of downstream pathways. Furthermore, among the significantly different metabolites, only serine was downregulated, and the relationship with other gene-regulated metabolites was reversed. In addition, a TF (MYBrelated) and the metabolite mannose-6-phosphate were significantly positively correlated, which led to a positive correlation between the expression of this TF and metabolites. Pathway information for
5. Conclusions In this study, we sequenced and analyzed the transcriptome and metabolome in mature and young ginkgo leaves. A total of 39,404 unigenes were obtained from 57.5 million clean reads. A total of 1,256 DEGs were identified in the ginkgo transcriptome, including 561 upregulated and 695 downregulated genes in mature leaves. The total number of detectable metabolites was 283, including 38 differential metabolites. A total of 141, 51 and 59 unigenes that participated in the biosynthesis of phenylpropanoid, flavonoids and terpenoids were identified through Swiss-Prot database annotation, respectively. A significant negative correlation was detected between one gene in the phenylalanine pathway and serine in metabolites. A combined analysis
Fig. 6. Correlation analysis of the expression of differentially expressed genes (DEGs) and metabolites (TOP20). A: Red indicates a positive correlation, and blue indicates a negative correlation. A deeper color and a larger circle indicate stronger correlations. B: Results of the correlation analysis between DEGs and metabolites. Positive correlations are indicated by a red line, and negative correlations are indicated by a green line. The thickness of the line represents the magnitude of the correlation coefficient. 8
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of the metabolome and transcriptome is an effective method for analyzing the relationship between key genes and metabolites in biosynthetic pathways. This study also offers valuable data and results regarding phenylpropanoid, flavonoid and terpenoid synthsis in ginkgo (young and mature leaves), but the specific mechanism still needs to be further studied and explored.
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Integrated analysis of the transcriptome and metabolome in young and mature leaves of Ginkgo biloba L.
T
Jing Guoa,1, Yaqiong Wua,1, Guibin Wanga,*, Tongli Wangb, Fuliang Caoa a b
Nanjing Forestry University, Co-Innovation Center for Sustainable Forestry in Southern China, 159 Longpan Road, Nanjing, 210037, China Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia, Vancouver, V6T 1Z4, Canada
A R T I C LE I N FO
A B S T R A C T
Keywords: Ginkgo biloba L. Metabolites Comparative transcriptome Differentially expressed genes Biosynthesis pathway
Ginkgo (Ginkgo biloba L.) is a unique medicinal tree species that mainly depends on the flavonoids and terpenoids found in its leaves. However, the biosynthesis of these metabolites is controlled and regulated by gene actions which remain poorly understood. To improve our understanding of this process, we produced a transcriptome library from young and mature leaves of ginkgo trees using the Illumina HiSeq X Ten sequencing platformin this study. We obtained a total of 57.5 million clean reads, compared with seven databases and revealed 39,404 annotated unigenes. The key differentially expressed genes (DEGs) in young and mature leaves of ginkgo were explored through GO and KEGG metabolic pathway analysis. A total of 1,256 DEGs, including 561 upregulated and 695 downregulated genes were obtained in the ginkgo transcriptome in the mature leaves compared with that of the young leaves. These genes included 23 bHLH, 9 MYB, 5 WRKY, and 4 bZIP genes that act as regulators in flavonoid and terpenoid biosynthesis. One key gene involved in the diterpenoid pathway was downregulated. A gene in the phenylpropanoid biosynthesis pathway was significantly upregulated and significantly negatively correlated with serine in metabolites. Additionally, a metabolomics analysis detected 283 metabolites, and 38 significantly different metabolites were identified. The combined metabolome and transcriptome approach employed in this study constitutes an effective method for assessing the relationships between the expression of critical genes and metabolites related to biosynthetic pathways. Our study provides new ideas for analyzing biosynthesis at the molecular level and will encourage further development of the ginkgorelated pharmaceutical industry.
1. Introduction Plant secondary metabolites are organic compounds which have great avail for plants to deal with abiotic stresses as well as plant development and growth (Loke et al., 2017). Secondary metabolites are primarily produced in leaves, among which terpenoid derivatives, with a (C5H8)n general formula and oxygen with different saturation degrees, constitute a class of important compounds in herbal medicine (Koksal et al., 2011; Vranova et al., 2012). Similar to terpenoids, flavonoids also play important roles in both plants and animals. Due to the functional diversity of these secondary metabolites, widespread attention have received in many kinds of plants globally (Jan et al., 2010). Ginkgo (Ginkgo biloba L.) is a unique medicinal tree species that mainly depends
on the flavonoids and terpenoids in its leaves, these medicinal ingredients also play important roles in dealing with abiotic stresses and plant development in ginkgo. Ginkgo, which is commonly known as a “living fossil”, originated in China and the only remaining species belonging to Ginkgoales (Li et al., 2018; Zhou and Zheng, 2003). Ginkgoales was widely distributed in the Northern Hemisphere during the Jurassic period of the Mesozoic era, began to decline in the late Cretaceous, and became extinct owing to the fourth ice age, miraculously preserved only in China (Crane, 2013; Tralau, 1967). Ginkgo has been cultivated for its medicinal properties and ornamental value for several thousands of years, and it has become a popular medicinal plant in China and many countries and regions of the world in recent years (Zhang et al., 2015). Ginkgo biloba extract
Abbreviations: bHLH, helix-loop-helix; BP, biological process; bZIP, basic region-leucine zipper; CC, cellular component; DAG, directed acyclic graph; DEGs, differentially expressed genes; GBE, Ginkgo biloba extract; Gb_M, ginkgo mature leaves; Gb_Y, ginkgo young leaves; MF, molecular function; MSD, mass selective detection; MYB, v-Myb avian myeloblastosis viral oncogene homolog; NIST, National Institute of Standards and Technology; OPLS-DA, orthogonal projections to latent structures discriminant analysis; PLS-DA, partial least squares DA; QCs, quality controls; TIC, total ion current; VIP, variable influence on projection ⁎ Corresponding author. E-mail address: [email protected] (G. Wang). 1 Jing Guo and Yaqiong Wu contributed equally to this work. https://doi.org/10.1016/j.indcrop.2019.111906 Received 28 January 2019; Received in revised form 21 September 2019; Accepted 23 October 2019 0926-6690/ © 2019 Elsevier B.V. All rights reserved.
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time point. The freshly collected leaves were immediately stored in liquid nitrogen and then stored at -80 ℃ until RNA and metabolite extraction.
(GBE) has become famous for its conspicuous healing efficacy and pharmacological activities and has been used to cure cardiovascular, cerebrovascular and Alzheimer’s diseases (Lin et al., 2002; Rodriguez et al., 2007). GBEs contain quite complex ingredients, among which terpenoids and flavonoids are the major medical compounds. The flavonoids quercetin, catechin, and kaempferol typically originated from phenylpropanoids (Korkina, 2007; Treutter and Treutter, 2006). Ginkgolides and bilobalides, belonging to a class of terpenoids, are ginkgo secondary metabolites synthesized through the isoprenoid biosynthesis pathway (Kim et al., 2006). Many genes work together to regulate the secondary metabolites biosynthesis which usually been detected using genomic approaches. High-throughput sequencing technologies provide insights into variation at the molecular level, and among these technologies, transcriptome sequencing has been widely used as a routine experimental method. RNA-sequencing (RNA-seq) provides solutions for novel gene discovery and transcript identification. The application of these platforms is making outstanding contributions to the discovery and identification of genes that participate in secondary metabolite biosynthesis (Jayakodi et al., 2015; Wu et al., 2018). A large number of functional genes have been annotated via transcriptome sequencing for many plants, such as Camellia nitidissima (Zhou et al., 2017), Dracaena cambodiana (Zhu et al., 2016), Salvia miltiorrhiza (Xu et al., 2015) and Torreya grandis (Suo et al., 2019). Although many studies have considered differential expression in the ginkgo transcriptome, the molecular regulatory pathways and secondary metabolites in young and mature leaves were not considered. Metabolomics, similar to transcriptomics and proteomics, is an important part of systematic biology that focuses on the quantitative analysis of all metabolites in organisms (Wishart et al., 2007). Metabolomics provides insight into ongoing intracellular activities regulated by metabolites, such as cell signaling and energy transfer (Saito and Matsuda, 2010). Over the past few years, combined metabolic and other omics tools have contributed to the identification of functional genes and elucidation of pathways involved in plant metabolism processes (Kusano et al., 2011; Matsuda et al., 2010). For example, the accumulation of anthocyanin in green and purple Asparagus officinalis L. was analyzed through metabolomics and transcriptomics, and analysis of related genes was also performed (Dong et al., 2019). A broad consensus have been reached that the physiological and biochemical features between young and mature leaves were significantly differed (Xue et al., 2014). However, few studies have attempt to investigate the gene expression and metabolic differences between young and mature leaves in plants. In this study, we systematically investigated the gene expression and metabolic differences between young and mature ginkgo leaves. We performed a transcriptomic analysis of two growth stages of ginkgo (young and mature leaves) to identify differentially expressed genes (DEGs) related to secondary metabolism. Additionally, to further understand the difference in the metabolism between young and mature leaves, metabolome analyses were carried out. Our results will enrich plant databases, help direct the selection of ginkgo secondary substances for extraction, and provide more useful information for the application of ginkgo leaves in the practical pharmaceutical industry.
2.2. Metabolome analysis The metabolome analysis mainly consisted of five steps. First, sample preparation was conducted as previously described (Ge et al., 2018). Briefly, 60 mg accurately weighed sample was transferred to a 1.5-mL tube and abstracted with 360 μL cold methanol and 40 μL 2chloro-l-phenylalanine (0.3 mg mL−1), after placed at −80 °C for 2 min and then grounded at 60 Hz for 2 min. The samples were ultrasonicated at 25 °C for 30 min followed by the addition of 200 μL chloroform and vortexed for 1 min. 400 μL water subsequently added and 1 min vortex was conducted again. After 30 min ultrasonication at 25 °C and 10 min centrifugation (12,000 rpm and 4℃). 300 μL supernatant of the samples were transferred to a glass vial for vacuum-dry at 25 °C. After that, 80 μL 15 mg mL−1 methoxylamine hydrochloride in pyridine was added. The obtained mixture was vortexed for 2 min and incubated at 37 °C for 90 min. 80 μL BSTFA (with 1% TMCS) and 20 μL n-hexane was added, following 2 min vortex at 25 °C and 60 min derivatization at 70 °C. After 30 min cooling, the samples for next step GC–MS analysis were prepared. Second, gas chromatography-mass spectrometry (GC–MS) analysis was performed according to a previously described method (Chen et al., 2017). The Agilent 7890B GC system and the Agilent 5977A mass selective detection (MSD) system were used for this analysis (Agilent Technologies Inc., CA, USA). To obtain data that could be evaluated for repeatability, the quality controls (QCs) were injected regularly throughout the analytical run in this analysis step. Third, the detection of differential metabolites were conducted following the previous procedure (Xia et al., 2018), whose fundamental approach was based on the combined statistically significant threshold of variable influence on projection (VIP) values obtained from an orthogonal projections to latent structures discriminant analysis model and P-values obtained from a two-tailed Student’s t-test of the normalized peak areas from different groups. Fourth, ChemStation (version E.02.02.1431, Agilent, USA) software and ChromaTOF software (version 4.34, LECO, St Joseph, MI) were used for data processing. The metabolites were annotated as described by Ning et al. (2019), using the Fiehn or National Institute of Standards and Technology (NIST) database. Finally, for metabolome analysis, the data were log2 transformed in Excel 2016 (Microsoft, USA) (replacing 0 with 0.000001 before transformation), and the resulting data matrix was then imported into SIMCA software (14.0, Umetrics, Umeå, Sweden). We subsequently performed the metabolome analysis following a previously described method (Xiong et al., 2018). 2.3. RNA-seq and differential expression analysis An RNA-seq analysis was performed according to our previously described approach (Wu et al., 2018). Briefly, the total RNA was extracted, purified, and verified for integrity. The standard-compliant samples (RNA integrity number (RIN) ≥7) were prepared and the following analysis were carried out using a TruSeq Stranded mRNA LT Sample Prep Kit (Illumina, San Diego, CA, USA) for the library construction. An Illumina sequencing platform (HiSeq X 10) was used for library sequencing, and 150 bp paired-end reads were generated. The following de novo assembly and functional annotation were performed. In addition, we added two public databases (eggNOG and Pfam) to the analysis. A differential expression analysis was performed following our previously described approach (Wu et al., 2018). Briefly, the fragments per kilobase per million reads (FPKM) method was used for calculating the unigene expression abundance. DEG identification was performed
2. Materials and methods 2.1. Plant materials Three thirty-year-old ginkgo trees showing uniform growthon the campus of Nanjing Forestry University (32°04′47″N, 118°48′56″E), Nanjing, China, were selected for sampling. The selected trees belonged to the same variety and were free of pests and diseases. Mature fully expanded leaves (second healthy leaf from the base of one-year-old branches) and young leaves (second healthy leaf from the top of oneyear-old branches) were collected separately from each tree at the same 2
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Gb_Y1, Gb_Y2 and Gb_Y3 for young leaves) were prepared and analyzed. In total, 59.2 million raw reads were obtained from the six libraries. The raw reads in this study were deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database (accession number: SRP178917). For further analysis, low-quality sequences were filtered out, and 57.5 million clean reads were obtained, among which the average GC content was 45.39% (Table 1). Trinity software was used to assemble the obtained clean reads, resulting in 39,404 unigenes with an N50 length of 2,165 bp and an average length of 1,365 bp (min length: 304 and max length: 15,729). The length of 9,148 (23.22%) of these unigenes exceeded 2 kb. A total of 39,404 unigenes of ginkgo were annotated by BLAST alignment with seven public databases (NR, Swiss-Prot, KEGG, EuKaryotic Orthologous Groups (KOG), eggNOG, Gene Ontology (GO), and Pfam). In summary, 23,820 unigenes with significant matches to the Nr database were obtained, representing 60.45% of the total—the highest percentage (Fig. 2). According to sequence homology analysis, three categories with 47 GO terms were obtained and 13,044 annotated unigenes were included (Figure S2). Three categories were defined as biological process (BP), cellular component (CC), and molecular function (MF). Within the BP category, genes matched 21 GO terms, and the largest subcategory was ‘cellular process’. Within the CC category, genes matched 15 GO terms, and the largest subcategory was ‘cell’. For the MF category, the two most abundant subcategories were ‘binding’ and ‘catalytic activity’, containing 11 GO terms. Through KOG analysis, these unigenes were classified into 25 groups (Figure S3). Among these groups, R, which considered general function prediction only, was the largest group. The KEGG annotation system was used to determine the synthetic pathway of bioactive components in ginkgo through mapping the assembled unigenes (Figure S4). The ‘signal transduction’ (795 unigenes), ‘translation’ (768), and ‘carbohydrate metabolism’ (748 unigenes) pathways had a markedly higher number of unigenes than the other pathways.
using the R package DESeq (http://bioconductor.org/packages/ release/bioc/html/DESeq.html), and specific parameters were set (Wu et al., 2018). Furthermore, the gene expression patterns were obtained through hierarchical clustering analysis (Wang et al., 2010). 2.4. Quantitative real-time PCR (qRT-PCR) The expression of selected candidate genes was determined by qRTPCR. Gene-specific primers were designed for 14 candidate genes based on the obtained sequences (Table S.1). qRT-PCR was performed on the same instruments used by Xu et al. (2018) according to the manufacturer’s instructions. The PCR was performed at 50 °C and 95 °C for 2 min. The thermal cycling conditions were as follows: 45 cycles at 95 °C for 1 s for denaturation and 60 °C for 30 s for annealing and extension. After the reactions, a dissociation curve analysis was conducted to evaluate the specificity of the primers. The glyceraldehyde-3-phosphate dehydrogenase gene in ginkgo (F: GGTGCCAAAAAGGTGGTCAT and R: CAACAACGAACATGGGAGCAT) was used as a reference. The 2−ΔΔCt method was used to calculate the relative changes in the gene expression levels (Livak and Schmittgen, 2001). All the reactions in all experiments were repeated three times. 2.5. Integrated transcriptome and metabolome analysis Based on the gene expression and the metabolite content data, Pearson correlation tests were used to detect associations between discriminant gene expression and discriminant metabolite content, only the detected associations with a P-value≤0.05 were selected. In addition, DEGs and differential metabolites were mapped to the KEGG pathway database to obtain their common pathway information. 3. Results 3.1. Metabolomics profiling
3.3. GO enrichment and KEGG pathway analysis of DEGs The total ion current (TIC) visual examination of all samples revealed a strong instrumental analysis signal, a large peak capacity and good retention time reproducibility (Figure S.1). The compositions of essential metabolites in the young and mature leaves were determined by GC-MS detection, and this analysis identified 283 compounds in the leaves. Based on the component analysis, organic acids were the most abundant metabolites, accounting for 33.21% of the total. Overall, the number of species and quantity of primary metabolites were greater than those of secondary metabolites, indicating that the ginkgo leaves had vigorous primary metabolic activities. For these metabolites, principal component analysis (PCA) accurately grouped all samples into distinct clusters, which reflected the obvious differences between the young and the mature leaves (Fig. 1.A). The scores obtained from partial least squares DA (PLS-DA) and OPLS-DA are shown in Fig. 1.B and C, respectively. The 200-response sorting tests of the OPLS-DA model were performed as shown in Fig. 1D. In general, if the slope of the R2Y and Q2Y lines is closer to the horizontal line, then the model is more likely to be overfit. For response permutation testing (RPT), Q2 was better than zero. Combined multidimensional analysis and unidimensional analysis were used to screen the differential metabolites between the Gb_M and Gb_Y groups, and 38 differential metabolites were obtained (Fig. 1E). Among which, the amounts of 3 hydroxy-3methylglutaric acid and lactitol were 215 and 84 times greater in Gb_Y group than in Gb_M group. And the glutamic acid contents were the most significant differential metabolite among Gb_M and Gb_Y groups (P = 8.5096E-05) which were only two differential metabolites (together with serine) obtained the higher content in Gb_M group.
The FPKM method was applied to calculate the unigene expression. A total of 1,256 significant DEGs (561 upregulated and 695 downregulated genes) between the Gb_M and Gb_Y libraries were detected through the hierarchical clustering of DEGs (Fig. 3A, B). In the heat map, similar color displayed by DEGs represented the high correlation coefficients. Red indicated the highest expression level, and blue represented the lowest expression level. The expression patterns of the young and mature leaf libraries were compared, revealing that the colors of Gb_Y1, Gb_Y2 and Gb_Y3 were similar and that these libraries were classified into the same cluster. We functionally categorized the differentially expressed unigenes using GO terms. A total of 948 unigenes of DEGs were annotated, including 420 upregulated and 528 downregulated unigenes in the two enriched GO term libraries. The subcategories with the highest enrichment degree was ‘cellular process’, followed by ‘metabolic process’, and then the ‘single-organism process’ were the three, as shown in the GO enrichment histogram. ‘Cell’, ‘cell part’ and ‘organelle’ were the three subcategories with the highest enrichment degree in the CC category. In the MF category, the ‘binding’ and ‘catalytic activity’ subcategories represented the two largest groups with the highest enrichment degrees (Fig. 4A). A directed acyclic graph (DAG) was used to display the GO structure results; a node represented a GO term, and an arrow represented a parent node (Fig. 4B). Similar to the functional categories, three GO DAGs were constructed. Enriched GO terms obtained in the DAG of the BP, CC, and MF category were 2, 1, and 3, respectively. KEGG analysis constitutes a crucial tool that enabled us to thoroughly understand specific metabolites synthesis processes, relevant gene functioning and multiple genes interactions at the transcriptome level. Based on the KEGG enrichment analysis, the most significantly
3.2. Assembly and annotation of the ginkgo transcriptome Six RNA libraries (Gb_M1, Gb_M2 and Gb_M3 for mature leaves and 3
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Fig. 1. Metabolomics profiling of young and mature ginkgo leaves. Plots of principal components analysis (PCA) (A), partial least squares discriminant analysis (PLSDA) (B) and orthogonal projections to latent structures DA (OPLS-DA) (C) scores for the analysis of the metabolites in young and mature leaves. D. The 200-response sorting tests of the OPLS-DA model. E. The Z-score is a value converted from the relative metabolite content and used to measure the relative content of other metabolites at the same level.
4
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significantly upregulated in mature ginkgo leaves.
Table 1 Summary of the sequencing quality of six RNA libraries of ginkgo. Sample
raw_reads
raw_bases
clean_reads
Q30
GC
3.5. Analysis of transcripts encoding transcription factors (TFs)
Gb_M1 Gb_M2 Gb_M3 Gb_Y1 Gb_Y2 Gb_Y3
98818352 97836606 98834462 98263300 99533302 98937068
14822752800 14675490900 14825169300 14739495000 14929995300 14840560200
96051404 94972066 96034390 95500032 96751684 95714746
95.43% 95.44% 95.46% 95.59% 95.46% 95.01%
45.35% 45.33% 45.37% 45.37% 45.44% 45.48%
The identification of TFs was performed through comparing the sequences with known TF gene families. A total of 13,849 putative transcripts similar to the TF genes in ginkgo were identified (Supplementary Figure S6). For example, WRKY, v-Myb avian myeloblastosis viral oncogene homolog (MYB), and basic region-leucine zipper (bZIP) among others were included in TF families. Among these TFs, helix-loop-helix (bHLH) and NAC genes were the most abundant. Members of the MYB and bHLH families regulate flavonoid biosynthesis in ginkgo, and 393 and 2,412 unigenes in these families were identified, respectively. Four unigenes encoding bZIP proteins were differentially expressed between young and mature leaves of ginkgo. Among these DEGs, three were upregulated, and one was downregulated. Six MYBrelated genes exhibited differential expression, and three MYB genes were downregulated. Differential expression was detected for 23 bHLH genes, among which eight were upregulated. The expression of five genes encoding WRKY protein exhibited significant differences. For the ERF family, four genes were upregulated, and seven were downregulated.
enriched pathway was ‘environmental information processing–signal transduction’. This pathway was composed mainly of downregulated DEGs, while the ‘genetic information processing–replication and repair’ and ‘metabolism-nucleotide metabolism’ pathways were composed mainly of upregulated DEGs (Figure S5). 3.4. Identification of unigenes related to phenylpropanoid, flavonoid, and terpenoid biosynthesis We obtained 141 and 51 candidate unigenes in the phenylpropanoid biosynthesis (ko00940) pathway and flavonoid biosynthesis (ko00941) pathway from the ginkgo transcriptome, respectively. In the phenylpropanoid biosynthesis pathway, one unigene was significantly upregulated. Two genes were obtained in the flavone and flavonol biosynthesis (ko00944) pathway. We obtained 59 candidate unigenes in the terpenoid backbone (ko00900) biosynthesis pathway from this transcriptome. In the monoterpenoid (ko00902) and diterpenoid biosynthesis (ko00904) pathways, 10 and 31 unigenes were found, respectively. Four unigenes were identified in the sesquiterpenoid and triterpenoid biosynthesis (ko00909) pathway. Among these unigenes, one key gene involved in the diterpenoid pathway was downregulated. In the phenylpropanoid biosynthesis (ko00940) pathway, one gene was
3.6. qRT-PCR validation of differential expression To verify the reliability of the transcriptome data, the transcriptome levels of 14unigenes was determined by qRT-PCR of three biological replicates (Fig. 5). Thirteen of these unigenes displayed expression patterns that were similar to those obtained in the RNA-Seq analysis. Hence, our completed RNA-seq analysis results showed high reproducibility and reliability and would be useful for further studies those focus on key genes that participate in phenylpropanoid, flavonoid and terpenoid accumulation in young and mature leaves of ginkgo. In
Fig. 2. Annotation information obtained from seven different databases. 5
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Fig. 3. Analysis of differentially expressed genes (DEGs) in ginkgo. A. Comparison of the gene transcription profile of the Gb_M and Gb_Y libraries. The red dots represent the significantly upregulated genes, and the green dots represent the significantly downregulated genes. B. Heat map of DEGs based on hierarchical clustering analysis.
2010). Flavonoids are a group of polyphenolic secondary metabolites with a variety of physiological functions (Buer et al., 2010; Saito et al., 2013). Among the 1,256 DEGs, we identified one key gene in the phenylpropanoid biosynthesis (ko00940) pathway that was significantly upregulated in mature leaves. This gene is likely an important regulatory gene upstream of flavonoid biosynthesis in ginkgo. Extensive research has been performed to study the genes related to terpenoid biosynthesis (Yang et al., 2005). Terpenoids are a family of natural compounds for which the basic units are five-carbon (C5) units; for this reason, the scaffold chemical space available for discovering bioactive molecules is limited (Zhou, 2018). The regulation of gene expression in the plant methylerythritol phosphate (MEP) and mevalonate (MVA) pathways mainly occurs at the transcriptional level (Vranova et al., 2013). Moreover, multiple unigenes may be annotated as the same enzyme, mainly because these unigenes belong to different selective splicing transcripts as well as a specific gene family (Yang et al., 2005); a similar situation exists in ginkgo. The analysis of the ginkgo unigenes revealed that one expressed unigene involved in the diterpenoid pathway was downregulated. This unigene might increase the production of terpenoids via the genetic regulation of secondary metabolic pathways.
addition, the RNA-Seq and qRT-PCR results demonstrated that the data were able to evaluate the up-regulation and down-regulation of gene expression. 3.7. Integrated analysis of the transcriptome and metabolome 38 differential metabolites were mainly distributed in 10 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Highly positive correlations were obtained among most of the differential metabolites, and the only exception was serine which negatively correlated with all other differential metabolites. The correlation analysis between DEGs and differential metabolites also showed that serine was negatively correlated with all DEGs (Fig.6A, Table S.2). The combined metabolomics and transcriptome analysis revealed a significant negative correlation between the TRINITY_DN23031_c0_g1_i4_2 gene and serine (correlation coefficient = -0.93) in metabolites (Fig. 6B). In addition, a TF (MYB-related) and the metabolite mannose-6-phosphate were significantly positively correlated. Four genes (TRINITY_DN30689_c0_g1_i1_2, TRINITY_DN28755_c0_g1_i1_1, TRINITY_DN26876_c0_g1_i1_1, and TRINITY_DN2726_c0_g1_i1_1) were significantly correlated with many metabolites (Fig. 6B), and their regulatory relationships need to be further studied.
4.2. Impact of the developmental stage on TFs 4. Discussion Various biological processes, such as developmental regulation, stress responses, and defense elicitation, involve TFs (Feng et al., 2018; Zhao et al., 2018; Shen et al., 2018). The genes in the flavonoid and terpenoid pathways subjected to regulation at the transcriptional level (Winkel-Shirley, 2001). Many studies have revealed that several TFs participated in the flavonoid and terpenoid biosynthesis pathways in plants, and acted as regulator (Allan et al., 2008; Palapol et al., 2009). The MBW complex are consist of R2R3-MYB and bHLH TFs, with the participation of WD40 proteins. This complex regulates flavonoid biosynthesis pathway in plants through controlling transcription of some key genes (Hichri et al., 2011; Jones, 2004; Kumar et al., 2016). Specifically, specific binding to motifs in the promoter region is the main
4.1. Impact of developmental stage on phenylpropanoid, flavonoids and terpenoids The assemblage of all transcription products in cells under certain physiological conditions or at a specific developmental stage is known as the transcriptome, which provides information about gene expression and regulation related to primary as well as secondary metabolite biosynthesis (Li et al., 2012; Liu et al., 2012). Flavonoids are products of phenylpropanoid metabolism, and p-coumaroyl-CoA acts as a precursor in the flavonoid biosynthesis pathway as well as a bridge connecting primary and secondary metabolism (Loke et al., 2017; Vogt, 6
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Fig. 4. Functional GO and KEGG pathway classification of DEGs. A. The enriched GO terms are shown on the x-axis, and the numbers and percentages of the up- and downregulated DEGs are shown on the y-axis. B. Directed acyclic graphs (DAGs) of three main categories are displayed in thumbnail view. The nodes are colored based on the q-value, and a darker color indcates a higher confidance level. The GO terms are presented at the horizontal node position.
spatial regulation of target gene transcription (Hobert, 2008).
way to realize this regulation (Dare et al., 2008). In this study, 23 bHLH and 9 (3 MYB and 6 MYB-related) TF genes were identified through RNA-seq analysis. The subsequent expression analysis revealed that five MYB-related genes were significantly upregulated relative to young leaves and might play key roles in flavonoid biosynthesis in young and mature leaves. In addition, the AP2, ERF, WRKY, bZIP and bHLH genes are all key genes contributing to the terpenoid secondary metabolic pathways (Cheng et al., 2012; Dai et al., 2009; Ma et al., 2009; Memelink and Gantet, 2007). Fifty-two AP2 TFs were obtained in this study and are worth attention because TcAP2 is related to the isoprene metabolic pathway found in Taxus (Dai et al., 2009). CAD1 is a sesquiterpene cyclase that catalyzes the synthesis of sesquiterpene phytoantitoxin and is a key gene in gossypol synthesis. CAD1-A belongs to the CAD1 family, and its promoter contains a TTGAC sequence (also known as the W-box), which can specifically bind to the GaWRKY1 protein. Therefore, three upregulated and two downregulated WRKY genes identified in this study might help regulate terpenoids. In short, TFs are crucial for gene expression regulation through temporary and
4.3. Integrated analysis of the transcriptome and metabolome Metabolites are the intermediate or final products produced in the plant growth process and strongly regulate plant growth and development. The total number of plant metabolites is estimated to be as high as 200,000, which indicates the diversity of natural metabolites in plants (Pichersky and Lewinsohn, 2011; Schwab, 2003). Plant metabolites are not only of great importance to plants themselves, but also important for human energy acquisition and health (De Luca et al., 2012). Plant metabolites can generally be divided into two categories, primary metabolites and secondary metabolites. The high metabolite diversity in plants makes metabolites an ideal target for studying the regulatory mechanism of metabolic biosynthesis. The total number of detectable metabolites in our study was 283, including 38 differential metabolites. These differential metabolites were the substances with the greatest differences between young and mature ginkgo leaves, with 7
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Fig. 5. Quantitative real-time PCR (qRT-PCR) validation of select genes in young and mature ginkgo leaves. A. Expression patterns of 14 unigenes based on the FPKM level. B. qRT-PCR was performed to obtain the relative expression levels. The error bars represent the standard deviations from three replicates.
phenylalanine tyrosine and tryptophan biosynthesis was obtained by mapping DEGs and differential metabolites to the KEGG pathway database at the same time; TRINITY_DN13541_c0_g1_i1_2 was significantly upregulated by 4.733-fold in ko00950, and metabolite tyramine was upregulated 81.73-fold.
which the references can be provided for future studies. The mechanisms underlying plant metabolite changes and the associated genetic mechanisms have been studied in recent years (Meihls et al., 2013; Quadrana et al., 2014; Weng et al., 2012). Combined transcriptome and metabolome information from Arabidopsis provided insight into the association between expression regulation of important metabolites and genes (Saito et al., 2010). In this study, combined metabolomics and transcriptome analysis uncovered a significant negative correlation between the synthesis of a gene (TRINITY_DN23031_c0_g1_i4_2) in the phenylalanine pathway (00940) and serine in metabolites (P < 0.01) (Fig. 6B), and this finding provides a reference for the correlations between secondary metabolites and genes. The effect of this gene is likely to initiate the metabolism of downstream pathways. Furthermore, among the significantly different metabolites, only serine was downregulated, and the relationship with other gene-regulated metabolites was reversed. In addition, a TF (MYBrelated) and the metabolite mannose-6-phosphate were significantly positively correlated, which led to a positive correlation between the expression of this TF and metabolites. Pathway information for
5. Conclusions In this study, we sequenced and analyzed the transcriptome and metabolome in mature and young ginkgo leaves. A total of 39,404 unigenes were obtained from 57.5 million clean reads. A total of 1,256 DEGs were identified in the ginkgo transcriptome, including 561 upregulated and 695 downregulated genes in mature leaves. The total number of detectable metabolites was 283, including 38 differential metabolites. A total of 141, 51 and 59 unigenes that participated in the biosynthesis of phenylpropanoid, flavonoids and terpenoids were identified through Swiss-Prot database annotation, respectively. A significant negative correlation was detected between one gene in the phenylalanine pathway and serine in metabolites. A combined analysis
Fig. 6. Correlation analysis of the expression of differentially expressed genes (DEGs) and metabolites (TOP20). A: Red indicates a positive correlation, and blue indicates a negative correlation. A deeper color and a larger circle indicate stronger correlations. B: Results of the correlation analysis between DEGs and metabolites. Positive correlations are indicated by a red line, and negative correlations are indicated by a green line. The thickness of the line represents the magnitude of the correlation coefficient. 8
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of the metabolome and transcriptome is an effective method for analyzing the relationship between key genes and metabolites in biosynthetic pathways. This study also offers valuable data and results regarding phenylpropanoid, flavonoid and terpenoid synthsis in ginkgo (young and mature leaves), but the specific mechanism still needs to be further studied and explored.
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