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Article

An Integrative Volatile Terpenoid Profiling and Transcriptomics Analysis in Hoya cagayanensis, Hoya lacunosa and Hoya coriacea (Apocynaceae, Marsdenieae)

by
Syazwani Basir
1,
Muhamad Afiq Akbar
1,
Noraini Talip
1,*,
Syarul Nataqain Baharum
2 and
Hamidun Bunawan
2,*
1
Department of Biological Sciences and Biotechnology, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
2
Institute of Systems Biology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
*
Authors to whom correspondence should be addressed.
Horticulturae 2022, 8(3), 224; https://doi.org/10.3390/horticulturae8030224
Submission received: 30 December 2021 / Revised: 25 February 2022 / Accepted: 1 March 2022 / Published: 4 March 2022
(This article belongs to the Special Issue Genomics and Bioinformatics Applications in Horticulture)
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Abstract

:
Hoya’s R.Br. attractive flower shapes and unique scents make it suitable to be exploited as a new source of tropical fragrance. Therefore, this study aims to elucidate the biosynthesis of secondary metabolites using phytochemical and transcriptomic approaches to understand the mechanisms of scents biosynthesis, especially terpenoid in Hoya. Three Hoya flower species were selected in this study: Hoya cagayanensis, Hoya lacunosa, and Hoya coriacea. The secondary metabolite profiles characterizing scents on flowers were performed using head space solid phase microextraction (HS-SPME). Gas chromatography-mass spectrometry (GC-MS) revealed 23 compounds from H. cagayanensis, 14 from H. lacunose, and 36 from H. coriacea. Volatiles from the three species had different fragrance profiles, with β-ocimene and methyl salicylate compounds dominating the odor in H. cagayanensis. The 1-octane-3-ol was found highest in H. lacunosa, and (Z)-acid butyric, 3-hexenyl ester was found highest in H. coriacea. Subsequent studies were conducted to identify the biosynthesis pathway of secondary metabolites responsible for the aroma profile released by Hoya flowers through transcriptome sequencing using the Illumina Hiseq 4000 platform. A total of 109,240 (75.84%) unigenes in H. cagayanensis, 42,479 (69.00%) in H. lacunosa and 72,610 (70.55%) in H. coriacea of the total unigenes were successfully annotated using public databases such as NCBI-Nr, KEGG, InterPro, and Gene Ontology (GO). In conclusion, this study successfully identified the complete outline of terpenoid biosynthesis pathways for the first time in Hoya. This discovery could lead to the exploitation of new knowledge in producing high-value compounds using the synthetic biology approach.

1. Introduction

Hoya is an epiphytic climbing plant known as a wax plant due to the appearance of its leaves and flowers [1]. Robert Brown discovered Hoya in 1810 [2]. It is one of the major genera in Apocynaceae, subfamily Asclepioideae [2]. This genus can be found in various environments, from lowland areas to hill forest areas ranging from 0 to 800 m [3,4]. More than 500 species have been recorded, with 300 known to exist in tropical Asia, the tropical Pacific Island, and northern Australia [5]. Southeast Asia had the most Hoya species, with 104 in the Philippines, 74 in Malaysia, 51 in Thailand, 43 in Sumatra Indonesia, 40 in Southern China, 40 in Vietnam, 27 in Brunei, 21 in Lao PDR, 13 in Singapore, and 8 species in Cambodia [6,7,8,9,10,11,12].
Most Hoya species synthesize and emit different scent metabolites. Hoya species are believed to have distinctive scents such as citrus aromas from H. vittelinoides and H. cummingiana, chocolate aromas from H. carnosa and H. sheperdii and spice aromas from H. cagayanensis [13,14]. The unique scents released from Hoya flowers have a high potential to become a new source of fragrance. Therefore, a complete understanding of secondary metabolite fragrance pathways and genes involved in biosynthesis is required. The construction of the Hoya biosynthesis pathway using secondary metabolite profiling and transcriptomic data is a valuable method.
Therefore, this study aims to understand the biosynthesis of secondary metabolites in Hoya using floral phytochemistry and transcriptomics approaches. The research began with identifying secondary metabolite profiles produced by the flower on the studied species through solid-phase microextraction (SPME) and gas chromatography-mass spectrometry (GC-MS). The chemical composition and the abundance profile of these secondary metabolites were analyzed to characterize the active compounds that contribute to the scents in this species. Transcriptome sequencing by Illumina HiSeq 4000 platform was performed to identify the secondary metabolite biosynthesis pathways on the scents profile emitted by the flower. The integration of flower phytochemical and transcriptome data is essential to identify the biosynthesis mechanisms and pathways of secondary metabolites produced in species studied.

2. Materials and Methods

2.1. Plant Materials

The specimens of H. cagayanensis, H. lacunosa, and H. coriacea used in this study were fresh specimens that fully bloomed on the first day, as the level of scent release was found to be higher in the early phases of flower opening and to decrease as the bloom wilted. The specimens were collected between 12 pm to 1 pm at Kedah, Malaysia, in October 2020. The selection of these three species is due to distinct morphologies of flowers, different floral fragrances, but nearly identical color groups (whitish to yellow). For phytochemical and transcriptomics analysis, the flowers specimens were weighed and placed in 20 mL vials for three biologicals replicated for each species. Parafilm was used to wrap the vial to ensure that no air escaped or entered the vial before being placed in an icebox containing dry ice. The samples were stored at −80 °C immediately upon arrival at the laboratory. Complete information on the locality for the three flower species in this study is summarized in Table 1. The complete voucher specimens H. cagayanensis (ID031/2020), H. lacunosa (ID033/2020), and H. coriacea (ID032/2020) were deposited in the Herbarium Universiti Kebangsaan Malaysia (UKMB).

2.2. Volatile Sampling

2.2.1. Solid Phase Micro Microextraction (SPME)

Extraction was carried out using SPME fiber, DVB/C-WR/PDMS/10, grey color, needle 23 with 80 µm SPME (Agilent Technologies, Santa Clara, CA, USA) fiber coating. The SPME fiber was activated for 30 min by preheating at temperature 250 °C in the injector hole of the CG-MS before the extraction process. The vials containing a 5-g sample was then heated for 30 min at a temperature of 35 °C. The SPME fiber then was injected into the sample vials through a septum, and the PDMS-coated fiber portion was exposed to the sample space portion. GC-MS parameters: gas chromatography (Agilent 7890A) with mass spectrometry detector (Agilent 5975C), MSD (DB-5MS UI) measuring 30 m × 0.25 mm × 0.25-µm with 5% phenyl methylpolyxyxane fiber coating. The column temperature was increased from 50 °C to 250 °C at a heat rate of 3 °C/min and raised to 250 °C at the rate of 5 °C/min. The experiment was repeated with three replications for each species.

2.2.2. Gas Chromatography-Mass Spectrometry (GC-MS) Analysis

The peak table for each sample was generated using MSD ChemStation software and identified based on library search using the National Institute of Standards and Technology (NIST) database version 2.0. Compounds that were not derived from the plant were excluded from the analysis.

2.2.3. Statistical and Multivariate Analyses

Data obtained were analyzed using one-way analysis of variance (ANOVA) in MetaboAnalyst 5.0 online software (https://www.metaboanalyst.ca, accessed online on 17 December 2020). The difference was significant at p ≤ 0.05. The normalized data were analyzed using the Pareto scale to reduce interference signals [15]. Finally, the data were analyzed using principal component analysis (PCA) in MetaboAnalysts V5.0 online software. PCA was used to identify the resulting patterns from the profiled metabolites and produced a score plot model that provided a visual image of the differences between the three samples from all aspects. Other than that, the default Euclidean distance was used to present the heat map.

2.3. Transcriptomic

2.3.1. RNA Extraction for Transcriptomic Analysis

The flower samples were ground into powder using mortar and pestle with liquid nitrogen. Total RNA was extracted using Trizol reagent (Sigma-Aldrich, St. Louis, MO, USA). Three biological replicates were used for sequencing. The contamination and RNA degradation were assessed using electrophoresis on agarose gel (1%). The purity of RNA extracts was examined using a NanoDrop® ND-1000 UV-Vis Spectrophotometer (Thermo Fischer Scientific, Waltham, MA, USA). RNA integrity was carried out using 2100B Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). A total of 1 μg high-quality RNA samples with RIN value seven or above was used for sequencing library preparation [16].

2.3.2. cDNA Library Construction and Illumina Sequencing

The sequencing library was constructed following the NEBNext® Ultra™ RNA Library Prep Kit for Illumina® protocol. The RNA library was prepared using 1 μg RNA, which was analyzed using high-throughput Illumina® sequencing technology at the GENEWIZ Company (Suzhou, China). Isolation Module (NEB) or Ribo-Zero™ rRNA Removal Kit (Illumina). The mRNA fragmentation and priming were performed using NEBNext First Strand Synthesis Reaction Buffer and NEBNext Random Primers to break them into minute pieces. The poly (A) mRNA isolation was performed using NEBNext Poly(A) mRNA Magnetic. The first strand of cDNA was synthesized with ProtoScript® II Reverse Transcriptase, while the second strand was synthesized with Second Strand Synthesis Enzyme Mix. Then, the End Prep Enzyme Mix was then used to purify double-stranded cDNA, repair both ends, and add a dA-tailing in one reaction. This step was followed by adding adaptors to both ends using a T-A ligation. The Adaptor-ligated DNA selection size was then conducted by AxyPrep Mag PCR Clean-up kit (Axygen, Union City, CA, USA), and fragments of ~360 bp (with the approximate insert size of 300 bp) was recovered. After that, each sample was amplified by PCR for 11 cycles using P5 and P7 primers carrying sequences that could anneal with flow cell to perform bridge PCR and P7 primer carrying a six-base index for multiplexing. The PCR products were cleaned with AxyPrep Mag PCR Clean-up (Axygen, Union City, CA, USA), verified with Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA), and quantified using Qubit 2.0 Fluorometer (Invitrogen, Carlsbad, CA, USA). The Illumina HiSeq 4000 (Illumina, San Diego, CA, USA) platform was used to sequence the cDNA libraries.

2.3.3. De Novo Transcriptome Assembly

Low quality reads from the raw read data from Illumina sequencing were eliminated using Trimmomatic version 3.6 [17], followed by a raw data filter to remove reads with Phred quality values Q < 20. The remaining clean reads from the sample library were assembled, and the FastQC software was used to verify the accuracy of the quality readings. Trinity version 2.8 software assembled the sequences using de novo assembly [18], which compiled the entire transcript using de Bruijn’s algorithm. The RSEM was used to calculate the expression level of each constructed transcript [19], and lowly expressed transcripts (expression level below 1 FPKM) were eliminated. Finally, the most extended transcript was selected as the unigene [20].

2.3.4. Gene Annotation and Classification

The transcriptome assembly sequence was compared with two protein databases, NCBI non-excess protein (NCBI Nr) and UniProtKB (Swiss-Prot and TrEMBL) using the blastx program with parameter e value ≤ 0.00001 [21]. InterProScan 5 program was used to predict domains for ORF sequences and identity homologous protein domains across 15 databases, including Phobius, TMHMM, Pfam, ProDom, Gene3D, PANTHER, SUPERFAMILY, COILS, SMART, PROSITE Profiles, PRINTS, SignalP, PIRSF, TIGRFAMs, and HAMAP [22]. The Blast2GO 5.2 program was then used to perform gene ontology (GO). The GO keywords associated with unigene matching results would match protein sequences in the NCBI Nr database. The Blast2GO 5.2 program was used to match the single GO words in this study and integrate the GO terms from the InterPro matching findings with the present GO terms. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to designate pathways [23]. TransDecoder was used to identify the coding section of the transcripts (http://transdecoder.sf.net, accessed online on 9 January 2021).

3. Results

3.1. Phytochemical Analysis

HS-SPME and GC-MS techniques were used to evaluate flower volatiles of H. cagayanensis, H. lacunosa, and H. coriacea. A total of 59 metabolite compounds with varying percentages were found. Nine chemical classes were found consisting of terpenoids, alcohols, alkenes, aldehydes, benzenoids, ester, ketone, acid, and sulfur groups. Twenty-three metabolites were identified in H. cagayanensis, 14 in H. lacunosa, and 36 in H. coriacea. Many profiled metabolites were found to be unique to a particular species only, with 15 unique metabolites in H. cagayanensis, 6 unique metabolites in the H. lacunose and only 28 unique metabolites in the H. coriacea (Table 2). Only six metabolites (β-ocimene, (Z)-3-hexen-1-ol, 1-octen-3-ol, butanoic acid (Z)-2-methyl-3-hexenyl ester, (Z)-butyric acid-3-hexenyl ester, and methyl salicylate) from terpenoid, alcohol, ester, and the benzenoid group were found to have a relative amount more than 10% in all species studied (Table 2).
The terpenoids group (39.51%) contribute the most aroma of the flower part in H. cagayanensis with the metabolite β-ocimene (terpenoid) and methyl salicylate (benzenoid) with a relative percentage of 25.78% and 24.67%, respectively. On the other hand, the alcohols group (29.59%) was the most prominent in the H. lacunose, with 1-octen-3-ol metabolite dominating with relative percentages of 26.1%. Meanwhile, the ester group (55.45%) was found highest in H. coriacea with (Z)-butyric acid-3-hexenyl ester metabolite dominating with a relative percentage of 29.36% (Table 2).
Based on principal component analysis (PCA), the score plot separated the metabolites composition trends between the three species (Supplementary S1). It was found that H. coriacea differed far from H. cagayanensis and H. lacunosa with a total PC1 of 66.7%, whereas H. cagayanensis and H. lacunosa were further separated at PC2 by 29.6% (Figure 1). This suggests the metabolites compositions of the three species are significantly different from one another, but that H. cagayanensis was especially divergent. This might be due to species-specific volatiles that may serve as marker compounds. The metabolites abundance marked the differences between the flowers of all species studied as statistically significant with a p-value less than 0.05. These are depicted in the heatmap (Figure 2).

3.2. Transcriptome Analysis

3.2.1. Assembly of Flower Hoya Species Transcriptomes

The transcriptome cDNA library was generated from the flower section using Illumina HiSeq 4000 platform. After removing low-quality sequences, a total of 65.06 million high-quality readings were successfully generated from all three species. De novo assembly in Trinity software transcript filtering with expression value yielded 144,042 final unigenes covering 89.21 Mpb for H. cagayanensis, 156,830 unigenes with 101.87 Mpb for H. lacunosa and about 102,914 unigenes with a total of 93.59 Mpb for H. coriacea. In addition, the values of N50 are 989 pb (H. cagayanensis), 1178 pb (H. lacunosa) and 1545 pb (H. coriacea). Other than that, the average values of unigene length were 619.35 pb, 649.54 pb, and 908.36 pb for H. cagayanensis, H. lacunosa, and H. coriacea (Table 3).
The net data readings were mapped back to the final unigene database using HISAT2 to evaluate the transcriptome quality of the de novo assembly. Next, the number of unigenes with complete sequence (full-length ORF) was also screened using a comparison between the transcriptome unigene sequence with the sequence found in several databases. The results showed that 32,385 unigenes represented full-length proteins for H. cagayanensis and 27,839 unigenes for H. lacunosa and 31,043 unigenes for H. coriacea.

3.2.2. Annotation of Unigenes

The NCBI, KEGG, InterPro and GO databases were used to compare all of the obtained unigenes. For H. cagayanensis, only 109,240 (75.85%) of the 144,042 unigenes were compared to the NCBI Nr database and produced significant matches, whereas the InterPro database annotation results reported a total of 54,331 (37.72%) significant matches (Table 4). A total of 20.92% (22,854 unigenes) of the unigene annotation results against the NCBI Nr database showed homology with proteins from Coffea arabica (Figure 3a). Other matches with the NCBI Nr database were also dominated by proteins from other plant groups such as Coffea eugenioides (10,752 unigenes), Citrobacter freundii (6596 unigenes), and Coffea canephora (6492 unigenes). In terms of predicted protein similarity distribution, 96% of the unigene sequences had similarities higher than 60%, while 40% of unigene sequences showed similarities between 28% and 60% (Figure 4a).
Meanwhile, for H. lacunosa, the results showed that 42,479 (69 %) unigenes were successful compared with 61,598 database unigenes in NCBI Nr. The annotation results of the InterPro database showed 55,079 (89.41%) matches (Table 3). Approximately 17% (7217 unigenes) of the unigene annotation results with the NCBI Nr database showed homology with protein from Coffea arabica (Figure 3b). Other protein matches in the NCBI Nr database were also dominated by other plant groups such as Coffea eugenioides (3668 unigenes) and Coffea canephora (2358 unigenes). The predicted protein similarity distribution found that 95.15% of unigene sequences had similarities higher than 60%, and 4.85% of unigene sequences were between 31% and 60% (Figure 4b).
Analysis of H. coriacea showed that 72,610 (70.55%) unigenes were successfully matched with 102,914 unigenes in the NCBI Nr database. Moreover, 52,269 (50.79%) corresponding unigenes were in the InterPro database (Table 3). Unigen annotation results with NCBI Nr database showed 29.21% (21,207 unigenes) of homology with protein Coffea arabica, Coffea eugenioides (11,767 unigenes), C. canephora (7215 unigenes), Nyssa sinensis (3274 unigenes), Sesamum indicum (2124 unigenes), and Olea europaea var. sylvestris (489 unigenes) (Figure 3c). The protein similarity distribution found that 93% of unigene sequences had similarities higher than 60%. Meanwhile, 7% of unigenes sequences were between 30% and 60% (Figure 4c).
Further analysis using the InterPro database, the distribution of enzyme classes identified in flower part transcripts of the three study species (Figure 5) and the protein class transferases dominated the enzyme class. In contrast, the enzyme hydrolases contributed to the second-highest distribution of protein classes.
In H. cagayanensis, a total of 25,186 unigenes were classified into 4018 KEGG orthological groups and 413 KEGG pathways. The distribution of these KEGG orthological groups with different cell function categories is shown in Figure 6a. The protein families: genetic information process group (5044 unigenes) and the genetic information processing group (4785 unigenes) were the largest KEGG orthological groups. For H. lacunosa, a total of 24,703 unigenes were classified into 3706 KEGG orthological groups covering 413 KEGG pathways. The orthology group of the protein families: genetic information process group (5964 unigenes) and the genetic information processing group (4667 unigenes) were found to be the largest orthological groups of KEGG (Figure 6b). While for H. coriacea, a total of 23,752 unigenes were classified into 3643 KEGG orthological groups and 408 KEGG pathways through the GhostKOALA program. The greatest orthological group of KEGG were discovered to be the protein families: genetic information process group (4873 unigenes) and the genetic information processing group (4682 unigenes) (Figure 6c).
Based on the unigene InterPro database, samples were classified into functions according to the classification from the GO database using Blast2GO software. A total of 98,203 GO H. cagayanensis, 335,81 GO H. lacunosa, and 56,914 GO H. coriacea terms were matched in the unigene database. The unigenes distributions successfully matched with the GO database showed elevations in the biological processes, between levels 8–10 for H. cagayanensis; levels 7–9 for H. lacunosa; and levels 6–9 for H. coriacea. For molecular functional processes, the highest concentration levels were 5–6 for all species study. Meanwhile, in the cellular component, all species are highest in levels 5–6 (Figure 7).
Furthermore, for the category of BP in this study, the main term GO is ‘organic substance metabolic process’ for all species study; H. cagayanensis (GO:0071704; 43,785 unigenes), H. lacunosa (GO:0071704; 17,101 unigenes), and H. coriacea (GO:0071704; 28,533 unigenes). Meanwhile, the category of MF was dominated by the term GO ‘organic cyclic compound binding’ with H. cagayanensis (GO:0097159; 30,966 unigenes), H. lacunosa (GO:0097159; 11,901 unigenes), and H. coriacea (GO:0097159; 19,357 unigenes). Next, the CC category was monopolized by the term GO ‘Intracellular anatomical structure’ with H. cagayanensis (GO:0005622; 36,653 unigenes), H. lacunosa (GO:0005622; 15,785 unigenes), and H. coriacea (GO:0005622; 29,351 unigenes) (Figure 8).

3.2.3. Transcriptome Analysis of Secondary Metabolite Biosynthesis Pathways

Based on the analysis of metabolites obtained in this study, it was found that terpenoid groups dominated the biosynthesis pathway sites that have been successfully identified. Transcriptome analysis in this study using GhostKOALA software led to the identification of the major enzyme responsible for the final pathway site in the terpenoid biosynthesis, namely alpha-farnesene synthase (AFS1), terpene synthase (TPs), and germacrene D synthase (GDS1). In the flower part of H. cagayanensis, the AFS1 gene reacts with the substrate (2E,6E)-alpha farnesyl diphosphate to produce (3E,6E)-alpha-Farnesene as a product. While TPs in H. lacunosa also play a role in producing terpenoid compounds. GDS1 in H. coriacea reacted with the substrate (2E,6E)-farnesyl diphosphate to produce (-)-germacrene D as the final product. The genes involved and the final metabolites biosynthesized in the Hoya flower terpenoid pathway were discovered for the first time in the genus. The study results also showed KEGG pathway analysis using MVA and MEP pathways to produce terpenoid compounds (Figure 9).

4. Discussion

Floral aroma and scent release in plants are often associated with the secretory structure, secondary metabolites produced in specific cells and genes involved in metabolite biosynthesis [24]. Secondary metabolites in flowers were discovered to have multiple functions such as protecting plants from pathogens, repelling pests, attracting pollination agents, and as a mechanism of plants interaction [25,26]. Flowering plant species have been found to synthesize and release various unique and specific aromas to attract particular pollinators [27,28]. β-ocimene, limonene, myrcene, linalool, pinene, benzaldehyde, methyl salicylate, and benzyl alcohol metabolites are among the significant aromatic metabolites commonly found in most flower species and has a specific function on plants [29,30].
Linalool, methyl salicylate, geraniol, and eugenol metabolites are found in the floral aromas of the species Satyrium microrrhynchum are attractive for beetles of the family Cetoniidae to visit the flowers [31,32]. Farré-Armengol et al. [29] discovered various ocimene metabolites in plants with trans- β-ocimene (47.5%) found in floral aromas of the 291 plants studied. The abundance of β-ocimene metabolites in plants suggests that these metabolites act as pollinator attractors and defense against herbivores. In the Mediterranean region, several low-density plant species such as Muscari neglectum, Ranunculus gramineus, Euphorbia flavicoma, and Iris lutescens were found to release high amounts of β-ocimene to compete with two other dominant plant species, namely Rosmarinus officinalis and Thymus vulgris to attract pollinating agents to compensate for low species abundance [33,34]. Meanwhile, methyl salicylate was found to act as a major attractor to Eulaema, Euglossa and Euplisia bee, pollinators in most Catasetum orchid species [35].
In an insect behavioral study of the Maruca vitrata butterfly on Vigna unguiculata (Fabaceae), it was found that the antenna of M. vitrata detected four metabolites consistent with the 1-octen-2-ol (alcohol) metabolites identified as a major compound of V. unguiculata leaf extract [36]. However, the alcohol group as a major compound in Hoya has not been reported in any study. Other than that, a study of 29 cultivars of the flowering plant Osmanthus fragrans found the metabolite (Z)-butyric acid- 3-hexenyl ester (ester) is among the metabolites found in some of these cultivars [37].
To attract pollinators, flowering plants use olfactory signaling factors and visual cues. Insect pollinators will be attracted to flowers with attractive colors and a pleasant aroma [38]. Other species of Hoya, H. incrassate and H. heuschkeliana have also been reported to have less attractive flower color and, therefore, may cause these two species to release (Z)-ocimene (26.4%) and (E)-ocimene (37.5%) metabolites with the highest percentages to attract the pollinator [39]. In this study, H. cagayanesis, H. lacunosa, and H. coriacea have less attractive flower colors. The release of various dominant floral aromas metabolites at high amounts may attract specific pollinators insects as a mechanism in the success of the pollination process. In contrast, other metabolites such as phenol and alkaloids have a selective effect on pollinators to prevent other visiting insects from visiting flowers [40]. Plant-insect-specific pollinating interactions will increase pollen selection while avoiding interspecific competition between different insect niches, especially to overcome problems when the flowering seasons co-occur [41,42].
The presence of a diverse range of metabolites indicates that each metabolite has another role in the flowers other than pollinator attraction. Other metabolites are present in most flowers’ function to regulate high temperatures and reduce damage due to oxidative stress [43,44]. For example, caryophyllene metabolite, often found in flowering plants, was highly reactive to ozone stress [45]. Other than that, the terpenoid metabolites found in plants’ essential oils have long been used as a flavouring and fragrance [46,47,48]. For example, Bernotienë et al. [49] reported the myrcene, α-humulene, β-caryophyllene and linalool metabolites in lupulin glandular trichomes in the Humulus lupulus species was used as a flavoring in brewing. In addition, monoterpene metabolites such as limonene, linalool, 1,8-cineole are widely used to favour lemon or lime-flavored beverages [50].
In this study, the β-ocimene metabolite from the terpenoid compound found in H. cagayanensis has a high potential to be exploited and commercialized since it has an aroma comparable to neroli oil and is widely used in manufacturing essential oils [51]. Furthermore, terpenoid compounds (E)-β-Ocimene and myrcene are crucial compounds in the floral fragrances of many plant species [51,52]. Due to their prevalence and abundance in floral odors, Ocimene is believed to have a significant ecological role in flowers. According to the list of identified floral-scent compounds compiled by Knudsen et al. [30], trans- β-Ocimene can be found in the floral scents from 71% of the 90 plant families in the list of the discovered floral-scent compound. Furthermore, trans-β-Ocimene was detected in the floral fragrances from 47% of the 291 plant species and 75% of the 63 plant families studied [29].
In addition to its abundance in the flower odors, there is evidence that β-Ocimene acts as a pheromone in attracting pollinating agents such as bees, moths, and butterflies [33,34,51]. For example, in one observation at Mediterranean shrubland area, several low-density plant species such as Muscari neglectum, Ranunculus gramineus, Euphorbia flavicoma, and Iris lutescens were found to release high amounts of β-Ocimene to compete with two other dominant plant species, Rosmarinus officinalis and Thymus vulgaris, to attract pollinating agents to compensate for low species abundance [33,34]. Moreover, another study of floral scent composition of two Hoya species, Hoya incrassate and Hoya heuschkeliana, found that both species emitted high levels of ocimene metabolites, (Z)-Ocimene (26.4%) for H. incrassate and (E)-Ocimene (37.5%) for H. heuschkeliana [39]. Furthermore, based on the morphology, both species were found to have less attractive flower colors similar to H. cagayanensis. Therefore, the release of a high amount of ocimene from the terpenoid group in Hoya could be suggested to attract pollinating insects as a mechanism in the success of the pollination process.
In general, terpenoids are important in pollinator attraction, reproduction, plant seed dispersal for pollination success [38,46,53], and defense against herbivorous and pathogenic attacks [54,55]. In addition, terpenoids are widely used in the perfume and cosmetics industries and used as additives and flavors in food due to their distinctive scents and taste [56,57]. Terpenoid metabolites also frequently have high medicinal value [58,59,60]. For example, Paclitaxel (Taxol®) is an anticancer medicine, whereas artemisinin is antimalarial. Both are new-generation medications based on terpenoid compounds with modern medicinal applications [61]. The terpenoid group has been found to dominate volatile scents in most flowering plants, including orchids species such as Phalaenopsis bellina (geraniol and linalool), Cymbidium goeringii (farnesol, methyl epi-jasmonate, (E)-β-farnesene and nerolidol), Vanda Mimi Palmer (ocimene, linalool oxide and linalool), and Maxillaria tenufolia (β-caryophyllene) [62,63,64,65]. In addition, several compounds of terpenoids-based antimicrobial substances have also been produced by several bacterial strains from Streptomyces sp. [58,66].
The presence of major enzymes in terpenoids biosynthesis pathways such as AFS1 has also been reported in other plants such as Actinidia deliciosa [67]. This enzyme is involved in the synthesis of cyclic terpene metabolites. As with other plants, metabolites of the terpenoids group in these flowers are produced through synthesis from the universal five-carbon precursors, isopentenyl diphosphate (IPP), and dimethylallyl diphosphate (DMAPP) synthesized via mevalonate pathway (MVA) or non-mevalonate pathway (MEP) [55]. In plants, monoterpenoids, diterpenoids, carotenoids, ubiquinone and phytol metabolites are produced in plastids through MEP pathways. In contrast, other terpenoid metabolites such as cyclic terpenoids, triterpenoids and polyterpenoids are produced using MVA pathways in the cytosol [65]. The results of this study can be used to undertake additional research on gene expression at terpenoid pathway sites to understand further the role of genes in the release of aromatic scents in Hoya species.

5. Conclusions

In conclusion, this study discovered compounds that are known to be generated through the terpenoid biosynthesis pathway and putative unigenes that are potential homologs to enzymes in the terpenoid pathway. To the best of our knowledge, the transcriptome profile presented in this study is the first comprehensively founded on this genus. The findings will provide a good foundation for understanding the terpenoid biosynthesis in Hoya and are essential for future reconfiguration of these metabolites pathways to generate these metabolites using the synthetic biology approach.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/horticulturae8030224/s1, Supplementary S1: Chemical composition data for PCA analysis.

Author Contributions

Conceptualization, H.B.; methodology, S.B.; formal analysis, S.B., H.B. and M.A.A.; writing-draft preparation, review and edition, S.B., H.B., N.T. and S.N.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Universiti Kebangsaan Malaysia, grant number GP-2020-K020959.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw read sequences of transcriptome assembly obtained from Illumina Hiseq4000 (150PE) platform have been deposited in Sequence Read Archive (SRA) under the accession number SRR18131961, SRR18131963 and SRR18131962 in NCBI and details of the sample can be viewed via BioProject id: PRJNA808778. The transcriptome shotgun assembly in this study has been deposited under the same BioProject.

Acknowledgments

We would like to thank the Institute of Systems Biology, Universiti Kebangsaan Malaysia (UKM) and CRIM, UKM, for the instruments and facilities provided for this research.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Principal component analysis (PCA) plot of metabolites from three species of Hoya. Each colour in scores plot represents a different species; H. cagayanensis (red), H. lacunosa (green), and H. coriacea (blue).
Figure 1. Principal component analysis (PCA) plot of metabolites from three species of Hoya. Each colour in scores plot represents a different species; H. cagayanensis (red), H. lacunosa (green), and H. coriacea (blue).
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Figure 2. Heat map represents metabolites of different abundance between the flowers of Hoya species. Red hues indicate high metabolite content and blue hues indicate low metabolite content.
Figure 2. Heat map represents metabolites of different abundance between the flowers of Hoya species. Red hues indicate high metabolite content and blue hues indicate low metabolite content.
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Figure 3. The homological species distribution annotation of (a) H. cagayanensis; (b) H. lacunosa; and (c) H. coriacea unigenes against the NCBI-NR database.
Figure 3. The homological species distribution annotation of (a) H. cagayanensis; (b) H. lacunosa; and (c) H. coriacea unigenes against the NCBI-NR database.
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Figure 4. A annotation unigenes of flower part of (a) H. cagayanensis; (b) H. lacunose; and (c) H. coriacea from NR database: the percentage of similarity of sequences identified using blastx program.
Figure 4. A annotation unigenes of flower part of (a) H. cagayanensis; (b) H. lacunose; and (c) H. coriacea from NR database: the percentage of similarity of sequences identified using blastx program.
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Figure 5. Distribution of protein classes identified on flower part of three species Hoya; (a) H. cagayanensis; (b) H. lacunosa, and (c) H. coriacea. Each area of the pie represents a value from the unigen.
Figure 5. Distribution of protein classes identified on flower part of three species Hoya; (a) H. cagayanensis; (b) H. lacunosa, and (c) H. coriacea. Each area of the pie represents a value from the unigen.
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Figure 6. Classification of KEGG function on the flower part of three species Hoya; (a) H. cagayanensis; (b) H. lacunosa, and (c) H. coriacea. Unigen functions were identified and categorized using GhostKOALA software over the entire eukaryotic database.
Figure 6. Classification of KEGG function on the flower part of three species Hoya; (a) H. cagayanensis; (b) H. lacunosa, and (c) H. coriacea. Unigen functions were identified and categorized using GhostKOALA software over the entire eukaryotic database.
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Figure 7. GO category classification in three Hoya species (a) H. cagayanensis; (b) H. lacunosa; and (c) H. coriacea: Division of terms in GO from various levels across the categories of cellular component (CC), molecular function (MF) and biological process (BP).
Figure 7. GO category classification in three Hoya species (a) H. cagayanensis; (b) H. lacunosa; and (c) H. coriacea: Division of terms in GO from various levels across the categories of cellular component (CC), molecular function (MF) and biological process (BP).
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Figure 8. Annotation of GO terms from categories of biological process (BP), molecular function (MF), and cellular components (CC) for (a) H. cagayanensis; (b) H. lacunosa, and (c) H. coriacea. Only the top 20 terms for each category shown.
Figure 8. Annotation of GO terms from categories of biological process (BP), molecular function (MF), and cellular components (CC) for (a) H. cagayanensis; (b) H. lacunosa, and (c) H. coriacea. Only the top 20 terms for each category shown.
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Figure 9. The terpenoid biosynthesis pathway in Hoya species based on KEGG database.
Figure 9. The terpenoid biosynthesis pathway in Hoya species based on KEGG database.
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Table
Table 1. List of flower species and collection locality.
Table 1. List of flower species and collection locality.
SpeciesSample IDLocation Site, StateCoordinates
H. cagayanensisHA 01Baling, Kedah5°30′06.8″ N 100°46′19.1″ E
H. lacunosaHA 02Baling, Kedah5°34′02.7″ N 100°52′05.8″ E
H. coriaceaHA 03Sik, Kedah5°51′42.2″ N 100°50′11.1″ E
Table
Table 2. List of secondary metabolites compounds found in flowers of H. cagayanensis, H. lacunosa, and H. coriacea species. The relative amount (in %) of scent compounds are listed according to compound class.
Table 2. List of secondary metabolites compounds found in flowers of H. cagayanensis, H. lacunosa, and H. coriacea species. The relative amount (in %) of scent compounds are listed according to compound class.
Formula and Chemical GroupCAS. NoCompoundPlant Species/
Relative Percentage (%)
H. cagayanensisH. lacunosaH. coriacea
Terpenoids
Monoterpene
C10H16013877-91-3β-Ocimene25.78--
C10H16005989-27-5d-Limonene--0.05
C10H18O000106-24-1(E)-Geraniol1.64--
C10H18O000106-25-2cis-Geraniol0.28--
C10H18O000078-70-6Linalool9.343.22-
C10H16O000141-27-5(E)-Citral0.33--
C10H16O000106-26-3(Z)-Citral0.27--
C10H16O000432-25-7β-Cyclocitral--0.04
C10H16003779-61-1trans-β-Ocimene0.54--
C10H18O2034995-77-2trans-Linalool oxide (furanoid)0.14--
C10H16007216-56-0Allo-ocimene0.79--
Sesquiterpene
C15H24017699-14-8α-Cubebene--0.18
C15H241000360-33-0α-Copaene-0.440.17
C15H241000374-19-0α-Ylangene--0.02
C15H241000374-18-9β-Copaene--0.07
C15H24000502-61-4α-Farnesene0.18-0.19
C15H24000087-44-5β-Caryophyllene0.221.000.06
C15H24017699-05-7cis-α-Bergamotene-0.37-
C15H24023986-74-5Germacrena D--1.01
C15H24029837-07-8cis-α-Bisabolene--0.03
C15H24000483-75-0delta-Cadinene--0.65
Alcohols
C6H12O000928-96-1(Z)-3-Hexen-1-ol-0.5519.92
C6H14O000111-27-3Hexanol--1.16
C8H16O003391-86-41-Octen-3-ol-26.1-
C10H18O2014049-11-7Linalool oxide pyranoside2.030.740.07
C10H18O004117-14-0Decanol--0.22
C8H10O000060-12-8Phenylethyl alcohol-2.20-
C5H10O000556-82-1Prenol0.7--
C8H18O1000144-07-1(S)-3-Ethyl-4-methylpentanol7.66--
Ester
C8H14O2003681-82-1(3E)-Hexenyl acetate--0.16
C11H18O21000373-74-1(E)-Hex-3-enyl (E)-2-methylbut-2-enoate--8.14
C10H18O2069727-41-9(4Z)-4-Hexenyl butyrate--0.61
C7H13ClO2005326-92-1Acetic acid, chloro-,3-methylbutyl ester1.22--
C11H20O2053398-85-9Butanoic acid (Z)-2-methyl-3-hexenyl ester,--11.78
C10H18O2016491-36-4(Z)-Butyric acid, 3-hexenyl ester--29.36
C12H24O2000110-39-4Butanoic acid, octyl ester--3.15
C12H22O2031501-11-8cis-Hexanoic acid, 3-hexenyl ester--1.43
C9H18O2000106-27-4Butyric acid, isopentyl ester--0.31
C7H12O2013894-62-7Z-Methyl 3-hexenoate--0.06
C9H16O2007785-66-2Tiglic acid n-butyl ester--0.05
C10H20O2002349-07-7Isobutyric acid, hexyl ester--0.4
Benzenoid
C6H5CHO000100-52-7Benzaldehyde5.72.111.3
C11H14O2000103-37-7Benzyl butyrate--0.31
C10H12O2000097-53-0p-Eugenol7.12.55-
C8H8O3000119-36-8Methyl salicylate24.671.750.66
C7H8O000100-51-6Phenylmethanol0.36-2.05
C7H8O000106-44-5p-Cresol0.13--
Alkenes ---
C15H24054274-73-6(+)-epi-Bicyclosesquiphellandrene--0.17
C6H10000693-89-01-Methylcyclopentene -0.3
C10H14000460-01-5Cosmene0.65-
C7H12000591-47-94-Methylcyclohexene--0.03
C6H12000625-27-42-Methyl-2-pentene0.48--
Aldehydes
C6H12O000066-25-1Hexaldehyde1.19--
C8H8O000122-78-1Phenylacetaldehyde-2.36-
C9H14O017587-33-6(E,E)-2,6-Nonadienal--1.73
Ketone
C8H14O010408-15-86-Methyl-6-hepten-2-one-4.71-
C13H22O003796-70-1Geranyl acetone-0.42-
Acid
C5H8O2000080-59-1Tiglic acid--0.94
Sulphur
C2H6S000075-08-1Ethanethiol--0.11
Table
Table 3. Statistical summary of transcriptome assemblages for all species.
Table 3. Statistical summary of transcriptome assemblages for all species.
StatisticH. cagayanensisH. lacunosaH. coriacea
Unigenes
Total unigenes144,042156,830102,914
GC%42.6842.6641.49
Contig N50 (bp)98911781545
Minimum contig length (bp)186187199
Maximum contig length (bp)966511,3039825
Median contig length (bp)350315547
Average contig length (bp)619.35649.54909.36
Total bases89,212,671101,866,95493,585,886
Table
Table 4. A annotated unigenes from flower part of H. cagayanensis, H. lacunosa, and H. coriacea by databases.
Table 4. A annotated unigenes from flower part of H. cagayanensis, H. lacunosa, and H. coriacea by databases.
StatisticH. cagayanensisH. lacunosaH. coriacea
Unigenes Annotation
NCBI-Nr109,240 (75.84%)42,479 (69.00%)72,610 (70.55%)
KEGG25,186 (17.49%)24,703 (40.10%)23,752 (23.08%)
InterPRO54,331 (37.72%)55,079 (89.41%)52,269 (50.79%)
Gene Ontology (GO)89,203 (61.93%)33,581 (54.52%)56,914 (55.30%)
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Basir, S.; Akbar, M.A.; Talip, N.; Baharum, S.N.; Bunawan, H. An Integrative Volatile Terpenoid Profiling and Transcriptomics Analysis in Hoya cagayanensis, Hoya lacunosa and Hoya coriacea (Apocynaceae, Marsdenieae). Horticulturae 2022, 8, 224. https://doi.org/10.3390/horticulturae8030224

AMA Style

Basir S, Akbar MA, Talip N, Baharum SN, Bunawan H. An Integrative Volatile Terpenoid Profiling and Transcriptomics Analysis in Hoya cagayanensis, Hoya lacunosa and Hoya coriacea (Apocynaceae, Marsdenieae). Horticulturae. 2022; 8(3):224. https://doi.org/10.3390/horticulturae8030224

Chicago/Turabian Style

Basir, Syazwani, Muhamad Afiq Akbar, Noraini Talip, Syarul Nataqain Baharum, and Hamidun Bunawan. 2022. "An Integrative Volatile Terpenoid Profiling and Transcriptomics Analysis in Hoya cagayanensis, Hoya lacunosa and Hoya coriacea (Apocynaceae, Marsdenieae)" Horticulturae 8, no. 3: 224. https://doi.org/10.3390/horticulturae8030224

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