Deep sequencing identifies miRNAs and their target genes involved in the biosynthesis of terpenoids in Cinnamomum camphora

Industrial Crops & Products 145 (2020) 111853

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Deep sequencing identifies miRNAs and their target genes involved in the biosynthesis of terpenoids in Cinnamomum camphora

T

Caihui Chena,b, Yongda Zhongb, Faxin Yub, Meng Xua,* a b

Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, 159 Longpan Road, Nanjing 210037, China Institute of Biological Resources, Jiangxi Academy of Sciences, Nanchang, Jiangxi, China

A R T I C LE I N FO

A B S T R A C T

Keywords: Cinnamomum camphora MicroRNA Terpenoid biosynthesis Degradome sequencing

Cinnamomum camphora is a tree of considerable economic and industrial importance. Terpenoids derived from the leaf essential oil of C. camphora are considered valuable components in the cosmetic and pharmaceutical industries. However, the possible gene-regulatory roles of microRNAs in C. camphora terpenoid biosynthesis are poorly understood. Here, we profiled the microRNA population and their targets in C. camphora with small RNA sequencing and degradome sequencing technology. Three hundred sixty-four known microRNAs and 117 novel microRNAs were detected in the linalool and borneol chemotype leaves of C. camphora. Fourteen differentially expressed microRNAs were identified between the linalool (F_L) and borneol (L_L) chemotype libraries, including 9 upregulated and 5 downregulated microRNAs in the borneol chemotype relative to the linalool chemotype. Among 12 selected miRNAs, the expression profiles of 11 microRNAs were consistent with the results of small RNA sequencing, and the results were verified by stem-loop quantitative real-time PCR. Based on the degradome sequencing analysis of the combined leaf samples, 363 and 144 target unigenes were predicted for 43 conserved microRNA families and 15 novel microRNAs, respectively. Target identification analyses revealed that some of the microRNAs, including miR4995, miR5021 and miR6300, might be related to the regulation of terpenoid biosynthesis. The analysis of microRNAs and their target unigenes in C. camphora not only contributes to our understanding of the gene-regulatory roles in differential terpenoid accumulation in C. camphora but also provides valuable resources for essential oil-related bioengineering studies.

1. Introduction Cinnamomum camphora belongs to the Lauraceae family and is a valuable timber and aromatic tree with at least 2000 years of cultivation history in China (Babu et al., 2003). Plants of this species contain volatile chemical compounds in all parts, and the essential oil distilled from leaves is used as a food additive and a raw material in the pharmaceutical and cosmetic industries. Plant-derived chemical compounds also exhibit strong anticancer and antimicrobial activities and have potential for modern clinical treatments (Bua et al., 2018; Cannas et al., 2015; Chaves-Lopez et al., 2018). As a traditional Chinese medicine, the essential oil of C. camphora has been widely applied for the treatment of sprains, rheumatism, and abdominal pain (Shi et al., 2016). Many components have been identified in the essential oil of C. camphora, such as camphor, linalool, borneol, safrole and cineole, which are broadly categorized as monoterpenes, sesquiterpenes and diterpenes (Shi et al., 1989). The terpenoids in the leaf essential oil of C. camphora are considered contributors to the beneficial properties of this plant

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(Pragadheesh et al., 2013). Moreover, terpenes play an important role as environmentally friendly compounds for attracting and trapping damaging insects by imitating their pheromones (Breitmaier, 2006). Significant variation in the major compounds has been found among the essential oils distilled from various chemotype leaves of C. camphora; for example, linalool and borneol are the signature constituents of the linalool and borneol types of oils, respectively (Chen et al., 2018; Guo et al., 2017; Shi et al., 1989). The biosynthesis and accumulation of secondary metabolites are strictly regulated by plant biosynthetic mechanisms (Gupta et al., 2017; Wu et al., 2018b). In our previous study, candidate unigenes involved in the biosynthesis of terpenoids in different C. camphora chemotypes were identified (Chen et al., 2018). However, little attention has been paid to the possible regulatory mechanisms of terpenoid biosynthesis in this species. MicroRNAs (miRNAs) are 21–24 nt length endogenous RNAs that play crucial gene-regulatory roles in plant biological processes, including diverse growth and developmental events, reproduction and responses to abiotic and biotic stress (Bartel, 2009; Matthew et al.,

Corresponding author. E-mail addresses: [email protected] (C. Chen), [email protected] (Y. Zhong), [email protected] (F. Yu), [email protected] (M. Xu).

https://doi.org/10.1016/j.indcrop.2019.111853 Received 13 January 2019; Received in revised form 13 May 2019; Accepted 8 October 2019 0926-6690/ © 2019 Elsevier B.V. All rights reserved.

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2.3. Identification of known and novel miRNAs

2006; Sunkar et al., 2012). The regulatory mechanisms of miRNAs that mediate gene silencing are primarily the cleavage of messenger RNA (mRNA) and posttranscriptional inhibition (Jones-Rhoades et al., 2006). Recently, the roles of miRNAs and their targets in the regulation of the biosynthesis and accumulation of flavonoids, terpenoids, alkaloids and other N-containing metabolites in plants have been extensively reported (Bulgakov and Avramenko, 2015; Gupta et al., 2017; Trumbo et al., 2015). In Arabidopsis thaliana, the miRNA156-targeted transcription factor SPL9 (Squamosa promoter binding-like 9) has been shown to participate in the regulation of anthocyanin and flavonoid biosynthesis (Gou et al., 2011). In addition, the miR156-SPL9 module regulates the biosynthesis of sesquiterpenoids in Pogostemon cablin by directly binding to and activating the promoter of the terpene synthase 21 (Yu et al., 2015). The potential roles of miRNAs in regulating the biosynthesis and accumulation of terpenoids in different chemical types of C. camphora warrant further investigation. Cloning was the initial method used to discover miRNAs in plants (Lagos-Quintana et al., 2001). Recently, small RNA (sRNA) sequencing technology and bioinformatics approaches have been widely used for miRNA identification of medicinal plants, e.g., Taxus mairei (Hao et al., 2012), Xanthium strumarium L. (Fan et al., 2015), opium poppy (Boke et al., 2015), and Mentha spp. (Singh et al., 2016). Degradome sequencing combines 5′ rapid amplification of cDNA ends and highthroughput sequencing technology and allows experimental identification of potential regulatory targets of corresponding miRNAs (German et al., 2008). Numerous targets of miRNAs have been revealed by degradome sequencing in Arabidopsis (Addo-Quaye et al., 2008), rice (Li et al., 2010), Populus euphratica (Li et al., 2011) and soybean (Song et al., 2011). In the current study, C. camphora leaf miRNAs and their targets were identified, and the regulatory functions of miRNAmRNA modules were analyzed, with a focus on their roles in terpenoid biosynthesis.

The ACGT101-miRNA program was used to remove poly-N (N percentage > 10%), low-quality reads, 3′ adapter null reads, 5′ adapter contaminated reads and poly-T/A/C/G reads from the raw reads of the sRNA sequences. Then, the screened 18–30 nt clean reads were mapped to the Rfam and RepeatMasker databases to remove the tags originating from non-coding RNAs (ribosomal RNAs, small nuclear RNAs, small nucleolar RNAs and transfer RNAs), repeat sequences and proteincoding genes. Subsequently, the remaining clean reads of sRNA were mapped to plant mature miRNAs in miRBase v22.0 for the identification of known miRNAs (conserved miRNAs). MiREvo (Wen, 2012) and miRDeep2 (Friedlander et al., 2012) software were used to predict novel miRNAs in C. camphora. 2.4. Identification of differentially expressed miRNAs To determine the significance of differences in miRNA expression level, the miRNA expression levels in the F_L1, F_L2, L_L1 and L_L2 sources were normalized by transcripts per million (TPM) (Zhou et al., 2010). The differentially expressed miRNAs between the linalool type (F_L) and borneol type (L_L) were screened by the DESeq2 program based on a negative binomial distribution (Love et al., 2014). The Qvalue was used to adjust the resulting p-values (Storey and Tibshirani, 2003). MiRNAs with a |log2 (fold change) | value of > 1 and an adjusted Q-value of < 0.05 were considered differentially expressed. 2.5. Degradome sequencing and target gene identification RNA samples of approximately 5 μg extracted from the borneol and linalool chemotypes were mixed to construct a degradome library according to previous methods (Ma et al., 2010). Then, an Illumina HiSeq 2500 system was used to perform single-end high-throughput sequencing with a read length of 50 bp. The library construction and sequencing were performed at Hangzhou LC-BIO, China. Raw sequencing data were filtered using Illumina’s Pipeline v1.5 software. The miRNA targets and their cleavage sites were predicted by CleaveLand v3.0 (Addoquaye et al., 2009). Then, the predicted miRNA targets were mapped to the publicly available transcriptome data for C. camphora (https://www.ncbi.nlm.nih.gov/sra/?term=SRP127892). Based on the hyperaccumulation abundance (or signatures) of unigenes in the C. camphora transcriptome data, ‘target plots’ were established for efficient analysis of the potential targets of the miRNAs. To further investigate the miRNAs and their targets, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) analyses were performed for all targets.

2. Materials and methods 2.1. Plant materials In our previous study, a gas chromatography-mass spectrometry (GC-MS) analysis revealed that terpenoids are the main components of C. camphora essential oil and that the most abundant component in extracts of the borneol chemotype was (-)-borneol, whereas that in linalool-chemotype extracts was beta-linalool (Chen et al., 2018). A GCMS diagram of four samples is shown in Supplementary Fig. A.1. In this study, leaf samples from borneol (L_L1 and L_L2) and linalool (F_L1 and F_L2) chemotypes were collected on July 10th from the same trees as previously reported (Chen et al., 2018). The sampled leaves were among the 4th to 8th leaves below the terminal bud of the new branch of each 9-year-old tree. Two biological repeats of each chemotype were collected. All samples were immediately frozen in liquid nitrogen and then stored at −80 °C for RNA isolation.

2.6. Stem-loop qRT-PCR validation Twelve miRNAs were selected, and their expression levels were verified by stem-loop real-time quantitative PCR (qRT-PCR), which is a simple and sensitive method for detecting miRNA expression (Chen et al., 2005). The qRT-PCR assays were performed on an ABI ViiA7 Real-Time PCR platform (ABI, Carlsbad, USA) with FastStart Universal SYBR Green Master Mix (Roche, Indianapolis, IN, USA). Then, 200 ng total RNA was reverse-transcribed with miRNA-specific RT primers (Table A.1). Sample cycle threshold (Ct) values were normalized to the reference gene CcACT (KM086738.1) Ct value. The relative expression levels of miRNA were estimated by the 2−△Ct method with the threshold of the PCR cycle.

2.2. sRNA library construction and sequencing RNA isolation from C. camphora leaves and the determination of RNA integrity and concentration were performed as previously described (Chen et al., 2018). Two biological repeats of each of the borneol and linalool chemotypes were analyzed. The four small RNA libraries of C. camphora were constructed using 3 μg RNA of each sample and NEBNext® Multiplex Small RNA Library Prep Set for Illumina® (NEB, Boston, Massachusetts, USA) according to the method used to construct a tamarisk sRNA library (Wang et al., 2018). Then, an Illumina HiSeq 2500 platform (Illumina, San Diego, CA, USA) was employed to perform single-end (50 bp) high-throughput sequencing of the prepared sRNA libraries following the manufacturer’s procedure.

3. Results 3.1. sRNAs generated from sequencing The total reads generated by high-throughput sequencing ranged 2

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Table 1 sRNA deep sequencing profiles of the two chemotypes of C. camphora. Category

F_L1

F_L2

L_L1

L_L2

Average

Raw reads Clean reads Clean reads of sRNA rRNA, etc.* reads Mapped sRNA reads Conserved miRNA reads Unique conserved miRNA reads Conserved miRNAs Novel miRNA reads Unique novel miRNA reads Novel miRNAs

19879915 19187910 15093411 801490 10790950 154292 984 291 116027 1021 104

19446882 18780105 13162885 921821 10134951 73268 752 212 41688 730 96

19193303 18500459 13539142 1207462 10567529 59932 902 248 103186 1028 104

18546300 17845865 13035215 1591450 11086440 54860 761 237 22316 572 93

19266600 18578585 13707663 1130556 10644968 85588 850 247 70804 838 99

* rRNA/snRNA/snoRNA/tRNA.

Fig. 1. miRNAs identified from leaves of the borneol and linalool chemotypes. (a) Number of miRNAs in the two chemotypes. (b) Length distribution of the identified miRNAs. Four hundred eighty-one miRNAs were found and divided into two classes: known miRNAs and novel miRNAs.

Fig. 2. Conservation of the identified known miRNAs in C. camphora.

(Wu et al., 2014), opium poppy (Boke et al., 2015) and Sedum alfredii (Han et al., 2016). Approximately 8.25% (1,130,556) of the sRNA reads were mapped to noncoding RNAs (tRNAs, snoRNAs, snRNAs and rRNAs) deposited in the Pfam database. More than 10 million reads (77.66% on average across the four libraries) were mapped to the C. camphora transcriptome. An average of 85,588 mapped reads per library were identified as known miRNAs, and 70,840 mapped reads were novel miRNAs. Detailed information for the four libraries is provided in Table 1.

from 18.5 to 19.8 million in the four C. camphora libraries (F_L1, F_L2, L_L1 and L_L2). The sequencing output has been submitted to the Sequence Read Archive of the National Center for Biotechnology Information under accession number PRJNA509985. After the poly-N (N percentage > 10%), low-quality, adapter and poly-T/A/C/G reads were removed, the number of sRNA tags with a length of 18–30 nt varied from 13.0 to 15.1 million. As shown in the figure of the length distribution of sRNAs, the most abundant sequences were 24 nt sRNA tags (Supplementary Fig. A.2), which is similar to the results from pear 3

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Table 2 Differential expression of miRNAs between different chemotypes of C. camphora. miRNA

L read count

F read count

Log2 fold changea

novel_116 osa-miR166h-5p novel_134 csi-miR396a-3p osa-miR166d-5p smo-miR396 osa-miR166b-5p novel_20 ath-miR399d novel_99 gma-miR4995 novel_4 novel_26 novel_151

2239.28 74.89 5.12 20.75 608.14 80.38 24.46 235.18 12.52 0.82 69.80 5211.85 35.52 107.44

475.67 7.88 34.00 102.86 201.67 342.68 3.65 62.32 0.56 11.25 10.97 696.96 125.69 6.48

1.7725 2.0182 −1.681 −1.5114 1.2605 −1.4294 1.5118 1.3293 1.3698 −1.3816 1.3904 1.3606 −1.2318 1.2676

Down/Up Up Up Down Down Up Down Up Up Up Down Up Up Down Up

Degradomeb N N N N N Y N N N N Y N N N

a. miRNAs with |log2 (fold change) | > 1 were considered significantly upregulated, and miRNAs with |log2 (fold change) | ≤1 were considered significantly downregulated. b. Degradome detection results of the target unigenes of miRNAs: Y and N indicate that the target genes were in and not in the degradome, respectively.

Fig. 3. Stem-loop qRT-PCR validation of miRNAs in C. camphora. The relative expression obtained by small RNA sequencing is shown by gray bars (right y-axis), and the black bars represent the level of expression of miRNAs calculated by qRT-PCR (left y-axis). Error bars represent the standard errors of three technical replicates.

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Table 3 Targets of known miRNAs identified in C. camphora by degradome analysis. MiRNA family

Target transcripts

Category

Alignment range

Cleavage site

Transcript function annotation

miR156

Cluster-13185.23477 Cluster-13185.58157 Cluster-13185.59080 Cluster-13185.80758 Cluster-13185.83476 Cluster-13185.99006 Cluster-13185.99009 Cluster-14727.0 Cluster-13185.10758 Cluster-13185.97251 Cluster-13185.30936 Cluster-13185.62079 Cluster-13185.60929 Cluster-13185.23946 Cluster-13185.59597 Cluster-34195.0 Cluster-13185.54742 Cluster-13185.62203 Cluster-13185.6542 Cluster-13185.97248 Cluster13185.102045 Cluster-13185.58214 Cluster-13185.77966 Cluster-13185.45561 Cluster-13185.6904 Cluster-13185.6905 Cluster-13185.25274 Cluster-13185.25277 Cluster-13185.74253 Cluster-13185.83217 Cluster-13185.52314 Cluster-13185.55964 Cluster-13185.59354 Cluster-13185.54726 Cluster-13185.56661 Cluster-13185.57005 Cluster-13185.57368 Cluster-13185.78780 Cluster-13185.19444 Cluster-13185.45720 Cluster-13185.53821 Cluster-13185.56518 Cluster13185.103436 Cluster-13185.51888 Cluster-13185.69132 Cluster-13185.77197 Cluster-13185.78353 Cluster13185.101067 Cluster-13185.25456 Cluster-13185.61061 Cluster-13185.59268 Cluster-13185.8 Cluster-13185.81858 Cluster-13854.0 Cluster-33739.1 Cluster-36567.0 Cluster-13185.19817 Cluster-13185.48258 Cluster-13185.39388 Cluster-13185.43171 Cluster-13185.62163 Cluster-13185.60652 Cluster-13185.65064 Cluster-13185.28814 Cluster-13185.61800 Cluster-13185.71728 Cluster-13185.37642 Cluster-13185.37643 Cluster-13185.62027 Cluster-13185.50303 Cluster-13185.44001 Cluster-3060.0 Cluster-13185.59358 Cluster-13185.65744

2 2 0 2 0 0 0 2 0 0 2 2 2 2 2 0 2 2 0 0 0 0 0 2 0 0 0 0 0 0 2 2 2 2 2 2 0 2 2 2 2 2 2 2 2 2 0 2 2 2 2 0 2 0 0 2 2 2 2 2 2 2 2 2 0 2 2 2 2 2 2 0 2 2

1115-1134 973-990 1308-1327 3560-3578 2467-2486 1181-1200 2482-2501 1090-1109 1295-1312 1397-1414 501-520 719-738 194-214 222-241 345-365 5-25 1766-1786 3386-3405 621-641 1542-1562 1526-1546 2579-2599 2152-2172 591-611 1081-1101 492-512 325-344 364-383 1237-1256 1215-1234 877-897 531-551 877-897 3081-3101 4168-4188 4096-4116 638-658 942-962 1106-1124 702-722 1338-1358 1105-1125 539-558 504-522 1846-1866 501-519 2205-2223 1706-1726 1624-1644 2528-2547 784-804 442-463 880-900 657-678 754-775 636-655 685-705 429-448 155-175 831-851 871-891 1269-1289 222-242 47-68 262-281 239-258 1373-1394 1513-1534 289-310 311-331 570-590 121-142 1328-1349 1317-1338

1125 982 1318 3569 2477 1191 2492 1100 1303 1405 511 729 205 232 356 16 1777 3397 632 1553 1537 2590 2163 602 1092 503 335 374 1247 1225 888 542 888 3092 4179 4107 649 953 1115 713 1349 1116 549 513 1857 510 2215 1717 1635 2538 795 453 891 668 765 647 696 439 166 842 882 1279 233 59 272 250 1384 1524 300 322 581 133 1340 1329

SBP domain protein C2C2-GATA SBP domain protein extracellular region SBP domain protein SBP domain protein SBP domain protein SBP domain protein MYB transcription factor MYB transcription factor SBP domain protein proton transport sorting-associated protein WRKY transcription factor 3-ketoacyl-CoA synthase plant-pathogen interaction TCP family transcription factor protein targeting Golgi hypothetical protein MYB transcription factor auxin response factor auxin response factor auxin response factor UDP-glucuronic acid decarboxylase UDP-glucuronic acid decarboxylase UDP-glucuronic acid decarboxylase regulation of transcription regulation of transcription HB transcription factor HB transcription factor drug transmembrane transport transmembrane transport drug transmembrane transport DNA-directed RNA polymerase auxin response factor auxin response factor translation initiation factor 2C translation initiation factor 2C pto-interacting protein DELLA protein GAI DELLA protein RGA DELLA protein RGA DNA binding N-acetylglucosaminyl transferase AP2 transcription factor N-acetylglucosaminyl transferase PolyC-binding proteins alphaCP-1 FHA domain protein FHA domain protein protein SPA1-RELATED 60S ribosomal protein growth regulating factor melanosome growth regulating factor growth regulating factor transmembrane transport L-ascorbate oxidase-like regulation of transcription obsolete electron transport ubiquitin-conjugating enzyme ubiquitin-conjugating enzyme sulfotransferase activity 6-phosphogluconate dehydrogenase Ran-binding protein RANBP1 40S ribosomal protein NAC transcription factor 60 ribosomal protein L14 60 ribosomal protein L14 60 ribosomal protein L14 SPX domain protein MYB transcription factor MYB transcription factor SAP DNA binding domain protein SAP DNA binding domain protein

miR156, 157

miR156, 159, 319 miR156, 535 miR157 miR159

miR159, 319

miR160

miR164

miR165, 166

miR166

miR167

miR168 miR171

miR172

miR319 miR396

miR397 miR398 miR399 miR476 miR482 miR529 miR530 miR535

miR827

miR1507

(continued on next page) 5

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Table 3 (continued) MiRNA family

miR1513 miR395l miR4413 miR5021

Target transcripts

Category

Alignment range

Cleavage site

Transcript function annotation

Cluster-13185.56571 Cluster-13185.60881 Cluster-13185.62013 Cluster-13185.36272 Cluster-13185.55047 Cluster-13185.61188 Cluster-13185.75729 Cluster-13185.78126 Cluster-13185.79029 Cluster-13185.81794 Cluster-13185.84693 Cluster-13185.66747 Cluster-13185.86244 Cluster-13185.58317 Cluster-13185.55128

2 2 2 2 2 2 2 2 2 2 2 3 2 3 3

1198-1217 1760-1780 1657-1675 42-61 559-578 1414-1433 1-20 700-719 3-22 151-170 515-534 574-593 958-977 1320-1339 1263-1282

1208 1771 1666 52 569 1424 11 710 13 161 525 584 968 1330 1273

dnaJ protein ERDJ3B chromatin binding Gpi-anchor transamidase SPX domain protein Coactivator p15 response to stress serine/threonine-protein kinase glutathione S-transferase bHLH transcription factor glycine metabolic process Cytochrome P450 subfamilies Cytochrome P450 71A3-like glutathione S-transferase chalcone synthase chalcone synthase

Fig. 4. MiRNA regulatory network in C. camphora leaf.

example, 242 pre-miRNAs in C. camphora were homologous with those of G. max (Fig. 2). In addition, several miRNAs showed a high degree of conservation among a variety of plant species, such as miR156, miR159, miR171, miR166, miR396, miR167 and miR172, which were conserved in 54, 54, 51, 46, 38, 37 and 33 plant species, respectively, suggesting their conserved roles in plant biological processes (JonesRhoades et al., 2006).

3.2. Identification of miRNAs in C. Camphora leaf A total of 481 miRNAs were identified from the linalool and borneol chemotypes, with 336 miRNAs shared by the two chemotypes, 82 miRNAs unique to the borneol type and 63 miRNAs unique to the linalool type (Fig. 1a). Among these miRNAs, the known miRNAs included 364 members perfectly matched to mature miRNAs deposited in miRBase v22.0 with no nucleotide mismatches. The novel miRNAs included 117 miRNA members that were not matched to mature miRNAs in miRBase but corresponded to one strand of miRNA precursors. These novel miRNAs are temporarily named novel_number forms, such as novel_1. The sequences of the known and novel miRNAs are shown in Supplementary Table A.2. Among the known miRNAs, 21 nt miRNAs were the most abundant, and among the novel miRNAs, 24 nt miRNAs were the most abundant (Fig. 1b). The distribution was similar to that observed in barley (Wu et al., 2018a). The known miRNAs corresponded to 583 pre-miRNAs in miRBase v22.0. Among these pre-miRNAs, most were highly homologous with those from Glycine max, Malus domestica, Oryza sativa, Populus trichocarpa, Zea mays, Medicago truncatula and Linum usitatissimum. For

3.3. Differential expression of miRNAs in C. Camphora The differentially expressed miRNAs in the F_L and L_L libraries were identified by DESeq2 with an adjusted Q-value of < 0.05 and a |log2 (fold change) | > 1. Fourteen miRNAs had significant differences in expression between the two chemotypes, comprising 9 upregulated and 5 downregulated miRNAs in the borneol chemical type relative to the linalool chemical type (Table 2). In the stem-loop qRT-PCR verification, among the 12 selected miRNAs, the expression profiles of 11 miRNAs (i.e., all miRNAs except novel_134) were consistent with the sRNA sequencing results (Fig. 3). 6

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Fig. 5. Annotation information for miRNA target unigenes in C. camphora. (a) GO classification of targets in C. camphora. (b) Target KEGG annotation. The x-axis represents the percentage of targets annotated to the pathway out of the total number of targets annotated. The y-axis represents the KEGG metabolic pathway.

listed in Table 3. Notably, in some cases, an individual conserved miRNA was involved in the regulation of multiple target genes; for example, miR156, miR159, miR172, miR963 and miR5021 had more than 20 targets (Fig. 5). Highly similar motifs paired with miRNA sequences may explain the target gene universality (Bonnet et al., 2004). However, all of the novel miRNAs (specific) except novel_10 had a limited number of targets (Supplementary Table A.4). In addition, in some cases, a unigene was regulated by more than one miRNA; for example, Cluster-13185.10758 and Cluster-13185.97251 were potentially coregulated by miR156, miR157 and miR319 (Fig. 4 Table 3). The targeted cleavage sequences of miRNAs were predominantly plant

3.4. Identification of miRNA targets by degradome sequencing Degradome sequencing technology was applied to identify the potential regulatory targets of miRNAs in C. camphora leaf. miRNA-guided cleavage usually takes place between the 10th and 11th nucleotides from the 5′ end of the miRNA in the complementary region of the target transcript (Addo-Quaye et al., 2008; German et al., 2008). According to this feature, 477 miRNA target unigenes were identified by CleaveLand v3.0, including 363 targets of 43 conservatively known miRNA families (Supplementary Table A.3) and 114 targets of 15 novel miRNAs (Supplementary Table A.4). Portions of the targets of known miRNAs are 7

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Fig. 6. MiRNA target functions involved in terpenoid and polyketide biosynthesis. MiRNA cleavage is represented by solid lines with “T” arrows.

whereas in the cellular component, the targets were grouped into “membrane” and “integral component of membrane”. Based on the KEGG pathway analysis, the targets matched 19 metabolic pathways, of which “Translation” (158), “Carbohydrate metabolism” (137) and “Environmental adaptation” (95) were the three richest pathways (Fig. 5b). Moreover, 38 of the targets matched the “Metabolism of terpenoids and polyketides” pathway, such as the gmamiR6300 target abscisic acid 8′-hydroxylase 4 (Cluster-13185.83504, Cluster-13185.83505 and Cluster-13185.53179) and novel_10 target cytochrome P450 724B1-like (Cluster-13185.51660, Cluster13185.68806 and Cluster-13185.72493). Interestingly, geranylgeranyl diphosphate reductase (GGDR, Cluster-13185.61050), which was cleaved by ath-miR5021, was identified as involved in the “Terpenoid backbone biosynthesis” and “Porphyrin and chlorophyll metabolism” pathways (Fig. 6).

transcription factors (Khan Barozai et al., 2008; Nodine and Bartel, 2010; Addo-Quaye et al., 2008; Jones-Rhoades et al., 2006; Li et al., 2010). Similarly, many miRNA targets were annotated as transcription factors in C. camphora leaf, such as SBP, MYB, TCP, WRKY, bHLH and ARF (Supplementary Table A.3), suggesting that the miRNAs of C. camphora may have the ability to target transcription factor gene families. Based on the abundance signatures of targets corresponding to miRNAs relative to the abundances of other mapped signatures, the cleaved targets were classified into 5 categories (Xu et al., 2013). The targets with unique miRNA-directed cleavage sites and the highest abundance signatures were defined as category 0. In category 1, the target’s cleavage signature is most abundant among all signatures. The targets with a splitting signal abundance higher than the median were classified as category 2. Category 3 targets had cut signature abundances lower than the median. Targets with only 1 miRNA-complemented read were classified as category 4. Among the 364 identified target transcripts of known miRNAs, 203 targets (55.7%) belonged to category 0, 1 or 2 (Supplementary Table A.3), whereas 161 targets were grouped into category 3 or 4. For novel miRNAs, 93 and 21 targets fell into category 0, 1, or 2 and category 3 or 4, respectively (Supplementary Table A.4). The prediction of miRNA-directed cleaving targets in categories 0, 1 and 2 may be more accurate because the abundance signatures of targets in these categories are significantly higher than those of other transcripts (Song et al., 2011).

4. Discussion C. camphora is an important industrial tree species for essential oil in China, especially natural linalool and borneol oil. In recent years, miRNA has been increasingly recognized as an instrument for manipulating all biosynthetic pathways (Bulgakov and Avramenko, 2015). An increasing number of publications have reported the roles of miRNAs in regulating economically important plant secondary metabolites (Trumbo et al., 2015). Nevertheless, the possible gene-regulatory roles of miRNAs in C. camphora remain unclear. Therefore, the bioinformatics approaches of sRNA sequencing and degradome sequencing were performed here to explore the potential miRNA regulatory network in the leaves of C. camphora. Among the 363 identified targets in the known miRNA regulatory network, most targets have conservative functions, including transcription regulation, cell growth and development, pathogen responses and secondary metabolism (Table 3, Supplementary Table A.3). Interestingly, indole-3-pyruvate monooxygenase (Cluster-13185.61534),

3.5. GO and KEGG analyses of miRNA targets The GO analysis summarized the functions of the targets into 3 main categories and divided them into 50 GO terms (Fig. 5a). In the biological process category, the number of target genes was highest in “oxidation-reduction process”, “regulation of transcription” and “protein phosphorylation”. In the category of molecular function, the targets were mainly related to “protein binding” and “DNA binding”, 8

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Fig. 7. Expression patterns of novel miRNAs and their targets in C. camphora leaf. Novel miRNA expression patterns are indicated on the left with values of log2 (TPM + 1), and the values on the right represent miRNA targets based on log2 (FPKM + 1).

and methyltransferase (Okamoto et al., 2011; Wang et al., 2013). In a previous transcriptome sequencing study, oxidoreductase activity was found to be significantly enriched in the borneol and linalool chemotypes of C. camphora (Chen et al., 2018). In Picrorhiza kurroa, the miR4995 target 3-deoxy-7-phosphoheptulonate synthase was reported to affect the production of picroside-I by regulating the biosynthesis of terpenoids (Vashisht et al., 2015). Accordingly, gma-miR4995 may play a vital regulatory role in terpene biosynthesis in the linalool and borneol chemical types of C. camphora. Several miRNAs related to plant secondary metabolism have been reported (Gupta et al., 2017), including miR5021, and miRNA regulation in the production of secondary metabolites has been frequently

which is the most likely target of the differentially expressed miRNA gma-miR4995, was identified in C. camphora. Plant terpenoids are biosynthesized in cells through the mevalonate acid (MVA) pathway and the 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway (Lichtenthaler, 1999; Rohdich et al., 2006). Under the catalysis of terpene synthetases (TPSs), various terpenoids have been synthesized with the condensation of 5-carbon precursor (Chen et al., 2011; Tholl, 2006). One reason for the diversity of terpenoids in plants is that the direct products of TPSs are modified by reactions including hydroxylation, glycosylation, methylation, epoxidation, reduction, and halogenation. The postmodified enzymes include cytochrome P450-dependent monooxygenases, dehydrogenases and reductases, glycosyltransferase 9

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reported. In Catharanthus roseus, miR5021 was found to play a regulatory role of in the biosynthesis of the terpenoid backbone and modulate the expression of geranylgeranyl diphosphate (Pani and Mahapatra, 2013). In the upstream isoprenoid pathway, the miR5021 targets hydroxymethylglutaryl-CoA synthase (HMGR), isopentenyl diphosphate isomerase (IDI) and diphosphate synthase (IDS) have been predicted in Xanthium strumarium (Fan et al., 2015). In the current study, three miRNAs with their targets were found to be related to terpenoid and polyketide metabolism (Fig. 6). The miR5021 target GGDR was located in the downstream branch of the isoprenoid pathway, which can catalyze geranylgeranyl diphosphate (GGPP) to form phytyl pyrophosphate (phytyl-pp). In addition, the miR6300 target abscisic acid 8′-hydroxylase participates in carotenoid biosynthesis. However, GGPP is a common substrate for the biosynthesis of diterpenoids, carotenoids and other polyketides (Tholl, 2006). Thus, miR5021 and miR6300 may be involved in the regulation of terpenoid accumulation in C. camphora. Moreover, two targets of miR5021 (Cluster-13185.58317 and Cluster-13185.55128) were annotated as chalcone synthase, suggesting that the miRNA may be involved in flavonoid biosynthesis. The transcriptome data of C. camphora allowed us to identify 114 targets of 15 novel miRNAs (Supplementary Table A.4). For many modules, the expression patterns of novel miRNAs were opposite to those of their target unigenes, suggesting that these novel miRNAs have negative regulatory effects on their targets (Fig. 7). Novel_1 miRNA with six targets, including two plant-pathogen interaction genes, three MYB transcription factors and one WRKY transcription factor, may participate in defense regulation. MYB and WRKY proteins are key factors in the regulatory networks that control plant metabolism, development and responses to biotic or abiotic stresses (Dubos et al., 2010; Rushton et al., 2010). Among the 67 targets potentially cleaved by novel_10, three target unigenes were annotated as cytochrome P450 724B1-like protein, suggesting that they may be involved in brassinosteroid biosynthesis (Fig. 6). However, the functions of many of the putative novel miRNA targets are unknown and showed no homology with the genes of other species, which suggests that these genes may underlie specific functions in C. camphora. Further experiments are needed to identify the functions of these miRNAs.

This work was supported by grants from the National Natural Science Foundation of China (31860079),the Forestry Science and Technology Innovation Special of Jiangxi(201701),the Postdoctoral Science Foundation of the Jiangxi Academy of Sciences, the Qinglan Project of the Jiangsu Education Department, the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) and the Collaborative Innovation Plan of Jiangsu Higher Education (CIP). Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.indcrop.2019.111853. References Addo-Quaye, C., Eshoo, T.W., Bartel, D.P., Axtell, M.J., 2008. Endogenous siRNA and miRNA targets identified by sequencing of the Arabidopsis degradome. Curr. Biol. 18, 758–762. https://doi.org/10.1016/j.cub.2008.04.042. Addoquaye, C., Miller, W., Axtell, M.J., 2009. CleaveLand: a pipeline for using degradome data to find cleaved small RNA targets. 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5. Conclusions Although miRNAs play crucial regulatory roles in plant life cycles, their functions in C. camphora, an economically important industrial tree widely cultivated in South China, are poorly understood. In this study, we examined and identified miRNA members in C. camphora. Using our previous transcriptome data, miRNAs and their targets were annotated. After functional annotation, the miRNAs and their targets related to the biosynthesis of terpenoids were analyzed. The findings expand our understanding of the regulatory mechanisms underlying the biosynthesis of terpenoids and essential oils in C. camphora.

6. Author contributions M.X. participated in the conception and design of the experiments and reviewed a draft of the manuscript. C.C. participated in conducting the experiments and analyzing the data and drafted the manuscript. Y.Z. participated in conducting the experiments and analyzing the data. F.Y. participated in the conception and design of the experiments. All authors read and approved the final version of the manuscript.

Declarations of Competing Interest The authors declare that they have no conflicts of interest. 10

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