Combined metabolomic and transcriptomic analysis reveals key candidate genes involved in the regulation of flavonoid accumulation in Anoectochilus roxburghii

Journal Pre-proof Combined metabolomic and transcriptomic analysis reveals key candidate genes involved in the regulation of flavonoid accumulation in Anoectochilus roxburghii Ying Chen, Wangyun Pan, Sha Jin, Sizu Lin

PII:

S1359-5113(19)31088-8

DOI:

https://doi.org/10.1016/j.procbio.2020.01.004

Reference:

PRBI 11889

To appear in:

Process Biochemistry

Received Date:

19 July 2019

Revised Date:

2 January 2020

Accepted Date:

8 January 2020

Please cite this article as: Chen Y, Pan W, Jin S, Lin S, Combined metabolomic and transcriptomic analysis reveals key candidate genes involved in the regulation of flavonoid accumulation in Anoectochilus roxburghii, Process Biochemistry (2020), doi: https://doi.org/10.1016/j.procbio.2020.01.004

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Combined metabolomic and transcriptomic analysis reveals key candidate genes involved in the regulation of flavonoid accumulation in Anoectochilus roxburghii

Ying Chen a,b, Wangyun Pan b, Sha Jin, b Sizu Lina*

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a College of Forestry, Fujian Agriculture and Forestry University, FuZhou, FUJIAN,CHINA; b College of landscape Architecture,Fujian Agriculture and Forestry University, FuZhou, FUJIAN,CHINA

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Graphical abstract

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Highlights

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A. roxburghii was first characterized using high-throughput sequencing technologies and metabolomics. Correlation analysis between metabolites and regulatory genes revealed regulatory genes that were relevant to flavonoid accumulation. This study screened and obtained key genes potentially involved in the accumulation of flavonoids in A. roxburghii. The combined approach in this study provides an in-depth understanding of the molecular regulatory mechanisms underlying flavonoid accumulation in A. roxburghii

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Abstract

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Anoectochilus roxburghii, a species used in Chinese herbal medicine, has unique characteristics. This plant has important medicinal and ornamental value and is distributed primarily in China, including Fujian, Zhejiang, Jiangxi, and Guizhou provinces, and in Taiwan. Flavonoids are involved in leaf pigment formation and are major pharmacodynamic substances. However, the molecular mechanisms that regulate accumulation of flavonoids remain unclear, which has significantly limited their application. To elucidate the molecular mechanisms underlying the regulation of flavonoid accumulation in A. roxburghii, root, stem and leaf samples were collected for constructing transcriptomic and metabolomic datasets using RNA sequencing and liquid chromatography-mass spectrometry (LC-MS) techniques. Sequencing of the transcriptomes of the different organs generated 60.2 Gb of data, which were assembled into 186,865 unigenes. Metabolomic analysis resulted in the identification of 10,690 metabolites. Based on the ESI+ mode, 4,010, 4,008 and 4,013 metabolites were annotated in roots, stems, and leaves, respectively; based on the ESI– mode, 1,530, 1,530, and 1,531 metabolites were annotated, respectively. Differential analyses of the transcriptome and flavonoid metabolism of the different organs revealed the greatest significant differences between roots and leaves, followed by differences between stems and leaves; differences between roots and stems were the smallest. According to analysis of differentially expressed genes (DEGs), the secondary metabolism-related DEGs between roots and stems, between roots and leaves, and between leaves and stems were classified into 16, 21, and 19 secondary metabolic Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, respectively. Among these pathways, flavonoid biosynthesis, flavone and flavonol biosynthesis, and isoflavonoid biosynthesis significantly differed in terms of both gene expression and secondary metabolism. Moreover, the key genes 2

involved in flavonoid accumulation were comprehensively analyzed using metabolic and transcriptomic data, and ten transcription factor-encoding genes and fourteen flavonoid biosynthesis genes were identified. Specifically, four of the ten transcription factor-encoding genes appear to activate CHS, CHI, CYP75A and ANR gene expression. The combined approach used in this study provides an in-depth understanding of the molecular regulatory mechanisms underlying flavonoid accumulation in A. roxburghii and is a powerful tool that can uncover valuable information for plant breeders. Keywords: A. roxburghii, transcriptome, metabolomics, flavonoid metabolic pathway, molecular regulatory mechanism

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Introduction

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Anoectochilus roxburghii (Wall.) Lindl is a perennial evergreen plant species of the Orchidaceae family. A. roxburghii has unique characteristics and value for use in Chinese herbal medicine; indeed, it is often used as an important medicinal and health care product in China and other Asian countries[1]. This plant is known as “the king of medicine” because of its multiple pharmacological effects and is applied for treating liver diseases, cardiovascular diseases, diabetes, cancer, and nephritis[1-4]. Because it is a small, exquisite plant with gracefully shaped leaves and networks of golden yellow veins, A. roxburghii also has high ornamental value and is prized for its indoor foliage[2]. Owing to its important medicinal and ornamental value, A. roxburghii is receiving increased attention. The secondary metabolites of A. roxburghii, especially flavonoids, are involved in leaf pigment formation and are a major source of the pharmacological activities of this species[5, 6]. Therefore, it is important to study the molecular regulatory mechanisms underlying flavonoid accumulation in A. roxburghii. Furthermore, the contents of flavonols, such as quercetin, kaempferol and isorhamnetin, are often used as indicators to measure the quality of A. roxburghii[1, 6]. Additionally, A. roxburghii is rich in flavonoid compounds, including dihydroquercetin, quercetin, kaempferol, and myricetin[2, 6], and differences in the contents of quercetin, kaempferol, myricetin, and total flavonoids can affect pigment formation[6, 7]. In recent years, long-term harvesting of A. roxburghii has led to a scarcity of wild resources of this species, pushing it to the verge of extinction. Although wild A. roxburghii was once the only source, the success of artificial cultivation techniques has revolutionized the procurement of the plant[8, 9]. Recent studies regarding the medicinal value of A. roxburghii have focused primarily on resource collection of varieties, quality evaluation, active ingredient analysis, and pharmacological effects. Conversely, few studies have investigated the 3

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molecular genetics of A. roxburghii, including molecular identification and genetic diversity analyses. Genomic information concerning A. roxburghii is also not available, which has been the primary bottleneck for molecular genetics research in this species[2, 8, 9]. However, there has been marked progress in plant functional genomics in recent years. In general, transcriptomics is widely used to elucidate the biosynthesis pathways and regulatory mechanisms of key drug-related metabolites in different medicinal plant species[10, 11]. Additionally, metabolomic analyses of medicinal plants have greatly facilitated the identification of the metabolic pathways of active pharmaceutical compounds[12]. Hence, integration of transcriptomics and metabolomics has been widely applied to identify the accumulation mechanisms underlying key metabolic pathways, especially the biosynthetic mechanisms of nonmodel plant species[11, 13]. In this study, a transcriptomic database of A. roxburghii organs was constructed using a sequencing platform and bioinformatic analysis. Analysis of the expression profiles of different organs utilizing this database will aid in the identification of functional genes in A. roxburghii, laying the foundation for molecular genetics investigations in this species. To date, the molecular regulatory mechanisms of flavonoid accumulation in A. roxburghii affecting leaf pigment production and drug efficacy remain unclear, yet the metabolic pathways and metabolites in different organs, including roots, stems, and leaves, can be analyzed via metabolomics to identify differences in flavonoid accumulation. The data obtained in this study provide important technical support for future research on the accumulation and metabolic regulation of flavonoids in different organs of A. roxburghii and will assist in the identification of other active drug compounds.

Materials and Methods

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Sample collection Sample materials were collected from Nanjing, Zhangzhou, Fujian Province, China, and identified as A. roxburghii. In March 2016, A. roxburghii was cultivated in Nanjing according to the underwood planting method. The planting site is located at 24’ 26” north, 117’ 36” east, at approximately 300 m above sea level, and is characterized as a southern Fujian hilly landform. The annual average temperature is 20.4°C, and the annual rainfall is 2,001.3 mm. The forest is dominated by evergreen broad-leaved trees. After A. roxburghii was cultivated for 16 weeks, healthy plants with an average height of approximately 15 cm were randomly selected. Seven plants were included in each group, with five replicates. Roots, stems, and leaves were collected, quickly immersed in liquid nitrogen and stored at -80°C for use in transcriptomic and metabolomic analyses. 4

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Library preparation and Illumina HiSeq X Ten sequencing Total RNA was extracted and isolated from the samples using to the cetyltrimethylammonium bromide (CTAB) method. The total RNA concentration was measured, and quality control (QC) was performed using Agilent 2100 (Agilent, USA) and NanoDrop (LabTech, USA) instruments. After the DNA in the qualified RNA samples was digested with DNase I, eukaryotic mRNA was enriched with oligo(dT) magnetic beads, and the mRNA was broken into small fragments in a thermomixer with a disruption reagent at a suitable temperature. The mRNA fragments were subsequently used as templates to synthesize single-stranded cDNA, which was then used in a twostranded synthesis reaction system.The cDNA was purified, and sticky ends were added; an “A” based was added, and 3’ ends were joined.. The cDNA was then selected on the basis of size and polymerase chain reaction (PCR) amplification. The constructed library was qualitatively examined using an Agilent 2100 Bioanalyzer and an ABI StepOnePlus Real-Time PCR System and sequenced with an Illumina HiSeq X Ten instrument[14]. The raw reads obtained were filtered to remove reads of low quality, with

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linker contamination, or containing high numbers of unknown (N) bases. After

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filtering, the clean reads were assembled to obtain unigenes, which were then functionally annotated. The expression levels of unigenes in each sample were calculated, and differentially expressed genes (DEGs) among the different samples were assessed according to specific requirements, after which they were subjected to in-depth cluster and functional enrichment analyses. De novo assembly and annotation The raw sequence data included low-quality reads, reads with joint contamination, and reads with a high content of N bases, all of which needed to be removed prior to the data analysis to ensure the reliability of the results. De novo assembly of clean reads (removal of PCR repeats to improve assembly efficiency) was performed with Trinity followed by Tgicl to cluster the assembled transcripts, remove redundant results and obtain unigenes. Trinity consists of three independent modules (Inchworm, Chrysalis, and Butterfly) that sequentially process numerous reads. Numerous separate de Bruijn plots were constructed from the reads in Trinity, and the full-length splicing subtypes of the transcripts were extracted from each plot[15]. The databases NCBI nucleotide (NT) and nonredundant (NR), Geno Ontology (GO), Clusters of Orthologous Genes (COG), Kyoto Encyclopedia of Genes and Genomes (KEGG), SwissProt, and InterPro were used in this study. Unigenes were annotated using BLAST in NT, NR, COG, KEGG, and SwissProt, with specific comparison conditions, including an e-value×10-5. GO annotations were performed using Blast2GO and the NR database, and InterPro annotations were conducted using InterProScan 5[16, 17]. 5

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Differential expression analysis The clean reads were aligned to unigenes with Bowtie2, and the gene expression levels in each sample were calculated using RNA sequencing by expectation maximization (RSEM). Principal component analysis (PCA) was then performed under the princomp function in R software. DEGs were detected according to the NOIseq and PossionDis methods as needed. The NOIseq method detects DEGs on the basis of the noise distribution principle, whereas the PossionDis method detects DEGs on the basis of the Poisson distribution principle. The DEGs were classified into different biological pathways according to KEGG annotation results and official classification and were simultaneously subjected to enrichment analysis via the phyper function in R. The p-values were then corrected in accordance with the false discovery rate (FDR). Typically, a pathway with an FDR value less than or equal to 0.01 is considered significantly enriched[18]. qRT-PCR assays Fluorescence quantitative PCR analysis was performed with specific primers designed using Primer 5.0. Fluorescence quantitative PCR amplification was performed under the following thermocycling conditions: 50°C for 2 min; 95°C for 10 min; and 40 cycles of 15 s at 95°C, 15 s at 58°C, and 20 s at 72°C. The dissolution curve program used to determine the specificity of the reaction was as follows: 60°C for 15 s followed by 95°C for 15 s. Relative expression of specific genes was calculated according to the 2 -ΔΔCT method, and three biological replicates were analyzed for each gene assayed [19]. Sample preparation for mass spectrum analysis Samples stored at -80°C were incubated at -20°C for 30 min and then at 4°C. Twenty-five milligrams of tissue was weighed and transferred to an Eppendorf (EP) tube, into which 800 μl of frozen dichloromethane:methanol (3:1) solution and two small steel balls were added. The sample was ground for 5 min using a TissueLyser instrument set at a frequency of 60 Hz. The tubes were centrifuged at 25,000 × g at 4°C for 20 min. Two aliquots of 200 µl of supernatant were removed from each tube: one was used for sample analysis and the other for QC, with five biological repeats. Mass spectrum parameter settings and data collection Chromatographic separation was performed using an Acquity UPLC BEH C18 column (100 mm×2.1 mm, 1.7 μm, Waters, UK) with a column temperature of 50°C and a flow rate of 0.4 ml/min. Mobile phase A consisted of water and 0.1% formic acid; mobile phase B consisted of methanol and 0.1% formic acid. Metabolites were eluted using the following gradients: 0-2 min, 100% mobile phase A; 2-11 min, 0-100% mobile phase B; 11-13 min, 100% mobile phase B; and 13-15 min, 0-100% mobile phase A. The loading volume for each sample was 10 μl. 6

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The small molecules eluted from the chromatographic column were acquired via ESI+ and ESI- modes using a Xevo G2-XS QTOF high-resolution tandem mass spectrometer (Waters, UK)[20]. The capillary voltage and cone hole voltage were 2 kV and 40 V, respectively, in ESI+ mode and 2 kV and 40 V, respectively, in ESI- mode. Centroid data acquisition was performed via the time-dependent data scan mode (MSE). The first-stage scan range was 501200 Da, with a scan time of 0.2 s. All parent ions were disrupted with an energy of 20 to 40 eV, and fragment information was collected. During the data acquisition process, real-time quality correction of the LE signals was performed every 3 s. Additionally, to evaluate the stability of the instrumentation during sample collection, a QC sample was collected every 10 samples. The raw mass spectrometry (MS) data were imported into Progenesis QI version 2.2 (hereafter referred to as QI) for peak extraction and acquisition of metabolite-relevant information, including the mass-to-charge ratio, retention time, and ion area[21]. The QI workflow consisted of the following steps: peak alignment, extraction, and identification. Low-mass ions (those that lost more than 50 or 80% in the QC or actual samples, respectively) were subsequently removed from the extracted data, after which the ions with a relative standard deviation (RSD) of >30% in all QC samples were filtered and removed (ions with RSD>30% fluctuated greatly during the experiment and were not included in the subsequent statistical analysis). The final ion statistics were generated using the following modes: ESI+ mode, which generates positive adduct ions (e.g., H+, NH4+, Na+, and K+), and ESI- mode, which generates negative adduct ions (e.g., -H, -Cl, and -OAc), when substances ionize at the ion source. Univariate analysis was performed using t-tests and fold change (FC) analysis. Multivariate analysis methods included PCA and partial least squaresdiscrimination analysis (PLS-DA). Integrative metabolomic and transcriptomic analysis Transcriptomic and metabolomic data for A. roxburghii roots, stems, and leaves with clear differences were used for analysis. According to the metabolite content of different organs in the metabolic dataset and the gene expression values in the transcriptomic dataset, differentially expressed flavonoid biosynthesis genes and differentially abundant flavonoids in each comparison group (three replicates each) were subjected to correlation analysis. On the basis of the above analysis, correlations (Spearman’s correlation coefficient) between flavonoid biosynthesis genes and transcription factorencoding genes were calculated by selecting genes with correlation coefficients greater than or equal to 0.85 and correlation p-values less than or equal to 0.05. The transcription factor-encoding genes related to multiple key enzymeencoding genes were selected for analysis, and a network diagram was constructed.

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Results

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1. Construction and functional analysis of the transcriptome libraries of A. roxburghii roots, stems and leaves The transcriptomes of the root, stem, and leaf libraries of A. roxburghii were sequenced, resulting in 60.2 Gb of data. After raw data were filtered and removed, the average sizes of clean reads of the transcriptomic data were 44.60, 44.58, and 44.61 Mb for roots, stems, and leaves, accounting for 78.87%, 78.68%, and 77.24% of the total reads, respectively. The Q20 (%) clean reads accounted for 97.99%, 97.70%, and 97.69% of the clean reads from the root, stem, and leaf libraries, respectively (Table 1), indicating that high-quality clean reads were obtained from the three libraries. The root, stem, and leaf clean reads were assembled in Trinity, and the transcripts were clustered to remove redundant results and to generate unigenes. In total, 186,865 unigenes were obtained, with a total length of 185,656,989 bp. The average unigene length was 993 bp, with an N50 of 1,882 bp. The GC content of the unigenes was calculated to be 41.38%. In general, if the N50 length of unigenes is greater than or equal to 800 bp, the sequence integrity of the assembly is considered good[11, 13]. As the N50 lengths of all sequenced sample fragments were greater than 800 bp, the assembly integrity was considered to be relatively high (Supplementary Table S1). To determine the function of these A. roxburghii unigenes, the unigene sequences were aligned with sequences in the NR, NT, Swiss-Prot, KEGG, COG, InterPro, and GO databases using BLAST. Homology-based annotation was performed for all A. roxburghii unigenes, yielding 96,854 unigenes with annotated information. The total annotation percentage was 51.83%, among which 87,781 unigenes had homologous sequences in the NR database and 59,736 in the SwissProt database. GO annotations were available for 15,418 unigenes and KEGG annotations for 66,542 unigenes (Supplementary Table S2). Based on GO functional classification (Supplementary Table S3) analysis, 36,841, 33,940, and 32,883 unigenes from the transcriptomic databases of the roots, stems, and leaves, respectively, were classified into 49 functional categories. Regarding the three categories biological processes, cellular components, and molecular functions, there were 13,867, 14,899, and 8,075 root unigenes, respectively, 12,995, 13,502, and 7,443 stem unigenes, respectively, and 12,651, 13,002, and 7,230 leaf unigenes, respectively. Among the categorized unigenes, the GO terms metabolic process, cellular process, and catalytic activity were enriched with additional genes. In particular, metabolic process was enriched with 3,608, 3,339, and 3,249 root, stem, and leaf unigenes, respectively, accounting for 9.79%, 9.84% and 9.88% of the total 8

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unigenes, respectively. With respect to the KEGG pathway annotation, 71,512, 69,737, and 68,916 unigenes from the transcriptomic databases of A. roxburghii roots, stems, and leaves, respectively, were classified into 19 functional categories grouped into five sections: cellular processes, environmental information processes, genetic information processing, metabolism, and organismal systems (Table 2). Among all KEGG subclasses, the metabolic subclass had the most genes and contained the following 11 functional classes: amino acid metabolism, biosynthesis of other secondary metabolites, carbohydrate metabolism, energy metabolism, global and overview maps, biosynthesis and metabolism of sugars, lipid metabolism, cofactor and vitamin metabolism, metabolism of other amino acids, metabolism of terpenoids and ketones, and nucleotide metabolism. Among the unigenes associated with these pathways, 1,661, 1,654, and 1,648 were associated with terpenoid and ketone metabolism in the roots, stems, and leaves, respectively, accounting for 2.32, 2.37, and 2.39% of the total unigenes, respectively.

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2. Analysis of the transcriptomes of different A. roxburghii organs DEGs in A. roxburghii roots, stems, and leaves were identified by comparative analysis to assess expression patterns. During the screening process, an FDR < 0.01 and FC ≥ 2 were used as screening criteria, where FC represents the ratio of gene expression between two samples. DEGs were identified by analyzing the gene expression profiles of roots, stems, and leaves. In total, 1,877 DEGs between roots and stems were identified, among which 351 were unique; 3,679 DEGs between roots and leaves were identified, among which 2,202 were unique. A total of 2,286 DEGs between stems and leaves were identified, among which 1,024 were unique. The data indicated enriched gene expression differences, with the most DEGs being detected between roots and leaves, followed by stems and leaves and roots and stems, as shown in Figure 1a. According to hierarchical clustering analysis of the DEGs in each pairwise comparison of the three organs, the most DEGs with high FCs were detected between roots and leaves, followed by roots and stems and stems and leaves. The expression patterns of the DEGs between roots and leaves were similar to those of the DEGs between roots and stems, but both of these expression patterns were different from those of the DEGs detected between leaves and stems (Figure 1b). These findings suggest that A. roxburghii stems and leaves have similar biological processes. The pathways of enriched DEGs in different organs reflect their primary biological functions. For example, expression of genes associated with photosynthesis and starch and sugar metabolic pathways was upregulated in leaves compared with roots and stems, indicating that leaves play an important 9

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role in providing material and energy for the growth and development of A. roxburghii. In addition, expression of genes involved in phytohormones, ribosomes, pyrimidine metabolism, and pathogen interactions was downregulated in leaves compared with roots. In contrast, expression of genes related to plant hormones, pyrimidine metabolism, and pathogen interaction was highly upregulated in leaves compared to stems. The above results reveal major functional differences among the organs of A. roxburghii and support the validity of the transcriptome sequencing results (Figure 2). Additionally, KEGG enrichment analysis of DEGs showed that many are associated with the primary metabolic pathways of the three organs (Table 3). Pathways associated with secondary metabolism are important in medicinal plants, and in this study, secondary metabolism-related DEGs between roots and stems, roots and leaves, and leaves and stems were classified into 16, 21, and 19 secondary metabolic KEGG pathways, respectively. Among them, the phenylpropanoid biosynthesis, cyanoamino acid metabolism, flavonoid biosynthesis, diterpenoid biosynthesis, flavonoid and flavonol biosynthesis, steroid biosynthesis, and isoflavonoid biosynthesis pathways were enriched in all three organs.

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3. Metabolomic analysis of A. roxburghii roots, stems, and leaves The key secondary metabolic pathways in A. roxburghii uncovered by analyzing the unigenes identified in the transcriptomic analysis were further confirmed by detecting the presence of metabolites. MS data for the roots, stems, and leaves of A. roxburghii were collected using a Xevo G2-XS QTOF instrument (Waters, UK) and statistically analyzed by Progenesis QI (version 2.2) (Waters, USA) as well as the metabolomics metaX package implemented in R. To identify the metabolic pathways that produce metabolites in the roots, stems, and leaves of A. roxburghii and to determine the distribution of metabolites in different metabolic pathways, root, stem, and leaf metabolites were subjected to KEGG enrichment analysis. In this study, the metabolites in the roots, stems, and leaves of A. roxburghii were analyzed to characterize metabolic pathways and to provide a foundation for future analysis of the differences in metabolic pathways among these tissues. In roots, 4,010 metabolites were identified via the ESI+ mode and were enriched in 106 metabolic pathways; 1,530 metabolites were identified via the ESI- mode and were enriched in 98 metabolic pathways. In stems, 4,008 metabolites were identified via the ESI+ mode and were enriched in 106 metabolic pathways; 1,530 metabolites were identified via the ESI- mode and were enriched in 98 metabolic pathways. In leaves, 4,013 metabolites were identified via the ESI + mode and were enriched in 106 metabolic pathways; 1,531 metabolites were identified via the ESI- mode and were enriched in 98 metabolic pathways. Fourteen key metabolic pathways related to the drug efficacy of A. roxburghii 10

were selected for further analysis (Supplementary Table S4). Among them, key secondary metabolic pathways, including those of flavonoid biosynthesis, isoflavone biosynthesis, anthocyanin biosynthesis, flavonoid and flavonol biosynthesis, and sesquiterpene and triterpenoid biosynthesis, differed significantly.

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4. Differential metabolite accumulation in different organs of A. roxburghii To analyze variations in metabolites among the roots, stems and leaves of A. roxburghii, we performed multivariate statistical analysis, and the results of PCA and PLS-DA showed that the metabolic differences between any two groups of the three samples were significant in both ESI+ and ESI- modes (Supplementary Figure 1). We also performed KEGG metabolic pathway enrichment analysis to evaluate the distribution of the differentially accumulated metabolites in the metabolic pathways of roots, stems and leaves. The results showed that between the roots and stems of A. roxburghii, 1,748 differentially accumulated metabolites were classified into 103 KEGG pathways by ESI + analysis and that 681 differentially accumulated metabolites were classified into 76 KEGG pathways by ESI- analysis. Between roots and leaves, 2,034 differentially accumulated metabolites were classified into 101 KEGG pathways by ESI+ analysis, and 866 differentially accumulated metabolites were classified into 92 KEGG pathways by ESI- analysis. Regarding stems and leaves, 1,516 differentially accumulated metabolites were classified into 94 KEGG metabolic pathways by ESI+ analysis; 611 differentially accumulated metabolites were classified into 80 KEGG metabolic pathways by ESI- analysis. Enrichment analysis of the pathways associated with these differentially accumulated metabolites and the subsequent analysis of 14 key different metabolic pathways (Table 4) indicated significant differences in key secondary metabolic pathways, including flavonoid biosynthesis, isoflavone biosynthesis, anthocyanin biosynthesis, flavonoid and flavonol biosynthesis, and sesquiterpene and triterpenoid biosynthesis. Flavonoids are considered to be the major constituents responsible for the drug activity of A. roxburghii. According to the results of KEGG analysis, the differentially accumulated metabolites showed significant enrichment in the various A. roxburghii organs and were associated with the following pathways: flavonoid biosynthesis, isoflavonoid biosynthesis, anthocyanin biosynthesis, and flavonoid and flavonol biosynthesis (Table 2). Therefore, elucidation of the key molecular regulatory mechanisms underlying flavonoid accumulation in different organs is important for understanding the accumulation of these compounds, which are the active components of A. roxburghii. 5. Transcription factor classification and differential expression analysis By regulating the expression level of multiple genes involved in secondary 11

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metabolite synthesis, transcription factors can effectively activate or inhibit entire pathways to regulate the synthesis and accumulation of specific secondary metabolites. Therefore, screening and identifying transcription factors that control secondary metabolism is of crucial importance for understanding the molecular mechanisms underlying the accumulation of metabolites[22, 23]. Thus, we sought to determine whether the DEGs among the roots, stems, and leaves of A. roxburghii encode transcription factors. Additionally, the classification and differential expression of transcription factors were examined, providing data for further analysis of the molecular mechanisms underlying differences in metabolism. The unigenes that encode transcription factors in the roots, stems, and leaves of A. roxburghii were predicted on the basis of the assembly results. Additionally, the predicted transcription factors were classified according to family functions. Previous analyses revealed significant differences in transcription between A. roxburghii roots and leaves and between leaves and stems, suggesting that analysis of transcriptional regulation is a promising approach. Therefore, we compared differences in gene expression of transcription factors between roots and leaves and between leaves and stems. The results showed that 129 DEGs between A. roxburghii roots and leaves could be classified into 27 transcription factor families, including MYB, MYBrelated, bHLH, MADS, G2-like, and GRAS transcription factor families, as shown in Supplementary Table S5. Among these DEGs, expression of 44 and 85 was upregulated and downregulated, respectively, in leaves compared with roots. Notably, the number of upregulated DEGs was significantly lower than that of downregulated DEGs. Sixty-four DEGs between leaves and stems were classified into 21 transcription factor families, including MYB-related family, MYB family, bHLH family, HB family, and TCP families, as shown in Supplementary Table S6. Among these DEGs, expression of 50 and 14 DEGs was upregulated and downregulated, respectively, in stems compared with leaves. As for the comparison above, the number of upregulated DEGs was significantly higher than that of downregulated DEGs.

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6. Analysis of the regulatory networks of flavonoid accumulation To better understand the relationship between metabolites and genes involved in the central flavonoid synthesis pathways in A. roxburghii, a detailed, integrated analysis of the levels of transcripts and metabolites associated with flavonoid biosynthesis pathways was performed, with the goal of more intuitively discerning the relationship between gene expression and metabolite accumulation (Figure 3a, Figure 3b). In the flavonoid biosynthesis pathway, 26 differentially accumulated metabolites were identified among roots, stems and 12

leaves, including desmethylxanthohumol, pinocembrin, prunin, hesperetin, neohesperidin, eriodictyol, dihydrokaempferol, pelargonidin, dihydroquercetin, leucocyanidin, cyanidin, kaempferol, and quercetin, among others. Additionally, 29 DEGs encoding flavonoid metabolism-related enzymes were identified. These genes include chalcone synthase (CHS), chalcone isomerase (CHI), flavonoid 3',5'-hydroxylase (CYP75A), anthocyanidin reductase (ANR), flavonol synthase (FLS), bifunctional dihydroflavonol 4-reductase (DFR), shikimate Ohydroxycinnamoyltransferase (HCT) and caffeoyl-CoA O-methyltransferase (CCOAOMT). The flavonoid metabolism pathway of A. roxburghii was outlined and analyzed according to these identified differentially expressed metabolites and DEGs encoding key enzymes (Figure 3a, Figure 3b). p-Coumaroyl-CoA is

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an important metabolic substrate for the flavonoid metabolic pathway. Two key branches in the pathway produce various flavonoids: one branch involves the synthesis of caffeoyl-CoA, feruloyl-CoA and homoeriodictyol by enzymes such

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as HCT and CCOAOMT; the other involves the production of the metabolite naringenin chalcone by CHS and the conversion of naringenin chalcone to naringenin by CHI, followed by the production of various flavonoids, such as dihydroquercetin, kaempferol, quercetin, leucocyanidin, and myricetin, by enzymes such as CYP75A, ANR and FLS.

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The results of integrated analysis comparing the gene expression patterns and accumulation of intermediate metabolites in the flavonoid biosynthesis pathway showed different accumulation patterns of the metabolites in the flavonoid biosynthesis pathway in the different organs of A. roxburghii. The trend of most of the genes was the same as that of their downstream metabolites. First, 11 metabolites, xanthohumol, pinostrobin, 5deoxyleucopelargonidin, prunin, 8-C-glucosylnaringenin, neohesperidin, dihydrokaempferol, dihydroquercetin, leucocyanidin, cyanidin, and (-)epicatechin, accumulated more in roots than in stems and leaves. Moreover, the gene expression patterns of CHS (CL2206. Contig3_All; CL1293. Contig3_All; CL2206. Contig11_All), CHI (CL16012. Contig2_All), CYP75A (CL14822. Contig1_All; CL14822. Contig2_All), and ANR (CL3891. Contig1_All; CL3891. Contig2_All; CL3891. Contig3_All; CL3891. Contig5_All) tended to be the same as the accumulation patterns of their 11 corresponding downstream metabolites, xanthohumol, pinostrobin, 5-deoxyleucopelargonidin, prunin, 8-Cglucosylnaringenin, neohesperidin, dihydrokaempferol, dihydroquercetin, leucocyanidin, cyanidin, and (-)-epicatechin, and the expression level in roots was greater than that in stems and leaves. Second, 8 metabolites, aureusidin 6-O-glucoside, 2,3,4,4,6-peptahydroxy-chalcone 4-O-glucoside, bracteatin 613

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O-glucoside, kaempferol, quercetin, myricetin, dihydromyricetin and leucodelphinidin, accumulated more in leaves than in roots and stems. Moreover, the gene expression patterns of FLS (CL318. Contig5_All; CL318. Contig6_All; CL8413. Contig3_All) and DFR (CL15569. Contig1_All) displayed the same trend as did the accumulation patterns of their 4 corresponding downstream metabolites: kaempferol, quercetin, myricetin and leucodelphinidin. The expression level in leaves was greater than that in roots and stems. Third, only eriodictyol accumulated more in stems than in roots and leaves. Hence, these fourteen flavonol biosynthesis genes may be the key in the regulation of flavonoid biosynthesis in A. roxburghii (Figure 3a). In general, transcription factors are known to function by binding to the promoters of structural genes. Thus, we speculate that the ten transcription factors RAX3 MYB (CL10099. Contig1_All), MYB36 (CL10857. Contig1_All), MYB36 (CL10857. Contig2_All), MYB (CL12674. Contig4_All), MYB6 (CL13482. Contig1_All), RAX2 MYB (CL6760. Contig2_All), RAX2 MYB (Unigene1094_All), Hv1 MYB (Unigene28668_All), RAX3 MYB (CL10099. Contig4_All) and RAX3 MYB (CL10099. Contig5_All) in the roots of A. roxburghii activate expression of more than three genes, such as CHS, CHI, CYP75A and ANR, by binding to their promoters and inducing accumulation of the 11 metabolites associated with flavonoid biosynthesis described above. Specifically, it is speculated that MYB6 (CL13482. Contig1_All), RAX2 MYB (CL6760. Contig2_All), RAX2 MYB (Unigene1094_All) and MYB36 (CL10857. Contig2_All) activate expression of CHS, CHI, CYP75A and ANR. These transcription factor-encoding genes are therefore candidates identified in this study that might be used in future studies to reveal the regulatory mechanism of flavonoid accumulation in A. roxburghii (Figure 3a) (Table 5).

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7. Quantitative gene expression and analysis In this study, ten genes among the identified candidates were selected for assessing their expression profiles in different organs by qRT-PCR (Figure 4). The results showed that the genes encoding CHS (CL2206. Contig3_All; CL1293. Contig3_All), CHI (CL16012. Contig2_All), CYP75A (CL14822. Contig1All; CL14822. Contig2_All), ANR (CL3891. Contig3_All), MYB36 (CL10857. Contig1_All), and MYB (CL12674. Contig4_All) were expressed more in roots than in leaves and stems. In contrast, genes encoding FLS (CL318. Contig5_All; CL318. Contig6_All) were more highly expressed in leaves. All of these results are consistent with those from the transcriptome sequencing analysis, i.e., the expression patterns of all genes in the different A.

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roxburghii organs quantified by qRT-PCR are consistent with the transcriptome sequencing results.

Discussion

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Transcription factors are involved in many biological processes, and their gene expression is likely related to different traits of different organs; however, flavonoid accumulation is also involved in many biological processes. Therefore, genes related to the different traits of different organs might also be linked to flavonoid accumulation processes[24-26]. In addition, previous studies have demonstrated that the identification of candidate genes involved in critical metabolic pathways relies on the comparison of gene expression among different organs[27-32]. For example, the key genes involved in metabolism in Jerusalem artichoke were identified by comparing the transcriptomes of its roots, stems, leaves, flowers and tubers[27], and critical metabolic genes involved in rotenoid biosynthesis in the medicinal plant Mirabilis himalaica were found by comparing transcriptomic and metabolomic data between roots, stems and leaves[28]. Similarly, genes involved in the biosynthetic pathways of triterpenes and some mono- and sesquiterpenes in Ferula gummosa were analyzed by assessing the transcriptome and metabolome of four organs (roots, flowers, stems, and leaves)[29]. Comparative transcriptomic analysis of the roots, leaves, stems, and flowers of Ferula asafoetida was combined with computational annotation to identify candidate genes with probable roles in terpenoid and coumarin biosynthesis[30]. Moreover, five key enzyme-encoding genes involved in the terpenoid pathway of Dendrobium huoshanense were identified by analyzing the transcriptomes of roots, stems and leaves[31], and 13 genes involved in regulating the synthesis of volatile terpenoids in Chamaemelum nobile were identified by analyzing transcripts in roots, stems, leaves, and flower[32]. Therefore, these studies indicate that the preliminary identification of candidate genes involved in flavonoid accumulation through comparison of different organs and further examination of the function of candidate genes by genetic analysis is a reliable and cost-effective method. In the present study, transcriptome libraries of the roots, stems, and leaves of A. roxburghii were constructed via high-throughput sequencing technology to identify active pharmaceutical compounds and their biosynthesis mechanisms. Root, stem, and leaf metabolites A. roxburghii were identified by liquid chromatography-mass spectrometry (LC-MS), which yielded 60.2 Gb of data. After the sequences were assembled, 186,865 unigenes were identified, and we identified 10,690 metabolites in A. roxburghii using metabolomic techniques. These transcriptomic and metabolomic data provide an important basis for further exploration of metabolic pathways in A. roxburghii. Flavonoids are considered to be one of the major pharmaceutical components of A. 15

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roxburghii. According to the results of KEGG analysis, metabolites differentially accumulate in the different organs of A. roxburghii and are involved in flavonoid, isoflavonoid, anthocyanin, and flavone and flavonol biosynthesis pathways, which were significantly enriched. This is important information needed to elucidate the molecular regulatory mechanisms underlying flavonoid accumulation. The results of metabolomic analysis showed greater levels of flavonoid compounds, including aureusidin 6-O-glucoside, 2,3,4,4,6-peptahydroxychalcone 4-O-glucoside, bracteatin 6-O-glucoside, kaempferol, quercetin, myricetin, dihydromyricetin and leucodelphinidin, in leaves than in roots and stems. Studies have shown that flavonoids such as kaempferol and quercetin have antiallergic, anti-inflammatory, antimalignant cell metastasis, and anticancer activities[33, 34]; myricetin and dihydromyricetin exert antioxidative and anti-inflammatory effects[35-38]. Compared with stems and leaves, the roots of A. roxburghii showed significantly increased levels of metabolites, including xanthohumol, pinostrobin, prunin, 5-deoxyleucopelargonidin, 8-Cglucosylnaringenin, dihydrokaempferol, dihydroquercetin, leucocyanidin, cyanidin, and (-)-epicatechin. Only eriodictyol accumulated more in the stems than in the roots and leaves of A. roxburghii, and the medicinal activities of these compounds are associated with the pharmaceutical value of this plant. In general, A. roxburghii is used as a whole-plant herbal medicine. Nonetheless, analysis of these metabolites in the different organs demonstrated that the metabolites involved in the flavonoid biosynthesis pathway exhibit different accumulation patterns in the different organs of A. roxburghii, suggesting that suitable manipulation of the different organs of this plant can lead to better flavonoid use and increased efficacy. Therefore, our findings contribute to the evaluation of medicinal plants and increase our understanding of the applications of different A. roxburghii tissues.

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A lack of genetic data has delayed the investigation of metabolic pathways in A. roxburghii. Thus, to elucidate the regulation of flavonoid accumulation in the different organs of this species, the DEGs in different organs identified by transcriptome sequencing were subjected to KEGG pathway enrichment analysis. The results showed enrichment of the flavonoid biosynthesis, flavone and flavonol biosynthesis, isoflavonoid biosynthesis, and upstream phenylpropanoid biosynthesis pathways in all three organs. Through correlation analysis between the transcriptomic and metabolomic data, genes encoding CHS and CHI were found to correlate significantly with downstream flavonoids. Indeed, CHS and CHI are important for the synthesis of flavonoids and are closely related to their contents. CHS controls the rate of flavonoid synthesis via an associated reaction with CHI[39, 40], indicating that transcription of the 16

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genes encoding CHS and CHI in different A. roxburghii organs plays an important role in the upstream catalytic process of flavonoid synthesis. CYP75A, which is involved in the biosynthesis of anthocyanin pigments, has a critical function in color formation in plants[41, 42] and is very important in the biosynthesis of bioactive components in medicinal plants[43]. ANR catalyzes a key step in proanthocyanidin biosynthesis and is important for human health and for plant responses to abiotic stress[44]. The expression levels of the genes encoding CHS, CHI, CYP75A and ANR were greater in roots than in stems and leaves, and the combined expression of these four genes may account for the increased accumulation of major flavonoids in the roots of A. roxburghii. Additionally, FLS catalyzes the conversion of dihydroflavonol to flavonol[45], dihydrokaempferol to kaempferol, dihydroquercetin to quercetin, and dihydromyricetin to myricetin. Therefore, FLS expression is related to the type and quantity of flavonols. DFR is the first committed enzyme of anthocyanin and proanthocyanidin biosynthesis in the late flavonoid biosynthesis pathway, contributing to the regulation of flower color[46, 47]. In our study, expression of FLS and DFR was upregulated in leaves compared with stems and roots, leading to a greater accumulation of flavonoids, which included kaempferol, quercetin, myricetin, dihydromyricetin and leucodelphinidin, in A. roxburghii leaves than in stems and roots. Because the basic metabolic pathways of flavonoids in plants have been elucidated, this study focused on the molecular regulatory network of flavonoids in A. roxburghii. By regulating expression of key genes involved in secondary metabolite synthesis pathways, transcription factors can activate or inhibit entire metabolic pathways, modulating the synthesis and accumulation of specific secondary metabolites. The screening and identification of transcription factors that control secondary metabolism are crucial steps in elucidating the molecular mechanisms underlying the accumulation of metabolites [23]. For example, studies have shown that overexpression of the MYB transcription factor encoded by the PAP1 gene induces the production of eight novel anthocyanins[48]. This study focused on comparing differences in transcription factor expression between the roots and leaves and between the leaves and stems of A. roxburghii. According to the results, 129 DEGs between roots and leaves can be classified into 27 transcription factor families, and 64 DEGs between leaves and stems can be classified into 21 transcription factor families. Differential expression of these transcription factors is particularly important for understanding the transcriptional regulatory mechanisms underlying the metabolic differences in A. roxburghii organs. The regulation of metabolites involves complex molecular regulatory networks. Transcription factor regulation is one of the most important mechanisms involved in the regulation of secondary metabolism in plants [13]. Thus, to explore the regulatory mechanism of flavonoid biosynthesis in A. 17

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roxburghii, flavonoids, metabolic enzyme-encoding genes and transcription factor-encoding genes were selected for transcriptomic and metabolomic correlation analyses. Many studies have indicated that flavonoid accumulation is regulated by the MBW complex, which consists of MYB, bHLH, and WD40 transcription factors[49, 50]. We screened target transcription factor-encoding genes that correlated strongly with different flavonoid biosynthesis genes. Ten transcription factor-encoding genes, RAX3 MYB (CL10099. Contig1_All), MYB36 (CL10857. Contig1_All), MYB36 (CL10857. Contig2_All), MYB (CL12674. Contig4_All), MYB6 (CL13482. Contig1_All), RAX2 MYB (CL6760. Contig2_All), RAX2 MYB (Unigene1094_All), Hv1 MYB (Unigene28668_All), RAX3 MYB (CL10099. Contig4_All) and RAX3 MYB (CL10099. Contig5_All), were identified, all of which belong to the MYB transcription factor family. Conversely, no transcription factor-encoding genes in the bHLH and WD40 families correlated strongly with the various flavonoid biosynthesis genes. RAX2 and RAX3 are closely related R2R3 MYB transcription factors[51]. Consistent with the results of our study, many reports have shown that R2R3 MYB transcription factors are involved in the flavonoid biosynthesis pathway in different plant species[52-54]. MYB6, which belongs to the R2R3 MYB transcription factor family, promotes anthocyanin and proanthocyanidin biosynthesis in Populus tomentosa[55]. Recent studies have demonstrated that MYB36 is involved in yellow-green peel color regulation in cucumber and that the expression levels of MYB36 are greater in roots than in leaves and stems[56]. Hv1 MYB was reported to be involved in the stress response in the roots of a drought-tolerant sugarcane cultivar[57]. Specifically, the results of the present study indicate that MYB6 (CL13482. Contig1_All), RAX2 MYB (CL6760. Contig2_All; Unigene1094_All) and MYB36 (CL10857. Contig2_All) activate expression of the CHS, CHI, CYP75A and ANR genes. These results show that these genes may be key transcription factor-encoding genes involved in the flavonoid biosynthesis pathway and that they may be examined in future studies to reveal the regulatory mechanism of flavonoid biosynthesis in A. roxburghii. The combined use of metabolomic and transcriptomic data is therefore an effective analytical method for interpreting the relationship between key genes and metabolites involved in biosynthesis pathways. Using this method, we screened key genes involved in the accumulation of flavonoids, including both metabolic enzyme- and transcription factor-encoding genes. These data will increase our understanding of the molecular regulatory mechanisms underlying the accumulation of active flavonoids in A. roxburghii.

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Conclusion

Author Contributions Statement

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In this study, A. roxburghii was for the first time characterized by highthroughput sequencing and metabolomic techniques. In total, 186,865 unigenes and 10,690 metabolites were identified by transcriptomic and metabolomic approaches. By comprehensively analyzing metabolomic and transcriptomic data, we investigated the regulatory network underlying flavonoid accumulation in different A. roxburghii organs. Correlation analysis between the metabolites and genes revealed regulatory genes related to flavonoids. Moreover, screening resulted in the identification of ten transcription factor-encoding genes and fourteen flavonoid biosynthesis genes that may be involved in flavonoid accumulation. It is speculated that four of the ten transcription factor-encoding genes activate expression of the CHS, CHI, CYP75A and ANR genes. This study provides new insight into the regulatory mechanism of flavonoid accumulation in A. roxburghii. Additionally, we screened and identified key transcription factor-encoding genes potentially involved in the accumulation of flavonoids. Thus, the results of this study provide a deep understanding of the molecular regulatory mechanisms underlying the accumulation of active flavonoids in A. roxburghii.

Funding

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SL obtained the funding and designed and supervised all the experiments. YC performed most of the experiments, analyzed the results, and wrote the article. JS performed some of the experiments. WP critically reviewed the manuscript. All authors read and approved the final manuscript.

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This work was financially supported by grants from the Department of Technology and Science of Fujian Provincial Government (Grant No. 2017J01614), the Fujian Department of Finance Scientific Research Project (Grant No. k81139231) and the Education Department Project of Fujian Province (Grant No. JAT160169). Conflict of Interest Statement The authors declare that they have no competing interests.

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Acknowledgments The authors would like to acknowledge the staff of the State Forestry Ad ministration Engineering Research Center of Chinese Fir for their excellent technical support. Figure Legends

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Figure 1 Statistical analysis of DEGs among different organs of Anoectochilus roxburghii via pairwise comparisons. a: Venn diagram showing the numbers of DEGs identified in pairwise comparisons of the different organs of A. roxburghii. b: Heat map analysis of the DEGs identified by pairwise comparisons of the different organs of A. roxburghii. The analysis was based on genes with |FC| > 2 and FDR ≤ 0.001.

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Figure 2 Major functional differences identified by pairwise comparisons of different organs of Anoectochilus roxburghii shown by the top 13 enriched pathways associated with DEGs: phenylpropanoid biosynthesis, starch and sucrose metabolism, plant hormone signal transduction, ribosome, photosynthesis, pyrimidine metabolism, purine metabolism, plant-pathogen interaction, carbon metabolism, RNA polymerase, endocytosis, RNA transport and RNA degradation pathways.

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Figure 3 Regulatory network of flavonoid biosynthesis in Anoectochilus roxburghii, as shown by different accumulation and expression patterns of metabolites and enzymes related to flavonoid biosynthesis in different organs. a: Differential accumulation of metabolites in different organs. The three red circles in a represent the different organs, with R representing roots, S stems, and L leaves. The color scale indicates the relative content of each metabolite in the different organs. The solid frame arrow represents only one step of a process; the dotted frame arrow represents more than one step of a process. The bold arrows represent the regulation of flavonoid biosynthesis gene expression. b: Differential expression of genes encoding key catalytic enzymes in different tissues. The color scale represents the transformed log10 (fragments per kilobase of transcript per million mapped reads [FPKM]) value of the unigenes in different organs. CHS: chalcone synthase, CHI: chalcone isomerase, CYP75A: flavonoid 3',5'-hydroxylase, ANR: anthocyanidin reductase, FLS: flavonol synthase, DFR: bifunctional dihydroflavonol 4-reductase, HCT: shikimate O-hydroxycinnamoyltransferase, and CCOAOMT: caffeoyl-CoA O20

methyltransferase. Figure 4 qRT-PCR verification of key genes associated with the flavonoid metabolic pathway in Anoectochilus roxburghii. mRNA levels were measured by qRT-PCR. R represents roots, S stems and L leaves. All values are expressed as the mean FC values ± SEMs relative to those of roots (n = 3 per group). ** p < 0.01.

Table Legends

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Table 1 Summary of the sequencing reads obtained from different organs after filtering

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Table 2 Functional distribution of KEGG annotations among the roots, stems and leaves of Anoectochilus roxburghii

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Table 3 Pathways and numbers of DEGs related to secondary metabolites in different organs of Anoectochilus roxburghii

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Table 4 Partial metabolic pathway distribution of differentially accumulated metabolite ions acquired from ESI+ and ESI- modes in the different organs of Anoectochilus roxburghii

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Table 5 Transcription factor-encoding genes correlated with flavonoid-related genes in Anoectochilus roxburghii

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Figure 1 Statistical analysis of DEGs among different organs of Anoectochilus roxburghii via

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pairwise comparisons. a: Venn diagram showing the numbers of DEGs identified in pairwise comparisons of the different organs of A. roxburghii. b: Heat map analysis of the DEGs identified

Jo

ur

na

with |FC| > 2 and FDR ≤ 0.001.

lP

by pairwise comparisons of the different organs of A. roxburghii. The analysis was based on genes

25

26

ro of

-p

re

lP

na

ur

Jo

Figure 2 Major functional differences identified by pairwise comparisons of different organs of Anoectochilus roxburghii shown by the top 13 enriched pathways associated with DEGs: phenylpropanoid biosynthesis, starch and sucrose metabolism, plant hormone signal transduction, ribosome, photosynthesis, pyrimidine metabolism, purine metabolism, plant-pathogen interaction, carbon metabolism, RNA polymerase, endocytosis, RNA transport and RNA degradation pathways.

Jo

ur

na

lP

re

-p

ro of

a

27

ur

na

lP

re

-p

ro of

b

Figure 3 Regulatory network of flavonoid biosynthesis in Anoectochilus roxburghii, as shown

Jo

by different accumulation and expression patterns of metabolites and enzymes related to flavonoid biosynthesis in different organs. a: Differential accumulation of metabolites in different organs. The three red circles in a represent the different organs, with R representing roots, S stems, and L leaves. The color scale indicates the relative content of each metabolite in the different organs. The solid frame arrow represents only one step of a process; the dotted frame arrow represents more than one step of a process. The bold arrows represent the regulation of flavonoid biosynthesis gene expression.b: Differential expression of genes encoding key catalytic enzymes in different tissues. The color scale represents the transformed log10 (fragments per kilobase of transcript per million 28

mapped reads [FPKM]) value of the unigenes in different organs. CHS: chalcone synthase, CHI: chalcone isomerase, CYP75A: flavonoid 3',5'-hydroxylase, ANR: anthocyanidin reductase, FLS: flavonol synthase, DFR: bifunctional dihydroflavonol 4-reductase, HCT: shikimate O-

re

-p

ro of

hydroxycinnamoyltransferase, and CCOAOMT: caffeoyl-CoA O-methyltransferase.

Figure 4 qRT-PCR verification of key genes associated with the flavonoid metabolic pathway in Anoectochilus roxburghii. mRNA levels were measured by qRT-PCR. R represents

lP

roots, S stems and L leaves. All values are expressed as the mean FC values ± SEMs relative to

na

those of roots (n = 3 per group). ** p < 0.01.

Jo

ur

Table1 Summary of the sequencing reads obtained from different organs after

Sample

filtering

Total Raw

Total Clean Total Clean Clean Reads Clean Reads Clean Reads

Reads(Mb Reads(Mb)

Bases(Gb)

Q20(%)

Q30(%)

Ratio(%)

) Root sample 1

54.77

44.39

6.66

98.56

95.80

81.05

Root sample 2

56.68

44.59

6.69

97.85

94.16

78.67

29

58.30

44.82

6.72

97.55

93.41

76.89

Stem sample 1

56.68

44.32

6.65

97.62

93.57

78.20

Stem sample 2

55.06

44.18

6.63

97.75

93.87

80.25

Stem sample 3

58.30

45.23

6.78

97.72

93.82

77.59

Leaf sample 1

56.68

44.30

6.64

97.79

94.00

78.16

Leaf sample 2

58.30

44.81

6.72

97.63

93.58

76.86

Leaf sample 3

58.30

44.71

6.71

97.66

93.67

76.70

ro of

Root sample 3

-p

Table2 Functional distribution of KEGG annotations among the roots, stems and leaves of Anoectochilus roxburghii

Number of unigenes

Function

Category

Stem

Leaf

Cellular Processes Transport and catabolism

5256

5148

5062

Environmental

1313

1291

1256

Signal transduction

2047

2024

2019

Folding，sorting and degradation

5589

5490

5397

Replication and repair

1400

1396

1380

Transcription

4192

4130

4065

Translation

6633

6191

6037

Amino acid metabolism

3177

3087

3086

3369

3302

3280

Carbohydrate metabolism

5333

5158

5114

Energy metabolism

2176

2022

2002

Genetic Information

Jo

ur

Processing

lP

Processing

na

Information

Membrane transport

re

Root

Metabolism

Biosynthesis of other secondary metabolites

30

15777

15428

15330

Glycan biosynthesis and metabolism

1506

1497

1464

Lipid metabolism

3664

3640

3610

Metabolism of cofactors and vitamins

1962

1994

1970

Metabolism of other amino acids

1572

1528

1497

Metabolism of terpenoids and polyketides

1661

1654

1648

Nucleotide metabolism

2148

2091

2048

2710

2639

2615

Environmental adaptation

ro of

Organismal

Global and overview maps

-p

Systems

lP

re

Table 3 Pathways and numbers of DEGs related to secondary metabolites in different organs of Anoectochilus roxburghii

DEGs with pathway annotation

biosynthesis of secondary metabolites pathway

Number(Rate)

Pathway ID

Leaf vs Stem

（5948）

（5948）

Phenylpropanoid biosynthesis

121(6.828%)

62(5.860%)

34(5.191%)

ko00940

Cyanoamino acid metabolism

31(1.749%)

16(1.512%)

12(1.832%)

ko00460

Flavone and flavonol biosynthesis

23(1.298%)

9(0.851%)

7(1.069%)

ko00944

Carotenoid biosynthesis

21(1.185%)

3(0.284%)

3(0.458%)

ko00906

Steroid biosynthesis

21(1.185%)

9(0.851%)

7(1.069%)

ko00100

Flavonoid biosynthesis

18(1.016%)

9(0.851%)

11(1.679%)

ko00941

Diterpenoid biosynthesis

17(0.959%)

3(0.284%)

7(1.069%)

ko00904

Jo

ur

na

Root vs Leaf

31

Root vs Stem （5948)

Tropane，piperidine and pyridine

9(0.851%)

1(0.153%)

ko00960

Terpenoid backbone biosynthesis

8(0.451%)

3(0.284%)

1(0.153%)

ko00900

Isoflavonoid biosynthesis

5(0.282%)

6(0.567%)

5(0.763%)

ko00943

5(0.282%)

5(0.473%)

5(0.763%)

ko00945

Betalain biosynthesis

4(0.226%)

1(0.095%)

4(0.611%)

ko00965

Isoquinoline alkaloid biosynthesis

4(0.226%)

2(0.189%)

3(0.458%)

ko00950

3(0.169%)

1(0.095%)

2(0.305%)

ko00909

2(0.113%)

/

/

ko00261

2(0.113%)

2(0.189%)

/

ko00760

2(0.113%)

ko00130

Stilbenoid，diarylheptanoid and gingerol biosynthesis

Sesquiterpenoid and triterpenoid biosynthesis Monobactam biosynthesis Nicotinate and nicotinamide

-p

metabolism

ro of

10(0.564%)

alkaloid biosynthesis

2(0.305%)

Benzoxazinoid biosynthesis

1(0.056%)

1(0.095%)

/

ko00402

Brassinosteroid biosynthesis

1(0.056%)

/

/

ko00905

lP

quinone biosynthesis

6(0.567%)

re

Ubiquinone and other terpenoid-

1(0.056%)

1(0.095%)

2(0.305%)

ko00232

Monoterpenoid biosynthesis

1(0.056%)

1(0.095%)

/

ko00902

ur

na

Caffeine metabolism

Jo

Table 4 Partial metabolic pathway distribution of differentially accumulated metabolite ions acquired from ESI+ and ESI- modes in the different organs of Anoectochilus roxburghii

Metabolites Number (Rate)

Pathway Root vs Leaf 32

Leaf vs Stem

Root vs Stem

ESI+

ESI–

(2034)

(2034)

24

9

ESI+ ESI– (1516) (611) 13

8

Flavonoid biosynthesis (1.18%)

(0.44%) (0.86%) (1.47%)

20

9

35

4

Isoflavonoid biosynthesis (0.44%) (2.31%) (0.65%)

9

8

Anthocyanin biosynthesis (0.44%)

21

Flavone and flavonol biosynthesis

re

3

21

6

10

38

17

(1.23%)

(0.54%)

na

2

(0.15%) (1.39%) (0.33%)

11

Indole alkaloid biosynthesis

23

(1.03%) (2.11%) (3.76%)

lP

alkaloid biosynthesis

32

-p

(1.43%)

Tropane,piperidine and pyridine

5

(0.39%) (0.20%) (0.82%)

29

25

3

2

(0.29%) (0.66%) (0.33%)

17

8

Jo

ur

Isoquinoline alkaloid biosynthesis

(1.87%)

(0.84%) (1.12%) (1.31%)

15

2

6

1

Pyrimidine metabolism

Sesquiterpenoid and triterpenoid biosynthesis

(0.74%)

(0.10%) (0.40%) (0.16%)

15

2

(0.74%)

8

1

(0.10%) (0.53%) (0.16%)

33

16 (0.91% ） 29 (1.66% ） 9

ESI– (681) 4 (0.59%)

8 (1.17%)

3

ro of

(0.98%)

ESI+ (1748)

(0.51% ） 22

(1.26% ） 20 (1.14% ） 15 (0.86% ） 35 (2.00% ） 12 (0.69% ） 8 (0.46% ）

(0.44%)

7

(1.03%)

0 (0.00%)

6 (0.88%)

7 (1.03%)

0 (0.00%)

3 (0.44%)

8

1

10

6

4

Terpenoid backbone biosynthesis (0.39%)

(0.05%) (0.66%) (0.65%)

43

3

42

(0.15%) (2.77%) (0.33%)

5

gingerol biosynthesis

3

(0.25%)

quinone biosynthesis

7

(1.08%)

9

19

34

-p

3

Diterpenoid biosynthesis

）

4 (0.59%)

4 (0.59%)

(1.14% ） 18

3

(0.15%) (2.24%) (0.49%)

(1.03% ）

3

(0.44%)

14

(2.06%)

lP

re

(1.87%)

(0.51%

20

4

(0.34%) (1.25%) (0.65%)

38

）

2

(0.15%) (0.46%) (0.33%)

22

Ubiquinone and other terpenoid-

7

(0.74%

(0.15%)

ro of

Stilbenoid,diarylheptanoid and

） 13

2

Monoterpenoid biosynthesis (2.11%)

(0.34%

1

na

Table 5 Transcription factor-encoding genes correlated with flavonoid-related genes in Anoectochilus roxburghii transcription factor

flavonol biosynthesis genes

cor

p_value

CHI（CL16012.Contig2_All）

0.89605442

0.00107556

CHS（CL2206.Contig3_All）

0.85540783

0.00327612

RAX3 MYB

ANR（CL3891.Contig1_All）

0.9025494

0.00086378

(CL10099.Contig1_All)

ANR（CL3891.Contig2_All）

0.93190237

0.00025374

ANR（CL3891.Contig3_All）

0.90873731

0.00069085

ANR（CL3891.Contig5_All）

0.88137536

0.00168256

CHS（CL2206.Contig3_All）

0.93567272

0.00020866

Jo

ur

NO.

1

2

MYB36(CL10857.Contig1_All)

34

CYP75A

0.95557837

5.82E-05

0.91740824

0.00049138

ANR（CL3891.Contig1_All）

0.94019167

0.00016243

ANR（CL3891.Contig2_All）

0.91348962

0.00057567

ANR（CL3891.Contig3_All）

0.92537101

0.00034736

ANR（CL3891.Contig5_All）

0.9163275

0.0005137

CHI（CL16012.Contig2_All）

0.85026555

0.00368288

（CL14822.Contig1_All） CYP75A

0.94037782

0.0001607

0.96398553

2.82E-05

-p

0.98155904

2.75E-06

ANR（CL3891.Contig1_All）

0.8685169

0.00238078

re

ro of

（CL14822.Contig2_All）

ANR（CL3891.Contig2_All）

0.86484165

0.00261213

ANR（CL3891.Contig3_All）

0.87272239

0.00213387

CHS（CL2206.Contig3_All）

0.90906082

0.00068254

0.93739176

0.00019011

0.93969865

0.00016709

ANR（CL3891.Contig1_All）

0.86176261

0.00281752

ANR（CL3891.Contig3_All）

0.85402911

0.00338198

CHI（CL16012.Contig2_All）

0.85752078

0.00311834

CHS（CL2206.Contig3_All）

0.97273075

1.07E-05

0.95420249

6.47E-05

CHS（CL2206.Contig3_All） CYP75A

（CL14822.Contig1_All） 3

MYB36(CL10857.Contig2_All)

CYP75A

Jo 5

MYB6(CL13482.Contig1_All)

CYP75A

（CL14822.Contig1_All）

MYB(CL12674.Contig4_All)

ur

4

na

lP

（CL14822.Contig2_All）

CYP75A （CL14822.Contig2_All）

CYP75A （CL14822.Contig1_All）

35

0.871109

0.00222639

ANR（CL3891.Contig1_All）

0.98959481

3.75E-07

ANR（CL3891.Contig2_All）

0.96463806

2.64E-05

ANR(CL3891.Contig3_All)

0.98317172

2.00E-06

ANR(CL3891.Contig5_All)

0.98337408

1.92E-06

CHI(CL16012.Contig2_All)

0.93913391

0.00017253

CHS(CL2206.Contig3_All)

0.97944687

4.02E-06

CYP75A(CL14822.Contig1_All)

0.94782538

0.00010149

RAX2

CYP75A(CL14822.Contig2_All)

0.87678819

0.00191256

MYB(CL6760.Contig2_All)

ANR(CL3891.Contig1_All)

0.99286804

1.00E-07

ANR(CL3891.Contig2_All)

0.9958929

1.46E-08

ANR(CL3891.Contig3_All)

0.99719843

3.82E-09

ANR(CL3891.Contig5_All)

0.96671674

2.14E-05

re

CYP75A

CHI(CL16012.Contig2_All)

0.93840755

0.00017972

CHS(CL2206.Contig3_All)

0.9740331

9.06E-06

CYP75A(CL14822.Contig1_All)

0.98302085

2.07E-06

CYP75A(CL14822.Contig2_All)

0.95444289

6.35E-05

ANR(CL3891.Contig1_All)

0.96324917

3.02E-05

ANR(CL3891.Contig2_All)

0.9681391

1.84E-05

ANR（CL3891.Contig3_All）

0.96799019

1.87E-05

ANR（CL3891.Contig5_All）

0.90820021

0.00070481

CHS（CL2206.Contig3_All）

0.95059023

8.41E-05

CHS（CL2206.Contig11_All）

0.86488534

0.00260929

0.96180085

3.46E-05

0.9557689

5.74E-05

na

RAX2 MYB(Unigene1094_All)

Jo

ur

7

lP

-p

6

ro of

（CL14822.Contig2_All）

8

CYP75A

Hv1(Unigene28668_All)

（CL14822.Contig1_All） CYP75A （CL14822.Contig2_All） 36

ANR（CL3891.Contig1_All）

0.87083204

0.00224255

ANR（CL3891.Contig2_All）

0.85523412

0.00328933

ANR（CL3891.Contig3_All）

0.87932321

0.00178293

CHS（CL2206.Contig3_All）

0.89177157

0.00123346

CHS（CL1293.Contig3_All）

0.87178131

0.00218751

0.88610956

0.00146605

ANR（CL3891.Contig1_All）

0.93237553

0.00024774

ANR（CL3891.Contig2_All）

0.88651514

0.00144845

ANR（CL3891.Contig3_All）

0.91065651

0.00064257

ANR（CL3891.Contig5_All）

0.94829233

9.84E-05

CHS（CL2206.Contig3_All）

0.89400006

0.00114942

0.88247707

0.00163032

re

0.89996926

0.00094405

0.94582318

0.00011555

ANR（CL3891.Contig2_All）

0.89481906

0.00111957

ANR（CL3891.Contig3_All）

0.92262485

0.00039311

ANR（CL3891.Contig5_All）

0.96838392

1.79E-05

CYP75A RAX3 MYB(CL10099.Contig4_All)

-p

ro of

9

（CL14822.Contig1_All）

CHS（CL1293.Contig3_All） CYP75A

RAX3 MYB(CL10099.Contig5_All)

ANR（CL3891.Contig1_All）

Jo

ur

na

lP

10

（CL14822.Contig1_All）

37