Transcriptome and metabolome analysis of Atractylodes lancea across different developmental stages

Atractylodes lancea (Thunb.) DC is a medicinal plant known for its rhizome's production of valuable sesquiterpenoids, although the molecular mechanisms underlying their biosynthesis are not well understood. This study utilized integrated metabolomic and transcriptomic analyses to examine terpenoid dynamics across four developmental stages (June, July, September, November) in A. lancea. Metabolite profiling indicated distinct accumulation patterns: monoterpenoids reached their peak in July, while sesquiterpenoids were most abundant in September. Transcriptome analysis revealed the presence of 36 structural genes linked to the mevalonate (MVA) and methylerythritol phosphate (MEP) pathways, alongside 55 terpene synthase (TPS) genes. Subsequent phylogenetic analysis categorized the TPS genes into distinct subfamilies, and within the TPS-a subfamily, a comprehensive screening process considering significant correlations with terpenoid metabolites and the preservation of key conserved motifs identified eight candidate genes, including AlTPS21 and AlTPS42. Functional characterization demonstrated that the AlTPS21 protein catalyzes the conversion of farnesyl diphosphate to δ-cadinene and α-cadinol, while the AlTPS42 protein catalyzes the conversion of farnesyl diphosphate to δ-cadinene and α-copaene. Subcellular localization studies showed that both enzymes are localized to the nucleus and cell membrane. These findings enhance the understanding of the temporal regulation of terpenoid biosynthesis in A. lancea and provide crucial genetic insights for future metabolic engineering efforts.

Introduction

A. lancea is a perennial medicinal plant within the genus Atractylodes, part of the Asteraceae family. The dried rhizome of A. lancea is a key medicinal material of traditional Chinese medicine, extensively utilized in East Asian traditional medical practices for its documented therapeutic to alleviate dampness, fortify the spleen, dispel wind and cold, and enhance vision (Jun et al., 2018). Contemporary pharmacological research has identified terpenoids found in its essential oil, such as atractylone, hinesol, β-eudesmol, and atractylodin, as the primary bioactive constituents of A. lancea (Xu et al., 2022). These terpenoids not only impart the characteristic aroma of A. lancea but are also intricately linked to a range of pharmacological activities, including anti-inflammatory (Kawada et al., 2024), antibacterial (Xu et al., 2025), antitumor (Saeheng et al., 2024; Guo et al., 2019), and regulation of gastrointestinal function (Chen et al., 2025).

Plant terpenoids are predominantly synthesized via the mevalonate (MVA) pathway in the cytoplasm and the methylerythritol phosphate (MEP) pathway in plastids (Li et al., 2023; Vranová et al., 2012), leading to the production of the essential five-carbon units, isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP). These units are subsequently converted by terpene synthases (TPSs) into a diverse array of monoterpenes, sesquiterpenes and diterpenes (Tholl, 2006; Chen et al., 2011). Despite the elucidation of these biosynthetic pathways, current research is often constrained to single time-point analyses or primarily concentrates on metabolic pathway examination (Bergman et al., 2019). The precise regulatory mechanisms that modulate these pathways throughout different developmental stages in A. lancea, leading to variations in metabolite accumulation, remain insufficiently characterized. Furthermore, although high-throughput sequencing has identified numerous putative TPS genes (Chen et al., 2023; Ahmed et al., 2016), the majority have not undergone experimental validation to determine their functions, substrate specificities, and roles both in vivo and in vitro, indicating a need for further investigation.

In recent years, the rapid advancement of omics technologies has significantly enhanced the application of integrated transcriptomic and metabolomic analyses in the investigation of secondary metabolite biosynthesis in medicinal plants (Yang et al., 2023; Zhao and Rhee, 2022). This integrative approach effectively correlates gene expression patterns with metabolite accumulation, offering a comprehensive perspective for elucidating biosynthetic pathways and regulatory mechanisms (Wei et al., 2025; Hua et al., 2023; Wang et al., 2023). Although prior research on A. lancea has predominantly concentrated on its phytochemical composition and pharmacological properties (Qiu et al., 2023; Koonrungsesomboon et al., 2014), the dynamic changes in terpenoid accumulation during rhizome development and the associated molecular regulatory networks remain inadequately explored.

This study employs an integration of metabolomic and transcriptomic analyses to examine the dynamic changes in terpenoid accumulation and gene expression in the Dabieshan variety of A. lancea across four developmental stages (June, July, September, November). Through differential expression analysis, KEGG enrichment, and gene-metabolite correlation analysis, key genes involved in terpenoid biosynthesis were identified. AlTPS21 and AlTPS42 were subsequently cloned, expressed heterologously, and functionally validated, elucidating their catalytic functions and subcellular localization. These findings provide novel insights into the developmental regulation mechanisms of terpenoid biosynthesis in A. lancea and lay the groundwork for varietal improvement and quality enhancement.

Materials and methods

Plant materials and treatment

Fresh three-year-old A. lancea samples were collected from Baisangguan Town, Yunyang District, Shiyan City, Hubei Province, and were authenticated by Associate Researcher Liangping Zha from Anhui University of Chinese Medicine. Rhizomes were harvested on April 20, May 20, June 18, July 19, August 20, September 20, October 20, and November 22, 2022, with three biological replicates per time point. Each sample was promptly rinsed with sterile water, and a portion was flash-frozen in liquid nitrogen and stored at –80°C for subsequent metabolomic and transcriptomic analyses. The residual material was subjected to drying at 40°C, subsequently ground, and sieved using a No. 3 mesh before being utilized for gas chromatography–mass spectrometry (GC–MS) analysis.

GC-MS detection

Approximately 0.1 g of the powdered sample from each time point was precisely weighed and placed in a centrifuge tube for extraction with 3 mL of n-hexane via ultrasonication (40 kHz, 500 W) for 30 minutes. Following the replenishment of any weight loss, the mixture was filtered through a 0.22 μm membrane. The analysis was conducted using an Agilent 7890B–7000B GC–MS system, which was equipped with a flame ionization detector and a DB-5 MS column (60 m × 0.25 mm, 0.25 μm) under high-purity helium as the carrier gas. The GC–MS parameters were consistent with those reported in previous studies (Xu et al., 2022; Yu et al., 2019).

Metabolomic profiling analysis

A metabolomic analysis was performed on rhizomes at four developmental stages (June, July, September, November), with three biological replicates each. Samples were ground in liquid nitrogen, mixed with saturated NaCl and an internal standard (3-Hexanone-2,2,4,4-d4), and volatile compounds were extracted using HS-SPME with a 120 μm DVB/CWR/PDMS fiber at 60°C for 15 minutes, followed by desorption at 250°C for 5 minutes. GC–MS separation used a DB-5MS column with helium at 1.2 mL/min. The temperature program was: 40°C for 3.5 minutes, increased by 10°C/min to 100°C, then 7°C/min to 180°C, and finally 25°C/min to 280°C for 5 minutes. Mass spectrometry used an EI ion source in SIM mode. Metabolites were identified with Metware’s database and analyzed with MassHunter software. PCA and OPLS-DA were used for statistical analysis, identifying differentially accumulated metabolites with VIP > 1 and FC ≥ 2 or ≤ 0.5 (Wu et al., 2023).

RNA extraction and transcriptome analysis

The transcriptomic analysis was conducted in parallel with metabolomic studies, utilizing the same four developmental stages: June, July, September, and November. Total RNA was extracted and purified employing BGI’s Plant Total RNA Extraction Kit, with RNA integrity assessed via a Standard Sensitivity RNA Analysis Kit. Following mRNA enrichment using Oligo(dT) beads, RNA was fragmented and reverse transcribed using random hexamers to construct double-stranded cDNA libraries. These libraries underwent end-repair, A-tailing, adapter ligation, PCR amplification, and circularization, preparing them for sequencing on the MGISEQ-2000RS platform. Initial raw data were processed using SOAPnuke to yield clean reads. Functional annotation was performed with reference to the KEGG and GO databases, with a particular emphasis on terpenoid biosynthesis pathways (Ko00900, Ko00902, Ko00904, Ko00909). Differentially expressed genes (DEGs) were identified using DEGseq2, applying thresholds of |Log₂FC| > 1 and Q ≤ 0.05. Subsequently, KEGG enrichment analysis was conducted using the Metware Cloud platform (https://cloud.metware.cn) (Liu et al., 2024b).

Quantitative real-time PCR

RNA extracted from rhizomes at four developmental stages was reverse-transcribed into complementary DNA (cDNA) utilizing the All-in-One First-Strand Synthesis MasterMix, which includes dsDNase. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was conducted on a Bio-Rad CFX96 Touch system employing Taq SYBR^® Green qPCR Premix. The thermal cycling program consisted of an initial denaturation step at 95°C for 30 seconds, followed by 40 cycles of denaturation at 95°C for 15 seconds, annealing at 60°C for 30 seconds, and extension at 72°C for 15 seconds. This was succeeded by a final step at 65°C for 5 seconds and a melting curve analysis ranging from 65°C to 95°C at increments of 0.5°C every 0.5 seconds. Primers were designed using Primer Premier 5 and synthesized by General Biol (Anhui). Gene expression levels were normalized to the internal reference gene AlUBQ2 and quantified using the 2^–ΔΔCt method (Livak and Schmittgen, 2001; Arya et al., 2005). The sequences of the primers utilized are provided in Supplementary Table S1.

Phylogenetic analysis

Protein sequence alignment was conducted utilizing MEGA version 11.0 software, and a phylogenetic tree was subsequently constructed employing the neighbor-joining method with 1000 bootstrap replicates. The amino acid sequences of Artemisia annua terpene synthases (TPSs) were sourced from the GPGenome database (http://www.gpgenome.com/species/92) as reported by Liao et al. (2022). The resulting phylogenetic tree was visualized and annotated using the Interactive Tree of Life (iTOL) tool (https://itol.embl.de/).

Cloning of AlTPS genes

Total RNA was reverse-transcribed into complementary DNA (cDNA) using the First-Strand cDNA Synthesis Mix. Target gene sequences were amplified via polymerase chain reaction (PCR) using TransStart^® FastPfu DNA Polymerase with specific primers designed through Primer Premier 5.0. PCR products were separated by electrophoresis, and target bands were excised and purified using the EasyPure^® Quick Gel Extraction Kit. The purified products were ligated into the pEASY^®-Blunt Zero Cloning Vector and subsequently transformed into Escherichia coli Trans-T1 competent cells. Positive clones were identified through colony PCR and sequenced by General Biol (Anhui). Plasmids from verified clones were extracted using the EasyPure HiPure Plasmid MiniPrep Kit and stored at –20°C for future use.

Heterologous expression in Saccharomyces cerevisiae and activity assay

The Saccharomyces cerevisiae strain BY-T15 was employed as the host organism for conducting in vivo enzyme assays. The pESC-His vector, harboring AlTPS genes, along with the empty pESC-His vector, were introduced into yeast competent cells via transformation. Selection of transformants was performed on SD-His-Leu solid medium. The recombinant strains were initially cultivated in SD His-Leu dropout liquid medium supplemented with glucose at 30°C. Amplification of AlTPS genes was achieved using primers AlTPS-pESC-His-F and AlTPS-pESC-His-R for the construction of a eukaryotic expression vector. Polymerase chain reaction (PCR) was conducted to validate the recombinant strains. Subsequently, yeast cells were subjected to centrifugation, washing, and resuspension in SC-His-Leu medium containing 2.0% D-galactose to induce the expression of the target protein. Following galactose induction, the cultures were transferred to headspace vials and incubated at 30°C with agitation at 200 rpm for a duration of 2–3 days. The analysis of volatile metabolites was carried out using headspace solid-phase microextraction gas chromatography–mass spectrometry (HS-SPME-GC–MS). Extraction was conducted at 60°C for 50 min, followed by desorption at 250°C for 10 min. GC separation used a DB-5MS column (60 m × 0.25 mm × 0.25 μm) with an inlet temperature of 250°C and a split ratio of 20:1. The temperature program was: 60°C hold for 5 min, ramp at 20°C/min to 130°C, then 2°C/min to 220°C, and finally 20°C/min to 280°C hold for 5 min. Helium was used as the carrier gas at 1 mL/min. Mass spectrometry (MS) detection employed an electron ionization (EI) source with 70 eV electron energy, scanning a range of 33–600 amu. Product identification was confirmed by comparing retention times and mass spectra with authentic standards: δ-cadinene (Shanghai Yuanye Bio-Technology Co., Ltd, Cat# S22805, purity ≥95%) and α-copaene (Bio-Fount, Cat# HCC319638, purity ≥98%).

Conserved motifs and protein structure analysis

Conserved motifs within AlTPS protein sequences were identified utilizing the MEME online tool (https://meme-suite.org/meme/tools/meme) as described by Liang et al. (2025), to facilitate the screening of candidate AlTPS genes. The results were subsequently visualized using TBtools. For multiple sequence alignment of the target proteins, AlTPS21 and AlTPS42, along with their homologs, ClustalW was employed. Homology modeling of the three-dimensional structures was performed via SWISS-MODEL (https://swissmodel.expasy.org/), using A0A5P8H4P8.1.A as a template, with visualization conducted through PyMOL.

Subcellular localization of AlTPSs

To investigate the subcellular localization of AlTPS21 and AlTPS42 proteins, their open reading frames (ORFs) were cloned into the pBI121-GFP vector. The verified recombinant plasmids were then introduced into Agrobacterium tumefaciens GV3101 and infiltrated into the abaxial side of Nicotiana benthamiana leaves using a needleless syringe. Following an overnight incubation in darkness and a subsequent 2–3 days of standard cultivation, the epidermal layers were peeled and examined for the localization of AlTPS-GFP fusion proteins using a confocal laser scanning microscope (FV 3000, Olympus, Japan).

Result

Analysis of volatile products of A. lancea at different stages

Gas chromatography-mass spectrometry (GC-MS) analysis of A. lancea rhizomes across eight developmental stages (April through November) revealed the presence of 16 volatile components, comprising 14 sesquiterpenes, one monoterpene, and one polyacetylene (Supplementary Table S2). The cumulative relative content of the three primary active constituents—hinesol, β-eudesmol, and atractylodin—ranged from 76.19% to 86.11% (Supplementary Figure S1A). Furthermore, elemol and α-eudesmol were identified as relatively abundant sesquiterpenes, with relative contents ranging from 2.93% to 5.99% and 1.10% to 2.40%, respectively.

Heatmap analysis of these five major components indicated that their concentrations were lowest in June, increased to a peak in July, and subsequently declined from August onward. Notably, three of these components (hinesol, α-eudesmol, and atractylodin) exhibited a secondary increase in September before reaching their lowest levels in November (Supplementary Figure S1B). These findings underscore significant variations in the accumulation of major volatile compounds in A. lancea across different developmental stages, with pronounced fluctuations observed in June, July, September, and November.

Developmental stages drive distinct metabolomic profiles and terpenoid accumulation dynamics

A metabolomic analysis was conducted on samples from four distinct developmental stages (June, July, September, and November) that demonstrated significant differences. Utilizing HS-SPME-GC-MS, a comprehensive total of 677 compounds were identified in A. lancea (Da Bie Shan type) across these stages, comprising 178 terpenoids, 121 esters, 87 heterocyclic compounds, 52 ketones, and 47 alcohols (Supplementary Figure S2A).

To further elucidate the variations in metabolite accumulation patterns throughout these developmental stages, principal component analysis (PCA) was employed (Figure 1A). The PCA results revealed that principal components 1 and 2 accounted for 37.11% and 25.50% of the total variance, respectively, and effectively distinguished samples from the different developmental stages. The three biological replicates for each stage exhibited close clustering, indicating high data reliability and substantial metabolic differences between the stages.

Figure 1

Figure 1. Analysis of terpenoid metabolites in A. lancea across four developmental stages. (A) Principal component analysis (PCA) of metabolites. (B) Heatmap analysis of the top 50 most abundant terpenoid compounds. (C) Differential terpenoid metabolites in different comparison groups.

A total of 50 terpenoid compounds with the highest abundance were selected to compare variations across developmental stages, comprising 38 sesquiterpenes and 12 monoterpenes (Figure 1B). Cluster analysis of these terpenoid compounds revealed distinct segregation patterns: the 12 monoterpenes were divided into two subgroups, while the 38 sesquiterpenes formed four subgroups. Within the monoterpenes, 11 compounds in Group I exhibited highly consistent accumulation dynamics, with peak levels observed in July and November and the lowest levels in June and September. Conversely, the single compound in Group II displayed a distinctly different accumulation pattern. For the sesquiterpenes, Groups III and IV reached maximum abundance in June and November, respectively, whereas Groups V and VI peaked in July and September. Notably, heatmap analysis of hinesol and β-eudesmol, both recognized as key bioactive constituents of A. lancea, indicated that hinesol concentration peaked in September, whereas β-eudesmol accumulation was most pronounced in July. These observations are corroborated by the GC-MS results. In summary, metabolite accumulation in A. lancea exhibited significant variation across different developmental stages.

A differential metabolite analysis was performed using adjacent developmental stages as comparative groups, with differential metabolites being identified for each group based on a variable importance in projection (VIP) score greater than 1. The analysis revealed that the July versus June comparison group identified 84 differential metabolites, comprising 54 upregulated and 30 downregulated metabolites. In the September versus July comparison group, 148 differential metabolites were identified, with 28 upregulated and 120 downregulated. The November versus September comparison group identified 184 differential metabolites, of which 143 were upregulated and 41 were downregulated (Supplementary Figure 2B). Further metabolomic analysis elucidated distinct stage-specific patterns in terpenoid biosynthesis. Specifically, monoterpenoids, exemplified by D-limonene, exhibited significant accumulation in July, whereas sesquiterpenoids, represented by β-elemene and germacrene D, became predominant in September. This observation collectively illustrates a dynamic shift in the terpenoid backbone flux from monoterpenes to sesquiterpenes (Figure 1B). KEGG enrichment analysis demonstrated that these differentially accumulated metabolites were predominantly enriched in two pathways: the biosynthesis of secondary metabolites (ko01110) and metabolic pathways (ko01100) (Supplementary Figure S3). Furthermore, an analysis of the terpenoid components among the differential metabolites in the three comparison groups was conducted. The July versus June group identified 18 terpenoid components, the September versus July group identified 34, and the November versus September group identified 42 terpenoid components (Figure 1C).

Transcriptional landscape reveals stage-specific regulation of terpenoid biosynthesis pathways

To achieve a comprehensive understanding of the regulatory mechanisms governing terpenoid biosynthesis in A. lancea across various developmental stages, transcriptome identification and quantification were conducted utilizing RNA samples from each stage, with three biological replicates per stage. A total of 12 libraries were constructed, corresponding to the months of June, July, September, and November, each represented in triplicate, resulting in approximately 6.3–6.4 Gb of clean data per sample. The four developmental stages yielded 42.59, 42.44, 42.30, and 42.56 million clean reads, respectively, with Q30 values exceeding 93.82%. The average alignment rate to the reference genome was 93.22%, with 65.83% of the reads successfully mapped to the gene set. Quality assessment of RNA extracted from the rhizomes of A. lancea at the four stages indicated that all quality indicators, including RNA concentration, RIN value, and 28S/18S ratio, conformed to the required standards (Supplementary Table S3). The FPKM distribution boxplot (Supplementary Figure 4A) and expression density plots (Supplementary Figure S4B) depict the dispersion of gene expression levels and the range of expression abundance, respectively. Overall, the findings demonstrate a high sequencing quality that is well-suited for subsequent analyses.

In the Gene Ontology (GO) functional annotation, a total of 34,551 genes were annotated and categorized into 41 terms spanning three principal categories: Biological Process, Cellular Component, and Molecular Function. The Biological Process category encompassed 20 terms, with “cellular process” being the most populous, comprising 14,944 genes. The Cellular Component category was limited to two terms: “cellular anatomical entity” and “protein-containing complex.” Within the Molecular Function category, which included 19 terms, “binding” emerged as the most prevalent function, represented by 19,642 genes, followed by “catalytic activity” with 17,499 genes (Figure 2A).

Figure 2

Figure 2. Functional annotation and DEG analysis of transcriptome genes across four developmental stages. (A) Gene Ontology (GO) functional annotation. (B) Kyoto Encyclopedia of Genes and Genomes (KEGG) functional annotation. (C) Volcano plots of DEGs in three comparison groups: Jun-vs-Jul, Jul-vs-Sep, and Sep-vs-Nov. (D) KEGG enrichment of DEGs involved in terpenoid and polyketide metabolism across the three comparison groups.

Regarding the KEGG functional annotation, 20,578 genes were classified into five primary categories and 19 subcategories based on KEGG pathways. The five major categories were Cellular Processes, Environmental Information Processing, Genetic Information Processing, Metabolism, and Organismal Systems. Among the subcategories, “Global and overview maps,” “Carbohydrate metabolism,” and “Folding, sorting, and degradation” contained the highest numbers of annotated genes. The subcategory “Metabolism of terpenoids and polyketides” was annotated with 722 genes (Figure 2B).

Stage-specific gene expression activates terpenoid biosynthesis

To examine the dynamic patterns of gene expression across four developmental stages (June vs. July, July vs. September, September vs. November), stage-specific differentially expressed genes (DEGs) were identified. The comparison between June and July revealed 4,563 DEGs, with 2,062 genes up-regulated and 2,501 down-regulated. In contrast, the comparisons between July and September, and September and November, identified 1,318 DEGs (447 up-regulated, 871 down-regulated) and 893 DEGs (479 up-regulated, 414 down-regulated), respectively (Figure 2C). Venn diagram analysis identified 34 DEGs common to all three comparisons, while 3,992, 800, and 606 unique DEGs were identified in the June vs. July, July vs. September, and September vs. November comparisons, respectively (Supplementary Figure S5).

KEGG enrichment analysis indicated significant enrichment in the “Plant-pathogen interaction,” “Plant hormone signal transduction,” and “MAPK signaling pathway” across all comparisons (Supplementary Figures S6A-C). In the “Metabolism of terpenoids and polyketides” category, differentially expressed genes (DEGs) were significantly enriched across nine pathways, including “Terpenoid backbone biosynthesis,” “Monoterpenoid biosynthesis,” and “Sesquiterpenoid and triterpenoid biosynthesis.” The comparative analysis between June and July revealed a substantially higher number of DEGs in both the “Terpenoid backbone biosynthesis” and “Sesquiterpenoid and triterpenoid biosynthesis” pathways compared to other temporal comparisons, suggesting the activation of key terpenoid biosynthetic genes commencing in July. This observation is consistent with the detected accumulation patterns of monoterpenoid metabolites, as illustrated in Figure 2D.

Integrated metabolomic and transcriptomic analysis uncovers key genes governing sesquiterpenoid biosynthesis

An analysis was conducted on the pathways associated with terpenoid biosynthesis, as illustrated in Figure 3. In plants, the mevalonate (MVA) and methylerythritol phosphate (MEP) pathways represent the two principal metabolic routes for terpenoid biosynthesis. Within the MVA pathway, 18 genes encoding six key enzymes—AACT, HMGS, HMGR, MVK, PMVK, and MVD—were identified. Notably, seven genes (HMGS1/2, HMGR1/3/6, PMVK1, and MVD2) exhibited significantly elevated expression levels in July, while six genes (AACT1/2/3, PMVK2/3, and MVD1) were highly expressed in September. This suggests an enhanced activity of the MVA pathway during these months, which may be linked to the accumulation of sesquiterpenoids. In the MEP pathway, 11 genes encoding seven key enzymes (DXS, DXR, MCT, CMK, MDS, HDS, and HDR) were identified. Three genes (DXS2/4 and HDS) demonstrated high expression in June, whereas another three (DXR1/2 and MDS) were highly expressed in July, potentially contributing to monoterpenoid accumulation during this period.

Figure 3

Figure 3. The biosynthetic pathways of terpenoids and the heat map of 36 genes related to the synthetic pathways.

The enzyme isopentenyl diphosphate isomerase (IDI) facilitates the interconversion of isopentenyl pyrophosphate (IPP) and dimethylallyl pyrophosphate (DMAPP), while geranyl pyrophosphate synthase (GPPS), farnesyl pyrophosphate synthase (FPPS), and geranylgeranyl pyrophosphate synthase (GGPPS) serve as branch-point enzymes essential for the biosynthesis of monoterpenes, sesquiterpenes, and diterpenes, respectively. In this study, we identified two IDI genes, two FPPS genes, one GPPS gene, and two GGPPS genes. Notably, IDI2, FPPS1/2, and GGPPS1 exhibited high expression levels in July, suggesting their significant roles in monoterpene and sesquiterpene synthesis during this period. The expression levels of these 36 genes associated with the terpenoid biosynthesis pathway are detailed in Supplementary Table S4. Furthermore, 55 terpene synthase (TPS) genes, including those encoding α-farnesene synthase, nerolidol synthase, and (-)-germacrene D synthase, were identified within terpenoid biosynthesis-related pathways (Supplementary Table S5).

A correlation analysis was performed to investigate the relationships between the 50 most abundant metabolites across four developmental stages and 36 genes implicated in terpenoid biosynthesis pathways. The analysis identified significant regulatory interactions, with 42 terpenoid compounds exhibiting notable correlations with 31 genes, thereby uncovering distinct patterns of metabolic regulation (P < 0.05) (Supplementary Figure S7). Notably, the expression level of AlPMVK1 demonstrated a significant positive correlation with β-eudesmol content. Similarly, AlMVD1 expression was significantly positively correlated with hinesol content, whereas AlDXS4 expression showed a negative correlation with hinesol content. Additionally, AlGPPS expression was negatively correlated with atractylon content. These robust associations between upstream genes and terpenoid metabolites indicate that these genes are pivotal in the biosynthesis of key sesquiterpenoids in A. lancea. This underscores the necessity for further functional studies on downstream TPS genes and their regulatory effects on metabolites.

Quantitative reverse transcription PCR (qRT-PCR) validation was conducted on six selected genes from the terpenoid biosynthesis pathways. The expression patterns of these genes across four developmental stages were largely consistent with their fragments per kilobase of transcript per million mapped reads (FPKM) values (Supplementary Figure S8), thereby confirming the reliability of the transcriptome sequencing data.

Terpene synthase gene discovery in A. lancea: AlTPS21 and AlTPS42 as multiproduct sesquiterpene synthases

A comprehensive analysis of the transcriptome data led to the identification of 55 AlTPSs, which were categorized into 33 TPS-a, 11 TPS-b, 2 TPS-e/f, and 9 TPS-g family members (Supplementary Figure S9; Supplementary Tables S6-7). Given that members of the TPS-a subfamily are predominantly involved in sesquiterpene synthesis, the 33 TPS-a genes were prioritized for an in-depth investigation into the mechanisms underlying sesquiterpenoid biosynthesis in A. lancea. A correlation analysis was conducted between these TPS-a genes and the top 50 most abundant terpenoid compounds. The findings indicated that 27 genes exhibited significant correlations with 35 terpenoid compounds (Figure 4). Notably, AlTPS1 and AlTPS40 demonstrated a highly significant positive correlation with Gansongone (P < 0.001). Furthermore, AlTPS5 was found to be highly significantly positively correlated with Selina-3,7(11)-diene (P < 0.001), while AlTPS13 showed a significantly positive correlation with (E)-gamma-Bisabolene (P < 0.01). AlTPS21 exhibited a highly significant positive correlation with Aristolochene (P < 0.001), a significant positive correlation with γ-Curcumene, and positive correlations with δ-Cadinene and (-)-α-Gurjunene. The study identified significant positive correlations between specific terpene synthase genes and terpenoid components. AlTPS34 and AlTPS45 were strongly correlated with α-Cuprenene and cis-α-Bisabolene (P < 0.01), while AlTPS39 and AlTPS41 exhibited highly significant correlations with Sabinene (P < 0.001). AlTPS42 showed a significant positive correlation with α-Ocimene (P < 0.01). Additionally, AlTPS46 demonstrated significant correlations with both Sabinene and Gansongone. AlTPS53 was significantly correlated with β-Cedrene, α-Bisabolene, and Germacrone (P < 0.01). In total, 12 terpene synthase genes were significantly associated with various terpenoid components.

Figure 4

Figure 4. Correlation analysis between the top 50 most abundant terpenoid compounds and 33 AlTPS genes. Note: Orange indicates a positive correlation, blue indicates a negative correlation; significance levels are denoted by asterisks: * for 0.01 < p < 0.05, ** for 0.001 < p < 0.01, and *** for p ≤ 0.001.

Further analysis of the conserved protein motifs in 12 terpene synthase genes was conducted using the MEME online tool. The analysis identified that AlTPS45 and AlTPS53 lack the “RRX8W” motif (motif 11), which is crucial for cyclization. Additionally, the “NSE/DTE” motif (motif 7), which plays a role in metal-dependent ionization and substrate binding, was absent in AlTPS13 and AlTPS41. Following a screening process, eight protein sequences containing complete conserved motifs were identified: AlTPS1, AlTPS5, AlTPS21, AlTPS34, AlTPS39, AlTPS40, AlTPS42, and AlTPS46, as detailed in Supplementary Figure S10.

Following the successful identification of eight candidate genes from the TPS-a subfamily, we cloned four of these genes (AlTPS1, AlTPS21, AlTPS40, AlTPS42) into the pESC-His vector for functional characterization. The remaining four candidate genes (AlTPS5, AlTPS34, AlTPS39, AlTPS46) were excluded from functional analysis due to unsuccessful cloning attempts, which precluded the acquisition of correct recombinant plasmids. Volatile metabolites from the yeast cultures were analyzed using headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS), with the pESC-His empty vector strain serving as a negative control. As illustrated in Figure 5, the expression of AlTPS21 resulted in the production of δ-cadinene (peak 1) and α-cadinol (peak 2). In the case of AlTPS42, two sesquiterpenoid products were detected in the yeast system: δ-cadinene (peak 2) and α-copaene (peak 1). No products were identified for AlTPS1 and AlTPS40 under the same experimental conditions, as shown in Supplementary Figure S11. We propose that a plausible explanation for these findings is that the native substrates for these enzymes may not be the universal precursor farnesyl diphosphate (FPP) naturally produced by the BY-T15 yeast strain. Moreover, their catalytic activity might necessitate plant-specific cofactors that are absent in this heterologous expression system. These findings provide significant avenues for future research, indicating that functional validation should be conducted through in planta experiments or by utilizing optimized expression platforms.

Figure 5

Figure 5. GC–MS analysis of catalytic products of AlTPS21 and AlTPS42. (A) Reaction products of AlTPS21 and the pESC-HIS empty vector. GC detection of the standard compound δ-cadinene. (B) MS analysis of the catalytic product of pESC-HIS-AlTPS21 and the δ-cadinene standard at 27.392 min; MS identification of AlTPS21 Peak2 confirmed it as α-cadinol. (C) Reaction products of AlTPS42 and the pESC-HIS empty vector. GC detection of standard compounds δ-cadinene and α-copaene. (D) MS analysis of the catalytic product of pESC-HIS-AlTPS42 and the α-copaene standard at 21.562 min, and MS analysis of the catalytic product and δ-cadinene standard at 27.406 min.

The tertiary structures of AlTPS21 and AlTPS42 were predicted using SWISS-MODEL, employing the template A0A5P8H4P8.1.A. The sequence similarity scores were 77.21% for AlTPS21 and 75.37% for AlTPS42, with both models achieving a GMQE value of 0.93. Three-dimensional protein structures were constructed and visualized using PyMOL in cartoon representation. Conserved motifs were highlighted in colored stick models: the RRX8W motif in orange, DDXXD in green, NSE/DTE in yellow, and RXR in red, as depicted in Supplementary Figure S12.

Subcellular localization of AlTPS gene

Subcellular localization analysis was conducted through the construction of GFP-tagged expression vectors. The GFP fluorescence of AlTPS proteins, along with empty vector controls, was examined using confocal laser scanning microscopy. Microscopic analysis revealed that both AlTPS21 and AlTPS42 were localized to the nucleus and cell membrane (Figure 6). To further elucidate their subcellular localization, co-localization experiments were performed utilizing a chloroplast marker. Green fluorescence indicated the expression and distribution of the GFP-tagged proteins, while red fluorescence denoted the distribution of chloroplasts labeled by the marker. Regions of overlap between green and red fluorescence appeared yellow. The merged results demonstrated that the AlTPS21 protein was localized to the cell membrane, whereas AlTPS42 was localized to both the nucleus and the cell membrane.

Figure 6

Figure 6. Subcellular localization of AlTPS21 and AlTPS42 proteins. GFP served as a negative control. Green fluorescence indicates the localization of the fusion proteins. Chlorophyll autofluorescence (CHI) shows the chloroplast channel. Merged images represent the overlay of GFP and chlorophyll signals. Scale bar = 25 μm.

Discussion

Recent research on A. lancea has predominantly concentrated on its volatile oils and pharmacological properties (Peng et al., 2022; Qu et al., 2022), whereas comprehensive studies on the dynamic accumulation patterns of metabolites across various developmental stages are still scarce. Metabolomic analysis not only aids in identifying key signaling metabolites involved in regulatory processes within plants but also provides a crucial foundation for integrating multi-omics strategies to identify key genes in sesquiterpenoid biosynthesis pathways (Okada et al., 2010; Scossa et al., 2018). This study conducted an analysis of the metabolic profiles of A. lancea across various developmental stages, revealing distinct temporal patterns in terpenoid accumulation. Specifically, monoterpenoids exhibited a rapid increase in July, while sesquiterpenoids, which were initially low in June, began to accumulate in July, peaked in September, and subsequently declined by November. These findings illustrate clear dynamics dependent on developmental stages. Such stage-specific accumulation patterns are consistent with observations in other medicinal plants. For instance, Wang et al. (2024) reported that in the essential oil of Citrus aurantium peel, oxygenated monoterpenes, such as citronellol, peaked during mid-growth stages and subsequently decreased with maturation, whereas sesquiterpenes, like elemene, gradually increased during the ripening process. Similarly, research on Magnolia biondii flower buds (Hu et al., 2018) and Satureja macrantha essential oil across various phenological stages (Aghbash et al., 2020) has consistently demonstrated a significant influence of developmental stage on the composition and content of volatile oils. The findings of this study not only elucidate the dynamic accumulation patterns of terpenoids in the Dabieshan variety of A. lancea but also provide a scientific basis for determining optimal harvesting times. This study offers insights into the quality assessment and sustainable use of A. lancea medicinal materials.

Terpenoids, which are essential secondary metabolites in plants, are primarily synthesized through the cytoplasmic mevalonate (MVA) pathway and the plastidial 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway (Ajikumar et al., 2010; Miziorko, 2011). A transcriptomic analysis of Dabieshan-type A. lancea across four developmental stages revealed a significant upregulation of MVA pathway genes, specifically HMGR and PMVK, during the months of July and September, coinciding with peaks in sesquiterpenoid accumulation. This observation supports the predominant role of the MVA pathway, as also documented by Zhao et al. (2013). Furthermore, the expression of HMGR exhibited a positive correlation with sesquiterpenoid levels, corroborating findings in Panax ginseng (Kim et al., 2014) and Artemisia annua (Aquil et al., 2009). In contrast, MEP pathway genes, including DXS and DXR, demonstrated heightened expression in June, facilitating the supply of monoterpene precursors. In this study, we identified two paralogs of DXR (DXR1 and DXR2) that exhibited developmental stage-specific expression patterns, thereby extending the established role of DXR in the regulation of monoterpenes, as previously reported in mint (Mahmoud and Croteau, 2001) and lily (Zhang et al., 2018). The simultaneous increase in IDI2 expression alongside terpenoid production in July further substantiates the hypothesis of cross-pathway regulation (Bergman et al., 2019).

The TPS family is pivotal in terpenoid biosynthesis and is categorized into eight subfamilies (TPS-a to TPS-h) (Jia et al., 2022). Integrated transcriptomic and metabolomic analyses enable the identification of key genes by correlating metabolite accumulation with gene expression within relevant pathways (Liu et al., 2024a; Lin et al., 2020). Employing this methodology, we identified eight candidate genes, successfully cloned four, and conducted functional characterization of AlTPS21 and AlTPS42. Notably, the expression peaks of AlTPS21 and AlTPS42 align with the peak of sesquiterpenoid accumulation in September, identifying them as crucial agents in translating developmental and environmental signals into specific metabolic phenotypes. Prior research on Asteraceae species, including Artemisia annua and Xanthium strumarium, has effectively characterized TPS enzymes through heterologous expression systems in E. coli and S. cerevisiae (Li et al., 2013, 2016). In the present study, expression in S. cerevisiae revealed that AlTPS21 synthesizes δ-cadinene and α-cadinol, whereas AlTPS42 produces δ-cadinene and α-copaene. This functional diversification, coupled with their stage-specific expression, directly influences the dynamic sesquiterpenoid flux and chemodiversity observed during rhizome development. Intriguingly, experimental results demonstrated that both AlTPS21 and AlTPS42 are localized in both the nucleus and the cell membrane. This unique distribution pattern suggests that these enzymes may have dual functional roles: potentially modulating gene activity within the nucleus while also participating in intercellular signaling at the membrane level. The coordinated activities at these two cellular sites appear to collectively impact the biosynthesis of sesquiterpenoid compounds. Although both enzymes generate δ-cadinene, their distinct product spectra highlight the catalytic promiscuity of TPSs and imply that functional variation may result from subtle differences in active site residues (Christianson, 2017). Our findings reveal significant parallels with the study on maize ZmTPS8 by Saldivar et al. (2023), as both investigations underscore the catalytic versatility of plant terpene synthases. Specifically, ZmTPS8 has been demonstrated to produce δ-cadinene and α-copaene. In this study, we establish that AlTPS42 similarly catalyzes the formation of these sesquiterpenes, while AlTPS21 predominantly generates δ-cadinene and its oxygenated derivative, α-cadinol. This functional conservation across species provides valuable insights into the evolutionary mechanisms underpinning terpenoid-based defense metabolism in plants. Previous research has documented the pharmacological properties of these sesquiterpenes, including anticancer and insect-repellent activities (Magnani et al., 2025; Ferraz et al., 2013). The identification of these functionally characterized and ecologically significant genes lays the foundation for future endeavors aimed at enhancing the production of bioactive terpenoids through metabolic engineering, thereby advancing the development of plant-derived pharmaceuticals.

This study employed an integrated multi-omics approach to elucidate the temporal regulation of terpenoid biosynthesis in A. lancea rhizomes. Utilizing this methodology, we identified key structural and regulatory genes and successfully characterized the functional properties of two sesquiterpene synthases, AlTPS21 and AlTPS42. Our findings indicate that intrinsic developmental programming predominantly governs terpenoid accumulation patterns, as demonstrated by the stage-specific expression of biosynthetic pathway genes and terpenoid synthases. Nonetheless, the interaction between developmental programs and environmental factors warrants further exploration. For instance, the unique metabolic profile observed in September may result from both developmental timing and plant adaptations to early autumn changes in photoperiod and temperature (Zhang et al., 2021). Such environmental cues may modulate metabolic production through direct regulation of key gene expression or via phytohormone-mediated pathways, with jasmonic acid being a well-documented inducer of terpenoid biosynthesis (Zhang et al., 2025; He and Zhao, 2015). This integrated methodology, encompassing controlled environment experiments, multi-regional field monitoring, and concurrent phytohormone and genomic profiling, facilitates a systematic analysis of individual environmental factors and their interactions with gene networks. This comprehensive strategy aims to elucidate the fundamental principles underlying the formation of medicinal components in A. lancea.

Conclusion

This study provides a comprehensive elucidation of the developmental regulatory mechanisms governing terpenoid biosynthesis in A. lancea, employing an integrated multi-omics approach coupled with functional validation. Our findings disclose distinct stage-specific accumulation patterns of terpenoids, with monoterpenoids and sesquiterpenoids peaking in July and September, respectively. Additionally, we demonstrate the temporal coordination between the mevalonate (MVA) and methylerythritol phosphate (MEP) pathways, highlighting their complementary regulatory roles in terpenoid biosynthesis. Notably, we have identified two novel terpene synthase genes, AlTPS21 and AlTPS42, which exhibit unique catalytic specificity for the production of multiple sesquiterpenoids, with expression patterns that align with the peaks of sesquiterpenoid accumulation. Their atypical subcellular localization further suggests potential non-canonical roles in plant secondary metabolism. These findings enhance our understanding of the regulation of terpenoid biosynthesis in medicinal plants from a spatiotemporal perspective. The outcomes of this research identify specific targets for the molecular breeding of A. lancea and establish a robust research framework that extends from gene discovery to functional validation, offering valuable insights for the investigation of secondary metabolism in other medicinal plants.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

Author contributions

GZ: Writing – original draft. ZZ: Writing – review & editing. YL: Data curation, Writing – review & editing. LZ: Data curation, Writing – review & editing. JL: Writing – review & editing, Software. KQ: Writing – review & editing, Validation. HL: Supervision, Writing – review & editing. LPZ: Project administration, Writing – review & editing. JX: Funding acquisition, Writing – review & editing.

Funding

The author(s) declare financial support was received for the research and/or publication of this article. This work was supported by the National Natural Science Foundation of China (Grant number (82574542), Anhui Provincial Natural Science Foundation (2508085J051, 2208085Y30), Key Project Foundation of Natural Science Research in Universities of Anhui Province in China (2023AH050634), Research Funds of Joint Research Center for Chinese Herbal Medicine of Anhui of IHM (grant number yjzx2023002), Science Research Project at the Universities of Anhui Province for Distinguished Young Scholars (2023AH020036), Young Elite Scientists Sponsorship Program by CACM (CACM-2023-QNRC2-B23), The University Synergy Innovation Program of Anhui Province (GXXT-2023-072), Research Funds of Center for Xin’an Medicine and Modernization of Traditional Chinese Medicine of IHM(2023CXMMTCM008), Traditional Chinese Medicine High-Level Key Discipline Construction Project of National Administration of Traditional Chinese Medicine Science of Chinese Medicinal Material Resources (pharmaceutical botany) (zyyzdxk-2023095).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Correction note

This article has been corrected with minor changes. These changes do not impact the scientific content of the article.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2025.1720423/full#supplementary-material

References

Aghbash, B. N., Pouresmaeil, M., Dehghan, G., Nojadeh, M. S., Mobaiyen, H., and Maggi, F. (2020). Chemical Composition, Antibacterial and Radical Scavenging Activity of Essential Oils from Satureja macrantha C.A.Mey. at Different Growth Stages. Foods 9, 494. doi: 10.3390/foods9040494

PubMed Abstract | Crossref Full Text | Google Scholar

Ahmed, S., Zhan, C., Yang, Y., Wang, X., Yang, T., Zhao, Z., et al. (2016). The transcript profile of a traditional chinese medicine, atractylodes lancea, revealing its sesquiterpenoid biosynthesis of the major active components. PloS One 11, e0151975. doi: 10.1371/journal.pone.0151975

PubMed Abstract | Crossref Full Text | Google Scholar

Ajikumar, P. K., Xiao, W. H., Tyo, K. E., Wang, Y., Simeon, F., Leonard, E., et al. (2010). Isoprenoid pathway optimization for Taxol precursor overproduction in Escherichia coli. Science 330, 70–74. doi: 10.1126/science.1191652

PubMed Abstract | Crossref Full Text | Google Scholar

Aquil, S., Husaini, A. M., Abdin, M. Z., and Rather, G. M. (2009). Overexpression of the HMG-CoA reductase gene leads to enhanced artemisinin biosynthesis in transgenic Artemisia annua plants. Plant. Med. 75, 1453–1458. doi: 10.1055/s-0029-1185775

PubMed Abstract | Crossref Full Text | Google Scholar

Arya, M., Shergill, I. S., Williamson, M., Gommersall, L., Arya, N., and Patel, H. R. (2005). Basic principles of real-time quantitative PCR. Expert Rev. Mol. Diagn. 5, 209–219. doi: 10.1586/14737159.5.2.209

PubMed Abstract | Crossref Full Text | Google Scholar

Bergman, M. E., Davis, B., and Phillips, M. A. (2019). Medically useful plant terpenoids: biosynthesis, occurrence, and mechanism of action. Molecules 24, 3961. doi: 10.3390/molecules24213961

PubMed Abstract | Crossref Full Text | Google Scholar

Chen, F., Tholl, D., Bohlmann, J., and Pichersky, E. (2011). The family of terpene synthases in plants: a mid-size family of genes for specialized metabolism that is highly diversified throughout the kingdom. Plant J. 66, 212–229. doi: 10.1111/j.1365-313X.2011.04520.x

PubMed Abstract | Crossref Full Text | Google Scholar

Chen, L., Zhang, S., Wang, Y., Sun, H., Wang, S., Wang, D., et al. (2023). Integrative analysis of transcriptome and metabolome reveals the sesquiterpenoids and polyacetylenes biosynthesis regulation in Atractylodes lancea (Thunb.) DC. Int. J. Biol. Macromol. 253, 127044. doi: 10.1016/j.ijbiomac.2023.127044

PubMed Abstract | Crossref Full Text | Google Scholar

Chen, Y., Ma, S., Cui, J., Huang, A., Chen, Z., Pi, D., et al. (2025). Atractylodes lancea (Thunb.) DC polysaccharide structural properties and its protective impact on cisplatin-induced gastrointestinal harm by restoring the intestinal barrier and gut flora. Int. J. Biol. Macromol. 318, 145232. doi: 10.1016/j.ijbiomac.2025.145232

PubMed Abstract | Crossref Full Text | Google Scholar

Christianson, D. W. (2017). Structural and chemical biology of terpenoid cyclases. Chem. Rev. 117, 11570–11648. doi: 10.1021/acs.chemrev.7b00287

PubMed Abstract | Crossref Full Text | Google Scholar

Ferraz, R. P., Cardoso, G. M., da Silva, T. B., Fontes, J. E., Prata, A. P., Carvalho, A. A., et al. (2013). Antitumour properties of the leaf essential oil of Xylopia frutescens Aubl. (Annonaceae). Food Chem. 141, 196–200. doi: 10.1016/j.foodchem.2013.02.114

PubMed Abstract | Crossref Full Text | Google Scholar

Guo, W., Liu, S., Ju, X., Du, J., Xu, B., Yuan, H., et al. (2019). The antitumor effect of hinesol, extract from Atractylodes lancea (Thunb.) DC. by proliferation, inhibition, and apoptosis induction via MEK/ERK and NF-κB pathway in non-small cell lung cancer cell lines A549 and NCI-H1299. J. Cell. Biochem. 120, 18600–18607. doi: 10.1002/jcb.28696

PubMed Abstract | Crossref Full Text | Google Scholar

He, Y. and Zhao, Y. (2015). A key link between jasmonic acid signaling and auxin biosynthesis. Sci. China Life Sci. 58, 311–312. doi: 10.1007/s11427-015-4821-8

PubMed Abstract | Crossref Full Text | Google Scholar

Hu, M., Bai, M., Ye, W., Wang, Y., and Wu, H. (2018). Variations in Volatile Oil Yield and Composition of “Xin-yi” (Magnolia biondii Pamp. Flower Buds) at Different Growth Stages. J. Oleo. Sci. 67, 779–787. doi: 10.5650/jos.ess17229

PubMed Abstract | Crossref Full Text | Google Scholar

Hua, Y., Dai, B., Luo, Y., and Ding, Y. (2023). Integrated analysis of multiple metabolome and transcriptome revealed the accumulation of flavonoids and associated molecular regulation mechanisms in Rubus chingii Hu at different developmental stages. Plant Physiol. Biochem. 204, 108085. doi: 10.1016/j.plaphy.2023.108085

PubMed Abstract | Crossref Full Text | Google Scholar

Jia, Q., Brown, R., Köllner, T. G., Fu, J., Chen, X., Wong, G. K., et al. (2022). Origin and early evolution of the plant terpene synthase family. Proc. Natl. Acad. Sci. U. S. A. 119, e2100361119. doi: 10.1073/pnas.2100361119

PubMed Abstract | Crossref Full Text | Google Scholar

Jun, X., Fu, P., Lei, Y., and Cheng, P. (2018). Pharmacological effects of medicinal components of Atractylodes lancea (Thunb.) DC. Chin. Med. 13, 59. doi: 10.1186/s13020-018-0216-7

PubMed Abstract | Crossref Full Text | Google Scholar

Kawada, K., Ishida, T., Morisawa, S., Jobu, K., Higashi, Y., Aizawa, F., et al. (2024). Atractylodes lancea (Thunb.) DC. [Asteraceae] rhizome-derived exosome-like nanoparticles suppress lipopolysaccharide-induced inflammation in murine microglial cells. Front. Pharmacol. 15. doi: 10.3389/fphar.2024.1302055

PubMed Abstract | Crossref Full Text | Google Scholar

Kim, Y. J., Lee, O. R., Oh, J. Y., Jang, M. G., and Yang, D. C. (2014). Functional analysis of 3-hydroxy-3-methylglutaryl coenzyme a reductase encoding genes in triterpene saponin-producing ginseng. Plant Physiol. 165, 373–387. doi: 10.1104/pp.113.222596

PubMed Abstract | Crossref Full Text | Google Scholar

Koonrungsesomboon, N., Na-Bangchang, K., and Karbwang, J. (2014). Therapeutic potential and pharmacological activities of Atractylodes lancea (Thunb.) DC. Asian Pac. J. Trop. Med. 7, 421–428. doi: 10.1016/S1995-7645(14)60069-9

PubMed Abstract | Crossref Full Text | Google Scholar

Li, Y., Chen, F., Li, Z., Li, C., and Zhang, Y. (2016). Identification and functional characterization of sesquiterpene synthases from xanthium strumarium. Plant Cell Physiol. 57, 630–641. doi: 10.1093/pcp/pcw019

PubMed Abstract | Crossref Full Text | Google Scholar

Li, J. X., Fang, X., Zhao, Q., Ruan, J. X., Yang, C. Q., Wang, L. J., et al. (2013). Rational engineering of plasticity residues of sesquiterpene synthases from Artemisia annua: product specificity and catalytic efficiency. Biochem. J. 451, 417–426. doi: 10.1042/BJ20130041

PubMed Abstract | Crossref Full Text | Google Scholar

Li, C., Zha, W., Li, W., Wang, J., and You, A. (2023). Advances in the biosynthesis of terpenoids and their ecological functions in plant resistance. Int. J. Mol. Sci. 24, 11561. doi: 10.3390/ijms241411561

PubMed Abstract | Crossref Full Text | Google Scholar

Liang, H., Liu, W., Zhao, Z., Li, Y., and Zha, L. (2025). Genome-wide identification and expression analysis of the WRKY transcription factors related to sesquiterpenes biosynthesis in Atractylodes lancea. Front. Genet. 16. doi: 10.3389/fgene.2025.1551991

PubMed Abstract | Crossref Full Text | Google Scholar

Liao, B., Shen, X., Xiang, L., Guo, S., Chen, S., Meng, Y., et al. (2022). Allele-aware chromosome-level genome assembly of Artemisia annua reveals the correlation between ADS expansion and artemisinin yield. Mol. Plant 15, 1310–1328. doi: 10.1016/j.molp.2022.05.013

PubMed Abstract | Crossref Full Text | Google Scholar

Lin, W., Li, Y., Lu, Q., Lu, H., and Li, J. (2020). Combined analysis of the metabolome and transcriptome identified candidate genes involved in phenolic acid biosynthesis in the leaves of cyclocarya paliurus. Int. J. Mol. Sci. 21, 1337. doi: 10.3390/ijms21041337

PubMed Abstract | Crossref Full Text | Google Scholar

Liu, L., Si, L., Zhang, L., Guo, R., Wang, R., Dong, H., et al. (2024a). Metabolomics and transcriptomics analysis revealed the response mechanism of alfalfa to combined cold and saline-alkali stress. Plant J. 119, 1900–1919. doi: 10.1111/tpj.16896

PubMed Abstract | Crossref Full Text | Google Scholar

Liu, L., Yang, A., Liu, T., Liu, S., Hu, P., Chen, C., et al. (2024b). Transcriptome and metabolome analyses of leaves from cutting rejuvenation of ancient cinnamomum camphora. Int. J. Mol. Sci. 25, 7664. doi: 10.3390/ijms25147664

PubMed Abstract | Crossref Full Text | Google Scholar

Livak, K. J. and Schmittgen, T. D. (2001). Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 25, 402–408. doi: 10.1006/meth.2001.1262

PubMed Abstract | Crossref Full Text | Google Scholar

Magnani, R. F., Volpe, H. X. L., Luvizotto, R. A. G., Mulinari, T. A., Agostini, T. T., Bastos, J. K., et al. (2025). α-Copaene is a potent repellent against the Asian Citrus Psyllid Diaphorina citri. Sci. Rep. 15, 3564. doi: 10.1038/s41598-025-86369-1

PubMed Abstract | Crossref Full Text | Google Scholar

Mahmoud, S. S. and Croteau, R. B. (2001). Metabolic engineering of essential oil yield and composition in mint by altering expression of deoxyxylulose phosphate reductoisomerase and menthofuran synthase. Proc. Natl. Acad. Sci. U. S. A. 98, 8915–8920. doi: 10.1073/pnas.141237298

PubMed Abstract | Crossref Full Text | Google Scholar

Miziorko, H. M. (2011). Enzymes of the mevalonate pathway of isoprenoid biosynthesis. Arch. Biochem. Biophys. 505, 131–143. doi: 10.1016/j.abb.2010.09.028

PubMed Abstract | Crossref Full Text | Google Scholar

Okada, T., Afendi, F. M., Altaf-Ul-Amin, M., Takahashi, H., Nakamura, K., and Kanaya, S. (2010). Metabolomics of medicinal plants: the importance of multivariate analysis of analytical chemistry data. Curr. Comput. Aided. Drug Des. 6, 179–196. doi: 10.2174/157340910791760055

PubMed Abstract | Crossref Full Text | Google Scholar

Peng, Z., Guo, X., Xiang, Z., Liu, D., Yu, K., Sun, K., et al. (2022). Maize intercropping enriches plant growth-promoting rhizobacteria and promotes both the growth and volatile oil concentration of Atractylodes lancea. Front. Plant Sci. 13. doi: 10.3389/fpls.2022.1029722

PubMed Abstract | Crossref Full Text | Google Scholar

Qiu, H., Shan, C., Fei, C., Xue, P., Zhou, Y., Yuan, J., et al. (2023). Geoherbalism metabolomic analysis of atractylodes lancea (Thunb.) DC. by LC-triple TOF-MS/MS and GC-MS. Molecules 28, 5974. doi: 10.3390/molecules28165974

PubMed Abstract | Crossref Full Text | Google Scholar

Qu, L., Liu, C., Ke, C., Zhan, X., Li, L., Xu, H., et al. (2022). Atractylodes lancea rhizoma attenuates DSS-induced colitis by regulating intestinal flora and metabolites. Am. J. Chin. Med. 50, 525–552. doi: 10.1142/S1995-7645(14)60069-9

Crossref Full Text | Google Scholar

Saeheng, T., Karbwang, J., Cheomung, A., Tongsiri, N., Plengsuriyakarn, T., and Na-Bangchang, K. (2024). Pharmacokinetic analysis of prognostic factors in patients with advanced-stage intrahepatic cholangiocarcinoma following the administration of capsule formulation of the standardized extract of atractylodes lancea (Thunb) DC. Integr. Cancer Ther. 23, 15347354231223967. doi: 10.1177/15347354231223967

PubMed Abstract | Crossref Full Text | Google Scholar

Saldivar, E. V., Ding, Y., Poretsky, E., Bird, S., Block, A. K., Huffaker, A., et al. (2023). Maize terpene synthase 8 (ZmTPS8) contributes to a complex blend of fungal-elicited antibiotics. Plants (Basel). 12, 1111. doi: 10.3390/plants12051111

PubMed Abstract | Crossref Full Text | Google Scholar

Scossa, F., Benina, M., Alseekh, S., Zhang, Y., and Fernie, A. R. (2018). The integration of metabolomics and next-generation sequencing data to elucidate the pathways of natural product metabolism in medicinal plants. Plant. Med. 84, 855–873. doi: 10.1055/a-0630-1899

PubMed Abstract | Crossref Full Text | Google Scholar

Tholl, D. (2006). Terpene synthases and the regulation, diversity and biological roles of terpene metabolism. Curr. Opin. Plant Biol. 9, 297–304. doi: 10.1016/j.pbi.2006.03.014

PubMed Abstract | Crossref Full Text | Google Scholar

Vranová, E., Coman, D., and Gruissem, W. (2012). Structure and dynamics of the isoprenoid pathway network. Mol. Plant 5, 318–333. doi: 10.1093/mp/sss015

PubMed Abstract | Crossref Full Text | Google Scholar

Wang, H., Zhang, S., Fu, Q., Wang, Z., Liu, X., Sun, L., et al. (2023). Transcriptomic and metabolomic analysis reveals a protein module involved in preharvest apple peel browning. Plant Physiol. 192, 2102–2122. doi: 10.1093/plphys/kiad064

PubMed Abstract | Crossref Full Text | Google Scholar

Wang, H., Zhou, X., Deng, Y., Zhang, R., Fu, K., Huang, J., et al. (2024). Variations in volatile components and biological activities of essential oils from Citrus aurantium ‘changshanhuyou’ at different growth and ripening stages. Food Res. Int. 197, 115303. doi: 10.1016/j.foodres.2024.115303

PubMed Abstract | Crossref Full Text | Google Scholar

Wei, J., Mu, X., Wang, S., Wei, Q., Zhu, L., Zhang, X., et al. (2025). Integrated metabolome and transcriptome analysis provides insights into the mechanisms of terpenoid biosynthesis in tea plants (Camellia sinensis). Food Res. Int. 201, 115542. doi: 10.1016/j.foodres.2024.115542

PubMed Abstract | Crossref Full Text | Google Scholar

Wu, J., Hu, J., Yu, H., Lu, J., Jiang, L., Liu, W., et al. (2023). Full-length transcriptome analysis of two chemotype and functional characterization of genes related to sesquiterpene biosynthesis in Atractylodes lancea. Int. J. Biol. Macromol. 225, 1543–1554. doi: 10.1016/j.ijbiomac.2022.11.210

PubMed Abstract | Crossref Full Text | Google Scholar

Xu, R., Lu, J., Wu, J., Yu, D., Chu, S., Guan, F., et al. (2022). Comparative analysis in different organs and tissue-specific metabolite profiling of Atractylodes lancea from four regions by GC-MS and laser microdissection. J. Sep. Sci. 45, 1067–1079. doi: 10.1002/jssc.202100924

PubMed Abstract | Crossref Full Text | Google Scholar

Xu, X., Xiong, X., He, Z., Lu, Q., and Wang, L. (2025). Optimization of ultrasound pretreatment combined with solvent-free microwave for extracting volatile oil from Atractylodes lancea and its chemical composition and antimicrobial activity. Talanta 297, 128724. doi: 10.1016/j.talanta.2025.128724

PubMed Abstract | Crossref Full Text | Google Scholar

Yang, L., Yang, Y., Huang, L., Cui, X., and Liu, Y. (2023). From single- to multi-omics: future research trends in medicinal plants. Brief. Bioinform. 24, bbac485. doi: 10.1093/bib/bbac485

PubMed Abstract | Crossref Full Text | Google Scholar

Yu, D. Q., Han, X. J., Shan, T. Y., Xu, R., Hu, J., Cheng, W. X., et al. (2019). Microscopic characteristic and chemical composition analysis of three medicinal plants and surface frosts. Molecules 24, 4548. doi: 10.3390/molecules24244548

PubMed Abstract | Crossref Full Text | Google Scholar

Zhang, A., Liu, M., Gu, W., Chen, Z., Gu, Y., Pei, L., et al. (2021). Effect of drought on photosynthesis, total antioxidant capacity, bioactive component accumulation, and the transcriptome of Atractylodes lancea. BMC Plant Biol. 21, 293. doi: 10.1186/s12870-021-03048-9

PubMed Abstract | Crossref Full Text | Google Scholar

Zhang, T., Sun, M., Guo, Y., Shi, X., Yang, Y., Chen, J., et al. (2018). Overexpression of liDXS and liDXR from lily (Lilium ‘Siberia’) enhances the terpenoid content in tobacco flowers. Front. Plant Sci. 9. doi: 10.3389/fpls.2018.00909

PubMed Abstract | Crossref Full Text | Google Scholar

Zhang, Z. A., Xue, X. Y., Wu, J. Y., Zhao, X., Zhang, H., Sun, K., et al. (2025). CYP71P2 regulated by MYB44 modulates oxygenated sesquiterpenoids biosynthesis in Atractylodes lancea. Plant Physiol. Biochem. 229, 110700. doi: 10.1016/j.plaphy.2025.110700

PubMed Abstract | Crossref Full Text | Google Scholar

Zhao, L., Chang, W. C., Xiao, Y., Liu, H. W., and Liu, P. (2013). Methylerythritol phosphate pathway of isoprenoid biosynthesis. Annu. Rev. Biochem. 82, 497–530. doi: 10.1146/annurev-biochem-052010-100934

PubMed Abstract | Crossref Full Text | Google Scholar

Zhao, K. and Rhee, S. Y. (2022). Omics-guided metabolic pathway discovery in plants: Resources, approaches, and opportunities. Curr. Opin. Plant Biol. 67, 102222. doi: 10.1016/j.pbi.2022.102222

PubMed Abstract | Crossref Full Text | Google Scholar