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J Plant Biotechnol (2024) 51:033-049

Published online February 19, 2024

https://doi.org/10.5010/JPB.2024.51.004.033

© The Korean Society of Plant Biotechnology

Transcriptome analysis of a tropical medicinal plant, Pistacia weinmannifolia

Mi Kyung Choi ・Bimpe Suliyat Azeez ・Sang Woo Lee ・Wan Yi Li ・Sangho Choi ・Ik-Young Choi ・Ki-Young Choi ・Jong-Kuk Na

Department of Agriculture and Industries, Kangwon National University, Chuncheon, Kangwon 24341, Republic of Korea
International Biological Material Research Center, KRIBB, Daejeon 34141, Republic of Korea
Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming, Yunnan 650200, P.R. China
Department of SmartFarm and Agricultural Industry, Kangwon National University, Chuncheon, Kangwon 24341, Republic of Korea

Correspondence to : e-mail: jongkook@kangwon.ac.kr

Received: 22 January 2024; Revised: 7 February 2024; Accepted: 7 February 2024; Published: 19 February 2024.

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Pistacia weinmannifolia has long been used as an herbal medicine for treating various illnesses. The genomic information of P. weinmannifolia will help elucidate the chemical constituents that exert medicinal effects; however, genomic studies have rarely been performed. Therefore, we conducted a transcriptome analysis of P. weinmannifolia using the Illumina RNA sequencing system. We obtained a total of 18 million high-quality paired-end reads with 2,230 Mbp. De novo assembly of high-quality reads generated a total of 18,956 non-redundant contigs with an average length of 901 bp, of which 18,296 contigs (96.5%) were annotated. The total length of all unigenes was 17,080,830 bp, and the GC content and N50 were 43.2% and 1,137 bp, respectively. Annotation using The Kyoto Encyclopedia of Genes and Genomes (KEGG) assigned a total of 5,095 unigenes (26.9%), of which 3,166 were mapped to 410 different KEGG metabolic pathways. A comparison of unigenes between P. weinmannifolia and Pistacia chinensis showed that 8,825 unigenes were highly similar to each other. Simple sequence repeats were mined, and valuable data for further comparative and functional genomic studies were obtained to uncover the mechanisms underlying the medicinal properties of P. weinmannifolia as an important medicinal plant. Several genes of P.weinmannifolia involved in the biosynthesis of eugenol and isoeugenol were also identified in this study.

Keywords RNA sequencing, Medicinal plant, Simple sequence repeat, Repetitive sequence

Pistacia weinmannifolia J. Poisson ex Franch belongs to the family Anacardiaceae and is a shrub localized predominantly to Southwestern China, particularly in the provinces of Yunnan, Sichuan, Guangxi, Guizhou, and Tibet (Mien and Ming 1980). It is also distributed in Vietnam and Myanmar (Mien and Ming 1980), and Iran and Western Mediterranean countries as well (Rauf et al. 2017). P. weinmannifolia has been used as a popular decorative plant for miniascapes because of its elegant profile. On the otherhand, P. weinmannifolia has been used as a herbal medicine to treat various illnesses (Rauf et al. 2017).

Various studies on the genus Pistacia for their pharmacological and industrial properties using various parts of the plants have shown that they contain diverse and valuable secondary metabolites (Bozorgi et al. 2013; Rauf et al. 2017), with several members of this genus being in long-term use for numerous medicinal purposes (Akhtar et al. 2013; Rauf et al. 2017). Also, many important phytochemicals were identified from the genus Pistacia, such as camphene, pistacigerrimones, masticadienolic acid, and ellagic acid (Rauf et al. 2017). P.weinmannifolia contains high contents of gallotannins, pistafollins A and B, polyphenols, and flavonoid glycosides (Hou et al. 2000). P. weinmannifolia leaves are used to cure dysentery, enteritis, headache, influenza, and lung cancer (Zhao et al. 2005). P. chinensis has been used for medicinal purposes to relieve dysentery, inflammatory swelling, psoriasis, and rheumatism in China (Tang et al. 2012) and also to treat jaundice and

liver diseases in Pakistan (Akhtar et al. 2013). The essential oil of P. chinensis is in consideration for valuable biodiesel and is also an effective insect repellant. Various parts of Pistacia lentiscus have been used to treat stomach aches, heartburn, stomach ulcers, and influenza (Bozorgi et al. 2013). In the Middle East and Mediterranean regions, P. atlantica has been used widely for coughs, renal disorders, seizures, and nausea (Mahjoub et al. 2018). Also, P. terebinthus, P. khinjuk, P. integerrima, P. palaestina, P. eurycarpa, and P. vera are used to relieve diarrhea, inflammation, nausea, stomach ache, liver disorder, or skin infections (Bozorgi et al. 2013; Rauf et al. 2017).

Biosynthetic pathways can be used to understand how these invaluable phytochemicals are produced in the genus Pistacia, which can be possible through genomic information. Except for P. vera, however, only very limited genomic information is available for other Pistacia species. To gain a deeper understanding of the genetics of the genus Pistacia, as well as to find promising compounds and important genes that are involved in therapeutic and medicinal activities, it is crucial to improve genomic and genetic resources.

In this study, RNA sequencing was carried out to examine the genomic makeup of P. weinmannifolia. The sequencing data was used for de novo assembly, and the resulting unigene set was run on gene ontology (GO) analysis, KEGG metabolic pathway analysis, and SSR mining. In addition, the transcriptome data was compared to the transcriptome of P. chinensis previously reported (Choi et al. 2019) to examine any differences in genomic features between P. weinmannifolia and P. chinensis. So far, genomic resources for P. weinmannifolia are too limited, so the genomic data presented in this study would be very useful for genetic and genomic studies of this species.

Plant materials and sequencing

Fresh leaf samples of P. weinmannifolia were collected in Yunnan, China in June 2015. Immediately after harvest, leaf tissues were frozen in liquid N2 and submerged into RNAlater solution (Ambion Inc, USA) for transportation. Leaf samples in RNAlater solution were stored in a -20°C freezer before use. Total RNA extraction of the leaf samples was performed using TRIzol reagent by following the manufacturer’s instructions. The Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA) was used to check the purity and concentration of the total RNAs. The subsequent RNA sequencing processes were carried out using the methods described previously (Bae et al. 2018; Eum et al. 2019).

De novo assembly and gene annotation

Low quality reads, such as adapter sequences and empty reads, were removed using NGS toolkits and the Ion-Torrent server. For de novo assembly, reads with less than 50 bp in length and low-quality reads (Q20) were removed by the Trimmomatic tool (Bolger et al. 2014). The filtered and trimmed reads were assembled using three separate assemblers as described previously (Choi et al. 2019) to improve gene identification efficiency. CD-HIT-EST (Li and Godzik 2006) was used to combine identical or nearly identical contigs into a single unigene after clustering contigs with an identity greater than 90% and a coverage of 100%. Then, all resulting assembly data were merged into a single assembly. NCBI Non-Redundant (NR) Protein Database was used to annotate identified unigenes. The succeeding procedures were carried out following the methods described previously (Bae et al. 2018).

GO analysis and KEGG pathway search

The unigenes were annotated separately using the Blast2GO analysis tool, and WEGO software was used to classify the functional properties of the annotated unigenes. All unigene sequences were subjected to BlastKOALA (KEGG Orthology and Links Annotation) to assign KO numbers for the KEGG pathway search (http://www.genome.jp/kegg), and the assigned K numbers were used for the reconstruction of KEGG pathways.

Identification of SSR and repetitive sequences

Using MISA (pgrc.ipk-gatersleben.de/misa), a tool for microsatellite identification, SSRs were identified to assess SSR composition in the transcriptome of P. weinmannifolia at criteria of a minimum motif repeat with four and a minimum length of 12 bp. To examine SSR compositions in other Pistacia species, the identified SSR data was compared to that from the transcriptome data of P. chinensis reported previously (Choi et al. 2019). To investigate the content of repetitive sequences in the transcriptome of P. weinmannifolia, RepeatMasker (v. 4.0.7) was run at default mode using the reference library RepBaseRepeatMakerEdition-20170127 (www.girinst.org).

De novo assembly and annotation

RNA sequencing of P. weinmannifolia produced 10.4 million raw reads with a length of about 2.6 Gb, from which a total of 9.2 million clear reads (equivalent to about 2.23 Gb) after filtering raw quality reads. The clear reads were used for de novo assembly, resulting in a total of 18,956 unigenes (contigs) with a total length of 17,080,830 bp. The size range of unigenes was 257~9,942 bp, and 6,048 unigenes were in full length. GC content and N50 were 43.2% and 1,137 bp, respectively (Table 1). Length distribution of all identified unigenes was 6066 (32.0%) unigenes between 297~500 bp, followed by 3583 (18.9%) between 501~700 bp, 2453 (12.9%) from 701~900, and the number of unigenes longer than 2,501 bp was 600 (3.2%) (Fig. 1). To examine the similarity of unigenes in two Pistacia species, the transcriptomes of P. chinensis (Choi et al. 2019) and P. weinmannifolia were compared, and it was found that 8,825 unigenes (46.6%) are homologous in two species (Suliyat et al. 2024).

Fig. 1. Length distribution of unigenes in the transcriptome of Pistacia weinmannifolia

Table 1 . Summary of sequencing data for Pistacia weinmannifolia

DataP. chinensisP. weinmannifolia
Total number of raw reads10,621,05910,388,991
Total length of raw reads (bp)2,676,506,8682,618,025,732
Number of filtered reads used for assembly9,625,6769,161,308
Total length of filtered reads (bp)2,358,007,3932,230,762,660
Number of assembled contigs (unigenes)18,52418,956
Total length of assembled contigs (bp)16,174,68317,080,830
Average length (bp)873901
Length of largest contig (bp)9,9429,783
N50 (bp)1,1041,137
GC content (%)43.443.2


From annotation based on NCBI non-redundant protein database, a total of 18,296 contigs (96.5%) were annotated, while 660 (4.5%) did not show similarity to any known protein.

The top five plant species showing the most similarity to unigenes of P. weinmannifolia were Citrus sinensis with 9,286 (49.1%), Theobroma cacao with 2,745 (14.5%), Vitis vinifera with 929 (4.9%), Populus euphratica with 893 (4.7%), and also, Ricinus communis with 662 (3.5%) unigenes, respectively (Fig. 2).

Fig. 2. Top-five plant species with the highest number of homologous genes with unigenes in Pistacia weinmannifolia

GO functional classifications of P. weinmannifolia transcriptome

Go functional analysis of P. weinmannifolia transcriptome classified 4,986 unigenes into cellular components, 15,205 unigenes into biological processes, and 12,258 unigenes into molecular functions (Fig. 3). However, the number of unique unigenes excluding unigenes involved in more than two sub-functional categories was 1,683 (34%) for cellular components, 4,944 (32%) for biological processes, and 6,394 (52%) for molecular functions. In addition, in the molecular function category, most of the unigenes were classified into two subcategories, binding and catalytic activity. In total, 6,394 genes (52%) were classified into binding and 4,693 genes (38%) into catalytic activity (Fig. 3). In the biological process category, the top three subcategories were the metabolic process with 4,944 (32%) unigenes, the cellular process with 4,358 (28%), and the single-organism process consisting of 2,863 (19%) unigenes. In the cellular component category, most genes were classified into four subcategories, cell component consisting of 1,683 genes (34%), membrane with 1,247 genes (25%), organelle component with 1,127 (23%), and macromolecular complex with 820 (16%) unigenes, respectively (Fig. 3).

Fig. 3. Gene Ontology functional categorization of annotated unigenes of Pistacia weinmannifolia

Analysis of unigenes involved in the KEGG metabolic pathway

As P. weinmannifolia is an important medicinal plant, it was of interest to examine unigenes involved in metabolic pathways. All unigenes were run on the BlastKOALA tool (http://www.genome.jp/kegg) to allocate KEGG orthology number, known as KO number, resulting in 5,095 unigenes being annotated by being assigned for KO numbers. Among them, 3,166 unigenes were mapped into 410 different KEGG metabolic pathways (Table 2), in which 2,265 unigenes were shown to be involved in multiple pathways. The pathways of metabolism were found to contain the largest unigenes, 5,527 in 141 pathways, while the pathways of genetic information processing contained the fewest associated unigenes with 1,192 in 26 pathways (Table 2). The top five pathways with the highest number of entry enzyme hits among all pathways were the metabolic pathways (map01100) with 796 entry enzymes, the biosynthesis of secondary metabolites (map01110) with 409, microbial metabolism in diverse environments (map01120) with 140, Ribosome with 115 (map03010), and 01240 Biosynthesis of cofactors with 107 (map01240), respectively (Fig. 4).

Fig. 4. Top-ten Kyoto Encyclopedia of Genes and Genomes pathways with the most entry enzymes identified from the transcriptome of Pistacia weinmannifolia. The number for each pathway denotes the total entry enzymes identified

Table 2 . Categories of KEGG metabolic pathways and their associated entries and unigenes

CategoryNumber of sub-categoriesNumber of pathwaysNumber of entriesNumber of associated genes
Metabolism121413,2265,527 (1,387)*
Genetic Information Processing6268111,192 (1,014)
Environmental Information Processing3363791,288 (498)
Cellular Processes5345481,045 (589)
Organismal Systems10855351,299 (660)
Human Diseases12881,5152,856 (896)

*Numbers in parenthesis represent the number of non-redundant unigenes only. A total of 5,095 non-redundant unigenes were assigned, of which 3,166 were mapped in the KEGG pathways. KEGG, Kyoto Encyclopedia of Genes and Genomes.



Several metabolic pathways, including those involved in the metabolism of terpenoids, the formation of phenylpropanoids, flavonoids, and alkaloids, are closely connected to the biosynthesis of many secondary metabolites. Therefore, we examined several pathways related to metabolism of terpenoids and biosynthesis of phenylpropanoid and flavonoid (Table 3; Supplementary Table S1). Five unigenes were found to be involved in monoterpenoid biosynthesis (map00902), while 20 unigenes were found for 16 entries of carotenoid biosynthesis pathway (map00906) (Table 3). For the biosynthesis of phenylpropanoid and flavonoid, 25 unigenes were found to be involved in the flavonoid biosynthetic pathway (map00941) (Supplementary Fig. S1), while 49 unigenes were found to encode enzymes for 14 entries involved in the phenylpropanoid biosynthesis pathway (map00940) (Table 3; Supplementary Fig. S2).

Table 3 . KEGG metabolic pathways related to the biosynthesis of various medicinal metabolites

Metabolic pathwaysKEGG map IDNumber of entriesNumber of unigenes*
Metabolism of terpenoids
Monoterpenoid biosynthesis0090235
Carotenoid biosynthesis009061620
Diterpenoid biosynthesis0090447
Sesquiterpenoid and triterpenoid biosynthesis00909711
Biosynthesis of phenylpropanoids and flavonoids
Flavonoid biosynthesis009411525
Flavone and flavonol biosynthesis0094444
Isoquinoline alkaloid biosynthesis0095077
Phenylpropanoid biosynthesis009401449

*The number of unigenes is higher than the number of entries because some entries encoded by redundant unigenes. KEGG, Kyoto Encyclopedia of Genes and Genomes.



One of Pistacia species, Pistacia lentiscus is another important ethnobotanical plant, which has long been used to excrete Mastic, a resin. The Mastic has been used to cure ulcers by killing Helicobacter pylori, and its mode of action is reported to be exerted by α-terpineol and (E)- methyl isoeugenol derived from the monoterpenoid or the phenylpropanoid biosynthetic pathway. Therefore, it was of interest to examine whether P. weinmannifolia also contains genes involved in α-terpineol and (E)-methyl isoeugenol biosynthesis, key compounds of Mastic. We found six unigenes (CYM02514, CYM15186, CYM16691, CYM00763, CYM05910, and CYM09652) that potentially involve in Mastic biosynthesis in P. weinmannifolia (Supplementary Data).

Accumulation of repetitive sequences in P. weinmannifolia transcriptome

RepeatMasker (http://www.repeatmasker.org) was used to analyze the content of repetitive sequences in the transcriptome of P. weinmannifolia. It was found that repetitive and low-complexity sequences occupied 148,307 bp (0.87%) of the P. weinmannifolia transcriptome (Supplementary Table S2). Most of the repetitive sequences were simple sequence repeats, with 2,618 elements occupying 114,244 bp (0.67%) (Supplementary Table S2).

SSR search allocated a total of 2,599 perfect SSRs from 2,019 SSR-containing unigenes of P. weinmannifolia (Supplementary Table S3). Among SSR-containing unigenes, 399 had more than one SSR, of which 151 had compound SSRs. The frequencies of total identified SSRs of P. weinmannifolia were examined and compared to those of another Pistacia species, P. chinensis. The SSR frequencies were 162.5 per one million base pairs (Mbp) for P. chinensis and 152.2 Mbp for P. weinmannifolia (Supplementary Table S3). For both P. chinensis and P. weinmannifolia, rinucleotide SSRs were the most abundant SSRs with 2,343 (89.1%) and 2,334 (89.8%) occurrences, followed by di-nucleotide SSRs with 142 (5.4%) and 130 (5.0%) occurrences, respectively (Fig. 5a). Furthermore, the frequencies of trinucleotide SSRs for the two species were 144.9 per Mbp and 136.6 per Mbp with penta-nucleotide SSRs having the least frequencies of 0.5 per Mbp and 0.4 per Mbp, respectively (Fig. 5b). The number of SSR occurrence by individual motif in the two species ranged from 704 and 699 in AAG/CTT followed by 389 and 412 in ATC/ATG motif to nine and eight in motif AC/GT in P. chinensis and P. weinmannifolia respectively (Fig. 6a). The highest SSR occurrence by motif unit was AAG/CTT motif for both species with 43.5 and 40.9 occurrences per Mbp, followed by ATC/ATG motif with 24.0 and 24.1 occurrences per Mbp for P. chinensis and P. weinmannifolia, respectively (Fig. 6b; Supplementary Table S3).

Fig. 5. SSR occurrence and frequencies by repeat unit size. A. SSR frequency. B. SSR distribution. The numbers on the top of each pair of bars denote the SSR occurrence or frequency for Pw and Pc. Pw, Pistacia weinmannifolia; Pc, Pistacia chinensis; SSR, simple sequence repeat

Fig. 6. SSR occurrence and frequencies by motif type. A. SSR occurrence. B. SSR frequency. The numbers on the top of each pair of bars denote the SSR occurrence or frequency for Pw and Pc. Pw, Pistacia weinmannifolia; Pc, Pistacia chinensis; SSR, simple sequence repeat

Plants are a significant source of traditional medicine for the treatment of numerous diseases (Bako et al. 2005) because numerous plants contain a wide variety of different active ingredients with important therapeutic effects on viral infection, cancer, and tuberculosis (Ishtiyak and Hussain 2017). These medicinal effects are mostly due to certain plant secondary metabolites. Therefore, such metabolites with therapeutic effects are highly valued for their potential applications in various medicinal treatments (Bao et al. 2023). Nowadays, an increasing number of research is being done on medicinal plants to find new secondary metabolites with significant therapeutic effects. Also, it is very important to elucidate plant secondary metabolic pathways related to the biosynthesis of new lead molecules. However, it can be a very challenging task to learn about how those molecules are made in those plants in the absence of adequate genomic information. P. weinmannifolia has been used for various medicinal purposes (Tang et al. 2014), but there is very limited genetic and genomic information available. In this study, RNA sequencing was carried out to enrich the genomic information of P. weinmannifolia.

Transcriptome analysis of Pistacia species

De novo assembly of the RNA sequencing data generated the total of 18,956 unigenes, among which 18,296 unigenes (96.5%) were annotated. Similarly, it was reported that a comparable number of unigenes were identified and annotated from the transcriptome study of P. chinensis (Choi et al. 2019). However, the number of unigenes identified in this study was significantly lower than reports from the transcriptome studies of both P. vera (Karci et al. 2020; Moazzzam Jazi et al. 2017; Zeng et al. 2019) and P. chinensis (Cheng et al. 2022; Dong et al. 2016). Since several other Pistacia species, including P. atlantica, P. lentiscus, and P. weinmannifolia, are also well known as valuable medicinal plants, it was thought that many genomic studies would have been conducted on these species. However, most studies for these species have focused on medicinal effects or discoveries of chemical constituents (Hou et al. 2000; Liu et al. 2008; Rashed et al. 2016; Rauf et al. 2017; 2023; Shi and Zuo 1992; Zhao et al. 2005; Zhu et al. 2006) rather than genomic studies, and except for P. vera and P. chinensis, there are very limited genomic resources available for the rest of Pistacia species. Therefore, the transcriptome data presented in this study would be very useful as valuable resources for genomic and genetic studies of P. weinmannifolia and other Pistacia species.

In previous studies, unigenes from P. chinensis and P. vera had the highest BLAST hits to genes from Citrus sinensis, followed by Theobroma cacao other than other Citrus species in terms of species distribution in annotation (Choi et al. 2019; Moazzzam Jazi et al. 2017). The percentage of homologous genes was 39.7% between P. vera and C. sinensis (Moazzzam Jazi et al. 2017) and 48.5% between P. chinensis and C. sinensis (Choi et al. 2019). Similarly, a significant number of unigenes in the present study also showed the highest BLAST hits to genes of Citrus sinensis (49.1%), followed by Theobroma cacao (Fig. 2), suggesting that Pistacia species are very closely related to C. sinensis. Intriguingly, the number of homologous genes is significantly higher between Pistacia species and C. sinensis, which even comparable to the percentage of homologous genes between two Pistacia species, P. chinensis and P. weinmannifolia (Suliyat et al. 2024), in which only 47% were homologous.

Analysis of genes involved in biosynthesis pathways of secondary metabolites

Since many useful metabolites are known to be synthesized through routes involved in biosynthetic pathways of secondary metabolites in plants, and a variety of these secondary metabolites have significant therapeutic effects (Choi et al. 2019). In P. chinensis, 4,061 unigenes were reported to be mapped into 391 KEGG metabolic pathways (Choi et al. 2019), while in P. weinmannifolia, KEGG pathway mapping revealed that 3,166 unigenes are involved in 410 different metabolic pathways (Table 2). Among the associated unigenes to metabolic pathways, 20, 25, and 49 unigenes were found being involved in the carotenoid biosynthesis, phenylpropanoid biosynthesis, and flavonoid biosynthesis pathway (Table 3; Supplementary Table S1).

Mastic gum, a resin from Pistacia lentiscus, is used to cure ulcers by killing Helicobacter pylori, of which mode of action is reported through α-terpineol and (E)-methyl isoeugenol derived from the monoterpenoid or the phenylpropanoid biosynthetic pathway (Miyamoto et al. 2014). Several genes of P. weinamanifolia and P. chinensis that are involved in the biosynthesis of eugenol and isoeugenol were identified in this study.

Resources for potential SSR marker development for genetic study

Transcriptome data is very useful to generate key DNA markers for genetic and population studies. Such markers have been used frequently to investigate genetic relationships through cross-amplification among closely related species in plants. Also, it has been exploited in the analysis of genetic relationships among Pistacia species as well, in which many SSR markers, primarily from P. vera, have been used successfully for the cross-amplification among different Pistacia species (Zaloglu et al. 2015; Ziya Motalebipour et al. 2016). Therefore, additional SSR markers from other Pistacia species would increase the depth of SSR marker development and applications for genetic and population studies of Pistacia species or closely related species. In this study, 2,618 perfect SSRs were identified from P. weinmannifolia, of which frequencies were 152.2 per one Mbp, which was similar to that in P. chinensis with 162.5 per one Mbp (Supplementary Table S3). According to several previous research (Bae et al. 2018; Eum et al. 2019; Kotwal et al. 2016; Zhang et al. 2019), trinucleotide SSRs were the most prevalent SSRs, and also trinucleotide SSRs were the most abundant SSRs with the frequency of 136.6 per Mbp in P. weinmannifolia (Fig. 5b). Nevertheless, the transcriptome data of P. weinmannifolia can be used not only for new SSR marker development but also for a detailed insight into understanding the genomic features of Pistacia species.

This work was supported by a research grant from Kangwon National University in 2022 and by a grant from the KRIBB Initiative Program [KGM4582423] of the Republic of Korea.

This Transcriptome Shotgun Assembly (TSA) projects have been deposited at DDBJ/EMBL/GenBank under the accession GKQH00000000 for P. chinensis and GKQG00000000 for P. weinmannifolia. The TSA version described in this paper is the first version, GKQH01000000 and GKQG01000000. Raw sequencing data can be accessed at BioProject data accession number PRJNA566127.

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Article

Research Article

J Plant Biotechnol 2024; 51(1): 33-49

Published online February 19, 2024 https://doi.org/10.5010/JPB.2024.51.004.033

Copyright © The Korean Society of Plant Biotechnology.

Transcriptome analysis of a tropical medicinal plant, Pistacia weinmannifolia

Mi Kyung Choi ・Bimpe Suliyat Azeez ・Sang Woo Lee ・Wan Yi Li ・Sangho Choi ・Ik-Young Choi ・Ki-Young Choi ・Jong-Kuk Na

Department of Agriculture and Industries, Kangwon National University, Chuncheon, Kangwon 24341, Republic of Korea
International Biological Material Research Center, KRIBB, Daejeon 34141, Republic of Korea
Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming, Yunnan 650200, P.R. China
Department of SmartFarm and Agricultural Industry, Kangwon National University, Chuncheon, Kangwon 24341, Republic of Korea

Correspondence to:e-mail: jongkook@kangwon.ac.kr

Received: 22 January 2024; Revised: 7 February 2024; Accepted: 7 February 2024; Published: 19 February 2024.

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Pistacia weinmannifolia has long been used as an herbal medicine for treating various illnesses. The genomic information of P. weinmannifolia will help elucidate the chemical constituents that exert medicinal effects; however, genomic studies have rarely been performed. Therefore, we conducted a transcriptome analysis of P. weinmannifolia using the Illumina RNA sequencing system. We obtained a total of 18 million high-quality paired-end reads with 2,230 Mbp. De novo assembly of high-quality reads generated a total of 18,956 non-redundant contigs with an average length of 901 bp, of which 18,296 contigs (96.5%) were annotated. The total length of all unigenes was 17,080,830 bp, and the GC content and N50 were 43.2% and 1,137 bp, respectively. Annotation using The Kyoto Encyclopedia of Genes and Genomes (KEGG) assigned a total of 5,095 unigenes (26.9%), of which 3,166 were mapped to 410 different KEGG metabolic pathways. A comparison of unigenes between P. weinmannifolia and Pistacia chinensis showed that 8,825 unigenes were highly similar to each other. Simple sequence repeats were mined, and valuable data for further comparative and functional genomic studies were obtained to uncover the mechanisms underlying the medicinal properties of P. weinmannifolia as an important medicinal plant. Several genes of P.weinmannifolia involved in the biosynthesis of eugenol and isoeugenol were also identified in this study.

Keywords: RNA sequencing, Medicinal plant, Simple sequence repeat, Repetitive sequence

Introduction

Pistacia weinmannifolia J. Poisson ex Franch belongs to the family Anacardiaceae and is a shrub localized predominantly to Southwestern China, particularly in the provinces of Yunnan, Sichuan, Guangxi, Guizhou, and Tibet (Mien and Ming 1980). It is also distributed in Vietnam and Myanmar (Mien and Ming 1980), and Iran and Western Mediterranean countries as well (Rauf et al. 2017). P. weinmannifolia has been used as a popular decorative plant for miniascapes because of its elegant profile. On the otherhand, P. weinmannifolia has been used as a herbal medicine to treat various illnesses (Rauf et al. 2017).

Various studies on the genus Pistacia for their pharmacological and industrial properties using various parts of the plants have shown that they contain diverse and valuable secondary metabolites (Bozorgi et al. 2013; Rauf et al. 2017), with several members of this genus being in long-term use for numerous medicinal purposes (Akhtar et al. 2013; Rauf et al. 2017). Also, many important phytochemicals were identified from the genus Pistacia, such as camphene, pistacigerrimones, masticadienolic acid, and ellagic acid (Rauf et al. 2017). P.weinmannifolia contains high contents of gallotannins, pistafollins A and B, polyphenols, and flavonoid glycosides (Hou et al. 2000). P. weinmannifolia leaves are used to cure dysentery, enteritis, headache, influenza, and lung cancer (Zhao et al. 2005). P. chinensis has been used for medicinal purposes to relieve dysentery, inflammatory swelling, psoriasis, and rheumatism in China (Tang et al. 2012) and also to treat jaundice and

liver diseases in Pakistan (Akhtar et al. 2013). The essential oil of P. chinensis is in consideration for valuable biodiesel and is also an effective insect repellant. Various parts of Pistacia lentiscus have been used to treat stomach aches, heartburn, stomach ulcers, and influenza (Bozorgi et al. 2013). In the Middle East and Mediterranean regions, P. atlantica has been used widely for coughs, renal disorders, seizures, and nausea (Mahjoub et al. 2018). Also, P. terebinthus, P. khinjuk, P. integerrima, P. palaestina, P. eurycarpa, and P. vera are used to relieve diarrhea, inflammation, nausea, stomach ache, liver disorder, or skin infections (Bozorgi et al. 2013; Rauf et al. 2017).

Biosynthetic pathways can be used to understand how these invaluable phytochemicals are produced in the genus Pistacia, which can be possible through genomic information. Except for P. vera, however, only very limited genomic information is available for other Pistacia species. To gain a deeper understanding of the genetics of the genus Pistacia, as well as to find promising compounds and important genes that are involved in therapeutic and medicinal activities, it is crucial to improve genomic and genetic resources.

In this study, RNA sequencing was carried out to examine the genomic makeup of P. weinmannifolia. The sequencing data was used for de novo assembly, and the resulting unigene set was run on gene ontology (GO) analysis, KEGG metabolic pathway analysis, and SSR mining. In addition, the transcriptome data was compared to the transcriptome of P. chinensis previously reported (Choi et al. 2019) to examine any differences in genomic features between P. weinmannifolia and P. chinensis. So far, genomic resources for P. weinmannifolia are too limited, so the genomic data presented in this study would be very useful for genetic and genomic studies of this species.

Materials and Methods

Plant materials and sequencing

Fresh leaf samples of P. weinmannifolia were collected in Yunnan, China in June 2015. Immediately after harvest, leaf tissues were frozen in liquid N2 and submerged into RNAlater solution (Ambion Inc, USA) for transportation. Leaf samples in RNAlater solution were stored in a -20°C freezer before use. Total RNA extraction of the leaf samples was performed using TRIzol reagent by following the manufacturer’s instructions. The Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA) was used to check the purity and concentration of the total RNAs. The subsequent RNA sequencing processes were carried out using the methods described previously (Bae et al. 2018; Eum et al. 2019).

De novo assembly and gene annotation

Low quality reads, such as adapter sequences and empty reads, were removed using NGS toolkits and the Ion-Torrent server. For de novo assembly, reads with less than 50 bp in length and low-quality reads (Q20) were removed by the Trimmomatic tool (Bolger et al. 2014). The filtered and trimmed reads were assembled using three separate assemblers as described previously (Choi et al. 2019) to improve gene identification efficiency. CD-HIT-EST (Li and Godzik 2006) was used to combine identical or nearly identical contigs into a single unigene after clustering contigs with an identity greater than 90% and a coverage of 100%. Then, all resulting assembly data were merged into a single assembly. NCBI Non-Redundant (NR) Protein Database was used to annotate identified unigenes. The succeeding procedures were carried out following the methods described previously (Bae et al. 2018).

GO analysis and KEGG pathway search

The unigenes were annotated separately using the Blast2GO analysis tool, and WEGO software was used to classify the functional properties of the annotated unigenes. All unigene sequences were subjected to BlastKOALA (KEGG Orthology and Links Annotation) to assign KO numbers for the KEGG pathway search (http://www.genome.jp/kegg), and the assigned K numbers were used for the reconstruction of KEGG pathways.

Identification of SSR and repetitive sequences

Using MISA (pgrc.ipk-gatersleben.de/misa), a tool for microsatellite identification, SSRs were identified to assess SSR composition in the transcriptome of P. weinmannifolia at criteria of a minimum motif repeat with four and a minimum length of 12 bp. To examine SSR compositions in other Pistacia species, the identified SSR data was compared to that from the transcriptome data of P. chinensis reported previously (Choi et al. 2019). To investigate the content of repetitive sequences in the transcriptome of P. weinmannifolia, RepeatMasker (v. 4.0.7) was run at default mode using the reference library RepBaseRepeatMakerEdition-20170127 (www.girinst.org).

Results

De novo assembly and annotation

RNA sequencing of P. weinmannifolia produced 10.4 million raw reads with a length of about 2.6 Gb, from which a total of 9.2 million clear reads (equivalent to about 2.23 Gb) after filtering raw quality reads. The clear reads were used for de novo assembly, resulting in a total of 18,956 unigenes (contigs) with a total length of 17,080,830 bp. The size range of unigenes was 257~9,942 bp, and 6,048 unigenes were in full length. GC content and N50 were 43.2% and 1,137 bp, respectively (Table 1). Length distribution of all identified unigenes was 6066 (32.0%) unigenes between 297~500 bp, followed by 3583 (18.9%) between 501~700 bp, 2453 (12.9%) from 701~900, and the number of unigenes longer than 2,501 bp was 600 (3.2%) (Fig. 1). To examine the similarity of unigenes in two Pistacia species, the transcriptomes of P. chinensis (Choi et al. 2019) and P. weinmannifolia were compared, and it was found that 8,825 unigenes (46.6%) are homologous in two species (Suliyat et al. 2024).

Figure 1. Length distribution of unigenes in the transcriptome of Pistacia weinmannifolia

Table 1 . Summary of sequencing data for Pistacia weinmannifolia.

DataP. chinensisP. weinmannifolia
Total number of raw reads10,621,05910,388,991
Total length of raw reads (bp)2,676,506,8682,618,025,732
Number of filtered reads used for assembly9,625,6769,161,308
Total length of filtered reads (bp)2,358,007,3932,230,762,660
Number of assembled contigs (unigenes)18,52418,956
Total length of assembled contigs (bp)16,174,68317,080,830
Average length (bp)873901
Length of largest contig (bp)9,9429,783
N50 (bp)1,1041,137
GC content (%)43.443.2


From annotation based on NCBI non-redundant protein database, a total of 18,296 contigs (96.5%) were annotated, while 660 (4.5%) did not show similarity to any known protein.

The top five plant species showing the most similarity to unigenes of P. weinmannifolia were Citrus sinensis with 9,286 (49.1%), Theobroma cacao with 2,745 (14.5%), Vitis vinifera with 929 (4.9%), Populus euphratica with 893 (4.7%), and also, Ricinus communis with 662 (3.5%) unigenes, respectively (Fig. 2).

Figure 2. Top-five plant species with the highest number of homologous genes with unigenes in Pistacia weinmannifolia

GO functional classifications of P. weinmannifolia transcriptome

Go functional analysis of P. weinmannifolia transcriptome classified 4,986 unigenes into cellular components, 15,205 unigenes into biological processes, and 12,258 unigenes into molecular functions (Fig. 3). However, the number of unique unigenes excluding unigenes involved in more than two sub-functional categories was 1,683 (34%) for cellular components, 4,944 (32%) for biological processes, and 6,394 (52%) for molecular functions. In addition, in the molecular function category, most of the unigenes were classified into two subcategories, binding and catalytic activity. In total, 6,394 genes (52%) were classified into binding and 4,693 genes (38%) into catalytic activity (Fig. 3). In the biological process category, the top three subcategories were the metabolic process with 4,944 (32%) unigenes, the cellular process with 4,358 (28%), and the single-organism process consisting of 2,863 (19%) unigenes. In the cellular component category, most genes were classified into four subcategories, cell component consisting of 1,683 genes (34%), membrane with 1,247 genes (25%), organelle component with 1,127 (23%), and macromolecular complex with 820 (16%) unigenes, respectively (Fig. 3).

Figure 3. Gene Ontology functional categorization of annotated unigenes of Pistacia weinmannifolia

Analysis of unigenes involved in the KEGG metabolic pathway

As P. weinmannifolia is an important medicinal plant, it was of interest to examine unigenes involved in metabolic pathways. All unigenes were run on the BlastKOALA tool (http://www.genome.jp/kegg) to allocate KEGG orthology number, known as KO number, resulting in 5,095 unigenes being annotated by being assigned for KO numbers. Among them, 3,166 unigenes were mapped into 410 different KEGG metabolic pathways (Table 2), in which 2,265 unigenes were shown to be involved in multiple pathways. The pathways of metabolism were found to contain the largest unigenes, 5,527 in 141 pathways, while the pathways of genetic information processing contained the fewest associated unigenes with 1,192 in 26 pathways (Table 2). The top five pathways with the highest number of entry enzyme hits among all pathways were the metabolic pathways (map01100) with 796 entry enzymes, the biosynthesis of secondary metabolites (map01110) with 409, microbial metabolism in diverse environments (map01120) with 140, Ribosome with 115 (map03010), and 01240 Biosynthesis of cofactors with 107 (map01240), respectively (Fig. 4).

Figure 4. Top-ten Kyoto Encyclopedia of Genes and Genomes pathways with the most entry enzymes identified from the transcriptome of Pistacia weinmannifolia. The number for each pathway denotes the total entry enzymes identified

Table 2 . Categories of KEGG metabolic pathways and their associated entries and unigenes.

CategoryNumber of sub-categoriesNumber of pathwaysNumber of entriesNumber of associated genes
Metabolism121413,2265,527 (1,387)*
Genetic Information Processing6268111,192 (1,014)
Environmental Information Processing3363791,288 (498)
Cellular Processes5345481,045 (589)
Organismal Systems10855351,299 (660)
Human Diseases12881,5152,856 (896)

*Numbers in parenthesis represent the number of non-redundant unigenes only. A total of 5,095 non-redundant unigenes were assigned, of which 3,166 were mapped in the KEGG pathways. KEGG, Kyoto Encyclopedia of Genes and Genomes..



Several metabolic pathways, including those involved in the metabolism of terpenoids, the formation of phenylpropanoids, flavonoids, and alkaloids, are closely connected to the biosynthesis of many secondary metabolites. Therefore, we examined several pathways related to metabolism of terpenoids and biosynthesis of phenylpropanoid and flavonoid (Table 3; Supplementary Table S1). Five unigenes were found to be involved in monoterpenoid biosynthesis (map00902), while 20 unigenes were found for 16 entries of carotenoid biosynthesis pathway (map00906) (Table 3). For the biosynthesis of phenylpropanoid and flavonoid, 25 unigenes were found to be involved in the flavonoid biosynthetic pathway (map00941) (Supplementary Fig. S1), while 49 unigenes were found to encode enzymes for 14 entries involved in the phenylpropanoid biosynthesis pathway (map00940) (Table 3; Supplementary Fig. S2).

Table 3 . KEGG metabolic pathways related to the biosynthesis of various medicinal metabolites.

Metabolic pathwaysKEGG map IDNumber of entriesNumber of unigenes*
Metabolism of terpenoids
Monoterpenoid biosynthesis0090235
Carotenoid biosynthesis009061620
Diterpenoid biosynthesis0090447
Sesquiterpenoid and triterpenoid biosynthesis00909711
Biosynthesis of phenylpropanoids and flavonoids
Flavonoid biosynthesis009411525
Flavone and flavonol biosynthesis0094444
Isoquinoline alkaloid biosynthesis0095077
Phenylpropanoid biosynthesis009401449

*The number of unigenes is higher than the number of entries because some entries encoded by redundant unigenes. KEGG, Kyoto Encyclopedia of Genes and Genomes..



One of Pistacia species, Pistacia lentiscus is another important ethnobotanical plant, which has long been used to excrete Mastic, a resin. The Mastic has been used to cure ulcers by killing Helicobacter pylori, and its mode of action is reported to be exerted by α-terpineol and (E)- methyl isoeugenol derived from the monoterpenoid or the phenylpropanoid biosynthetic pathway. Therefore, it was of interest to examine whether P. weinmannifolia also contains genes involved in α-terpineol and (E)-methyl isoeugenol biosynthesis, key compounds of Mastic. We found six unigenes (CYM02514, CYM15186, CYM16691, CYM00763, CYM05910, and CYM09652) that potentially involve in Mastic biosynthesis in P. weinmannifolia (Supplementary Data).

Accumulation of repetitive sequences in P. weinmannifolia transcriptome

RepeatMasker (http://www.repeatmasker.org) was used to analyze the content of repetitive sequences in the transcriptome of P. weinmannifolia. It was found that repetitive and low-complexity sequences occupied 148,307 bp (0.87%) of the P. weinmannifolia transcriptome (Supplementary Table S2). Most of the repetitive sequences were simple sequence repeats, with 2,618 elements occupying 114,244 bp (0.67%) (Supplementary Table S2).

SSR search allocated a total of 2,599 perfect SSRs from 2,019 SSR-containing unigenes of P. weinmannifolia (Supplementary Table S3). Among SSR-containing unigenes, 399 had more than one SSR, of which 151 had compound SSRs. The frequencies of total identified SSRs of P. weinmannifolia were examined and compared to those of another Pistacia species, P. chinensis. The SSR frequencies were 162.5 per one million base pairs (Mbp) for P. chinensis and 152.2 Mbp for P. weinmannifolia (Supplementary Table S3). For both P. chinensis and P. weinmannifolia, rinucleotide SSRs were the most abundant SSRs with 2,343 (89.1%) and 2,334 (89.8%) occurrences, followed by di-nucleotide SSRs with 142 (5.4%) and 130 (5.0%) occurrences, respectively (Fig. 5a). Furthermore, the frequencies of trinucleotide SSRs for the two species were 144.9 per Mbp and 136.6 per Mbp with penta-nucleotide SSRs having the least frequencies of 0.5 per Mbp and 0.4 per Mbp, respectively (Fig. 5b). The number of SSR occurrence by individual motif in the two species ranged from 704 and 699 in AAG/CTT followed by 389 and 412 in ATC/ATG motif to nine and eight in motif AC/GT in P. chinensis and P. weinmannifolia respectively (Fig. 6a). The highest SSR occurrence by motif unit was AAG/CTT motif for both species with 43.5 and 40.9 occurrences per Mbp, followed by ATC/ATG motif with 24.0 and 24.1 occurrences per Mbp for P. chinensis and P. weinmannifolia, respectively (Fig. 6b; Supplementary Table S3).

Figure 5. SSR occurrence and frequencies by repeat unit size. A. SSR frequency. B. SSR distribution. The numbers on the top of each pair of bars denote the SSR occurrence or frequency for Pw and Pc. Pw, Pistacia weinmannifolia; Pc, Pistacia chinensis; SSR, simple sequence repeat

Figure 6. SSR occurrence and frequencies by motif type. A. SSR occurrence. B. SSR frequency. The numbers on the top of each pair of bars denote the SSR occurrence or frequency for Pw and Pc. Pw, Pistacia weinmannifolia; Pc, Pistacia chinensis; SSR, simple sequence repeat

Discussion

Plants are a significant source of traditional medicine for the treatment of numerous diseases (Bako et al. 2005) because numerous plants contain a wide variety of different active ingredients with important therapeutic effects on viral infection, cancer, and tuberculosis (Ishtiyak and Hussain 2017). These medicinal effects are mostly due to certain plant secondary metabolites. Therefore, such metabolites with therapeutic effects are highly valued for their potential applications in various medicinal treatments (Bao et al. 2023). Nowadays, an increasing number of research is being done on medicinal plants to find new secondary metabolites with significant therapeutic effects. Also, it is very important to elucidate plant secondary metabolic pathways related to the biosynthesis of new lead molecules. However, it can be a very challenging task to learn about how those molecules are made in those plants in the absence of adequate genomic information. P. weinmannifolia has been used for various medicinal purposes (Tang et al. 2014), but there is very limited genetic and genomic information available. In this study, RNA sequencing was carried out to enrich the genomic information of P. weinmannifolia.

Transcriptome analysis of Pistacia species

De novo assembly of the RNA sequencing data generated the total of 18,956 unigenes, among which 18,296 unigenes (96.5%) were annotated. Similarly, it was reported that a comparable number of unigenes were identified and annotated from the transcriptome study of P. chinensis (Choi et al. 2019). However, the number of unigenes identified in this study was significantly lower than reports from the transcriptome studies of both P. vera (Karci et al. 2020; Moazzzam Jazi et al. 2017; Zeng et al. 2019) and P. chinensis (Cheng et al. 2022; Dong et al. 2016). Since several other Pistacia species, including P. atlantica, P. lentiscus, and P. weinmannifolia, are also well known as valuable medicinal plants, it was thought that many genomic studies would have been conducted on these species. However, most studies for these species have focused on medicinal effects or discoveries of chemical constituents (Hou et al. 2000; Liu et al. 2008; Rashed et al. 2016; Rauf et al. 2017; 2023; Shi and Zuo 1992; Zhao et al. 2005; Zhu et al. 2006) rather than genomic studies, and except for P. vera and P. chinensis, there are very limited genomic resources available for the rest of Pistacia species. Therefore, the transcriptome data presented in this study would be very useful as valuable resources for genomic and genetic studies of P. weinmannifolia and other Pistacia species.

In previous studies, unigenes from P. chinensis and P. vera had the highest BLAST hits to genes from Citrus sinensis, followed by Theobroma cacao other than other Citrus species in terms of species distribution in annotation (Choi et al. 2019; Moazzzam Jazi et al. 2017). The percentage of homologous genes was 39.7% between P. vera and C. sinensis (Moazzzam Jazi et al. 2017) and 48.5% between P. chinensis and C. sinensis (Choi et al. 2019). Similarly, a significant number of unigenes in the present study also showed the highest BLAST hits to genes of Citrus sinensis (49.1%), followed by Theobroma cacao (Fig. 2), suggesting that Pistacia species are very closely related to C. sinensis. Intriguingly, the number of homologous genes is significantly higher between Pistacia species and C. sinensis, which even comparable to the percentage of homologous genes between two Pistacia species, P. chinensis and P. weinmannifolia (Suliyat et al. 2024), in which only 47% were homologous.

Analysis of genes involved in biosynthesis pathways of secondary metabolites

Since many useful metabolites are known to be synthesized through routes involved in biosynthetic pathways of secondary metabolites in plants, and a variety of these secondary metabolites have significant therapeutic effects (Choi et al. 2019). In P. chinensis, 4,061 unigenes were reported to be mapped into 391 KEGG metabolic pathways (Choi et al. 2019), while in P. weinmannifolia, KEGG pathway mapping revealed that 3,166 unigenes are involved in 410 different metabolic pathways (Table 2). Among the associated unigenes to metabolic pathways, 20, 25, and 49 unigenes were found being involved in the carotenoid biosynthesis, phenylpropanoid biosynthesis, and flavonoid biosynthesis pathway (Table 3; Supplementary Table S1).

Mastic gum, a resin from Pistacia lentiscus, is used to cure ulcers by killing Helicobacter pylori, of which mode of action is reported through α-terpineol and (E)-methyl isoeugenol derived from the monoterpenoid or the phenylpropanoid biosynthetic pathway (Miyamoto et al. 2014). Several genes of P. weinamanifolia and P. chinensis that are involved in the biosynthesis of eugenol and isoeugenol were identified in this study.

Resources for potential SSR marker development for genetic study

Transcriptome data is very useful to generate key DNA markers for genetic and population studies. Such markers have been used frequently to investigate genetic relationships through cross-amplification among closely related species in plants. Also, it has been exploited in the analysis of genetic relationships among Pistacia species as well, in which many SSR markers, primarily from P. vera, have been used successfully for the cross-amplification among different Pistacia species (Zaloglu et al. 2015; Ziya Motalebipour et al. 2016). Therefore, additional SSR markers from other Pistacia species would increase the depth of SSR marker development and applications for genetic and population studies of Pistacia species or closely related species. In this study, 2,618 perfect SSRs were identified from P. weinmannifolia, of which frequencies were 152.2 per one Mbp, which was similar to that in P. chinensis with 162.5 per one Mbp (Supplementary Table S3). According to several previous research (Bae et al. 2018; Eum et al. 2019; Kotwal et al. 2016; Zhang et al. 2019), trinucleotide SSRs were the most prevalent SSRs, and also trinucleotide SSRs were the most abundant SSRs with the frequency of 136.6 per Mbp in P. weinmannifolia (Fig. 5b). Nevertheless, the transcriptome data of P. weinmannifolia can be used not only for new SSR marker development but also for a detailed insight into understanding the genomic features of Pistacia species.

Acknowledgement

This work was supported by a research grant from Kangwon National University in 2022 and by a grant from the KRIBB Initiative Program [KGM4582423] of the Republic of Korea.

Conflict of Interests

The authors declare that there is no conflict of interest.

Data Accession

This Transcriptome Shotgun Assembly (TSA) projects have been deposited at DDBJ/EMBL/GenBank under the accession GKQH00000000 for P. chinensis and GKQG00000000 for P. weinmannifolia. The TSA version described in this paper is the first version, GKQH01000000 and GKQG01000000. Raw sequencing data can be accessed at BioProject data accession number PRJNA566127.

Fig 1.

Figure 1.Length distribution of unigenes in the transcriptome of Pistacia weinmannifolia
Journal of Plant Biotechnology 2024; 51: 33-49https://doi.org/10.5010/JPB.2024.51.004.033

Fig 2.

Figure 2.Top-five plant species with the highest number of homologous genes with unigenes in Pistacia weinmannifolia
Journal of Plant Biotechnology 2024; 51: 33-49https://doi.org/10.5010/JPB.2024.51.004.033

Fig 3.

Figure 3.Gene Ontology functional categorization of annotated unigenes of Pistacia weinmannifolia
Journal of Plant Biotechnology 2024; 51: 33-49https://doi.org/10.5010/JPB.2024.51.004.033

Fig 4.

Figure 4.Top-ten Kyoto Encyclopedia of Genes and Genomes pathways with the most entry enzymes identified from the transcriptome of Pistacia weinmannifolia. The number for each pathway denotes the total entry enzymes identified
Journal of Plant Biotechnology 2024; 51: 33-49https://doi.org/10.5010/JPB.2024.51.004.033

Fig 5.

Figure 5.SSR occurrence and frequencies by repeat unit size. A. SSR frequency. B. SSR distribution. The numbers on the top of each pair of bars denote the SSR occurrence or frequency for Pw and Pc. Pw, Pistacia weinmannifolia; Pc, Pistacia chinensis; SSR, simple sequence repeat
Journal of Plant Biotechnology 2024; 51: 33-49https://doi.org/10.5010/JPB.2024.51.004.033

Fig 6.

Figure 6.SSR occurrence and frequencies by motif type. A. SSR occurrence. B. SSR frequency. The numbers on the top of each pair of bars denote the SSR occurrence or frequency for Pw and Pc. Pw, Pistacia weinmannifolia; Pc, Pistacia chinensis; SSR, simple sequence repeat
Journal of Plant Biotechnology 2024; 51: 33-49https://doi.org/10.5010/JPB.2024.51.004.033

Table 1 . Summary of sequencing data for Pistacia weinmannifolia.

DataP. chinensisP. weinmannifolia
Total number of raw reads10,621,05910,388,991
Total length of raw reads (bp)2,676,506,8682,618,025,732
Number of filtered reads used for assembly9,625,6769,161,308
Total length of filtered reads (bp)2,358,007,3932,230,762,660
Number of assembled contigs (unigenes)18,52418,956
Total length of assembled contigs (bp)16,174,68317,080,830
Average length (bp)873901
Length of largest contig (bp)9,9429,783
N50 (bp)1,1041,137
GC content (%)43.443.2

Table 2 . Categories of KEGG metabolic pathways and their associated entries and unigenes.

CategoryNumber of sub-categoriesNumber of pathwaysNumber of entriesNumber of associated genes
Metabolism121413,2265,527 (1,387)*
Genetic Information Processing6268111,192 (1,014)
Environmental Information Processing3363791,288 (498)
Cellular Processes5345481,045 (589)
Organismal Systems10855351,299 (660)
Human Diseases12881,5152,856 (896)

*Numbers in parenthesis represent the number of non-redundant unigenes only. A total of 5,095 non-redundant unigenes were assigned, of which 3,166 were mapped in the KEGG pathways. KEGG, Kyoto Encyclopedia of Genes and Genomes..


Table 3 . KEGG metabolic pathways related to the biosynthesis of various medicinal metabolites.

Metabolic pathwaysKEGG map IDNumber of entriesNumber of unigenes*
Metabolism of terpenoids
Monoterpenoid biosynthesis0090235
Carotenoid biosynthesis009061620
Diterpenoid biosynthesis0090447
Sesquiterpenoid and triterpenoid biosynthesis00909711
Biosynthesis of phenylpropanoids and flavonoids
Flavonoid biosynthesis009411525
Flavone and flavonol biosynthesis0094444
Isoquinoline alkaloid biosynthesis0095077
Phenylpropanoid biosynthesis009401449

*The number of unigenes is higher than the number of entries because some entries encoded by redundant unigenes. KEGG, Kyoto Encyclopedia of Genes and Genomes..


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