The genome of Dioscorea zingiberensis sheds light on the biosynthesis, origin and evolution of the medicinally important diosgenin saponins

Yi Li; Chao Tan; Zihao Li; Jingzhe Guo; Song Li; Xin Chen; Chen Wang; Xiaokang Dai; Huan Yang; Wei Song; Lixiu Hou; Jiali Xu; Ziyu Tong; Anran Xu; Xincheng Yuan; Weipeng Wang; Qingyong Yang; Lingling Chen; Zongyi Sun; Kai Wang; Bo Pan; Jianghua Chen; Yinghua Bao; Faguang Liu; Xiaoquan Qi; David R Gang; Jun Wen; Jiaru Li

doi:10.1093/hr/uhac165

. 2022 Jul 25;9:uhac165. doi: 10.1093/hr/uhac165

The genome of Dioscorea zingiberensis sheds light on the biosynthesis, origin and evolution of the medicinally important diosgenin saponins

Yi Li ¹, Chao Tan ², Zihao Li ³, Jingzhe Guo ⁴, Song Li ⁵, Xin Chen ⁶, Chen Wang ⁷, Xiaokang Dai ⁸, Huan Yang ⁹, Wei Song ¹⁰, Lixiu Hou ¹¹, Jiali Xu ¹², Ziyu Tong ¹³, Anran Xu ¹⁴, Xincheng Yuan ¹⁵, Weipeng Wang ¹⁶, Qingyong Yang ¹⁷, Lingling Chen ¹⁸, Zongyi Sun ¹⁹, Kai Wang ²⁰, Bo Pan ²¹, Jianghua Chen ²², Yinghua Bao ²³, Faguang Liu ²⁴, Xiaoquan Qi ²⁵, David R Gang ²⁶, Jun Wen ²⁷, Jiaru Li ^28,^✉

¹State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China

²State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China

³State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China

⁴State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China

⁵State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China

⁶State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China

⁷State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China

⁸State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China

⁹State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China

¹⁰State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China

¹¹State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China

¹²State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China

¹³State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China

¹⁴State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China

¹⁵State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China

¹⁶State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China

¹⁷Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China

¹⁸College of Life Science and Technology, Guangxi University, Nanning, 530004, China

¹⁹ Grandomics Biosciences, Beijing 102200, China

²⁰ Grandomics Biosciences, Beijing 102200, China

²¹Center for Integrative Conservation, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Menglun, 666303, China

²² CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, CAS Center for Excellence in Molecular Plant Sciences, Xishuangbanna Tropical Botanical Garden, Kunming, 650223, China

²³Henry Fok College of Biology and Agriculture, Shaoguan University, Shaoguan, 512005, China

²⁴Henry Fok College of Biology and Agriculture, Shaoguan University, Shaoguan, 512005, China

²⁵Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China

²⁶ Institute of Biological Chemistry, Washington State University, Pullman, WA, 99164, USA

²⁷Department of Botany, National Museum of Natural History, Smithsonian Institution, Washington, DC, 20013-7012, USA

²⁸State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China

^✉

Corresponding author. E-mail: jrli@whu.edu.cn

PMCID: PMC9531337 PMID: 36204203

Abstract

Diosgenin saponins isolated from Dioscorea species such as D. zingiberensis exhibit a broad spectrum of pharmacological activities. Diosgenin, the aglycone of diosgenin saponins, is an important starting material for the production of steroidal drugs. However, how plants produce diosgenin saponins and the origin and evolution of the diosgenin saponin biosynthetic pathway remain a mystery. Here we report a high-quality, 629-Mb genome of D. zingiberensis anchored on 10 chromosomes with 30 322 protein-coding genes. We reveal that diosgenin is synthesized in leaves (‘source’), then converted into diosgenin saponins, and finally transported to rhizomes (‘sink’) for storage in plants. By evaluating the distribution and evolutionary patterns of diosgenin saponins in Dioscorea species, we find that diosgenin saponin-containing may be an ancestral trait in Dioscorea and is selectively retained. The results of comparative genomic analysis indicate that tandem duplication coupled with a whole-genome duplication event provided key evolutionary resources for the diosgenin saponin biosynthetic pathway in the D. zingiberensis genome. Furthermore, comparative transcriptome and metabolite analysis among 13 Dioscorea species suggests that specific gene expression patterns of pathway genes promote the differential evolution of the diosgenin saponin biosynthetic pathway in Dioscorea species. Our study provides important insights and valuable resources for further understanding the biosynthesis, evolution, and utilization of plant specialized metabolites such as diosgenin saponins.

Introduction

Diosgenin is a steroidal sapogenin first isolated from the rhizomes of Dioscorea tokoro in the 1930s [1]. Diosgenin saponins, with diosgenin acting as the aglycone backbone, are important bioactive constituents widely distributed in Dioscorea spp. Diosgenin saponins isolated from the rhizomes or tubers of Dioscorea plants are mainly divided into spirostanol-type and furostanol-type (Fig. 1), which have a broad spectrum of pharmacological activities, including antioxidative, anti-cancer, anti-inflammatory, anti-fungal, and hypolipidemic functions, and are widely used in pharmacological research and medical treatment [2–4].

Putative biosynthetic pathway of diosgenin saponins in D. *zingiberensis*. The diosgenin saponin biosynthetic pathway can be divided into the following stages: synthesis of squalene, generation of cholesterol/sitosterol, biosynthesis of diosgenin, and biosynthesis of diosgenin saponins. Isopentenyl diphosphate (IPP) is synthesized through the mevalonate (MVA) and methylerythritol 4-phosphate (MEP) pathways. Three enzymes (GPPS, geranylgeranyl pyrophosphate synthase; FPPS, farnesyl diphosphate synthase; and SQS, squalene synthase) involved in the synthesis of squalene are depicted in gray. Eleven enzymes [SQE, squalene epoxidase; CAS, cycloartenol synthase; SMT, sterol C-24 methyltransferase; SMO, C-4 sterol methyl oxidase; CPI1, cyclopropylsterol isomerase; CYP51, sterol C-14 demethylase; C14-R, sterol C-14 reductase; 8,7 SI, sterol 8;7 isomerase; CD-SD, sterol C-5(6) desaturase; 7-DR, 7-dehydrocholesterol reductase; and DWF1, δ-(24)-sterol reductase] that participate in plant sterol (cholesterol and β-sitosterol) biosynthetic pathways are marked in salmon, and four key enzymes (CYP90B, sterol 22-α-hydroxylase; CYP72A/CYP94, sterol 26-α-hydroxylase; S3GT, sterol 3-β-glucosyltransferase; and F26G) involved in the synthesis of diosgenin saponins are marked in green. Dashed arrows indicate multiple steps in the pathway.

The genus Dioscorea of the family Dioscoreaceae consists of ~600 species [5], which are mainly divided into the following lineages: Dioscorea sect. Stenophora, sect. Combilium, sect. Opsophyton, sect. Enantiophyllum, and New World I/II [6–8]. Dioscorea species are cultivated in many parts of the world not only for their synthesis of diosgenin saponins, but also as an important food crop. The tubers of some Dioscorea plants, such as D. rotundata, D. alata and D. oppositifolia, are often used as edible yams because of their high starch content, while other Dioscorea species, such as D. zingiberensis, D. panthaica, and D. nipponica, are used as medicinal plants due to their abundance of diosgenin saponins [6, 8]. More than 190 diosgenin saponins have been isolated from Dioscorea plants; however, only 137 species of Dioscorea contain diosgenin saponins [9]. Moreover, the contents and types of diosgenin saponins in these Dioscorea plants are different, and only 41 species contained >1% of diosgenin [9]. About 70, 30, and <10 diosgenin saponins are identified from sect. Stenophora species such as D. zingiberensis, D. panthaica, and D. nipponica, respectively [10–12]. However, in other lineages, ~15 diosgenin saponins are isolated from D. esculenta (sect. Combilium), and even fewer from D. bulbifera (sect. Opsophyton) [13, 14]. Diosgenin saponins are typically found in Dioscorea plants; however, why only a few species of Dioscorea are rich in diosgenin saponins still remains unclear.

D. zingiberensis, a traditional medicinal plant species rich in diosgenin saponins and diosgenin (up to 16.15% of dry weight) [15] (Supplementary Data Fig. 1), has been used in folk medicines since as early as 2000 years ago [10]. It is also used as an ideal steroid hormone source plant in the world, so it is considered an important model for investigating the biosynthesis of diosgenin saponins [16–18]. Based on previous studies, the biosynthesis of diosgenin saponins can be divided into the following stages: biosynthesis of the 30-carbon squalene via mevalonate (MVA) and methylerythritol 4-phosphate (MEP) pathways; biosynthesis of cholesterol or sitosterol as precursor; the catalysis of sterol side chains to synthesize diosgenin (cytochrome P450s, CYP450); and finally glycosylation to form diosgenin saponins (UDP-glycosyltransferases, UGTs) [19–24] (Fig. 1). Notably, the β-glucosidase furostanol glycoside 26-O-β-glucosidase (F26G) may be involved in the catalysis of diosgenin formation and the conversion of furostanol-type saponins to spirostanol-type saponins [20, 22] (Fig. 1). At present, the MVA, MEP, squalene, and cholesterol biosynthetic pathways are ubiquitous in plants, and these pathways are also associated with other pathways [21]. Therefore, the key to diosgenin saponin biosynthesis lies downstream of cholesterol or β-sitosterol, and this is the focus of further research. Revealing the biosynthesis of diosgenin saponins in plants will help us to understand the molecular mechanism of the formation and evolution of diosgenin saponins in plants.

In recent years, more studies have been devoted to the biosynthesis of diosgenin saponins in plants [15, 17, 24, 25], but a refined chromosome-scale reference genome of D. zingiberensis is still the key to deciphering the molecular mechanisms underlying this biosynthetic pathway. Here we report a high-quality chromosome-level reference genome of D. zingiberensis. Through comparative genomic, transcriptomic, and metabolomic analyses, we have obtained novel insights into the mechanisms driving the formation and evolution of the diosgenin saponin biosynthetic pathway. Our study lays an important foundation for further research in the molecular mechanism of diosgenin saponin biosynthesis and evolution in plants, and also provides a reference for understanding the formation and evolution of plant specialized metabolites.

Results

Genome assembly and annotation

Based on genome survey results, we estimated that the genome size of D. zingiberensis was 716 Mb with a high level of heterozygosity (1.56%) (Supplementary Data Fig. 2a); furthermore, D. zingiberensis is a diploid with a karyotype of 2n = 20 (Supplementary Data Fig. 2b). To overcome the difficulties in assembling the highly heterozygous genome, a combined strategy was used to produce 91.34 Gb of Illumina sequencing data (~110× coverage) for the genome survey, 117.18 Gb of Oxford Nanopore Technologies (ONT) long reads (N50 length of 19 kb, ~146×), 106.97 Gb of 10X Genomics Linked-Reads (~170×), and 194.77 Gb of Hi-C sequencing data (~310×) for genome assembly (Supplementary Data Table 1), generating a chromosome-scale genome (~629 Mb) with contig N50 of 1.16 Mb and scaffold N50 of 55.78 Mb (Table 1, Supplementary Data Table 2). Approximately 93.39% of the genomic sequences were anchored on 10 linkage groups (Supplementary Data Fig. 3, Supplementary Data Table 3). We further evaluated the quality and integrity of the assembled genome, and found that 99.30% of Illumina reads and 99.35% of Nanopore reads could be mapped to the scaffolds (Supplementary Data Table 4). In addition, Benchmarking Universal Single-Copy Orthologs (BUSCO) [26] analysis revealed that 96.84% of 1614 core eukaryotic genes were found complete in our D. zingiberensis genome assembly (Supplementary Data Table 5). Moreover, analysis using a Conserved Core Eukaryotic Gene Mapping approach (CEGMA) [27] showed that 97.98% of 248 conserved core eukaryotic genes from CEGMA were captured in our assembled genome (Supplementary Data Table 5). These results suggest that our genome assembly is of high quality.

Table 1.

Statistics of genome assembly and annotation of D. zingiberensis.

Genomic feature
Genome assembly
	Assembled sequences (bp)	629 184 705
	Contig N50 (bp)	1 164 246
	Scaffold N50 (bp)	55 778 752
	GC content of the genome (%)	39.51
Genome annotation
	Number of protein-coding genes	30 322
	Mean gene length (bp)	4820
	Mean coding sequence length (bp)	1215
	Mean exon length (bp)	219
	Mean intron length (bp)	792

Open in a new tab

About 56.50% of the sequences in the D. zingiberensis genome were transposable elements (TEs) (Supplementary Data Table 6), and the distribution density of TEs in the genome generally increased from the distal to proximal regions of the chromosome arms, whereas gene density was reversed (Fig. 2a), which was consistent with previous results [28, 29], indicating the high accuracy of our assembly. We predicted a total of 30 322 protein-coding genes, and the mean lengths of the gene and coding sequence were 4820 and 1215 bp, respectively (Supplementary Data Fig. 4, Table 1, Supplementary Data Table 7). Over 94% of these predicted genes had functional annotation matches to the public databases (Supplementary Data Fig. 5, Supplementary Data Table 8). On average, >93% of RNA-seq reads from the leaves, stems, and rhizomes of D. zingiberensis could be mapped to the genome (Supplementary Data Table 9), reflecting the high reliability of genome assembly and annotation.

Genome assembly characterization and genome evolution of *D. zingiberensis*. a Circos view of the *D. zingiberensis* genome. (i) TE density distributions in each linkage group. (ii) Gene density: numbers of genes in 1-Mb non-overlapping windows. (iii) Gene expression levels: gene expression levels of each gene in leaves, stems, and rhizomes of *D. zingiberensis*. The distribution of TE density shows a peak in the middle, while that of gene density shows two peaks, with one at each end. b Phylogenetic tree showing the divergence times and evolution of gene family sizes for 13 plant species. The value at each node represents the divergence time in millions of years ago (Mya) relative to the present. Expansion: number of gene families expanded (green); Contraction: number of gene families contracted (red); Total: total number of gene families (black). c WGD events detected in D. *zingiberensis*. Frequency plot of K_s distributions of paralogs identified from D. *zingiberensis*, *D. rotundata*, and *A. officinalis*.

Comparative genomic analysis

To investigate the evolutionary history of the D. zingiberensis genome, the D. zingiberensis genome was compared with those of 12 other representative plant species. In total, we identified 13 144 gene families in the D. zingiberensis genome by OrthoMCL [30], of which 745 were specific to D. zingiberensis (Supplementary Data Fig. 6, Supplementary Data Table 10). We found 7741 genes that were specific to D. zingiberensis compared with these 12 selected plant species (Supplementary Data Fig. 7, Supplementary Data Tables 10 and 11). Then, we examined the functions of D. zingiberensis-specific genes based on both the KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontogeny) databases, and found that these genes were mainly enriched in ‘secondary metabolic processes’, especially UGT activity (GO:0008194, P < .001) (Supplementary Data Fig. 8), potentially representing several key steps in the biosynthesis of diosgenin saponins.

We then constructed the phylogenetic relationships and estimated the divergence times of the 13 angiosperm plant species, based on the 430 single-copy orthologous genes retrieved using OrthoMCL [30] (Fig. 2b). The results suggested that D. zingiberensis is sister to D. rotundata with an estimated divergence time of ~47.49 million years ago (Mya). The diosgenin-containing species Phoenix dactylifera of Palmae [31] and Asparagus officinalis of Asparagaceae [32] are in a clade sister to the Dioscorea clade (Fig. 2b).

Whole-genome duplication (WGD) events are important in the duplication and retention of genes, especially those related to plant species-specific phenotypic traits, and are ubiquitous in terrestrial plant genomes [33–35]. Here we identified 21 257, 14 589, and 14 128 paralogous gene pairs in the D. zingiberensis, D. rotundata, and A. officinalis genome, respectively. Two peaks of synonymous substitutions per synonymous site (K_s) distribution were detected for the D. zingiberensis genome at ~0.14 and ~ 0.74, respectively, suggesting that two WGD events occurred around 12–22 and 83–93 Mya in the evolutionary history of D. zingiberensis (Fig. 2b, Supplementary Data Fig. 9). Similarly, we found two peaks of K_s distribution of D. rotundata, while the second one (~0.41, ~44–54 Mya) nearly coincided with the K_s between D. zingiberensis and D. rotundata (~0.40, ~48 Mya) (Fig. 2c, Supplementary Data Fig. 9). The younger WGD event around 12–22 Mya in D. zingiberensis was after the divergence of D. zingiberensis and D. rotundata at ~47.49 Mya (Fig. 2b and c), indicating a species-specific WGD event in the genome of the D. zingiberensis lineage.

By comparing orthologous genes between D. zingiberensis and 12 selected plant species, we found that expanded gene families (4051 gene families, including 6873 genes) substantially outnumbered contracted gene families (867 gene families, including 1088 genes) in the D. zingiberensis genome (Fig. 2b, Supplementary Data Table 12). KEGG and GO enrichment analysis suggested that these expanded genes were mainly enriched in steroid biosynthesis (ath00100, P < .001) and UGT activity (GO:0008194, P < .001), respectively (Supplementary Data Fig. 10). The expanded gene families included OSC (PF13243), CYP450 (PF00067), and UGT (PF00201), whose members function in several key steps in the biosynthesis of diosgenin saponins [19], suggesting an evolutionary basis for the higher capacity for diosgenin saponin biosynthesis in D. zingiberensis compared with other plant species.

Biosynthesis of diosgenin saponins in D. zingiberensis

In order to fully understand the spatial and temporal change of diosgenin saponins in D. zingiberensis, we measured the contents of diosgenin and diosgenin saponins in three different tissues (leaves, stems, and rhizomes) of D. zingiberensis. The diosgenin content first peaked in leaves in July, and then in rhizomes in August. The content of diosgenin in stems was always lower than that in leaves and rhizomes (Fig. 3a).

Diosgenin distributions and gene expression levels in different tissues of *D. zingiberensis*. a Diosgenin contents in different tissues of D. *zingiberensis* during the development period of 7 months. b–f Immunohistochemical localization of diosgenin in stem (b), leaf (c, d), and rhizome (e, f) tissues. Blue color in sections represents the diosgenin signal. Diosgenin is detected in the epidermis (Ep), ground tissue (Gt), and phloem (Ph) of the stem cross-section. Blue signals localized in the phloem are observed. The diosgenin signal is concentrated in palisade tissue (Pat), spongy tissue (Spt), and phloem in the leaf. Xy represents the xylem. Scale bar = 100 μm. g Schematic diagram of the transport assay. The aerial part of the plant with leaves attached (exp1), stem connected to the rhizome (exp2), and stem of the aerial part with leaves detached were immersed in flasks containing deionized water (exp3). The aerial part with leaves attached was also inserted into a flask containing 1.5 mM dinitrophenol (exp4). The diosgenin content secreted into the water in each treatment is shown in the histogram. h Expression profiles of pathway genes based on RNA-seq data from three tissues (leaves, stems, and rhizomes). Gene expression values (FPKM) were normalized by log2, and the expression pattern was visualized using the R package pheatmap. Three biological replicates for each sample were collected in this study, and the error bar represents the standard error.

Histological localization of diosgenin was determined using a rabbit polyclonal anti-diosgenin antibody that we developed previously [36]. Strong diosgenin signals were found in the phloem of vascular tissues in the leaf vein, stem, and rhizome (Fig. 3b, c, and f). Diosgenin signals were also observed in the palisade and spongy parenchyma tissues of the leaf (Fig. 3d). In the rhizome, the diosgenin signal appeared as condensed particles scattered inside the parenchyma cells (Fig. 3e and f), and control slides are shown in Supplementary Data Fig. 11. Diosgenin signals were also observed in the epidermis and ground tissues of the stem. As the phloem is mainly responsible for the downward transport of organic compounds, our results suggest that phloem in vascular tissues may play a role in diosgenin transport, while leaves and rhizomes act as sites of its biosynthesis and accumulation, respectively.

To test this hypothesized transport route, we measured diosgenin content in the exudate obtained from cut sites in dissected plant tissues (Fig. 3g). The stem connected to the rhizome secreted a negligible amount of diosgenin (1.41 μg/ml). However, the diosgenin content obtained from the aerial parts was substantially higher (8.22 μg/ml, Fig. 3g), suggesting that diosgenin may be transported in a downward manner. On the other hand, the aerial part with all leaves removed secreted as little as 0.42 μg/ml diosgenin (Fig. 3g). The transport of diosgenin was investigated further by immersing the aerial part of the plant in 1.5 mM 2,4-dinitrophenol solution to inhibit active transport via phloem in the stem, and the diosgenin content measured under this condition was only 0.34 μg/ml (Fig. 3g). These results support the crucial function of phloem in diosgenin transport, and also indicate that the phloem, rather than xylem, in the stem is responsible for the transport of diosgenin in D. zingiberensis.

The possibility that the rhizome was capable of diosgenin synthesis as well as storage was also investigated. We focused on the upstream genes (SQE, CAS) and downstream gene (F26G) of the diosgenin saponin biosynthetic pathway. Based on the expression profiles of genes analyzed by real-time qPCR, we found a trend in the gene expression levels of SQE and CAS in leaves, stems, and rhizomes: the expression of these two genes in leaves first peaked in July, but peaked in August in stems and rhizomes (Supplementary Data Fig. 12a and b). In contrast, the expression of F26G was also highest in leaves in July, but highest in August and September in stems and rhizomes, respectively (Supplementary Data Fig. 12c). The variation trend of total diosgenin saponins in the three tissues of D. zingiberensis was the same as that of diosgenin (Supplementary Data Fig. 12d). With the aid of our assembled genome sequences for D. zingiberensis, transcriptomic data were used to analyze the expression levels of 167 genes involved in diosgenin saponin biosynthesis in the leaves, stems, and rhizomes of D. zingiberensis (Supplementary Data Table 13). Our results showed that the highest expression levels of most of these pathway genes were detected in leaves, while the lowest levels were detected in rhizomes (Fig. 3h).

In summary, our above results indicate that diosgenin is first synthesized in leaves (‘source’), then converted into diosgenin saponins, and finally transported to underground rhizomes (‘sink’) for storage, while the leaves are essential for the synthesis of diosgenin saponins.

Origin of diosgenin saponins in Dioscorea species

According to the chemical structure of aglycone backbone, diosgenin saponins can be classified into two types, spirostanol-type and furostanol-type [19, 22]. In order to uncover the origin and evolution of the diosgenin saponin biosynthetic pathway, we first measured seven furostanol-type saponins (PF, parvifloside; PD, protodioscin; PB, protobioside; PG, protogracillin; PSG, pseudoprotogracillin; PSD, pseudoprotodioscin; PDL, protodeltonin), three spirostanol-type saponins (DC, dioscin; GR, gracillin; ZN, zingiberensis newsaponins), and diosgenin (DG) in leaf samples of 13 Dioscorea species by liquid chromatography–mass spectrometry (LC–MS) (Fig. 4a, Supplementary Data Tables 14 and 15). The average content of spirostanol-type saponins in the species of Dioscorea sect. Stenophora was 1.63 mg/g, significantly higher than that in non-Stenophora species (~3.4-fold on average, P < .05). Similarly, furostanol-type saponins varied in sect. Stenophora plants, but the average content of 4.7 mg/g was 3-fold more than that of non-Stenophora species (1.52 mg/g, P < .05) (Supplementary Data Table 15). The average content of DG in sect. Stenophora was 0.31 mg/g, which was ~3.9-fold higher than that in non-Stenophora samples (P < .05) (Supplementary Data Table 15). It is worth noting that diosgenin saponin contents are also high in D. composita, which belongs to the earliest diverging Dioscorea clade (New World I) [8] (Fig. 4a). The content of diosgenin saponins in D. composita can account for 3.68% of dry weight, and the yield of D. composita tubers is 45–90 tons per hectare, which is also a potential plant source of steroid drugs [37]. Our results suggest that diosgenin saponins are mainly distributed in sect. Stenophora of the Dioscorea genus, and D. zingiberensis contains all 10 types of diosgenin saponins, but D. bulbifera, D. composita, and D. esculenta in non-Stenophora, only contain certain types of diosgenin saponins (Fig. 4a, Supplementary Data Table 15).

Distribution of diosgenin saponins and reconstruction of ancestral states in 13 *Dioscorea* species. a Left panel represents the phylogenetic relationship of the 13 *Dioscorea* species; plants from sect. *Stenophora* (sect. Sten) and non-*Stenophora* (sect. non-Sten) are represented by salmon and cyan, respectively. The content of diosgenin and diosgenin saponin detected in the leaves of each *Dioscorea* plant is displayed in the right panel. The content values are normalized using the z-score method. b–d Ancestral state reconstructions of three chemical traits: (b) furostanol-type saponins, (c) spirostanol-type saponins, and (d) diosgenin (DG). The maximum likelihood tree was constructed from the transcriptome data of 13 *Dioscorea* species and ancestral states were estimated using the R package phytools. The pie charts at the tips and nodes indicate the states of each *Dioscorea* species and the probability that ancestors contained diosgenin saponins (brown) or no diosgenin (blue).

To investigate the evolution and distribution pattern of diosgenin saponin in Dioscorea species (Fig. 4a), the ancestral states of binary traits (presence/absence of diosgenin saponins) were reconstructed based on the maximum likelihood tree of the transcriptome data of these 13 Dioscorea species. Several strong phylogenetic signals were detected in furostanol-type saponins such as PF, PB, PSG, and PSD (P < .05), and the estimated λ values were close to 1 (0.999), indicating that furostanol-type saponins in Dioscorea species were as expected under the Brownian motion model (Fig. 4b, Supplementary Data Table 16). The phylogenetic signals of most diosgenin saponins (furostanol-type and spirostanol-type) were mainly randomly distributed in the Stenophora clade, while those of DC and DG were also distributed in the non-Stenophora clade, such as D. composita, D. esculenta, and D. sansibarensis (Fig. 4c and d,Supplementary Data Table 16).

In addition, we reconstructed the ancestral state of diosgenin saponin on a larger species scale using the collected binary diosgenin saponin phenotypes of 91 Dioscorea species. Our results showed that a significant phylogenetic signal was detected in Dioscorea species (λ = 0.7659, P < .001), and diosgenin saponin was mainly distributed in the sect. Stenophora species, the early diverged lineage in Dioscorea [8], and was scattered in non-Stenophora species (Supplementary Data Fig. 13). These results indicated that diosgenin saponins were mainly distributed in the early diverged lineages of Dioscorea, and their distribution in Dioscorea showed phylogenetic conservation. Furthermore, almost all evaluated ancestral nodes were located at the base of the species tree, further indicating that diosgenin saponin-containing may be an ancestral trait in Dioscorea.

Evolution of the diosgenin saponin biosynthetic pathway

Based on the results of comparative genomic analysis, we found a large number of expanded gene families in D. zingiberensis (Fig. 2b). Considering the key roles of OSC, CYP90B, CYP72A, CYP94, S3GT, and F26G in diosgenin saponin biosynthesis [19–24], we identified these six genes in D. zingiberensis and 93 other representative plants, including angiosperms, gymnosperms, pteridophytes, bryophytes, and algae. The results showed that the CYP90B, CYP72A, and CYP94 gene families were expanded in D. zingiberensis compared with 93 other plants, and the F26G gene had the most copies (Fig. 5a, Supplementary Data Table 17). Not only that, the CYP90B, CYP72A, and CYP94 gene families in D. zingiberensis accounted for a higher proportion of the CYP450 gene family than in other plants (Supplementary Data Table 17).

Expansion of *OSC* and *CYP72A* gene families in *D. zingiberensis*. a Heat map of copy number of the six key genes (*CAS*, *CYP90B*, *CYP72A*, *CYP94*, *S3GT*, *F26G*) in 32 representative plant species. The right panels show angiosperms, gymnosperms, algae, pteridophytes, and bryophytes from top to bottom. The color in each block shows the copy number of these six key genes identified in each plant. b, c Phylogenetic trees show *OSC* (b) and *CYP72A* (c) gene families in *D. zingiberensis* and seven other plant species (*Aquiligia coerulea*, *A. officinalis*, *Brassica napus*, *Coffea arabica*, *D. rotundata*, *P. dactylifera*, and *Vitis vinifera*). The dots at the end of the branches in (b) represent the *CAS* genes in the OSC gene family of each species. The dots at the end of the branches in (c) represent the *CYP72A* genes of each species. The tandem-duplicated genes are shown on the chromosomes of the *D. zingiberensis* genome.

In plants, the OSC gene family is involved in sterol and triterpene biosynthesis, and the CAS genes are responsible for sterols and diosgenin saponin biosynthesis [19, 38]. Although there were only 14 OSC copies in the D. zingiberensis genome, D. zingiberensis had a higher proportion of CAS genes (8 of 14) than the other plant species (Supplementary Data Table 17). According to the phylogenetic tree, the CAS genes were grouped into single clade (Fig. 5b). In contrast, compared with other plants, D. zingiberensis had both the highest copy number and the highest proportion of CYP72A in the CYP450 gene family (Fig. 5b, Supplementary Data Table 17), and the CYP72A genes were mainly clustered into two clades (Fig. 5c). The phylogenomic analyses indicated that the CAS and CYP72A genes are duplicated in D. zingiberensis.

When we counted the number of gene copies produced by tandem duplication according to the gene locations, we found that 7 out of 8 CAS, 13 out of 19 CYP90B, 21 out of 25 CYP72A, 6 out of 23 CYP94, and 13 out of 18 F26G genes were produced by tandem duplication (Supplementary Data Fig. 14, Supplementary Data Table 18). Our results suggested that tandem duplication acted as an evolutionary driver for the expansion of pathway gene families. Further phylogenetic analysis showed that the CYP90B, CYP72A, CYP94, and F26G genes from Dioscorea species, and two diosgenin saponin-containing species (P. dactylifera and A. officinalis) were more similar (Supplementary Data Fig. 15).

Furthermore, based on the K_s value of each gene pair in these 13 Dioscorea species, we evaluated the gene duplication time and the contribution of paralogous duplication to the evolution of diosgenin saponin biosynthetic pathways. The results showed that only a few diosgenin saponin biosynthesis genes from diosgenin-containing Dioscorea species duplicated when the WGD event occurred, while the majority of the pathway genes duplicated more recently (~1–10 Mya) (Supplementary Data Fig. 16, Supplementary Data Table 19). In contrast, we found that, unlike other Dioscorea species, most of the diosgenin saponin biosynthetic pathway genes in D. zingiberensis, especially CAS, CYP90B, CYP94, CYP72A, S3GT, and F26G, were duplicated via the WGD event that occurred ~12–22 Mya, while most CYP94 seemed to be duplicated earlier (Supplementary Data Fig. 9, Supplementary Data Table 19). A total of 8 CAS, 19 CYP90B, 23 CYP94, 22 CYP72A, 7 S3GT, and 18 F26G genes were found in the D. zingiberensis genome, of which 4 CAS, 4 CYP90B, 2 CYP94, 6 CYP72A, 2 S3GT, and 6 F26G genes were generated through the WGD event (Supplementary Data Fig. 9, Supplementary Data Table 19). Our findings indicate that tandem duplication and the WGD event may be the driving force for the evolution of the diosgenin saponin biosynthetic pathway.

Expression pattern of the key genes involved in diosgenin saponin biosynthesis

Genes involved in specific metabolic pathways usually show a lineage-specific pattern of gene expression or co-expression within the transcriptome [39, 40]. Given that genes in the same metabolic pathway are often co-expressed with each other [40], we used transcriptome data of D. zingiberensis to explore whether there is co-expression among all genes involved in diosgenin saponin biosynthesis. The results of co-expression analysis indicated that there is indeed co-expression between genes upstream and downstream of cholesterol. Specifically, we found that CYP94 genes were highly co-expressed with MVK, DXS, and GPPS genes, and CYP72A, CYP90B, and F26G genes were highly co-expressed with GPPS, SQS, SMT, SMO, and CAS genes (Fig. 6a). In particular, almost all CAS genes were co-expressed with CYP90B, CYP72A, and F26G genes; interestingly, most CAS and CYP72A genes were located on chromosome 1. At the same time, there was also co-expression among the downstream genes, such as CYP90B, CYP72A, CYP94, S3GT, and F26G (Fig. 6a).

Relationships between metabolites and gene expression levels. a Co-expression network of diosgenin saponin biosynthetic pathway genes. Pathway genes with a Pearson correlation coefficient >.9 (P ≤ .01) are displayed on the network. Genes marked in gray, green, and salmon represent genes in the pathways of squalene synthesis, sterol synthesis, and diosgenin and diosgenin saponin synthesis, respectively. b Expression profiles of genes involved in diosgenin saponin biosynthesis in 13 *Dioscorea* species, with pathway genes shown in columns. The left panel is the phylogeny of 13 *Dioscorea* species. Expression data are plotted as log2 values. c Correlation analysis of gene expression with the contents of diosgenin, furostanol-type saponins, and spirostanol-type saponins. The horizontal axis of the bubble plot shows the Pearson’s correlation coefficients, and the vertical axis represents the P value. Circle sizes correspond to the value of Pearson’s correlation coefficient. The significance thresholds of correlations among the pathway genes are set at Pearson’s correlation coefficient value >.8 and P-value <.05.

We further analyzed the association between the expression of diosgenin saponin biosynthetic pathway genes in 13 Dioscorea plants and their diosgenin saponin content. The expression levels of almost all diosgenin saponin synthesis pathway genes, especially CAS, SMO, CPI1, C14-R, 8,7 SI, C5-SD, 7-DR, DWF1, CYP94, and S3GT, were highly expressed in D. zingiberensis, and D. composita (Fig. 6b). It is worth noting that these nine genes were expressed at a low level in Dioscorea species that lacked diosgenin saponins, and that no expression of SMT2, CPI1, 8,7 SI, CYP72A, and F26G or their homologs was detected in these latter species (Fig. 4a and b). In fact, transcriptome and metabolite correlation analysis showed that the expression patterns of CAS, CYP90B, CYP72A, CYP94, S3GT, and F26G in these 13 Dioscorea plants was significantly correlated with the content of diosgenin saponins (Pearson’s correlation coefficient >0.9, P < .05) (Fig. 6b and c).

Combined with the results of gene co-expression analysis, these key genes, such as CYP90B, CYP72A, CYP94, S3GT, and F26G, are potentially involved in the biosynthesis of diosgenin saponins. Moreover, the expression patterns of genes involved in diosgenin saponin biosynthesis correlate well with phenotypic differentiation of diosgenin saponins in different Dioscorea species.

Discussion

In this study, we report a high-quality reference genome sequence of D. zingiberensis at the chromosome level, providing the genetic basis for studying the biosynthesis and evolution of diosgenin saponins, a group of specialized metabolites of great medicinal and economic value. Our genomic database of D. zingiberensis at the chromosomal scale is particularly important for enhancing the breeding improvement of the diosgenin saponin-containing Dioscorea species.

Although recent transcriptome analyses of several diosgenin-containing plants, including D. composita, D. zingiberensis, Trillium govanianum, and Trigonella foenum-graecum, have provided insights into key genes involved in diosgenin saponin biosynthesis [37, 41–43], the biosynthetic sites of diosgenin saponins in plants remain unclear. Integrating genomic and transcriptomic data and immunohistochemical localization, our results suggested that although the rhizomes of D. zingiberensis are used as the medicinal parts of the plant due to being the site of accumulation, diosgenin saponins are synthesized in the leaves, and then transported through the stem vascular tissues to the underground rhizomes for storage (Fig. 3). Therefore, the leaves play a decisive role in diosgenin saponin biosynthesis in plants.

By reconstructing the ancestral state of diosgenin saponins in Dioscorea species, we evaluated ancestral nodes at the base of the Dioscorea species tree (Fig. 4b–d, Supplementary Data Fig. 13). In addition, the results of a phylogenetic signal test also showed that the distribution of diosgenin saponins in Dioscorea is phylogenetically conserved or phylogenetically clustered (Supplementary Data Fig. 13, Supplementary Data Table 16). All these results indicate that diosgenin saponin-containing may be an ancestral trait in Dioscorea species and originates from the early diverged lineages of Dioscorea species, sect. Stenophora [8]. This chemical trait (diosgenin saponin) might have formed long ago, but gradually disappeared in many Dioscorea species during evolution. For example, a few Dioscorea species, such as D. alata, D. oppositifolia, and D. fordii, have been artificially selected and domesticated as edible yams and appear to have lost their ability to synthesize diosgenin saponins.

The expansion or contraction of gene families is an important factor driving species differentiation, phenotypic diversification, and adaptation based on natural variation [44, 45]. Similarly, WGD events are also considered to be the major driving force in species diversification and trait variation [33–35]. In the D. zingiberensis genome, we found that >70% of the key genes for diosgenin saponin biosynthesis (CAS, CYP90B, CYP72A, and F26G) were replicated by tandem duplication and WGD (Fig. 5, Supplementary Data Fig. 14, Supplementary Data Tables 18 and 19). Gene duplication can help plants acquire more gene copies, which may lead to new gene functions that are more adaptable than those of single-copy or low-copy genes [35], and this also applies to D. zingiberensis and other Dioscorea plants containing diosgenin saponins. For example, sterol side-chain catalytic genes (such as CYP90B, CYP94, and CYP72A) played important roles in the synthesis of diosgenin saponins [25, 46, 47]. We found that the CYP90B genes in plants without diosgenin saponins were involved in catalyzing the conversion of cholesterol to 22S-hydroxycholesterol, en route to brassinosteroid hormones, while in plants with diosgenin saponins, such as Dioscorea and Paris, some CYP90B genes produced by duplication evolved sterol 16,22-polyhydroxylase activity, leading to the biosynthesis of the core structure of diosgenin [24, 25]. Although some studies have shown that CYP72A genes in plants are usually involved in plant-specific metabolism and the 13-hydroxylation of gibberellins in plants [48, 49], CYP72A genes identified from diosgenin saponin-containing plants have been shown to be involved in the biosynthesis of diosgenin saponins [23, 24, 47].

Furthermore, the WGD event can also provide genetic resources for the formation and evolution of plant-specific metabolite biosynthetic pathways, which have been revealed in some plants, such as tea, yew, and Scutellaria baicalensis [50–52]. Therefore, the expansion of CAS, CYP90B, CYP94, CYP72A, F26G, and S3GT genes by tandem duplication and a WGD event provided genetic and evolutionary resources for the core modification of diosgenin saponins, and also provided opportunities for the formation of the diosgenin saponin biosynthetic pathway in D. zingiberensis.

Gene expression patterns were also associated with phenotypic differences of diosgenin saponins in plants, which was confirmed in the correlation analysis of gene expression and diosgenin saponin content (Fig. 6c). Specifically, we detected high contents of diosgenin saponins in sect. Stenophora of the Dioscorea genus, such as D. panthaica, D. deltoidea, D. nipponica, and D. zingiberensis, and found high expression levels of genes related to diosgenin saponin biosynthesis in these plants. In D. sansibarensis, only a small amount of diosgenin saponins were detected, and its CYP90B, CYP72A, and F26G genes were also expressed at low levels (Fig. 6b). Moreover, we found that there was co-expression between the genes upstream and downstream of cholesterol identified in D. zingiberensis (Fig. 6a). Our results indicate that genes involved in diosgenin saponin biosynthesis often exhibit lineage-specific patterns of gene expression or co-expression, which is an important mechanism of specialized metabolism evolution [40].

In summary, our findings suggest that tandem duplications and a WGD event may be the driving forces for the species-specific evolution of diosgenin saponin biosynthesis in D. zingiberensis. Through tandem duplication and a WGD event, an increasing number of genes were produced in plants to modify the diosgenin precursor cholesterol, ultimately resulting in diosgenin saponins, and the species-specific expression patterns of pathway genes promote phenotypic differentiation of diosgenin saponins in Dioscorea species. Our study provides a high-quality genome of D. zingiberensis, and sets an important foundation for future research on the molecular mechanism of diosgenin saponin biosynthesis and evolution in plants.

Materials and methods

Plant materials

For genome sequencing, seeds of D. zingiberensis were collected from Shiyan City, Hubei Province, China, and cultivated in the greenhouse of Wuhan University in October 2015. Fresh tender leaves, stems, and rhizomes of D. zingiberensis were harvested in July 2018. These samples were immediately frozen in liquid nitrogen after collection, followed by preservation at −80°C in the laboratory prior to genome sequencing, transcriptome sequencing, and metabolite analysis.

In addition, we collected 12 Dioscorea plant species, among which D. sansibarensis, D. composita, D. alata, and D. bulbifera were collected from Xishuangbanna, Yunnan Province; D. deltoidea and D. panthaica were collected from Lijiang, Yunnan Province. D. pentaphylla, D. esculenta, and D. fordii were collected from Shaoguan, Guangdong Province; D. oppositifolia was collected from Xinxiang, Henan province; D. arachidna was collected from Wenshan, Yunnan Province; D. zingiberensis was collected from Xunyang, Shaanxi Province; and D. nipponica was collected from Fushun, Liaoning Province (Supplementary Data Table 14). These collected Dioscorea plants included seven lineages: sect. Stenophora, sect. Enantiophyllum, sect. Lasiophyton, sect. Opsophyton, sect. Combilium, New World I clade, and the Malagasy clade. We planted these Dioscorea plants in the greenhouse of Wuhan University, and collected the youngest leaves. All the leaf samples for transcriptome sequencing and metabolite analysis comprised three and six biological replicates, respectively.

Genome sequencing

We adopted a combination of three sequencing technologies, including ONT, 10X Genomics, Hi-C Technologies, and other Illumina second-generation sequencing platforms, for genome sequencing, survey, and assembly. Before genome sequencing, we conducted a genome survey. Briefly, we extracted genomic DNA from D. zingiberensis leaves and broke it into ~350 bp fragments. Subsequently, the Illumina libraries were constructed and sequenced on the Illumina Novaseq platform. High-molecular-weight genomic DNA was extracted from tender leaves of D. zingiberensis with the Qiagen Genomic DNA extraction kit. For ONT sequencing, high-quality genomic DNA libraries were loaded into Nanopore Grid ION X5 sequencer (Oxford Nanopore Technologies) flow cells for single-molecule real-time sequencing. The 10X Genomics libraries were prepared from genomic DNA, and Illumina Hiseq XTen was used for sequencing. The Hi-C libraries were prepared using an optimized protocol [53], and then quantified and sequenced using the Illumina Hiseq platform.

Genome assembly

To estimate the genome size and heterozygosity of D. zingiberensis, we performed K-mer analysis using KMC [54] and GenomeScope [55] with 350 bp of Illumina paired-end reads. First, we analyzed the K-mer frequency distribution. Then, we calculated the genome size based on the formula: G = K-mer number/K-mer depth, where G is genome size, the K-mer number is the total number of K-mers (K = 17), and K-mer depth is estimated from the K-mer distribution.

All ONT sequencing data were corrected using Nextdenovo (https://github.com/Nextomics/NextDenovo). Then, SMARTdenovo (https://github.com/ruanjue/smartdenovo) was used for genome assembly. To further improve the accuracy of genome assembly, genome correction was performed using Illumina reads mapped using minimap2 [56] and NextPolish [57]. Then, 10X Genomics Linked-Reads were used to refine assembly using ARKS and LINKS [58, 59]. Finally, we combined the filtered Hi-C data and used the de novo assembly pipeline in LACHESIS [60] software to produce chromosome groups. The completeness of the genome assembly was evaluated using BUSCO [26], and CEGMA [27].

Genome annotation

We predicted and identified protein-coding genes using a combined strategy that incorporated repeat sequence annotation, gene structure prediction, and gene function annotation. For repeat sequence annotation, LTR_FINDER [61], MITE-Hunter [62], and RepeatMasker (http://www.repeatmasker.org/) were used to predict repeat sequences in the D. zingiberensis genome. Gene structure prediction was based on ab initio gene prediction and homologous protein prediction, which were performed using Augustus [63] and GeMoMa [64] software, respectively. Then, PASA [65] and TransDecoder (http://transdecoder.github.io/) were used to predict unigene sequences based on the transcriptome data. Finally, all of these predicted gene models were integrated and functionally annotated by mapping to KEGG (https://www.genome.jp/kegg/), KOG (http://www.ncbi.nlm.nih.gov/COG/), Swiss-Prot (http://www.gpmaw.com/html/swiss-prot.html), and NCBI NR (http://www.ncbi.nlm.nih.gov/RefSeq/) databases, We also used InterProScan methods (https://www.ebi.ac.uk/interpro/about/interproscan/) to predict the conserved sequences and structure domain against the GO database (http://geneontology.org/).

Comparative genomic analyses

We selected five sequenced monocots (A. officinalis, Brachypodium distachyon, D. rotundata, Oryza sativa, and P. dactylifera) and six sequenced eudicots (Arabidopsis thaliana, Artemisia annua, Populus trichocarpa, Solanum lycopersicum, Salvia miltiorrhiza, and Vitis vinifera) for comparative genomic analysis, with Amborella trichopoda as outgroup. These plants include three monocots that can synthesize diosgenin saponins: D. rotundata, P. dactylifera, and A. officinalis. The eudicots comprised S. lycopersicum, which can also use cholesterol as a precursor to synthesize steroidal glycoalkaloids [19], and the two medicinal plants S. miltiorrhiza, A. annua. We used OrthoMCL [30] software to identify single-copy genes and ortholog genes in each species. Phylogenetic relationships between D. zingiberensis and the 12 other plant species were constructed by using RAxML [66] based on 430 single-copy orthologous genes identified. Then, the divergence times among these 13 plant species were estimated using the MCMCTREE program [67]. Finally, CAFÉ [68] was used to predict gene family expansion (gain) or contraction (loss) in these 13 plants. Meanwhile, we performed gene functional enrichment analysis of specific and expanded gene families in D. zingiberensis using the KEGG and GO databases.

Genome evolution

To investigate the WGD events in the D. zingiberensis genome, we used the FASTKs pipeline (https://github.com/mrmckain/FASTKs) to estimate the synonymous substitution rate of the paralogous gene pairs in D. zingiberensis, D. rotundata, and A. officinalis. The protein sequences of these three plants were aligned using BLASTN with E-value <1e⁻⁴⁰. For each BLAST pair, protein sequences were aligned using MUSCLE [69] and converted to codon-based alignments using PAL2NAL [70]. The K_s and the non-synonymous substitution rate (K_a) for paralogous gene pairs in the genomes of D. zingiberensis, D. rotundata, and A. officinalis were calculated using PAML [67]. The K_s distribution plots were then drawn by R packages ggplot2 (https://cran.r-project.org/web/packages/ggplot2/index.html) and Mclust [71].

Immunohistochemical localization and transport assay of diosgenin in D. zingiberensis

To investigate the spatial and temporal changes of diosgenin content in D. zingiberensis, we used a previously developed rabbit polyclonal antibody specific for diosgenin to determine the synthetic sites of diosgenin in leaves, stems, and rhizomes by immunochemical tissue localization [36]. For the transport assay of diosgenin in plants, the aerial part of the plant with leaves attached, the stem connected to the rhizome, and the stem of the aerial part with leaves detached were immersed in flasks containing deionized water. In addition, we used 2,4-dinitrophenol to inhibit active transport within the phloem. Finally, the amount of diosgenin in the solution was measured as described previously [36]. In addition, expression profiles of SQE, CAS, and F26G in various tissues of D. zingiberensis were obtained using qRT–PCR. The above experiments used three biological replicates.

Gene expression analyses

We mapped all clean RNA-seq reads of leaves, stems, and rhizomes of D. zingiberensis to our assembled reference genome of D. zingiberensis by using STAR [72]. Then, we used HTSeq [73] to obtain the read numbers mapped to each gene. The number of fragments per kilobase of exon per million fragments mapped (FPKM) of each gene was calculated using RNS-Seq by Expectation Maximization (RESM; http://deweylab.github.io/RSEM/). The diosgenin saponin biosynthetic pathway genes were identified using BLASTP. The highest expressed genes in three D. zingiberensis tissues were selected to be visualized using the R package pheatmap (https://cran.r-project.org/web/packages/pheatmap/index.html).

In addition, we also collected RNA-seq data of three tissues from our study and existing transcriptome data of D. zingiberensis [43] to construct the co-expression network of all diosgenin saponin biosynthetic pathway genes by the Pearson correlation coefficient method in R software. We screened genes with correlation coefficient >.9 and then used these genes to construct gene co-expression networks with Cytoscape software [74].

Molecular evolution of genes involved in diosgenin saponin biosynthesis

To elucidate the molecular evolution of the OSC (PF13243), CYP90B, CYP72A, CYP94, S3GT, F26G, CYP450 (PF0067), and UGT (PF00201) genes in D. zingiberensis, we first downloaded the hmm files of these gene families from Pfam (http://pfam.xfam.org/). Then, we identified OSC, CYP450, and UGT genes from the proteins of 13 Dioscorea plants and 93 other selected species using HMMER [75] software with the default parameters, and checked all candidate protein sequences using InterPro (http://www.ebi.ac.uk/interpro/search/sequence), SMART (http://smart.embl-heidelberg.de/), and the NCBI Conserved Domain Database (https://www.ncbi.nlm.nih.gov/Structure/cdd). Second, we used the protein sequences of CAS, S3GT, and F26G downloaded from NCBI database to identify homologous genes of all selected plants by BLASTP (E-value ≤1e−10, identity ≥50%). The CYP90B, CYP94, and CYP72A gene families were defined by the CYP450 database [76].

Finally, phylogenetic trees for all homologous genes were constructed with RAxML [66]. To detect possible tandem duplications in each gene family within this pathway, we extracted and analyzed the locations of all pathway genes on the chromosomes of the D. zingiberensis genome. We used the PAML [67] program to calculate the K_s values of all duplicated genes and estimated the divergence time of each diosgenin saponin biosynthetic pathway gene in Dioscorea plants using the formula T = K_s/2r, where T represents the divergence time and r represents the synonymous substitution rate; the synonymous substitution rate of Dioscorea is 4.19 × 10⁻⁹ mutations per site per year.

Determination of diosgenin and diosgenin saponin contents of the 13 Dioscorea species

To determine diosgenin saponin and diosgenin contents in Dioscorea plants, the metabolites were extracted from the fresh and young leaves from all 13 Dioscorea species with reference to the established protocol [18, 24, 47]. The relative contents of diosgenin and diosgenin saponins in different Dioscorea species were measured by LC–MS as described below.

To determine diosgenin saponin levels in Dioscorea species, we homogenized frozen leaf tissues with a chilled mortar and pestle, and then suspended 100 mg of the homogenate in 1.5 ml of 80% ethanol containing 0.04 mg/ml ginsenoside Rb1 (internal standard). Samples were sonicated in ice water for 30 minutes, held for at least 4 hours at 4°C, and centrifuged at 13 000 g for 15 minutes to remove precipitates. Finally, the supernatant was transferred to glass sample vials. The diosgenin saponin content in extracts was determined with a Vanquish ultra-performance liquid chromatography (UPLC) system (Thermo Scientific), which was equipped with a Hypersil GOLD C18 column (2.1 mm × 100 mm, 3 μm, Thermo Scientific) and coupled to a Q Exactive Hybrid Quadrupole-Orbitrap Mass Spectrometer (Q Exactive MS/MS, Thermo Scientific).

We extracted diosgenin from tissues using an established protocol with some modifications [24]. Briefly, 1 ml isopropanol was added to 100 mg homogenate from leaf samples, and homogenized thoroughly. These samples were extracted at 50°C with shaking at 200 rpm for 30 minutes, followed by two rounds of sonication for 20 minutes for further extraction. The supernatant was centrifuged at 13 000 g for 10 minutes, and analyzed using UPLC–MS/MS (Thermo Scientific). All raw data were processed using XCalibur software (Thermo Scientific). The metabolite content values were normalized using the z-score method and visualized using the pheatmap package (https://cran.r-project.org/web/packages/pheatmap/index.html) of R software.

Comparative transcriptomic analysis of Dioscorea species

High-quality RNA was extracted from fresh leaves of the 13 Dioscorea plants. Sequencing libraries of each sample were generated using the NEBNext^® Ultra™ RNA Library Prep Kit for Illumina (NEB) according to the manufacturer’s instructions. Then, libraries were sequenced using the Illumina Hiseq 2500 platform. After raw sequencing data were filtered, a total of ~252 Gb of clean data containing 125- to 150-bp paired-end reads were generated for transcriptome assembly and gene functional annotation.

All clean paired-end reads were first mapped to our assembled D. zingiberensis genome using STAR [72]. The mapping rates of other Dioscorea plants, which were <70%, were analyzed using the de novo transcriptome assembly strategy. Transcriptome assembly was accomplished using Trinity [77]. Clean reads for these Dioscorea species were mapped back to their own de novo assembled transcriptomes, and the expression of each transcript in each sample was quantified using the RSEM software package [78]. The FPKM value of each gene was calculated based on the read counts obtained from RSEM results.

To investigate the expression levels of diosgenin saponin biosynthetic pathway genes in all 13 selected Dioscorea species, we used BLASTP (E-value <1e−10) to identify the homologous genes involved in the biosynthesis of diosgenin saponins. Then, we selected the best-hit homologous pathway genes in each Dioscorea plant and visualized them using the R package pheatmap (https://cran.r-project.org/web/packages/pheatmap/index.html).

Ancestral state reconstructions and phylogenetic signal analysis

For ancestral state reconstruction, the presence/absence of diosgenin saponins was treated as the binary chemical trait, and the content of diosgenin saponins was treated as a continuous trait. In addition, we also collected the phenotypic data of diosgenin saponin for a total of 91 Dioscorea species and converted them into binary data [18, 22], and constructed the phylogenetic relationships using RAxML with 1000 bootstrap replicates [66, 79]. We then reconstructed ancestral states for each data set using the R package APE [80], and visualized them using the R package phytools [81].

To estimate the phylogenetic signal, the function phylosig of the phytools package was used to estimate the value of λ and K for continuous traits and binary traits [82, 83]. Based on Blomberg’s K statistic, K > 1 indicates that the phylogenetic signal is strong, and the similarity between close relatives is higher than expected under a Brownian threshold model [83]. λ or K ≈ 0 indicates that the trait distribution is phylogenetically random, suggesting that there is no correlation in the direction of evolution. On the contrary, λ or K ≈ 1 means non-random trait distribution.

Further details of the methods are provided in Supplementary Information.

Acknowledgements

We thank Professors Jianbo Wang, Huabin Zhao, Yu Zhou (College of Life Sciences, Wuhan University), Professor Hui Ye (Department of Biology, Loyola University Chicago), and Professor Kai Ye (School of Electronics and Information Engineering, Xi’an Jiaotong University) for critical reading and valuable suggestions on this manuscript; Professor Yingjun Zhang (State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences) for suggestions on the chemical structure of metabolites; and Novogene Corporation for sequencing and preliminary analysis of transcriptome data. This work was supported by grants from the National Natural Science Foundation of China (30370152, 31270345, and 31470388, all to J.L.); and D.R.G.’s involvement was supported in part by the USDA National Institute of Food and Agriculture, Hatch project 227700.

Author contributions

J.L. conceived and supervised the project; J.L. and Y.L. designed the experiments; Y.L. performed most of the experiments; C.T., Z.L., J.G., S.L., X.C., C.W., X.D., H.Y., W.S., L.H., Z.T., A.X., X.Y., W.W., Z.S., K.W., B.P., Y.B., and F.L. performed some of the experiments; Y.L., Z.L., S.L., X.D., H.Y., L.H., J.X., Z.T., X.Y., Z.S., and K.W. analyzed data; Y.L., Q.Y., L.C., J.C., X.Q., D.R.G., J.W., and J.L. wrote the manuscript. All authors read and approved the manuscript.

Data availability

All assembled sequence and annotation data of the D. zingiberensis genome have been deposited in the NCBI database as a BioProject under accession number PRJNA716093. The RNA-seq raw data described in this paper have been deposited in the National Genomics Data Center, Beijing Institute of Genomics (China National Center for Bioinformation), Chinese Academy of Sciences, under the BioProject accession number PRJCA009925 and the Genome Sequence Archive (GSA) under accession number CRA007170 (https://bigd.big.ac.cn/gsa).

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary data

Supplementary data is available at Horticulture Research online.

Supplementary Material

Web_Material_uhac165

Click here for additional data file.^{(6.5MB, zip)}

Contributor Information

Yi Li, State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.

Chao Tan, State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.

Zihao Li, State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.

Jingzhe Guo, State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.

Song Li, State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.

Xin Chen, State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.

Chen Wang, State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.

Xiaokang Dai, State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.

Huan Yang, State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.

Wei Song, State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.

Lixiu Hou, State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.

Jiali Xu, State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.

Ziyu Tong, State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.

Anran Xu, State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.

Xincheng Yuan, State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.

Weipeng Wang, State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.

Qingyong Yang, Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China.

Lingling Chen, College of Life Science and Technology, Guangxi University, Nanning, 530004, China.

Zongyi Sun, Grandomics Biosciences, Beijing 102200, China.

Kai Wang, Grandomics Biosciences, Beijing 102200, China.

Bo Pan, Center for Integrative Conservation, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Menglun, 666303, China.

Jianghua Chen, CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, CAS Center for Excellence in Molecular Plant Sciences, Xishuangbanna Tropical Botanical Garden, Kunming, 650223, China.

Yinghua Bao, Henry Fok College of Biology and Agriculture, Shaoguan University, Shaoguan, 512005, China.

Faguang Liu, Henry Fok College of Biology and Agriculture, Shaoguan University, Shaoguan, 512005, China.

Xiaoquan Qi, Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China.

David R Gang, Institute of Biological Chemistry, Washington State University, Pullman, WA, 99164, USA.

Jun Wen, Department of Botany, National Museum of Natural History, Smithsonian Institution, Washington, DC, 20013-7012, USA.

Jiaru Li, State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.

References

1. Marker RE, Tsukamoto T, Turner DL. Sterols. C. Diosgenin1. J Am Chem Soc. 1940;62:2525–32. [Google Scholar]
2. Li H, Huang W, Wen Yet al. Anti-thrombotic activity and chemical characterization of steroidal saponins from Dioscorea zingiberensis C.H. Wright. Fitoterapia. 2010;81:1147–56. [DOI] [PubMed] [Google Scholar]
3. Tan Y, Xiao X, Yao Jet al. Syntheses and anti-cancer activities of glycosylated derivatives of diosgenin. Chem Res Chin Univ. 2017;33:80–6. [Google Scholar]
4. Sethi G, Shanmugam M, Warrier Set al. Pro-apoptotic and anti-cancer properties of diosgenin: a comprehensive and critical review. Nutrients 2018;10:645. [DOI] [PMC free article] [PubMed] [Google Scholar]
5. Yi T, Fan LL, Chen HLet al. Comparative analysis of diosgenin in Dioscorea species and related medicinal plants by UPLC-DAD-MS. BMC Biochem. 2014;15:19. [DOI] [PMC free article] [PubMed] [Google Scholar]
6. Sautour M, Mitaine-Offer AC, Lacaille-Dubois MA. The Dioscorea genus: a review of bioactive steroid saponins. J Nat Med. 2007;61:91–101. [Google Scholar]
7. Hsu KM, Tsai JL, Chen MYet al. Molecular phylogeny of Dioscorea (Dioscoreaceae) in east and Southeast Asia. Blumea. 2013;58:21–7. [Google Scholar]
8. Viruel J, Segarra-Moragues JG, Raz Let al. Late cretaceous-early Eocene origin of yams (Dioscorea, Dioscoreaceae) in the Laurasian Palaearctic and their subsequent Oligocene-Miocene diversification. J Biogeogr. 2016;43:750–62. [Google Scholar]
9. Shen L, Xu J, Luo Let al. Predicting the potential global distribution of diosgenin-contained Dioscorea species. Chin Med. 2018;13:58. [DOI] [PMC free article] [PubMed] [Google Scholar]
10. Zhang X, Jin M, Tadesse Net al. Dioscorea zingiberensis C. H. Wright: an overview on its traditional use, phytochemistry, pharmacology, clinical applications, quality control, and toxicity. J Ethnopharmacol. 2018;220:283–93. [DOI] [PubMed] [Google Scholar]
11. Wang W, Zhao Y, Jing Wet al. Ultrahigh-performance liquid chromatography-ion trap mass spectrometry characterization of the steroidal saponins of Dioscorea panthaica Prain et Burkill and its application for accelerating the isolation and structural elucidation of steroidal saponins. Steroids. 2015;95:51–65. [DOI] [PubMed] [Google Scholar]
12. Lin S, Wang D, Yang Det al. Characterization of steroidal saponins in crude extract from Dioscorea nipponica Makino by liquid chromatography tandem multi-stage mass spectrometry. Anal Chim Acta. 2007;599:98–106. [DOI] [PubMed] [Google Scholar]
13. Lee HJ, Watanabe B, Nakayasu Met al. Novel steroidal saponins from Dioscorea esculenta (Togedokoro). Biosci Biotechnol Biochem. 2017;81:2253–60. [DOI] [PubMed] [Google Scholar]
14. Tapondjou LA, Jenett-Siems K, Böttger Set al. Steroidal saponins from the flowers of Dioscorea bulbifera var. sativa. Phytochemistry. 2013;95:341–50. [DOI] [PubMed] [Google Scholar]
15. Ye T, Song W, Zhang JJet al. Identification and functional characterization of DzS3GT, a cytoplasmic glycosyltransferase catalyzing biosynthesis of diosgenin 3-O-glucoside in Dioscorea zingiberensis. Plant Cell Tissue Organ Cult. 2017;129:399–410. [Google Scholar]
16. Ding ZZ, Tang SR, Qin HZet al. Resource Plant of Steroid Hormone. Chapter 1 Dioscorea species. In: Zhai RK. Beijing: Publishing House of Science; 1983:14–113. [Google Scholar]
17. Song W, Yan S, Li Yet al. Functional characterization of squalene epoxidase and NADPH-cytochrome P450 reductase in Dioscorea zingiberensis. Biochem Biophys Res Commun. 2019;509:822–7. [DOI] [PubMed] [Google Scholar]
18. Li X, Wang Y, Sun Jet al. Chemotaxonomic studies of 12 Dioscorea species from China by UHPLC-QTOF-MS/MS analysis. Phytochem Anal. 2020;31:164–82. [DOI] [PubMed] [Google Scholar]
19. Moses T, Papadopoulou KK, Osbourn A. Metabolic and functional diversity of saponins, biosynthetic intermediates and semi-synthetic derivatives. Crit Rev Biochem Mol Biol. 2014;49:439–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
20. Nakayasu M, Kawasaki T, Lee HJet al. Identification of furostanol glycoside 26-O-β-glucosidase involved in steroidal saponin biosynthesis from Dioscorea esculenta. Plant Biotechnol (Tokyo). 2015;32:299–308. [Google Scholar]
21. Sonawane PD, Pollier J, Panda Set al. Plant cholesterol biosynthetic pathway overlaps with phytosterol metabolism. Nat Plants. 2017;3:16205. [DOI] [PubMed] [Google Scholar]
22. Upadhyay S, Jeena GS, Shukla RK. Recent advances in steroidal saponins biosynthesis and in vitro production. Planta. 2018;248:519–44. [DOI] [PubMed] [Google Scholar]
23. Mohammadi M, Mashayekh T, Rashidi-Monfared Set al. New insights into diosgenin biosynthesis pathway and its regulation in Trigonella foenum-graecum L. Phytochem Anal. 2020;31:229–41. [DOI] [PubMed] [Google Scholar]
24. Christ B, Xu C, Xu Met al. Repeated evolution of cytochrome P450-mediated spiroketal steroid biosynthesis in plants. Nat Commun. 2019;10:1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
25. Cheng J, Chen J, Liu Xet al. The origin and evolution of the diosgenin biosynthetic pathway in yam. Plant Commun. 2021;2:100079. [DOI] [PMC free article] [PubMed] [Google Scholar]
26. Simão FA, Waterhouse RM, Ioannidis Pet al. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015;31:3210–2. [DOI] [PubMed] [Google Scholar]
27. Parra G, Bradnam K, Korf I. CEGMA: a pipeline to accurately annotate core genes in eukaryotic genomes. Bioinformatics. 2007;23:1061–7. [DOI] [PubMed] [Google Scholar]
28. Gottlieb A, Müller HG, Massa ANet al. Insular organization of gene space in grass genomes. PLoS One. 2013;8:e54101. [DOI] [PMC free article] [PubMed] [Google Scholar]
29. Luo MC, Gu YQ, Puiu Det al. Genome sequence of the progenitor of the wheat D genome Aegilops tauschii. Nature. 2017;551:498–502. [DOI] [PMC free article] [PubMed] [Google Scholar]
30. Li L, Stoeckert CJJ, Roos DS. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res. 2003;13:2178–89. [DOI] [PMC free article] [PubMed] [Google Scholar]
31. Abbas FA, Ateya AM. Estradiol, esteriol, estrone and novel flavonoids from date palm pollen. Aust J Basic Appl Sci. 2011;5:606–14. [Google Scholar]
32. Wang L, Wang X, Yuan Xet al. Simultaneous analysis of diosgenin and sarsasapogenin in Asparagus officinalis byproduct by thin-layer chromatography. Phytochem Anal. 2011;22:14–7. [DOI] [PubMed] [Google Scholar]
33. Jiao Y, Wickett NJ, Ayyampalayam Set al. Ancestral polyploidy in seed plants and angiosperms. Nature. 2011;473:97–100. [DOI] [PubMed] [Google Scholar]
34. Clark JW, Donoghue PCJ. Whole-genome duplication and plant macroevolution. Trends Plant Sci. 2018;23:933–45. [DOI] [PubMed] [Google Scholar]
35. Ren R, Wang H, Guo Cet al. Widespread whole genome duplications contribute to genome complexity and species diversity in angiosperms. Mol Plant. 2018;11:414–28. [DOI] [PubMed] [Google Scholar]
36. Li J, Yang D, Yu Ket al. Determination of diosgenin content in medicinal plants with enzyme-linked immunosorbent assay. Planta Med. 2010;76:1915–20. [DOI] [PubMed] [Google Scholar]
37. Wang X, Chen D, Wang Yet al. De novo transcriptome assembly and the putative biosynthetic pathway of steroidal sapogenins of Dioscorea composita. PLoS One. 2015;10:e0124560. [DOI] [PMC free article] [PubMed] [Google Scholar]
38. Gas-Pascual E, Berna A, Bach TJet al. Plant oxidosqualene metabolism: cycloartenol synthase-dependent sterol biosynthesis in Nicotiana benthamiana. PLoS One. 2014;9:e109156. [DOI] [PMC free article] [PubMed] [Google Scholar]
39. Gang DR. Evolution of flavors and scents. Annu Rev Plant Biol. 2005;56:301–25. [DOI] [PubMed] [Google Scholar]
40. Chae L, Kim T, Nilo-Poyanco Ret al. Genomic signatures of specialized metabolism in plants. Science. 2014;344:510–3. [DOI] [PubMed] [Google Scholar]
41. Singh P, Singh G, Bhandawat Aet al. Spatial transcriptome analysis provides insights of key gene(s) involved in steroidal saponin biosynthesis in medicinally important herb Trillium govanianum. Sci Rep. 2017;7:45295. [DOI] [PMC free article] [PubMed] [Google Scholar]
42. Ciura J, Szeliga M, Grzesik Met al. Changes in fenugreek transcriptome induced by methyl jasmonate and steroid precursors revealed by RNA-Seq. Genomics. 2018;110:267–76. [DOI] [PubMed] [Google Scholar]
43. Li J, Liang Q, Li Cet al. Comparative transcriptome analysis identifies putative genes involved in dioscin biosynthesis in Dioscorea zingiberensis. Molecules. 2018;23:1–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
44. Chen S, Xu J, Liu Cet al. Genome sequence of the model medicinal mushroom Ganoderma lucidum. Nat Commun. 2012;3:913. [DOI] [PMC free article] [PubMed] [Google Scholar]
45. Guo YL. Gene family evolution in green plants with emphasis on the origination and evolution of Arabidopsis thaliana genes. Plant J. 2013;73:941–51. [DOI] [PubMed] [Google Scholar]
46. Zhou C, Yang Y, Tian Jet al. 22R- but not 22S-hydroxycholesterol is recruited for diosgenin biosynthesis. Plant J. 2022;109:940–51. [DOI] [PubMed] [Google Scholar]
47. Hou L, Yuan X, Li Set al. Genome-wide identification of CYP72A gene family and expression patterns related to jasmonic acid treatment and steroidal saponin accumulation in Dioscorea zingiberensis. Int J Mol Sci 2021;22:10953 [DOI] [PMC free article] [PubMed] [Google Scholar]
48. Prall W, Hendy O, Thornton LE. Utility of a phylogenetic perspective in structural analysis of CYP72A enzymes from flowering plants. PLoS One. 2016;11:e0163024. [DOI] [PMC free article] [PubMed] [Google Scholar]
49. He J, Chen Q, Xin Pet al. CYP72A enzymes catalyse 13-hydrolyzation of gibberellins. Nat Plants. 2019;5:1057–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
50. Zhao Q, Yang J, Cui MYet al. The reference genome sequence of Scutellaria baicalensis provides insights into the evolution of wogonin biosynthesis. Mol Plant. 2019;12:935–50. [DOI] [PubMed] [Google Scholar]
51. Cheng J, Wang X, Liu Xet al. Chromosome-level genome of Himalayan yew provides insights into the origin and evolution of the paclitaxel biosynthetic pathway. Mol Plant. 2021;14:1199–209. [DOI] [PubMed] [Google Scholar]
52. Xia EH, Zhang HB, Sheng Jet al. The tea tree genome provides insights into tea flavor and independent evolution of caffeine biosynthesis. Mol Plant. 2017;10:866–77. [DOI] [PubMed] [Google Scholar]
53. Belton JM, McCord RP, Gibcus JHet al. Hi-C: a comprehensive technique to capture the conformation of genomes. Methods. 2012;58:268–76. [DOI] [PMC free article] [PubMed] [Google Scholar]
54. Kokot M, Dlugosz M, Deorowicz S. KMC 3: counting and manipulating k-mer statistics. Bioinformatics. 2017;33:2759–61. [DOI] [PubMed] [Google Scholar]
55. Vurture GW, Sedlazeck FJ, Nattestad Met al. GenomeScope: fast reference-free genome profiling from short reads. Bioinformatics. 2017;33:2202–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
56. Li H. Minimap and miniasm: fast mapping and de novo assembly for noisy long sequences. Bioinformatics. 2016;32:2103–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
57. Hu J, Fan J, Sun Zet al. NextPolish: a fast and efficient genome polishing tool for long-read assembly. Bioinformatics. 2020;36:2253–5. [DOI] [PubMed] [Google Scholar]
58. Coombe L, Zhang J, Vandervalk BPet al. ARKS: chromosome-scale scaffolding of human genome drafts with linked read kmers. BMC Bioinformatics. 2018;19:234. [DOI] [PMC free article] [PubMed] [Google Scholar]
59. Warren RL, Yang C, Vandervalk BPet al. LINKS: scalable, alignment-free scaffolding of draft genomes with long reads. GigaScience. 2015;4:35. [DOI] [PMC free article] [PubMed] [Google Scholar]
60. Burton JN, Adey A, Patwardhan RPet al. Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions. Nat Biotechnol. 2013;31:1119–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
61. Xu Z, Wang H. LTR-FINDER: an efficient tool for the prediction of full-length LTR retrotransposons. Nucleic Acids Symp Ser. 2007;35:W265–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
62. Han Y, Wessler SR. MITE-hunter: a program for discovering miniature inverted-repeat transposable elements from genomic sequences. Nucleic Acids Symp Ser. 2010;38:e199–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
63. Stanke M, Schöffmann O, Morgenstern Bet al. Gene prediction in eukaryotes with a generalized hidden Markov model that uses hints from external sources. BMC Bioinformatics. 2006;7:1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
64. Keilwagen J, Wenk M, Erickson JLet al. Using intron position conservation for homology-based gene prediction. Nucleic Acids Res. 2016;44:e89–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
65. Haas BJ, Salzberg SL, Zhu Wet al. Automated eukaryotic gene structure annotation using evidence modeler and the program to assemble spliced alignments. Genome Biol. 2008;9:R7–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
66. Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3. [DOI] [PMC free article] [PubMed] [Google Scholar]
67. Yang Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol Biol Evol. 2007;24:1586–91. [DOI] [PubMed] [Google Scholar]
68. De Bie T, Cristianini N, Demuth JPet al. CAFE: a computational tool for the study of gene family evolution. Bioinformatics. 2006;22:1269–71. [DOI] [PubMed] [Google Scholar]
69. Edgar RC. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics. 2004;5:113–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
70. Suyama M, Torrents D, Bork P. PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments. Nucleic Acids Res. 2006;34:W609–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
71. Scrucca L, Fop M, Murphy TBet al. Mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. R J. 2016;8:289–317. [PMC free article] [PubMed] [Google Scholar]
72. Dobin A, Davis CA, Schlesinger Fet al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
73. Anders S, Pyl PT, Huber W. HTSeq – a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31:166–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
74. Shannon P, Markiel A, Ozier Oet al. A software environment for integrated models. Genome Res. 1971;13:2498–504. [DOI] [PMC free article] [PubMed] [Google Scholar]
75. Johnson LS, Eddy SR, Portugaly E. Hidden Markov model speed heuristic and iterative HMM search procedure. BMC Bioinformatics. 2010;11:431. [DOI] [PMC free article] [PubMed] [Google Scholar]
76. Nelson DR. The cytochrome P450 homepage. Hum Genomics. 2009;4:59–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
77. Grabherr MG, Haas BJ, Yassour Met al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011;29:644–52. [DOI] [PMC free article] [PubMed] [Google Scholar]
78. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011;12:323. [DOI] [PMC free article] [PubMed] [Google Scholar]
79. Zanne AE, Tank DC, Cornwell WKet al. Three keys to the radiation of angiosperms into freezing environments. Nature. 2014;506:89–92. [DOI] [PubMed] [Google Scholar]
80. Paradis E, Claude J, Strimmer K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics. 2004;20:289–90. [DOI] [PubMed] [Google Scholar]
81. Revell LJ. Phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol Evol. 2012;3:217–23. [Google Scholar]
82. Pagel M. Inferring evolutionary processes from phylogenies. Zool Scr. 1997;26:331–48. [Google Scholar]
83. Blomberg SP, Garland T, Ives AR. Testing for phylogenetic signal in comparative data: behavioral traits are more labile. Evolution. 2003;57:717–45. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Web_Material_uhac165

Click here for additional data file.^{(6.5MB, zip)}

Data Availability Statement

[ref1] 1. Marker RE, Tsukamoto T, Turner DL. Sterols. C. Diosgenin1. J Am Chem Soc. 1940;62:2525–32. [Google Scholar]

[ref2] 2. Li H, Huang W, Wen Yet al. Anti-thrombotic activity and chemical characterization of steroidal saponins from Dioscorea zingiberensis C.H. Wright. Fitoterapia. 2010;81:1147–56. [DOI] [PubMed] [Google Scholar]

[ref3] 3. Tan Y, Xiao X, Yao Jet al. Syntheses and anti-cancer activities of glycosylated derivatives of diosgenin. Chem Res Chin Univ. 2017;33:80–6. [Google Scholar]

[ref4] 4. Sethi G, Shanmugam M, Warrier Set al. Pro-apoptotic and anti-cancer properties of diosgenin: a comprehensive and critical review. Nutrients 2018;10:645. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref5] 5. Yi T, Fan LL, Chen HLet al. Comparative analysis of diosgenin in Dioscorea species and related medicinal plants by UPLC-DAD-MS. BMC Biochem. 2014;15:19. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref6] 6. Sautour M, Mitaine-Offer AC, Lacaille-Dubois MA. The Dioscorea genus: a review of bioactive steroid saponins. J Nat Med. 2007;61:91–101. [Google Scholar]

[ref7] 7. Hsu KM, Tsai JL, Chen MYet al. Molecular phylogeny of Dioscorea (Dioscoreaceae) in east and Southeast Asia. Blumea. 2013;58:21–7. [Google Scholar]

[ref8] 8. Viruel J, Segarra-Moragues JG, Raz Let al. Late cretaceous-early Eocene origin of yams (Dioscorea, Dioscoreaceae) in the Laurasian Palaearctic and their subsequent Oligocene-Miocene diversification. J Biogeogr. 2016;43:750–62. [Google Scholar]

[ref9] 9. Shen L, Xu J, Luo Let al. Predicting the potential global distribution of diosgenin-contained Dioscorea species. Chin Med. 2018;13:58. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref10] 10. Zhang X, Jin M, Tadesse Net al. Dioscorea zingiberensis C. H. Wright: an overview on its traditional use, phytochemistry, pharmacology, clinical applications, quality control, and toxicity. J Ethnopharmacol. 2018;220:283–93. [DOI] [PubMed] [Google Scholar]

[ref11] 11. Wang W, Zhao Y, Jing Wet al. Ultrahigh-performance liquid chromatography-ion trap mass spectrometry characterization of the steroidal saponins of Dioscorea panthaica Prain et Burkill and its application for accelerating the isolation and structural elucidation of steroidal saponins. Steroids. 2015;95:51–65. [DOI] [PubMed] [Google Scholar]

[ref12] 12. Lin S, Wang D, Yang Det al. Characterization of steroidal saponins in crude extract from Dioscorea nipponica Makino by liquid chromatography tandem multi-stage mass spectrometry. Anal Chim Acta. 2007;599:98–106. [DOI] [PubMed] [Google Scholar]

[ref13] 13. Lee HJ, Watanabe B, Nakayasu Met al. Novel steroidal saponins from Dioscorea esculenta (Togedokoro). Biosci Biotechnol Biochem. 2017;81:2253–60. [DOI] [PubMed] [Google Scholar]

[ref14] 14. Tapondjou LA, Jenett-Siems K, Böttger Set al. Steroidal saponins from the flowers of Dioscorea bulbifera var. sativa. Phytochemistry. 2013;95:341–50. [DOI] [PubMed] [Google Scholar]

[ref15] 15. Ye T, Song W, Zhang JJet al. Identification and functional characterization of DzS3GT, a cytoplasmic glycosyltransferase catalyzing biosynthesis of diosgenin 3-O-glucoside in Dioscorea zingiberensis. Plant Cell Tissue Organ Cult. 2017;129:399–410. [Google Scholar]

[ref16] 16. Ding ZZ, Tang SR, Qin HZet al. Resource Plant of Steroid Hormone. Chapter 1 Dioscorea species. In: Zhai RK. Beijing: Publishing House of Science; 1983:14–113. [Google Scholar]

[ref17] 17. Song W, Yan S, Li Yet al. Functional characterization of squalene epoxidase and NADPH-cytochrome P450 reductase in Dioscorea zingiberensis. Biochem Biophys Res Commun. 2019;509:822–7. [DOI] [PubMed] [Google Scholar]

[ref18] 18. Li X, Wang Y, Sun Jet al. Chemotaxonomic studies of 12 Dioscorea species from China by UHPLC-QTOF-MS/MS analysis. Phytochem Anal. 2020;31:164–82. [DOI] [PubMed] [Google Scholar]

[ref19] 19. Moses T, Papadopoulou KK, Osbourn A. Metabolic and functional diversity of saponins, biosynthetic intermediates and semi-synthetic derivatives. Crit Rev Biochem Mol Biol. 2014;49:439–62. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref20] 20. Nakayasu M, Kawasaki T, Lee HJet al. Identification of furostanol glycoside 26-O-β-glucosidase involved in steroidal saponin biosynthesis from Dioscorea esculenta. Plant Biotechnol (Tokyo). 2015;32:299–308. [Google Scholar]

[ref21] 21. Sonawane PD, Pollier J, Panda Set al. Plant cholesterol biosynthetic pathway overlaps with phytosterol metabolism. Nat Plants. 2017;3:16205. [DOI] [PubMed] [Google Scholar]

[ref22] 22. Upadhyay S, Jeena GS, Shukla RK. Recent advances in steroidal saponins biosynthesis and in vitro production. Planta. 2018;248:519–44. [DOI] [PubMed] [Google Scholar]

[ref23] 23. Mohammadi M, Mashayekh T, Rashidi-Monfared Set al. New insights into diosgenin biosynthesis pathway and its regulation in Trigonella foenum-graecum L. Phytochem Anal. 2020;31:229–41. [DOI] [PubMed] [Google Scholar]

[ref24] 24. Christ B, Xu C, Xu Met al. Repeated evolution of cytochrome P450-mediated spiroketal steroid biosynthesis in plants. Nat Commun. 2019;10:1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref25] 25. Cheng J, Chen J, Liu Xet al. The origin and evolution of the diosgenin biosynthetic pathway in yam. Plant Commun. 2021;2:100079. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref26] 26. Simão FA, Waterhouse RM, Ioannidis Pet al. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015;31:3210–2. [DOI] [PubMed] [Google Scholar]

[ref27] 27. Parra G, Bradnam K, Korf I. CEGMA: a pipeline to accurately annotate core genes in eukaryotic genomes. Bioinformatics. 2007;23:1061–7. [DOI] [PubMed] [Google Scholar]

[ref28] 28. Gottlieb A, Müller HG, Massa ANet al. Insular organization of gene space in grass genomes. PLoS One. 2013;8:e54101. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref29] 29. Luo MC, Gu YQ, Puiu Det al. Genome sequence of the progenitor of the wheat D genome Aegilops tauschii. Nature. 2017;551:498–502. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref30] 30. Li L, Stoeckert CJJ, Roos DS. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res. 2003;13:2178–89. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref31] 31. Abbas FA, Ateya AM. Estradiol, esteriol, estrone and novel flavonoids from date palm pollen. Aust J Basic Appl Sci. 2011;5:606–14. [Google Scholar]

[ref32] 32. Wang L, Wang X, Yuan Xet al. Simultaneous analysis of diosgenin and sarsasapogenin in Asparagus officinalis byproduct by thin-layer chromatography. Phytochem Anal. 2011;22:14–7. [DOI] [PubMed] [Google Scholar]

[ref33] 33. Jiao Y, Wickett NJ, Ayyampalayam Set al. Ancestral polyploidy in seed plants and angiosperms. Nature. 2011;473:97–100. [DOI] [PubMed] [Google Scholar]

[ref34] 34. Clark JW, Donoghue PCJ. Whole-genome duplication and plant macroevolution. Trends Plant Sci. 2018;23:933–45. [DOI] [PubMed] [Google Scholar]

[ref35] 35. Ren R, Wang H, Guo Cet al. Widespread whole genome duplications contribute to genome complexity and species diversity in angiosperms. Mol Plant. 2018;11:414–28. [DOI] [PubMed] [Google Scholar]

[ref36] 36. Li J, Yang D, Yu Ket al. Determination of diosgenin content in medicinal plants with enzyme-linked immunosorbent assay. Planta Med. 2010;76:1915–20. [DOI] [PubMed] [Google Scholar]

[ref37] 37. Wang X, Chen D, Wang Yet al. De novo transcriptome assembly and the putative biosynthetic pathway of steroidal sapogenins of Dioscorea composita. PLoS One. 2015;10:e0124560. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref38] 38. Gas-Pascual E, Berna A, Bach TJet al. Plant oxidosqualene metabolism: cycloartenol synthase-dependent sterol biosynthesis in Nicotiana benthamiana. PLoS One. 2014;9:e109156. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref39] 39. Gang DR. Evolution of flavors and scents. Annu Rev Plant Biol. 2005;56:301–25. [DOI] [PubMed] [Google Scholar]

[ref40] 40. Chae L, Kim T, Nilo-Poyanco Ret al. Genomic signatures of specialized metabolism in plants. Science. 2014;344:510–3. [DOI] [PubMed] [Google Scholar]

[ref41] 41. Singh P, Singh G, Bhandawat Aet al. Spatial transcriptome analysis provides insights of key gene(s) involved in steroidal saponin biosynthesis in medicinally important herb Trillium govanianum. Sci Rep. 2017;7:45295. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref42] 42. Ciura J, Szeliga M, Grzesik Met al. Changes in fenugreek transcriptome induced by methyl jasmonate and steroid precursors revealed by RNA-Seq. Genomics. 2018;110:267–76. [DOI] [PubMed] [Google Scholar]

[ref43] 43. Li J, Liang Q, Li Cet al. Comparative transcriptome analysis identifies putative genes involved in dioscin biosynthesis in Dioscorea zingiberensis. Molecules. 2018;23:1–13. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref44] 44. Chen S, Xu J, Liu Cet al. Genome sequence of the model medicinal mushroom Ganoderma lucidum. Nat Commun. 2012;3:913. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref45] 45. Guo YL. Gene family evolution in green plants with emphasis on the origination and evolution of Arabidopsis thaliana genes. Plant J. 2013;73:941–51. [DOI] [PubMed] [Google Scholar]

[ref46] 46. Zhou C, Yang Y, Tian Jet al. 22R- but not 22S-hydroxycholesterol is recruited for diosgenin biosynthesis. Plant J. 2022;109:940–51. [DOI] [PubMed] [Google Scholar]

[ref47] 47. Hou L, Yuan X, Li Set al. Genome-wide identification of CYP72A gene family and expression patterns related to jasmonic acid treatment and steroidal saponin accumulation in Dioscorea zingiberensis. Int J Mol Sci 2021;22:10953 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref48] 48. Prall W, Hendy O, Thornton LE. Utility of a phylogenetic perspective in structural analysis of CYP72A enzymes from flowering plants. PLoS One. 2016;11:e0163024. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref49] 49. He J, Chen Q, Xin Pet al. CYP72A enzymes catalyse 13-hydrolyzation of gibberellins. Nat Plants. 2019;5:1057–65. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref50] 50. Zhao Q, Yang J, Cui MYet al. The reference genome sequence of Scutellaria baicalensis provides insights into the evolution of wogonin biosynthesis. Mol Plant. 2019;12:935–50. [DOI] [PubMed] [Google Scholar]

[ref51] 51. Cheng J, Wang X, Liu Xet al. Chromosome-level genome of Himalayan yew provides insights into the origin and evolution of the paclitaxel biosynthetic pathway. Mol Plant. 2021;14:1199–209. [DOI] [PubMed] [Google Scholar]

[ref52] 52. Xia EH, Zhang HB, Sheng Jet al. The tea tree genome provides insights into tea flavor and independent evolution of caffeine biosynthesis. Mol Plant. 2017;10:866–77. [DOI] [PubMed] [Google Scholar]

[ref53] 53. Belton JM, McCord RP, Gibcus JHet al. Hi-C: a comprehensive technique to capture the conformation of genomes. Methods. 2012;58:268–76. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref54] 54. Kokot M, Dlugosz M, Deorowicz S. KMC 3: counting and manipulating k-mer statistics. Bioinformatics. 2017;33:2759–61. [DOI] [PubMed] [Google Scholar]

[ref55] 55. Vurture GW, Sedlazeck FJ, Nattestad Met al. GenomeScope: fast reference-free genome profiling from short reads. Bioinformatics. 2017;33:2202–4. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref56] 56. Li H. Minimap and miniasm: fast mapping and de novo assembly for noisy long sequences. Bioinformatics. 2016;32:2103–10. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref57] 57. Hu J, Fan J, Sun Zet al. NextPolish: a fast and efficient genome polishing tool for long-read assembly. Bioinformatics. 2020;36:2253–5. [DOI] [PubMed] [Google Scholar]

[ref58] 58. Coombe L, Zhang J, Vandervalk BPet al. ARKS: chromosome-scale scaffolding of human genome drafts with linked read kmers. BMC Bioinformatics. 2018;19:234. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref59] 59. Warren RL, Yang C, Vandervalk BPet al. LINKS: scalable, alignment-free scaffolding of draft genomes with long reads. GigaScience. 2015;4:35. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref60] 60. Burton JN, Adey A, Patwardhan RPet al. Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions. Nat Biotechnol. 2013;31:1119–25. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref61] 61. Xu Z, Wang H. LTR-FINDER: an efficient tool for the prediction of full-length LTR retrotransposons. Nucleic Acids Symp Ser. 2007;35:W265–8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref62] 62. Han Y, Wessler SR. MITE-hunter: a program for discovering miniature inverted-repeat transposable elements from genomic sequences. Nucleic Acids Symp Ser. 2010;38:e199–8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref63] 63. Stanke M, Schöffmann O, Morgenstern Bet al. Gene prediction in eukaryotes with a generalized hidden Markov model that uses hints from external sources. BMC Bioinformatics. 2006;7:1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref64] 64. Keilwagen J, Wenk M, Erickson JLet al. Using intron position conservation for homology-based gene prediction. Nucleic Acids Res. 2016;44:e89–11. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref65] 65. Haas BJ, Salzberg SL, Zhu Wet al. Automated eukaryotic gene structure annotation using evidence modeler and the program to assemble spliced alignments. Genome Biol. 2008;9:R7–22. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref66] 66. Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref67] 67. Yang Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol Biol Evol. 2007;24:1586–91. [DOI] [PubMed] [Google Scholar]

[ref68] 68. De Bie T, Cristianini N, Demuth JPet al. CAFE: a computational tool for the study of gene family evolution. Bioinformatics. 2006;22:1269–71. [DOI] [PubMed] [Google Scholar]

[ref69] 69. Edgar RC. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics. 2004;5:113–9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref70] 70. Suyama M, Torrents D, Bork P. PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments. Nucleic Acids Res. 2006;34:W609–12. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref71] 71. Scrucca L, Fop M, Murphy TBet al. Mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. R J. 2016;8:289–317. [PMC free article] [PubMed] [Google Scholar]

[ref72] 72. Dobin A, Davis CA, Schlesinger Fet al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref73] 73. Anders S, Pyl PT, Huber W. HTSeq – a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31:166–9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref74] 74. Shannon P, Markiel A, Ozier Oet al. A software environment for integrated models. Genome Res. 1971;13:2498–504. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref75] 75. Johnson LS, Eddy SR, Portugaly E. Hidden Markov model speed heuristic and iterative HMM search procedure. BMC Bioinformatics. 2010;11:431. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref76] 76. Nelson DR. The cytochrome P450 homepage. Hum Genomics. 2009;4:59–65. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref77] 77. Grabherr MG, Haas BJ, Yassour Met al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011;29:644–52. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref78] 78. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011;12:323. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref79] 79. Zanne AE, Tank DC, Cornwell WKet al. Three keys to the radiation of angiosperms into freezing environments. Nature. 2014;506:89–92. [DOI] [PubMed] [Google Scholar]

[ref80] 80. Paradis E, Claude J, Strimmer K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics. 2004;20:289–90. [DOI] [PubMed] [Google Scholar]

[ref81] 81. Revell LJ. Phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol Evol. 2012;3:217–23. [Google Scholar]

[ref82] 82. Pagel M. Inferring evolutionary processes from phylogenies. Zool Scr. 1997;26:331–48. [Google Scholar]

[ref83] 83. Blomberg SP, Garland T, Ives AR. Testing for phylogenetic signal in comparative data: behavioral traits are more labile. Evolution. 2003;57:717–45. [DOI] [PubMed] [Google Scholar]

PERMALINK

The genome of Dioscorea zingiberensis sheds light on the biosynthesis, origin and evolution of the medicinally important diosgenin saponins

Yi Li

Chao Tan

Zihao Li

Jingzhe Guo

Song Li

Xin Chen

Chen Wang

Xiaokang Dai

Huan Yang

Wei Song

Lixiu Hou

Jiali Xu

Ziyu Tong

Anran Xu

Xincheng Yuan

Weipeng Wang

Qingyong Yang

Lingling Chen

Zongyi Sun

Kai Wang

Bo Pan

Jianghua Chen

Yinghua Bao

Faguang Liu

Xiaoquan Qi

David R Gang

Jun Wen

Jiaru Li

Abstract

Introduction

Figure 1.

Results

Genome assembly and annotation

Table 1.

Figure 2.

Comparative genomic analysis

Biosynthesis of diosgenin saponins in D. zingiberensis

Figure 3.

Origin of diosgenin saponins in Dioscorea species

Figure 4.

Evolution of the diosgenin saponin biosynthetic pathway

Figure 5.

Expression pattern of the key genes involved in diosgenin saponin biosynthesis

Figure 6.

Discussion

Materials and methods

Plant materials

Genome sequencing

Genome assembly

Genome annotation

Comparative genomic analyses

Genome evolution

Immunohistochemical localization and transport assay of diosgenin in D. zingiberensis

Gene expression analyses

Molecular evolution of genes involved in diosgenin saponin biosynthesis

Determination of diosgenin and diosgenin saponin contents of the 13 Dioscorea species

Comparative transcriptomic analysis of Dioscorea species

Ancestral state reconstructions and phylogenetic signal analysis

Acknowledgements

Author contributions

Data availability

Conflict of interest

Supplementary data

Supplementary Material

Contributor Information

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases