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. 2025 Oct 13;24(1):346–360. doi: 10.1111/pbi.70398

A Comprehensive Multiomics Approach Illuminates the Biosynthetic Mechanism of Scoparone in Artemisia capillaris

Fuyou Guo 1, Hong Zhou 1,2,✉, Fangfang Yan 3, Junjie Chen 1, Qiuyu Zhu 1, Yu Tan 1, Yutong Guo 1, Xiangning Lai 1, Zongquan Li 1, Miao Zhang 1, Yi Ni 1, Matthana Klakong 1, Jiamin Yu 4, Liang Yang 1, Shili Li 1, Wei Ding 1,✉
PMCID: PMC12854908  PMID: 41078345

ABSTRACT

Artemisia capillaris, a traditional Chinese medicinal plant, has displayed favourable effects in the treatment of jaundice, inflammation of the liver and cholecystitis for thousands of years. However, the biosynthesis of scoparone, the major active component in A. capillaris , remains unclear. Here, a full‐length transcriptomic and integrated metabolomic dataset of A. capillaris was generated for the first time. A nearly complete map of the biosynthesis pathway of scoparone was subsequently proposed. Notably, a novel O‐methyltransferase, AcOMT1, which catalyses the methylation of scopoletin and isoscopoletin as the final step in the biosynthesis of scoparone, was annotated and characterised using a weighted gene correlation network, phylogenetic analysis, subcellular localisation analysis and enzyme assays. Molecular docking and dynamics simulations demonstrated that AcOMT1 has a stronger and more stable affinity for scopoletin than for isoscopoletin, explaining its greater efficiency and preference for the methylation of scopoletin. Ultimately, by coexpressing AcOMT1, along with the crucial genes responsible for scopoletin biosynthesis in Nicotiana benthamiana, we developed a system for scoparone biosynthesis, yielding 3.03 μg.g−1 dry weight. This study provides not only a comprehensive understanding of the biosynthetic pathway of scoparone but also new avenues for scoparone production in A. capillaris via synthetic biology and metabolic engineering.

Keywords: Artemisia capillaris, biosynthesis, O‐methyltransferase, scoparone

1. Introduction

Artemisia capillaris Thunb., known as Yin Chen in Chinese, is a traditional Chinese herb that is frequently used in the management of jaundice (Yang et al. 2023) and chronic liver conditions (Cai et al. 2020). Extensive biological and pharmacological investigations have demonstrated its efficacy in the clinical treatment of cholestasis (Cai et al. 2024), hepatitis C (Hussain 2022), primary biliary cirrhosis (Lee et al. 2007), liver fibrosis (Feng et al. 2024) and cholestatic diseases (Song et al. 2023). This herb is prevalent in various regions, including China, Korea, Japan, Pakistan, India, Central Europe, Saudi Arabia and Iran (Ding et al. 2021). With more than two millennia of use, A. capillaris holds a significant place in traditional herbal medicine practices (Yen et al. 2018). More than 100 Yin Chen‐related prescription drugs (https://db.yaozh.com/chufang) have been launched. Yin Chen has also become a popular health drink (YinChen tea) because of its multiple medicinal properties.

Scoparone, a widely recognised polymethoxylated coumarin, is considered the primary and significant active component of A. capillaris (Dai et al. 2021; Ma et al. 2006; Yin et al. 2012). Numerous studies have illustrated the diverse health benefits of scoparone, including its anti‐tumour (Shen et al. 2023), choleretic and liver‐protective (Fang et al. 2016; Liu et al. 2020), anti‐asthmatic (Fang et al. 2003), anti‐allergic (Choi and Yan 2009) and anti‐inflammatory (Feng et al. 2022) effects. Furthermore, previous research has indicated that scoparone has significant potential as a crop protection agent for the management of widespread and damaging mite pests in agriculture (Wan et al. 2020; Yao et al. 2022; Zhou et al. 2021, 2024). Nevertheless, A. capillaris plants exhibit limited and inconsistent accumulation of the bioactive substance scoparone. Meeting the growing demand for plant‐derived scoparone in the pharmaceutical and agricultural sectors poses a significant challenge due to its low production levels. Metabolic engineering offers a potential solution to these issues. Despite the increasing attention given to this minor compound, the biosynthesis of scoparone in A. capillaris , particularly the downstream O‐methylation steps, remains poorly understood. Based on a detailed examination of the structure of scoparone (6,7‐dimethylesculetin), it has been proposed that scopoletin (6‐methylesculetin) or isoscopoletin (7‐methylesculetin) is a potential precursor for scoparone biosynthesis and may involve an O‐methylation reaction. Omics technology has allowed for the comprehensive analysis of multiomics data to effectively elucidate metabolic pathways in various plant species (Chen et al. 2023; Jiang et al. 2021; Polturak et al. 2018; Wen et al. 2024), including nonmodel plant species. It is well known that, beginning with phenylalanine, a series of enzymes—including phenylalanine ammonia lyase (PAL), cinnamate‐4‐hydroxylase (C4H), coumarate‐3‐hydroxylase (C3H), caffeoyl‐CoA O‐methyltransferase (CCoAOMT), 4‐coumarate‐CoA ligase (4CL), caffeate 3‐O‐methyltransferase (COMT), coumarin synthase (COSY) and feruloyl‐CoA 6′‐hydroxylase (F6'H1)—drive the biosynthesis of scopoletin in Arabidopsis thaliana (Kai et al. 2008; Vanholme et al. 2019) (Figure S1). Accordingly, the putative scoparone biosynthetic pathway has been predicted (Yang et al. 2024; Zhao et al. 2019); however, very little information concerning the biosynthetic genes, especially O‐methyltransferase genes, of this important biosynthetic pathway in this non‐model species is available.

In this study, we utilised the popular medicinal plant A. capillaris to elucidate the biosynthetic pathway of scoparone and identify the missing methyltransferase. Conjoint analysis of the transcriptome and metabolome was used to screen out the genes and metabolites responsible for putative scoparone biosynthesis. Notably, a novel O‐methyltransferase, AcOMT1, which catalyses the methylation of scopoletin and isoscopoletin, as the final step in the biosynthesis of scoparone, was identified. By leveraging the identified biosynthetic genes, we subsequently achieved successful de novo heterologous biosynthesis of scoparone in N. benthamiana leaves. Our results provide significant insights into the biosynthesis of scoparone and offer a theoretical foundation for the development of potential biotechnological strategies for its production via synthetic biology.

2. Materials and Methods

2.1. Plant Materials and Chemical Reagents

In September 2021, A. capillaris plants in the late reproductive stage were collected in Xi'an city, Shanxi, China. Fresh plant organs, specifically the roots, stems, leaves and flowers of A. capillaris, were promptly separated, flash frozen in liquid nitrogen and preserved at −80°C for subsequent analysis. The seeds of Nicotiana benthamiana were maintained in storage within our laboratory. N. benthamiana plants were cultivated from seeds that were germinated and grown in potting soil under controlled greenhouse conditions, maintained at a temperature of 25°C with a photoperiod of 16 h of light and 8 h of darkness, until they reached an age of 4–6 weeks, after which they were used for Agrobacterium‐mediated transient transformation using standard protocols.

High‐performance liquid chromatography (HPLC)‐grade methanol and formic acid were obtained from Chengdu Kelong Chemical Co. Ltd. Chemical reagents, including scoparone, scopoletin, isoscopoletin and S‐adenosyl‐L‐methionine (SAM), were obtained from Yuanye Biotech Co. Ltd. (Shanghai, China).

2.2. Widely Targeted Metabolomics Analysis of A. capillaris

Sample preparation and metabolite extraction, identification and quantification for leaf and flower samples (each in triplicate) were performed at MetWare Biotech Co. Ltd. (Wuhan, China) following previously described methods (Zhang et al. 2022, 2020). In brief, freeze‐dried samples were ground into powder using a mixer mill (MM 400, Retsch) for 1.5 min at 30 Hz. Next, 50 mg of lyophilised powder was dissolved in 1.2 mL of 70% methanol extraction solution and vortexed for 30 s every 30 min. This mixing step was repeated six times. Following centrifugation at 12 000 rpm and 4°C for 3 min, the extracts were filtered through a 0.22 μm microporous membrane. The sample extracts were analysed on an ultraperformance liquid chromatography‐tandem mass spectrometry (UPLC–MS/MS) system. Finally, a quality control sample (mix) was prepared by mixing equal amounts of all the samples to monitor the stability of the analytical conditions for analysis. A UPLC–ESI–MS/MS system was used to detect metabolite profiles. Qualitative analysis of all metabolites was performed via a database constructed by MetWare Biotechnology, Wuhan, China. Mass spectrometry data were analysed and quantified using Analyst software v1.6.3 and MultiQuant software v3.0.2. Orthogonal partial least‐squares discriminant analysis (OPLS‐DA) and principal component analysis (PCA) were conducted to analyse and verify the differences and reliability of the metabolites in the samples.

2.3. Determination of Scoparone Content

Quantitative analysis of scoparone in different tissues of A. capillaris and the leaves of transgenic N. benthamiana was performed via HPLC. Different tissues (roots, stems, leaves and flowers) of A. capillaris and different leaves of transgenic N. benthamiana were collected and freeze‐dried to obtain powders. The powder (1.0 g) of each sample was mixed with 10 mL of methanol by ultrasonication (180 W power, 40 kHz frequency) for 60 min and then centrifuged at 12 000 × g for 30 min. The resulting extracts were dried via a rotary evaporator and dissolved in methanol. The supernatant was filtered through a 0.22 μm microporous membrane and then subjected to HPLC (Agilent) to determine the content of scoparone.

Content analysis was performed using an Agilent 1260 Infinity II HPLC system equipped with a C18 column (5 μm × 250 mm; Phenomenex, USA) at 30°C and 343 nm. The mobile phase consisted of methanol (40%) and 1% formic acid (60%) in isocratic mode, with a flow rate of 1.0 mL·min−1, and the injection volume was 10 μL. A scoparone standard was used for quantification and qualitative analysis.

2.4. RNA Extraction, Illumina and PacBio Sequencing and Analyses

Total RNA was extracted using a TRIzol kit (TIANGENT) following the manufacturer's instructions. The concentration, purity, integrity and overall status of the RNA were subsequently assessed to ensure optimal quality. The libraries were sequenced by MetWare Biotechnology Co. Ltd. (https://www.metware.cn/). For Illumina sequencing, libraries from leaf and flower tissues were prepared using an NEBNext Ultra RNA Library Prep Kit (Illumina Inc.) and sequenced on an Illumina NovaSeq 6000 sequencer (Illumina) in paired‐end mode (PE150). To construct the library for PacBio sequencing, total RNA from the tissues, including leaves and flowers, was mixed equally. The Iso‐Seq library was constructed following the isoform sequencing protocol (Iso‐Seq), utilising the Clontech SMARTer PCR cDNA Synthesis Kit protocol as outlined by Pacific Biosciences. The PacBio Sequel sequencing platform was utilised to generate subreads, which were subsequently self‐corrected and assembled into circular consensus sequences (CCSs). These CCSs, containing primers at both ends and poly‐A sequences at the 3′ ends, were screened for full‐length nonchimerics (FLNCs) with no chimeric sequences. Next, self‐correction and data correction using second‐generation sequencing were conducted. The corrected FLNCs were further processed for redundancy using CD‐HIT software with a similarity threshold of 95%. This process yielded high‐quality full‐length transcripts. The full‐length transcripts were functionally annotated through the utilisation of seven databases, namely, the non‐redundant protein sequences database (NR), SwissProt, Kyoto Encyclopedia of Genes and Genomes (KEGG), the Protein Family Database (Pfam), Gene Ontology (GO), Clusters of EuKaryotic Orthologous Groups (KOG) and Translation of EMBL (TrEMBL).

The full‐length transcripts were regarded as the reference genome. All the clean reads from the samples were mapped to the reference genome using Bowtie2 (Langmead and Salzberg 2012). The RSEM tool was employed to quantify gene and isoform abundances, and normalisation of mapped reads was performed, followed by the calculation of FPKM (fragments per kilobase of transcript per million mapped reads) (Li and Dewey 2011). According to the raw read count values of each sample, differentially expressed genes (DEGs) were identified in pairwise comparisons between groups using DESeq2 software (Love et al. 2014) employing a threshold of |log2 FoldChange| ≥ 1 and a false discovery rate (FDR) of < 0.05 (Anders 2010). The clusterProfiler R package was used to carry out GO and KEGG pathway enrichment analyses of the DEGs.

2.5. Real‐Time Quantitative Reverse Transcription‐PCR Analysis

The RNA (1 μg) from each sample was reverse transcribed to cDNA via an Evo M‐MLV Plus cDNA Synthesis Kit (AG11615; Accurate Biotechnology, China) in a 20 μL reaction mixture. qRT–PCR was performed on a CFX96 system (Bio‐Rad, Hercules, CA, USA) using SsoFast EvaGreen Supermix (Bio‐Rad, USA). The reactions were performed as follows: an initial denaturation step at 95°C for 30 s, followed by 40 cycles of denaturation at 95°C for 5 s and annealing at 60°C for 30 s. Melting curve analysis was performed to confirm PCR specificity, with a heat dissociation protocol from 60°C to 95°C following the final PCR cycle. Relative expression levels were normalised to that of the actin gene as an internal control (de Oliveira et al. 2022) and then calculated using the 2−△△Ct method (Schmittgen and Livak 2008). The qRT–PCR primers used are listed in Table S1. Each assay included three biological replicates.

2.6. Coexpression Analysis

The WGCNA package (v1.70–3) (Langfelder and Horvath 2008) was used to perform weighted gene correlation network analysis using FPKM values obtained from various tissues. Module eigengene values were calculated within the WGCNA framework and then correlated with the accumulation patterns of potential metabolites in different plant tissues. Cytoscape (v3.9.0) (Shannon et al. 2003) was used for the analysis and visualisation of gene coexpression networks.

2.7. Bioinformatics Analysis

The amino acid sequences of the AcOMT homologues were obtained from the National Center for Biotechnology Information (NCBI, https://www.ncbi.nlm.nih.gov/) and aligned using MAFFT (v.7.310) (Katoh et al. 2019). Phylogenetic tree construction was performed using MEGA X software with the neighbour–joining method and 1000 bootstrap replicates (Kumar et al. 2018). iTOL (https://itol.embl.de/) was utilised for the annotation and visualisation of the phylogenetic trees (Letunic and Bork 2021). Sequence similarity and secondary structure information were analysed using the ESPript 3.0 web server (https://espript.ibcp.fr/).

2.8. Protein Expression and Purification

The complete coding sequence (CDS) of AcOMT1‐3 (Table S2) was amplified from the A. capillaris cDNA library and subsequently subcloned and inserted into the pET28a (+) vector via the ClonExpress II One Step Cloning Kit (Vazyme, China). Following sequence validation, the construct was introduced into the E. coli strain BL21 (DE3) (Takara, China), and protein expression was induced with 1 mM IPTG overnight at 180 rpm and 16°C. The recombinant protein was subsequently purified using Ni‐NTA agarose beads (Vazyme, China). Protein concentrations were determined using Bradford reagent (Karimi et al. 2022) and further analysed by 10% SDS–PAGE.

2.9. In Vitro Assays of AcOMTs

The enzymatic activity assays were conducted in accordance with previously published methods (Zhao et al. 2024), with slight modifications. Specifically, a 200 μL reaction mixture was prepared containing 100 mM Tris–HCl (pH 7.5), 5 mM SAM, 1 mM DTT, 1 mM substrates and 1 mg of crude recombinant protein. If necessary, 1 mM MgCl2 was included in the mixture. The pET28a (+) empty vector served as a negative control. The assays were carried out at 30°C for 2 h, and the resulting mixture was extracted twice with an equal volume of ethyl acetate. The upper organic phase was dried, dissolved in methanol and filtered through a 0.22 μm filter for subsequent analysis by HPLC.

2.10. Kinetics Measurement of AcOMT1

The biochemical properties of AcOMT1 were investigated using scopoletin or isoscopoletin as the methyl receptor and SAM as the methyl donor. The experimental procedure involved the use of Tris–HCl buffer (pH 6.0, 7.0, 7.5, 8.0 or 9.0) and 4 μg of purified AcOMT1, with the reaction conducted at 30°C for 2 h to determine the optimal pH. Additionally, the reaction was carried out at temperatures ranging from 25°C to 45°C for 2 h to determine the optimal temperature. The kinetic parameters (K m and K cat ) were assessed via reactions with scopoletin or isoscopoletin at concentrations ranging from 0.0625–1.50 mM for 30 min, followed by analysis through nonlinear regression curve fitting using Michaelis–Menten bundled in GraphPad Prism 8.3 software (GraphPad, USA).

2.11. Substrate Binding Analysis

Purified AcOMT1 was subjected to binding titration analysis to determine the K d values for substrate binding, following a previously described method (Guo et al. 2021) with modifications. A concentration of 1 μM AcOMT1 was established as the baseline, followed by the addition of substrate at concentrations ranging from 0 to 100 μM. After 10 min of incubation at 25°C, the differences in absorbance between 200 and 500 nm were recorded using a UV–visible 2550 spectrophotometer (Shimadzu, Kyoto, Japan), and the data were plotted to determine the binding affinities at 343 nm. The binding affinities were estimated using GraphPad Prism 8.3 software, as outlined in previous studies (Wang et al. 2023).

2.12. Subcellular Localisation Assay

Subcellular localisation prediction of AcOMT1 was performed using WoLF PSORT (https://wolfpsort.hgc.jp/). To generate the AcOMT1‐GFP fusion protein, the CDS of the AcOMT1 gene was cloned and inserted into the pBI121‐GFP vector using the primers listed in Table S1. The constructs were subsequently sequenced and transferred into the A. tumefaciens strain GV3101, which was then co‐transformed into 6‐week‐old N. benthamiana leaves (Li et al. 2019). After 48 h of cultivation, the GFP fluorescence signal was visualised using a confocal laser scanning microscope (Zeiss LSM 710 NLO, Oberkochen, Germany).

2.13. Protein Structure Prediction, Molecular Docking and Molecular Dynamics Analysis

The structural model of AcOMT1 was generated using Alphafold2 (Bryant et al. 2022). Molecular docking of the protein with ligands was performed using AutoDock 4.2 (Forli et al. 2016). The resulting models were visualised by AutoDockTools v1.5.6 and PyMOL 2.5 (http://www.pymol.org). Molecular dynamics (MD) simulations were conducted to analyse the structures of AcOMT1 docked with scopoletin and isoscopoletin by employing the GROMACS 2019 software package (Abraham et al. 2015). The topology and coordinate files for the compounds were generated using AmberTools18 and subsequently converted into GROMACS‐compatible topology and coordinate files using the ACPYPE program (Kagami et al. 2023). The protein topology and coordinate files were generated in accordance with the Amber ff99SB force field using the pdb2gmx tool in GROMACS 2019. The topology and coordinate files of both the protein and ligand were subsequently combined and embedded within a dodecahedral periodic box by employing the SPC216 explicit solvation model. The system was then neutralised by the addition of five Na+ ions. Energy minimisation was conducted utilising the steepest descent algorithm, followed by a position‐restraint protocol under the NVT and NPT ensembles at 298 K and 1.0 bar. The MD simulations produced a 100 ns trajectory based on the Leapfrog integrator with a 2 fs time step. Constraints on all bonds involving hydrogen atoms were enforced using the LINCS algorithm. Long‐range electrostatic interaction energies were computed via the particle–mesh Ewald (PME) method. Atomic coordinates and energies were recorded every 10 ps for subsequent analysis. The binding free energy of the protein–ligand complex in solvent was determined using the molecular mechanics Poisson–Boltzmann surface area (MM‐PBSA) approach, as implemented in the gmx_MMPBSA tool (Valdés‐Tresanco et al. 2021).

2.14. Heterologous Production of Scoparone in N. Benthamiana

With respect to the de novo biosynthesis of scoparone in N. benthamiana, the genes AtF6'H1 (Hoengenaert et al. 2022), AtCOSY (Vanholme et al. 2019) and AcOMT1 were coexpressed in tobacco leaves. The coding sequences of these genes were connected using a 2A peptide linker from porcine teschovirus (P2A) (Table S2) and then integrated into the pBI121‐GFP vector at the BamHI/SacI site to create transient expression vectors. This procedure was carried out by Huada BioTech (Beijing, China). These transient expression vectors were subsequently introduced into A. tumefaciens GV3101, which was then used to transform tobacco leaves. The plants were then subjected to extended periods of light exposure for 2 days to ensure adequate accumulation of protein. Fluorescence was visualised with a hand‐held lamp (LUYOR‐3415UR, LUYOR, USA). Leaves were then harvested for further analysis.

2.15. Statistical Analyses

Statistical analysis was conducted using GraphPad Prism 8.3, and p‐values were determined through Student's t‐test (*p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001; ns, not significant).

3. Results

3.1. Metabolomic Profiling and Scoparone Determination in A. capillaris

Tissues with different levels of secondary metabolites can be used to identify biosynthetic genes and intermediate metabolites. Four organs, namely, the roots, stems, leaves and flowers, were sampled (Figure 1A), and HPLC was used to determine the scoparone content in A. capillaris . The content of scoparone varied among the different tissues (Figure 1B). All four organs analysed contained scoparone, with the highest level in flowers (10.1 mg·g−1 DW), a moderate level in leaves (3.60 mg·g−1 DW) and the lowest amounts in stems (0.236 mg·g−1 DW) and roots (0.265 mg·g−1 DW), indicating that flowers and leaves are the primary sites of scoparone biosynthesis and accumulation in A. capillaris .

FIGURE 1.

FIGURE 1

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Metabolomic profiles of A. capillaris . (A) Isolation of scoparone from A. capillaris . (B) Scoparone contents in the flower, leaf, stem and root of A. capillaris . DW, dry weight. The bars represent the average (±SD) values. Different letters above the error bars indicate significant differences (ANOVA, Tukey's test; p < 0.05). (C) Principal component analysis (PCA) of the metabolome data from different repeats. (D) Orthogonal partial least‐squares discriminant analysis (OPLS‐DA) score plot for six samples. (E) Differences in the contents of intermediates related to scoparone biosynthesis between the two tissues (Student's t‐test, **p < 0.01, ***p < 0.001, ****p < 0.0001).

To further understand and elucidate the scoparone biosynthesis pathway, we selected leaves and flowers for broadly targeted metabolomics profiling. Principal component analysis (PCA) and orthogonal partial least‐squares discriminant analysis (OPLS‐DA) revealed that the leaf and flower groups were significantly separated from each other (Figure 1C,D). Metabolites that may be intermediates for scoparone biosynthesis, such as scopoletin and isoscopoletin, were identified (Table S3). The relative contents of these compounds (represented as peak areas) are shown in Figure 1E. Scoparone was the major bioactive polymethoxylated coumarin compound, whose abundance was 2.0‐fold greater in flowers than in leaves. Two additional methoxylated coumarins with chemical structures similar to scoparone, scopoletin and isoscopoletin also presented relatively high levels in flowers, with 3.0‐ and 1.5‐fold greater accumulation than in leaves, respectively, which was similar to the accumulation trend of scoparone. Moreover, 8 of the 12 possible annotated metabolites associated with scoparone synthesis significantly differed between the leaves and flowers (Figure 1E). Overall, the tissue‐specific accumulation of coumarins in leaves and flowers suggests that these tissues may serve as valuable sources for the further identification of genes involved in scoparone biosynthesis.

3.2. Transcriptome Analysis Based on SMRT Sequencing and NGS in Leaves and Flowers

To further elucidate the scoparone biosynthesis pathway at the transcriptional level in A. capillaris , a hybrid approach involving short‐read next‐generation sequencing (NGS) and long‐read single‐molecule real‐time (SMRT) sequencing was used to obtain complete cDNA sequences. For NGS, six cDNA libraries of two different tissues (leaf and flower, each in triplicate) of A. capillaris were constructed and subsequently sequenced using the Illumina platform. High‐quality sequencing data were obtained with a Q30 score > 92% and an average of 47.90 M raw reads per sample read (Table S4). The numbers of total clean reads from the leaves and flowers were 141.65 and 143.62 M, respectively, which were obtained via the removal of contaminated adapters and low‐quality reads.

To generate the full‐length transcriptome of A. capillaris , mixed RNA samples from the leaves and flowers were collected for SMRT sequencing using the PacBio Sequel platform. After the adapter sequences were removed, 28 564 923 subreads were obtained from 760 788 polymerase reads, totaling 54.04 Gb of bases (Tables S5 and S6, Figure S2A,B). These subreads were processed through the PacBio SMRT analysis pipeline, which resulted in 582 104 circular consensus sequences (CCSs) with a total base count of 1 171 578 502 bp and an average length of 2013 bp (Table S7). Among these reads, 88.61% (515 825) were identified as full‐length nonchimeric reads (FLNCs) (Table S8, Figure S2C). The FLNC sequences were analysed using IsoSeq in SMRTLink to cluster similar sequences, correct errors and create consensus transcripts. In total, 53 237 accurate transcripts were identified, with 45 666 non‐redundant transcripts remaining after redundancies were removed via CD‐HIT (c = 0.90) (Tables S9 and S10). The TransDecoder program was used to predict the coding sequences, and the length distribution is shown in Figure S2D. The constructed set of full‐length transcripts was used as a reference dataset for subsequent analyses.

3.3. Functional Annotation of the Full‐Length A. capillaris Transcriptome

To fully annotate the A. capillaris transcriptome, 45 666 transcripts were aligned with seven public databases. Additionally, 45 107 (98.78%) annotated unigenes were obtained from at least one database, and 13 969 (30.59%) unigenes were annotated in seven databases (Table S11). The NR database was used for the majority of the annotations (98.32%). The predicted homologous species of A. capillaris were also identified through alignment with the NR database. The greatest number of sequences was found in Artemisia annua (73.93%), followed by Tanacetum cinerariifolium (14.49%) and Quercus suber (1.21%) (Figure S3A). In the GO classification, 41 172 transcripts were clustered into three major categories and 46 subcategories. In the second‐level classification, most terms belonged to the ‘cellular anatomical entity’ category (35 818), ‘binding’ category (26 011), ‘catalytic activity’ category (22 081 transcripts) and ‘cellular process’ category (29 212 transcripts; Figure S3C). Enriched metabolic pathways were categorised using the KEGG pathway database. A total of 744 transcripts were linked to the phenylpropanoid biosynthesis pathway, which included pathways such as phenylalanine metabolism (185 transcripts); phenylalanine, tyrosine and tryptophan biosynthesis (166 transcripts) and phenylpropanoid biosynthesis (393 transcripts) (Figure S3B).

3.4. Differentially Expressed Gene Identification and Enrichment Analyses

To determine the prevalence of organ‐specific transcripts, a comparative transcriptome analysis was carried out to identify differentially expressed genes (DEGs) between the leaf (CK) and flower tissues (Figure 2A). A total of 18 297 unigenes (40.07%) were identified as DEGs, with 7986 upregulated in flower tissue (Table S12, Figure 2B). To explore the biological functions of these DEGs, they were annotated using the GO database. GO enrichment analysis classified the DEGs into three main groups: molecular function, cellular component biological process, including phenylpropanoid biosynthesis process, caffeate O‐methyltransferase activity and methyltransferase complex (Figure 2D). Additionally, KEGG enrichment analysis revealed that the DEGs were significantly enriched in various pathways, including the phenylpropanoid biosynthesis pathway (Figure 2E). The results of the transcriptomic analysis provided evidence that the observed metabolic differences influenced scoparone biosynthesis in A. capillaris .

FIGURE 2.

FIGURE 2

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Analysis of differentially expressed genes (DEGs) between the leaves and flowers. (A) Diagram of the candidate genes as determined by RNA‐seq. (B) The number of genes whose expression levels were upregulated and downregulated in leaves (CK) vs. flowers. (C) Venn diagram of DEGs expressed in the two tissues. (D, E) Top 20 enriched GO (D) and KEGG (E) pathways of the differentially expressed genes. The names of the terms are indicated on the vertical axis, and the enrichment factor is indicated on the horizontal axis. The size of the bubble represents the number of genes, and the colour represents the p‐value.

3.5. DEGs Involved in Proposed Scoparone Biosynthesis

Scoparone biosynthesis occurs downstream of the phenylpropanoid pathway. Based on previous studies (Wu et al. 2021), we reconstructed the putative biosynthetic pathway of scoparone in A. capillaris (Figure 3). A total of 186 DEGs (leaf (CK) vs. flower) were significantly enriched in the phenylpropanoid biosynthesis pathway (Figure 2E). Among these genes, 74 DEGs were annotated as eight candidate core enzymes in the proposed scoparone pathway, including 21 PALs, 3 C4Hs, 21 4CLs, 2 C3'Hs, 8 CCoAOMTs, 4 F6'Hs, 19 COSYs and 12 COMTs. However, a small number of these biosynthetic genes presented relatively high expression levels in flowers. For example, two of the four F6'H genes (transcript_48324 and transcript_41590), which are responsible for the synthesis of coumarins, were upregulated approximately 4.7 and 1.4‐fold, respectively, in flower samples compared with those in leaves. Similarly, three OMT genes presented approximately 1.0‐ to 2.1‐fold greater expression in flowers than in leaves.

FIGURE 3.

FIGURE 3

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Putative pathways involved in scoparone biosynthesis and a cluster heatmap of the expression of the candidate enzyme‐encoding genes in A. capillaris tissues. Red and green indicate high and low expression levels, respectively. PAL, phenylalanine ammonia lyase; C4H, cinnamate 4‐hydroxylase; 4CL, 4‐coumaroyl CoA ligase; C3H, 4‐coumarate‐3‐hydroxylase; CCoAOMT, caffeoyl‐CoA O‐methyltransferase; F6'H, feruloyl‐CoA 6′‐hydroxylase; COSY, coumarin synthase; COMT, caffeic acid 3‐O‐methyltransferase.

To further verify the reliability of the gene expression data obtained from the transcriptome sequencing of A. capillaris , we randomly selected 12 transcripts with full‐length ORFs related to coumarin biosynthesis for qRT–PCR. The expression trends were similar between the RNA‐seq and qRT–PCR results (Figure S4). These results suggest that the RNA‐seq data are accurate and reliable.

3.6. Identification of AcOMT1‐3 as Candidate Genes Responsible for Scoparone Accumulation

To identify OMT genes potentially involved in polymethoxylated coumarin (scoparone) biosynthesis, we performed weighted gene coexpression network analysis (WGCNA) and gene coexpression correlation analysis. The transcriptome data from A. capillaris were divided into 13 modules based on their expression patterns (Figure 4A). Analysis of the module–trait relationships revealed that the MEturquoise module was significantly positively correlated with the contents of methoxylated coumarins (scopoletin, isoscopoletin and scoparone) (R > 0.96, p < 0.01; Figure 4B). Therefore, the MEturquoise module was selected for further study to understand the biosynthesis of scoparone in A. capillaris . Within the MEturquoise module, three candidate OMTs (AcOMT1, AcOMT2 and AcOMT3) were screened, along with two F6'Hs, which are core enzymes in the biosynthesis of coumarins (Beesley et al. 2023). Moreover, coexpression and correlation network analyses were conducted to identify the downstream OMTs that were strongly associated with the key genes (F6'Hs) (Figure 4C). These results suggested that AcOMT1–3 play crucial roles in scoparone biosynthesis. Therefore, these genes were selected as target genes for subsequent functional analysis.

FIGURE 4.

FIGURE 4

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Identification of candidate AcOMT genes involved in scoparone biosynthesis by WGCNA and coexpression analysis. (A) The top half of the image indicates the cluster tree, whereas the bottom half indicates the gene module. The genes that clustered together were grouped into the same module. (B) Module‐trait associations with scoparone content. Every row represents a module eigengene, and every column represents a characteristic. The colours, ranging from blue to red, represent correlations that vary from low to high. (C) Coexpression networks associated with AcOMT1‐3 and F6'Hs. The orange lines indicate positive correlations and green lines indicate negative correlations. (D) Phylogenetic tree based on AcOMT1‐3 genes screened in A. capillaris and OMT genes in other species.

3.7. Molecular Cloning and Bioinformatic Analysis of AcOMT1–3

The full‐length sequence of AcOMT3 contains a 693 bp ORF encoding a protein of 230 amino acids, which is consistent with the calculated molecular weight of 26.90 kDa. In contrast, AcOMT1 and AcOMT2 encoded proteins with 359 and 350 amino acids, respectively, corresponding to calculated molecular weights of 40.08 and 39.60 kDa, respectively. The isoelectric points of AcOMT1–3 were 7.17, 7.03 and 7.12, respectively. Subcellular localisation analysis indicated that AcOMT1–3 are cytoplasmic proteins (Table S13). AcOMT1–2 contained two conserved domains, the Methyltransf_2 domain and the dimerisation domain, whereas AcOMT3 contained only the Methyltransf_2 domain (Figure S5). Moreover, five conserved motifs, which were proposed as signatures for plant OMT, also existed in the sequences of AcOMT1–3 (Figure S6). A phylogenetic analysis was conducted on three O‐methyltransferases (OMTs) and other plant OMTs (Table S14), which revealed that AcOMT1 and AcOMT2 are classified as Type I OMTs within the caffeic OMT (COMT) and COMT‐like subgroups, respectively (Figure 4D) (Tan et al. 2022). Additionally, AcOMT1 demonstrated an amino acid sequence identity of 88.3% with TcCOMT from T. cinerariifolium , whereas AcOMT2 exhibited a similarity of 77.7% with the COMT‐like protein in A. annua (Table S15). AcOMT3 was placed within a clade of Type II OMTs and displayed 70.9% amino acid sequence similarity to AaCCoAOMT from A. annua .

3.8. AcOMT1 Catalyses the Last Step of Scoparone Biosynthesis in A. capillaris

Based on the structural similarities and accumulation patterns observed, we hypothesised that scoparone may be a methylated derivative of either scopoletin or isoscopoletin in A. capillaris . To test this hypothesis, we utilised recombinant AcOMT1, AcOMT2 and AcOMT3 proteins expressed in E. coli BL21 (DE3) cells to investigate their methylating activity toward the potential precursors (Figure 5A,B). Our results indicate that AcOMT1 successfully catalysed the conversion of scopoletin or isoscopoletin into scoparone, whereas AcOMT2, AcOMT3 and the control did not exhibit this activity (Figure 5C). To decipher the subcellular organisation of C‐6/7 decorations of scopoletin or isoscopoletin, we analysed the subcellular localisation of AcOMT1 using the transient expression of green fluorescent protein (GFP) fusion proteins within N. benthamiana leaves. As shown in Figure 5D, the fluorescence signals from AcOMT1‐GFP were localised in the cytoplasm, suggesting that further modifications of scopoletin or isoscopoletin mediated by AcOMT1 took place within the cytoplasm. Furthermore, the expression levels of AcOMT1 across various tissues were assessed using qRT–PCR. The results indicated that AcOMT1 was highly expressed in flowers, followed by leaves, whereas its expression was minimal in stems and roots (Figure S8A). This expression pattern was consistent with the accumulation pattern of scoparone (Figure S8B). Overall, these results indicated that both scopoletin and isoscopoletin might act as immediate precursors for scoparone production, suggesting the potential involvement of multiple pathways in the biosynthesis of scoparone. Additionally, the findings indicated that the newly discovered multifunctional O‐methyltransferase (AcOMT1) plays a role in the final stage of scoparone biosynthesis in A. capillaris .

FIGURE 5.

FIGURE 5

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Functional verification of candidate AcOMT1‐3. (A) SDS–PAGE analysis of the expression of recombinant candidate AcOMT1‐3 in E. coli BL21 (DE3). M: Protein molecular weight marker; T: Total protein of the cells; S: Supernatant of the cell lysate. (B) Schematic representation of AcOMT‐catalysed conversion from scopoletin/isoscopoletin to scoparone in A. capillaris . (C) HPLC chromatograms of the reaction dynamics of AcOMTs with SAM and scopoletin/isoscopoletin as substrates. SAH: S‐adenosyl‐L‐homocysteine. Substrates incubated with the empty vector (EV: PET28a (+)) are indicated in black. (D) Subcellular localisation of AcOMT1 (scale bars = 20 μm). (E–H) Substrate specificities and binding affinities of the recombinant A. capillaris O‐methyltransferase (AcOMT1) proteins. Kinetic analysis of AcOMT1 on scopoletin (E) and isoscopoletin (F). Dissociation constants of AcOMT1 with scopoletin (G) and isoscopoletin (H).

3.9. AcOMT1 Shows a Greater Substrate Preference for Scopoletin Than for Isoscopoletin

To gain a deeper understanding of the roles of AcOMT1 in the methylation of isoscopoletin and scopoletin, we tested their kinetic parameters using purified AcOMT1 (Figure S7). AcOMT1 exhibited maximum activity at 40°C (Figure S9A,B) and pH 7.5 (Figure S9C,D), with apparent K cat /K m values of 900.90 and 1759.88 M−1.s−1, respectively (Figure 5E,F). The results demonstrated that although both isoscopoletin and scopoletin can be methylated by AcOMT1, AcOMT1 exhibited superior substrate affinity and catalytic efficiency for scopoletin. To further verify this substrate preference, the binding affinities of AcOMT1 molecules involved in hydrogen bonding to scopoletin or isoscopoletin were assessed through fluorescence‐binding assays. As indicated in Figure 5G,H, AcOMT1 had a higher binding affinity for scopoletin (K d = 5.8 μM) than for isoscopoletin (K d = 7.3 μM). These results highlighted that AcOMT1 exhibited a substrate preference for scopoletin. These results suggest that manipulating the biosynthesis of scopoletin could have a substantial effect on the accumulation of scoparone in A. capillaris .

3.10. Structural Insights Into AcOMT1 Substrate Recognition and Preference

The exceptional catalytic properties of AcOMT1 make it a compelling subject for further exploration into the structural underpinnings of its unique catalytic behaviour. Therefore, the protein structure of AcOMT1 was predicted using AlphaFold2 (Figure S10B). Molecular docking was subsequently employed to predict the binding modes of scopoletin/isoscopoletin with the AcOMT1 protein (Figure 6A,B). The docking results indicated that two pockets in AcOMT1, one binding to SAM and the other binding to the substrate, were considered to belong to the two active centers of the protein (Figure S10A). The binding energies of AcOMT1 with scopoletin and isoscopoletin were −6.5 and −6.0 kcal·mol−1, respectively. Interaction analysis revealed that scopoletin displayed three strong hydrogen bond interactions with ASP245, ASP225 and GLY202, whereas isoscopoletin formed two stable hydrogen bond interactions with ASN170 and TRP265.

FIGURE 6.

FIGURE 6

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Reconstitution of the scoparone biosynthetic pathway in N. benthamiana. Molecular docking of AcOMT1 with scopoletin (A) or isoscopoletin (B). Time evolution of the RMSD (C) or RMSF (D) of the protein backbone and substrate via molecular dynamics simulations. (E) Three key scoparone biosynthesis genes, connected by a 2A peptide linker and driven by a 35S CaMV promoter, were transiently co‐overexpressed in N. benthamiana. (F) Photos of transiently transformed leaves showing the characteristic fluorescence of scoparone and GFP under ultraviolet illumination. (G) HPLC chromatogram of N. benthamiana leaf extracts after the transient overexpression of scoparone biosynthetic pathway genes. (H) The contents of scoparone. The control represents tobacco leaves expressing GFP. #OE represents tobacco leaves coexpressing AtF6'H1, AtCOSY and AcOMT1.

Molecular dynamics simulations conducted over a 100 ns timescale demonstrated that scopoletin was more stable within the AcOMT1 complex than was isoscopoletin (Figure 6C,D). MM/PBSA calculations also revealed that compared with isoscopoletin, scopoletin had a lower binding free energy of −18.19 kcal·mol−1 (−5.84 kcal·mol−1). These results demonstrate that scopoletin can form stronger and more stable interactions with the active site residues of AcOMT1 than can isoscopoletin.

3.11. De Novo Biosynthesis of Scoparone in N. Benthamiana

To further assess the function of AcOMT1 and its application in scoparone engineering in tobacco, we reconstructed the scoparone biosynthetic pathway in the leaves of N. benthamiana. Two upstream biosynthesis genes (AtF6'H1, Arabidopsis thaliana feruloyl‐CoA 6′‐hydroxylase; AtCOSY, Arabidopsis thaliana coumarin synthase), together with AcOMT1, were transiently coexpressed in tobacco leaves (Figure 6E). Control experiments in which green fluorescent protein (GFP) was expressed were conducted. RT–PCR confirmed the expression of AtF6'H1, AtCOSY and AcOMT1 only in leaves transformed with the transgene‐encoding construct but not in the controls (Figure S8). Additionally, the fluorescence results revealed that the leaves expressing the biosynthesis pathway of scoparone presented blue fluorescence, whereas the control leaves presented green fluorescence (Figure 6F) and did not yield detectable amounts of scoparone or intermediates (Figure 6G). The highest content of scoparone was detected in tobacco leaves, at 3.03 μg.g−1 DW (Figure 6H).

4. Discussion

4.1. High‐Throughput Sequencing Facilitates Research on Scoparone‐Producing Plants

The extensive availability of nucleotide sequence data, coupled with the accessibility of advanced technologies, has facilitated the development of computational methodologies for identifying genes and pathways associated with plant‐specific metabolism (Li et al. 2023; Xu et al. 2023). A key method is to analyse the metabolite levels and gene expression patterns of orthologous genes in tissues that produce specific metabolites (Gao et al. 2024; Xu et al. 2021).

A. capillaris is widely acknowledged as a medicinal plant utilised in the treatment of jaundice and chronic liver diseases (Yang et al. 2023). The methoxylated coumarins scoparone and scopoletin have been recognised as the principal bioactive constituents (Ding et al. 2021). Through metabolomics analysis, these three methoxylated coumarins were shown to exhibit tissue‐specific accumulation (Figure 1). These characteristics make A. capillaris an ideal candidate for investigating scoparone biosynthesis via high‐throughput sequencing (Figure 2A). Hence, datasets were generated to predict the genes and metabolites associated with scoparone biosynthetic pathways in A. capillaris (Figure 3). These datasets will serve as valuable resources for advancing the understanding of the genes and metabolites implicated in these secondary metabolic pathways, as elaborated upon in other contributions to this special issue. Specifically, the identification of candidate genes involved in specific biosynthetic pathways can be facilitated through a comparative analysis of gene expression and metabolite accumulation, as exemplified by a similar recent study (Wen et al. 2024).

4.2. AcOMT1 Specifically Catalyses Scopoletin Methylation, the Last Committed Step in Scoparone Biosynthesis

Methyltransferases (MTs) constitute a diverse class of proteins responsible for catalysing methylation reactions, including C‐methyltransferases (CMTs), N‐methyltransferases (NMTs) and O‐methyltransferases (OMTs) (Tan et al. 2022), which result in changes in the solubility, stability and other characteristics of the substrate, thereby increasing its versatility (Hui et al. 2020; Luo et al. 2022). Because of their structural properties, OMTs are likely involved in the biosynthesis of scoparone.

In the present study, a newly discovered multifunctional O‐methyltransferase gene was shown to play a crucial role in the final stage of scoparone biosynthesis. This discovery was made through the examination of in vitro enzymatic reactions, transcriptome sequencing and analysis of scoparone accumulation in various tissues of A. capillaris . Notably, the flowers presented relatively high expression levels of AcOMT1 (Figure S8A) and the greatest concentrations of scoparone (Figure 1B), indicating a potential correlation between scoparone accumulation and AcOMT1 gene expression in this particular tissue (Figure S8B). Usually, genes within the biosynthetic pathway of plant metabolites undergo coregulation, which results in a correlation between the expression of biosynthetic genes and the accumulation of their corresponding end products (Wei et al. 2018). Furthermore, phylogenetic analysis revealed a close relationship between AcOMT1 and the class II OMT group TcOMT, indicating high substrate specificity (Song et al. 2022; Xu et al. 2022; Zhao et al. 2019). Class II OMTs are known to methylate a wide range of secondary metabolites, such as flavonoids, flavonols, stilbenes, phenylpropenes and various volatile phenolics (Hao et al. 2023; Wu et al. 2024). Notably, plant class II OMTs are primarily responsible for para‐O‐methylation activity (Zhao et al. 2020). AcOMT1, in particular, can methylate scopoletin and is involved in the final step of methylation to produce scoparone (Figure 5B,C). AcOMT1 exhibited a greater affinity for scopoletin than for isoscopoletin, demonstrating increased catalytic efficiency and binding affinity (Figure 5E,F). Consequently, AcOMT1 has emerged as a promising candidate for the regulation of scoparone metabolism.

4.3. Metabolic Engineering of Scoparone Production in Heterologous Hosts

Scoparone is among the most abundant coumarins identified in A. capillaris and is known to possess rich pharmacological properties, such as anti‐inflammatory (Feng et al. 2022). The scoparone content is insufficient to satisfy commercial demand. Consequently, addressing the increasing need for potential plant‐derived scoparone benefits to both human health and agriculture presents a considerable challenge. Metabolic engineering offers a promising solution to this issue (De La Peña and Sattely 2021; Martí et al. 2020). For example, the simultaneous introduction of stilbene synthase and O‐methyltransferase led to significant heterologous production of pterostilbene (52 μg.g−1 FW) in N. benthamiana (Martinez‐Marquez et al. 2018). Similarly, the transgenic expression of Rhodiola salidroside biosynthetic genes enables the heterologous production of salidroside (20 mg.g−1 DW) in N. benthamiana (Torrens‐Spence et al. 2018). This study presents the novel induction of scoparone production in a heterologous plant through transient gene expression without the requirement for substrate feeding. This was facilitated by the discovery of the additional scopoletin methylation activity of AcOMT1. The coexpression of AcOMT1, AtF6'H1 and AtCOSY was determined to be adequate for the biosynthesis of scoparone, eliminating the need for external supplementation, albeit still at a very low level (3.03 μg.g−1 DW), indicating that there is substantial room for engineering N. benthamiana for high‐yield scoparone production. An adequate supply of endogenous substrates is crucial for the efficient production of products in plants. It would be useful to be able to induce the coumarins biosynthetic pathway more efficiently using transcription factors, as not only are biosynthetic pathway genes induced by Delila and Rosea1, but also upstream genes involved in the biosynthesis of the precursor phenylalanine (Outchkourov et al. 2018). Higher amounts of anthocyanin (C3G: 1.22 mg.g−1 FW) can be produced by transient coexpression of heterologous pathway genes together with the MYB transcription factors Rosea1 or PAP1 than when only pathway genes are used (Grützner et al. 2023). Furthermore, coexpressing the upstream phenylpropanoid biosynthesis genes with type II GmCHI1B1 markedly increased the yield of liquiritigenin (0.838 mg.g−1 DW) (Xie et al. 2025). In the future, we will develop synergistic engineering strategies to optimise scoparone synthesis in N. benthamiana, including the remodelling of endogenous fluxes for scoparone production with transcription factors (TFs) that upregulate the upstream phenylpropanoid pathway. Nonetheless, this investigation offers a promising approach for enhancing scoparone production via metabolic engineering in nonnative organisms.

5. Conclusion

In summary, this work presents the first comprehensive transcriptomic and metabolomic analysis of the traditional medicinal plant A. capillaris , offering significant insights into the biosynthesis of scoparone. The resulting gene coexpression networks enabled us to directly connect candidate genes to metabolites at the scoparone pathway level based on correlations between transcript and metabolite abundances. Furthermore, a novel O‐methyltransferase, AcOMT1, which is highly correlated with scoparone levels, was functionally characterised via phylogenetic analysis, subcellular localisation analysis, enzyme assays and molecular docking techniques. Finally, we managed to induce scoparone biosynthesis in tobacco. Our findings expand upon those of previous studies by elucidating the complete biosynthetic pathway for scoparone, which involves the incorporation of a missing last methylation step essential for its production. These findings provide a theoretical basis for the genetic engineering and molecular breeding of A. capillaris cultivars with increased medicinal value. In addition, the methodology employed in this study is broadly applicable for future investigations of other largely unexplored specialised metabolic pathways in non‐model plants.

Author Contributions

Fuyou Guo, Hong Zhou and Wei Ding conceived and designed the research. Fuyou Guo and Fangfang Yan assembled and annotated the full‐length transcriptome. Matthana Klakong, Yu Tan, Yutong Guo and Junjie Chen collected the samples. Miao Zhang, Xiangning Lai, Zongquan Li and Yi Ni conducted the bioinformatics analysis. Fuyou Guo, Shili Li, Liang Yang and Jiamin Yu performed the experiments and analysed the data. Fuyou Guo and Hong Zhou wrote and revised the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Figure S1: pbi70398‐sup‐0001‐FiguresS1‐S11.docx.

PBI-24-346-s001.docx (4.6MB, docx)

Table S1: pbi70398‐sup‐0002‐TablesS1‐S15.xlsx.

PBI-24-346-s002.xlsx (6.9MB, xlsx)

Acknowledgements

This research was supported by the National Natural Science Foundation of China (32302394 and 32202398), the Key Project of China Tobacco Corporation Sichuan Branch (SCYC202301 and SCYC202517), the Fundamental Research Fund for the Central Universities of China (SWU‐KR25018), and the Sichuan Science and Technology Program (2025ZNSFSC1120).

Funding: This work was supported by the National Natural Science Foundation of China (32302394 and 32202398), the Key Project of China Tobacco Corporation Sichuan Branch (SCYC202301 and SCYC202517), the Fundamental Research Fund for the Central Universities of China (SWU‐KR25018), and the Sichuan Science and Technology Program (2025ZNSFSC1120).

Contributor Information

Hong Zhou, Email: 15730534453@163.com.

Wei Ding, Email: dingw@swu.edu.cn.

Data Availability Statement

The RNA‐seq datasets obtained from two distinct tissues of A. capillaris were archived in the NCBI Sequence Read Archive (SRA) database under the BioProject accession number PRJNA1164833.

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Associated Data

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

Supplementary Materials

Figure S1: pbi70398‐sup‐0001‐FiguresS1‐S11.docx.

PBI-24-346-s001.docx (4.6MB, docx)

Table S1: pbi70398‐sup‐0002‐TablesS1‐S15.xlsx.

PBI-24-346-s002.xlsx (6.9MB, xlsx)

Data Availability Statement

The RNA‐seq datasets obtained from two distinct tissues of A. capillaris were archived in the NCBI Sequence Read Archive (SRA) database under the BioProject accession number PRJNA1164833.

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